This function searchs [GDS](https://www.ncbi.nlm.nih.gov/gds) database, and return a data.frame for all the search results.
Arguments
- query
character, the search term. The NCBI uses a search term syntax which can be associated with a specific search field with square brackets. So, for instance "Homo sapiens\[ORGN\]" denotes a search for `Homo sapiens` in the “Organism” field. Details see <https://www.ncbi.nlm.nih.gov/geo/info/qqtutorial.html>. The names and definitions of these fields can be identified using [entrez_db_searchable][rentrez::entrez_db_searchable].
- step
the number of records to fetch from the database each time. You may choose a smaller value if failed.
Details
The NCBI allows users to access more records (10 per second) if they register for and use an API key. [set_entrez_key][rentrez::set_entrez_key] function allows users to set this key for all calls to rentrez functions during a particular R session. You can also set an environment variable `ENTREZ_KEY` by [Sys.setenv][base::Sys.setenv]. Once this value is set to your key rentrez will use it for all requests to the NCBI. Details see <https://docs.ropensci.org/rentrez/articles/rentrez_tutorial.html#rate-limiting-and-api-keys>
Examples
GEOquery::searchGEO("diabetes[ALL] AND Homo sapiens[ORGN] AND GSE[ETYP]")
#> Title
#> 1 Shifts in the Immunoepigenomic Landscape of Monocytes in Response to a Diabetes-Specific Social Support Intervention: A Pilot Study Among Native Hawaiian Adults with Diabetes
#> 2 Vitreous of proliferative diabetic retinopathy patients
#> 3 Circulating small non-coding RNA profiling as potential biomarkers of atherosclerotic plaque composition in Type 1 diabetes
#> 4 Independent phenotypic plasticity axes define mammalian metabolic and obesity sub-types [RNA-seq, human]
#> 5 Transcriptional regulation of liver lipotoxicity in non-alcoholic steatohepatitis [RNA-seq]
#> 6 Transcriptional regulation of liver lipotoxicity in non-alcoholic steatohepatitis [ATAC-seq]
#> 7 Distinctive exercise-induced inflammatory response and exerkine induction in skeletal muscle of people with type 2 diabetes
#> 8 Bioinformatic analysis of the mechanism by which metformin enhances chemosensitivity of head and neck squamous cell carcinoma cells
#> 9 Glucagon-like Peptide-1 (GLP-1) Rescue Diabetic Cardiac Dysfuntions in Human iPSC-Derived Cardiomyocytes
#> 10 DNA Methylation Profiling Reveals Novel Pathway Implicated in Cardiovascular Diseases of Diabetes
#> 11 Transcriptome analysis of Newly Diagnosed Type 2 Diabetes Subjects identifies genes to predict Metformin drug Response
#> 12 Hepatic senescence is associated with clinical progression of NAFLD/NASH: Role of BMP4 and its antagonist Gremlin1 (Visceral adipose cells)
#> 13 Single-cell Transcriptome Atlas of the Human Corpus Cavernosum
#> 14 Genome-wide placental gene methylations in gestational diabetes mellitus, fetal growth and metabolic health biomarkers in cord blood
#> 15 Development of a physiological insulin resistance model in human stem cell-derived adipocytes
#> 16 Bulk RNA-seq on mouse model of diabetic nephropathy and in vitro model of SRSF7 knockdown
#> 17 Altered expressions of transfer RNA-derived small RNAs and microRNAs in the vitreous humour of proliferative diabetic retinopathy.
#> 18 Synovial inflammatory pathways characterize anti-TNF-responsive rheumatoid arthritis patients
#> 19 Self-amplifying Loop of NF-κB and Periostin Initiated by PIEZO1 Accelerates Mechano-induced Senescence of Nucleus Pulposus Cells and Intervertebral Disc Degeneration
#> 20 Patient iPSCs with NEUROG3 mutation reveal pancreatic insufficiency
#> 21 Gene expression data from human omental adipose tissue
#> 22 Expression profiles of placenta and umbilical cord blood with or without gestational diabetes mellitus (GDM)
#> 23 Reduced representation bisulfite sequencing (RRBS) methylation profiles of placenta and umbilical cord blood with or without gestational diabetes mellitus (GDM)
#> 24 Methylation profiling (RRBS) and expression profiling (RNA-seq) of placenta and umbilical cord blood with gestational diabetes mellitus (GDM)
#> 25 Deciphering protective mechanism against human type 2 diabetes through in vitro β cell differentiation
#> 26 High-throughput analysis of ANRIL circRNA isoforms in human pancreatic islets
#> 27 LncRNA LYPLAL1-DT screening from type 2 diabetes with macrovascular complication contributes protective effects on human umbilical vein endothelial cells via regulating the miR-204-5p/SIRT1 axis.
#> 28 DNA Methylation-Based Prediction of Post-Operative Atrial Fibrillation
#> 29 DNA Methylation-Based Prediction of Post-Operative Atrial Fibrillation II
#> 30 DNA Methylation-Based Prediction of Post-Operative Atrial Fibrillation I
#> 31 RNA-seq profiling of tubulointerstitial tissue reveals a potential therapeutic role of dual anti-phosphatase 1 in kidney diseases
#> 32 Bone metabolism-related serum miRNAs to diagnose postmenopausal osteoporosis in middle-aged and elderly women
#> 33 Transcription Factor Binding Analysis of Wild Type and HHEX-/- ES-derived Pancreatic Progentiors
#> 34 Chromatin Landscape Analysis of Wild Type and HHEX-/- ES-derived Pancreatic Progentiors
#> 35 Transcription Landscape Analysis of Wild Type and HHEX-/- ES-derived Pancreatic Progentiors
#> 36 Effect of O-GlcNAc Transferase (OGT) siRNA in trophoblastic BeWo cells
#> 37 Polysome profiling quantified by RNA sequencing in PANC1 cells treated with MNK2 inhibitors or DMSO
#> 38 Circulating extracellular vesicles exhibit a differential miRNA profile in gestational diabetes mellitus pregnancies
#> 39 Serum miRNA profile in diabetic patients with ischemic heart disease (IHD) as a promising non-invasive biomarker.
#> 40 Identification of significant immune-related genes for diabetic foot ulcers: validated by scRNA-seq
#> 41 Spatial Environment Affects HNF4A Mutation-Specific Proteome Signatures and Cellular Morphology in hiPSC-Derived β-Like Cells
#> 42 Genome-wide Analysis Reflects Novel 5-Hydroxymethylcytosines Implicated in Diabetic Nephropathy
#> 43 Transcriptome analysis and weighted gene co-expression network reveal candidate genes and pathways responses to lactate dehydrogenase inhibition (oxamate) in hyperglycemic human renal proximal epithelial tubular cells
#> 44 Fourteen-weeks combined exercise epigenetically modulated 118 genes of menopausal women with prediabetes
#> 45 Human placental tissues:control group vs non-diabetic fetal macrosomia (NDFMS) group
#> 46 Multi-dimensional modeling disrupted synapse formation underlying psychiatric disorders of Wolfram syndrome reveals essentiality of astrocytes
#> 47 Transcriptional and chromatin accessibility changes underlying progression from islet autoantibody positivity to type 1 diabetes
#> 48 Transcriptional changes underlying progression from islet autoantibody positivity to type 1 diabetes
#> 49 Chromatin accessibility changes underlying progression from islet autoantibody positivity to type 1 diabetes
#> 50 In-depth molecular profiling specifies human retinal microglia identity
#> 51 Probiotic normalization of systemic inflammation in siblings of Type 1 diabetes patients
#> 52 Characterization of peripheral blood TCR in patients with Type 1 Diabetes Mellitus by BD Rhapsody™ VDJ CDR3 Assay
#> 53 Diverging metabolic effects of two energy restricted diets differing in nutrient quality: a 12-week randomized controlled trial in subjects with abdominal obesity
#> 54 RNA aptamers specific for transmembrane p24 trafficking protein 6 and Clusterin for the targeted delivery of imaging reagents and RNA therapeutic to human β cells
#> 55 Bariatric surgery mediated weight loss reduces breast cancer risk by reducing estrogen receptor alpha activity
#> 56 RNA-seq profiles between human parental and 5-FU drug resistant HCT116 and SW480 colorectal cancer cell lines
#> 57 Identification of Key LncRNAs and Pathways in Prediabetes and Type 2 Diabetes Mellitus for Hypertriglyceridemia Patients Based on Weighted Gene Co-Expression Network Analysis
#> 58 Increased insulin secretion in ZNT8 mutant stem-cell derived beta cells
#> 59 Differentially-expressed mRNAs, microRNAs and long noncoding RNAs in intervertebral disc degeneration identified by RNA-sequencing
#> 60 Human Tubular Epithelial Cells Activate a Coordinated Stress Response after Serum Exposure [RNAseq-pid2019]
#> 61 Human Tubular Epithelial Cells Activate a Coordinated Stress Response after Serum Exposure [RNAseq-pid1830]
#> 62 VPA-treatment of Panc-1-cells to study epigenetic impact mediated by histone acetylation on epithelial-mesenchymal transmission
#> 63 Human Pluripotent Stem Cell-derived Islets Ameliorate Diabetes in Nonhuman Primates
#> 64 Human Pluripotent Stem Cell-derived Islets Ameliorate Diabetes in Nonhuman Primates [human_singlecell]
#> 65 Human Pluripotent Stem Cell-derived Islets Ameliorate Diabetes in Nonhuman Primates [human_bulk]
#> 66 RNA-seq of human adipose tissue macrophage subtypes in obesity
#> 67 Genome-wide DNA methylation profiling in anorexia nervosa discordant identical twins
#> 68 Loci-specific differences in blood DNA methylation in HBV-negative populations at risk for hepatocellular carcinoma development - post-diagnostic HCC blood samples
#> 69 Loci-specific differences in blood DNA methylation in HBV-negative populations at risk for hepatocellular carcinoma development - pre-diagnostic HCC blood samples
#> 70 MYCL-mediated in vivo reprogramming expands pancreatic insulin-producing cells to reverse diabetes
#> 71 Single-cell RNA-sequencing reveals the heterogeneity of microglia in fibrous membrane derived from proliferative diabetic retinopathy and proliferative vitreoretinopathy
#> 72 HAMSAB supplement enhances SCFA production associated with microbiota and immune modulation in type 1 diabetes
#> 73 An HNF1A truncation associated with maturity-onset diabetes of the young impairs pancreatic progenitor differentiation by antagonising HNF1B function
#> 74 Limited extent and consequences of pancreatic SARS-CoV-2 infection
#> 75 Exosomal RNA expression profiles and their prediction performance in gestational diabetes mellitus patients with macrosomia
#> 76 circRNA profiles of diabetic retinopathy
#> 77 A global analysis on the differential regulation of RNA binding proteins (RBPs) by TNF–α as potential modulators of metabolic syndromes
#> 78 RNA sequencing of control and PTPN2 knocked down transcriptomes in EndoC- H1 cells with or without the treatment of pro-inflammatory cytokines
#> 79 Pharmacologically enhanced regulatory hematopoietic stem cells (HSC.Regs) reverts experimental autoimmune diabetes
#> 80 A miR-125 / Sirtuin-7 pathway drives pro-calcific potential of myeloid cells in diabetic vascular disease
#> 81 Exploring the mechanism of Jiangtang Tiaozhi Recipe in the treatment of obese T2DM patients with dyslipidemia based on transcriptomics
#> 82 A single cell atlas of human adipose tissue
#> 83 Characterization of the stromal vascular fraction (SVF) of human subcutaneous adipose tissue (SAT)
#> 84 Epigenomic and Transcriptional Basis of Human Insulin Resistance
#> 85 Prevalence of inflammatory pathways over immuno-tolerance in peripheral blood mononuclear cells of recent-onset type 1 diabetes
#> 86 Inflammatory pathways in peripheral blood expression profile of recent-onset type 1 diabetes
#> 87 Integrated analysis of the transcriptome-wide m6A methylome in gestational diabetes mellitus and healthy control placentas
#> 88 RNA sequence of gestational diabetes mellitus (GDM) and healthy control placentas
#> 89 Integrated analysis of the transcriptome-wide m6A methylome in gestational diabetes mellitus and healthy control placentas [meRIP-seq]
#> 90 HO1 activates autophagy to protect intervertebral disc degeneration
#> 91 Heterogeneous Gene Expression Patterns of Tuberculosis-Diabetes Interaction in Diverse Cohorts
#> 92 Epigenetic alterations are associated with gastric emptying disturbances in Diabetes Mellitus
#> 93 Integratome analysis of adipose tissues reveals abnormal epigenetic regulation of adipogenesis, inflammation, and insulin signaling in obese individuals with type 2 diabetes
#> 94 Whole Transcriptomic analysis of placenta and its released extracellular vesicles in normal and preeclampsia pregnancies: insigths into novel biomarkers and mechanisms of disease
#> 95 SmallRNA analysis of placenta and its released extracellular vesicles in normal and preeclampsia pregnancies: insigths into novel biomarkers and mechanisms of disease
#> 96 Transcriptomic analysis of placenta and its released extracellular vesicles in normal and preeclampsia pregnancies: insigths into novel biomarkers and mechanisms of disease
#> 97 Persistent Coxsackievirus B1 infection results in extensive changes in the transcriptome of a pancreatic cell line
#> 98 Germline-like TCR alpha chains dominate shared self-reactive T cell receptors in type 1 diabetes
#> 99 Human Tongue Fungiform Papilla Transcriptome and Proteome Reveal Sex Differences in Long Intergenic Noncoding RNA, Immune Response and Metabolism Genes [array]
#> 100 DNA methylation profiling of cord blood progenitor endothelial cells from overweight and GDM pregnancies
#> 101 Single cell trajectory modeling identifies a primitive trophoblast state defined by BCAM enrichment
#> 102 TGF-β-induced miR143/145 influences differentiation, insulin signaling and exercise response in human skeletal muscle [small RNA-seq]
#> 103 TGF-β-induced miR143/145 influences differentiation, insulin signaling and exercise response in human skeletal muscle [RNA-seq]
#> 104 LncRNA expression profile and target gene prediction of calcification in human aortic smooth muscle cells induced by DPP4
#> 105 Abnormal exocrine-endocrine cell crosstalk promotes β-cell dysfunction and loss in MODY8
#> 106 Exploratory study reveals far reaching systemic and cellular effects of verapamil treatment in subjects with type 1 diabetes
#> 107 Changes in CIDEA expression associate with adipocytes size and functionality in adolescent obese girls
#> 108 A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases
#> 109 A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases [study of 13 diseases]
#> 110 Effect of salivary exosomal miR-25-3p on periodontitis with insulin resistance
#> 111 Alterations of 5-Hydroxymethylcytosines in Circulating Cell-free DNA Reflect Retinopathy in Type 2 Diabetes
#> 112 Adipocyte Precursor Cells from First Degree Relatives of type 2 diabetic patients feature changes of hsa-mir-23a-5p, -193a-5p, and -193b-5p and Insulin-Like Growth Factor 2 expression
#> 113 Adipocyte Precursor Cells from First Degree Relatives of type 2 diabetic patients feature changes of hsa-mir-23a-5p, -193a-5p, and -193b-5p and Insulin-Like Growth Factor 2 expression [smallRNA-seq]
#> 114 Adipocyte Precursor Cells from First Degree Relatives of type 2 diabetic patients feature changes of hsa-mir-23a-5p, -193a-5p, and -193b-5p and Insulin-Like Growth Factor 2 expression [RNA-Seq]
#> 115 Transcriptome dataset of two different adipose tissues in gestational diabetes patients.
#> 116 Gene-expression profiles of whole blood cells from a Han Chinese population with or without Type-2 Diabetes Mellitus or/and its complications in nephropathy and retinopathy
#> 117 RNAsequencing of control and STAT3 knocked down transriptomes of EndoC cells
#> 118 Perturb-Seq using T2D islets
#> 119 scGOF-Seq using ND islets
#> 120 BACH2 inhibition reverses β-cell failure in type 2 diabetes models
#> 121 Isoforms of SEMA3E-containing supernatant treated gene expression in human aortic endothelial cells
#> 122 Single Cell Transcriptomic Landscape of Diabetic Foot Ulcers
#> 123 In-depth molecular characterization of neovascular membranes suggests a role for hyalocytes-to-myofibroblasts transdifferentiation in proliferative diabetic retinopathy
#> 124 Impaired Skeletal Muscle Repair in Healthy Young Adults with Type 1 Diabetes Mellitus
#> 125 Spatial transcriptomics of healing and non-healing diabetic foot ulcers
#> 126 Adipocyte-derived extracellular vesicles promote breast cancer progression in type 2 diabetes
#> 127 Dysregulated lncRNA and mRNA may promote the progression of ischemic stroke via immune and inflammatory pathways: results from RNA sequencing and bioinformatics analysis
#> 128 Lnc-SLC15A1-1 Up-regulates CXCL10/CXCL8 Expression in Endothelial Cells by Sponging MicroRNAs (RNA-Seq)
#> 129 Distinct hepatic gene expression patterns characterize progressive disease in NAFLD
#> 130 Transcriptome-wide N6-methyladenine profiling in low input multiplex samples by a kit-free multi-barcode method
#> 131 ENTPD3 Marks Mature Stem Cell Derived Beta Cells Formed by Self-Aggregation in Vitro
#> 132 RNA-seq analysis for wild-type fibroblasts and patient fibroblasts bearing the m.3243A>G mutatioin
#> 133 Progressive ER stress over time due to human insulin gene mutation contributes to pancreatic β-cell dysfunction, islet inflammation and compensatory responses
#> 134 Altered Human Alveolar Bone Gene Expression in Type 2 Diabetes
#> 135 Impaired bone fracture healing in type 2 diabetes is caused by defective functions of skeletal progenitor cells
#> 136 Increased adipose tissue fibrogenesis, not impaired expandability, is associated with nonalcoholic fatty liver disease
#> 137 Role of microRNA-143, -150 and 126 in pathological retinal angiogenesis
#> 138 10X genomics single cell GEX and VDJ 5' sequencing of PBMC from Type 1 Diabetes patients treated with Treg therapy alone or plus low dose IL-2
#> 139 Early developmental alteration of neurite outgrowth occurs besides late-appearing neurodegenerative processes in Wolfram syndrome
#> 140 Transcriptome analysis of human pancreatic preadipocytes and in vitro differentiated adipocytes
#> 141 Novel diabetes gene discovery through comprehensive characterization and integrative analysis of longitudinal gene expression changes
#> 142 Lipid droplets protect human β-cells from lipotoxic-induced stress and cell identity changes
#> 143 Acetylation State of Histone Core Defines Macrophage Dynamics in Diabetic Wounds
#> 144 Permutational immune analysis reveals architectural similarities between inflammaging, Down syndrome and autoimmunity
#> 145 Combinatorial transcription factor profiles predict mature and functional human islet α and β cells
#> 146 Heme-Oxygenase 1 is a Master Regulator of Cell Fate Following Oxidative Stress Response in Endothelial Cells
#> 147 ATAC-seq and multi-omics analysis of human liver highlight a hepatocyte-specific enhancer for ACOT1 regulating the balance of acyl-CoA and free fatty acids in type 2 diabetes.
#> 148 Microvessels support engraftment and functionality of human islets and hESC-derived pancreatic progenitors in diabetes models
#> 149 Epigenetic impairment and blunted transcriptional response to Mycobacterium tuberculosis of alveolar macrophages from persons living with HIV
#> 150 Epigenetic impairment and blunted transcriptional response to Mycobacterium tuberculosis of alveolar macrophages from persons living with HIV (RNA-Seq)
#> 151 Epigenetic impairment and blunted transcriptional response to Mycobacterium tuberculosis of alveolar macrophages from persons living with HIV (ATAC-Seq)
#> 152 Circulating circRNA signature in pregnancies with gestational diabetes mellitus
#> 153 High-throughput mediation analysis of human proteome and metabolome identifies mediators of post-bariatric surgical diabetes control
#> 154 mRNA-seq read counts of peripheral blood mononuclear cells from congenital generalized lipodystrophy patients and their gender/aged-matched controls
#> 155 Disrupted Circadian Oscillations in Type 2 Diabetes are Linked to Altered Rhythmic Mitochondrial Metabolism in Skeletal Muscle
#> 156 Disrupted Circadian Oscillations in Type 2 Diabetes are Linked to Altered Rhythmic Mitochondrial Metabolism in Skeletal Muscle [Affymetrix]
#> 157 Disrupted Circadian Oscillations in Type 2 Diabetes are Linked to Altered Rhythmic Mitochondrial Metabolism in Skeletal Muscle [RNA-seq]
#> 158 Identification of circulating miRNA molecular signature for erectile dysfunction in type 2 diabetes
#> 159 High resolution chromosome conformation capture from gene promoters at COVID-19, T1D, AS and RBC GWAS loci
#> 160 Angiogenin Released from ABCB5+ Stromal Precursors Improves Healing of Diabetic Wounds by Promoting Angiogenesis
#> 161 A Critical Role of Hepatic GABA in The Metabolic Dysfunction and Hyperphagia of Obesity
#> 162 Islet Sympathetic Innervation and Islet Neuropathology in Patients with Type 1 Diabetes
#> 163 Gene expression signatures for human non-diabetic (hND) islets and human type 2 diabetes mellitus (hT2DM) islets
#> 164 RNA-seq analysis with isolated human pancreatic islets treated with human breast cancer cell secreted Evs or control
#> 165 Modelling HNF1B-associated monogenic diabetes using human iPSCs reveals an early stage impairment of the pancreatic developmental program
#> 166 TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [RNA-Seq]
#> 167 TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [ChIP-Seq]
#> 168 Serum miRNA profiling for early PDAC diagnosis and prognosis: a retrospective study
#> 169 Transcriptomic phenotyping of human labor myometrium.
#> 170 RNA Sequencing of Blood in Coronary Artery Disease; Involvement of Regulatory T Cell Imbalance
#> 171 RNA Sequencing of Blood in Coronary Artery Disease; Involvement of Regulatory T Cell Imbalance [Discovery Cohort]
#> 172 Using single-nucleus RNA-sequencing to interrogate transcriptomic profiles of archived human pancreatic islets
#> 173 Effects of oral-glucose load on the gene expression of peripheral blood mono-nuclear cells in Asian-Indian men.
#> 174 Genetic variants associated with development of colorectal cancer, type 1 diabetes, Hodgkin lymphoma and Diffuse large B-cell lymphoma
#> 175 Self-Renewing Tri-Potent Stem/Progenitor-like Cells from Adult Human Pancreas
#> 176 Areca catechu-(Betel-nut)-induced whole transcriptome changes in a human monocyte cell line that may have relevance to diabetes and obesity; a pilot study
#> 177 Generation of Human Islet Cell-Type-Specific Identity Genesets
#> 178 Identification of potential genomic alterations in primary and recurrent synovial sarcoma
#> 179 Transcriptome sequencing in serum exosomes from proliferative diabetic retinopathy (PDR)
#> 180 RECK isoforms are differentially expressed in patients with stable and unstable coronary artery disease: A pilot study.
#> 181 Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits [snRNA-seq]
#> 182 Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits [snATAC-seq]
#> 183 Profiling of CD14+ monocytes from humans with Type diabetes and without diabetes
#> 184 Profiling of CD14+ monocytes from humans with Type diabetes and without diabetes [CHIRP-seq]
#> 185 Profiling of CD14+ monocytes from humans with Type diabetes and without diabetes [RNA-Seq]
#> 186 Genome wide methylation of cord blood from gestational diabetes mellitus
#> 187 Modeling pancreatic beta cell senescence by induction of DNA double-strand breaks
#> 188 RNA-seq of HUVECs stimulated with HG and oxLDL
#> 189 Pancreatic Differentiation of stem cells reveals pathogenesis of a syndrome of Ketosis-Prone Diabetes
#> 190 The effect of homocysteine on Human Aortic Endothelial Cells [miRNA]
#> 191 The effect of homocysteine on Human Aortic Endothelial Cells [RNA]
#> 192 Circulating exosomal miRNA signature in pregnancies with gestational diabetes mellitus across gestation
#> 193 RNA Sequencing Facilitates Quantitative Analysis of Transcriptomes of adipose stem cells from diabetic, old and young patients
#> 194 DNA methylation in skeletal muscle of patients with hypertension and diabetes undergoing coronary artery bypass grafting surgery
#> 195 Muscle transcriptomic profiling of chronological aging and metabolic syndrome in men
#> 196 Single cell RNA-sequencing reveals placenta cellular heterogeneity in adverse pregnancy
#> 197 Nicotinamide mononucleotide increases muscle insulin sensitivity in women with prediabetes
#> 198 scRNA-seq analysis of SARS-CoV-2 infected human islets
#> 199 In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy
#> 200 In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy (smallRNA)
#> 201 In-depth transcriptomic analyses investigating molecular mechanisms underlying diabetic retinopathy (totalRNA)
#> 202 An inter-dependent network of enhancers regulates INK4a/ARF locus
#> 203 Multi-omics profiling of living human pancreatic islet donors reveals heterogeneous beta cell trajectories toward type 2 diabetes
#> 204 Temporal evolution of cellular heterogeneity during the progression to advanced, AR-negative prostate cancer
#> 205 Obese Insulin Resistant Humans with Compensatory Hyperinsulinemia Dissociate Lipolysis from Glycemia as Possible Adaptive Response to Fatness
#> 206 The effects of a novel oral nutritional supplement as compared to standard care on body composition, physical function and skeletal muscle mRNA expression in Dutch older adults with (or at risk of) undernutrition
#> 207 Neonatal diabetes mutations disrupt a chromatin pioneering function that activates the human insulin gene
#> 208 Single-cell transcriptomic resolution of human pancreatic islets reveals cellular states and intercellular interactions associated with type 1 diabetes
#> 209 Identification of human glucocorticoid response markers using integrated multi-omic analysis
#> 210 Identification of human glucocorticoid response markers using integrated multi-omic analysis [Adipose Tissue]
#> 211 Identification of human glucocorticoid response markers using integrated multi-omic analysis [Peripheral blood mononuclear cells]
#> 212 Expression data from peripheral blood mononuclear cells（PBMCs) in newly diagnosed type 2 diabetes
#> 213 Unravelling the Biological Functions of Type 1 Diabetes Associated Noncoding Single-Nucleotide Polymorphism in Human Pancreatic β Cells
#> 214 Glucocorticoid signaling in pancreatic islets modulates gene regulatory programs and genetic risk of type 2 diabetes
#> 215 Human Placental Exosomes in Gestational Diabetes Mellitus Carry a Specific Set of miRNAs Associated with Skeletal Muscle Insulin Sensitivity
#> 216 Unique human beta-cell senescence-associated secretory phenotype (SASP) reveal conserved signaling pathways and heterogeneous factors
#> 217 Interpreting type 1 diabetes risk with genetics and single cell epigenomics
#> 218 Transcriptional deregulation in subcutaneous adipose tissue from severely obese patients is associated with cancer: focus on gender differences and role of type 2 diabetes
#> 219 ALTERED DUODENAL MUCOSAL MITOCHONDRIAL GENE EXPRESSION IS ASSOCIATED WITH DELAYED GASTRIC EMPTYING IN DIABETIC GASTROENTEROPATHY
#> 220 ALTERED DUODENAL MUCOSAL MITOCHONDRIAL GENE EXPRESSION IS ASSOCIATED WITH DELAYED GASTRIC EMPTYING IN DIABETIC GASTROENTEROPATHY [miRNA-Seq]
#> 221 ALTERED DUODENAL MUCOSAL MITOCHONDRIAL GENE EXPRESSION IS ASSOCIATED WITH DELAYED GASTRIC EMPTYING IN DIABETIC GASTROENTEROPATHY [mRNA-Seq]
#> 222 Population and single cell RNAseq analysis of CD4+ T cells in FOXP3 mutant mice ( scurfy") and IPEX patients
#> 223 Gene expression analysis of human mortal renal tubular epithelial cells chronically exposed to elevated levels of glucose
#> 224 miRNA expression during BMSCs from human jaw in Type 2 diabetics.
#> 225 Single-cell RNAseq (10x Genomics) analysis of human CD4+ T cells in IPEX patients, healthy donors and heterozygous mothers (blood).
#> 226 Impaired peripheral mononuclear cell metabolism in patients at risk of developing sepsis
#> 227 A histological and transcriptional characterization of the pancreatic acinar tissue in type 1 diabetes
#> 228 Transcriptional analysis of islets of Langerhans from organ donors of different ages
#> 229 Analysis of the transcriptome and DNA methylome in response to acute and recurrent low glucose in human primary astrocytes (RNA-Seq)
#> 230 Analysis of the transcriptome and DNA methylome in response to acute and recurrent low glucose in human primary astrocytes (BeadChip)
#> 231 DNA methylation data throughout human muscle cell differentiation in individuals with type 2 diabetes and controls
#> 232 DNA methylation data for human muscle cells from individuals with type 2 diabetes and controls
#> 233 mRNA expression data throughout human muscle cell differentiation in individuals with type 2 diabetes and controls
#> 234 mRNA expression data for human muscle cells from individuals with type 2 diabetes and controls
#> 235 Integrative analysis of DNA methylation and gene expression data among preterm and/or small for gestational age infants during perinatal period
#> 236 Integrative analysis of DNA methylation and gene expression data among preterm and/or small for gestational age infants during perinatal period [methylation]
#> 237 Large-scale single-cell analysis reveals critical immune characteristics of COVID-19 patients
#> 238 Urinary single cell profiling captures cellular diversity of the kidney
#> 239 SARS-CoV-2 infection of human pancreatic islets
#> 240 Transcriptomic characterization of the delayed wound healing response in a diabetic skin humanized mice model
#> 241 Exposure to Gestational Diabetes Mellitus In Utero Alters DNA Methylation in Placenta and Fetal Cord Blood
#> 242 Exposure to Gestational Diabetes Mellitus In Utero Alters DNA Methylation in Placenta and Fetal Cord Blood [Placenta]
#> 243 Exposure to Gestational Diabetes Mellitus In Utero Alters DNA Methylation in Placenta and Fetal Cord Blood [Cord]
#> 244 Pancreatic progenitor epigenome maps prioritize type 2 diabetes risk genes with roles in development
#> 245 Ayurvedic herbal preparation supplementation does not improve metabolic health in impaired glucose tolerance subjects; observations from a randomised placebo controlled trial
#> 246 Single cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk [Hi-C]
#> 247 Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk
#> 248 Single-cell chromatin accessibility identifies pancreatic islet cell type- and state-specific regulatory programs of diabetes risk [RNA-seq]
#> 249 Single-cell chromatin accessibility identifies pancreatic islet cell type– and state-specific regulatory programs of diabetes risk [snATAC-seq]
#> 250 Type 2 Diabetes Mellitus is Associated with Transcriptome Alterations in Cortical Neurones and Associated Neurovascular Unit Cells in the Ageing Brain
#> 251 Hyperglycemic memory of innate immune cells promotes in vitro proinflammatory responses of human monocytes and murine macrophages
#> 252 Expression profiles of human adipose tissue, adipocytes and stromal-vascular pellet cells from multiple body sites
#> 253 miRNA sequencing profile of human plasma samples from healthy, diabetic, and gastroparesis patients
#> 254 Pericardial fluid exosome miRNA profiling
#> 255 Long non-coding RNA screening for type 2 diabetes
#> 256 GLP-1 receptor signaling increases PCSK1 and beta-cell features in human alpha-cells
#> 257 Distinct exhausted-like CD8 T cell populations are linked to C-peptide preservation in alefacept-treated, recent onset T1D subjects
#> 258 Identification of SRSF6 splicing regulatory map and its impact on diabetes susceptibility genes regulation
#> 259 Transcriptomic Signatures of Kidney Injury in Human Renal Biopsy Specimens
#> 260 The human aortic endothelium undergoes dose-dependent DNA methylation in response to transient hyperglycemia
#> 261 Distinct exhausted CD8 T cell populations are linked to C-peptide preservation in alefacept-treated, recent onset T1D subjects
#> 262 Clinical, histopathologic and molecular features of idiopathic and diabetic nodular mesangial sclerosis in humans
#> 263 Array comparative genomic hybridization analysis of metastatic lung tumors
#> 264 Drug-drug interaction between metformin and sorafenib alters antitumor effect in hepatocellular carcinoma cells
#> 265 Estrogen-driven control of diabetogenic gene networks is associated with reduced levels of miR-224/452 circulating in extracellular vesicles [miRNA-Seq]
#> 266 The postprandial transcriptomic response of adipose tissue to high fat meals in middle-aged men with metabolic syndrome
#> 267 Human PBMCs: Healthy vs Diabetic nephropathy vs ESRD
#> 268 Multi-locus imprinting disturbances in a family harboring a ZFP57 truncation
#> 269 Whole transcriptome sequencing of peripheral blood mononuclear cells from patients with COVID-19
#> 270 Gene cascade analysis in human granulosa tumor cells (KGN) following exposure to high levels of free fatty acids and insulin
#> 271 Relationship between insulin sensitivity and gene expression in human skeletal muscle
#> 272 Relationship between insulin sensitivity and gene expression in human skeletal muscle (Study B)
#> 273 Relationship between insulin sensitivity and gene expression in human skeletal muscle (Study A)
#> 274 Identification of a human gut-derived LEAP2 fragment as a novel insulin secretagogue
#> 275 RNA-seq of human dendritic cells cultured with PSAB-liposomes and/or Liraglutide
#> 276 Epigenome analysis of cord blood DNA from infants born into the UPBEAT study
#> 277 Integrative omics analyses reveal epigenetic memory in diabetic cells regulating genes associated with kidney dysfunction.
#> 278 Integrative omics analyses reveal epigenetic memory in diabetic cells regulating genes associated with kidney dysfunction. [sequencing]
#> 279 Integrative omics analyses reveal epigenetic memory in diabetic cells regulating genes associated with kidney dysfunction. [microarray]
#> 280 Phospho-antibody microarray anayses for control and DG-LRG1 treated HUVEC cells.
#> 281 Persistent or Transient Human β-cell Dysfunction Induced by Metabolic Stress Associated with Specific Signatures and Shared Gene Expression of Type 2 Diabetes
#> 282 Single-cell analysis of adipose tissue T cells in diabetic persons with HIV reveals high proportions of clonally expanded CMV-like CD4+ T cells with cytotoxic RNA transcriptomes
#> 283 Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes
#> 284 Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes [RNA-seq]
#> 285 Cell-free DNA Methylation and Transcriptomic Signature Prediction of Pregnancies with Adverse Outcomes [WGBS]
#> 286 Type 2 Diabetes reduces the enteroendocrine GLP-1 cell lineage in human obesity: characterization in enriched human enteroendocrine cells
#> 287 A whole-genome CRISPRa screening metformin resistance related gene in prostate cancer
#> 288 A high glycemic burden drives functional and metabolic alterations of human monocytes in patients with type 1 diabetes
#> 289 Single cell transcriptomics of human islet ontogeny defines the molecular basis of beta cell dedifferentiation in T2D
#> 290 Single cell lineage analysis reveals cell fate determination events during directed β-cell differentiation
#> 291 RNAseq from human islets treated with brefeldin A as a model of Golgi stress
#> 292 Deregulated immune signature orchestrated by FOXM1 impairs human diabetic wound healing
#> 293 Two distinct immunopathological profiles in lungs of lethal COVID-19
#> 294 Baseline assessment of circulating microRNAs near diagnosis of type 1 diabetes predicts future stimulated insulin secretion
#> 295 Liver-specific knockdown of class IIa HDACs has limited efficacy on glucose metabolism but entails severe organ side effects in mice
#> 296 Immune Gene Expression Profile of Tr1 Skewed Tregs
#> 297 Transcriptomic analysis of peripheral blood mononuclear cells (PBMC) of patients with type 2 Diabetes Melittus(T2DM), Dyslipidemia (DL) and Periodontitis (P)
#> 298 Genomewide transcriptional analysis of growth hormone-treated human podocytes
#> 299 Differential effects of voclosporin and tacrolimus on insulin secretion from human islets
#> 300 Genome-Wide Profiling of DNA Methylation and Gene Expression Identifies Candidate Genes for Human Diabetic Neuropathy
#> 301 Genome-Wide Profiling of DNA Methylation and Gene Expression Identifies Candidate Genes for Human Diabetic Neuropathy (RRBS)
#> 302 Genome-Wide Profiling of DNA Methylation and Gene Expression Identifies Candidate Genes for Human Diabetic Neuropathy (RNA-Seq)
#> 303 Comparison of Regulatory Type of Macrophages and PCMO Cells from perspective of RNAseq data.
#> 304 Comparative transcriptome analysis of human skeletal muscle in response to cold acclimation and exercise training in human volunteers.
#> 305 Comparative transcriptome analysis of human skeletal muscle in response to cold acclimation and exercise training in human volunteers. [A391]
#> 306 Comparative transcriptome analysis of human skeletal muscle in response to cold acclimation and exercise training in human volunteers. [A294]
#> 307 Stress-induced RNA–chromatin interactions promote endothelial dysfunction (scRNA-seq human vascular)
#> 308 Stress-induced RNA–chromatin interactions promote endothelial dysfunction (RNA-seq)
#> 309 Stress-induced RNA–chromatin interactions promote endothelial dysfunction (iMARGI replicate 2)
#> 310 Stress-induced RNA–chromatin interactions promote endothelial dysfunction
#> 311 Stress-induced RNA–chromatin interactions promote endothelial dysfunction (scRNA-seq)
#> 312 Stress-induced RNA–chromatin interactions promote endothelial dysfunction (iMARGI)
#> 313 Stress-induced RNA–chromatin interactions promote endothelial dysfunction (Hi-C)
#> 314 Combined transcriptome and proteome profiling of the pancreatic β-cell response to palmitate unveils key pathways of β-cell lipotoxicity
#> 315 Human pancreatic islets methylation array
#> 316 Induced Expression of VEGFC, ANGPT, and EFNB2 and Their Receptors Characterizes Neovascularization in Proliferative Diabetic Retinopathy
#> 317 DNA microarray analysis of blood before and after ingesting carotenoid-rich vegitable beverage for 8 weeks, a randomized and double-blinded controlled clinical trial
#> 318 Modeling human T-cell mediated beta cell destruction
#> 319 The role of long noncoding RNAs during pancreas development
#> 320 RNA-sequencing analysis of forearm skin in diabetic patients with or without foot ulcerations
#> 321 Transcriptomic and Chromatin accessibility profiling of functional brown adipocytes derived from human pluripotent stem cells
#> 322 The role of TCF7L2 rs290487 variant in hepatic glucose metabolism: an integrated analysis of clinical and multi-omics data
#> 323 Increased long noncoding RNA maternally expressed gene 3 contributes to podocyte injury induced by high glucose through regulation of mitochondrial fission
#> 324 Identification of an Anti-diabetic, Orally Available Small Molecule that Regulates TXNIP Expression and Glucagon Action
#> 325 Derivation and Characterization of a UCP1 Reporter Human ES Cell Line
#> 326 A common genetic trait through multistep hepatocarcinogenesis in a case with chronic hepatitis C
#> 327 Circulating miRNAs as a predictive biomarkers of progression from prediabetes to diabetes: outcomes of 5-year prospective observational study
#> 328 Vitamin C supplementation reduces expression of circulating miR-451a in poorly controlled type 2 diabetes mellitus
#> 329 Expression data from of FACS separated acinar and duct cell at day 4 of suspension cultured human pancreatic exocrine cells
#> 330 Expression data from day of isolation and day 4 suspension cultured human pancreatic exocrine cells
#> 331 Immune dysfunction in intermediate hyperglycaemia and diabetes patients in tuberculosis
#> 332 Whole-blood transcriptome profiling reveals signatures of metformin and its therapeutic response
#> 333 Transcriptional Profiling of Normal, Stenotic, and Regurgitant Human Aortic Valves
#> 334 Identified differentially expressed lncRNAs in type 2 diabetes patients
#> 335 Unique molecular signatures of microRNAs in ocular fluids and plasma in diabetic retinopathy
#> 336 Differenatiation of ceRNA (circRNA, lncRNA and mRNA) expression in PBMCs (peripheral blood mononuclear cells)
#> 337 Differenatiation of miRNA expression in PBMCs (peripheral blood mononuclear cells)
#> 338 Discovery of CD80 and CD86 as recent activation markers on regulatory T cells by protein-RNA single-cell analysis
#> 339 RIPK1 gene variants associate with increased obesity in humans and can be therapeutically silenced to improve metabolic dysfunction in obese mice
#> 340 MicroRNA arrays for early diagnosis of diabetic kidney disease
#> 341 Epithelial membrane protein 2 (EMP2) regulates hypoxia induced angiogenesis in retinal epithelial cells
#> 342 Successful Preclinical Islet Transplantation in the Subcutaneous Space for Type 1 Diabetes
#> 343 Transimmunom whole blood RNA-seq data from type 1 diabetic patients and healthy volunteers
#> 344 The PPAR agonist Rosiglitazone induces paracrine signaling in melanoma cells that activate stromal cells and enhances tumor growth.
#> 345 Innate immune stimulation of whole blood reveals IFN-1 hyper-responsiveness in type 1 diabetes
#> 346 Gene expression profiles of human retinal microvascular pericytes (HRMVPC) and human lipoaspirate derived mesenchymal stromal cells (adipose stromal cells, ASC)
#> 347 DNA methylation analysis of human peripheral blood mononuclear cell collected in the AIRWAVE study
#> 348 The omentum of obese girls harbors small adipocytes and browning transcrips
#> 349 Patient iPSCs identify vascular smooth muscle AADAC as protective against atherosclerosis
#> 350 Next generation sequencing identifies differentially expressed genes between breast cancer with diabetes and breast cancer without diabetes
#> 351 Gestational diabetes and human amniocytes
#> 352 Transcriptomic changes in response to modulation of long-non-coding RNA LINC00473
#> 353 Single Cell Sequencing Analysis for Wolfram Syndrome (WS4) Unedited and Corrected Stem Cell-Derived Beta Cells
#> 354 Transcriptomic changes in response to modulation of long-non-coding RNA, LINC00473
#> 355 An Exome-Wide Association Study Identifies New Susceptibility Loci for the Risk of Nicotine Dependence in European-American Populations
#> 356 An integrated multi-omics approach identifies the landscape of interferon-a-mediated responses of human pancreatic beta cells [RNA-seq 2]
#> 357 An integrated multi-omics approach identifies the landscape of interferon-a-mediated responses of human pancreatic beta cells [ATAC-seq]
#> 358 An integrated multi-omics approach identifies the landscape of interferon-a-mediated responses of human pancreatic beta cells [RNA-seq]
#> 359 Inhibition of Grb14, a negative modulator of insulin signaling, improves glucose homeostasis without causing cardiac dysfunction
#> 360 An Exome-Wide Association Study Identifies New Susceptibility Loci for the Risk of Nicotine Dependence in African-American Populations
#> 361 Beta cell-specific CD8+ T cells maintain stem-cell memory-associated epigenetic programs during type 1 diabetes
#> 362 Beta cell-specific CD8+ T cells maintain stem-cell memory-associated epigenetic programs during type 1 diabetes (scATAC-seq)
#> 363 Beta cell-specific CD8+ T cells maintain stem-cell memory-associated epigenetic programs during type 1 diabetes (WGBS)
#> 364 VEGF-B Signaling Impairs Endothelial Glucose Transcytosis via an LDLR-dependent Decrease in Membrane Cholesterol Loading [HBMEC]
#> 365 A long noncoding RNA, LOC157273, is the effector transcript at the chromosome 8p23.1-PPP1R3B metabolic traits and type 2 diabetes risk locus
#> 366 Analysis of association between LPHN3 markers and substance use disorder
#> 367 BET bromodomain containing epigenetic reader proteins regulate vascular smooth muscle cell proliferation and neointima formation
#> 368 Skeletal muscle enhancer interactions identify genes controlling whole body metabolism in humans [RNA-seq]
#> 369 Skeletal muscle enhancer interactions identify genes controlling whole body metabolism in humans [cHiC-seq]
#> 370 Skeletal muscle enhancer interactions identify genes controlling whole body metabolism in humans [ChIP-seq]
#> 371 Single Cell RNA sequencing of MAFB +/+ and -/- cells at the pancreatic progenitor and beta-like stages
#> 372 Differential DNA methylation encodes proliferation and senescence programs in human adipose-derived mesenchymal stem cells
#> 373 A method for the generation of human stem cell-derived alpha cells
#> 374 Mendelian randomization identifies FLCN expression as a mediator of diabetic retinopathy
#> 375 Longitudinal DNA methylation differences precede type 1 diabetes
#> 376 Interfering with DNA replication improves beta cell differentiation and maturation from human pluripotent stem cells
#> 377 Transcriptional responses to TNF-alpha in germline A20 haploinsufficiency
#> 378 Pancreas single cell patch-seq links physiologic dysfunction in diabetes to transcriptomic phenotypes
#> 379 Molecular characterization of clonal human renal forming cells
#> 380 Sexually dimorphic methylation of CD3+ T-lymphocyte DNA in offspring of overweight and obese mothers in a high risk, minority population in the Bronx
#> 381 Whole genome bisulfite sequencing of human spermatozoa reveals differentially methylated patterns from type 2 diabetic patients
#> 382 A composite immune signature parallels disease progression across T1D subjects
#> 383 A composite immune signature parallels disease progression across T1D subjects (RNA-Seq Cohort 0 WB)
#> 384 5-Hydroxymethylcytosines in Circulating Cell-free DNA Reveal Vascular Complications of Type 2 Diabetes
#> 385 Genome Wide Analysis of Gene Expression Changes in Skin from Patients with Type 2 Diabetes
#> 386 Whole genome transcriptomics of pre-access veins and hemodialysis arteriovenous fistula (AVF) samples from two-stage AVF patients with different maturation outcomes (matured vs. failed)
#> 387 Angiogenin derived from ABCB5+ mesenchymal stem cells improves diabetic wound via enhancing angiogenesis
#> 388 Vascular Progenitors Generated from Tankyrase Inhibitor-Regulated Naive Diabetic Human iPSC Potentiate Efficient Revascularization of Ischemic Retina
#> 389 In vivo hyperglycemia exposure elicits distinct period-dependent effects on human pancreatic progenitor differentiation, conveyed by oxidative stress
#> 390 Skeletal muscle regeneration is compromised in advanced diabetic peripheral neuropathy
#> 391 Adipocyte serine uptake curbs ROS generation and visceral adiposity [Human]
#> 392 Knockdown PTPRN expression inhibits U87 cell line growth
#> 393 Neutrophil extracellular trap induced dendritic cell activation leads to Th1 polarization in type 1 diabetes
#> 394 Chromatin state of MCF-7 breast cancer cells treated with proteasome inhibitor MG132 [histone_mod_chip_seq]
#> 395 DNA methylation profiles in Taiwanese patients of Type-2 Diabetes (T2D) associated to Nephropathy (DN) and Retinopathy (DR)
#> 396 Fat_challenge_tests
#> 397 Profiling of RNAs from human islet-derived exosomes in a model of type 1 diabetes
#> 398 Loss of ER and nuclear envelope-associated neutral sphingomyelinase SMPD4 causes a severe neurodevelopmental disorder with microcephaly and congenital arthrogryposis
#> 399 bulk RNA-seq of human nucleusus pulposus from scoliosis patients
#> 400 Identified differentially expressed lncRNAs in Type 1 Diabetes Patients
#> 401 bulk RNA-seq of human nucleusus pulposus cell differentiations from embryonic stem cells and iPSCs
#> 402 Transient PAX8 Expression in Islets During Pregnancy Correlates With β-Cell Survival, Revealing a Novel Candidate Gene in Gestational Diabetes Mellitus.
#> 403 CD31 positive-extracellular vesicles from patients with type 2 diabetes: a miRNA signature
#> 404 Transcriptome analysis-identified long noncoding RNA CRNDE in maintaining endothelial cell proliferation, migration, and tube formation
#> 405 A MAFG-lncRNA axis links systemic nutrient abundance to hepatic glucose metabolism.
#> 406 A MAFG-lncRNA axis links systemic nutrient abundance to hepatic glucose metabolism: Liver RNA profiles of lean non-diabetic, obese non-diabetic as well as obese diabetic humans.
#> 407 Comparison of Kidney Transcriptomic Profiles of Early and Advanced Diabetic Nephropathy Reveals Potential New Mechanisms for Disease Progression
#> 408 Expression data for patients with myocardial infarction (MI) vs healthy patients
#> 409 Transcriptome analysis between primary and iPS-derived monocytes and macrophages and comparison of iPS-derived macrophages between CCR5 patients and healthy controls
#> 410 Transcriptome signatures reveal candidate key genes in the whole blood of patients with lumbar disc prolapse
#> 411 Inhibition of PARP 1 Protects Against Hyperglycemic-induced Neointimal Hyperplasia by Upregulating TFPI-2 Activity
#> 412 Urinary sediment transcriptomic and longitudinal data to investigate renal function decline in type 1 diabetes
#> 413 Identification of microRNA-dependent gene regulatory networks driving human pancreatic endocrine cell differentiation [Islet ATAC-Seq]
#> 414 Identification of microRNA-dependent gene regulatory networks driving human pancreatic endocrine cell differentiation [Islet RNA-Seq]
#> 415 miRNA142-3p targets Tet2 and impairs Treg differentiation and stability in models of type 1 diabetes
#> 416 miRNA142-3p targets Tet2 and impairs Treg differentiation and stability in models of type 1 diabetes
#> 417 Combined use of astragalus polysaccharide and berberine attenuates insulin resistance in IR-HepG2 cells via regulation of the gluconeogenesis signaling pathway
#> 418 Identification of microRNA-dependent gene regulatory networks driving human pancreatic endocrine cell differentiation [RNA-Seq III]
#> 419 miRNA142-3p targets Tet2 and impairs Treg differentiation and stability in models of type 1 diabetes
#> 420 Identification of microRNA-dependent gene regulatory networks driving human pancreatic endocrine cell differentiation [ATAC-seq]
#> 421 Identification of microRNA-dependent gene regulatory networks driving human pancreatic endocrine cell differentiation [H1 RNA-seq]
#> 422 Identification of microRNA-dependent gene regulatory networks driving human pancreatic endocrine cell differentiation [small RNA-seq]
#> 423 6mer seed toxicity in viral microRNAs
#> 424 Transcriptome profiling of subcutaneous and visceral adipose tissue from obese individuals
#> 425 Single Cell RNASeq profiling of stromal vascular fraction from Subcutaneous and visceral adipose tissue
#> 426 Multi-Parameter Analysis of Biobanked Human Bone Marrow Stromal Cells Shows Little Influence for Donor Age and Mild Comorbidities on Phenotypic and Functional Properties
#> 427 Signaling protein antibody microarray analyses for islets of control and IFT88 knockout mice [SET100]
#> 428 Phospho-antibody microarray analyses for islets of control and IFT88 knockout mice [PEX100]
#> 429 Human Islet Response to Selected Type 1 Diabetes Associated Bacteria
#> 430 Edematous Severe Acute Malnutrition is Characterized by Hypomethylation of DNA
#> 431 Cellular recruitment by podocyte-derived pro-migratory factors in assembly of the human renal filter
#> 432 Circular RNA expression profiling in diabetic foot ulcers and human normal acute wounds
#> 433 A global transcriptome analysis of human epidermal keratinocytes upon knockdown of hsa_circ_0084443
#> 434 Large-scale gene expression profiling of hepatocellular adenomas
#> 435 Placental Accreta Spectrum: Upregulated Cytotrophoblast DOCK4 Contributes to Over Invasion
#> 436 Genome-wide analysis of hepatic gene expression in patients with non-alcoholic steatohepatitis (NASH) before and after 1 year supplementation with n-3 polyunsaturated fatty acids (PUFA) from fishoil
#> 437 Effect of high glucose on transcriptomic expression of cholangiocarcinoma cells
#> 438 Comparative Analysis of the Transcriptome of Latent Autoimmune Diabetes (LADA) Patients from Eastern China
#> 439 The impact of pro-inflammatory cytokines on the β-cell regulatory landscape provides insights into the genetics of type 1 diabetes
#> 440 The impact of pro-inflammatory cytokines on the β-cell regulatory landscape provides insights into the genetics of type 1 diabtes [UMI-4C]
#> 441 The impact of pro-inflammatory cytokines on the β-cell regulatory landscape provides insights into the genetics of type 1 diabtes [H3K27ac ChIP-seq]
#> 442 The impact of pro-inflammatory cytokines on the β-cell regulatory landscape provides insights into the genetics of type 1 diabtes [ATAC-seq]
#> 443 Differential messenger RNA expression in Granulosa Cells from polycystic ovary syndrome with Normoandrogen and Hyperandrogen: Identification of gene sets through bioinformatic Filtering analysis
#> 444 Proteomics in gastroparesis: Unique and overlapping protein signatures in diabetic and idiopathic gastroparesis
#> 445 Metformin-induced alterations in peripheral blood cell trancriptome of healthy individuals
#> 446 The Single Cell Transcriptomic Landscape of Early Human Diabetic Nephropathy
#> 447 Systematic assessment of blood-borne microRNAs highlights molecular profiles of endurance sport and carbohydrate uptake
#> 448 Single-cell sequencing reveals the relationship between phenotypes and genotypes of Klinefelter syndrome
#> 449 Hyperglycemia promotes an aggressive phenotype in breast cancer cells
#> 450 Environmental Factors Influence the Epigenetic Signature of Newborns from Mothers with Gestational Diabetes
#> 451 miRNA-27b-3p and miRNA-1228-3p correlate with the progression of Kidney Fibrosis in Diabetic Nephropathy
#> 452 SC-beta Cell in vivo Maturation
#> 453 RNA sequencing human monocytes
#> 454 N6-methyladenosine (m6A) profiling of EndoC-bH1 cell line and RNA seq of Mettl14 knockout mice beta cell
#> 455 Phospho-antibody microarray analyses for islets of control and Mettl14 knock-out mice
#> 456 N6-methyladenosine (m6A) profiling of type II diabetes islets
#> 457 Hyperglycemia acts in synergy with hypoxia to maintain the pro-inflammatory phenotype of macrophages
#> 458 Transcriptome as marker for nutrition-related health: added value of time course analyses during challenge tests before and after energy restriction
#> 459 A transcriptomic analysis of primary mature adipocytes from lean, obese, and type 2 diabetic subjects: role of the extracellular matrix
#> 460 Point mutations in the PDX1 transactivation domain impair human β-cell development and function (RNA-Seq)
#> 461 Point mutations in the PDX1 transactivation domain impair human β-cell development and function (ChIP-Seq)
#> 462 Point mutations in the PDX1 transactivation domain impair human β-cell development and function (mRNA microarray)
#> 463 Metformin alters human host responses to Mycobacterium tuberculosis in-vitro and in healthy human subjects [PBMC RNA-Seq]
#> 464 Metformin alters human host responses to Mycobacterium tuberculosis in-vitro and in healthy human subjects [Ex vivo Blood RNA-Seq]
#> 465 scRNA-seq analysis of the dual expressors, B cells and T cells of a diabetes patient
#> 466 A single-nucleus RNA-sequencing pipeline to decipher the molecular anatomy and pathophysiology of human kidneys
#> 467 Phenotypic cooperation of a KCNQ2 exon 7 partial duplication and compound copy number variations in genes associated to a severe epileptic and neurodevelopmental delay
#> 468 Liver transcriptome of statin-treated patients
#> 469 Charting in vitro beta cell differentiation by single cell RNA sequencing
#> 470 Identification of metabolically distinct adipocyte progenitor cells in human adipose tissues
#> 471 Association of cord blood methylation with neonatal leptin: an epigenome wide association study
#> 472 MAIT cell RNA sequencing
#> 473 Host response to IAV infections in human patients
#> 474 RNA sequence data in whole cell extracts of differentiated human podocytes
#> 475 HNF1A deficiency impairs β-cell fate, granule maturation and function (scRNA-seq of 309 hESC-derived cells: Differentiation day 25)
#> 476 Acute Effects of Single Doses of Bonito Fish Peptides and Vitamin D on Whole Blood Gene Expression Levels
#> 477 Quantitative variation in m.3243A>G mutation produce discrete changes in energy metabolism
#> 478 ATAC-seq on human pancreatic islets
#> 479 Culture of mature adipocytes under a permeable membrane and comparative analysis with different cell culture models
#> 480 Plasma circulating extracellular RNAs in left ventricular remodeling post-myocardial infarction
#> 481 Novel risk variants affecting enhancers of TH1 and TREG cells in type 1 diabetes
#> 482 Novel risk variants affecting enhancers of TH1 and TREG cells in type 1 diabetes [RNA-seq]
#> 483 Novel risk variants affecting enhancers of TH1 and TREG cells in type 1 diabetes [ChIP-seq]
#> 484 A co-expression analysis of the placental transcriptome in association with maternal pre-pregnancy BMI and newborn birth weight
#> 485 HNF1A deficiency impairs β-cell fate, granule maturation and function
#> 486 Epigenetic modulation of β-cells by interferon-α via PNPT11-miR-26a-TET2 triggers autoimmune diabetes
#> 487 Epigenetic modulation of β-cells by interferon-α via PNPT11-miR-26a-TET2 triggers autoimmune diabetes [RNA-seq]
#> 488 Epigenetic modulation of β-cells by interferon-α via PNPT11-miR-26a-TET2 triggers autoimmune diabetes [methylation array]
#> 489 Serological autoantibody profiling of type 1 diabetes by protein arrays.
#> 490 ChIA-PET from MSiPS (ENCSR778FXH)
#> 491 ChIA-PET from fibroblast (ENCSR732QOH)
#> 492 ChIA-PET from MSLCL (ENCSR452NHL)
#> 493 Cell type-specific immune phenotypes predict loss of insulin secretion in new-onset type 1 diabetes
#> 494 B lymphocyte alterations accompany abatacept resistance in new-onset type 1 diabetes
#> 495 Transcriptomic Profiling of Trophoblast Fusion Using BeWo and JEG-3 Cell Lines
#> 496 Integrated analysis of genetic variants regulating retinal transcriptome (GREx) identifies genes underlying age-related macular degeneration
#> 497 Preeclamptic placentae release factors that damage neurons in vitro
#> 498 Short-term low calorie diet remodels skeletal muscle lipid profile and metabolic gene expression in obese adults
#> 499 Metformin reverses gene expression signautres in hyperglycaemics endothelial cells
#> 500 DNA Hypermethylation at Loci Associated with Diabetes, Obesity and Cardiac Abnormalities in CD3+ Lymphocytes of Intrauterine Growth Restricted Newbors
#> 501 The diurnal rhythm of adipose tissue gene expression is reduced in obese patients with type 2 diabetes
#> 502 Transcriptomics analysis of Colon tumor xenograft model in streptozotocin-induced diabetic mice
#> 503 Transcriptomics analysis of paired tumor and normal mucosa samples in a cohort of patients with colon cancer, with and without T2DM.
#> 504 Patient adipose stem cell-derived adipocytes reveal genetic variation that predicts anti-diabetic drug response
#> 505 Circulating Exosomal miR-20b-5p is Elevated in Type 2 Diabetes and Could Impair Insulin Action in Human Skeletal Muscle.
#> 506 EndoC-βH1 multiomic profiling defines gene regulatory programs intrinsic to human β cell identity and function
#> 507 Genome-Wide Analyses Identify Filamin-A (FLNA) as a Novel Downstream Target for Insulin and IGF1 Action.
#> 508 miRNA seq of feto-placental arterial endothelial cells (pfEC) after normal pregnancy vs pregnancy complicated by gestational diabetes (GDM)
#> 509 Identification of molecular signatures of cystic fibrosis disease status using plasma-based functional genomics
#> 510 High-resolution map of copy number variations in motor cortex of Control and Sporadic Amyotrphic Lateral Sclerosis patients by using a customized exon-centric comparative genomic hybridization array.
#> 511 Genome–wide gene expression profile of adipocytes and infiltration macrophages obtained from abdominal (visceral and subcutaneous) and peripheral (thigh) adipose depots from Normal Glucose Tolerant and Type 2 Diabetics Asian Indians
#> 512 Affymetrix microarray analysis of the effects of isonicotinamide on HEK293 cells
#> 513 Affymetrix microarray analysis of the effects of nicotinamide on HEK293 cells
#> 514 Primate fetal hepatic response to maternal obesity: epigenetic signaling pathways and lipid accumulation [gene expression]
#> 515 ENPP6 as a neural regulator of visceral adiposity
#> 516 Single cell transcriptome profiling of mouse and hESC-derived pancreatic progenitors
#> 517 Diabetes Remission Using Glucose-Responsive Insulin-Producing Human alpha-Cells
#> 518 Expression analysis of λH1-hESC derived β-like cells
#> 519 Diabetes Remission Using Glucose-Responsive Insulin-Producing Human α-Cells
#> 520 Therapeutic potential of targeting miR-141 in intervertebral disc degeneration: first steps toward the clinic
#> 521 Elevated T cell levels in peripheral blood predict poor clinical response following rituximab treatment in new-onset type 1 diabetes
#> 522 Effects of Cadmium Exposure on DNA Methylation at Imprinting Control Regions and Genome-Wide in Mothers and Newborn Children.
#> 523 Single-cell RNA sequencing enables transcriptomic analysis of iPSC-derived beta-cells in a model of neonatal diabetes caused by insulin mutations.
#> 524 Integrative molecular and clinical analysis of intrahepatic cholangiocarcinoma reveals two prognostic subclassees
#> 525 Abnormal neutrophil signature in the blood and pancreas of pre-symptomatic and symptomatic type 1 diabetes
#> 526 Pan-senescence transcriptome analysis identified RRAD as a marker and negative regulator of cellular senescence
#> 527 Profiling of vascular organoid endothelial cells and pericytes from iPS cells
#> 528 Stable oxidative cytosine modifications accumulate in cardiac mesenchymal cells from Type2 diabetes patients: rescue by alpha-ketoglutarate and TET-TDG
#> 529 Stable oxidative cytosine modifications accumulate in cardiac mesenchymal cells from Type2 diabetes patients: rescue by alpha-ketoglutarate and TET-TDG functional reactivation [human cells RNA-seq]
#> 530 Diabetes Mellitus Drives Extracellular Vesicle Secretion and Promotes Increased Internalization by Circulating Leukocytes
#> 531 Specific targeting of the common gamma chain blocks cooperative reprogramming of human tissue-resident cytotoxic T lymphocytes by IL-15 and IL-21
#> 532 Human Pancreatic Islets Expressing HNF1A Variant Have Defective β cell Transcriptional Regulatory Networks
#> 533 High-throughput single cell transcriptome analysis and CRISPR screen identify key β cell-specific disease genes
#> 534 NTPDase3 antibody targets adult human pancreatic β-cells for in vitro and in vivo analysis
#> 535 Identification of early biological changes in palmitate-treated isolated human islets
#> 536 Gene array of laser capture microdissectioned human diabetic vs non-diabetic plaque macrophages
#> 537 Circadian misalignment induces fatty acid metabolism gene profiles and induces insulin resistance in human skeletal muscle.
#> 538 JCAD/KIAA1462, a coronary artery disease-associated gene product, regulates endothelial function
#> 539 Conventional and neo-antigenic peptides naturally processed and presented by beta cells are targeted by circulating naïve CD8+ T cells in type 1 diabetic and healthy donors
#> 540 Human Feto-placental Arterial and Venous Endothelial Cells are Differentially Programmed by Gestational Diabetes Mellitus Resulting in Cell-specific Barrier Function
#> 541 Human Feto-placental Arterial and Venous Endothelial Cells are Differentially Programmed by Gestational Diabetes Mellitus Resulting in Cell-specific Barrier Function Changes
#> 542 Endothelial cells derived from iPSC in response to diabetic medium
#> 543 Exon Level Expression Profiling of Diabetic Nephropathy
#> 544 Functional Genomics Analysis of Islets from Recent and Longstanding T1D Reveals the Need for Distinct Approaches to Therapy
#> 545 Innate immune activity differentiate subtypes in new onset Type 1 diabetes that predict duration of the post-onset partial remission and correlate with responsiveness to CTLA4-Ig treatment
#> 546 A new axis linking diabetes to cancer: Glucose regulates tumor suppressor TET2 and 5hmC through AMPK
#> 547 Expression data of A2058-TET2WT, A2058-TET2M, and Mock cells treated under high-g and normal-g
#> 548 Dysregulated circRNAs in plasma from active tuberculosis patients
#> 549 Fine-mapping and functional studies highlight potential causal variants for rheumatoid arthritis and type 1 diabetes
#> 550 RNA Expression data for early diabetic nephropathy (EDN)
#> 551 Discovering human diabetes-risk gene function with genetics and physiological assays
#> 552 Propargite, an environmental chemical, interacts with GWAS identified diabetes genes to impact human pancreatic β-cell death
#> 553 Propargite, an environmental chemical, interacts with GWAS identified diabetes genes to impact human pancreatic β-cell death [PTPN2 knockout]
#> 554 Propargite, an environmental chemical, interacts with GWAS identified diabetes genes to impact human pancreatic β-cell death [propargite treatment]
#> 555 CRISPR/Cas9-targeted removal of unwanted sequences from small-RNA sequencing libraries
#> 556 Association of Elevated Urinary miR-126, miR-155 and miR-29b with Diabetic Kidney Disease
#> 557 Expression data from childhood obesity
#> 558 Integrated transcriptomics network analysis of miRNA and mRNA in human myometrium in term and preterm labor.
#> 559 Effect of TDNC1 ectopic expression on global gene expression pattern
#> 560 A global transcriptome analysis of human epidermal keratinocytes upon inhibition of lncRNA WAKMAR1
#> 561 Discovery of a Drug Candidate for GLIS3-Associated Diabetes
#> 562 The role of CFTR in islet function
#> 563 Pure epicatechin and inflammatory gene expression profiles in circulating immune cells in (pre) hypertensive adults; a randomized double-blind, placebo-controlled, crossover trial
#> 564 Differential metabolic effects of insulin detemir versus NPH in patients with type 2 diabetes
#> 565 Effect of rosiglitazone treatment on insulin sensitivity in type 2 diabetic patients skeletal muscle
#> 566 De novo reconstruction of human adipose reveals conserved lncRNAs as regulators of brown adipogenesis
#> 567 Altered adipose lipid mobilization predicts long-term weight gain and impaired glucose metabolism
#> 568 Bioinformatics analysis of microRNAs related to blood stasis syndrome in diabetes mellitus patients
#> 569 Bioinformatics analysis of transcriptome related to blood stasis syndrome in diabetes mellitus patients
#> 570 GABA regulates release of inflammatory cytokines from peripheral blood mononuclear cells and CD4+ T cells and is immunosuppressive in type 1 diabetes
#> 571 Placental methylation arrays for the assessment of epigenetic regulation in transcriptional subtypes of human preeclampsia
#> 572 Genome-wide analysis of PDX1 target genes in human pancreatic progenitors [expression profiling]
#> 573 Transcriptomes of iPSC-derived and post-mortum Hypothalamus Neurons from obese and control donors
#> 574 The lipodystrophic hotspot lamin A p.R482W mutation deregulates the mesodermal inducer T/Brachyury and early vascular differentiation gene networks
#> 575 Unique Circulating MicroRNA profiles in HIV Infection
#> 576 Heart failure patients' peripheral blood mononuclear cell gene expression profiles before mechanical circulatory support
#> 577 A SRp55-regulated alternative splicing network controls pancreatic beta cell survival and function
#> 578 Expression data from SOX9 overexpressing EndoC-ßH1 cells
#> 579 Expression data from PolyIC treated EndoC-ßH1 cells
#> 580 Expression profiling of circular RNAs in human islet samples
#> 581 FABP4 overexpressed in intratumoral hepatic stellate cells within hepatocellular carcinoma with metabolic risk factors (part 2)
#> 582 FABP4 overexpressed in intratumoral hepatic stellate cells within hepatocellular carcinoma with metabolic risk factors (part 1)
#> 583 Tubulointerstitial transcriptome from ERCB subjects with chronic kidney disease and living donor biopsies.
#> 584 Glomerular Transcriptome from European Renal cDNA Bank subjects and living donors
#> 585 Transcription factors operate across disease loci: EBNA2 in autoimmunity
#> 586 Asynchronous remodeling is a driver of failed regeneration in Duchenne muscular dystrophy
#> 587 Valproic acid attenuates hyperglycemia induced complement and coagulation cascade gene expression
#> 588 Dermal endothelial cells of type 2 diabetic patients
#> 589 RNA sequencing data of whole blood cells of normal glucose tolerant (NGT) and gestational diabetes (GDM) pregnant women
#> 590 Genomic Profiling of Diabetic Foot Ulcers Identifies miR-15b-5p as a Major Regulator that Leads to Suboptimal Inflammatory Response and Diminished DNA Repair Mechanisms
#> 591 α Cell Function and Gene Expression Are Compromised in Type 1 Diabetes
#> 592 Affymetrix profiling of IMIDIA biobank samples from organ donors and partially pancreatectomized patients
#> 593 Affymetrix profiling of IMIDIA biobank samples from organ donors and partially pancreatectomized patients [partially pancreatectomized patient cohort]
#> 594 Affymetrix profiling of IMIDIA biobank samples from organ donors and partially pancreatectomized patients [organ donor cohort]
#> 595 HDAC inhibitor SAHA reverses inflammatory gene expression in diabetic endothelial cells
#> 596 RNA-seq in neutrophils from patients with intracranial aneurysms
#> 597 Glucose inhibits cardiac maturation through nucleotide biosynthesis
#> 598 Altered intestinal functions and increased local inflammation in insulin-resistant obese subjects: a gene-expression profile analysis.
#> 599 Clinical Evidence Supports a Protective Role for CXCL5 in Coronary Artery Disease Progression in the Elderly
#> 600 Gene expression data from Phase 2 of the SAMARA study (Supporting a Multi-disciplinary Approach to Researching Atherosclerosis)
#> 601 Genotyping data from Phase 2 of the SAMARA study (Supporting a Multi-disciplinary Approach to Researching Atherosclerosis)
#> 602 Small RNA-seq analysis of circulating miRNAs to identify phenotypic variability in Friedreich's ataxia patients
#> 603 Single cell transcriptome analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns.
#> 604 Real-time quantitative PCR analysis of 232 microRNAs in human oral tissues
#> 605 Small RNA-seq during acute maximal exercise reveal RNAs involved in vascular inflammation and cardiometabolic health
#> 606 Discovery and validation of a gene expression profile for human islet integrity and transplant functionality
#> 607 DNA methylation in blood from neonatal screening cards and the association with BMI and insulin sensitivity in early childhood
#> 608 Plasma-derived exosome characterization reveals a distinct microRNA signature in long duration Type 1 diabetes
#> 609 Preclinical evaluation of the BET bromodomain inhibitor BAY 1238097 for the treatment of lymphoma
#> 610 lncRNA Expression Signatures in Response to Jiangtang Tiaozhi Formular in T2DM with Obesity and Hyperlipidemia
#> 611 Global gene expression profiling and senescence biomarker analysis of hESC exposed to H2O2 induced non-cytotoxic oxidative stress
#> 612 Effects of isoxazole (ISX) on long-term cultured human islets
#> 613 Transcriptomic profile in lymphomonocytes of healthy subjects identifies an early signature of insulin resistance and related diseases
#> 614 Transcriptome-based network analysis reveals renal cell type-specific dysregulation of hypoxia-associated transcripts
#> 615 Transcriptome-based network analysis reveals renal cell type-specific dysregulation of hypoxia-associated transcripts [glomeruli]
#> 616 Transcriptome-based network analysis reveals renal cell type-specific dysregulation of hypoxia-associated transcripts [Tub-FE]
#> 617 Dysregulation of a miR-23b/27b-p53 axis impairs muscle differentiation in humans with type 2 diabetes
#> 618 Single-cell transcriptomics of East-Asian pancreatic islets cells
#> 619 DNA methylation profiles in sibling pairs discordant for intrauterine exposure to maternal gestational diabetes
#> 620 Proteomic Comparison of Acute Myocardial Infarction and Stress Cardiomyopathy in Women
#> 621 Acute Exercise Bout Effects on GH and IGF1 in Prediabetic and Healthy African Americans
#> 622 Human monocyte subsets are transcriptionally and functionally altered in aging in response to pattern recognition receptor agonists [ExVivo]
#> 623 Human monocyte subsets are transcriptionally and functionally altered in aging in response to pattern recognition receptor agonists [InVitro]
#> 624 Entrainment of Breast Cell Lines Results in Rhythmic Fluctuations of MicroRNAs
#> 625 Effect of hyperglycemia on the transcriptional profile of primary human macrophages
#> 626 Expression data from liver of obese patients
#> 627 IL-6/Stat3-Dependent Induction of Distinct, Obesity-Associated Natural Killer Cells Deteriorates Energy and Glucose Homeostasis
#> 628 Glucose impairs tamoxifen sensitivity modulating CTGF in breast cancer cells
#> 629 Using hESCs to Probe the Interaction of CDKAL1 and MT1E, Two GWAS identified Diabetes Associated Genes
#> 630 Rader HHDL and BioBank genotyping
#> 631 Sirt6 Deficiency Exacerbates Podocyte Injury and Proteinuria through Targeting Notch Signaling
#> 632 Serum miRNAs from Drug-induced liver injury, Hepatitis B, Liver cirrhosis and Type 2 Diabetes patients
#> 633 Single cell RNA-seq reveals expansion of IGRP-reactive CD4+ T cells in recent onset type I diabetes
#> 634 Single cell RNA-seq reveals expansion of IGRP-reactive CD4+ T cells in recent onset type I diabetes (single-cell RNA-seq of CD4+ pooled islet antigen-reactive T cells)
#> 635 Single cell RNA-seq reveals expansion of IGRP-reactive CD4+ T cells in recent onset type I diabetes (single-cell RNA-seq of CD8+ influenza-reactive T cells)
#> 636 Single cell RNA-seq reveals expansion of IGRP-reactive CD4+ T cells in recent onset type I diabetes (bulk RNA-seq of T-cell clone)
#> 637 Single cell RNA-seq reveals expansion of IGRP-reactive CD4+ T cells in recent onset type I diabetes (single-cell RNA-seq of T-cell clone)
#> 638 Glucose upregulates a limited number of genes in human beta cells.
#> 639 Acute and chronic treatment of trametinib plus lapatinib in patient-derived xenografts (PDX) of pancreatic adenocarcinoma (PDAC)
#> 640 A DNA methylation atlas of the human eye and its diseases
#> 641 Aberrantly Expressed Long Non-coding RNAs In CD8+ T Cells Response to Active Tuberculosis
#> 642 Prenatal Pesticide Exposure Interacts with a Common Polymorphism in the PON1 Gene Leading to DNA Methylation Changes
#> 643 Characterizing the global changes in miRNA expression in human atrial appendages with persistent atrial fibrillation.
#> 644 Circulating miRNAs for gestational diabetes mellitus
#> 645 RNA-sequencing of human skeletal myocytes from healthy, obese, and type 2 diabetic subjects
#> 646 Transcriptomic Analysis of Endothelial Cells from Fibrovascular Membranes in Proliferative Diabetic Retinopathy
#> 647 Epigenetic signatures of gestational diabetes mellitus on ATP5A1, PRKCH, SLC17A4 and HIF3A cord blood methylation
#> 648 Enhanced Protein Translation Underlies Improved Metabolic and Physical Adaptations to Different Exercise Training Modes in Young and Old Humans
#> 649 Microarray analysis of CD9high and CD9low progenitors isolated from adipose tissue
#> 650 Microarray analysis of CD9high and CD9low progenitors isolated from omental adipose tissue of morbid obese individuals
#> 651 Interaction between mitoNEET and NAF-1 in cancer cells
#> 652 Open chromatin profiling of human postmortem brain infers functional roles for non-coding schizophrenia loci
#> 653 Differential expression analysis between Microadenoma and Macroadenoma in Cushing's Disease
#> 654 Transcriptional profiling of diabetic peripheral neuropathy patients, diabetic patients, and healthy participants
#> 655 Tissue-Specific and Genetic Regulation of Insulin Sensitivity-Associated Transcripts in African Americans [skeletal muscle]
#> 656 Tissue-Specific and Genetic Regulation of Insulin Sensitivity-Associated Transcripts in African Americans [subcutaneous adipose]
#> 657 Characterization of molecular functions, pathways and protein classes affected by aging-related changes of miRNA expression in peripheral blood mononuclear cells
#> 658 Pathogenic Implications for Autoimmune Mechanisms Derived by Comparative eQTL Analysis of CD4+ Versus CD8+ T cells
#> 659 Serum microRNA profile of human type 1 diabetes mellitus
#> 660 Impact of Visceral Fat Adiposity on Gene Expression Profile in Peripheral Blood Cells
#> 661 Adipose tissue gene expression is differentially regulated with different rates of weight loss in overweight and obese humans
#> 662 Transcriptional Profiling of Dysregulated lncRNAs in B cells Response to Active Tuberculosis
#> 663 Artemisinins target GABA receptor signaling to induce alpha to beta cell transdifferentiation
#> 664 Epigenome-wide association in the METSIM cohort identifies 22 novel loci for diabetes and metabolic syndrome traits
#> 665 Serum microRNA signatures are indicative of skeletal fractures in post-menopausal women with and without type 2 diabetes and influence osteo-genic differentiation of mesenchymal stem cells
#> 666 Sequential global gene expression analysis of glucose stimulated human islets
#> 667 Transcriptome profiles of differentiated hepatoma cells infected with oncogenic hepatitis C virus
#> 668 Healthy glucocorticoid receptor N363S SNP carriers and metabolic syndrome
#> 669 Partially exhausted CD8+ T cells are associated with clinically beneficial response to Teplizumab in new onset type I diabetes (single-cell RNA-seq of sorted CD8+ T-cells)
#> 670 Partially exhausted CD8+ T cells are associated with clinically beneficial response to Teplizumab in new onset type I diabetes (microarray)
#> 671 Conserved recurrent gene mutations correlate with pathway deregulation and clinical outcomes of lung adenocarcinoma in never-smokers
#> 672 Partially exhausted CD8+ T cells are associated with clinically beneficial response to Teplizumab in new onset type I diabetes
#> 673 Partially exhausted CD8+ T cells are associated with clinically beneficial response to Teplizumab in new onset type I diabetes (whole blood RNA-seq)
#> 674 Partially exhausted CD8+ T cells are associated with clinically beneficial response to Teplizumab in new onset type I diabetes (bulk RNA-seq of sorted CD8+ T-cells)
#> 675 Gene expression in the peripheral whole blood of established Type 1 diabetes patients
#> 676 Single cell transcriptomics defines human islet cell signatures and reveals cell-type-specific expression changes in type 2 diabetes
#> 677 Single cell transcriptomics defines human islet cell signatures and reveals cell-type-specific expression changes in type 2 diabetes [single cell]
#> 678 Single cell transcriptomics defines human islet cell signatures and reveals cell-type-specific expression changes in type 2 diabetes [bulk]
#> 679 Genome-wide analysis of hepatic gene expression in patients with non-alcoholic fatty liver disease and in healthy donors in relation to hepatic fatty acid composition and other nutritional factors
#> 680 Pleiotropic Analysis of Lung Cancer and Blood Triglycerides
#> 681 Transcriptome comparison of PAX6 ablated mouse beta cells to WT beta cells, ChIP-seq analysis of PAX6 bound sites both in mouse and human beta cell lines (Min6 and EndoC), and ChIP-seq analysis fo histone mark H3K9ac on mouse pancreatic beta cells.
#> 682 Differences in genome-wide gene expression response in PBMCs between young and old men upon caloric restriction
#> 683 Hepatocyte Nuclear Factor 1 coordinates multiple functions of intestinal epithelial cells
#> 684 Continuous Aging of the Human DNA Methylome Throughout the Human Lifespan
#> 685 A single-cell transcriptome atlas of the human pancreas [CEL-seq2]
#> 686 Potential Epigenetic Biomarkers of Obesity Related Insulin Resistance in Human Whole Blood
#> 687 Integrative Analysis of miRNA and mRNA Paired Expression Profiling of Primary Fibroblast Derived from Diabetic Foot Ulcers Reveals Multiple Impaired Cellular Functions
#> 688 A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure
#> 689 Comparative study of the transcriptome of HUVECs from infants born to mothers diagnosed with GDM and controls.
#> 690 Enhanced T cell responses to IL-6 in type 1 diabetes are associated with early clinical disease and increased IL-6 receptor expression
#> 691 Differentially Expressed Gene Transcripts Using RNA Sequencing from the Blood of Immunosuppressed Kidney Allograft Recipients
#> 692 Genome-wide RNA-sequencing of human islets 48 hour after transduction with adenoviruses expressing either GFP (control), or histone chaperone ASF1B.
#> 693 Expression profiling of cutaneous squamous cell carcinoma with perineural invasion implicates the p53 pathway in the process
#> 694 RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes
#> 695 Response of peripheral blood mononuclear cells from 20 healthy donor and 15 patients with type 1 diabetes to type 1 diabetogenic protein (IGRP, PPI) derived peptide stimulation.
#> 696 Response of peripheral blood mononuclear cells from 15 healthy donor and 15 patients with type 1 diabetes to type 1 diabetogenic protein (GAD65, IGRP, PPI, ZnT8) and influenza virus M derived peptide stimulation.
#> 697 The expression profiling of circular RNAs (circRNA) in human intervertebral disc degeneration (IDD)
#> 698 Tacrolimus in diabetic nephropathy
#> 699 The epigenetic signature of systemic insulin resistance in obese women [SC]
#> 700 The epigenetic signature of systemic insulin resistance in obese women [OM]
#> 701 The epigenetic signature of systemic insulin resistance in obese women [BL]
#> 702 Conversion of Human Gastric Epithelial Cells to Multipotent Endodermal Progenitors using Defined Small Molecules [array]
#> 703 Conversion of Human Gastric Epithelial Cells to Multipotent Endodermal Progenitors using Defined Small Molecules [DNA methylation]
#> 704 Conversion of Human Gastric Epithelial Cells to Multipotent Endodermal Progenitors using Defined Small Molecules [gene expression]
#> 705 High-throughput sequencing reveals key genes and immune homeostatic pathways activated in myeloid dendritic cells by Porphyromonas gingivalis 381 and its fimbrial mutants
#> 706 Discovery of a Drug that Targets a Diabetes Gene identified by GWAS
#> 707 Single cell RNA-seq of human pancreatic endocrine cells from Juvenile, adult control and type 2 diabetic donors.
#> 708 DNA methylation anaylsis of placenta samples exposed to variable levels of arsenic during pregnancy
#> 709 Revisiting the microRNA expression profiling of human intervertebral disc degeneration (IDD)
#> 710 TGFβ contributes to impaired exercise response by suppression of mitochondrial key regulators in skeletal muscle
#> 711 A single-cell transcriptome atlas of the human pancreas
#> 712 RNA sequencing of pancreatic adenocarcinoma tumors yields novel expression patterns associated with long-term survival and reveals a role for *ANGPTL4*
#> 713 RNA-sequencing of human pancreatic adenocarcinoma cancer tissues
#> 714 Novel Regions of Variable DNA Methylation in Human Placenta associated with Newborn Neurobehavioral Traits
#> 715 Hyperglycemia induced microRNAs in endothelial dysfunction
#> 716 Comparsion of IGRP reactive CD8 T cell clones
#> 717 DNA-methylation profiling of Whole blood genomic DNAs collected at EDIC baseline and monocytes at EDIC years 16/17 yrs from participants of DCCT/EDIC study
#> 718 DNA-methylation profiling of monocyte genomic DNAs collected from participants of Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study
#> 719 DNA-methylation profiling of Whole blood genomic DNAs collected from participants of Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study
#> 720 Nfib promotes Metastasis through a Widespread Increase in Chromatin Accessibility
#> 721 Nfib promotes Metastasis through a Widespread Increase in Chromatin Accessibility [ATAC-seq]
#> 722 5-hydroxymethylcytosine-mediated alteration of transposon activity associated with Preeclampsia
#> 723 Embryonic atrazine exposure alters zebrafish and human miRNAs associated with angiogenesis, cancer, and neurodevelopment
#> 724 Epigenomic landscapes of human primary pancreatic cell types
#> 725 Study of Topoisomerase I in human
#> 726 Methylome-wide analysis of chronic HIV infected patients and healthy controls
#> 727 Generation of Stem Cell-Derived β Cells from Type 1 Diabetic Patients
#> 728 Altered DNA methylation of glycolytic and lipogenic genes in liver from obese and type 2 diabetic patients [methylome analysis]
#> 729 Altered DNA methylation of glycolytic and lipogenic genes in liver from obese and type 2 diabetic patients [transcriptome analysis]
#> 730 fibroblasts and iPS cells from patients with insulin receptor mutations
#> 731 Omics profiling of 21 novel primary and metastatic colorectal cancer cell lines
#> 732 SNP-array profiling of 21 novel primary and metastatic colorectal cancer cell lines [Illumina HumanExome-12 v1.2 BeadChip]
#> 733 SNP-array profiling of 21 novel primary and metastatic colorectal cancer cell lines [Illumina HumanExome-12 v1.0 BeadChip]
#> 734 Systematic Evaluation Of Genes And Genetic Variants Associated With Type 1 Diabetes Susceptibility
#> 735 Gene expression response to mitochondrial DNA depletion
#> 736 Gene expression profiling in human precision-cut liver slices upon treatment with the FXR agonist obeticholic acid [human]
#> 737 Perivascular Progenitor Cells Derived from Human Embryonic Stem Cells Exhibit Functional Characteristics of Pericytes, and Improve the Retinal Vasculature in a Rodent Model of Diabetic Retinopathy
#> 738 Integrated Analysis of Dysregulated lncRNA and mRNA Expression profiles of myocardial sleevesof pulmonary veins in atrial fibrillation
#> 739 Maternal-diabetes induced gene expression changes in the umbilical cord
#> 740 Palmitate-induced gene expression in human gingival fibroblasts (HGF)
#> 741 An integrative analysis of renal miRNA- and mRNA-expression signatures in progressive chronic kidney disease
#> 742 An integrative analysis of renal miRNA- and mRNA-expression signatures in progressive chronic kidney disease [validation cohort]
#> 743 An integrative analysis of renal miRNA- and mRNA-expression signatures in progressive chronic kidney disease [discovery cohort]
#> 744 Altered microRNA expression in individuals at high risk of type 1 diabetes
#> 745 Integration of ATAC-seq and RNA-seq Identifies Human Alpha Cell and Beta Cell Signature Genes
#> 746 Tetraspanin-2 promotes glucotoxic apoptosis by regulating JNK/β-catenin signaling pathway in human pancreatic β-cells
#> 747 Rader HHDL genotyping
#> 748 Human skeletal muscle gene expression analysis on Lean, obese insulin sensitive, obese insulin resistant and obese Type II diabetic subjects
#> 749 Tissue Transcriptome Driven Identification of Epidermal Growth Factor as a Chronic Kidney Disease Biomarker
#> 750 Mexican Patients with Breast Cancer
#> 751 Gene Expression of Mexican Patients with Breast Cancer
#> 752 miRNAs Expression of Mexican Patients with Breast Cancer
#> 753 Capture Hi-C reveals novel candidate genes and complex long-range interactions with related autoimmune risk loci
#> 754 Blood transcriptional biomarkers for active TB among US patients: A case-control study with systematic cross-classifier evaluation.
#> 755 Peripheral blood transcriptome profiles from an RNA Pilot Study within the United States Health and Retirement Study (HRS)
#> 756 Near-whole-genome transcriptome analysis of gene expression in human skeletal muscle tissue at baseline in obese individuals with Type 2 Diabetes
#> 757 Pancreatic Beta Cell Enhancers Regulate Rhythmic Transcription of Exocyst Triggering and Diabetes
#> 758 Genome-wide Circadian Control of Transcription at Active Enhancers Regulates Insulin Secretion and Diabetes Risk
#> 759 Expression data from MSC-treated monocytes
#> 760 Transcriptome profile of peripheral blood from pancreatic ductal adenocarcinoma patients
#> 761 miRNA profile in the vitreous of proliferative vitreoretinal disease patients
#> 762 Gene expression in the pancreas of healthy control, auto-antibody positive, and type 1 diabetic subjects
#> 763 Chlorella ingestion and suppression of resistin gene expression in borderline diabetics: a randomized, placebo-controlled study
#> 764 Genetic and epigenetic variation in the lineage specification of regulatory T cells
#> 765 Plasma induced signatures reveal an extracellular milieu possessing an immunoregulatory bias in treatment naïve inflammatory bowel disease
#> 766 Canakinumab treatment for recent-onset type 1 diabeties mellitus: a multicenter randomized, placebo-controlled trial
#> 767 Interleukin-1 receptor antagonist for recent-onset type 1 diabeties mellitus: a multicenter randomized, placebo-controlled trial
#> 768 Influence of muscle activity on paralyzed muscle
#> 769 Preserved DNA Damage Checkpoint Pathway Protects From Complications in Long-standing Type 1 Diabetes
#> 770 Epigenome-wide and Transcriptome-wide Analyses Reveal Gestational Diabetes is Associated with Alterations in the Human Leukocyte Antigen Complex
#> 771 Epigenome-wide and Transcriptome-wide Analyses Reveal Gestational Diabetes is Associated with Alterations in the Human Leukocyte Antigen Complex [gene expression]
#> 772 Epigenome-wide and Transcriptome-wide Analyses Reveal Gestational Diabetes is Associated with Alterations in the Human Leukocyte Antigen Complex [methylation]
#> 773 Expression data from insulin-treated human primary fibroblasts and effects of U0126 on insulin-induced gene expression
#> 774 Gene Expression Profiling in Omental Adipose Tissue of Morbidly Obese Diabetic African Americans
#> 775 Inhibition of ZEB1 expression induces redifferentiation of adult human β cells expanded in vitro
#> 776 Comparative analysis of gene expression profiles in lymphoma cells after treatment by Dexamethasone or CpdA
#> 777 Comparative analysis of gene expression profiles in prostate cancer cells after treatment by Dexamethasone or CpdA
#> 778 BisPCR2 method for targeted bisulfite sequencing
#> 779 Gene expression annalysis of peripheral blood cells in patients with chronic kidney disease
#> 780 Genome-wide blood transcriptional profiling in critically ill patients - MARS consortium
#> 781 Cold acclimation improves insulin sensitivity in patients with type 2 diabetes mellitus.
#> 782 Effect of type 2 diabetes on transcriptional signatures during exercise and recovery
#> 783 Differences in platelet miRNA profiles between patients with coronary artery disease and healthy controls.
#> 784 miRNA regulation in diabetes associated impaired wound healing
#> 785 Novel Observations from Next Generation RNA Sequencing of Highly Purified Human Adult and Fetal Islet Cell Subsets
#> 786 A Blood Transcriptional Diagnostic Assay for Septicemic Melioidosis
#> 787 Genetic background of immune complications
#> 788 Differential mRNA expression profile regulated by HNF4α in Hep3B cells
#> 789 Fibroblast growth factor 21 is elevated in metabolically unhealthy obesity and affects lipid deposition, adipogenesis, and adipokine secretion of human abdominal subcutaneous adipocytes
#> 790 Skeletal muscle gene expression changes with exercise mode, duration and intensity: STRRIDE study
#> 791 caArray_beer-00153: Gene-expression profiles predict survival of patients with lung adenocarcinoma
#> 792 Gene expression responses to chronic low dose arsenite exposure
#> 793 Comparative genomic, microRNA, and tissue analyses reveal subtle differences between non-diabetic and diabetic foot skin
#> 794 Comparative genomic, microRNA, and tissue analyses reveal subtle differences between non-diabetic and diabetic foot skin [nanoString nCounter miR expression assay]
#> 795 Comparative genomic, microRNA, and tissue analyses reveal subtle differences between non-diabetic and diabetic foot skin [microRNA PCR panel]
#> 796 Comparative genomic, microRNA, and tissue analyses reveal subtle differences between non-diabetic and diabetic foot skin [gene expression]
#> 797 MicroRNA signature in skeletal muscle in type 2 Diabetes and insulin resistant rats
#> 798 MicroRNA signature in skeletal muscle in type 2 Diabetes [human]
#> 799 Differential adipose tissue gene expression profiles in abacavir treated patients that may contribute to cardiovascular risk: a microarray study
#> 800 Salivary Transcriptomic Biomarkers for Insulin Resistance
#> 801 RNA-sequencing of healthy human skeletal myocytes
#> 802 Transcriptome analysis of Myotonic Dystrophy type 2 (DM2) patients.
#> 803 Noncoding RNAs in human intervertebral disc degeneration: an integrated microarray study
#> 804 Mitoscriptome analysis to understand the pathogenesis of Diabetic Retinopathy using tissue microarray
#> 805 Age-associated DNA methylation changes within 5 years after birth in human blood leukocytes
#> 806 Genome-Wide Gene Expression Profiles in the Pancreatic Lymph Nodes of At-Risk Autoantibody Positive Individuals
#> 807 A Whole Blood Molecular Signature for the Identification of Acute Myocardial Infarction Without Relying Upon Myonecrosis (microarray)
#> 808 Blood methylomic signatures of pre-symptomatic dementia in elderly subjects with Type 2 Diabetes Mellitus
#> 809 Genome wide analysis of copy number variation in NAFLD spectrum
#> 810 Differential microarray expression profile analysis of long non-coding RNAs in umbilical cord vein plasma from normal and gestational diabetes-induced macrosomia
#> 811 Imporatance of substantial weight loss for altering gene expression during intensive cardiovascular lifestyle modification
#> 812 miRNA expression profiling of primary melanoma tumors
#> 813 miRNA expression profiling of primary melanoma tumors (cohort I)
#> 814 The effects of moderate weight gain in adipose tissue gene expression in metabolically-normal (MNO) and metabolically-abnormal (MAO) subjects
#> 815 Mouse-human experimental epigenetic analysis unmasks dietary targets and genetic liability for diabetic phenotypes
#> 816 Next generation sequencing of human immune cell subsets across diseases
#> 817 MicroRNA Expression Profiles identify genes of apoptosis, anabolism and catabolism in patients with Intervertebral Disc Degeneration different from Spinal Trauma
#> 818 Gene expression profiling in blood of patients with chronic respiratory failure
#> 819 Determinants of excess genetic risk of acute myocardial infarction – a matched case-control study
#> 820 p38 MAPK activation upregulates pro-inflammatory pathways in skeletal muscle cells from insulin resistant type 2 diabetic patients
#> 821 Differential regulation of microRNAs in skeletal muscle from monozygotic twins discordant for type 2 diabetes
#> 822 The long noncoding RNA expression profile of human intervertebral disc degeneration identified by microarray analysis
#> 823 White-to-brown metabolic conversion of human adipocytes by JAK inhibition
#> 824 Gene expression profiling of myxoid liposarcomas (validation set INT-B)
#> 825 Gene expression profiling of myxoid liposarcomas (training set INT-A)
#> 826 Gene expression profiles of HMEC-1 after exposure to the chemotherapeutic drugs bleomycin and cisplatin with untreated samples as control
#> 827 PRC2 loss amplifies Ras-driven transcription and confers sensitivity to BRD4-based therapies [expression array]
#> 828 PRC2 loss amplifies Ras-driven transcription and confers sensitivity to BRD4-based therapies [ChIP-seq]
#> 829 SNP genotyping data from human iPSCs and human fibroblast cells
#> 830 Generation of functional human pancreatic beta cells in vitro
#> 831 Global mRNA/LncRNA expression analysis of pancreatic tumors causing type3C Diabetes Mellitus
#> 832 Exonic variants associated with development of Aspirin Exacerbated Respiratory Diseases
#> 833 Using RNA sequencing for identifying gene imprinting and random monoallelic expression in human placenta
#> 834 Using RNA sequencing for identifying gene imprinting and random monoallelic expression in human placenta (SNP genotyping)
#> 835 Redifferentiation of adult human β cells expanded in vitro by inhibition of the WNT pathway
#> 836 Molecular and cellular characterization of Cord Blood derived, IDO expressing human fibrocystic Myeloid Derived Suppressor Cells
#> 837 Ras-induced epigenetic inactivation of RRAD promotes glucose uptake in a human ovarian cancer model [DGE-Seq]
#> 838 Ras-induced epigenetic inactivation of RRAD promotes glucose uptake in a human ovarian cancer model (RRBS-Seq]
#> 839 FKBP5 expression in human adipose tissue increases following dexamethasone exposure and is associated with insulin resistance
#> 840 Gene Expression Profile of Fibrovascular Membrane Associated with Proliferative Diabetic Retinopathy
#> 841 Peripheral blood mononuclear cell in patients with type 1 diabetes compared with normal controls
#> 842 microRNA expression data from peripheral blood mononuclear cell in patients with type 1 diabetes compared with normal controls.
#> 843 Expression data from peripheral blood mononuclear cell in patients with type 1 diabetes compared with normal controls
#> 844 Identification of Novel Auto-antibodies in Type 1 Diabetic Patients using a High-density Protein Microarray
#> 845 Global transcriptomic analysis of human pancreatic islets reveals novel genes influencing glucose metabolism [expression array]
#> 846 microRNA profiling of HepG2 cells: control vs treatment with cacao, grape seed proanthocyanidin extract or epigallocatechin gallate
#> 847 Human cells infected with Mucormycosis-causing strains from clinical settings
#> 848 Maternal microRNAs secreted by the endometrium act as transcriptomic regulators of the pre-implantation embryo
#> 849 Towards epigenetic understanding and therapy of insulin resistance by intranuclear insulin
#> 850 Resistance to aerobic exercise training causes metabolic dysfunction and reveals novel exercise-regulated signaling networks
#> 851 Molecular signatures differentiate immune states in Type 1 Diabetes families
#> 852 Dietary fat quality, more than dietary fat quantity, impacts genome-wide DNA methylation patterns in Greek preadolescents
#> 853 Resveratrol improves adipose insulin signaling and reduces the inflammatory response in adipose tissue of rhesus monkeys on a high-fat, high-sugar diet.
#> 854 Risk of T1D progression in islet autoantibody positive children can be further stratified using expression patterns of multiple genes implicated in peripheral blood lymphocyte activation and function
#> 855 Identification of Type 1 Diabetes-Associated DNA Methylation Variable Positions That Precede Disease Diagnosis
#> 856 A conditionally immortalized human pancreatic beta cell line
#> 857 Platelet micro-RNA expression in type 2 diabetes mellitus
#> 858 Whole Blood Transcriptional Modules generated on Illumina Hu-6 V2 Beadchips
#> 859 Expression microarray analysis of human pancreatic islets reveals CD59 function
#> 860 Expression data of Participants of Ornish intervention and Control group
#> 861 Genome-wide expression kinetics of children with Type 1 diabetes (T1D) -associated autoantibodies or progression towards clinical T1D, compared to healthy matched controls .
#> 862 Gene expression changes during Type 1 diabetes pathogenesis
#> 863 Genome-wide espression kinetics of children progressing to clinical Type 1 diabetes (T1D).
#> 864 Genome-wide expression kinetics of children with T1D-associated autoantibodies compared to healthy matched controls II
#> 865 Genome-wide expression kinetics of children with T1D-associated autoantibodies compared to healthy matched controls I
#> 866 Differential miRNA expression profiling of urinary exosomes from normo- and microalbuminuric type 1 diabetic patients
#> 867 Genome wide analysis of Visceral adipose tissue CD14+ cells from Obese and obese diabetic subjects
#> 868 Prenatal arsenic exposure and the epigenome: altered gene expression profiles in newborn cord blood
#> 869 Prenatal arsenic exposure and the epigenome: altered miRNA expression profiles in newborn cord blood
#> 870 RNA-sequencing identifies dysregulation of the human pancreatic islet transcriptome by the saturated fatty acid palmitate
#> 871 gene expression Fulminant type 1 diabetes vs classical type 1A diabetes vs healthy controls
#> 872 Gene expression Fulminant type 1 diabetes vs classical type 1A diabetes
#> 873 mRNA and microRNA profile in colon cancer
#> 874 Complementary Strand MicroRNAs Mediate Acquisition of Metastatic Potential in Colonic AdenocarcinomamRNA and microRNA profile in colon cancer
#> 875 mRNA and microRNA profile in colon cancer [mRNA data]
#> 876 Impact of Visceral Fat on Gene Expression Profile in Peripheral Blood Cells
#> 877 Epigenomic Approaches to explaining Metabolic Memory in the Epidemiology of Diabetes Intervention and Complications (EDIC) Study
#> 878 Human islets exposed to cytokines IL-1β and IFN-γ
#> 879 Expression of the placental transcriptome in fetal growth restriction in the Baboon is Dependent on Fetal Sex
#> 880 Molecular signatures of human iPSCs highlight sex differences and cancer genes
#> 881 Epigenetic regulation of the MEG3-DLK1 microRNA cluster in human Type 2 diabetic islets
#> 882 DNA methylation differences between multiple sclerosis and controls in frontal lobe white matter
#> 883 Differential genes in adipocytes induced from polycystic ovary syndrome-derived and non- polycystic ovary syndrome-derived human embryonic stem cells
#> 884 Epigenetic regulation of the MEG3-DLK1 microRNA cluster in human Type 2 diabetic islets
#> 885 Differentially Expressed Wound Healing-Related microRNAs in the Human Diabetic Cornea
#> 886 Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants (ChIP-seq)
#> 887 Chromatin stretch enhancer states drive cell-specific gene regulation and harbor human disease risk variants (RNA-seq)
#> 888 Increases in Insulin Sensitivity among Obese Youth are Associated with Gene Expression Changes in Whole Blood.
#> 889 SBV - Gene Expression Profiles of Lung Cancer Tumors - Adenocarcinomas and Squamous Cell Carcinomas
#> 890 Adipose tissue gene expression associated with weight gain in kidney transplant recipients
#> 891 Effects of 30 days resveratrol supplementation on adipose tissue morphology and gene expression patterns in obese men
#> 892 Expression data from kidney biopsies of liver disease patients
#> 893 DNA methylation differences between human regulatory T cells and conventional T cells
#> 894 Gene expression profiles in 74 laser microdissected colorectal cancer tissues
#> 895 Epigenomic plasticity enables human pancreatic alpha to beta cell reprogramming
#> 896 Expression data from open bariatric surgery patients - various adipose samples
#> 897 Ficolin-1 is upregulated in leukocytes and glomeruli from microscopic polyangiitis patients
#> 898 Differential expression profiles of human primary endothelial cells (HUVECs) from umbilical cords of Gestational Diabetic mothers
#> 899 Analyses of a deactivation genetic variation in Ha-Ras proto oncogene identified in a patient wit premature aging and insulin resistance
#> 900 A mutation in the c-Fos gene associated with congenital generalized lipodystrophy
#> 901 In silico nano-dissection: defining cell type specificity at transcriptional level in human disease
#> 902 In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (tubulointerstitium)
#> 903 In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (glomeruli)
#> 904 Cyclodextrin protects podocytes in diabetic kidney disease [HumanHT-12 V4.0 array]
#> 905 Cyclodextrin protects podocytes in diabetic kidney disease.
#> 906 Cyclodextrin protects podocytes in diabetic kidney disease [HumanWG-6 v3.0 array]
#> 907 The Heritage (HEalth, RIsk factors, exercise Training And GEnetics) family study
#> 908 miRNA-sequencing of human pancreatic islets and enriched beta-cells
#> 909 Temporal induction of immunoregulatory processes coincides with age-dependent resistance to viral-induced type 1 diabetes
#> 910 Temporal induction of immunoregulatory processes coincides with age-dependent resistance to viral-induced type 1 diabetes [human]
#> 911 Cluster analysis reveals differential transcript profiles associated with resistance training-induced human skeletal muscle hypertrophy
#> 912 Genetic Risk Factors for Type 2 Diabetes: A Trans-Regulatory Genetic Architecture?
#> 913 A transcriptomic analysis of a Caucasian family cohort of high risks for the metabolic syndrome [HumanWG-6 v3.0]
#> 914 A transcriptomic analysis of a Caucasian family cohort of high risks for the metabolic syndrome [HumanWG-6 v2.0]
#> 915 A stem cell model of diabetes due to glucokinase deficiency
#> 916 Gene expression from human pancreatic islet
#> 917 Transcription dependent dynamic supercoiling is a short-range genomic force
#> 918 Transcription dependent dynamic supercoiling in Raji human B cells in vivo
#> 919 Gene expression assay from Raji human B cells
#> 920 Gene expression profiling in endothelial precursor cells of patients protected from microvascular complications
#> 921 Human transcriptome analysis of acute responses to glucose ingestion reveals a role of leukocytes in hyperglycemia induced inflammation.
#> 922 PBEF Knockdown in HMVEC-LBI
#> 923 Genome-Wide Analysis of DNA Methylation Differences in Muscle and Fat from Monozygotic Twins Discordant for Type 2 Diabetes
#> 924 The anti-inflammatory IL-1 receptor antagonist (IL-1ra) protects against the development of islet autoimmunity.
#> 925 An eQTL study in the Japanese population [genotype]
#> 926 An eQTL study in the Japanese population [gene expression_3]
#> 927 An eQTL study in the Japanese population [gene expression_2]
#> 928 An eQTL study in the Japanese population [gene expression_1]
#> 929 Global Gene Expression Profiles of Visceral Adipose in Females with Type 2 Diabetes.
#> 930 Global Gene Expression Profiles of Subcutaneous Adipose in Females with Type 2 Diabetes.
#> 931 Global Gene Expression Profiles of Skeletal Muscle in Males with Type 2 Diabetes.
#> 932 Global gene expression profile of coronary artery disease in Asian Indians
#> 933 Dynamic regulation of miRNA and mRNA signatures during in vitro pancreatic differentiation (mRNA)
#> 934 Dynamic regulation of miRNA and mRNA signatures during in vitro pancreatic differentiation (miRNA)
#> 935 Differential Gene Expression in Granulosa Cells from Polycystic Ovary Syndrome Patients with and without Insulin Resistance: Identification of Susceptibility Gene Sets through Network Analysis
#> 936 Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes [T1D_114]
#> 937 Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes [Pneu_S3S24_10Pneu_4HC]
#> 938 Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes
#> 939 Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes [H1N1_S5_5Pre_5D0]
#> 940 Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes [CF_S1S3_5Auto_20CF_10HC]
#> 941 microRNAs expression profile in Myotonic Dystrophy type-2 (DM2) patients
#> 942 Genomic Multivariate Predictors of Response to Adjuvant Chemotherapy in Ovarian Carcinoma: Predicting Platinum Resistance
#> 943 microRNA profiling of formalin-fixed, paraffin-embedded human sarcoma specimens
#> 944 Plasticity of adult human pancreatic duct cells by neurogenin3-mediated reprogramming
#> 945 Expression data from human pancreatic islets
#> 946 Paradigm Test Set
#> 947 Paradigm Test Set Expression Array
#> 948 Cell type-specific binding patterns reveal that TCF7L2 can be tethered to the genome by association with GATA3
#> 949 Dermal lymphatic endothelial cell response to type 2 diabetes [Homo sapiens]
#> 950 Profiles of Epigenetic Histone Post-translational Modifications at Type 1 Diabetes Susceptible Genes
#> 951 ChIP-chip of lymphocytes using H3K9Ac, H3K4me3, H3K9me3, H3K27me3 and H4K16Ac antibodies
#> 952 ChIP-chip of monocytes using H3K9Ac, H3K4me3, H3K9me2 and H4K16Ac antibodies
#> 953 Gene expression data from human lymphocytes
#> 954 Peripheral Blood Monocyte Gene Expression in Recent-Onset Type 1 Diabetes
#> 955 Incisional hernia recurrence through genomic profiling: a pilot study
#> 956 Transcriptome analysis of circulating monocytes in obese patients before and three months after bariatric surgery
#> 957 microRNA expression analysis of circulating monocytes in obese patients
#> 958 Evaluation of a novel clinical platform for cardiovascular drug development
#> 959 Mid-gestational gene expression profile in placenta and link to pregnancy complications
#> 960 Amorfrutins are selective PPARγ agonists with potent antidiabetic properties
#> 961 Blood biomarkers of pancreatic cancer associated diabetes identified by peripheral blood-based gene expression profiles
#> 962 Genome-wide analysis of SPARC(secreted protein acidic and rich in cysteine)-responsive gene expression in HTR-8/SVneo cells
#> 963 DNA methylation profiling of male human pancreatic islets identifies loci for type 2 diabetes
#> 964 Gene expression analysis of bone biospies from nine patients with endogenous Cushings syndrome before and after treatment
#> 965 Hyperglycemia and a Common Variant of GCKR Are Associated with the Levels of Eight Amino Acids in 9,371 Finnish Men
#> 966 RNA-sequencing of TGF-ß1-driven gene expression in human kidney cell line
#> 967 Expression data from cytoplasmic hybrid (cybrid) and rho0 cells
#> 968 Transcriptome analysis of diabetic and non diabetic patients affected by post-ischemic heart failure
#> 969 Personal Omics Profiling Reveals Dynamic Molecular Phenotypes and Actionable Medical Risks
#> 970 Autoantibody profile timecourse of UNK
#> 971 The human pancreatic islet transcriptome: impact of pro-inflammatory cytokines
#> 972 Polyunsaturated fatty acids acutely affect triacylglycerol-derived skeletal muscle fatty acid uptake and increases postprandial insulin sensitivity
#> 973 Expression data from peripheral blood mononuclear cell in patients with type 1 diabetes before and after peripheral stem cell transplantation
#> 974 Differential gene expression in adipose tissue from obese human subjects during weight loss and weight maintenance
#> 975 Specific post-translational histone mod. of neutrophil extracellular traps as immunogens & potential SLE Ab targets.
#> 976 MicroRNAs expression profiling of human nucleus pulposus cells: control vs. degeneration
#> 977 Blood genomic expression profile for ischemic stroke (IS)
#> 978 Gene expression profiling in arterial tissue from type 2 diabetic patients
#> 979 Calorie restriction-like effects of 30 days of resveratrol supplementation on energy metabolism and metabolic profile in obese humans
#> 980 Molecular markers of predictive value associated with low birth weight
#> 981 Genome-wide survey reveals predisposing diabetes type 2-related DNA methylation variations in human peripheral blood
#> 982 Human oocytes reprogram somatic cells to a pluripotent state
#> 983 Gene expression in blastomeres after transfer of somatic cells into human oocytes
#> 984 Gene expression in pluripotent stem cells derived after somatic cell genome transfer into human oocytes
#> 985 To investigate how the glycosylation of podocyte proteins changes during diabetic kidney disease
#> 986 Formalin Fixation at Low Temperature Better Preserves Nucleic Acid Integrity
#> 987 Correlation between maternal age and newborn DNA methylation
#> 988 Gene expression profiles in 132 laser microdissected colorectal cancer tissues
#> 989 A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence
#> 990 Transcriptome Analysis of Human Diabetic Kidney Disease (Control Glomeruli vs. Control Tubuli)
#> 991 Transcriptome Analysis of Human Diabetic Kidney Disease (DKD Tubuli vs. Control Tubuli)
#> 992 Transcriptome Analysis of Human Diabetic Kidney Disease (DKD Glomeruli vs. Control Glomeruli)
#> 993 Transcriptome Analysis of Human Diabetic Kidney Disease
#> 994 Differences in subcutaneous adipose tissue gene expression between obese African Americans and Hispanic Youths
#> 995 Genome-wide mRNA profiling of adult human pancreatic beta and duct cells in comparison to other human tissues
#> 996 Insulin-producing cells generated from dedifferentiated human pancreatic beta cells expanded in vitro
#> 997 HIV Infection and Antiretroviral Therapy Have Divergent Effects on Mitochondria in Adipose Tissue
#> 998 Expression in Huh7 cells 72 hours after treatment with scramble, SPTLC123, or DEGS siRNA
#> 999 Gene-chip studies of adipogenesis-regulated microRNAs in mouse primary adipocytes and human obesity
#> 1000 An early inflammatory gene profile in visceral adipose tissue in children
#> 1001 Gene-chip studies of adipogenesis-regulated microRNAs in mouse primary adipocytes and human obesity (Affymetrix)
#> 1002 Genome-wide profiling of H3K56 acetylation and transcription factor binding sites in human adipocytes
#> 1003 TGFß1-driven epithelial to mesenchymal transition in human kidney cell line
#> 1004 Comparative miRNA Expression Profiles in Individuals with Latent and Active Tuberculosis
#> 1005 Transcriptome profile of peripheral blood mononuclear cells in patients with type I diabetes and their first grade relatives
#> 1006 Expression Data from HNF4a RNAi Knockdown in HepG2 cells
#> 1007 Increased SRF Transcriptional Activity is a Novel Signature of Insulin Resistance in Humans and Mice
#> 1008 Mapping of INS promoter interactions reveals its role in long-range regulation of SYT8 transcription
#> 1009 Dioxin exposure of human CD34+ hemopoietic cells induces gene expression modulation that recapitulates its in vivo clinical and biological effects
#> 1010 Adenosine-treated endothelial progenitor cells
#> 1011 Resolution of Type 2 Diabetes Following Bariatric Surgery is Associated with Changes in Whole Blood Gene Expression
#> 1012 Growth hormone receptor deficiency is associated with a major reduction in pro-aging signaling, cancer, and diabetes in humans
#> 1013 Genomic expression profiles of blood and placenta in Chinese women with gestational diabetes
#> 1014 Type 2 Diabetes mellitus: mRNA and miRNA profiling
#> 1015 MicroRNA 144 impairs insulin signaling by inhibiting the expression of insulin receptor substrate 1 in Type 2 Diabetes mellitus
#> 1016 DNA methylation patterns associated with arsenicosis
#> 1017 Investigation of somatic copy number variation in MZ twins
#> 1018 Expression data from type 2 diabetic and non-diabetic isolated human islets
#> 1019 Methylated DNA Immunoprecipitation (MeDIP) using a custom type 2 diabetes and related phenotypes array
#> 1020 Effect of insulin on the stromal and adipocyte cells within hMSC derived adipocytes
#> 1021 Skeletal muscle mitochondrial dysfunction is secondary to T2DM
#> 1022 Genome-wide binding of the orphan nuclear receptor TR4 suggests its general role in fundamental biological processes
#> 1023 BI Human Reference Epigenome Mapping Project: Characterization of chromatin modification by ChIP-Seq in human subject
#> 1024 Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci
#> 1025 Analysis of transcriptome in ectopic versus orthotopic thyroid tissue.
#> 1026 microRNA and mRNA expression profiles of human pancreatic islet-like cell clusters
#> 1027 Sural nerve of progressive and non-progressive diabetic neuropathy patients
#> 1028 Insulin resistance induced by physical inactivity is associated with multiple transcriptional changes in skeletal muscle in young men
#> 1029 Sera-induced transcriptional signatures in human leukemia cell lines
#> 1030 DNA methylation data from non-immortalized lymphocyte samples from participants of the AGES Reykjavik Study
#> 1031 Peripheral blood gene expression profiles in metabolic syndrome, coronary artery disease and type 2 diabetes
#> 1032 Phenothiazine Neuroleptics Signal To The Human Insulin Promoter As Revealed By A Novel Human b-Cell Line Based Screen
#> 1033 Human lung squamous cell carcinoma expression profiling
#> 1034 Gene expression changes in Peripheral Blood Mononuclear cells (PBMC) induced by physical activity
#> 1035 Effect of IL6 level on gene expression changes in Peripheral Blood Mononuclear cells (PBMC) induced by physical activity
#> 1036 Expression data from human liver with or without type 2 diabetes
#> 1037 Systematic analysis of a human renal transcript dataset
#> 1038 Expression data from human skeletal muscle
#> 1039 Transcriptional response in human umbilical vein endothelial cells exposed to insulin
#> 1040 miRNA expression profile of human subcutaneous adipose
#> 1041 Blood microRNA profiles and upregulation of hsa-miR-144 in males with type 2 diabetes mellitus.
#> 1042 A Transcriptional Signature and Common Gene Networks Link Cancer with Metabolic Syndrome and Auto-immune Diseases
#> 1043 Human skeletal muscle - type 2 diabetes and family history positive individuals - Mexican American
#> 1044 Expression levels in immortalized B cells from unrelated individuals and twins undergoing ER stress
#> 1045 Gene expression profiles of beta-cell enriched tissue obtained by Laser Capture Microdissection from subjects with type 2 diabetes
#> 1046 Genome wide DNA methylation profiling of diabetic nephropathy in type 1 diabetes mellitus
#> 1047 A restricted repertoire of cytosine methylation changes in neonates following intrauterine growth restriction
#> 1048 C-peptide and/or transforming growth factor beta 1 effect on human proximal tubular cell line
#> 1049 mRNA expression data from skeletal muscle of type 2 diabetes
#> 1050 miRNA expression signatures for human stomach biopsy samples, H. pylori positive versus negative
#> 1051 University of Washington Human Reference Epigenome Mapping Project
#> 1052 Preadipocytes of T2DM patients display an intrinsic gene expression profile of decreased differentiation capacity
#> 1053 Folic acid supplementation normalizes the endothelial progenitor cell transcriptome of patients with type 1 diabetes
#> 1054 Differential Expression of MicroRNAs in Mouse Liver under Aberrant Energy Metabolic Status
#> 1055 Mitochondrial dysregulation and oxidative stress in patients with chronic kidney disease
#> 1056 Stable Patterns of Gene Expression Regulating Carbohydrate Metabolism Determined by Geographic Ancestry
#> 1057 THE EFFECTS OF ALCOHOLISM ON THE HUMAN BASOLATERAL AMYGDALA
#> 1058 Gene expression analysis of chronically inflamed human peri-implant and periodontal ligament cells in vivo
#> 1059 Thrombospondin-1: A Pro-Atherosclerotic Protein Augmented by Hyperglycemia
#> 1060 UCSD Human Reference Epigenome Mapping Project
#> 1061 Meta analysis of gene expression in human islets after in vitro expansion.
#> 1062 Expression in adipose tissue and liver from a spontaneous rat model of Type 2 diabetes
#> 1063 MiRNA expression in adipose tissue and liver from a spontaneous rat model of Type 2 diabetes
#> 1064 miRNA prognostic profiles in lung cancer
#> 1065 Genomic Transcriptional Profiling Identifies a Blood Biomarker Signature for the Diagnosis of Septicemic Melioidosis
#> 1066 A synthetic gene-metabolic circuit preferentially increased fatty acid metabolism in human hepatocytes
#> 1067 Genome wide gene expression profiling of visceral adipose tissue among Asian Indian diabetics
#> 1068 Expression data from liver of obese (with or without type 2 diabetes) and lean human subjects.
#> 1069 Gene expression of innate immune response in endothelial cells
#> 1070 Acetaminophen-induced gene expression profiles in sandwich-cultured primary rat hepatoctyes
#> 1071 MicroRNA expression profiling in diabetic GK rat model
#> 1072 Transcriptomes in Healthy and Diseased Gingival Tissues
#> 1073 Circulating Cells in Coronary Collateral Artery Growth II
#> 1074 Gene expression profiling in skeletal muscle of PCOS after pioglitazone therapy
#> 1075 Transcription profiling of myotubes from patients with type 2 diabetes
#> 1076 Construction of a modular analysis framework for blood Genomics Studies
#> 1077 A Modular Analysis Framework for Blood Genomics Studies: Application to Systemic Lupus Erythematosus
#> 1078 Transcriptional changes in blood from metabolic syndrome patients after a period of high intensity interval training
#> 1079 Mapping the Genetic Architecture of Gene Expression in Human Liver
#> 1080 Profiling Gene Expression in Human Placentae of Different Gestational Ages: an OPRU Network and UW SCOR Study
#> 1081 Gene expression data on human optic nerve head astrocytes in normal Caucasian and African americans
#> 1082 Expression profiles of peripheral blood monocytes in periodontal therapy
#> 1083 gene expression in monkey aorta with aging and gender
#> 1084 Reduced expression of mitochondrial oxidative metabolism genes in skeletal muscle of women with PCOS
#> 1085 Expression profiling of human adipose tissue in obese and lean subjects and in various clinical conditions
#> 1086 Expression profile of human preadipocytes cultured with activated macrophages medium
#> 1087 Effect of Acute Physiologic Hyperinsulinemia on Gene Expression in Human Skeletal Muscle in vivo
#> 1088 Gene expression in PBMCs from children with diabetes
#> 1089 Myocardial gene expression of hibernating and control tissue from patients with ischemic left ventricular dysfunction
#> 1090 Specific inhibition of p300-HAT alters Global Gene Expression and Repress HIV replication
#> 1091 Effect of insulin infusion on human skeletal muscle
#> 1092 Changes in transcription profile in pelvic organ fibroblasts in response to stretch
#> 1093 Dysregulation of the circulating and tissue-based renin-angiotensin system in preeclampsia
#> 1094 Effects of laughter on gene expression profiles patients with type 2 diabetes (Tenri)
#> 1095 Comparison of expression profile between human Müller cells, HMCL-I and HMCL-II
#> 1096 Effects of laughter on gene expression profiles of patients with type 2 diabetes
#> 1097 PCOS patients vs control subjects
#> 1098 Skeletal muscle and insulin regulated genes
#> 1099 Target genes of the transcription factors HNF1beta and HNF1alpha in the human embryonic kidney cell line HEK293
#> 1100 Gene Expression Signature Shared in Autoimmune Diseases Not in Unaffected Family Members
#> 1101 Comparative profiling in 13 muscle disease groups
#> 1102 Colon cancer profiling
#> 1103 Gestational Diabetes Induces Placental Genes for Chronic Stress and Inflammatory Pathways
#> 1104 laughter regulates postprandial blood glucose levels and gene expression
#> 1105 Diabetic nephropathy
#> 1106 Muscle - atypical diabetes protein expression
#> 1107 Type 2 diabetes and insulin resistance
#> Summary
#> 1 Genome-wide DNA methylation profiling of bead-enriched total monocytes collected from Native Hawaiian participants with known type 2 diabetes mellitus enrolled in a 3 month diabetes-specific social support education intervention. DNA methylation profiling was performed across ~450,000 CpGs from monocytes using the Illumina Infinium HumanMethylation450 BeadChip. Samples included 8 participants with paired DNA methylation data collected at pre-intervention and post-intervention (3 months), and 2 non-diabetic donors.
#> 2 We identified lncRNA expression profiles in vitreous samples between proliferative diabetic retinopathy (PDR) patients and idiopathic macular hole (IMH) patients, and between PDR patients who had received preoperative anti-vascular endothelial growth factor (anti-VEGF) therapy and PDR patients who had received surgery alone. There had been the systemic expression differences in vitreous at the microarray level from PDR patients and IMH patients, and from PDR patients after anti-VEGF treatment and untreated PDR patients.
#> 3 Cardiovascular disease (CVD) accounts for the majority of deaths in patients with type 1 diabetes (T1D); however, the determinants of plaque composition are unknown in this population. MicroRNAs (miRNAs), the most abundant class of circulating small non-coding RNA (sncRNAs) regulating gene expression, participate in the development of atherosclerosis and represent promising biomarkers of CVD. This study analyzed the circulating miRNA expression profile in T1D with carotid calcified and fibrous plaque. more...
#> 4 Studies in genetically identical individuals indicate that as much as 50% of complex trait variation cannot be traced to either genetics or to the environment. The mechanisms that generate this ‘unexplained’ phenotypic variation (UPV) remain largely unknown. Here, we identify neuronatin (NNAT) as a conserved factor that buffers against unexplained phenotypic variation. We find that Nnat deficiency in isogenic F1 mice triggers the emergence of a novel, bi-stable polyphenism, where isogenic littermates emerge into adulthood either ‘normal’ or ‘overgrown’, without intermediates. more...
#> 5 Non-alcoholic fatty liver disease is continuum of disorders among which non-alcoholic steatohepatitis (NASH) is particularly associated with a negative prognosis. Hepatocyte lipotoxicity is one of the main pathogenic factors of liver fibrosis and NASH. However, the molecular mechanisms regulating this process are poorly understood. Here, we integrated transcriptomic and chromatin accessibility analyses from human liver and mouse hepatocytes to identify lipotoxicity-sensitive transcription factors. more...
#> 6 Non-alcoholic fatty liver disease is continuum of disorders among which non-alcoholic steatohepatitis (NASH) is particularly associated with a negative prognosis. Hepatocyte lipotoxicity is one of the main pathogenic factors of liver fibrosis and NASH. However, the molecular mechanisms regulating this process are poorly understood. Here, we integrated transcriptomic and chromatin accessibility analyses from human liver and mouse hepatocytes to identify lipotoxicity-sensitive transcription factors. more...
#> 7 Mechanistic insights into the molecular events by which exercise enhances the skeletal muscle phenotype are lacking, particularly in the context of type 2 diabetes. Here we unravel a fundamental role for exercise-responsive cytokines (exerkines) on skeletal muscle development and growth in individuals with normal glucose tolerance or type 2 diabetes. Acute exercise triggered an inflammatory response in skeletal muscle, concomitant with an infiltration of immune cells. more...
#> 8 Metformin is one of the first-line drugs for clinical treatment of type II diabetes, and recent studies have found that metformin can inhibit the development of multiple malignant tumors. When metformin is combined with chemotherapeutic drugs to treat head and neck squamous cell carcinoma（HNSCC）, it can effectively enhance the efficacy of chemotherapy. The aim of this study was to define the signaling pathways regulated by metformin in HNSCC, and the underlying mechanisms by which metformin sensitizes HNSCC chemotherapy. more...
#> 9 We investigate the effects of GLP-1 on diabetic cardiomyocytes (DCMs) model established by human induced pluripotent stem cells-derived cardiomyocytes (iPSC-CMs). Two subtypes of GLP-1, GLP-17-36 and GLP-19-36, were evaluated for their efficacy on hypertrophic phenotype, impaired calcium homeostasis and electrophysiological properties. RNA-seq was performed to reveal the underlying molecular mechanism of GLP-1. more...
#> 10 Epigenetics was reported to mediate the effects of environmental risk factors on disease pathogenesis. To unleash the role of DNA methylation modification in the pathological process of cardiovascular diseases in diabetes, we screened differentially methylated genes by methylated DNA immunoprecipitation chip (MeDIP-chip) among the enrolled participants.
#> 11 Aims: Metformin is a widely used, primary drug of choice to treat individuals with type 2 diabetes (T2D). Clinically, inter-individual variability of drug response is of significant concern. The targets and precise mechanisms of action for metformin is still under interrogation. In the present study, a whole transcriptome analysis was performed with an intent to identify predictive biomarkers of metformin response in T2D individuals. more...
#> 12 To understand the role of adipose tissue senescence in NAFLD/NASH, RNA sequencing was performed in the visceral adipose tissue of NAFLD and NASH pateints.
#> 13 Single-cell transcriptomes of corpus cavernosum from three males with normal erections and five organic erectile dysfunction (ED) patients.
#> 14 Gestational diabetes mellitus (GDM) “program” an elevated risk of metabolic syndrome in the offspring. Epigenetic alterations are a suspected mechanism. GDM has been associated with placental DNA methylation changes in some epigenome-wide association studies. It remains unclear which genes or pathways are affected, and whether any placental differential gene methylations are correlated to fetal growth or circulating metabolic health biomarkers. more...
#> 15 Adipocytes are key regulators of human metabolism, and their dysfunction in insulin signaling is central to metabolic diseases including type II diabetes mellitus (T2D). However, the progression of insulin resistance into T2D is still poorly understood. This limited understanding is due, in part, to the dearth of suitable models of insulin signaling in human adipocytes. Traditionally, adipocyte models fail to recapitulate in vivo insulin signaling, possibly due to exposure to supraphysiological nutrient and hormone conditions. more...
#> 16 In this dataset, we utilized the db/db, uninephrectomy and renin-hypertension mouse model. We performed bulk RNA-seq and compared vehicle to ACE inhibitor, Rosiglitizone, SGLT2 inhibitor, ACEi + Rosiglitizone and ACEi + SGLT2i at two time points (2 days and 2 weeks). To study the mechanism, we also performed bulk RNA-seq on human primary tubular epithelial cells with or without SRSF7 siRNA knockdown.
#> 17 To reveal the expression profiles of transfer RNA-derived small RNA (tsRNA)s and microRNA (miRNA)s in the vitreous humour of proliferative diabetic retinopathy (PDR).
#> 18 Objectives: This study was undertaken to understand the mechanistic basis of response to anti-TNF therapies and determine if transcriptomic changes in the synovium are reflected in peripheral protein markers. Methods: Synovial tissue from 46 RA patients was profiled with RNA sequencing before and 12 weeks after treatment with anti-TNF therapies. Pathway and gene signature analyses were performed on RNA expression profiles of synovial biopsies to identify mechanisms that could discriminate among EULAR good, moderate and non-responders. more...
#> 19 Abnormal mechanical load is a main risk factor of intervertebral disc degeneration (IDD), and cellular senescence is a pathological change in IDD. Additionally, extracellular matrix (ECM) stiffness promotes human nucleus pulposus cells (hNPCs) senescence. However, the molecular mechanism underlying mechano-induced cellular senescence and IDD progression is not yet fully elucidated. First, we demonstrated that mechano-stress promoted hNPCs senescence via NF-κB signaling. more...
#> 20 Patients with NEUROGENIN3 mutations have enteric endocrinopathy and diabetes mellitus. We generated pluripotent stem cells from a patient’s fibroblasts to investigate if gene editing restores endocrine differentiation. Corrected cell lines differentiated into all pancreatic lineages while native cell lines failed to activate pancreatic progenitor and lineage determination genes, suggesting that the mutation disrupts pancreatic organogenesis and results in endocrine and exocrine dysfunction. more...
#> 21 Among 226 morbidly obese patients who underwent gastric bypass surgery between 2013 and 2014 as part of the A Biological Atlas of Severe Obesity (ABOS) study (ClinicalTrials.gov; NCT01129297), 18 women who gave informed consent were recruited in this study for immunophenotyping and microarray analyses of omental adipose tissue (AT). We characterized T and NK cell populations in omental AT from morbidly obese women with varying levels of IR. more...
#> 22 Objective: To explore the characteristics and underlying molecular mechanisms of genome-scale expression profiles of women with- or without- GDM and their offspring. Materials and Methods: We recruited a group of 21 pregnant women with GDM and 20 healthy pregnant women as controls. For each pregnant women, RNA-seq were performed using the placenta and paired neonatal umbilical cord blood specimens. more...
#> 23 Objective: To explore the characteristics and underlying molecular mechanisms of genome-scale expression profiles of women with- or without- gestational diabetes mellitus and their offspring. Materials and Methods: We recruited a group of 21 pregnant women with gestational diabetes mellitus (GDM) and 20 healthy pregnant women as controls. For each pregnant women, RRBS were performed using the placenta and paired neonatal umbilical cord blood specimens. more...
#> 24 This SuperSeries is composed of the SubSeries listed below.
#> 25 To systemically investigate the role of ZnT8 in β cell maturation, we performed single cell RNA-seq in both WT and KO β cells at both S6 (immature) and S7 (mature) stages. Both WT and KO β cells were obtained from FACS as positive for both INS and NKX6.1. Single cell RNA-seq results revealed that SLC30A8 is mainly involved in β cell maturation process, and further showed that SLC30A8 LOF accelerates β cell maturation and upregulates insulin secretion pathway in mature β cells.
#> 26 The antisense non-coding RNA in the INK locus (ANRIL), which originates from the CDKN2A/B (INK4-ARF) locus, has been identified as a hotspot for genetic variants associated with cardiometabolic disease including coronary artery disease (CAD) and Type 2 diabetes (T2D). We recently found that ANRIL abundance in human pancreatic islets was increased in donors carrying certain T2D risk-SNPs, and that a T2D risk-SNP located within exon2 of ANRIL conferred reduced beta cell proliferation index, pointing to a role for ANRIL in the regulation of T2D pathogenicity via an impact on insulin secretory capacity. more...
#> 27 Long noncoding RNAs (lncRNAs) are involved in diabetes related diseases. However, the role of lncRNAs in the pathogenesis of type 2 diabetes with macrovascular complication (DMC) has seldomly been recognized. This study screened lncRNA profiles of leukocytes from DMC patients and explored protective role of lncRNA LYPLAL1-DT in endothelial cells (EC) under high glucose (HG) and inflammatory conditions (IS). more...
#> 28 This SuperSeries is composed of the SubSeries listed below.
#> 29 Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. more...
#> 30 Background: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and post-operative atrial fibrillation (POAF) is a major healthcare burden, contributing to an increased risk of stroke, kidney failure, heart attack and death. Genetic studies have identified associations with AF, but no molecular diagnostic exists to predict POAF based on pre-operative measurements. Such a tool would be of great value for perioperative planning to improve patient care and reduce healthcare costs. more...
#> 31 We profiled manually microdissected tubulointerstitial tissue from 43 IgA nephropathy, 3 diabetes mellitus nephropathy, 3 focal segmental glomerulosclerosis, 3 lupus nephritis, 4 membranous nephropathy, and 9 minimal change disease biopsy cores and 22 nephrectomy controls by RNA sequencing. The 3 outliers which were not included in our main analysis were also uploaded in this database.
#> 32 Serum miRNAs could be powerful classifiers for the detection of patients with postmenopausal osteoporosis.
#> 33 The pancreas and liver arise from a common pool of progenitors in the foregut endoderm; however, the underlying molecular mechanisms driving this lineage diversification are not fully understood. We combined human pluripotent stem cell guided differentiation and sequential CRISPR-Cas9 loss-of-function screening to uncover regulators of pancreatic specification. Here we report the discovery of a cell-intrinsic requirement for HHEX, a transcription factor (TF) associated with diabetes susceptibility. more...
#> 34 The pancreas and liver arise from a common pool of progenitors in the foregut endoderm; however, the underlying molecular mechanisms driving this lineage diversification are not fully understood. We combined human pluripotent stem cell guided differentiation and sequential CRISPR-Cas9 loss-of-function screening to uncover regulators of pancreatic specification. Here we report the discovery of a cell-intrinsic requirement for HHEX, a transcription factor (TF) associated with diabetes susceptibility. more...
#> 35 The pancreas and liver arise from a common pool of progenitors in the foregut endoderm; however, the underlying molecular mechanisms driving this lineage diversification are not fully understood. We combined human pluripotent stem cell guided differentiation and sequential CRISPR-Cas9 loss-of-function screening to uncover regulators of pancreatic specification. Here we report the discovery an unexpected, cell-intrinsic requirement for HHEX, a transcription factor (TF) associated with diabetes susceptibility. more...
#> 36 Every year, about 18 million babies are born from mothers with gestational diabetes mellitus (GDM). While diabetic symptoms usually resolve after delivery, lasting complications can occur for both mother and child, including fetal overgrowth, type 2 diabetes (T2D), cardiovascular diseases, and obesity. The rapid progression of GDM is unique to pregnancies, and likely arises from placental dysfunction. more...
#> 37 Regenerating pancreatic b-cells is a potential curative approach for diabetes. We previously identified the small molecule CID661578 as a potent inducer of b-cell regeneration but its target and mechanism of action have remained unknown. We now screened 257 million yeast clones and determined that CID661578 targets MAP kinase-interacting serine/threonine kinase 2 (MNK2), an interaction that was genetically validated in vivo. more...
#> 38 We have reported differntial abundance of miRNAs present in the secretory Extracellular vesicles during Gestetional Diabetes Mellitus or Ischemic placental disease
#> 39 The aim of this study was to conduct a baseline comparison of serum-circulating miRNA in diabetic patients with and without ischemic heart disease. We analysed the expression levels of 798 serum miRNAs using the NanoString nCounter Technology Platform. The prediction of the putative miRNAs targets was performed by the Ingenuity Pathway Analysis (IPA) software. Receiver operating characteristic (ROC) analysis was used to assess the diagnostic value of identified miRNAs. more...
#> 40 We performed RNA-seq on tissue biopsies derived from patients with DFU and compared it to healthy controls who had similar foot surgery to identify the significant immune related differentially expressed genes between normal and DFU samples. Our results identified that there was a total of 8800 DEGs detected by RNA-seq data analysis, among which 2351 were upregulated and 6449 downregulated genes in DFU. more...
#> 41 Studies of monogenic diabetes are particularly useful as we can gain insight into the molecular events of pancreatic β-cell failure. Maturity-onset diabetes of the young 1 (MODY1) is a monogenic diabetes form, caused by a mutation in the HNF4A gene. Human induced pluripotent stem cells (hiPSC) provide an excellent tool for disease modelling by subsequent directed differentiation toward desired pancreatic islet cells, but cellular phenotypes in terminally differentiated cells are notoriously difficult to detect. more...
#> 42 Background: Long-term complications of type 2 diabetes (T2D) are the major causes for T2D-related disability and mortality. Notably, diabetic nephropathy (DN) has become the most frequent cause of end-stage renal disease (ESRD) in most countries. Understanding epigenetic contributors to DN can provide novel insights into this complex disorder and lay the foundation for more effective monitoring tools and preventive interventions, critical for achieving the ultimate goal of improving patient care and reducing healthcare burden.
#> 43 Diabetic kidney disease (DKD) is the leading cause of both chronic kidney disease (CKD) and end-stage renal disease (ESRD). In this study, we performed transcriptome gene expression profiling of kidney tissues in human renal proximal epithelial tubular cell line (HK-2) treated with high D-glucose (HG) for 7 days before the addition of 40 mM oxamate for a further 24 hours in the presence of HG. Afterwards, we analyzed the differentially expressed (DE) genes and investigated gene relationships based on weighted gene co-expression network analysis (WGCNA). more...
#> 44 Background: Pre-diabetes condition precedes the Diabetes Mellitus (DM) disease and is a critical period for hyperglycemia treatment, especially for menopausal women, considering all metabolic alterations due to hormonal changes. Recently, the literature has demonstrated the role of physical exercise in epigenetic reprogramming to modulate the gene expression patterns of metabolic conditions, such as hyperglycemia, preventing DM development. more...
#> 45 To investigate whether aberrant lncRNA expression in the placenta is involved in the pathogenesis of NDFMS and to elucidate its biological mechanisms. The expression profile of lncRNAs in the placentas of pregnant women with NDFMS was investigated using an Agilent Human LncRNA Microarray. Differentially expressed lncRNAs were selected for validation using reverse transcription-quantitative polymerase chain reaction (RT-qPCR).
#> 46 Dysregulated neurite outgrowth and synapse formation underlie many psychiatric disorders. Wolfram syndrome (WS) mainly caused by WFS1 deficiency is a monogenic genetic disease manifested by severe psychiatric disorders. Due to athe lack of proper human disease models, the underlying mechanism is poorly understood. Particularly, whether and how WFS1 deficiency affects synapse formation remain elusive. more...
#> 47 This SuperSeries is composed of the SubSeries listed below.
#> 48 Type 1 diabetes (T1D) usually has a preclinical phase identified by the presence of circulating autoantibodies to pancreatic islet antigens, and most young children who have multiple autoantibodies progress to diabetes within 10 years. While autoantibodies denote underlying islet autoimmunity, how this process is initiated and then progresses to clinical diabetes on a background of genetic susceptibility is not clearly understood. more...
#> 49 Type 1 diabetes (T1D) usually has a preclinical phase identified by the presence of circulating autoantibodies to pancreatic islet antigens, and most young children who have multiple autoantibodies progress to diabetes within 10 years. While autoantibodies denote underlying islet autoimmunity, how this process is initiated and then progresses to clinical diabetes on a background of genetic susceptibility is not clearly understood. more...
#> 50 Microglia are the tissue-resident macrophages of the retina and brain, being critically involved in organ development, tissue homeostasis, and response to cellular damage. Until now, little is known about the transcriptional profile of human retinal microglia and how they differentiate from peripheral monocytes, as well as from brain microglia. Additionally, the degree to which mice are suitable models for human retinal microglia is still not clear. more...
#> 51 BACKGROUND. The incidence of Type 1 Diabetes (T1D) has significantly increased in recent decades and coincides with lifestyle changes that have likely altered the composition of the gut microbiota. Dysbiosis and gut barrier dysfunction are associated with T1D, and notably, our studies have identified an inflammatory state in T1D families that is consistent microbial antigen exposure. METHODS. We conducted a 6-week, single-arm, open-label trial to investigate whether daily multi-strain probiotic (Bifidobacteria, Lactobacillus, and Streptococcus) supplementation could reduce the familial inflammatory state in 25 unaffected siblings of diabetes patients. RESULTS. Probiotic supplementation was found safe and well-tolerated; there were no adverse events and participant adherence was 93%. Bacterial 16S rDNA gene sequencing of stool revealed that community alpha and beta diversity were not altered between the pre- and post-supplement samplings. LEfSe analyses identified post-supplement enrichment of the family Lachnospiraceae, producers of the anti-inflammatory short chain fatty acid butyrate. Systemic inflammation was measured by plasma induced transcription and quantified with a gene ontology-based composite inflammatory index (I.I.com). After supplementation, I.I.com was reduced (p=0.017), and pathway analysis predicted inhibition of IL17A, lipopolysaccharide, NFkB, IL1B, and TNF (Z-score≤-2.0) and activation of IL10RA (Z-score=2.0). Post-supplement plasma levels of IL12p40, IL-13, IL-15, IL-18, CCL2, CCL24 were reduced (p<0.05), while butyrate levels trended 2.4-fold higher (p=0.06). CONCLUSION. There is a substantial need for safe, broadly applicable therapies to reduce T1D susceptibility. This study indicates that investigations of prebiotic and probiotic strategies are warranted as they may be efficacious either alone or in combination with other therapeutic agents.
#> 52 We used the latest technology, BD Rhapsody, to analyze the pairing of α and β chains that constitute the TCR of PBMCs from patients with type 1 diabetes at the single-cell level.
#> 53 Background: Despite the established relation between energy restriction and metabolic health, the most beneficial nutrient composition of a weight-loss diet is still subject of debate. Objectives: The aim of the study was to examine the additional effects of nutrient quality on top of energy restriction(ER). Methods: A parallel-designed 12-week 25%ER dietary intervention study was conducted. Participants aged 40-70 years with abdominal obesity were randomized over three groups: a 25%ER high nutrient quality diet (n = 40); a 25%ER low nutrient quality diet (n = 40); or a habitual diet (n = 30). more...
#> 54 The ability to detect and target β cells in vivo can drastically refine the way diabetes is studied and treated. By an unsupervised Systematic evolution of ligands by exponential enrichment (SELEX) we identified two RNA aptamers that specifically recognize mouse and human β cells in vitro and in vivo. Here we took advantage of commercially available high density protein arrays to identify putative target of the two islet specific aptamers. more...
#> 55 Bariatric surgery mediated weight loss has been shown to significantly reduce breast cancer incidence in women. We hypothesize that loss of excessive adiposity, reduces net Estrogen Receptor Alpha activation which in turn lowers breast cancer risk. A differential gene expression analysis and subsequent pathway enrichment analysis would reveal the relevant molecular mechanism behind the preventive effect of weight loss. more...
#> 56 Colorectal cancer (CRC) is one of the most frequently diagnosed and lethal malignancy. Several key factors including poor dietary habits, smoking, alcohol consumption, genetic predisposition, obesity, diabetes mellitus, and sedentary lifestyle – all result in a significantly increased risk for developing CRC. Current treatment modalities for patients with CRC include surgery, which is often followed with adjuvant chemotherapy, especially in patients with a stage II and III disease. more...
#> 57 We used WGCNA to construct a co‐expression network and obtain modules related to blood glucose, thus detecting key lncRNAs, and providing a reference for searching potential biomarkers of prediabetes and T2DM in hypertriglyceridemia patients.
#> 58 A rare truncating p. Arg138* variant (R138X) in zinc transporter is associated with a 65% reduced risk for type 2 diabetes. To address the mechanism of how this variant protects from type 2 diabetes, we derived human pluripotent stem cells carrying this mutation and differentiated them into insulin-producing cells. We found that human pluripotent stem cells with R138X mutation and the null mutation have normal efficiency of differentiation towards insulin-producing cells, but these cells were depleted of zinc and presented large diffused insulin granules. more...
#> 59 This study aimed to identify the crucial molecules and explore the function of noncoding RNAs and related pathways in IDD. We randomly selected 3 samples each from an IDD and a spinal cord injury group (control) for RNA-sequencing. We identified 463 differentially-expressed long noncoding RNAs (lncRNAs), 47 differentially-expressed microRNAs (miRNAs), and 1,334 differentially-expressed mRNAs in IDD. more...
#> 60 Proteinuria, the spillage of serum proteins into the urine, is a feature of glomerulonephritides, podocyte disorders and diabetic nephropathy. However, the response of tubular epithelial cells to serum protein exposure has not been systematically characterized. Using transcriptomic profiling we studied serum-induced changes in primary human tubular epithelial cells cultured in 3D microphysiological devices. more...
#> 61 Proteinuria, the spillage of serum proteins into the urine, is a feature of glomerulonephritides, podocyte disorders and diabetic nephropathy. However, the response of tubular epithelial cells to serum protein exposure has not been systematically characterized. Using transcriptomic profiling we studied serum-induced changes in primary human tubular epithelial cells cultured in 3D microphysiological devices. more...
#> 62 Non-mesenchymal pancreatic cells are a potential source for cell replacement therapies aiming to restore the endocrine capacity lost during diabetes mellitus. Although a highly complex network of transcription factors underlies the differentiation, growth, and specification of pancreatic precursors, several studies indicated that the transdifferentiation of non-mesenchymal cells can be achieved by epigenetic regulation. more...
#> 63 This SuperSeries is composed of the SubSeries listed below.
#> 64 Pluripotent stem cell-derived islets (hPSC-islets) are a promising cell resource for diabetes treatment. Here, we demonstrate that transplantation of pluripotent stem cell-derived islets into diabetic nonprimates effectively restored endogenous insulin secretion and improved glycemic control. Single-cell RNA sequencing analysis of S6D2 clusters confirmed the existence of the three major pancreatic endocrine cell populations (β cells, α-like cells and δ-like cells) and their proportions, which altogether accounted for 80%. more...
#> 65 Human pluripotent stem cell-derived islets (hPSC-islets) are a promising cell resource for diabetes treatment. Here, we demonstrate that transplantation of human pluripotent stem cell-derived islets into diabetic nonhuman primates effectively restored endogenous insulin secretion and improved glycemic control. Single-cell RNA sequencing analysis of S6D2 clusters confirmed the existence of the three major pancreatic endocrine cell populations (β cells, α-like cells and δ-like cells) and their proportions, which altogether accounted for 80%. more...
#> 66 We profiled three prominent ATM subtypes from human visceral omental adipose tissue in obesity by RNA-seq. In the related manuscript, we evaluated differences in their signatures and their relationship to type 2 diabetes: Visceral (VAT) and subcutaneous (SAT) adipose tissue samples were collected from diabetic and non-diabetic obese subjects to evaluate cellular content and gene expression. VAT CD206+CD11c− ATMs were increased in diabetic subjects, scavenger receptor-rich with low intracellular lipids, secreted proinflammatory cytokines, and diverged significantly from two CD11c+ ATM subtypes, which were lipid-laden, lipid antigen presenting, and overlapped with monocyte signatures. more...
#> 67 Up until now, no study has looked specifically at epigenomic landscapes throughout twin samples, discordant for Anorexia nervosa (AN). Our goal was to find evidence to confirm the hypothesis that epigenetic variations play a key role in the aetiology of AN. In this study, we quantified genome-wide patterns of DNA methylation using the Infinium Human DNA Methylation EPIC BeadChip array (850K) in DNA samples isolated from whole blood collected from a group of 7 monozygotic twin pairs discordant for AN. more...
#> 68 Genome wide DNA methylation profiling of blood samples collected from patients after diagnosis with hepatocellular carcinoma (HCC) (cases) vs. blood samples collected from healthy individuals without family history of cancer (controls). The Illumina Infinium 450K Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 450,000 CpGs in human samples corresponding to cases (post-diagnostic HCC) and controls. more...
#> 69 Genome wide DNA methylation profiling of blood samples collected from patients prior to diagnosis with hepatocellular carcinoma (HCC) vs. blood samples collected from healthy individuals without family history of cancer. The Illumina Infinium 450K Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 450,000 CpGs in human samples corresponding to cases (pre-diagnostic HCC) and controls. more...
#> 70 β cell proliferation rates decline with age and adult β cells have limited self-duplicating activity for regeneration, which predisposes to diabetes. Here we show that, among MYC family members, Mycl was expressed preferentially in proliferating immature endocrine cells. Genetic ablation of Mycl caused a modest reduction in cell proliferation of pancreatic endocrine cells in neonatal mice. By contrast, systemic expression of Mycl in mice stimulated proliferation in pancreatic islet cells and resulted in expansion of pancreatic islets without forming tumors in other organs. more...
#> 71 Fibrous membrane (FM), the hallmark for proliferative diabetic retinopathy (PDR) and proliferative vitreoretinopathy (PVR), can cause hemorrhages and retinal detachment, which may lead to blindness if not properly treated. However, little is known about the pathophysiology of FM. In this study, we successfully employed single-cell RNA sequencing on the small-sized vitreous FMs, and generated a comprehensive cell atlas of FMs derived from PVR and PDR. more...
#> 72 Purpose: Short chain fatty acids (SCFAs) produced by the gut microbiota have dual beneficial anti-inflammatory and anti-dysbiotic effects associated with the prevention of type 1 diabetes (T1D) in mice. We have conducted a single-arm trial of a dietary supplement (HAMSAB), to determine the effects of increasing SCFA delivery in adults with long-standing T1D. Particularly, we examined blood transcriptome in these patients using RNA-seq. more...
#> 73 Purpose: The goal of this study is to conduct and compare NGS-derived transcriptome profiling (RNA-seq) of progenitor lines derived from 3 HNF1A-WT and 3 HNF1A-CRISPR (with p291fsinsC mutation) human induced pluripotent stem cell lines. Methods: mRNA profiles of WT/CRISPR pancreatic progenitor cells obtained after in-vitro differentiation for 14 days were generated by deep sequencing using Illumina HiSeq 2000 sequencer. more...
#> 74 Several studies have suggested a relationship between SARS-CoV-2 infection and diabetes. This study examined the consequences of infection of human pancreatic islets with SARS-CoV-2 virus. This GEO submission contains the raw and processed data from single-cell RNA sequencing (scRNAseq) experiments evaluating the tropism of SARS-CoV-2 in pancreatic islets and transcriptional changes induced by infection of these cells. more...
#> 75 Exosomal RNAs in cord blood may allow intercellular communication between maternal and fetus. We aimed to establish exosomal RNA expression profiles in cord blood exosomes from gestational diabetes mellitus (GDM) patients with macrosomia.We used microarray technology to establish the differential mRNA, lncRNA and circRNA expression profiles in cord blood exosomes from GDM patients with macrosomia compared with normal controls. more...
#> 76 To compare the circRNA expression profile of diabetic retinopathy with that of diabetes mellitus and controls, peripheral blood mononuclear cell samples were obtained and extracted from healthy controls and diabetes mellitus patients (with or without diabetic retinopathy). CircRNA Capital Bio Technology Human CircRNA Array v2 was performed to detect circRNA expression profiles. To further check differentiate circRNA, qRT_PCR assay was performed to detect the level of 5 candidates.
#> 77 Purpose:Metabolic syndrome (MetS) is associated with a group of conditions including diabetes, obesity, insulin resistance etc. The goal of our study is to identify the differentially regulated genes under metabolic syndromes induced by TNF-α. Methods:A Homosapines Reference based Transcriptome sequencing is performed to understand the genes that are diffrerentially regulated under metabolic synromes with TNF-α as upstream. more...
#> 78 Type 1 diabetes (T1D) results from autoimmune destruction of β-cells in the pancreas. Protein tyrosine phosphatases (PTPs) are candidate genes for T1D and play a key role in autoimmune disease development and β-cell function. Here, we assessed the global protein and individual PTP profile in the pancreas from diabetic NOD mice treated with anti-CD3 monoclonal antibody and IL-1 receptor antagonist (IL-1RA). more...
#> 79 Ex-vivo pharmacological modulation enhances the immunoregulatory and trafficking properties of HSCs which mitigated autoimmune diabetes and other autoimmune disorders. We used GeneChip microarrays to compare the whole transcriptomes of vehicle (DMSO) and dmPGE2 (10uM) + Dexamenthasone (uM) modulated human CD34+ HSPCs.
#> 80 Aims/hypothesis. Ectopic calcification is a typical feature of diabetic vascular disease and resembles an accelerated aging phenotype. We previously found an excess of myeloid calcifying cells (MCCs) in diabetic patients. We herein examined molecular and cellular pathways linking atherosclerotic calcification with calcification by myeloid cells in the diabetic milieu. Methods. We first examined the associations among coronary calcification, MCC levels, and mononuclear cell gene expression in a cross-sectional study of 87 type 2 diabetic patients undergoing elective coronary angiography. more...
#> 81 Objective: To explore the mechanism of Jiangtang Tiaozhi Recipe in the treatment of obese T2DM patients with dyslipidemia based on transcriptomics. Methods: We chose 6 patients with obese type 2 diabetes mellitus and dyslipidemia (syndrome of excess of gastrointestinal heat) who were treated by JTTZR for 24 weeks, while 6 cases included in the healthy control group. We selected 6 cases in each group (disease group before treatment, disease group after treatment and healthy control group) to start the research of lncRNA microarray. more...
#> 82 White adipose tissue (WAT), once regarded as morphologically and functionally bland, is now recognized to be dynamic, plastic, heterogenous, and involved in a wide array of biological processes including energy homeostasis, glucose and lipid handling, blood pressure control, and host defense. High fat feeding and other metabolic stressors cause dramatic changes in adipose morphology, physiology, and cellular composition, and alterations in adiposity are associated with insulin resistance, dyslipidemia, and Type 2 diabetes (T2D). more...
#> 83 We report single-cell RNA-seq (Drop-seq) data from the stromal vascular fraction (SVF) of human subcutaneous adipose tissue (SAT).
#> 84 Development of insulin resistance is a key pathogenic component underlying metabolic syndrome and Type 2 diabetes (T2DM). Despite its importance, the molecular mechanisms underlying insulin resistance are poorly understood. Genome-wide association studies for T2DM and other metabolic traits have led to the identification of many candidate SNPs, but the majority of these SNPs are noncoding and determination of associated causal genes and/or specific tissue sites of action have been difficult. more...
#> 85 Background Changes in innate and adaptive immunity occurring in/around pancreatic islets had been observed in peripheral blood mononuclear cells (PBMC) of Caucasian T1D patients by some, but not all researchers. The aim of our study was to investigate whether gene expression patterns of PBMC of the highly admixed Brazilian population could add knowledge about T1D pathogenic mechanisms. METHODS: We assessed global gene expression in PBMC from two groups matched for age, sex and BMI: 20 patients with recent-onset T1D (≤ 6 months from diagnosis, in a time when the autoimmune process is still highly active), testing positive for one or more islet autoantibodies and 20 islet autoantibody-negative healthy controls. more...
#> 86 Changes in innate and adaptive immunity occurring in and around pancreatic islets can also be observed in peripheral blood mononuclear cells (PBMC) of T1D patients in Caucasians. The aim of our study was to investigate whether gene expression patterns of PBMC could complement islet autoantibodies for T1D pathogenic mechanisms in the higlty admixed Brazilian population. Methods: We assessed global gene expression in PBMC from two groups mached for age, sex and BMI: The T1D group with 20 patients with recent-onset T1D (≤ 6 months from diagnosis, in a time when the autoimmune process is still highly active), testing positive for one or more islet autoantibodies and 20 islet autoantibody-negative healthy controls (Control group). more...
#> 87 This SuperSeries is composed of the SubSeries listed below.
#> 88 GDM is a multi-system disorder that is primarily characterised by new-onset hypertension accompanied by proteinuria during gestation. This disease is one of the leading causes of maternal and perinatal morbidity and mortality. In this work, placental samples were collected from GDM and control patients. RNA-seq was performed to identify differences in gene expression. Significantly differentially expressed genes between the GDM and control samples included 64 up-regulated and 296 down-regulated genes. more...
#> 89 N6-methyladenosine (m6A) is the most prevalent modification in eukaryotic mRNA and potential regulatory functions of m6A have been shown by mapping the RNA m6A modification landscape. M6A modification in active gene regulation manifests itself as altered methylation profiles. However, the profiling of m6A modification and its potential role in gestational diabetes mellitus (GDM) has not yet been studied. more...
#> 90 Intervertebral disc degeneration (IDD) is majorly resulted from disordered extracellular matrix (ECM) metabolism, including decreased anabolism and increased catabolism activities in the nucleus pulposus (NP) cells of discs. Pro-inflammatory cytokines such as interleukin-1β (IL-1β) are considered to be potent mediators of ECM loss. We reported previously that hemeoxygenase-1 (HO-1) inducer cobalt protoporphyrin IX (CoPP) could attenuate the ECM breakdown which induced by IL-1β, however, the underlying mechanism remains elusive. more...
#> 91 Background. Diabetes mellitus (DM) increases tuberculosis (TB) severity. We previously reported baseline blood microarray data in a South Indian pulmonary TB cohort with or without DM, finding no qualitative or quantitative differences in immune pathway gene expression. To extend those observations, we compared baseline and longitudinal blood gene expression in TB patients from South India and Brazil. more...
#> 92 Introduction. Epigenetic modifications have been implicated to mediate several complications of diabetes mellitus (DM), especially nephropathy and retinopathy. Our aims were to ascertain if epigenetic alterations in whole blood discriminate among DM patients with normal, delayed and rapid gastric emptying (GE). Methods. Using ChIP-seq (Chromatin immunoprecipitation combined with next generation sequencing) assays, we compared the genome-wide enrichment of three histone modifications (ie, H3K4me3, H3K9ac and H3K27ac) in buffy coats from 20 DM patients with normal (n=6), delayed (n=8), or rapid (n=6) GE. more...
#> 93 Type 2 diabetes (T2D) is associated with cardiovascular-renal complications and premature death. Although most patients with T2D are obese, not all obese individuals develop T2D. Thus, an understanding of the mechanistic relationships between obesity and T2D is crucial. In this study, using subcutaneous (SAT) and visceral adipose tissues (VAT) from obese individuals with or without T2D collected during metabolic surgery, integration of the transcriptomes and methylomes of VAT and SAT with publicly available tissue-specific regulatory networks, we discovered the close relation between T2D and inflammatory response in both SAT and VAT in obese individuals, although less differences were observed respectively in transcriptome or methylome. more...
#> 94 This SuperSeries is composed of the SubSeries listed below.
#> 95 Purpose: The goals of this study is to compare and profile the smallRNA transcriptome of the placenta in preeclamptic and normal patients using RNA sequencing. Methods: Placental and Placental vesicles (STB-EVs) smallRNA profiles of normal and preeclamptic patients were generated by deep sequencing using Illumina HISEQ. FASTq.gz files were compressed with OASIS compressor and alignment was done with OASIS 2.0 ( by trimmimng with trimmomatic, aligning using default OASIS 2.0 aligning papameters). more...
#> 96 Purpose: The goals of this study is to compare and profile the transcriptome of the placenta in preeclamptic and normal patients using RNA sequencing. Methods: Placental and Placental vesicles (STB-EVs) mRNA profiles of normal and preeclamptic patients were generated by deep sequencing using Illumina HISEQ. The sequence reads that passed quality filters were analyzed at the gene level HISAT2 followed by featureCounts. more...
#> 97 Enteroviruses, particularly the group B Coxsackieviruses have been associated with the development of type 1 diabetes. Several CVB serotypes can establish chronic infection in human cells in vivo and in vitro. However, the mechanisms of leading to enterovirus persistency and, possibly, bell-cell autoimmunity are not fully understood. We established a carrier-state persistent infection model in human pancreatic ductal-like cell line PANC-1 using two distinct CVB1 strains and profiled infection-induced changes in cellular transcriptome. more...
#> 98 Human islet antigen reactive CD4+ memory T cells (IAR T cells) play a key role in the pathogenesis of autoimmune type 1 diabetes (T1D). Using single cell RNA-sequencing (scRNA-seq) to identify T cell receptors (TCRs) in IAR T cells, we have identified a class of TCRs that share TCR alpha chains between individuals (“public”).
#> 99 Fungiform papillae (FP) are visible protrusions on the anterior tongue surface that contain taste buds, their nerves, and capillaries, epithelial cells, stromal cells, and immune-surveilling cells. As FP are easily biopsied in a minimally invasive procedure and have been shown to regrow, we compared three different mechanical methods of FP protein extraction and found that mechanical disruption of FP under liquid nitrogen or bead beating were more efficient than mincing in terms of yield and proteomic profile. more...
#> 100 Fetal progenitor endothelial cells (endothelial colony forming cells; ECFC) are recruited for repair, vascular growth and angiogenesis and their high abundance perinatally suggests a function in postnatal vasculogenesis and angiogenesis. In this study we profiled ECFCs from pregnancies of control, overweight and diabetic mothers to study if adverse pregnancies are associated with epigenetic variation in ECFCs.
#> 101 The establishment and function of the human placenta is dependent on specialized cells called trophoblasts. Unfortunately, little is known about the cellular and molecular processes controlling human trophoblast stem cell maintenance and differentiation into mature trophoblast sub-populations/cell states. To address this, we here report transcriptomic data from n=7 first trimester human placental tissues, n=3 regenerative human trophoblast stem cell (hTSC) derived trophoblast organoids, and n=3 EVT-differentiated hTSC derived organoid cultures at single-cell resolution. more...
#> 102 Physical training improves insulin sensitivity and can prevent type 2 diabetes. However, approximately 20% of individuals lack a beneficial outcome in glycemic control. TGF-β, identified as a possible upstream regulator involved in this low response is also a potent regulator of microRNAs (miRs). Aim of this study was to elucidate the potential impact of TGF-β-driven miRNAs on individual exercise response. more...
#> 103 Physical training improves insulin sensitivity and can prevent type 2 diabetes. However, approximately 20% of individuals lack a beneficial outcome in glycemic control. TGF-β, identified as a possible upstream regulator involved in this low response is also a potent regulator of microRNAs (miRs). Aim of this study was to elucidate the potential impact of TGF-β-driven miRNAs on individual exercise response. more...
#> 104 Arterial media calcification caused by diabetes is an important cause of vascular calcification. Dipeptidyl peptidase-4 (DPP4) is associated with diabetic arterial media calcification. At the same time, long non-coding RNA(lncRNA) is closely related to the evolution of a variety of cardiovascular diseases, but the involvement of lncRNA in vascular calcification induced by DPP4 has not been reported in details. more...
#> 105 MODY8 (maturity-onset diabetes of the young, type 8) is a dominantly inherited monogenic form of diabetes associated with frameshift mutations in the carboxyl ester lipase (CEL) gene expressed by pancreatic acinar cells. Patients carrying the mutation develop childhood-onset exocrine pancreas dysfunction followed by the manifestation of diabetes during adulthood. However, it is unclear how CEL mutations cause diabetes. more...
#> 106 Currently, no oral medications are available for individuals suffering from type 1 diabetes (T1D). Our randomized placebo-controlled phase 2 trial recently revealed that oral verapamil has short- term beneficial effects in subjects with new-onset type 1 diabetes (T1D) 1. However, what exact biological changes verapamil elicits in humans with T1D, how long they may last, and how to best monitor any associated therapeutic success has remained elusive. more...
#> 107 About 20% of youth are obese with higher risk for cardiovascular disease and type 2 diabetes (T2D). We have recently reported that in obese adolescents altered pattern of fat distribution is associated with insulin resistance and T2D. In particular, the high ratio of visceral AT depot (VAT) to abdominal subcutaneous AT depot (SAT) (high VAT/(VAT+SAT)) was associated with a metabolically unhealthy phenotype with high risk for insulin resistance and T2D. more...
#> 108 This SuperSeries is composed of the SubSeries listed below.
#> 109 We conducted prospective clinical studies to validate the importance of CD4+ T cells in 13 diseases from the following ICD-10-CM chapters: Neoplasms (breast cancer, chronic lymphocytic leukemia); endocrine, nutritional and metabolic diseases (type I diabetes, obesity); diseases of the circulatory system (atherosclerosis); diseases of the respiratory system (acute tonsillitis, influenza, seasonal allergic rhinitis, asthma); diseases of the digestive system (Crohn’s disease [CD], ulcerative colitis [UC]); and diseases of the skin and subcutaneous tissue (atopic eczema, psoriatic diseases). more...
#> 110 Salivary exsomal miRNAs may play important role in the pathogenesis of chronic inflammatory disease, such as periodontitis. There are many studies which suggested the connection between periodontitis and systemic disease, however, the role of specific miRNA as a intersection of periontitis and diabetes are not elucidated. We suggested miR-25-3p as possible common mediator in the pathogenesis of periodontitis and diabetes.
#> 111 Diabetic retinopathy (DR) is a common microvascular complication that may cause severe visual impairment and blindness in patients with type 2 diabetes mellitus (T2DM). Early detection of DR will provide opportunities for more treatment options and better control of disease progression. Effective biomarkers, which are not currently available, may improve clinical outcomes through precise diagnosis and prognosis. more...
#> 112 This SuperSeries is composed of the SubSeries listed below.
#> 113 First-degree relatives (FDRs) of type 2 diabetics (T2D) feature dysfunction of subcutaneous adipose tissue (SAT) long before T2D onset. miRNAs have a role in adipocyte precursor cells (APC) differentiation and in adipocyte identity. Thus, impaired miRNA expression may contribute to SAT dysfunction in FDRs. In the present work, we have explored changes of miRNA expression associated with T2D family history which may affect gene expression in SAT APCs from FDRs. more...
#> 114 First-degree relatives (FDRs) of type 2 diabetics (T2D) feature dysfunction of subcutaneous adipose tissue (SAT) long before T2D onset. miRNAs have a role in adipocyte precursor cells (APC) differentiation and in adipocyte identity. Thus, impaired miRNA expression may contribute to SAT dysfunction in FDRs. In the present work, we have explored changes of miRNA expression associated with T2D family history which may affect gene expression in SAT APCs from FDRs. more...
#> 115 Using next generation RNA sequencing (RNA-seq), this study evaluated the whole transcriptome of subcutaneous (SC) and omental (OM) adipose tissues from patients with gestational diabetes (GD) and healthy matching controls that were collected during cesarean delivery (C-section). Results show a strong separation of the transcriptomic profiles based on anatomical location and reveal specific RNA expression patterns unique to GD patients
#> 116 This SuperSeries is composed of the SubSeries listed below.
#> 117 Most obese and insulin resistant individuals do not develop diabetes. This is the result of the capacity of β-cells to adapt and produce enough insulin to cover the needs of the organism. The underlying mechanism of β-cell adaptation in obesity, however, remains unclear. Previous studies have suggested a role for STAT3 in mediating β-cell development and human glucose homeostasis, but little is known about its role in β-cells in obesity. more...
#> 118 Type 2 diabetes is associated with defective insulin secretion and reduced β-cell mass. Available treatments provide a temporary reprieve, but secondary failure rates are high, making insulin supplementation necessary. Reversibility of b-cell failure is a key translational question. Here, we reverse-engineered and interrogated pancreatic islet-specific regulatory networks to discover T2D-specific subpopulations characterized by metabolic-inflexibility and endocrine-progenitor/stem cell features. more...
#> 119 Type 2 diabetes is associated with defective insulin secretion and reduced β-cell mass. Available treatments provide a temporary reprieve, but secondary failure rates are high, making insulin supplementation necessary. Reversibility of b-cell failure is a key translational question. Here, we reverse-engineered and interrogated pancreatic islet-specific regulatory networks to discover T2D-specific subpopulations characterized by metabolic-inflexibility and endocrine-progenitor/stem cell features. more...
#> 120 Type 2 diabetes is associated with defective insulin secretion and reduced β-cell mass. Available treatments provide a temporary reprieve, but secondary failure rates are high, making insulin supplementation necessary. Reversibility of b-cell failure is a key translational question. Here, we reverse-engineered and interrogated pancreatic islet-specific regulatory networks to discover T2D-specific subpopulations characterized by metabolic-inflexibility and endocrine-progenitor/stem cell features. more...
#> 121 The expression of SEMA3E isoforms changes in mouse circulation with type 1 diabetes. The alterations in the transcriptional profiles of human aortic endothelial cells (HAECs) in response to PCS1 (Processing consensus sequences)-cleaved SEMA3E and PCS1-uncleaved SEMA3E were examined.
#> 122 Diabetic foot ulcers (DFUs) are a devastating complication of diabetes. In order to identify systemic and local factors associated with DFU healing, we examined the cellular landscape of DFUs by single-cell RNA-seq analysis of foot and forearm skin specimens, as well as PBMC samples, from 10 non-diabetic subjects, and 17 diabetic patients, 11 with, and 6 without DFU. Our analysis shows enrichment of a unique inflammatory fibroblast population in DFU patients with healing wounds. more...
#> 123 Background: Proliferative diabetic retinopathy (PDR) is hallmarked by the formation of retinal neovascularization (RNV) membranes, which can lead to a tractional retinal detachment, the primary reason for severe vision loss in end-stage disease. The aim of this study was to characterize the molecular and cellular features of RNV in order to unravel potential novel drug treatments for PDR. Methods: A total of 42 patients undergoing vitrectomy for PDR, macular pucker or macular hole (control patients) were included in this study. more...
#> 124 Preclinical models of type 1 diabetes mellitus exhibit marked declines in skeletal muscle health including significant impairments in muscle repair. The present study investigated, for the first time, whether muscle repair was altered in young adults with uncomplicated type 1 diabetes (T1D) following damaging exercise.In this cohort study, eighteen physically-active young adults (M=22.1, SEM=0.9 years) with T1D (n, male/female=4/5; MHbA1c= 58, SEMHbA1c=5.9 mmol/mol) and without T1D (n, male/female=4/5) performed 300 unilateral eccentric contractions (90°s-1) of the knee extensors. more...
#> 125 Diabetic foot ulcers (DFUs) are a devastating complication of diabetes. To better understand the molecular mechanisms and cell types implicated in DFU healing, we used NanoString’s GeoMx Digital Spatial profiling platform on DFU tissue sections and compared gene expression of areas within the same ulcer as well as between patients who in 12 weeks following surgery healed their DFU (Healers, N=2) vs those who did not (Non-Healers, N=2).
#> 126 This SuperSeries is composed of the SubSeries listed below.
#> 127 Long non-coding RNAs (lncRNAs) are widely involved in gene transcription regulation and which act as epigenetic modifiers. To determine whether lncRNAs are involved in ischemic stroke (IS), we analyzed the expression profile of lncRNAs and mRNAs in IS. RNA sequencing was performed on the blood of three pairs of IS patients and heathy controls. Differential expression analysis was used to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs). more...
#> 128 Cardiovascular disease (CVD) is the leading cause of mortality in diabetes mellitus (DM). However, the molecular factors that cause this disproportiona increase in CVD in the DM/chronic kidney disease (CKD) population are still unknown.Human endothelial cells treated with high glucose to mimic DM and with the uremic toxin indoxyl sulfate (IS) to mimic the endothelial injury associated with CKD. Differentially expressed lncRNAs in these conditions were analyzed by RNA sequencing.Lnc-SLC15A1-1 expression was significantly increased upon IS treatment versus high glucose alone.
#> 129 The pathogenesis of non-alcoholic fatty liver disease is not fully understood. Transcriptomic analysis of a large cohort of 318 patients provides evidence of gene perturbations related to inflammation, complement and coagulation pathways, and tissue remodeling in distinct states of NAFLD.
#> 130 N6-methyladnine, which is the most abundant post-transcriptional RNA modification in eukaryotic mRNA, has been proved to be essential in various biological processes and related to numerous diseases. Transcriptome-wide m6A profiling by next generation sequencing is widely used to explore the distributions as well as quantity of m6A modifications. As traditional m6A-seq demands large amount of starting RNA which limited its application to clinical samples, we present a strategy of low input multi-barcode m6A-seq (SLIM-m6A-seq) to realize simplified m6A profiling of mixed clinical samples. more...
#> 131 Stem cell derived beta-like cells (sBC) carry the promise of providing an abundant source of insulin-producing cells for use in cell replacement therapy for patients with diabetes, potentially allowing widespread implementation of a practical cure. To achieve their clinical promise, sBC need to function comparably to mature adult beta cells, but as yet they display varying degrees of maturity. Indeed, detailed knowledge of the events resulting in human beta cell maturation remains obscure. more...
#> 132 The goal of this study is to compare RNA-seq of wild-type fibroblasts and patient fibroblasts bearing the m.3243A>G mutatioin. When comparing patient fibroblasts to wild-type ones and using a significance level of false discovery rate (FDR) < 0.05, we identified 3394 transcripts of which 1849 were upregulated and 1545 were downregulated.
#> 133 Heterozygous human INS gene mutations are known to promote ER stress, leading to β-cell dysfunction and neonatal diabetes. Recent literature challenged the long-standing notion that neonatal diabetes occurs due to ER stress-induced β-cell apoptosis. Importantly, mechanisms of β-cell failure during the disease progression and why the other wild-type (WT) INS allele is unable to function still remain unclear. more...
#> 134 The objective of this study is to investigate alveolar bone gene expression in health and diabetes through RNA-sequencing and bioinformatics analysis.
#> 135 The mechanisms of obesity and type 2 diabetes (T2D)-associated impaired fracture healing are poorly studied. In a murine model of T2D reflecting both hyperinsulinemia induced by high fat diet (HFD) and insulinopenia induced by treatment with streptozotocin (STZ), we examined bone healing in a tibia cortical bone defect. A delayed bone healing was observed during hyperinsulinemia as newly formed bone was reduced by – 28.4±7.7% and was associated with accumulation of marrow adipocytes at the defect site +124.06±38.71%, and increased density of SCA1+ (+74.99± 29.19%) but not Runx2+osteoprogenitor cells. more...
#> 136 BACKGROUND AND AIMS: It is proposed that impaired expansion of subcutaneous adipose tissue (SAT), caused in part by an increase in adipose tissue fibrosis, redirects fatty acids to the liver and other organs, leading to ectopic lipid accumulation and insulin resistance. We therefore evaluated whether a decrease in SAT expandability, assessed by measuring SAT lipogenesis (triglyceride production), and an increase in SAT fibrogenesis (collagen production) are associated with nonalcoholic fatty liver disease (NAFLD) and insulin resistance in people with obesity. more...
#> 137 Retinal neovascularization is a severe complication of proliferative diabetic retinopathy. We have previously identified that miRNAs is directly involved in the development of retinal neovascularization. Here, we explored the role of miRNAs and its underlying mechanism in modulating angiogenesis.
#> 138 Background: A previous Phase I study showed that the infusion of autologous Treg cells expanded ex-vivo into recent onset Type 1 Diabetes (T1D) patients had an excellent safety profile, however, the majority of the infused Tregs could no longer be detected in the peripheral blood three months post-infusion (NCT01210664-Treg-T1D Trial). Interleukin-2 (IL-2) is a well-characterized cytokine that has been shown to enhance human Treg cell survival and expansion at low doses in vivo. more...
#> 139 Developmental alteration in brain wiring that would make it more susceptible to later pathological processes has been suggested as a basis for the occurrence of neurodegenerative diseases, but mechanisms have remained elusive. A recent series of magnetic resonance imaging studies have demonstrated that, in Wolfram syndrome, neurodegenerative processes appear during childhood and adolescence on top of a clinically silent global defect in brain development. more...
#> 140 The aim of this study is to investigate the impact of the metabolic status on the transcriptome of isolated preadipocytes and in vitro differentiated adipocytes. We identified 38654 transcripts in pancreatic fat cells. We report that preadipocyte differentiation increased the abundance of mRNA levels of proteins related to adipogenesis and lipid metabolism. These changes in the transcriptome were absent or less pronounced in fat cells obtained from patients with prediabetes and type 2 diabetes. more...
#> 141 Type 2 diabetes is a complex, systemic disease affected by both genetic and environmental factors. Previous research has identified genetic variants associated with type 2 diabetes risk, however gene regulatory changes underlying progression to disease are still largely unknown. We investigated RNA expression changes that occur during diabetes progression using a two-stage approach. In our discovery stage, we compared changes in gene expression using two longitudinally collected blood samples from subjects who transitioned to type 2 diabetes between the time points against those who did not with a novel analytical network approach. more...
#> 142 Free fatty acids (FFAs) are often stored in lipid droplet (LD) depots for eventual metabolic and/or synthetic use in many cell types, such a muscle, liver, and fat. In pancreatic islets, overt LD accumulation was detected in humans but not mice. LD buildup in islets was principally observed after roughly 11 years of age, increasing throughout adulthood under physiologic conditions, and also enriched in type 2 diabetes. more...
#> 143 Dysregulation of macrophage populations at the wound site is responsible for the non-healing state of chronic wounds. The molecular mechanisms underlying macrophage dysfunction and its control in diabetes are largely unexplored on an epigenetic level. Here, we report that acetyl histone-H3 (Lys27), an epigenetic mark regulating the macrophage transcriptome, is lost in the hostile tissue microenvironment in diabetes. more...
#> 144 Permutational analysis of immune landscape reveals advanced immune aging in people with Down syndrome and in people with type 1 diabetes.
#> 145 Islet-enriched transcription factors (TFs) exert broad control over cellular processes in pancreatic α and β cells and changes in their expression are associated with developmental state and diabetes. However, the implications of heterogeneity in TF expression across islet cell populations are not well understood. To define this TF heterogeneity and its consequences for cellular function, we profiled >40,000 cells from normal human islets by scRNA-seq and stratified α and β cells based on combinatorial TF expression. more...
#> 146 Purpose: Excess oxidative stress (OS) impairs endothelial function and plays an important role in vascular diseases, diabetes, and neuronal disorders. Several consequences of OS including cell recovery and apoptosis have been described previously. In this study, we report systems model of the temporal dynamics of the oxidative stress response in Human Umbilical Vein Endothelial Cells (HUVECs) and characterize HMOX1 as a master regulator in orchestrating the response to oxidative stress. more...
#> 147 ATAC-seq (assay for transposase-accessible chromatin followed by sequencing) is widely used to decode chromatin accessibility. Here we performed high-sensitive ATAC-seq in 9 human liver samples from normal and T2D donors, and identified a set of differentially accessible regions (DARs). DARs were overlapped with publicly available CREs databases and integrated with multi-omics data to identify candidates for further experimental validations. more...
#> 148 Single nuclei sequencing of grafts developed 14 weeks post transplantation of human embryonic stem cell derived pancreatic progenitors alone (PP) or with rat adipose derived microvessels (PPMV) into the subcutaneous pocket of diabetic (STZ-induced) Scid-beige mice.
#> 149 This SuperSeries is composed of the SubSeries listed below.
#> 150 Persons living with HIV (PLWH) are at increased risk of tuberculosis (TB). HIV-associated TB is mainly the result of recent infection with Mycobacterium tuberculosis (Mtb) followed by rapid progression to disease. Alveolar macrophages (AM) are the first cells of the innate immune system that engage Mtb, but how HIV and antiretroviral therapy (ART) impact on the anti-mycobacterial response of AM is not known. more...
#> 151 Persons living with HIV (PLWH) are at increased risk of tuberculosis (TB). HIV-associated TB is mainly the result of recent infection with Mycobacterium tuberculosis (Mtb) followed by rapid progression to disease. Alveolar macrophages (AM) are the first cells of the innate immune system that engage Mtb, but how HIV and antiretroviral therapy (ART) impact on the anti-mycobacterial response of AM is not known. more...
#> 152 Circular RNA can regulate blood glucose levels by targeting mRNA expression, but the role of circRNA in GDM is still unknown. Therefore, a joint microarray analysis of circRNAs and their targeting mRNAs using the peripheral blood of GDM patients and healthy pregnant women was carried out for the first time. In our study, high-throughput microarray sequencing technique was used to analyze the expression profile of circRNA and transcripts mRNA in the peripheral blood of GDM patients, in order to comprehensively evaluate the role of circRNAs targets and their parents genes in the signal pathways related to the pathogenesis of GDM. more...
#> 153 To improve the power of mediation in high-throughput studies, here we introduce High-throughput mediation analysis (Hitman), which accounts for direction of mediation and applies empirical Bayesian linear modeling. We apply Hitman in a retrospective, exploratory analysis of the SLIMM-T2D clinical trial in which participants with type 2 diabetes were randomized to Roux-en-Y gastric bypass (RYGB) or nonsurgical diabetes/weight management, and fasting plasma proteome and metabolome were assayed up to 3 years. more...
#> 154 Congenital generalized lipodystrophy (CGL) is an autosomal recessive disorder characterized by defective adipose tissue, extreme insulin resistance, and early onset of diabetes. There are four types of congenital generalized lipodystrophy based on the causative genetic alterations. The symptoms and the degrees of disease progression are varied among all affected individuals, which might be due to unknown genetic modifiers. more...
#> 155 This SuperSeries is composed of the SubSeries listed below.
#> 156 Circadian rhythms are generated by an auto-regulatory feedback loop composed of transcriptional activators and repressors. Disruption of circadian rhythms contributes to Type 2 diabetes (T2D) pathogenesis. We elucidated whether altered circadian rhythmicity of clock genes is associated with metabolic dysfunction in T2D. Transcriptional cycling of core clock genes BMAL1, CLOCK, and PER3 was altered in skeletal muscle from individuals with T2D and this was coupled with reduced number and amplitude of cycling genes and disturbed circadian oxygen consumption. more...
#> 157 Circadian rhythms are generated by an auto-regulatory feedback loop composed of transcriptional activators and repressors. Disruption of circadian rhythms contributes to Type 2 diabetes (T2D) pathogenesis. We elucidated whether altered circadian rhythmicity of clock genes is associated with metabolic dysfunction in T2D. Transcriptional cycling of core clock genes BMAL1, CLOCK, and PER3 was altered in skeletal muscle from individuals with T2D and this was coupled with reduced number and amplitude of cycling genes and disturbed circadian oxygen consumption. more...
#> 158 we apply miRNA sequencing from blood samples of 10 DMED patients and 10 DM controls to study the mechanism of miRNAs action on DMED.
#> 159 Genome Wides Association Studies (GWAS) have identified tens of thousands of associations between human genetic variation and common disease. The majority of causative variants lie in regulatory elements that are located some distance from their target genes. High resolution chromosome conformation capture (3C) has proven useful for identifying enhancer-promoter interaction. We employed targeted Capture-C at loci with GWAS for severe COVID-19, Type-1 Diabetes (T1D), Ankylosing spondylitis (AS) and red blood cell traits (RBC)
#> 160 Severe angiopathy is a major driver for diabetes associated secondary complications. Knowledge on underlying mechanisms essential for advanced therapies to attenuate these pathologies is limited. Injection of ABCB5+ stromal precursors (SPs) at the edge of non-healing diabetic wounds in a murine db/db model, closely mirroring human type II diabetes, profoundly accelerates wound closure. Strikingly, enhanced angiogenesis was substantially enforced by the release of the ribonuclease angiogenin from ABCB5+ SPs. more...
#> 161 Hepatic lipid accumulation is a hallmark of type 2 diabetes (T2D) and associated with hyperinsulinemia, insulin resistance, and hyperphagia. Hepatic synthesis of GABA, catalyzed by GABA-transaminase (GABA-T), is upregulated in obese mice. To assess the role of hepatic GABA production in obesity-induced metabolic and energy dysregulation, we treated mice with two pharmacologic GABA-T inhibitors and also knocked down hepatic GABA-T expression using an antisense oligonucleotide. more...
#> 162 Dysregulation of glucagon secretion in type 1 diabetes (T1D) involves hypersecretion during postprandial states, but insufficient secretion during hypoglycemia. The sympathetic nervous system regulates glucagon secretion. To investigate islet sympathetic innervation in T1D, sympathetic tyrosine hydroxylase (TH) axons were analyzed in control non-diabetic organ donors, non-diabetic islet autoantibody-positive individuals (AAb), and age-matched persons with T1D. more...
#> 163 We employed a microarray as a discovery platform to identify the differential gene expressions between hND islets and hT2DM islets. 4805 genes with differential expression (fold change >2) were manifested in hT2DM islets. Inflammatory response and immune response were the mostly upregulated biological processes distinguished betwee hND islets and T2DM islets. Results provided insight into the molecular mechanisms in T2DM.
#> 164 Epidemiological evidence has identified an association between breast cancer (BC) and systemic dysregulation of glucose metabolism. However, how BC influences glucose homeostasis remains unknown. Here we show that BC-derived extracellular vesicles (EVs) suppress pancreatic endocrine secretion to systemically reset glucose homeostasis. In pancreatic β-cells, miR-122 delivered in BC-derived EVs targets PKM to suppress glycolysis and ATP-dependent insulin exocytosis. more...
#> 165 Heterozygous mutations in HNF1B in humans result in a multi-system disorder, including pancreatic hypoplasia and diabetes mellitus. The underlying mechanisms that contribute to disease pathogenesis remain largely unknown, partially accounted by the fact that mouse models with heterozygous deletions in Hnf1b do not develop diabetes, in contrast to the phenotypes observed in MODY patients. Here we used a well-controlled human induced pluripotent stem cell pancreatic differentiation model to elucidate the molecular mechanisms underlying HNF1B-associated diabetes and pancreatic hypoplasia. more...
#> 166 Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the effect of body mass index (BMI) which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in BD and BMI interaction remained unclear. more...
#> 167 Bipolar disorder (BD) and obesity are highly comorbid. We previously performed a genome-wide association study (GWAS) for BD risk accounting for the effect of body mass index (BMI) which identified a genome-wide significant single-nucleotide polymorphism (SNP) in the gene encoding the transcription factor 7 like 2 (TCF7L2). However, the molecular function of TCF7L2 in the central nervous system (CNS) and its possible role in BD and BMI interaction remained unclear. more...
#> 168 Background: Tumor stage predicts pancreatic cancer (PDAC) prognosis, but prolonged and short survivals have been described in patients with early-stage tumors. Circulating microRNA (miRNA) are an emerging class of suitable biomarkers for PDAC prognosis. Our aim was to identify whether serum miRNA signatures predict survival of early-stage PDAC. Methods: Se-rum RNA from archival 15 stage I-III PDAC patients and 4 controls was used for miRNAs ex-pression profile (Agilent microarrays). more...
#> 169 Myometrial biopsies were collected from 31 women undergoing primary cesarean sections and were carefully phenotyped with respect to gestational age (GA), circumstances of labor onset, and clinical status at the start and end of the intervention. Cases were aggregated into groups as follows: Group 1: term birth following spontaneous onset of term labor (TL, n=5); Group 2: term birth by elective cesarean section not in labor (TNL, n=5); Group 3: PTB following spontaneous preterm labor with intact membranes (n=6); Group 4: preterm birth following PPROM (n=8); and Group 5: provider-initiated preterm birth in the absence of active labor contractions, cervical dilation or membrane rupture (n=7). more...
#> 170 This SuperSeries is composed of the SubSeries listed below.
#> 171 Background: Cardiovascular disease had a global prevalence of 523 million cases and 18.6 million deaths in 2019. The current standard for diagnosing coronary artery disease (CAD) is coronary angiography. Surprisingly, despite well-established clinical indications, up to 40% of the one million invasive cardiac catheterizations return a result of ‘no blockage’. The present studies employed RNA sequencing of whole blood to identify an RNA signature in patients presenting with a clinical suspicion of CAD. more...
#> 172 Human pancreatic islets, including insulin secreting beta-cells are a major focus of transplantation strategies aimed at identifying new therapeutic approaches to counteract hyperglycemia in patients with diabetes. Identifying the transcriptomic signature of human islet cells provides insights into regulatory pathways that can be harnessed for planning therapeutic strategies. In this context, single-cell RNA-sequencing (scRNA-seq) has been used mostly in vitro. more...
#> 173 The objective of this study was to perform a global, non-targeted gene expression analysis by microarray, to understand the immune cell gene regulation at fasting and in response to oral glucose load and how this regulation is different in Asian-Indian men with normal glucose tolerance (NGT) and pre-diabetes (PD). Through observing real-time gene expression changes, this study highlights 1. the importance of acute metabolic challenges like oral glucose load in regulating the immune cell gene expression and function. more...
#> 174 In our genome-wide association study, we searched for an association of genetic variants with colorectal cancer, type 1 diabetes, Hodgkin lymphoma and Diffuse large B-cell lymphoma among Polish population.
#> 175 Stem and progenitor cells in the adult human pancreas provide an under-explored resource for regenerative medicine. Using micro-manipulation and methylcellulose-containing colony/organoid assays, we identified cells within the human cadaveric exocrine pancreas that fulfill the definition of a stem cell: able to self-renew and differentiate. Exocrine tissues were collected after the isolation of endocrine cells, dissociated into single cells, and plated into a 3-dimensional semisolid medium. We found that some pancreatic ductal cells gave rise to cystic colonies/organoids containing pancreatic duct, acinar, and endocrine lineage cells. These cells self-renewed and expanded approximately 300-fold over 9 weeks. When transplanted into diabetic mice, colonies/organoids lowered blood glucose levels and gave rise to insulin-expressing endocrine cells. Thus, stem/progenitor-like cells capable of self-renewal and differentiation either preexist in the adult human pancreas or readily adapt in culture. more...
#> 176 Betel-nut consumption is the fourth most common addictive habit globally and there is good evidence linking the habit to obesity, type 2 diabetes (T2D) and the metabolic syndrome. The aim of our pilot study was to identify gene expression relevant to obesity, T2D and the metabolic syndrome using a genome-wide transcriptomic approach in a human monocyte cell line incubated with arecoline and its nitrosated products.
#> 177 Generation of mature cells with stable functional identities is crucial for developing cell-based replacement therapies. Current global efforts to produce insulin-secreting beta-like cells to treat diabetes are hampered by the lack of tools to reliably assess cellular identity. We conducted a thorough single-cell transcriptomics meta-analysis to generate robust genesets defining the identity of human adult alpha-, beta-, gamma- and delta-cells. more...
#> 178 This study aimed to analyze the mutated genes of primary and recurrent SSs (PRSSs), to discover whether these sarcomas exhibit some potential mutated genes between primary and recurrent cases Illumina Infinium whole genome genotyping (WGG) arrays are increasingly being applied in cancer genomics to study gene copy number alterations and allele-specific aberrations such as loss-of-heterozygosity (LOH). more...
#> 179 Purpose: To detect serum exosomal ncRNA profiles of proliferative diabetic retinopathy (PDR) by High-throughput sequencing Methods: serum exosomal non-coding RNA (ncRNA) profiles profiles of PDR and MH were generated by deep sequencing, only in once, using IlluminaHiSeq 3000. After analyzing the base composition and quality of the data, according to the analysis results of the original data, the data were filtered to remove the joint sequence and the contaminated part, and to remove low-quality base sequences.If it is paired-ended sequencing data, the filtered data should be further screened to retain the paired sequences and obtain clean data. more...
#> 180 Four male patients were enrolled for this study in collaboration with the Cardiology Unit of Policlinico Tor Vergata-Fondazione PTV (Rome). We have performed RNA-Sequencing using NextSeq 500 ILLUMINA platform on PBMCs of patients with clinically proven healthy coronary arteries (CTR) and patients with chronic coronary artery disease (CAD) confirmed by coronary angiography. RNA sequencing results showed differentially Altenative Splicing (AS) events and filtering for a statistically significant Splicing-Index Fold-Change≥ ±1.5 (p≤0.05) we observed 113 differentially regulated AS events (24 up and 89 down-regulated) from 86 genes. more...
#> 181 Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. more...
#> 182 Skeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases and mobility. It is composed of several different cell and muscle fiber types. Here, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. more...
#> 183 This SuperSeries is composed of the SubSeries listed below.
#> 184 We identified a novel lncRNA DRAIR that is downregulated in CD14+ monocytes from type 2 diabetes relative to controls. Functional studies showed that DRAIR regulates anti-inflammatory genes and its knockdown enhances proinflammatoory phentype of monocytes. To examine mechanisms of DRAIR actions, we performed Chromatin isolation by RNA purification (ChIRP) assays using DRAIR biotinylated tiling oligonucleotides to identify chromatin inding sites in THP-1 monocytes.
#> 185 Monocyte activation by high glucose and free fatty acids promotes inflammation implicated in vascular complications associated with Type 2 diabetes (T2D). Emerging evidence shows that long non coding RNA (lncRNA)s regulate inflammation, but their role in T2D induced monocyte dysfunction is unclear. To examine this, we profiled the transcriptome of CD14+ monocytes from volunteers with T2D and without diabetes (n=5 each) using strand-specific RNA-seq on Illumina HiSeq 2500. more...
#> 186 Genome wide DNA methylation profiling of cord blood cells obtained from gestational diabetes mellitus (GDM) pregnancies. The Illumina EPIC methylation beadchip array was used to obtain DNA methylation profiles across approximately 850,000 CpG dinucleotide methylation loci in DNA isolated from cord blood. Samples include 165 GDM subjects.
#> 187 Pancreatic beta cell senescence occurs during the development of Type 1 Diabetes. To model the transcriptional responses of islet cells to DNA damage, we previously developed a human islet culture model in which the DNA damage response and senescence can be induced via double strand-breaks with the agent bleomycin. Here, we report the transcriptome-wide changes in human pancreatic islet cells following bleomycin exposure.
#> 188 We investigated the role of SNO-GNIA2 in HG+oxLDL-induced endothelial inflammation during the development of diabetes-accelerated atherosclerosis and found that SNO-GNAI2 could promote endothelial inflammation through dysregulating Hippo-YAP . We hypothesized that SNO-GNAI2 induced Hippo-YAP dysfunction through enhancing coupling and activating G-protein coupling receptors (GPCRs).
#> 189 We describe an unusual course of Ketosis Prone Diabetes and investigate potential mechanisms using induced pluripotent stem cell technology with high throughput mRNA sequencing and validation of a lecuine sensitive mTOR pathway.
#> 190 Hyperhomocysteinemia (HHcy) is an established and potent independent risk factor for degenerative diseases, including cardiovascular disease (CVD), Alzheimer disease, type II diabetes mellitus, and chronic kidney disease. HHcy has been shown to inhibit proliferation and promote inflammatory responses in endothelial cells (EC), and impair endothelial function, a hallmark for vascular injury. However, metabolic processes and molecular mechanisms mediating HHcy-induced endothelial injury remains to be elucidated. more...
#> 191 Hyperhomocysteinemia (HHcy) is an established and potent independent risk factor for degenerative diseases, including cardiovascular disease (CVD), Alzheimer disease, type II diabetes mellitus, and chronic kidney disease. HHcy has been shown to inhibit proliferation and promote inflammatory responses in endothelial cells (EC), and impair endothelial function, a hallmark for vascular injury. However, metabolic processes and molecular mechanisms mediating HHcy-induced endothelial injury remains to be elucidated. more...
#> 192 The aim of this study was to establish the exosomal miRNA profile across gestation in normal and GDM pregnancies and to determine the signaling pathways associated with the changes in the miRNA profile in GDM.
#> 193 Purpose: Whole-transcriptome sequencing technology and bioinformatics analysis were applied to systematically analyze the differentially expressed mRNAs, lncRNAs, circRNAs and miRNAs in adipose stem cells (ASCs) from diabetic, old and young patients. Methods: MRNAs, lncRNAs and cirRNAs profiles of adipose stem cells were generated by RNA sequencing, in triplicate, using Illumina HiSeq X Ten . MiRNAs profiles of adipose stem cells were generated by RNA sequencing, in triplicate, using BGISEQ-500. more...
#> 194 Patients with hypertension alone, hypertension plus controlled diabetes and hypertension plus uncontrolled diabetes, and control patients without these conditions underwent coronary artery bypass grafting surgery. Skeletal muscle biopsy specimens were taken at the beginning ('pre-operative') and at the end ('post-operative') of the surgery.
#> 195 Skeletal muscle aging is characterized by a progressive decline in muscle mass and function, which is referred to as sarcopenia. Aging is also a primary risk factor for metabolic syndrome (SX), which is a cluster of risk factors for cardiovascular diseases and type 2 diabetes. However, the molecular mechanisms implicated in sarcopenia and changes in muscle proteome associated with SX in elderly men remain unclear. more...
#> 196 The placenta is a highly heterogeneous organ and is closely related to adverse pregnancy. The previous bulk sequencing of whole tissue could not show the characteristics of individual cells and the interactions between cells. Here, we select the placental tissues of the gestational diabetes group(GDM), preeclampsia group(PE), advanced age group(GL) and normal control group for single-cell sequencing in order to explain the mechanism of related diseases in more depth.nated spatial and temporal regulation of gene expression in the murine hindlimb determines the identity of mesenchymal progenitors and the development of diversity of musculoskeletal tissues they form. more...
#> 197 Studies in rodents have shown obesity and aging impair tissue nicotinamide adenine dinucleotide (NAD+) biosynthesis, which contributes to metabolic dysfunction. The availability of nicotinamide mononucleotide (NMN) is an important rate-limiting factor in mammalian NAD+ biosynthesis. We conducted a 10-week, randomized, placebo-controlled, double-blind trial to evaluate the effect of NMN supplementation on metabolic function in 25 postmenopausal women with prediabetes who were overweight/obese. more...
#> 198 Recent clinical data has suggestedsed a bi-directional relationship between Coronavirus disease 19 (COVID-19) and diabetes. Here, we showdemonstrateed the detection of SARS-CoV-2 in pancreatic endocrine cells in autopsy samples derived fromof COVID-19 patients. Single cell RNA-seq and immunostaining confirmed that multiple types of pancreatic islet cells can be infected byare susceptible to SARS-CoV-2, eliciting a cellular stress response and the induction of chemokines. more...
#> 199 This SuperSeries is composed of the SubSeries listed below.
#> 200 Diabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Here we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 80 patients diagnosed with various stages of DR. more...
#> 201 Diabetic Retinopathy (DR) is among the major global causes for vision loss. With the rise in diabetes prevalence, an increase in DR incidence is expected. Current understanding of both the molecular etiology and pathways involved in the initiation and progression of DR is limited. Here we analyzed mRNA and miRNA expression profiles of 80 human post-mortem retinal samples from 80 patients diagnosed with various stages of DR. more...
#> 202 By functionally dissecting densest enhancer cluster in the gene desert at 9P21 locus, we identified a non-redundant inter-dependent enhancer network that functions over long distances, the perturbation in any enhancer in the network results in the complete collapse of entire enhancer cluster and target genes activity. The enhancer network can be targeted to regulate INK4a/ARF locus in associated pathophysiologies and cancers.
#> 203 To gain insight into the history of islet cell deterioration along the progression from normal glycemic regulation to T2D, we collected surgical pancreatic tissue samples from 133 metabolically phenotyped pancreatectomized patients (PPP). Gene expression profiles of islets isolated by laser capture microdissection (LCM) from resected and snap-frozen pancreas samples were assessed by RNA sequencing.
#> 204 This SuperSeries is composed of the SubSeries listed below.
#> 205 High blood levels of free fatty acids link obesity with type-2 diabetes, but this connection remains poorly understood. We have investigated lipolysis and glucose homeostasis in recently diagnosed obese type-2 diabetics; in obese insulin resistant non-diabetic subjects (obese-IR) matched for age, sex, body composition and fasting insulin levels; and in healthy lean individuals. Our results show that obese-IR dissociate lipolysis from glycemic control, revealing that the action of compensatory hyperinsulinemia on blood glucose is not mediated by reduced lipolysis. more...
#> 206 In a randomized controlled trial, 82 older adults (>65y) with (or at risk of) undernutrition (n=82) were randomly allocated to 12 weeks of supplementation with a novel supplement (586 kcal, 22 g protein of which 50% whey and 50% casein, 206 mg ursolic acid, 7 g free BCAAs, 11 µg vitamin D) or standard care (600 kcal, 24g protein of which 100% casein, 4 µg vitamin D). Body weight increased significantly in the 12 weeks, both in the intervention group (+1.6 ± 0.2 kg, p<.0001) and in the standard care group (+1.8 ± 0.2 kg, p<.0001). more...
#> 207 Despite the central role of chromosomal context in gene transcription, human noncoding DNA variants are generally studied outside of their endogenous genomic location. This limits our understanding of disease-causing regulatory variants. INS promoter mutations cause recessive neonatal diabetes. We studied 60 patients with such mutations, and show that all single base mutations disrupt a CC dinucleotide, while none affect elements important for INS promoter function in episomal assays. more...
#> 208 Our transcriptomic phenotyping of pancreatic cell types provides novel insights into pancreas biology, as well as the initial pathogenic events in T1D.
#> 209 This SuperSeries is composed of the SubSeries listed below.
#> 210 A multi-omic approach in a clinical experimental study identified circulating biomarkers reflecting glucocorticoid exposure. Background: Endogenous glucocorticoids (GC) are mechanistically linked to common diseases and are important as drugs in the treatment of many disorders. There is no marker that can measure and quantify GC action. Our aim was to identify circulating biomarkers of GC action using a clinical experimental study. more...
#> 211 A multi-omic approach in a clinical experimental study identified circulating biomarkers reflecting glucocorticoid exposure. Background: Endogenous glucocorticoids (GC) are mechanistically linked to common diseases and are important as drugs in the treatment of many disorders. There is no marker that can measure and quantify GC action. Our aim was to identify circulating biomarkers of GC action using a clinical experimental study. more...
#> 212 Type 2 diabetes mellitus is mainly affected by genetic and environmental factors, and long noncoding RNAs (lncRNAs) have been shown to be correlated with diabetes.LncRNA is expected to be a target for the treatment and prediction of type 2 diabetes. We used microarrays to detail the lncRNAs and mRNAs expression in type 2 diabetes patients and healthy controls and obtained differentially expressed genes. more...
#> 213 Over 90% of disease associated single nucleotide polymorphisms (SNPs) identified by genome wide association studies (GWAS) are noncoding variants. Platform to efficiently validate the biological function of variants thus discovered remain distinctly lacking. Here, we used β-like cells derived from isogenic human pluripotent stem cells (hPSCs), carrying the type 1 diabetes (T1D)-associated noncoding SNP rs2542151T>G or the knockout of the SNP-associated gene PTPN2−/−, to systematically examine the role of the T1D associated noncoding variant in β cell function and survival. more...
#> 214 Glucocorticoids are key regulators of glucose homeostasis and pancreatic islet function. In this study we used ATAC-seq and RNA-seq to map chromatin accessibility and gene expression from eleven primary human islet samples cultured in vitro with the glucocorticoid dexamethasone at multiple doses and durations. We identified thousands of accessible chromatin sites and genes with significant changes in activity in response to glucocorticoids. more...
#> 215 The objective of this study was to investigate whether placental exosomes in gestational diabetes mellitus (GDM) carries a specific set of miRNAs associated with skeletal muscle insulin sensitivity. Exosomes were isolated from chorionic villi-conditioned media and plasma from normal and GDM pregnancies. A specific set of miRNAs was identified to be selectively enriched within exosomes when compared to their cells of origin indicating specific packaging of miRNAs into exosomes. more...
#> 216 The aging of pancreatic beta-cells may undermine their ability to compensate for insulin resistance, leading to the development of type 2 diabetes (T2D). Aging beta-cells acquire markers of cellular senescence and develop a senescence-associated secretory phenotype (SASP) that can lead to senescence and dysfunction of neighboring cells through paracrine actions, contributing to beta-cell failure. Herein, we defined the beta-cell SASP signature based on unbiased proteomic analysis of conditioned media of cells obtained from human senescent beta-cells. more...
#> 217 Genetic risk variants identified in genome-wide association studies (GWAS) of complex disease are primarily non-coding, and translating risk variants into mechanistic insight requires detailed gene regulatory maps in disease-relevant cell types. Here, we combined a GWAS of type 1 diabetes (T1D) in 520,580 samples with candidate cis-regulatory elements (cCREs) in pancreas and peripheral blood mononuclear cell types defined using single nucleus ATAC-seq (snATAC-seq) of 131,554 nuclei. more...
#> 218 Obesity is a major risk factor for a high number of secondary diseases, including cancer. Specific insights into the role of gender differences and secondary co-morbidities, such as type 2 diabetes (T2D) and cancer risk, are yet to be fully obtained. The aim of this study is thus to find a correlation between the transcriptional deregulation present in the subcutaneous adipose tissue of obese patients and the risk of cancer, in the presence of T2D, and considering gender differences. more...
#> 219 This SuperSeries is composed of the SubSeries listed below.
#> 220 Introduction. Hindered by a limited understanding of the molecular mechanisms responsible for diabetic gastroenteropathy (DGE), patients are managed by symptom-based therapies. We investigated the duodenal mucosal expression of protein-coding genes and miRNAs in DGE and related these abnormalities to clinical features. Methods. mRNA and micro RNA (miRNA) expression and ultrastructure of duodenal mucosal biopsies were investigated in 39 DGE patients and 21 healthy controls. more...
#> 221 Introduction. Hindered by a limited understanding of the molecular mechanisms responsible for diabetic gastroenteropathy (DGE), patients are managed by symptom-based therapies. We investigated the duodenal mucosal expression of protein-coding genes and miRNAs in DGE and related these abnormalities to clinical features. Methods. mRNA and micro RNA (miRNA) expression and ultrastructure of duodenal mucosal biopsies were investigated in 39 DGE patients and 21 healthy controls. more...
#> 222 This SuperSeries is composed of the SubSeries listed below.
#> 223 This study was designed to simulate the effect of hyperglycemia on the proximal tubule. This portion of the kidney is responsible for reabsorption of the filtered glucose, and thus, the amount reabsorbed is not regulated by insulin. A long-term exposure was designed to model aspects of renal demise seen in diabetes. We utilized mortal cultures of human renal tubule epithelial cells isolated from renal cortical tissue. more...
#> 224 The association between T2 DM and BMSCs osteogenic differentiation has been documented in experimental settings. We examine miRNA expression specific for BMSCs from human jaw in Type 2 diabetics.
#> 225 Single-cell RNAseq (10x Genomics) analysis of human CD4+ T cells in IPEX patients, healthy donors and heterozygous mothers (blood). Human CD4+T cells from IPEX, HD and mothers were isolated from frozen peripheral blood mononuclear cells by flow cytometry as DAPI–CD3+CD4+ cells. In cohort 1, cells from separate donor were encapsulated in separate channel following 10x Genomics Single Cell 3′ Reagent Kit (V2 chemistry). more...
#> 226 Mitochondrial respiration and gene expression related to mitochondrial function were measured from the peripheral blood of infection and sepsis patients as well as healthy controls
#> 227 Despite reduced function and volume of the exocrine pancreas in type 1 diabetes, literature describing the histology and the molecular biological profile in this area is limited. Here, the density of acinar cells was examined adjacent to and at varying distances from islets and the transcriptome was analyzed on laser capture microdissected (LCM) tissue from organ donors with and without type 1 diabetes. more...
#> 228 Islet function diminishes with age and as such the incidence of type 2 diabetes increases. The cause of this is unknown. In this study whole islets were extracted with laser capture microdissection from organ donors 1-81 years of age. Increasing age was associated with a downregulation of pathways associated with the cell cycle and increase in markers of senescence e.g. CDKN2A. Among novel genes increasing with age was SPP1.
#> 229 Aims/hypothesis: Recurrent hypoglycaemia (RH) is a major side-effect of intensive insulin therapy for people with diabetes. Changes in hypoglycaemia sensing by the brain contribute to the development of impaired counterregulatory responses to and awareness of hypoglycaemia. Little is known about the intrinsic changes in human astrocytes in response to acute and recurrent low glucose (RLG) exposure. Methods: Human primary astrocytes (HPA) were exposed to zero, one, three or four bouts of low glucose (0.1 mmol/l) for three hours per day for four days to mimic RH. On the fourth day, DNA and RNA were collected. Differential gene expression and ontology analyses were performed using DESeq2 and GOseq respectively. DNA methylation was assessed using the Infinium MethylationEPIC BeadChip platform. Results: 24 differentially expressed genes (DEGs) were detected (after correction for multiple comparisons). One bout of low glucose exposure had the largest effect on gene expression. Pathway analyses revealed that endoplasmic-reticulum (ER) stress-related genes such as HSPA5, XBP1, and MANF, involved in the unfolded protein response (UPR), were all significantly increased following LG exposure, which was diminished following RLG. There was little correlation between differentially methylated positions and changes in gene expression yet the number of bouts of LG exposure produced distinct methylation signatures. Conclusions/interpretation: These data suggest that exposure of human astrocytes to transient LG triggers activation of genes involved in the UPR linked to endoplasmic reticulum (ER) stress. Following RLG, the activation of UPR related genes was diminished, suggesting attenuated ER stress. This may be mediated by metabolic adaptations to better preserve intracellular and/or ER ATP levels, but this requires further investigation.
#> 230 Aims/hypothesis: Recurrent hypoglycaemia (RH) is a major side-effect of intensive insulin therapy for people with diabetes. Changes in hypoglycaemia sensing by the brain contribute to the development of impaired counterregulatory responses to and awareness of hypoglycaemia. Little is known about the intrinsic changes in human astrocytes in response to acute and recurrent low glucose (RLG) exposure. Methods: Human primary astrocytes (HPA) were exposed to zero, one, three or four bouts of low glucose (0.1 mmol/l) for three hours per day for four days to mimic RH. On the fourth day, DNA and RNA were collected. Differential gene expression and ontology analyses were performed using DESeq2 and GOseq respectively. DNA methylation was assessed using the Infinium MethylationEPIC BeadChip platform. Results: 24 differentially expressed genes (DEGs) were detected (after correction for multiple comparisons). One bout of low glucose exposure had the largest effect on gene expression. Pathway analyses revealed that endoplasmic-reticulum (ER) stress-related genes such as HSPA5, XBP1, and MANF, involved in the unfolded protein response (UPR), were all significantly increased following LG exposure, which was diminished following RLG. There was little correlation between differentially methylated positions and changes in gene expression yet the number of bouts of LG exposure produced distinct methylation signatures. Conclusions/interpretation: These data suggest that exposure of human astrocytes to transient LG triggers activation of genes involved in the UPR linked to endoplasmic reticulum (ER) stress. Following RLG, the activation of UPR related genes was diminished, suggesting attenuated ER stress. This may be mediated by metabolic adaptations to better preserve intracellular and/or ER ATP levels, but this requires further investigation.
#> 231 DNA methylation data throughout human muscle cell differentiation in n=14 individuals with type 2 diabetes and n=14 controls
#> 232 DNA methylation data for both proliferating myoblasts and differentiated myotubes from n=14 individuals with type 2 diabetes and n=14 controls
#> 233 mRNA expression data throughout human muscle cell differentiation in n=13 individuals with type 2 diabetes and n=13 controls
#> 234 mRNA expression data for both proliferating myoblasts and differentiated myotubes from n=13 individuals with type 2 diabetes and n=13 controls
#> 235 This SuperSeries is composed of the SubSeries listed below.
#> 236 Preterm or small for gestational age (SGA) infants are to be at high risk of noncommunicable diseases in adolescence, because they are exposed to hypoxia and malnutrition in and ex utero during perinatal period. Epigenetics could be one of the most important mechanisms of DOHaD.In the field of premature babies, previous studies investigated the methylation alterations related to gestational age and birthweight by using cord blood samples. more...
#> 237 We profiled scRNA-seq of 284 samples collected from 196 individuals, including 22 patients with mild/moderate symptoms, 54 hospitalized patients with severe symptoms, and 95 recovered convalescent persons, as well as 25 healthy controls. The samples were obtained from various tissue types, including human peripheral blood mononuclear cells (249), bronchoalveolar lavage fluid (12) and pleural pleural effusion (1)/sputum (22).
#> 238 We performed single cell transcriptomic analysis on 17 urine samples obtained from five subjects at two different occasions using both spot and 24-hour urine collection. In addition, we used a combined spot urine samples of five healthy individuals as a control sample. We sequenced a total of 71,667 cells. After quality control and downstream analysis, we found that epithelial cells were the most common cell types in the urine. more...
#> 239 We performed RNA-sequencing in uninfected, SARS-CoV-2-infected, and additionally remdesivir treated ex vivo cultured human islets from two donors to shed light on the transcriptional changes occurring upon viral infection.
#> 240 Previously, we developed a new model of diabetes-induced wound healing impairment in skin-humanized mice models that faithfully recapitulates the major histo-physiological features of such skin repair-deficient condition. Aiming to dissect the molecular mechanisms responsible for the delayed wound closure, global gene expression studies were performed.
#> 241 This SuperSeries is composed of the SubSeries listed below.
#> 242 Genome wide DNA Methylation in fetal cord blood and placenta from mother with GDM compared to mother with normal glucose tolerance
#> 243 Genome wide DNA Methylation in fetal cord blood and placenta from mother with GDM compared to mother with normal glucose tolerance
#> 244 Genetic variants associated with type 2 diabetes (T2D) risk affect gene regulation in metabolically relevant tissues, such as pancreatic islets. Here, we investigated contributions of regulatory programs active during pancreatic development to T2D risk. Interrogation of chromatin maps from developmental precursors throughout pancreatic differentiation of human embryonic stem cells (hESCs) identifies enrichment of T2D variants in pancreatic progenitor-specific stretch enhancers that are not active in islets. more...
#> 245 The increased usage of alternative Ayurvedic treatments as potential health-beneficial therapies emphasizes the importance of studying its efficacy in sound placebo-controlled intervention trials. An example of such a traditional Ayurvedic herbal preparation is Mohana Choorna, a mixture composed of 20 different herbs and used to prevent and treat type 2-diabetes (T2D). We studied the efficacy of “Mohana Choorna” on T2D-related parameters in subjects with impaired glucose tol-erance. more...
#> 246 Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. more...
#> 247 Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. more...
#> 248 Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type-specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. more...
#> 249 Single-nucleus assay for transposase-accessible chromatin using sequencing (snATAC-seq) creates new opportunities to dissect cell type–specific mechanisms of complex diseases. Since pancreatic islets are central to type 2 diabetes (T2D), we profiled 15,298 islet cells by using combinatorial barcoding snATAC-seq and identified 12 clusters, including multiple alpha, beta and delta cell states. We cataloged 228,873 accessible chromatin sites and identified transcription factors underlying lineage- and state-specific regulation. more...
#> 250 Type 2 diabetes mellitus (T2D), characterised by peripheral insulin resistance, is a risk factor for dementia. In addition to its contribution to small and large vessel disease, T2D may directly damage cells of the brain neurovascular unit. In this study, we investigated the transcriptomic changes in cortical neurones, and associated astrocytes and endothelial cells of the neurovascular unit, in the ageing brain
#> 251 It has been well established that the presence of diabetes is accompanied by a chronic inflammatory state promoting various diabetes-associated complications. One potential driver of this enhanced inflammatory state in patients with diabetes is hyperglycemia. Even after blood glucose control is achieved, diabetes-associated complications persist, suggesting the presence of a “hyperglycemic memory.” Innate immune cells, critically involved in various complications associated with diabetes, can build nonspecific, immunological memory (trained immunity) via epigenetic regulation. more...
#> 252 Adipose tissue is found throughout the human body. The diversity of physiological specialization of fat depots is reflected in the depot-specific alterations seen in lipodystrophies and links between specific patterns of fat distribution and susceptibility to diseases, including Type II Diabetes. We compared gene expression patterns in seven anatomically diverse fat depots and in adipocytes and stromal-vascular cells isolated from each sample. more...
#> 253 We report the high-throughput miRNA sequencing of plasma isolated from human patients with type 2 diabetes & gastroparesis, idiopathic gastroparesis alone, and healthy control patients.
#> 254 Pericardial sac surrounding the heart contains pericardial fluid (PF), which is rich in exosomes. PF exosomes increase angiogenesis in hypoxic endothelial cells and in animal model of hindlimb ischemia by passing the proangiogenic miRNAs to recipient cells. However, under pathological conditions such as diabetes, exosome cargo composition changes and harmful miRNAs can be transferred to the recipient cells and induce more deleterious effects in target tissues. more...
#> 255 Improving the early diagnosis and treatment of type 2 diabetes (T2D) can effectively control blood glucose. To investigate new long non-coding RNAs (lncRNAs) as molecular markers we used microarrays to identify differentially expressed lncRNAs and mRNAs in peripheral blood mononuclear cells from T2D patients and controls.
#> 256 Glucagon-like peptide-1 (GLP-1) is an incretin hormone that potentiates glucose stimulated insulin secretion. GLP-1 is classically produced by gut L cells; however, under certain circumstances alpha-cells can express the prohormone convertase required for proglucagon processing to GLP-1, prohormone convertase 1/3 (PC1/3), and can produce GLP-1. However, the mechanisms through which this occurs are poorly defined. more...
#> 257 This SuperSeries is composed of the SubSeries listed below.
#> 258 The downregulation of diabetes susceptibility gene GLIS3 contributes to pancreatic beta cell demise, at least in part, through downregulation of the splicing factor SRSF6. Here, we used individual-nucleotide UV crosslinking and immunoprecipitation (iCLIP) to map the RNA binding landscape of SRSF6 in pancreatic beta cells.
#> 259 The gene expression signature of the human kidney interstitium is not fully understood. Transcript expression of laser microdissected cortical interstitium (excluding tubules, glomeruli and large vessels) in 9 human reference nephrectomies was compared to 6 human diabetic kidney biopsy specimens. This transcriptomic data revealed novel interstitial markers and enrichment of relevant pathways. Analysis of diabetic interstitium uncovered genes with unchanged as well as down-regulated expression when compared to reference samples. more...
#> 260 Glycemic control is a strong predictor of long-term cardiovascular risk in patients with diabetes mellitus, and poor glycemic control influences long-term risk of cardiovascular disease even decades after optimal medical management. This phenomenon, termed glycemic memory, has been proposed to occur due to stable programs of cardiac and endothelial cell gene expression. This transcriptional remodeling has been shown to occur in the vascular endothelium through a yet undefined mechanism of cellular reprogramming. more...
#> 261 Bulk RNA-sequencing of sorted CD8 T cells from recent-onset T1D subjects treated with alefacept.
#> 262 Idiopathic nodular mesangial sclerosis, also called idiopathic nodular glomerulosclerosis (ING) is a rare clinical entity with unclear pathogenesis. The hallmark of this disease is the presence of nodular mesangial sclerosis on histology without clinical evidence of diabetes mellitus or other predisposing diagnoses. To achieve insights into its pathogenesis, we queried the clinical, histopathologic and transcriptomic features of ING and nodular diabetic nephropathy (DN)
#> 263 Comparative genomic hybridization analysis for detection of recurring gene copy number variation (CNV) among a set of lung cancer mestastatic brain tumors DNA was isolated and analyzed in a two-color experiment using Cancer CGH+SNP 180Kx4 arrays from Agilent and Agilent SureScan system: Cy5-labeled specimen DNA and Cy3-labeled Agilent characterized normal human reference DNA
#> 264 Hepatocellular carcinoma (HCC) is the most common primary liver malignancy and is one of the leading causes of cancer-related deaths worldwide. The multi‐target inhibitor sorafenib is a first-line treatment for patients with advanced unresectable HCC. Recent clinical studies have evidenced that patients treated with sorafenib together with the anti-diabetic drug metformin have a survival disadvantage compared to patients receiving sorafenib only. more...
#> 265 Women with diabetes have a higher prevalence of cardiovascular complications than men, suggesting that sex-steroid hormones like estrogen may impact on female health in diabetes. Here we demonstrate that estrogen suppletion and insulin resistance in male-to-female transgenders coincides with lower plasma levels of miR-224 and miR-452 carried in extracellular vesicles. Systemic silencing of miR-224 and miR-452 in mice triggered a prediabetic phenotype with higher plasma insulin levels, increased white adipose lipogenesis and less glucose uptake and mitochondrial respiration in brown adipose tissue. more...
#> 266 This study aimed to examine the postprandial transcriptome of adipose tissue middle-aged men selectively recruited on the basis of MetS (defined by the International Diabetes Federation (IDF) criteria) and healthy control participants. Two breakfast meals that provided different macronutrient composition, and were indicative of major patterns of dietary habits (animal-based versus plant-based) were given, postprandial adipose gene expression was measured by microarray at fasting (0 h) and 4 hours post-meal.
#> 267 Transcriptional profiling of human PBMCs comparing healthy controls, patients with diabetic nephropathy and patients with ESRD. PBMCs were analyzed as they mediate inflammatory injury. Goal was to determine effects of increasing severity of diabetic nephropathy on global PBMC gene expression. Microarray analysis of PBMCs taken from patients with varying degrees of diabetic nephropathy.
#> 268 We previously reported a child with transient neonatal diabetes mellitus (TNDM), who upon molecular diagnosis was homozygous for a one base-pair deletion in ZFP57, inheriting the mutations from both heterozygous parents. Methylation profiling at diagnosis revealed severe hypomethylation at PLAGL1 and mosaic loss-of-methylation (LOM) at GRB10, NAP1L5 and GNAS-XL DMRs. Some years after the first child, a second sibling was born with a comparable clinical presentation. more...
#> 269 Understanding the process of immune remodeling and regulation in SARS-CoV-2 infected patients from hospitalization to rehabilitation is crucial for therapy of patients with COVID-19. Here, we performed a longitudinal whole-transcriptome RNA sequencing on peripheral blood mononuclear cell (PBMC) samples of 18 patients with mild, moderate or severe COVID-19 symptoms during the treatment, convalescence and rehabilitation stages. more...
#> 270 Maternal metabolic disorders such as obesity and diabetes are detrimental factors that compromise fertility and the success rates of medically assisted procreation (MAP) procedures. During metabolic stress, adipose tissue is more likely to release free fatty acids (FFA) in the serum resulting in an increase of FFA levels not only in blood, but also in follicular fluid (FF). In humans, high concentrations of palmitic acid (PA) and stearic acid (SA) reduced granulosa cell survival and were associated with poor cumulus-oocyte complex (COC) morphology. more...
#> 271 The aim of this study was therefore to investigate molecular mechanisms associated with insulin sensitivity in skeletal muscle by relating global skeletal muscle gene expression with a surrogate measure of insulin sensitivity, i.e. homeostatic model assessment of insulin resistance (HOMA-IR). To identify genes with skeletal muscle expression related to insulin sensitivity, we obtained muscle biopsies from 38 non-diabetic participants in study A. more...
#> 272 We studied 9 healthy young non-diabetic men without any family history of diabetes. The mean age and body mass index (BMI) were 25.33 ± 0.33 years and 24.57 ± 0.62 kg/m2, respectively, and the mean 1/ homeostatic model assessment of insulin resistance (HOMA-IR) was 1.17 ± 0.12. We included baseline gene expression profile data (i.e. only before bed rest)
#> 273 To identify genes correlated to insulin sensitivity in skeletal muscle, we studied 38 non-diabetic men from Malmö, Sweden. Briefly, the Malmö Exercise Intervention cohort consists of sedentary but otherwise healthy male subjects from southern Sweden. They all have European ancestry and 18 of them have a first-degree family member with T2D. The mean age and body mass index (BMI) were 37.71 ± 0.71 years and 28.47 ± 0.48 kg/m2, respectively, and the mean 1/the homeostatic model assessment-insulin resistance (HOMA-IR) was 0.69 ± 0.04
#> 274 The mechanisms underlying Roux-en-Y gastric bypass (RYGB) surgery-induced weight loss and the immediate postoperative beneficial metabolic effects associated with the operation remain uncertain. We aimed to identify novel gut-derived peptides with therapeutic potential in obesity and/or diabetes by determining genome-wide expression patterns in isolated human small intestinal enteroendocrine cells (EECs) obtained from 20 obese subjects undergoing RYGB and again three months later by upper enteroscopy. more...
#> 275 RNA-seq data of monocyte-derived human Dendritic cells (huDCs) cultured with PSAB-liposomes and/or Liraglutide
#> 276 Genome-wide DNA methylation profiling of umbilical cord blood buffy coat DNA samples. The Illumina Infinium MethylationEPIC array was used to obtain DNA methylation profiles across approximately 850,000 CpGs. Samples included 557 cord blood samples born to obese women in the UPBEAT trial, with and without gestational diabetes mellitus (GDM), to determine the association between maternal GDM and hyperglycaemia during pregnancy on the methylation in the infant.
#> 277 This SuperSeries is composed of the SubSeries listed below.
#> 278 We report novel epigenetic mechanisms of epigenetic memory and its role in regulation of transporter genes in diabetic renal proximal tubules. We have generated RNA-seq, ATAC-seq and Infinium EPIC methylation array datasets from human primary proximal tubule epithelial cells from non-diabetic healthy controls and from patients with history of Type II Diabetes.
#> 279 We report novel epigenetic mechanisms of epigenetic memory and its role in regulation of transporter genes in diabetic renal proximal tubules. We have generated RNA-seq, ATAC-seq and Infinium EPIC methylation array datasets from human primary proximal tubule epithelial cells from non-diabetic healthy controls and from patients with history of Type II Diabetes. Analyses of RNA-seq, ATAC-seq and Methylaiton EPIC array data.
#> 280 Deglycosylated-leucine-rich α-2-glycoprotein1 (DG-LRG1) as well as LRG1 was discovered to promote angiogenesis under diabetes mellitus condition through TGF-β independent binding to endoglin. To examine the signaling pathways triggered by DG-LRG1, we subjected whole-cell protein lysates of control and DG-LRG1 treated HUVECs to a Phospho Explorer antibody array analysis using commercial antibody array assay kit (Full Moon Biosystems, Inc.). more...
#> 281 Pancreatic β-cell failure is key to type 2 diabetes (T2D) onset and progression. We assessed whether human β-cell dysfunction induced by metabolic stress is reversible, evaluated the molecular pathways underlying persistent or transient damage, and explored the relationships with T2D islet traits. Twenty-six human islet preparations were exposed to several lipo- and/or glucotoxicity conditions, some of which impaired insulin release depending on stressor type, concentration and combination. more...
#> 282 Persons with HIV have a disproportionate burden of metabolic disease, including type 2 diabetes. We hypothesized that the accumulation of chronically activated T cells in the adipose tissue of HIV+ persons is a central mechanism promoting local macrophage activation, impaired adipocyte function, and the development of HIV-associated glucose intolerance. Prior studies of immune activation and HIV-associated metabolic disease have only measured circulating T cell subsets. more...
#> 283 This SuperSeries is composed of the SubSeries listed below.
#> 284 Although analysis of maternal plasma cell-free content has been employed for screening of genetic abnormalities within a pregnancy, limited attention has been paid to its use for the detection of adverse pregnancy outcomes (APOs) based on placental function. We investigated the cell-free RNA content of 102 maternal, 25 cord plasma samples and 7 non pregnant women as control. using cell-free RNA sequencing, APOs revealed seventy-one differentially expressed genes early in pregnancy. more...
#> 285 Although analysis of maternal plasma cell-free content has been employed for screening of genetic abnormalities within a pregnancy, limited attention has been paid to its use for the detection of adverse pregnancy outcomes (APOs) based on placental function. Here we investigated cell-free DNA and RNA content of 102 maternal and 25 cord plasma samples. Employing a novel deconvolution methodology, we found that during the first trimester, placenta-specific DNA increased prior to the subsequent development of gestational diabetes with no change in patients with preeclampsia while decreasing with maternal obesity. more...
#> 286 Obesity and type 2 diabetes (T2D) can be associated with altered secretion of enterohormones in condition that remains to be understood in depth. Here, we aimed to decipher the mechanisms by which a major enterohormone GLP-1, is decreased in human obese patients according to their diabetic status.
#> 287 Metformin is a classic type II diabetes drug which has possessed anti-tumor properties for various cancers. However, different cancers do not respond to metformin with the same effectiveness or acquire resistance. Thus, searching for vulnerabilities of metformin-resistant prostate cancer is a promising strategy to improve the therapeutic efficiency. A genome-scale CRISPR-Cas9 activation library targeting 23430 genes is conducted to identify the genes that confer resistance to metformin in prostate cancer cells.
#> 288 Diabetes mellitus is associated with serious long-term complications, including increased cardiovascular risk and a higher occurrence of infections. These diabetes-related complications are suggestive of altered responses of the innate immune system. Recent studies have shown that energy metabolism of monocytes is a crucial determinant of their functionality. Here we investigate whether metabolism and function of monocytes are changed in patients with diabetes and aim to identify diabetes-associated factors driving these alterations. more...
#> 289 Dedifferentiation of pancreatic beta cells may reduce islet function in type 2 diabetes (T2D). However, the prevalence, plasticity and functional consequences of this cellular state remain unknown. We employed single-cell RNAseq to detail the maturation program of alpha and beta cells during human ontogeny. We show that although both alpha and beta cells mature in part by repressing non-endocrine genes, alpha-cells retain hallmarks of an immature state, while beta-cells attain a full beta-cell specific gene expression program. more...
#> 290 In vitro differentiation of human ES cells into insulin-producing β-like cells offers new opportunities for pancreatic development modeling and potential diabetes therapy. However, the precise molecular events associated with this multi-stage process remain unclear. Here, we generated 95,308 single cell transcriptome data encompassing the entire differentiation process, and reconstructed a tree delineating the fate choices of all major cell populations in both endocrine and non-endocrine lineages. more...
#> 291 Type 1 diabetes (T1D) is characterized by immune mediated destruction of insulin producing β cells. Biomarkers capable of identifying T1D risk and dissecting disease-related heterogeneity represent an unmet clinical need. Aims: Towards the goal of informing T1D biomarker strategies, we profiled different classes of RNAs in human islet-derived exosomes and identified RNAs that were differentially expressed under cytokine stress conditions. more...
#> 292 We performed RNA-seq on tissue biopsies derived from patients with DFUs and compared it to human oral and skin wounds to identify the molecular mechanisms and transcriptional networks that are deregulated in DFUs. Our results identified a unique inflammatory transcriptional signature unique to oral and skin wounds involved in promoting cell proliferation and cell survival of immune cells that are deficient in DFUs. more...
#> 293 Immune responses in lungs of Coronavirus Disease 2019 (COVID-19) are poorly characterized. We conducted transcriptomic, histologic and cellular profiling of post mortem COVID-19 and normal lung tissues. Two distinct immunopathological reaction patterns were identified. One pattern showed high expression of interferon stimulated genes (ISGs) and cytokines, high viral loads and limited pulmonary damage, the other pattern showed severely damaged lungs, low ISGs, low viral loads and abundant immune infiltrates. more...
#> 294 We investigated whether circulating microRNAs (miRNAs) are associated with residual insulin secretion at diagnosis and predict the severity of its future decline. We studied 53 newly diagnosed subjects enrolled in placebo groups of TrialNet clinical trials. We measured serum levels of 2,083 miRNAs using RNAseq technology, in fasting samples from the baseline visit (<100 days from diagnosis), during which residual insulin secretion was measured with a mixed meal tolerance test (MMTT). more...
#> 295 Histone deacetylases (HDACs) are important regulators of epigenetic gene modification that are involved in the transcriptional control of metabolism. In particular class IIa HDACs have been shown to affect hepatic gluconeogenesis and previous approaches revealed that their inhibition reduces blood glucose in type 2 diabetic mice. In the present study, we aimed to evaluate the potential of class IIa HDAC inhibition as a therapeutic opportunity for the treatment of metabolic diseases. more...
#> 296 IL-12 and IL-18 synergize to promote TH1 responses and have been implicated as accelerators of autoimmune pathogenesis in type 1 diabetes (T1D). We therefore investigated the influence of these cytokines on phenotype and function of immune cells that are involved in disease progression. To understand how IL-12 and IL-18 may synergize to impair Treg function and phenotype, we conducted transcriptional profiling of Treg expanded under normal conditions or in the presence of IL-12 and IL-18. more...
#> 297 We identified that PBMC of individuals simultaneously affected by a combination of T2DM, dyslipidemia and periodontitis, showed altered molecular profile mainly associated to inflammatory response, immune cell trafficking, and infectious disease pathways Patients were divided into: T2DMpoorly-DL-P (n=5, Grupo 1), T2DMwell-DL-P (n=7, Grupo 2), DL-P (n=6, Grupo 3), P (n=6, Grupo 4) and Healthy (n=6, Control). more...
#> 298 The growth hormone plays a significant role in normal renal function and overactive growth hormone signaling has been implicated in proteinuria in diabetes. Earlier studies from our group have shown that the glomerular podocytes, which play an essential role in renal filtration, express the growth hormone receptor, suggesting the direct action of growth hormone on these cells. Nevertheless, the precise mechanism and the downstream pathways that are induced by the excess growth hormone in these podocytes leading to diabetic nephropathy are not clearly established. more...
#> 299 The incidence of new onset diabetes after transplant (NODAT) has increased over the past decade, likely due to calcineurin inhibitor-based immunosuppressants, including tacrolimus (TAC) and cyclosporin (CsA). Voclosporin (VCS), a next generation calcineurin inhibitor is reported to cause fewer incidences of NODAT but the reason is unclear. Whilst calcineurin signaling plays important roles in pancreatic beta-cell survival, proliferation, and function, its effects on human beta-cells remain understudied. more...
#> 300 This SuperSeries is composed of the SubSeries listed below.
#> 301 We perfomed transcriptomic and methylomic analysis of sural nerve biopsies from type 2 diabetic patients with neuropathy. Sural nerve transcriptomic and methylomic profiles were integrated and subsequent biological meaning investigated using KEGG pathway analysis of overlapping differentially expressed genes (DEGs) and differentially methylated genes (DMGs). A gene interation network was also generated including DEGs and DMGs, and common biological pathways were identified.
#> 302 We perfomed transcriptomic and methylomic analysis of sural nerve biopsies from type 2 diabetic patients with neuropathy. Sural nerve transcriptomic and methylomic profiles were integrated and subsequent biological meaning investigated using KEGG pathway analysis of overlapping differentially expressed genes (DEGs) and differentially methylated genes (DMGs). A gene interation network was also generated including DEGs and DMGs, and common biological pathways were identified.
#> 303 Gene expression plasticity is central for macrophages? timely responses to cues from the microenvironment permitting phenotypic adaptation from pro-inflammatory (M1) to wound healing and tissue-regenerative (M2, with several subclasses). Regulatory macrophages (Mreg) are a distinct macrophage type, partially sharing some functionalities with both M1 and M2 cells. Mreg possess immunoregulatory, anti-inflammatory, and angiogenic properties, and are considered as a potential allogeneic cell therapy product to treat clinical conditions, e.g., non-healing diabetic foot ulcers. more...
#> 304 This SuperSeries is composed of the SubSeries listed below.
#> 305 Background: Cold acclimation and exercise training were previously shown to increase peripheral insulin sensitivity in human volunteers with type 2 diabetes. Although cold is a potent activator of brown adipose tissue, the increase in peripheral insulin sensitivity by cold is largely mediated by events occurring in skeletal muscle and at least partly involves GLUT4 translocation, as is also observed for exercise training. more...
#> 306 Background: Cold acclimation and exercise training were previously shown to increase peripheral insulin sensitivity in human volunteers with type 2 diabetes. Although cold is a potent activator of brown adipose tissue, the increase in peripheral insulin sensitivity by cold is largely mediated by events occurring in skeletal muscle and at least partly involves GLUT4 translocation, as is also observed for exercise training. more...
#> 307 Chromatin-associated RNA (caRNA) has been proposed as a type of epigenomic modifier. Here, we test whether environmental stress can induce cellular dysfunction through modulating RNA-chromatin interactions. We induce endothelial cell (EC) dysfunction with high glucose and TNFα (H + T), that mimic the common stress in diabetes mellitus. We characterize the H + T-induced changes in gene expression by single cell (sc)RNA-seq, DNA interactions by Hi-C, and RNA-chromatin interactions by iMARGI. more...
#> 308 Chromatin-associated RNA (caRNA) has been proposed as a type of epigenomic modifier. Here, we test whether environmental stress can induce cellular dysfunction through modulating RNA-chromatin interactions. We induce endothelial cell (EC) dysfunction with high glucose and TNFα (H + T), that mimic the common stress in diabetes mellitus. We characterize the H + T-induced changes in gene expression by single cell (sc)RNA-seq, DNA interactions by Hi-C, and RNA-chromatin interactions by iMARGI. more...
#> 309 Chromatin-associated RNA (caRNA) has been proposed as a type of epigenomic modifier. Here, we test whether environmental stress can induce cellular dysfunction through modulating RNA-chromatin interactions. We induce endothelial cell (EC) dysfunction with high glucose and TNFα (H + T), that mimic the common stress in diabetes mellitus. We characterize the H + T-induced changes in gene expression by single cell (sc)RNA-seq, DNA interactions by Hi-C, and RNA-chromatin interactions by iMARGI. more...
#> 310 This SuperSeries is composed of the SubSeries listed below.
#> 311 Chromatin-associated RNA (caRNA) has been proposed as a type of epigenomic modifier. Here, we test whether environmental stress can induce cellular dysfunction through modulating RNA-chromatin interactions. We induce endothelial cell (EC) dysfunction with high glucose and TNFα (H + T), that mimic the common stress in diabetes mellitus. We characterize the H + T-induced changes in gene expression by single cell (sc)RNA-seq, DNA interactions by Hi-C, and RNA-chromatin interactions by iMARGI. more...
#> 312 Chromatin-associated RNA (caRNA) has been proposed as a type of epigenomic modifier. Here, we test whether environmental stress can induce cellular dysfunction through modulating RNA-chromatin interactions. We induce endothelial cell (EC) dysfunction with high glucose and TNFα (H + T), that mimic the common stress in diabetes mellitus. We characterize the H + T-induced changes in gene expression by single cell (sc)RNA-seq, DNA interactions by Hi-C, and RNA-chromatin interactions by iMARGI. more...
#> 313 Chromatin-associated RNA (caRNA) has been proposed as a type of epigenomic modifier. Here, we test whether environmental stress can induce cellular dysfunction through modulating RNA-chromatin interactions. We induce endothelial cell (EC) dysfunction with high glucose and TNFα (H + T), that mimic the common stress in diabetes mellitus. We characterize the H + T-induced changes in gene expression by single cell (sc)RNA-seq, DNA interactions by Hi-C, and RNA-chromatin interactions by iMARGI. more...
#> 314 Background: Prolonged exposure to elevated free fatty acids induces β-cell failure (lipotoxicity) and contributes to the pathogenesis of type 2 diabetes. In vitro exposure of β-cells to the saturated free fatty acid palmitate is a valuable model of lipotoxicity, reproducing features of β-cell failure observed in type 2 diabetes. In order to map the β-cell response to lipotoxicity, we combined RNA-sequencing of palmitate-treated human islets with iTRAQ proteomics of insulin-secreting INS-1E cells following a time course exposure to palmitate. more...
#> 315 Circulating cell-free unmethylated DNA fragments arising from the human INS gene have been proposed as biomarkers of β-cell death for the presymptomatic detection of diabetes. However, given the variability of CpG methylation in the INS gene in different cell types, this gene alone may not yield sufficiently specific information to unambiguously report β-cell damage. We employed an unbiased approach using data from a human DNA methylation gene array to identify the CHTOP gene as a candidate biomarker whose CpGs show a greater frequency of unmethylation in human islets. more...
#> 316 Proliferative diabetic retinopathy (PDR) is the advanced stage of diabetic retinopathy (DR), coupling with irregular neovascularization, and is the leading cause of blindness in working-age people; but the molecular mechanism of vascular differentiation in PDR remains poorly characterized. In our study, we obtained the transcriptome profile of neovascular proliferative membrane specimens from patients with PDR via high-throughput sequencing and advanced bioinformatics. more...
#> 317 Metabolic syndrome, whose main diagnostic component is obesity, is a risk factor for lifestyle-related diseases, type 2 diabetes, and cardiovascular disease. Diet is known to affect the prevalence of metabolic syndrome. However, the effect of diet on metabolic syndrome in Japanese subjects has not been thoroughly explored. In the present study, we investigated the effect of carotenoid-rich vegetables, particularly lycopene- and lutein-rich vegetables, on the metabolic syndrome in obese Japanese men. more...
#> 318 Autoimmune destruction of pancreatic β cells underlies type 1 diabetes (T1D). To understand T-cell mediated immune impact on human pancreatic β cells, we combine β cell specific expression of a model antigen CD19 and anti-CD19 chimeric antigen receptor T (CAR-T) cells. Co-culturing CD19-expressing -like cells and CD19 CAR-T cells results in T-cell mediated β-like cell death with release of activated T cell cytokines. more...
#> 319 LncRNAs are developmentally regulated and highly cell type-specific non-coding RNAs that have emerged as important regulators of cell fate commitment and maintenance. In this study, we dissected the role of lncRNAs in human pancreas development by classifying lncRNAs based on their dynamic regulation, subcellular localization, and engagement with ribosomes during the stepwise differentiation of human embryonic stem cells (hESCs) towards pancreatic fate. more...
#> 320 Diabetic foot ulcers (DFUs) and associated impaired healing, represent a major problem, that significantly impairs the quality of life of diabetic patients, leading to prolonged hospitalization and resulting in more than 70,000 lower extremity amputations per year in the USA alone. In the present study, we prospectively followed a large group of DFU patients for 12 weeks and aimed to identify systemic and local factors that are associated with DFU healing. more...
#> 321 Brown adipocytes (BAs) are a potential therapeutic cell source for the treatment of metabolic disease such as type 2 diabetes. In this report, human pluripotent stem cells (hPSCs) are subject to directed differentiation to brown dipocytes through a paraxial mesoderm intermediate at high-efficiency. RNA-Seq and ATAC-seq was performed to characterized hPSCs derived paraxial mesoderm and brown adipocytes generated in this study.
#> 322 TCF7L2 rs290487 C allele increases diabetic risk in Chinese, however the mechanism remains unclear. We herein evaluated the role of rs290487 variant in hepatic glucose homeostasis by integrating clinical and multi-omics data (ChIP-seq, ATAC-seq, RNA-seq, and metabolomics) from CRISPR/Cas9 edited PLC-PRF-5 cell lines (C/C vs. C/T). In clinical cohort, C/C genotype was associated with higher insulin resistance index and higher incidence of hepatogenous diabetes as compared to C/T heterozygote and T/T homozygote genotypes. more...
#> 323 Excessive mitochondrial fission plays a key role in podocyte injury in diabetic kidney disease (DKD), and long noncoding RNAs (lncRNAs) are important in the development and progression of DKD. However, lncRNA regulation of mitochondrial fission in podocytes is poorly understood. Here, we want to identify how lncRNA changes in human podocytes cultured with high glucose.
#> 324 Diabetes is characterized by hyperglycemia, loss of functional islet beta cell mass, deficiency of glucose-lowering insulin, and persistent alpha cell secretion of gluconeogenic glucagon. Still, no therapies that target these underlying processes are available. We therefore performed high-throughput screening of 300,000 compounds and extensive medicinal chemistry optimization and here report the discovery of SRI-37330, an orally bioavailable, non-toxic small molecule, which effectively rescued mice from streptozotocin- and obesity-induced (db/db) diabetes. more...
#> 325 Interest in human brown fat as a novel therapeutic target to tackle the growing obesity and diabetes epidemic has increased dramatically in recent years. While much insight into brown fat biology has been gained from murine cell lines and models, few resources are available to study human brown fat in-vitro. In this study, we detail the derivation and characterization of a novel human ES UCP1 reporter cell line that marks UCP1 positive adipocytes in-vitro. more...
#> 326 Common genetic traits are not well defined in hepatocellular carcinoma (HCC), because necroinflammation lasting long in prior to hepatocarcinogenesis embeds highly heterogenous genetic background in hepatocytes over the liver. We experienced a rare case with chronic hepatitis C, in which multiple liver tumors at different stages in multistep hepatocarcinogenesis were observed at the same time. Under the same genetic and etiological backgrounds, comparisons of expression profiles among dysplastic nodules (DN), well differentiated HCC (WEL), and moderately differentiated HCC (MOD) would provide critical genetic information for the initiation and progression of HCC.
#> 327 The aim of this study was to conduct a baseline comparison of serum-circulating miRNA in prediabetic individuals with the distinction between those who later progressed to type 2 diabetes (T2DM) and those who did not. The expression level of 798 miRNAs using NanoString technology was examined. Spearman correlation, ROC curve analysis, and logistic regression modeling were performed. Gene ontology (GO), canonical pathways analysis were used to explore the biological functions of the miRNA target genes. more...
#> 328 Four miRNAs showed significantly different expression post-vitamin C supplementation including the down-regulation of miR-451a, and up-regulation of miR-1253, miR-1290 and miR-644a. Subsequent validation study showed only miR-451a expression was significantly different with supplementation.
#> 329 Donor pancreata were obtained from the Beta Cell Bank of the JDRF Centre for Beta Cell Therapy in Diabetes (Brussels, Belgium), from Pancreatic Islet Processing (ECIT center) of Diabetes Research Institute at the IRCCS San Raffaele Scientific Institute (Milan, Italy) and from the DRWF Human Islet Isolation Facility (Oxford, England). Full written consent for use of donor material for research was obtained according to Belgian, Italian and English laws. more...
#> 330 Donor pancreata were obtained from the Beta Cell Bank of the JDRF Centre for Beta Cell Therapy in Diabetes (Brussels, Belgium), from Pancreatic Islet Processing (ECIT center) of Diabetes Research Institute at the IRCCS San Raffaele Scientific Institute (Milan, Italy) and from the DRWF Human Islet Isolation Facility (Oxford, England). Full written consent for use of donor material for research was obtained according to Belgian, Italian and English laws. more...
#> 331 People living with diabetes have an increased risk of developing active tuberculosis. The effects of diabetes (HbA1c ≥6.5%) and intermediate hyperglycaemia (HbA1c 5.7-6.5%), on this transcriptomic signature were investigated by RNA-seq, to enhance understanding of immunological susceptibility in diabetes-tuberculosis comorbidity.Diabetes increased the magnitude of gene expression change in the host transcriptome in tuberculosis, characterised by an increase in innate, and decrease in adaptive immune responses. more...
#> 332 Metformin, a biguanide agent, is the first-line treatment for type 2 diabetes mellitus due to its glucose-lowering effect. Despite its wide application in the treatment of multiple health conditions, the glycemic response to metformin is highly variable, emphasizing the need for reliable biomarkers. We chose the RNA-Seq-based comparative transcriptomics approach to evaluate the systemic effect of metformin and highlight potential predictive biomarkers of metformin response in drug-naïve type 2 diabetes patient volunteers in vivo. more...
#> 333 Purpose: The goal of this study is to characterize the gene expression profiles and identify genes of interest (GOI) in stenotic (AS) and regurgitant (AI) human aortic valves using RNA sequencing technology. Methods: Aortic valve leaflets were collected from non-matched transplant donor hearts (NC) and from aortic valve replacement operations (AS or AI). Leaflets were washed in cold PBS, snap frozen, and stored at -80°C until RNA extraction. more...
#> 334 Long noncoding RNAs (lncRNAs) is already evidently involved in a variety of biological functions and pathophysiological mechanisms underlying the diabetes. However, the role of lncRNAs in the type 2 diabetes (T2D) has not been explored clearly yet. The aim of this study was to determine the circulating lncRNA profile and confirmed the differentially expressed lncRNA between T2D patients.
#> 335 PURPOSE: To investigate the circulatory microRNA (miRNA) profiles of aqueous, vitreous, and plasma in order to identify biomarkers in aqueous humor or plasma that are reflecting changes in vitreous of patients with diabetes. METHODS: Aqueous, vitreous and plasma samples were collected from a total of 27 patients - 11 controls (macular pucker or macular hole patients) and 16 patients with diabetes mellitus (DM) undergoing vitreoretinal surgery: DM-Type I with proliferative diabetic retinopathy (PDR) (DMI-PDR), DM Type II with PDR (DMII-PDR) and DM Type II with nonproliferative DR (DMII-NPDR). more...
#> 336 To determine ceRNA transcribed during the PBMCs, we have employed whole genome microarray expression profiling as a discovery platform to identify ceRNA expression in PBMCs donated by T1DM (type 1 diabetes mellitus) patients and healthy volunteers.
#> 337 To determine miRNA transcribed during the PBMCs, we have employed whole genome microarray expression profiling as a discovery platform to identify miRNA expression in PBMCs donated by T1DM (type 1 diabetes mellitus) patients and healthy volunteers.
#> 338 Background: Traditionally, the transcriptomic and proteomic characterisation of CD4+ T cells at the single-cell level has been performed by two largely exclusive types of technologies: single-cell RNA-sequencing (scRNA-seq) and antibody-based cytometry. Here we present a multi-omics approach allowing the simultaneous targeted quantification of mRNA and protein expression in single-cells and investigate its performance to dissect the heterogeneity of human immune cell populations. more...
#> 339 Obesity is a major public health burden worldwide, greatly increasing the risk of diabetes, cardiovascular diseases and cancer. Obesity and associated insulin resistance are characterized by chronic low-grade inflammation driven by the cooperation of the innate immune system and dysregulated metabolism in adipose tissue and other metabolic organs. RIPK1 (Receptor-Interacting serine/threonine Protein Kinase 1) is a central regulator of inflammatory cell function that coordinates inflammation, apoptosis and necroptosis in response to inflammatory stimuli. more...
#> 340 We collected the mid-morning urine samples, and centrifuged at 2000g for ten minutes in order to remove cells and debris, and then stored in -80 degree refrigerator. we selected 2 samples per group for the microRNA arrays in the following four groups: normal control, IGT with renal impairment, diabetes, diabetic kidney disease. In IGT renal impairment group, we have found that the expression of two microRNAs were changed. more...
#> 341 Pathologic retinal neovascularization is a potentially blinding consequence seen in many common diseases including diabetic retinopathy, retinopathy of prematurity, and retinal vascular occlusive diseases, among others. The use of therapeutics targeting pro-angiogenesis factors such as vascular endothelial growth factor (VEGF) has proven to be highly effective, however considerable side effects exist and serial anti-VEGF treatment has been shown to decrease effectiveness over time. more...
#> 342 The intrahepatic milieu is inhospitable to intraportal islet allografts, limiting their applicability to ameliorate Type 1 Diabetes (T1D). Islet viability in the subcutaneous space represents an unfulfilled paradigm that is crucial to ensure widespread adoption and safety of clinical islet transplantation. Herein we report that human islets transplanted subcutaneously uniformly promote long-term euglycemia when admixed with a device-free Islet Viability Matrix (IVM), through a previously unknown anti-apoptotic mechanism.
#> 343 Patients affected by type 1 diabetes are recruited in the departments of Diabetology and Healthy Volunteers (HV) are selected based on internal records in the same hospital. Total RNA from whole blood has been extracted following a two-step procedure. First, RNA from blood collected on PAX-Gene tubes has been extracted using Maxwell 16 LEV simplyRNA blood kit (Promega) following manufacturer recommendations and second b-globin, dominant RNA from red blood cells, has been removed using the GLOBINclear kit (Ambion) on extracted RNA. RNA sequencing has been performed from using the TruSeq Stranded mRNA preparation kit (Illumina) on 500 ng b-globin depleted RNA with a RNA Integrity Number > 8 (measured on Bioanalyzer following manufacturer recommendations), and then sequenced following a pair-end 2x75 bp protocol on NextSeq 500 or HiSeq 4000 (Illumina) at LIGAN Equipex (Lille, France).
#> 344 Besides improving insulin sensitivity in type 2 diabetes, the thiazolidinedione family of compounds and the pharmacologic activation of their best characterized target PPARg has been proposed as a therapeutic option for cancer treatment. In the present study, we reveal a new mechanism by which the thiazolidinedione rosiglitazone contributes to tumorigenesis, which limits its therapeutic potential in cancer. more...
#> 345 TruCulture human whole blood ex vivo stimulation was performed on 17 healthy individuals and 17 post-onset type 1 diabetics, then gene expression was analyzed using Nanostring to characterize stimulated innate immune responses. Ex vivo whole blood stimulation revealed higher induced IFN-1 responses in type 1 diabetes as compared to healthy controls.
#> 346 Comparative gene expression profiles of human retinal pericytes (HRMPC) and lipoaspirate derived mesenchymal stromal cells (adipose stromal cells, ASC) cultivated either in normal (1g/l) or high (4.5g/l) glucose medium to identify similarities and discrepancies and elucidate high glucose effects considering cell-based therapies in diabetic retinopathy. A hallmark of diabetic retinopathy is pericyte- dropout increased vascular permeability and progressive vascular occlusion. more...
#> 347 Markers of biological ageing have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry in urine and serum, within a large sample (N=2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. more...
#> 348 Severe obesity (SO) affects about 6% of youth in US, augmenting the risks for cardiovascular disease and Type 2 diabetes. Herein, we obtained paired omental (omVAT) and abdominal subcutaneous (SAT) adipose tissue biopsies from obese girls with SO, undergoing sleeve gastrectomy (SG), to test whether differences in cellular and transcriptomic profiles between omVAT and SAT depots affect insulin sensitivity differentially. more...
#> 349 Cardiovascular disease (CVD) is the most common cause of death in patients with type II diabetes mellitus (T2DM). Although susceptibility to CVD is different for every patient, why some patients with T2DM develop CVD while others are protected has not yet been clarified. Patient-derived induced pluripotent stem cells (iPSCs) have been utilized to reveal the influence of genotype on phenotype and have the potential to connect a clinical phenotype to a causal gene. more...
#> 350 We report the application of RNA-sequencing for high-throughput profiling of transcriptomes in tumor tissues from patients with breast cancer and diabetes, and in tumor tissues from breast cancer patients without diabetes.
#> 351 Context: Context: Gestational diabetes (GDM) has profound effects on the intrauterine metabolic milieu and is linked to obesity and diabetes in offspring, but the mechanisms driving these effects remain largely unknown. Alterations gene expression in amniocytes exposed to GDM in utero may identify potential mechanisms leading to metabolic dysfunction later in life. Objective: Objective: To profile changes in the transcriptome in human amniocytes exposed to GDM Methods: A nested case-control study was performed in second trimeseter amniocytes matched for offspring sex, maternal race/ethnicity, maternal age, gestational age at amniocentesis, gestational age at birth and gestational diabetes status. more...
#> 352 Human thermogenic adipose tissue mitigates metabolic disease, raising much interest in understanding its development and function. Here, we show that human thermogenic adipocytes specifically express a primate-specific long non-coding RNA, LINC00473 which is highly correlated with UCP1 expression and decreased in obesity and type-2 diabetes. LINC00473 is detected in progenitor cells, and increases upon differentiation and in response to cAMP. more...
#> 353 We revealed transcriptome differences between corrected and unedited (diseased) Wolfram Syndrome patient stem cell-derived beta cells. We also identified several endocrine, pancreatic, and non-pancreatic cell types in the samples populations.
#> 354 Human thermogenic adipose tissue mitigates metabolic disease, raising much interest in understanding its development and function. Here, we show that human thermogenic adipocytes specifically express a primate-specific long non-coding RNA, LINC00473 which is highly correlated with UCP1 expression and decreased in obesity and type-2 diabetes. LINC00473 is detected in progenitor cells, and increases upon differentiation and in response to cAMP. more...
#> 355 Cigarette smoking is one of the largest causes of preventable death worldwide. Smoking behaviors, including age at smoking initiation (ASI), smoking dependence (SD), and smoking cessation (SC), are all complex phenotypes determined by both genetic and environmental factors as well as their interactions. To identify susceptibility loci for each smoking phenotype, numerous studies have been conducted, with approaches including genome-wide linkage scans, candidate gene-based association analysis, and genome-wide association study (GWAS). more...
#> 356 Proinflammatory cytokines are important mediators of pancreatic beta cell dysfunction and demise in type 1 diabetes (T1D). We presently characterized human beta cell responses to IFNa by combining ATAC-seq, RNA-seq and proteomics assays. The initial beta cell response to IFNa was characterized by major chromatin remodeling, followed by marked changes in transcriptional and translational regulation. IFNa-induced changes in alternative splicing (AS) and first exon usage increased the diversity of transcripts expressed by beta cells. This, combined with changes observed on protein modification/degradation, ER stress and MHC class I, may significantly expand the peptide repertoire presented by beta cells to the immune system. On the other hand, beta cells up-regulated checkpoint proteins, such as PDL1 and HLA-E, that may protect them against the autoimmune assault. Data mining of the present multi-omics analysis led to the identification of two compound classes that revert IFNa effects on human beta cells and may be translated to clinical trials.
#> 357 Proinflammatory cytokines are important mediators of pancreatic beta cell dysfunction and demise in type 1 diabetes (T1D). We presently characterized human beta cell responses to IFNa by combining ATAC-seq, RNA-seq and proteomics assays. The initial beta cell response to IFNa was characterized by major chromatin remodeling, followed by marked changes in transcriptional and translational regulation. IFNa-induced changes in alternative splicing (AS) and first exon usage increased the diversity of transcripts expressed by beta cells. This, combined with changes observed on protein modification/degradation, ER stress and MHC class I, may significantly expand the peptide repertoire presented by beta cells to the immune system. On the other hand, beta cells up-regulated checkpoint proteins, such as PDL1 and HLA-E, that may protect them against the autoimmune assault. Data mining of the present multi-omics analysis led to the identification of two compound classes that revert IFNa effects on human beta cells and may be translated to clinical trials.
#> 358 Proinflammatory cytokines are important mediators of pancreatic beta cell dysfunction and demise in type 1 diabetes (T1D). We presently characterized human beta cell responses to IFNa by combining ATAC-seq, RNA-seq and proteomics assays. The initial beta cell response to IFNa was characterized by major chromatin remodeling, followed by marked changes in transcriptional and translational regulation. IFNa-induced changes in alternative splicing (AS) and first exon usage increased the diversity of transcripts expressed by beta cells. This, combined with changes observed on protein modification/degradation, ER stress and MHC class I, may significantly expand the peptide repertoire presented by beta cells to the immune system. On the other hand, beta cells up-regulated checkpoint proteins, such as PDL1 and HLA-E, that may protect them against the autoimmune assault. Data mining of the present multi-omics analysis led to the identification of two compound classes that revert IFNa effects on human beta cells and may be translated to clinical trials.
#> 359 Insulin resistance increases patient’s risk of developing type 2 diabetes (T2D), nonalcoholic steatohepatitis (NASH) and a host of other comorbidities including cardiovascular disease and cancer. At the molecular level, insulin exerts its function through the insulin receptor (IR), a transmembrane receptor tyrosine kinase. Data from human genetic studies have shown that Grb14 functions as a negative modulator of IR activity, and germline Grb14-knockout (KO) mice have improved insulin signaling in liver and muscle tissues. more...
#> 360 Cigarette smoking is one of the largest causes of preventable death worldwide. Smoking behaviors, including age at smoking initiation (ASI), smoking dependence (SD), and smoking cessation (SC), are all complex phenotypes determined by both genetic and environmental factors as well as their interactions. To identify susceptibility loci for each smoking phenotype, numerous studies have been conducted, with approaches including genome-wide linkage scans, candidate gene-based association analysis, and genome-wide association study (GWAS). more...
#> 361 This SuperSeries is composed of the SubSeries listed below.
#> 362 The pool of beta cell-specific CD8+ T-cells in type 1 diabetes (T1D) sustains an autoreactive potential despite having access to a constant source of antigen. To investigate the long-lived nature of these cells, we established a DNA methylation-based T cell “multipotency index” and found that beta cell-specific CD8+ T-cells retained a stem-like epigenetic multipotency score. Single cell ATAC-seq analysis confirmed the co-existence of naive and effector-associated epigenetic programs in individual beta cell-specific CD8+ T-cells. more...
#> 363 The pool of beta cell-specific CD8+ T-cells in type 1 diabetes (T1D) sustains an autoreactive potential despite having access to a constant source of antigen. To investigate the long-lived nature of these cells, we established a DNA methylation-based T cell “multipotency index” and found that beta cell-specific CD8+ T-cells retained a stem-like epigenetic multipotency score. Single cell ATAC-seq analysis confirmed the co-existence of naive and effector-associated epigenetic programs in individual beta cell-specific CD8+ T-cells. more...
#> 364 Regulation of endothelial nutrient transport is poorly understood. Vascular endothelial growth factor (VEGF)-B signaling in endothelial cells promotes uptake and transcytosis of fatty acids (FA) from the bloodstream to the underlying tissue, advancing pathological lipid accumulation and lipotoxicity in diabetic complications. Here we demonstrate a VEGF-B dependent obstruction of endothelial glucose transport attributed to plasma membrane lipid alterations affecting glucose transporter 1 function, which was independent of FA uptake. more...
#> 365 Genome wide association studies (GWAS) identified a chromosome 8 locus associated with fasting glucose (FG), insulin (FI) and lipid levels that is located near PPP1R3B (a gene which encodes the catalytic subunit of a serine/threonine protein phosphatase that promotes hepatic glycogen storage upon insulin signaling). The lead SNP rs4841132 lies in a long non-coding RNA (lncRNA) LOC157273, 175 kb away and is not in linkage disequilibrium with PPP1R3B. more...
#> 366 Genetic factors are strongly implicated in the susceptibility to develop externalizing syndromes such as attention deficit/hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, and substance use disorder (SUD). Variants in the ADGRL3 (LPHN3) gene predispose to ADHD and predict ADHD severity, disruptive behaviors comorbidity, long-term outcome, and response to treatment. In this study, we investigated whether variants within ADGRL3 are associated with SUD, a disorder that is frequently co-morbid with ADHD. more...
#> 367 Recent studies revealed that the bromodomain and extraterminal (BET) epigenetic reader proteins resemble key regulators in the underlying pathophysiology of cancer, diabetes or cardiovascular disease. However, whether they also regulate vascular remodeling processes by direct effects on vascular cells is unknown. In this study we investigated the effects of the BET proteins on neointima formation in response to vascular injury in vivo and on human smooth muscle cell function in vitro. more...
#> 368 Obesity and type 2 diabetes (T2D) are metabolic disorders influenced by lifestyle and genetic factors, and characterized by insulin resistance in skeletal muscle, a prominent site of glucose disposal. Numerous genetic variants have been associated with obesity and T2D, of which the majority is located in non-coding DNA regions. This suggest that most variants mediate their effect by altering the activity of gene-regulatory elements, including enhancers. more...
#> 369 Obesity and type 2 diabetes (T2D) are metabolic disorders influenced by lifestyle and genetic factors, and characterized by insulin resistance in skeletal muscle, a prominent site of glucose disposal. Numerous genetic variants have been associated with obesity and T2D, of which the majority is located in non-coding DNA regions. This suggest that most variants mediate their effect by altering the activity of gene-regulatory elements, including enhancers. more...
#> 370 Obesity and type 2 diabetes (T2D) are metabolic disorders influenced by lifestyle and genetic factors, and characterized by insulin resistance in skeletal muscle, a prominent site of glucose disposal. Numerous genetic variants have been associated with obesity and T2D, of which the majority is located in non-coding DNA regions. This suggest that most variants mediate their effect by altering the activity of gene-regulatory elements, including enhancers. more...
#> 371 Pancreatic Beta-cells are essential for regulating blood glucose levels. Much of our knowledge relating to human Beta-cell development and function has depended on rodent models, which have provided a blueprint to confirm important cellular features in humans. The advent of next generation sequencing studies, however, has highlighted discrepancies in Beta-cells which exist between mice and men. The precise contribution of such differences has not yet been fully appreciated. more...
#> 372 Adipose tissue-derived mesenchymal stem cells (ASC’s) constitute a vital population of multipotent cells capable of differentiating into end-organ tissues. However, scientific endeavors to harness the regenerative potential of ASC’s for regenerative medicine are currently limited by an incomplete understanding of the mechanisms that determine cell-lineage commitment and stemness. In the current study, we used reduced representation bisulfite sequencing (RRBS) analysis to identify epigenetic gene targets and cellular processes that are responsive to 5-azathioprine, a potent inducer of DNA methylation. more...
#> 373 The generation of pancreatic cell types from renewable cell sources holds promise for cell replacement therapies for diabetes. Although most effort has focused on generating pancreatic beta cells, there is considerable evidence that glucagon secreting alpha cells are critically involved in disease progression and proper glucose control. Here we report on the generation of stem cell-derived human pancreatic alpha (SC-alpha) cells from pluripotent stem cells via a transient pre-alpha cell intermediate. more...
#> 374 The goal of the study was to identify genes whose aberrant expression can contribute to diabetic retinopathy. We determined differential response in gene expression to high glucose in lymphoblastoid cell lines derived from matched type 1 diabetic individuals with and without retinopathy. Those genes exhibiting the largest difference in glucose response between diabetic subjects with and without retinopathy were assessed for association to diabetic retinopathy utilizing genotype data from a meta-genome-wide association study. more...
#> 375 DNA methylation may be involved in development of type 1 diabetes (T1D), but previous epigenome-wide association studies were conducted among cases with clinically diagnosed diabetes. Using multiple pre-disease peripheral blood samples on the Illumina 450K and EPIC platforms, we investigated longitudinal methylation differences between 87 T1D cases and 87 controls from the prospective Diabetes Autoimmunity Study in the Young (DAISY) cohort. more...
#> 376 Cell cycle progression plays an important role in mediating the transition from a differentiated state to a pluripotent stem cell. Conversely, establishing limitations in proliferative potential may be important to achieve functional maturity as well as to prevent abnormal growths after transplantation of stem cell derived products. Here we induced exit from the cell cycle in pancreatic progenitors by interfering with the progression of DNA replication and determined growth potential, differentiation and maturation to insulin producing endocrine cells. more...
#> 377 Early-onset complex autoimmunity can arise from monogenic activating mutations in inflammatory signalling pathways or loss of function mutations of immunoregulatory molecules. We sought to define the molecular basis of severe early-onset autoimmunity, characterised by autoimmune diabetes, cytopenias, hepatitis, enteropathy and interstitial lung disease, in a child without pathogenic variants in STAT3 and FOXP3. more...
#> 378 Combined single-cell RNAseq and electrophysiological profiling of human pancreatic islet cells (pancreas patch-seq) to link transcriptomic phenotypes of islet cells to their physiologic properties.
#> 379 The adult kidney replaces lost cells in-vivo via proliferation of cells functioning as committed clonal progenitors. Here we combined the generation of single cell derived clonal cultures from human adult kidney with transcriptomic analysis for molecular characterization of in-vitro clonal behavior at inception and after propagation. We discovered two types of clones; rapidly proliferating de-differentiated fibroblast-like (FL) originating from the proximal tubule and stably proliferating cuboidal epithelial-like (EL) originating from distal segments that efficiently propagate with one cell giving rise to 3.3*10(6) cells. more...
#> 380 Maternal obesity impacts the health of offspring, increasing the risk of developing obesity and/or other metabolic dysregulation in childhood or later in life. Using a genome-wide methylation assay, we identified sex-dependent dysregulation of the methylome of CD3+ T-lymphocytes, a cell type that plays an important role in obesity and inflammatory diseases, in newborn offspring of overweight and obese mothers. more...
#> 381 We investigated the genome-wide DNA methylation profiles in sperm by comparing 8 individuals with T2DM and 9 non-diabetic controls using whole genome bisulfite sequencing (WGBS) method. Our study provides the first evidence that T2DM reprograms sperm DNA methylome and provides new insights into the intricate mechanisms of susceptibility to T2DM in offspring.
#> 382 This SuperSeries is composed of the SubSeries listed below.
#> 383 Technical replicate testing was performed to determine the measurement precision of each analyte. Peripheral blood samples were collected in tempus tubes from a healthy control subject with no family history of autoimmunity (n=1) and from subjects with Type 1 diabetes (n=4). Total RNA was isolated independently from 3 replicate aliquots of the same tempus samples and then globin-reduced. RNAseq libraries were prepared from the globin-reduced RNA.
#> 384 BACKGROUND: Long-term complications of type 2 diabetes (T2D), such as macrovascular and microvascular events, are the major causes for T2D-related disability and mortality. A clinically convenient, non-invasive approach for monitoring the development of these complications would improve the overall life quality of patients with T2D and help reduce healthcare burden through preventive interventions. METHODS: A selective chemical labeling strategy for 5-hydroxymethylcytosines (5hmC-Seal) was used to profile genome-wide 5hmCs, an emerging class of epigenetic markers implicated in complex diseases including diabetes, in circulating cell-free DNA (cfDNA) from a collection of Chinese patients (n = 62). Differentially modified 5hmC markers between patients with T2D with and without macrovascular/microvascular complications were analyzed under a case-control design. RESULTS: Statistically significant changes in 5hmC markers were associated with T2D-related macrovascular/microvascular complications, involving genes and pathways relevant to vascular biology and diabetes, including insulin resistance and inflammation. A 16-gene 5hmC marker panel accurately distinguished patients with vascular complications from those without (testing set: AUC = 0.85, 95%CI, 0.73-0.96), outperforming conventional clinical variables such as urinary albumin. In addition, a separate 13-gene 5hmC marker panel could distinguish patients with single complications from those with multiple complications (testing set: AUC = 0.84, 95%CI, 0.68-0.99), showing superiority over conventional clinical variables. CONCLUSIONS: The 5hmC markers in cfDNA reflected the epigenetic changes in patients with T2D who developed macrovascular/microvascular complications. The 5hmC-Seal assay has the potential to be a clinically convenient, non-invasive approach that can be applied in the clinic to monitor the presence and severity of diabetic vascular complications.
#> 385 We investigated genome wide changes in gene expression in skin between patients with type 2 diabetes and non-diabetic patients using next generation sequencing. We compared the gene expression in skin samples taken from 27 patients (13 with type 2 diabetes and 14 non-diabetic).
#> 386 We report the RNA expression profiles of native veins used for AVF creation and of remodeled AVF samples obtained 6-15 weeks later at the time of transposition (if vein matured) or salvage procedure (if vein failed). We perfomed RNA-seq on native veins and AVFs with different maturation outcomes (matured vs. failed). The "matured" and "failed" subgroups were similar in terms of demographics (age range, sex distribution, ethnicity distribution), clinical characteristics (proportion of diabetes mellitus and coronary artery disease, previous hemodialysis access history), and time interval between first-stage and second-stage surgeries. more...
#> 387 Severe angiopathy has been postulated as a major driver for diabetes associated secondary complications. So far the knowledge on underlying mechanisms and thereon based therapeutic options to attenuate these pathologies are limited. Here we systematically administered ABCB5+ MSCs for the treatment of chronic non-healing diabetic wounds employing db/db mice, a type II diabetes model as their number markedly declined during diabetes. more...
#> 388 We propose that reprogramming of patient donor cells to tankyrase inhibitor-regulated naive hiPSC (N-hiPSC) improves the functionality of differentiated progenitors for subsequent regenerative therapies, and more effectively erases donor epigenetic aberrations sustained from chronic diseases such as diabetes .
#> 389 Aim: The loss of insulin-secreting β-cells, ultimately characterizing most diabetes forms, demands the development of cell replacement therapies. The common endpoint for all ex vivo strategies is transplantation into diabetic patients. However, the effects of hyperglycemia environment on the transplanted cells were not yet properly assessed. Thus, the main goal of this study was to characterize global effect of brief and prolonged in vivo hyperglycemia exposure on the cell fate acquisition and maintenance of transplanted human pancreatic progenitors. more...
#> 390 DPN muscle exhibits features of degeneration with attempted regeneration. In the most severely pathological muscle samples, regeneration appears to be stymied and our data suggest that this may be partly due to intrinsic dysfunction of the satellite cell pool in addition to extrinsic structural pathology (e.g. nerve damage).
#> 391 Obesity, and visceral adiposity in particular, increases the risk of common metabolic diseases, including type 2 diabetes, cardiovascular disease, and several forms of cancer. However, the molecular mechanisms responsible for regional fat storage remain poorly characterized, preventing therapeutic innovation. We here applied a systematic genome-wide screen and translational approach, where human primary preadipocytes were isolated from liposuction aspirate and differentiated. more...
#> 392 Protein Tyrosine Phosphatase Receptor Type N (PTPRN) plays an important role in diabetes and many cancers but its role in glioma remain poorly defined. Here, we firstly verified PTPRN expression was negatively correlated with overall survival of glioblastoma patients. Moreover, suppression of PTPRN expression reduced both U87 and U343 cell viability, suppressed proliferation, induced cell cycle arrest and inhibited glioma growth in vivo. more...
#> 393 We performed a comparison of transcriptome between monocyte-derived dendritic cells (moDC) cultured with neutrophil extracellular traps (NETs) from healthy donors or type 1 diabetes (T1D) patients. The source of moDCs is healthy donors and T1D patients
#> 394 We report whole genome chromatin immunoprecipitation followed by sequencing (ChIP-seq) of histone modifications in MCF-7 breast cancer cells treated with vehicle (UNTR) or the proteasome inhibitor MG132 for 4 (MG4H) or 24 (MG24H) hours. We find that MG132 treatment results in the spreading of the H3-trimethyl lysine 4 mark into gene bodies of a subset of induced genes in MCF-7 cells. The spreading of the H3K4me3 is concomitant with hyperacetylation (H3K27ac, K122ac and K9/14ac) of the corresponding gene TSS. more...
#> 395 There are the differential levels of methylation in the groups with and without complication to its control groups as non-diabetes and T2D without complication, two of which were classified into male and female, affects strictly the gene regulation to diabetic pathogenesis. To screen specific candidates for detecting classification of Type-2 Diabetes without or with retinopathy or nephropathy, the level of DNA methylation is the one of considerable factors to influence the differential gene expression was inspected involvement of chromosome modification. more...
#> 396 Exploration of new markers that define impaired metabolic flexibility using an acute postprandial challenge test. Healthy subjects underwent a 4-week high-fat high-calorie diet. High-fat challenges were performed in these subjects before and after the diet and in subjects with the metabolic syndrome.
#> 397 Type 1 diabetes (T1D) is characterized by immune mediated destruction of insulin producing β cells. Biomarkers capable of identifying T1D risk and dissecting disease-related heterogeneity represent an unmet clinical need. Aims: Towards the goal of informing T1D biomarker strategies, we profiled different classes of RNAs in human islet-derived exosomes and identified RNAs that were differentially expressed under cytokine stress conditions. more...
#> 398 Several neurodevelopmental processes including neuronal survival, migration and differentiation are controlled by sphingolipid metabolism. Sphingomyelin is an abundant component of cell membranes. Sphingomyelinases generate ceramide from sphingomyelin as a second messenger in intracellular signaling pathways involved in cell proliferation, differentiation, or apoptosis. While the role of acid sphingomyelinase is well established, the role of neutral sphingomyelinases in human neurodevelopment has remained elusive. more...
#> 399 Intervertebral disc degeneration (IDD) leads to low back pain and disability globally. Progressive loss of nucleus pulposus cells (NPCs) are associated with the onset of IDD. Cell-based therapy has been shown the promising for many diseases, including the IDD in preclinical studies. However, the limited availability of human NPCs has hurdled such application for IDD. This study aimed to define strategies to derive NPCs from human ESC/iPSC. more...
#> 400 Long noncoding RNAs (lncRNAs) is already evidently involved in a variety of biological functions and pathophysiological mechanisms underlying the diabetes. However, the role of lncRNAs in the type 1 diabetes (T1D) has not been explored clearly yet. The aim of this study was to determine the circulating lncRNA profile and confirmed the differentially expressed lncRNA between T1D patients and healthy control.
#> 401 Progressive loss of nucleus pulposus cells (NPCs) is associated with the onset of intervertebral disc degeneration (IDD). Transplantation of NPCs, derived from human pluripotent stem cells including hESC/iPSCs, may offer a novel therapy for IDD. To date, effective in vitro differentiations of notochordal and NP cells remained to be demonstrated. Towards this end, we developed a three-step protocol to directly differentiate hESC/iPSC towards mesodermal, then notochordal and finally NPCs. more...
#> 402 Transient Pax8 expression was reported in mouse islets during gestation, whereas a genome-wide linkage and admixture mapping study highlighted PAX8 as a candidate gene for diabetes mellitus (DM). We sought the significance of PAX8 expression in mouse and human islet biology. PAX8 was induced in gestating mouse islets and in human islets treated with recombinant prolactin. Global gene expression profiling of human and mouse islets overexpressing the corresponding species-specific PAX8 revealed the modulation of distinct genetic pathways that converge on cell survival. more...
#> 403 Comparative profiling of miRNA content within CD31+EVs comparing Ctrl and T2DM patients (5 vs 5 samples with each sample prepared from the pooled plasma of 4 subjects).
#> 404 Obesity is a leading risk factor for type-2 diabetes. Diabetes often leads to the dysregulation of angiogenesis, although, the mechanism is not fully understood. Previously, long noncoding RNAs (lncRNAs) have been found to modulate angiogenesis. In this study, we asked how the expression levels of lncRNAs change in endothelial cells in response to excessive palmitic acid treatment, an obesity-like condition. more...
#> 405 This SuperSeries is composed of the SubSeries listed below.
#> 406 Obesity and type 2 diabetes mellitus are global emergencies and long noncoding RNAs (lncRNAs) are regulatory transcrips with elusive functions in metabolism. Here we report that an unexpectedly high fraction of lncRNAs, but not protein-coding mRNAs, is repressed during diet-induced obesity (DIO) and refeeding, whilst nutrient deprivation specifically induced lncRNAs in mouse liver. Similarly, lncRNAs were lost in diabetic humans. more...
#> 407 To identify the factors mediating the progression of di- abetic nephropathy (DN), we performed RNA sequencing of kidney biopsy samples from patients with early DN, advanced DN, and normal kidney tissue from nephrectomy samples. A set of genes that were upregulated at early but downregulated in late DN were shown to be largely renoprotective, which included genes in the retinoic acid pathway and glucagon-like peptide 1 receptor. more...
#> 408 Myocardial infarction (MI) is one of the most severe manifestations of coronary artery disease (CAD) and the leading cause of death from non-infectious diseases worldwide. It is known, that the central component of CAD pathogenesis is a chronic vascular inflammation. However, the mechanisms underlying the changes that occur in T, B and NK-lymphocytes, monocytes and other immune cells during CAD and MI are still poorly understood. more...
#> 409 iPS-derived monocytes and macrophages are similar with primary monocytes and macrophages compared to iPS cells from the genome-wide overview and have similar gene expression patterns.
#> 410 The present study aimed to investigate differentially expressed genes in whole blood obtained from patients with lumbar disc prolapse and healthy volunteers. A total of 8 patients with lumbar disc prolapse and 8 healthy volunteers were recruited. An Agilent SurePrint G3 human gene expression microarray 8x60 K was used to perform the microarray analyses.
#> 411 To investigate the function and potential mechanism of PARP-1 poly(ADP-ribose) polymerase 1 (PARP1) in diabetic neointimal hyperplasia. Type 1 diabetes mellitus was induced using streptozotocin (STZ) in wild-type mice and PARP1-/- mice, and ligation of the left carotid artery was performed to induce neointimal hyperplasia. Ligated carotid arteries from diabetic mice developed more extensive neointimal hyperplasia and showed greater proliferation and migration than arteries from nondiabetic mice. more...
#> 412 Using a discovery/validation approach we investigated associations between a panel of genes selected from a transcriptomic study and the renal function decline across time in a cohort of type 1 diabetes patients.
#> 413 MicroRNAs (miRNAs) are small non-coding RNA molecules that have the ability to drive cell lineage decisions by regulating the expression of hundreds of genes. Although evidence indicates that miRNAs have roles in pancreas development and endocrine cell function, the role of miRNAs in pancreatic endocrine cell differentiation has not been systematically explored. To address this, we performed genome-wide small RNA sequencing analysis in pancreatic progenitor cells differentiated in vitro from human embryonic stem cells and endocrine cells isolated from whole human islets. more...
#> 414 MicroRNAs (miRNAs) are small non-coding RNA molecules that have the ability to drive cell lineage decisions by regulating the expression of hundreds of genes. Although evidence indicates that miRNAs have roles in pancreas development and endocrine cell function, the role of miRNAs in pancreatic endocrine cell differentiation has not been systematically explored. To address this, we performed genome-wide small RNA sequencing analysis in pancreatic progenitor cells differentiated in vitro from human embryonic stem cells and endocrine cells isolated from whole human islets. more...
#> 415 This SuperSeries is composed of the SubSeries listed below.
#> 416 In type 1 diabetes (T1D), the appearance of multiple islet autoantibodies indicates the onset of islet autoimmunity, often many years before clinical symptoms arise. However, the underlying molecular mechanisms in T cells that can promote aberrant activation thereby triggering autoimmune progression remain poorly understood. Here, we show that during early stages of islet autoimmunity a miRNA142-3p/Tet2 signaling axis in murine and human CD4+T cells interferes with the efficient induction of regulatory T (Treg) cells accompanied by impairments in Treg stability. more...
#> 417 Insulin resistance (IR) is likely to induce metabolic syndrome and type 2 diabetes mellitus (T2DM). Gluconeogenesis (GNG) is a complex metabolic process that may result in glucose generation from certain non-carbohydrate substrates. Chinese herbal medicine astragalus polysaccharides and berberine have been documented to ameliorate IR, and combined use of astragalus polysaccharide (AP) and berberine (BBR) are reported to synergistically produce an even better effect. more...
#> 418 MicroRNAs (miRNAs) are small non-coding RNA molecules that have the ability to drive cell lineage decisions by regulating the expression of hundreds of genes. Although evidence indicates that miRNAs have roles in pancreas development and endocrine cell function, the role of miRNAs in pancreatic endocrine cell differentiation has not been systematically explored. To address this, we performed genome-wide small RNA sequencing analysis in pancreatic progenitor cells differentiated in vitro from human embryonic stem cells and endocrine cells isolated from whole human islets. more...
#> 419 In type 1 diabetes (T1D), the appearance of multiple islet autoantibodies indicates the onset of islet autoimmunity, often many years before clinical symptoms arise. However, the underlying molecular mechanisms in T cells that can promote aberrant activation thereby triggering autoimmune progression remain poorly understood. Here, we show that during early stages of islet autoimmunity a miRNA142-3p/Tet2 signaling axis in murine and human CD4+T cells interferes with the efficient induction of regulatory T (Treg) cells accompanied by impairments in Treg stability. more...
#> 420 MicroRNAs (miRNAs) are small non-coding RNA molecules that have the ability to drive cell lineage decisions by regulating the expression of hundreds of genes. Although evidence indicates that miRNAs have roles in pancreas development and endocrine cell function, the role of miRNAs in pancreatic endocrine cell differentiation has not been systematically explored. To address this, we performed genome-wide small RNA sequencing analysis in pancreatic progenitor cells differentiated in vitro from human embryonic stem cells and endocrine cells isolated from whole human islets. more...
#> 421 MicroRNAs (miRNAs) are small non-coding RNA molecules that have the ability to drive cell lineage decisions by regulating the expression of hundreds of genes. Although evidence indicates that miRNAs have roles in pancreas development and endocrine cell function, the role of miRNAs in pancreatic endocrine cell differentiation has not been systematically explored. To address this, we performed genome-wide small RNA sequencing analysis in pancreatic progenitor cells differentiated in vitro from human embryonic stem cells and endocrine cells isolated from whole human islets. more...
#> 422 MicroRNAs (miRNAs) are small non-coding RNA molecules that have the ability to drive cell lineage decisions by regulating the expression of hundreds of genes. Although evidence indicates that miRNAs have roles in pancreas development and endocrine cell function, the role of miRNAs in pancreatic endocrine cell differentiation has not been systematically explored. To address this, we performed genome-wide small RNA sequencing analysis in pancreatic progenitor cells differentiated in vitro from human embryonic stem cells and endocrine cells isolated from whole human islets. more...
#> 423 We provide evidence that viral miRNAs use 6mer seed toxicity to kill cells
#> 424 Distinct characteristics of adipose tissue at different localization of human body has shown greater significance in development of metabolic disorders. Visceral adipose tissue in particular is known to be associated with obesity related metabolic complications that include type II diabetes. In this experiment, we attempt to profile transcriptome signatures of adipocyte, stromal vascular fraction (SVF) and adipose tissue from subcutaneous and visceral adipose tissue from obese individuals.
#> 425 The complex relationship between metabolic disease risk and body fat distribution in humans involves cellular characteristics which are specific to each body fat compartment. We applied single-cell RNA sequencing (scRNA-Seq) to identify these depot-specific differences in the stromal vascular fraction of visceral (VAT) and subcutaneous (SAT) adipose tissue of obese individuals. We characterized multiple immune cells, endothelial cells, fibroblasts, adipose and hematopoietic stem cell progenitors. more...
#> 426 Heterogeneous populations of human bone marrow-derived stromal cells (BMSC) are among the most frequently tested cellular therapeutics for treating degenerative and immune disorders, which occur predominantly in the aging population. Currently, it is unclear whether advanced donor age and commonly associated comorbidities affect the properties of ex vivo-expanded BMSCs. Thus, we stratified cells from adult and elderly donors from our biobank (n = 10 and n = 13, mean age 38 and 72 years, respectively) and compared their phenotypic and functional performance, using multiple assays typically employed as minimal criteria for defining multipotent mesenchymal stromal cells (MSCs).We found that BMSCs from both cohorts meet the standard criteria for MSC, exhibiting similar morphology, growth kinetics, gene expression profiles, and pro-angiogenic and immunosuppressive potential and the capacity to differentiate toward adipogenic, chondrogenic, and osteogenic lineages.We found no substantial differences between cells from the adult and elderly cohorts. more...
#> 427 β-cell specific IFT88 knock-out mice recapitulate human diabetes with impaired insulin secretion and altered islet hormone paracrine regulation. To examine the signaling pathways regulating islet cell function, we subjected protein lysates of whole islets from control and IFT88 knockout mice to a commercial signaling-protein array analysis (Full Moon Bio, Inc). Samples were probed against 1358 antibodies with 2 replicates per antibody on 76 x 25 x 1mm glass slides.
#> 428 β-cell specific IFT88 knock-out mice recapitulate human diabetes with impaired insulin secretion and altered islet hormone paracrine regulation. To examine the signaling pathways regulating islet cell function, we subjected protein lysates of whole islets from control and IFT88 knockout mice to a commercial phospho-antibody array analysis (Full Moon Bio, Inc). Samples were probed against 1318 site-specific and phospho-specific antibodies with 2 replicates per antibody on 76 x 25 x 1mm glass slides.
#> 429 Type 1 diabetes (T1D) is a chronic autoimmune disease that results from destruction of pancreatic β-cells. T1D subjects were recently shown to harbor distinct intestinal microbiome profiles. Based on these findings, the role of gut bacteria in T1D is being intensively investigated. The mechanism connecting intestinal microbial homeostasis with the development of T1D is unknown. Specific gut bacteria such as Bacteroides dorei (BD) and Ruminococcus gnavus (RG) show markedly increased abundance prior to the development of autoimmunity. more...
#> 430 A slower transmethylation of one-carbon substrates in the edematous form of severe acute malnutrition (ESAM) suggests that downstream aberrations in DNA methylation could drive differences in acute pathogenesis between ESAM and non-edematous malnutrition (NESAM). Here, we integrate genome-wide assessments of DNA methylation with corresponding gene expression profiles and sequence variation to show that relative to NESAM, acute ESAM is characterized by significant hypomethylation at 99% of differentially methylated loci in two SAM cohorts, whereas recovered adults show no significant differences in methylation. more...
#> 431 Analysis of monogenic kidney disease-causing genes, and secondary pathology resulting from systemic diseases including diabetes and hypertension, highlight the importance of the kidney’s filtering system, the renal corpuscles. To elucidate the developmental processes that establish the renal corpuscle, we employed single-nucleus droplet-based sequencing to capture single nuclei from the human fetal kidney. more...
#> 432 Circular RNA (circRNA) microarray analysis was performed to examine the expression profiles of circRNAs in diabetic foot ulcers (DFU) and in human excisional skin wounds 7 days after injury.
#> 433 Hsa_circ_0084443 expression level is down-regulated during normal skin wound healing and higher level of hsa_circ_0084443 was found in chronic non-healing diabetic foot ulcers compared to normal wounds. However, the biological function of hsa_circ_0084443 in epidermal keratinocytes during wound repair has not been studied. To study the genes regulated by hsa_circ_0084443, we transfected siRNA targeting hsa_circ_0084443 diagnostic junction into human primary epidermal keratinocytes to knockdown hsa_circ_0084443 expression. more...
#> 434 Hepatocellular adenomas (HCA) are rare benign tumors mainly developed in women after 2 years of oral contraceptive use (Rooks et al., 1979). HCA are also related to other risk factors (obesity, vascular diseases, androgen and alcohol intake) or to different genetic diseases (Mac Cune Albright syndrome, glycogen storage diseases type 1a and MODY3 diabetes caused by HNF1A germline mutation) (Calderaro et al., 2013; Nault et al., 2013a). more...
#> 435 In humans, a subset of placental cytotrophoblasts (CTBs) invades the uterus and its vasculature, anchoring the pregnancy and ensuring adequate blood flow to the fetus. Appropriate depth is critical. Shallow invasion increases the risk of pregnancy complications, e.g., severe preeclampsia. Overly deep invasion, the hallmark of placenta accreta spectrum (PAS), increases the risk of pre-term delivery, hemorrhage and death. more...
#> 436 Fishoil or n-3 PUFA supplementation has shown some beneficial effects in patients with NASH. It is known that n-3 PUFA can influence hepatic gene expression. However, the effect of n-3 PUFA supplementation on hepatic gene expression has not been examined in patients with NASH. Aim of this pilot study was to examine the effect of n-3 PUFA supplementation on liver n-3 PUFA levels, hepatic gene expression and liver histology in patients with NASH. more...
#> 437 This project was aimed to study the transciptomic profiles of cholangiocarcinoma cells cultured in different concentration of glucose. The established human cholangiocarcinoma cell line; KKU-213, and highly metastatic subline; KKU-213L5, were used. KKU-213 were cultured in either Dulbecco Modified Eagle's Medium (DMEM) with normal (5.6 mM) or high glucose (25 mM) and labeled as KKU-213NG or KKU-213HG according to thier cuture medium and KKU-213L5 was cultured in high glucose DMEM medium. more...
#> 438 We did the transcriptome analysis of peripheral blood mononuclear cells of LADA patients and healthy controls
#> 439 Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic β cells. We show here that exposure to pro-inflammatory cytokines unmasks a marked plasticity of the β-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the β-cell transcriptome, proteome and 3D chromatin structure. more...
#> 440 Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic β cells. We show here that exposure to pro-inflammatory cytokines unmasks a striking plasticity of the β-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the β-cell transcriptome, proteome and 3D chromatin structure. more...
#> 441 Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic β cells. We show here that exposure to pro-inflammatory cytokines unmasks a striking plasticity of the β-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the β-cell transcriptome, proteome and 3D chromatin structure. more...
#> 442 Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic β cells. We show here that exposure to pro-inflammatory cytokines unmasks a striking plasticity of the β-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the β-cell transcriptome, proteome and 3D chromatin structure. more...
#> 443 PCOS is a widespread disease that primarily caused in-pregnancy in pregnant-age women. Normoandrogen (NA) and Hyperandrogen (HA) PCOS are distinguished under distinct level of testosterone, while markers and expression patterns for both subtypes were not adequately studied. Text-mining analysis stated the correlation for PCOS with granusola cells and thus we performed microarray analysis on granusola cells from HA PCOS, NA PCOS and normal tissue from individuals, and afterwards downloaded RNA-seq and microarray data from NCBI GEO database on granusola cells from PCOS and normal ovary. more...
#> 444 Background: Macrophage-based immune dysregulation plays a critical role in development of delayed gastric emptying in animal models of diabetes. Human studies have also revealed loss of anti-inflammatory macrophages and increased expression of genes associated with pro-inflammatory macrophages in full thickness gastric biopsies from gastroparesis patients. Aim: We aimed to determine broader protein expression (proteomics) and protein-based signaling pathways in full thickness gastric biopsies of diabetic (DG) and idiopathic gastroparesis (IG) patients. more...
#> 445 Metformin is a commonly used antihyperglycaemic agent for the treatment of type 2 diabetes. Nevertheless, the exact mechanisms of action, underlying the various therapeutic effects of metformin, remain elusive. The goal of this study was to evaluate the alterations in longitudinal whole-blood transcriptome profiles of healthy individuals after a one-week metformin intervention in order to identify the novel molecular targets and further prompt the discovery of predictive biomarkers of metformin response. more...
#> 446 We report the early transcriptional changes in human diabetic nephropathy by single nucleus RNA sequencing
#> 447 Multiple studies endorsed the positive effect of regular exercising on mental and physical health. However, the molecular mechanisms underlying training-induced fitness in combination with personal life-style remain largely unexplored. Circulating biomarkers such as microRNAs (miRNAs) offer themselves for studying systemic and cellular changes since they can be collected from the bloodstream in a low-invasive manner. more...
#> 448 One of the most common congenital disorders of male infertility is Klinefelter syndrome. Because of its extreme heterogeneity in clinical and genetic presentation, the relationship between transcriptome and the clinical phenotype and the associated co-morbidities seen in KS has not been fully clarified yet. We reported here a 47 XXY karyotype Chinese male (KS) with infertility and analyzed the differences in gene expression patterns of peripheral blood mononuclear cells (PBMCs) from a Chinese male and a female control with normal karyotype by single-cell sequencing. more...
#> 449 Diabetes and breast cancer are common diseases with a major impact on the health sector in Mexico and worldwide. Epidemiological and experimental works support the link between type 2 diabetes and breast cancer; these data support that insulin resistance, hyperglycemia, hyperinsulinemia, and elevated levels of IGF-1 in patients with type II diabetes mellitus promote growth and invasiveness of tumor cells. more...
#> 450 Here we investigated the degree by which epigenetic signatures in children from mothers with obesity or gestational diabetes mellitus are influenced by environmental factors. We profiled the DNA methylation signature of whole blood from lean, obese and gestational diabetes mellitus mothers and their respective newborns. DNA methylation profiles of mothers showed high similarity across groups, while on the contrary, newborns from GDM mothers showed a marked distinct epigenetic profile compared to newborns of both lean and obese mothers. more...
#> 451 Diabetic Nephropathy (DN) is a chronic complication of diabetes and the primary cause of end stage renal disease. DN can be differentially diagnosed only through histological investigation. Therefore, there is need for molecular biomarkers, such as miRNAs, to discriminate among different histological lesions in diabetics. Aim of this study was to identify a pattern of differentially expressed miRNAs in kidney biopsies of DN patients and to assess their potential as differential diagnostic biomarkers. more...
#> 452 Stem cell-derived β (SC-β) cells are an emerging regenerative therapy to compensate for loss of functional β cell mass in diabetes. Glucose-stimulated insulin secretion in SC-β cells is variable in vitro but stabilizes after transplantation and maturation under the kidney capsule of mice. We identified mechanisms correlated with functional maturation using RNA-sequencing and co-expression network analysis. more...
#> 453 Aim: To improve risk stratification in patients with stable coronary artery disease (CAD), we aimed to identify genes in monocytes predictive of new ischemic events in patients with CAD and determine to what extent expression of these transcripts resembles expression in acute myocardial infarction (AMI). Results: COX10 and ZNF484 distinguished between AMI and the whole group of stable CAD patients with an accuracy of 90%. more...
#> 454 In type 2 diabetes, pancreatic beta-cells fail to compensate for the presence of insulin resistance in target tissues and represent a central player in the disease development. Identifying and studying innovative molecular mechanisms that lead to beta-cell failure in diabetes represent an interesting line of research and are necessary. N6-Methyladenosine (m6A) is the most abundant modification in mRNA and is found virtually in all mammals. more...
#> 455 β-cell specific Mettl14 knock-out mice display reduced N6-methyladenosine (m6A) levels and recapitulate human Type II diabetes (T2D) islet phenotype with early diabetes onset and mortality secondary to decreased β-cell proliferation and insulin degranulation. To gain insights into the role of m6A in regulating the IGF1/insulin -> AKT - > PDX1 pathway and to dissect the signaling networks modulating AKT phosphorylation, we subjected freshly isolated islets from control and Mettl14 knock-out mice to phospho-antibody microarrays.
#> 456 In type 2 diabetes, pancreatic beta-cells fail to compensate for the presence of insulin resistance in target tissues and represent a central player in the disease development. Identifying and studying innovative molecular mechanisms that lead to beta-cell failure in diabetes represent an interesting line of research and are necessary. N6-Methyladenosine (m6A) is the most abundant modification in mRNA and is found virtually in all mammals. more...
#> 457 Diabetic foot ulcers (DFUs) are characterized by a chronic inflammation state which prevents cutaneous wound healing, andDFUs eventually lead to infection and leg amputation. Macrophages located in DFUs are locked in an pro-inflammatory phenotype. In this study, the effect of hyperglycemia and hypoxia on the macrophage phenotype was analyzed. For this purpose, a microarray was performed to study the gene expression profile of macrophages cultivated in a high glucose concentration. more...
#> 458 Phenotypic flexibility is used as a measure for health and can be studied during nutritional challenge tests. Changes in gene expression are early markers and give insight into mechanisms. Energy restriction (ER) has a variety of beneficial health effects and can be used to investigate different health states to study postprandial changes during challenge tests. Objective: We aimed to determine the postprandial effects of a 20% ER diet on whole genome expression profiles of peripheral blood mononuclear cells (PBMCs). more...
#> 459 Type 2 diabetes is associated with obesity, and is characterized by insulin resistance in target tissues of the hormone combined with insufficient systemic insulin. Insulin resistance apparently begins in subcutaneous adipocytes that fail to further accumulate triacylglycerol. To understand the pathogenesis of transition from lean to obesity and to diabetes, we performed transcriptome profiling by RNA-sequencing of isolated primary human adipocytes. more...
#> 460 Missense mutations in coding region of PDX1 predispose to type-2 diabetes mellitus as well as cause MODY through largely unexplored mechanisms. Here, we screened a large cohort of subjects with increased risk for diabetes and identified two subjects with impaired glucose tolerance carrying heterozygous missense mutations in the PDX1 coding region leading to single amino acid exchanges (P33T, C18R) in its transactivation domain. more...
#> 461 Missense mutations in coding region of PDX1 predispose to type-2 diabetes mellitus as well as cause MODY through largely unexplored mechanisms. Here, we screened a large cohort of subjects with increased risk for diabetes and identified two subjects with impaired glucose tolerance carrying heterozygous missense mutations in the PDX1 coding region leading to single amino acid exchanges (P33T, C18R) in its transactivation domain. more...
#> 462 Missense mutations in coding region of PDX1 predispose to type-2 diabetes mellitus as well as cause MODY through largely unexplored mechanisms. Here, we screened a large cohort of subjects with increased risk for diabetes and identified two subjects with impaired glucose tolerance carrying heterozygous missense mutations in the PDX1 coding region leading to single amino acid exchanges (P33T, C18R) in its transactivation domain. more...
#> 463 Metformin, the most widely administered diabetes drug, has been proposed as a candidate for host directed therapy for tuberculosis although very little is known about its effects on human host responses to Mycobacterium tuberculosis. When added in vitro to PBMCs isolated from healthy non-diabetic volunteers, metformin increased glycolysis, inhibited the mTOR targets, strongly reduced M. tuberculosis induced production of TNF-alpha (-58%), IFN-gamma (-47%) and IL-beta (-20%), while increasing phagocytosis. more...
#> 464 Metformin, the most widely administered diabetes drug, has been proposed as a candidate for host directed therapy for tuberculosis although very little is known about its effects on human host responses to Mycobacterium tuberculosis. When added in vitro to PBMCs isolated from healthy non-diabetic volunteers, metformin increased glycolysis, inhibited the mTOR targets, strongly reduced M. tuberculosis induced production of TNF-α (-58%), IFN-gamma (-47%) and IL-1β (-20%), while increasing phagocytosis. more...
#> 465 We identified a rare subset of autoreactive lymphocytes with a hybrid phenotype of T and B cells including coexpression of TCR and BCR and key lineage markers of both cell types (hereafter referred to as dual expressers or DEs). To investigate the dual phenotype of DEs at single cell resolution, we examined their transcriptomes using single cell RNA sequencing (scRNA-seq). We sorted individual DEs, Bcon and Tcon cells from PBMCs of one type I diabetes patient and analyzed the transcriptomes of 34 DEs, 20 Bcon , and 23 Tcon using the plate-based SMART-seq2 protocol (Tirosh and Suva, 2018; Tirosh et al., 2016). more...
#> 466 Defining cellular and molecular identities within the kidney is necessary to understand its organization and function in health and disease. Here we demonstrate a reproducible method with minimal artifacts for single-nucleus Droplet-based RNA sequencing (snDrop-Seq) that we use to resolve thirty distinct cell populations in human adult kidney. We define molecular transition states along more than ten nephron segments spanning two major kidney regions. more...
#> 467 A 6-year-old boy, second son of healthy parents affected with epileptic encephalopathy of neonatal onset. Pregnancy with gestational diabetes controlled with diet. Delivery was uneventful. Since 48 hours of life, he presented episodes of cyanosis, generalized hypertonia, and tonic asymmetric postures followed by apnea. Video-EEG at 5 days of life showed bilateral and asynchronous spike-and-wave. Seizures were refractory to phenobarbital but were controlled with phenytoin. more...
#> 468 Background: Clinical data identified an association between the use of HMG-CoA reductase inhibitors (statins) and incident diabetes in patients with underlying diabetes risk factors such as obesity, hypertension and dyslipidemia. The molecular mechanisms however are unknown. Methods: An observational cross-sectional study included 910 severely obese patients, mean (SD) body mass index 46.7 (8.7), treated with or without statins (ABOS cohort: a biological atlas of severe obesity). more...
#> 469 In vitro differentiation of human stem cells can produce pancreatic beta cells, the insulin-secreting cell type whose loss underlies Type 1 Diabetes. As a step towards mastery of this process, we report on transcriptional profiling of >100,000 individual cells sampled during in vitro beta cell differentiation and describe the cells that emerge. We resolve populations corresponding to beta cells, alpha-like poly-hormonal cells, non-endocrine cells that resemble pancreatic exocrine cells and a previously unreported population resembling enterochromaffin cells. more...
#> 470 Adipocyte progenitor cells (APCs) provide the reservoir of regenerative cells to produce new adipocytes, although their identity in humans remains elusive. Using FACS analysis, gene expression profiling and metabolic and proteomic analyses, we identified three APCs subtypes in human white adipose tissues. The APC subtypes are molecularly distinct but possess similar proliferative and adipogenic capacities. more...
#> 471 This is a study of 114 newborns aimed at identifying associations of cord blood methylation profiles with measures of newborn adiposity. Neonatal adiposity is a risk factor for childhood obesity. Investigating contributors to neonata adiposity is important for understanding early life obesity risk. Epigenetic changes of metabolic genes in cord blood may contribute to excessive neonatal adiposity and subsequent childhood obesity. more...
#> 472 Obesity underpins the development of numerous chronic diseases such as type II diabetes mellitus. It is well established that obesity negatively alters immune cell frequencies and functions. Mucosal Associated Invariant T (MAIT) cells are a population of innate T cells, which we have previously reported are dysregulated in obesity, with altered circulating and adipose tissue frequencies and a reduction in their IFN-gamma production, which is a critical effector function of MAIT cells in host defence. more...
#> 473 Using influenza infection as a disease model, we used a systems biology approach to analyse host response and to identify immune pathways that might contribute to disease progression. We recruited influenza patients with varying severity of infection (n=154) and collected peripheral blood samples within 24 hours of their presentation to hospitals. Gene-expression arrays of these samples were analysed using weighted gene co-expression network analysis to detect disease-driving modules. more...
#> 474 Sphingomyelin phosphodiesterase acid-like 3b (SMPDL3b) is a lipid raft enzyme that regulates plasma membrane (PM) fluidity. Here we report that SMPDL3b excess, as observed in podocytes in diabetic kidney disease (DKD), impairs insulin receptor isoform B-dependent pro-survival insulin signaling by interfering with insulin receptor isoforms binding to caveolin-1 in PM. SMPDL3b excess affects the production of active sphingolipids resulting in decreased ceramide-1-phosphate (C1P) content as observed in human podocytes in vitro and in kidney cortexes of diabetic db/db mice in vivo. more...
#> 475 Mutations in HNF1A cause Maturity Onset Diabetes of the Young type 3, the second most frequent form of diabetes caused by single gene mutation. We generated human pancreatic stem cell-derived endocrine cells with mutations in HNF1A and show that HNF1A deficiency impairs scβ-cell fate, insulin granule maturation and the secretion of insulin in a glucose responsive manner. Single-cell RNA sequencing reveals that HNF1A orchestrates a network of genes involved in glucose metabolism, zinc transport, calcium ion binding and hormone exocytosis. more...
#> 476 Fish contains high quality proteins and essential nutrients including vitamin D (VitD3). Fish peptide consumption can lower cardiovascular disease (CVD) risk factors and studies showed an association between VitD3 deficiency, CVD and CVD risk factors such as diabetes. This study investigated acute effects of a single dose of VitD3, bonito fish peptide hydrolysate (BPH), or a combination of both on CVD risk factors and whole blood gene expression levels. more...
#> 477 Mitochondrial DNA (mtDNA) 3243A>G tRNALeu(UUR) heteroplasmic mutation (m.3243A>G) exhibits clinically heterogeneous phenotypes. While the high mtDNA heteroplasmy exceeding a critical threshold causes mitochondrial encephalomyopathy, lactic acidosis with stroke-like episodes (MELAS) syndrome, the low mtDNA heteroplasmy causes maternally inherited diabetes with or without deafness (MIDD) syndrome. How quantitative differences in mtDNA heteroplasmy produces distinct pathological states has remained elusive. more...
#> 478 We have studied the impact of T2D on open chromatin in human pancreatic islets. We used assay for transposase-accessible chromatin using sequencing (ATAC-seq) to profile open chromatin in islets from T2D and non-diabetic donors. We identified ATAC-seq peaks representing open chromatin regions in islets of non-diabetic and diabetic donors. The majority of ATAC-seq peaks mapped near transcription start sites. more...
#> 479 White adipose tissue (WAT) is a central factor in the development of type 2 diabetes. Despite the epidemiological importance of WAT there is a paucity of translational models to study long term changes in mature adipocytes. Here, we describe a novel method for the culture of mature white adipocytes under a permeable membrane. Compared to existing culture methods such as adipose tissue explants and adipocyte ceiling culture, Membrane mature Adipocyte Aggregate Cultures (MAAC) are superior at maintaining adipogenic gene expression through 2 weeks of culture, do not dedifferentiate, and are under reduced hypoxic stress relative to adipose tissue explants. more...
#> 480 Despite substantial declines in mortality following myocardial infarction (MI), subsequent left ventricular remodelling (LVRm) remains a significant long-term complication. Extracellular small non-coding RNAs (exRNAs) have been associated with cardiac inflammation and fibrosis and we hypothesized that they are associated with post-MI LVRm phenotypes. RNA sequencing of exRNAs was performed on plasma samples from patients with “beneficial” (decrease LVESVI ≥20%, n=11) and “adverse” (increase LVESVI ≥15%, n=11) LVRm. more...
#> 481 This SuperSeries is composed of the SubSeries listed below.
#> 482 We generated expression profiles of TH1 and TREG cells from T1D and healthy subjects by RNA-Seq. By integrating RNA-Seq dta with other data sets, we predicted and validated serveral T1D risk SNPs.
#> 483 Most type 1 diabets (T1D) associated SNPs are located in non-coding regions, making it hard to understand their functional impact. We performed epigenomic profiling of two enhancer marks, H3K4me1 and H3K27ac, using primary TH1 and TREG cells from healthy and T1D subjects. By integrating enhancers predicted using these ChIP-Seq data, T1D associated SNPs and additional supporting data, we found and validated several novel risk SNPs for T1D.
#> 484 We conducted a genome-wide placental transcriptome study aiming at the identification of functional pathways representing the molecular link between maternal pre-pregnancy BMI and fetal growth. We used RNA microarray (Agilent 8 X 60 K), medical records, and questionnaire data from 183 mother-newborn pairs from the ENVIRONAGE birth cohort study (Flanders, Belgium). We applied a weighted gene co-expression network analysis (WGCNA) and identified genes modules and hub genes that were associated with maternal BMI as well as newborn birth weight. more...
#> 485 Mutations in HNF1A cause Maturity Onset Diabetes of the Young type 3, the second most frequent form of diabetes caused by single gene mutation. We generated human pancreatic stem cell-derived endocrine cells with mutations in HNF1A and show that HNF1A deficiency impairs scβ-cell fate, insulin granule maturation and the secretion of insulin in a glucose responsive manner. Single-cell RNA sequencing reveals that HNF1A orchestrates a network of genes involved in glucose metabolism, zinc transport, calcium ion binding and hormone exocytosis. more...
#> 486 This SuperSeries is composed of the SubSeries listed below.
#> 487 Type 1 diabetes (T1D) is caused by autoimmune destruction of pancreatic β cells. Mounting evidence supports a central role for β-cell alterations in triggering the activation of self-reactive T-cells in T1D. However, the early deleterious events that occur in β cells, underpinning islet autoimmunity are not known. We hypothesized that epigenetic modifications induced in β cells by inflammatory mediators play a key role in initiating the autoimmune response. more...
#> 488 Type 1 diabetes (T1D) is caused by autoimmune destruction of pancreatic β cells. Mounting evidence supports a central role for β-cell alterations in triggering the activation of self-reactive T-cells in T1D. However, the early deleterious events that occur in β cells, underpinning islet autoimmunity are not known. We hypothesized that epigenetic modifications induced in β cells by inflammatory mediators play a key role in initiating the autoimmune response. more...
#> 489 The study was aimed to identify novel autoantibody(AAB) biomarkers for Type 1 diabetes.
#> 490 RAD21 ChIA-PET in human MSiPS cells For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODE_Data_Use_Policy_for_External_Users_03-07-14.pdf
#> 491 RAD21 ChIA-PET in human MSFIB cells (fibroblast from skin from donor Michael Snyder) For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODE_Data_Use_Policy_for_External_Users_03-07-14.pdf
#> 492 RAD21 ChIA-PET in human MSLCL cells (B-cell-derived lymphoblastoid cell line from donor Michael Snyder) For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODE_Data_Use_Policy_for_External_Users_03-07-14.pdf
#> 493 Peripheral blood samples were collected from control-arm subjects enrolled in 6 clinical trials conducted by the Immune Tolerance Network and Type 1 Diabetes TrialNet. The included trials evaluated immune-modifying therapy in new-onset T1D, with similar trial timecourses, primary outcomes, and data and sample collection. Total RNA was isolated from whole blood samples and then globin-reduced. RNAseq libraries were prepared from the globin-reduced RNA.
#> 494 Peripheral blood samples were collected from subjects enrolled in the TrialNet study TN-09. This was a phase II study of the effects of the T cell costimulation inhibitor CTLA4-Ig (abatacept) in new-onset T1D. Total RNA was isolated from whole blood samples and then globin-reduced. RNAseq libraries were prepared from the globin-reduced RNA.
#> 495 Introduction: In human placenta, alteration in trophoblast differentiation has a major impact on placental maintenance and integrity. Moreover, abnormal syncytial fusion seems to be implicated in the development of many complications including pre-eclampsia and intra-uterine growth restriction (IUGR). However, little is known about the mechanisms that control cytotrophoblast fusion into syncytiotrophoblast. more...
#> 496 Age-related macular degeneration (AMD) is a complex multifactorial disease with at least 34 loci contributing to genetic susceptibility. To gain functional understanding of AMD genetics, we generated transcriptional profiles of retina from 453 individuals including both controls and cases at distinct stages of AMD. We integrated retinal transcriptomes, covering 13,662 protein-coding and 1,462 noncoding genes, with genotypes at over 9 million common single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTL) analysis of a tissue not included in Genotype-Tissue Expression (GTEx) and other large datasets. more...
#> 497 Prenatal development is a critical period for programming of neurological disease. Preeclampsia, a pregnancy complication involving oxidative stress in the placenta, has been associated with long-term health implications for the child, including an increased risk of developing schizophrenia and autism spectrum disorders in later life. We have shown previously, in a rodent model of placental oxidative stress, that culture medium conditioned by the placenta alters neuronal characteristics when applied to primary cortical cultures in vitro and mimics many of the neurodevelopmental changes observed in the offspring brain. more...
#> 498 OBJECTIVE Diet intervention in obese adults is the first strategy to induce weight loss and to improve insulin sensitivity. We hypothesized that improvements in insulin sensitivity after weight loss from a short-term dietary intervention tracks with alterations in expression of metabolic genes and abundance of specific lipid species. RESEARCH DESIGN AND METHODS Eight obese, insulin resistant, non-diabetic adults were recruited to participate in a three-week low calorie diet intervention study (1000 kcal/day). more...
#> 499 Metformin is a well tolerated and often prescribed treatment for type 2 diabetes. However, the effect of metformin on gene expression in endothelial cells remains unknown. We used RNA-seq to profile gene expression in primary human aortic endothelial cells stimulated with metformin in normoglycaemic and hyperglycaemic conditions. We identified novel pathways in hyperglycaemic endothelial cells that may be involved in the development of endothelial dysfunction. more...
#> 500 Intrauterine growth restriction (IUGR) is associated with increased susceptibility to obesity, metabolic syndrome and type 2 diabetes. Although the mechanisms underlying the fetal origin of metabolic disease are poorly understood, evidence suggests epigenomic alterations play a critical role. We sought to identify changes in DNA methylation patterns that define IUGR in CD3+ T-cells purified from umbilical cord blood obtained from appropriate for gestational age (Control) and IUGR male newborns using a genome-wide assay. more...
#> 501 Animal studies have linked disturbed adipose tissue clock gene rhythms to the pathophysiology of the metabolic syndrome. However, data on molecular clock rhythms in human patients are limited. Therefore, in a standardized real life setting, we compared diurnal gene expression profiles in subcutaneous adipose tissue between obese patients with type 2 diabetes and age-matched healthy lean control subjects, using RNA sequencing. more...
#> 502 This is a transcriptomics analysis contributing to a bigger project that tries to shed light on the role of type 2 diabetes mellitus (T2DM) as a risk factor for colon cancer (CC). Here we present a gene expression screening of 7 colon tumor xenograft samples, 2 with diabetic mice and 5 with normal blood glucose levels. For xenograft model details see: Prieto I, et al. (2017) Colon cancer modulation by a diabetic environment: A single institutional experience. more...
#> 503 This is a transcriptomics analysis contributing to a bigger project that tries to shed light on the role of type 2 diabetes mellitus (T2DM) as a risk factor for colon cancer (CC). Here we present a gene expression screening of paired tumor and normal colon mucosa samples in a cohort of 42 CC patients, 23 of them with T2DM. Using gene set enrichment, we identified an unexpected overlap of pathways over-represented in diabetics compared to non-diabetics, both in tumor and normal mucosa, including diabetes-related metabolic and signaling processes. more...
#> 504 Thiazolidinedione drugs (TZDs) target the transcriptional activity of PPARg to reverse insulin resistance in type 2 diabetes, but side effects limit their clinical use. Here, using human adipose stem cell-derived adipocytes, we demonstrate that single-nucleotide polymorphisms (SNPs) were enriched at sites of patient-specific PPARg binding, which correlated with the individual-specific effects of TZD rosiglitazone (rosi) on gene expression. more...
#> 505 MicroRNAs (miRNAs) are noncoding RNAs representing an important class of gene expression modulators. Extracellular circulating miRNAs are both candidate biomarkers for disease pathogenesis and mediators of cell-to-cell communication. We examined the miRNA expression profile of total serum and serum derived exosome-enriched extracellular vesicles in people with normal glucose tolerance or type 2 diabetes. more...
#> 506 EndoC-βH1 is emerging as a critical human β cell model to study the genetic and environmental etiologies of β cell (dys)function and diabetes. Comprehensive knowledge of its molecular landscape is lacking, yet required, for effective use of this model. Here, we report chromosomal (spectral karyotyping), genetic (genotyping), epigenomic (ChIP-seq and ATAC-seq), chromatin interaction (Hi-C and Pol2 ChIA-PET), and transcriptomic (RNA-seq and miRNA-seq) maps of EndoC-βH1. more...
#> 507 Identification of filamin-A as a target for insulin and IGF1 action. Insulin analogues have been developed to achieve further improvement in the therapy of diabetes. However, modifications introduced into the insulin molecule might enhance their affinity to the insulin-like growth factor-1 receptor (IGF1R). Most tumors, including endometrial cancers, express high levels of IGF1R. The present study was aimed at identifying the entire set of genes that are differentially activated by insulin glargine or detemir, in comparison to regular insulin and IGF1, in Type 1 and Type 2 endometrial cancer cell lines (ECC-1 and USPC-1, respectively). more...
#> 508 We report that the effect of GDM on gene expression differs between feto-placental endothelial cells of male vs female progeny, i.e. after pregnancy with a male or female offspring.
#> 509 The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). more...
#> 510 Amyotrophic lateral sclerosis (ALS) is an incurable and fatal neurodegenerative disease. Increasing the chances of success for future clinical strategies requires more in-depth knowledge of the molecular basis underlying disease heterogeneity. We recently laid the foundation for a molecular taxonomy of ALS by whole transcriptome expression profiling of motor cortex from sporadic ALS (SALS) patients. more...
#> 511 We have identified molecular-level alternations in different adipose depots (thigh, visceral and subcutaneous) of Asian Indians (both male and female) suffering from type-2 diabetes as compared to age and BMI matched normal glucose tolerant subjects by functional analysis of differentially expressed genes, and correlation of gene expression estimates with measured intermediate traits associated with T2D and its related co-morbidities (Hb1Ac, HOMA-B, HOMA-R, NEFA, Triglyceride, Total Cholesterol, HDL, LDL, VLDL, Leptin, Adiponectin, TNF-α, Serum- Creatinine, IL-6, High sensitivity - serum-creatinine (hs-CRP) and also size of adipocytes). more...
#> 512 Sirtuin deacetylases and forkhead box class O (FOXO) transcription factors are central regulators of cell survival, cell cycle and cellular resistance to stress in response to signals from hormones, growth factors and oxidative stress. FOXO activity is modulated by the sirtuins, which function in a NAD+-dependent manner. Sirtuin activity, on the other hand is subject to inhibition by a natural compound nicotinamide (NAM). more...
#> 513 Sirtuin deacetylases and forkhead box class O (FOXO) transcription factors are central regulators of cell survival, cell cycle and cellular resistance to stress in response to signals from hormones, growth factors and oxidative stress. FOXO activity is modulated by the sirtuins, which function in a NAD+-dependent manner. Sirtuin activity, on the other hand is subject to inhibition by a natural compound nicotinamide (NAM). more...
#> 514 The liver is a major site for synthesis, storage and redistribution of carbohydrates, proteins and lipids. In addition, it is well-known that maternal obesity (MO) increases risk of offspring cardiovascular disease (CVD), diabetes and obesity. However, the mechanisms by which the MO intrauterine environment predisposes offspring to CVD and metabolic dysregulation are unknown. The goal of this study was to assess the impact of MO on primate fetal liver and identify underlying molecular mechanisms by which MO increases disease risk. more...
#> 515 Accumulation of visceral fat around internal organs, is a strong risk predictor for cardiometabolic disease. Although fat deposition at distinct anatomical sites is influenced by genetic factors their functional mechanism remains poorly understood. Here, we show ENPP6 as a neural determinant of selectively visceral adiposity. Through dual-energy X-ray absorptiometry (DXA) body composition analysis in 1,301 individuals from the isolated population of Orkney, we identified low-frequency variants at 4q35.1 associated with a reduction of DXA fat distribution (rs144607341/rs17583822, P = 2.7 x 10-10/ 2.0 x 10-9). more...
#> 516 Human embryonic stem cells (hESCs) are a potential unlimited source of insulin-producing β-cells for diabetes treatment. A greater understanding of how β-cells form during embryonic development will improve current hESC differentiation protocols. As β-cells are formed from NEUROG3-expressing endocrine progenitors, this study focused on characterizing the single-cell transcriptomes of mouse and hESC-derived endocrine progenitors. more...
#> 517 Natural and stable cell identity switches, where terminally-differentiated cells convert into different cell-types when stressed, represent a widespread regenerative strategy in animals, yet they are poorly documented in mammals. In mice, some glucagon-producing pancreatic α-cells become insulin expressers upon ablation of insulin-secreting β-cells, promoting diabetes recovery. Whether human islets also display this plasticity for reconstituting β-like cells, especially in diabetic conditions, remains unknown. more...
#> 518 Purpose: Pancreatic islet transplantation is an effective cell therapy for type 1 diabetes (T1D), but its clinical application is limited by the shortage of donor pancreata. Among the potential alternatives, the differentiation of human embryonic stem cells (hESc) into insulin-producing β-cells has taken an early lead. However, while the proportion of β-cells obtained through current methods is relatively high, a significant percentage of undefined non-endocrine cell types are still generated. more...
#> 519 Natural and stable cell identity switches, where terminally-differentiated cells convert into different cell-types when stressed, represent a widespread regenerative strategy in animals, yet they are poorly documented in mammals. In mice, some glucagon-producing pancreatic α-cells become insulin expressers upon ablation of insulin-secreting β-cells, promoting diabetes recovery. Whether human islets also display this plasticity for reconstituting β-like cells, especially in diabetic conditions, remains unknown. more...
#> 520 Understanding the molecular mechanisms regulating the maintenance and destruction of intervertebral disc may lead to the development of new therapies for intervertebral disc degeneration (IDD). Here we present evidence from miRNA microarray analyses of clinical data sets along with in vitro and in vivo experiments that miR-141 is a key regulator of IDD. Gain- and loss-of-function studies show that miR-141 drives IDD by inducing nucleus pulposus (NP) apoptosis. more...
#> 521 Peripheral blood samples were collected from subjects enrolled in the TrialNet study TN-05. This was a phase II study of the effects of the anti-CD20 monoclonal antibody rituximab in new-onset T1D. Total RNA was isolated from whole blood samples and then globin-reduced. RNAseq libraries were prepared from the globin-reduced RNA.
#> 522 Background: Imprinted genes are defined by their preferential expression from one of the two parental alleles. This unique mode of gene expression is dependent on allele-specific DNA methylation profiles established at regulatory sequences called imprinting control regions. These loci are frequently used as biosensors to study how environmental exposures affect methylation and transcription. However, a critical unanswered question is whether they are more, less or equally sensitive to environmental stressors as the rest of the genome. more...
#> 523 Insulin gene mutations are a leading cause of neonatal diabetes. They can lead to proinsulin misfolding and its retention in endoplasmic reticulum (ER). This results in increased ER-stress suggested to trigger beta-cell apoptosis. In humans, the mechanisms underlying beta-cell failure remain unclear. Here we show that misfolded proinsulin impairs developing beta-cell proliferation without increasing apoptosis. more...
#> 524 Intrahepatic cholangiocarcioma has two molecular classification of intrahepatic CCA with distinct clinical, pathological, biological and prognostic differences
#> 525 Analysis of neutrophils purified from peripheral blood of patients with symptomatic and pre-symptomatic type 1 diabetes (T1D), at risk of T1D, and healthy controls.
#> 526 Cellular senescence, an irreversible proliferative arrest, functions in tissue remodeling during development and is implicated in multiple aging-associated diseases. While senescent cells often manifest an array of senescence-associated phenotypes, such as cell cycle arrest, altered heterochromatin architecture, reprogrammed metabolism and senescence-associated secretory phenotype(SASP), the identification of senescence cells has been hindered by lack of specific and universal biomarkers. more...
#> 527 Diabetes is prevalent worldwide and associated with severe health complications, including blood vessel damage that leads to cardiovascular disease and death. Here we report the development of a 3D blood vessel organoid culture system from human pluripotent stem cells. These human blood vessel organoids contain endothelial cells and pericytes that self-assemble into interconnected capillary networks enveloped by a basement membrane. more...
#> 528 This SuperSeries is composed of the SubSeries listed below.
#> 529 Background: Here, the role of α-ketoglutarate (αKG) in the epi-metabolic control of DNA demethylation has been investigated in therapeutically relevant cardiac mesenchymal cells (CMSCs) isolated from controls and type 2 diabetes donors. Methods & results: Quantitative global analysis, methylated and hydroxymethylated DNA sequencing and gene specific GC methylation detection revealed an accumulation of 5mC, 5hmC and 5fC in the genomic DNA of human CMSCs isolated from diabetic (D) donors (D-CMSCs). more...
#> 530 Type 2 diabetes mellitus is a chronic age-associated degenerative metabolic disease that reflects relative insulin deficiency and resistance. Extracellular vesicles (EVs; exosomes, microvesicles and apoptotic bodies) are small (50-400 nM) lipid-bound vesicles capable of shuttling functional proteins, nucleic acids, and lipids as part of intercellular communication systems. Recent studies in mouse models and in cell culture suggest that EVs may modulate insulin signaling. more...
#> 531 The common gamma chain (γc) is required for productive signaling by interleukin (IL)-15, IL-21 and IL-2, which are critically involved in immune activation and regulation. IL-21 and IL-15 are implicated in the pathogenesis of type-1 diabetes, graft-versus-host disease, and celiac disease (CeD), a gluten-mediated autoimmune-like enteropathy. Attempts to treat type-1 diabetes and graft-versus-host disease with biologics targeting one particular cytokine have failed. more...
#> 532 Using an integrated approach to characterize the pancreatic tissue and isolated islets from a 33-year-old with 17 years of type 1 diabetes (T1D), we found donor islets contained β cells without insulitis and lacked glucose-stimulated insulin secretion despite a normal insulin response to cAMP-evoked stimulation. With these unexpected findings for T1D, we sequenced the donor DNA and found a pathogenic heterozygous variant in hepatocyte nuclear factor 1 alpha (HNF1A). more...
#> 533 Pancreatic endocrine cells orchestrate the precise control of blood glucose levels, but the contribution of each cell type to diabetes or obesity remains elusive. Here we used a massively parallel single-cell RNA-seq technology (Drop-Seq) to analyze the transcriptome of 26,677 pancreatic islets cells from both healthy and type II diabetic (T2D) donors. We have analyzed cell type-specific gene signatures, and detected several rare α or β cell subpopulations with high sensitivity. more...
#> 534 Identification of cell surface markers specific to human pancreatic β-cells would allow in vivo analysis and imaging. Here we introduce a biomarker – ectonucleoside triphosphate diphosphohydrolase-3 (NTPDase3) – that is expressed on the cell surface of essentially all adult human β-cells, including those from individuals with type 1 or type 2 diabetes (T1D, T2D). NTPDase3 is expressed dynamically during postnatal human pancreas development, appearing first in acinar cells at birth, but several months later its expression declines in acinar cells while concurrently emerges in islet β-cells. more...
#> 535 Background: Long-term exposure to elevated levels of free fatty acids (FFAs) is deleterious for beta-cell function and may contribute to development of type 2 diabetes mellitus (T2DM). Whereas mechanisms of impaired glucose-stimulated insulin secretion (GSIS) in FFA-treated beta-cells have been intensively studied, biological events preceding the secretory failure, when GSIS is accentuated, are poorly investigated. more...
#> 536 The aim of this study was to understand if gene expression in atherosclerotic plaque macrophages is altered by diabetes. Laser capture microdissection (LCM) was used to specifically isolate macrophage enriched regions from human carotid atherosclerotic plaque samples. RNA isolated was then sent for sequencing using the Illumina bead array system. Gene expression data revealed that 106 genes from diabetic macrophages are differentially expressed (FDR<0.2) and provide mechanistic evidence for the involvement of Runt-related transcription factor 1 (RUNX1) in the development of diabetic atherosclerosis.
#> 537 Circadian misalignment, such as in shift work, has been associated with obesity and type 2 diabetes, however, direct effects of circadian misalignment on skeletal muscle insulin sensitivity and muscle molecular circadian clock have never been investigated in humans. Here we investigated insulin sensitivity and muscle metabolism in fourteen healthy young lean men (age 22.4 ± 2.8 years; BMI 22.3 ± 2.1 kg/m2 [mean ± SD]) after a 3-day control protocol and a 3.5-day misalignment protocol induced by a 12-h rapid shift of the behavioral cycle. more...
#> 538 Recent genome-wide association studies (GWAS) have identified gene variants associated with coronary artery disease including ADAMTS7, PHACTR1, KIAA1462/JCAD (Junctional Protein Associated with Coronary Artery Disease) and many others. JCAD has been identified as a novel component of endothelial cell-cell junctions (Akashi et al., 2011, BBRC) and regulates angiogenesis (Hara et al, ATVB, 2017). In our study, we observed that JCAD is a 148-KDa protein identified by mass spectrometry, but display a band shift to around 180-200 KDa, suggesting that JCAD is subject to multiple post-translatinonal modification. more...
#> 539 Human CD8+ T cells are the final mediators of autoimmune β-cell destruction in type 1 diabetes. However, their target epitopes have not been demonstrated to be naturally processed and presented by β cells. We therefore performed an epitope discovery study combining HLA Class I peptidomics and transcriptomics strategies. Inflammatory cytokines increased β-cell peptide presentation in vitro, paralleling upregulation of HLA Class I expression. more...
#> 540 We performed genome-wide methylation analysis of primary feto-placental arterial and venous endothelial cells from healthy (AEC and VEC) and GDM complicated pregnancies (dAEC and dVEC). Parallel transcriptome analysis identified variation in gene expression linked to GDM-associated DNA methylation, implying a direct functional link. Pathway analysis found that genes altered by exposure to GDM clustered to functions associated with ’Cell Morphology’ and ’Cellular Movement’ in both AEC and VEC. more...
#> 541 We performed genome-wide methylation analysis of primary feto-placental arterial and venous endothelial cells from healthy (AEC and VEC) and GDM complicated pregnancies (dAEC and dVEC). Parallel transcriptome analysis identified variation in gene expression linked to GDM-associated DNA methylation, implying a direct functional link. Pathway analysis found that genes altered by exposure to GDM clustered to functions associated with ’Cell Morphology’ and ’Cellular Movement’ in both AEC and VEC. more...
#> 542 Diabetes is prevalent worldwide and associated with severe health complications, including blood vessel damage that leads to cardiovascular disease and death. We report the development of 3D blood vessel organoids from human embryonic and induced pluripotent stem cells. These human blood vessel organoids contain endothelium, perivascular pericytes, and basal membranes, and self-assemble into lumenized interconnected capillary networks. more...
#> 543 Transcriptome comparison of glomeruli from kidneys with diabetic nephropathy (DN) and glomeruli from the unaffected portion of tumor nephrectomies. Transcritomics profile of glomeruli in DN patients explored SRGAP2 was strongly associated with proteinuria and involved in podocyte cytoskeleton organization
#> 544 Our current understanding of the pathogenesis of T1D arose in large part from studies using the non-obese diabetic (NOD) mouse model of type 1 diabetes (T1D). Of concern, therapeutic interventions shown to significantly dampen or even reverse disease in mouse models have not successfully translated into interventions in human T1D. The present study addresses this disconnect in research translation by directly analyzing human donor islets from individuals with T1D, aiming to provide insight into disease mechanisms and identify potential target pathways for therapeutic intervention. more...
#> 545 New measures are needed to predict type 1 diabetes disease trajectory. We have developed a sensitive array-based bioassay whereby patient plasma is used to induce transcription in healthy “reporter” leukocytes. Here we report a refined gene ontology-based inflammatory index (I.I.359) that is based upon expression levels of 359 transcripts identified in cross-sectional studies of new onset Type 1 diabetes patients and controls, where higher scores reflect greater inflammatory bias. more...
#> 546 This SuperSeries is composed of the SubSeries listed below.
#> 547 Diabetes is a complex metabolic syndrome characterized by prolonged high blood glucose levels. It is known that diabetes is associated with an elevated risk of cancer, however, the underlying molecular mechanisms are largely unknown. In particular, it remains unclear as to how hyperglycemia may affect epigenetic checkpoints and tumor suppressor pathways, thus enabling oncogenic transformation. Here we show that long-term hyperglycemic conditions adversely impact the anti-tumor epigenetic mark DNA 5-hydroxymethylcytosine (5hmC) through direct regulation of the tumor suppressor and DNA 5mC hydroxymethylase, TET2. more...
#> 548 Role of circRNAs in active tuberculosis (TB) remains unknown. The present study was aimed to determine plasma circRNA expression profile in active TB patients to identify potential biomarker by circRNA microarrays.
#> 549 We investigated whether variants fine-mapped for Rheumatoid Arthritis (RA) and Type 1 Diabetes overlap with open chromatin regions specifically after stimulation. We show that rs117701653, a potentially causal variant for RA near CD28, overlaps open chromatin regions only after stimulation. We futhermore observe a small increase in enhancer activity for this variant under stimulatory conditions using a luciferase assay. more...
#> 550 There is a temporal window from the time diabetes is diagnosed to the appearance of overt kidney disease during which time the disease progresses quietly without detection. Currently, there is no way to detect early diabetic nephropathy (EDN). Here we performed an unbiased assessment of gene-expression analysis of postmortem human kidneys using microarrays to identify candidate genes that may contribute to EDN.
#> 551 To delineate the effects of BCL11A (a type 2 diabetes risk gene) in human beta cell function we knockdown BCL11A in primary human beta cells and generated and sequenced RNA-seq libraries from 4 human donors
#> 552 This SuperSeries is composed of the SubSeries listed below.
#> 553 This paper describes the first time a high-content environmental chemicals screen using pancreatic β-like cells derived from human pluripotent stem cells (hPSCs), and discovered that a commonly used pesticide, propargite, induces pancreatic β-cell DNA damage and necrosis. More interestingly, we found out the genetic background of β-like cells affects their response to propargite-induced toxicity, based on isogenic hPSC platform, including for variants GWAS identified associated with T1D, since isogenic GSTT1-/- and PTPN2-/- pancreatic β-like cells are hypersensitive to propargite-induced β-cell death both in vitro and in vivo. more...
#> 554 This paper describes the first time a high-content environmental chemicals screen using pancreatic β-like cells derived from human pluripotent stem cells (hPSCs), and discovered that a commonly used pesticide, propargite, induces pancreatic β-cell DNA damage and necrosis. More interestingly, we found out the genetic background of β-like cells affects their response to propargite-induced toxicity, based on isogenic hPSC platform, including for variants GWAS identified associated with T1D, since isogenic GSTT1-/- and PTPN2-/- pancreatic β-like cells are hypersensitive to propargite-induced β-cell death both in vitro and in vivo. more...
#> 555 In our study, we generated and sequenced small RNA libraries from commercially available brain total RNA or human blood plasma samples. These samples were generated with MAD-DASH, a method we developed employing CRISPR/Cas9 ribonucleoprotein targeting specific overabundant sequences such as adapter dimer or miRNAs to reduce these sequences from final libraries. We sequenced treated and untreated samples to demonstrate specificity, efficacy, and reproducibility of our MAD-DASH small-RNA sequencing protocol.
#> 556 TaqMan®Array Human MicroRNA Cards were used to profile the differential expression of human microRNAs in patients with CKD versus heathy controls
#> 557 In the past few decades, the prevalence of overweight and obesity has sharply increased in children and adolescents. Childhood obesity life are associated with increased risk of cardiovascular disease (CVD), diabetes mellitus, metabolic syndrome, sleep disturbances and certain cancers in adulthood. Childhood obesity has become a serious global public health challenge. Long noncoding RNAs (lncRNAs) have an important role in adipose tissue function and energy metabolism homeostasis, and abnormalities may lead to obesity. more...
#> 558 Myometrial biopsies were collected from 20 women undergoing primary cesarean sections in well-characterized clinical scenarios: 1) term labor of spontaneous onset (TL, n=5); 2) term non-labor (TNL, n=5); 3) spontaneous PTB in the setting of chorioamnionitis (PTB-HCA) and 4) indicated preterm birth (PTB) non-labor (PTB-NL, n=5). RNAs were profiled using 2nd-generation RNA sequencing.
#> 559 Type 2 Diabetes (T2D) is a complex metabolic disorder due to a progressive insulin secretory defect on the background of insulin resistance. We found a muscle specific lncRNA we named TDNC1(T2D down-regulated non-coding RNA 1) whose expression is reduced in T2D patients as well as young individuals with family history of T2D. We used Microarray to assess for global gene expression pattern following ectopic expression of this lncRNA.
#> 560 Recent study has revealed that long non-coding RNAs (lncRNAs) perform as important regulators of cellular physiology and pathology, which makes them promising therapeutic and diagnostic entities. We found lncRNA WAKMAR1 is significantly down-regulated in wound-edge keratinocytes from venous ulcer and diabetic foot ulcer compared to the normal wounds. To study the genes regulated by WAKMAR1, we transfected lncRNA GapmeRs into human primary epidermal keratinocytes to inhibit its expression. more...
#> 561 Through development and use of a minimal component protocol for derivation of late stage pancreatic progenitors and beta-like cells, we compared WT and GLIS3-/- pancreatic cells at different stages and discovered that GLIS3-/- cells show an ectopic activation of TGF-beta signaling.
#> 562 Cystic fibrosis (CF)-related diabetes (CFRD) is an increasingly common and devastating comorbidity of CF, affecting ~35% of adults with CF. However, the underlying causes of CFRD are unclear. Here, we examined cystic fibrosis transmembrane conductance regulator (CFTR) islet expression and whether the CFTR participates in islet endocrine cell function using murine models of b cell CFTR deletion, and normal and CF human pancreas and islets. more...
#> 563 Introduction: There is increasing evidence that consumption of cocoa products have a beneficial effect on cardio-metabolic health, but the underlying mechanisms remain unclear. Cocoa contains a complex mixture of flavan-3-ols. Epicatechin, a major monomeric flavan-3-ol, is considered to contribute to the cardio-protective effects of cocoa. We investigated effects of pure epicatechin supplementation on whole genome gene expression profiles of circulating immune cells. more...
#> 564 We investigate whether lower weight gain with insulin detemir is due to a decrease in energy intake or an increase in energy output and whether any change in energy balance is accompanied by changes in hormones, lipid metabolism and muscle gene expression.
#> 565 The goal of this study was to analyse the effect of a 12 weeks treatment with rosiglitazone on insulin sensitivity in the muscle of type 2 diabetic patients. Ten diabetic patients were submitted to a 3 hours euglycemic-hyperinsulinemic clamp. Skeletal muscle biopsies were taken before and after the clamp. Samples from the same patients (obtained before and after the clamp) were hybridized on the same microarray. more...
#> 566 Obesity has emerged as a formidable health crisis due to its association with metabolic risk factors such as diabetes, dyslipidaemia and hypertension. Recent work has demonstrated the multifaceted roles of lncRNAs in regulating mouse adipose development, but its implication in human adipocytes remain largely unknown at least partially due to the lack of a comprehensive lncRNA catalog, particularly those specifically expressed in brown adipose tissue (BAT). more...
#> 567 Factors predicting body weight gain and associated disturbed glucose metabolism remain to be established. Here we assessed the role of subcutaneous adipocyte lipid mobilization (lipolysis) in spontaneous long-term (>10 years) body weight changes. In two independent clinical cohorts we found that low stimulated lipolysis at baseline correlated inversely with body mass index changes over time. Disturbed lipolysis gave odds ratios of ≥4.6 for weight gain and ≥3.2 for development of insulin resistance and impaired fasting glucose/type 2 diabetes. more...
#> 568 In traditional Chinese medicine (TCM), blood stasis syndrome (BSS) is mainly manifested by the increase of blood viscosity, platelet adhesion rate and aggregation, and the change of microcirculation, resulting in vascular endothelial injury. It is an important factor in the development of diabetes mellitus (DM). The aim of this study was to screen out the potential candidate microRNAs (miRNAs) in DM patients with BSS by high-throughput sequencing (HTS) and bioinformatics analysis. more...
#> 569 In traditional Chinese medicine (TCM), blood stasis syndrome (BSS) is mainly manifested by the increase of blood viscosity, platelet adhesion rate and aggregation, and the change of microcirculation, resulting in vascular endothelial injury. It is an important factor in the development of diabetes mellitus (DM). According to the differences in the internal and external environment of the individual disease, BSS were divided into qi-deficiency and blood stasis syndrome (QDBS), qi-stagnation and blood stasis syndrome (QSBS), cold-coagulation and blood stasis syndrome (CCBS), heat-accumulation and blood stasis syndrome (HABS). more...
#> 570 This study compared the transcriptome profiling (RNA-seq) of CD3+ T cells from nondiabetic (ND) individuals and patients with type 1 diabetes (T1D).
#> 571 Preeclampsia (PE) is a complex, heterogeneous disorder of pregnancy, demonstrating considerable variability in observed maternal symptoms and fetal outcomes. We recently identified five clusters of placentas within a large gene expression microarray dataset (N=330, GSE75010), of which four contained a substantial number of PE samples. However, while transcriptional analysis of placentas can subtype patients, we hypothesized that the addition of epigenetic information should reveal gene regulatory mechanisms behind the distinct PE pathologies. more...
#> 572 Objective: Homozygous loss-of-function mutations in the gene coding for the homeobox transcription factor (TF) PDX1 leads to pancreatic agenesis, whereas heterozygous mutations can cause Maturity-Onset Diabetes of the Young 4 (MODY4). Although the function of Pdx1 is well studied in pre-clinical models during insulin-producing β-cell development and homeostasis, it remains elusive how this TF controls human pancreas development by regulating a downstream transcriptional program. more...
#> 573 We sequenced the transcriptomes of seven samples of hypothalamic neurons dervied from pluripotent stem cells taken from healthy patients; five samples of hypothalamic neurons derived from pluripotent stem cells of constitutionally obese donors; five samples of sectioned hypothalami from post-mortem dissection of brains, and two samples for motor neurons derived from pluripotent stem cells of healthy donors in order to assess the similarity of our iPSC-derived cells against those of sectioned brains and to identify possible transcriptional disfunction which might underlie extreme, inherited obesity.
#> 574 The p.R482W hotspot mutation in A-type nuclear lamins causes familial partial lipodystrophy of Dunnigantype (FPLD2), a lipodystrophic syndrome complicated by early-onset atherosclerosis. Molecular mechanisms underlying endothelial cell dysfunction conferred by the lamin A mutation remain elusive. However, lamin A regulates epigenetic developmental pathways and mutations could perturb these functions. more...
#> 575 We aimed to determine the association between extracellular miRs and HIV infection, and have demonstrated unique expression profile of 29 miRs in HIV+ subjects and 34 miRs in elite controllers as compared to HIV- subjects. Elite HIV+ subjects are those who are HIV+, not on antiretroviral therapy, and with HIV viral load <200 copies/mL.
#> 576 This data represents whole transcriptome total RNA gene expression profiles of peripheral blood mononuclear cells collected from 29 advanced heart failure patients on the day before undergoing mechanical circulatory support surgery. Keywords = Advanced Heart Failure. Keywords = Mechanical Circulatory Support. Keywords = Biological Age. Keywords = PBMC. Keywords = Immune Response. Keywords = Peripheral Blood. more...
#> 577 Progressive failure of insulin-producing beta cells is the central event leading to diabetes, yet the signalling networks controlling beta cell fate remain poorly understood. Here we show that SRp55, a splicing factor regulated by the diabetes susceptibility gene GLIS3, has a major role in maintaining function and survival of human beta cells. RNA-seq analysis revealed that SRp55 regulates the splicing of genes involved in cell survival and death, insulin secretion and JNK signalling. more...
#> 578 Human ß cell dedifferentiation as a potent mechanism of diabetes is gaining prominence. Several data suggest an upregulation of the transcription factor SOX9, a progenitor and duct cell marker during ß cell dedifferentiation. However, its targets in such cells need more understanding. Here, we overexpressed SOX9 and a constitutively active mutant (VP16-SOX9∆TAD) in Human pancreatic beta EndoC-ßH1 cells in order to understand its targets.
#> 579 Type 1 diabetes (T1D) is a chronic disease characterized by an autoimmune-mediated destruction of insulin-producing pancreatic β cells. Environmental factors such as viruses play an important role in the onset of T1D and interact with predisposing genes. Recent data suggest that viral infection of human islets leads to a decrease in insulin production rather than β cell death, suggesting loss of β cell identity. more...
#> 580 There is already strong evidence indicating that different types of non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs, are key players in the regulation of β-cell functions and in the development of diabetes. However, the role of the newly discovered class of circular RNAs remains to be elucidated. We therefore analysed circular RNA expression in human islet samples.
#> 581 BACKGROUND & AIMS: Metabolic syndrome is a newly identified risk factor for hepatocellular carcinoma (HCC), however the molecular mechanisms still remain unclear. To elucidate this issue, cross-species analysis was performed to compare gene expression patterns of HCC from human patients and melanocortin 4 receptor-knockout (MC4R-KO) mice, developing HCC with obesity, insulin resistance and dyslipidemia. more...
#> 582 BACKGROUND & AIMS: Metabolic syndrome is a newly identified risk factor for hepatocellular carcinoma (HCC), however the molecular mechanisms still remain unclear. To elucidate this issue, cross-species analysis was performed to compare gene expression patterns of HCC from human patients and melanocortin 4 receptor-knockout (MC4R-KO) mice, developing HCC with obesity, insulin resistance and dyslipidemia. more...
#> 583 summary : Tubulointerstitial transcriptome from ERCB subjects with chronic kidney disease and living donor biopsies. Samples included in this analysis have been previously analyzed using older CDF definitions and are included under previous GEO submissions - GSE47184 (chronic kidney disease samples), and GSE32591 (IgA nephropathy samples).
#> 584 summary : Glomerular Transcriptome from European Renal cDNA Bank subjects and living donors. Samples included in this analysis have been previously analyzed using older CDF definitions and are included under previous GEO submissions - GSE47183 (chronic kidney disease samples), and GSE32591 (IgA nephropathy samples).
#> 585 Explaining the genetics of many diseases is challenging because most associations localize to regulatory regions. We present a novel computational method for discovering disease-driving mechanisms acting across multiple disease-associated, non-coding genomic regions. Application to a matrix of 213 phenotypes and 1,544 transcription factor (TF) binding datasets identifies 2,264 significant associations for hundreds of TFs in 92 phenotypes, including prostate and breast cancers. more...
#> 586 49 human patient mRNA profiles was generated using HG-U133 Plus 2.0 microarrays. Procesed in Affymetrix Expression console using Plier normalization method and later processed in Partek Genomics Suite. The clustering figure was generated using HCE clustering software. We sought to determine the mechanisms underlying failure of muscle regeneration that is observed in dystrophic muscle through hypothesis generation using muscle profiling data (human dystrophy and murine regeneration). more...
#> 587 Diabetes is a multifactorial disorder and epigenetics changes are increasingly appreciated to influence the development of diabetic complications. Chromatin remodeling and histone acetylation are implicated in activation of the inflammatory response. Recently, histone deacetylase (HDAC) inhibitors (HDACi) have proved to reduce the severity of inflammatory diseases. We have previously shown that chromatin alterations regulated by HDACi in HepG2 cells stimulated by hyperglycemia reduced hepatic glucose production. more...
#> 588 The prevalence of type 2 diabetes mellitus (T2D) is increasing constantly and various risk factors such as obesity, aging, nutritional states and physical inactivity, in addition to genetic pre-dispositions in different populations has been identified. The consequences of high blood glucose include damaged blood vessels, leading to arteriosclerosis and chronic diabetic microangiopathies. These changes lead to occlusive angiopathy, altered vascular permeability, or tissue hypoxia, resulting in complications such as heart disease, strokes, kidney disease, blindness, impaired wound healing, chronic skin ulcers, or amputations. more...
#> 589 Total RNA was isolated from WBCs. For the analysis of genome-wide expression differences in small non-coding RNAs (sncRNAs) and long-coding and non-coding RNAs (mRNAs and lncRNAs), NGT and GDM pregnant women were selected. Twenty-nine GDM-associated mature micro-RNAs (miRNAs) with increased expression and one hundred sixty-three mRNAs with reduced expression associated with GDM were found (P<0.05 and FDR<0.1).
#> 590 Diabetic foot ulcers (DFUs) are the leading cause of lower leg amputations in diabetic population. To better understand molecular pathophysiology of DFUs we used patients’ specimens and genomic profiling. We identified 3900 genes specifically regulated in DFUs. Moreover, we compared DFU to human skin acute wound (AW) profiles and found DNA repair mechanisms and regulation of gene expression among the processes specifically suppressed in DFUs, whereas essential wound healing-related processes, inflammatory/immune response or cell migration, were not activated properly. more...
#> 591 Many patients with type 1 diabetes (T1D) have residual beta cells producing small amounts of C-peptide long after disease onset, but develop an inadequate glucagon response to hypoglycemia following T1D diagnosis. The features of these residual beta cells and alpha cells persisting in the islet endocrine compartment are largely unknown due to difficulty of comprehensive investigation. By studying the T1D pancreas and isolated islets, we show that remnant beta cells appeared to maintain several aspects of regulated insulin secretion. more...
#> 592 This SuperSeries is composed of the SubSeries listed below.
#> 593 Pancreatic islet beta cell failure causes type 2 diabetes (T2D). The IMIDIA consortium has used a strategy entailing a stringent comparative transcriptomics analysis of islets isolated enzymatically or by laser microdissection from two large cohorts of non-diabetic (ND) and T2D organ donors (OD) or partially pancreatectomized patients (PPP). This work led to the identification of a signature of genes that were differentially expressed between T2D and ND regardless of the sample type (OD or PPP). more...
#> 594 Pancreatic islet beta cell failure causes type 2 diabetes (T2D). The IMIDIA consortium has used a strategy entailing a stringent comparative transcriptomics analysis of islets isolated enzymatically or by laser microdissection from two large cohorts of non-diabetic (ND) and T2D organ donors (OD) or partially pancreatectomized patients (PPP). This work led to the identification of a signature of genes that were differentially expressed between T2D and ND regardless of the sample type (OD or PPP). more...
#> 595 While histone deacetylase (HDAC) inhibitors are thought to regulate gene expression by post-translational modification of histone as well as non-histone proteins. While histone hyperacetylation has long been considered the paradigmatic mechanism of action, recent genome-wide profiles indicate more complex interactions with the epigenome. In particular, HDAC inhibitors also induce histone deacetylation at the promoters of highly active genes, resulting in gene suppression. more...
#> 596 In this study, we explored transcriptional differences in human neutrophils from patients with intracranial aneurysms and a demographic and comorbidity paired population of controls
#> 597 In summary, we discovered (1) that glucose dose-dependently inhibits cardiac maturation in vitro and in vivo, (2) that the maturation-inhibitory effect is dependent on nucleotide biosynthesis via the PPP, (3) that the developing heart accomplishes glucose deprivation condition by limiting the glucose uptake at late gestational stages during normal embryogenesis, and (4) that perturbation of the glucose deprivation in gestational diabetes affects natural cardiomyocyte maturation and potentially contributes to congenital heart disease.
#> 598 Metabolic alterations relevant to postprandial dyslipidemia were previously identified in the intestine of obese subjects with systemic insulin resistance. These dysregulations were closely associated with an amplification of intestinal lipogenesis and lipoprotein output, which was triggered by insulin resistance likely sustained by oxidative stress and inflammation. The aim of the study was to identify the genes deregulated by the presence of systemic insulin resistance in the intestine of severely obese subjects. more...
#> 599 This SuperSeries is composed of the SubSeries listed below.
#> 600 Our goal was to measure molecular phenotypes associated with coronary atherosclerosis severity in a geriatric cohort.
#> 601 Our goal was to measure molecular phenotypes associated with coronary atherosclerosis severity in a geriatric cohort.
#> 602 Friedreich’s ataxia (FRDA; OMIM 229300), an autosomal recessive neurodegenerative mitochondrial disease, is the most prevalent hereditary ataxia. In addition, FRDA patients showed additional non-neurological features such as scoliosis, diabetes and cardiac complications. Hypertrophic cardiomyopathy, which is found in two thirds of patients at the time of diagnosis, is the primary cause of death in these patients. more...
#> 603 As organisms age, cells accumulate genetic and epigenetic changes that eventually lead to impaired organ function or catastrophic failure such as cancer. Here we describe a single-cell transcriptome analysis of 2544 human pancreas cells from donors, spanning six decades of life. We find that islet cells from older donors have increased levels of disorder as measured both by noise in the transcriptome and by the number of cells which display inappropriate hormone expression, revealing a transcriptional instability associated with aging. more...
#> 604 Oral squamous cell carcinoma (OSCC) is the sixth most common cause of cancer mortality worldwide, and the five-year survival rate remains low in patients with advanced OSCC. Many studies indicate that microRNAs (miRNAs) may paly critical roles in OSCC carcinogenesis, but the dynamic composition and functions of miRNAs-mRNAs regulatory networks in OSCC pathogenesis remain largely unknown. Thus, detailed investigations of OSCC-associated miRNAs and their regulated networks may provide insights into mechanistic understanding of OSCC progression and development of new strategies of OSCC management. more...
#> 605 Circulating ex-RNAs altered in plasma after acute exercise target pathways involved in inflammation, including miR-181b-5p.
#> 606 There is growing evidence that transplantation of cadaveric human islets is an effective therapy for type 1 diabetes. However, gauging the suitability of islet samples for clinical use remains a challenge. We hypothesized that islet quality is reflected in the expression of specific genes. Therefore, gene expression in 59 human islet preparations was analyzed and correlated with diabetes reversal after transplantation in diabetic mice. more...
#> 607 There is increasing evidence that metabolic diseases originate in early life, and epigenetic changes have been implicated as key drivers of this early life programming. This led to the hypothesis that epigenetic marks present at birth may predict an individual’s future risk of obesity and type 2 diabetes. In this study, we assessed whether epigenetic marks in blood of newborn children were associated with BMI and insulin sensitivity later in childhood. more...
#> 608 Type 1 diabetes mellitus (T1DM) results from an autoimmune attack against the insulin-producing ß cells which leads to chronic hyperglycemia. Exosomes are lipid vesicles derived from cellular multivesicular bodies that are enriched in specific miRNAs, potentially providing a disease-specific diagnostic signature. To assess the value of exosome miRNAs as biomarkers for T1DM, miRNA expression in plasma-derived exosomes was measured. more...
#> 609 The epigenome is often deregulated in cancer and treatment with inhibitors of bromodomain and extra-terminal proteins, the readers of epigenetic acetylation marks, represents a novel therapeutic approach. Here, we have characterized the anti-tumour activity of the novel bromodomain and extra-terminal (BET) inhibitor BAY 1238097 in preclinical lymphoma mod- els. BAY 1238097 showed anti-proliferative activity in a large panel of lym- phoma-derived cell lines, with a median 50% inhibitory concentration between 70 and 208 nmol/l. more...
#> 610 In the study, patients with type 2 diabetes with obesity and hyperlipidemia were treated by traditional Chinese medicine Jiangtang Tiaozhi Prescription of 24 weeks, we chosed 6 effective cases, 6 invalid cases and 6 health people as control to analysis the molecular mechanism of TCM treatment. According to the research of LncRNA microarray, GO analysis, Pathway analysis, we found out the target LncRNAs, as well as their associated mRNAs were contribute to the good outcome of Jiangtang Tiaozhi Formula. more...
#> 611 Human embryonic stem cells (hESCs) potentially offer new routes to study, on the basis of the Developmental Origins of Health and Disease (DOHaD) concept, how the maternal environment during pregnancy influences the offspring health and can predispose to chronic disease in later life. Reactive Oxygen Species (ROS), antioxidant defences, and cellular redox status state play an important role in the regulation of gene expression and are involved in diabetes and metabolic syndromes as in aging. more...
#> 612 Novel strategies are needed to modulate β-cell differentiation and function as potential β-cell replacement or restorative therapies for diabetes. We previously demonstrated that small molecules based on the isoxazole scaffold drive neuroendocrine phenotypes. The nature of the effects of isoxazole compounds on β cells was incompletely defined. We find that isoxazole largely induced genes that support neuroendocrine and β-cell phenotypes, and suppressed a set of genes important for proliferation. more...
#> 613 Insulin resistance is considered to be a pathogenetic mechanism in several and diverse diseases (e.g. type 2 diabetes, atherosclerosis) often antedating them in apparently healthy subjects. The aim of this study was to investigate whether IR per se is characterized by a specific pattern of gene expression. We analyzed the transcriptomic profile of peripheral blood mononuclear cells in two groups (10 subjects each) of healthy individuals, with extreme insulin resistance or sensitivity, matched for BMI, age and gender, selected within the MultiKnowledge Study cohort (n=148). more...
#> 614 This SuperSeries is composed of the SubSeries listed below.
#> 615 Accumulating evidence suggests that dysregulation of hypoxia-regulated transcriptional mechanisms is involved in development of chronic kidney diseases (CKD). However, it remains unclear how hypoxia-induced transcription factors (HIFs) and subsequent biological processes contribute to CKD development and progression. In our study, genome-wide expression profiles of more than 200 renal biopsies from patients with different CKD stages revealed significant correlation of HIF-target genes with eGFR in glomeruli and tubulointerstitium. more...
#> 616 Accumulating evidence suggests that dysregulation of hypoxia-regulated transcriptional mechanisms is involved in development of chronic kidney diseases (CKD). However, it remains unclear how hypoxia-induced transcription factors (HIFs) and subsequent biological processes contribute to CKD development and progression. In our study, genome-wide expression profiles of more than 200 renal biopsies from patients with different CKD stages revealed significant correlation of HIF-target genes with eGFR in glomeruli and tubulointerstitium. more...
#> 617 Impaired skeletal muscle function is a central feature in the pathophysiology of type 2 diabetes (T2DM). The disease phenotype could be due to immature muscle cell development, which in turn may occur as the result of disturbed microRNA-mediated regulation of muscle differentiation in T2DM. To address this hypothesis, we assessed global miRNA expression during in vitro differentiation of muscle stem cells derived from T2DM patients and healthy controls. more...
#> 618 Single-cell RNA-seq (scRNA-seq) of pancreatic islets have reported on α- and β-cell gene expression in mice and subjects of predominantly European ancestry. We aimed to assess these findings in East-Asian islet-cells. 448 islet-cells were captured from three East-Asian non-diabetic subjects for scRNA-seq. Hierarchical clustering using pancreatic cell lineage genes was used to assign cells into cell-types. more...
#> 619 Intrauterine exposure to hyperglycemic environment is reported to confer increased metabolic risk in later life, supporting the “developmental origins of health and disease” hypothesis. Epigenetic alterations are suggested as one of the possible underlying mechanisms. We measured DNA methylation using Infinium HumanMethylation450 BeadChip in siblings discordant for maternal gestational diabetes mellitus (GDM), which may allow possible genetic and environmental confounding effects to be reduced. more...
#> 620 Background: Stress cardiomyopathy (SCM) is a unique form of LV dysfunction that more often occurs in women. Patients with SCM have a higher Troponin I/B-type natriuretic peptide ratio than AMI, but little is known about other circulating proteins. The goals of this study were to compare plasma proteins in SCM and AMI to learn about the pathophysiology of SCM and also to identify putative biomarkers of SCM. more...
#> 621 The incidence of pre-diabetes (PD) and Type-2 Diabetes Mellitus (T2D) is a worldwide epidemic. African American (AA) individuals are disproportionately more likely to become diabetic than other ethnic groups. Over the long-term, metabolic complications related to diabetes result in significant alterations in growth hormone (GH) and insulin-like growth factor-1 (IGF-1). Considering the limited exercise-related studies in the area of gene expression changes with disease progression, the objective of this study was to examine differences in exercise-induced gene expression related to the GH and IGF-1 pathways in peripheral blood mononuclear cells (PBMCs) of healthy (CON) and PD AA individuals. more...
#> 622 Age-related alterations in immunity have been linked to increased incidence of infections and decreased responses to vaccines in the aging population. Human peripheral blood monocytes are known to promote antigen presentation and antiviral activities; however, the impact of aging on monocyte functions remains an open question. We present an in-depth global analysis examining the impact of aging on classical (CD14+CD16-), intermediate (CD14+CD16+), and non-classical (CD14dimCD16+) monocytes. more...
#> 623 Age-related alterations in immunity have been linked to increased incidence of infections and decreased responses to vaccines in the aging population. Human peripheral blood monocytes are known to promote antigen presentation and antiviral activities; however, the impact of aging on monocyte functions remains an open question. We present an in-depth global analysis examining the impact of aging on classical (CD14+CD16-), intermediate (CD14+CD16+), and non-classical (CD14dimCD16+) monocytes. more...
#> 624 Circadian rhythms are essential for temporal (~24 h) regulation of molecular processes in diverse species. Dysregulation of circadian gene expression has been implicated in the pathogenesis of various disorders, including hypertension, diabetes, depression, and cancer. Recently, microRNAs (miRNAs) have been identified as critical modulators of gene expression post-transcriptionally, and perhaps involved in circadian clock architecture or their output functions. more...
#> 625 Hyperglycemia is an essential factor leading to micro- and macrovascular diabetic complications. Macrophages are key innate immune regulators of inflammation that undergo 2 major directions of functional polarization: classically (M1) and alternatively (M2) activated macrophages. The aim of the study was to examine the effect of hyperglycemia on transcriptional activation of M0, M1 and M2 human macrophages.
#> 626 Non-alcoholic fatty liver disease (NAFLD) encompasses a spectrum of histological findings, from simple steatosis to steatohepatitis (NASH), the latter presenting a higher risk of cardiovascular and kidney diseases, type 2 diabetes and end-stage liver disease. NAFLD is seen as the hepatic manifestation of the metabolic syndrome and affects up to 70-80% of obese patients. There are currently no approved pharmacological therapies for NASH, thus the only option is lifestyle intervention or bariatric surgery in order to lose weight and to improve insulin resistance. more...
#> 627 Natural killer (NK) cells contribute to the development of obesity-associated insulin resistance. We demonstrate that in mice obesity promotes the expansion of interleukin-6 receptor (IL6Ra)-expressing NK cells, which also express a number of other myeloid lineage genes such as the colony-stimulating-factor 1 receptor (Csf1r). Selective ablation of Csf1r- expressing NK cells prevents obesity and insulin resistance. more...
#> 628 Metabolic diseases, including type 2 diabetes and obesity are relevant negative prognostic factor in patients with breast cancer (BC). We have investigated the mechanisms through which elevated glucose levels affect tamoxifen sensitivity of estrogen receptor positive (ER+) BC cells. We found that MCF7 BC cell sensitivity to tamoxifen was 2-fold reduced in 25mM glucose (HG), a concentration mimicking hyperglycaemia, compared to 5.5 mM glucose (LG), resembling normal fasting glucose levels in humans. more...
#> 629 We report the RNA expression of insulin-GFP+ cells derived from CDKAL1-/- hESCs and CDKAL1-/-hESCs overexpressing MT1E
#> 630 The presence of a coding variant affecting plasma high density lipoprotein cholesterol (HDLC) levels was evaluated in subjects with elevated or normal plasma levels of HDLC. Carriers of the variant were further analyzed phenotypically
#> 631 Podocyte injury is a major determinant in proteinuric kidney disease and identification of potential therapeutic targets for preventing podocyte injury has clinical importance. Here, we show that histone deacetylase Sirt6 protects against podocyte injury through epigenetic regulation of Notch signaling. Sirt6 is downregulated in renal biopsies from patients with podocytopathies and its expression negatively correlates withglomerular filtration rate. more...
#> 632 The dataset comprises of circulating miRNAs in human subjects with various types of liver impairments. In our study, we analyzed a total 48 serum samples from a group of 42 subjects that included subjects with accidental acetaminophen overdose (APAP), hepatitis B infection (HBV), liver cirrhosis (LC) and type 2 diabetes mellitus (T2DM) subjects with alanine amino transference (ALT) elevation. As a control 16 sex and age matched healthy controls from subjects with no evidence of liver disease were analyzed. more...
#> 633 This SuperSeries is composed of the SubSeries listed below.
#> 634 Islet-reactive T cells found in peripheral blood of type 1 diabetes (T1D) subjects are thought to be involved in disease pathogenesis, but full understanding of their role is complicated by their presence also in blood of in healthy subjects. To elucidate their role in T1D, we have combined flow cytometry and single cell RNA sequencing (RNA-seq) techniques to link prior antigen exposure, inferred from expanded TCR clonotypes, and functional capacities of islet antigen-reactive CD4+ memory T cells. more...
#> 635 Islet-reactive T cells found in peripheral blood of type 1 diabetes (T1D) subjects are thought to be involved in disease pathogenesis, but full understanding of their role is complicated by their presence also in blood of in healthy subjects. To elucidate their role in T1D, we have combined flow cytometry and single cell RNA sequencing (RNA-seq) techniques to link prior antigen exposure, inferred from expanded TCR clonotypes, and functional capacities of islet antigen-reactive CD4+ memory T cells. more...
#> 636 Islet-reactive T cells found in peripheral blood of type 1 diabetes (T1D) subjects are thought to be involved in disease pathogenesis, but full understanding of their role is complicated by their presence also in blood of in healthy subjects. To elucidate their role in T1D, we have combined flow cytometry and single cell RNA sequencing (RNA-seq) techniques to link prior antigen exposure, inferred from expanded TCR clonotypes, and functional capacities of islet antigen-reactive CD4+ memory T cells. more...
#> 637 Islet-reactive T cells found in peripheral blood of type 1 diabetes (T1D) subjects are thought to be involved in disease pathogenesis, but full understanding of their role is complicated by their presence also in blood of in healthy subjects. To elucidate their role in T1D, we have combined flow cytometry and single cell RNA sequencing (RNA-seq) techniques to link prior antigen exposure, inferred from expanded TCR clonotypes, and functional capacities of islet antigen-reactive CD4+ memory T cells. more...
#> 638 Although a large set of data is available concerning organogenesis in animal models, information remains scarce on human organogenesis. In this work, we performed temporal mapping of human fetal pancreatic organogenesis using cell surface markers. We demonstrate that in the human fetal pancreas at 7 weeks of development, the glycoprotein 2 (GP2) marks a multipotent cell population that will differentiate either into the acinar, ductal and endocrine lineages. more...
#> 639 Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with limited effective treatment options. PDAC tumors frequently harbor the constitutively activated form of KRAS which drives proliferative signaling, but directly targeting KRAS has so far been unsuccessful. To overcome this limitation, combinatorial treatment strategies have been developed to inhibit upstream activators and downstream effectors of KRAS signaling. more...
#> 640 Genome-scale DNA methylation profiling using the Infinium DNA methylation BeadChip platform and samples from normal human eye and five ocular- related diseases
#> 641 Dysregulated expression of long noncoding RNAs (lncRNAs) has been demonstrated as being implicated in a variety of human diseases. In the study we aimed to determine lncRNA profile in CD8+ T cells response to active tuberculosis (TB).
#> 642 Prenatal environmental conditions may influence disease risk in later life. We previously found a gene-environment interaction between the paraoxonase 1 (PON1) Q192R genotype and prenatal pesticide exposure leading to a cardio-metabolic risk profile at school age. However, the molecular mechanisms involved have not yet been resolved. It has been hypothesized that epigenetics might be involved. The aim of the present study was to investigate whether DNA methylation patterns in blood cells were related to prenatal pesticide exposure level, PON1 Q192R genotype, and associated metabolic effects observed in the children. more...
#> 643 Atrial fibrillation (AF), the most common cardiac rhythm disorder, is a major cause of cardiovascular morbidity and mortality. AF is characterized by the rapid and irregular activation of the atrium with diverse abnormalities, including electrical, structural, metabolic, neurohormonal, or molecular alterations.3 Although the pathophysiology of AF is complex, it has traditionally been treated with antiarrhythmic drugs that control the rhythm by altering cardiac electrical properties, principally by modulating ion channel function. more...
#> 644 We compared the plasma miRNA expression profiles between healthy and GDM women by microarray analysis.Our study offers new insights into circulating biomarkers of GDM and thus provides a valuable resource for future investigations.
#> 645 Skeletal muscle is one of the primary tissues involved in the development of type 2 diabetes (T2D). Obesity is tightly associated with T2D, making it challenging to isolate specific effects attributed to the disease alone. By using an in vitro myocyte model system we were able to isolate the inherent properties retained in myocytes originating from donor muscle precursor cells, without being confounded by varying extracellular factors present in the in vivo environment of the donor. more...
#> 646 Purpose: Identification of RUNX1 via next-generation sequencing (NGS) of fibrovascular membranes in patients with proliferative diabetic retinopathy. Methods: Transcriptomic analysis with Illumina HiSeq2000 of fibrovascular membrane and control retina CD31+ samples. The sequence reads were analyzed with ANOVA (ANOVA) and targets with significance (fold change > +/-1.5 and p-value < 0.05) were selected for with Cufflinks, DeSeq2, Partek E/M, and EdgeR. more...
#> 647 Background: Intrauterine exposure to gestational diabetes mellitus (GDM) confers a lifelong increased risk for metabolic and other complex disorders to the offspring. GDM-induced epigenetic modifications modulating gene regulation and persisting into later life are generally assumed to mediate these increased disease risks. To identify candidate genes for fetal programming, we compared genome-wide methylation patterns of fetal cord bloods (FCBs) from GDM and control pregnancies. more...
#> 648 The molecular transducers of benefits from different exercise modalities remain incompletely defined. Here we report that 12 weeks of high-intensity aerobic interval (HIIT), resistance (RT), and combined exercise training enhanced insulin sensitivity and lean mass, but only HIIT and combined training improved aerobic capacity and skeletal muscle mitochondrial respiration. HIIT revealed a more robust increase in gene transcripts than other exercise modalities, particularly in older adults, although little overlap with corresponding individual protein abundance was noted. more...
#> 649 This SuperSeries is composed of the SubSeries listed below.
#> 650 Obesity-induced white adipose tissue (WAT) fibrosis is believed to accelerate WAT dysfunction. Two progenitor populations could be distinguished in omental white adipose tissue (oWAT) of morbidly obese individuals based on CD9 expression. In addition, the frequency of CD9high progenitors in oWAT correlates with oWAT fibrosis level, insulin-resistance severity and type 2 diabetes. To further gain insight into the functional differences between the CD9high and CD9low progenitor subsets, we performed transcriptomic profiling of FACS-sorted progenitor populations isolated from oWAT of obese individuals. more...
#> 651 The NEET proteins mitoNEET (mNT) and nutrient-deprivation autophagy factor-1 (NAF-1) are required for cancer cell proliferation and resistance to oxidative stress. MitoNEET and NAF-1 are also implicated in a number of other human pathologies including diabetes, neurodegeneration and heart disease, as well as in development, differentiation and aging. Previous studies suggested that mNT and NAF-1 could function in the same pathway in cancer cells, preventing the over-accumulation of iron and reactive oxygen species (ROS) in mitochondria. more...
#> 652 Open chromatin provides access to DNA binding proteins for the correct spatiotemporal regulation of gene expression. Mapping chromatin accessibility has been widely used to identify the location of cis regulatory elements (CREs) including promoters and enhancers. CREs show tissue- and cell-type specificity and disease-associated variants are often enriched for CREs in the tissues and cells that pertain to a given disease. more...
#> 653 ACTH-dependent hypercortisolism caused by a pituitary adenoma [Cushing’s disease (CD)] is the most common cause of endogenous Cushing’s syndrome. CD is often associated with several morbidities, including hypertension, diabetes, osteoporosis/bone fractures, secondary infections, and increased cardiovascular mortality. While the majority (≈80%) of the corticotrophinomas visible on pituitary magnetic resonance imaging are microadenomas (MICs, <10 mm of diameter), some tumors are macroadenomas (MACs, ≥10 mm) with increased growth potential and invasiveness, exceptionally exhibiting malignant demeanor. more...
#> 654 Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus (DM). It is not diagnosed or managed properly in the majority of patients, because its pathogenesis remains controversial. In this study, using microarray-based genome-wide expression analyses, we sought to identify both common and distinct mechanisms underlying the pathogenesis of DM and DPN. The results demonstrated that down-regulation of the neurotrophin-MAPK signaling pathway may be the major mechanism of DPN pathogenesis, thus providing a potential approach for DPN treatment.
#> 655 Context: Compared with European Americans, African Americans (AAs) are more insulin resistant, have a higher insulin secretion response to glucose, and develop type 2 diabetes more often. Molecular processes and/or genetic variations contributing to altered glucose homeostasis in high-risk AAs remain uncharacterized. Objective: Adipose and muscle transcript expression profiling and genotyping were performed in 260 AAs to identify genetic regulatory mechanisms associated with insulin sensitivity (SI). more...
#> 656 Context: Compared with European Americans, African Americans (AAs) are more insulin resistant, have a higher insulin secretion response to glucose, and develop type 2 diabetes more often. Molecular processes and/or genetic variations contributing to altered glucose homeostasis in high-risk AAs remain uncharacterized. Objective: Adipose and muscle transcript expression profiling and genotyping were performed in 260 AAs to identify genetic regulatory mechanisms associated with insulin sensitivity (SI). more...
#> 657 Epigenetic drift, an aging-associated change of the epigenome is one of the factors that can influence the rate and course of aging. In fact, even subtle changes of the miRnome can affect cellular functions. Therefore, changes of aging-associated miRNA expression in peripheral blood mononuclear cells (PBMC) of long-lived humans (n=24, mean age 94.8±3.9 years) and healthy, young individuals (n=24, 28.0±4.0 years) was evaluated using next generation sequencing. more...
#> 658 Inappropriate activation or inadequate regulation of CD4+ and CD8+ T cells may contribute to the initiation and progression of multiple autoimmune and inflammatory diseases. Studies on disease-associated genetic polymorphisms have highlighted the importance of biological context for many regulatory variants, which is particularly relevant in understanding the genetic regulation of the immune system and its cellular phenotypes. more...
#> 659 To evaluate whether serum micoRNAs can be biomarkers for diagnosis of type 1 diabetes mellitus, we analyzed the serum microRNA expression profiles in 6 patients with new-onset type 1 diabetes mellitus and 6 age- and gender-matched healthy controls. A difference was observed in 31 miRNAs between the patients and controls (fold change ≥ 2, P < 0.05)
#> 660 We profiled gene expression in peripheral blood cells from 17 obese patients by microarray analysis and revealed that visceral fat adiposity impact on gene expression profile in peripheral blood cells compared to subcutaneous fat accumulation.
#> 661 Background: Moderate weight loss can ameliorate adverse health effects associated with obesity, reflected by an improved adipose tissue (AT) gene expression profile. However, the effect of rate of weight loss on the AT transcriptome is unknown. Objective: We investigated the global AT gene expression profile before and after two different rates of weight loss that resulted in similar total weight loss, and after a subsequent weight stabilization period. more...
#> 662 Role of lncRNAs in human adaptive immune response to active tuberculosis (TB) infection is largely unexplored. The objective of this study was to characterize lncRNA expression profile in primary human B cell response to active TB infection using mcroarray assay.
#> 663 Type 1 diabetes is characterized by the destruction of pancreatic beta cells, and generating new insulin-producing cells from other cell types is a major aim of regenerative medicine. One promising approach is transdifferentiation of developmentally related pancreatic cell types including glucagon-producing alpha cells. In a genetic model, overexpression of the master regulatory transcription factor Pax4 or loss of its counterplayer Arx are sufficient to induce the conversion of alpha cells to functional beta-like cells. more...
#> 664 In this study, we examined the association of DNA methylation with metabolic traits in humans using adipose tissue samples from the Metabolic Syndrome in Men (METSIM) cohort. The METSIM cohort has been thoroughly characterized for longitudinal clinical data of metabolic traits including a 3-point oral glucose tolerance test, cardiovascular disorders, diabetes complications, drug and diet questionnaire, as well as high density genotyping, and genome-wide expression in adipose. more...
#> 665 The objective of this study was the identification of serum microRNAs that can differentiate osteoporotic fracture patients with and without type-2 diabetes from healthy control subjects. For that purpose circulating microRNAs were profiled by real-time quantitative PCR using a custom 384-well panel in 200 µl serum samples. Univariate and multivariate statistical tools were used in order to identify single as well as combinations of circulating microRNas that were characteristic of patients with prevalent osteoporotic fractures: a qRT-PCR-based classifier consisting of miR-550a-5p, miR-96-5p, miR-32-3p and miR-486-5p can distinguish T2D women with (DMFx) and without fragility fractures (DM) with high specifitiy and sensitivity (AUC = 0.93). more...
#> 666 Islet transplantation has the potential to benefit patients with type I diabetes, but this cellular therapy is limited by a shortage of islets, which necessitates the collection or production of islets from alternative sources. If islets produced from stem cells are to be used for transplant therapy they should precisely replicate beta-cell function. Characterization of the unique molecular mechanisms underlying the beta-cell’s response to glucose stimulation will allow a better understanding of critical elements that the alternative cells must possess. more...
#> 667 BACKGROUND & AIMS: Although patients infected by genotype 1b hepatitis C virus (HCV) with Q(70) and/or M(91)core gene mutations have an almost five-fold increased risk of developing hepatocellular carcinoma (HCC) and increased insulin resistance, the absence of a suitable experimental system has precluded direct experimentation on the effects of these mutations on cellular gene expression. METHODS: HuH7 cells were treated long-term with human serum to induce differentiation and to produce a model system for testing high-risk and control HCV. more...
#> 668 Differences in gene regulation between healthy glucocorticoid receptor N363S single nucleotide polymorphism carriers and noncarrier controls may underlie the emergence of metabolic syndrome, Type 2 diabetes and cardiovascular disease associated with the N363S polymorphism.
#> 669 Biologic agents active in other autoimmune settings have had variable effectiveness in newly diagnosed type 1 diabetes (T1D) where treatment across therapeutic targets is accompanied by transient stabilization of C-peptide levels in some patients, followed by progression at the same rate as in control groups. Why disparate treatments lead to similar clinical courses is currently unknown. Here, we use integrated systems biology and flow cytometry approaches to elucidate immunologic mechanisms associated with C-peptide stabilization in T1D subjects treated with the anti-CD3 monoclonal antibody, teplizumab. more...
#> 670 Biologic agents active in other autoimmune settings have had variable effectiveness in newly diagnosed type 1 diabetes (T1D) where treatment across therapeutic targets is accompanied by transient stabilization of C-peptide levels in some patients, followed by progression at the same rate as in control groups. Why disparate treatments lead to similar clinical courses is currently unknown. Here, we use integrated systems biology and flow cytometry approaches to elucidate immunologic mechanisms associated with C-peptide stabilization in T1D subjects treated with the anti-CD3 monoclonal antibody, teplizumab.
#> 671 Background. Novel and targetable mutations are needed for improved understanding and treatment of lung cancer in never-smokers. Methods. Twenty-seven lung adenocarcinomas from never-smokers were sequenced by both exome and mRNA-seq with respective normal tissues. Somatic mutations were detected and compared with pathway deregulation, tumor phenotypes and clinical outcomes. Results. Although somatic mutations in DNA or mRNA ranged from hundreds to thousands in each tumor, the overlap mutations between the two were only a few to a couple of hundreds. more...
#> 672 This SuperSeries is composed of the SubSeries listed below.
#> 673 Biologic agents active in other autoimmune settings have had variable effectiveness in newly diagnosed type 1 diabetes (T1D) where treatment across therapeutic targets is accompanied by transient stabilization of C-peptide levels in some patients, followed by progression at the same rate as in control groups. Why disparate treatments lead to similar clinical courses is currently unknown. Here, we use integrated systems biology and flow cytometry approaches to elucidate immunologic mechanisms associated with C-peptide stabilization in T1D subjects treated with the anti-CD3 monoclonal antibody, teplizumab. more...
#> 674 Biologic agents active in other autoimmune settings have had variable effectiveness in newly diagnosed type 1 diabetes (T1D) where treatment across therapeutic targets is accompanied by transient stabilization of C-peptide levels in some patients, followed by progression at the same rate as in control groups. Why disparate treatments lead to similar clinical courses is currently unknown. Here, we use integrated systems biology and flow cytometry approaches to elucidate immunologic mechanisms associated with C-peptide stabilization in T1D subjects treated with the anti-CD3 monoclonal antibody, teplizumab. more...
#> 675 This study was performed to measure gene expression in peripheral whole blood RNA samples of established Type 1 diabetics.
#> 676 This SuperSeries is composed of the SubSeries listed below. Grant ID: Award No. W81XWH-16-1-0130 Grant title: Peer Reviewed Medical Research Program Funding Source: Assistant Secretary of Defense for Health Affairs Affiliation: Jackson Laboratory for Genomic Medicine, Farmington, CT Name: Michael Stitzel
#> 677 Blood glucose levels are tightly controlled by the coordinated action of at least five cell types constituting pancreatic islets. Changes in the proportion and/or function of these cells are associated with genetic and molecular pathophysiology of monogenic, type 1, and type 2 diabetes (T2D). Cellular heterogeneity impedes precise understanding of the molecular components of each islet cell type that govern islet dysfunction, particularly the less abundant delta and gamma/pancreatic polypeptide (PP) cells. more...
#> 678 Blood glucose levels are tightly controlled by the coordinated action of at least five cell types constituting pancreatic islets. Changes in the proportion and/or function of these cells are associated with genetic and molecular pathophysiology of monogenic, type 1, and type 2 diabetes (T2D). Cellular heterogeneity impedes precise understanding of the molecular components of each islet cell type that govern islet dysfunction, particularly the less abundant delta and gamma/pancreatic polypeptide (PP) cells. more...
#> 679 In nonalcoholic fatty liver disease (NAFLD), hepatic gene expression and fatty acid (FA) composition have been reported independently but a comprehensive gene expression profiling in relation to FA composition is lacking. The aim was to assess this relationship. In a cross-sectional study, hepatic gene expression (Illumina Microarray) was first compared among 20 patients with simple steatosis (SS), 19 with nonalcoholic steatohepatitis (NASH), and 24 healthy controls (HC). more...
#> 680 Epidemiologically related traits may share genetic risk factors, and pleiotropic analysis could identify individual loci associated with these traits. Because of their shared epidemiological associations, we conducted pleiotropic analysis of genome-wide association studies of lung cancer (12 160 lung cancer case patients and 16 838 control subjects) and cardiovascular disease risk factors (blood lipids from 188 577 subjects, type 2 diabetes from 148 821 subjects, body mass index from 123 865 subjects, and smoking phenotypes from 74 053 subjects). more...
#> 681 Here, we report a key role for the transcription factor Pax6 in the maintenance of adult beta-cell identity and function. Pax6 is down regulated in beta-cells of diabetic db/db mice and in wild type mice treated with an insulin receptor antagonist, revealing metabolic control of expression. Deletion of Pax6 in beta-cells of adult mice leads to lethal hyperglycemia and ketosis, due to loss of beta-cell function and expansion of alpha-cells. more...
#> 682 Caloric restriction (CR) is considered to increase lifespan and to prevent various age-related diseases in different non-human organisms. Only a limited number of CR studies have been performed in humans, and results put CR as a beneficial tool to decrease risk factors in several age-related diseases. The question remains at what age CR should be implemented to be most effective with respect to healthy aging. more...
#> 683 Background and Aims: Hepatocyte nuclear factor 1 (HNF1) transcription factors direct tissue specific gene regulation in liver, pancreas and kidney and are associated with diabetes. Here we investigate the transcriptional network governed by HNF1 in an intestinal epithelial cell line. Methods: Chromatin immunoprecipitation followed by direct sequencing (ChIP-seq) was used to identify HNF1 binding sites genome-wide. more...
#> 684 DNA methylation plays an important role in development of disease and the process of aging. In this study we examine DNA methylation at 476,366 sites throughout the genome of white blood cells from a population cohort (N = 421) ranging in age from 14 to 94 years old. Age affects DNA methylation at almost one third (29%) of the sites (Bonferroni adjusted P-value <0.05), of which 60.5% becomes hypomethylated and 39.5% hypermethylated with increasing age. more...
#> 685 To understand organ function it is important to have an inventory of the cell types present in the tissue and of the corresponding markers that identify them. This is a particularly challenging task for human tissues like the pancreas, since reliable markers are limited. Transcriptome-wide studies are typically done on pooled islets of Langerhans, which obscures contributions from rare cell types and/or potential subpopulations. more...
#> 686 Obesity is a critical health concern, and identifying new biomarkers has become essential for better understanding the progression to disease such as type 2 diabetes. DNA methylation has become a useful epigenetic biomarker in part due to its susceptibility to disease influence. Detecting methylation changes in blood is important as it is an easily accessible, compared to the insulin responsive tissue skeletal muscle. more...
#> 687 Diabetic foot ulcers (DFUs) are one of the major complications of diabetes. Its molecular pathology remains poorly understood, impeding the development of effective treatments. Although it has been established that multiple cell types, including fibroblasts, keratinocytes, macrophages and endothelial cells, all contribute to inhibition of healing, less is known regarding individual contributions of each cell type. more...
#> 688 While the function of the mammalian pancreas hinges on complex interactions of distinct cell types, gene expression profiles have primarily been described with bulk mixtures of cells. Here, we invoked inDrop, a droplet-based single-cell RNA-Seq method, to determine the transcriptomes of over 12,000 individual pancreatic cells from four human donors and two strains of mice. Cells could be divided into 15 clusters that matched previously characterized cell types: all endocrine cell types, including rare ghrelin-expressing epsilon-cells, exocrine cell types, vascular cells, Schwann cells, quiescent and activated pancreatic stellate cells, and four types of immune cells. more...
#> 689 The prevalence of metabolic syndrome comprising obesity, type 2 diabetes mellitus and cardiovascular disease has been on the rise world-wide in recent years. As non-communicable diseases such as type 2 diabetes mellitus have their roots in prenatal development and conditions such as maternal gestational diabetes (GDM), we aimed to test this hypothesis in primary cells derived from the offspring of GDM mothers compared to control subjects. more...
#> 690 IL-6 is a proinflammatory cytokine implicated in multiple autoimmune diseases. Here we show that IL-6 induced STAT3 and STAT1 phosphorylation is enhanced in CD4 and CD8 T cells from patients with T1D compared to healthy controls. Enhanced IL-6/pSTAT3 is associated with increased surface IL-6R and early clinical disease. The transcriptome of IL-6 treated CD4 T cells from T1D patients reveals upregulation of genes involved in T cell migration. more...
#> 691 We performed RNA sequencing (RNAseq) on peripheral blood mononuclear cells (PBMCs) to identify differentially expressed gene transcripts (DEGs) after kidney transplantation and after the start of immunosuppressive drugs. RNAseq is superior to microarray to determine DEGs because it’s not limited to available probes, has increased sensitivity, and detects alternative and previously unknown transcripts. more...
#> 692 Comparison of gene expression in pancreatic islets of BTBR-ob/ob mouse model of obesity-induced type 2 diabetes, and in non-diabetic control mice, B6-ob/ob identified Asf1b as an important gene candidate predictive of diabetic outcome. Asf1B expression was suppressed in response to age in both B6 and BTBR islets, induced by obesity only in B6 islets. This is consistent with other studies reporting a decline in -cell proliferation with age. more...
#> 693 Squamous cell carcinoma (SCC) is the second most common cancer worldwide and accounts for approximately 30% of all keratinocyte cancers. The vast majority of cutaneous SCCs of the head and neck (cSCCHN) are readily curable with surgery and/or radiotherapy unless high-risk features are present. Perineural invasion (PNI) is recognized as one of these high-risk features. The molecular changes during clinical PNI in cSCCHN have not been previously investigated. more...
#> 694 Pancreatic islet cells are critical for maintaining normal blood glucose levels and their malfunction underlies diabetes development and progression. We used single-cell RNA sequencing to determine the transcriptomes of 1,492 human pancreatic α-, β-, δ- and PP cells from non-diabetic and type 2 diabetes organ donors. We identified cell type specific genes and pathways as well as 245 genes with disturbed expression in type 2 diabetes. more...
#> 695 CD4 T cell responses are characterized based on a limited number of molecular markers selected from exisiting knowledge. The goal of the experiment was to assess antigenic-peptide specific T-cell responses in vitro without bias using microarrays.
#> 696 CD4 T cell responses are characterized based on a limited number of molecular markers selected from exisiting knowledge. The goal of the experiment was to assess antigenic-peptide specific T-cell responses in vitro without bias using microarrays.
#> 697 Circular RNA expression profiling of human nucleus pulposus derived from patients with IDD in comparison with those derived from cadaveric disc as normal control. We have identified the expression profiles of miRNAs (GSE63492), lncRNAs, mRNAs (GSE56081) in IDD using 5 normal discs as control and 5 IDD discs. Accumulating evidence indicates that circRNAs are key regulators of gene expression by interacting with miRNAs. more...
#> 698 Validation of predicted gene expression of human mesangial cells after 24h Tacrolimus stimulus Objective: To evaluate tacrolimus as therapeutic option for diabetic nephropathy (DN) based on molecular profile and network-based molecular model comparisons. Materials and Methods: We generated molecular models representing pathophysiological mechanisms of DN and tacrolimus mechanism of action (MoA) based on literature derived data and transcriptomics datasets. more...
#> 699 Impaired ability of insulin to stimulate cellular glucose uptake and regulate metabolism, that is insulin resistance (IR), links adiposity to metabolic disorders such as type 2 diabetes (T2D), dyslipidemia and cardiovascular disease (Langenberg, 2012). Both genetic and epigenetic factors are implicated in development of systemic IR (Vaag, 2001). IR is characterized by elevated levels of fasting insulin in the general circulation. more...
#> 700 Impaired ability of insulin to stimulate cellular glucose uptake and regulate metabolism, that is insulin resistance (IR), links adiposity to metabolic disorders such as type 2 diabetes (T2D), dyslipidemia and cardiovascular disease (Langenberg, 2012). Both genetic and epigenetic factors are implicated in development of systemic IR (Vaag, 2001). IR is characterized by elevated levels of fasting insulin in the general circulation. more...
#> 701 Impaired ability of insulin to stimulate cellular glucose uptake and regulate metabolism, that is insulin resistance (IR), links adiposity to metabolic disorders such as type 2 diabetes (T2D), dyslipidemia and cardiovascular disease (Langenberg, 2012). Both genetic and epigenetic factors are implicated in development of systemic IR (Vaag, 2001). IR is characterized by elevated levels of fasting insulin in the general circulation. more...
#> 702 Endodermal stem/progenitor cells have diverse potential applications in research and regenerative medicine, so a readily available source could have widespread uses. Here we describe derivation of human induced endodermal progenitor cells (hiEndoPCs) from gastrointestinal epithelial cells using a cocktail of defined small molecules along with support from tissue-specific mesenchymal feeders. The hiEndoPCs show clonal expansion in culture and give rise to hepatocytes, pancreatic endocrine cells, and intestinal epithelial cells when treated with defined soluble molecules directing differentiation. more...
#> 703 Endodermal stem/progenitor cells have diverse potential applications in research and regenerative medicine, so a readily available source could have widespread uses. Here we describe derivation of human induced endodermal progenitor cells (hiEndoPCs) from gastrointestinal epithelial cells using a cocktail of defined small molecules along with support from tissue-specific mesenchymal feeders. The hiEndoPCs show clonal expansion in culture and give rise to hepatocytes, pancreatic endocrine cells, and intestinal epithelial cells when treated with defined soluble molecules directing differentiation. more...
#> 704 Endodermal stem/progenitor cells have diverse potential applications in research and regenerative medicine, so a readily available source could have widespread uses. Here we describe derivation of human induced endodermal progenitor cells (hiEndoPCs) from gastrointestinal epithelial cells using a cocktail of defined small molecules along with support from tissue-specific mesenchymal feeders. The hiEndoPCs show clonal expansion in culture and give rise to hepatocytes, pancreatic endocrine cells, and intestinal epithelial cells when treated with defined soluble molecules directing differentiation. more...
#> 705 Periodontitis affects 47.1% of adult population in the U.S. Porphyromonas gingivalis is an opportunistic oral pathogen that colonizes the oral mucosa, invades myeloid dendritic cells and accesses the bloodstream, brain, placenta and other organs in human with periodontitis. Periodontitis also sustains a chronic long-term pro-inflammatory immune disorder, potentially contributing to other systemic conditions such as cardiovascular disease, type 2 diabetes mellitus, adverse pregnancy outcomes, and osteoporosis. more...
#> 706 A robust system using disease relevant cells to systematically evaluate the role in diabetes for loci identified through genome wide association studies (GWAS) is urgently needed. Toward this goal, we created isogenic mutant human embryonic stem cell (hESC) lines in GWAS-identified candidate diabetes genes including CDKAL1, KCNQ1 and KCNJ11, and used directed differentiation to evaluate the function of derivative human beta-like cells. more...
#> 707 We successfully sequenced and annotated more than 400 cells from child, adult control, type 1 diabetes and type 2 diabetes donors. We detect donor-type specific transcript variation. We also report that cells from child donors have less defined gene signature. Cells from type 2 diabetes donors resemble juvenile cells in gene expression.
#> 708 Genome-wide profiling of placental DNA methylation in relation to arsenic exposure. The Illumina 450k methylation array was used to profile 343 samples for which 3 different measurements of arsenic exposure were available during gestation. These samples have been collected from the New Hampshire Birth Cohort Study (NHBCS).
#> 709 MicroRNA expression profiling of human nucleus pulposus derived from patients with IDD in comparison with those derived from cadaveric disc as normal control. We have identified the expression profiles of miRNAs in IDD using scoliotic nucleus pulposus as controls (GSE19943). It is noteworthy that scoliotic discs are not strictly normal discs. Therefore, the microRNA expression profiles were revisited using normal discs as control.
#> 710 substantial number of people at risk to develop type 2 diabetes could not improve insulin sensitivity by physical training intervention. We studied the mechanisms of this impaired exercise response in 20 middle-aged individuals who performed a controlled eight weeks cycling and walking training at 80 % individual VO2max. Participants identified as non-responders in insulin sensitivity (based on Matsuda index) did not differ in pre-intervention parameters compared to high responders. more...
#> 711 To understand organ (dys)function it is important to have a complete inventory of its cell types and the corresponding markers that unambiguously identify these cell types. This is a challenging task, in particular in human tissues, because unique cell-type markers are typically unavailable, necessitating the analysis of complex cell type mixtures. Transcriptome-wide studies on pancreatic tissue are typically done on pooled islet material. more...
#> 712 This SuperSeries is composed of the SubSeries listed below.
#> 713 RNA-seq profiling was conducted on clinically-annotated human pancreatic adenocarcinoma cancer tissues
#> 714 Genome-wide profiling of placental DNA methylation in relation to neurobehavioral development. The Illumina 450k methylation array was used to profile 335 samples. These samples have been collected from the Rhode Island Child Health Study (RICHS).
#> 715 Hyperglycemia is a hallmark in prediabetes and type 2 diabetes mellitus (T2DM) which increases risk of micro and macrovascular complications such as diabetic retinopathy, diabetic nephropathy (microvascular complications), and peripheral vascular disease, cerebrovascular disease and cardiovascular diseases (macrovascular complications). Endothelial cells are affected in both cases. In this study, we investigated the miRNA expression changes in HUVECs during different glucose treatment (5mM, 10mM, 25mM and 40mM glucose) at various time intervals (6, 12, 24 and 48hrs). more...
#> 716 Autoreactive CD8+ T-cells recognizing autoantigens expressed by pancreatic islets lead to the destruction of insulin-producing β-cells in type 1 diabetes, but these T-cell also occur in healthy subjects. We tested the hypothesis that uncontrolled expansion of diabetogenic T-cells in patients occurs, resulting from failure to activate apoptosis. We compared function, transcriptome and epigenetic regulation thereof in relation with fate upon repeated exposure to islet-autoantigen of islet autoreactive T-cells from healthy and type 1 diabetic donors with identical islet epitope specificity and HLA-A2 restriction. more...
#> 717 DNA-methylation profiling of Whole blood genomic DNAs collected at EDIC baseline and monocytes at EDIC years 16/17 years from participants of Diabetes Control and Complications Trial/ Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study This SuperSeries is composed of the SubSeries listed below.
#> 718 Study the association of DNA-methylation and metabolic memory by examing DNA-methylation alternation between cases (received conventional therapy in DCCT and showing retinopathy or albuminuria progression at EDIC year-10) and Controls(in DCCT intensive treatment group and did not have retinopathy or nephropathy progression during EDIC]
#> 719 Study the association of DNA-methylation and metabolic memory by examing DNA-methylation alternation between cases (received conventional therapy in DCCT and showing retinopathy or albuminuria progression at EDIC year-10) and Controls (in DCCT intensive treatment group and did not have retinopathy or nephropathy progression during EDIC).
#> 720 This SuperSeries is composed of the SubSeries listed below.
#> 721 We describe Nfib as an important regulator of chromatin accessibility in Small cell lung cancer (SCLC).
#> 722 Preeclampsia and gestational diabetes mellitus (GDM) are two of the most common clinical conditions during pregnancy that could result in adverse utero environments of the fetus. Fetal exposure to poor environments in uterus also raises the risk of future adulthood disorders known as fetal origins of adult disease (FOAD). Epigenetic modifications like cytosine methylation and histone modification have been proposed to be involved in FOAD. more...
#> 723 The study of epigenetic mechanisms of gene regulation and the role of these mechanisms in developmental reprogramming of the genome and disease susceptibility has increased in recent years. Molecular epigenetic mechanisms regulating gene expression include DNA methylation, histone modifications, and small non-coding RNAs (e.g., microRNAs). MicroRNAs (miRNAs) are short, single-stranded RNA that regulate post-transcriptional control of the translation of mRNA. more...
#> 724 Intensive efforts are focused on identifying regulators of human pancreatic islet cell growth and maturation to accelerate development of therapies for diabetes. After birth, islet cell growth and function are dynamically regulated; however, establishing these age-dependent changes in humans has been challenging. Here we describe a multimodal strategy for isolating pancreatic endocrine and exocrine cells from children and adults to identify age-dependent gene expression and chromatin changes on a genomic scale. more...
#> 725 We report a mechanism through which the transcription machinery directly controls topoisomerase 1 (TOP1) activity to adjust DNA topology throughout the transcription cycle. By comparing TOP1 occupancy using ChIP-Seq, versus TOP1 activity using TOP1-Seq, a method reported here to map catalytically engaged TOP1, TOP1 bound at promoters was discovered to become fully active only after pause-release. This transition coupled the phosphorylation of the carboxyl-terminal-domain (CTD) of RNA polymerase II (RNAPII) with stimulation of TOP1 above its basal rate, enhancing its processivity. more...
#> 726 Current therapy has turned HIV infection into a chronic condition. Clinically, some patients suffer prematurely from ailments associated with advanced age; however, the relationship between HIV and aging is unclear. Here we have collected a large cohort of HAART treated HIV+ subjects with both recent and chronic infection and recapitulated the shared phenotype of HIV and age. To further understand this signal, we applied validated models of DNA methylation-based biological age to establish a clear link between HIV infection and molecular age advancement. more...
#> 727 We recently reported the scalable in vitro production of functional stem cell-derived β cells. Here we extend this approach to generate SC-β cells from Type 1 diabetic patients (T1D), a cell type that is destroyed during disease progression and has not been possible to extensively study. These cells express β cell markers, respond to glucose both in vitro and in vivo, prevent alloxan-induced diabetes in mice, and respond to anti-diabetic drugs. more...
#> 728 Type 2 diabetes is a complex disease associated with many underlying pathomechanisms. Epigenetic regulation of gene expression by DNA methylation has become increasingly recognized as an important component in the etiology of type 2 diabetes. We performed genome-wide methylome and transcriptome analysis in liver from severely obese patients with or without type 2 diabetes to discover aberrant pathways underlying the development of insulin resistance. more...
#> 729 Type 2 diabetes is a complex disease associated with many underlying pathomechanisms. Epigenetic regulation of gene expression by DNA methylation has become increasingly recognized as an important component in the etiology of type 2 diabetes. We performed genome-wide methylome and transcriptome analysis in liver from severely obese patients with or without type 2 diabetes to discover aberrant pathways underlying the development of insulin resistance. more...
#> 730 Insulin resistance is central to diabetes and metabolic syndrome. To define the consequences of genetic insulin resistance distinct from those secondary to cellular differentiation or in vivo regulation, we generated induced pluripotent stem cells (iPSCs) from individuals with insulin receptor mutations and age-appropriate control subjects and studied insulin signaling and gene expression compared with the fibroblasts from which they were derived. more...
#> 731 This SuperSeries is composed of the SubSeries listed below.
#> 732 Background: In vitro models are an essential tool towards understanding the molecular characteristics of colorectal cancer (CRC) and the testing of therapies for CRC. To this end we established 21 novel CRC cell lines of which six were derived from liver metastases. Extensive genetic, genomic, transcriptomic and methylomic profiling was performed in order to characterize these new cell lines and all data is made publically available. more...
#> 733 Background: In vitro models are an essential tool towards understanding the molecular characteristics of colorectal cancer (CRC) and the testing of therapies for CRC. To this end we established 21 novel CRC cell lines of which six were derived from liver metastases. Extensive genetic, genomic, transcriptomic and methylomic profiling was performed in order to characterize these new cell lines and all data is made publically available. more...
#> 734 Application of Systems Genetics analysis for systematic evaluation of candidate causal genes associated with risk of Type 1 Diabetes along with follow-up bioinformatics pathway analysis.
#> 735 Mitochondrial defects are associated with a spectrum of human disorders, ranging from rare, inborn errors of metabolism to common, age-associated diseases such as diabetes and neurodegeneration. In lower organisms, genetic “retrograde” signaling programs have been identified that promote cellular and organism survival in the face of mitochondrial dysfunction. Here, we characterized the transcriptional component of the human mitochondrial retrograde response in an inducible model of mitochondrial dysfunction. more...
#> 736 Background: The bile acid-activated farnesoid X receptor (FXR) is a nuclear receptor regulating bile acid, glucose and cholesterol homeostasis. Obeticholic acid (OCA; also known as INT-747 or 6α-ethyl-chenodeoxycholic acid), a promising drug for the treatment of non-alcoholic steatohepatitis (NASH) and type 2 diabetes, activates FXR. Mouse studies demonstrated that FXR activation by OCA (INT-747) alters hepatic expression of many genes. more...
#> 737 Diabetic retinopathy (DR) is the leading cause of blindness in working-age people. Pericyte loss is one of the pathologic cellular events in DR, which weakens the retinal microvessels. Damages to the microvascular networks are irreversible and permanent, thus further progression of DR is inevitable. In this study, we hypothesize that multipotent perivascular progenitor cells derived from human ESCs (hESC-PVPCs) improve the damaged retinal vasculature in the streptozotocin (STZ)-induced diabetic rodent models. more...
#> 738 Atrial fibrillation (AF) is currently the most prevalent arrhythmia worldwide.Recent clinical data implicate the additional contribution of non-coding RNAs in the pathogenesis of AF，which include microRNAs(miRNAs), endogenous small interfering RNAs, PIWIinteracting RNAs, and lncRNA. Notably, a growing number of lncRNAs have been implicated in disease etiology, although an association with AF has not been reported. more...
#> 739 Analysis of umbilical cord tissue in newborns of type 1 diabetic mothers at gene expression level. The hypothesis tested in the present study was that intrauterine diabetic milieu may effect of fetal umbilical cord gene expression, and via umbilical cord, the alterations may be produced in other fetal tissues as well. Results provide an information of the differentially expressed genes and enriched pathways, such as the dowregulated genes on the pathway on blood vessel development in umbilical cords from diabetic pregnancies.
#> 740 DNA microarray analysis was performed to investigate the expression of genes in HGF stimulated with palmitate Type 2 diabetes (T2D) is characterized by decreased insulin sensitivity and higher concentrations of free fatty acids (FFAs) in plasma. Among FFAs, saturated fatty acids (SFAs), such as palmitate, have been proposed to promote inflammatory responses. Although many epidemiological studies have shown a link between periodontitis and T2D, little is known about the clinical significance of SFAs in periodontitis. more...
#> 741 This SuperSeries is composed of the SubSeries listed below.
#> 742 MicroRNAs (miRNAs) contribute to chronic kidney disease progression via negatively regulating mRNA abundance. However, their association with clinical outcome remains poorly understood. We performed large-scale miRNA and mRNA expression profiling on cryo-cut renal biopsy sections from a discovery (n=43) and a validation (n=29) cohort. In the discovery cohort (GEO Series accession number GSE45980), miRNAs differentiating stable and progressive cases were determined, and putative target mRNAs showing inversely correlated expression profiles were identified. more...
#> 743 MicroRNAs (miRNAs) significantly contribute to chronic kidney disease (CKD) progression via regulating mRNA expression and abundance. However, their association with clinical outcome remains poorly understood. We performed large scale miRNA and mRNA expression profiling on cryo-cut renal biopsy sections from n=43 subjects. miRNAs differentiating stable and progressive cases were determined, and putative target mRNAs showing inversely correlated expression profiles were identified and further characterized. more...
#> 744 Type 1 diabetes (T1D) is an autoimmune disease characterized by the destruction of pancreatic insulin-producing β cells. CD4+ T cells are integral to the pathogenesis of T1D, but biomarkers that define their pathogenic status in T1D are lacking. miRNAs have essential functions in a wide range of tissues/organs, including the immune system. We reasoned that CD4+ T cells from individuals at high risk for T1D (pre-T1D) might be distinguished by an miRNA signature. more...
#> 745 Objective: Although glucagon-secreting α-cells and insulin-secreting β-cells have opposing functions in regulating plasma glucose levels, the two cell types share a common developmental origin and have overlaps in their transcriptome and epigenome profiles. Notably, destruction of one of these cell populations can stimulate repopulation via transdifferentiation of the other cell type, at least in mice, suggesting plasticity between these cell fates. more...
#> 746 Diabetes mellitus is a complex and heterogeneous disease that has β cell dysfunction at its core. Glucose toxicity affects pancreatic islets where it leads to β cell apoptosis. However, the role of JNK/β-catenin signaling pathway in glucotoxic β-cell apoptosis is poorly understood. To identify the potential genes whose expression changed in response to high glucose, we performed microarray analysis of gene expression in the RNAKT-15 cells for 48 h. more...
#> 747 The presence of a coding variant affecting plasma high density lipoprotein cholesterol (HDLC) levels was evaluated in subjects with elevated plasma levels of HDLC.
#> 748 The obese people with abnormal BMI are predisposed to insulin resistance and diabetes. At the same time, human subjects with obesity and high BMI that are otherwise insulin sensitive are an interesting group to study the underlying gene expression patterns which provide them with such protective phenotype. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. more...
#> 749 We identified EGF as the top candidates predicting kidney function through an intrarenal transcriptome-driven approach, and demonstrated it is an independent risk predictor of CKD progression and can significantly improve prediction of renal outcome by established clinical parameters in diverse populations with CKD from a wide spectrum of causes and stages
#> 750 This SuperSeries is composed of the SubSeries listed below.
#> 751 Gene expression of tumor sample of mexican patients with breast cancer. Samples obtained from the Hospital San Jose Tec de Monterrey.
#> 752 miRNAs expression of tumor sample of mexican patients with breast cancer. Samples obtained from the Hospital San Jose Tec de Monterrey.
#> 753 Genome-wide association studies have been tremendously successful in identifying genetic variants associated with complex diseases. The majority of association signals are intergenic and evidence is accumulating that a high proportion of signals lie in enhancer regions.We use Capture Hi-C to investigate, for the first time, the interactions between associated variants for four autoimmune diseases and their functional targets in B- and T-cell lines. more...
#> 754 In a prospective case-control study, we identified novel transcriptional classifiers for TB among US patients and systematically compared their accuracy to other classifiers in published studies.
#> 755 Individual differences in peripheral blood transcriptomes in older adults as a function of demographic, socio-economic, psychological, and health history characteristics.
#> 756 Analysis of gene expression associated with exercise response. The hypothesis tested was that individuals with Type 2 Diabetes that failed to demonstrate exercise-induced metabolic improvements would also reflect this lack of response in their skeletal muscle transcriptional profile at baseline. Of 186 genes identified by microarray analysis, 70% were upregulated in Responders and downregulated in Non-responders. more...
#> 757 This SuperSeries is composed of the SubSeries listed below.
#> 758 The molecular clock is a transcriptional oscillator present in brain and peripheral cells that coordinates behavior and physiology with the solar cycle. Here we reveal that the clock gates insulin secretion through genome-wide transcriptional control of the pancreatic exocyst across species. Clock transcription factors bind to unique enhancer sites in cycling genes in beta cells that diverge from those in liver, revealing the dynamics of inter-tissue clock control of genomic and physiologic processes important in glucose homeostasis.
#> 759 Chronic inflammation leading to pro-inflammatory macrophage infiltration contributes to the pathogenesis of type 2 diabetes and subsequently the development of diabetic nephropathy. Mesenchymal stem cells (MSCs) possess unique immunomodulatory and cytoprotective properties making them an ideal candidate for therapeutic intervention We used microarrays to detail changes in the gene expression profile of monocytes isolated from type 2 diabetic patients with end-stage renal disease and non-diabetic control subjects following co-culture with MSCs.
#> 760 Analysis of expression profile of peripheral blood from pancreatic ductal adenocarcinoma patients RNA expression profile of peripheral blood from pancreatic ductal adenocarcinoma patients
#> 761 The microRNA profiles in the vitreous of proliferative vitreoretinal disease (PVD) such as proliferative diabetic retinopathy with fibrovascular membrane and macular hole (MH) patients were studied by RT-PCR.
#> 762 These experiments were performed to identify differentially expressed genes in the pancreas of healthy humans, auto-antibody positive and type 1 diabetic patients. All samples were obtained from the network of pancreatic organ donors with diabetes (nPOD). ID numbers are specified. Patient information can be obtained at http://www.jdrfnpod.org/
#> 763 Diabetes and Arteriosclerosis progression are frequently observed in borderline Type 2 diabetes cases. Onset of complications (arteriosclerosis and renal damage) due to Type 2 diabetes is well documented; it is extremely important to prevent or delay their progression. Type 2 diabetes onset and progression has been controlled through dietary habits and exercise, although these remain insufficient. Chlorella ingestion improves blood glucose and cholesterol concentrations in mice and humans, although no reports have evaluated Chlorella effects in borderline diabetics. more...
#> 764 ChIP-seq of H3K27ac was performed in regulatory T cells (resting and activated) and conventional T cells (naïve, effector, memory) in mouse and human. A small number of regulatory elements were lineage specific in both mouse and human and represented the 'core' lineage specification program. Regulatory element acetylation levels were associated with genetic variation in humans and lineage-specific loci were enriched for autoimmune risk-alleles (especially type 1 diabetes) identified in classic and fine-resolution genome-wide association studies.
#> 765 The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). more...
#> 766 Background: Blocking the action of the pro-inflammatory cytokine interleukin-1 (IL-1) reduces beta-cell secretory dysfunction and apoptosis in vitro, diabetes incidence in animal models of Type 1 diabetes mellitus (T1D), and glycaemia via improved beta-cell function in patients with T2D. We hypothesised that canakinumab, a monoclonal antibody to IL-1B, improves beta-cell function in patients with new-onset T1D. more...
#> 767 Background: Blocking the action of the pro-inflammatory cytokine interleukin-1 (IL-1) reduces beta-cell secretory dysfunction and apoptosis in vitro, diabetes incidence in animal models of Type 1 diabetes mellitus (T1D), and glycaemia via improved beta-cell function in patients with T2D. We hypothesised that anakinra, a recombinant human IL-1 receptor antagonist, improves beta-cell function in patients with new-onset T1D. more...
#> 768 We investigated the short and long term effects of electrically induced exercise on mRNA expression of human paralyzed muscle. We developed an exercise dose that activated the muscle for 0.6% of the day. The short term effects were assessed 3 hours after a single dose of exercise, while the long term effects were assessed after training 5 days per week for at least one year (adherence 81%). A single dose of electrical stimulation increased the mRNA expression of transcriptional, translational, and enzyme regulators of metabolism important to shift muscle toward an oxidative phenotype (PGC-1a, NR4A3, IFRD1, ABRA, PDK4). more...
#> 769 Gene expression analyses of fibroblasts obtained from healthy controls, Medalist -C patients and Medalist +C patients. Type 1diabetes (T1D) is associated with late complications, mechanisms underscoring which are poorly understood. We report the derivation of induced pluripotent stem cells (iPSCs) from patients with longstanding T1D (disease duration ≥ 50years) with severe (designated Medalist +C) or absent to mild complications (designated Medalist -C). more...
#> 770 This SuperSeries is composed of the SubSeries listed below.
#> 771 Gestational diabetes mellitus (GDM) affects approximately 18% of pregnancies in the United States and increases the risk of adverse health outcomes in the offspring. These adult disease propensities may be set by anatomical and molecular alterations in the placenta associated with GDM. To assess the mechanistic aspects of fetal programming, we measured genome-wide methylation (Infinium HumanMethylation450 Beadchips) and expression (Affymetrix Transcriptome Microarrays) in placental tissue of 41 GDM cases and 41 matched pregnancies without maternal complications from the Harvard Epigenetic Birth Cohort. more...
#> 772 Gestational diabetes mellitus (GDM) affects approximately 18% of pregnancies in the United States and increases the risk of adverse health outcomes in the offspring. These adult disease propensities may be set by anatomical and molecular alterations in the placenta associated with GDM. To assess the mechanistic aspects of fetal programming, we measured genome-wide methylation (Infinium HumanMethylation450 Beadchips) and expression (Affymetrix Transcriptome Microarrays) in placental tissue of 41 GDM cases and 41 matched pregnancies without maternal complications from the Harvard Epigenetic Birth Cohort. more...
#> 773 We carried out a high throughput analysis of insulin-induced kinase signaling pathways in primary fibroblasts from 35 unrelated individuals. We found that extensive individual variation exists in induction of various signaling pathways. ERK signaling displayed the greatest variation, which led to extensive variation in expression of downstream target genes. Our results suggest that phenotypic variation in kinase signaling mediates variation in downstream processes of insulin response. more...
#> 774 Adipose tissues play an important role in the pathophysiology of obesity-related disease including type 2 diabetes. To describe gene expression patterns and functional pathways in obesity-related type 2 diabetes, we performed global transcript profiling of omental adipose tissue in morbidly obese individuals with or without diabetes.
#> 775 In-vitro expansion of functional adult human β-cells is an attractive approach for generating insulin-producing cells for transplantation. However, human islet cell expansion in culture results in loss of β-cell phenotype and epithelial-mesenchymal transition (EMT). This process activates expression of ZEB1 and ZEB2, two members of the zinc-finger homeobox family of E-cadherin repressors, which play key roles in EMT. more...
#> 776 The project is directed to the development of selective glucocorticoid receptor agonists for anticancer therapy. Glucocorticoids (GC) are widely used in treatment of many types of cancer due to its ability to induce apoptosis in malignant cells (in blood cancer therapy) and to prevent nausea and emesis (in the chemotherapy of solid tumors). However, severe dose-limiting side effects occur, including osteoporosis, muscle wasting, diabetes and other metabolic complications. more...
#> 777 The project is directed to the development of selective glucocorticoid receptor agonists for anticancer therapy. Glucocorticoids (GC) are widely used in treatment of many types of cancer due to its ability to induce apoptosis in malignant cells (in blood cancer therapy) and to prevent nausea and emesis (in the chemotherapy of solid tumors). However, severe dose-limiting side effects occur, including osteoporosis, muscle wasting, diabetes and other metabolic complications. more...
#> 778 We present a novel method, termed BisPCR2, for targeted bisulfite sequencing and apply it in the setting of validating type 2 diabetes CpG susceptibility loci
#> 779 Genes related to sleep and wakefulness were evaluated by RNA microarray in patients, including CKD,HD patients and control subjects.
#> 780 The host response in critically ill patients with sepsis, septic shock remains poorly defined. Considerable research has been conducted to accurately distinguish patients with sepsis from those with non-infectious causes of disease. Technological innovations have positioned systems biology at the forefront of biomarker discovery. Analysis of the whole-blood leukocyte transcriptome enables the assessment of thousands of molecular signals beyond simply measuring several proteins in plasma, which for use as biomarkers is important since combinations of biomarkers likely provide more diagnostic accuracy than the measurement of single ones or a few. more...
#> 781 Background: The prevalence of type 2 diabetes has increased dramatically in recent decades. Increasing brown adipose tissue (BAT) mass and activity has recently emerged as an interesting approach to not only increase energy expenditure, but also improve glucose homeostasis. BAT can be recruited by prolonged cold exposure in lean, healthy humans. Here, we tested whether cold acclimation could have therapeutic value for patients with type 2 diabetes by improving insulin sensitivity. more...
#> 782 We performed gene expression microarray analysis of skeletal muscle biopsies from normal glucose tolerant subjects and type 2 diabetes subjects obtained during a 60 min bicycle ergometer exercise and the 180 min of recovery phase
#> 783 Coronary artery disease (CAD) is the leading cause of human morbidity and mortality worldwide, underscoring the need to improve diagnostic strategies. Platelets play a major role, not only in the process of acute thrombosis during plaque rupture, but also in the formation of atherosclerosis itself. MicroRNAs are endogenous small non-coding RNAs that control gene expression and are expressed in a tissue and disease-specific manner. more...
#> 784 Circulating miRNAs constitute a novel class of disease biomarkers, which are altered in diabetes but the effect of diabetes associated inflammation as seen in chronic wounds is unknown. We here compared the miRNA pattern in diabetic patients in presence or absence of chronic wound with PAD.
#> 785 Understanding distinct gene expression patterns of normal adult and developing fetal human pancreatic a and b cells is crucial for developing stem cell therapies, islet regeneration strategies, and therapies designed to increase b cell function in patients with diabetes (type 1 or 2). Toward that end, we have developed methods to highly purify a, b, and d cells from human fetal and adult pancreata by intracellular staining for the cell-specific hormone content, sorting the sub-populations by flow cytometry and, using next generation RNA sequencing, we report on the detailed transcriptomes of fetal and adult a and b cells. more...
#> 786 Melioidosis, caused by Gram negative bacteria Burkholderia pseudomallei, is a major type of community-acquired septicemia in Southeast Asia and Northern Australia with high mortality and morbidity rate. More accurate and rapid diagnosis is needed for improving the management of septicemic melioidosis. We previously identified 37-gene candidate signature to distinguish septicemic melioidosis from sepsis due to other pathogens. more...
#> 787 Differencies between groups between pre and post haematopoietic stem cell transplantation children Immune reactions are among the most serious complications observed after hematopoietic stem cell transplantation (HSCT) in children. Microarray technique allows for simultaneous assessment of expression of nearly all human genes. The objective of the study was to compare the whole genome expression in children before and after HSCT. more...
#> 788 A previous study from this laboratory demonstrated that up-regulating HNF4a could reverse the malignant phenotypes of HCC by inducing redifferentiation of HCC cells to hepatocytes. To study the mechanisms of the hepatic differentiation effect by HNF4α, we used the cDNA microarray to detect differential gene expression profiles of Hep3B infected with AdHNF4α and AdGFP. Expression profile analysis revealed that HNF4α positively regulated 1218 mRNAs and negatively regulated 1411 mRNAs for more than 2 times. more...
#> 789 Background/aims: Serum concentrations of the hepatokine fibroblast growth factor (FGF) 21 are elevated in obesity, type‐2 diabetes, and the metabolic syndrome. We asked whether FGF21 levels differ between subjects with metabolically healthy vs. unhealthy obesity (MHO vs. MUHO) opening the possibility that FGF21 is a cross‐talker between liver and adipose tissue in MUHO. Furthermore, we studied the effects of chronic FGF21 treatment on adipocyte differentiation, lipid storage, and adipokine secretion. more...
#> 790 Skeletal muscle adapts to exercise training of various modes, intensities and durations with a programmed gene expression response. This study dissects the independent and combined effects of exercise mode, intensity and duration to identify which exercise has the most positive effects on skeletal muscle health. Full details on exercise groups can be found in: Kraus et al Med Sci Sports Exerc. 2001 Oct;33(10):1774-84 and Bateman et al Am J Cardiol. more...
#> 791 Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. more...
#> 792 Arsenic (As) exposure is a significant worldwide environmental health concern. Low dose, chronic arsenic exposure has been associated with higher risk of skin, lung, and bladder cancer, as well as cardiovascular disease and diabetes. While arsenic-induced biological changes play a role in disease pathology, little is known about the dynamic cellular changes due to arsenic exposure and withdrawal. In these studies, we seek to understand the molecular mechanisms behind the biological changes induced by chronic low doses of arsenic exposure. more...
#> 793 This SuperSeries is composed of the SubSeries listed below.
#> 794 Diabetes Mellitus (DM) is a chronic, severe disease rapidly increasing in incidence and prevalence and is associated with numerous complications. Patients with DM are at high risk of developing diabetic foot ulcers (DFU) that often lead to lower limb amputations, long term disability, and a shortened lifespan. Despite this, the effects of DM on human foot skin biology are largely unknown. Thus, the focus of this study was to determine whether DM changes foot skin biology predisposing it for healing impairment and development of DFU. more...
#> 795 Diabetes Mellitus (DM) is a chronic, severe disease rapidly increasing in incidence and prevalence and is associated with numerous complications. Patients with DM are at high risk of developing diabetic foot ulcers (DFU) that often lead to lower limb amputations, long term disability, and a shortened lifespan. Despite this, the effects of DM on human foot skin biology are largely unknown. Thus, the focus of this study was to determine whether DM changes foot skin biology predisposing it for healing impairment and development of DFU. more...
#> 796 Diabetes Mellitus (DM) is a chronic, severe disease rapidly increasing in incidence and prevalence and is associated with numerous complications. Patients with DM are at high risk of developing diabetic foot ulcers (DFU) that often lead to lower limb amputations, long term disability, and a shortened lifespan. Despite this, the effects of DM on human foot skin biology are largely unknown. Thus, the focus of this study was to determine whether DM changes foot skin biology predisposing it for healing impairment and development of DFU. more...
#> 797 This SuperSeries is composed of the SubSeries listed below.
#> 798 The current study aimed to address the hypothesis that programmed expression of key miRNAs in skeletal muscle mediates the development of insulin resistance, and consequently long-term health. We thus examined microRNA signatures in skeletal muscle of unmedicated newly diagnosed human pre-diabetics and type 2 diabetics.
#> 799 Objective: to compare changes in gene expression by microarray from subcutaneous adipose tissue from HIV treatment naïve patients treated with efavirenz based regimens containing abacavir (ABC), tenofavir (TDF) or zidovidine (AZT). There were significant divergence between ABC and the other two groups 6 months after treatment in genes controlling cell adhesion and environmental information processin, with some convergence at 18 months. more...
#> 800 We have performed gene expression microarray analysis to profile transcriptomic signatures between insulin resistance high risk subjects and insulin resistance low risk subjects
#> 801 Skeletal myocytes are metabolically active and susceptible to insulin resistance, thus implicated in type 2 diabetes (T2D). This complex disease involves systemic metabolic changes and their elucidation at the systems level requires genome-wide data and biological networks. Genome-scale metabolic models (GEMs) provide a network-context to integrate high-throughput data. We generated myocyte-specific RNA-seq data and investigated their correlation with proteome data. more...
#> 802 Myotonic Dystrophy Type-2 (DM2) is an autosomal dominant disease caused by the expansion of a CCTG tetraplet repeat. It is a multisystemic disorder, affecting skeletal muscles, the heart, the eye, the central nervous system and the endocrine system. Whole mRNAs expression was measured in the muscle of DM2 patients and compared it to controls.We identified distinct genes modulated in DM2 patients compared to controls.
#> 803 This SuperSeries is composed of the SubSeries listed below. Each of the SubSeries contained in this SuperSeries represents identical RNA samples used for hybridization to different array platforms.
#> 804 Diabetic Retinopathy (DR) is a progressive disease affecting the structure and cellular composition of the microvasculature. Several factors like genetic, environmental and biochemical are involved in the development of DR. However, the inheritance pattern of this disease is multi factorial resulting from the interaction of one or more genes but the exact mechanism is not completely understood. Over the decade there has been alot of molecular genetics work done in nuclear genome for DR in different ethnic groups and identified several candidate genes involved in disease pathogenesis but the role of these genes in the development of disease is not yet clearly understood. more...
#> 805 Age-related changes in DNA methylation occurring in blood leukocytes during early childhood may reflect epigenetic maturation. We hypothesized that some of these changes involve gene networks of critical relevance in leukocyte biology and conducted a prospective study to elucidate the dynamics of DNA methylation. Serial blood samples were collected at 3, 6, 12, 24, 36, 48 and 60 months after birth in 10 healthy girls born in Finland and participating in the Type 1 Diabetes Prediction and Prevention Study. more...
#> 806 Type 1 diabetes (T1D) is a polygenic autoimmune disorder caused by autoreactive T cells that recognize pancreatic islet antigens and subsequently destroy insulin-producing β-cells. Pancreatic lymph nodes (PLN) are an essential site for the development of T1D, where tolerance to pancreatic self-antigens is first broken and the autoimmune responses are amplified. The purpose of this study was to identify candidate genes and pathways in the PLN that may contribute to the pathogenesis of T1D.
#> 807 Despite the significant reduction in the overall burden of cardiovascular disease (CVD) over the past decade, CVD still accounts for a third of all deaths in the United States and worldwide each year. While efforts to identify and reduce risk factors for atherosclerotic heart disease (i.e. hypertension, dyslipidemia, diabetes mellitus, cigarette smoking, inactivity) remain the focus of primary prevention, the inability to accurately and temporally predict acute myocardial infarction (AMI) impairs our ability to further improve patient outcomes. more...
#> 808 Due to an increasingly aging population, the incidence of dementias such as Alzheimer’s disease are steadily rising, with recent estimates predicting >115million dementia sufferers by 2050. The ability to identify early markers in blood, which appear before the onset of clinical symptoms is of considerable interest to allow early intervention, particularly in “high risk” groups such as those with Type 2 Diabetes (T2D). more...
#> 809 Despite some success in identifying CNVs responsible for metabolic phenotypes including obesity and diabetes mellitus, there are as yet no data available to suggest whether or not CNVs might be involved in the etiology of the NAFLD spectrum. This report is a comprehensive analysis of copy number in Malaysian patients with NAFLD.
#> 810 To investigate the umbilical cord lncRNA profiles in gestational diabetes-induced macrosomia, the umbilical cord vein blood from normal and gestational diabetes-induced macrosomia was hybridized to a microarray containing probes representing 33,000 lncRNA genes. Quantitative real-time polymerase chain reaction (qPCR) was used to validate selected differentially expressed lncRNAs. The gene ontology (GO), pathway and network analysis were performed. more...
#> 811 The objective of this study was to examine relationships between weight loss through changes in lifestyle and peripheral blood gene expression profiles. Substantial weight loss (-15.2+3.8%) in lifestyle participants was associated with improvement in selected cardiovascular risk factors and significant changes in peripheral blood gene expression from pre- to post-intervention: 132 unique genes showed significant expression changes related to immune function and inflammatory responses involving endothelial activation. more...
#> 812 This SuperSeries is composed of the SubSeries listed below.
#> 813 We performed global microRNA expression profiling of a cohort of primary melanoma patient samples linked to a well-annotated clinical database. The goal of this study was to identify microRNA that are associated to or correlated with various clinical parameters and patient outcomes. Candidate microRNA were identified for building prognostic models and functional testing.
#> 814 Obesity is associated with insulin resistance and increased intrahepatic triglyceride (IHTG) content, which are key risk factors for diabetes and cardiovascular disease. However, a subset of obese people does not develop these metabolic complications. We tested the hypothesis that MNO, but not MAO, people are protected from the adverse metabolic effects of weight gain. To this end, global transcriptional profile in adipose tissue before and after weight gain was evaluated by microarray analyses.
#> 815 Using a functional approach to investigate the epigenetics of Type 2 Diabetes (T2D), we combine three lines of evidence – diet-induced epigenetic dysregulation in mouse, epigenetic conservation in humans, and T2D clinical risk evidence – to identify genes implicated in T2D pathogenesis through epigenetic mechanisms related to obesity. Beginning with dietary manipulation of genetically homogeneous mice, we identify differentially DNA-methylated genomic regions. more...
#> 816 This study compared whole transcriptome signatures of 6 immune cell subsets and whole blood from patients with an array of immune-associated diseases. Fresh blood samples were collected from healthy subjects and subjects diagnosed type 1 diabetes, amyotrophic lateral sclerosis, and sepsis, as well as multiple sclerosis patients before and 24 hours after the first treatment with IFN-beta. At the time of blood draw, an aliquot of whole blood was collected into a Tempus tube (Invitrogen), while the remainder of the primary fresh blood sample was processed to highly pure populations of neutrophils, monocytes, B cells, CD4 T cells, CD8 T cells, and natural killer cells. more...
#> 817 The pathogenesis of IDD is still unclear, and microRNA has been reported playing an important role in occurrence and development of many diseases. But to date, the research about the role of microRNA in IDD is rare.
#> 818 Genes dysregulated in cystic fibrosis (CF) and primary pulmonary arterial hypertension (PAH) at a late stage of pulmonary failure are still largely unknown. Blood samples taken in the frame of the French cohort of lung transplantation COLT offers the opportunity to identify in blood specific gene signatures of each disease and a common gene signature for both pathologies. A microarray analysis was performed with homogeneous groups of CF patients (n=23), PAH (n=13) patients and healthy volunteers (n=28). more...
#> 819 Peripheral blood samples of patients with acute myocardial infarction were matched with those of control patients to identify possible differences in corresponding gene expression profiles. The controls were matched to cases based on gender, age, status of diabetes mellitus and smoking status. Six months cardiovascular survival status of the cases was used to identify two distinct subgroups among the cases. more...
#> 820 Skeletal muscle is the key site of peripheral insulin resistance in type 2 diabetes. Insulin-stimulated glucose uptake is decreased in differentiated diabetic myotubes in keeping with a retained genetic/epigenetic defect of insulin action. Microarray analysis was used to investigate differences in gene expression with differentiation in diabetic cultures compared to controls.
#> 821 Analysis of microRNA expression in vastus lateralis muscle biopsies from 11 genetically identical twin pairs discordant for type 2 diabetes. This eliminates the influence of genotype and leads to the identification of microRNAs that are exclusively influenced by environmental (non-genetic) factors.
#> 822 In addition to the well-known short noncoding RNAs as miRNAs, increasing evidence suggests that long noncoding RNAs (lncRNAs) act as key regulators in a wide aspect of biologic processes. Dysregulated expression of lncRNAs has been demonstrated being implicated in a variety of human diseases. However, there is relative paucity of information regarding the role of lncRNAs in intervertebral disc degeneration (IDD) hitherto. more...
#> 823 The rising incidence of obesity and related disorders such as diabetes and heart disease has focused considerable attention on the discovery of novel therapeutics. One promising approach has been to increase the number or activity of brown-like adipocytes in white adipose depots, as this has been shown to prevent diet-induced obesity and reduce the incidence and severity of type 2 diabetes. Thus, the conversion of fat-storing cells into metabolically active thermogenic cells has become an appealing therapeutic strategy to combat obesity. more...
#> 824 FUS-CHOP and EWS-CHOP balanced translocations characterize myxoid liposarcoma which encompasses myxoid (ML) and round cell (RC) variants initially believed to be distinct diseases. Currently, myxoid and RC liposarcoma are regarded to represent the well differentiated and the poorly differentiated ends, respectively, within spectrum of myxoid liposarcoma where the fusion proteins blocking lipogenic differentiation play a role in tumor initiation while molecular determinants associated to progression to RC remain poorly understood. more...
#> 825 FUS-CHOP and EWS-CHOP balanced translocations characterize myxoid liposarcoma which encompasses myxoid (ML) and round cell (RC) variants initially believed to be distinct diseases. Currently, myxoid and RC liposarcoma are regarded to represent the well differentiated and the poorly differentiated ends, respectively, within spectrum of myxoid liposarcoma where the fusion proteins blocking lipogenic differentiation play a role in tumor initiation while molecular determinants associated to progression to RC remain poorly understood. more...
#> 826 Chemotherapy-related endothelial damage contributes to the early development of cardiovascular morbidity in testicular cancer patients. We aimed to identify relevant mechanisms of and search for candidate biomarkers for this endothelial damage. Human micro-vascular endothelial cells (HMEC-1) were exposed to bleomycin or cisplatin with untreated samples as control. 18k cDNA microarrays were used. Gene expression differences were analysed at single gene level and in gene sets clustered in biological pathways and validated by qRT-PCR. more...
#> 827 The polycomb repressive complex 2 (PRC2) exerts oncogenic effects in many tumour types1. However, loss-of-function mutations in PRC2 components occur in a subset of haematopoietic malignancies, sug- gesting that this complex plays a dichotomous and poorly understood role in cancer2,3. Here we provide genomic, cellular, and mouse mod- elling data demonstrating that the polycomb group gene SUZ12 func- tions as tumour suppressor in PNS tumours, high-grade gliomas and melanomas by cooperating with mutations in NF1. more...
#> 828 The polycomb repressive complex 2 (PRC2) exerts oncogenic effects in many tumour types1. However, loss-of-function mutations in PRC2 components occur in a subset of haematopoietic malignancies, suggesting that this complex plays a dichotomous and poorly understood role in cancer2,3. Here we provide genomic, cellular, and mouse mod- elling data demonstrating that the polycomb group gene SUZ12 func- tions as tumour suppressor in PNS tumours, high-grade gliomas and melanomas by cooperating with mutations in NF1. more...
#> 829 Human induced pluripotent stem cells changed the face of stem cell biology as they represent a renewable source of stem cells with the potential to differentiated into multiple lineages in a manner akin to embryonic stem cells that can be collected without the need for the destruction of an embryo. The potential of these cells as research tools is vast as they can be pushed to generate different cell types depending on research interest. more...
#> 830 The generation of insulin-producing pancreatic cells from stem cells in vitro would provide an unprecedented cell source for drug discovery and cell transplantation therapy in diabetes. However, insulin-producing cells previously generated from human pluripotent stem cells (hPSC) lack many functional characteristics of bona fide β cells. Here we report a scalable differentiation protocol that can generate hundreds of millions of glucose-responsive β cells from hPSC in vitro. more...
#> 831 Pancreatic tumors with small size can cause type3C Diabetes Mellitus (PCA-DM) but the mechanism is unknown. In this study we aimed at revealing the mRNA and long noncoding RNA (LncRNA) expression patterns of pancreatic tumors that triggered PCA-DM. Four pancreatic tumors from patients with PCA-DM (A1-A4), four pancreatic tumors from patients without PCA-DM (B1-B4), and four pancreatic tissues from patients with pancreatitis were individually profiled with Agilent microarrays(Arraystar Human LncRNA Array v3.0).
#> 832 Aspirin-exacerbated respiratory disease (AERD) is one phenotype of asthma, often in the form of a severe and sudden attack. Due to time consuming and laborious oral aspirin challenge (OAC) for diagnosis of AERD, non-invasive biomarkers have been searched. Therefore, we scrutinize AERD-associated exonic SNPs and examine the diagnostic potential of combination of these candidate SNPs to predict AERD
#> 833 This SuperSeries is composed of the SubSeries listed below.
#> 834 Given the possible critical importance of placental gene imprinting and random monoallelic expression on fetal and infant health, most of those genes must be identified, in order to understand the risks that the baby might meet during pregnancy and after birth. Therefore, the aim of the current study was to introduce a workflow and tools for analyzing imprinted and random monoallelic gene expression in human placenta, by applying whole-transcriptome (WT) RNA sequencing of placental tissue and genotyping of coding DNA variants in family trios. more...
#> 835 In vitro expansion of adult human islet β cells is an attractive solution for the shortage of tissue for cell replacement therapy of type 1 diabetes. Using a lineage tracing approach, we have demonstrated that β-cell-derived (BCD) cells rapidly dedifferentiate in culture and can proliferate for up to 16 population doublings. Dedifferentiation is associated with changes resembling epithelial-mesenchymal transition (EMT). more...
#> 836 By restraining T cell activation and promoting regulatory T cell (Treg) expansion, myeloid-derived suppressor cells (MDSC) and tolerogenic dendritic cells (DC) (tDC) can control self-reactive and anti-graft effector T cells in autoimmunity and transplantation. Their therapeutic use and characterization, however, is limited by their scarce availability in the peripheral blood of tumor-free donors. In the present study we describe and characterize a novel population of myeloid suppressor cells, named fibrocytic MDSC (f-MDSC), that are differentiated from umbilical cord blood (UCB) precursors by a 4 day culture with FDA approved cytokines (rh-GM-CSF and rh-G-CSF). more...
#> 837 Ras-related associated with diabetes (RRAD) is a small Ras-related GTPase that is frequently inactivated by DNA methylation of the CpG island in its promoter region in cancer tissues. However, the role of the methylation-induced RRAD inactivation in tumorigenesis remains unclear. In this study, the Ras regulated-transcriptome and epigenome were profiled by comparing T29H (a RasV12-transformed human ovarian epithelial cell line) with T29 (an immortalized but non-transformed cell line) through Reduced representation bisulfite sequencing (RRBS-seq) and Digital gene expression (DGE) . more...
#> 838 Ras-related associated with diabetes (RRAD) is a small Ras-related GTPase that is frequently inactivated by DNA methylation of the CpG island in its promoter region in cancer tissues. However, the role of the methylation-induced RRAD inactivation in tumorigenesis remains unclear. In this study, the Ras regulated-transcriptome and epigenome were profiled by comparing T29H (a RasV12-transformed human ovarian epithelial cell line) with T29 (an immortalized but non-transformed cell line) through Reduced representation bisulfite sequencing (RRBS-seq) and Digital gene expression (DGE) . more...
#> 839 Glucocorticoid excess is linked to central obesity, adipose tissue insulin resistance and type 2 diabetes mellitus. The aim of our study was to investigate the effects of dexamethasone on gene expression in human subcutaneous and omental adipose tissue, in order to identify potential novel mechanisms and biomarkers for glucocorticoid-induced insulin resistance in adipose tissue. Dexamethasone changed the expression of 527 genes in both subcutaneous and omental adipose tissue. more...
#> 840 Proliferative diabetic retinopathy (PDR) is a vision-threatening disorder characterized by the formation of cicatricial fibrovascular membranes leading to traction retinal detachment. Despite the recent advance in the treatment of PDR such as vitreoretinal surgery with use of anti-vascular endothelial growth factor (VEGF) drug as an adjunct, it still remains vision-threatening disease. In order to identify genes associated with the pathogenesis of PDR, we performed gene expression analyses in fibrovascular membrane in patients with PDR using DNA microarray technology.
#> 841 This SuperSeries is composed of the SubSeries listed below.
#> 842 Type 1 diabetes mellitus (T1D) is a common autoimmune disease mediated by autoimmune attack against pancreatic b cells. It has been reported that dys-regulation of microRNAs (miRNAs) may contribute to the pathogenesis of autoimmune diseases, including T1D. This study sought to identify T1D associated miRNAs in the peripheral blood mononuclear cell (PBMC).
#> 843 Type 1 diabetes mellitus (T1D) is a common autoimmune disease mediated by autoimmune attack against pancreatic b cells.Dys-regualtion of the component of peripheral blood mononuclear cells (PBMCs), including T-cells and B-cells, and smaller amounts of NK cells and dendritic cells, have all been implicated in this process This study sought to identify T1D associated differently expressed genes in the peripheral blood mononuclear cell (PBMC).
#> 844 Type 1 diabetes mellitus (T1DM) results from immune mediated destruction of pancreatic beta cells. However, clinical and immunologic phenotypes of T1DM are variable. Several auto-antibodies including GADA, IA-2A, and ZnT8A, were identified in T1DM, but the prevalence of these auto-antibodies varied for a broad spectrum of T1DM. Here, we systemically profiled auto-antibodies from serum samples of 16 T1DM, 16 type 2 diabetes (T2DM) patients, and 27 healthy controls with normal glucose tolerance (NGT) using protein microarrays containing 9,480 proteins. more...
#> 845 Here we harnessed the potential of expression arrays in 89 human pancreatic islet donors (different levels of blood glucose (HbA1c)) to identify genes regulated in this relevant tissue for type 2 diabetes (T2D).
#> 846 Mi(cro)RNAs are small non-coding RNAs of 18-25 nucleotides in length that modulate gene expression at the post-transcriptional level. These RNAs have been shown to be involved in a several biological processes, human diseases and metabolic disorders. Proanthocyanidins, which are the most abundant polyphenol class in the human diet, have positive heath effects on a variety of metabolic disorders such as inflammation, obesity, diabetes and insulin resistance. more...
#> 847 Mucormycosis is an increasingly common, life-threatening fungal infection caused by fungi belonging to the subphylum Mucormycotina, order Mucorales. The major risk factors for mucormycosis include uncontrolled diabetes mellitus, treatment with corticosteroids, organ or bone marrow transplantation, neutropenia, trauma and burns, malignant hematological disorders, and deferoxamine-therapy in patients receiving hemodialysis. more...
#> 848 The developmental origins of adult disease are now recognized to reflect intrauterine conditions during embryonic and fetal life. Cell-cell communication between the maternal endometrium and the pre-implantation embryo can occur by several means. Here, we show that maternal miRNAs are secreted by the endometrial epithelium to the endometrial fluid. Microarray assessments revealed the presence of specific miRNAs that are associated with the window of implantation and therefore in direct contact with the human preimplantation embryo. more...
#> 849 This SuperSeries is composed of the SubSeries listed below.
#> 850 Low aerobic exercise capacity is a risk factor for diabetes and strong predictor of mortality; yet some individuals are exercise resistant, and unable to improve exercise capacity through exercise training. To test the hypothesis that resistance to aerobic exercise training underlies metabolic disease-risk, we used selective breeding for 15 generation to develop rat models of low- and high-aerobic response to training. more...
#> 851 The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). more...
#> 852 The type and the amount of dietary fat have a significant influence on the metabolic pathways involved in the development of obesity, metabolic syndrome, diabetes type 2 and cardiovascular diseases. However, it is unknown to what extent this modulation is achieved through DNA methylation. We assessed the effects of cholesterol intake, the proportion of energy intake derived from fat, the ratio of polyunsaturated fatty acids (PUFA) to saturated fatty acids (SFA), the ratio of monounsaturated fatty acids (MUFA) to SFA, and the ratio of (MUFA+PUFA) to SFA on genome-wide DNA methylation patterns in normal-weight and obese children. more...
#> 853 Obesity is associated with a chronic, low-grade, systemic inflammation that may contribute to the development of insulin resistance and type 2 diabetes. Resveratrol, a natural compound with anti-inflammatory properties, is shown to improve glucose tolerance and insulin sensitivity in obese mice and humans. Here we tested the effect of a 2-year resveratrol administration on the pro-inflammatory profile and insulin resistance caused by a high-fat, high-sugar (HFS) diet in white adipose tissue (WAT) from rhesus monkeys. more...
#> 854 To identify genes with expression levels that are associated with T1D progression from AbP (islet autoantibody positive), global gene expression changes were analyzed in AbP subjects with different T1D progression rate.
#> 855 Monozygotic (MZ) twin pair discordance for childhood-onset Type 1 Diabetes (T1D) is ~50%, implicating roles for genetic and non-genetic factors in the aetiology of this complex autoimmune disease. Although significant progress has been made in elucidating the genetics of T1D in recent years, the non-genetic component has remained poorly defined. We hypothesized that epigenetic variation could underlie some of the non-genetic component of T1D aetiology and, thus, performed an epigenome-wide association study (EWAS) for this disease. more...
#> 856 Access to an unlimited number of human pancreatic beta cells represents a major challenge in the field of diabetes to better dissect human beta cell functions and to make significant progress in drug discovery and cell replacement therapies. We previously reported the generation of the EndoC-bH1 human beta cell line that was generated by targeted oncogenesis in human fetal pancreases followed by in vivo cell differentiation in mice. more...
#> 857 Results Platelets in non-diabetic patients demonstrated miRNA expression profiles comparable to previously published data. The miRNA expression profiles of platelets in diabetics were similar. Statistical analysis unveiled only three miRNAs (miR-377-5p, miR-628-3p, miR-3137) with high reselection probabilities in resampling techniques, corresponding to signatures with only modest discriminatory performance. more...
#> 858 This dataset was used to establish whole blood transcriptional modules (n=260) that represent groups of coordinately expressed transcripts that exhibit altered abundance within individual datasets or across multiple datasets. This modular framework was generated to reduce the dimensionality of whole blood microarray data processed on the Illumina Beadchip platform yielding data-driven transcriptional modules with biologic meaning.
#> 859 Pancreatic islets are central in type 2-diabetes development, which coincides with increased activity of innate immunity. Intriguingly, human pancreatic islets express many complement genes. The most highly expressed gene was the complement inhibitor CD59 that is GPI anchored to the cell membrane, which unexpectedly was found in high amounts intracellularly in beta cells. Silencing of CD59 strongly suppressed insulin secretion. more...
#> 860 Intensive lifestyle modification is believed to mediate cardiovascular disease (CVD) risk through traditional pathways that affect endothelial function and progression of atherosclerosis; however, the extent, persistence, and clinical significance of molecular change during lifestyle modification are not well known. Our study reveals that gene expression signatures are significantly modulated by rigorous lifestyle behaviors and track with CVD risk profiles over time.
#> 861 To unravel genes and molecular pathways involved in the pathogenesis of type 1 diabetes (T1D), we performed genome-wide gene expression profiling of prospective venous blood samples from children developing T1D-associated autoantibodies or progressing towards clinical diagnosis.
#> 862 This SuperSeries is composed of the SubSeries listed below. Due to privacy concerns, the SNP data is not available with unrestricted access.
#> 863 To unravel genes and molecular pathways involved in the pathogenesis of type 1 diabetes (T1D), we performed genome-wide gene expression profiling of prospective venous blood samples from children developing T1D-associated autoantibodies or progressing towards clinical diagnosis.
#> 864 To unravel genes and molecular pathways involved in the pathogenesis of type 1 diabetes (T1D), we performed genome-wide gene expression profiling of prospective venous blood samples from children developing T1D-associated autoantibodies or progressing towards clinical diagnosis.
#> 865 To unravel genes and molecular pathways involved in the pathogenesis of type 1 diabetes (T1D), we performed genome-wide gene expression profiling of prospective venous blood samples from children developing T1D-associated autoantibodies or progressing towards clinical diagnosis.
#> 866 The aim of this study was to compare miRNA expression in urinary exosomes from type 1 diabetic patients with and without incipient diabetic nephropathy. Overnight urine collections were obtained from normo- and microalbuminuric type 1 diabetic patients. Urines were pre-cleared by both centrifugation and filtration, urinary exosomes were isolated by two consecutive ultracentrifugation steps and total RNA extracted. more...
#> 867 Identification of the inflammatory signature in visceral adipose tissue CD14+ cells (adipose tissue macrophage) Total RNA obtained from CD14+ cells (Immunoselcted cells from stromal adipose tissue cells)
#> 868 The Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort in Gómez Palacio, Mexico was recently established to better understand the impacts of prenatal exposure to inorganic arsenic (iAs). In this study, we examined a subset (n = 40) of newborn cord blood samples for microRNA (miRNA) expression changes associated with in utero arsenic exposure. Levels of iAs in maternal drinking water (DW-iAs) and maternal urine were assessed. more...
#> 869 The Biomarkers of Exposure to ARsenic (BEAR) pregnancy cohort in Gómez Palacio, Mexico was recently established to better understand the impacts of prenatal exposure to inorganic arsenic (iAs). In this study, we examined a subset (n = 40) of newborn cord blood samples for microRNA (miRNA) expression changes associated with in utero arsenic exposure. Levels of iAs in maternal drinking water (DW-iAs) and maternal urine were assessed. more...
#> 870 Pancreatic beta-cell dysfunction and death are central in the pathogenesis of type 2 diabetes. Saturated fatty acids cause beta-cell failure and contribute to diabetes development in genetically predisposed individuals. Here we used RNA-sequencing to map transcripts expressed in five palmitate-treated human islet preparations, observing 1,325 modified genes. Palmitate induced fatty acid metabolism and endoplasmic reticulum (ER) stress. more...
#> 871 We determined the genes that were differentially expressed between fulminant type 1 diabetes and classical type 1A diabetes or healthy control using gene expression microarray in peripheral blood cells.
#> 872 We determined the genes that were differentially expressed between fulminant type 1 diabetes and classical type 1A diabetes using gene expression microarray in peripheral blood cells.
#> 873 This SuperSeries is composed of the SubSeries listed below.
#> 874 With broad high-throughput evaluation of microRNA expression across the spectrum of colon cancer stages, we identidied a microRNA signature that is associated with more aggressive disease
#> 875 Compariosn of mRNA and miRNA profile in colon cancer
#> 876 We profiled gene expression in peripheral blood cells from 28 obese patients by microarray analysis and visceral fat accumulation caused the gene expression proliles especially in circadian rhythm, inflammation, oxidative stress, and immune response.
#> 877 This SuperSeries is composed of the SubSeries listed below.
#> 878 In the context of T1 Diabetes, pro-inflammatory cytokines IL-1β and IFN-γ are known to contribute to β-cell apoptosis; The measurement of mRNA expression following β-cell exposure to these cytokines gives a picture of the changes in gene expression characterizing the path to β-cell dysfunction and death.
#> 879 Fetal growth restriction (FGR) develops when fetal nutrient availability is compromised and increases the risk for perinatal complications and predisposes for offspring obesity, diabetes and cardiovascular disease later in life. Emerging evidence implicates changes in placental function in altered fetal growth and the subsequent development of adult disease. The susceptibility for disease in response to an adverse intrauterine environment differs distinctly between boys and girls, with girls typically having better outcomes. more...
#> 880 We compared human female hiPSC lines (all derived from IMR-90 fibroblasts) that were XIST RNA-positive and XIST RNA-negative. We also examined the gene expression patterns for 2 female hIPSCs (derived from different disease model fibroblasts) that were also negative for XIST RNA. hiPS 12D-1 is derived from Huntington's Disease patient and 6C-1 is derived from a Type I Diabetes Mellitus patient (Park et al Nature 2008).
#> 881 Type 2 diabetes mellitus (T2DM) is a multi-factorial disease characterized by the inability of beta-cells in the endocrine pancreas to produce sufficient amounts of insulin to overcome insulin resistance in peripheral tissue. To investigate the function of miRNAs in T2DM, we sequenced the small RNAs of human islets cells from diabetic and non-diabetic organ donors and identified a cluster of miRNAs in an imprinted locus on human chromosome 14 to be dramatically down-regulated in T2DM islets. more...
#> 882 Using the Illumina 450K array and a stringent statistical analysis with age and gender correction, we report genome-wide differences in DNA methylation between pathology-free regions derived from human multiple sclerosis–affected and control brains. Differences were subtle, but widespread and reproducible in an independent validation cohort. The transcriptional consequences of differential DNA methylation were further defined by genome-wide RNA-sequencing analysis and validated in two independent cohorts. more...
#> 883 To explore the molecular mechanisms of obesity and insulin resistance in the patients with polycystic ovary syndrome (PCOS) at the level of human embryonic stem cells (hESCs).Three PCOS-derived and one non-PCOS-derived hESC lines were induced into adipocytes, and then total mRNA was extracted from these adipocytes. The differential genes between PCOS-derived and non-PCOS-derived adipocytes were identified with GeneChip, and then were validated with real-time PCR.There were 153 differential genes. more...
#> 884 Type 2 diabetes mellitus (T2DM) is a complex disease characterized by the inability of the insulin-producing β-cells in the endocrine pancreas to overcome insulin resistance in peripheral tissues. To determine if microRNAs are involved in the pathogenesis of human T2DM, we sequenced the small RNAs of human islets from diabetic and non-diabetic organ donors. We identified a cluster of miRNAs in an imprinted locus on human chromosome 14q32 that is highly and specifically expressed in human β-cells and dramatically down-regulated in islets from T2DM organ donors. more...
#> 885 MicroRNAs are powerful gene expression regulators, but their corneal repertoire and potential changes in corneal diseases remain unknown. Our purpose was to identify miRNAs altered in the human diabetic cornea by microarray analysis, and to examine their effects on wound healing in cultured telomerase-immortalized human corneal epithelial cells (HCEC) in vitro. Using microarrays, 29 miRNAs were identified as differentially expressed in diabetic samples. more...
#> 886 Chromatin-based functional genomic analyses and genomewide association studies (GWASs) together implicate enhancers as critical elements influencing gene expression and risk for common diseases. Here, we performed systematic chromatin and transcriptome pro- filing in human pancreatic islets. Integrated analysis of islet data with those generated by the ENCODE project in nine cell types identified specific and significant enrichment of type 2 diabetes and related quantitative trait GWAS variants in islet enhancers. more...
#> 887 Chromatin-based functional genomic analyses and genomewide association studies (GWASs) together implicate enhancers as critical elements influencing gene expression and risk for common diseases. Here, we performed systematic chromatin and transcriptome profiling in human pancreatic islets. Integrated analysis of islet data with those generated by the ENCODE project in nine cell types identified specific and significant enrichment of type 2 diabetes and related quantitative trait GWAS variants in islet enhancers. more...
#> 888 Lifestyle intervention can improve insulin sensitivity in obese youth yet few studies have examined the biological mechanisms underlying improvements. Therefore, the purpose of this study was to explore biological pathways associated with intervention-induced improvements in insulin sensitivity. Fifteen (7M/8F) overweight/obese (BMI percentile=96.3±1.1) Latino adolescents (15.0±0.9 years) completed a 12-week lifestyle intervention that included weekly nutrition education and 180 minutes of moderate-vigorous exercise per week. more...
#> 889 This dataset encompassing the profiles of 150 lung cancer tumors was developed to serve as test dataset in the SBV IMPROVER Diagnostic Signature Challenge (sbvimprover.com). The aim of this subchallenge was to verify that it is possible to extract a robust diagnostic signature from gene expression data that can identify stages of different types of lung cancer. Participants were asked to develop and submit a classifier that can stratify lung cancer patients in one of four groups – Stage 1 of Adenocarcinoma (AC Stage 1), Stage 2 of Adenocarcinoma (AC Stage 2), Stage 1 of Squamous cell carcinoma (SCC Stage 1) or Stage 2 of Squamous cell carcinoma (SCC Stage 2). more...
#> 890 The aim of this study was to investigate the association of gene expression profiles in subcutaneous adipose tissue with percent of total body weight change in 26 kidney transplant recipients. Using multivariate linear regression analysis controlled for race and gender, expression levels of 1553 genes were significantly (p<0.05) associated with weight change.
#> 891 Polyphenolic compounds, such as resveratrol, have recently received widespread interest due to their ability to mimic effects of calorie restriction. The objective of the present study was to gain more insight into the effects of 30 days resveratrol supplementation on adipose tissue morphology and underlying processes. Nine healthy obese men were supplemented with placebo and 150mg/day resveratrol for 30 days, separated by a 4-week washout period. more...
#> 892 Glomerular abnormalities have been demonstrated in kidney biopsies of patient after orthotopic liver transplantation (OLT). We hypothesize that these changes exist prior to OLT and may play a role in the development of renal failure after OLT. We use gene expression microarrays to investigate the mechanism of kidney disease in patients listed for OLT. Gene expression profiles of biopsies of cirrhotic patients were compared with pre-implantation living donor biopsies. more...
#> 893 Regulatory T cells (Treg) prevent the emergence of autoimmune disease. Prototypic natural Treg (nTreg) are programmed by Forkhead-box P3 (FOXP3) and can be reliably identified by demethylation at the FOXP3 locus. To explore the nTreg methylation landscape we performed genome-wide methylation studies on human naïve resting nTreg (rTreg) and conventional naïve CD4+ T cells (Naïve). We detected 2,315 differentially methylated CpGs between these two cell types, many of which clustered into 127 regions of differential methylation (RDMs). more...
#> 894 Tissues from another series of 74 patients with colorectal cancer were collected by laser micro-dissection with the Leica Laser Microdissection System (Leica Microsystems).
#> 895 Insulin-secreting β cells and glucagon-secreting α cells maintain physiological blood glucose levels, and their malfunction drives diabetes development. Using ChIP sequencing and RNA sequencing analysis, we determined the epigenetic and transcriptional landscape of human pancreatic α, β, and exocrine cells. We found that, compared with exocrine and β cells, differentiated α cells exhibited many more genes bivalently marked by the activating H3K4me3 and repressing H3K27me3 histone modifications. more...
#> 896 Diabetes and obesity are widespread diseases with signifciant socioeconomic implications. We used three different types of human adipose tissue (epigastric, visceral, and subcutaneous) in order to determine differences in global gene expression between these adipose depots in severely obese patients. In this dataset, we include the expression data obtained from three types of adipose tissue; epigastric, subcutaneous, and visceral all obtained through open gastric bypass surgery.
#> 897 To search for new markers of active lesions that might help better understand the molecular basis of MPA and aid in its diagnosis, DNA microarray analysis was performed with peripheral blood mononuclear cells (PBMCs).
#> 898 Primary endothelial cells from umbilical cord vein (HUVEC) obtained at delivery from gestational diabetic (GD) women, represent an expedient model for the study of the effects of chronic HG in vivo. In fetal tissues genome-wide epigenetic changes are likely to occur with specific long term and even trans-generational effects. We have utilized this model to study the effects of chronic hyperglycemia on the transcriptome and to verify the presence of specific epigenetic changes associated to chronic HG in vascular cells.
#> 899 Inherited genetic variants of insulin receptor induced cellular signaling have long been suspected to contribute to the development of type-2- diabetes mellitus. In this report we discuss a heterozygous mutation in the first coding exon of the proto-oncogene Ha-Ras (Ha-RasA11P) that we have identified in a patient with familial premature aging syndrome. The patient has atopic sklerodermic skin alterations, insulin resistance as well as disturbances in lipid metabolism. more...
#> 900 Lipodystrophies resemble syndromes of disturbed adipocyte biology or development and severe congenital forms (CGL) lack adipose tissue. The ubiquitous immediate-early gene c-fos is one essential transcription factor to initiate adipocyte differentiation. In a CGL patient we identified a single homozygous point mutation in the promoter of c-fos gene. The mutation facilitates the formation of a novel specific protein/ DNA complex and ubiquitously reduces basal and inducible c-fos transcription activity. more...
#> 901 This SuperSeries is composed of the SubSeries listed below.
#> 902 To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. more...
#> 903 To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. more...
#> 904 Analysis of gene expression changes in differentiated human podocytes treated with the serum from patients with (DKD+) or without (DKD-) diabetic kidney disease when compared to normal subjects (C). The hypothesis is that the three groups can be distinghed by their differential gene expression pattern. The results obtained revealed important information regarding differences in gene expression in human podocytes treated with the serum from patients with (DKD+) or without (DKD-) diabetic kidney disease when compared to normal subjects (C).
#> 905 This SuperSeries is composed of the SubSeries listed below.
#> 906 Analysis of gene expression changes in differentiated human podocytes treated with the serum from patients with (DKD+) or without (DKD-) diabetic kidney disease when compared to normal subjects (C). The hypothesis is that the three groups can be distinghed by their differential gene expression pattern. The results obtained revealed important information regarding differences in gene expression in human podocytes treated with the serum from patients with (DKD+) or without (DKD-) diabetic kidney disease when compared to normal subjects (C).
#> 907 The overall objective of the heritage project is to study the role of the genotype in cardiovascular,metabolic and hormonal responses to aerobic exercise training and the contribution of regular exercise to changes in several cardiovascular disease and diabetes risk factors. PLEASE NOTE THE POST-TRAINING GENE CHIP FILES HAVE NEVER BEEN RELEASED ON GEO. PLEASE ALSO NOTE THAT DUE TO THE OUTDATED INSULIN ASSAY UTILISED IN THE HERITAGE STUDY, THE INSULIN DATA WAS NOT COMPARABLE WITH ANY MORE RECENT MODERN STUDIES.
#> 908 Recent advances in the understanding of the genetics of type 2 diabetes (T2D) susceptibility have focused attention on the regulation of transcriptional activity within the pancreatic beta-cell. MicroRNAs (miRNAs) represent an important component of regulatory control, and have proven roles in the development of human disease and control of glucose homeostasis. We set out to establish the miRNA profile of human pancreatic islets and of enriched beta-cell populations, and to explore their potential involvement in T2D susceptibility. more...
#> 909 This SuperSeries is composed of the SubSeries listed below.
#> 910 A need exists for biomarkers in T1D that can 1) sensitively and specifically detect disease-related immune activity prior to, and independent of, measurement of auto-antibodies towards islet cell antigens; 2) define immunopathological mechanisms; and 3) monitor changes in the inflammatory state associated with disease progression or response to therapeutic intervention. In an effort to fill this gap, we have applied a novel bioassay to both human and BB rat T1D whereby the complex milieu of inflammatory mediators present in plasma can be indirectly detected through their ability to drive transcription in peripheral blood mononuclear cells drawn from healthy, unrelated donors. more...
#> 911 Overall, the widely disparate transcriptomes identified prior to RT among the three clusters support the notion that at least some of the inter-individual heterogeneity in propensity for RT-induced myofiber hypertrophy is likely pre-determined.
#> 912 Transcriptional Profiling of Insulin Sensitive and Insulin Resistant Samples
#> 913 Metabolic Syndrome (MetS) is a strong predictor for diabetes and cardiovascular disease and is defined by a constellation of phenotypes including increased and adverse body fat distribution, insulin resistance, abnormalities in lipids and lipoproteins, malfunctional cardiovascular performance, and abnormal levels of adipokines and cytokines. We assayed in a subset of our family cohort phentoyped for MetS phentoypes, the genome-wde transcript levels using the Illumina Human WG-6 v3 expression arrays.
#> 914 Metabolic Syndrome (MetS) is a strong predictor for diabetes and cardiovascular disease and is defined by a constellation of phenotypes including increased and adverse body fat distribution, insulin resistance, abnormalities in lipids and lipoproteins, malfunctional cardiovascular performance, and abnormal levels of adipokines and cytokines. We assayed in a subset of our family cohort phentoyped for MetS phentoypes, the genome-wde transcript levels using the Illumina Human WG-6 v2 expression arrays.
#> 915 Diabetes is a disorder characterized by loss of beta cell mass and/or beta cell function, leading to deficiency of insulin relative to metabolic need. To determine whether stem cell derived-beta cells faithfully reflect the phenotypes of a diabetic subject, we generated induced pluripotent stem cells from diabetic subjects (MODY2) with heterozygous loss-of-function of the gene encoding glucokinase (GCK). more...
#> 916 Expression profiling of cell cycle genes in human pancreatic islets with and without type 2 diabetes
#> 917 This SuperSeries is composed of the SubSeries listed below.
#> 918 Transcription has the capacity to modify mechanically DNA topology, DNA structure, and nucleosome arrangement. Resulting from ongoing transcription, these modifications in turn, may provide instant feedback to the transcription machinery. To substantiate the connection between transcription and DNA dynamics, we charted an ENCODE map of transcription-dependent dynamic supercoiling in human Burkitt lymphoma cells using psoralen photobinding to probe DNA topology in vivo. more...
#> 919 Nuclear RNA from Raji human B cells was hybridized to NimbleGen arrays to quantify gene expression levels.
#> 920 In this study, we examined the impact of modulating the TGF-β/PAI-1 axis in CD34+ cells function from diabetic patients and controls. Using gene array studies, we found that diabetics, protected from microvascular complications despite suboptimal glycemic control, had reduced level of TGF- β1 and PAI-1 transcripts in their CD34+ cells compared to age, sex, duration and degree of glycemic control -matched diabetics with microvascular complications. more...
#> 921 Objective was to examine acute gene expression responses to physiologic oral glucose ingestion in human circulating leukocytes. Microarray study of human circulating leukocytes sampled before, 1 hour after and 2 hours after glucose ingestion was performed. The present study demonstrated 36 genes which showed acute gene expression change in human leukocytes within 1 hour after glucose ingestion and suggest that leukocytes participate in the inflammatory process induced by acute hyperglycemia.
#> 922 This study employed Affymetrix GeneChips to profile transcriptome of human pulmonary microvascular endothelial cells (HMVEC-L) treated with PBEFsiRNA to gain insight into transcriptional regulations of PBEF on the endothelial function. We isolated and labeled mRNAs from PBEF siRNA transfected HMVEC-L and hybridized them to Affymetrix GeneChip HG-U133 plus 2. Differentially expressed genes and canonical pathways were analyzed. more...
#> 923 Monozygotic twins discordant for type 2 diabetes constitute an ideal model to study environmental contributions to type 2 diabetic traits. We aimed to examine whether global DNA methylation differences exist in major glucose metabolic tissues from twelve 53–80 year-old monozygotic discordant twin pairs.
#> 924 Through gene expression profiling in cultured lymphocytes and PBMCs from a large set of T1D patients and controls, we demonstrate that IL-1ra may protect against the development of islet autoimmunity and T1D through down-regulating a large number of inflammatory genes and pathways. Keywords: autoimmunity; IL-1Ra;Type 1 diabetes (T1D)
#> 925 A genome-wide eQTL analysis was performed in whole blood samples collected from 76 Japanese subjects. RNA microarray analysis was performed for 3 independent samples that were genotyped in a genome-wide scan. The correlations between the genotypes of 534,404 autosomal single nucleotide polymorphisms (SNPs) and the expression levels of 30,465 probes were examined for each sample. The SNP-probe pairs with combined correlation coefficients of all 3 samples corresponding to P < 3.10 × 10-12 (i.e., Bonferroni-corrected P < 0.05) were considered significant. more...
#> 926 A genome-wide eQTL analysis was performed in whole blood samples collected from 76 Japanese subjects. RNA microarray analysis was performed for 3 independent samples that were genotyped in a genome-wide scan. The correlations between the genotypes of 534,404 autosomal single nucleotide polymorphisms (SNPs) and the expression levels of 30,465 probes were examined for each sample. The SNP-probe pairs with combined correlation coefficients of all 3 samples corresponding to P < 3.10 × 10-12 (i.e., Bonferroni-corrected P < 0.05) were considered significant. more...
#> 927 A genome-wide eQTL analysis was performed in whole blood samples collected from 76 Japanese subjects. RNA microarray analysis was performed for 3 independent samples that were genotyped in a genome-wide scan. The correlations between the genotypes of 534,404 autosomal single nucleotide polymorphisms (SNPs) and the expression levels of 30,465 probes were examined for each sample. The SNP-probe pairs with combined correlation coefficients of all 3 samples corresponding to P < 3.10 × 10-12 (i.e., Bonferroni-corrected P < 0.05) were considered significant. more...
#> 928 A genome-wide eQTL analysis was performed in whole blood samples collected from 76 Japanese subjects. RNA microarray analysis was performed for 3 independent samples that were genotyped in a genome-wide scan. The correlations between the genotypes of 534,404 autosomal single nucleotide polymorphisms (SNPs) and the expression levels of 30,465 probes were examined for each sample. The SNP-probe pairs with combined correlation coefficients of all 3 samples corresponding to P < 3.10 × 10-12 (i.e., Bonferroni-corrected P < 0.05) were considered significant. more...
#> 929 Gene expression profiles of biopsy samples of visceral adipose of three female patients of type 2 diabetes and three non-diabetic female patients were generated using Illumina HumanHT-12 v3 Expression BeadChip arrays. The primary indications of surgery were non-infective and non-malignant conditions, namely, cholelethiasis, hernia and trauma.
#> 930 Gene expression profiles of biopsy samples of subcutaneous adipose of three female patients of type 2 diabetes and three non-diabetic female patients were generated using Illumina HumanHT-12 v3 Expression BeadChip arrays. The primary indications of surgery were non-infective and non-malignant conditions, namely, cholelethiasis, hernia and trauma.
#> 931 Gene expression profiles of biopsy samples of skeletal muscle of three male patients of type 2 diabetes and three non-diabetic male patients were generated using Illumina HumanHT-12 v3 Expression BeadChip arrays. The primary indications of surgery were non-infective and non-malignant conditions, namely, cholelethiasis, hernia and trauma.
#> 932 Global gene expression profile of whole blood in patients with coronary artery disease (CAD) showed significant upregulation of 343 genes and down regulation of 151 genes as compared to controls (p<0.05). There was predominant differential regulation of inflammatory and immune response genes as well as early growth response genes in our dataset. Of the ten candidate genes selected for validation by real time PCR in an independent cohort, CXCL1, EGR3, IL8, PTGS2 and CD69 genes were up regulated and IFNG and FASLG down regulated in cases relative to controls.
#> 933 The remarkable differentiation capacity of pluripotent stem cells into any adult cell types have enabled researchers to model human embryonic development and disease process in dishes, as well as deriving specialized cells for replacing damaged tissues. Type 1 diabetes is a degenerative disease characterized by autoimmune destruction of the insulin-producing beta islet cells in the pancreas. Recent advances have led to the establishment of different methods to direct differentiation of human or mouse pluripotent stem cells toward beta cell lineages. more...
#> 934 The remarkable differentiation capacity of pluripotent stem cells into any adult cell types have enabled researchers to model human embryonic development and disease process in dishes, as well as deriving specialized cells for replacing damaged tissues. Type 1 diabetes is a degenerative disease characterized by autoimmune destruction of the insulin-producing beta islet cells in the pancreas. Recent advances have led to the establishment of different methods to direct differentiation of human or mouse pluripotent stem cells toward beta cell lineages. more...
#> 935 Polycystic ovary Syndrome (PCOS) is a heterogeneous endocrine disorder that shows evidence of genetic predidposition among affected individuals. We have utilized the Microarray data from granulosa cells of normal and PCOS women for network construction.
#> 936 The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). more...
#> 937 The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). more...
#> 938 This SuperSeries is composed of the SubSeries listed below.
#> 939 The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). more...
#> 940 The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). more...
#> 941 Myotonic Dystrophy Type-2 (DM2) is an autosomal dominant disease caused by the expansion of a CCTG tetraplet repeat. It is a multisystemic disorder, affecting skeletal muscles, the heart, the eye, the central nervous system and the endocrine system The expression of 365 miRNAs was measured in the muscle of DM2 patients and compared it to controls and were identified distinct miRNAs modulated in DM2 patients compared to controls.
#> 942 Purpose: Despite advances in radical surgery and chemotherapy delivery, ovarian cancer is the most lethal gynecologic malignancy. Most of these patients are treated with platinum-based chemotherapies, but there is no biomarker model to guide their responses to these therapeutic agents. We have developed and independently tested our novel multivariate molecular predictors for forecasting patients' responses to individual drugs on a cohort of 58 ovarian cancer patients. more...
#> 943 A study which examines differences in microRNA expression profiles across different sarcoma histological subtypes
#> 944 Aims/hypothesis: Duct cells isolated from adult human pancreas can be reprogrammed to express islet beta cell genes by adenoviral transduction of the developmental transcription factor neurogenin3 (Ngn3). In this study we aimed to fully characterize the extent of this reprogramming and intended to improve it. Methods: The extent of the Ngn3-mediated duct-to-endocrine cell reprogramming was measured employing genome wide mRNA profiling. more...
#> 945 Close to 50 genetic loci have been associated with type 2 diabetes (T2D), but they explain only 15% of the heritability. In an attempt to identify additional T2D genes, we analyzed global gene expression in human islets from 63 donors.
#> 946 This SuperSeries is composed of the SubSeries listed below.
#> 947 Expression data was used in Paradigm analysis for exploration of networks affected by copy number and gene expression changes based on mutation spectra of recurrently mutated genes in breast cancer.
#> 948 The TCF7L2 transcription factor is linked to a variety of human diseases, including type 2 diabetes and cancer. One mechanism by which TCF7L2 could influence expression of genes involved in diverse diseases is by binding to distinct regulatory regions in different tissues. To test this hypothesis, we performed ChIP-seq for TCF7L2 in 6 human cell lines. We identified 116,000 non-redundant TCF7L2 binding sites, with only 1,864 sites common to the 6 cell lines. more...
#> 949 Analysis of ex vivo isolated lymphatic endothelial cells from the dermis of patients to define type 2 diabetes-induced changes. Results preveal aberrant dermal lymphangiogenesis and provide insight into its role in the pathogenesis of persistent skin inflammation in type 2 diabetes. The ex vivo dLEC transcriptome reveals a dramatic influence of the T2D environment on multiple molecular and cellular processes, mirroring the phenotypic changes seen in T2D affected skin. more...
#> 950 This SuperSeries is composed of the SubSeries listed below.
#> 951 Both genetic and environmental factors are implicated in Type 1 Diabetes (T1D). Since environmental factors can trigger epigenetic changes, we hypothesized that variations in histone posttranslational modifications (PTMs) at the promoter/enhancer regions of T1D susceptible genes may be associated with T1D. We therefore evaluated histone PTM variations at known T1D susceptible genes in blood cells from T1D patients versus healthy non-diabetic controls, and explored their connections to T1D. more...
#> 952 Both genetic and environmental factors are implicated in Type 1 Diabetes (T1D). Since environmental factors can trigger epigenetic changes, we hypothesized that variations in histone posttranslational modifications (PTMs) at the promoter/enhancer regions of T1D susceptible genes may be associated with T1D. We therefore evaluated histone PTM variations at known T1D susceptible genes in blood cells from T1D patients versus healthy non-diabetic controls, and explored their connections to T1D. more...
#> 953 Both genetic and environmental factors are implicated in Type 1 Diabetes (T1D). Since environmental factors can trigger epigenetic changes, we hypothesized that variations in histone posttranslational modifications (PTMs) at the promoter/enhancer regions of T1D susceptible genes may be associated with T1D. We therefore evaluated histone PTM variations at known T1D susceptible genes in blood cells from T1D patients versus healthy non-diabetic controls, and explored their connections to T1D. more...
#> 954 OBJECTIVE: Novel biomarkers of disease progression after type 1 diabetes onset are needed. RESEARCH DESIGN AND METHODS: We profiled peripheral blood (PB) monocyte gene expression in 6 healthy subjects and 16 children with type 1 diabetes diagnosed ~3 months previously, and analyzed clinical features from diagnosis to 1 year. RESULTS: Monocyte expression profiles clustered into two distinct subgroups, representing mild and severe deviation from healthy controls, along the same continuum. more...
#> 955 Skin and intact fascia were collected from 15 normal control (NC) patients with no hernia history and 18 patients presenting for recurrent incisional hernia (RH) repair. Microarray analysis was performed using whole genome microarray chips on NC (n = 8) and RH (n = 9). These samples were further investigated using a pathway-specific PCR array containing fibrosis-related genes.
#> 956 Low-grade chronic inflammation plays an important role in the development of obesity and obesity-associated disorders such as insulin resistance, type 2 diabetes, the metabolic syndrome and atherosclerosis. One possible link between obesity and inflammation is the enhanced activation of circulating monocytes making them more prone to infiltration into the adipose and vascular tissues of obese persons. more...
#> 957 Low-grade chronic inflammation plays an important role in the development of obesity and obesity-associated disorders such as insulin resistance, type 2 diabetes, the metabolic syndrome and atherosclerosis. One possible link between obesity and inflammation is the enhanced activation of circulating monocytes making them more prone to infiltration into the adipose and vascular tissues of obese persons. more...
#> 958 Objectives –To determine whether inflammation biomarkers can be used as indicators of therapeutic response, an exploratory study was performed to ascertain whether short term improvements in risk parameters will have measureable effects on a pre-defined panel of plaque inflammation biomarkers. Methods and Results – Patients (n=121) with peripheral arterial disease were enrolled into one of three sub-studies based upon the presence of hypercholesterolemia, hypertension, or diabetes. more...
#> 959 Data on the temporal dynamics of human placental gene expression is scarce. We have completed the first whole-genome profiling of human placental gene expression dynamics (GeneChips, Affymetrix®) from early to mid- gestation (10 samples; gestational weeks 5 to 18) and report 154 genes with considerable change in transcript levels (FDR P<0.1). Functional enrichment analysis revealed >200 GO categories that are statistically over-represented among 105 genes with dynamically increasing transcript levels. more...
#> 960 Faced by an alarming incidence of metabolic diseases including obesity and type 2 diabetes worldwide, there is an urgent need for effective strategies for preventing and treating these common diseases. The nuclear receptor PPARγ (peroxisome proliferator-activated receptor gamma) plays a crucial role in metabolism. We isolated the amorfrutins from edible parts of the plants Glychyrrhiza foetida and Amorpha fruticosa, and identified these natural products as a new chemical class to treat insulin resistance and diabetes by selectively activating PPARγ. more...
#> 961 The prognosis of pancreatic cancer is still very poor, how to detect pancreatic cancer from high-risk group in an early stage is essential for improving its long-time survival. Therefore, the purpose of this study was to explore specific biomarkers that can differentiate pancreatic cancer-associated diabetes from type-2 diabetes for the early detection of pancreatic cancer. In the current study, we used global gene transcription analysis with affymetrix gene chip to identify genes specifically expressed in pancreatic cancer-associated diabetes mellitus from peripheral blood samples in stead of from tissue samples.
#> 962 SPARC is a matricellular glycoprotein that plays critical roles in the pathologies associated with obesity and diabetes, as well as tumorigenesis. The objective of this study was to investigate the role of SPARC in the process of trophoblast invasion which shares many similarities with tumor cells invasion. Our results reveals that hormones, cell adhesion molecules, ECM molecules, growth factors and cytokines all are mediated by SPARC in EVT invasion.
#> 963 Little is known about the contribution of the epigenome to the pathophysiology of type 2 diabetes (T2D). Here we have used genome-wide DNA methylation profiling to obtain the first comprehensive DNA methylation data set for human T2D pancreatic islets. Therefore, we analyzed the methylation profile of 27,578 CpG sites affiliated to more than 14,000 genes in 16 samples of pancreatic islets, 11 normal and 5 type 2-diabetic. more...
#> 964 Glucose intolerance and diabetes mellitus are classical parts of endogenous Cushing’s syndrome (CS), and insulin resistance is a feature of cortisol excess. CS patients display characteristics including hyperglycemia, abdominal obesity, reduced high-density lipoprotein cholesterol levels and elevated triglycerides, and arterial hypertension. Hypercortisolism is a well known cause of bone loss, and patients with CS frequently display low bone mass and fragility fractures. more...
#> 965 Objective: Insulin regulates amino acid metabolism. We investigated whether glycemia and 43 genetic risk variants for hyperglycemia/type 2 diabetes affect amino acid levels in a large population-based cohort. Subjects and Methods: A total of 9,371 non-diabetic or newly-diagnosed type 2 diabetic Finnish men from the population-based METSIM Study were studied. Proton NMR spectroscopy was used to measure plasma levels of 8 amino acids. more...
#> 966 TGF-beta1 is the major cytokine driver of fibrotic scarring observed in diabetic nephropathy and other fibrosis-related diseases. RNA-sequencing offers the potential for more sensitive assessment of the TGF-ß1-driven transcriptome.
#> 967 Mitochondria have been implicated in insulin resistance and beta cell dysfunction, both of which comprise the core pathophysiology of type 2 diabetes mellitus (T2DM). It has also recently been found that mtDNA haplogroups are distinctively associated with susceptibility to T2DM at least in Koreans and Japanese. To investigate the functional consequences of different mtDNA, we compared gene expression profiles between cybrid clones harboring three different mtDNA haplogroups (D5, F, and N9a). more...
#> 968 Increased morbidity and mortality associated with post-ischemic heart failure (HF) in diabetic patients underscore the need for a better understanding of the underlying molecular events. Indeed, effective HF therapy in diabetic patients requires a complex strategy encompassing the development of improved diagnostic and prognostic markers and innovative pharmacological approaches. Whole mRNAs expression was measured in the heart of patients with heart failure (HF) with or without concomitant Type 2 diabetes mellitus (T2DM) and compared it to control non-failing hearts. more...
#> 969 We have determined the whole genome sequence of an individual at high accuracy and performed an integrated analysis of omics profiles over a 1.5 year period that included healthy and two virally infected states. Omics profiling of transcriptomes, proteomes, cytokines, metabolomes and autoantibodyomes from blood components have revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways that occurred during healthy and disease states. more...
#> 970 We have determined the whole genome sequence of an individual at high accuracy and performed an integrated analysis of omics profiles over a 1.5 year period that included healthy and two virally infected states. Omics profiling of transcriptomes, proteomes, cytokines, metabolomes and autoantibodyomes from blood components have revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways that occurred during healthy and disease states. more...
#> 971 We have used RNA-seq to identify transcripts, including splice variants, expressed in human islets of Langerhans under control condition or following exposure to the pro-inflammatory cytokines interleukin-1β (IL-1β) and interferon-γ (IFN-γ). A total of 29,776 transcripts were identified as expressed in human islets. Expression of around 20% of these transcripts was modified by pro-inflammatory cytokines, including apoptosis- and inflammation-related genes. more...
#> 972 Dietary fat quality may influence skeletal muscle lipid handling and fat accumulation, thereby modulating insulin sensitivity. Objective: To examine acute effects of meals with various fatty acid (FA) compositions on skeletal muscle FA handling and postprandial insulin sensitivity in obese insulin resistant men. Design: In a single-blinded randomized crossover study, 10 insulin resistant men consumed three high-fat mixed-meals (2.6MJ). more...
#> 973 Autologous nonmyeloablative hematopoietic stem cell transplantation (AHST) was the first therapeutic approaches that can improveβcell function in type 1 diabetic (T1D) patients. This study was designed to investigate the potential mechanisms involved.We applied AHST to nine T1D patients diagnosed within six months and analyzed the acute response in peripheral blood genomic expression profiling at the six-month follow-up.
#> 974 Background. Differential gene expression in adipose tissue during diet-induced weight loss followed by a weight stability period is not well characterized. Markers of these processes may provide a deeper understanding of the underlying mechanisms. Objective. To identify differentially expressed genes in human adipose tissue during weight loss and weight maintenance after weight loss. Design. RNA from subcutaneous abdominal adipose tissue from nine obese subjects was obtained and analyzed at baseline, after weight reduction on a low calorie diet (LCD), and after a period of group therapy in order to maintain weight stability. more...
#> 975 This SuperSeries is composed of the SubSeries listed below.
#> 976 MicroRNAs expression profiling of human nucleus pulposus cells derived from patients with disc degeneration in comparison with those derived from patients with scoliosis as control.
#> 977 Stroke is a “brain attack” cutting off vital blood, and consequently the nutrients and oxygen vital to the brain cells that control everything we do. Stroke is a complex disease with unclear pathogenesis resulting from environmental and genetic factors. To better understand IS´s etiology, we performed genomic expression profiling of patients and controls.
#> 978 Gene expression profiling in arterial tissue from type 2 diabetic patients
#> 979 Resveratrol is a naturally occurring compound that profoundly affects energy metabolism and mitochondrial function and serves as a calorie restriction mimetic, at least in animal models of obesity. Here we treated 10 healthy, obese men with placebo and 150 mg/day resveratrol in a randomized double-blind cross-over study for 30 days. Resveratrol supplementation significantly reduced sleeping- and resting metabolic rate. more...
#> 980 Sub-optimal fetal development is associated with an increased risk of developing cardiovascular disease, type 2 diabetes (T2D) and adiposity later in life. However, definitions of intrauterine growth restriction (IUGR) and small for gestational age (SGA) are based on simple statistical approaches that may misclassify infants with a normal developmental profile and vice versa. We used an unbiased global profiling approach to identify gene expression patterns in umbilical cord tissue from 38 infants and identified a set of 466 genes which separated the subjects into 2 distinct groups – one biased towards lower birth weight and one biased towards normal birth weight. more...
#> 981 Inter-individual DNA methylation variations were frequently hypothesized to alter individual susceptibility to Type 2 Diabetes Mellitus (T2DM). Sequence-influenced methylations were described in T2DM-associated genomic regions, but evidence for direct, sequence-independent association with disease risk is missing. Here we explore disease-contributing DNA methylation through a stepwise study design: first, a pool-based, genome-scale screen among 1,169 case and control individuals revealed an excess of differentially methylated sites in genomic regions that were previously associated with T2DM through genetic studies. more...
#> 982 This SuperSeries is composed of the SubSeries listed below.
#> 983 The exchange of the oocyte's genome with the genome of a somatic cell, followed by the derivation of pluripotent stem cells, could enable the generation of specific cell types affected in degenerative human diseases. Such cells, carrying the patient's genome, might be useful for cell replacement. Here we report that the development of human oocytes activated after genome exchange invariably arrests at the late cleavage stages in association with transcriptional abnormalities. more...
#> 984 The exchange of the oocyte’s genome with the genome of a somatic cell, followed by the derivation of pluripotent stem cells, could enable the generation of specific cell types affected in degenerative human diseases. Such cells, carrying the patient’s genome, might be useful for cell replacement. Here we report that the development of human oocytes activated after genome exchange invariably arrests at the late cleavage stages in association with transcriptional abnormalities. more...
#> 985 Podocytes are cells of the visceral epithelium in the kidneys and form a crucial component of the glomerular filtration barrier, contributing to size selectivity and maintaining a massive filtration surface.
#> 986 INTRODUCTION. Fixation with formalin, a widely adopted procedure to preserve tissue samples, leads to extensive degradation of nucleic acids and thereby compromises procedures like microarray-based gene expression profiling. We hypothesized that RNA fragmentation is caused by activation of RNAses during the interval between formalin penetration and tissue fixation. To prevent RNAse activation, a series of tissue samples were kept under-vacuum at 4°C until fixation and then fixed at 4°C, for 24 hours, in formalin followed by 4 hours in ethanol 95%. more...
#> 987 Background: Changes in DNA methylation patterns with age frequently have been observed and implicated in the normal aging process and its associated increasing risk of disease, particularly cancer. Additionally, the offspring of older parents are at significantly increased risk of cancer, diabetes, and neurodevelopmental disorders. Only a proportion of these increased risks among the children of older parents can be attributed to nondisjunction and chromosomal rearrangements. more...
#> 988 Tissues from another series of 132 patients with colorectal cancer were collected by laser micro-dissection with the Leica Laser Microdissection System (Leica Microsystems).
#> 989 Samples were prospectively collected from patients with histologically normal surgical resection margins. 96 tissue samples (histologically normal margins, oral carcinoma and adjacent normal tissues) from 24 patients comprised the training set. Our study design was guided by the hypothesis that the expression of genes present in oral squamous cell carcinoma (OSCC) but not in healthy oral tissues would be indicative of recurrence in advance of histological alteration. more...
#> 990 We identified 1,700 differentially expressed probesets in DKD glomeruli and 1,831 in diabetic tubuli; 330 probesets were commonly differentially expressed in both compartments. The canonical complement signaling pathway was determined to be statistically differentially regulated in both DKD glomeruli and tubuli and was associated with increased glomerulosclerosis even in an additional set of DKD samples.
#> 991 We identified 1,700 differentially expressed probesets in DKD glomeruli and 1,831 in diabetic tubuli; 330 probesets were commonly differentially expressed in both compartments. The canonical complement signaling pathway was determined to be statistically differentially regulated in both DKD glomeruli and tubuli and was associated with increased glomerulosclerosis even in an additional set of DKD samples.
#> 992 We identified 1,700 differentially expressed probesets in DKD glomeruli and 1,831 in diabetic tubuli; 330 probesets were commonly differentially expressed in both compartments. The canonical complement signaling pathway was determined to be statistically differentially regulated in both DKD glomeruli and tubuli and was associated with increased glomerulosclerosis even in an additional set of DKD samples.
#> 993 This SuperSeries is composed of the SubSeries listed below.
#> 994 Obesity is a major risk factor for several chronic diseases including diabetes, fatty liver disease and cancer. Despite similar propensities for obesity, Hispanics and African Americans exhibit unique and distinct differences in obesity related outcomes such as greater risk of, obesity-related cancers in AA and non alcoholic fatty liver disease (NAFLD) in Hispanics. This study was aimed to determine whether differences in subcutaneous adipose tissue (SAT) gene expression in obese, Hispanic and AA young adults might explain ethnic differences in obesity-related phenotypes.
#> 995 Aims: establishment of reference samples to investigate gene expression selective for endocrine or ductal-exocrine cells within the adult human pancreas. To this end, human islet endocrine cells, FACS-enriched in insulin+ cells, (n=3) and human exocrine ductal cells (n=2) are compared on Affymetrix HG133A platform with duplicate hybridizations of a panel of other primary human tissues.
#> 996 Expansion of beta cells from the limited number of adult human islet donors is an attractive prospect for increasing cell availability for cell therapy of diabetes. However, while evidence supports the replicative capacity of adult beta cells in vivo, attempts at expanding human islet cells in tissue culture resulted in loss of beta-cell phenotype. Using a genetic lineage-tracing approach we have provided evidence for massive proliferation of beta-cell-derived (BCD) cells within these cultures. more...
#> 997 Exploratory microarray analysis identified significant changes in gene expression in adipose tissue. These included changes in genes regulating lipid and steroid metabolic processes and electron carrier activity in HIV-infected patients receiving antiretroviral therapy (ART). Additional genes involved in metabolic processes and mitochondrial function were found to be up-regulated in the adipose tissue of HIV-positive patients compared with HIV-negative controls.
#> 998 Dysregulation of ceramide synthesis has been associated with metabolic disorders such as atherosclerosis and diabetes mellitus. Using a human hepatoma cell line (Huh7), we investigated the changes in lipid homeostasis and gene expression when the synthesis of ceramide is perturbed by knocking down serine transferases subunits 1, 2 and 3 (SPTLC123) or dihydroceramide desaturase (DEGS1). While the inhibition of serine palmitoyl transferase (SPTLC) affects ceramide production differently at the subspecies level depending upon which SPTLC subunit is silenced; depleting DEGS1 is sufficient to produce a similar outcome as knocking down all SPTLC subunits. more...
#> 999 This SuperSeries is composed of the SubSeries listed below.
#> 1000 The aim of this study was to characterize expression profiles of visceral and subcutaneous adipose tissue in children. Adipose tissue samples were collected from children having elective surgery (n=71, [54 boys], 6.0 +- 4.3 years). Affymetrix microarrays (n=20) were performed to characterize the functional profile and identify genes of interest in adipose tissue. Visceral adipose tissue had an overrepresentation of Gene Ontology themes related to immune and inflammatory responses and subcutaneous adipose tissue had an overrepresentation of themes related to adipocyte growth and development. more...
#> 1001 Adipose tissue abundance relies partly on the factors that regulate adipogenesis, i.e. proliferation and differentiation of adipocytes. While the transcriptional program that initiates adipogenesis is well-known, the importance of microRNAs in adipogenesis is less well studied. We thus set out to investigate whether miRNAs would be actively modulated during adipogenesis and obesity. Several models exist to study adipogenesis in vitro, of which the cell line 3T3-L1 is probably the most well known, albeit not the most physiologically appropriate. more...
#> 1002 We use ChIP-seq to discover the genome-wide sites of acetylation of lysine 56 of the histone H3 (H3K56), which is a target of three histone modifying enzymes with known roles in diabetes and insulin resistance, in human adipocytes derived from mesenchymal stem cells. Surprisingly, we find that a very large fraction of genes show some level of acetylation on H3K56, but the highest levels of acetylation are associated with genes previously reported to be involved in type 2 diabetes. more...
#> 1003 TGFbeta is the major cytokine driver of fibrosis in the kidney and other tissue. Epithelial-mesenchymal transition has been postulated to contibrute to renal fibrosis in diseases such as diabetic nephropathy. We wished to identify novel genes that were upregulated in human kidney epithelial cells in response to TGFb1.The transcriptional responses for human proximal tubule epithelial cells to 10 ng/ml TGFbeta1 was examined over 24 and 48 hr
#> 1004 To study the role of miRNAs in the transition from latent to active TB and to discover candidate biomarkers that may help predict TB progression, we have employed miRNA microarray expression profiling as a discovery platform to probe the transcriptome of peripheral blood mononuclear cells (PBMCs) with active TB, latent TB infection (LTBI), and healthy donors.Patients were recruited at the Shanghai Public Health Clinical Centre (Shanghai, China) from December, 2008 to May, 2009. more...
#> 1005 Type 1 Diabetes (T1D) is considered to be a Th1 autoimmune disease characterised by an absolute lack of insulin caused by an autoimmune destruction of the insulin producing pancreatic beta cells. Th1 lymphocytes are responsible for the infiltration of the islets of Langerhans and for the cytokine release that supports cytotoxic (Tc) lymphocytes to mediate destruction of the beta cells. The preclinical disease stage is characterized by the generation of the self-reactive lymphocytes that infiltrate the pancreas and selectively destroy the insulin-producing beta cells present in the islets. more...
#> 1006 HNF4a is an important liver transcription factor that regulates at least a thousand genes in the liver. Here we used expression profiling in HepG2 cells, a hepatocellular carcinoma cell line, in which HNF4a was knocked down by RNAi to identify some of those target genes. This dataset accompanies the article in Hepatology 2010 Feb;51(2):642-53. Integrated approach for the identification of human hepatocyte nuclear factor 4alpha target genes using protein binding microarrays by Bolotin E, Liao H, Ta TC, Yang C, Hwang-Verslues W, Evans JR, Jiang T, Sladek FM.
#> 1007 Insulin resistance in skeletal muscle is a key phenotype associated with type 2 diabetes (T2D) and is even present in offspring of diabetic parents. However, molecular mediators of insulin resistance remain unclear. We find that the top-ranking gene set in expression analysis of muscle from humans with T2D and normoglycemic insulin resistant subjects with parental family history (FH+) of T2D is increased expression of actin cytoskeleton genes regulated by serum response factor (SRF) and its coactivator MKL1. more...
#> 1008 Insulin (INS) synthesis and secretion from pancreatic β cells are tightly regulated; their deregulation causes diabetes. Here we map INS-associated loci in human pancreatic islets by 4C and 3C techniques and show that the INS gene physically interacts with the SYT8 gene, located over 300 kb away. This interaction is elevated by glucose and accompanied by increases in SYT8 expression. Inactivation of the INS promoter by promoter-targeting siRNA reduces SYT8 gene expression. more...
#> 1009 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) has a large number of biological effects, including skin, cardiovascular, neurologic disease, diabetes, infertility and cancer. We analysed the in vitro TCDD effects on human CD34+ cells and tested the gene expression modulation by means of microarray analyses before and after TCDD exposure. We identified 253 differentially modulated probe sets, identifying 217 well-characterized genes. more...
#> 1010 The goal of this study was to investigate the effects of the cardioprotective nucleoside adenosine on gene expression in early and late endothelial progenitor cells. Adenosine mod
#> 1011 To investigate the effects of bariatric surgery on gene expression profile changes in whole blood in obese subjects with type 2 diabetes in a pilot study setting. Whole blood from eleven obese subjects with type 2 diabetes was collected in PAXgene tubes prior to and 6-12 months after bariatric surgery. Total RNA was isolated, amplified, labeled and hybridized to Illumina gene expression microarrays. more...
#> 1012 Mutations in growth signaling pathways extend life span, as well as protect against age-dependent DNA damage in yeast and decrease insulin resistance and cancer in mice. To test their effect in humans, we monitored for 22 years Ecuadorian individuals who carry mutations in the growth hormone receptor (GHR) gene that lead to severe GHR and IGF-1 (insulin-like growth factor-1) deficiencies. We combined this information with surveys to identify the cause and age of death for individuals in this community who died before this period. more...
#> 1013 Genomic expression profiles of blood and placenta reveal significant immune-related pathways and categories in Chinese women with Gestational Diabetes Gestational diabetes mellitus (GDM) is a complex metabolic disease which occurs in pregnancy with high prevalence, and its pathogenesis remains elusive. Thus far, there has been no comprehensive gene expression profiling in Chinese women with GDM. In this study, we attempt to define the genes and/or pathways that are involved in GDM with Chinese ethnicity, by the Illumina microarray technique. more...
#> 1014 This SuperSeries is composed of the SubSeries listed below.
#> 1015 Dysregulation in expression of microRNAs (miRNAs) in various tissues has been linked to a wide spectrum of diseases, including Type 2 Diabetes mellitus (T2D). In this study, we compared the expression profiles of miRNAs in blood samples from Impaired Fasting Glucose (IFG) and T2D male patients with tissues from T2D rat models. Healthy adult males with no past history of T2D (n=158) and with desirable cholesterol and blood pressure profiles were enrolled in this study. more...
#> 1016 DNA methylation patterns were analyzed in blood samples from humans unexposed or exposed to arsenic Using a state-of-the-art technique to map the methylomes of our study subjects, we identified a large interactome of hypermethylated genes that are enriched for their involvement in arsenic-associated diseases, such as cancer, heart disease, and diabetes. Notably, we have uncovered an arsenic-induced “suppressorome” - a complex of 17 known and putative tumor suppressors silenced in human cancers. more...
#> 1017 There is growing evidence that genomic DNA sequence changes occur in individual somatic cells during the lifetime of an individual and accumulation of these changes may influence aging and disease. In light of this, and contradicting reports regarding discordant copy number profiles between MZ twins(BARANZINI et al. 2010; BRUDER et al. 2008), we set out to identify de novo somatic copy number mutations in DNA from blood for MZ twin pairs of Mexican American descent who were participants of the San Antonio Family Heart Study (SAFHS) or San Antonio Family Diabetes/Gallbladder study (SAFDGS). more...
#> 1018 We performed microarray analysis to evaluate differences in the transcriptome of type 2 diabetic human islets compared to non-diabetic islet samples.
#> 1019 Custom array designed to tile Linkage Disequilibrium Blocks of T2D GWAS SNPs, monogenic candidates for T2D and Obesity, and all plausible imprinted loci from human and mouse data.
#> 1020 There are an estimated 21million diabetics in the United States and 150 million diabetics worldwide. The World Health Organization anticipates that these numbers will double in the next 20 years. Metabolic syndrome is a well recognized set of symptoms that increases a patient’s risk of developing diabetes. Insulin resistance is a factor in both metabolic syndrome and Type 2 diabetes. It is characterized by decreased insulin stimulated glucose uptake in peripheral tissues, decreased adiponectin levels, increased adipocyte FFA and cytokine production, and increased insulin and hepatic glucose output. more...
#> 1021 Skeletal muscle mitochondrial dysfunction is secondary to T2DM and can be improved by long-term regular exercise training Mitochondrial dysfunction has long been implicated to play a causative role in development of type 2 diabetes (T2DM). However, a growing number of recent studies provide data that mitochondrial dysfunction is a consequence of T2DM development. The aim of our study is to clarify in further detail the causal role of mitochondrial dysfunction in T2DM by a comprehensive ex vivo analysis of mitochondrial function combined with global gene expression analysis in muscle of pre-diabetic newly diagnosed untreated T2DM subjects and long-standing insulin treated T2DM subjects compared with age- and BMI-matched controls. more...
#> 1022 The orphan nuclear receptor TR4 (human testicular receptor 4 or NR2C2) plays a pivotal role in a variety of biological and metabolic processes. With no known ligand and few known target genes, the mode of TR4 function was unclear. We report the first genome-wide identification and characterization of TR4 in vivo binding. Using chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq), we identified TR4 binding sites in 4 different human cell types and found that the majority of target genes were shared among different cells. more...
#> 1023 Genome scale characterization of chromatin modification, RNA expression, and cytosine methylation in a diverse panel of primary human cells and tissues, stem cells, and iPS cells derived from deidentified human subjects **************** For data usage terms and conditions, please refer to: http://www.drugabuse.gov/funding/funding-opportunities/nih-common-fund/epigenomics-data-access-policies ****************
#> 1024 Identifying cis-regulatory elements is important to understand how human pancreatic islets modulate gene expression in physiologic or pathophysiologic (e.g., diabetic) conditions. We conducted genome-wide analysis of DNase I hypersensitive sites, histone H3 lysine methylation marks (K4me1, K4me3, K79me2), and CCCTC factor (CTCF) binding in human islets. This identified ~18,000 putative promoters (several hundred novel and islet-active). more...
#> 1025 Congenital hypothyroidism from thyroid dysgenesis (CHTD) is a sporadic disease characterized by defects in the differentiation, migration or growth of thyroid tissue. Of these defects, incomplete migration resulting in ectopic thyroid tissue is the most common (up to 80%). We obtained flashfrozen samples of ectopic thyroid tissue removed from 3 girls aged 8, 10 and 15 yr, because it caused local symptoms. more...
#> 1026 Type 1 diabetes is an autoimmune destruction of pancreatic islet beta cell disease, and it is important to find new alternative source of the islet beta cells to replace the damaged cells. Human embryonic stem (hES) cells possess unlimited self-renewal and pluripotency and thus have the potential to provide an unlimited supply of different cell types for tissue replacement. The hES-T3 cells with normal female karyotype were first differentiated into embryoid bodies and then induced to generate the pancreatic islet-like cell clusters, which expressed pancreatic islet cell-specific markers of insulin, glucagon and somatostatin. more...
#> 1027 Diabetic neuropathy (DN) is a common complication of diabetes. While multiple pathways are implicated in the pathophysiology of DN, there are no specific treatments for DN and currently it is not possible to predict DN onset or progression. To examine gene expression signatures related to DN, microarray experiments were performed on a subset of human sural nerves collected during a 52-week clinical trial of acetyl-L-carnitine. more...
#> 1028 Rationale: Physical inactivity is a risk factor for insulin resistance. We examined the effect of nine days of bed rest on basal and insulin stimulated expression of genes potentially involved in insulin action by applying hypothesis-generating microarray in parallel with candidate gene real-time PCR approaches in 20 healthy, young men. Furthermore, we investigated whether bed rest affected DNA methylation in the promoter region of the peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PPARGC1A) gene. more...
#> 1029 Inflammation is common to many disorders and responsible for tissue and organ damage. However, the associated peripheral cytokine milieu is frequently dilute and difficult to measure, necessitating development of more sensitive and informative biomarkers for mechanistic studies, earlier diagnosis, and monitoring therapeutic interventions. Previously, we have shown that sera from type 1 diabetes (T1D) patients induces a unique disease-specific pro-inflammatory transcriptional profile in fresh peripheral blood mononuclear cells (PBMCs) compared to sera of healthy controls. more...
#> 1030 Genome-wide DNA methylation was studied to identify regions with extreme inter-individual variability, half of which show stability within-person, and some of which show covariation with body mass index consistently and are located in or near genes previously implicated in regulating body weight or diabetes.
#> 1031 Gene expression in peripheral blood is shown sufficient to differentiate patients with metabolic disorders from control. The signatures of metabolic syndrome, coronary artery disease and type 2 diabetes also have significant overlap.
#> 1032 (original Title) Phenothiazine Neuroleptics Signal To The Human Insulin Promoter As Revealed By A Novel Human b-Cell Line Based High-Throughput Screen. To address the current deficiency in human beta-cell models, we have developed a cell line from human islets in which the expression of insulin and other beta-cell restricted genes are modulated by an inducible form of the bHLH transcription factor E47. more...
#> 1033 Lung squamous cell carcinoma gene expression (LSCC) is highly variable. This study discovered and validated LSCC gene expression subtypes.
#> 1034 Gene expression changes in Peripheral Blood Mononuclear cells (PBMC) induced by physical activity was investigated in sedentary middle-aged men (mean age 52.6 years and BMI 29.1) who undertook a 24-week physical activity programme with blood sampling in the pre-exercise period , at the end of 24-weeks prescribed physical activity , and following a two-week detraining period.
#> 1035 A number of chronic, age-related diseases are associated with elevated markers of inflammation such as interleukin-6 (IL-6). In this study, we investigated the hypothesis that sedentary individuals with disparate basal serum IL-6 respond differentially to a structured physical activity programme. Gene expression changes in Peripheral Blood Mononuclear cells (PBMC) induced by physical activity was investigated in sedentary, middle-aged men (mean age 52.6 years and BMI 29.1), with relatively high or low basal serum IL-6 levels (mean of 2.13 and 0.59pg/ml respectively), who undertook a 24-week physical activity programme with blood sampling in the pre-exercise period and at the end of 24-weeks prescribed physical activity.
#> 1036 The liver may regulate glucose homeostasis by modulating the sensitivity/resistance of peripheral tissues to insulin, by way of the production of secreted proteins, termed hepatokines. To identify hepatic secretory proteins involved in insulin resistance, we performed liver biopsies in humans with or without type 2 diabetes and conducted a comprehensive analysis of gene expression profiles.
#> 1037 Glomerular diseases account for the majority of cases with chronic renal failure. Several genes have been identified with key relevance for glomerular function. Quite a few of these genes show a specific or preferential mRNA expression in the renal glomerulus. To identify additional candidate genes involved in glomerular function in humans we generated a human renal glomerulus-specific transcript dataset (GTD) by comparing gene expression profiles from human glomeruli and tubulointerstitium obtained from six transplant living donors using Affymetrix HG-U133A arrays. more...
#> 1038 Insulin is a potent pleiotropic hormone that affects processes such as cellular growth, differentiation, apoptosis, ion flux, energy expenditure, and carbohydrate, lipid, and protein metabolism. We used microarrays to detail the global programme of gene expression underlying the influence of insulin in human skeletal muscle collected from different human individuals including 20 insulin sensitive, 20 insulin resistant and 15 diabetic patients. more...
#> 1039 Background: In diabetes chronic hyperinsulinemia is responsible for the instability of the atherosclerotic plaque and stimulates cellular proliferation through the activation of the MAP kinases, which in turn regulate cellular proliferation. However, it is not known whether insulin itself could increase the transcription of specific genes for cellular proliferation in the endothelium. Hence, the characterization of transcriptional modifications in endothelium is an important step for a better understanding of the mechanism of insulin action and the relationship between endothelial cell dysfunction and insulin resistance. more...
#> 1040 Objective: Potential regulators of adipogenesis include microRNAs (miRNAs), small non-coding RNAs that have been recently shown related to adiposity and differentially expressed in fat depots. However, to date no study is available regarding the relationship of miRNAs expression profile, biological pathway and cellular phenotype during human adipogenesis. Thereby, the aim of this study was to investigate whether miRNA expression profile in human adipocytes is related to adipogenesis and to test whether miRNA profile in human subcutaneous adipose tissue is associated to human obesity and co-morbidities. more...
#> 1041 In this study, we compared the expression profiles of miRNAs in blood samples from Impaired Fasting Glucose (IFG) and T2D male patients. Healthy adult males with no past history of T2D (n=158) and with desirable cholesterol and blood pressure profiles were enrolled in this study. They were then classified according to fasting glucose levels to have T2D, IFG or as healthy controls (CTL), for comparison of miRNA expression profiles. more...
#> 1042 Epidemiological studies have revealed concurrence of specific cancers with other disease states such as metabolic syndrome, inflammatory disease and autoimmune disease. Patients with these chronic conditions have a higher incidence of various cancers, more aggressive tumors, and a higher mortality rate. It has been proposed that obesity, inflammation and chronic disease should be correlated with cancer at the molecular level, but common gene signatures or networks have yet to be described. more...
#> 1043 Type 2 diabetes mellitus (DM) is characterized by insulin resistance and pancreatic beta-cell dysfunction. In high-risk subjects, the earliest detectable abnormality is insulin resistance in skeletal muscle. Impaired insulin-mediated signaling, gene expression, and glycogen synthesis, and accumulation of intramyocellular triglycerides have all been linked with insulin resistance, but no specific defect responsible for insulin resistance and DM has been identified in humans. more...
#> 1044 The accumulation of unfolded or misfolded proteins in the endoplasmic reticulum (ER) results in the condition called “ER stress” which induces the unfolded protein response (UPR) which is a complex cellular process that includes changes in expression of many genes. Failure to restore homeostasis in the ER is associated with human diseases. To identify the underlying changes in gene expression in response to ER stress, we induced ER stress in human B-cells and then measured gene expression at 10 time-points. more...
#> 1045 Changes in gene expression in pancreatic beta-cells from type 2 diabetes could provide insights into their abnormal insulin secretion and beta-cell turnover. The laser capture microdissection technique was used to acquire beta-cells from pancreatic tissue sections obtained from type 2 diabetic (T2D) and non-diabetic controls. We found that 4% of analyzed transcripts were differentially expressed between the two groups at the lower confidence bound cutoff of 1.2, and, among the differentially expressed transcripts, 62% were up-regulated and 38% down-regulated in samples of T2D subjects compared to non-diabetic controls. more...
#> 1046 The Illumina Infinium 27k Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 27,000 CpGs in whole blood samples from a case-control study of 192 Irish patients with type 1 diabetes mellitus (T1D). Cases had T1D and nephropathy whereas controls had T1D but no evidence of renal disease. emails: christopher.bell@cancer.ucl.ac.uk, a.teschendorff@ucl.ac.uk Keywords: DNA methylation
#> 1047 Perturbations of the intrauterine environment can affect fetal development during critical periods of plasticity, and can increase susceptibility to a number of age-related diseases (e.g. type 2 diabetes mellitus; T2DM), manifesting sometimes decades later. We hypothesized that this biological memory is mediated by permanent alterations of the epigenome in stem cell populations. Our studies focused specifically on DNA methylation in CD34+ hematopoietic stem and progenitor cells from cord blood, and utilized a two-stage design involving genome-wide discovery followed by quantitative, single-locus validation. more...
#> 1048 Microarray analysis reveals up-regulation of retinoic acid and hepatocyte growth factor related signaling pathways by pro-insulin C-peptide in kidney proximal tubular cells: Antagonism of the pro-fibrotic effects of TGF-b1 Novel signaling roles for C-peptide have recently been discovered with evidence that it can ameliorate complications of type 1 diabetes. Here we sought to identify new pathways regulated by C-peptide of relevance to the pathophysiology of diabetic nephropathy. more...
#> 1049 We used microarray technology to profile mRNA expression in the skeletal muscle of normal (NGT), glucose intolerant (IGT) and type 2 diabetic (DM) subjects. Groups were classified using WHO criteria and, importantly, the DM group were free of anti hypoglycaemic medication for one week prior to biopsy.
#> 1050 We have employed whole microRNA microarray with the potential to distinguish H.pylori infection.Among the 470 human miRNAs represented on the array chip, 228 were undetectable or expressed below the background and so were eliminated, leaving 242 miRNAs for the supervised analysis. When comparing 10 H. pylori-negative and nine H. pylori-positive subjects, 55 miRNAs were deemed significantly different on the basis of microRNA arrays.
#> 1051 The NIH Roadmap Epigenomics Mapping Consortium aims to produce a public resource of epigenomic maps for stem cells and primary ex vivo tissues selected to represent the normal counterparts of tissues and organ systems frequently involved in human disease. Study of chromatin accessibility and expression using exon arrays. **************** For data usage terms and conditions, please refer to: http://www.drugabuse.gov/funding/funding-opportunities/nih-common-fund/epigenomics-data-access-policies ****************
#> 1052 Insulin resistance and Type 2 Diabetes Mellitus (T2DM) are associated with increased adipocyte size, altered secretory pattern and decreased differentiation of preadipocytes. To identify the underlying molecular processes in preadipocytes of T2DM patients that are a characteristic of the development of T2DM, preadipocyte cell cultures were prepared from subcutaneous fat biopsies of T2DM patients and compared with age- and BMI matched control subjects. more...
#> 1053 Background: Endothelial progenitor cells play an important role in vascular wall repair. Patients with type 1 diabetes have reduced levels of endothelial progenitor cells of which their functional capacity is impaired. Reduced nitric oxide bioavailability and increased oxidative stress play a role in endothelial progenitor cell dysfunction in these patients. Folic acid, a B-vitamin with anti-oxidant properties, may be able to improve endothelial progenitor cell function. more...
#> 1054 Despite years of effort, exact pathogenesis of non-alcoholic fatty liver disease (NAFLD) remains obscure. To gain an insight into the regulatory roles of microRNAs (miRNAs) in aberrant energy metabolic status and pathogenesis of NAFLD, we analyzed the expression of miRNAs in livers of ob/ob mice, streptozotocin (STZ)-induced type 1 diabetic mice and normal C57BL/6 mice by miRNA microarray. Compared to normal C57BL/6 mice, ob/ob mice showed up-regulation of 8 miRNAs and down-regulation of 4 miRNAs in fatty livers. more...
#> 1055 Several reports have focused on the identification of biological elements involved in the development of abnormal systemic biochemical alterations in chronic kidney disease, but this abundant literature results most of the time fragmented. To better define the cellular machinery associated to this condition, we employed an innovative high-throughput approach based on a whole transcriptomic analysis and classical biomolecular methodologies. more...
#> 1056 Individuals of African descent in the United States suffer disproportionately from diseases with a metabolic etiology (obesity, metabolic syndrome, and diabetes), and from the pathological consequences of these disorders (hypertension and cardiovascular disease). Using a combination of genetic/genomic and bioinformatics approaches, we identified a large number of genes that were both differentially expressed between American subjects self-identified to be of either African or European ancestry and that also contained single nucleotide polymorphisms that distinguish distantly related ancestral populations. more...
#> 1057 Alcohol affects gene expression in several brain regions. The amygdala is a key structure in the brain’s emotional system and in recent years the crucial importance of the amygdala in drug-seeking and relapse has been increasingly recognized. In this study gene expression screening was used to identify genes involved in alcoholism in the human basolateral amygdala. The results show that alcoholism affects a broad range of genes and many systems including genes involved in synaptic transmission, neurotransmitter transport, structural plasticity, metabolism, energy production, transcription and RNA processing and the circadian cycle. more...
#> 1058 The goal of this study was to compare the gene expression profiles of chronically inflamed human peri-implant and chronically inflamed human periodontal tissues in order to elucidate potential changes at the molecular level. Cells out of the pocket depth of the inflamed peri-implant and periodontal ligament as well as from the middle third of healthy periodontal ligament were applied. Genome-wide gene expression was compared with the help of microarray analysis, and the data were validated by real-time RT-PCR. more...
#> 1059 Diabetes is associated with a more aggressive form of atherosclerosis. Thrombospondin-1 (TSP-1), an extracellular matrix protein, is an acute phase reactant that induces vascular smooth muscle (VSMC) migration and proliferation in areas of vascular injury, and is also upregulated in VSMCs exposed to hyperglycemia. We hypothesized that hyperglycemia amplifies the expression of genes induced by TSP-1 in VSMCs. more...
#> 1060 The human embryonic stem cells (hESCs) are a unique model system for investigating the mechanisms of human development due to their ability to replicate indefinitely while retaining the capacity to differentiate into a host of functionally distinct cell types. In addition, these cells could be potentially used as therapeutic agents in regenerative medicine. Differentiation of hESCs involves selective activation or silencing of genes, a process controlled in part by the epigenetic state of the cell. more...
#> 1061 Pancreatic islet transplantation as a cure for type 1 diabetes (T1D) cannot be scaled up due to a scarcity of human pancreas donors. In vitro expansion of beta cells from mature human pancreatic islets provides an alternative source of insulin-producing cells. The exact nature of the expanded cells produced by diverse expansion protocols, and their potential for differentiation into functional beta cells, remain elusive. more...
#> 1062 This SuperSeries is composed of the SubSeries listed below.
#> 1063 MicroRNAs (miRNAs) are non-coding RNA molecules involved in post-transcriptional control of gene expression of a wide number of genes, including those involved in glucose homeostasis. Type 2 diabetes (T2D) is characterized by hyperglycaemia and defects in insulin secretion and action at target tissues. Using a miRNA microarray platform, we sought to establish differences in miRNA expression in two insulin-target tissues (liver and adipose tissue) from seven-month-old spontaneously diabetic (Goto-Kakizaki [GK]) and non-diabetic (Brown-Norway [BN]) rats. more...
#> 1064 Non-small-cell lung cancer (NSCLC), which is comprised mainly of adenocarcinoma and squamous cell carcinoma (SCC), is the cause of 80% of all lung cancer deaths in the US. NSCLC is also associated with a high rate of relapse following clinical treatment and therefore requires robust prognostic markers to better manage therapy options. The aim of this study was to identify miRNA expression profiles in squamous cell carcinoma (SSC) of the lung that would better predict prognosis.
#> 1065 Melioidosis is a severe infectious disease caused by Burkholderia pseudomallei, a gram-negative bacillus classified by the NIAID as a category B priority agent. Septicemia is the most common presentation of the disease with 40% mortality rate even with appropriate treatments. Faster diagnostic procedures are required to improve therapeutic response and survival rates. We have used microarray technology to generate genome-wide transcriptional profiles (>48,000 transcripts) of whole blood obtained from patients with septicemic melioidosis (n=32), patients with sepsis caused by other pathogens (n=31), and uninfected controls (n=29). more...
#> 1066 Obesity is becoming increasingly widespread in developed countries, and is often associated with heart diseases and diabetes. Elevated levels of plasma free fatty acids are a biochemical hallmark of obesity. Unlike plants and bacteria, mammals cannot utilize fatty acids to generate glucose because of the lack of glyoxylate shunt enzymes. Instead, fatty acids are used for energy storage, and their utilization is regulated at multiple levels ranging from hormonal to metabolic ensuring that glucose is preferentially oxidized before fatty acids. more...
#> 1067 Genes showing differential expression in visceral adipose tissue obtained from Asia Indian obese women suffering from type-2 diabetes mellitus as compared to age and BMI matched normal glucose tolerant women were identified by genome wide transcriptomic profiling in 5 diabetic and 5 control subjects respectively.
#> 1068 Hepatic lipid accumulation is an important complication of obesity linked to risk for type 2 diabetes. To identify novel transcriptional changes in human liver which could contribute to hepatic lipid accumulation and associated insulin resistance and type 2 diabetes (DM2), we evaluated gene expression and gene set enrichment in surgical liver biopsies from 13 obese (9 with DM2) and 5 control subjects, obtained in the fasting state at the time of elective abdominal surgery for obesity or cholecystectomy. more...
#> 1069 Gene expression in human umbilical vein endothelial cells (HUVEC) was investigated by microarray analysis after 4 h infection with S. aureus isolated from healthy nasal carriers (n=5) and from blood (n=5) of septic patients. All bacterial isolates were spa-typed and characterized with a DNA microarray to determine the presence of virulence genes. Keywords: infection studies, pathogen, S. aureus
#> 1070 The frequent use of rodent hepatic in vitro systems in pharmacological and toxicological investigations challenges extrapolation of in vitro results to the situation in vivo and interspecies extrapolation from rodents to humans. The toxicogenomics approach may aid in evaluating relevance of these model systems for human risk assessment by direct comparison of toxicant-induced gene expression profiles and infers mechanisms between several systems. more...
#> 1071 MicroRNAs (miRNAs), which are a newly identified class of small single-stranded non-coding RNAs, regulate their target genes via post-transcriptional pathway. It has been proved that miRNAs play important roles in many biological processes. To better understand miRNA function with type 2 diabetes, we have used an oligonucleotide microarray to monitor miRNA expression profiles of GK and Wistar rats’ muscle. more...
#> 1072 We examined gene expression signatures in healthy and diseased gingival tissues in 90 patients. Analysis of the gingival tissue transcriptome in states of periodontal health and disease may reveal novel insights of the pathobiology of periodontitis. Keywords: gingival tissue disease state analysis
#> 1073 In the present study, we performed transcriptome expression analyses in three independent peripheral blood-derived monocyte subpopulations from patients with chronic coronary occlusions (CTO) and tested for arteriogenesis. Whole-genome mRNA expression analyses were performed on these three monocyte subpopulations, namely: (1) unstimulated-, (2) 3 hours LPS-stimulated-, (3) monocyte-derived macrophages. more...
#> 1074 Insulin resistance is a common metabolic abnormality in women with PCOS and leads to an elevated risk of type 2 diabetes. Studies have shown that thiazolidinediones (TZD) improve metabolic disturbances in PCOS patients. We hypothesized that the effect of TZD in PCOS is in part mediated by changes in the transcriptional profile of muscle favoring insulin sensitivity. Using Affymetrix microarrays, we examined the effect of pioglitazone (30 mg/day for 16 weeks) on gene expression in skeletal muscle of 10 obese women with PCOS metabolically characterized by a euglycemic-hyperinsulinemic clamp. more...
#> 1075 Microarray-based studies of skeletal muscle from patients with type 2 diabetes and high-risk individuals have demonstrated that insulin resistance and reduced mitochondrial biogenesis co-exist early in the pathogenesis of type 2 diabetes independent of hyperglycaemia and obesity. It is unknown whether reduced mitochondrial biogenesis or other transcriptional alterations co-exist with impaired insulin-responsiveness in primary human muscle cells from patients with type 2 diabetes. more...
#> 1076 We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. Mapping changes in gene expression at the module level generated disease-specific transcriptional fingerprints that provide a stable framework for the visualization and functional interpretation of microarray data.
#> 1077 The analysis of patient blood transcriptional profiles offers a means to investigate the immunological mechanisms relevant to human diseases on a genome-wide scale. In addition, such studies provide a basis for the discovery of clinically relevant biomarker signatures. We designed a strategy for microarray analysis that is based on the identification of transcriptional modules formed by genes coordinately expressed in multiple disease data sets. more...
#> 1078 Abstract The metabolic syndrome is a cluster of conditions that predispose for diabetes and cardiovascular disease. Nine metabolic syndrome patients were recruited to 48 workouts of interval training. At the end of the study, all patients significantly reduced their risk of cardiovascular disease (in terms of VO2max, blood pressure and plasma lipid). Exercise-induced transcriptional changes may provide new mechanistically insights in the area of improved health by exercise. more...
#> 1079 To uncover the genetic determinants affecting expression in a metabolically active tissue relevant to the study of obesity, diabetes, atherosclerosis, and other common human diseases, we profiled 427 human liver samples on a comprehensive gene expression microarray targeting greater than 40,000 transcripts and genotyped DNA from each of these samples at greater than 1,000,000 SNPs. The relatively large sample size of this study and the large number of SNPs genotyped provided the means to assess the relationship between genetic variants and gene expression and it provided this look for the first time in a non-blood derived, metabolically active tissue. more...
#> 1080 We used a whole genome approach to identify major functional gene categories (including xenobiotic transporters and metabolizing enzymes) whose expression depends on gestational age. STUDY DESIGN: We compared gene expression profiles of 1st (45-59 days) and 2nd trimester (109-115 days), and C-section term placentae. RESULTS: In 1st trimester placentae, genes related to cell cycle, DNA, aminoacids and carbohydrate metabolism were significantly overrepresented, while genes related to signal transduction were downregulated. more...
#> 1081 To determine whether optic nerve head astrocytes, a key cellular component of glaucomatous neuropathy, exhibit differential gene expression in primary culture of astrocytes from normal African American donors, compared to astrocytes from normal Caucasian American donors. All donors have no histories of eye disease, diabetes, or chronic CNS disease. Keywords: Gene expression profile
#> 1082 Periodontal infections have been associated with systemic inflammation and risk for atherosclerosis and vascular disease. We investigated the effects of comprehensive periodontal therapy on gene expression of peripheral blood monocytes. Approximately 1/3 of the patients showed substantial changes in expression in genes relevant to innate immunity, apoptosis, and cell signaling. We concluded that periodontal therapy may alter monocytic gene expression in a manner consistent with a systemic anti-inflammatory effect. more...
#> 1083 Although increased vascular stiffness is more prominent in aging males than females, and males are more prone to vascular disease with aging, no study has investigated the genes potentially responsible for gender differences in vascular aging. We tested the hypothesis that the transcriptional adaptation to aging differs in males and females using a monkey model, which is not only physiologically and phylogenetically closer to humans than the more commonly studied rodent models, but also is not afflicted with the most common forms of vascular disease that accompany the aging process in humans, e.g., atherosclerosis, hypertension, and diabetes. more...
#> 1084 Recently, abnormalities in mitochondrial oxidative phosphorylation (OXPHOS) have been implicated in the pathogenesis of skeletal muscle insulin resistance in type 2 diabetes. In the present study, we hypothesized that decreased expression of OXPHOS genes could be of similar importance for insulin resistance in the polycystic ovary syndrome (PCOS). Using the HG-U133 Plus 2.0 expression array from Affymetrix, we analyzed gene expression in skeletal muscle from obese women with PCOS (n=16) and age- and body mass index-matched control women (n=13) metabolically characterized by euglycemic-hyperinsulinemic clamp and indirect calorimetry. more...
#> 1085 This SuperSeries is related to a manuscript published in Genome Biology. The abstract of this manuscript follows here: Background Investigations performed in mice and humans have acknowledged obesity as a low-grade inflammatory disease. Several molecular mechanisms have been convincingly involved in activating inflammatory processes and altering cell composition in white adipose tissue (WAT); however, the overall importance of these alterations, and their long-term impact on the metabolic functions of the WAT and on its morphology, remain unclear. more...
#> 1086 Extracellular matrix (ECM) remodelling occurs during tissue repair and inflammation-related pathologies with deposition of specific proteins. White adipose tissue (WAT) was recently shown to be the site of substantial interstitial fibrosis. ECM components, such as fibronectin, and their receptors integrins control cell migration, proliferation and differentiation. Adipocyte differentiation which is under the control of a specific transcriptional network is associated with decrease of fibronectin-rich matrix. more...
#> 1087 This study was undertaken to test the hypothesis that short term exposure (4 hours) to physiologic hyperinsulinemia in normal, healthy subjects without a family history of diabetes would induce a low grade inflammatory response, independently of glycemic status. We performed euglycemic hyperinsulinemic (80 mU/m2/min) clamps in 12 healthy, insulin sensitive subjects with no family history of diabetes followed by biopsies of the vastus lateralis muscle taken basally and after 30 and 240 minutes of insulin infusion. more...
#> 1088 Objective: We hypothesized that type 1 diabetes (T1D) is accompanied by changes in gene expression in peripheral blood mononuclear cells (PBMCs) due to dysregulation of adaptive and innate immunity, counterregulatory responses to immune dysregulation, insulin deficiency and hyperglycemia. Research Design and Methods: Microarray analysis was performed on PBMCs from 43 patients with newly diagnosed T1D, 12 patients with newly diagnosed type 2 diabetes (T2D) and 24 healthy controls. more...
#> 1089 Objectives: While cardiac scar tissue is damaged irreversibly, hibernating myocardium is characterized by reversible contractile dysfunction. Limited data are available in humans regarding the molecular biology of hibernating myocardium. The aim of this study was to identify new molecular mechanisms distinctive for human hibernating myocardium by gene expression analysis. Results: Of 4,171 transcripts examined, we identified 86 to be differentially expressed. more...
#> 1090 Reversible acetylation of histone and nonhistone proteins plays pivotal role in cellular homeostasis. Dysfunction of histone acetyltransferases (HATs) leads to several diseases including cancer, neurodegenaration, asthma, diabetes, AIDS and cardiac hypertrophy. We describe the synthesis and characterization of a set of p300-HAT specific small molecule inhibitors from a natural nonspecific HAT inhibitor, garcinol, which is highly toxic to cells. more...
#> 1091 To identify insulin responsive genes in humans, in the first protocol, skeletal muscle biopsies from six non-diabetic subjects were obtained before and after a two-hour of hyperinsulinaemic (infusion rate 40 mU/m2/min) euglycemic clamp. A variable infusion of glucose (180 g/l) enriched with tritiated glucose (100 μCi/500 ml) maintained euglycemia during insulin infusion, with monitoring of plasma glucose concentration every 5 to 10 min during the basal and clamp periods using an automated glucose oxidation method (Glucose Analyzer 2, Beckman Instruments, Fullerton, CA). more...
#> 1092 Failure of ligamentous support of the genital tract to resist intra-abdominal pressure is a plausible underlying mechanism for the development of pelvic organ prolapse, but the nature of molecular response of pelvic tissue support remains unknown. We hypothesized that the expression of genes coding for proteins involved in maintaining the cellular and extracellular integrity would be altered in cases of pelvic organ prolapse. more...
#> 1093 Preeclampsia complicates more than 3% of all pregnancies in the United States and Europe. High-risk populations include women with diabetes, dyslipidemia, thrombotic disorders, hyperhomocysteinemia, hypertension, renal diseases, previous preeclampsia, twin pregnancies, and low socioeconomic status. In the latter case, the incidence may increase to 20% to 25%. Preeclampsia is a major cause of maternal and fetal morbidity and mortality. more...
#> 1094 Sample tissue: peripheral blood Samples for gene expression analysis were obtained before and after the event. Keywords: time course, event response
#> 1095 Müller cells are the principal glial cells in the retina. Alterations in Müller cell behaviour are observed in retinal tissue from patients with proliferative diabetic retinopathy. The purpose of this study was to compare gene and protein expression profile of normal human Müller cells (NHMC) with two spontaneously human Müller cell lines generated from type 1 (HMCL-I) and type 2 (HMCL-II) diabetic donors using Serial Analysis Gene Expression (SAGE). more...
#> 1096 Sample tissue: peripheral blood Disease: normal subject, patients with type 2 diabetes (diabetic nephropathy +, -) Samples for gene expression analysis were obtained before and after the event. Keywords: equivalent probe, disease response
#> 1097 This experiment was designed to study if there are differences in gene expression in the adipose tissue of women affected by polycystic ovary syndrome (PCOS) compared to non-hyperandrogenic women. PCOS is the most common endocrinopathy in women of reproductive age, and is characterized by hyperandrogenism and chronic anovulation. This disease is frequently associated with obesity, insulin resistance, and defects in insulin secretion, predisposing these women to type 2 diabetes, atherosclerosis, and cardiovascular disease. more...
#> 1098 Insulin action in target tissues involved precise regulation of gene expression. To define the set of insulin-regulated genes in human skeletal muscle, we analyzed the global changes in mRNA levels during a 3-h hyperinsulinemic euglycemic clamp in vastus lateralis muscle of six healthy subjects. Using 29,308 cDNA element microarrays, we found that the mRNA expression of 762 genes, including 353 expressed sequence tags, was significantly modified during insulin infusion. more...
#> 1099 Hepatocyte nuclear factor 1beta (HNF1beta, TCF2) is a tissue-specific transcription factor whose mutation in humans leads to renal cysts, genital malformations, pancreas atrophy and maturity onset diabetes of the young (MODY5). Furthermore, HNF1beta overexpression has been observed in clear cell cancer of the ovary. To identify potential HNF1beta target genes whose activity may be deregulated in human patients we established a human embryonic kidney cell line (HEK293) expressing HNF1beta conditionally. more...
#> 1100 Even though autoimmune diseases are heterogeneous, believed to result from the interaction between genetic and environmental components, patients with these disorders exhibit reproducible patterns of gene expression in their peripheral blood mononuclear cells. A portion of this gene expression profile reflects family resemblance rather than the actual presence of an autoimmune disease. Here we wanted to identify that portion of this gene expression pattern that is independent of family resemblance and determine if it is a product of disease duration, disease onset, or other factors. more...
#> 1101 Summary: Genetic disorders of muscle cause muscular dystrophy, and are some of the most common inborn errors of metabolism. Muscle also rapidly remodels in response to training and innervation. Muscle weakness and wasting is important in such conditions as aging, critical care medicine, space flight, and diabetes. Finally, muscle can also be used to investigate systemic defects, and the compensatory mechansisms invoked by cells to overcome biochemical and genetic abnormalities. more...
#> 1102 Expression profile was obtained for GeneChip probe sets among colon cancer specimens with or without the methylation of MLH1 promoter. Keywords: other
#> 1103 A physiological state of insulin resistance is required to preferentially direct maternal nutrients toward the feto-placental unit, allowing adequate growth of the fetus. When women develop gestational diabetes mellitus (GDM), insulin resistance is more severe and disrupts the intrauterine milieu, resulting in accelerated fetal development with increased risk of macrosomia. As a natural interface between mother and fetus, the placenta is the obligatory target of such environmental changes. more...
#> 1104 Sample tissue: peripheral blood Disease: diabetes Samples for gene expression analysis were obtained before the meal and 1.5 hours after the event. Event: listening to a Japanese comic story or a monotonous academic lecture without humor. Keywords: equivalent probe
#> 1105 Gene expression profiling in glomeruli from human kidneys with diabetic nephropathy Keywords = Diabetes Keywords = kidney Keywords = glomeruli Keywords: other
#> 1106 Skeletal muscle biopsies from atypical diabetics at presentation and remission. Protein expression determined with antibody arrays Keywords: other
#> 1107 Global transcript profiling to identify differentially expressed skeletal muscle genes in insulin resistance, a major risk factor for Type II (non-insulin-dependent) diabetes mellitus. Compared gene expression profiles of skeletal muscle tissues from 18 insulin-sensitive versus 17 insulin-resistant equally obese, non-diabetic Pima Indians. Keywords: other
#> Organism
#> 1 Homo sapiens
#> 2 Homo sapiens
#> 3 Homo sapiens
#> 4 Homo sapiens
#> 5 Homo sapiens; Mus musculus
#> 6 Mus musculus; Homo sapiens
#> 7 Homo sapiens
#> 8 Homo sapiens
#> 9 Homo sapiens
#> 10 Homo sapiens
#> 11 Homo sapiens
#> 12 Homo sapiens
#> 13 Homo sapiens
#> 14 Homo sapiens
#> 15 Homo sapiens
#> 16 Homo sapiens; Mus musculus
#> 17 Homo sapiens
#> 18 Homo sapiens
#> 19 Homo sapiens
#> 20 Homo sapiens
#> 21 Homo sapiens
#> 22 Homo sapiens
#> 23 Homo sapiens
#> 24 Homo sapiens
#> 25 Homo sapiens
#> 26 Homo sapiens
#> 27 Homo sapiens
#> 28 Homo sapiens
#> 29 Homo sapiens
#> 30 Homo sapiens
#> 31 Homo sapiens
#> 32 Homo sapiens
#> 33 Homo sapiens
#> 34 Homo sapiens
#> 35 Homo sapiens
#> 36 Homo sapiens
#> 37 Homo sapiens
#> 38 Homo sapiens
#> 39 Homo sapiens
#> 40 Homo sapiens
#> 41 Homo sapiens
#> 42 Homo sapiens
#> 43 Homo sapiens
#> 44 Homo sapiens
#> 45 Homo sapiens
#> 46 Homo sapiens
#> 47 Homo sapiens
#> 48 Homo sapiens
#> 49 Homo sapiens
#> 50 Homo sapiens
#> 51 Homo sapiens
#> 52 Homo sapiens
#> 53 Homo sapiens
#> 54 Homo sapiens
#> 55 Homo sapiens
#> 56 Homo sapiens
#> 57 Homo sapiens
#> 58 Homo sapiens
#> 59 Homo sapiens
#> 60 Homo sapiens
#> 61 Homo sapiens
#> 62 Homo sapiens
#> 63 Macaca mulatta; Homo sapiens
#> 64 Homo sapiens
#> 65 Homo sapiens
#> 66 Homo sapiens
#> 67 Homo sapiens
#> 68 Homo sapiens
#> 69 Homo sapiens
#> 70 Mus musculus; Homo sapiens
#> 71 Homo sapiens
#> 72 Homo sapiens
#> 73 Homo sapiens
#> 74 Homo sapiens
#> 75 Homo sapiens
#> 76 Homo sapiens
#> 77 Homo sapiens
#> 78 Homo sapiens
#> 79 Homo sapiens
#> 80 Homo sapiens
#> 81 Homo sapiens
#> 82 Homo sapiens; Mus musculus
#> 83 Homo sapiens
#> 84 Homo sapiens
#> 85 Homo sapiens
#> 86 Homo sapiens
#> 87 Homo sapiens
#> 88 Homo sapiens
#> 89 Homo sapiens
#> 90 Homo sapiens
#> 91 Homo sapiens
#> 92 Homo sapiens
#> 93 Homo sapiens
#> 94 Homo sapiens
#> 95 Homo sapiens
#> 96 Homo sapiens
#> 97 Homo sapiens
#> 98 Homo sapiens
#> 99 Homo sapiens
#> 100 Homo sapiens
#> 101 Homo sapiens
#> 102 Homo sapiens
#> 103 Homo sapiens
#> 104 Homo sapiens
#> 105 Homo sapiens
#> 106 Homo sapiens
#> 107 Homo sapiens
#> 108 Homo sapiens
#> 109 Homo sapiens
#> 110 Homo sapiens
#> 111 Homo sapiens
#> 112 Homo sapiens
#> 113 Homo sapiens
#> 114 Homo sapiens
#> 115 Homo sapiens
#> 116 synthetic construct; Homo sapiens
#> 117 Homo sapiens
#> 118 Homo sapiens
#> 119 Homo sapiens
#> 120 Homo sapiens
#> 121 Homo sapiens
#> 122 Homo sapiens
#> 123 Homo sapiens
#> 124 Homo sapiens
#> 125 Homo sapiens
#> 126 Homo sapiens
#> 127 Homo sapiens
#> 128 Homo sapiens
#> 129 Homo sapiens
#> 130 Homo sapiens; Mus musculus
#> 131 Homo sapiens
#> 132 Homo sapiens
#> 133 Homo sapiens
#> 134 Homo sapiens
#> 135 Homo sapiens
#> 136 Homo sapiens
#> 137 Homo sapiens
#> 138 Homo sapiens
#> 139 Homo sapiens
#> 140 Homo sapiens
#> 141 Homo sapiens
#> 142 Homo sapiens
#> 143 Homo sapiens; Mus musculus
#> 144 Homo sapiens
#> 145 Homo sapiens
#> 146 Homo sapiens
#> 147 Homo sapiens
#> 148 Mus musculus; Rattus norvegicus; Homo sapiens
#> 149 Homo sapiens
#> 150 Homo sapiens
#> 151 Homo sapiens
#> 152 Homo sapiens
#> 153 Homo sapiens
#> 154 Homo sapiens
#> 155 Homo sapiens
#> 156 Homo sapiens
#> 157 Homo sapiens
#> 158 Homo sapiens
#> 159 Homo sapiens
#> 160 Homo sapiens; Mus musculus
#> 161 Homo sapiens
#> 162 Homo sapiens
#> 163 Homo sapiens
#> 164 Homo sapiens
#> 165 Homo sapiens
#> 166 Homo sapiens
#> 167 Homo sapiens
#> 168 Homo sapiens
#> 169 Homo sapiens
#> 170 Homo sapiens
#> 171 Homo sapiens
#> 172 Homo sapiens
#> 173 Homo sapiens
#> 174 Homo sapiens
#> 175 Homo sapiens
#> 176 Homo sapiens
#> 177 Homo sapiens
#> 178 Homo sapiens
#> 179 Homo sapiens
#> 180 Homo sapiens
#> 181 Homo sapiens; Rattus norvegicus
#> 182 Rattus norvegicus; Homo sapiens
#> 183 Homo sapiens
#> 184 Homo sapiens
#> 185 Homo sapiens
#> 186 Homo sapiens
#> 187 Homo sapiens
#> 188 Homo sapiens
#> 189 Homo sapiens
#> 190 synthetic construct; Homo sapiens
#> 191 Homo sapiens
#> 192 Homo sapiens
#> 193 Homo sapiens
#> 194 Homo sapiens
#> 195 Homo sapiens
#> 196 Homo sapiens
#> 197 Homo sapiens
#> 198 Homo sapiens
#> 199 Homo sapiens
#> 200 Homo sapiens
#> 201 Homo sapiens
#> 202 Homo sapiens
#> 203 Homo sapiens
#> 204 Homo sapiens; Mus musculus
#> 205 Homo sapiens
#> 206 Homo sapiens
#> 207 Homo sapiens
#> 208 Homo sapiens
#> 209 Homo sapiens
#> 210 Homo sapiens
#> 211 Homo sapiens
#> 212 Homo sapiens
#> 213 Homo sapiens
#> 214 Homo sapiens
#> 215 Homo sapiens
#> 216 Homo sapiens
#> 217 Homo sapiens
#> 218 Homo sapiens
#> 219 Homo sapiens
#> 220 Homo sapiens
#> 221 Homo sapiens
#> 222 Homo sapiens; Mus musculus
#> 223 Homo sapiens
#> 224 Homo sapiens; synthetic construct
#> 225 Homo sapiens
#> 226 Homo sapiens
#> 227 Homo sapiens
#> 228 Homo sapiens
#> 229 Homo sapiens
#> 230 Homo sapiens
#> 231 Homo sapiens
#> 232 Homo sapiens
#> 233 Homo sapiens
#> 234 Homo sapiens
#> 235 Homo sapiens
#> 236 Homo sapiens
#> 237 Homo sapiens
#> 238 Homo sapiens
#> 239 Homo sapiens
#> 240 Homo sapiens
#> 241 Homo sapiens
#> 242 Homo sapiens
#> 243 Homo sapiens
#> 244 Homo sapiens
#> 245 Homo sapiens
#> 246 Homo sapiens
#> 247 Homo sapiens
#> 248 Homo sapiens
#> 249 Homo sapiens
#> 250 Homo sapiens
#> 251 Homo sapiens
#> 252 Homo sapiens
#> 253 Homo sapiens
#> 254 Homo sapiens
#> 255 Homo sapiens
#> 256 Homo sapiens
#> 257 Homo sapiens
#> 258 Homo sapiens
#> 259 Homo sapiens
#> 260 Homo sapiens
#> 261 Homo sapiens
#> 262 Homo sapiens
#> 263 Homo sapiens
#> 264 Homo sapiens
#> 265 Homo sapiens
#> 266 Homo sapiens
#> 267 Homo sapiens
#> 268 Homo sapiens
#> 269 Homo sapiens
#> 270 Homo sapiens
#> 271 Homo sapiens
#> 272 Homo sapiens
#> 273 Homo sapiens
#> 274 Homo sapiens
#> 275 Homo sapiens
#> 276 Homo sapiens
#> 277 Homo sapiens
#> 278 Homo sapiens
#> 279 Homo sapiens
#> 280 Homo sapiens
#> 281 Homo sapiens
#> 282 Homo sapiens
#> 283 Homo sapiens
#> 284 Homo sapiens
#> 285 Homo sapiens
#> 286 Homo sapiens
#> 287 Homo sapiens
#> 288 Homo sapiens
#> 289 Homo sapiens
#> 290 Homo sapiens
#> 291 Homo sapiens
#> 292 Homo sapiens
#> 293 Homo sapiens
#> 294 Homo sapiens
#> 295 Homo sapiens
#> 296 Homo sapiens
#> 297 Homo sapiens
#> 298 Homo sapiens
#> 299 Homo sapiens
#> 300 Homo sapiens
#> 301 Homo sapiens
#> 302 Homo sapiens
#> 303 Homo sapiens
#> 304 Homo sapiens
#> 305 Homo sapiens
#> 306 Homo sapiens
#> 307 Homo sapiens
#> 308 Homo sapiens
#> 309 Homo sapiens
#> 310 Homo sapiens
#> 311 Homo sapiens
#> 312 Homo sapiens
#> 313 Homo sapiens
#> 314 Homo sapiens
#> 315 Homo sapiens
#> 316 Homo sapiens
#> 317 Homo sapiens
#> 318 Homo sapiens
#> 319 Homo sapiens
#> 320 Homo sapiens
#> 321 Homo sapiens
#> 322 Homo sapiens
#> 323 Homo sapiens
#> 324 Homo sapiens
#> 325 Homo sapiens
#> 326 Homo sapiens
#> 327 Homo sapiens
#> 328 Homo sapiens
#> 329 Homo sapiens
#> 330 Homo sapiens
#> 331 Homo sapiens
#> 332 Homo sapiens
#> 333 Homo sapiens
#> 334 Homo sapiens
#> 335 synthetic construct; Homo sapiens
#> 336 Homo sapiens
#> 337 Homo sapiens
#> 338 Homo sapiens
#> 339 Homo sapiens; Mus musculus
#> 340 Homo sapiens
#> 341 Homo sapiens
#> 342 Homo sapiens
#> 343 Homo sapiens
#> 344 Homo sapiens
#> 345 Homo sapiens
#> 346 Homo sapiens
#> 347 Homo sapiens
#> 348 Homo sapiens
#> 349 Homo sapiens
#> 350 Homo sapiens
#> 351 Homo sapiens
#> 352 Homo sapiens
#> 353 Homo sapiens
#> 354 Homo sapiens
#> 355 Homo sapiens
#> 356 Homo sapiens
#> 357 Homo sapiens
#> 358 Homo sapiens
#> 359 Homo sapiens
#> 360 Homo sapiens
#> 361 Mus musculus; Homo sapiens
#> 362 Homo sapiens
#> 363 Homo sapiens; Mus musculus
#> 364 Homo sapiens
#> 365 Homo sapiens
#> 366 Homo sapiens
#> 367 Homo sapiens
#> 368 Homo sapiens
#> 369 Homo sapiens
#> 370 Homo sapiens
#> 371 Homo sapiens
#> 372 Homo sapiens
#> 373 Homo sapiens
#> 374 Homo sapiens
#> 375 Homo sapiens
#> 376 Homo sapiens
#> 377 Homo sapiens
#> 378 Homo sapiens
#> 379 Homo sapiens
#> 380 Homo sapiens
#> 381 Homo sapiens
#> 382 Homo sapiens
#> 383 Homo sapiens
#> 384 Homo sapiens
#> 385 Homo sapiens
#> 386 Homo sapiens
#> 387 Homo sapiens
#> 388 Homo sapiens
#> 389 Homo sapiens
#> 390 Homo sapiens
#> 391 Homo sapiens
#> 392 Homo sapiens
#> 393 Homo sapiens
#> 394 Homo sapiens
#> 395 Homo sapiens
#> 396 Homo sapiens
#> 397 Homo sapiens
#> 398 Homo sapiens
#> 399 Homo sapiens
#> 400 Homo sapiens
#> 401 Homo sapiens
#> 402 Homo sapiens; Mus musculus
#> 403 Homo sapiens
#> 404 Homo sapiens
#> 405 Homo sapiens; Mus musculus
#> 406 Homo sapiens
#> 407 Homo sapiens
#> 408 Homo sapiens
#> 409 Homo sapiens
#> 410 Homo sapiens
#> 411 Homo sapiens
#> 412 Homo sapiens
#> 413 Homo sapiens
#> 414 Homo sapiens
#> 415 Homo sapiens
#> 416 Homo sapiens
#> 417 Homo sapiens
#> 418 Homo sapiens
#> 419 Homo sapiens
#> 420 Homo sapiens
#> 421 Homo sapiens
#> 422 Homo sapiens
#> 423 Homo sapiens
#> 424 Homo sapiens
#> 425 Homo sapiens
#> 426 Homo sapiens
#> 427 Mus musculus; Homo sapiens
#> 428 Mus musculus; Homo sapiens
#> 429 Homo sapiens
#> 430 Homo sapiens
#> 431 Homo sapiens
#> 432 Homo sapiens
#> 433 Homo sapiens
#> 434 Homo sapiens
#> 435 Homo sapiens
#> 436 Homo sapiens
#> 437 Homo sapiens
#> 438 Homo sapiens
#> 439 Homo sapiens
#> 440 Homo sapiens
#> 441 Homo sapiens
#> 442 Homo sapiens
#> 443 Homo sapiens
#> 444 Homo sapiens
#> 445 Homo sapiens
#> 446 Homo sapiens
#> 447 Homo sapiens
#> 448 Homo sapiens
#> 449 Homo sapiens
#> 450 Homo sapiens
#> 451 Homo sapiens
#> 452 Homo sapiens
#> 453 Homo sapiens
#> 454 Homo sapiens; Mus musculus
#> 455 Homo sapiens; Mus musculus
#> 456 Homo sapiens
#> 457 Homo sapiens
#> 458 Homo sapiens
#> 459 Homo sapiens
#> 460 Homo sapiens
#> 461 Homo sapiens
#> 462 Homo sapiens
#> 463 Homo sapiens
#> 464 Homo sapiens
#> 465 Homo sapiens
#> 466 Homo sapiens
#> 467 Homo sapiens
#> 468 Homo sapiens
#> 469 Homo sapiens
#> 470 Homo sapiens
#> 471 Homo sapiens
#> 472 Homo sapiens
#> 473 Homo sapiens
#> 474 Homo sapiens
#> 475 Homo sapiens
#> 476 Homo sapiens
#> 477 Homo sapiens
#> 478 Homo sapiens
#> 479 Homo sapiens
#> 480 Homo sapiens
#> 481 Homo sapiens
#> 482 Homo sapiens
#> 483 Homo sapiens
#> 484 Homo sapiens
#> 485 Homo sapiens
#> 486 Homo sapiens
#> 487 Homo sapiens
#> 488 Homo sapiens
#> 489 Homo sapiens
#> 490 Homo sapiens
#> 491 Homo sapiens
#> 492 Homo sapiens
#> 493 Homo sapiens
#> 494 Homo sapiens
#> 495 Homo sapiens
#> 496 Homo sapiens
#> 497 Homo sapiens
#> 498 Homo sapiens
#> 499 Homo sapiens
#> 500 Homo sapiens
#> 501 Homo sapiens
#> 502 Homo sapiens
#> 503 Homo sapiens
#> 504 Homo sapiens
#> 505 Homo sapiens
#> 506 Homo sapiens
#> 507 Homo sapiens
#> 508 Homo sapiens
#> 509 Homo sapiens
#> 510 Homo sapiens
#> 511 Homo sapiens
#> 512 Homo sapiens
#> 513 Homo sapiens
#> 514 Papio hamadryas; Homo sapiens
#> 515 Homo sapiens
#> 516 Mus musculus; Homo sapiens
#> 517 Homo sapiens
#> 518 Homo sapiens
#> 519 Homo sapiens
#> 520 Homo sapiens
#> 521 Homo sapiens
#> 522 Homo sapiens
#> 523 Homo sapiens
#> 524 Homo sapiens
#> 525 Homo sapiens
#> 526 Homo sapiens
#> 527 Homo sapiens
#> 528 Homo sapiens; Mus musculus
#> 529 Homo sapiens
#> 530 Homo sapiens
#> 531 Homo sapiens
#> 532 Homo sapiens
#> 533 Mus musculus; Homo sapiens
#> 534 Homo sapiens
#> 535 Homo sapiens
#> 536 Homo sapiens
#> 537 Homo sapiens
#> 538 Homo sapiens
#> 539 Homo sapiens
#> 540 Homo sapiens
#> 541 Homo sapiens
#> 542 Homo sapiens
#> 543 Homo sapiens
#> 544 Homo sapiens
#> 545 Homo sapiens
#> 546 Homo sapiens
#> 547 Homo sapiens
#> 548 Homo sapiens
#> 549 Homo sapiens
#> 550 Homo sapiens
#> 551 Homo sapiens
#> 552 Homo sapiens
#> 553 Homo sapiens
#> 554 Homo sapiens
#> 555 Homo sapiens
#> 556 Homo sapiens
#> 557 Homo sapiens
#> 558 Homo sapiens
#> 559 Homo sapiens
#> 560 Homo sapiens
#> 561 Homo sapiens
#> 562 Mus musculus; Homo sapiens
#> 563 Homo sapiens
#> 564 Homo sapiens
#> 565 Homo sapiens
#> 566 Homo sapiens
#> 567 Homo sapiens
#> 568 Homo sapiens
#> 569 Homo sapiens
#> 570 Homo sapiens
#> 571 Homo sapiens
#> 572 Homo sapiens
#> 573 Homo sapiens
#> 574 Homo sapiens
#> 575 Homo sapiens
#> 576 Homo sapiens
#> 577 Homo sapiens
#> 578 Homo sapiens
#> 579 Homo sapiens
#> 580 Homo sapiens
#> 581 Homo sapiens
#> 582 Homo sapiens
#> 583 Homo sapiens
#> 584 Homo sapiens
#> 585 Homo sapiens
#> 586 Homo sapiens
#> 587 Homo sapiens
#> 588 Homo sapiens
#> 589 Homo sapiens
#> 590 Homo sapiens
#> 591 Homo sapiens
#> 592 Homo sapiens
#> 593 Homo sapiens
#> 594 Homo sapiens
#> 595 Homo sapiens
#> 596 Homo sapiens
#> 597 Homo sapiens
#> 598 Homo sapiens
#> 599 Homo sapiens
#> 600 Homo sapiens
#> 601 Homo sapiens
#> 602 Homo sapiens
#> 603 Homo sapiens
#> 604 Homo sapiens
#> 605 Homo sapiens
#> 606 Homo sapiens
#> 607 Homo sapiens
#> 608 Homo sapiens
#> 609 Homo sapiens
#> 610 Homo sapiens
#> 611 Homo sapiens
#> 612 Homo sapiens
#> 613 Homo sapiens
#> 614 Homo sapiens
#> 615 Homo sapiens
#> 616 Homo sapiens
#> 617 Homo sapiens; Macacine alphaherpesvirus 1; Merkel cell polyomavirus; Human gammaherpesvirus 4; JC polyomavirus; Human immunodeficiency virus 1; Human gammaherpesvirus 8; Human alphaherpesvirus 2; Saimiriine gammaherpesvirus 2; Betapolyomavirus hominis; Human alphaherpesvirus 1; Human betaherpesvirus 5; Human betaherpesvirus 6B; Betapolyomavirus macacae
#> 618 Homo sapiens
#> 619 Homo sapiens
#> 620 Homo sapiens
#> 621 Homo sapiens
#> 622 Homo sapiens
#> 623 Homo sapiens
#> 624 Homo sapiens
#> 625 Homo sapiens
#> 626 Homo sapiens
#> 627 Homo sapiens; Mus musculus
#> 628 Homo sapiens
#> 629 Homo sapiens
#> 630 Homo sapiens
#> 631 Homo sapiens
#> 632 Homo sapiens
#> 633 Homo sapiens
#> 634 Homo sapiens
#> 635 Homo sapiens
#> 636 Homo sapiens
#> 637 Homo sapiens
#> 638 Homo sapiens
#> 639 Homo sapiens
#> 640 Homo sapiens
#> 641 Homo sapiens
#> 642 Homo sapiens
#> 643 Homo sapiens
#> 644 Homo sapiens
#> 645 Homo sapiens
#> 646 Homo sapiens
#> 647 Homo sapiens
#> 648 Homo sapiens
#> 649 Homo sapiens; Mus musculus
#> 650 Homo sapiens
#> 651 Homo sapiens
#> 652 Homo sapiens
#> 653 Homo sapiens
#> 654 Homo sapiens
#> 655 Homo sapiens
#> 656 Homo sapiens
#> 657 Homo sapiens
#> 658 Homo sapiens
#> 659 Homo sapiens
#> 660 Homo sapiens
#> 661 Homo sapiens
#> 662 Homo sapiens
#> 663 Homo sapiens
#> 664 Homo sapiens
#> 665 Homo sapiens
#> 666 Homo sapiens
#> 667 Homo sapiens
#> 668 Homo sapiens
#> 669 Homo sapiens
#> 670 Homo sapiens
#> 671 Homo sapiens
#> 672 Homo sapiens
#> 673 Homo sapiens
#> 674 Homo sapiens
#> 675 Homo sapiens
#> 676 Homo sapiens
#> 677 Homo sapiens
#> 678 Homo sapiens
#> 679 Homo sapiens
#> 680 Homo sapiens
#> 681 Mus musculus; Homo sapiens
#> 682 Homo sapiens
#> 683 Homo sapiens
#> 684 Homo sapiens
#> 685 Homo sapiens
#> 686 Homo sapiens
#> 687 Homo sapiens
#> 688 Homo sapiens; Mus musculus
#> 689 Homo sapiens
#> 690 Homo sapiens
#> 691 Homo sapiens
#> 692 Homo sapiens
#> 693 Homo sapiens
#> 694 Homo sapiens
#> 695 Homo sapiens
#> 696 Homo sapiens
#> 697 Homo sapiens
#> 698 Homo sapiens
#> 699 Homo sapiens
#> 700 Homo sapiens
#> 701 Homo sapiens
#> 702 Homo sapiens
#> 703 Homo sapiens
#> 704 Homo sapiens
#> 705 Homo sapiens
#> 706 Homo sapiens
#> 707 Homo sapiens
#> 708 Homo sapiens
#> 709 Homo sapiens
#> 710 Homo sapiens
#> 711 Homo sapiens
#> 712 Homo sapiens
#> 713 Homo sapiens
#> 714 Homo sapiens
#> 715 Homo sapiens
#> 716 Homo sapiens
#> 717 Homo sapiens
#> 718 Homo sapiens
#> 719 Homo sapiens
#> 720 Homo sapiens; Mus musculus
#> 721 Homo sapiens; Mus musculus
#> 722 Homo sapiens
#> 723 Homo sapiens; Danio rerio
#> 724 Homo sapiens
#> 725 Homo sapiens
#> 726 Homo sapiens
#> 727 Homo sapiens
#> 728 Homo sapiens
#> 729 Homo sapiens
#> 730 Homo sapiens
#> 731 Homo sapiens
#> 732 Homo sapiens
#> 733 Homo sapiens
#> 734 Homo sapiens
#> 735 Homo sapiens
#> 736 Homo sapiens
#> 737 Homo sapiens
#> 738 Homo sapiens
#> 739 Homo sapiens
#> 740 Homo sapiens
#> 741 Homo sapiens
#> 742 Homo sapiens
#> 743 Homo sapiens
#> 744 Homo sapiens
#> 745 Homo sapiens
#> 746 Homo sapiens
#> 747 Homo sapiens
#> 748 Homo sapiens
#> 749 Homo sapiens
#> 750 Homo sapiens
#> 751 Homo sapiens
#> 752 Homo sapiens
#> 753 Homo sapiens
#> 754 Homo sapiens
#> 755 Homo sapiens
#> 756 Homo sapiens
#> 757 Homo sapiens; Mus musculus
#> 758 Mus musculus; Homo sapiens
#> 759 Homo sapiens
#> 760 Homo sapiens
#> 761 Homo sapiens
#> 762 Homo sapiens
#> 763 Homo sapiens
#> 764 Homo sapiens; Mus musculus
#> 765 Homo sapiens
#> 766 Homo sapiens
#> 767 Homo sapiens
#> 768 Homo sapiens
#> 769 Homo sapiens
#> 770 Homo sapiens
#> 771 Homo sapiens
#> 772 Homo sapiens
#> 773 Homo sapiens
#> 774 Homo sapiens
#> 775 Homo sapiens
#> 776 Homo sapiens
#> 777 Homo sapiens
#> 778 Homo sapiens
#> 779 Homo sapiens
#> 780 Homo sapiens
#> 781 Homo sapiens
#> 782 Homo sapiens
#> 783 Homo sapiens
#> 784 synthetic construct; Homo sapiens
#> 785 Homo sapiens
#> 786 Homo sapiens
#> 787 Homo sapiens
#> 788 Homo sapiens
#> 789 Homo sapiens
#> 790 Homo sapiens
#> 791 Homo sapiens
#> 792 Homo sapiens
#> 793 Homo sapiens
#> 794 Homo sapiens
#> 795 Homo sapiens
#> 796 Homo sapiens
#> 797 Rattus norvegicus; Homo sapiens
#> 798 Homo sapiens
#> 799 Homo sapiens
#> 800 Homo sapiens
#> 801 Homo sapiens
#> 802 Homo sapiens
#> 803 Homo sapiens
#> 804 Homo sapiens
#> 805 Homo sapiens
#> 806 Homo sapiens
#> 807 Homo sapiens
#> 808 Homo sapiens
#> 809 Homo sapiens
#> 810 Homo sapiens
#> 811 Homo sapiens
#> 812 Homo sapiens; synthetic construct
#> 813 Homo sapiens; synthetic construct
#> 814 Homo sapiens
#> 815 Homo sapiens; Mus musculus
#> 816 Homo sapiens
#> 817 Human gammaherpesvirus 8; Mus musculus cytomegalovirus 2; Betapolyomavirus macacae; Homo sapiens; Human betaherpesvirus 5; Murid gammaherpesvirus 4; Betapolyomavirus hominis; Human alphaherpesvirus 1; Human alphaherpesvirus 2; Human gammaherpesvirus 4; Mus musculus; Rattus norvegicus; Murid betaherpesvirus 1; JC polyomavirus; Human immunodeficiency virus 1; Merkel cell polyomavirus
#> 818 Homo sapiens
#> 819 Homo sapiens
#> 820 Homo sapiens
#> 821 JC polyomavirus; Human gammaherpesvirus 8; Betapolyomavirus macacae; Rattus norvegicus; Mus musculus; Human alphaherpesvirus 1; Human betaherpesvirus 5; Human gammaherpesvirus 4; Human immunodeficiency virus 1; Homo sapiens; Betapolyomavirus hominis
#> 822 Homo sapiens
#> 823 Homo sapiens
#> 824 Homo sapiens
#> 825 Homo sapiens
#> 826 Homo sapiens
#> 827 Homo sapiens
#> 828 Homo sapiens
#> 829 Homo sapiens
#> 830 Homo sapiens
#> 831 Homo sapiens
#> 832 Homo sapiens
#> 833 Homo sapiens
#> 834 Homo sapiens
#> 835 Homo sapiens
#> 836 Homo sapiens
#> 837 Homo sapiens
#> 838 Homo sapiens
#> 839 Homo sapiens
#> 840 Homo sapiens
#> 841 Homo sapiens; synthetic construct
#> 842 synthetic construct; Homo sapiens
#> 843 Homo sapiens
#> 844 Homo sapiens
#> 845 Homo sapiens
#> 846 Homo sapiens
#> 847 Homo sapiens
#> 848 Homo sapiens
#> 849 Homo sapiens; Mus musculus
#> 850 Mus musculus; Human alphaherpesvirus 1; Human gammaherpesvirus 4; Rattus norvegicus; Murid betaherpesvirus 1; JC polyomavirus; Human gammaherpesvirus 8; Betapolyomavirus macacae; Homo sapiens; Human betaherpesvirus 5; Human immunodeficiency virus; Murid gammaherpesvirus 4; Betapolyomavirus hominis
#> 851 Homo sapiens
#> 852 Homo sapiens
#> 853 Macaca mulatta; Homo sapiens
#> 854 Homo sapiens
#> 855 Homo sapiens
#> 856 Homo sapiens
#> 857 Homo sapiens
#> 858 Homo sapiens
#> 859 Homo sapiens
#> 860 Homo sapiens
#> 861 Homo sapiens
#> 862 Homo sapiens
#> 863 Homo sapiens
#> 864 Homo sapiens
#> 865 Homo sapiens
#> 866 Homo sapiens
#> 867 Homo sapiens
#> 868 Homo sapiens
#> 869 Homo sapiens
#> 870 Homo sapiens
#> 871 Homo sapiens
#> 872 Homo sapiens
#> 873 Homo sapiens
#> 874 Homo sapiens
#> 875 Homo sapiens
#> 876 Homo sapiens
#> 877 Homo sapiens
#> 878 Homo sapiens
#> 879 Homo sapiens; Papio hamadryas
#> 880 Homo sapiens
#> 881 Homo sapiens
#> 882 Homo sapiens
#> 883 Homo sapiens
#> 884 Homo sapiens
#> 885 synthetic construct; Homo sapiens
#> 886 Homo sapiens
#> 887 Homo sapiens
#> 888 Homo sapiens
#> 889 Homo sapiens
#> 890 Homo sapiens
#> 891 Homo sapiens
#> 892 Homo sapiens
#> 893 Homo sapiens
#> 894 Homo sapiens
#> 895 Homo sapiens
#> 896 Homo sapiens
#> 897 Homo sapiens
#> 898 Homo sapiens
#> 899 Homo sapiens
#> 900 Homo sapiens
#> 901 Homo sapiens
#> 902 Homo sapiens
#> 903 Homo sapiens
#> 904 Homo sapiens
#> 905 Homo sapiens
#> 906 Homo sapiens
#> 907 Homo sapiens
#> 908 Homo sapiens
#> 909 Rattus norvegicus; Homo sapiens
#> 910 Homo sapiens
#> 911 Homo sapiens
#> 912 Homo sapiens
#> 913 Homo sapiens
#> 914 Homo sapiens
#> 915 Homo sapiens
#> 916 Homo sapiens
#> 917 Homo sapiens
#> 918 Homo sapiens
#> 919 Homo sapiens
#> 920 Homo sapiens
#> 921 Homo sapiens
#> 922 Homo sapiens
#> 923 Homo sapiens
#> 924 Homo sapiens
#> 925 Homo sapiens
#> 926 Homo sapiens
#> 927 Homo sapiens
#> 928 Homo sapiens
#> 929 Homo sapiens
#> 930 Homo sapiens
#> 931 Homo sapiens
#> 932 Homo sapiens
#> 933 Homo sapiens
#> 934 Homo sapiens
#> 935 Homo sapiens
#> 936 Homo sapiens
#> 937 Homo sapiens
#> 938 Homo sapiens
#> 939 Homo sapiens
#> 940 Homo sapiens
#> 941 Homo sapiens
#> 942 Homo sapiens
#> 943 Homo sapiens
#> 944 Homo sapiens
#> 945 Homo sapiens
#> 946 Homo sapiens
#> 947 Homo sapiens
#> 948 Homo sapiens
#> 949 Homo sapiens
#> 950 Homo sapiens
#> 951 Homo sapiens
#> 952 Homo sapiens
#> 953 Homo sapiens
#> 954 Homo sapiens
#> 955 Homo sapiens
#> 956 Homo sapiens
#> 957 Human gammaherpesvirus 8; Mus musculus cytomegalovirus 2; Betapolyomavirus macacae; Rattus norvegicus; Human alphaherpesvirus 2; JC polyomavirus; Merkel cell polyomavirus; Mus musculus; Human alphaherpesvirus 1; Human betaherpesvirus 5; Human gammaherpesvirus 4; Human immunodeficiency virus 1; Homo sapiens; Murid gammaherpesvirus 4; Betapolyomavirus hominis
#> 958 Homo sapiens
#> 959 Homo sapiens
#> 960 Mus musculus; Homo sapiens
#> 961 Homo sapiens
#> 962 Homo sapiens
#> 963 Homo sapiens
#> 964 Homo sapiens
#> 965 Homo sapiens
#> 966 Homo sapiens
#> 967 Homo sapiens
#> 968 Homo sapiens
#> 969 Homo sapiens
#> 970 Homo sapiens
#> 971 Homo sapiens
#> 972 Homo sapiens
#> 973 Homo sapiens
#> 974 Homo sapiens
#> 975 Gallus gallus; Oryctolagus cuniculus; Mus musculus; Escherichia coli; Bos taurus; Homo sapiens
#> 976 Homo sapiens
#> 977 Homo sapiens
#> 978 Homo sapiens
#> 979 Homo sapiens
#> 980 Homo sapiens
#> 981 Homo sapiens
#> 982 Homo sapiens
#> 983 Homo sapiens
#> 984 Homo sapiens
#> 985 Homo sapiens
#> 986 Homo sapiens
#> 987 Homo sapiens
#> 988 Homo sapiens
#> 989 Homo sapiens
#> 990 Homo sapiens
#> 991 Homo sapiens
#> 992 Homo sapiens
#> 993 Homo sapiens
#> 994 Homo sapiens
#> 995 Homo sapiens
#> 996 Homo sapiens
#> 997 Homo sapiens
#> 998 Homo sapiens
#> 999 Rattus norvegicus; Murid gammaherpesvirus 4; Betapolyomavirus hominis; Human alphaherpesvirus 1; Human betaherpesvirus 5; Betapolyomavirus macacae; Homo sapiens; Mus musculus; Murid betaherpesvirus 1; Human gammaherpesvirus 4; JC polyomavirus; Human immunodeficiency virus 1; Human gammaherpesvirus 8
#> 1000 Homo sapiens
#> 1001 Homo sapiens
#> 1002 Homo sapiens
#> 1003 Homo sapiens
#> 1004 Homo sapiens
#> 1005 Homo sapiens
#> 1006 Homo sapiens
#> 1007 Homo sapiens
#> 1008 Homo sapiens
#> 1009 Homo sapiens
#> 1010 Homo sapiens
#> 1011 Homo sapiens
#> 1012 Homo sapiens
#> 1013 Homo sapiens
#> 1014 Human gammaherpesvirus 8; Betapolyomavirus macacae; Homo sapiens; Human betaherpesvirus 5; Murid gammaherpesvirus 4; Betapolyomavirus hominis; Mus musculus; Human alphaherpesvirus 1; Human gammaherpesvirus 4; Rattus norvegicus; Murid betaherpesvirus 1; JC polyomavirus; Human immunodeficiency virus 1
#> 1015 JC polyomavirus; Human gammaherpesvirus 8; Betapolyomavirus macacae; Rattus norvegicus; Mus musculus; Human alphaherpesvirus 1; Human betaherpesvirus 5; Murid betaherpesvirus 1; Human gammaherpesvirus 4; Human immunodeficiency virus 1; Homo sapiens; Murid gammaherpesvirus 4; Betapolyomavirus hominis
#> 1016 Homo sapiens
#> 1017 Homo sapiens
#> 1018 Homo sapiens
#> 1019 Homo sapiens
#> 1020 Homo sapiens
#> 1021 Homo sapiens
#> 1022 Homo sapiens
#> 1023 Homo sapiens
#> 1024 Homo sapiens
#> 1025 Homo sapiens
#> 1026 Homo sapiens
#> 1027 Homo sapiens
#> 1028 Homo sapiens
#> 1029 Homo sapiens
#> 1030 Homo sapiens
#> 1031 Homo sapiens
#> 1032 Homo sapiens
#> 1033 Homo sapiens
#> 1034 Homo sapiens
#> 1035 Homo sapiens
#> 1036 Homo sapiens
#> 1037 Homo sapiens
#> 1038 Homo sapiens
#> 1039 Homo sapiens
#> 1040 Homo sapiens
#> 1041 Human gammaherpesvirus 8; Betapolyomavirus macacae; Rattus norvegicus; Murid betaherpesvirus 1; JC polyomavirus; Mus musculus; Human alphaherpesvirus 1; Human betaherpesvirus 5; Human gammaherpesvirus 4; Human immunodeficiency virus 1; Homo sapiens; Murid gammaherpesvirus 4; Betapolyomavirus hominis
#> 1042 Homo sapiens
#> 1043 Homo sapiens
#> 1044 Homo sapiens
#> 1045 Homo sapiens
#> 1046 Homo sapiens
#> 1047 Homo sapiens
#> 1048 Homo sapiens
#> 1049 Homo sapiens
#> 1050 Homo sapiens
#> 1051 Homo sapiens
#> 1052 Homo sapiens
#> 1053 Homo sapiens
#> 1054 Homo sapiens; Mus musculus; Rattus norvegicus
#> 1055 Homo sapiens
#> 1056 Homo sapiens
#> 1057 Homo sapiens
#> 1058 Homo sapiens
#> 1059 Homo sapiens
#> 1060 Homo sapiens
#> 1061 Homo sapiens
#> 1062 Homo sapiens; Mus musculus; Rattus norvegicus
#> 1063 Homo sapiens; Mus musculus; Rattus norvegicus
#> 1064 Mus musculus; Rattus norvegicus; Homo sapiens
#> 1065 Homo sapiens
#> 1066 Homo sapiens
#> 1067 Homo sapiens
#> 1068 Homo sapiens
#> 1069 Homo sapiens
#> 1070 Rattus norvegicus; Homo sapiens
#> 1071 Homo sapiens; Rattus norvegicus; Mus musculus
#> 1072 Homo sapiens
#> 1073 Homo sapiens
#> 1074 Homo sapiens
#> 1075 Homo sapiens
#> 1076 Homo sapiens
#> 1077 Homo sapiens
#> 1078 Homo sapiens
#> 1079 Homo sapiens
#> 1080 Homo sapiens
#> 1081 Homo sapiens
#> 1082 Homo sapiens
#> 1083 Macaca fascicularis; Homo sapiens
#> 1084 Homo sapiens
#> 1085 Homo sapiens
#> 1086 Homo sapiens
#> 1087 Homo sapiens
#> 1088 Homo sapiens
#> 1089 Homo sapiens
#> 1090 Homo sapiens
#> 1091 Homo sapiens
#> 1092 Homo sapiens
#> 1093 Homo sapiens
#> 1094 Homo sapiens
#> 1095 Homo sapiens
#> 1096 Homo sapiens
#> 1097 Homo sapiens
#> 1098 Homo sapiens
#> 1099 Homo sapiens
#> 1100 Homo sapiens
#> 1101 Homo sapiens
#> 1102 Homo sapiens
#> 1103 Homo sapiens
#> 1104 Homo sapiens
#> 1105 Homo sapiens
#> 1106 Homo sapiens
#> 1107 Homo sapiens
#> Type
#> 1 Methylation profiling by genome tiling array
#> 2 Expression profiling by array; Non-coding RNA profiling by array
#> 3 Non-coding RNA profiling by high throughput sequencing
#> 4 Expression profiling by high throughput sequencing
#> 5 Expression profiling by high throughput sequencing
#> 6 Genome binding/occupancy profiling by high throughput sequencing
#> 7 Expression profiling by high throughput sequencing
#> 8 Expression profiling by high throughput sequencing
#> 9 Expression profiling by high throughput sequencing
#> 10 Methylation profiling by array
#> 11 Expression profiling by high throughput sequencing
#> 12 Expression profiling by high throughput sequencing
#> 13 Expression profiling by high throughput sequencing
#> 14 Methylation profiling by genome tiling array
#> 15 Expression profiling by high throughput sequencing
#> 16 Expression profiling by high throughput sequencing
#> 17 Non-coding RNA profiling by high throughput sequencing
#> 18 Expression profiling by high throughput sequencing
#> 19 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 20 Expression profiling by high throughput sequencing
#> 21 Expression profiling by array
#> 22 Expression profiling by high throughput sequencing
#> 23 Methylation profiling by high throughput sequencing
#> 24 Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing
#> 25 Expression profiling by high throughput sequencing
#> 26 Non-coding RNA profiling by high throughput sequencing
#> 27 Expression profiling by high throughput sequencing
#> 28 Methylation profiling by high throughput sequencing
#> 29 Methylation profiling by high throughput sequencing
#> 30 Methylation profiling by high throughput sequencing
#> 31 Expression profiling by high throughput sequencing
#> 32 Non-coding RNA profiling by array
#> 33 Genome binding/occupancy profiling by high throughput sequencing
#> 34 Genome binding/occupancy profiling by high throughput sequencing
#> 35 Expression profiling by high throughput sequencing
#> 36 Expression profiling by high throughput sequencing
#> 37 Expression profiling by high throughput sequencing
#> 38 Non-coding RNA profiling by high throughput sequencing
#> 39 Non-coding RNA profiling by array
#> 40 Expression profiling by high throughput sequencing
#> 41 Expression profiling by high throughput sequencing
#> 42 Methylation profiling by high throughput sequencing
#> 43 Expression profiling by high throughput sequencing
#> 44 Methylation profiling by genome tiling array
#> 45 Expression profiling by array; Non-coding RNA profiling by array
#> 46 Expression profiling by high throughput sequencing
#> 47 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 48 Expression profiling by high throughput sequencing
#> 49 Genome binding/occupancy profiling by high throughput sequencing
#> 50 Expression profiling by high throughput sequencing
#> 51 Expression profiling by array
#> 52 Expression profiling by high throughput sequencing
#> 53 Expression profiling by array
#> 54 Protein profiling by protein array
#> 55 Expression profiling by high throughput sequencing
#> 56 Expression profiling by high throughput sequencing
#> 57 Non-coding RNA profiling by high throughput sequencing
#> 58 Expression profiling by high throughput sequencing; Other
#> 59 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 60 Expression profiling by high throughput sequencing
#> 61 Expression profiling by high throughput sequencing
#> 62 Expression profiling by high throughput sequencing
#> 63 Expression profiling by high throughput sequencing
#> 64 Expression profiling by high throughput sequencing
#> 65 Expression profiling by high throughput sequencing
#> 66 Expression profiling by high throughput sequencing
#> 67 Methylation profiling by genome tiling array
#> 68 Methylation profiling by genome tiling array
#> 69 Methylation profiling by array
#> 70 Expression profiling by high throughput sequencing
#> 71 Expression profiling by high throughput sequencing
#> 72 Expression profiling by high throughput sequencing
#> 73 Expression profiling by high throughput sequencing
#> 74 Expression profiling by high throughput sequencing
#> 75 Expression profiling by array; Non-coding RNA profiling by array
#> 76 Non-coding RNA profiling by array
#> 77 Expression profiling by high throughput sequencing
#> 78 Expression profiling by high throughput sequencing
#> 79 Expression profiling by array
#> 80 Expression profiling by high throughput sequencing
#> 81 Non-coding RNA profiling by array; Expression profiling by array
#> 82 Expression profiling by high throughput sequencing
#> 83 Expression profiling by high throughput sequencing
#> 84 Expression profiling by high throughput sequencing
#> 85 Expression profiling by array
#> 86 Expression profiling by array
#> 87 Expression profiling by high throughput sequencing; Other
#> 88 Expression profiling by high throughput sequencing
#> 89 Other
#> 90 Expression profiling by high throughput sequencing
#> 91 Expression profiling by high throughput sequencing
#> 92 Genome binding/occupancy profiling by high throughput sequencing
#> 93 Methylation profiling by array
#> 94 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 95 Non-coding RNA profiling by high throughput sequencing
#> 96 Expression profiling by high throughput sequencing
#> 97 Expression profiling by high throughput sequencing
#> 98 Expression profiling by high throughput sequencing; Other
#> 99 Expression profiling by array
#> 100 Methylation profiling by genome tiling array
#> 101 Expression profiling by high throughput sequencing
#> 102 Non-coding RNA profiling by high throughput sequencing
#> 103 Expression profiling by high throughput sequencing
#> 104 Expression profiling by array
#> 105 Expression profiling by high throughput sequencing
#> 106 Expression profiling by high throughput sequencing
#> 107 Expression profiling by high throughput sequencing
#> 108 Expression profiling by array
#> 109 Expression profiling by array
#> 110 Non-coding RNA profiling by high throughput sequencing
#> 111 Methylation profiling by high throughput sequencing
#> 112 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 113 Non-coding RNA profiling by high throughput sequencing
#> 114 Expression profiling by high throughput sequencing
#> 115 Expression profiling by high throughput sequencing
#> 116 Expression profiling by array; Non-coding RNA profiling by array
#> 117 Expression profiling by high throughput sequencing
#> 118 Expression profiling by high throughput sequencing
#> 119 Expression profiling by high throughput sequencing
#> 120 Expression profiling by high throughput sequencing
#> 121 Expression profiling by high throughput sequencing
#> 122 Expression profiling by high throughput sequencing
#> 123 Expression profiling by high throughput sequencing
#> 124 Expression profiling by array
#> 125 Expression profiling by high throughput sequencing; Other
#> 126 Expression profiling by RT-PCR
#> 127 Non-coding RNA profiling by high throughput sequencing
#> 128 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 129 Expression profiling by array
#> 130 Expression profiling by high throughput sequencing
#> 131 Expression profiling by high throughput sequencing
#> 132 Expression profiling by high throughput sequencing
#> 133 Expression profiling by high throughput sequencing
#> 134 Expression profiling by high throughput sequencing
#> 135 Expression profiling by high throughput sequencing
#> 136 Expression profiling by high throughput sequencing
#> 137 Expression profiling by high throughput sequencing
#> 138 Expression profiling by high throughput sequencing
#> 139 Expression profiling by high throughput sequencing
#> 140 Expression profiling by high throughput sequencing
#> 141 Expression profiling by high throughput sequencing
#> 142 Expression profiling by high throughput sequencing
#> 143 Expression profiling by high throughput sequencing
#> 144 Expression profiling by high throughput sequencing
#> 145 Expression profiling by high throughput sequencing
#> 146 Expression profiling by high throughput sequencing
#> 147 Genome binding/occupancy profiling by high throughput sequencing
#> 148 Expression profiling by high throughput sequencing
#> 149 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 150 Expression profiling by high throughput sequencing
#> 151 Genome binding/occupancy profiling by high throughput sequencing
#> 152 Non-coding RNA profiling by array
#> 153 Protein profiling by protein array
#> 154 Expression profiling by high throughput sequencing
#> 155 Expression profiling by high throughput sequencing; Expression profiling by array
#> 156 Expression profiling by high throughput sequencing
#> 157 Expression profiling by high throughput sequencing
#> 158 Non-coding RNA profiling by high throughput sequencing
#> 159 Other
#> 160 Expression profiling by high throughput sequencing
#> 161 Expression profiling by high throughput sequencing
#> 162 Expression profiling by high throughput sequencing
#> 163 Expression profiling by array; Non-coding RNA profiling by array
#> 164 Expression profiling by high throughput sequencing
#> 165 Expression profiling by high throughput sequencing
#> 166 Expression profiling by high throughput sequencing
#> 167 Genome binding/occupancy profiling by high throughput sequencing
#> 168 Non-coding RNA profiling by array
#> 169 Expression profiling by high throughput sequencing
#> 170 Expression profiling by high throughput sequencing
#> 171 Expression profiling by high throughput sequencing
#> 172 Expression profiling by high throughput sequencing
#> 173 Expression profiling by array
#> 174 Genome variation profiling by SNP array
#> 175 Expression profiling by high throughput sequencing
#> 176 Expression profiling by high throughput sequencing
#> 177 Expression profiling by high throughput sequencing
#> 178 Genome variation profiling by SNP array; SNP genotyping by SNP array
#> 179 Non-coding RNA profiling by high throughput sequencing
#> 180 Expression profiling by high throughput sequencing
#> 181 Expression profiling by high throughput sequencing
#> 182 Genome binding/occupancy profiling by high throughput sequencing
#> 183 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 184 Genome binding/occupancy profiling by high throughput sequencing
#> 185 Expression profiling by high throughput sequencing
#> 186 Methylation profiling by genome tiling array
#> 187 Expression profiling by high throughput sequencing
#> 188 Expression profiling by high throughput sequencing
#> 189 Expression profiling by high throughput sequencing
#> 190 Non-coding RNA profiling by array
#> 191 Expression profiling by array
#> 192 Non-coding RNA profiling by high throughput sequencing
#> 193 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 194 Methylation profiling by genome tiling array
#> 195 Expression profiling by array
#> 196 Expression profiling by high throughput sequencing
#> 197 Expression profiling by high throughput sequencing
#> 198 Expression profiling by high throughput sequencing
#> 199 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 200 Non-coding RNA profiling by high throughput sequencing
#> 201 Expression profiling by high throughput sequencing
#> 202 Expression profiling by high throughput sequencing
#> 203 Expression profiling by high throughput sequencing
#> 204 Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 205 Expression profiling by high throughput sequencing
#> 206 Expression profiling by array
#> 207 Genome binding/occupancy profiling by high throughput sequencing
#> 208 Expression profiling by high throughput sequencing
#> 209 Expression profiling by array
#> 210 Expression profiling by array
#> 211 Expression profiling by array
#> 212 Expression profiling by array; Non-coding RNA profiling by array
#> 213 Expression profiling by high throughput sequencing
#> 214 Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 215 Non-coding RNA profiling by high throughput sequencing
#> 216 Protein profiling by protein array
#> 217 Genome binding/occupancy profiling by high throughput sequencing
#> 218 Expression profiling by high throughput sequencing
#> 219 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 220 Non-coding RNA profiling by high throughput sequencing
#> 221 Expression profiling by high throughput sequencing
#> 222 Expression profiling by high throughput sequencing
#> 223 Expression profiling by array
#> 224 Non-coding RNA profiling by array
#> 225 Expression profiling by high throughput sequencing
#> 226 Other
#> 227 Expression profiling by high throughput sequencing
#> 228 Expression profiling by high throughput sequencing
#> 229 Expression profiling by high throughput sequencing
#> 230 Methylation profiling by array
#> 231 Methylation profiling by genome tiling array
#> 232 Methylation profiling by genome tiling array
#> 233 Expression profiling by array
#> 234 Expression profiling by array
#> 235 Expression profiling by array; Methylation profiling by genome tiling array
#> 236 Methylation profiling by genome tiling array
#> 237 Expression profiling by high throughput sequencing
#> 238 Expression profiling by high throughput sequencing
#> 239 Expression profiling by high throughput sequencing
#> 240 Expression profiling by array
#> 241 Methylation profiling by genome tiling array
#> 242 Methylation profiling by genome tiling array
#> 243 Methylation profiling by genome tiling array
#> 244 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 245 Expression profiling by array
#> 246 Other
#> 247 Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Other
#> 248 Expression profiling by high throughput sequencing
#> 249 Genome binding/occupancy profiling by high throughput sequencing
#> 250 Expression profiling by array
#> 251 Expression profiling by array
#> 252 Expression profiling by array
#> 253 Non-coding RNA profiling by high throughput sequencing
#> 254 Expression profiling by RT-PCR
#> 255 Non-coding RNA profiling by array
#> 256 Expression profiling by high throughput sequencing
#> 257 Expression profiling by high throughput sequencing
#> 258 Other
#> 259 Expression profiling by high throughput sequencing
#> 260 Methylation profiling by high throughput sequencing
#> 261 Expression profiling by high throughput sequencing
#> 262 Expression profiling by high throughput sequencing
#> 263 Genome variation profiling by array
#> 264 Expression profiling by array
#> 265 Non-coding RNA profiling by high throughput sequencing
#> 266 Expression profiling by array
#> 267 Expression profiling by array
#> 268 Methylation profiling by genome tiling array; Methylation profiling by SNP array
#> 269 Non-coding RNA profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 270 Expression profiling by high throughput sequencing
#> 271 Expression profiling by array
#> 272 Expression profiling by array
#> 273 Expression profiling by array
#> 274 Expression profiling by high throughput sequencing
#> 275 Expression profiling by high throughput sequencing
#> 276 Methylation profiling by genome tiling array
#> 277 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by array
#> 278 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 279 Methylation profiling by array
#> 280 Protein profiling by protein array
#> 281 Expression profiling by high throughput sequencing
#> 282 Expression profiling by high throughput sequencing
#> 283 Methylation profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 284 Expression profiling by high throughput sequencing
#> 285 Methylation profiling by high throughput sequencing
#> 286 Expression profiling by high throughput sequencing
#> 287 Other
#> 288 Expression profiling by array
#> 289 Expression profiling by high throughput sequencing
#> 290 Expression profiling by high throughput sequencing
#> 291 Expression profiling by high throughput sequencing
#> 292 Expression profiling by high throughput sequencing
#> 293 Expression profiling by high throughput sequencing
#> 294 Non-coding RNA profiling by high throughput sequencing
#> 295 Expression profiling by array
#> 296 Expression profiling by array
#> 297 Expression profiling by array
#> 298 Expression profiling by high throughput sequencing
#> 299 Expression profiling by high throughput sequencing
#> 300 Expression profiling by high throughput sequencing; Methylation profiling by high throughput sequencing
#> 301 Methylation profiling by high throughput sequencing
#> 302 Expression profiling by high throughput sequencing
#> 303 Expression profiling by high throughput sequencing
#> 304 Expression profiling by array
#> 305 Expression profiling by array
#> 306 Expression profiling by array
#> 307 Expression profiling by high throughput sequencing
#> 308 Expression profiling by high throughput sequencing
#> 309 Other
#> 310 Expression profiling by high throughput sequencing; Other; Genome binding/occupancy profiling by high throughput sequencing
#> 311 Expression profiling by high throughput sequencing
#> 312 Other
#> 313 Genome binding/occupancy profiling by high throughput sequencing
#> 314 Expression profiling by high throughput sequencing
#> 315 Methylation profiling by genome tiling array
#> 316 Expression profiling by high throughput sequencing
#> 317 Expression profiling by array
#> 318 Expression profiling by high throughput sequencing
#> 319 Expression profiling by high throughput sequencing
#> 320 Expression profiling by high throughput sequencing
#> 321 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 322 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 323 Expression profiling by array; Non-coding RNA profiling by array
#> 324 Expression profiling by high throughput sequencing
#> 325 Expression profiling by high throughput sequencing
#> 326 Expression profiling by array
#> 327 Non-coding RNA profiling by array
#> 328 Non-coding RNA profiling by array
#> 329 Expression profiling by array
#> 330 Expression profiling by array
#> 331 Expression profiling by high throughput sequencing
#> 332 Expression profiling by high throughput sequencing
#> 333 Expression profiling by high throughput sequencing
#> 334 Non-coding RNA profiling by high throughput sequencing
#> 335 Non-coding RNA profiling by array
#> 336 Expression profiling by array; Non-coding RNA profiling by array
#> 337 Non-coding RNA profiling by array
#> 338 Other
#> 339 Expression profiling by high throughput sequencing
#> 340 Non-coding RNA profiling by array
#> 341 Expression profiling by high throughput sequencing
#> 342 Non-coding RNA profiling by high throughput sequencing
#> 343 Expression profiling by high throughput sequencing
#> 344 Expression profiling by high throughput sequencing
#> 345 Expression profiling by array
#> 346 Expression profiling by array
#> 347 Methylation profiling by genome tiling array
#> 348 Expression profiling by high throughput sequencing
#> 349 Expression profiling by array
#> 350 Expression profiling by high throughput sequencing
#> 351 Expression profiling by high throughput sequencing
#> 352 Expression profiling by high throughput sequencing
#> 353 Expression profiling by high throughput sequencing
#> 354 Expression profiling by high throughput sequencing
#> 355 Genome variation profiling by genome tiling array
#> 356 Expression profiling by high throughput sequencing
#> 357 Genome binding/occupancy profiling by high throughput sequencing
#> 358 Expression profiling by high throughput sequencing
#> 359 Expression profiling by high throughput sequencing
#> 360 Genome variation profiling by array; SNP genotyping by SNP array
#> 361 Methylation profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 362 Genome binding/occupancy profiling by high throughput sequencing
#> 363 Methylation profiling by high throughput sequencing
#> 364 Expression profiling by array
#> 365 Expression profiling by high throughput sequencing
#> 366 SNP genotyping by SNP array
#> 367 Expression profiling by array
#> 368 Expression profiling by high throughput sequencing
#> 369 Other
#> 370 Genome binding/occupancy profiling by high throughput sequencing
#> 371 Expression profiling by high throughput sequencing
#> 372 Methylation profiling by high throughput sequencing
#> 373 Expression profiling by high throughput sequencing
#> 374 Expression profiling by array
#> 375 Methylation profiling by genome tiling array
#> 376 Expression profiling by high throughput sequencing
#> 377 Expression profiling by high throughput sequencing
#> 378 Expression profiling by high throughput sequencing
#> 379 Expression profiling by high throughput sequencing
#> 380 Methylation profiling by high throughput sequencing
#> 381 Methylation profiling by high throughput sequencing
#> 382 Expression profiling by array; Expression profiling by high throughput sequencing
#> 383 Expression profiling by high throughput sequencing
#> 384 Methylation profiling by high throughput sequencing
#> 385 Expression profiling by high throughput sequencing
#> 386 Expression profiling by high throughput sequencing
#> 387 Expression profiling by high throughput sequencing
#> 388 Expression profiling by high throughput sequencing
#> 389 Expression profiling by high throughput sequencing
#> 390 Expression profiling by high throughput sequencing
#> 391 Expression profiling by high throughput sequencing
#> 392 Expression profiling by high throughput sequencing
#> 393 Expression profiling by high throughput sequencing
#> 394 Genome binding/occupancy profiling by high throughput sequencing
#> 395 Methylation profiling by genome tiling array
#> 396 Expression profiling by array
#> 397 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 398 Expression profiling by high throughput sequencing
#> 399 Expression profiling by high throughput sequencing
#> 400 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 401 Expression profiling by high throughput sequencing
#> 402 Expression profiling by array
#> 403 Other
#> 404 Expression profiling by high throughput sequencing
#> 405 Expression profiling by high throughput sequencing
#> 406 Expression profiling by high throughput sequencing
#> 407 Expression profiling by high throughput sequencing
#> 408 Expression profiling by array
#> 409 Expression profiling by high throughput sequencing
#> 410 Expression profiling by array
#> 411 Expression profiling by array
#> 412 Expression profiling by high throughput sequencing
#> 413 Genome binding/occupancy profiling by high throughput sequencing
#> 414 Expression profiling by high throughput sequencing
#> 415 Other; Non-coding RNA profiling by high throughput sequencing
#> 416 Non-coding RNA profiling by high throughput sequencing
#> 417 Expression profiling by high throughput sequencing
#> 418 Expression profiling by high throughput sequencing
#> 419 Other
#> 420 Genome binding/occupancy profiling by high throughput sequencing
#> 421 Expression profiling by high throughput sequencing
#> 422 Non-coding RNA profiling by high throughput sequencing
#> 423 Expression profiling by high throughput sequencing
#> 424 Expression profiling by high throughput sequencing
#> 425 Expression profiling by high throughput sequencing
#> 426 Expression profiling by high throughput sequencing
#> 427 Protein profiling by protein array
#> 428 Protein profiling by protein array
#> 429 Expression profiling by high throughput sequencing
#> 430 Methylation profiling by genome tiling array
#> 431 Expression profiling by high throughput sequencing
#> 432 Expression profiling by array
#> 433 Expression profiling by array
#> 434 Expression profiling by array
#> 435 Expression profiling by array
#> 436 Expression profiling by array
#> 437 Expression profiling by high throughput sequencing
#> 438 Expression profiling by high throughput sequencing
#> 439 Expression profiling by high throughput sequencing
#> 440 Other
#> 441 Genome binding/occupancy profiling by high throughput sequencing
#> 442 Genome binding/occupancy profiling by high throughput sequencing
#> 443 Expression profiling by array
#> 444 Protein profiling by protein array
#> 445 Expression profiling by high throughput sequencing
#> 446 Expression profiling by high throughput sequencing
#> 447 Non-coding RNA profiling by array
#> 448 Expression profiling by high throughput sequencing
#> 449 Expression profiling by array
#> 450 Methylation profiling by high throughput sequencing
#> 451 Non-coding RNA profiling by array
#> 452 Expression profiling by high throughput sequencing
#> 453 Expression profiling by high throughput sequencing
#> 454 Expression profiling by high throughput sequencing; Other
#> 455 Protein profiling by protein array
#> 456 Methylation profiling by high throughput sequencing
#> 457 Expression profiling by array
#> 458 Expression profiling by array
#> 459 Expression profiling by high throughput sequencing
#> 460 Expression profiling by high throughput sequencing
#> 461 Genome binding/occupancy profiling by high throughput sequencing
#> 462 Expression profiling by array
#> 463 Expression profiling by high throughput sequencing
#> 464 Expression profiling by high throughput sequencing
#> 465 Expression profiling by high throughput sequencing
#> 466 Expression profiling by high throughput sequencing
#> 467 Genome variation profiling by SNP array
#> 468 Expression profiling by array
#> 469 Expression profiling by high throughput sequencing
#> 470 Expression profiling by high throughput sequencing
#> 471 Methylation profiling by genome tiling array
#> 472 Expression profiling by high throughput sequencing
#> 473 Expression profiling by array
#> 474 Expression profiling by high throughput sequencing
#> 475 Expression profiling by high throughput sequencing
#> 476 Expression profiling by array
#> 477 Expression profiling by array
#> 478 Genome binding/occupancy profiling by high throughput sequencing
#> 479 Expression profiling by high throughput sequencing
#> 480 Expression profiling by RT-PCR
#> 481 Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 482 Expression profiling by high throughput sequencing
#> 483 Genome binding/occupancy profiling by high throughput sequencing
#> 484 Expression profiling by array
#> 485 Expression profiling by high throughput sequencing
#> 486 Methylation profiling by genome tiling array; Expression profiling by high throughput sequencing
#> 487 Expression profiling by high throughput sequencing
#> 488 Methylation profiling by genome tiling array
#> 489 Protein profiling by protein array
#> 490 Genome binding/occupancy profiling by high throughput sequencing; Other
#> 491 Genome binding/occupancy profiling by high throughput sequencing; Other
#> 492 Genome binding/occupancy profiling by high throughput sequencing; Other
#> 493 Expression profiling by high throughput sequencing
#> 494 Expression profiling by high throughput sequencing
#> 495 Expression profiling by array
#> 496 Expression profiling by high throughput sequencing
#> 497 Other
#> 498 Expression profiling by array
#> 499 Expression profiling by high throughput sequencing
#> 500 Methylation profiling by high throughput sequencing
#> 501 Expression profiling by high throughput sequencing
#> 502 Expression profiling by array
#> 503 Expression profiling by array
#> 504 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 505 Expression profiling by array
#> 506 Other; Genome binding/occupancy profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 507 Expression profiling by array
#> 508 Expression profiling by high throughput sequencing
#> 509 Expression profiling by array
#> 510 Genome variation profiling by array
#> 511 Expression profiling by array
#> 512 Expression profiling by array
#> 513 Expression profiling by array
#> 514 Expression profiling by array
#> 515 Other
#> 516 Expression profiling by high throughput sequencing
#> 517 Expression profiling by high throughput sequencing
#> 518 Expression profiling by high throughput sequencing
#> 519 Expression profiling by high throughput sequencing
#> 520 Non-coding RNA profiling by array
#> 521 Expression profiling by high throughput sequencing
#> 522 Methylation profiling by high throughput sequencing
#> 523 Expression profiling by high throughput sequencing
#> 524 Expression profiling by high throughput sequencing
#> 525 Expression profiling by high throughput sequencing
#> 526 Expression profiling by array; Non-coding RNA profiling by array
#> 527 Expression profiling by high throughput sequencing
#> 528 Expression profiling by high throughput sequencing
#> 529 Expression profiling by high throughput sequencing
#> 530 Expression profiling by array
#> 531 Expression profiling by high throughput sequencing
#> 532 Expression profiling by high throughput sequencing
#> 533 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 534 Expression profiling by high throughput sequencing
#> 535 Expression profiling by array
#> 536 Expression profiling by array
#> 537 Expression profiling by array
#> 538 Expression profiling by high throughput sequencing
#> 539 Expression profiling by high throughput sequencing
#> 540 Methylation profiling by genome tiling array
#> 541 Expression profiling by array
#> 542 Expression profiling by high throughput sequencing
#> 543 Expression profiling by array
#> 544 Expression profiling by high throughput sequencing
#> 545 Expression profiling by array
#> 546 Expression profiling by array; Methylation profiling by high throughput sequencing
#> 547 Expression profiling by array
#> 548 Non-coding RNA profiling by array
#> 549 Genome binding/occupancy profiling by high throughput sequencing
#> 550 Expression profiling by array
#> 551 Expression profiling by high throughput sequencing
#> 552 Expression profiling by high throughput sequencing
#> 553 Expression profiling by high throughput sequencing
#> 554 Expression profiling by high throughput sequencing
#> 555 Non-coding RNA profiling by high throughput sequencing
#> 556 Expression profiling by RT-PCR
#> 557 Non-coding RNA profiling by array; Expression profiling by array
#> 558 Non-coding RNA profiling by high throughput sequencing
#> 559 Expression profiling by array
#> 560 Expression profiling by array
#> 561 Expression profiling by high throughput sequencing
#> 562 Expression profiling by high throughput sequencing
#> 563 Expression profiling by array
#> 564 Expression profiling by array
#> 565 Expression profiling by array
#> 566 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 567 Expression profiling by array
#> 568 Non-coding RNA profiling by high throughput sequencing
#> 569 Expression profiling by high throughput sequencing
#> 570 Expression profiling by high throughput sequencing
#> 571 Expression profiling by array; Methylation profiling by genome tiling array
#> 572 Expression profiling by array
#> 573 Expression profiling by high throughput sequencing
#> 574 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 575 Other
#> 576 Expression profiling by high throughput sequencing
#> 577 Expression profiling by high throughput sequencing
#> 578 Expression profiling by array
#> 579 Expression profiling by array
#> 580 Non-coding RNA profiling by array
#> 581 Expression profiling by array
#> 582 Expression profiling by array
#> 583 Expression profiling by array
#> 584 Expression profiling by array
#> 585 Expression profiling by high throughput sequencing
#> 586 Expression profiling by array
#> 587 Expression profiling by high throughput sequencing
#> 588 Expression profiling by high throughput sequencing
#> 589 Non-coding RNA profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 590 Expression profiling by array
#> 591 Expression profiling by high throughput sequencing
#> 592 Expression profiling by array
#> 593 Expression profiling by array
#> 594 Expression profiling by array
#> 595 Expression profiling by high throughput sequencing
#> 596 Expression profiling by high throughput sequencing
#> 597 Expression profiling by high throughput sequencing
#> 598 Expression profiling by array
#> 599 Expression profiling by array; Genome variation profiling by SNP array
#> 600 Expression profiling by array
#> 601 Genome variation profiling by SNP array
#> 602 Non-coding RNA profiling by high throughput sequencing
#> 603 Expression profiling by high throughput sequencing
#> 604 Expression profiling by SNP array
#> 605 Non-coding RNA profiling by array
#> 606 Expression profiling by array
#> 607 Methylation profiling by genome tiling array
#> 608 Non-coding RNA profiling by array
#> 609 Expression profiling by array
#> 610 Non-coding RNA profiling by array
#> 611 Expression profiling by array
#> 612 Expression profiling by array
#> 613 Expression profiling by array
#> 614 Expression profiling by array
#> 615 Expression profiling by array
#> 616 Expression profiling by array
#> 617 Non-coding RNA profiling by array
#> 618 Expression profiling by high throughput sequencing
#> 619 Methylation profiling by array
#> 620 Protein profiling by protein array
#> 621 Expression profiling by array
#> 622 Expression profiling by array
#> 623 Expression profiling by array
#> 624 Non-coding RNA profiling by array
#> 625 Expression profiling by array
#> 626 Expression profiling by array
#> 627 Expression profiling by high throughput sequencing
#> 628 Expression profiling by high throughput sequencing
#> 629 Expression profiling by high throughput sequencing
#> 630 SNP genotyping by SNP array; Genome variation profiling by SNP array
#> 631 Expression profiling by array
#> 632 Non-coding RNA profiling by high throughput sequencing
#> 633 Expression profiling by high throughput sequencing
#> 634 Expression profiling by high throughput sequencing
#> 635 Expression profiling by high throughput sequencing
#> 636 Expression profiling by high throughput sequencing
#> 637 Expression profiling by high throughput sequencing
#> 638 Expression profiling by array
#> 639 Expression profiling by array
#> 640 Methylation profiling by array
#> 641 Expression profiling by array; Non-coding RNA profiling by array
#> 642 Methylation profiling by array
#> 643 Non-coding RNA profiling by array
#> 644 Non-coding RNA profiling by array
#> 645 Expression profiling by high throughput sequencing
#> 646 Expression profiling by high throughput sequencing
#> 647 Methylation profiling by genome tiling array
#> 648 Expression profiling by high throughput sequencing
#> 649 Expression profiling by array
#> 650 Expression profiling by array
#> 651 Expression profiling by high throughput sequencing
#> 652 Genome binding/occupancy profiling by high throughput sequencing
#> 653 Expression profiling by array
#> 654 Expression profiling by array
#> 655 Expression profiling by array
#> 656 Expression profiling by array
#> 657 Non-coding RNA profiling by high throughput sequencing
#> 658 Expression profiling by array
#> 659 Non-coding RNA profiling by array
#> 660 Expression profiling by array
#> 661 Expression profiling by array
#> 662 Non-coding RNA profiling by array
#> 663 Expression profiling by high throughput sequencing
#> 664 Methylation profiling by high throughput sequencing
#> 665 Other
#> 666 Expression profiling by array
#> 667 Expression profiling by array
#> 668 Expression profiling by array
#> 669 Expression profiling by high throughput sequencing
#> 670 Expression profiling by array
#> 671 Expression profiling by high throughput sequencing
#> 672 Expression profiling by high throughput sequencing; Expression profiling by array
#> 673 Expression profiling by high throughput sequencing
#> 674 Expression profiling by high throughput sequencing
#> 675 Expression profiling by array
#> 676 Expression profiling by high throughput sequencing
#> 677 Expression profiling by high throughput sequencing
#> 678 Expression profiling by high throughput sequencing
#> 679 Expression profiling by array
#> 680 Methylation profiling by high throughput sequencing
#> 681 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 682 Expression profiling by array
#> 683 Genome binding/occupancy profiling by high throughput sequencing
#> 684 Methylation profiling by high throughput sequencing
#> 685 Expression profiling by high throughput sequencing
#> 686 Methylation profiling by high throughput sequencing
#> 687 Non-coding RNA profiling by array
#> 688 Expression profiling by high throughput sequencing
#> 689 Expression profiling by array
#> 690 Expression profiling by high throughput sequencing
#> 691 Expression profiling by high throughput sequencing
#> 692 Expression profiling by high throughput sequencing
#> 693 Expression profiling by array
#> 694 Expression profiling by high throughput sequencing
#> 695 Expression profiling by array
#> 696 Expression profiling by array
#> 697 Non-coding RNA profiling by array
#> 698 Expression profiling by array
#> 699 Methylation profiling by genome tiling array
#> 700 Methylation profiling by genome tiling array
#> 701 Methylation profiling by genome tiling array
#> 702 Expression profiling by array
#> 703 Methylation profiling by high throughput sequencing
#> 704 Expression profiling by high throughput sequencing
#> 705 Expression profiling by high throughput sequencing
#> 706 Expression profiling by high throughput sequencing
#> 707 Expression profiling by high throughput sequencing
#> 708 Methylation profiling by genome tiling array
#> 709 Non-coding RNA profiling by array
#> 710 Expression profiling by array
#> 711 Expression profiling by high throughput sequencing
#> 712 Expression profiling by high throughput sequencing
#> 713 Expression profiling by high throughput sequencing
#> 714 Methylation profiling by array
#> 715 Non-coding RNA profiling by array
#> 716 Expression profiling by array
#> 717 Methylation profiling by genome tiling array
#> 718 Methylation profiling by genome tiling array
#> 719 Methylation profiling by genome tiling array
#> 720 Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 721 Genome binding/occupancy profiling by high throughput sequencing
#> 722 Methylation profiling by high throughput sequencing
#> 723 Non-coding RNA profiling by array
#> 724 Genome binding/occupancy profiling by high throughput sequencing
#> 725 Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 726 Methylation profiling by array
#> 727 Expression profiling by array
#> 728 Methylation profiling by array
#> 729 Expression profiling by array
#> 730 Expression profiling by array
#> 731 Methylation profiling by genome tiling array; SNP genotyping by SNP array
#> 732 SNP genotyping by SNP array
#> 733 SNP genotyping by SNP array
#> 734 Expression profiling by array
#> 735 Expression profiling by array
#> 736 Expression profiling by array
#> 737 Expression profiling by array
#> 738 Expression profiling by array
#> 739 Expression profiling by array
#> 740 Expression profiling by array
#> 741 Expression profiling by array; Non-coding RNA profiling by array
#> 742 Expression profiling by array; Non-coding RNA profiling by array
#> 743 Expression profiling by array; Non-coding RNA profiling by array
#> 744 Non-coding RNA profiling by high throughput sequencing
#> 745 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 746 Expression profiling by array
#> 747 SNP genotyping by SNP array
#> 748 Expression profiling by array
#> 749 Expression profiling by array
#> 750 Expression profiling by array; Non-coding RNA profiling by array
#> 751 Expression profiling by array
#> 752 Non-coding RNA profiling by array
#> 753 Other
#> 754 Expression profiling by array
#> 755 Expression profiling by array
#> 756 Expression profiling by array
#> 757 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 758 Expression profiling by high throughput sequencing
#> 759 Expression profiling by array
#> 760 Expression profiling by array
#> 761 Expression profiling by RT-PCR; Other
#> 762 Expression profiling by array
#> 763 Expression profiling by array
#> 764 Genome binding/occupancy profiling by high throughput sequencing
#> 765 Expression profiling by array
#> 766 Expression profiling by array
#> 767 Expression profiling by array
#> 768 Expression profiling by array
#> 769 Expression profiling by array
#> 770 Expression profiling by array; Methylation profiling by array
#> 771 Expression profiling by array
#> 772 Methylation profiling by array
#> 773 Expression profiling by array
#> 774 Expression profiling by array
#> 775 Expression profiling by array
#> 776 Expression profiling by array
#> 777 Expression profiling by array
#> 778 Methylation profiling by high throughput sequencing
#> 779 Expression profiling by array
#> 780 Expression profiling by array
#> 781 Expression profiling by array
#> 782 Expression profiling by array
#> 783 Non-coding RNA profiling by array
#> 784 Non-coding RNA profiling by array
#> 785 Expression profiling by high throughput sequencing
#> 786 Expression profiling by array
#> 787 Expression profiling by array
#> 788 Expression profiling by array
#> 789 Expression profiling by array
#> 790 Expression profiling by array
#> 791 Expression profiling by array
#> 792 Expression profiling by array
#> 793 Expression profiling by array; Other; Non-coding RNA profiling by array
#> 794 Non-coding RNA profiling by array
#> 795 Other
#> 796 Expression profiling by array
#> 797 Non-coding RNA profiling by array
#> 798 Non-coding RNA profiling by array
#> 799 Expression profiling by array
#> 800 Expression profiling by array
#> 801 Expression profiling by high throughput sequencing
#> 802 Expression profiling by array
#> 803 Non-coding RNA profiling by array
#> 804 Expression profiling by array
#> 805 Methylation profiling by genome tiling array
#> 806 Expression profiling by array
#> 807 Expression profiling by array
#> 808 Methylation profiling by genome tiling array
#> 809 Genome variation profiling by genome tiling array
#> 810 Expression profiling by array
#> 811 Expression profiling by array
#> 812 Non-coding RNA profiling by array
#> 813 Non-coding RNA profiling by array
#> 814 Expression profiling by array
#> 815 Methylation profiling by genome tiling array
#> 816 Expression profiling by high throughput sequencing
#> 817 Non-coding RNA profiling by array
#> 818 Expression profiling by array
#> 819 Expression profiling by array
#> 820 Expression profiling by array
#> 821 Non-coding RNA profiling by array
#> 822 Non-coding RNA profiling by array; Expression profiling by array
#> 823 Expression profiling by high throughput sequencing
#> 824 Expression profiling by array
#> 825 Expression profiling by array
#> 826 Expression profiling by array
#> 827 Expression profiling by array
#> 828 Genome binding/occupancy profiling by high throughput sequencing
#> 829 Genome variation profiling by SNP array
#> 830 Expression profiling by array
#> 831 Expression profiling by array; Non-coding RNA profiling by array
#> 832 SNP genotyping by SNP array
#> 833 Genome variation profiling by SNP array; SNP genotyping by SNP array; Expression profiling by high throughput sequencing
#> 834 SNP genotyping by SNP array; Genome variation profiling by SNP array
#> 835 Expression profiling by array
#> 836 Expression profiling by array
#> 837 Expression profiling by high throughput sequencing
#> 838 Methylation profiling by high throughput sequencing
#> 839 Expression profiling by array
#> 840 Expression profiling by array
#> 841 Expression profiling by array; Non-coding RNA profiling by array
#> 842 Non-coding RNA profiling by array
#> 843 Expression profiling by array
#> 844 Protein profiling by protein array
#> 845 Expression profiling by array
#> 846 Non-coding RNA profiling by array
#> 847 Expression profiling by high throughput sequencing; Third-party reanalysis
#> 848 Non-coding RNA profiling by array
#> 849 Expression profiling by array; Methylation profiling by genome tiling array
#> 850 Non-coding RNA profiling by array
#> 851 Expression profiling by array
#> 852 Methylation profiling by array
#> 853 Expression profiling by array
#> 854 Expression profiling by array
#> 855 Methylation profiling by array
#> 856 Expression profiling by array; Third-party reanalysis
#> 857 Non-coding RNA profiling by array
#> 858 Expression profiling by array
#> 859 Expression profiling by array
#> 860 Expression profiling by array
#> 861 Expression profiling by array
#> 862 Expression profiling by array
#> 863 Expression profiling by array
#> 864 Expression profiling by array
#> 865 Expression profiling by array
#> 866 Expression profiling by RT-PCR
#> 867 Expression profiling by array
#> 868 Expression profiling by array
#> 869 Non-coding RNA profiling by array
#> 870 Expression profiling by high throughput sequencing; Third-party reanalysis
#> 871 Expression profiling by array
#> 872 Expression profiling by array
#> 873 Expression profiling by array; Other
#> 874 Other
#> 875 Expression profiling by array
#> 876 Expression profiling by array
#> 877 Genome binding/occupancy profiling by genome tiling array
#> 878 Expression profiling by array
#> 879 Expression profiling by array
#> 880 Expression profiling by array
#> 881 Non-coding RNA profiling by high throughput sequencing
#> 882 Methylation profiling by array
#> 883 Expression profiling by array
#> 884 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 885 Non-coding RNA profiling by array
#> 886 Genome binding/occupancy profiling by high throughput sequencing
#> 887 Expression profiling by high throughput sequencing
#> 888 Expression profiling by array
#> 889 Expression profiling by array
#> 890 Expression profiling by array
#> 891 Expression profiling by array
#> 892 Expression profiling by array
#> 893 Methylation profiling by array
#> 894 Expression profiling by array
#> 895 Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing
#> 896 Expression profiling by array
#> 897 Expression profiling by array
#> 898 Expression profiling by array
#> 899 Expression profiling by array
#> 900 Expression profiling by array
#> 901 Expression profiling by array
#> 902 Expression profiling by array
#> 903 Expression profiling by array
#> 904 Expression profiling by array
#> 905 Expression profiling by array
#> 906 Expression profiling by array
#> 907 Expression profiling by array
#> 908 Non-coding RNA profiling by high throughput sequencing
#> 909 Expression profiling by array
#> 910 Expression profiling by array
#> 911 Expression profiling by array
#> 912 Expression profiling by array
#> 913 Expression profiling by array
#> 914 Expression profiling by array
#> 915 Expression profiling by array
#> 916 Expression profiling by array
#> 917 Other; Genome variation profiling by genome tiling array
#> 918 Genome variation profiling by genome tiling array
#> 919 Other
#> 920 Expression profiling by array
#> 921 Expression profiling by array
#> 922 Expression profiling by array
#> 923 Methylation profiling by array
#> 924 Expression profiling by array
#> 925 Genome variation profiling by SNP array; SNP genotyping by SNP array
#> 926 Expression profiling by array
#> 927 Expression profiling by array
#> 928 Expression profiling by array
#> 929 Expression profiling by array
#> 930 Expression profiling by array
#> 931 Expression profiling by array
#> 932 Expression profiling by array
#> 933 Expression profiling by array
#> 934 Non-coding RNA profiling by array
#> 935 Expression profiling by array
#> 936 Expression profiling by array
#> 937 Expression profiling by array
#> 938 Expression profiling by array
#> 939 Expression profiling by array
#> 940 Expression profiling by array
#> 941 Expression profiling by RT-PCR
#> 942 Expression profiling by array
#> 943 Non-coding RNA profiling by array
#> 944 Expression profiling by array
#> 945 Expression profiling by array
#> 946 Expression profiling by array; Genome variation profiling by genome tiling array
#> 947 Expression profiling by array
#> 948 Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing
#> 949 Expression profiling by array
#> 950 Expression profiling by array; Genome binding/occupancy profiling by genome tiling array
#> 951 Genome binding/occupancy profiling by genome tiling array
#> 952 Genome binding/occupancy profiling by genome tiling array
#> 953 Expression profiling by array
#> 954 Expression profiling by array
#> 955 Expression profiling by array
#> 956 Expression profiling by array
#> 957 Non-coding RNA profiling by array
#> 958 Expression profiling by array
#> 959 Expression profiling by array
#> 960 Expression profiling by array
#> 961 Expression profiling by array
#> 962 Expression profiling by array
#> 963 Methylation profiling by array
#> 964 Expression profiling by array
#> 965 Expression profiling by array
#> 966 Expression profiling by high throughput sequencing
#> 967 Expression profiling by array
#> 968 Expression profiling by array
#> 969 Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 970 Protein profiling by protein array
#> 971 Expression profiling by high throughput sequencing
#> 972 Expression profiling by array
#> 973 Expression profiling by array
#> 974 Expression profiling by array
#> 975 Protein profiling by protein array
#> 976 Non-coding RNA profiling by array
#> 977 Expression profiling by array
#> 978 Expression profiling by array
#> 979 Expression profiling by array
#> 980 Expression profiling by array
#> 981 Methylation profiling by SNP array
#> 982 Expression profiling by array
#> 983 Expression profiling by array
#> 984 Expression profiling by array
#> 985 Expression profiling by array
#> 986 Expression profiling by array
#> 987 Methylation profiling by array
#> 988 Expression profiling by array
#> 989 Expression profiling by array
#> 990 Expression profiling by array
#> 991 Expression profiling by array
#> 992 Expression profiling by array
#> 993 Expression profiling by array
#> 994 Expression profiling by array
#> 995 Expression profiling by array
#> 996 Expression profiling by array
#> 997 Expression profiling by array
#> 998 Expression profiling by array
#> 999 Non-coding RNA profiling by array; Expression profiling by array
#> 1000 Expression profiling by array
#> 1001 Expression profiling by array
#> 1002 Genome binding/occupancy profiling by high throughput sequencing
#> 1003 Expression profiling by array
#> 1004 Non-coding RNA profiling by array
#> 1005 Expression profiling by array
#> 1006 Expression profiling by array
#> 1007 Expression profiling by array
#> 1008 Other
#> 1009 Expression profiling by array
#> 1010 Expression profiling by array
#> 1011 Expression profiling by array
#> 1012 Expression profiling by array
#> 1013 Expression profiling by array
#> 1014 Expression profiling by array; Non-coding RNA profiling by array
#> 1015 Non-coding RNA profiling by array
#> 1016 Methylation profiling by genome tiling array
#> 1017 Genome variation profiling by SNP array
#> 1018 Expression profiling by array
#> 1019 Methylation profiling by genome tiling array
#> 1020 Expression profiling by array
#> 1021 Expression profiling by array
#> 1022 Genome binding/occupancy profiling by high throughput sequencing
#> 1023 Genome binding/occupancy profiling by high throughput sequencing
#> 1024 Genome binding/occupancy profiling by high throughput sequencing
#> 1025 Expression profiling by array
#> 1026 Expression profiling by array
#> 1027 Expression profiling by array
#> 1028 Expression profiling by array
#> 1029 Expression profiling by array
#> 1030 Methylation profiling by genome tiling array
#> 1031 Expression profiling by array
#> 1032 Expression profiling by array
#> 1033 Expression profiling by array
#> 1034 Expression profiling by array
#> 1035 Expression profiling by array
#> 1036 Expression profiling by array
#> 1037 Expression profiling by array
#> 1038 Expression profiling by array
#> 1039 Expression profiling by array
#> 1040 Non-coding RNA profiling by array
#> 1041 Expression profiling by array; Non-coding RNA profiling by array
#> 1042 Expression profiling by array
#> 1043 Expression profiling by array
#> 1044 Expression profiling by array
#> 1045 Expression profiling by array
#> 1046 Methylation profiling by array
#> 1047 Methylation profiling by genome tiling array
#> 1048 Expression profiling by array
#> 1049 Expression profiling by array
#> 1050 Non-coding RNA profiling by array
#> 1051 Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by array; Expression profiling by high throughput sequencing
#> 1052 Expression profiling by array
#> 1053 Expression profiling by array
#> 1054 Non-coding RNA profiling by array
#> 1055 Expression profiling by array
#> 1056 Expression profiling by array
#> 1057 Expression profiling by array
#> 1058 Expression profiling by array
#> 1059 Expression profiling by array
#> 1060 Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing; Expression profiling by high throughput sequencing; Non-coding RNA profiling by high throughput sequencing
#> 1061 Expression profiling by array
#> 1062 Expression profiling by array; Non-coding RNA profiling by array
#> 1063 Non-coding RNA profiling by array
#> 1064 Non-coding RNA profiling by array
#> 1065 Expression profiling by array
#> 1066 Expression profiling by array
#> 1067 Expression profiling by array
#> 1068 Expression profiling by array
#> 1069 Expression profiling by array
#> 1070 Expression profiling by array
#> 1071 Non-coding RNA profiling by array
#> 1072 Expression profiling by array
#> 1073 Expression profiling by array
#> 1074 Expression profiling by array
#> 1075 Expression profiling by array
#> 1076 Expression profiling by array
#> 1077 Expression profiling by array
#> 1078 Expression profiling by array
#> 1079 Expression profiling by array
#> 1080 Expression profiling by array
#> 1081 Expression profiling by array
#> 1082 Expression profiling by array
#> 1083 Expression profiling by array
#> 1084 Expression profiling by array
#> 1085 Expression profiling by array
#> 1086 Expression profiling by array
#> 1087 Expression profiling by array
#> 1088 Expression profiling by array
#> 1089 Expression profiling by array
#> 1090 Expression profiling by array
#> 1091 Expression profiling by array
#> 1092 Expression profiling by array
#> 1093 Expression profiling by array
#> 1094 Expression profiling by array
#> 1095 Expression profiling by SAGE
#> 1096 Expression profiling by array
#> 1097 Expression profiling by array
#> 1098 Expression profiling by array
#> 1099 Expression profiling by array
#> 1100 Expression profiling by array
#> 1101 Expression profiling by array
#> 1102 Expression profiling by array; Methylation profiling by array
#> 1103 Expression profiling by array
#> 1104 Expression profiling by array
#> 1105 Expression profiling by array
#> 1106 Protein profiling by protein array
#> 1107 Expression profiling by array
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#> 718 GPL16304 61 Samples
#> 719 GPL16304 63 Samples
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#> 848 GPL14767 20 Samples
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#> 860 GPL571 378 Samples
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#> 867 GPL10558 12 Samples 5167
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#> 932 GPL13607 24 Samples
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#> 937 GPL570 28 Samples
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#> 1105 GPL8300 6 Samples 961
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#> 1107 <NA> 5 related Platforms 50 Samples
#> FTP download
#> 1 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE197nnn/GSE197881/
#> 2 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE191nnn/GSE191210/
#> 3 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE207nnn/GSE207901/
#> 4 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE205nnn/GSE205668/
#> 5 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173735/
#> 6 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173734/
#> 7 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE202nnn/GSE202295/
#> 8 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE207nnn/GSE207122/
#> 9 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE197nnn/GSE197850/
#> 10 GEO (PAIR, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE188nnn/GSE188395/
#> 11 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153315/
#> 12 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE200nnn/GSE200678/
#> 13 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE206nnn/GSE206528/
#> 14 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE200nnn/GSE200659/
#> 15 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE201nnn/GSE201908/
#> 16 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE199nnn/GSE199437/
#> 17 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE199nnn/GSE199852/
#> 18 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE198nnn/GSE198520/
#> 19 GEO (NARROWPEAK, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185728/
#> 20 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE176nnn/GSE176145/
#> 21 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE133nnn/GSE133666/
#> 22 GEO (TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE203nnn/GSE203346/
#> 23 GEO (BEDGRAPH, RDATA) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE203nnn/GSE203169/
#> 24 GEO (BEDGRAPH, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE203nnn/GSE203353/
#> 25 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135076/
#> 26 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE192nnn/GSE192541/
#> 27 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE202nnn/GSE202151/
#> 28 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE194nnn/GSE194156/
#> 29 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE194nnn/GSE194155/
#> 30 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE194nnn/GSE194154/
#> 31 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE175nnn/GSE175759/
#> 32 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE201nnn/GSE201543/
#> 33 GEO (BIGWIG, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE181nnn/GSE181478/
#> 34 GEO (BIGWIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE181nnn/GSE181412/
#> 35 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE180nnn/GSE180521/
#> 36 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE200nnn/GSE200983/
#> 37 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE200nnn/GSE200477/
#> 38 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE186nnn/GSE186883/
#> 39 GEO (RCC) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185845/
#> 40 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE199nnn/GSE199939/
#> 41 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE188nnn/GSE188827/
#> 42 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE186nnn/GSE186021/
#> 43 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182138/
#> 44 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE199nnn/GSE199700/
#> 45 GEO (TXT, XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE199nnn/GSE199148/
#> 46 GEO (FA, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185052/
#> 47 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185191/
#> 48 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185190/
#> 49 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185189/
#> 50 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE193nnn/GSE193161/
#> 51 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162622/
#> 52 GEO (CSV, FASTA) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE197nnn/GSE197456/
#> 53 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE197nnn/GSE197285/
#> 54 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162273/
#> 55 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE126nnn/GSE126803/
#> 56 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE196nnn/GSE196900/
#> 57 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE193nnn/GSE193436/
#> 58 GEO (CSV, TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166641/
#> 59 GEO (CSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE167nnn/GSE167199/
#> 60 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159586/
#> 61 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159554/
#> 62 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166640/
#> 63 GEO (CSV, TAR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185038/
#> 64 GEO (CSV, TAR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185036/
#> 65 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185035/
#> 66 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE189nnn/GSE189849/
#> 67 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE186nnn/GSE186766/
#> 68 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE113nnn/GSE113409/
#> 69 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE113nnn/GSE113392/
#> 70 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163947/
#> 71 GEO (TAR, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE165nnn/GSE165784/
#> 72 GEO (TXT, XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE176nnn/GSE176230/
#> 73 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166822/
#> 74 GEO (H5) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE194nnn/GSE194061/
#> 75 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE194nnn/GSE194119/
#> 76 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE193nnn/GSE193974/
#> 77 GEO (GTF, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182259/
#> 78 GEO (XLS, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE172nnn/GSE172148/
#> 79 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE167nnn/GSE167269/
#> 80 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE193nnn/GSE193510/
#> 81 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE193nnn/GSE193626/
#> 82 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE176nnn/GSE176171/
#> 83 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE176nnn/GSE176067/
#> 84 GEO (TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE174nnn/GSE174475/
#> 85 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE193nnn/GSE193273/
#> 86 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156035/
#> 87 GEO (BEDGRAPH) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154415/
#> 88 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154414/
#> 89 GEO (BEDGRAPH) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154413/
#> 90 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE113nnn/GSE113199/
#> 91 GEO (CSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE181nnn/GSE181143/
#> 92 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE142nnn/GSE142178/
#> 93 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162166/
#> 94 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE190nnn/GSE190973/
#> 95 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE190nnn/GSE190972/
#> 96 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE190nnn/GSE190971/
#> 97 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE184nnn/GSE184831/
#> 98 GEO (CSV, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182870/
#> 99 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE190nnn/GSE190832/
#> 100 GEO (CSV, IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE180nnn/GSE180355/
#> 101 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE174nnn/GSE174481/
#> 102 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE188nnn/GSE188235/
#> 103 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE188nnn/GSE188234/
#> 104 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE189nnn/GSE189923/
#> 105 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185430/
#> 106 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE181nnn/GSE181328/
#> 107 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159955/
#> 108 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123088/
#> 109 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123086/
#> 110 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE189nnn/GSE189107/
#> 111 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140842/
#> 112 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162135/
#> 113 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162133/
#> 114 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE161nnn/GSE161989/
#> 115 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE188nnn/GSE188799/
#> 116 GEO (CEL, CHP, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE189nnn/GSE189007/
#> 117 GEO (CSV, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162837/
#> 118 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137766/
#> 119 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE136nnn/GSE136887/
#> 120 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE98nnn/GSE98887/
#> 121 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE186nnn/GSE186524/
#> 122 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE165nnn/GSE165816/
#> 123 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE179nnn/GSE179568/
#> 124 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182494/
#> 125 GEO (DCC, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166120/
#> 126 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE186nnn/GSE186432/
#> 127 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE180nnn/GSE180470/
#> 128 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185598/
#> 129 GEO (RCC) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163211/
#> 130 GEO (CSV, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE185nnn/GSE185749/
#> 131 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE142nnn/GSE142290/
#> 132 GEO (ZIP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE175nnn/GSE175477/
#> 133 GEO (H5) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE141nnn/GSE141319/
#> 134 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182923/
#> 135 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE180nnn/GSE180504/
#> 136 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159924/
#> 137 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE139nnn/GSE139947/
#> 138 GEO (H5AD) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE178nnn/GSE178991/
#> 139 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156911/
#> 140 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE169nnn/GSE169514/
#> 141 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE184nnn/GSE184050/
#> 142 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE184nnn/GSE184016/
#> 143 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE183nnn/GSE183965/
#> 144 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE183nnn/GSE183701/
#> 145 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE183nnn/GSE183568/
#> 146 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE72nnn/GSE72991/
#> 147 GEO (BW, NARROWPEAK) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173277/
#> 148 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173228/
#> 149 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE165nnn/GSE165709/
#> 150 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE165nnn/GSE165708/
#> 151 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE165nnn/GSE165703/
#> 152 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182737/
#> 153 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE122nnn/GSE122279/
#> 154 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159337/
#> 155 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182121/
#> 156 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182120/
#> 157 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182117/
#> 158 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE182nnn/GSE182053/
#> 159 GEO (BED, TAB) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159867/
#> 160 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE181nnn/GSE181881/
#> 161 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144414/
#> 162 GEO (XLS, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE181nnn/GSE181674/
#> 163 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118139/
#> 164 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173276/
#> 165 GEO (TAR, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE168nnn/GSE168071/
#> 166 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE179nnn/GSE179921/
#> 167 GEO (BED, TDF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE179nnn/GSE179762/
#> 168 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE168nnn/GSE168996/
#> 169 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163773/
#> 170 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE180nnn/GSE180083/
#> 171 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE180nnn/GSE180081/
#> 172 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150212/
#> 173 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153837/
#> 174 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156411/
#> 175 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153834/
#> 176 GEO (H5, JSON, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE179nnn/GSE179143/
#> 177 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150724/
#> 178 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE178nnn/GSE178828/
#> 179 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE178nnn/GSE178721/
#> 180 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173983/
#> 181 GEO (BED, BW) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE178nnn/GSE178734/
#> 182 GEO (BED, BROADPEAK, BW) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE178nnn/GSE178733/
#> 183 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156122/
#> 184 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156121/
#> 185 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156061/
#> 186 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE122nnn/GSE122086/
#> 187 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE176nnn/GSE176324/
#> 188 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173669/
#> 189 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE165nnn/GSE165385/
#> 190 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE175nnn/GSE175745/
#> 191 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE175nnn/GSE175735/
#> 192 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE114nnn/GSE114860/
#> 193 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE174nnn/GSE174502/
#> 194 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173613/
#> 195 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE136nnn/GSE136344/
#> 196 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE173nnn/GSE173193/
#> 197 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE157nnn/GSE157988/
#> 198 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159556/
#> 199 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE160nnn/GSE160310/
#> 200 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE160nnn/GSE160308/
#> 201 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE160nnn/GSE160306/
#> 202 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153410/
#> 203 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE164nnn/GSE164416/
#> 204 GEO (BW, H5, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151426/
#> 205 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE141nnn/GSE141432/
#> 206 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE136nnn/GSE136395/
#> 207 GEO (BED, BW) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151405/
#> 208 GEO (H5) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148073/
#> 209 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148642/
#> 210 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148640/
#> 211 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148639/
#> 212 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE168nnn/GSE168437/
#> 213 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166785/
#> 214 GEO (BW, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE167nnn/GSE167250/
#> 215 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE112nnn/GSE112168/
#> 216 GEO (DATA) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162521/
#> 217 GEO (BED, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163160/
#> 218 GEO (DAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166047/
#> 219 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151497/
#> 220 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151496/
#> 221 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151495/
#> 222 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE168nnn/GSE168492/
#> 223 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE168nnn/GSE168072/
#> 224 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE168nnn/GSE168327/
#> 225 GEO (CSV, MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE167nnn/GSE167976/
#> 226 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE167nnn/GSE167914/
#> 227 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162689/
#> 228 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE165nnn/GSE165121/
#> 229 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166847/
#> 230 GEO (CSV, IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166845/
#> 231 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166787/
#> 232 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166652/
#> 233 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166502/
#> 234 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE166nnn/GSE166467/
#> 235 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE110nnn/GSE110829/
#> 236 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE110nnn/GSE110828/
#> 237 GEO (CSV, MTX, TSV, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE158nnn/GSE158055/
#> 238 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE157nnn/GSE157640/
#> 239 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159717/
#> 240 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE147nnn/GSE147890/
#> 241 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153221/
#> 242 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153220/
#> 243 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153219/
#> 244 GEO (BIGWIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE149nnn/GSE149148/
#> 245 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE164nnn/GSE164934/
#> 246 GEO (HIC) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163610/
#> 247 GEO (HIC) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE160nnn/GSE160474/
#> 248 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE160nnn/GSE160473/
#> 249 GEO (BED, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE160nnn/GSE160472/
#> 250 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE161nnn/GSE161355/
#> 251 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE164nnn/GSE164338/
#> 252 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE25nnn/GSE25194/
#> 253 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE139nnn/GSE139577/
#> 254 GEO (TXT, XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118103/
#> 255 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163980/
#> 256 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163744/
#> 257 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163732/
#> 258 GEO (BW) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150172/
#> 259 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163603/
#> 260 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE163nnn/GSE163510/
#> 261 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE158nnn/GSE158292/
#> 262 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162830/
#> 263 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE157nnn/GSE157515/
#> 264 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE162nnn/GSE162557/
#> 265 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE147nnn/GSE147965/
#> 266 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE142nnn/GSE142401/
#> 267 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE142nnn/GSE142153/
#> 268 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE149nnn/GSE149568/
#> 269 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE157nnn/GSE157859/
#> 270 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE161nnn/GSE161914/
#> 271 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE161nnn/GSE161721/
#> 272 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE161nnn/GSE161720/
#> 273 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE161nnn/GSE161719/
#> 274 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154306/
#> 275 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144348/
#> 276 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE141nnn/GSE141065/
#> 277 GEO (IDAT, NARROWPEAK, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE145nnn/GSE145747/
#> 278 GEO (NARROWPEAK, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE145nnn/GSE145746/
#> 279 GEO (CSV, IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE145nnn/GSE145745/
#> 280 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE160nnn/GSE160005/
#> 281 GEO (CSV, TAB, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159984/
#> 282 GEO (CSV, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159759/
#> 283 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154378/
#> 284 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154377/
#> 285 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154348/
#> 286 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132831/
#> 287 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE159nnn/GSE159467/
#> 288 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154609/
#> 289 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154126/
#> 290 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE143nnn/GSE143783/
#> 291 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE152nnn/GSE152615/
#> 292 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE134nnn/GSE134431/
#> 293 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151764/
#> 294 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE157nnn/GSE157177/
#> 295 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154554/
#> 296 GEO (RCC) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE143nnn/GSE143690/
#> 297 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156993/
#> 298 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140308/
#> 299 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156903/
#> 300 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148061/
#> 301 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148060/
#> 302 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148059/
#> 303 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE146nnn/GSE146028/
#> 304 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156249/
#> 305 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156248/
#> 306 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156247/
#> 307 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156341/
#> 308 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156340/
#> 309 GEO (BEDPE) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156339/
#> 310 GEO (BEDPE, MTX, TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135357/
#> 311 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135356/
#> 312 GEO (BEDPE) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135355/
#> 313 GEO (BEDPE) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135354/
#> 314 GEO (GTF, TSV, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE156nnn/GSE156109/
#> 315 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE143nnn/GSE143209/
#> 316 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102485/
#> 317 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151683/
#> 318 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE155nnn/GSE155713/
#> 319 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144682/
#> 320 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE143nnn/GSE143735/
#> 321 GEO (NARROWPEAK, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE131nnn/GSE131169/
#> 322 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE138nnn/GSE138781/
#> 323 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE155nnn/GSE155188/
#> 324 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151588/
#> 325 GEO (XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE109nnn/GSE109163/
#> 326 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153565/
#> 327 GEO (RCC) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148961/
#> 328 GEO (RCC, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154647/
#> 329 GEO (CEL, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154629/
#> 330 GEO (CEL, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE154nnn/GSE154628/
#> 331 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE114nnn/GSE114192/
#> 332 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153792/
#> 333 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE153nnn/GSE153555/
#> 334 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE134nnn/GSE134594/
#> 335 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140959/
#> 336 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE133nnn/GSE133225/
#> 337 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE133nnn/GSE133217/
#> 338 GEO (CSV, FASTA) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150060/
#> 339 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151889/
#> 340 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE121nnn/GSE121221/
#> 341 GEO (DOCX, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE151nnn/GSE151610/
#> 342 GEO (XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE145nnn/GSE145593/
#> 343 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123658/
#> 344 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115221/
#> 345 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE146nnn/GSE146338/
#> 346 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144605/
#> 347 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE147nnn/GSE147740/
#> 348 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE145nnn/GSE145284/
#> 349 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE113nnn/GSE113969/
#> 350 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150586/
#> 351 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150621/
#> 352 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE150nnn/GSE150119/
#> 353 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE139nnn/GSE139535/
#> 354 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148896/
#> 355 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148812/
#> 356 GEO (GTF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148058/
#> 357 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE133nnn/GSE133219/
#> 358 GEO (GTF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE133nnn/GSE133218/
#> 359 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE143nnn/GSE143319/
#> 360 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE148nnn/GSE148375/
#> 361 GEO (BED, MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144693/
#> 362 GEO (BED, MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144692/
#> 363 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144691/
#> 364 GEO (CEL, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE146nnn/GSE146108/
#> 365 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE142nnn/GSE142385/
#> 366 GEO (CSV, IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE112nnn/GSE112652/
#> 367 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE138nnn/GSE138323/
#> 368 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE126nnn/GSE126101/
#> 369 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE126nnn/GSE126100/
#> 370 GEO (BED, BW, CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE126nnn/GSE126099/
#> 371 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE145nnn/GSE145347/
#> 372 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE139nnn/GSE139157/
#> 373 GEO (MTX, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE138nnn/GSE138857/
#> 374 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE146nnn/GSE146615/
#> 375 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE142nnn/GSE142512/
#> 376 GEO (CSV, MTX, TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE139nnn/GSE139949/
#> 377 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE95nnn/GSE95078/
#> 378 GEO (TAB) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE124nnn/GSE124742/
#> 379 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144908/
#> 380 GEO (BEDGRAPH) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE94nnn/GSE94729/
#> 381 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE138nnn/GSE138598/
#> 382 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE131nnn/GSE131528/
#> 383 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE131nnn/GSE131526/
#> 384 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE125nnn/GSE125929/
#> 385 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144441/
#> 386 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE119nnn/GSE119296/
#> 387 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE144nnn/GSE144169/
#> 388 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE141nnn/GSE141639/
#> 389 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE141nnn/GSE141309/
#> 390 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE143nnn/GSE143979/
#> 391 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135155/
#> 392 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE143nnn/GSE143495/
#> 393 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE143nnn/GSE143143/
#> 394 GEO (BIGWIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE142nnn/GSE142009/
#> 395 GEO (CEL, CHP, TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE121nnn/GSE121820/
#> 396 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE124nnn/GSE124534/
#> 397 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE139nnn/GSE139932/
#> 398 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE133nnn/GSE133264/
#> 399 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE131nnn/GSE131065/
#> 400 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE130nnn/GSE130279/
#> 401 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE122nnn/GSE122429/
#> 402 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE94nnn/GSE94846/
#> 403 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE142nnn/GSE142553/
#> 404 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE141nnn/GSE141126/
#> 405 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE121nnn/GSE121346/
#> 406 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE121nnn/GSE121344/
#> 407 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE142nnn/GSE142025/
#> 408 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE141nnn/GSE141512/
#> 409 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE141nnn/GSE141410/
#> 410 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE124nnn/GSE124272/
#> 411 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE141nnn/GSE141193/
#> 412 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140627/
#> 413 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140404/
#> 414 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140403/
#> 415 GEO (BED, TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140065/
#> 416 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE140nnn/GSE140064/
#> 417 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE139nnn/GSE139929/
#> 418 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135197/
#> 419 GEO (BED, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE124nnn/GSE124264/
#> 420 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115326/
#> 421 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115312/
#> 422 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115306/
#> 423 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135453/
#> 424 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135776/
#> 425 GEO (MTX, TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129363/
#> 426 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE139nnn/GSE139073/
#> 427 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE138nnn/GSE138885/
#> 428 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE138nnn/GSE138856/
#> 429 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE131nnn/GSE131320/
#> 430 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE112nnn/GSE112893/
#> 431 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE114nnn/GSE114569/
#> 432 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE114nnn/GSE114248/
#> 433 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE114nnn/GSE114236/
#> 434 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE88nnn/GSE88839/
#> 435 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE136nnn/GSE136048/
#> 436 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96971/
#> 437 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137803/
#> 438 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE136nnn/GSE136053/
#> 439 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137136/
#> 440 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE136nnn/GSE136865/
#> 441 GEO (BW) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE133nnn/GSE133135/
#> 442 GEO (BW) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123404/
#> 443 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137684/
#> 444 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE130nnn/GSE130672/
#> 445 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE137nnn/GSE137317/
#> 446 GEO (RDS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE131nnn/GSE131882/
#> 447 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE133nnn/GSE133910/
#> 448 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE136nnn/GSE136353/
#> 449 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE136nnn/GSE136277/
#> 450 GEO (COV, TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE128nnn/GSE128289/
#> 451 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE51nnn/GSE51674/
#> 452 GEO (CSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE135nnn/GSE135944/
#> 453 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129935/
#> 454 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132306/
#> 455 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132111/
#> 456 GEO (TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE120nnn/GSE120024/
#> 457 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE132nnn/GSE132187/
#> 458 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE88nnn/GSE88794/
#> 459 GEO (CSV, TAB) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE133nnn/GSE133099/
#> 460 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE125nnn/GSE125769/
#> 461 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE125nnn/GSE125768/
#> 462 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE125nnn/GSE125590/
#> 463 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102677/
#> 464 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102633/
#> 465 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129112/
#> 466 GEO (CSV, TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE121nnn/GSE121862/
#> 467 GEO (CEL, CYCHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE122nnn/GSE122584/
#> 468 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE130nnn/GSE130991/
#> 469 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE114nnn/GSE114412/
#> 470 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129042/
#> 471 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129841/
#> 472 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE126nnn/GSE126169/
#> 473 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE101nnn/GSE101702/
#> 474 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129666/
#> 475 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129653/
#> 476 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129604/
#> 477 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129091/
#> 478 GEO (BEDGRAPH) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE129nnn/GSE129383/
#> 479 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115020/
#> 480 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE112nnn/GSE112690/
#> 481 GEO (BED, WIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE112nnn/GSE112342/
#> 482 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE112nnn/GSE112341/
#> 483 GEO (BED, WIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE112nnn/GSE112337/
#> 484 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE128nnn/GSE128381/
#> 485 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE128nnn/GSE128331/
#> 486 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE124nnn/GSE124811/
#> 487 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE124nnn/GSE124810/
#> 488 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE124nnn/GSE124809/
#> 489 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE45nnn/GSE45515/
#> 490 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE127nnn/GSE127045/
#> 491 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE127nnn/GSE127042/
#> 492 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE127nnn/GSE127033/
#> 493 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE124nnn/GSE124400/
#> 494 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE124nnn/GSE124284/
#> 495 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE127nnn/GSE127170/
#> 496 GEO (TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115828/
#> 497 GEO (RCC, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE110nnn/GSE110786/
#> 498 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE103nnn/GSE103682/
#> 499 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE77nnn/GSE77522/
#> 500 GEO (WIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE77nnn/GSE77268/
#> 501 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE104nnn/GSE104674/
#> 502 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115329/
#> 503 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115313/
#> 504 GEO (BIGWIG, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115421/
#> 505 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102295/
#> 506 GEO (BED, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118588/
#> 507 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE109nnn/GSE109022/
#> 508 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE104nnn/GSE104297/
#> 509 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE71nnn/GSE71799/
#> 510 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE107nnn/GSE107375/
#> 511 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE78nnn/GSE78721/
#> 512 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE9nnn/GSE9707/
#> 513 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7319/
#> 514 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97554/
#> 515 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE108nnn/GSE108403/
#> 516 GEO (FA, GTF, MTX, TSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE120nnn/GSE120522/
#> 517 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123844/
#> 518 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE108nnn/GSE108056/
#> 519 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE117nnn/GSE117454/
#> 520 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE116nnn/GSE116726/
#> 521 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE112nnn/GSE112594/
#> 522 GEO (BED, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE123nnn/GSE123279/
#> 523 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115257/
#> 524 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE107nnn/GSE107943/
#> 525 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE110nnn/GSE110914/
#> 526 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE116nnn/GSE116761/
#> 527 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE117nnn/GSE117469/
#> 528 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE106nnn/GSE106181/
#> 529 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE106nnn/GSE106177/
#> 530 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE105nnn/GSE105167/
#> 531 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE120nnn/GSE120904/
#> 532 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE120nnn/GSE120299/
#> 533 GEO (BW, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE101nnn/GSE101207/
#> 534 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE116nnn/GSE116559/
#> 535 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118230/
#> 536 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE118nnn/GSE118481/
#> 537 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE106nnn/GSE106800/
#> 538 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102498/
#> 539 GEO (GTF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE108nnn/GSE108413/
#> 540 GEO (IDAT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE106nnn/GSE106099/
#> 541 GEO (CEL, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE103nnn/GSE103552/
#> 542 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE89nnn/GSE89475/
#> 543 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96804/
#> 544 GEO (GTF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102371/
#> 545 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102234/
#> 546 GEO (BEDGRAPH, CEL, NARROWPEAK) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86376/
#> 547 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE85nnn/GSE85990/
#> 548 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE106nnn/GSE106953/
#> 549 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE116nnn/GSE116497/
#> 550 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE111nnn/GSE111154/
#> 551 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE116nnn/GSE116369/
#> 552 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE83nnn/GSE83782/
#> 553 GEO (XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE83nnn/GSE83781/
#> 554 GEO (XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE83nnn/GSE83699/
#> 555 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE116nnn/GSE116029/
#> 556 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE114nnn/GSE114477/
#> 557 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE104nnn/GSE104815/
#> 558 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE99nnn/GSE99853/
#> 559 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70365/
#> 560 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE114nnn/GSE114908/
#> 561 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE114nnn/GSE114051/
#> 562 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE110nnn/GSE110935/
#> 563 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE84nnn/GSE84453/
#> 564 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE37nnn/GSE37785/
#> 565 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12844/
#> 566 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97205/
#> 567 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE113nnn/GSE113080/
#> 568 GEO (XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE109nnn/GSE109266/
#> 569 GEO (XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE109nnn/GSE109265/
#> 570 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE111nnn/GSE111876/
#> 571 GEO (CEL, CSV, IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE98nnn/GSE98224/
#> 572 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE106nnn/GSE106813/
#> 573 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE95nnn/GSE95243/
#> 574 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE98nnn/GSE98675/
#> 575 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE110nnn/GSE110552/
#> 576 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE104nnn/GSE104423/
#> 577 GEO (GTF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE98nnn/GSE98485/
#> 578 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE104nnn/GSE104195/
#> 579 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE104nnn/GSE104190/
#> 580 GEO (GPR, XLS, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE105nnn/GSE105096/
#> 581 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102080/
#> 582 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102079/
#> 583 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE104nnn/GSE104954/
#> 584 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE104nnn/GSE104948/
#> 585 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE93nnn/GSE93709/
#> 586 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE109nnn/GSE109178/
#> 587 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE109nnn/GSE109140/
#> 588 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE92nnn/GSE92724/
#> 589 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE92nnn/GSE92772/
#> 590 GEO (CEL, CHP, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE80nnn/GSE80178/
#> 591 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE106nnn/GSE106148/
#> 592 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76896/
#> 593 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76895/
#> 594 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76894/
#> 595 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE77nnn/GSE77108/
#> 596 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE106nnn/GSE106520/
#> 597 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE84nnn/GSE84814/
#> 598 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63992/
#> 599 GEO (CEL, CHP, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE90nnn/GSE90076/
#> 600 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE90nnn/GSE90074/
#> 601 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE90nnn/GSE90073/
#> 602 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE105nnn/GSE105052/
#> 603 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81547/
#> 604 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE103nnn/GSE103931/
#> 605 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE103nnn/GSE103393/
#> 606 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE75nnn/GSE75062/
#> 607 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE103nnn/GSE103657/
#> 608 GEO (RCC, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97123/
#> 609 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE93nnn/GSE93032/
#> 610 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE85nnn/GSE85192/
#> 611 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE90nnn/GSE90999/
#> 612 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE74nnn/GSE74240/
#> 613 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE87nnn/GSE87005/
#> 614 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE99nnn/GSE99340/
#> 615 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE99nnn/GSE99339/
#> 616 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE99nnn/GSE99325/
#> 617 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86069/
#> 618 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97655/
#> 619 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE102nnn/GSE102177/
#> 620 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE95nnn/GSE95368/
#> 621 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE101nnn/GSE101931/
#> 622 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE94nnn/GSE94497/
#> 623 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE94nnn/GSE94496/
#> 624 GEO (GPR, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE101nnn/GSE101461/
#> 625 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86298/
#> 626 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE83nnn/GSE83452/
#> 627 GEO (TXT, XLS, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE99nnn/GSE99068/
#> 628 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97647/
#> 629 GEO (XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97591/
#> 630 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE100nnn/GSE100271/
#> 631 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE100nnn/GSE100185/
#> 632 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE90nnn/GSE90028/
#> 633 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96569/
#> 634 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96568/
#> 635 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96564/
#> 636 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96563/
#> 637 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96562/
#> 638 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE98nnn/GSE98501/
#> 639 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE98nnn/GSE98399/
#> 640 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE57nnn/GSE57362/
#> 641 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97530/
#> 642 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE90nnn/GSE90117/
#> 643 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68475/
#> 644 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE98nnn/GSE98043/
#> 645 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81965/
#> 646 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE94nnn/GSE94019/
#> 647 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE88nnn/GSE88929/
#> 648 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE97nnn/GSE97084/
#> 649 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE84nnn/GSE84823/
#> 650 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE84nnn/GSE84821/
#> 651 GEO (DIFF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE87nnn/GSE87626/
#> 652 GEO (BW) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE83nnn/GSE83345/
#> 653 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE72nnn/GSE72490/
#> 654 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE95nnn/GSE95849/
#> 655 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE95nnn/GSE95675/
#> 656 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE95nnn/GSE95674/
#> 657 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE89nnn/GSE89360/
#> 658 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE78nnn/GSE78840/
#> 659 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE94nnn/GSE94649/
#> 660 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE85nnn/GSE85226/
#> 661 GEO (CEL, PDF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE77nnn/GSE77962/
#> 662 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE89nnn/GSE89552/
#> 663 GEO (TSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE84nnn/GSE84714/
#> 664 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE87nnn/GSE87893/
#> 665 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70318/
#> 666 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24555/
#> 667 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE64nnn/GSE64605/
#> 668 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE87nnn/GSE87000/
#> 669 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE85nnn/GSE85527/
#> 670 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE84nnn/GSE84934/
#> 671 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE87nnn/GSE87340/
#> 672 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE85nnn/GSE85573/
#> 673 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE85nnn/GSE85531/
#> 674 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE85nnn/GSE85530/
#> 675 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE89nnn/GSE89022/
#> 676 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86473/
#> 677 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86469/
#> 678 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86468/
#> 679 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE89nnn/GSE89632/
#> 680 GEO (BW) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE65nnn/GSE65319/
#> 681 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE87nnn/GSE87530/
#> 682 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63117/
#> 683 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67740/
#> 684 GEO (IDAT, TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE87nnn/GSE87571/
#> 685 GEO (CSV, PDF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE85nnn/GSE85241/
#> 686 GEO (BW) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE85nnn/GSE85928/
#> 687 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE84nnn/GSE84971/
#> 688 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE84nnn/GSE84133/
#> 689 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE87nnn/GSE87295/
#> 690 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE78nnn/GSE78922/
#> 691 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86884/
#> 692 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86611/
#> 693 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE86nnn/GSE86544/
#> 694 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81608/
#> 695 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE72nnn/GSE72377/
#> 696 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE72nnn/GSE72376/
#> 697 GEO (TXT, XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67566/
#> 698 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE84nnn/GSE84908/
#> 699 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76398/
#> 700 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76394/
#> 701 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76285/
#> 702 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE71nnn/GSE71301/
#> 703 GEO (BED, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE69nnn/GSE69705/
#> 704 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE58nnn/GSE58557/
#> 705 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67141/
#> 706 GEO (XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76308/
#> 707 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE83nnn/GSE83139/
#> 708 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE71nnn/GSE71678/
#> 709 GEO (GPR, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63492/
#> 710 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE72nnn/GSE72462/
#> 711 GEO (CSV, RTF, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81076/
#> 712 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE79nnn/GSE79670/
#> 713 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE79nnn/GSE79668/
#> 714 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE75nnn/GSE75248/
#> 715 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE74nnn/GSE74296/
#> 716 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE80nnn/GSE80569/
#> 717 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76171/
#> 718 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76170/
#> 719 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76169/
#> 720 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81258/
#> 721 GEO (BED, BROADPEAK, BW, CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE81nnn/GSE81255/
#> 722 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE75nnn/GSE75941/
#> 723 GEO (TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE78nnn/GSE78805/
#> 724 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE65nnn/GSE65793/
#> 725 GEO (TXT, WIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE57nnn/GSE57628/
#> 726 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67705/
#> 727 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70901/
#> 728 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE65nnn/GSE65057/
#> 729 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE64nnn/GSE64998/
#> 730 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE78nnn/GSE78891/
#> 731 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67775/
#> 732 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67774/
#> 733 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67773/
#> 734 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE77nnn/GSE77350/
#> 735 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55311/
#> 736 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76161/
#> 737 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE77nnn/GSE77114/
#> 738 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76899/
#> 739 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE51nnn/GSE51546/
#> 740 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62761/
#> 741 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE60nnn/GSE60861/
#> 742 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE60nnn/GSE60860/
#> 743 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE45nnn/GSE45980/
#> 744 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE44nnn/GSE44639/
#> 745 GEO (BED, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76268/
#> 746 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76189/
#> 747 GEO (CSV, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE76nnn/GSE76065/
#> 748 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE73nnn/GSE73034/
#> 749 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE69nnn/GSE69438/
#> 750 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE75nnn/GSE75685/
#> 751 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE75nnn/GSE75678/
#> 752 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE75nnn/GSE75669/
#> 753 GEO (BED, BEDPE) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE69nnn/GSE69600/
#> 754 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE73nnn/GSE73408/
#> 755 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68526/
#> 756 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE74nnn/GSE74782/
#> 757 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70961/
#> 758 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE69nnn/GSE69889/
#> 759 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE65nnn/GSE65561/
#> 760 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE74nnn/GSE74629/
#> 761 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE74nnn/GSE74559/
#> 762 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE72nnn/GSE72492/
#> 763 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE41nnn/GSE41767/
#> 764 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE73nnn/GSE73418/
#> 765 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE71nnn/GSE71730/
#> 766 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68049/
#> 767 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE37nnn/GSE37025/
#> 768 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63423/
#> 769 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70752/
#> 770 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70494/
#> 771 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70493/
#> 772 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70453/
#> 773 GEO (CEL, CHP, TXT, XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE21nnn/GSE21891/
#> 774 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE71nnn/GSE71416/
#> 775 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE69nnn/GSE69658/
#> 776 GEO (RDATA) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE71nnn/GSE71102/
#> 777 GEO (RDATA) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE71nnn/GSE71099/
#> 778 GEO (TAB) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE69nnn/GSE69595/
#> 779 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE70nnn/GSE70528/
#> 780 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE65nnn/GSE65682/
#> 781 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67297/
#> 782 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE59nnn/GSE59363/
#> 783 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE59nnn/GSE59421/
#> 784 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE49nnn/GSE49885/
#> 785 GEO (XLSX) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67543/
#> 786 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE69nnn/GSE69528/
#> 787 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE69nnn/GSE69421/
#> 788 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE66nnn/GSE66785/
#> 789 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67279/
#> 790 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE48nnn/GSE48278/
#> 791 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68571/
#> 792 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE60nnn/GSE60760/
#> 793 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68186/
#> 794 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68185/
#> 795 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68184/
#> 796 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68183/
#> 797 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68226/
#> 798 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE68nnn/GSE68224/
#> 799 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62117/
#> 800 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67738/
#> 801 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63887/
#> 802 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE37nnn/GSE37084/
#> 803 GEO (GPR, TIFF, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE67nnn/GSE67567/
#> 804 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE53nnn/GSE53257/
#> 805 GEO (IDAT, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62219/
#> 806 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE66nnn/GSE66413/
#> 807 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE66nnn/GSE66360/
#> 808 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62003/
#> 809 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55645/
#> 810 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE65nnn/GSE65737/
#> 811 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE66nnn/GSE66175/
#> 812 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62372/
#> 813 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62370/
#> 814 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62832/
#> 815 GEO (TXT, XYS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE63nnn/GSE63981/
#> 816 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE60nnn/GSE60424/
#> 817 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE45nnn/GSE45856/
#> 818 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE38nnn/GSE38267/
#> 819 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE34nnn/GSE34198/
#> 820 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55650/
#> 821 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE38nnn/GSE38835/
#> 822 GEO (TIFF, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE56nnn/GSE56081/
#> 823 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE57nnn/GSE57896/
#> 824 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55465/
#> 825 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55464/
#> 826 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62523/
#> 827 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62500/
#> 828 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE62nnn/GSE62499/
#> 829 GEO (CEL, CHP, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE61nnn/GSE61769/
#> 830 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE61nnn/GSE61714/
#> 831 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE61nnn/GSE61166/
#> 832 GEO (IDAT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE61nnn/GSE61129/
#> 833 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE56nnn/GSE56781/
#> 834 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE56nnn/GSE56685/
#> 835 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE60nnn/GSE60803/
#> 836 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE52nnn/GSE52376/
#> 837 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55567/
#> 838 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55566/
#> 839 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42902/
#> 840 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE60nnn/GSE60436/
#> 841 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55100/
#> 842 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55099/
#> 843 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE55nnn/GSE55098/
#> 844 GEO (GPR, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE50nnn/GSE50866/
#> 845 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE50nnn/GSE50397/
#> 846 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30575/
#> 847 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE58nnn/GSE58634/
#> 848 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE44nnn/GSE44558/
#> 849 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE57nnn/GSE57928/
#> 850 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE57nnn/GSE57880/
#> 851 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE52nnn/GSE52724/
#> 852 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE57nnn/GSE57484/
#> 853 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE50nnn/GSE50005/
#> 854 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE51nnn/GSE51058/
#> 855 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE56nnn/GSE56606/
#> 856 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE48nnn/GSE48101/
#> 857 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE44nnn/GSE44093/
#> 858 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29536/
#> 859 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE54nnn/GSE54279/
#> 860 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE46nnn/GSE46097/
#> 861 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE43nnn/GSE43488/
#> 862 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30211/
#> 863 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30210/
#> 864 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30209/
#> 865 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30208/
#> 866 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE48nnn/GSE48318/
#> 867 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE54nnn/GSE54350/
#> 868 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE48nnn/GSE48354/
#> 869 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE48nnn/GSE48353/
#> 870 GEO (GTF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE53nnn/GSE53949/
#> 871 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE44nnn/GSE44314/
#> 872 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE44nnn/GSE44313/
#> 873 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29623/
#> 874 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29622/
#> 875 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29621/
#> 876 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE28nnn/GSE28038/
#> 877 GEO (GFF, PAIR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE47nnn/GSE47385/
#> 878 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE53nnn/GSE53454/
#> 879 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE40nnn/GSE40878/
#> 880 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE36nnn/GSE36233/
#> 881 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE52nnn/GSE52314/
#> 882 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE40nnn/GSE40360/
#> 883 GEO (LSR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE52nnn/GSE52422/
#> 884 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE51nnn/GSE51924/
#> 885 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE52nnn/GSE52233/
#> 886 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE51nnn/GSE51311/
#> 887 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE51nnn/GSE51310/
#> 888 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE50nnn/GSE50800/
#> 889 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE43nnn/GSE43580/
#> 890 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE33nnn/GSE33070/
#> 891 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42432/
#> 892 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE50nnn/GSE50892/
#> 893 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE49nnn/GSE49667/
#> 894 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35279/
#> 895 GEO (BED) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE50nnn/GSE50386/
#> 896 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42715/
#> 897 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE41nnn/GSE41744/
#> 898 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE49nnn/GSE49524/
#> 899 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE49nnn/GSE49566/
#> 900 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE39nnn/GSE39825/
#> 901 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE47nnn/GSE47185/
#> 902 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE47nnn/GSE47184/
#> 903 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE47nnn/GSE47183/
#> 904 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE46nnn/GSE46899/
#> 905 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE46nnn/GSE46900/
#> 906 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE46nnn/GSE46897/
#> 907 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE47nnn/GSE47874/
#> 908 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE47nnn/GSE47720/
#> 909 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE40nnn/GSE40498/
#> 910 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE40nnn/GSE40496/
#> 911 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42507/
#> 912 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE40nnn/GSE40234/
#> 913 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE45nnn/GSE45986/
#> 914 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE45nnn/GSE45792/
#> 915 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE45nnn/GSE45777/
#> 916 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE44nnn/GSE44035/
#> 917 GEO (GFF, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE43nnn/GSE43752/
#> 918 GEO (GFF, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE43nnn/GSE43751/
#> 919 GEO (GFF, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE43nnn/GSE43750/
#> 920 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE43nnn/GSE43950/
#> 921 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE32nnn/GSE32909/
#> 922 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE34nnn/GSE34512/
#> 923 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE38nnn/GSE38291/
#> 924 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE14nnn/GSE14368/
#> 925 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42487/
#> 926 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42229/
#> 927 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42228/
#> 928 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42227/
#> 929 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29231/
#> 930 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29226/
#> 931 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29221/
#> 932 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42148/
#> 933 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42094/
#> 934 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE42nnn/GSE42093/
#> 935 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE34nnn/GSE34526/
#> 936 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35725/
#> 937 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35716/
#> 938 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35713/
#> 939 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35712/
#> 940 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35711/
#> 941 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE37nnn/GSE37794/
#> 942 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30161/
#> 943 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35851/
#> 944 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30802/
#> 945 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE38nnn/GSE38642/
#> 946 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35191/
#> 947 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35186/
#> 948 GEO (GFF, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE38nnn/GSE38447/
#> 949 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE38nnn/GSE38396/
#> 950 GEO (CEL, GFF, PAIR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE36nnn/GSE36403/
#> 951 GEO (GFF, PAIR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE36nnn/GSE36402/
#> 952 GEO (GFF, PAIR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE36nnn/GSE36397/
#> 953 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE36nnn/GSE36084/
#> 954 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE33nnn/GSE33440/
#> 955 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE19nnn/GSE19637/
#> 956 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE32nnn/GSE32575/
#> 957 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE34nnn/GSE34223/
#> 958 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE37nnn/GSE37824/
#> 959 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE37nnn/GSE37901/
#> 960 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE28nnn/GSE28384/
#> 961 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE15nnn/GSE15932/
#> 962 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE37nnn/GSE37639/
#> 963 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE21nnn/GSE21232/
#> 964 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30159/
#> 965 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE32nnn/GSE32512/
#> 966 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29660/
#> 967 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE26nnn/GSE26244/
#> 968 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE26nnn/GSE26887/
#> 969 GEO (BAM) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE32nnn/GSE32874/
#> 970 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE32nnn/GSE32691/
#> 971 GEO (BED, GTF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35296/
#> 972 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE31nnn/GSE31901/
#> 973 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29908/
#> 974 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE35nnn/GSE35411/
#> 975 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE32nnn/GSE32544/
#> 976 GEO (GPR, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE19nnn/GSE19943/
#> 977 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE22nnn/GSE22255/
#> 978 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE13nnn/GSE13760/
#> 979 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE32nnn/GSE32357/
#> 980 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24818/
#> 981 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE33nnn/GSE33032/
#> 982 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE28nnn/GSE28024/
#> 983 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE28nnn/GSE28022/
#> 984 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE27nnn/GSE27507/
#> 985 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE32nnn/GSE32553/
#> 986 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE27nnn/GSE27175/
#> 987 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE27nnn/GSE27317/
#> 988 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE21nnn/GSE21815/
#> 989 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE31nnn/GSE31056/
#> 990 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30566/
#> 991 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30529/
#> 992 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30528/
#> 993 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30122/
#> 994 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE23nnn/GSE23506/
#> 995 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30803/
#> 996 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30732/
#> 997 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE30nnn/GSE30310/
#> 998 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE28nnn/GSE28059/
#> 999 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE27nnn/GSE27951/
#> 1000 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29718/
#> 1001 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE27nnn/GSE27949/
#> 1002 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24326/
#> 1003 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE23nnn/GSE23338/
#> 1004 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29190/
#> 1005 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29142/
#> 1006 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE29nnn/GSE29084/
#> 1007 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE25nnn/GSE25462/
#> 1008 GEO (BED, GRAPH) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE25nnn/GSE25862/
#> 1009 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24193/
#> 1010 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE26nnn/GSE26744/
#> 1011 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE19nnn/GSE19790/
#> 1012 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE21nnn/GSE21980/
#> 1013 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE19nnn/GSE19649/
#> 1014 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE26nnn/GSE26168/
#> 1015 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE26nnn/GSE26167/
#> 1016 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE26nnn/GSE26073/
#> 1017 GEO (IDAT, PDF) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE25nnn/GSE25826/
#> 1018 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE25nnn/GSE25724/
#> 1019 GEO (PAIR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE20nnn/GSE20553/
#> 1020 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24422/
#> 1021 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE19nnn/GSE19420/
#> 1022 GEO (BAM, BED, BIGWIG, TAGALIGN) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24685/
#> 1023 GEO (BED, WIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE25nnn/GSE25249/
#> 1024 GEO (WIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE23nnn/GSE23784/
#> 1025 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE16nnn/GSE16804/
#> 1026 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE14nnn/GSE14503/
#> 1027 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24290/
#> 1028 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24215/
#> 1029 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE24nnn/GSE24147/
#> 1030 GEO (XYS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE23nnn/GSE23858/
#> 1031 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE23nnn/GSE23561/
#> 1032 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE18nnn/GSE18821/
#> 1033 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE17nnn/GSE17710/
#> 1034 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12385/
#> 1035 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12384/
#> 1036 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE23nnn/GSE23343/
#> 1037 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE21nnn/GSE21785/
#> 1038 GEO (CEL, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE22nnn/GSE22309/
#> 1039 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE21nnn/GSE21989/
#> 1040 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE18nnn/GSE18470/
#> 1041 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE21nnn/GSE21321/
#> 1042 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE17nnn/GSE17941/
#> 1043 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE21nnn/GSE21340/
#> 1044 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE19nnn/GSE19519/
#> 1045 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE20nnn/GSE20966/
#> 1046 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE20nnn/GSE20067/
#> 1047 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE17nnn/GSE17727/
#> 1048 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE20nnn/GSE20247/
#> 1049 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE18nnn/GSE18732/
#> 1050 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE19nnn/GSE19769/
#> 1051 GEO (BAM, BED, CEL, TXT, WIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE18nnn/GSE18927/
#> 1052 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE15nnn/GSE15790/
#> 1053 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE17nnn/GSE17635/
#> 1054 GEO (LSR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE13nnn/GSE13840/
#> 1055 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE15nnn/GSE15072/
#> 1056 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12959/
#> 1057 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE18nnn/GSE18212/
#> 1058 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE8nnn/GSE8908/
#> 1059 GEO (CEL, CHP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE17nnn/GSE17556/
#> 1060 GEO (BAM, BED, WIG) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE16nnn/GSE16256/
#> 1061 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE15nnn/GSE15543/
#> 1062 GEO (GPR, TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE17nnn/GSE17060/
#> 1063 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE17nnn/GSE17058/
#> 1064 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE16nnn/GSE16025/
#> 1065 GEO (XLS) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE13nnn/GSE13015/
#> 1066 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE5nnn/GSE5903/
#> 1067 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE16nnn/GSE16415/
#> 1068 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE15nnn/GSE15653/
#> 1069 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE13nnn/GSE13736/
#> 1070 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE13nnn/GSE13465/
#> 1071 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE13nnn/GSE13920/
#> 1072 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE10nnn/GSE10334/
#> 1073 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE13nnn/GSE13290/
#> 1074 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE8nnn/GSE8157/
#> 1075 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE12nnn/GSE12643/
#> 1076 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE11nnn/GSE11908/
#> 1077 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE11nnn/GSE11907/
#> 1078 GEO (TXT) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE10nnn/GSE10540/
#> 1079 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE9nnn/GSE9588/
#> 1080 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE9nnn/GSE9984/
#> 1081 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE9nnn/GSE9939/
#> 1082 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE6nnn/GSE6751/
#> 1083 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE6nnn/GSE6599/
#> 1084 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE6nnn/GSE6798/
#> 1085 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE9nnn/GSE9157/
#> 1086 GEO (GPR) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE9nnn/GSE9017/
#> 1087 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE9nnn/GSE9105/
#> 1088 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE9nnn/GSE9006/
#> 1089 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE4nnn/GSE4704/
#> 1090 GEO (CSV) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7818/
#> 1091 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE7nnn/GSE7146/
#> 1092 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE6nnn/GSE6862/
#> 1093 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE6nnn/GSE6573/
#> 1094 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE4nnn/GSE4901/
#> 1095 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE3nnn/GSE3118/
#> 1096 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE4nnn/GSE4117/
#> 1097 GEO (CEL, EXP) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE5nnn/GSE5090/
#> 1098 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE3nnn/GSE3881/
#> 1099 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE3nnn/GSE3308/
#> 1100 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE3nnn/GSE3447/
#> 1101 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE3nnn/GSE3307/
#> 1102 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE2nnn/GSE2138/
#> 1103 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE2nnn/GSE2956/
#> 1104 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE1322/
#> 1105 GEO (CEL) ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE1nnn/GSE1009/
#> 1106 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSEnnn/GSE634/
#> 1107 GEO ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSEnnn/GSE121/
#> Series Accession ID
#> 1 GSE197881 200197881
#> 2 GSE191210 200191210
#> 3 GSE207901 200207901
#> 4 GSE205668 200205668
#> 5 GSE173735 200173735
#> 6 GSE173734 200173734
#> 7 GSE202295 200202295
#> 8 GSE207122 200207122
#> 9 GSE197850 200197850
#> 10 GSE188395 200188395
#> 11 GSE153315 200153315
#> 12 GSE200678 200200678
#> 13 GSE206528 200206528
#> 14 GSE200659 200200659
#> 15 GSE201908 200201908
#> 16 GSE199437 200199437
#> 17 GSE199852 200199852
#> 18 GSE198520 200198520
#> 19 GSE185728 200185728
#> 20 GSE176145 200176145
#> 21 GSE133666 200133666
#> 22 GSE203346 200203346
#> 23 GSE203169 200203169
#> 24 GSE203353 200203353
#> 25 GSE135076 200135076
#> 26 GSE192541 200192541
#> 27 GSE202151 200202151
#> 28 GSE194156 200194156
#> 29 GSE194155 200194155
#> 30 GSE194154 200194154
#> 31 GSE175759 200175759
#> 32 GSE201543 200201543
#> 33 GSE181478 200181478
#> 34 GSE181412 200181412
#> 35 GSE180521 200180521
#> 36 GSE200983 200200983
#> 37 GSE200477 200200477
#> 38 GSE186883 200186883
#> 39 GSE185845 200185845
#> 40 GSE199939 200199939
#> 41 GSE188827 200188827
#> 42 GSE186021 200186021
#> 43 GSE182138 200182138
#> 44 GSE199700 200199700
#> 45 GSE199148 200199148
#> 46 GSE185052 200185052
#> 47 GSE185191 200185191
#> 48 GSE185190 200185190
#> 49 GSE185189 200185189
#> 50 GSE193161 200193161
#> 51 GSE162622 200162622
#> 52 GSE197456 200197456
#> 53 GSE197285 200197285
#> 54 GSE162273 200162273
#> 55 GSE126803 200126803
#> 56 GSE196900 200196900
#> 57 GSE193436 200193436
#> 58 GSE166641 200166641
#> 59 GSE167199 200167199
#> 60 GSE159586 200159586
#> 61 GSE159554 200159554
#> 62 GSE166640 200166640
#> 63 GSE185038 200185038
#> 64 GSE185036 200185036
#> 65 GSE185035 200185035
#> 66 GSE189849 200189849
#> 67 GSE186766 200186766
#> 68 GSE113409 200113409
#> 69 GSE113392 200113392
#> 70 GSE163947 200163947
#> 71 GSE165784 200165784
#> 72 GSE176230 200176230
#> 73 GSE166822 200166822
#> 74 GSE194061 200194061
#> 75 GSE194119 200194119
#> 76 GSE193974 200193974
#> 77 GSE182259 200182259
#> 78 GSE172148 200172148
#> 79 GSE167269 200167269
#> 80 GSE193510 200193510
#> 81 GSE193626 200193626
#> 82 GSE176171 200176171
#> 83 GSE176067 200176067
#> 84 GSE174475 200174475
#> 85 GSE193273 200193273
#> 86 GSE156035 200156035
#> 87 GSE154415 200154415
#> 88 GSE154414 200154414
#> 89 GSE154413 200154413
#> 90 GSE113199 200113199
#> 91 GSE181143 200181143
#> 92 GSE142178 200142178
#> 93 GSE162166 200162166
#> 94 GSE190973 200190973
#> 95 GSE190972 200190972
#> 96 GSE190971 200190971
#> 97 GSE184831 200184831
#> 98 GSE182870 200182870
#> 99 GSE190832 200190832
#> 100 GSE180355 200180355
#> 101 GSE174481 200174481
#> 102 GSE188235 200188235
#> 103 GSE188234 200188234
#> 104 GSE189923 200189923
#> 105 GSE185430 200185430
#> 106 GSE181328 200181328
#> 107 GSE159955 200159955
#> 108 GSE123088 200123088
#> 109 GSE123086 200123086
#> 110 GSE189107 200189107
#> 111 GSE140842 200140842
#> 112 GSE162135 200162135
#> 113 GSE162133 200162133
#> 114 GSE161989 200161989
#> 115 GSE188799 200188799
#> 116 GSE189007 200189007
#> 117 GSE162837 200162837
#> 118 GSE137766 200137766
#> 119 GSE136887 200136887
#> 120 GSE98887 200098887
#> 121 GSE186524 200186524
#> 122 GSE165816 200165816
#> 123 GSE179568 200179568
#> 124 GSE182494 200182494
#> 125 GSE166120 200166120
#> 126 GSE186432 200186432
#> 127 GSE180470 200180470
#> 128 GSE185598 200185598
#> 129 GSE163211 200163211
#> 130 GSE185749 200185749
#> 131 GSE142290 200142290
#> 132 GSE175477 200175477
#> 133 GSE141319 200141319
#> 134 GSE182923 200182923
#> 135 GSE180504 200180504
#> 136 GSE159924 200159924
#> 137 GSE139947 200139947
#> 138 GSE178991 200178991
#> 139 GSE156911 200156911
#> 140 GSE169514 200169514
#> 141 GSE184050 200184050
#> 142 GSE184016 200184016
#> 143 GSE183965 200183965
#> 144 GSE183701 200183701
#> 145 GSE183568 200183568
#> 146 GSE72991 200072991
#> 147 GSE173277 200173277
#> 148 GSE173228 200173228
#> 149 GSE165709 200165709
#> 150 GSE165708 200165708
#> 151 GSE165703 200165703
#> 152 GSE182737 200182737
#> 153 GSE122279 200122279
#> 154 GSE159337 200159337
#> 155 GSE182121 200182121
#> 156 GSE182120 200182120
#> 157 GSE182117 200182117
#> 158 GSE182053 200182053
#> 159 GSE159867 200159867
#> 160 GSE181881 200181881
#> 161 GSE144414 200144414
#> 162 GSE181674 200181674
#> 163 GSE118139 200118139
#> 164 GSE173276 200173276
#> 165 GSE168071 200168071
#> 166 GSE179921 200179921
#> 167 GSE179762 200179762
#> 168 GSE168996 200168996
#> 169 GSE163773 200163773
#> 170 GSE180083 200180083
#> 171 GSE180081 200180081
#> 172 GSE150212 200150212
#> 173 GSE153837 200153837
#> 174 GSE156411 200156411
#> 175 GSE153834 200153834
#> 176 GSE179143 200179143
#> 177 GSE150724 200150724
#> 178 GSE178828 200178828
#> 179 GSE178721 200178721
#> 180 GSE173983 200173983
#> 181 GSE178734 200178734
#> 182 GSE178733 200178733
#> 183 GSE156122 200156122
#> 184 GSE156121 200156121
#> 185 GSE156061 200156061
#> 186 GSE122086 200122086
#> 187 GSE176324 200176324
#> 188 GSE173669 200173669
#> 189 GSE165385 200165385
#> 190 GSE175745 200175745
#> 191 GSE175735 200175735
#> 192 GSE114860 200114860
#> 193 GSE174502 200174502
#> 194 GSE173613 200173613
#> 195 GSE136344 200136344
#> 196 GSE173193 200173193
#> 197 GSE157988 200157988
#> 198 GSE159556 200159556
#> 199 GSE160310 200160310
#> 200 GSE160308 200160308
#> 201 GSE160306 200160306
#> 202 GSE153410 200153410
#> 203 GSE164416 200164416
#> 204 GSE151426 200151426
#> 205 GSE141432 200141432
#> 206 GSE136395 200136395
#> 207 GSE151405 200151405
#> 208 GSE148073 200148073
#> 209 GSE148642 200148642
#> 210 GSE148640 200148640
#> 211 GSE148639 200148639
#> 212 GSE168437 200168437
#> 213 GSE166785 200166785
#> 214 GSE167250 200167250
#> 215 GSE112168 200112168
#> 216 GSE162521 200162521
#> 217 GSE163160 200163160
#> 218 GSE166047 200166047
#> 219 GSE151497 200151497
#> 220 GSE151496 200151496
#> 221 GSE151495 200151495
#> 222 GSE168492 200168492
#> 223 GSE168072 200168072
#> 224 GSE168327 200168327
#> 225 GSE167976 200167976
#> 226 GSE167914 200167914
#> 227 GSE162689 200162689
#> 228 GSE165121 200165121
#> 229 GSE166847 200166847
#> 230 GSE166845 200166845
#> 231 GSE166787 200166787
#> 232 GSE166652 200166652
#> 233 GSE166502 200166502
#> 234 GSE166467 200166467
#> 235 GSE110829 200110829
#> 236 GSE110828 200110828
#> 237 GSE158055 200158055
#> 238 GSE157640 200157640
#> 239 GSE159717 200159717
#> 240 GSE147890 200147890
#> 241 GSE153221 200153221
#> 242 GSE153220 200153220
#> 243 GSE153219 200153219
#> 244 GSE149148 200149148
#> 245 GSE164934 200164934
#> 246 GSE163610 200163610
#> 247 GSE160474 200160474
#> 248 GSE160473 200160473
#> 249 GSE160472 200160472
#> 250 GSE161355 200161355
#> 251 GSE164338 200164338
#> 252 GSE25194 200025194
#> 253 GSE139577 200139577
#> 254 GSE118103 200118103
#> 255 GSE163980 200163980
#> 256 GSE163744 200163744
#> 257 GSE163732 200163732
#> 258 GSE150172 200150172
#> 259 GSE163603 200163603
#> 260 GSE163510 200163510
#> 261 GSE158292 200158292
#> 262 GSE162830 200162830
#> 263 GSE157515 200157515
#> 264 GSE162557 200162557
#> 265 GSE147965 200147965
#> 266 GSE142401 200142401
#> 267 GSE142153 200142153
#> 268 GSE149568 200149568
#> 269 GSE157859 200157859
#> 270 GSE161914 200161914
#> 271 GSE161721 200161721
#> 272 GSE161720 200161720
#> 273 GSE161719 200161719
#> 274 GSE154306 200154306
#> 275 GSE144348 200144348
#> 276 GSE141065 200141065
#> 277 GSE145747 200145747
#> 278 GSE145746 200145746
#> 279 GSE145745 200145745
#> 280 GSE160005 200160005
#> 281 GSE159984 200159984
#> 282 GSE159759 200159759
#> 283 GSE154378 200154378
#> 284 GSE154377 200154377
#> 285 GSE154348 200154348
#> 286 GSE132831 200132831
#> 287 GSE159467 200159467
#> 288 GSE154609 200154609
#> 289 GSE154126 200154126
#> 290 GSE143783 200143783
#> 291 GSE152615 200152615
#> 292 GSE134431 200134431
#> 293 GSE151764 200151764
#> 294 GSE157177 200157177
#> 295 GSE154554 200154554
#> 296 GSE143690 200143690
#> 297 GSE156993 200156993
#> 298 GSE140308 200140308
#> 299 GSE156903 200156903
#> 300 GSE148061 200148061
#> 301 GSE148060 200148060
#> 302 GSE148059 200148059
#> 303 GSE146028 200146028
#> 304 GSE156249 200156249
#> 305 GSE156248 200156248
#> 306 GSE156247 200156247
#> 307 GSE156341 200156341
#> 308 GSE156340 200156340
#> 309 GSE156339 200156339
#> 310 GSE135357 200135357
#> 311 GSE135356 200135356
#> 312 GSE135355 200135355
#> 313 GSE135354 200135354
#> 314 GSE156109 200156109
#> 315 GSE143209 200143209
#> 316 GSE102485 200102485
#> 317 GSE151683 200151683
#> 318 GSE155713 200155713
#> 319 GSE144682 200144682
#> 320 GSE143735 200143735
#> 321 GSE131169 200131169
#> 322 GSE138781 200138781
#> 323 GSE155188 200155188
#> 324 GSE151588 200151588
#> 325 GSE109163 200109163
#> 326 GSE153565 200153565
#> 327 GSE148961 200148961
#> 328 GSE154647 200154647
#> 329 GSE154629 200154629
#> 330 GSE154628 200154628
#> 331 GSE114192 200114192
#> 332 GSE153792 200153792
#> 333 GSE153555 200153555
#> 334 GSE134594 200134594
#> 335 GSE140959 200140959
#> 336 GSE133225 200133225
#> 337 GSE133217 200133217
#> 338 GSE150060 200150060
#> 339 GSE151889 200151889
#> 340 GSE121221 200121221
#> 341 GSE151610 200151610
#> 342 GSE145593 200145593
#> 343 GSE123658 200123658
#> 344 GSE115221 200115221
#> 345 GSE146338 200146338
#> 346 GSE144605 200144605
#> 347 GSE147740 200147740
#> 348 GSE145284 200145284
#> 349 GSE113969 200113969
#> 350 GSE150586 200150586
#> 351 GSE150621 200150621
#> 352 GSE150119 200150119
#> 353 GSE139535 200139535
#> 354 GSE148896 200148896
#> 355 GSE148812 200148812
#> 356 GSE148058 200148058
#> 357 GSE133219 200133219
#> 358 GSE133218 200133218
#> 359 GSE143319 200143319
#> 360 GSE148375 200148375
#> 361 GSE144693 200144693
#> 362 GSE144692 200144692
#> 363 GSE144691 200144691
#> 364 GSE146108 200146108
#> 365 GSE142385 200142385
#> 366 GSE112652 200112652
#> 367 GSE138323 200138323
#> 368 GSE126101 200126101
#> 369 GSE126100 200126100
#> 370 GSE126099 200126099
#> 371 GSE145347 200145347
#> 372 GSE139157 200139157
#> 373 GSE138857 200138857
#> 374 GSE146615 200146615
#> 375 GSE142512 200142512
#> 376 GSE139949 200139949
#> 377 GSE95078 200095078
#> 378 GSE124742 200124742
#> 379 GSE144908 200144908
#> 380 GSE94729 200094729
#> 381 GSE138598 200138598
#> 382 GSE131528 200131528
#> 383 GSE131526 200131526
#> 384 GSE125929 200125929
#> 385 GSE144441 200144441
#> 386 GSE119296 200119296
#> 387 GSE144169 200144169
#> 388 GSE141639 200141639
#> 389 GSE141309 200141309
#> 390 GSE143979 200143979
#> 391 GSE135155 200135155
#> 392 GSE143495 200143495
#> 393 GSE143143 200143143
#> 394 GSE142009 200142009
#> 395 GSE121820 200121820
#> 396 GSE124534 200124534
#> 397 GSE139932 200139932
#> 398 GSE133264 200133264
#> 399 GSE131065 200131065
#> 400 GSE130279 200130279
#> 401 GSE122429 200122429
#> 402 GSE94846 200094846
#> 403 GSE142553 200142553
#> 404 GSE141126 200141126
#> 405 GSE121346 200121346
#> 406 GSE121344 200121344
#> 407 GSE142025 200142025
#> 408 GSE141512 200141512
#> 409 GSE141410 200141410
#> 410 GSE124272 200124272
#> 411 GSE141193 200141193
#> 412 GSE140627 200140627
#> 413 GSE140404 200140404
#> 414 GSE140403 200140403
#> 415 GSE140065 200140065
#> 416 GSE140064 200140064
#> 417 GSE139929 200139929
#> 418 GSE135197 200135197
#> 419 GSE124264 200124264
#> 420 GSE115326 200115326
#> 421 GSE115312 200115312
#> 422 GSE115306 200115306
#> 423 GSE135453 200135453
#> 424 GSE135776 200135776
#> 425 GSE129363 200129363
#> 426 GSE139073 200139073
#> 427 GSE138885 200138885
#> 428 GSE138856 200138856
#> 429 GSE131320 200131320
#> 430 GSE112893 200112893
#> 431 GSE114569 200114569
#> 432 GSE114248 200114248
#> 433 GSE114236 200114236
#> 434 GSE88839 200088839
#> 435 GSE136048 200136048
#> 436 GSE96971 200096971
#> 437 GSE137803 200137803
#> 438 GSE136053 200136053
#> 439 GSE137136 200137136
#> 440 GSE136865 200136865
#> 441 GSE133135 200133135
#> 442 GSE123404 200123404
#> 443 GSE137684 200137684
#> 444 GSE130672 200130672
#> 445 GSE137317 200137317
#> 446 GSE131882 200131882
#> 447 GSE133910 200133910
#> 448 GSE136353 200136353
#> 449 GSE136277 200136277
#> 450 GSE128289 200128289
#> 451 GSE51674 200051674
#> 452 GSE135944 200135944
#> 453 GSE129935 200129935
#> 454 GSE132306 200132306
#> 455 GSE132111 200132111
#> 456 GSE120024 200120024
#> 457 GSE132187 200132187
#> 458 GSE88794 200088794
#> 459 GSE133099 200133099
#> 460 GSE125769 200125769
#> 461 GSE125768 200125768
#> 462 GSE125590 200125590
#> 463 GSE102677 200102677
#> 464 GSE102633 200102633
#> 465 GSE129112 200129112
#> 466 GSE121862 200121862
#> 467 GSE122584 200122584
#> 468 GSE130991 200130991
#> 469 GSE114412 200114412
#> 470 GSE129042 200129042
#> 471 GSE129841 200129841
#> 472 GSE126169 200126169
#> 473 GSE101702 200101702
#> 474 GSE129666 200129666
#> 475 GSE129653 200129653
#> 476 GSE129604 200129604
#> 477 GSE129091 200129091
#> 478 GSE129383 200129383
#> 479 GSE115020 200115020
#> 480 GSE112690 200112690
#> 481 GSE112342 200112342
#> 482 GSE112341 200112341
#> 483 GSE112337 200112337
#> 484 GSE128381 200128381
#> 485 GSE128331 200128331
#> 486 GSE124811 200124811
#> 487 GSE124810 200124810
#> 488 GSE124809 200124809
#> 489 GSE45515 200045515
#> 490 GSE127045 200127045
#> 491 GSE127042 200127042
#> 492 GSE127033 200127033
#> 493 GSE124400 200124400
#> 494 GSE124284 200124284
#> 495 GSE127170 200127170
#> 496 GSE115828 200115828
#> 497 GSE110786 200110786
#> 498 GSE103682 200103682
#> 499 GSE77522 200077522
#> 500 GSE77268 200077268
#> 501 GSE104674 200104674
#> 502 GSE115329 200115329
#> 503 GSE115313 200115313
#> 504 GSE115421 200115421
#> 505 GSE102295 200102295
#> 506 GSE118588 200118588
#> 507 GSE109022 200109022
#> 508 GSE104297 200104297
#> 509 GSE71799 200071799
#> 510 GSE107375 200107375
#> 511 GSE78721 200078721
#> 512 GSE9707 200009707
#> 513 GSE7319 200007319
#> 514 GSE97554 200097554
#> 515 GSE108403 200108403
#> 516 GSE120522 200120522
#> 517 GSE123844 200123844
#> 518 GSE108056 200108056
#> 519 GSE117454 200117454
#> 520 GSE116726 200116726
#> 521 GSE112594 200112594
#> 522 GSE123279 200123279
#> 523 GSE115257 200115257
#> 524 GSE107943 200107943
#> 525 GSE110914 200110914
#> 526 GSE116761 200116761
#> 527 GSE117469 200117469
#> 528 GSE106181 200106181
#> 529 GSE106177 200106177
#> 530 GSE105167 200105167
#> 531 GSE120904 200120904
#> 532 GSE120299 200120299
#> 533 GSE101207 200101207
#> 534 GSE116559 200116559
#> 535 GSE118230 200118230
#> 536 GSE118481 200118481
#> 537 GSE106800 200106800
#> 538 GSE102498 200102498
#> 539 GSE108413 200108413
#> 540 GSE106099 200106099
#> 541 GSE103552 200103552
#> 542 GSE89475 200089475
#> 543 GSE96804 200096804
#> 544 GSE102371 200102371
#> 545 GSE102234 200102234
#> 546 GSE86376 200086376
#> 547 GSE85990 200085990
#> 548 GSE106953 200106953
#> 549 GSE116497 200116497
#> 550 GSE111154 200111154
#> 551 GSE116369 200116369
#> 552 GSE83782 200083782
#> 553 GSE83781 200083781
#> 554 GSE83699 200083699
#> 555 GSE116029 200116029
#> 556 GSE114477 200114477
#> 557 GSE104815 200104815
#> 558 GSE99853 200099853
#> 559 GSE70365 200070365
#> 560 GSE114908 200114908
#> 561 GSE114051 200114051
#> 562 GSE110935 200110935
#> 563 GSE84453 200084453
#> 564 GSE37785 200037785
#> 565 GSE12844 200012844
#> 566 GSE97205 200097205
#> 567 GSE113080 200113080
#> 568 GSE109266 200109266
#> 569 GSE109265 200109265
#> 570 GSE111876 200111876
#> 571 GSE98224 200098224
#> 572 GSE106813 200106813
#> 573 GSE95243 200095243
#> 574 GSE98675 200098675
#> 575 GSE110552 200110552
#> 576 GSE104423 200104423
#> 577 GSE98485 200098485
#> 578 GSE104195 200104195
#> 579 GSE104190 200104190
#> 580 GSE105096 200105096
#> 581 GSE102080 200102080
#> 582 GSE102079 200102079
#> 583 GSE104954 200104954
#> 584 GSE104948 200104948
#> 585 GSE93709 200093709
#> 586 GSE109178 200109178
#> 587 GSE109140 200109140
#> 588 GSE92724 200092724
#> 589 GSE92772 200092772
#> 590 GSE80178 200080178
#> 591 GSE106148 200106148
#> 592 GSE76896 200076896
#> 593 GSE76895 200076895
#> 594 GSE76894 200076894
#> 595 GSE77108 200077108
#> 596 GSE106520 200106520
#> 597 GSE84814 200084814
#> 598 GSE63992 200063992
#> 599 GSE90076 200090076
#> 600 GSE90074 200090074
#> 601 GSE90073 200090073
#> 602 GSE105052 200105052
#> 603 GSE81547 200081547
#> 604 GSE103931 200103931
#> 605 GSE103393 200103393
#> 606 GSE75062 200075062
#> 607 GSE103657 200103657
#> 608 GSE97123 200097123
#> 609 GSE93032 200093032
#> 610 GSE85192 200085192
#> 611 GSE90999 200090999
#> 612 GSE74240 200074240
#> 613 GSE87005 200087005
#> 614 GSE99340 200099340
#> 615 GSE99339 200099339
#> 616 GSE99325 200099325
#> 617 GSE86069 200086069
#> 618 GSE97655 200097655
#> 619 GSE102177 200102177
#> 620 GSE95368 200095368
#> 621 GSE101931 200101931
#> 622 GSE94497 200094497
#> 623 GSE94496 200094496
#> 624 GSE101461 200101461
#> 625 GSE86298 200086298
#> 626 GSE83452 200083452
#> 627 GSE99068 200099068
#> 628 GSE97647 200097647
#> 629 GSE97591 200097591
#> 630 GSE100271 200100271
#> 631 GSE100185 200100185
#> 632 GSE90028 200090028
#> 633 GSE96569 200096569
#> 634 GSE96568 200096568
#> 635 GSE96564 200096564
#> 636 GSE96563 200096563
#> 637 GSE96562 200096562
#> 638 GSE98501 200098501
#> 639 GSE98399 200098399
#> 640 GSE57362 200057362
#> 641 GSE97530 200097530
#> 642 GSE90117 200090117
#> 643 GSE68475 200068475
#> 644 GSE98043 200098043
#> 645 GSE81965 200081965
#> 646 GSE94019 200094019
#> 647 GSE88929 200088929
#> 648 GSE97084 200097084
#> 649 GSE84823 200084823
#> 650 GSE84821 200084821
#> 651 GSE87626 200087626
#> 652 GSE83345 200083345
#> 653 GSE72490 200072490
#> 654 GSE95849 200095849
#> 655 GSE95675 200095675
#> 656 GSE95674 200095674
#> 657 GSE89360 200089360
#> 658 GSE78840 200078840
#> 659 GSE94649 200094649
#> 660 GSE85226 200085226
#> 661 GSE77962 200077962
#> 662 GSE89552 200089552
#> 663 GSE84714 200084714
#> 664 GSE87893 200087893
#> 665 GSE70318 200070318
#> 666 GSE24555 200024555
#> 667 GSE64605 200064605
#> 668 GSE87000 200087000
#> 669 GSE85527 200085527
#> 670 GSE84934 200084934
#> 671 GSE87340 200087340
#> 672 GSE85573 200085573
#> 673 GSE85531 200085531
#> 674 GSE85530 200085530
#> 675 GSE89022 200089022
#> 676 GSE86473 200086473
#> 677 GSE86469 200086469
#> 678 GSE86468 200086468
#> 679 GSE89632 200089632
#> 680 GSE65319 200065319
#> 681 GSE87530 200087530
#> 682 GSE63117 200063117
#> 683 GSE67740 200067740
#> 684 GSE87571 200087571
#> 685 GSE85241 200085241
#> 686 GSE85928 200085928
#> 687 GSE84971 200084971
#> 688 GSE84133 200084133
#> 689 GSE87295 200087295
#> 690 GSE78922 200078922
#> 691 GSE86884 200086884
#> 692 GSE86611 200086611
#> 693 GSE86544 200086544
#> 694 GSE81608 200081608
#> 695 GSE72377 200072377
#> 696 GSE72376 200072376
#> 697 GSE67566 200067566
#> 698 GSE84908 200084908
#> 699 GSE76398 200076398
#> 700 GSE76394 200076394
#> 701 GSE76285 200076285
#> 702 GSE71301 200071301
#> 703 GSE69705 200069705
#> 704 GSE58557 200058557
#> 705 GSE67141 200067141
#> 706 GSE76308 200076308
#> 707 GSE83139 200083139
#> 708 GSE71678 200071678
#> 709 GSE63492 200063492
#> 710 GSE72462 200072462
#> 711 GSE81076 200081076
#> 712 GSE79670 200079670
#> 713 GSE79668 200079668
#> 714 GSE75248 200075248
#> 715 GSE74296 200074296
#> 716 GSE80569 200080569
#> 717 GSE76171 200076171
#> 718 GSE76170 200076170
#> 719 GSE76169 200076169
#> 720 GSE81258 200081258
#> 721 GSE81255 200081255
#> 722 GSE75941 200075941
#> 723 GSE78805 200078805
#> 724 GSE65793 200065793
#> 725 GSE57628 200057628
#> 726 GSE67705 200067705
#> 727 GSE70901 200070901
#> 728 GSE65057 200065057
#> 729 GSE64998 200064998
#> 730 GSE78891 200078891
#> 731 GSE67775 200067775
#> 732 GSE67774 200067774
#> 733 GSE67773 200067773
#> 734 GSE77350 200077350
#> 735 GSE55311 200055311
#> 736 GSE76161 200076161
#> 737 GSE77114 200077114
#> 738 GSE76899 200076899
#> 739 GSE51546 200051546
#> 740 GSE62761 200062761
#> 741 GSE60861 200060861
#> 742 GSE60860 200060860
#> 743 GSE45980 200045980
#> 744 GSE44639 200044639
#> 745 GSE76268 200076268
#> 746 GSE76189 200076189
#> 747 GSE76065 200076065
#> 748 GSE73034 200073034
#> 749 GSE69438 200069438
#> 750 GSE75685 200075685
#> 751 GSE75678 200075678
#> 752 GSE75669 200075669
#> 753 GSE69600 200069600
#> 754 GSE73408 200073408
#> 755 GSE68526 200068526
#> 756 GSE74782 200074782
#> 757 GSE70961 200070961
#> 758 GSE69889 200069889
#> 759 GSE65561 200065561
#> 760 GSE74629 200074629
#> 761 GSE74559 200074559
#> 762 GSE72492 200072492
#> 763 GSE41767 200041767
#> 764 GSE73418 200073418
#> 765 GSE71730 200071730
#> 766 GSE68049 200068049
#> 767 GSE37025 200037025
#> 768 GSE63423 200063423
#> 769 GSE70752 200070752
#> 770 GSE70494 200070494
#> 771 GSE70493 200070493
#> 772 GSE70453 200070453
#> 773 GSE21891 200021891
#> 774 GSE71416 200071416
#> 775 GSE69658 200069658
#> 776 GSE71102 200071102
#> 777 GSE71099 200071099
#> 778 GSE69595 200069595
#> 779 GSE70528 200070528
#> 780 GSE65682 200065682
#> 781 GSE67297 200067297
#> 782 GSE59363 200059363
#> 783 GSE59421 200059421
#> 784 GSE49885 200049885
#> 785 GSE67543 200067543
#> 786 GSE69528 200069528
#> 787 GSE69421 200069421
#> 788 GSE66785 200066785
#> 789 GSE67279 200067279
#> 790 GSE48278 200048278
#> 791 GSE68571 200068571
#> 792 GSE60760 200060760
#> 793 GSE68186 200068186
#> 794 GSE68185 200068185
#> 795 GSE68184 200068184
#> 796 GSE68183 200068183
#> 797 GSE68226 200068226
#> 798 GSE68224 200068224
#> 799 GSE62117 200062117
#> 800 GSE67738 200067738
#> 801 GSE63887 200063887
#> 802 GSE37084 200037084
#> 803 GSE67567 200067567
#> 804 GSE53257 200053257
#> 805 GSE62219 200062219
#> 806 GSE66413 200066413
#> 807 GSE66360 200066360
#> 808 GSE62003 200062003
#> 809 GSE55645 200055645
#> 810 GSE65737 200065737
#> 811 GSE66175 200066175
#> 812 GSE62372 200062372
#> 813 GSE62370 200062370
#> 814 GSE62832 200062832
#> 815 GSE63981 200063981
#> 816 GSE60424 200060424
#> 817 GSE45856 200045856
#> 818 GSE38267 200038267
#> 819 GSE34198 200034198
#> 820 GSE55650 200055650
#> 821 GSE38835 200038835
#> 822 GSE56081 200056081
#> 823 GSE57896 200057896
#> 824 GSE55465 200055465
#> 825 GSE55464 200055464
#> 826 GSE62523 200062523
#> 827 GSE62500 200062500
#> 828 GSE62499 200062499
#> 829 GSE61769 200061769
#> 830 GSE61714 200061714
#> 831 GSE61166 200061166
#> 832 GSE61129 200061129
#> 833 GSE56781 200056781
#> 834 GSE56685 200056685
#> 835 GSE60803 200060803
#> 836 GSE52376 200052376
#> 837 GSE55567 200055567
#> 838 GSE55566 200055566
#> 839 GSE42902 200042902
#> 840 GSE60436 200060436
#> 841 GSE55100 200055100
#> 842 GSE55099 200055099
#> 843 GSE55098 200055098
#> 844 GSE50866 200050866
#> 845 GSE50397 200050397
#> 846 GSE30575 200030575
#> 847 GSE58634 200058634
#> 848 GSE44558 200044558
#> 849 GSE57928 200057928
#> 850 GSE57880 200057880
#> 851 GSE52724 200052724
#> 852 GSE57484 200057484
#> 853 GSE50005 200050005
#> 854 GSE51058 200051058
#> 855 GSE56606 200056606
#> 856 GSE48101 200048101
#> 857 GSE44093 200044093
#> 858 GSE29536 200029536
#> 859 GSE54279 200054279
#> 860 GSE46097 200046097
#> 861 GSE43488 200043488
#> 862 GSE30211 200030211
#> 863 GSE30210 200030210
#> 864 GSE30209 200030209
#> 865 GSE30208 200030208
#> 866 GSE48318 200048318
#> 867 GSE54350 200054350
#> 868 GSE48354 200048354
#> 869 GSE48353 200048353
#> 870 GSE53949 200053949
#> 871 GSE44314 200044314
#> 872 GSE44313 200044313
#> 873 GSE29623 200029623
#> 874 GSE29622 200029622
#> 875 GSE29621 200029621
#> 876 GSE28038 200028038
#> 877 GSE47385 200047385
#> 878 GSE53454 200053454
#> 879 GSE40878 200040878
#> 880 GSE36233 200036233
#> 881 GSE52314 200052314
#> 882 GSE40360 200040360
#> 883 GSE52422 200052422
#> 884 GSE51924 200051924
#> 885 GSE52233 200052233
#> 886 GSE51311 200051311
#> 887 GSE51310 200051310
#> 888 GSE50800 200050800
#> 889 GSE43580 200043580
#> 890 GSE33070 200033070
#> 891 GSE42432 200042432
#> 892 GSE50892 200050892
#> 893 GSE49667 200049667
#> 894 GSE35279 200035279
#> 895 GSE50386 200050386
#> 896 GSE42715 200042715
#> 897 GSE41744 200041744
#> 898 GSE49524 200049524
#> 899 GSE49566 200049566
#> 900 GSE39825 200039825
#> 901 GSE47185 200047185
#> 902 GSE47184 200047184
#> 903 GSE47183 200047183
#> 904 GSE46899 200046899
#> 905 GSE46900 200046900
#> 906 GSE46897 200046897
#> 907 GSE47874 200047874
#> 908 GSE47720 200047720
#> 909 GSE40498 200040498
#> 910 GSE40496 200040496
#> 911 GSE42507 200042507
#> 912 GSE40234 200040234
#> 913 GSE45986 200045986
#> 914 GSE45792 200045792
#> 915 GSE45777 200045777
#> 916 GSE44035 200044035
#> 917 GSE43752 200043752
#> 918 GSE43751 200043751
#> 919 GSE43750 200043750
#> 920 GSE43950 200043950
#> 921 GSE32909 200032909
#> 922 GSE34512 200034512
#> 923 GSE38291 200038291
#> 924 GSE14368 200014368
#> 925 GSE42487 200042487
#> 926 GSE42229 200042229
#> 927 GSE42228 200042228
#> 928 GSE42227 200042227
#> 929 GSE29231 200029231
#> 930 GSE29226 200029226
#> 931 GSE29221 200029221
#> 932 GSE42148 200042148
#> 933 GSE42094 200042094
#> 934 GSE42093 200042093
#> 935 GSE34526 200034526
#> 936 GSE35725 200035725
#> 937 GSE35716 200035716
#> 938 GSE35713 200035713
#> 939 GSE35712 200035712
#> 940 GSE35711 200035711
#> 941 GSE37794 200037794
#> 942 GSE30161 200030161
#> 943 GSE35851 200035851
#> 944 GSE30802 200030802
#> 945 GSE38642 200038642
#> 946 GSE35191 200035191
#> 947 GSE35186 200035186
#> 948 GSE38447 200038447
#> 949 GSE38396 200038396
#> 950 GSE36403 200036403
#> 951 GSE36402 200036402
#> 952 GSE36397 200036397
#> 953 GSE36084 200036084
#> 954 GSE33440 200033440
#> 955 GSE19637 200019637
#> 956 GSE32575 200032575
#> 957 GSE34223 200034223
#> 958 GSE37824 200037824
#> 959 GSE37901 200037901
#> 960 GSE28384 200028384
#> 961 GSE15932 200015932
#> 962 GSE37639 200037639
#> 963 GSE21232 200021232
#> 964 GSE30159 200030159
#> 965 GSE32512 200032512
#> 966 GSE29660 200029660
#> 967 GSE26244 200026244
#> 968 GSE26887 200026887
#> 969 GSE32874 200032874
#> 970 GSE32691 200032691
#> 971 GSE35296 200035296
#> 972 GSE31901 200031901
#> 973 GSE29908 200029908
#> 974 GSE35411 200035411
#> 975 GSE32544 200032544
#> 976 GSE19943 200019943
#> 977 GSE22255 200022255
#> 978 GSE13760 200013760
#> 979 GSE32357 200032357
#> 980 GSE24818 200024818
#> 981 GSE33032 200033032
#> 982 GSE28024 200028024
#> 983 GSE28022 200028022
#> 984 GSE27507 200027507
#> 985 GSE32553 200032553
#> 986 GSE27175 200027175
#> 987 GSE27317 200027317
#> 988 GSE21815 200021815
#> 989 GSE31056 200031056
#> 990 GSE30566 200030566
#> 991 GSE30529 200030529
#> 992 GSE30528 200030528
#> 993 GSE30122 200030122
#> 994 GSE23506 200023506
#> 995 GSE30803 200030803
#> 996 GSE30732 200030732
#> 997 GSE30310 200030310
#> 998 GSE28059 200028059
#> 999 GSE27951 200027951
#> 1000 GSE29718 200029718
#> 1001 GSE27949 200027949
#> 1002 GSE24326 200024326
#> 1003 GSE23338 200023338
#> 1004 GSE29190 200029190
#> 1005 GSE29142 200029142
#> 1006 GSE29084 200029084
#> 1007 GSE25462 200025462
#> 1008 GSE25862 200025862
#> 1009 GSE24193 200024193
#> 1010 GSE26744 200026744
#> 1011 GSE19790 200019790
#> 1012 GSE21980 200021980
#> 1013 GSE19649 200019649
#> 1014 GSE26168 200026168
#> 1015 GSE26167 200026167
#> 1016 GSE26073 200026073
#> 1017 GSE25826 200025826
#> 1018 GSE25724 200025724
#> 1019 GSE20553 200020553
#> 1020 GSE24422 200024422
#> 1021 GSE19420 200019420
#> 1022 GSE24685 200024685
#> 1023 GSE25249 200025249
#> 1024 GSE23784 200023784
#> 1025 GSE16804 200016804
#> 1026 GSE14503 200014503
#> 1027 GSE24290 200024290
#> 1028 GSE24215 200024215
#> 1029 GSE24147 200024147
#> 1030 GSE23858 200023858
#> 1031 GSE23561 200023561
#> 1032 GSE18821 200018821
#> 1033 GSE17710 200017710
#> 1034 GSE12385 200012385
#> 1035 GSE12384 200012384
#> 1036 GSE23343 200023343
#> 1037 GSE21785 200021785
#> 1038 GSE22309 200022309
#> 1039 GSE21989 200021989
#> 1040 GSE18470 200018470
#> 1041 GSE21321 200021321
#> 1042 GSE17941 200017941
#> 1043 GSE21340 200021340
#> 1044 GSE19519 200019519
#> 1045 GSE20966 200020966
#> 1046 GSE20067 200020067
#> 1047 GSE17727 200017727
#> 1048 GSE20247 200020247
#> 1049 GSE18732 200018732
#> 1050 GSE19769 200019769
#> 1051 GSE18927 200018927
#> 1052 GSE15790 200015790
#> 1053 GSE17635 200017635
#> 1054 GSE13840 200013840
#> 1055 GSE15072 200015072
#> 1056 GSE12959 200012959
#> 1057 GSE18212 200018212
#> 1058 GSE8908 200008908
#> 1059 GSE17556 200017556
#> 1060 GSE16256 200016256
#> 1061 GSE15543 200015543
#> 1062 GSE17060 200017060
#> 1063 GSE17058 200017058
#> 1064 GSE16025 200016025
#> 1065 GSE13015 200013015
#> 1066 GSE5903 200005903
#> 1067 GSE16415 200016415
#> 1068 GSE15653 200015653
#> 1069 GSE13736 200013736
#> 1070 GSE13465 200013465
#> 1071 GSE13920 200013920
#> 1072 GSE10334 200010334
#> 1073 GSE13290 200013290
#> 1074 GSE8157 200008157
#> 1075 GSE12643 200012643
#> 1076 GSE11908 200011908
#> 1077 GSE11907 200011907
#> 1078 GSE10540 200010540
#> 1079 GSE9588 200009588
#> 1080 GSE9984 200009984
#> 1081 GSE9939 200009939
#> 1082 GSE6751 200006751
#> 1083 GSE6599 200006599
#> 1084 GSE6798 200006798
#> 1085 GSE9157 200009157
#> 1086 GSE9017 200009017
#> 1087 GSE9105 200009105
#> 1088 GSE9006 200009006
#> 1089 GSE4704 200004704
#> 1090 GSE7818 200007818
#> 1091 GSE7146 200007146
#> 1092 GSE6862 200006862
#> 1093 GSE6573 200006573
#> 1094 GSE4901 200004901
#> 1095 GSE3118 200003118
#> 1096 GSE4117 200004117
#> 1097 GSE5090 200005090
#> 1098 GSE3881 200003881
#> 1099 GSE3308 200003308
#> 1100 GSE3447 200003447
#> 1101 GSE3307 200003307
#> 1102 GSE2138 200002138
#> 1103 GSE2956 200002956
#> 1104 GSE1322 200001322
#> 1105 GSE1009 200001009
#> 1106 GSE634 200000634
#> 1107 GSE121 200000121
#> SRA Run Selector
#> 1 <NA>
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA726931
#> 6 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA726930
#> 7 <NA>
#> 8 <NA>
#> 9 <NA>
#> 10 <NA>
#> 11 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA642130
#> 12 <NA>
#> 13 <NA>
#> 14 <NA>
#> 15 <NA>
#> 16 <NA>
#> 17 <NA>
#> 18 <NA>
#> 19 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA770632
#> 20 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA735124
#> 21 <NA>
#> 22 <NA>
#> 23 <NA>
#> 24 <NA>
#> 25 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA557351
#> 26 <NA>
#> 27 <NA>
#> 28 <NA>
#> 29 <NA>
#> 30 <NA>
#> 31 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA733490
#> 32 <NA>
#> 33 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA752137
#> 34 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA751907
#> 35 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA748583
#> 36 <NA>
#> 37 <NA>
#> 38 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA777441
#> 39 <NA>
#> 40 <NA>
#> 41 https://www.ncbi.nlm.nih.gov/Traces/study/?acc=PRJNA780478
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#> 646 <NA> <NA>
#> 647 <NA> <NA>
#> 648 <NA> <NA>
#> 649 <NA> <NA>
#> 650 <NA> <NA>
#> 651 <NA> <NA>
#> 652 <NA> <NA>
#> 653 <NA> <NA>
#> 654 <NA> <NA>
#> 655 <NA> <NA>
#> 656 <NA> <NA>
#> 657 <NA> <NA>
#> 658 <NA> <NA>
#> 659 <NA> <NA>
#> 660 <NA> <NA>
#> 661 <NA> <NA>
#> 662 <NA> <NA>
#> 663 <NA> <NA>
#> 664 <NA> <NA>
#> 665 <NA> <NA>
#> 666 <NA> <NA>
#> 667 <NA> <NA>
#> 668 <NA> <NA>
#> 669 <NA> <NA>
#> 670 <NA> <NA>
#> 671 <NA> <NA>
#> 672 <NA> <NA>
#> 673 <NA> <NA>
#> 674 <NA> <NA>
#> 675 <NA> <NA>
#> 676 <NA> <NA>
#> 677 <NA> <NA>
#> 678 <NA> <NA>
#> 679 <NA> <NA>
#> 680 <NA> <NA>
#> 681 <NA> <NA>
#> 682 <NA> <NA>
#> 683 <NA> <NA>
#> 684 <NA> <NA>
#> 685 <NA> <NA>
#> 686 <NA> <NA>
#> 687 <NA> <NA>
#> 688 <NA> <NA>
#> 689 <NA> <NA>
#> 690 <NA> <NA>
#> 691 <NA> <NA>
#> 692 <NA> <NA>
#> 693 <NA> <NA>
#> 694 <NA> <NA>
#> 695 <NA> <NA>
#> 696 <NA> <NA>
#> 697 <NA> <NA>
#> 698 <NA> <NA>
#> 699 <NA> <NA>
#> 700 <NA> <NA>
#> 701 <NA> <NA>
#> 702 <NA> <NA>
#> 703 <NA> <NA>
#> 704 <NA> <NA>
#> 705 <NA> <NA>
#> 706 <NA> <NA>
#> 707 <NA> <NA>
#> 708 <NA> <NA>
#> 709 <NA> <NA>
#> 710 <NA> <NA>
#> 711 <NA> <NA>
#> 712 <NA> <NA>
#> 713 <NA> <NA>
#> 714 <NA> <NA>
#> 715 <NA> <NA>
#> 716 <NA> <NA>
#> 717 <NA> <NA>
#> 718 <NA> <NA>
#> 719 <NA> <NA>
#> 720 <NA> <NA>
#> 721 <NA> <NA>
#> 722 <NA> <NA>
#> 723 <NA> <NA>
#> 724 <NA> <NA>
#> 725 <NA> <NA>
#> 726 <NA> <NA>
#> 727 <NA> <NA>
#> 728 <NA> <NA>
#> 729 <NA> <NA>
#> 730 <NA> <NA>
#> 731 <NA> <NA>
#> 732 <NA> <NA>
#> 733 <NA> <NA>
#> 734 <NA> <NA>
#> 735 <NA> <NA>
#> 736 <NA> <NA>
#> 737 <NA> <NA>
#> 738 <NA> <NA>
#> 739 <NA> <NA>
#> 740 <NA> <NA>
#> 741 <NA> <NA>
#> 742 <NA> <NA>
#> 743 <NA> <NA>
#> 744 <NA> <NA>
#> 745 <NA> <NA>
#> 746 <NA> <NA>
#> 747 <NA> <NA>
#> 748 <NA> <NA>
#> 749 <NA> <NA>
#> 750 <NA> <NA>
#> 751 <NA> <NA>
#> 752 <NA> <NA>
#> 753 <NA> <NA>
#> 754 <NA> <NA>
#> 755 <NA> <NA>
#> 756 <NA> <NA>
#> 757 <NA> <NA>
#> 758 <NA> <NA>
#> 759 <NA> <NA>
#> 760 <NA> <NA>
#> 761 <NA> <NA>
#> 762 <NA> <NA>
#> 763 <NA> <NA>
#> 764 <NA> <NA>
#> 765 <NA> <NA>
#> 766 <NA> <NA>
#> 767 <NA> <NA>
#> 768 <NA> <NA>
#> 769 <NA> <NA>
#> 770 <NA> <NA>
#> 771 <NA> <NA>
#> 772 <NA> <NA>
#> 773 <NA> <NA>
#> 774 <NA> <NA>
#> 775 <NA> <NA>
#> 776 <NA> <NA>
#> 777 <NA> <NA>
#> 778 <NA> <NA>
#> 779 <NA> <NA>
#> 780 <NA> <NA>
#> 781 <NA> <NA>
#> 782 <NA> <NA>
#> 783 <NA> <NA>
#> 784 <NA> <NA>
#> 785 <NA> <NA>
#> 786 <NA> <NA>
#> 787 <NA> <NA>
#> 788 <NA> <NA>
#> 789 <NA> <NA>
#> 790 <NA> <NA>
#> 791 <NA> <NA>
#> 792 <NA> <NA>
#> 793 <NA> <NA>
#> 794 <NA> <NA>
#> 795 <NA> <NA>
#> 796 <NA> <NA>
#> 797 <NA> <NA>
#> 798 <NA> <NA>
#> 799 <NA> <NA>
#> 800 <NA> <NA>
#> 801 <NA> <NA>
#> 802 <NA> <NA>
#> 803 <NA> <NA>
#> 804 <NA> <NA>
#> 805 <NA> <NA>
#> 806 <NA> <NA>
#> 807 <NA> <NA>
#> 808 <NA> <NA>
#> 809 <NA> <NA>
#> 810 <NA> <NA>
#> 811 <NA> <NA>
#> 812 <NA> <NA>
#> 813 <NA> <NA>
#> 814 <NA> <NA>
#> 815 <NA> <NA>
#> 816 <NA> <NA>
#> 817 <NA> <NA>
#> 818 <NA> <NA>
#> 819 <NA> <NA>
#> 820 <NA> <NA>
#> 821 <NA> <NA>
#> 822 <NA> <NA>
#> 823 <NA> <NA>
#> 824 <NA> <NA>
#> 825 <NA> <NA>
#> 826 <NA> <NA>
#> 827 <NA> <NA>
#> 828 <NA> <NA>
#> 829 <NA> <NA>
#> 830 <NA> <NA>
#> 831 <NA> <NA>
#> 832 <NA> <NA>
#> 833 <NA> <NA>
#> 834 <NA> <NA>
#> 835 <NA> <NA>
#> 836 <NA> <NA>
#> 837 <NA> <NA>
#> 838 <NA> <NA>
#> 839 <NA> <NA>
#> 840 <NA> <NA>
#> 841 <NA> <NA>
#> 842 <NA> <NA>
#> 843 <NA> <NA>
#> 844 <NA> <NA>
#> 845 <NA> <NA>
#> 846 <NA> <NA>
#> 847 <NA> <NA>
#> 848 <NA> <NA>
#> 849 <NA> <NA>
#> 850 <NA> <NA>
#> 851 <NA> <NA>
#> 852 <NA> <NA>
#> 853 <NA> <NA>
#> 854 <NA> <NA>
#> 855 <NA> <NA>
#> 856 <NA> <NA>
#> 857 <NA> <NA>
#> 858 <NA> <NA>
#> 859 <NA> <NA>
#> 860 <NA> <NA>
#> 861 <NA> <NA>
#> 862 <NA> <NA>
#> 863 <NA> <NA>
#> 864 <NA> <NA>
#> 865 <NA> <NA>
#> 866 <NA> <NA>
#> 867 <NA> GDS
#> 868 <NA> <NA>
#> 869 <NA> <NA>
#> 870 <NA> <NA>
#> 871 <NA> <NA>
#> 872 <NA> <NA>
#> 873 <NA> <NA>
#> 874 <NA> <NA>
#> 875 <NA> <NA>
#> 876 <NA> <NA>
#> 877 <NA> <NA>
#> 878 <NA> <NA>
#> 879 <NA> <NA>
#> 880 <NA> <NA>
#> 881 <NA> <NA>
#> 882 <NA> <NA>
#> 883 <NA> <NA>
#> 884 <NA> <NA>
#> 885 <NA> <NA>
#> 886 <NA> <NA>
#> 887 <NA> <NA>
#> 888 <NA> <NA>
#> 889 <NA> <NA>
#> 890 <NA> <NA>
#> 891 <NA> <NA>
#> 892 <NA> <NA>
#> 893 <NA> <NA>
#> 894 <NA> <NA>
#> 895 <NA> <NA>
#> 896 <NA> <NA>
#> 897 <NA> <NA>
#> 898 <NA> <NA>
#> 899 <NA> <NA>
#> 900 <NA> <NA>
#> 901 <NA> <NA>
#> 902 <NA> <NA>
#> 903 <NA> <NA>
#> 904 <NA> <NA>
#> 905 <NA> <NA>
#> 906 <NA> <NA>
#> 907 <NA> <NA>
#> 908 <NA> <NA>
#> 909 <NA> <NA>
#> 910 <NA> <NA>
#> 911 <NA> <NA>
#> 912 <NA> <NA>
#> 913 <NA> <NA>
#> 914 <NA> <NA>
#> 915 <NA> <NA>
#> 916 <NA> <NA>
#> 917 <NA> <NA>
#> 918 <NA> <NA>
#> 919 <NA> <NA>
#> 920 <NA> <NA>
#> 921 <NA> <NA>
#> 922 <NA> GDS
#> 923 <NA> <NA>
#> 924 <NA> <NA>
#> 925 <NA> <NA>
#> 926 <NA> <NA>
#> 927 <NA> <NA>
#> 928 <NA> <NA>
#> 929 <NA> <NA>
#> 930 <NA> <NA>
#> 931 <NA> <NA>
#> 932 <NA> <NA>
#> 933 <NA> <NA>
#> 934 <NA> <NA>
#> 935 <NA> GDS
#> 936 <NA> <NA>
#> 937 <NA> <NA>
#> 938 <NA> <NA>
#> 939 <NA> <NA>
#> 940 <NA> <NA>
#> 941 <NA> <NA>
#> 942 <NA> <NA>
#> 943 <NA> <NA>
#> 944 <NA> <NA>
#> 945 <NA> GDS
#> 946 <NA> <NA>
#> 947 <NA> <NA>
#> 948 <NA> <NA>
#> 949 <NA> <NA>
#> 950 <NA> <NA>
#> 951 <NA> <NA>
#> 952 <NA> <NA>
#> 953 <NA> <NA>
#> 954 <NA> <NA>
#> 955 <NA> <NA>
#> 956 <NA> <NA>
#> 957 <NA> <NA>
#> 958 <NA> <NA>
#> 959 <NA> <NA>
#> 960 <NA> <NA>
#> 961 <NA> <NA>
#> 962 <NA> <NA>
#> 963 <NA> <NA>
#> 964 <NA> GDS
#> 965 <NA> <NA>
#> 966 <NA> <NA>
#> 967 <NA> <NA>
#> 968 <NA> GDS
#> 969 <NA> <NA>
#> 970 <NA> <NA>
#> 971 <NA> <NA>
#> 972 <NA> GDS
#> 973 <NA> <NA>
#> 974 <NA> <NA>
#> 975 <NA> <NA>
#> 976 <NA> <NA>
#> 977 <NA> GDS
#> 978 <NA> GDS
#> 979 <NA> GDS
#> 980 <NA> <NA>
#> 981 <NA> <NA>
#> 982 <NA> <NA>
#> 983 <NA> <NA>
#> 984 <NA> <NA>
#> 985 <NA> <NA>
#> 986 <NA> <NA>
#> 987 <NA> <NA>
#> 988 <NA> <NA>
#> 989 <NA> GDS
#> 990 <NA> <NA>
#> 991 <NA> <NA>
#> 992 <NA> <NA>
#> 993 <NA> <NA>
#> 994 <NA> <NA>
#> 995 <NA> GDS
#> 996 <NA> GDS
#> 997 <NA> GDS
#> 998 <NA> <NA>
#> 999 <NA> GDS
#> 1000 <NA> GDS
#> 1001 <NA> <NA>
#> 1002 <NA> <NA>
#> 1003 <NA> <NA>
#> 1004 <NA> <NA>
#> 1005 <NA> <NA>
#> 1006 <NA> GDS
#> 1007 <NA> GDS
#> 1008 <NA> <NA>
#> 1009 <NA> GDS
#> 1010 <NA> <NA>
#> 1011 <NA> GDS
#> 1012 <NA> <NA>
#> 1013 <NA> <NA>
#> 1014 <NA> GDS
#> 1015 <NA> <NA>
#> 1016 <NA> <NA>
#> 1017 <NA> <NA>
#> 1018 <NA> GDS
#> 1019 <NA> <NA>
#> 1020 <NA> <NA>
#> 1021 <NA> GDS
#> 1022 ENCODE <NA>
#> 1023 Roadmap Epigenomics <NA>
#> 1024 <NA> <NA>
#> 1025 <NA> <NA>
#> 1026 <NA> GDS
#> 1027 <NA> GDS
#> 1028 <NA> <NA>
#> 1029 <NA> <NA>
#> 1030 <NA> <NA>
#> 1031 <NA> <NA>
#> 1032 <NA> <NA>
#> 1033 <NA> <NA>
#> 1034 <NA> <NA>
#> 1035 <NA> <NA>
#> 1036 <NA> GDS
#> 1037 <NA> <NA>
#> 1038 <NA> GDS
#> 1039 <NA> <NA>
#> 1040 <NA> <NA>
#> 1041 <NA> <NA>
#> 1042 <NA> <NA>
#> 1043 <NA> <NA>
#> 1044 <NA> GDS4129 GDS
#> 1045 <NA> GDS
#> 1046 <NA> <NA>
#> 1047 <NA> <NA>
#> 1048 <NA> GDS
#> 1049 <NA> <NA>
#> 1050 <NA> <NA>
#> 1051 Roadmap Epigenomics <NA>
#> 1052 <NA> <NA>
#> 1053 <NA> GDS
#> 1054 <NA> <NA>
#> 1055 <NA> <NA>
#> 1056 <NA> <NA>
#> 1057 <NA> <NA>
#> 1058 <NA> <NA>
#> 1059 <NA> <NA>
#> 1060 Roadmap Epigenomics <NA>
#> 1061 <NA> GDS
#> 1062 <NA> <NA>
#> 1063 <NA> <NA>
#> 1064 <NA> <NA>
#> 1065 <NA> <NA>
#> 1066 <NA> <NA>
#> 1067 <NA> GDS
#> 1068 <NA> GDS
#> 1069 <NA> GDS
#> 1070 <NA> <NA>
#> 1071 <NA> <NA>
#> 1072 <NA> <NA>
#> 1073 <NA> GDS
#> 1074 <NA> GDS4132 GDS
#> 1075 <NA> GDS
#> 1076 <NA> <NA>
#> 1077 <NA> <NA>
#> 1078 <NA> <NA>
#> 1079 <NA> <NA>
#> 1080 <NA> GDS
#> 1081 <NA> <NA>
#> 1082 <NA> GDS
#> 1083 <NA> <NA>
#> 1084 <NA> GDS
#> 1085 <NA> <NA>
#> 1086 <NA> <NA>
#> 1087 <NA> GDS
#> 1088 <NA> GDS3874 GDS
#> 1089 <NA> <NA>
#> 1090 <NA> <NA>
#> 1091 <NA> GDS2790 GDS
#> 1092 <NA> <NA>
#> 1093 <NA> GDS
#> 1094 <NA> <NA>
#> 1095 <NA> <NA>
#> 1096 <NA> <NA>
#> 1097 <NA> GDS
#> 1098 <NA> <NA>
#> 1099 <NA> GDS
#> 1100 <NA> <NA>
#> 1101 <NA> GDS1956 GDS
#> 1102 <NA> <NA>
#> 1103 <NA> <NA>
#> 1104 <NA> <NA>
#> 1105 <NA> GDS
#> 1106 <NA> <NA>
#> 1107 <NA> GDS157 GDS158 GDS160 GDS161 GDS162