Mechanistic evaluation of the insulin response in H4IIE hepatoma cells: New endpoints for toxicity testing?
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ABSTRACT:
ORGANISM(S): Rattus norvegicus
PROVIDER: GSE35697 | GEO | 2012/06/18
SECONDARY ACCESSION(S): PRJNA152033
REPOSITORIES: GEO
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Project description:Transcriptional profiling of insulin exposed H4IIE cells at different time points (6h-24h) to 100 nM insulin, with untreated solvent control cells (blanks). The central role of hepatic insulin resistance in pandemic metabolic diseases urges investigation of the underlying mechanisms of its development and pathogenesis. Besides genetic susceptibility and lifestyle factors, environmental pollutants are suggested risk factors. In order to appraise the role of the thousands of pollutants we are daily exposed to, stable, robust and high throughput cell-based systems should be developed. In this context, it is of paramount importance to select an in vitro system which mimics in vivo insulin responses as close as possible. Therefore, this study was designed to evaluate if the rat H4IIE hepatoma cell line is a physiologically relevant model to study hepatic insulin responses. DNA microarray analysis, real-time PCR and flow cytometric cell cycle analysis were used to assess the relevance of the insulin response in H4IIE cells. Insulin dose dependently stimulated H4IIE growth. Time dependency of the insulin response was shown with real-time PCR for the known insulin responsive genes: Fasn, Pck1 and Irs2. Based on these results, microarray analysis was performed on H4IIE cells exposed to insulin (100 nM) for 6h and 24h. Genes related to carbohydrate (glycolysis and gluconeogenesis) and lipid metabolism (glycerolipid metabolism, cholesterol synthesis and fatty acid oxidation) were most profoundly afflicted, in accordance with in vivo insulin action in liver. Since changes in carbohydrate and lipid metabolism are pivotal in the pathogenesis of insulin resistance, the presence of a physiological relevant insulin response in H4IIE cells pleads for further testing of its potential use in research on pollutant-driven insulin resistance. Microarray analysis was performed on H4IIE cells exposed to insulin (100 nM) for 6h and 24h. A separate v+2 A-optimal hybridization design was used for each time-point. Using this design each exposure condition is represented by three biological replicates, with each biological replicate analysed in technical duplicate while applying the dye swap principle to correct for dye bias.
2012-06-17 | E-GEOD-35697 | biostudies-arrayexpress
Project description:Glycerol kinase (GK) is at the interface of fat and carbohydrate metabolism and has been implicated in insulin resistance and type 2 diabetes mellitus (T2DM). To define GK's role in insulin resistance, we examined gene expression in brown adipose tissue in a glycerol kinase (Gyk) knockout (KO) mouse model using microarray analysis. Global gene expression profiles of KO mice were distinct from wild type (WT) with 668 genes that were differentially expressed. These included genes involved in lipid metabolism, carbohydrate metabolism, insulin signaling, and insulin resistance. Real-Time (RT) PCR analysis confirmed the differential expression of selected genes involved in lipid and carbohydrate metabolism. PathwayAssist analysis confirmed direct and indirect connections between GK and genes in lipid metabolism, carbohydrate metabolism, insulin signaling, and insulin resistance. Network Component Analysis (NCA) showed that the transcription factors, peroxisome proliferator-activated receptor gamma (PPAR-γ), sterol regulatory element binding factor 1 (SREBP-1), SREBP-2, signal transducer and activator of transcription 3 (STAT3), STAT5, trans-acting transcription factor 1 (SP1), CCAAT/enhancer binding protein alpha (CEBP-α), cAMP responsive element binding protein 1 (CREB), glucocorticoid receptor (GR), and PPAR-α have altered activity in the KO mice. NCA also revealed the individual contribution of these transcription factors on the expression of genes altered in the microarray data. This study elucidates the transcription network of Gyk and further confirms a role for Gyk, a simple Mendelian disorder, in insulin resistance and T2DM, a common complex genetic disorder. Experiment Overall Design: 7 samples are analyzed; 3 wildtype and 4 KO. the WT samples are used as control and KO samples are used as the experimental group.
2007-11-22 | E-GEOD-4656 | biostudies-arrayexpress
Project description:Glycerol kinase (GK) is at the interface of fat and carbohydrate metabolism and has been implicated in insulin resistance and type 2 diabetes mellitus (T2DM). To define GK’s role in insulin resistance, we examined gene expression in brown adipose tissue in a glycerol kinase (Gyk) knockout (KO) mouse model using microarray analysis. Global gene expression profiles of KO mice were distinct from wild type (WT) with 668 genes that were differentially expressed. These included genes involved in lipid metabolism, carbohydrate metabolism, insulin signaling, and insulin resistance. Real-Time (RT) PCR analysis confirmed the differential expression of selected genes involved in lipid and carbohydrate metabolism. PathwayAssist analysis confirmed direct and indirect connections between GK and genes in lipid metabolism, carbohydrate metabolism, insulin signaling, and insulin resistance. Network Component Analysis (NCA) showed that the transcription factors, peroxisome proliferator-activated receptor gamma (PPAR-γ), sterol regulatory element binding factor 1 (SREBP-1), SREBP-2, signal transducer and activator of transcription 3 (STAT3), STAT5, trans-acting transcription factor 1 (SP1), CCAAT/enhancer binding protein alpha (CEBP-α), cAMP responsive element binding protein 1 (CREB), glucocorticoid receptor (GR), and PPAR-α have altered activity in the KO mice. NCA also revealed the individual contribution of these transcription factors on the expression of genes altered in the microarray data. This study elucidates the transcription network of Gyk and further confirms a role for Gyk, a simple Mendelian disorder, in insulin resistance and T2DM, a common complex genetic disorder. Keywords: genotype state analysis
2007-03-11 | GSE4656 | GEO
Project description:Intervention1: randomized controlled trial: one group will be given 400-600 ml of oral carbohydrate drink night before surgery and 200 ml 2 hours before surgery and the other group will not be given such carbohydrate drink,they will be kept fasted and from both groups of patient blood samples will be taken for glucose,insulin,CRP, IL-6,albumin and also subjective well being ,surgical outcome will be assessed
Intervention2: randomized controlled trial: interventional group will be given oral carbohydrate drink night before and 2 hours before surgery
Control Intervention1: nil: nil
Primary outcome(s): preoperative oral carbohydrate loading is effective in reducing insulin resistance and increased insulin sensitivity in patients undergoing colorectal surgeryTimepoint: Time of patient discharge
| 2594638 | ecrin-mdr-crc
Project description:The beneficial effects of glucocorticoids (GCs) in acute lymphoblastic leukemia (ALL) are based on their ability to induce apoptosis. Omics technologies such as DNA microarray analysis are widely used to study the changes in gene expression and have been successfully implemented in biomarker identification. In addition, time series studies of gene expression enable the identification of correlations between kinetic profiles of glucocorticoid receptor (GR) target genes and diverse modes of transcriptional regulation. This study presents a genome-wide microarray analysis of both our and published Affymetrix HG-U133 Plus 2.0 data in GCs-sensitive and -resistant ALL. GCs-sensitive CCRF-CEM-C7-14 cells were treated with dexamethasone at three time points (0 h, 2 h and 10 h). The treated samples were then compared to the control (0 h). The published data used were as follows: GSE2677: GSM51674: B-ALL-24-24h GSM51675: B-ALL-24-6h GSM51676: B-ALL-24-0h GSM51680: B-ALL-17-24h GSM51681: B-ALL-17-8h GSM51682: B-ALL-17-0h GSM51677: B-ALL-13-24h GSM51678: B-ALL-13-8h GSM51679: B-ALL-13-0h GSM51683: B-ALL-31-24h GSM51684: B-ALL-31-6h GSM51685: B-ALL-31-0h GSM51686: B-ALL-32-24h GSM51687: B-ALL-32-6h GSM51688: B-ALL-32-0h GSM51689: B-ALL-33-24h GSM51690: B-ALL-33-6h GSM51691: B-ALL-33-0h GSM51692: B-ALL-37-24h GSM51693: B-ALL-37-6h GSM51694: B-ALL-37-0h GSM51695: B-ALL-38-24h GSM51696: B-ALL-38-6h GSM51697: B-ALL-38-0h GSM51698: B-ALL-40-24h GSM51699: B-ALL-40-6h GSM51700: B-ALL-40-0h GSM51701: B-ALL-43-24h GSM51702: B-ALL-43-6h GSM51703: B-ALL-43-0h GSM51707: T-ALL-25-24h GSM51708: T-ALL-25-6h GSM51709: T-ALL-25-0h GSM51704: T-ALL-20-24h GSM51705: T-ALL-20-8h GSM51706: T-ALL-20-0h GSM51710: T-ALL-2-24h GSM51711: T-ALL-2-8h GSM51712: T-ALL-2-0h GSE2842 GSM60545: S-Line-PreB-6h-EtOH GSM60546: S-Line-PreB-6h-GC GSM60547: S-Line-PreB-24h-GC GSM60542: S-Line-C7H2-6h-EtOH GSM60543: S-Line-C7H2-6h-GC GSM60544: S-Line-C7H2-24h-GC GSM60560: R-Line-CEMC1-6h-EtOH GSM60561: R-Line-CEMC1-6h-GC GSM60562: R-Line-CEMC1-24h-GC GSM60564: R-Line-C7R1-6h-EtOH GSM60566: R-Line-C7R1-6h-GC GSM60576: R-Line-C7R1dim-low-6h-EtOH GSM60578: R-Line-C7R1dim-low-6h-GC GSM60579: R-Line-C7R1dim-low-24h-GC GSM60581: R-Line-PreB-6h-EtOH GSM60583: R-Line-PreB-6h-GC GSM60584: R-Line-PreB-24h-EtOH GSM60586: R-Line-PreB-24h-GC GSM60548: C-Line-CEMC1-ratGR-6h-EtOH GSM60549: C-Line-CEMC1-ratGR-6h-GC GSM60550: C-Line-CEMC1-ratGR-24h-GC GSM60551: C-Line-C7R1dim-high-6h-EtOH GSM60552: C-Line-C7R1dim-high-6h-GC GSM60553: C-Line-C7R1dim-high-24h-GC
2012-09-01 | GSE39338 | GEO
Project description:The beneficial effects of glucocorticoids (GCs) in acute lymphoblastic leukemia (ALL) are based on their ability to induce apoptosis. Omics technologies such as DNA microarray analysis are widely used to study the changes in gene expression and have been successfully implemented in biomarker identification. In addition, time series studies of gene expression enable the identification of correlations between kinetic profiles of glucocorticoid receptor (GR) target genes and diverse modes of transcriptional regulation. This study presents a genome-wide microarray analysis of both our and published Affymetrix HG-U133 Plus 2.0 data in GCs-sensitive and -resistant ALL. GCs-sensitive CCRF-CEM-C7-14 cells were treated with dexamethasone at three time points (0 h, 2 h and 10 h). The treated samples were then compared to the control (0 h). The published data used were as follows: GSE2677: GSM51674: B-ALL-24-24h GSM51675: B-ALL-24-6h GSM51676: B-ALL-24-0h GSM51680: B-ALL-17-24h GSM51681: B-ALL-17-8h GSM51682: B-ALL-17-0h GSM51677: B-ALL-13-24h GSM51678: B-ALL-13-8h GSM51679: B-ALL-13-0h GSM51683: B-ALL-31-24h GSM51684: B-ALL-31-6h GSM51685: B-ALL-31-0h GSM51686: B-ALL-32-24h GSM51687: B-ALL-32-6h GSM51688: B-ALL-32-0h GSM51689: B-ALL-33-24h GSM51690: B-ALL-33-6h GSM51691: B-ALL-33-0h GSM51692: B-ALL-37-24h GSM51693: B-ALL-37-6h GSM51694: B-ALL-37-0h GSM51695: B-ALL-38-24h GSM51696: B-ALL-38-6h GSM51697: B-ALL-38-0h GSM51698: B-ALL-40-24h GSM51699: B-ALL-40-6h GSM51700: B-ALL-40-0h GSM51701: B-ALL-43-24h GSM51702: B-ALL-43-6h GSM51703: B-ALL-43-0h GSM51707: T-ALL-25-24h GSM51708: T-ALL-25-6h GSM51709: T-ALL-25-0h GSM51704: T-ALL-20-24h GSM51705: T-ALL-20-8h GSM51706: T-ALL-20-0h GSM51710: T-ALL-2-24h GSM51711: T-ALL-2-8h GSM51712: T-ALL-2-0h GSE2842 GSM60545: S-Line-PreB-6h-EtOH GSM60546: S-Line-PreB-6h-GC GSM60547: S-Line-PreB-24h-GC GSM60542: S-Line-C7H2-6h-EtOH GSM60543: S-Line-C7H2-6h-GC GSM60544: S-Line-C7H2-24h-GC GSM60560: R-Line-CEMC1-6h-EtOH GSM60561: R-Line-CEMC1-6h-GC GSM60562: R-Line-CEMC1-24h-GC GSM60564: R-Line-C7R1-6h-EtOH GSM60566: R-Line-C7R1-6h-GC GSM60576: R-Line-C7R1dim-low-6h-EtOH GSM60578: R-Line-C7R1dim-low-6h-GC GSM60579: R-Line-C7R1dim-low-24h-GC GSM60581: R-Line-PreB-6h-EtOH GSM60583: R-Line-PreB-6h-GC GSM60584: R-Line-PreB-24h-EtOH GSM60586: R-Line-PreB-24h-GC GSM60548: C-Line-CEMC1-ratGR-6h-EtOH GSM60549: C-Line-CEMC1-ratGR-6h-GC GSM60550: C-Line-CEMC1-ratGR-24h-GC GSM60551: C-Line-C7R1dim-high-6h-EtOH GSM60552: C-Line-C7R1dim-high-6h-GC GSM60553: C-Line-C7R1dim-high-24h-GC
2012-09-01 | GSE39335 | GEO
Project description:We provide here the alterations in gene expression profiles of 3T3-L1 cells, a validated model for adipogenesis, exposed to quizalofop-p-ethyl for 6h, 24h and 12 days.
2019-04-18 | GSE121419 | GEO
Project description:Interventions: The control group: At preoperative night, patient drinks 800mL of water. From 2.5 to 3 hours before operation, patients drink 400mL of water.
The 12.5% of carbohydrate group: At preoperative night, patient drinks 800mL of 12.5% of carbohydrate. Before 2.5 to 3 hours to operation, patients drink 400mL of 12.5% of carbohydrate.
The 16.7% of carbohydrate group: At preoperative night, patient drinks 600mL of 16.7% of carbohydrate. From 2.5 to 3 hours before operation, patients drink 300mL of 16.7% of carbohydrate.
Primary outcome(s): Improvement of insulin resistance pre- and post- operation with HOMA-R Blood glucose Concentration of blood insulin
Study Design: Parallel Randomized
| 2634406 | ecrin-mdr-crc
Project description:Identification by Chromatin immunoprecipitation DNA-sequencing (ChIP-seq) of ChREBP binding sites in primary cultured mouse hepatocytes cultured for 24h with 25 mM glucose and 100 nM insulin.
2024-01-23 | GSE250437 | GEO
Project description:The fungal pathogen Sclerotinia sclerotiorum infects a broad range of dicotyledonous plant species and is the causative agent of stem rot in Brassica napus. To elucidate the mechanisms underlying the defense response, we studied the patterns of gene expression in a partially resistant variety of ZhongYou 821 (ZY821) and a susceptible line from Westar over five time points, 6, 12, 24, 48, and 72 hours post-inoculation (hpi) using a B. napus oligonucleotide microarray. Maximum differential gene expression was observed at 48 hpi in both genotypes with ZY821 responding more quickly than Westar. Specific sets of genes exhibited higher levels of expression at the earlier stages of the infection (6-12 hpi), including genes encoding defense-associated proteins such as chitinases, glucanases, osmotins and lectins and genes encoding transcription factors belonging to the zinc finger, WRKY, AP2, and MYB classes. Genes encoding enzymes involved in JA, ethylene, and auxin synthesis were induced in both genotypes, as were those for gibberellin degradation. Changes in metabolic pathways affecting carbohydrate and energy metabolism appeared to be directed toward shuttling carbon reserves to the TCA cycle. Genes involved in glucosinolate and phenylpropanoid biosynthesis were highly up-regulated suggesting that secondary metabolites are also important components of the response to S. sclerotiorum in B. napus. Keywords: Time course, infected vs mock infected Time course experiment (6hr, 12 hr, 24 hr, 48 hr, 72 hrs), sclerotinia infected vs mock-infected. Biological replicates: 3, independently harvested. Technical replicates: 2, dye swapped within each time for each biological replicate (total 6 slides per time point) time after innoculation: 6 hours: zy 6h inf vs control rep1, zy 6h control vs inf rep1, zy 6h inf vs control rep2, zy 6h control vs inf rep2, zy 6h inf vs control rep3, zy 6h control vs inf rep3 time after innoculation: 12 hours: zy 12h inf vs control rep1, zy 12h control vs inf rep1, zy 12h inf vs control rep2, zy 12h control vs inf rep2, zy 12h inf vs control rep3, zy 12h control vs inf rep3 time after innoculation: 24 hours: zy 24h inf vs control rep1, zy 24h control vs inf rep1, zy 24h inf vs control rep2, zy 24h control vs inf rep2, zy 24h inf vs control rep3, zy 24h control vs inf rep3 time after innoculation: 48 hours: zy 48h inf vs control rep1, zy 48h control vs inf rep1, zy 48h inf vs control rep2, zy 48h control vs inf rep2, zy 48h inf vs control rep3, zy 48h control vs inf rep3 time after innoculation: 72 hours: zy 72h inf vs control rep1, zy 72h control vs inf rep1, zy 72h inf vs control rep2, zy 72h control vs inf rep2, zy 72h inf vs control rep3, zy 72h control vs inf rep3 biological replicate: zy 6h inf vs control rep1, zy 6h inf vs control rep2, zy 6h inf vs control rep3 biological replicate: zy 6h control vs inf rep1, zy 6h control vs inf rep2, zy 6h control vs inf rep3 technical replicate - labeled-extract: zy 6h inf vs control rep1, zy 6h control vs inf rep1 technical replicate - labeled-extract: zy 6h inf vs control rep2, zy 6h control vs inf rep2 technical replicate - labeled-extract: zy 6h inf vs control rep3, zy 6h control vs inf rep3 biological replicate: zy 12h inf vs control rep1, zy 12h inf vs control rep2, zy 12h inf vs control rep3 biological replicate: zy 12h control vs inf rep1, zy 12h control vs inf rep2, zy 12h control vs inf rep3 technical replicate - labeled-extract: zy 12h inf vs control rep1, zy 12h control vs inf rep1 technical replicate - labeled-extract: zy 12h inf vs control rep2, zy 12h control vs inf rep2 technical replicate - labeled-extract: zy 12h inf vs control rep3, zy 12h control vs inf rep3 biological replicate: zy 24h inf vs control rep1, zy 24h inf vs control rep2, zy 24h inf vs control rep3 biological replicate: zy 24h control vs inf rep1, zy 24h control vs inf rep2, zy 24h control vs inf rep3 technical replicate - labeled-extract: zy 24h inf vs control rep1, zy 24h control vs inf rep1 technical replicate - labeled-extract: zy 24h inf vs control rep2, zy 24h control vs inf rep2 technical replicate - labeled-extract: zy 24h inf vs control rep3, zy 24h control vs inf rep3 biological replicate: zy 48h inf vs control rep1, zy 48h inf vs control rep2, zy 48h inf vs control rep3 biological replicate: zy 48h control vs inf rep1, zy 48h control vs inf rep2, zy 48h control vs inf rep3 technical replicate - labeled-extract: zy 48h inf vs control rep1, zy 48h control vs inf rep1 technical replicate - labeled-extract: zy 48h inf vs control rep2, zy 48h control vs inf rep2 technical replicate - labeled-extract: zy 48h inf vs control rep3, zy 48h control vs inf rep3 biological replicate: zy 72h inf vs control rep1, zy 72h inf vs control rep2, zy 72h inf vs control rep3 biological replicate: zy 72h control vs inf rep1, zy 72h control vs inf rep2, zy 72h control vs inf rep3 technical replicate - labeled-extract: zy 72h inf vs control rep1, zy 72h control vs inf rep1 technical replicate - labeled-extract: zy 72h inf vs control rep2, zy 72h control vs inf rep2 technical replicate - labeled-extract: zy 72h inf vs control rep3, zy 72h control vs inf rep3
2010-05-06 | E-GEOD-13256 | biostudies-arrayexpress