Dataset Information

E-GEOD-79636

ABSTRACT: NextGen Consortium: GENESiPS Study: Identifying the Gene Networks of Insulin Resistance

PROVIDER: E-GEOD-79636 | ExpressionAtlas | 2019-03-29

REPOSITORIES: ExpressionAtlas

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	E-GEOD-79636-analysis-methods.tsv	Tabular
	E-GEOD-79636-atlasExperimentSummary.Rdata	Rdata
	E-GEOD-79636-coexpressions.tsv.gz	Tabular
	E-GEOD-79636-configuration.xml	Xml
	E-GEOD-79636-factors.xml	Xml

Items per page:

1 - 5 of 317

Similar Datasets

Project description: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. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Gene expression from muscle biopsies of Lean, Obese insulin sensitive (OIS), Obese insulin resistant (OIR) and obese T2D patients (T2D) were compared for differential expression between the groups (n=7 in each group)

Project description:Insulin resistance is necessary but not sufficient for the development of type 2 diabetes. Diabetes results when pancreatic beta-cells fail to compensate for insulin resistance by increasing insulin production through an expansion of beta-cell mass or increased insulin secretion. Communication between insulin target tissues and beta-cells may initiate this compensatory response. Correlated changes in gene expression between tissues can provide evidence for such intercellular communication. We profiled gene expression in six tissues of mice from an obesity-induced diabetes-resistant and a diabetes-susceptible strain before and after the onset of diabetes. We studied the correlation structure of mRNA abundance and identified 105 co-expression gene modules. We provide an interactive gene network model showing the correlation structure between the expression modules within and among the six tissues. This resource also provides a searchable database of gene expression profiles for all genes in six tissues in lean and obese diabetes-resistant and diabetes-susceptible mice, at 4 and 10 weeks of age. A cell cycle regulatory module in islets predicts diabetes susceptibility. The module predicts islet replication; we found a strong correlation between ^2 H_2 O incorporation into islet DNA /in vivo/ and the expression pattern of the cell cycle module. This pattern is highly correlated with that of several individual genes in insulin target tissues, including IGF2, which has been shown to promote beta-cell proliferation, suggesting that these genes may provide a link between insulin resistance and beta-cell proliferation. Keywords: time course, mouse strain comparison, effect of obesity, Type 2 diabetes is a disorder that involves an increased demand for insulin brought about by insulin resistance, together with a failure to compensate with sufficient insulin production. Although Insulin resistance occurs in most obese individuals, diabetes is generally forestalled through compensation with increased insulin. This increase in insulin occurs through an expansion of beta-cell mass and/or increased insulin secretion by individual beta-cells. Failure to compensate for insulin resistance leads to type 2 diabetes. One way to understand the pathophysiology of diabetes is to examine the coordinate changes in gene expression that occur in insulin-responsive tissues and pancreatic islets in obese animals that either compensate for insulin resistance or progress to type 2 diabetes. In each case, there are groups of genes that undergo changes in expression in a highly correlated fashion. By identifying groups of correlated transcripts (gene expression modules) during the compensation and development of diabetes, we can gain insight into potential pathways and regulatory networks in obesity-induced diabetes. We study two strains of mice that differ in obesity-induced diabetes susceptibility. In this study, we surveyed gene expression in six tissues of lean and obese C57BL/6 (B6) and BTBR mice aged 4 wks and 10 wks. B6 mice remain essentially non-diabetic at all ages, irrespective of obesity. When obese, BTBR mice become severely diabetic by 10 weeks of age. By analyzing the correlation structure of the genes under three contrast conditions, obesity, strain, and age, we identified gene expression modules associated with the onset of diabetes and provide an interactive co-expression network model of type 2 diabetes. We found a key module that is comprised of cell cycle regulatory genes. In the islet, the expression profile of these transcripts accurately predicts diabetes and is highly correlated with islet cell proliferation.

Project description:Recent discovery reveals HFD insult can cause insulin resistance very rapidly, but the underlying mechanism is still not well understood. We performed a short term experiment in a Diet Induced Insulin resistance mouse model. Objective: Insulin resistance (IR) is one of the earliest predictors of type 2 diabetes. However, diagnosis of IR is limited. High fat fed mouse models provide key insights into IR. We hypothesized that early features of IR are associated with persistent changes in gene expression (GE) and endeavoured to (a) develop novel methods for improving signal:noise in analysis of human GE using mouse models; (b) identify a GE motif that accurately diagnoses IR in humans; and (c) identify novel biology associated with IR in humans. Methods: We integrated human muscle GE data with longitudinal mouse GE data and developed an unbiased three-level cross-species analysis platform (single-gene, gene-set and networks) to generate a gene expression motif (GEM) indicative of IR. A logistic regression classification model validated GEM in 3 independent human datasets (n =115). Results: This GEM of 93 genes substantially improved diagnosis of IR compared to routine clinical measures across multiple independent datasets. Individuals misclassified by GEM possessed other metabolic features raising the possibility that they represent a separate metabolic subclass. The GEM was enriched in pathways previously implicated in insulin action and revealed novel associations between Î²-catenin and Jak1 and IR. Functional analyses using small molecule inhibitors showed an important role for these proteins in insulin action. Conclusions: This study shows that systems approaches for identifying molecular signatures provides a powerful way to stratify individuals into discrete metabolic groups. Moreover, we speculate that the Î²-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation. Comparison of gene expression in muscle tissue during High Fat Diet (HFD) time course (day 5 and day 42). Chow diet will serve as control for HFD. 4 samples per group serve as experimental replicates.

Dataset Information

E-GEOD-79636

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure