Hepatic tissue expression profiles of insulin-sensitive obese mice, insulin-resistant mice and control mice
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1027791 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR26377700_1.fastq.gz | Fastqsanger.gz | |||
| SRR26377700_2.fastq.gz | Fastqsanger.gz | |||
| SRR26377701_1.fastq.gz | Fastqsanger.gz | |||
| SRR26377701_2.fastq.gz | Fastqsanger.gz | |||
| SRR26377702_1.fastq.gz | Fastqsanger.gz |
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Project description:Male C57BL/6J mice were fed a high-fat diet (HFD, 60 kcal% fat, D12492, Research Diets, Inc) or normal standard chow diet with 10 kal% fat (ND, D09100304, Research Diets, Inc). Specifically, 6-week-old mice were fed a HFD for 12 weeks to induce insulin resistance (HFD-12w group); 14-week-old mice were fed a HFD for 4 weeks to induce obesity (HFD-4w group). Control mice were fed a ND continuously for 12 weeks starting at 6 weeks of age (ND group). All mice reached the experimental endpoint at 18 weeks of age. Insulin sensitivity was measured by glucose tolerance test and insulin tolerance test. Mice that developed insulin resistance in HFD-12w group and obese mice with normal insulin sensitivity in HFD-4w group were used for further experiments. Mice in ND group were used as controls. Upon reaching the experimental endpoint, livers from three insulin-resistant mice, three insulin-sensitive obese mice, and three control mice were removed for RNA sequencing.
2023-10-14 | GSE245301 | GEO
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 β-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 β-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)
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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 β-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 β-catenin pathway may represent a novel biomarker for IR in humans that warrant future investigation.
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