Unknown,Transcriptomics,Genomics,Proteomics

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Insulin resistance in high fat diet mouse

ABSTRACT: 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.

ORGANISM(S): Mus musculus

SUBMITTER: Rima Chaudhuri

PROVIDER: E-GEOD-73036 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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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.

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:Diet-induced obesity is reported to induce a phenotypic switch in adipose tissue macrophages from an antiinflammatory M2 state to a proinflammatory M1 state. Telmisartan, an angiotensin II type 1 receptor antagonist and a peroxisome proliferator-activated receptor-gamma (PPAR-gamma) agonist, reportedly has beneficial effects on insulin sensitivity. We studied the effects of telmisartan on the adipose tissue macrophage phenotype in high fat-fed mice. Telmisartan was administered for 5 weeks to high fat-fed C57BL/6 mice. Insulin sensitivity, macrophage infiltration, and the gene expressions of M1 and M2 markers in epididymal fat tissues were examined. Insulin- or a glucose-tolerance test showed that telmisartan treatment improved insulin resistance, decreasing the body weight gain, visceral fat weight and adipocyte size without affecting the amount of food intake. Telmisartan treatment reduced the number of CD11c-positive cells and crown-like structures. Telmisartan reduced the mRNA expressions of M1 macrophage markers, such as TNF-alpha and IL-6, and increased the expression of M2 markers, such as IL-10 and Mgl2. The reduction of M1 macrophage markers, as well as the increased gene expression of M2 markers especially IL-10, is a possible mechanism for the improvement of insulin sensitivity by telmisartan. Six-week-old male C57BL/6J mice were purchased from CLEA Japan. The mice were fed a chow that contained 10% of its calories from fat (control) or a high-fat diet (HFD) that contained 30% of its calories from fat for 24 weeks. The high fat-fed mice were randomized to 3 groups. Either telmisartan (~3 mg/kg/day) in drinking water (HFD+Tel), candesartan (~3 mg/kg/day) in drinking water (HFD+Can), or a HFD without any drugs (HFD) was administered for the next 5 weeks. Two mice were treated per group. Epididymal adipose tissues were rapidly removed from each mouse. Gene expression in epididymal fat tissue was analyzed using a GeneChip® system with the Mouse Genome 430 2.0 Array, which was spotted with 45,101 probe sets (Affymetrix, Santa Clara, CA, USA). Sample preparation for the array hybridization was performed according to the manufacturer’s instructions. In short, 5 μg of total RNA was used to synthesize double-stranded cDNA using the GeneChip® Expression 3′-Amplification Reagents One-Cycle cDNA Synthesis Kit (Affymetrix). Biotin-labeled cRNA was then synthesized from the cDNA using GeneChip® Expression 3′-Amplification Reagents for IVT Labeling (Affymetrix). After fragmentation, the biotinylated cRNA was hybridized to arrays at 45 °C for 16 h. The arrays were washed, stained with streptavidin-phycoerythrin, and scanned using a probe array scanner. The scanned chip was analyzed using the GeneChip Analysis Suite software (Affymetrix). Hybridization intensity data were converted into a presence/absence call for each gene, and changes in gene expression between experiments were detected by a comparison analysis. Data was shown as the fold change relative to the expression level of normal chow-fed mice.

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Insulin resistance in high fat diet mouse

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