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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 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE73036 | GEO | 2015/12/07

SECONDARY ACCESSION(S): PRJNA296104

REPOSITORIES: GEO

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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.

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: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:Obesity and its accompanying metabolic complications have risen to epidemic proportions over the past 30 years. Together, these diseases are creating economic and social burdens that are expected to increase. Although multifactorial in their etiology and polygenic, the development of diseases such as metabolic syndrome in humans (obesity, fatty liver, insulin resistance [IR] and dyslipidemia) is facilitated by access to cheap, calorically dense foods. Many animal models, particularly laboratory rats and mice, have been developed to identify the underlying mechanisms involved in risk factors associated with metabolic diseases, especially Type II diabetes (T2D) and IR. Genetic models and dietary manipulations have aided our understanding of these disorders; however, it is important to recognize that examples of reversible obesity and IR occur in nature. Importantly, these conditions in animals are not associated with the negative consequences observed in humans. These observations together suggest that metabolic controls are sufficiently flexible to avoid the highly deleterious, maladapted process in humans and serve as a survival mechanism in some animals. An example of this is obesity of almost unlimited proportions (i.e., >50% body fat) and insulin resistance present in hibernating bears. Both phenotypes are reversible naturally. Our studies using cultured bear fat cells collected during hibernation recapitulate the natural insulin resistance observed in vivo. In addition, proteins or peptides found in active season serum can reverse this insulin resistance of cells from hibernating bears, indicating that circulating factors also play an important role in the seasonal modulation of insulin sensitivity. Experimentally, we can reverse the insulin resistance temporarily during hibernation by simply feeding glucose for a few weeks; thus, an important control can be added to minimize influences of body fat content, season, and diet composition. Bears therefore offer a unique model in which whole animal and cell culture systems can be used in highly standardized, easily manipulated, and controlled experiments to explore the underlying basis for reversible IR. By revealing the mechanisms involved in this reversible physiology we can gain a broader understanding of metabolic controls and the regulation of energy balance. The Washington State University (WSU) Bear Research, Education and Conservation Center is the only facility of its kind in the world dedicated to basic nutrition and physiological research using captive bears. Based on this rationale, the main objective of the proposed studies is to reveal the underlying molecular changes occurring in bears during different states of insulin sensitivity by identifying the serum proteins/peptides associated with hibernation by comparing serum from active season and hibernating bears, as well as bears that have restoring insulin sensitivity during hibernation.

Project description: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. Adipocytes are critical regulators of mammalian metabolic homeostasis, with important effects on appetite, satiety, glucose and lipid homeostasis, energy expenditure, blood pressure, and immune function. Although insulin resistance (IR) in skeletal muscle, the major source of glucose disposal, is responsible for the bulk of the hyperglycemia observed in T2DM, muscle IR KO mice are generally healthy, IR also occurs in adipocytes, and inflammation within adipose tissue has been proposed as a primary mediator of IR, resulting in excess release of free fatty acids as well as alterations in adipokine release. Absence of adipose tissue, as observed in various lipodystrophies, or adipocyte-specific knockout of genes such as GLUT4 or insulin receptor, also alter systemic IR and T2DM risk. Transcriptional changes in adipocytes associated with metabolic disease. Despite the importance of adipocytes to metabolic disease, we have a poor understanding of how the adipocyte transcriptome changes in the disease state. Studies investigating transcriptional changes in whole adipose tissue have shown decreases in genes involved in adipogenesis, as well as alterations in inflammation, mitochondrial metabolism, lipid metabolism, detoxification, and insulin signaling. However, the vast majority of these studies use total adipose tissue samples, which does not allow for the exclusive evaluation of gene expression in mature adipocytes due to the substantial number of nonadipocyte cells (immune cells, fibroblasts, endothelial cells, pre-adipocytes, and mesenchymal cells) that also reside in adipose tissue. This is particularly relevant when studying metabolic disorders related to obesity-associated insulin resistance because these conditions are characterized by an increased influx of inflammatory cells into the adipose tissue.

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Insulin resistance in high fat diet mouse

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