Dataset Information

Expression data from liver of obese (with or without type 2 diabetes) and lean human subjects.

ABSTRACT: Hepatic lipid accumulation is an important complication of obesity linked to risk for type 2 diabetes. To identify novel transcriptional changes in human liver which could contribute to hepatic lipid accumulation and associated insulin resistance and type 2 diabetes (DM2), we evaluated gene expression and gene set enrichment in surgical liver biopsies from 13 obese (9 with DM2) and 5 control subjects, obtained in the fasting state at the time of elective abdominal surgery for obesity or cholecystectomy. RNA was isolated for cRNA preparation and hybridized to Affymetrix U133A microarrays. Human liver samples were obtained from 5 lean control subjects undergoing elective cholecystectomy and 13 obese subjects (with or without Type 2 diabetes) undergoing gastric bypass surgery. Subjects with diabetes were classified as either well-controlled or poorly-controlled.

ORGANISM(S): Homo sapiens

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PROVIDER: S-DIXA-D-1033 | biostudies-other |

REPOSITORIES: biostudies-other

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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)

Dataset Information

Expression data from liver of obese (with or without type 2 diabetes) and lean human subjects.

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