Dataset Information

Global transcriptome profiling identifies KLF15 and SLC25A10 as regulators of adipocytes insulin sensitivity in obese women [siRNA]

ABSTRACT: The aim of this study was to identify new genes controlling insulin sensitivity in adipocytes from obese women with either insulin-resistant (OIR) or -sensitive (OIS) adipocytes. 432 genes were differentially expressed between the OIR and OIS group (FDR <5%). These genes are enriched in pathways related to glucose and amino acid metabolism, cellular respiration, and insulin signaling. Two IR-associated genes, KLF15 and SLC25A10, were selected for functional evaluation.

ORGANISM(S): Homo sapiens

PROVIDER: GSE94751 | GEO | 2017/06/29

SECONDARY ACCESSION(S): PRJNA373930

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

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)

Dataset Information

Global transcriptome profiling identifies KLF15 and SLC25A10 as regulators of adipocytes insulin sensitivity in obese women [siRNA]

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets