Dataset Information

Identification of a novel insulin receptor-binding protein from Momordica charantia by transcriptomic, proteomic, and docking analyses

ABSTRACT: Momordica charantia (MC) is a common vegetable in tropical areas and has been used for a long time as an alternative therapy for diabetes. Although several constituents of MC have displayed the hypoglycemic effects, the hypoglycemic targets of these constituents remain to be clarified. In this study, we analyzed and elucidated the therapeutic targets contributing to the hypoglycemic effect of aqueous extract of MC seeds (MCSE) by transcriptomic analysis. The protein ingredients aimed at the hypoglycemic target were further identified by proteomic, docking, and receptor-binding assays. Our data showed that MCSE, which was rich in small-molecular weight proteins, displayed hypoglycemic effects in normal and diabetic mice by glucose tolerance assay. MCSE significantly and primarily regulated the insulin signaling pathway in muscles and adipose tissues, suggesting that MCSE might target to insulin receptor (IR), stimulate the IR-downstream pathway, and subsequently display the hypoglycemic activity. We further identified that inhibitor against trypsin (TI) of MC directly docked into IR and activated the kinase activity of IR. In conclusion, our findings suggested that MCSE regulated glucose metabolism mainly via insulin signaling pathway. Moreover, we newly identified that TI was a novel IR-binding protein of MC that triggers the insulin signaling pathway via binding to IR.

ORGANISM(S): Mus musculus

PROVIDER: GSE39689 | GEO | 2015/07/01

SECONDARY ACCESSION(S): PRJNA171580

REPOSITORIES: GEO

ACCESS DATA

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)

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.

Dataset Information

Identification of a novel insulin receptor-binding protein from Momordica charantia by transcriptomic, proteomic, and docking analyses

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure