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Medicinal Chemistry

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ISSN (Print): 1573-4064
ISSN (Online): 1875-6638

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Research Article

Integrative Analysis of Whole-genome Expression Profiling and Regulatory Network Identifies Novel Biomarkers for Insulin Resistance in Leptin Receptor-deficient Mice

Author(s): Yuchi Zhang, Xinyu Wu, Cong Zhao, Kai Li, Yi Zheng, Jing Zhao and Pengling Ge*

Volume 16, Issue 5, 2020

Page: [635 - 642] Pages: 8

DOI: 10.2174/1573406415666191004135450

Price: $65

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Abstract

Background: Molecular characterization of insulin resistance, a growing health issue worldwide, will help to develop novel strategies and accurate biomarkers for disease diagnosis and treatment.

Objective: Integrative analysis of gene expression profiling and gene regulatory network was exploited to identify potential biomarkers early in the development of insulin resistance.

Methods: RNA was isolated from livers of animals at three weeks of age, and whole-genome expression profiling was performed and analyzed with Agilent mouse 4×44K microarrays. Differentially expressed genes were subsequently validated by qRT-PCR. Functional characterizations of genes and their interactions were performed by Gene Ontology (GO) analysis and gene regulatory network (GRN) analysis.

Results: A total of 197 genes were found to be differentially expressed by fold change ≥2 and P < 0.05 in BKS-db +/+ mice relative to sex and age-matched controls. Functional analysis suggested that these differentially expressed genes were enriched in the regulation of phosphorylation and generation of precursor metabolites which are closely associated with insulin resistance. Then a gene regulatory network associated with insulin resistance (IRGRN) was constructed by integration of these differentially expressed genes and known human protein-protein interaction network. The principal component analysis demonstrated that 67 genes in IRGRN could clearly distinguish insulin resistance from the non-disease state. Some of these candidate genes were further experimentally validated by qRT-PCR, highlighting the predictive role as biomarkers in insulin resistance.

Conclusion: Our study provides new insight into the pathogenesis and treatment of insulin resistance and also reveals potential novel molecular targets and diagnostic biomarkers for insulin resistance.

Keywords: Biomarkers, expression profiling, gene regulatory network, insulin resistance, integrative analysis, leptin receptor gene.

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