Integrative Analysis of Whole-genome Expression Profiling and Regulatory Network Identifies Novel Biomarkers for Insulin Resistance in Leptin Receptor-deficient Mice
| Autor: | Cong Zhao, Jing Zhao, Pengling Ge, Yi Zheng, Kai Li, Xinyu Wu, Yuchi Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Candidate gene
Gene regulatory network Computational biology Biology Mice 03 medical and health sciences 0302 clinical medicine Insulin resistance Drug Discovery medicine Animals Gene Regulatory Networks Gene Oligonucleotide Array Sequence Analysis 030304 developmental biology 0303 health sciences Leptin receptor Gene Expression Profiling medicine.disease Fold change Gene expression profiling 030220 oncology & carcinogenesis Receptors Leptin Insulin Resistance DNA microarray Biomarkers |
| Zdroj: | Medicinal Chemistry. 16:635-642 |
| ISSN: | 1573-4064 |
| DOI: | 10.2174/1573406415666191004135450 |
| Popis: | 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. Conclusions: 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. |
| Databáze: | OpenAIRE |
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