Differential gene expression analysis reveals metabolic pathways in normal, glucose-intolerant, and diabetic individuals with type 2 diabetes, suggesting novel insights for pharmacology.
Background: Glucose intolerance (GI) is a metabolic disorder that is a consequence of hyperglycemia. Glucose intolerance can, under some conditions, progress to type 2 diabetes mellitus (T2D), where insulin is insufficiently utilized. As a result of genetic and lifestyle effects, the incidence of T2D has increased worldwide. Pathophysiological consequences of the disease may include retinopathy, nephropathy, and neuropathy. Skeletal muscle is one of the major organs that regulates blood sugar homeostasis, both at rest and during exercise. Thus, understanding the molecular and genetic perspectives on the contribution of skeletal muscles to the predisposition to diabetes is a hot topic in diabetes research. In this study, we conducted a differential analysis of gene expression and compared the expression profiles of all the genes in the skeletal muscles of normal, glucose-intolerant, and diabetic individuals via the Affymetrix HGU133plus2 platform. Data were collected from the Gene-Expression Omnibus (GEO) series GSE18732. Gene Ontology enrichment and perturbed pathways were thoroughly analyzed. Results: We found that genes that were significantly differentially expressed between the different tissues contribute to metabolic pathways related to glucose homeostasis, as well as several signaling pathways related to insulin signaling, e.g., the MAPK, mTOR, Toll-like receptor (TLR), p53, WNT and neurotrophin signaling pathways. Furthermore, some genes related to several malignancies were also differentially expressed across the different clinical groups. Additionally, some of these genes are related to epigenetic regulation. Furthermore, other differentially expressed genes were connected to several myopathies. Conclusions: This study may serve as a gene-based analysis that contributes as a basis for further analysis. This investigation may include gene and protein networks that serve in understanding diabetes, the mechanism of action of the involved proteins, and pharmacology and drug design targeting T2D.
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Barghash et al. (2025) studied this question.
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