Deciphering the gene regulatory network associated with anti-apoptosis in the pancreatic islets of type 2 diabetes mice using computational approaches

Type 2 diabetes (T2D) is a major global health problem often caused by the inability of pancreatic islets to compensate for the high insulin demand due to apoptosis. However, the complex mechanisms underlying the activation of apoptosis and its counter process, anti-apoptosis, during T2D remain unclear. In this study, we employed bioinformatics and systems biology approaches to understand the anti-apoptosis-associated gene expression and the biological network in the pancreatic islets of T2D mice. First, gene expression data from four peripheral tissues (islets, liver, muscle and adipose) were used to identify differentially expressed genes (DEGs) in T2D compared to non-T2D mouse strains. Our comparative analysis revealed that Gm2036 is upregulated across all four tissues in T2D and is functionally associated with increased cytosolic Ca2+ levels, which may alter the signal transduction pathways controlling metabolic processes. Next, our study focused on islets and performed functional enrichment analysis, which revealed that upregulated genes are significantly associated with sucrose and fructose metabolic processes, as well as negative regulation of neuron apoptosis. Using the Ingenuity Pathway Analysis (IPA) tool of QIAGEN, gene regulatory networks and their biological effects were analyzed, which revealed that glucose is associated with the underlying change in gene expression in the islets of T2D; and an activated gene regulatory network—containing upregulated CCK, ATF3, JUNB, NR4A1, GAST and downregulated DPP4—is possibly inhibiting apoptosis of islets and β-cells in T2D. Our computational-based study has identified a putative regulatory network that may facilitate the survival of pancreatic islets in T2D; however, further validation in a larger sample size is needed. Our results provide valuable insights into the underlying mechanisms of T2D and may offer potential targets for developing more efficacious treatments.