Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration

Abstract

Background Type 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis. Methods The datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program. Results Module-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib. Conclusions We identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D.

Transcriptional heterogeneity in human diabetic foot wounds

“…Subclusters 1 and 2 were more prevalent in DFUs and expressed mRNAs encoding for complement, while subclusters 3 and 4 were mixed between DFUs and NDFUs ( Figure 4F-I ). Moreover, macrophages of DFUs overexpressed several collagen genes, and genes involved in insulin signaling and metabolism, like DPP4 and IGFR1 ( Figure 4J ), all of which have been associated with diabetes (Fernández et al, 2001, Liu et al, 2022, Röhrborn et al, 2015).…”

Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses

“… 29 These associations were replicated in a dataset-based multi-omics study. 30 Another study found that HLA-DR-related inflammation might be associated with beta-cell destruction in patients with type 2 diabetes, which may explain the associations with diabetic complications. 31 Still, the immune response to increased levels of HLA-DRA is complex.…”

Gene expression analysis reveals diabetes-related gene signatures

“…Nevertheless, while individual analysis pinpoints relevant mechanisms, the integrated and combined analysis of multiple levels emerges as a promising approach to comprehending disease-linked mechanisms. Integrated strategies have the power to expand our knowledge of disease-related determinants that can be used as biomarkers of disease onset and progression [ 36 ]. Despite the massive data provided by different studies, limitations and challenges still affect the results, from the choice of materials to the methods applied [ 35 ].…”