Multiomics Analysis Reveals Insights into Potential Drivers of Pancreatic Islet Pathology in Type 2 Diabetes

Despite the high prevalence of type 2 diabetes (T2D),
the mechanisms
driving pathology in pancreatic islet β cells remain poorly
understood. We utilized a multiomics approach to evaluate the transcriptional
and biochemical makeup of islets from human organ donors with T2D
and nondiabetic controls. Transcriptomic (N = 10),
proteomic (N = 6), and untargeted high-resolution
metabolomic (N = 10) data were analyzed individually
and then integrated using sparse partial least-squares regression,
and differential network analysis was performed. In individual data
sets, 25 transcripts, 30 proteins, and 30 metabolites were differentially
abundant between T2D and nondiabetic islets, representing some pathways
not previously characterized in T2D islets including purine and pyrimidine,
branched-chain amino acid, and histidine metabolism. Network analysis
of integrated data sets highlighted disrupted relationships among
features in T2D islets compared to those from nondiabetic individuals.
Fatty and amino acid metabolism and immune activity were identified
as prominent drivers of the distinctions in biochemical interactions
in T2D networks. Our findings also suggested greater abundance and
influence of industrial chemicals, including polychlorinated and polybrominated
biphenyls, in T2D islets. This pilot study demonstrates that multiomics
profiling can identify candidate molecules and mechanisms impacting
islet cell activity in T2D, which could represent targets for therapeutic
intervention.