Secondary analysis of publicly available data reveals superoxide and oxygen radical pathways are enriched for associations between type 2 diabetes and low-frequency variants

Identification of reversible and druggable pathways to improve beta-cell function and survival in Type 2 diabetes

Targeting β-cell failure could prevent, delay or even partially reverse Type 2 diabetes. However, development of such drugs is limited as the molecular pathogenesis is complex and incompletely understood. Further, while β-cell failure can be modeled experimentally, only some of the molecular changes will be pathogenic. Therefore, we used a novel approach to identify molecular pathways that are not only changed in a diabetes-like state but also are reversible and can be targeted by drugs. INS1E cells were cultured in high glucose (HG, 20 mM) for 72 h or HG for an initial 24 h followed by drug addition (exendin-4, metformin and sodium salicylate) for the remaining 48 h. RNAseq (Illumina TruSeq), gene set enrichment analysis (GSEA) and pathway analysis (using Broad Institute, Reactome, KEGG and Biocarta platforms) were used to identify changes in molecular pathways. HG decreased function and increased apoptosis in INS1E cells with drugs partially reversing these effects. HG resulted in upregulation of 109 pathways while drug treatment downregulated 44 pathways with 21 pathways in common. Interestingly, while hyperglycemia extensively upregulated metabolic pathways, they were not altered with drug treatment, rather pathways involved in the cell cycle featured more heavily. GSEA for hyperglycemia identified many known pathways validating the applicability of our cell model to human disease. However, only a fraction of these pathways were downregulated with drug treatment, highlighting the importance of considering druggable pathways. Overall, this provides a powerful approach and resource for identifying appropriate targets for the development of β-cell drugs.

From SNPs to pathways: Biological interpretation of type 2 diabetes (T2DM) genome wide association study (GWAS) results

Genome-wide association studies (GWAS) have become a common method for discovery of gene-disease relationships, in particular for complex diseases like Type 2 Diabetes Mellitus (T2DM). The experience with GWAS analysis has revealed that the genetic risk for complex diseases involves cumulative, small effects of many genes and only some genes with a moderate effect. In order to explore the complexity of the relationships between T2DM genes and their potential function at the process level as effected by polymorphism effects, a secondary analysis of a GWAS meta-analysis is presented. Network analysis, pathway information and integration of different types of biological information such as eQTLs and gene-environment interactions are used to elucidate the biological context of the genetic variants and to perform an analysis based on data visualization. We selected a T2DM dataset from a GWAS meta-analysis, and extracted 1,971 SNPs associated with T2DM. We mapped 580 SNPs to 360 genes, and then selected 460 pathways containing these genes from the curated collection of WikiPathways. We then created and analyzed SNP-gene and SNP-gene-pathway network modules in Cytoscape. A focus on genes with robust connections to pathways permitted identification of many T2DM pertinent pathways. However, numerous genes lack literature evidence of association with T2DM. We also speculate on the genes in specific network structures obtained in the SNP-gene network, such as gene-SNP-gene modules. Finally, we selected genes relevant to T2DM from our SNP-gene-pathway network, using different sources that reveal gene-environment interactions and eQTLs. We confirmed functions relevant to T2DM for many genes and have identified some-LPL and APOB-that require further validation to clarify their involvement in T2DM.