Systems Biology Approach Reveals Genome to Phenome Correlation in Type 2 Diabetes

<div><p>Genome-wide association studies (GWASs) have discovered association of several loci with Type 2 diabetes (T2D), a common complex disease characterized by impaired insulin secretion by pancreatic β cells and insulin signaling in target tissues. However, effect of genetic risk variants on continuous glycemic measures in nondiabetic subjects mainly elucidates perturbation of insulin secretion. Also, the disease associated genes do not clearly converge on functional categories consistent with the known aspects of T2D pathophysiology. We used a systems biology approach to unravel genome to phenome correlation in T2D. We first examined enrichment of pathways in genes identified in T2D GWASs at genome-wide or lower levels of significance. Genes at lower significance threshold showed enrichment of insulin secretion related pathway. Notably, physical and genetic interaction network of these genes showed robust enrichment of insulin signaling and other T2D pathophysiology related pathways including insulin secretion. The network also overrepresented genes reported to interact with insulin secretion and insulin action targeting antidiabetic drugs. The drug interacting genes themselves showed overrepresentation of insulin signaling and other T2D relevant pathways. Next, we generated genome-wide expression profiles of multiple insulin responsive tissues from nondiabetic and diabetic patients. Remarkably, the differentially expressed genes showed significant overlap with the network genes, with the intersection showing enrichment of insulin signaling and other pathways consistent with T2D pathophysiology. Literature search led our genomic, interactomic, transcriptomic and toxicogenomic evidence to converge on TGF-beta signaling, a pathway known to play a crucial role in pancreatic islets development and function, and insulin signaling. Cumulatively, we find that GWAS genes relate directly to insulin secretion and indirectly, through collaborating with other genes, to insulin resistance. This seems to support the epidemiological evidence that environmentally triggered insulin resistance interacts with genetically programmed β cell dysfunction to precipitate diabetes.</p> </div

Overlap between T2D interactome and T2D transcriptome.

<p>Compared to total interactome of 14,306 genes, the T2D interactome of 561 genes represent significantly greater number of genes differentially expressed at unadjusted <i>p</i> value threshold in microarray profiles, dubbed “T2D transcriptome” from now on. Bonferroni adjusted hypergeometric distribution <i>p</i> values for the overlaps are indicated.</p

Schematic representation of the workflow.

<p>T2D GWAS genes do not directly relate (indicated by ‘X’ on the left side) to pathways associated with disease pathophysiology. Conspicuously, effect of identified risk variants on continuous glycemic measures in nondiabetic subjects chiefly explains only perturbation of insulin secretion, not insulin resistance. Further, the genes found as associated with the disease do not clearly relate to processes and pathways consistent with the known aspects of T2D pathophysiology. The main aim of the present study was to ask the question (indicated by ‘?’ on the right side) if GWAS data when considered in conjunction with interactome, toxicogenome and disease transcriptome data reveal genome to phenome correlation in T2D. Data available in public domain for GWAS, interactome and toxicogenome was used in the analysis. For disease transcriptome, new experimental data was generated. We specifically examined if interaction network of genes reported in T2D GWAS, genes showing altered expression after treatment with various antidiabetic drugs, and genes that are differentially expressed in insulin responsive tissues in male and female T2D patients do converge on insulin secretion, insulin resistance and other T2D associated pathophysiological pathways.</p

Enrichment of pathways in genes reported in T2D GWASs at various association <i>p</i> value thresholds.

<p>Genes at different <i>p</i> value cutoffs were examined for pathway enrichment. The pathway along with corresponding genes and enrichment <i>p</i> values are indicated. Note highly significant enrichment of Maturity onset diabetes of the young in genes reported at 10⁻⁵<i>p</i> value threshold, dubbed “T2D genome” henceforth.</p

Enriched pathways in T2D interactome.

*<p>Bejamini-Hochberg correction.</p

Enriched pathways in differentially expressed genes between people with and without T2D.

*<p>Bejamini-Hochberg correction.</p

Dendrogram of samples based on gene expression profiling.

<p>Correlations between all the eight groups of samples analyzed in microarrays are plotted as a dendrogram. As expected, muscle and adipose form separate clusters. Also, in adipose cluster, subgroups of adipose type and gender are observed. Globally normalized data was used for constructing the dendrogram. Con, control subjects; T2D, diabetic patients; SA, subcutaneous adipose; VA, visceral adipose; SM, skeletal muscle.</p

Genome to phenome pathway of TGF-beta signaling.

<p>T2D genome, T2D interactome, T2D transcriptome and antidiabetic drug interacting genes are mapped on to KEGG pathway for TGF-beta signaling. Red: genome, interactome and transcriptome; brown: interactome, transcriptome and antidiabetic drug interacting genes; green: interactome and transcriptome; yellow: interactome; grey: antidiabetic drug interacting genes.</p

Enriched pathways in antidiabetic drug interacting genes.

*<p>Bejamini-Hochberg correction.</p

Enriched pathways in T2D inteactome-T2D transcriptome intersection without TGF-beta signaling genes.

*<p>Bejamini-Hochberg correction.</p