Abstract
Probing the dynamic control features of biological networks represents a new frontier in capturing the dysregulated pathways in complex diseases. Here, using patient samples obtained from a pancreatic islet transplantation program, we constructed a tissue-specific gene regulatory network and used the control centrality (Cc) concept to identify the high control centrality (HiCc) pathways, which might serve as key pathobiological pathways for Type 2 Diabetes (T2D). We found that HiCc pathway genes were significantly enriched with modest GWAS p-values in the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) study. We identified variants regulating gene expression (expression quantitative loci, eQTL) of HiCc pathway genes in islet samples. These eQTL genes showed higher levels of differential expression compared to non-eQTL genes in low, medium, and high glucose concentrations in rat islets. Among genes with highly significant eQTL evidence, NFATC4 belonged to four HiCc pathways. We asked if the expressions of T2D-associated candidate genes from GWAS and literature are regulated by Nfatc4 in rat islets. Extensive in vitro silencing of Nfatc4 in rat islet cells displayed reduced expression of 16, and increased expression of four putative downstream T2D genes. Overall, our approach uncovers the mechanistic connection of NFATC4 with downstream targets including a previously unknown one, TCF7L2, and establishes the HiCc pathways’ relationship to T2D.
Highlights
The pathobiological changes leading to a complex disease are most likely to be influenced by the disease genes that perturb the underlying biological networks in specific tissue types
We start by building a gene-regulatory network (GRN) using gene expression data from pancreatic islet samples of diabetic and nondiabetic cadaver donors obtained through the Nordic Islet Transplantation Programme
We found that control centrality (Cc) is comparable to or higher performing than a number of established methods to find the dysregulated pathways, such as HotNet[28] in capturing “T2Drelated” pathways, which are pathways significantly enriched in literature-mined Type 2 Diabetes (T2D) disease genes with experimental evidence from the DISEASES database,[16] both on the Extended gene-regulatory network (EGRN) and on generic networks (Supplementary Figure 3, see Supplementary Information for details on comparisons with other methods)
Summary
The pathobiological changes leading to a complex disease are most likely to be influenced by the disease genes that perturb the underlying biological networks in specific tissue types. Recent evidence suggests that these perturbations are not scattered randomly in the interactome; instead, they are localized in specific neighborhoods, or ‘disease modules’.1,2. In order to identify this disease-specific interactome neighborhood, we previously integrated human islet gene expression data, genetics, and protein interaction data to build a localized map of genes associated with islet cell dysfunction in Type 2 Diabetes (T2D).[3] Recently, we identified an asthma disease module by a connectivity-based model and validated it for functional and pathophysiological relevance to the disease.[2] Several tools based on the ‘guilt-byassociation principle’ predict potential candidate genes using networks.[4,5,6] inference tools such as ANAT identify the highest-confidence paths between pairs of proteins by viewing the local neighborhood of a set of proteins.[7] Other methods such as HotNet[2] use the heat diffusion process to analyze a gene’s mutation score and its local topology together to find the subnetworks in cancer.[8] the NetQTL approach combines eQTL and network flow to identify genes and dysregulated pathways.[9]
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