Inter-Tissue Gene Co-Expression Networks between Metabolically Healthy and Unhealthy Obese Individuals

Abstract

Background: Obesity is associated with severe co-morbidities such as type 2 diabetes and nonalcoholic steatohepatitis. However, studies have shown that 10-25 percent of the severely obese individuals are metabolically healthy. To date, the identification of genetic factors underlying the metabolically healthy obese (MHO) state is limited. Systems genetics approaches have led to the identification of genes and pathways in complex diseases. Here, we have used such approaches across tissues to detect genes and pathways involved in obesity-induced disease development.

Methods: Expression data of 60 severely obese individuals was accessible, of which 28 individuals were MHO and 32 were metabolically unhealthy obese (MUO). A whole genome expression profile of four tissues was available: liver, muscle, subcutaneous adipose tissue and visceral adipose tissue. Using insulin-related genes, we used the weighted gene co-expression network analysis (WGCNA) method to build within- and inter-tissue gene networks. We identified genes that were differentially connected between MHO and MUO individuals, which were further investigated by homing in on the modules they were active in. To identify potentially causal genes, we integrated genomic and transcriptomic data using an eQTL mapping approach.

Results: Both IL-6 and IL1B were identified as highly differentially co-expressed genes across tissues between MHO and MUO individuals, showing their potential role in obesity-induced disease development. WGCNA showed that those genes were clustering together within tissues, and further analysis showed different co-expression patterns between MHO and MUO subnetworks. A potential causal role for metabolic differences under similar obesity state was detected for PTPRE, IL-6R and SLC6A5.

Conclusions: We used a novel integrative approach by integration of co-expression networks across tissues to elucidate genetic factors related to obesity-induced metabolic disease development. The identified genes and their interactions give more insight into the genetic architecture of obesity and the association with co-morbidities.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. The adjacency matrix to construct the within- and inter-tissue gene network.

The matrix presents the adjacency for L = Liver, M = Muscle, S = Subcutaneous Adipose Tissue, V = Vat, for i till n genes. In red the within-tissue blocks, in black the inter-tissue blocks.

Fig 2. Network visualization of inter-tissue network modules.

Visualization of the genes in the (A) Greenyellow module (from MUO subnetwork) in the MHO and MUO individuals, and (B) Turquoise module (from MUO subnetwork) in the MHO and MUO individuals. Genes coming from liver are coloured yellow, from muscle orange, from VAT blue and from SAT green. IL1B and IL-6 are bordered red. Edges are coloured based on their correlation on a red-green scale representing a negative-positive correlation.

Fig 3. Network visualization of the inter-tissue network modules.

Visualization of the genes in the (A) Black module (from MUO subnetwork) in the MHO and MUO individuals, and (B) Salmon module (from MUO subnetwork) in the MHO and MUO individuals. Genes coming from liver are coloured yellow, from muscle orange, from VAT blue and from SAT green. IL1B and IL-6 are bordered red. Edges are coloured based on their correlation on a red-green scale representing a negative-positive correlation.