Identification of co-expression gene networks, regulatory genes and pathways for obesity based on adipose tissue RNA Sequencing in a porcine model

Abstract

BackgroundObesity is a complex metabolic condition in strong association with various diseases, like type 2 diabetes, resulting in major public health and economic implications. Obesity is the result of environmental and genetic factors and their interactions, including genome-wide genetic interactions. Identification of co-expressed and regulatory genes in RNA extracted from relevant tissues representing lean and obese individuals provides an entry point for the identification of genes and pathways of importance to the development of obesity. The pig, an omnivorous animal, is an excellent model for human obesity, offering the possibility to study in-depth organ-level transcriptomic regulations of obesity, unfeasible in humans. Our aim was to reveal adipose tissue co-expression networks, pathways and transcriptional regulations of obesity using RNA Sequencing based systems biology approaches in a porcine model.MethodsWe selected 36 animals for RNA Sequencing from a previously created F2 pig population representing three extreme groups based on their predicted genetic risks for obesity. We applied Weighted Gene Co-expression Network Analysis (WGCNA) to detect clusters of highly co-expressed genes (modules). Additionally, regulator genes were detected using Lemon-Tree algorithms.ResultsWGCNA revealed five modules which were strongly correlated with at least one obesity-related phenotype (correlations ranging from -0.54 to 0.72, P < 0.001). Functional annotation identified pathways enlightening the association between obesity and other diseases, like osteoporosis (osteoclast differentiation, P = 1.4E-7), and immune-related complications (e.g. Natural killer cell mediated cytotoxity, P = 3.8E-5; B cell receptor signaling pathway, P = 7.2E-5). Lemon-Tree identified three potential regulator genes, using confident scores, for the WGCNA module which was associated with osteoclast differentiation: CCR1, MSR1 and SI1 (probability scores respectively 95.30, 62.28, and 34.58). Moreover, detection of differentially connected genes identified various genes previously identified to be associated with obesity in humans and rodents, e.g. CSF1R and MARC2.ConclusionsTo our knowledge, this is the first study to apply systems biology approaches using porcine adipose tissue RNA-Sequencing data in a genetically characterized porcine model for obesity. We revealed complex networks, pathways, candidate and regulatory genes related to obesity, confirming the complexity of obesity and its association with immune-related disorders and osteoporosis.

REVIEW-ARTICLE Bioinformatics: an overview and its applications

“…Thus, it is possible to detect groups of highly coexpressed genes (modules) that share a common function for which they are believed to act cooperatively (guilt by association) in a metabolic pathway (Kogelman et al, 2014). The connectivity of the gene (k i ) describes the relative importance of the gene in the network.…”

“…The connectivity of the gene (k i ) describes the relative importance of the gene in the network. Genes with high k i are biologically relevant and reflect heavily regulated processes (Kogelman et al, 2014). Since the modules may correspond to biological pathways, it is possible to investigate whether the modules identified are associated with certain phenotypes as well as the significance of the gene on the traits under analysis (Zhao et al, 2010).…”

Systems Genetics of Complex Diseases Using RNA-Sequencing Methods

“…In recent times, based on high leverage of bioinformatics data that are produced, systems genetics have provided systems level understanding the biological phenomena [2]. This systems genetics approaches have been applied in livestock [3], [4], humans [5] and thoroughly reviewed [6]- [8]. However, most of these previous studies are based on chip-based or array based high throughput 'omics data.…”

“…We recently published our first study using RNA-Seq data in a porcine model for human obesity using a network approach [77]. The porcine model used was an F2-population, created by crossing the Gӧttingen Minipig (prone to obesity) with Duroc and Yorkshire sows (bred for centuries for lean meat content).…”

“…The key drivers of the modules are based on the phenotype(s) of interest and intra-modular characteristics. Previously, we have applied this method successfully on RNA-Seq data in a pig model for human obesity [3].…”