Dataset Information

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

ABSTRACT: BACKGROUND: Obesity 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. METHODS: We 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. RESULTS: WGCNA 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. CONCLUSIONS: To 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.

SUBMITTER: Kogelman LJ

PROVIDER: S-EPMC4183073 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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

Kogelman Lisette J A LJ Cirera Susanna S Zhernakova Daria V DV Fredholm Merete M Franke Lude L Kadarmideen Haja N HN

BMC medical genomics 20140930

<h4>Background</h4>Obesity 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 ...[more]

PMID: 25270054

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Project description:BackgroundSelection for feed efficiency is crucial for overall profitability and sustainability in dairy cattle production. Key regulator genes and genetic markers derived from co-expression networks underlying feed efficiency could be included in the genomic selection of the best cows. The present study identified co-expression networks associated with high and low feed efficiency and their regulator genes in Danish Holstein and Jersey cows. RNA-sequencing data from Holstein and Jersey cows with high and low residual feed intake (RFI) and treated with two diets (low and high concentrate) were used. Approximately 26 million and 25 million pair reads were mapped to bovine reference genome for Jersey and Holstein breed, respectively. Subsequently, the gene count expressions data were analysed using a Weighted Gene Co-expression Network Analysis (WGCNA) approach. Functional enrichment analysis from Ingenuity® Pathway Analysis (IPA®), ClueGO application and STRING of these modules was performed to identify relevant biological pathways and regulatory genes.ResultsWGCNA identified two groups of co-expressed genes (modules) significantly associated with RFI and one module significantly associated with diet. In Holstein cows, the salmon module with module trait relationship (MTR) = 0.7 and the top upstream regulators ATP7B were involved in cholesterol biosynthesis, steroid biosynthesis, lipid biosynthesis and fatty acid metabolism. The magenta module has been significantly associated (MTR = 0.51) with the treatment diet involved in the triglyceride homeostasis. In Jersey cows, the lightsteelblue1 (MTR = - 0.57) module controlled by IFNG and IL10RA was involved in the positive regulation of interferon-gamma production, lymphocyte differentiation, natural killer cell-mediated cytotoxicity and primary immunodeficiency.ConclusionThe present study provides new information on the biological functions in liver that are potentially involved in controlling feed efficiency. The hub genes and upstream regulators (ATP7b, IFNG and IL10RA) involved in these functions are potential candidate genes for the development of new biomarkers. However, the hub genes, upstream regulators and pathways involved in the co-expressed networks were different in both breeds. Hence, additional studies are required to investigate and confirm these findings prior to their use as candidate genes.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most lethal urologic cancer. Associations of both visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) with ccRCC have been reported, and underlying mechanisms of VAT perhaps distinguished from SAT, considering their different structures and functions. We performed this study to disclose different miRNA-mRNA networks of obesity-related ccRCC in VAT and SAT using datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA); and find out different RNAs correlated with the prognosis of ccRCC in VAT and SAT.MethodsWe screened out different expressed (DE) mRNAs and miRNAs of obesity, in both VAT and SAT from GEO datasets, and constructed miRNA-mRNA networks of obesity-related ccRCC. To evaluate the sensitivity and specificity of RNAs in networks of obesity-related ccRCC in both VAT and SAT, Receiver Operating Characteristic (ROC) analyses were conducted using TCGA datasets. Spearman correlation analyses were then performed to find out RNA pairs with inverse correlations. We also performed Cox regression analyses to estimate the association of all DE RNAs of obesity with the overall survival.Results136 and 185 DE mRNAs of obesity in VAT and SAT were found out. Combined with selected DE miRNAs, miRNA-mRNA networks of obesity-related ccRCC were constructed. By performing ROC analyses, RNAs with same trend as shown in networks and statistically significant ORs were selected to be paired. Three pairs were finally remained in Spearman correlation analyses, including hsa-miR-182&ATP2B2, hsa-miR-532&CDH2 in VAT, and hsa-miR-425&TFAP2B in SAT. Multivariable Cox regression analyses showed that several RNAs with statistically significant adjusted HRs remained consistent trends as shown in DE analyses of obesity. Risk score analyses using selected RNAs showed that the overall survival time of patients in the low-risk group was significantly longer than that in the high-risk group regardless of risk score models.ConclusionsWe found out different miRNA-mRNA regulatory networks of obesity-related ccRCC for both VAT and SAT; and several DE RNAs of obesity-related ccRCC were found to remain consistent performance in terms of ccRCC prognosis. Our findings could provide valuable evidence on the targeted therapy of obesity-related ccRCC.

Dataset Information

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

Publications

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

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