Dataset Information

A Systems Genetics Approach Identified GPD1L and its Molecular Mechanism for Obesity in Human Adipose Tissue.

ABSTRACT: To explore novel molecular mechanisms underlying obesity, we applied a systems genetics framework to integrate risk genetic loci from the largest body mass index (BMI) genome-wide association studies (GWAS) meta-analysis with mRNA and microRNA profiling in adipose tissue from 200 subjects. One module was identified to be most significantly associated with obesity and other metabolic traits. We identified eight hub genes which likely play important roles in obesity metabolism and identified microRNAs that significantly negatively correlated with hub genes. This module was preserved in other three test gene expression datasets, and all hub genes were consistently downregulated in obese subjects through the meta-analysis. Gene GPD1L had the highest connectivity and was identified a key causal regulator in the module. Gene GPD1L was significantly negatively correlated with the expression of miR-210, which was experimentally validated that miR-210 regulated GPD1L protein level through direct interaction with its mRNA three prime untranslated region (3'-UTR). GPD1L was found to be upregulated during weight loss and weight maintenance induced by low calorie diet (LCD), while downregulated during weight gain induced by high-fat diet (HFD). The results indicated that increased GPD1L in adipose tissue may have a significant therapeutic potential in reducing obesity and insulin resistance.

SUBMITTER: He H

PROVIDER: S-EPMC5431993 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Systems Genetics Approach Identified GPD1L and its Molecular Mechanism for Obesity in Human Adipose Tissue.

He Hao H Sun Dianjianyi D Zeng Yong Y Wang Ruifeng R Zhu Wei W Cao Shaolong S Bray George A GA Chen Wei W Shen Hui H Sacks Frank M FM Qi Lu L Deng Hong-Wen HW

Scientific reports 20170511 1

To explore novel molecular mechanisms underlying obesity, we applied a systems genetics framework to integrate risk genetic loci from the largest body mass index (BMI) genome-wide association studies (GWAS) meta-analysis with mRNA and microRNA profiling in adipose tissue from 200 subjects. One module was identified to be most significantly associated with obesity and other metabolic traits. We identified eight hub genes which likely play important roles in obesity metabolism and identified micro ...[more]

PMID: 28496128

Similar Datasets

Project description:Obesity is a multi-factorial health problem in which genetic factors play an important role. Limited results have been obtained in single-gene studies using either genomic or transcriptomic data. RNA sequencing technology has shown its potential in gaining accurate knowledge about the transcriptome, and may reveal novel genes affecting complex diseases. Integration of genomic and transcriptomic variation (expression quantitative trait loci [eQTL] mapping) has identified causal variants that affect complex diseases. We integrated transcriptomic data from adipose tissue and genomic data from a porcine model to investigate the mechanisms involved in obesity using a systems genetics approach.Using a selective gene expression profiling approach, we selected 36 animals based on a previously created genomic Obesity Index for RNA sequencing of subcutaneous adipose tissue. Differential expression analysis was performed using the Obesity Index as a continuous variable in a linear model. eQTL mapping was then performed to integrate 60 K porcine SNP chip data with the RNA sequencing data. Results were restricted based on genome-wide significant single nucleotide polymorphisms, detected differentially expressed genes, and previously detected co-expressed gene modules. Further data integration was performed by detecting co-expression patterns among eQTLs and integration with protein data.Differential expression analysis of RNA sequencing data revealed 458 differentially expressed genes. The eQTL mapping resulted in 987 cis-eQTLs and 73 trans-eQTLs (false discovery rate?<?0.05), of which the cis-eQTLs were associated with metabolic pathways. We reduced the eQTL search space by focusing on differentially expressed and co-expressed genes and disease-associated single nucleotide polymorphisms to detect obesity-related genes and pathways. Building a co-expression network using eQTLs resulted in the detection of a module strongly associated with lipid pathways. Furthermore, we detected several obesity candidate genes, for example, ENPP1, CTSL, and ABHD12B.To our knowledge, this is the first study to perform an integrated genomics and transcriptomics (eQTL) study using, and modeling, genomic and subcutaneous adipose tissue RNA sequencing data on obesity in a porcine model. We detected several pathways and potential causal genes for obesity. Further validation and investigation may reveal their exact function and association with obesity.

Dataset Information

A Systems Genetics Approach Identified GPD1L and its Molecular Mechanism for Obesity in Human Adipose Tissue.

Publications

A Systems Genetics Approach Identified GPD1L and its Molecular Mechanism for Obesity in Human Adipose Tissue.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets