Transcriptomic profiling in muscle and adipose tissue identifies genes related to growth and lipid deposition.
Xuan TaoYan LiangXuemei YangJianhui PangZhijun ZhongXiaohui ChenYuekui YangKai ZengRunming KangYunfeng LeiSancheng YingJianjun GongYiren GuXuebin LvPublished in: PloS one (2017)
Growth performance and meat quality are important traits for the pig industry and consumers. Adipose tissue is the main site at which fat storage and fatty acid synthesis occur. Therefore, we combined high-throughput transcriptomic sequencing in adipose and muscle tissues with the quantification of corresponding phenotypic features using seven Chinese indigenous pig breeds and one Western commercial breed (Yorkshire). We obtained data on 101 phenotypic traits, from which principal component analysis distinguished two groups: one associated with the Chinese breeds and one with Yorkshire. The numbers of differentially expressed genes between all Chinese breeds and Yorkshire were shown to be 673 and 1056 in adipose and muscle tissues, respectively. Functional enrichment analysis revealed that these genes are associated with biological functions and canonical pathways related to oxidoreductase activity, immune response, and metabolic process. Weighted gene coexpression network analysis found more coexpression modules significantly correlated with the measured phenotypic traits in adipose than in muscle, indicating that adipose regulates meat and carcass quality. Using the combination of differential expression, QTL information, gene significance, and module hub genes, we identified a large number of candidate genes potentially related to economically important traits in pig, which should help us improve meat production and quality.
Keyphrases
- genome wide
- network analysis
- adipose tissue
- single cell
- dna methylation
- insulin resistance
- skeletal muscle
- genome wide identification
- copy number
- high throughput
- fatty acid
- high fat diet
- immune response
- gene expression
- rna seq
- bioinformatics analysis
- genome wide analysis
- magnetic resonance
- genetic diversity
- type diabetes
- healthcare
- electronic health record
- machine learning
- south africa
- deep learning
- social media
- magnetic resonance imaging
- metabolic syndrome