Integration of multi-tissue transcriptomic data identifies key regulatory genes for porcine fat deposition.
- 1State Key Laboratory of animal Biotech Breeding, National Engineering Laboratory for Animal Breeding; Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100021, China.
- 2College of Animal Science and Technology, Beijing University of Agricultural, Beijing, 102206, China.
- 3State Key Laboratory of animal Biotech Breeding, National Engineering Laboratory for Animal Breeding; Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, China Agricultural University, Beijing, 100021, China. [email protected].
Abstract
Background
Fat deposition significantly influences both carcass composition and meat quality; however its molecular regulation is highly complex and displays pronounced variation across tissues and genetic backgrounds. Integrating transcriptomic data from multiple tissues and populations can help identify conserved regulatory programs that distinguish lean-type commercial pigs from fat-type indigenous pigs. This study aimed to combine transcriptomic datasets across multiple studies and tissues with differential expression analysis and gene co-expression network analysis to identify candidate regulators associated with fat deposition.Methods
We integrated 814 publicly available RNA sequencing samples from 32 pig populations, including muscle (n = 626), adipose tissue (n = 117) and liver (n = 71). All datasets were reprocessed with a unified workflow for read quality control, alignment and gene quantification, followed by batch-effect adjustment using surrogate variable analysis. Differentially expressed genes (DEGs) between lean-type commercial pigs and fat-type indigenous pigs were identified within each tissue using linear models with empirical Bayes moderation. Multiple testing was controlled by the false discovery rate (0.05), together with an absolute log₂-transformed fold change threshold of ≥ 1. Functional enrichment of DEG sets and modules was evaluated using hypergeometric tests based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes annotations. Weighted gene co-expression networks were constructed using Pearson correlations under a scale-free topology criterion, and module-trait associations were assessed by Pearson correlation; hub genes were prioritized by network connectivity and protein interaction information.Results
We identified 1,614 differentially expressed genes in muscle tissue, 4,647 in adipose tissue and 573 in liver between lean-type commercial pigs and fat-type indigenous pigs, respectively. Thirty-six genes were consistently differentially expressed across all three tissues and were enriched for mitochondrial energy metabolism, including oxidative phosphorylation and respiratory chain processes. Co-expression network analysis clustered 19,760 genes into 29 modules and revealed distinct tissue-group associated modules, including Cyan (liver tissue of lean-type commercial pigs), Orange (adipose tissue of fat-type indigenous pigs) and Tan (muscle tissue of lean-type commercial pigs). Integration of shared differentially expressed genes and trait-associated modules prioritized five candidate regulatory genes: ND6, UQCRC1, UQCRFS1, TBXA2R and LIFR.Conclusions
Integrative multi-tissue transcriptomic data with differential expression analysis and co-expression network analysis identified mitochondrial energy metabolism as a central program underlying fat deposition The prioritized candidate genes provide a basis for understanding the molecular regulatory mechanisms underlying fat deposition and may support molecular breeding strategies to optimize carcass traits and meat quality.
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BibTeX
@article{yang2026integration,
title = {{Integration of multi-tissue transcriptomic data identifies key regulatory genes for porcine fat deposition.}},
author = {Jiarui Yang and Yan Wang and Jiahao Wang and Lulu Wang and Zhuoye Zheng and Xinran Niu and Jianfeng Liu and Xihui Sheng and Chuduan Wang and Kai Xing},
journal = {BMC genomics},
year = {2026},
month = aug,
doi = {10.1186/s12864-026-13234-4},
url = {https://europepmc.org/article/MED/42968020},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Integration of multi-tissue transcriptomic data identifies key regulatory genes for porcine fat deposition.
AU - Jiarui Yang
AU - Yan Wang
AU - Jiahao Wang
AU - Lulu Wang
AU - Zhuoye Zheng
AU - Xinran Niu
AU - Jianfeng Liu
AU - Xihui Sheng
AU - Chuduan Wang
AU - Kai Xing
PY - 2026
DA - 2026/08/04
JO - BMC genomics
DO - 10.1186/s12864-026-13234-4
UR - https://europepmc.org/article/MED/42968020
AB - Background Fat deposition significantly influences both carcass composition and meat quality; however its molecular regulation is highly complex and displays pronounced variation across tissues and genetic backgrounds. Integrating transcriptomic data from multiple tissues and populations can help identify conserved regulatory programs that distinguish lean-type commercial pigs from fat-type indigenous pigs. This study aimed to combine transcriptomic datasets across multiple studies and tissues with differential expression analysis and gene co-expression network analysis to identify candidate regulators associated with fat deposition.Methods We integrated 814 publicly available RNA sequencing samples from 32 pig populations, including muscle (n = 626), adipose tissue (n = 117) and liver (n = 71). All datasets were reprocessed with a unified workflow for read quality control, alignment and gene quantification, followed by batch-effect adjustment using surrogate variable analysis. Differentially expressed genes (DEGs) between lean-type commercial pigs and fat-type indigenous pigs were identified within each tissue using linear models with empirical Bayes moderation. Multiple testing was controlled by the false discovery rate (0.05), together with an absolute log₂-transformed fold change threshold of ≥ 1. Functional enrichment of DEG sets and modules was evaluated using hypergeometric tests based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes annotations. Weighted gene co-expression networks were constructed using Pearson correlations under a scale-free topology criterion, and module-trait associations were assessed by Pearson correlation; hub genes were prioritized by network connectivity and protein interaction information.Results We identified 1,614 differentially expressed genes in muscle tissue, 4,647 in adipose tissue and 573 in liver between lean-type commercial pigs and fat-type indigenous pigs, respectively. Thirty-six genes were consistently differentially expressed across all three tissues and were enriched for mitochondrial energy metabolism, including oxidative phosphorylation and respiratory chain processes. Co-expression network analysis clustered 19,760 genes into 29 modules and revealed distinct tissue-group associated modules, including Cyan (liver tissue of lean-type commercial pigs), Orange (adipose tissue of fat-type indigenous pigs) and Tan (muscle tissue of lean-type commercial pigs). Integration of shared differentially expressed genes and trait-associated modules prioritized five candidate regulatory genes: ND6, UQCRC1, UQCRFS1, TBXA2R and LIFR.Conclusions Integrative multi-tissue transcriptomic data with differential expression analysis and co-expression network analysis identified mitochondrial energy metabolism as a central program underlying fat deposition The prioritized candidate genes provide a basis for understanding the molecular regulatory mechanisms underlying fat deposition and may support molecular breeding strategies to optimize carcass traits and meat quality.
SN - 1471-2164
ER -