Liver transcriptomic networks reveal main biological processes associated with feed efficiency in beef cattle

BMC Genomics. 2015 Dec 18:16:1073. doi: 10.1186/s12864-015-2292-8.

Abstract

Background: The selection of beef cattle for feed efficiency (FE) traits is very important not only for productive and economic efficiency but also for reduced environmental impact of livestock. Considering that FE is multifactorial and expensive to measure, the aim of this study was to identify biological functions and regulatory genes associated with this phenotype.

Results: Eight genes were differentially expressed between high and low feed efficient animals (HFE and LFE, respectively). Co-expression analyses identified 34 gene modules of which 4 were strongly associated with FE traits. They were mainly enriched for inflammatory response or inflammation-related terms. We also identified 463 differentially co-expressed genes which were functionally enriched for immune response and lipid metabolism. A total of 8 key regulators of gene expression profiles affecting FE were found. The LFE animals had higher feed intake and increased subcutaneous and visceral fat deposition. In addition, LFE animals showed higher levels of serum cholesterol and liver injury biomarker GGT. Histopathology of the liver showed higher percentage of periportal inflammation with mononuclear infiltrate.

Conclusion: Liver transcriptomic network analysis coupled with other results demonstrated that LFE animals present altered lipid metabolism and increased hepatic periportal lesions associated with an inflammatory response composed mainly by mononuclear cells. We are now focusing to identify the causes of increased liver lesions in LFE animals.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • Cattle
  • Computational Biology / methods
  • Gene Expression Profiling*
  • Gene Regulatory Networks*
  • Genetic Association Studies*
  • High-Throughput Nucleotide Sequencing
  • Liver / metabolism*
  • Quantitative Trait, Heritable*
  • Transcriptome*