4.7 Article

Landscape of multi-tissue global gene expression reveals the regulatory signatures of feed efficiency in beef cattle

期刊

BIOINFORMATICS
卷 35, 期 10, 页码 1712-1719

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OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bty883

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资金

  1. Alberta Livestock and Meat Agency Ltd. [2015P008R]
  2. Ministry of Alberta Agriculture and Forestry [AF2018F095R]
  3. NSERC Discovery grant

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Motivation Feed efficiency is an important trait for sustainable beef production that is regulated by the complex biological process, but the mode of action behinds it has not been clearly defined. Here, we aimed to elucidate the regulatory mechanisms of this trait through studying the landscape of the genome-wide gene expression of rumen, liver, muscle and backfat tissues, the key ones involved in the energy metabolism. Results The transcriptome of 189 samples across four tissues from 48 beef steers with varied feed efficiency were generated using Illumina HiSeq4000. The analysis of global gene expression profiles of four tissues, functional analysis of tissue-shared and -unique genes, co-expressed network construction of tissue-shared genes, weighted correlations analysis between gene modules and feed efficiency-related traits in each tissue were performed. Among four tissues, the transcriptome of muscle tissue was distinctive from others, while those of rumen and backfat tissues were similar. The associations between co-expressed genes and feed efficiency related traits at single or all tissues level exhibited that the gene expression in the rumen, liver, muscle and backfat were the most correlated with feed conversion ratio, dry matter intake, average daily gain and residual feed intake, respectively. The 19 overlapped genes identified from the strongest module-trait relationships in four tissues are potential generic gene markers for feed efficiency. Availability and implementation The distribution of gene expression data can be accessed at https://www.cattleomics.com/transcriptome. Supplementary information Supplementary data are available at Bioinformatics online.

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