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Open Access Highly Accessed Research article

Transcriptional profiling of mammary gland in Holstein cows with extremely different milk protein and fat percentage using RNA sequencing

Xiaogang Cui1, Yali Hou2, Shaohua Yang1, Yan Xie1, Shengli Zhang1, Yuan Zhang1, Qin Zhang1, Xuemei Lu2, George E Liu3 and Dongxiao Sun1*

Author Affiliations

1 Key Laboratory of Animal Genetics and Breeding of Ministry of Agriculture, National Engineering Laboratory of Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing 100193, China

2 Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100029, China

3 Bovine Functional Genomics Laboratory, ANRI, USDA-ARS, Beltsville, MD 20705, USA

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BMC Genomics 2014, 15:226  doi:10.1186/1471-2164-15-226

Published: 24 March 2014

Abstract

Background

Recently, RNA sequencing (RNA-seq) has rapidly emerged as a major transcriptome profiling system. Elucidation of the bovine mammary gland transcriptome by RNA-seq is essential for identifying candidate genes that contribute to milk composition traits in dairy cattle.

Results

We used massive, parallel, high-throughput, RNA-seq to generate the bovine transcriptome from the mammary glands of four lactating Holstein cows with extremely high and low phenotypic values of milk protein and fat percentage. In total, we obtained 48,967,376–75,572,578 uniquely mapped reads that covered 82.25% of the current annotated transcripts, which represented 15549 mRNA transcripts, across all the four mammary gland samples. Among them, 31 differentially expressed genes (p < 0.05, false discovery rate q < 0.05) between the high and low groups of cows were revealed. Gene ontology and pathway analysis demonstrated that the 31 differently expressed genes were enriched in specific biological processes with regard to protein metabolism, fat metabolism, and mammary gland development (p < 0.05). Integrated analysis of differential gene expression, previously reported quantitative trait loci, and genome-wide association studies indicated that TRIB3, SAA (SAA1, SAA3, and M-SAA3.2), VEGFA, PTHLH, and RPL23A were the most promising candidate genes affecting milk protein and fat percentage.

Conclusions

This study investigated the complexity of the mammary gland transcriptome in dairy cattle using RNA-seq. Integrated analysis of differential gene expression and the reported quantitative trait loci and genome-wide association study data permitted the identification of candidate key genes for milk composition traits.

Keywords:
Transcriptome; Differentially expressed gene; Mammary gland; Protein percentage; Fat percentage; RNA-Seq; Dairy cattle