| Summary: | Long non-coding RNAs (lncRNAs) have emerged as a novel class of regulatory molecules involved in various biological processes. However, their role in milk performance is unknown. Here, whole transcriptome RNA sequencing was used to generate the lncRNA transcriptome profiles in mammary tissue samples from 6 Chinese Holstein cows with 3 extremely high and 3 low milk protein percentage phenotypes. In this study, 6,450 lncRNA transcripts were identified through 5 stringent steps and filtration by coding potential. In total, 31 lncRNAs and 18 novel genes were identified to be differentially expressed in high milk protein samples (HP) relative to low milk protein samples (LP), respectively. Differentially expressed lncRNAs were selected to predict target genes through bioinformatics analysis, followed by the integration of differentially expressed mRNA data, gene function, gene ontology (GO) and pathway, genome wide association study (GWAS) and quantitative trait locus (QTL) information, as well as network analysis to further characterize potential interactions. Several lncRNAs were found (such as XLOC_059976) that could be used as candidate markers for milk protein content prediction. This is the first study to perform global expression profiling of lncRNAs and mRNAs related to milk protein traits in dairy cows. These results provide important information and insights into the synthesis of milk proteins, and potential targets for the future improvement of milk quality.
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