Deep Small RNA Sequencing Reveals Important miRNAs Related to Muscle Development and Intramuscular Fat Deposition in Longissimus dorsi Muscle From Different Goat Breeds

深度小RNA测序揭示了不同品种山羊背最长肌中与肌肉发育和肌内脂肪沉积相关的关键miRNA

阅读:1

Abstract

MicroRNAs (miRNAs) are a class of small non-coding RNAs that have been shown to play important post-transcriptional regulatory roles in the growth and development of skeletal muscle tissues. However, limited research into the effect of miRNAs on muscle development in goats has been reported. In this study, Liaoning cashmere (LC) goats and Ziwuling black (ZB) goats with significant phenotype difference in meat production performance were selected and the difference in Longissimus dorsi muscle tissue expression profile of miRNAs between the two goat breeds was then compared using small RNA sequencing. A total of 1,623 miRNAs were identified in Longissimus dorsi muscle tissues of the two goat breeds, including 410 known caprine miRNAs, 928 known species-conserved miRNAs and 285 novel miRNAs. Of these, 1,142 were co-expressed in both breeds, while 230 and 251 miRNAs were only expressed in LC and ZB goats, respectively. Compared with ZB goats, 24 up-regulated miRNAs and 135 miRNAs down-regulated were screened in LC goats. A miRNA-mRNA interaction network showed that the differentially expressed miRNAs would target important functional genes associated with muscle development and intramuscular fat deposition. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that the target genes of differentially expressed miRNAs were significantly enriched in Ras, Rap 1, FoxO, and Hippo signaling pathways. This study suggested that these differentially expressed miRNAs may be responsible for the phenotype differences in meat production performance between the two goat breeds, thereby providing an improved understanding of the roles of miRNAs in muscle tissue of goats.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。