In Frontiers in veterinary science ; h5-index 25.0
Sheep milk production is a dynamic and multifactorial trait regulated by diverse biological mechanisms. To improve the quality and production of sheep milk, it is necessary to understand the underlying non-coding transcriptomic mechanisms. In this study, ribonucleic acid-sequencing (RNA-seq) was used to profile the expression of microRNAs (miRNAs) and circular RNAs (circRNAs) in the sheep mammary gland at three key lactation time points (perinatal period, PP; early lactation, EL; and peak lactation, PL). A total of 2,369 novel circRNAs and 272 miRNAs were profiled, of which 348, 373, and 36 differentially expressed (DE) circRNAs and 30, 34, and 7 DE miRNAs were detected in the comparison of EL vs. PP, PL vs. PP, and PL vs. EL, respectively. A series of bioinformatics analyses including functional enrichment, machine learning prediction, and competing endogenous RNA (ceRNA) network analyses were conducted to identify subsets of the potential candidate miRNAs (e.g., oar_miR_148a, oar_miR_362, and oar_miR_432) and circRNAs (e.g., novel_circ_0011066, novel_circ_0010460, and novel_circ_0006589) involved in sheep mammary gland development. Taken together, this study offers a window into the dynamics of non-coding transcriptomes that occur during sheep lactation and may provide further insights into miRNA and circRNA that influence sheep mammary gland development.
Chen Weihao, Gu Xinyu, Lv Xiaoyang, Cao Xiukai, Yuan Zehu, Wang Shanhe, Sun Wei
2022
ceRNA, circRNA, lactation, machine learning, mammary gland, miRNA, sheep
