Identification of microRNAs in Wool Follicles during Anagen, Catagen, and Telogen Phases in Tibetan SheepReportar como inadecuado

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Wool quality is one of the most important economic traits in sheep. The wool fiber is derived from specialized skin cells that are referred to as wool follicles. To understand the roles of microRNAs miRNAs in wool fiber growth, we detected the expression patterns of miRNAs in wool follicles at the anagen, catagen, and telogen stages from Tibetan sheep through Solexa sequencing.


A total of 244 mature miRNAs were identified. Of these, only five miRNAs are listed in the database of sheep miRNAs miRBase Database V19, and the other 239 miRNAs have not been previously described in this species. Further analyses indicated that 204 miRNAs are evolutionarily conserved among mammal species, whereas 35 of the identified miRNAs were first found specifically in sheep. The expression pattern analyses showed that the expression levels of 39, 34, and 20 of the miRNAs significantly change between anagen and catagen, between anagen and telogen, and between catagen and telogen, respectively. The results of the bioinformatics analysis show that these differentially expressed miRNAs might regulate wool follicle development by targeting genes in many different pathways, such as the MAPK and Wnt pathways, as well as the pathways that regulate the actin cytoskeleton, focal adhesion, and tight junctions. Furthermore, we identified six differentially expressed miRNAs oar-miR-103-3P, oar-miR-148b-3P, oar-miR-320-3P, oar-miR-31-5P, oar-novel-1-5P, and oar-novel-2-3P that might target the key genes of the Wnt pathway. It has been reported that the Wnt pathway is critical for wool follicle development. Therefore, these miRNAs may regulate wool development through the Wnt pathway.


Our results provide new information on the identification and expression pattern of miRNAs in wool follicles. Our data might therefore aid in the understanding of the mechanisms of wool follicle development in sheep.

Autor: Guangbin Liu, Ruize Liu, Qinqun Li, Xiaohui Tang, Mei Yu, Xinyun Li, Jianhua Cao , Shuhong Zhao



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