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. 2021 Feb 23:9:e10796.
doi: 10.7717/peerj.10796. eCollection 2021.

Evaluation of key miRNAs during early pregnancy in Kazakh horse using RNA sequencing

Affiliations

Evaluation of key miRNAs during early pregnancy in Kazakh horse using RNA sequencing

LingLing Liu et al. PeerJ. .

Abstract

Background: miRNA has an important role in cell differentiation, biological development, and physiology. Milk production is an important quantitative trait in livestock and miRNA plays a role in the amount of milk produced.

Methods: The role of regulatory miRNAs involved in equine milk production is not fully understood. We constructed two miRNA libraries for Kazakh horse milk production from higher-producing (H group) and lower-producing (L group) individuals, and used RNA-Seq technology to identify the differentially expressed miRNAs between the two milk phenotypes of Kazakh horses.

Results: A total of 341 known and 333 novel miRNAs were detected from the H and L groups, respectively. Eighty-three differentially expressed miRNAs were identified between the H and L group s, of which 32 were known miRNAs (27 were up-regulated, five were down-regulated) and 51 were novel miRNAs (nine were up-regulated, 42 were down-regulated). A total of 2,415 genes were identified. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that these genes were annotated to mammary gland development, mammary gland morphogenesis, tissue development and PI3K-Akt signaling pathways, insulin signaling pathway and TGF-beta signaling pathway, among others. Five miRNAs (miR-199a-3p, miR143, miR145, miR221, miR486-5p) were identified as affecting horse milk production and these five miRNAs were validated using qRT-PCR.

Conclusions: We described a methodology for the transcriptome-wide profiling of miRNAs in milk, which may help the design of new intervention strategies to improve the milk yield of Kazakh horses.

Keywords: High-throughput sequencing; Kazakh horse; Milk yield; miRNAs.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Statistics of miRNA length distribution in H group and L group of horse libraries.
Figure 2
Figure 2. The differential miRNA expression profile in milk of Kazakh horse.
Figure 3
Figure 3. GO annotation analysis.
Figure 4
Figure 4. KEGG annotation analysis.
Figure 5
Figure 5. qRT-PCR validation of five miRNAs identified as differentially expressed by miRNA profiling.

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Grants and funding

This work was supported by the Natural Science Foundation of Xinjiang Uygur Autonomous (grant number 2019D01B14); the NSFC (No.31560620). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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