Uterus proliferative period ceRNA network of Yunshang black goat reveals candidate genes on different kidding number trait

Author:

Du Xiaolong,Liu Yufang,He Xiaoyun,Tao Lin,Fang Meiying,Chu Mingxing

Abstract

Pregnancy loss that occurs in the uterus is an important and widespread problem in humans and farm animals and is also a key factor affecting the fecundity of livestock. Understanding the differences in the fecundity of goats may be helpful in guiding the breeding of goats with high fecundity. In this study, we performed RNA sequencing and bioinformatics analysis to study the uterus of Yunshang black goats with high and low fecundity in the proliferative period. We identified mRNAs, long non-coding RNAs (lncRNAs), and microRNAs (miRNAs) by analyzing the uterine transcriptomes. The target genes of the identified miRNAs and lncRNAs were predicted, and miRNA–mRNA interaction and competitive endogenous RNA (ceRNA) networks were constructed. By comparisons between low- and high-fecundity groups, we identified 1,674 differentially expressed mRNAs (914 were upregulated, and 760 were downregulated), 288 differentially expressed lncRNAs (149 were upregulated, and 139 were downregulated), and 17 differentially expressed miRNAs (4 were upregulated, and 13 were downregulated). In addition, 49 miRNA–mRNA pairs and 45 miRNA–lncRNA pairs were predicted in the interaction networks. We successfully constructed a ceRNA interaction network with 108 edges that contained 19 miRNAs, 11 mRNAs, and 73 lncRNAs. Five candidate genes (PLEKHA7, FAT2, FN1, SYK, and ITPR2) that were annotated as cell adhesion or calcium membrane channel protein were identified. Our results provide the overall expression profiles of mRNAs, lncRNAs, and miRNAs in the goat uterus during the proliferative period and are a valuable reference for studies into the mechanisms associated with the high fecundity, which may be helpful to guide goat to reduce pregnancy loss.

Funder

National Natural Science Foundation of China

Publisher

Frontiers Media SA

Subject

Endocrinology, Diabetes and Metabolism

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