Dataset Information

Integrated analyses of miRNA-mRNA expression profiles of ovaries reveal the crucial interaction networks that regulate the prolificacy of goats in the follicular phase

ABSTRACT:

Background

Litter size is an important index of mammalian prolificacy and is determined by the ovulation rate. The ovary is a crucial organ for mammalian reproduction and is associated with follicular development, maturation and ovulation. However, prolificacy is influenced by multiple factors, and its molecular regulation in the follicular phase remains unclear.

Methods

Ten female goats with no significant differences in age and weight were randomly selected and divided into either the high-yielding group (n = 5, HF) or the low-yielding group (n = 5, LF). Ovarian tissues were collected from goats in the follicular phase and used to construct mRNA and miRNA sequencing libraries to analyze transcriptomic variation between high- and low-yield Yunshang black goats. Furthermore, integrated analysis of the differentially expressed (DE) miRNA-mRNA pairs was performed based on their correlation. The STRING database was used to construct a PPI network of the DEGs. RT–qPCR was used to validate the results of the predicted miRNA-mRNA pairs. Luciferase analysis and CCK-8 assay were used to detect the function of the miRNA-mRNA pairs and the proliferation of goat granulosa cells (GCs).

Results

A total of 43,779 known transcripts, 23,067 novel transcripts, 424 known miRNAs and 656 novel miRNAs were identified by RNA-seq in the ovaries from both groups. Through correlation analysis of the miRNA and mRNA expression profiles, 263 negatively correlated miRNA-mRNA pairs were identified in the LF vs. HF comparison. Annotation analysis of the DE miRNA-mRNA pairs identified targets related to biological processes such as “estrogen receptor binding (GO:0030331)”, “oogenesis (GO:0048477)”, “ovulation cycle process (GO:0022602)” and “ovarian follicle development (GO:0001541)”. Subsequently, five KEGG pathways (oocyte meiosis, progesterone-mediated oocyte maturation, GnRH signaling pathway, Notch signaling pathway and TGF-β signaling pathway) were identified in the interaction network related to follicular development, and a PPI network was also constructed. In the network, we found that CDK12, FAM91A1, PGS1, SERTM1, SPAG5, SYNE1, TMEM14A, WNT4, and CAMK2G were the key nodes, all of which were targets of the DE miRNAs. The PPI analysis showed that there was a clear interaction among the CAMK2G, SERTM1, TMEM14A, CDK12, SYNE1 and WNT4 genes. In addition, dual luciferase reporter and CCK-8 assays confirmed that miR-1271-3p suppressed the proliferation of GCs by inhibiting the expression of TXLNA.

Conclusions

These results increase the understanding of the molecular mechanisms underlying goat prolificacy. These results also provide a basis for studying interactions between genes and miRNAs, as well as the functions of the pathways in ovarian tissues involved in goat prolificacy in the follicular phase.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-021-08156-2.

SUBMITTER: Liu Y

PROVIDER: S-EPMC8582148 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Several studies have investigated miRNA and mRNA co-expression to identify regulatory networks at the transcriptional level. A typical finding of these studies is the presence of both negative and positive miRNA-mRNA correlations. Negative correlations are consistent with the expected, faster degradation of target mRNAs, whereas positive correlations denote the existence of feed-forward regulations mediated by transcription factors. Both mechanisms have been characterized at the molecular level, although comprehensive methods to represent miRNA-mRNA correlations are lacking. At present, genome-wide studies are able to assess the expression of more than 1000 mature miRNAs and more than 35,000 well-characterized human genes. Even if studies are generally restricted to a small subset of genes differentially expressed in specific diseases or experimental conditions, the number of potential correlations remains very high, and needs robust multivariate methods to be conveniently summarized by a small set of data.Nonparametric Kendall correlations were calculated between miRNAs and mRNAs differentially expressed in livers of patients with acute liver failure (ALF) using normal livers as controls. Spurious correlations due to the histopathological composition of samples were removed by partial correlations. Correlations were then transformed into distances and processed by multidimensional scaling (MDS) to map the miRNA and mRNA relationships. These showed: (a) a prominent displacement of miRNA and mRNA clusters in ALF livers, as compared to control livers, indicative of gene expression dysregulation; (b) a clustering of mRNAs consistent with their functional annotations [CYP450, transcription factors, complement, proliferation, HLA class II, monocytes/macrophages, T cells, T-NK cells and B cells], as well as a clustering of miRNAs with the same seed sequence; and (c) a tendency of miRNAs and mRNAs to populate distinct regions of the MDS plot. MDS also allowed to visualize the network of miRNA-mRNA target pairs.Different features of miRNA and mRNA relationships can be represented as thematic maps within the framework of MDS obtained from pairwise correlations. The symmetric distribution of positive and negative correlations between miRNA and mRNA expression suggests that miRNAs are involved in a complex bidirectional molecular network, including, but not limited to, the inhibitory regulation of miRNA targets.