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Identification and analysis of differentially expressed microRNAs in endometrium to explore the regulation of sheep fecundity

BMC Genomics volume 24, Article number: 600 (2023) Cite this article

Abstract

Background

MicroRNAs (miRNAs) play an important regulatory role in mammalian reproduction. Currently, most studies are primarily concentrated on ovarian miRNAs, ignoring the influence of endometrial miRNAs on the fecundity of female sheep. To uncover potential regulators of sheep fecundity, RNA-seq was used to comparatively analyze miRNA expression profiles of endometrium between high prolificacy sheep (HP, litter size = 3) and low prolificacy sheep (LP, litter size = 1) with FecB genotype.

Results

Firstly, genomic features of miRNAs from endometrium were analyzed. Furthermore, 58 differentially expressed (DE) miRNAs were found in the endometrium of Hu sheep with different litter size. A co-expression network of DE miRNAs and target genes has been constructed, and hub genes related litter size are included, such as DE miRNA unconservative_NC_019472.2_1229533 and unconservative_NC_019481.2_1637827 target to estrogen receptor α (ESR1) and unconservative_NC_019481.2_1637827 targets to transcription factor 7 (TCF7). Moreover, functional annotation analysis showed that the target genes (NRCAM and NEGR1) of the DE miRNAs were significantly enriched in cell adhesion molecules (CAMs) signaling pathway, which was related to uterine receptivity.

Conclusion

Taken together, this study provides a new valuable resource for understanding the molecular mechanisms underlying Hu sheep prolificacy.

Peer Review reports

Introduction

Hu sheep, as an excellent local breed in China, are famous for their high prolificacy and year-round estrus [1]. A variety of marker genes have been identified, and found to be involved with litter size by affecting sheep ovulation rate, including bone morphogenetic protein receptor (BMPR1B/FecB), bone morphogenetic protein 15 (BMP15/FecX), and growth differentiation factor9 (GDF9/FecG) [2, 3]. In the breeding process, some Hu sheep carrying the fecundity gene (FecB) of high ovulation rate still reveal low prolificacy [4]. Environmental variables may have a great influence on the model of a genome wide association study [5]. Therefore, it is meaningful to research the regulatory mechanism of sheep prolificacy at the level of transcription.

MicroRNAs (miRNAs) belong to endogenous small non-coding RNAs that perform important gene-regulatory functions in animals by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression [6]. During the past few years, studies have shown that miRNAs play important roles in female sheep reproduction [7, 8]. Especially, ovarian transcriptomic analysis revealed the interactions of miRNAs and lncRNAs were related to fecundity in different sheep [9, 10]. Furthermore, genome-wide analysis of miRNAs in the ovaries of multiple and uniparous goats were performed [11, 12], revealing that miRNAs may regulate follicular development and ovulation and consequent increase in litter size.

As we all known, ovulation and uterine receptivity are the two main controlling factors for mammalian fertility. Therefore, in addition to ovulation performance, the development and receptivity of uterus are closely related to reproductive efficiency of sheep to maintain large litters [13]. To date, 15 miRNAs in mice and 29 miRNAs have been reported in humans within various compartments of the endometrium that may potentially modulate receptivity [14]. Furthermore, miRNAs also have been found to play a pivotal role for the establishment of the proper uterine environment required for implantation [15]. Differentially expressed miRNAs in porcine ovaries have been identified as important regulators of the litter size [16]. However, there are few studies on endometrial miRNAs in the ovine fecundity.

In this study, RNA sequencing (RNA-seq) was applied to analyze the expression profiling of endometrial miRNAs between different fecundity Hu sheep, and screen the candidate miRNAs involved in high prolificacy. These results could provide useful information for under-standing the molecular basis of miRNAs in the regulation of endometrial functions, as well as the mechanism of Hu sheep prolificacy.

Methods

This study was carried out according to the Guide for the Care and Use of Laboratory Animals (permit no. DKY2021021) prepared by the Ethics Committee of Qingdao Agricultural University.

Animals and sample preparation

The high prolificacy sheep group (HP, n = 3, litter size = 3) and low prolificacy sheep group (LP, n = 3, litter size = 1) were selected from the nucleus herds of Hu sheep at Taizhou Sheep Industry according to their littering records (three consecutive lambing records) and polymorphism analysis of FecB [17], two groups of sheep in this study had similar numbers of dominant follicles and FecB genotype. All sheep were housed under the same conditions with free access to feed and water. Synchronous estrus of sheep was conducted according to previously described [18]. The estrous cycles of the ewes were adjusted by intravaginal progestagen sponges (30 mg; Ningbo Sansheng pharmaceutical Co., LTD, Zhejiang, China) for 11 days. Estrus was monitored by presentation of a buck fitted with an apron three times one day following sponge removal. The sheep were deeply anesthetized by intravenous administration of 3% pentobarbital sodium (30 mg/kg; Solarbio, P8410, China), and sacrificed by exsanguination in a healthy physiological stages at the second estrus (natural estrous), and endometrium was collected from the mid-part of uterine horns, and immediately frozen in liquid nitrogen for RNA extraction.

Library preparation and sequencing

According to the manufacturer’s instruction, total RNA from 6 endometrium samples was isolated by TRIzol reagent (Invitrogen, Carlsbad, CA, USA) for RNA sequencing (RNA-seq). The purity and concentration of RNA were assessed by NanoDrop 2000 spectrophotometer (Thermo Scientific, Wilmington, DE, United States). The integrity of RNA was assessed using the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, Palo Alto, CA, USA). Sequencing libraries were generated using the small RNA Sample Pre Kit (NEB, Ipswich, MA, USA). TruSeq PE Cluster Kit v4-cBot-HS (Illumia) was used to cluster the index-coded samples on a cBot Cluster Generation System according to the manufacturer’s instructions, then library preparations were sequenced on an Illumina Hiseq 2500 platform. Clean data with high quality was generated from Raw data (raw reads) of fastq format by quality control step. Clean data were obtained by removing reads containing ploy-N and low quality reads from raw data, retained 18–30 nt sequences. At the same time, the Q30 of the clean data were calculated. All the downstream analyses were based on clean data (Supplementary Table 1).

In order to filter ribosomal RNA (rRNA), transfer RNA (tRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA) and other ncRNA and repeats, Clean Reads respectively performed on Silva database, GtRNAdb database, Rfam database and Repbase database sequence alignment by Bowtie software [19]. Ovis_aries_v4.0_genomic was used as reference genomes for sequence alignment and subsequent analysis. The remaining reads were used to detect known miRNA and novel miRNA predicted by comparing with known miRNAs from miRBase (v21) [20], and miRDeep2 software package was used for novel miRNA [21].

Differential expression and target gene prediction analysis

According to the values of normalized transcripts per kilobase per million reads (TPM), the differentially expressed miRNAs were identified by a p-value threshold of < 0.05 and |log2(fold change)| > 1. The data of differently expressed mRNAs was taken from our previous study [22]. According to the sheep gene sequence information of known miRNAs and newly predicted miRNAs, target gene prediction was performed using miRanda [23] and RNAhybrid [24].

KEGG pathways and GO analysis

Kyoto Encyclopedia of Genes and Genomes (KEGG) (http://www.genome.jp/kegg) pathway analysis of target genes was performed by the KOBAS (v2.0) software [25]. Gene ontology (GO) enrichment analysis was performed by the software GOSeq (Release2.12) [26]. P-values (t-test) < 0.05 was indicated as significant enrichment.

Real-time quantitative PCR

We validated the RNA sequencing data using RT-qPCR. Primers were designed online using Primer 5 (Premier Biosoft, Palo Alto, CA, USA) software. RNA (1 mg) was reverse transcribed using the miRNA 1st Strand cDNA Synthesis kit (by stem-loop; Vazyme, Nanjing, China), and the SYBR green (Vazyme, Nanjing, China) method was used for RT-qPCR. The expression levels of genes were evaluated by 2−ΔΔCT, and U6 gene were used as reference genes for normalization of the miRNA data. The primer sequences used for RT-qPCR were listed in Supplementary Table 2.

Statistical data analysis

Further analysis of RNA-seq data and graphical representations were performed using the statistical R package (R, Auckland, NZL). SPSS 19.0 software (SPSS, Chicago, IL, USA) was used to analyze the RT-qPCR data, which were presented as the means ± standard deviations (SDs). Differential gene expression levels were calculated using a t-test, and Statistical significance was defined as p < 0.05.

Results

Genomic features of endometrial miRNAs

A total of 1726 unique miRNAs were screened from sheep endometrium, result of length distribution analysis showed that most miRNAs ranged from 21 to 22nt. The percentage of the 22nt miRNAs in the total miRNAs was 36.73% (Fig. 1A). Analysis of nucleotide bias distribution showed that the proportion of base to U is the largest, especially in the 16–24 nt miRNA, followed by base A, and G and C. In the first proportion of miRNA, (A + U) was found in most miRNAs with a percentage of 77.58% in average (Fig. 1B).

Fig. 1
figure 1

Genomic features of miRNAs from sheep endometrium. (A) Length distribution of miRNAs in the sheep endometrium libraries. (B) Nucleotide bias distribution of miRNAs.

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Screening of differentially expressed miRNAs

Further analysis identified 58 significant differential expression miRNAs (P < 0.05), including 39 up-regulated and 19 down-regulated miRNAs in high prolificacy samples compared with low prolificacy samples (Fig. 2A). In the up-regulated miRNAs, 5 known miRNAs (oar-miR-136, oar-miR-154b-3p, oar-miR-410-5p, oar-miR-431, oar-miR-665-3p) and 7 specific miRNAs expression were discovered in HP group (unconservative_NC_019478.2_1533152, unconservative_NC_019484.2_1771487, unconservative_NC_019471.2_1197647, unconservative_NC_019461.2_579869, unconservative_NC_019471.2_1208164, unconservative_NC_019468.2_1001447, unconservative_NC_019467.2_942834),

In the down-regulated miRNAs, 6 specific miRNAs expression were discovered in LP group (unconservative_NC_019473.2_1279266, unconservative_NC_019460.2_377698, unconservative_NC_019467.2_921965, unconservative_NC_019468.2_974797, unconservative_NC_019472.2_1249990, oar-miR-410-5p) (Fig. 2B; Supplementary Table 3).

Fig. 2
figure 2

Analysis of differentially expressed miRNAs. (A) Differentially expressed miRNA volcano diagram. (B) Differentially expressed miRNA cluster. HP, high prolificacy sheep; LP, low prolificacy sheep

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In order to verify the accuracy of the differentially expressed miRNA by RNA-seq, eight differentially expressed miRNAs were randomly selected for RT-qPCR. We found that the overall trend of this data was the same as our RNA-seq data (Fig. 3).

Fig. 3
figure 3

Validation of RNA-seq results by using RT-qPCR. The values are presented as means ± SDs. Means with different letters indicate significant differences (P < 0.05)

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Interaction network analysis of differentially expressed miRNAs and mRNAs

The targets of 10 down-regulated miRNAs (Fig. 4A) and 25 up-regulated miRNAs (Fig. 4B) were identified from high prolificacy sheep. A total of 538 miRNA-mRNA pairs were predicted in the HP vs. LP comparison (Supplementary Table 4). Among these miRNA-mRNA pairs, 258 pairs were negatively correlated in expression. In the network, the core gene estrogen receptor 1 (ESR1) was targeted by 2 miRNAs (unconservative_NC_019472.2_1229533 and unconservative_NC_019481.2_1637827). Furthermore, miRNA unconservative_NC_019481.2_1637827 also targeted to TCF7, which might play crucial roles in the molecular mechanism of the sheep prolific trait.

Fig. 4
figure 4

The interaction network of miRNAs-mRNAs in the comparison. (A) The target gene prediction of down-regulated miRNAs in the comparison. (B) The target gene prediction of up-regulated miRNAs in the comparison. red triangle: up-regulated miRNA; green triangle: down-regulated miRNA; red circle: up-regulated mRNA; green circle: down-regulated mRNA.

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Pathway enrichment analysis of miRNA targets

GO analysis of target gene functions showed developmental process and reproductive process were enriched in biological process analysis, cell junction was enriched in cellular component analysis (Fig. 5; Supplementary Table 5).

Fig. 5
figure 5

GO enrichment analysis for target genes of miRNA. red column: biological process; green column: cellular component; blue column: molecular function

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Results of KEGG pathway analysis revealed that DE miRNA targets were enriched in some pathways, including cell adhesion molecules (CAMs), tight junction, and other pathways involved in reproduction (Fig. 6; Supplementary Table 6). Especially, CAMs pathway was significantly enriched in KEGG analysis (qvalue < 0.05), which may participate in the regulation of sheep fecundity.

Fig. 6
figure 6

The top 20 KEGG pathways involved in reproduction of sheep. The size of point indicated the number of DE genes in pathway. The color of point indicated the range of qvalue

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As shown in Fig. 7A, DE miRNAs (unconservative_NC_019472.2_1249990 and unconservative_NC_019480.2_1604830) were screened from CAMs signaling pathway, which targeted to NEGR1; DE miRNAs (unconservative_NC_019462.2_631388 and unconservative_NC_019468.2_996653) in CAMs signaling pathway targeted to NRCAM. Results of RT-qPCR showed the expression levels of miRNAs were negative connected with their predicted target genes (Fig. 7B).

Fig. 7
figure 7

Analysis of miRNAs-mRNAs in CAMs signaling pathway. (A) The interaction network of miRNAs-mRNAs in CAMs signaling pathway [27,28,29]. red triangle: up-regulated miRNAs; green circle: down-regulated mRNAs; blue circle: no difference RNAs. (B) Analysis of miRNAs and mRNAs expression in CAMs by using RT-qPCR. The values are presented as means ± SDs. Means with different letters indicate significant differences (P < 0.05)

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Discussions

The Hu sheep is famous for high prolificacy and year-around estrus, so it has attracted much attention in the mechanism research of sheep reproduction. As reported, uterus were important organs in prolific breeds of ewes that possess an intrinsically high ovulation rate as well as enhanced uterine capacity to maintain large litters [13]. Our previous study discovered the high prolificacy sheep had higher densities of uterine glands and endometrial microvessel, which is critical for enhanced endometrial uterine receptivity [30]. However, the molecular mechanisms underlying the effects of different uterine morphology on sheep reproductive performance remain unclear. At present, there are many reports focused on non-coding RNA in the regulation of endometrial functions [31, 32], and the expression pattern of miRNAs in ovine endometrium was identified demonstrated that miRNAs modulate ovine endometrium during the peri-implantation [33, 34]. Recent research identified miRNA transcriptome profile, and constructed miRNA-mRNA interaction in adrenal gland tissue of small tail Han sheep, discovered the miR-370-3p might play a major role in the reproduction regulation process [7]. Furthermore, the genome-wide analysis of ovarian miRNAs and mRNAs identified the roles of miRNAs in fecundity regulation between different sheep species [35]. To better understand the molecular mechanisms underlying the high prolificacy of Hu sheep, endometrial miRNAs sequencing was performed.

In present study, analysis of length distribution of endometrial miRNAs showed intensive enriching effects for 22 nt miRNAs in all samples, which was consistent with previous researches [36, 37]. Due to restriction site specificity, the first base of a mature miRNA sequence is highly biased. Therefore, we further analyzed the frequency distribution of bases at various positions of the endometrial miRNAs, which was found to be very similar to the typical miRNAs [38]. Previous study discovered differentially expressed miRNAs in ovaries were involved in the prolificacy of goat [12, 39, 40]. Research of miRNAs in sheep ovaries indicates miRNAs regulate the process of follicular development during the periovulatory stage, which provides new insights into the molecular mechanisms affecting sheep prolificacy [41]. Endometrial capacity is crucial for prolific breeds of ewes to maintain large litters [13]. However, the function of miRNAs in endometrium related to sheep uterine receptivity and prolificacy remains unknown. In order to clarify the role of endometrial miRNA in prolificacy of sheep, 58 differentially expressed miRNAs were screened in high prolificacy and low prolificacy sheep endometrium by RNA-seq in this study, especially the known miRNA oar-miRNA-136, oar-miRNA-154b-3p, oar-miRNA-410-5p, oar-miRNA-431, and oar-miRNA-665-3p. As reported, miRNA-136 significantly suppressed the expression of PPP2R2A in deciduas, these results further indicate that differentially expressed miRNAs may be involved in the pathogenesis of pre-eclampsia [42]. MiRNA-410-5p participates in the pathogenesis of abortion by regulating the biological function of trophoblast [43]. The dysregulation of miRNA-665 was implicated in the initiation and progression of endometrial cancer [44]. These researches signify that they may be extensively involved in sheep fertility regulation. Furthermore, some specific miRNAs were also discovered in sheep endometrium that may be potential markers affecting fertility. The accuracy of RNA-seq was validated by RT-qPCR. As previously reported, miRNAs can regulate the expression of their target genes via post-transcriptional regulation [45]. In order to analysis roles of DE miRNAs, RNA-seq were used to identify the potential miRNA-mRNA pairs, provided candidate targets for studying high-prolificacy traits in sheep. As study showed, estrogen receptor (ESR) gene was related to litter traits [46]. Among these miRNA-mRNA pairs, the core genes ESR1 was identified the target of miRNAs unconservative_NC_019472.2_1229533 and unconservative_NC_019481.2_1637827. Furthermore, we also discovered unconservative_NC_019481.2_1637827 targeted to TCF7, which might play crucial roles in the molecular mechanism of the sheep prolific trait. As reported, the polymorphisms of TCF12 gene was related to litter size in pigs [47]. GO analysis of target gene discovered the developmental process, reproductive process, and cell junction were enriched. In the present study, the enriched KEGG pathways and GO pathways associated with reproduction clearly suggest that these miRNAs play a vital role in regulation of endometrial receptivity as well as the prolificacy of sheep. In present study, KEGG analysis of miRNA-targets revealed CAMs pathway as the candidate functional pathways was significantly enriched for high-prolificacy sheep. The major groups of CAMs involved in the embryo implantation are integrins, cadherins, selectins, immunoglobulins and mucins. These surface ligands mediate the adhesion between cells which are the maintenance of the structural integrity of tissue and receptivity of endometrium [48, 49]. These results indicated that CAMs may play a key role for high fecundity sheep by maintenance of endometrial receptivity. Furthermore, we discovered NRCAM and NEGR1 in CAMs signaling pathway were significantly enriched in high prolificacy sheep as the targets of DE miRNAs. Previous research indicated NRCAM secreted by endometrial stromal cells, which enhanced the progestin sensitivity through epigenetic modulation [50]. Endometrial angiogenesis is closely related to the process of the cyclical development of the endometrium and embryo implantation [51]. NEGR1, as an inducible protein, is associated with vascular cells proliferation, differentiation, and death pathways [52]. Research also showed NEGR1 could be regulated by miR-150-5p in spinal cord ischemia–reperfusion injury model of rats [53]. However, the functions of miRNAs and their predicted targets analyses in sheep should be carefully evaluated by further experiments.

Conclusion

In summary, this study provides the comprehensive analysis of endometrial miRNAs in Hu sheep with different fecundity, and discovered several target genes of miRNAs related to sheep prolificacy by the construction of miRNA-mRNA interaction network. In addition, CAMs signaling pathway was enriched in the endometrium of high prolificacy Hu sheep. Our study has crucial roles in understanding the regulatory mechanism of prolificacy in sheep.

Data Availability

Data are available in the Sequence Reads Archive (SRA): [https://www.ncbi.nlm.nih.gov/sra/PRJNA964457].

Abbreviations

CAMs:

Cell adhesion molecules

ESR1:

Estrogen receptorα

HP:

High prolificacy

LP:

Low prolificacy

miRNAs:

MicroRNAs

NEGR1:

Neuronal growth regulator 1

NRCAM:

Neuron cell adhesion molecule

TCF7:

Transcription factor 7

References

  1. Yue GH. Reproductive characteristics of chinese Hu sheep. Anim Reprod Sci. 1996;44(4):223–30.

    Google Scholar 

  2. Notter DR. Genetic aspects of reproduction in sheep. Reprod Domest Animals = Zuchthygiene. 2008;43(Suppl 2):122–8.

    PubMed  Google Scholar 

  3. Guan F, Liu SR, Shi GQ, Yang LG. Polymorphism of FecB gene in nine sheep breeds or strains and its effects on litter size, lamb growth and development. Anim Reprod Sci. 2007;99(1–2):44–52.

    CAS  PubMed  Google Scholar 

  4. Yao X, Gao X, Bao Y, El-Samahy MA, Yang J, Wang Z, Li X, Zhang G, Zhang Y, Liu W, et al. lncRNA FDNCR promotes apoptosis of granulosa cells by targeting the miR-543-3p/DCN/TGF-beta signaling pathway in Hu sheep. Mol Therapy Nucleic Acids. 2021;24:223–40.

    CAS  Google Scholar 

  5. Xu SS, Gao L, Xie XL, Ren YL, Shen ZQ, Wang F, Shen M, Eyorsdottir E, Hallsson JH, Kiseleva T, et al. Genome-wide Association analyses highlight the potential for different genetic mechanisms for litter size among Sheep Breeds. Front Genet. 2018;9:118.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell. 2009;136(2):215–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Chen Y, Liu Y, Chu M. miRNA-mRNA analysis of sheep adrenal glands reveals the network regulating reproduction. BMC Genomic data. 2022;23(1):44.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Liu Q, Di R, Ren C, He X, Wang X, Xia Q, Chu M, Zhang Z. Screening of differentially expressed genes and miRNAs in Hypothalamus and Pituitary Gland of Sheep under different photoperiods. Genes. 2022;13(6):1091.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Miao X, Luo Q, Zhao H, Qin X. An Integrated analysis of miRNAs and methylated genes encoding mRNAs and lncRNAs in Sheep Breeds with different fecundity. Front Physiol. 2017;8:1049.

    PubMed  PubMed Central  Google Scholar 

  10. Miao X, Luo Q, Zhao H, Qin X. Ovarian transcriptomic study reveals the differential regulation of miRNAs and lncRNAs related to fecundity in different sheep. Sci Rep. 2016;6:35299.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Miao X, Luo Q, Zhao H, Qin X. Genome-wide analysis of miRNAs in the ovaries of Jining Grey and Laiwu Black goats to explore the regulation of fecundity. Sci Rep. 2016;6:37983.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Ling YH, Ren CH, Guo XF, Xu LN, Huang YF, Luo JC, Zhang YH, Zhang XR, Zhang ZJ. Identification and characterization of microRNAs in the ovaries of multiple and uniparous goats (Capra hircus) during follicular phase. BMC Genomics. 2014;15(1):339.

    PubMed  PubMed Central  Google Scholar 

  13. Spencer TE, Bazer FW. Uterine and placental factors regulating conceptus growth in domestic animals. J Anim Sci. 2004;82(E–Suppl):E4–13.

    PubMed  Google Scholar 

  14. Shekibi M, Heng S, Nie G. MicroRNAs in the regulation of Endometrial receptivity for embryo implantation. Int J Mol Sci. 2022;23(11):6210.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Imakawa K, Matsuno Y, Fujiwara H. New Roles for EVs, miRNA and lncRNA in bovine embryo implantation. Front Veterinary Sci. 2022;9:944370.

    Google Scholar 

  16. Xu G, Hu Y, Yu D, Chen X, Li X, Duan S, Zhang N, Xu G, Hu J, Yang G, et al. Discovery of differentially expressed MicroRNAs in Porcine Ovaries with smaller and larger litter size. Front Genet. 2022;13:762124.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Feng X, Li F, Wang F, Zhang G, Pang J, Ren C, Zhang T, Yang H, Wang Z, Zhang Y. Genome-wide differential expression profiling of mRNAs and lncRNAs associated with prolificacy in Hu sheep. Biosci Rep. 2018;38(2):BSR20171350.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Zheng J, Wang Z, Yang H, Yao X, Yang P, Ren C, Wang F, Zhang Y. Pituitary Transcriptomic Study reveals the Differential Regulation of lncRNAs and mRNAs related to prolificacy in different FecB genotyping Sheep. Genes. 2019;10(2):E157.

    Google Scholar 

  19. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25.

    PubMed  PubMed Central  Google Scholar 

  20. Kozomara A, Birgaoanu M, Griffiths-Jones S. miRBase: from microRNA sequences to function. Nucleic Acids Res. 2019;47(D1):D155–62.

    CAS  PubMed  Google Scholar 

  21. Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40(1):37–52.

    PubMed  Google Scholar 

  22. Gao X, Yao X, Li X, Liang Y, Liu Z, Wang Z, Li K, Li Y, Zhang G, Wang F. Roles of WNT6 in Sheep Endometrial epithelial cell cycle progression and uterine glands Organogenesis. Veterinary Sci. 2021;8(12):316.

    Google Scholar 

  23. Betel D, Wilson M, Gabow A, Marks DS, Sander C. The microRNA.org resource: targets and expression. Nucleic Acids Res. 2008;36(Database issue):D149–153.

    CAS  PubMed  Google Scholar 

  24. Rehmsmeier M, Steffen P, Hochsmann M, Giegerich R. Fast and effective prediction of microRNA/target duplexes. RNA. 2004;10(10):1507–17.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Mao XZ, Cai T, Olyarchuk JG, Wei LP. Automated genome annotation and pathway identification using the KEGG Orthology (KO) as a controlled vocabulary. Bioinformatics. 2005;21(19):3787–93.

    CAS  PubMed  Google Scholar 

  26. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):R14.

    PubMed  PubMed Central  Google Scholar 

  27. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51(D1):D587–92.

    CAS  PubMed  Google Scholar 

  28. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28(11):1947–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Gao X, Yao X, Wang Z, Li X, Li X, An S, Wei Z, Zhang G, Wang F. Long non-coding RNA366.2 controls endometrial epithelial cell proliferation and migration by upregulating WNT6 as a ceRNA of miR-1576 in sheep uterus. Biochim et Biophys acta Gene Regul Mech. 2020;1863(9):194606.

    CAS  Google Scholar 

  31. Ferlita A, Battaglia R, Andronico F, Caruso S, Cianci A, Purrello M, Pietro CD. Non-coding RNAs in endometrial physiopathology. Int J Mol Sci. 2018;19(7):2120.

    PubMed  PubMed Central  Google Scholar 

  32. La Y, Tang J, He X, Di R, Wang X, Liu Q, Zhang L, Zhang X, Zhang J, Hu W, et al. Identification and characterization of mRNAs and lncRNAs in the uterus of polytocous and monotocous small tail Han sheep (Ovis aries). PeerJ. 2019;7:e6938.

    PubMed  PubMed Central  Google Scholar 

  33. Kose M, Hitit M, Kaya MS, Kirbas M, Dursun S, Alak I, Atli MO. Expression pattern of microRNAs in ovine endometrium during the peri-implantation. Theriogenology. 2022;191:35–46.

    CAS  PubMed  Google Scholar 

  34. Song Y, Zhang L, Liu X, Niu M, Cui J, Che S, Liu Y, An X, Cao B. Analyses of circRNA profiling during the development from pre-receptive to receptive phases in the goat endometrium. J Anim Sci Biotechnol. 2019;10:34.

    PubMed  PubMed Central  Google Scholar 

  35. Miao X, Qin QL. Genome-wide transcriptome analysis of mRNAs and microRNAs in Dorset and small tail Han sheep to explore the regulation of fecundity. Mol Cell Endocrinol. 2015;402(C):32–42.

    CAS  PubMed  Google Scholar 

  36. Li X, Li C, Ni W, Wang D, Hou X, Liu Z, Cao Y, Yao Y, Zhang X, Hu S. Identification and comparison of microRNAs in pituitary gland during prenatal and postnatal stages of sheep by deep sequencing. J Genet. 2018;97(4):965–75.

    CAS  PubMed  Google Scholar 

  37. Tian G, Yin X, Luo H, Xu X, Bolund L, Zhang X, Gan SQ, Li N. Sequencing bias: comparison of different protocols of microRNA library construction. BMC Biotechnol. 2010;10:64.

    PubMed  PubMed Central  Google Scholar 

  38. Ullah Y, Li C, Li X, Ni W, Yao R, Xu Y, Quan R, Li H, Zhang M, Liu L, et al. Identification and profiling of Pituitary microRNAs of Sheep during Anestrus and Estrus Stages. Animals: An open Access Journal from MDPI. 2020;10(3):402.

    PubMed  Google Scholar 

  39. Liu Y, Zhou Z, Guo S, Li K, Wang P, Fan Y, He X, Jiang Y, Lan R, Chen S, et al. Transcriptome analysis reveals key miRNA-mRNA pathways in ovarian tissues of Yunshang Black Goats with different kidding numbers. Front Endocrinol (Lausanne). 2022;13:883663.

    PubMed  Google Scholar 

  40. An X, Song Y, Hou J, Li G, Zhao H, Wang J, Cao B. Identification and profiling of microRNAs in the ovaries of polytocous and monotocous goats during estrus. Theriogenology. 2016;85(4):769–80.

    CAS  PubMed  Google Scholar 

  41. Yao X, Wang Z, Gao X, Li X, Yang H, Ei-Samahy MA, Bao Y, Xiao S, Meng F, Wang F. Unconservative_15_2570409 suppresses progesterone receptor expression in the granulosa cells of Hu sheep. Theriogenology. 2020;157:303–13.

    CAS  PubMed  Google Scholar 

  42. Zhao G, Zhou X, Chen S, Miao H, Fan H, Wang Z, Hu Y, Hou Y. Differential expression of microRNAs in decidua-derived mesenchymal stem cells from patients with pre-eclampsia. J Biomed Sci. 2014;21(1):81.

    PubMed  PubMed Central  Google Scholar 

  43. Wu S, Liu H, Zhou M, Shang Y, Luo L, Chen J, Yang J. The miR-410-5p /ITGA6 axis participates in the pathogenesis of recurrent abortion by regulating the biological function of trophoblast. J Reprod Immunol. 2022;152:103647.

    CAS  PubMed  Google Scholar 

  44. Wang J, Lei C, Shi P, Teng H, Lu L, Guo H, Wang X. LncRNA DCST1-AS1 promotes Endometrial Cancer Progression by modulating the MiR-665/HOXB5 and MiR-873-5p/CADM1 pathways. Front Oncol. 2021;11:714652.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Hwang HW, Wentzel EA, Mendell JT. A hexanucleotide element directs microRNA nuclear import. Science. 2007;315(5808):97–100.

    CAS  PubMed  Google Scholar 

  46. Rahman M, Phookan A, Zaman GU, Das A, Akhtar F, Tamuly S, Choudhury H, Sarma LM. Allelic variability of estrogen receptor (ESR) gene and its effect on litter traits of Doom pigs. Trop Anim Health Prod. 2021;53(2):316.

    PubMed  Google Scholar 

  47. Tao H, Mei S, Sun X, Peng X, Zhang X, Ma C, Wang L, Hua L, Li F. Associations of TCF12, CTNNAL1 and WNT10B gene polymorphisms with litter size in pigs. Anim Reprod Sci. 2013;140(3–4):189–94.

    CAS  PubMed  Google Scholar 

  48. You F, Du X, Zhang T, Wang Y, Lv Y, Zeng L. High-frequency electroacupuncture improves endometrial receptivity via regulating cell adhesion molecules and leukemia inhibitory factor / signal transducer and activator of transcription signaling pathway. Bioengineered. 2021;12(2):10470–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Governini L, Luongo FP, Haxhiu A, Piomboni P, Luddi A. Main actors behind the endometrial receptivity and successful implantation. Tissue Cell. 2021;73:101656.

    CAS  PubMed  Google Scholar 

  50. Cheng Y, Xie L, Xu Z, Hao M, Yang B, Shan W, Wang Y, Lv Q, Chen X. NrCAM secreted by endometrial stromal cells enhances the progestin sensitivity of endometrial cancer cells through epigenetic modulation of PRB. Cancer Gene Ther. 2022;29(10):1452–62.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Gao XX, Li XH, Zhang QF, Zhu M, Guo YX, Deng KP, Zhang GM, Wang F. Effects of l-arginine on endometrial microvessel density in nutrient-restricted Hu sheep. Theriogenology. 2018;119:252–8.

    CAS  PubMed  Google Scholar 

  52. Wang B, Guo H, Yu H, Chen Y, Xu H, Zhao G. The role of the transcription factor EGR1 in Cancer. Front Oncol. 2021;11:642547.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Wang Z, Liu J, Yang Q, Ma M. LncRNA MIAT Upregulates NEGR1 by Competing for miR-150-5p as a Competitive Endogenous RNA in SCIRI Rats. International journal of genomics 2022, 2022:2942633.

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Acknowledgements

We sincerely thank all the members of the N Liu’s laboratory who for their technical assistance during the preparation of the study.

Funding

This research was supported by the Natural Science Foundation of Shandong Province (ZR2022QC035; ZR2022QC243), Qingdao Agricultural University Doctoral Start-Up Fund (XJ2023000303), Shandong Province Nataral Science Foundation (ZR2020MC167), Earmarked Fund for Modern China Wool & Cashmere Technology Research System (CARS-39-05), and the Technology System of Modern Agricultural Industry in Shandong Province (SDAIT-10-03).

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Authors and Affiliations

  1. College of Animal Science and Technology, Qingdao Agricultural University, 700 Changcheng Road, Qingdao, 266109, Shandong, China

    Jihong Dong, Xuecheng Jiang, Nan Liu, Hegang Li, Jinshan Zhao, Jianning He & Xiaoxiao Gao

  2. College of Veterinary Medicine, Qingdao Agricultural University, Qingdao, 266109, Shandong, China

    Jihong Dong

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  1. Jihong Dong
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  2. Xuecheng Jiang
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  3. Nan Liu
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  4. Hegang Li
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  5. Jinshan Zhao
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  6. Jianning He
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  7. Xiaoxiao Gao
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Contributions

J. D. performed the experiments, data analysis, and manuscript writing. X. J. performed the experiments. H. L. and J. Z. performed data analysis. J. H. and X. G. conceived, designed, and reviewed the paper. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Jianning He or Xiaoxiao Gao.

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Ethics approval and consent to participate

All experimental protocols and animals in this study were approved by the Guide for the Care and Use of Laboratory Animals (permit no. DKY2021021) prepared by the Ethics Committee of Qingdao Agricultural University. This study was carried out in compliance with the ARRIVE guidelines 2.0 (https://arriveguidelines.org). All methods were performed in accordance with the relevant guidelines and regulations.

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Dong, J., Jiang, X., Liu, N. et al. Identification and analysis of differentially expressed microRNAs in endometrium to explore the regulation of sheep fecundity. BMC Genomics 24, 600 (2023). https://doi.org/10.1186/s12864-023-09681-y

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  • DOI: https://doi.org/10.1186/s12864-023-09681-y

Keywords

BMC Genomics

ISSN: 1471-2164

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