A lncRNA related to sheep fertility and its application

Abstract

The present invention relates to a lncRNA related to sheep fertility and its application. In order to explore effective breeding measures to improve the regularity of sheep lambing, the applicant conducted differential expression analysis on the ovarian lncRNAs of high- and low-fertility ewes, and performed a series of molecular verifications on subsequent targets. The verification results showed that ENSOART00020038447 was closely related to sheep fertility, and knocking out would lead to reduced fertility. The lncRNAs screened in this application are of great value in improving livestock fertility.

Description

Technical Field

[0001] The present invention relates to the fields of molecular biology research and bioinformatics research, and specifically to a lncRNA related to sheep fertility and an application thereof. Background Art

[0002] lncRNA is a group of RNAs with a length greater than 200 nucleotides, lacking a complete open reading frame and having no protein coding function. lncRNAs that bind to miRNAs and prevent them from interacting with their target mRNAs are considered competitive endogenous RNAs (ceRNAs), decoys or sponges. lncRNAs prevent miRNAs from completing their regulatory functions, so lncRNAs that act as sponges are actually positive regulators of mRNA transcripts, leading to increased expression of target mRNAs that would otherwise be inhibited by miRNAs. In animal ovarian tissue, several studies have explored the role of lncRNAs. For example, one study discovered a lncRNA called MSTRG.19869 that acts as a ceRNA by binding to miR-145 and regulating the expression of the target gene MYOCD involved in smooth muscle cell differentiation. Another study discovered several lncRNAs that can act as ceRNAs to regulate the expression of genes related to adipogenesis in sheep. These studies suggest that lncRNAs may play an important role in regulating gene expression in animal ovarian tissues. However, the function of lncRNAs in the ovaries of small-tailed Han sheep remains unclear, especially their role in regulating fertility is largely unknown. Summary of the invention

[0003] In order to improve the economic benefits of breeding, it is particularly important to improve its fertility. In order to explore effective breeding measures to improve the regularity of lambing in sheep, it is of great significance to perform differential expression analysis of lncRNAs in the ovaries of high- and low-fertility ewes. In addition, this study aims to identify lncRNAs and explore their role as ceRNAs in regulating fertility through systematic transcriptome analysis, co-expression networks, functional enrichment, and protein-protein analysis.

[0004] The present invention provides the use of a reagent for detecting the ENSOART00020038447 sequence in preparing a test for livestock fertility. Preferably, the cDNA sequence of ENSOART00020038447 is as shown in SEQ ID NO.1, and the sequence has more than 90% sequence homology with SEQ ID NO.1, and preferably, the sequence has more than 98% or 99% sequence homology with SEQ ID NO.1.

[0005] cDNA sequence of ENSOART00020038447 (3543 bp, SEQ ID NO. 1):

[0006] GAGTCAGTCTCGTCTATAAACGGGCGGTTGTGAGAGAAGAGTGAGTCAGTTCTTTTCATTTTAGACGGTGCTGT

[0007] TGCAGTCAGTCAGAGAGCGTTTGTGTGCCACCGACCTGGAAACGAGTTGTATAAATTGTACCCAGCCCAAATAC

[0008] TGAAATCTAATGAAGTGATATTTTCCCGCTAAAAAGAACTGTTTCCCACCAGTTCAGGTTTAAACATTCATACCACA

[0009] GTGGGAACACATGAATAGAAGAAGAAAATTTCTTCTTGCCTCAGTTCTTGCTCTCCAGAATTCTAGTTTTATATATCC

[0010] TTCATGCAAAAGTGCTTTTCTCGGATAATCCTGGTCTCCAAAAGGTCTAACTGTCCAAAATGTGGCTCTACTGGT

[0011] GAAGCTGAAAATGCCATTTACAGATACAAGCTTTCCTTAAAAGTTGCAGAATCAGACAAATTATTTGGCATTACTGT

[0012] GTTTGGAAGCTGCTTAGATGCATTTTTTGGTCTCACGGCCACTGGTTTGCACAGGTACATTCAGGATCCCAGTGA

[0013] AATTCCAGAAACTCTGGGCAGTGATACAACTCAAAACCTATTAACTAAAGCAGTGGAAACTTGCTTTGTTGGACAA

[0014] AACTTTGTTTTTGGAGTGACGAATTTTGAAAACCAGCATGGAAGTGGTTCAGGTTCAGTAACTTCTCAGAGCAGT

[0015] GCTCAGACCGCAAGAGAGTAGTTAAAGCACTCGTAGCTTGCCAGATCATTCCACCAGACCCAAGTGTTGTAGGCT

[0016] TCACTGTCATTGACTATTTCCGTCGGCTTTTGCAGCTTTCTGCTGTCAGGAAACTTCACTGTGGCTCCCAGACAC

[0017] CTAATAGCCACTTACTTGCTTTAGAAAGTTCAAATAGCGATCTCAGCAACTTATGTGGCCCTGACAGCAGTTCTTGT

[0018] TTTGAGCCCCATGACAAAGATTATTTTCCAGGATTCTGGCAGCTATCACTAGAACTCACTTCTGTTGTTTCACAACT

[0019] AACAGATGATGGTTTTTCAGCTTCAGAACAAAGCAAGGCCATTGGTACTCTTCACCAGAACAGAAAGTGCATCTC

[0020] CTCTGCAGAGGCCACTGGTTCCAATAGCTGCCTTGATACCATTCAGGGTTCGTGGAGCCTTGTTTCACATATGGA

[0021] CAAAAAGAGTTCAGCACAAAAGTTAGATGAAGAACCTGGCCTACAGGCTAATCAGCCAAGTGCAGTTCATAGCAA

[0022] TCATCATGAAATTGGAGTTCCTGACTCTAATTTATTCCCTATGAAAGTACAGGAGCGATTTGAACCAAGGAATACAA

[0023] AATCCTTCCACAGTGCAGTGGAAGTTACATATTCTCAGCATGAGATAACATGTCACCAGAATCAGGAGGTAGATAC

[0024] CCCCACCACGTTTCAGCAGAGATGTATGTGTTATCCATCTTCATCACTCAGACTTGAGGAAATAGCCAGTGGTTCC

[0025] CAGGACTGTGACCTCGAAATCTGGGATGACCTGCCACTCTCTGAAAGCCTAAACAAATTTCTGGCAGCTATCGAA

[0026] AGTAAAATCGCTATAAACCAGACAGATGCCAGTAGCAGGAAATGTTGTCTAGATAATGACATCAGTAAACTACATGT

[0027] GGACCACAGCAGGTTATCATTGACTCCACAAAGAACCCCTGGAGCCTTGCATACACCACATATAGCCTTAAGGTCA

[0028] CCACCAGCAACAGTCAAAGCAAACTCCAGCAGAGATAACTTCCTTTCCAACCATGAAGCAAATCCAAGTCCTAGC

[0029] GTTCGTAAGGAATCACAGCCAGAAAACAGAGCAGAGGCTCTCTCATTTAGTGGTAATAAAAGAGATATTTCTGAAC

[0030] ATTCTCTACCAAATGTTCATCTGTCAGCTCTGTTTCCATCTTCAAAAGGCTCAGGCACAACAGTTGCTCTTAAGTCT

[0031] ACGAGAATTCTACCACATGAGACTGAAATTTCCTGTAAGCAGAATATTTCAGAGGCTGACCGTTCTTGTCTCAGCA

[0032] GCAAATATAATAATGGATGTGGAGAAAAATCACTTTCAGAAATGAGTGAAAAGTTGACAACTTTGCATTCTGGGAG

[0033] GTATAATGTTGTTTCCAACTTTCCCAGCTTAGAAAATAAACAATGCTGTAGGTGGCCAAAGAATCAGGGCAACAGT

[0034] TTGACAATTTCCAGGAAACTCACATATCCTTTAGAAGCTCTTCACAGTACTCCAGACAGAAGTATGAATACATTGAG

[0035] AGCAATGCTTTATGAACACACTGGTAATAACCTAACACAGAACTGTTCTACTGGTCATGAAGGCAATTACAGTGCTT

[0036] CTGTGGATCTTTTTGATGATAGTCCTAAAGAGATGGACATTGTAACAGAAATGACCAAAAGGTCACAGGATATTTTA

[0037] TTACAGTGGGGAAAGTCTTTGGCAGAAAGTCATCATATAGAATCTGATTTTTCACTGAGATCACTTTCTGAAAATTC

[0038] CAGCCAGTCACCACAAAAATTATCCTTGCAAAACACATCTGCCTCTCTCTATCCAAGAACCTGTCCCTCTCCACCT

[0039] CATTTTCAGTCAGATTCAGAATATGATTTTGAATATAGCCAAGACTTTGTTCCATGTTCACAGTCAACTCCAGTTACT

[0040] GGATTCCACCAAAGTAGGATTCATGGGATGAAAGGAGCTTTCCAAAAATTACCTGCTTTTTATTTGGACCTTGATGC

[0041] CAACTATAAAAAAACAAGGATTTCCTCTGCAAATGATGCACAGCAAGCCACCCCTATCTGTCAAAAAAATATAAAGA

[0042] CACCTAGCCAGAAATCCAGAAACCCTACTGTATCTGGTATTACACCACCAGAGATCTCCAACAACCATCCTGTCAC

[0043] TGAATGCCTTGAAACTGATGCTGACGAATGGGTCCCTCCTACCACACAAAGGTGTTTCCTTCAGAAATGCTTGGA

[0044] TTCCGGGCCATGGGTCTAAAGAAATGCCATGCTGCTTATAATTCTCCTGATCAAAATGAGTTACCAAGAAAAAAACT

[0045] GAAAAATATGTAAAGCAAAGAACTGGTAAATGCTTAATTAAGAAGGAGTTAATTTAAAGAATATGCTTACAGCAGCAGT

[0046] TATAACAGAAAACTCCTGACTGTAACAATATAAGGTCAGGCTGGATTTCTAAAGAGTCAGTTTTGGGACGTGGT

[0047] TCTTGTGCGGAAGTCAAATGTTGCCTTCCATTTTCAGAAAATTGGTCACCTTCAGCACCTGAAGCTAAAAGTGCTT

[0048] GGTCTCCTGAATTGTTCTCACAAAATATCACCTAAACCCAATTTCTGAGCTTTTAAAGGTACGTCTCTTTGGAAACT

[0049] CTGCCTCCAATGTCATGGAAAGCAGACTTACCATTTTGAAGACTTGACGCAAATAGAAAAGAGGTGCTATTATGCC

[0050] TATAAACAGGGAAGCCATTGTTTTTCCTAGCGTTTTATCCAGAATGATCTGATTTCAGATAATTTGGCACTTTATCTT

[0051] GTATTGATTGTACTTAAGTAATATTGTTAATTTTGCTTGTTAAAAGTATTTGTTAAATCACACATACATGCAGAAATAAA

[0052] GCTTTGCAGACCAAAACTTAGTCTTTCTA

[0053] Further, the reagent is a reagent for detecting the expression level of ENSOART00020038447 in a sample by sequencing technology, nucleic acid hybridization technology and / or nucleic acid amplification technology. The nucleic acid amplification technology includes polymerase chain reaction, reverse transcription polymerase chain reaction, transcription-mediated amplification, ligase chain reaction, strand displacement amplification and nucleic acid sequence-based amplification.

[0054] Furthermore, the reagent is selected from: a probe that specifically recognizes ENSOART00020038447; or a primer that specifically amplifies ENSOART00020038447.

[0055] Furthermore, the sequence of the probe that specifically recognizes ENSOART00020038447 is shown in SEQ ID NO.2.

[0056] The invention provides a product for detecting the expression level of sample ENSOART00020038447 to detect the fertility of livestock. The product comprises a chip, a test kit and a nucleic acid membrane strip.

[0057] Furthermore, the chip includes an oligonucleotide probe that specifically recognizes ENSOART00020038447; the kit includes primers that specifically amplify ENSOART00020038447, or an oligonucleotide probe that specifically recognizes ENSOART00020038447; and the nucleic acid membrane strip includes an oligonucleotide probe that specifically recognizes ENSOART00020038447.

[0058] Furthermore, the kit further comprises one or more substances selected from the following group: a container, instructions for use, a positive control substance, a negative control substance, a buffer, an auxiliary agent or a solvent.

[0059] The present invention provides application of ENSOART00020038447 in preparing a product for improving livestock fertility.

[0060] Further, the product includes an agent that overexpresses ENSOART00020038447.

[0061] The present invention provides the use of ENSOART00020038447 in constructing a computational model for predicting fertility or screening candidate genes for improving fertility.

[0062] Preferably, the livestock comprises sheep; preferably, the livestock is sheep.

[0063] Instruction Manual

[0064] Figure 1This is a qRT-PCR verification result diagram of lncRNA, miRNA, and mRNA involved in the ceRNA network differentially expressed in ovarian tissues of Small Tail Han sheep and Duolang sheep provided by an embodiment of the present invention;

[0065] Figure 2 1 is a graph of in vitro experimental results provided by an embodiment of the present invention, A is a graph showing the knockdown results of siRNA-lncRNA-OV1 in human ovarian granulosa (KGN) cells; B is a graph showing the results of plasmid overexpression in human ovarian granulosa (KGN) cells; C is a graph showing the differential expression results of CACNG7 gene after siRNA transfection and plasmid overexpression in KGN cells verified by qRT-PCR; D is a graph showing the differential expression results of CCNB1 gene after siRNA transfection and plasmid overexpression in KGN cells verified by qRT-PCR; E is a graph showing the differential expression results of GLI1 gene after siRNA transfection and plasmid overexpression in KGN cells verified by qRT-PCR; F is a graph showing the differential expression results of CDC25C gene after siRNA transfection and plasmid overexpression in KGN cells verified by qRT-PCR; G is a graph showing the differential expression results of POLE gene after siRNA transfection and plasmid overexpression in KGN cells verified by qRT-PCR;

[0066] Figure 3 This is a result diagram of detecting the proliferation of lncRNA-OV1 in KGN cells transfected with an overexpression vector using CCK-8 provided in an embodiment of the present invention, F1-F4, representing different fields of view under a microscope of a control group or an overexpression group;

[0067] Figure 4 This is a graph showing the proliferation results of KGN cells measured using CCK-8 through siRNA transfection and pcDNA-lncRNA-OV1 overexpression vectors provided in an embodiment of the present invention;

[0068] Figure 5 ; is a graph showing the results of cell cycle analysis of lncRNA-OV1 knockdown and overexpression in KGN cells provided in an embodiment of the present invention;

[0069] Figure 6 ; These are the results of siRNA-lncRNAlncRNA-OV1 knockdown and plasmid overexpression in in vivo transgenic mice provided in an embodiment of the present invention; (A) qRT-PCR validation confirmed the expression of CCNB1 in transgenic mice, and (B) the results showed that compared with lncRNA-OV1 knockdown, the number of births of lncRNA-OV1 overexpressing transgenic mice was the largest. DETAILED DESCRIPTION

[0070] The present invention is further described below in conjunction with specific embodiments, which are only used to explain the present invention and are not to be construed as limiting the present invention. It will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and purposes of the present invention, and that the scope of the present invention is defined by the claims and their equivalents. The experimental methods for which specific conditions are not specified in the following embodiments are usually tested under conventional conditions or under conditions recommended by the manufacturer.

[0071] Example 1 Sequencing and candidate screening

[0072] 1 Identification of potential lncRNA candidates

[0073] lnRNAs longer than 200 nucleotides were identified from the own sequencing data. StringTie software was used to combine and summarize the reconstructed transcripts of each sample to generate a transcript set that can represent the sample transcriptome. The four softwares obtained candidate lncRNAs through a rigorous four-step screening method.

[0074] Step 1, the spliced ​​transcripts were compared and analyzed with the reference transcripts (reference_transcripts) to screen for new transcripts of known coding transcripts or known loci. In this step, CuffCompare software was used to compare merged_transcripts with reference_transcripts one by one to determine the positional type of the remaining transcripts. Then, through the screening of candidate lncRNA transcripts, transcripts containing 'i', 'u', 'x', and 'o' were retained and entered the second screening step.

[0075] Step 2, the candidate transcripts obtained from the first step screening with a length ≥ 200 nt, an exon number ≥ 2, and an ORF ≤ 300 bp were selected as primary screening candidates. The lack of protein coding ability was the key condition for judging lncRNA.

[0076] Step 3, CPC prediction analysis is performed on the transcripts obtained in the second step by using software with coding capability to eliminate transcripts.

[0077] Step 4: For species with known lncRNAs, the lncRNA sequences predicted in the above steps were compared with known lncRNAs using Blast software, and the alignment was identified as known lncRNAs, which were fused with known lncRNA sequences for quantitative analysis. For species without known lncRNAs, the lncRNA sequences predicted in step 3 were directly used for quantification. Finally, the newly predicted lncRNAs in sheep tissue samples were obtained, and known lncRNAs were identified from the NCBI and Ensemble databases.

[0078] 2 Expression quantification and differentially expressed lncRNA screening

[0079] The expression level of lncRNA was quantified by the transcripts per million reads (TPM) method, and the gene length and sequencing depth were unified. Therefore, the expression level in different samples is consistent. The expression level between genes can be more intuitive. For biological replicates, the raw counts were statistically analyzed using DESeq2 based on negative binomial distribution to generate differential expression based on p-values.

[0080] 3 Co-expression analysis of non-coding RNA and coding RNA

[0081] First, the negative co-expression regulation pattern between miRNA-lncRNA and miRNAmRNA was calculated by Pearson correlation coefficient of differentially expressed miRNAs, lncRNAs and mRNAs, and the threshold r was greater than or equal to -0.8 and p-value was less than 0.05. The co-regulation pairs with CC ranked in the top 0.05 and 0.1 percentiles and p-value less than 0.01 were calculated. The obtained results were compared and the network was constructed using MetScape. At the same time, the miranda v3.3a program was used to predict the binding between these miRNA-mRNA sequences and lncRNA-miRNA sequences. Next, the miRNA-lncRNA and miRNA-mRNA interactions mentioned in the first step were crossed, and then all interactions with common miRNAs formed the lncRNA-related ceRNA network.

[0082] 4. Analysis of significant ternary relationships and construction of ceRNA / PPI network

[0083] In order to select the ternary relationship of lncRNA-miRNA-mRNA, the ceRNA score of lncRNA-miRNA-mRNA obtained from the above steps was calculated using the MuTaME method. Then, the Pearson correlation coefficient of lncRNA and mRNA expression was calculated, and the mRNA-lncRNA pairs were screened by adjusting the threshold to the absolute value of the correlation coefficient greater than or equal to 0.80 and the p-value less than or equal to 0.05. Finally, the ceRNA score analysis results were crossed with the mRNA and lncRNA co-expression calculation results to finally determine the significant lncRNA-miRNA-mRNA ternary relationship. In addition, Cytoscape v.3.1.1 (https: / / cytoscape.org) was used to construct the ceRNA network (lncRNA-miRNA-mRNA) and witness the functional protein association network of ceRNA target genes by using the string database (https: / / stringdb.org). The PPI network data file was visualized using Cytoscape to identify key target genes.

[0084] Functional enrichment analysis of target mRNAs in the 5ceRNA network

[0085] GO was used for functional enrichment analysis of ceRNA target mRNAs, which were divided into cellular component (CC), molecular function (MF), and biological process (BP). KEGG enrichment analysis was performed to reveal pathways (metabolic and signaling pathways) where target mRNAs were significantly enriched. The calculated P value less than or equal to 0.05 was defined as the significant threshold, and genes were screened and pathways were enriched. Next, the significance of pathway enrichment analysis was corrected by FDR to obtain the corrected P value. The differentially expressed genes were further studied using GO and KEGG databases to study the functions of the genes and identify the pathways in which they were involved. If the p value was less than or equal to 0.05, the enrichment was considered significant.

[0086] The results show:

[0087] lncRNA identification, characterization and expression analysis

[0088] After analysis, 9614, 9657, and 7229 noncoding transcripts were found, and a total of 8,402 lncRNAs were identified, with a total length of 16,698,297 nt, an average length of 1987.42 nt, a minimum length of 137, a maximum length of 39907, and an N50 length of 2891 bp, as shown in Table 1. There were 2,650 lncRNAs with a length greater than 2000 nt and 17 lncRNAs with a length less than 200 nt. The frequency distribution of the GC content of lncRNAs was optimally 43-45%. According to the direction, 2882 antisense lncRNAs and 4198 sense lncRNAs had different types and positions. The number of exons of lncRNA was counted and it was found that most lncRNAs (2758) had 2 exons (maximum 31 exons, average 2.99 exons), which was significantly less than protein-coding genes (maximum 118 exons, average 9.64 exons), followed by 1580, 1495, 682 and 334 lncRNAs with 1, 3, 4, 5 exons, etc. The results showed that 534 lncRNAs were assigned to chromosome 1, while 419 lncRNAs were assigned to sex chromosomes.

[0089] Table 1 Identified lncRNA sequence information

[0090]

[0091] The expression levels of lncRNAs were analyzed according to the FPKM values ​​(Table 2). It was observed that the transcript expression density of the Small Tail Han sheep (X_LC) in the ovarian tissue was higher than that of the Duolang sheep (D_LC). The number of lncRNAs expressed by the samples in different expression intervals showed that the maximum number of lncRNAs in each sample ranged from 0 to 0.5. In addition, there were significant correlation coefficients between the sequenced samples, and the principal component analysis showed that the variance of PC1 was 41.23% and the variance of PC2 was 20.52%, indicating that the spatial distribution of the same group of samples was more concentrated.

[0092] Table 2 Statistics of FPKM values ​​of lncRNA

[0093]

[0094] Differential expression and co-expression analysis of lncRNA, miRNA and mRNA

[0095] Based on p-value less than 0.05 and log 2|FC|>1, analysis of differentially expressed lncRNAs in ovarian tissue showed that there were 253 differentially expressed long noncoding RNAs, 150 of which were upregulated and 103 of which were downregulated. Similarly, 35 differentially expressed miRNAs and 397 differentially expressed mRNAs were obtained by DESeq2.

[0096] In order to obtain lncRNA-miRNA-mRNA interactions, the correlation analysis between miRNA-mRNA and lncRNA-miRNA was determined, which was further used for ceRNA scoring analysis. A total of 2510 miRNA-mRNA pairs were obtained by calculating the negative co-expression regulatory patterns of these differentially expressed miRNAs and mRNAs. According to the action principle of miRNA and mRNA, 903 pairs of miRNA-mRNA relationships were screened, and the target binding sites between these miRNA-mRNA sequences were determined using the default parameters of miranda v3.3a. As a result, 337 pairs of DEmiRNA-DEmRNA interactions were obtained, including 22 DEmiRNAs and 165 DEmRNAs. The results showed that most miRNAs target multiple DEmRNAs, such as oar-miR-22-3p, novel 62_star, novel351_mature, novel14_mature, and novel457_mature targeting 29, 28, 25, 12, and 20 DEmRNAs, respectively. Similarly, 84 lncRNA-miRNA interactions were obtained from the negative co-expression regulation pattern and target gene prediction analysis. Interestingly, ENSOART00020038447 and ENSOART00020034965 lncRNAs contained 5 MREs (miRNA response elements) targeting various miRNAs. In addition, based on the differential expression of lncRNAs and miRNAs, 41 DElncRNA-DEmiRNA interactions were screened for co-expression network construction containing 16 DElncRNAs and 13 DEmiRNAs. We observed that most lncRNAs shared miRNA response elements (MREs) of multiple miRNAs, such as ENSOART00020038447, ENSOART00020018186 shared the upregulated MREs of oar-miR-22-3p, novel novel62_star, and novel14_mature.

[0097] To explore the function of lncRNAs as competing endogenous RNAs (ceRNAs) in X_LC vs D_LC ovarian tissues, ceRNA scores were calculated, resulting in the identification of 736 interacting lncRNA-miRNA-mRNA pairs. We hypothesized that the smaller the p-value of the ceRNA relationship, the more important the miRNA shared between two ceRNAs (mRNA and lncRNA). Then, 24,289 lncRNA-mRNA pairs were identified by co-expression correlation analysis of DElncRNA and DEmRNA with the criteria of r greater than or equal to 0.80 and p-value less than or equal to 0.05. Thus, 16,000 positively correlated regulatory pairs between mRNA-lncRNA were screened. Based on the lncRNA-associated ceRNA interaction pairs, combined with the results of ceRNA score analysis and co-expression computational analysis, 441 significant ternary relationships (lncRNA-miRNA-mRNA) were screened. Based on the differential expression of lncRNAs, miRNAs, and mRNAs, we constructed a ceRNET consisting of 148 ceRNA regulatory pairs, including 16 DE lncRNAs, 13 DEmiRNAs, and 91 DEGs. We observed that most lncRNAs involved in ceRNETs have binding sites for multiple miRNAs, and many lncRNAs share miRNA response elements (MREs) for multiple miRNAs. Similarly, most miRNAs regulate multiple mRNAs. Other lncRNAs, such as ENSOART00020038447, can regulate CCNB1 gene expression by binding to oar-miR-22-3p, while TCONS_00042138 can regulate CACNG7 gene expression by binding to novel43_star. Through PPI network construction, we obtained a high degree of coordination between candidate lncRNA target genes, including CACNG7, CDC25C, POLE, and CCNB1. GO and KEGG enrichment analysis of lncRNA target mRNAs in the network Functional enrichment analysis of target mRNAs involved in the ceRNA regulatory network was performed. Gene ontology (GO) analysis determined that 67, 69, and 76 genes were annotated by biological process (BP), cellular component (CC), and molecular function (MF) among the 126 DEGs. The main GOs included DNA binding, DNA dependence, ATPase activity, spindle microtubules, midbody, cell adhesion, meiotic nuclear division, and receptor activity. KEGG enrichment analysis of ceRNA-regulated genes showed that only 52 genes were annotated to KEGG pathways related to metabolic processes and biological processes, such as glutathione metabolism, antigen processing and presentation, cell adhesion molecules, hedgehog signaling pathway, MAPK signaling pathway, phagosome, progesterone-mediated signaling pathway, oocyte maturation, and phagosome.The following ceRNA target mRNAs CDC25C, CCNB1, PPP2R2B, CACNG7, GLI1, and POLE were significantly involved in pathways affecting reproduction, including cell cycle, oocyte meiosis, progesterone-mediated oocyte maturation, FoxO signaling pathway, p53 signaling pathway, Hippo signaling pathway, sphingolipid signaling pathway, PI3K-Akt signaling pathway, AMPK signaling pathway, tight junction, MAPK signaling pathway, oxytocin signaling pathway, Hedgehog signaling pathway, cAMP signaling pathway, purine metabolism, pyrimidine metabolism, and DNA replication. Furthermore, the results showed that each pathway was regulated by multiple target mRNAs; CLDN11, CD2, CDH2, LOC101110545 were enriched in the cell adhesion process, CACNG7, HSPA1A, CACNA2D3, LOC494436 were involved in the MAPK pathway, CDC25C, CCNB1 were involved in progesterone-mediated oocyte maturation, CLDN11, PPP2R2B were involved in tight junctions, and GGT7, GPX3, LOC101106291 were enriched in glutathione metabolism.

[0098] Example 2 qRT-PCR verification

[0099] QRT-PCR was used to verify the reliability of the sequencing results. Seven differentially expressed lncRNAs, miRNAs, and six mRNAs were randomly selected based on the ceRNA network (lncRNA-miRNA-mRNA), and their enrichment pathways related to reproduction. 0.5 μg of RNA was taken and the cDNA template was synthesized by GeneAmp PCR System 9700 (APPlied Biosystems, USA). qRT-PCR analysis was performed using a LightCycler 480II real-time PCR instrument (Roche, Switzerland). The reaction system included 1 μL cDNA, 5 μL 2×PerfectStart Green qPCR SuperMix, 0.2 μL 10 μM forward primer, 0.2 μL 10 μM reverse primer, and 3.6 μL nuclease-free H2O. The reaction conditions were as follows: 94°C for 30 s, 94°C for 5 s, and 60°C for 30 s for 45 cycles. After the cycle, the specificity of the product was detected by a melting curve: the temperature was slowly increased from 60°C to 97°C. The relative transcript abundance of genes between samples was calculated using the 2-ΔΔCt method. The acquired data were analyzed using GraphPad Prism (V8.0.1). The t-test (p < 0.05) was used for mean comparison. All results are presented in bar graphs with the mean and its standard deviation (± SD).

[0100] Primer sequence information:

[0101] CDC25C-F(SEQ ID NO.11)AAGTGGCCTATCGCTCCC

[0102] CDC25C-R(SEQ ID NO.12)CCCTGGTTAGAATCTTCTCCA

[0103] CCNB1-F(SEQ ID NO.9)AATAAGGCGAAGATCAACATGGC

[0104] CCNB1-R(SEQ ID NO.10)TTTGTTACCAATGTCCCCAAGAG

[0105] CACNG7-F(SEQ ID NO.13)ACAGTGCTACCGCAGAACC

[0106] CACNG7-R(SEQ ID NO.14)CTGCAAAGAAGCAGACTCGC

[0107] GLI1-F(SEQ ID NO.15)AGCGTGAGCCTGAATCTGTG

[0108] GLI1-R(SEQ ID NO.16)CAGCATGTACTGGGCTTTGAA

[0109] POLE-F(SEQ ID NO.17)TTGCGACCAGAAAGGGTTTGT

[0110] POLE-R(SEQ ID NO.18)TGATTTGGCAAGTCCAGATCCT

[0111] The results show:

[0112] To further validate the sequencing data, seven differentially expressed lncRNAs, miRNAs, and mRNAs were selected and their expression levels were detected by RT-qPCR. Figure 1As shown, ENSOART00020036454, ENSOART00020038447, XR_003587321.1, TCONS_00042138, ENSOART00020026697, and ENSOART00020034965 were downregulated, while oar-miR-22-3p, novel351_mature, novel45 7_mature, novel91_mature, novel43_star, novel14_mature, and novel62_star were upregulated in ovaries, X_LC, and X_LC. GLI1, CACNG7, CCNB1, CDC25C, POLE, and PPP2R2B were downregulated in X_LC. These results are consistent with the transcriptome data, demonstrating that lncRNAs regulate the expression of target genes through sponge miRNAs. qRT-PCR validation results of differentially expressed lncRNAs, miRNAs, and mRNAs involved in the ceRNA network in the ovarian tissues of Small-tailed Han sheep and Duolang sheep (X_LC-vs-D_LC).

[0113] Example 3 siRNA transfection and overexpression cell culture

[0114] According to the results of qRT-PCR and KEGG enrichment analysis, ENSOART00020038447 was selected and named “lncRNA-OV1” for further verification. Human ovarian granulosa (KGN) cells were first obtained, and then KGN cells were cultured in Dulbecco's Modified Eagle's Medium (DMEM) (Hyclone) containing 10% fetal bovine serum (Hyclone) and 1% (v / v) penicillin solution (Gibco). The cells were then transferred to a six-well plate and cultured until the cell density reached 50% to 60%. For siRNA transfection, siRNA-238, 1768 and 3239 were designed and synthesized by Beijing Bomade Gene Technology Co., Ltd. Using pcDNA 3.1 plasmid (preferably Invitrogen's plasmid), lncRNA-OV1 was overexpressed in KGN cells. Lipofectamine 3000 (Invitrogen, Karlsruhe, Germany) was used as a transfection reagent. Before transfection, cells were placed at 37°C and 5% CO 2Place the cells in a cell culture incubator for 24 hours. One hour before transfection, replace the culture medium with fresh serum-free medium and place in the incubator. To prepare the transfection solution, add 3 μg of DNA plasmid to 100 μL of dilution solution and mix gently. In addition, add 6 μL of Lipofectamine 3000 to 100 μL of dilution solution and place at room temperature for 5 minutes. Add the diluted Lipofectamine 3000 dropwise to the diluted DNA solution, mix gently, and incubate at room temperature for 20 minutes. Add the resulting transfection solution dropwise to the culture medium and incubate at 37°C, 5% CO 2 After transfection, the cells were incubated at 4 °C for 4 h. After transfection, fresh complete medium was replaced and cultured for 24 h. KGN cell proliferation assay was performed. qRT-PCR, FISH and CCK8 were used to detect changes in the cell cycle by flow cytometry.

[0115] The siRNA sequences are as follows:

[0116] siRNA-238:

[0117] siRNA-238F: AAAUUUUCUUCUUCUAUUCAU (SEQ ID NO.3)

[0118] siRNA-238R:GAAUAGAAGAAGAAAAUUUCU (SEQ ID NO.4)

[0119] siRNA-1768:

[0120] siRNA-1768F:UUUAUUACCACUAAAUGAGAG(SEQ ID NO.5)

[0121] siRNA-1768R: CUCAUUUAGUGGUAAUAAAAG (SEQ ID NO.6)

[0122] siRNA-3239:

[0123] siRNA-3239F: UUUAAAAGCUCAGAAAUUGGG (SEQ ID NO.7)

[0124] siRNA-3239R: CAAUUUCUGAGCUUUUAAAGG (SEQ ID NO.8)

[0125] The results show:

[0126] Based on previous studies (Small Tail Han Sheep and Duolang Sheep) and our functional enrichment analysis qRT-PCR, we selected ENSOART00020038447 named “lncRNA-OV1” to further investigate its role in fertility. pcDNA3.1 vectors for overexpression of lncRNA-OV1 were synthesized as well as small interfering RNA (siRNA) to knock down endogenous lncRNA-OV1 in ovarian granulosa (KGN) cells and cultured in vitro. To knock down the lncRNA, siRNA transfection was used. For this purpose, siRNA-238, 1768, and 3239 were synthesized, and interestingly, siRNA-238 was found to have the highest efficiency, as Figure 2 A, and was therefore used in subsequent experiments. Figure 2 Overexpression of lncRNA-OV1 was also observed in B. We determined the effect of lncRNA-OV1 on the expression levels of the following G1 / S checkpoint proteins, including CCNB1, CDC25C, POLE, GLI1, CACNG7 ( Figure 2 After ENSOART00020038447 overexpression, the expression levels of CDC25C, POLE, and GLI1 in ovarian granulosa (KGN) cells were reduced, but increased after lncRNA-OV1 knockdown (see Figure 2 ). At the same time, the expression level of CCNB1 encoding gene in KGN ovarian cells increased after lncRNA-OV1 overexpression ( Figure 2 D), but the expression level of CCNB1 encoding gene was reduced after lncRNA-OV1 knockdown. In vitro, siRNA-lncRNA-OV1 knockdown and plasmid overexpression in ovarian granulosa (KGN) cells. Figure 2 qRT-PCR validation in C, 2D, 2E, 2F, and 2G confirmed the expression of differentially expressed mRNAs after siRNA transfection and plasmid overexpression in KGN cells. lncRNA-OV1 knockdown downregulated CCNBI expression. Overexpression increased CCNBI expression in KGN cells. The subcellular localization of lncRNA-OV1 in KGN cells was analyzed by nuclear and cytoplasmic RNA isolation and FISH analysis. The results showed that lncRNA-OV1 was mainly located in the cells, and some lncRNAs were detected in the cytoplasm ( Figure 3). These findings suggest that the overexpression of lncRNA-OV1 in ovarian granulosa cells, whereas the lncRNA was not detected in the control group, may contribute to reproduction. CCK-8 was used to detect the proliferation of lncRNA-OV1 in KGN cells transfected with the overexpression vector. lncRNA-OV1 expression was determined by FISH probe with Cy3 (Fish probe: ACCTGAACTGGTGGGAAACA (SEQ ID NO.2)), F1-F4, representing different fields of view under the microscope of the control group or overexpression group. CCK-8 detection showed that lncRNA-OV1 knockdown significantly reduced the proliferation of KGN cells compared with the overexpression group and the control group ( Figure 4 ). These findings highlight that ENSOART00020038447 knockdown reduces fecundity. The proliferation of KGN cells was measured by siRNA transfection and pcDNA-lncRNA-OV1 overexpression vector using CCK-8. The results of this study showed the role of lncRNA-OV1 in promoting proliferation and cell cycle progression. The results showed that lncRNA-OV1 knockdown suppressed the mRNA and protein expression of CCNB1 ( Figure 6 A). In vivo experimental results showed that the overexpression transgenic mice had more litters than the control group, while the knockdown transgenic mice had fewer litters, e.g. Figure 6 B. These results suggest that lncRNA-OV1 can affect fertility and can be considered a useful biomarker for fertility. After knockdown and overexpression of lncRNA-OV1 in KGN cells, the S phase population was significantly increased. Figure 5 .

[0127] Although the present invention has been described with reference to various preferred embodiments, it is understood by those skilled in the art that various changes may be made, and equivalents may be substituted for its components without departing from the basic scope of the present invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present invention without departing from its basic scope.

[0128] Therefore, it is intended that the invention be limited to the particular embodiments disclosed herein for carrying out this invention; on the contrary, the invention is intended to include all embodiments falling within the scope of the claims.

Claims

1. Use of a reagent for detecting the expression level of lncRNA ENSOART00020038447 sequence in the preparation of a product for detecting sheep fertility, characterized in that: The cDNA sequence of ENSOART00020038447 is SEQ ID NO.

1.

2. The use according to claim 1, characterized in that: The reagents include reagents for detecting the expression level of ENSOART00020038447 in a sample by sequencing technology, nucleic acid hybridization technology, and nucleic acid amplification technology.

3. The use according to claim 1, characterized in that: The reagent is selected from: a probe that specifically recognizes ENSOART00020038447; Or primers that specifically amplify ENSOART00020038447.

4. The use according to claim 1, characterized in that: The products include chips, test kits, and nucleic acid membrane strips.

5. The use according to claim 4, characterized in that: The chip comprises an oligonucleotide probe that specifically recognizes ENSOART00020038447; the kit comprises a primer that specifically amplifies ENSOART00020038447, or an oligonucleotide probe that specifically recognizes ENSOART00020038447; and the nucleic acid membrane strip comprises an oligonucleotide probe that specifically recognizes ENSOART00020038447.

6. The use according to claim 4, characterized in that: The kit further comprises one or more substances selected from the following group: a container, instructions for use, a positive control substance, a negative control substance, a buffer, an auxiliary agent or a solvent.

7. Use of a reagent for overexpressing lncRNA ENSOART00020038447 in the preparation of a product for improving sheep fertility, wherein the cDNA sequence of ENSOART00020038447 is SEQ ID NO.

1.

8. The use according to claim 7, characterized in that: The overexpression method comprises constructing an overexpression vector containing the ENSOART00020038447 sequence.

9. The use according to claim 8, characterized in that: The vector is a pcDNA plasmid.

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