Boar sperm motility miRNA markers and uses thereof

Abstract

The application discloses six boar sperm motility miRNA markers, which are ssc-miR-486, ssc-miR-10386, ssc-miR-708-5p, ssc-miR-122-5p, ssc-miR-199a-3p and ssc-miR-31, wherein the ssc-miR-486, ssc-miR-10386, ssc-miR-708-5p, ssc-miR-122-5p and ssc-miR-199a-3p are highly expressed in low-motility semen sperm plasma exosomes and lowly expressed in high-motility semen sperm plasma exosomes, and the ssc-miR-31 is lowly expressed in low-motility semen sperm plasma exosomes and highly expressed in high-motility semen sperm plasma exosomes. The application has good prospects for identifying boar sperm motility, and the disclosed miRNA markers have accuracy and reliability.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biotechnology, and particularly relates to a boar sperm motility miRNA marker and application thereof. BACKGROUND

[0002] Boar sperm motility is an index of sperm movement parameters, and sperm gradually acquires the ability to swim forward through continuous development in epididymis, prostate and other accessory glands. Sperm plasma exosomes are derived from the male reproductive tract and are an important part of the sperm environment. They can transport proteins, miRNAs and other molecules to sperm for material exchange and information regulation, perfecting the structure and function of sperm and playing an important role in regulating sperm motility. Sperm plasma exosomes contain a large number of miRNAs. Studies have found that semen with different motilities has different miRNA expressions in some species, and abnormal sperm motility may be due to the influence of miRNA expression disorders during development, leading to incomplete sperm development.

[0003] miRNAs can silence mRNAs by forming complexes with AGO family proteins, regulate mRNA stability and protein synthesis, and ultimately affect the basic parameters of sperm and various aspects of the fertilization process. The functional state of the source cell of the secreted exosome can be reflected by the types and contents of miRNAs in the secreted exosomes, so miRNAs in sperm plasma exosomes have a very broad development space in becoming a non-invasive marker of sperm motility and predicting boar fertility.

[0004] Although there have been some research results on sperm plasma exosomes in general, the research on pigs in this species is still relatively scarce. Some studies have identified and analyzed miRNAs in pig sperm plasma exosomes ([1] Wu Z, Chen H, Liu J, et al. Identification and functional analysis of miRNAs in long white pig sperm plasma exosomes. Journal of Agricultural Biotechnology, 2021.), and found that some high-abundance miRNAs have potential regulatory effects on sperm function, but they did not reveal the expression differences of different miRNAs in sperm plasma exosomes of different motility pig semen, so there is still a blank in the research on the expression differences of different miRNAs in sperm plasma exosomes of different motility pig semen. SUMMARY

[0005] Therefore, the application provides a boar sperm motility miRNA marker and application thereof, which has reliability and accuracy in identifying pig sperm motility.

[0006] The first aspect of the present application provides boar sperm motility miRNA markers, characterized by comprising: ssc-miR-486, ssc-miR-10386, ssc-miR-708-5p, ssc-miR-122-5p, ssc-miR-199a-3p and ssc-miR-31.

[0007] In some embodiments, the ssc-miR-486, ssc-miR-10386, ssc-miR-708-5p, ssc-miR-122-5p and ssc-miR-199a-3p are highly expressed in low motility boar semen sperm plasma exosomes and lowly expressed in high motility boar semen sperm plasma exosomes, and the ssc-miR-31 is lowly expressed in low motility boar semen sperm plasma exosomes and highly expressed in high motility boar semen sperm plasma exosomes, wherein the high expression refers to the expression amount in low motility boar semen sperm plasma exosomes / high expression amount in high motility boar semen sperm plasma exosomes>1.5, and the low expression refers to the expression amount in low motility boar semen sperm plasma exosomes / high expression amount in high motility boar semen sperm plasma exosomes<0.85.

[0008] The present application provides five miRNAs that are highly expressed in low motility boar semen sperm plasma exosomes and lowly expressed in high motility boar semen sperm plasma exosomes, and one miRNA that is lowly expressed in low motility boar semen sperm plasma exosomes and highly expressed in high motility boar semen sperm plasma exosomes, so that the accuracy of boar sperm motility identification is improved and the probability of false positives is reduced by using different miRNAs as markers and including miRNA markers with different expression levels.

[0009] The second aspect of the present application claims the use of the above-mentioned markers in the preparation of a kit for identifying boar sperm motility.

[0010] Further, the nucleotide sequence of the ssc-miR-486 is shown in SEQ ID NO. 1, the nucleotide sequence of the ssc-miR-10386 is shown in SEQ ID NO. 2, the nucleotide sequence of the ssc-miR-708-5p is shown in SEQ ID NO. 3, the nucleotide sequence of the ssc-miR-122-5p is shown in SEQ ID NO. 4, the nucleotide sequence of the ssc-miR-199a-3p is shown in SEQ ID NO. 5, and the nucleotide sequence of the ssc-miR-31 is shown in SEQ ID NO. 6.

[0011] The third aspect of the present application provides primers for detecting the expression amount of the miRNA markers in boar sperm plasma exosomes, characterized by comprising:

[0012] an upstream primer of ssc-miR-486, the nucleotide sequence of which is shown as SEQ ID NO. 7;

[0013] an upstream primer of ssc-miR-10386, the nucleotide sequence of which is shown as SEQ ID NO. 8;

[0014] an upstream primer of ssc-miR-708-5p, the nucleotide sequence of which is shown as SEQ ID NO. 9;

[0015] an upstream primer of ssc-miR-122-5p, the nucleotide sequence of which is shown as SEQ ID NO. 10;

[0016] an upstream primer of ssc-miR-199a-3p, the nucleotide sequence of which is shown as SEQ ID NO. 11.

[0017] Further, an upstream primer of ssc-miR-31 is further included, the nucleotide sequence of which is shown as SEQ ID NO. 12.

[0018] In a fourth aspect of the present application, the above-mentioned primers are claimed for use in preparing a kit for identifying the sperm activity of a boar.

[0019] In a fifth aspect of the present application, a kit for identifying the sperm activity of a boar is provided, which contains the above-mentioned primers.

[0020] Compared with the prior art, the present application has the following advantages:

[0021] 1. The six miRNA markers of the present application are differentially expressed in low-activity and high-activity boar semen, including ssc-miR-486, ssc-miR-10386, ssc-miR-708-5p, ssc-miR-122-5p, ssc-miR-199a-3p which are up-regulated in low-activity boar semen, and ssc-miR-31 which is down-regulated in low-activity boar semen, thereby improving the reliability and accuracy of the identification of boar semen quality.

[0022] 2. Real-time fluorescent quantitative PCR of boar seminal plasma exosomes shows that the six differentially expressed miRNA markers have certain fold differences, indicating that the six differentially expressed miRNAs are excellent indicators for identifying boar sperm activity, and have good prospects for use in identifying boar sperm activity. The present application can be used for screening boar semen and breeding boars. BRIEF DESCRIPTION OF DRAWINGS

[0023] Figure 1 Figure 2 is a diagram of the activity difference between the sequencing sample groups;

[0024] Figure 2 This is a distribution map of sequence lengths in sequencing data.

[0025] Figure 3 Figure 1 shows the results of miRNA identification for each group;

[0026] Figure 4 This is a probability distribution map of miRNA expression levels.

[0027] Figure 5 Volcano plot for differentially expressed miRNAs;

[0028] Figure 6 Heatmap of known miRNAs upregulated in the low-activity group;

[0029] Figure 7 Heatmap of known miRNAs downregulated in the low activity group;

[0030] Figure 8 The graph shows the GO analysis results of the top 20 genes with the highest enrichment in biological processes.

[0031] Figure 9 The graph shows the GO analysis results of the top 20 gene-enriched items in cellular components.

[0032] Figure 10 The graph shows the GO analysis results of the top 20 gene enrichment items in molecular function;

[0033] Figure 11 Figure showing the results of KEGG enrichment analysis of differentially expressed miRNA target genes;

[0034] Figure 12 The image shows the validation results for differentially expressed miRNAs. Detailed Implementation

[0035] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Preferred embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a thorough and complete understanding of the disclosure of the invention.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention.

[0037] In this invention, terms such as "first aspect," "second aspect," "third aspect," and "fourth aspect" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or quantity, nor should they be construed as implicitly indicating the importance or quantity of the indicated technical features. Moreover, terms such as "first," "second," "third," and "fourth" serve only as a non-exhaustive enumeration and should be understood not to constitute a closed limitation on quantity.

[0038] Unless otherwise specified, the percentage content mentioned in this invention refers to mass percentage for solid-liquid mixtures and solid-phase-solid mixtures, and volume percentage for liquid-phase-liquid mixtures. Unless otherwise specified, the percentage concentration mentioned in this invention refers to the final concentration. The final concentration refers to the proportion of the added component in the system after its addition. Unless otherwise specified, the temperature parameters in this invention allow for both isothermal treatment and treatment within a certain temperature range. Isothermal treatment allows temperature fluctuations within the precision range controlled by the instrument.

[0039] The experimental instruments and reagents used in the following embodiments of the present invention are as follows:

[0040] The main instruments used in the experiment were as follows: ultracentrifuge (BeckMan, USA); refrigerated centrifuge (Eppendorf, Germany); 17℃ incubator (Zhu Xianzi, China); 2100 bioanalytical system (Agilent, USA); QuanStudio 3 real-time quantitative PCR instrument (Thermo Scientific, USA); PCR instrument (BioRad, USA); CASA (Hamilton Thorne, USA).

[0041] The main reagents and consumables used in the experiment are as follows: RNA extraction kit, Qiagen, USA; cDNA synthesis kit, Tiangen Biotech, China; miRNA quantitative PCR kit, Tiangen Biotech, China; primers, Sangon Biotech, China; RNA Nano6000 Assay Kit, Agilent, USA; QIAseq miRNA Library Kit, Qiagen, USA; TruSeq PE-Cluster Kit v3-cBot-HS, Illumina, USA; 0.45µm filter, Millipore, USA; 0.22µm filter, Millipore, USA; PBS solution, Solarbio, China; ultrafiltration tubes, BeckMan, USA.

[0042] Example 1: Detection and screening of differentially expressed miRNAs in seminal plasma exosomes with different sperm motility

[0043] 1.1 Collection of seminal plasma samples

[0044] Six 15-25 month old Landrace boars were provided by a boar station in northern China. All boars were fed and managed according to the standard program established by the boar station. Semen was collected using the hand-holding method. After collection, semen samples underwent CASA testing. Semen was collected from Landrace boars whose sperm motility was consistently above 90% over the past three months, with the sperm motility at the time of collection required to be above 90%. These were designated as the High Motility Group (three semen samples numbered HM1, HM2, and HM3). Semen was collected from Landrace boars whose sperm motility was below 80% five or more times in the past three months, with the sperm motility at the time of collection required to be below 80%. These were designated as the Low Motility Group (three semen samples numbered LM1, LM2, and LM3).

[0045] The sperm motility of Landrace boars used for isolation and sequencing was tested using a computer-aided analysis system (CASA) (Table 1). The abnormality rate was less than 20%, meeting the national standards for the quality of boar semen at room temperature. Independent t-test analysis of the motility test results showed a highly statistically significant difference in sperm motility between the two groups (P < 0.001). Figure 1 As shown.

[0046] Table 1. CASA test results of semen samples

[0047]

[0048] 1.2 Separation of seminal plasma exosomes

[0049] Exosomes from seminal plasma were separated and extracted using ultracentrifugation.

[0050] (1) Thaw the seminal plasma at 37°C;

[0051] (2) At 4℃, centrifuge at 3000g for 30min. At this time, the precipitate is cell fragments. Keep the pure supernatant in a new tube for the next step.

[0052] (3) Centrifuge at 12000g, 4℃ for 70min to separate extracellular macrovesicles and retain the supernatant;

[0053] (4) The obtained sample solution was filtered sequentially using 0.45μm and 0.22μm pinhole filters;

[0054] (5) Centrifuge the filtered liquid at 4°C and 100,000g for 80 minutes using an ultra-high speed centrifuge;

[0055] (6) After removing the supernatant, add PBS and repeatedly pipette to re-dissolve the exosomes. At this time, the white flocculent substance that can be observed with the naked eye is the exosome.

[0056] (7) The liquid obtained by redissolution was centrifuged again at 4°C and 100,000g for 80 min using an ultra-high speed centrifuge to purify the exosomes;

[0057] (8) Redissolve the exosomes in 400 μL PBS solution and store them at -80°C to ensure their activity.

[0058] 1.3 Total RNA extraction from exosomes

[0059] Following the instructions, total RNA was extracted from seminal plasma exosomes using the miRNeasy Serum / Plasma Advanced Kit. A portion of the RNA was then processed using the RNA Nano 6000 Assay Kit (Agilent). The concentration and purity of the total RNA were then measured using an Agilent 2100 bioanalyzer system. The detailed extraction steps for total RNA are as follows:

[0060] (1) Add 700 μl of QIAzol lysis buffer to 100 μl of sample and vortex mix.

[0061] (2) Homogenize for 5 minutes at 15-25℃.

[0062] (3) After the above treatment, add 140 μl of chloroform to each tube of sample, tighten the tube cap, and shake vigorously for 15 seconds.

[0063] (4) Incubate at room temperature for 5-10 minutes;

[0064] (5) Centrifuge at 12000g for 15 minutes at 4℃;

[0065] (6) Transfer the supernatant obtained by centrifugation to a new tube, avoiding the lower layer of liquid when aspirating, and then mix it with 1.5 times the volume of anhydrous ethanol.

[0066] (7) Take a 700 μl sample and place it in an RNeasy Mini column and a collection tube. Centrifuge at 8000g for 15 seconds at room temperature. Discard the liquid in the tube;

[0067] (8) Repeat step 7 to collect the remaining samples;

[0068] (9) Take 700 μl of Buffer RWT into the column, centrifuge at 8000g for 15 seconds, and discard the excess filtered liquid;

[0069] (10) Take 500 μl of Buffer RPF into the column, centrifuge at 8000g for 15 seconds, and discard the excess filter liquid;

[0070] (11) Take 500 μl of Buffer RPE into the column, centrifuge at 8000g for 15 seconds, and discard the excess filtered liquid;

[0071] (12) Place the RNeasy Mini column into a brand new collection tube and adjust the centrifuge to 12000g and run for 2 minutes;

[0072] (13) Take the RNeasy Mini column and place it into a brand new EP tube. Open the tube cap and let it stand at room temperature for 2 minutes.

[0073] (14) Add 35ul of RNA-free water to the RNeasy Mini column, cover it and let it stand at room temperature for 2 minutes;

[0074] (15) Centrifuge at 12000g for 2 minutes at 4℃;

[0075] (16) Add the centrifuged liquid center to the RNeasy Mini column, cover the chamber and let stand at room temperature for 2 minutes;

[0076] (17) Repeat step 15;

[0077] (18) Discard the centrifuge column and store the sample in a -80°C refrigerator.

[0078] 1.4 miRNA library construction

[0079] Following the instrument and kit instructions, 1-500 ng of total RNA was input from a single sequencing sample to construct the miRNA library for this sequencing. The QIAseq miRNA Library Kit (Qiagen) was used to generate the sequencing library. An additional index code was added to the attribute sequence of each sequencing sample. The quality of the small RNA library construction was finally evaluated using a Bioanalyzer 2100 (Agilent) and qPCR. Following the instrument manufacturer's instructions, the TruSeq PE-Cluster Kitv3-cBot-HS (Illumina) was used to cluster the samples by adding additional index codes. Based on the clustering results, the small RNA library was sequenced using the Illumina Hiseq platform, generating paired reads.

[0080] 1.5 Sequencing Data Processing and Analysis

[0081] The high-throughput sequencing raw images obtained by the sequencing platform are transformed into raw sequencing reads by base identification and finally saved as data files in FASTQ format. Sequence information and sequence-related quality information are stored in these files. The presence of some adapter sequences and low-quality sequences can affect the sequencing results. Therefore, it is necessary to remove relevant sequences from the raw sequences to ensure the accuracy of subsequent sequencing analysis and obtain high-quality sequences (i.e., Clean Reads) that can be used for identification and expression analysis. The following sequence quality control scheme is implemented: (1) Remove sequences with substandard quality values ​​from each sequencing sample; (2) Remove sequences when the proportion of unidentifiable base N in the sequencing sequence is ≥10%; (3) Remove sequences when there is no 3' adapter at the end of the sequencing sequence; (4) Cut off the 3' adapter sequence; (5) Remove sequencing sequences with a length <15nt and >35nt.

[0082] The Q30 of Clean Reads was calculated as the quality control standard. All subsequent analyses were based on high-quality sequences processed according to the quality control protocol. Bowtie is widely used in genetic material sequencing analysis. It can efficiently align short sequences to databases or genomes. Using Bowtie software, the high-quality sequences (CleanReads) obtained above were aligned with the Silva database (https: / / www.arb-silva.de / ) for obtaining filtered ribosomal RNA (rRNA), the GtRNAdb database (http: / / gtrnadb.ucsc.edu / ) for obtaining transfer RNA (tRNA), the Rfam database (http: / / rfam.xfam.org / ) for obtaining non-coding RNA, and the Repbase database (http: / / www.girinst.org / server / RepBase / index.php) for obtaining repetitive sequences. The sequences that did not align were called unannotated reads, which contained the required miRNA sequence information.

[0083] In this invention, three replicates were used for both the high-activity and low-activity groups, resulting in a total of 66,062,578 and 72,832,844 raw reads, respectively. After removing low-quality sequences, adapter sequences (containing 'N' reads), and sequencing sequences shorter than 15-35 nucleotides, the final number of clean reads used for further analysis was 47,795,289 and 28,008,495, respectively. The detailed results are shown in Table 2. The table shows that all samples contained no low-quality sequences, and after data filtering, the Q30 (error rate of 0.1%) was as low as 96.96%, indicating that the data could be used for further analysis. Sequence alignment was performed using Bowtie, and sRNAs were annotated to rRNA, tRNA, snRNA, snoRNA, Repbase, and unannotated reads containing miRNAs. The sRNA classification and annotation for both groups are shown in Table 3. Unannotated reads were used for subsequent miRNA identification and analysis. In the two groups, unannotated reads accounted for 76.37% and 64.62% of the filtered data, respectively.

[0084] Table 2 Raw Data Filtering and Quality Control

[0085]

[0086] Table 3 sRNA Classification Annotations

[0087]

[0088] 1.6 miRNA Identification

[0089] The unannotated sequences obtained above were aligned with the pig reference genome (Sus scrofa 11.1) using Bowtie software to obtain the location information (Mapped Reads) of the sequencing sequences in the pig reference genome. Mapped reads were then compared with mature miRNA sequences in the miRBase (v22) database. If a Mapped read matched a known miRNA sequence in the database, it was identified as a known miRNA. Based on the characteristics of miRNA processing, sequences that did not align successfully with the miRBase (v22) database were used to predict unknown miRNAs using miRDeep2 software. Library construction was performed using QsRNA-seq. UMI technology was employed during the sequencing library construction process to reduce errors in the polymerase chain reaction, resulting in more accurate miRNA expression levels. The TPM algorithm was used for normalization of miRNA expression levels, with the formula: TPM = Read count / Mapped read * 1000000. In this equation, Read count represents the number of sequences identified as a specific miRNA, and Mapped Reads represents the total number of sequences identified as miRNAs.

[0090] After comparing unannotated reads with pig genome data, the sequence length distribution of the sequencing data was analyzed as follows: Figure 2 It can be observed that most of the identified miRNAs in both groups are concentrated in the 21-23 nt range, accounting for 66.58% and 70.80% of the high-activity group and low-activity group, respectively. The peak value occurred at the typical miRNA length of 22 nt, accounting for 35.73% and 38.12% in the high-activity group and low-activity group, respectively.

[0091] Unannotated reads were compared with miRBase (v22) to identify known miRNAs, and miRDeep2 was used to predict unknown miRNAs. The identified known miRNAs and predicted unknown miRNAs in each sample are shown in Table 4, and their distribution in each group is as follows: Figure 3 As shown in the figure, the two sequencing datasets identified a total of 338 known miRNAs and predicted 508 novel miRNAs. The high-activity group contained 20 known miRNAs and 94 novel miRNAs expressed individually. The low-activity group contained 13 known miRNAs and 47 unknown miRNAs expressed individually.

[0092] Table 4. Results of miRNA identification in sequencing samples

[0093]

[0094] The expression levels of all identified miRNAs were normalized, and the TMP algorithm was used for calculation. After processing, the overall miRNA expression pattern of each sequencing sample was analyzed, and the results are as follows: Figure 4 As shown in the figure, the peak expression probability of both groups of samples occurred within the TPM range of 0-10. The peak values ​​of the three samples in the low-activity group were generally shifted to the right compared to the high-activity group, indicating that the miRNAs represented near the peak values ​​had higher expression levels in the low-activity samples.

[0095] 1.7 miRNA Expression Analysis

[0096] The expression levels and differential expression of miRNAs identified in the high-activity and low-activity groups were analyzed using the R software edgeR. Differential miRNAs were detected based on fold change (FC) and significance. p value( p The screening was conducted using two aspects: fold difference (the ratio of expression levels between sequencing sample groups) and Significance. p The value represents the probability that there is no difference in the expression levels of the components. When the values ​​of these two conditions satisfy FC > 1.5, p -Values ​​< 0.05 indicate differentially expressed miRNAs. Furthermore, to obtain more biologically meaningful analytical results, low-expression miRNAs were removed during expression analysis, leaving only miRNAs with an average expression level higher than 1 for further expression analysis.

[0097] Based on the identified miRNA expression levels, differential expression analysis was performed on exosomal miRNAs in the high-activity and low-activity groups. A fold change in expression >1.5 and a statistically significant difference (P < 0.05) were used as the criteria for screening differentially expressed miRNAs between the groups. After screening, a total of 49 significantly differentially expressed miRNAs were identified. Compared with the high-activity group, the low-activity group contained 38 highly expressed miRNAs and 11 low-expressed miRNAs, such as... Figure 5As shown. Among the 49 differentially expressed miRNAs, 17 were known miRNAs and 32 were unknown miRNAs. Cluster analysis of the known differentially expressed miRNAs based on TPM expression levels revealed that ssc-miR-10a-3p, ssc-miR-139-5p, ssc-miR-148b-5p, ssc-miR-31, ssc-miR-345-3p, ssc-miR-362, and ssc-miR-500-5p were highly expressed in the high-activity group, while ssc-miR-143-3p was not highly expressed. The following proteins were highly expressed in the low-activity group: ssc-miR-10386, ssc-miR-122-5p, ssc-miR-142-5p, ssc-miR-199a-3p, ssc-miR-199b-3p, ssc-miR-223, ssc-miR-451, ssc-miR-486, and ssc-miR-708-5p. The results are as follows: Figure 6 and Figure 7 As shown.

[0098] 1.8 Target gene prediction and pathway enrichment analysis

[0099] Based on the identified differentially expressed miRNAs, target genes of miRNAs were predicted using the TargetScan and miRDB databases, with the 3'UTR as the target sequence. Gene ontology (GO) databases are commonly used in gene research, helping to understand the enrichment of target genes in cellular components, biological processes, and molecular functions, such as their location of action and function. The Kyoto Encyclopedia of Genes and Genomes (KEGG) is a database resource for analyzing gene enrichment in pathways, facilitating the linking of genes to form pathway networks for holistic analysis. To further understand the relationships between target genes, this invention utilizes GO and KEGG pathway enrichment analysis to uncover their biological functions in the organism.

[0100] Based on the principle of miRNA base complementarity, two target gene prediction software programs, TargetScan and miRDB, were used to predict the target genes of 17 known differentially expressed miRNAs with 3'UTR as the target sequence. A total of 313 target genes were obtained from the two sequencing samples. To explore the location and mechanism of these genes' regulatory functions in vivo, this invention conducted further enrichment analysis using the GO and KEGG databases. The results are as follows: Figure 8 As shown.

[0101] like Figures 8-10As shown, GO analysis mainly includes three parts: cellular component (CC), molecular function (MF), and biological process (BP). Cellular component analysis results show that target genes primarily act in the nucleus, cytoplasm, and cytosol. ATP binding was the most enriched target gene item in molecular function, followed by RNA polymerase II binding to sequence-specific DNA, transcriptional activation activity, and DNA binding. In biological process, the most enriched genes were, in descending order, RNA polymerase II transcriptional regulation, positive regulation of gene expression, and intracellular signal transduction. Among the top 20 most enriched genes, four were related to protein phosphorylation.

[0102] like Figure 11 As shown, KEGG pathway enrichment analysis was performed. The color of the bubbles represents the significance of the enrichment analysis, and the volume of the bubbles represents the number of genes found in the pathway after enrichment analysis. The results showed that the five pathways with the highest significance were, in order, RNA polymerase II transcription activator activity, ATP binding, RNA polymerase II specific binding to DNA, tyrosine phosphatase activity, and insulin receptor binding. Among the top 20 pathways with the most significant enrichment, eight were related to RNA polymerase and transcription activities.

[0103] Example 2: Primer Design and qPCR Reaction

[0104] This invention selects six differentially expressed miRNAs from the above analysis results for qPCR experiments to verify the accuracy of miRNA sequencing analysis, and then screens out miRNA biomarkers that can be used to identify boar semen quality.

[0105] 2.1 Total RNA Extraction

[0106] This step is the same as step 1.3.

[0107] 2.2 Preparation of the reverse transcription system

[0108] The experiment used the miRcute Enhanced miRNA cDNA First-Strand Synthesis Kit (TIANGEN). This kit utilizes the tailing method for reverse transcription of cDNA. The total volume of the reverse transcription system was 20 μL, as shown in Table 5. The reaction conditions were as follows: 60 minutes at 42°C for adding PolyA to the miRNA tail and then reverse transcription to form cDNA; and 3 minutes at 95°C for inactivating PolyA polymerase and reverse transcriptase. The resulting cDNA was stored at -20°C.

[0109] Table 5 Reverse Transcription System

[0110]

[0111] 2.3 Primer Design

[0112] miRNA sequences were obtained from the miRBase database (https: / / www.mirbase.org / ). The U in the sequence was replaced with T. The upstream primers for miRNA were designed using the tailing method. To ensure compatibility with the subsequent kit, the annealing temperature Tm of the upstream primers must be around 65℃. The upstream primers were synthesized by Shanghai Sangon Biotech. The specific sequences are shown in Table 6 below.

[0113] Table 6. Upstream Primer Design

[0114]

[0115] 2.4 qPCR reaction system

[0116] The selected miRNAs were subjected to qPCR reactions using the miRcute enhanced miRNA quantitative PCR kit (TIANGEN), and quantification was performed using the QuantStudio3 real-time quantitative PCR system. The reaction system preparation is shown in Table 7 below. The reaction program was set as follows: initial template denaturation at 95℃ for 15 minutes, 1 cycle; template denaturation during PCR cycles: template denaturation at 94℃ for 20 seconds; annealing and extension at 60℃ for 34 seconds; the latter two parts totaled 40-45 cycles.

[0117] Table 7 qPCR reaction system

[0118]

[0119] 2.5 Data Processing and Analysis

[0120] Using U6 as an endogenous control, the fold change of each miRNA between the two groups was analyzed using the ΔΔCt method as a measure of relative expression level. Finally, the relative expression results were compared with miRNA-seq data.

[0121] To assess the accuracy of the miRNA expression analysis results in section 1.7, this invention selected six differentially expressed miRNAs from the above analysis results for qPCR experiments, including five upregulated miRNAs and one downregulated miRNA. The detection results were analyzed and expressed as fold change, representing the ratio of expression levels in exosomes of low-motility boars to those in exosomes of high-motility boars. This ratio was then compared with the miRNA sequencing results. Figure 9As shown in the figure. The results showed that the expression changes of the six differentially expressed miRNAs were synchronous with those of the sequencing results, indicating the accuracy of miRNA sequencing analysis. Among them, the fold change of the five upregulated miRNAs was >1.5, and the fold change of the one downregulated miRNA was <0.85.

[0122] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A boar sperm motility miRNA marker characterized in that, consisting of ssc-miR-486, ssc-miR-10386, ssc-miR-708-5p, ssc-miR-122-5p, ssc-miR-199a-3p and ssc-miR-31; the nucleotide sequence of the ssc-miR-486 is shown as SEQ ID NO. 1, the nucleotide sequence of the ssc-miR-10386 is shown as SEQ ID NO. 2, the nucleotide sequence of the ssc-miR-708-5p is shown as SEQ ID NO. 3, the nucleotide sequence of the ssc-miR-122-5p is shown as SEQ ID NO. 4, the nucleotide sequence of the ssc-miR-199a-3p is shown as SEQ ID NO. 5, and the nucleotide sequence of the ssc-miR-31 is shown as SEQ ID NO.

6.

2. Primers for detecting the expression of the miRNA markers of claim 1 in boar seminal plasma exosomes, characterized by, the primer comprises an upstream primer, and the upstream primer is: the upstream primer of ssc-miR-486, and the nucleotide sequence thereof is shown as SEQ ID NO. 7; the upstream primer of ssc-miR-10386, and the nucleotide sequence thereof is shown as SEQ ID NO. 8; the upstream primer of ssc-miR-708-5p, and the nucleotide sequence thereof is shown as SEQ ID NO. 9; the upstream primer of ssc-miR-122-5p, and the nucleotide sequence thereof is shown as SEQ ID NO. 10; the upstream primer of ssc-miR-199a-3p, and the nucleotide sequence thereof is shown as SEQ ID NO. 11; and the upstream primer of ssc-miR-31, and the nucleotide sequence thereof is shown as SEQ ID NO.

12.

3. Use of the primer of claim 2 in the preparation of a kit for identifying the sperm activity of a boar.

4. A kit for identifying boar sperm motility, characterized in that, the kit contains the primer of claim 2.

Images