Gene related to pig antiviral ability, and acquisition method, verification method and application thereof

Abstract

The invention provides a gene related to pig antiviral ability and a verification method thereof, and belongs to the technical field of biotechnology molecular breeding. The invention relates to a gene related to pig antiviral ability, and the gene is characterized in that the antiviral candidate gene is IFI44, the position of the antiviral candidate gene on a pig genome is chr6: 134633615-1346650203, and the position is determined based on a reference genome version of Sscrofa11.1 of an Ensepbl database. The IFI44 gene has the beneficial effects that the found IFI44 gene shows an obvious level difference in the activity score of an anti-virus related immune pathway, the provided gene provides a solid theoretical basis and a new material for assisting the disease-resistant breeding work of pigs, and the IFI44 gene has important significance in the pig breeding industry.

Description

Technical Field

[0001] The present invention belongs to the technical field of biotechnology molecular breeding, and specifically relates to the mining of genes regulating the antiviral ability of commercial pigs and the functional verification thereof. Background Art

[0002] Viral diseases are a major enemy of modern pig production, seriously threatening the health of pigs and humans, and causing huge economic losses. For decades, while people have been highly selecting for growth and reproductive traits in pigs, they have neglected the selection of immune and health traits. This has led to a reduction or loss of immune-related gene expression, resulting in modern commercial pigs lacking sufficient immunity and being unable to cope with the many sources of infection under intensive farming conditions.

[0003] The body's immune system is the source of livestock's disease resistance. It is composed of various immune tissues and the immune cells and cytokines they produce. It is the most effective weapon against foreign pathogens. However, there are few comprehensive and systematic studies on pig immune tissues. In addition, Poly(I:C) can be used as a virus mimic to study the body's immune response during viral infection. Summary of the invention

[0004] The present invention provides a candidate gene related to the antiviral function of pigs and a verification method thereof, which are used to improve the selection of pigs with stronger disease resistance during the breeding process, and also verify the effectiveness of the candidate gene related to the antiviral function of pigs.

[0005] In order to achieve the above-mentioned purpose of the invention, the present invention provides a gene related to the antiviral ability of pigs, characterized in that the antiviral candidate gene is IFI44, and its position on the pig genome is chr6:134633615-134650203, and the position is determined based on the reference genome version of Sscrofa11.1 of the Ensembl database.

[0006] The present invention provides an application of a gene related to pig's antiviral ability in pig disease-resistant breeding.

[0007] The present invention provides a method for obtaining genes related to pig antiviral ability, characterized in that the specific implementation steps are as follows:

[0008] S101: Collection and RNA extraction: Six immune tissues were collected from the same part of each Landrace pig at the same time, including thymus, lymph, bone marrow, spleen, small intestine, and peripheral blood. The peripheral blood was separated into peripheral blood mononuclear cells (PBMC) by density gradient centrifugation within 4 hours. The total RNA of the sample was extracted by Trizol method. After the RNA quality was tested to be qualified, the total RNA was used as a template for reverse transcription and PCR amplification to obtain amplified products. The amplified products were library-built and sequenced.

[0009] S102: Sequencing data quality control: Use trimmomatic software to perform quality control on the sequencing sequence rawreads, and use fastqc software to detect the quality control results and remove the reads with a quality score less than 25%;

[0010] S103: Genome alignment and expression profile acquisition: The cleanreads of the quality control results were aligned to the pig reference genome version Sscrofa11.1 using STAR software, and converted into transcriptome gene expression count files using featurecounts software; stringtie was used to evaluate the expression abundance of 35,670 genes in the pig Sscrofa11.1 reference genome, and genes with TPM greater than 0.01 and expressed in more than 20% of the samples were selected as expressed genes, and finally 20,893 screening genes were obtained for subsequent analysis;

[0011] S104: Immune pathway activity assessment: Pathways related to immune phenotypes were selected from the GO / KEGG database, and gene set variation analysis was performed using the R package GSVA to estimate changes in immune pathway activity; the TPM expression matrix of all samples was used as input, and the gene sets extracted from each immune pathway of interest were combined to assess pathway activity, and the immune pathway activity score corresponding to each individual was obtained;

[0012] S105: Co-expression network analysis: Based on the immune pathway activity score, it was used as an individual phenotype, and the R software package WGCNA was used to perform weighted gene co-expression network analysis to identify the co-expression modules in the six immune tissue expressed genes of Landrace pigs; the TPM expression matrix of the six immune tissue expressed genes of Landrace pigs was used as input, and the output was the gene modules divided according to the expression pattern, and the soft threshold was selected as 8 to ensure the scale-free topological structure; the module eigenvectors (MEs) were selected to represent the expression pattern, and the immune pathway activity score was used as the phenotype. The co-expression modules were analyzed for correlation, the key modules were identified, and the module member scores were calculated to find the hub gene with the highest connectivity, and the IFI44 candidate gene was obtained;

[0013] S106: SNP association analysis: GATK software was used to call SNPs from the transcriptome data of porcine peripheral blood mononuclear cells, and SNPs within 2kb upstream and downstream of immune candidate genes were screened. Association analysis was performed on each SNP based on a linear model, and SNPs were used as a bridge to determine the expression genes that affect the corresponding phenotypes. Immune candidate genes related to antiviral pathways were identified, and it was found that the SNP site SCC6:134641947 on the IFI44 gene was significantly correlated with the expression level of the IFI44 gene, and was significantly correlated with the IFN-α and IFN-β production pathways that are closely related to antiviral responses and the pathways that regulate pro-inflammatory responses, making it an antiviral candidate gene.

[0014] Furthermore, the formula for correlation analysis in step S106 is:

[0015] Y=β1X+β2T+ε

[0016] Among them, Y is the phenotypic value, i.e., the immune pathway activity score; X is the genotype vector of the tested SNP; β1 is the effect size of the SNP; T is the age vector of the individuals in the study; β2 is the effect vector of age; and ε is the residual vector.

[0017] Furthermore, the method for separating peripheral blood mononuclear cells (PBMC) in step S101 is as follows: using a pig peripheral blood mononuclear cell separation solution kit to perform separation operation:

[0018] S201: Take 6 ml of fresh anticoagulated blood + 6 ml of diluent in a 15 ml tube;

[0019] S202: Add 12 ml of separation solution to the 50 ml tube;

[0020] S203: slowly adding the diluted blood onto the separation solution along the tube wall;

[0021] S204: 1000 g horizontal centrifugation for 20 min;

[0022] S205: Aspirate the white film layer into a new 15 ml tube and add 10 ml of washing solution;

[0023] S206: 250g, horizontal centrifugation for 10 min, discard the supernatant, and keep 0.5 ml of water film;

[0024] S207: 250g, horizontal centrifugation for 10 min, discard the supernatant to obtain the cell pellet.

[0025] Further, the specific steps of calling SNPs from the transcriptome data of pig peripheral blood mononuclear cells using GATK software in step S106 are as follows:

[0026] S301: Use Picard tools to merge read groups, sort, mark duplicates, and index reads for each PBMC sample into bam files;

[0027] S302: Use SplitNCigarReads of GATK to process reads spanning introns and adjust mapping quality;

[0028] S303: Use GATK's BaseRecalibrator to recalibrate base quality scores based on known variant sites (NCBI dbSNP), and use GATK's HaplotypeCaller to perform genome-wide variant calling;

[0029] S304: In order to ensure high-confidence SNP calls, use GATK's VariantFiltration to apply a specific quality threshold, where the quality threshold is FS>30.0 and QD<2.0, for filtering; selectvariant of GATK is used to select and filter high-quality SNPs of SNP-type variants;

[0030] S305: The haplotype data of 1241 individuals downloaded from the SWIM genotype imputation platform were used, and the above genotype files were filled using beagle software. Finally, BCFtools was used to screen SNPs according to two criteria, namely, minimum minor allele frequency (MAF) < 0.1 and filling quality (DR2) > 0.8, and PLINK software was used to remove SNPs that did not conform to Hardy Weinberg equilibrium.

[0031] Furthermore, the soft threshold in step S105 is scale-free R 2 =0.84.

[0032] The present invention also provides a method for identifying genes related to pig antiviral ability, characterized in that the steps of the identification method are as follows:

[0033] S401: siRNA design and transfection: Design the small interfering RNA fragment siRNA-IFI44 based on the IFI44 candidate gene; isolate peripheral blood mononuclear cells from fresh pig blood and use RPMI-

[0034] The cells were resuspended in 1640 medium and inoculated into cell culture plates. Lipofectamine3000 transfection reagent was used to transfect siRNA-IFI44 into cells, and the levels of cytokines IFN-α, IFN-β, IL-6, and TNF-α were detected 24 hours later.

[0035] S402: Virus mimic treatment and detection: 24 hours after siRNA-IFI44 transfection, cells were treated with the virus mimic Poly(I:C), and cell apoptosis, necrosis and proliferation were detected 24 hours later.

[0036] Furthermore, the nucleotide sequence of the small interfering RNA fragment siRNA-IFI44 in step S107 is shown as SEQ ID NO.1 and SEQ ID NO.2.

[0037] Further, the specific operation steps of transfection in step S107 are as follows:

[0038] S501: Dilute Lipofectamine 3000: Take 3ul Lipofectamine 3000 transfection reagent and add 50ul Opti-MEM serum-free medium to dilute;

[0039] S502: dilute siRNA-IFI44: take 6ul siRNA-IFI44 small interfering RNA and add 50ul Opti-MEM serum-free medium to dilute;

[0040] S503: Add the diluent in step S501 to step S502, mix well, let stand for 10-15 minutes, and then transfect into cells;

[0041] S504: After transfection, centrifuge at low speed for 30 min at 1000 rpm and place in a cell culture incubator for culture;

[0042] 24 hours after transfection, the expression of IFI44 gene was detected by RT-qPCR method. The target gene IFI44 primers used were: the sequence of the upstream primer was shown in SEQ ID NO.3; the sequence of the downstream primer was shown in SEQ ID NO.4; the internal reference gene GAPDH primers used were: the upstream primer was shown in SEQ ID NO.5; the downstream primer was shown in SEQ ID NO.6.

[0043] After completing the above steps, the experimental results showed that the levels of two antiviral-related type I interferons, IFN-α and IFN-β, in the pig peripheral blood mononuclear cell IFI44 gene interference group were significantly increased, while the levels of proinflammatory cytokines IL-6 and TNF-α were significantly reduced; the cell necrosis in the pig peripheral blood mononuclear cell IFI44 gene interference group was significantly reduced, the cell apoptosis was significantly increased, and the cell proliferation was increased.

[0044] The above results indicate that the antiviral response is enhanced after interfering with the IFI44 gene. Necrosis is a passive form of cell death that can trigger a strong inflammatory response. Interfering with the IFI44 gene can reduce Poly(I:C)-induced cell necrosis, thereby reducing the excessive inflammatory response and tissue damage associated with viral infection. Apoptosis is a programmed cell death, and apoptotic cells can be quickly engulfed by macrophages to prevent the release of intracellular components. After interfering with the IFI44 gene, Poly(I:C)-induced apoptosis increased significantly, indicating that the body may have initiated an apoptosis program in an attempt to minimize the inflammatory area and prevent spread by eliminating damaged cells stimulated by Poly(I:C). In general, the IFI44 gene is a negative regulator in the body's antiviral response, and knocking out this gene can significantly enhance the host's antiviral ability.

[0045] The beneficial effects of the present invention are:

[0046] 1. The present invention can effectively identify disease-resistant candidate genes related to immune phenotypes by detecting the gene expression profiles in six immune tissues of Landrace pigs, combining immune pathway activity scoring evaluation and WGCNA co-expression network analysis;

[0047] 2. Weighted gene co-expression network analysis (WGCNA) is used to identify gene co-expression modules and key genes. By taking the immune pathway activity score as the phenotype and combining it with co-expression network analysis, key gene modules and hub genes related to the immune phenotype can be effectively identified. This not only improves the accuracy of gene screening, but also provides important candidate genes for subsequent functional verification;

[0048] 3. SNP association analysis can be used to call SNPs from transcriptome data using GATK software and perform association analysis in combination with linear models, which can accurately locate SNP sites associated with immune phenotypes. This can not only identify candidate genes related to antiviral pathways, but also reveal the complex relationship between genes and phenotypes, providing a new perspective for gene function research;

[0049] 4. Designing small interfering RNA (siRNA) based on candidate genes and performing functional verification at the cellular level can directly verify the effectiveness of gene function. By transfecting siRNA-IFI44 and detecting the content of cytokines, the specific role of the IFI44 gene in the antiviral response can be clarified. This method not only verifies the function of the gene, but also provides an important basis for subsequent gene editing and breeding work;

[0050] 5. In terms of virus mimics treatment and detection, using virus mimics Poly(I:C) to treat cells and detecting cell apoptosis, necrosis and proliferation is an innovative method of the present invention that simulates viral infection response. This method can further verify the specific role of candidate genes in antiviral response and provide more comprehensive evidence for gene function research;

[0051] 6. The IFI44 gene found by the present invention showed obvious level differences in the antiviral-related immune pathway activity scores;

[0052] 7. The genes provided by the present invention provide a solid theoretical basis and new materials for assisting the disease-resistant breeding of pigs, and are of great significance in the pig breeding industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 Pierce correlation heat map between co-expression modules and immune pathway activity scores in six pig immune tissues identified by WGCNA;

[0054] Figure 2 (Left) Interaction diagram of hub gene connectivity in the key immune module darkgrey; (Right) TPM expression levels of four hub genes IFI44, EIF2AK2, CMPK2, and MX2 in immune tissues and 11 non-immune tissues;

[0055] Figure 3 Gene expression of different genotypes of SNP (6_134641947) on the IFI44 gene;

[0056] Figure 4 Different genotypes of the SNP (6_134641947) on the IFI44 gene were significantly associated with antiviral-related immune activity pathways (humoral immune response; IFN-α production; INF-β production; positive and negative regulation of inflammatory response; NOD-like receptor signaling pathway);

[0057] Figure 5 After 24 hours of interfering with the IFI44 gene, gene expression was significantly reduced;

[0058] Figure 6 The levels of cytokines IFN-α, IFN-β, IL-6, and TNF-α 24 h after interfering with the IFI44 gene;

[0059] Figure 7 Cell apoptosis, necrosis, and proliferation after 24 h of poly(I:C) treatment;

[0060] Figure 8Immunofluorescence images of cell apoptosis and necrosis 24 hours after cells were treated with Poly(I:C) (apoptotic cells were stained green by YO-PRO-1, and necrotic cells were stained red by PI). DETAILED DESCRIPTION

[0061] Embodiment 1:

[0062] In order to achieve the purpose of the present invention, the following steps are adopted to obtain genes related to the ability of porcine viruses:

[0063] S101: Collection and RNA extraction:

[0064] Five Landrace piglets of the same age and weight were selected from the same pig farm. Six immune tissues (thymus, lymph, bone marrow, spleen, small intestine, and peripheral blood) were collected from the same part of each individual at the same time. The peripheral blood collected was separated into peripheral blood mononuclear cells (PBMC) by density gradient centrifugation within 4 hours. The specific separation method is as follows, using a porcine peripheral blood mononuclear cell separation solution kit for separation:

[0065] S201: Take 6 ml of fresh anticoagulated blood + 6 ml of diluent in a 15 ml tube;

[0066] S202: Add 12 ml of separation solution to the 50 ml tube;

[0067] S203: slowly adding the diluted blood onto the separation solution along the tube wall;

[0068] S204: 1000 g horizontal centrifugation for 20 min;

[0069] S205: Aspirate the white film layer into a new 15 ml tube and add 10 ml of washing solution;

[0070] S206: 250g, horizontal centrifugation for 10 min, discard the supernatant, and keep 0.5 ml of water film;

[0071] S207: 250g, horizontal centrifugation for 10 min, discard the supernatant to obtain the cell pellet.

[0072] Subsequently, the total RNA of 30 samples was extracted using the Trizol method. After the RNA quality was tested to be qualified, the total RNA was used as a template for reverse transcription and PCR amplification to obtain amplified products. The amplified products were library-built and sequenced.

[0073] S102: Sequencing data quality control:

[0074] The raw reads obtained by sequencing were quality controlled using trimmomatic software, and the quality control results were detected using fastqc software to remove reads with a quality score less than 25%.

[0075] S103: Genome alignment and expression profile acquisition:

[0076] The cleanreads of the quality-controlled data were aligned to the pig reference genome (Sscrofa11.1) using STAR software, and converted into transcriptome gene expression count files using Samtools and featurecounts software to obtain the gene expression profile in each sample. Finally, Stringtie was used to evaluate the expression abundance of 35,670 genes in the pig 11.1 reference genome, and genes with TPM greater than 0.01 and expressed in more than 20% of the samples were selected as expressed genes, and 20,893 genes were finally obtained for subsequent analysis.

[0077] S104: Assessment of immune pathway activity:

[0078] Pathways related to immune phenotypes were selected from the GO / KEGG database, and the R package Gene Set Variation Analysis (GSVA (v1.50.1)) software was used to estimate changes in immune pathway activity. Specifically, the TPM expression matrix of all samples was used as input, and the gene set extracted from each immune pathway of interest was combined to evaluate pathway activity. Each pathway corresponding to each sample can obtain the immune pathway activity score shown in Tables 1.1-1.6.

[0079] Table 1.1: Immune pathway activity scores (I)

[0080]

[0081]

[0082] Table 1.2 Immune pathway activity score (II)

[0083]

[0084]

[0085] Table 1.3 Immune pathway activity score (III)

[0086]

[0087]

[0088] Table 1.4 Immune pathway activity score (IV)

[0089]

[0090]

[0091] Table 1.5 Immune pathway activity score (V)

[0092]

[0093] Table 1.6 Immune pathway activity score (VI)

[0094]

[0095]

[0096] S105: Co-expression network analysis:

[0097] The obtained immune pathway activity scores were used as individual phenotypes, and weighted gene co-expression network analysis (WGCNA) was performed using the WGCNA (v1.72) R software package to identify the co-expression modules in the six immune tissue-expressed genes of Landrace pigs. Specifically, the TPM expression matrix of the six immune tissue-expressed genes of Landrace pigs was used as input, and the output was the gene modules divided according to the expression pattern, and the soft threshold was selected as 8 (scale-free R 2 =0.84) to ensure the scale-free topology. The module eigenvectors (MEs, i.e., the first principal component of the module) were selected to represent the expression pattern. Subsequently, the immune pathway activity scores of the six immune tissues were used as phenotypes, and the obtained co-expression modules were subjected to correlation analysis, as shown in Figure 2. Figure 1 As shown in the figure, the key modules were identified, among which the module darkgrey was significantly associated with the immune phenotype. For the genes in the key module, the module member score (MM) was calculated to find the hub gene with the highest connectivity. The higher the score, the more important the gene. The IFI44 candidate gene was obtained as shown in Table 2. Its expression level was compared with that of 11 other non-immune tissues of pigs downloaded from pigGTEx (https: / / piggtex.ipiginc.com / # / home). It was found that the hub gene was only highly expressed in immune tissues, as shown in Table 2. Figure 2 shown.

[0098] Table 2 Genes in the darkgray module and their module membership scores (MM)

[0099]

[0100]

[0101]

[0102] S106: SNP association analysis:

[0103] GATK software was used to call SNPs from the transcriptome data of peripheral blood mononuclear cells of 15 pigs. The specific steps are as follows:

[0104] S301: Use Picard tools to merge read groups, sort, mark duplicates, and index reads for each PBMC sample into bam files;

[0105] S302: Use SplitNCigarReads of GATK to process reads spanning introns and adjust mapping quality;

[0106] S303: Use GATK's BaseRecalibrator to recalibrate base quality scores based on known variant sites (NCBI dbSNP), and use GATK's HaplotypeCaller to perform genome-wide variant calling;

[0107] S304: In order to ensure high-confidence SNP calls, use GATK's VariantFiltration to apply a specific quality threshold, where the quality threshold is FS>30.0 and QD<2.0, for filtering; selectvariant of GATK is used to select and filter high-quality SNPs of SNP-type variants;

[0108] S305: The haplotype data of 1241 individuals downloaded from the SWIM genotype imputation platform were used, and the above genotype files were filled using beagle software. Finally, BCFtools was used to screen SNPs according to two criteria, namely, minimum minor allele frequency (MAF) < 0.1 and filling quality (DR2) > 0.8, and PLINK software was used to remove SNPs that did not conform to Hardy Weinberg equilibrium.

[0109] Then, SNPs within 2kb upstream and downstream of the immune candidate genes obtained above were screened, and association analysis was performed on each SNP based on a linear model:

[0110] Y=β1X+β2T+ε

[0111] Among them, y is the phenotypic value, i.e., the immune pathway activity score; X is the genotype vector of the tested SNP; β1 is the effect size of the SNP; T is the age vector of the individuals in the study; β2 is the effect vector of age; ε is the residual vector. Using SNP as a bridge to determine the expression genes that affect the corresponding phenotype, we can identify immune candidate genes related to the antiviral pathway. Among them, if Figure 3 As shown in Figure 2, it was found that the SNP site SCC6:134641947 on the IFI44 gene was significantly correlated with the expression level of the IFI44 gene, and was significantly correlated with the IFN-α and IFN-β production pathways that are closely related to the antiviral response, as well as the pathways that regulate the pro-inflammatory response. The results are as follows Figure 4As shown, they can be used as antiviral candidate genes. The specific results are shown in Tables 3.1-3.6.

[0112] Table 3.1 Correlation between humoral immune response immune pathway activity scores and SNPs within 2 kb upstream and downstream of the IFI44 gene

[0113]

[0114]

[0115] Table 3.2 Correlation between IFN-α production immune pathway activity score and SNPs within 2 kb upstream and downstream of the IFI44 gene

[0116]

[0117]

[0118] Table 3.3 Correlation between IFN-β production immune pathway activity score and SNPs within 2 kb upstream and downstream of the IFI44 gene

[0119]

[0120]

[0121] Table 3.4 Correlation between the negative regulatory immune pathway activity score of inflammatory response and SNPs within 2 kb upstream and downstream of the IFI44 gene

[0122]

[0123]

[0124] Table 3.5 Correlation between the activity score of the positive regulatory immune pathway of inflammatory response and SNPs within 2 kb upstream and downstream of the IFI44 gene

[0125] CHR SNP BP A1 TEST NMISS BETA STAT P 6 6_134632059_A 1.35E+08 G ADD 15 0.0313 0.3692 0.7184 6 6_134632339_C 1.35E+08 C ADD 15 0.07094 0.8199 0.4282 6 6_134632498_T 1.35E+08 C ADD 15 0.0313 0.3692 0.7184 6 6_134633287_C 1.35E+08 G ADD 15 0.0313 0.3692 0.7184 6 6_134633501_C 1.35E+08 T ADD 15 0.0313 0.3692 0.7184 6 6_134634305_G 1.35E+08 C ADD 15 0.2117 2.221 0.04635 6 6_134635444_G 1.35E+08 A ADD 15 0.2117 2.221 0.04635 6 6_134636006_A 1.35E+08 C ADD 15 0.2117 2.221 0.04635 6 6_134638528_G 1.35E+08 A ADD 15 0.0635 0.334 0.7441 6 6_134638850_C 1.35E+08 T ADD 15 0.0635 0.334 0.7441 6 6_134639328_C 1.35E+08 T ADD 15 0.0635 0.334 0.7441 6 6_134639815_A 1.35E+08 G ADD 15 0.003008 0.02481 0.9806 6 6_134640029_T 1.35E+08 G ADD 15 0.2886 3.065 0.009814 6 6_134640041_C 1.35E+08 A ADD 15 0.0635 0.334 0.7441 6 6_134640419_G 1.35E+08 GC ADD 15 0.2886 3.065 0.009814 6 6_134640421_C 1.35E+08 C ADD 15 0.2886 3.065 0.009814 6 6_134640827_C 1.35E+08 A ADD 15 0.0635 0.334 0.7441 6 6_134640903_C 1.35E+08 T ADD 15 0.2275 3.106 0.009094 6 6_134640912_C 1.35E+08 T ADD 15 0.0635 0.334 0.7441 6 6_134641653_A 1.35E+08 G ADD 15 0.2188 2.294 0.04067 6 6_134641947_C 1.35E+08 T ADD 15 0.1944 2.908 0.01312 6 6_134642099_C 1.35E+08 T ADD 15 0.2117 2.221 0.04635 6 6_134642480_G 1.35E+08 A ADD 15 0.2886 3.065 0.009814 6 6_134643436_A 1.35E+08 C ADD 15 0.2117 2.221 0.04635 6 6_134644735_TG 1.35E+08 T ADD 15 0.2886 3.065 0.009814 6 6_134645351_T 1.35E+08 C ADD 15 0.2886 3.065 0.009814 6 6_134645692_GC 1.35E+08 G ADD 15 0.2117 2.221 0.04635 6 6_134647202_T 1.35E+08 T ADD 15 0.2886 3.065 0.009814 6 6_134649318_G 1.35E+08 G ADD 15 NA NA NA 6 6_134649344_T 1.35E+08 C ADD 15 0.2117 2.221 0.04635 6 6_134649534_C 1.35E+08 G ADD 15 0.2886 3.065 0.009814 6 6_134649655_A 1.35E+08 A ADD 15 0.2886 3.065 0.009814 6 6_134649699_C 1.35E+08 T ADD 15 0.2886 3.065 0.009814 6 6_134649795_C 1.35E+08 C ADD 15 0.2886 3.065 0.009814 6 6_134649797_T 1.35E+08 TC ADD 15 0.2886 3.065 0.009814 6 6_134651235_C 1.35E+08 C ADD 15 0.2886 3.065 0.009814 6 6_134651268_A 1.35E+08 G ADD 15 0.2886 3.065 0.009814

[0126] Table 3.6 Correlation between NOD-like receptor signaling pathway immune pathway activity score and SNPs within 2 kb upstream and downstream of IFI44 gene

[0127] CHR SNP BP A1 TEST NMISS BETA STAT P 6 6_134640903_C 134640903 T ADD 15 0.1725 3.01 0.01087 6 6_134634305_G 134634305 C ADD 15 0.1961 2.945 0.01225 6 6_134635444_G 134635444 A ADD 15 0.1961 2.945 0.01225 6 6_134636006_A 134636006 C ADD 15 0.1961 2.945 0.01225 6 6_134642099_C 134642099 T ADD 15 0.1961 2.945 0.01225 6 6_134643436_A 134643436 C ADD 15 0.1961 2.945 0.01225 6 6_134645692_GC 134645692 G ADD 15 0.1961 2.945 0.01225 6 6_134649344_T 134649344 C ADD 15 0.1961 2.945 0.01225 6 6_134641947_C 134641947 T ADD 15 0.135 2.456 0.03027 6 6_134640029_T 134640029 G ADD 15 0.1638 1.934 0.07708 6 6_134640419_G 134640419 GC ADD 15 0.1638 1.934 0.07708 6 6_134640421_C 134640421 C ADD 15 0.1638 1.934 0.07708 6 6_134642480_G 134642480 A ADD 15 0.1638 1.934 0.07708 6 6_134644735_TG 134644735 T ADD 15 0.1638 1.934 0.07708 6 6_134645351_T 134645351 C ADD 15 0.1638 1.934 0.07708 6 6_134647202_T 134647202 T ADD 15 0.1638 1.934 0.07708 6 6_134649534_C 134649534 G ADD 15 0.1638 1.934 0.07708 6 6_134649655_A 134649655 A ADD 15 0.1638 1.934 0.07708 6 6_134649699_C 134649699 T ADD 15 0.1638 1.934 0.07708 6 6_134649795_C 134649795 C ADD 15 0.1638 1.934 0.07708 6 6_134649797_T 134649797 TC ADD 15 0.1638 1.934 0.07708 6 6_134651235_C 134651235 C ADD 15 0.1638 1.934 0.07708 6 6_134651268_A 134651268 G ADD 15 0.1638 1.934 0.07708 6 6_134632339_C 134632339 C ADD 15 0.1138 1.889 0.08329 6 6_134639815_A 134639815 G ADD 15 -0.1316 -1.538 0.15 6 6_134632059_A 134632059 G ADD 15 0.08284 1.351 0.2015 6 6_134632498_T 134632498 C ADD 15 0.08284 1.351 0.2015 6 6_134633287_C 134633287 G ADD 15 0.08284 1.351 0.2015 6 6_134633501_C 134633501 T ADD 15 0.08284 1.351 0.2015 6 6_134638528_G 134638528 A ADD 15 -0.1799 -1.304 0.2167 6 6_134638850_C 134638850 T ADD 15 -0.1799 -1.304 0.2167 6 6_134639328_C 134639328 T ADD 15 -0.1799 -1.304 0.2167 6 6_134640041_C 134640041 A ADD 15 -0.1799 -1.304 0.2167 6 6_134640827_C 134640827 A ADD 15 -0.1799 -1.304 0.2167 6 6_134640912_C 134640912 T ADD 15 -0.1799 -1.304 0.2167 6 6_134641653_A 134641653 G ADD 15 0.08308 0.9779 0.3474 6 6_134649318_G 134649318 G ADD 15 NA NA NA

[0128] Example 2

[0129] In order to identify the antiviral effectiveness of the SNP site SCC6:134641947 on the IFI44 gene, the following steps were used:

[0130] S401: siRNA design and transfection:

[0131] Primary PBMCs were isolated from fresh pig blood using the above method and promptly resuspended in RPMI-1640 medium supplemented with 10% fetal bovine serum (FBS). Subsequently, 2-3×10^6 cells were inoculated in a 24-well plate (1 ml per well) for culture. Small interfering RNA (siRNA-IFI44) targeting the IFI44 gene was transfected into the cells using Lipofectamine3000 transfection reagent. The siRNA double strands used were:

[0132] SEQ ID NO.1: Forward:5′-GCGAAAUAGGACCGCUUAATT-3′,

[0133] SEQ ID NO. 2: Reverse: 5′-UUAAGCGGCCUAUUUCGCTT-3′.

[0134] The specific steps of transfection are as follows (the amount added to each well):

[0135] S501: Dilute Lipofectamine 3000: Take 3ul Lipofectamine 3000 transfection reagent and add 50ul Opti-MEM serum-free medium to dilute;

[0136] S502: dilute siRNA-IFI44: take 6ul siRNA-IFI44 small interfering RNA and add 50ul Opti-MEM serum-free medium to dilute;

[0137] S503: Add the diluent in step S501 to step S502, mix well, let stand for 10-15 minutes, and then transfect into cells;

[0138] S504: After transfection, centrifuge at low speed for 30 min at 1000 rpm and place in a cell culture incubator for culture;

[0139] After 24 h of transfection, the expression of IFI44 gene was detected by RT-qPCR method ( Figure 5), the target gene IFI44 primers used were: upstream primer: SEQ ID NO.3: 5′-CCTTTGTGTTCAGTGCCTACTCT-3′; downstream primer: SEQ ID NO.4: 5′-GAGTGAGCAATACCACCTGTACC-3′. The internal reference gene GAPDH primers used were: upstream primer: SEQ ID NO.5: 5′-CCTTTGTGTTCAGTGCCTACTCT-3′; downstream primer: SEQ ID NO.6: 5′-CAGCATCAAAAGTGGAAGAGTG-3′. The results showed that after transfection of siRNA-IFI44, the expression of the IFI44 gene was significantly reduced.

[0140] At the same time, 24 hours after transfection, the levels of cytokines IFN-α, IFN-β, IL-6, and TNF-α were detected using Elisa kits (Shanghai ELISA Biologicals Co., Ltd.) according to the instructions. Figure 6 ), the results showed that the levels of two antiviral-related type I interferons, IFN-α and IFN-β, in the gene interference group were significantly increased, while the levels of proinflammatory cytokines IL-6 and TNF-α were significantly reduced.

[0141] S402: Virus Simulants Processing and Detection:

[0142] 24 h after transfection, cells were stimulated with the virus mimic Poly(I:C) at a concentration of 10 ug / ml. After 24 h of Poly(I:C) treatment, cells were seeded into 96-well plates (cell density was 2 × 10^6 cells / well, volume was 100 μL / well). Subsequently, apoptosis and necrosis were detected using a cell apoptosis and necrosis detection kit (Beyotime, Shanghai, China), and cell proliferation was detected using a cell CCK-8 kit (Beyotime, Shanghai, China). Figure 7 All assay steps were performed according to the manufacturer's instructions. Fluorescence and absorbance values ​​were measured using Varioskan LUX (Thermo Fisher Scientific). Cells treated with the kit were photographed using a Nikon ECLIPSETs2 fluorescence microscope (Nikon, Tokyo, Japan). Apoptotic cells were stained green with YO-PRO-1, and necrotic cells were stained red with PI (propiodine). Figure 8). The results showed that the cell necrosis in the gene interference group was significantly reduced, the cell apoptosis was significantly increased, and the cell proliferation was increased. Necrosis is a passive form of cell death that can trigger a strong inflammatory response. Interference with the IFI44 gene can reduce Poly(I:C)-induced cell necrosis, thereby reducing the excessive inflammatory response and tissue damage associated with viral infection. Apoptosis is a programmed cell death, and apoptotic cells can be quickly engulfed by macrophages to prevent the release of intracellular components. After interfering with the IFI44 gene, Poly(I:C)-induced cell apoptosis increased significantly, indicating that the body may have initiated the cell apoptosis program in an attempt to minimize the inflammatory area and prevent spread by eliminating damaged cells stimulated by Poly(I:C). In general, this experiment proves that the IFI44 gene is a negative regulator in the body's antiviral response, and knocking out this gene can significantly enhance the host's antiviral ability.

[0143] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A gene related to pig antiviral ability, characterized in that: The antiviral candidate gene is IFI44, and its position on the pig genome is chr6:134633615-134650203, which is determined based on the reference genome version of Sscrofa11.1 in the Ensembl database.

2. Use of a gene related to pig antiviral ability as claimed in claim 1 in pig disease resistance breeding.

3. A method for obtaining genes related to pig antiviral ability, characterized in that: The gene is the gene related to the antiviral ability of pigs as described in claim 1, and the specific implementation steps are as follows: S101: Collection and RNA extraction: Six immune tissues were collected from the same part of each Landrace pig at the same time, including thymus, lymph, bone marrow, spleen, small intestine, and peripheral blood. The peripheral blood was separated into peripheral blood mononuclear cells (PBMC) by density gradient centrifugation within 4 hours. The total RNA of the sample was extracted by Trizol method. After the RNA quality was tested to be qualified, the total RNA was used as a template for reverse transcription and PCR amplification to obtain amplified products. The amplified products were library-built and sequenced. S102: Sequencing data quality control: Use trimmomatic software to perform quality control on the sequencing sequence rawreads, and use fastqc software to detect the quality control results and remove the reads with a quality score less than 25%; S103: Genome alignment and expression profile acquisition: The cleanreads of the quality control results were aligned to the pig reference genome version Sscrofa11.1 using STAR software, and converted into transcriptome gene expression count files using featurecounts software; stringtie was used to evaluate the expression abundance of 35,670 genes in the pig Sscrofa11.1 reference genome, and genes with TPM greater than 0.01 and expressed in more than 20% of the samples were selected as expressed genes, and finally 20,893 screening genes were obtained for subsequent analysis; S104: Immune pathway activity assessment: Pathways related to immune phenotypes were selected from the GO / KEGG database, and gene set variation analysis was performed using the R package GSVA to estimate changes in immune pathway activity; the TPM expression matrix of all samples was used as input, and the gene sets extracted from each immune pathway of interest were combined to assess pathway activity, and the immune pathway activity score corresponding to each individual was obtained; S105: Co-expression network analysis: Based on the immune pathway activity scores, which were used as individual phenotypes, weighted gene co-expression network analysis was performed using the R software package WGCNA to identify co-expression modules among the genes expressed in the six immune tissues of Landrace pigs; The TPM expression matrix of six immune tissue-expressed genes of Landrace pigs was used as input, and the output was gene modules divided according to expression patterns. The soft threshold was selected as 8 to ensure a scale-free topological structure. Module eigenvectors (MEs) were selected to represent expression patterns, and the immune pathway activity score was used as the phenotype. The co-expression modules were analyzed for correlation, key modules were identified, and module member scores were calculated to find the hub gene with the highest connectivity, thus obtaining the IFI44 candidate gene. S106: SNP association analysis: GATK software was used to call SNPs from the transcriptome data of porcine peripheral blood mononuclear cells, and SNPs within 2kb upstream and downstream of immune candidate genes were screened. Association analysis was performed on each SNP based on a linear model, and SNPs were used as a bridge to determine the expression genes that affect the corresponding phenotypes. Immune candidate genes related to antiviral pathways were identified, and it was found that the SNP site SCC6:134641947 on the IFI44 gene was significantly correlated with the expression level of the IFI44 gene, and was significantly correlated with the IFN-α and IFN-β production pathways that are closely related to antiviral responses and the pathways that regulate pro-inflammatory responses, making it an antiviral candidate gene.

4. According to the method for obtaining genes related to pig antiviral ability according to claim 3, the formula for correlation analysis in step S106 is: Y=β1X+β2T+ε in, Y is the phenotypic value, i.e., the immune pathway activity score; X is the genotype vector of the tested SNP; β1 is the effect size of the SNP; T is the age vector of the individuals in the study; β2 is the effect vector of age; and ε is the residual vector.

5. The method for obtaining a gene related to pig antiviral ability according to claim 3, wherein the method for separating peripheral blood mononuclear cells (PBMC) in step S101 is as follows: using a pig peripheral blood mononuclear cell separation solution kit for separation operation: S201: Take 6 ml of fresh anticoagulated blood + 6 ml of diluent in a 15 ml tube; S202: Add 12 ml of separation solution to the 50 ml tube; S203: slowly adding the diluted blood onto the separation solution along the tube wall; S204: 1000 g horizontal centrifugation for 20 min; S205: Aspirate the white film layer into a new 15 ml tube and add 10 ml of washing solution; S206: 250g, horizontal centrifugation for 10 min, discard the supernatant, and keep 0.5 ml of water film; S207: 250g, horizontal centrifugation for 10 min, discard the supernatant to obtain the cell pellet.

6. The method for obtaining genes related to pig antiviral ability according to claim 3, wherein the step S106 of using GATK software to call SNPs from the transcriptome data of pig peripheral blood mononuclear cells comprises the following specific steps: S301: Use Picard tools to merge read groups, sort, mark duplicates, and index reads for each PBMC sample into bam files; S302: Use SplitNCigarReads of GATK to process reads spanning introns and adjust mapping quality; S303: Use GATK's BaseRecalibrator to recalibrate base quality scores based on known variant sites (NCBI dbSNP), and use GATK's HaplotypeCaller to perform genome-wide variant calling; S304: In order to ensure high-confidence SNP calls, use GATK's VariantFiltration to apply a specific quality threshold, where the quality threshold is FS>30.0 and QD<2.0, for filtering; selectvariant of GATK is used to select and filter high-quality SNPs of SNP-type variants; S305: The haplotype data of 1241 individuals downloaded from the SWIM genotype imputation platform were used, and the above genotype files were filled using beagle software. Finally, BCFtools was used to screen SNPs according to two criteria, namely, minimum minor allele frequency (MAF) < 0.1 and filling quality (DR2) > 0.8, and PLINK software was used to remove SNPs that did not conform to Hardy Weinberg equilibrium.

7. The method for obtaining genes related to pig antiviral ability according to claim 3, wherein the soft threshold in step S105 is a scale-free R 2 =0.

84.

8. A method for identifying genes related to pig antiviral ability, characterized in that: The gene is the gene related to the antiviral ability of pigs as claimed in claim 1, and the steps of the identification method are as follows: S401: siRNA design and transfection: Design the small interfering RNA fragment siRNA-IFI44 based on the IFI44 candidate gene; isolate the peripheral blood mononuclear cells from fresh pig blood, resuspend them in RPMI-1640 medium containing 10% fetal bovine serum and inoculate them in a cell culture plate; use Lipofectamine3000 transfection reagent to transfect siRNA-IFI44 into the cells, and detect the content of cytokines IFN-α, IFN-β, IL-6, and TNF-α after 24 hours; S402: Virus mimic treatment and detection: 24 hours after siRNA-IFI44 transfection, cells were treated with the virus mimic Poly(I:C), and cell apoptosis, necrosis and proliferation were detected 24 hours later.

9. The method for obtaining a gene related to pig antiviral ability according to claim 8, wherein the nucleotide sequence of the small interfering RNA fragment siRNA-IFI44 in step S107 is shown in SEQ ID NO.1 and SEQ ID NO.

2.

10. According to the method for obtaining a gene related to pig antiviral ability in claim 8, the specific operation steps of transfection in step S107 are as follows: S501: Dilute Lipofectamine 3000: Take 3ul Lipofectamine 3000 transfection reagent and add 50ul Opti-MEM serum-free medium to dilute; S502: dilute siRNA-IFI44: take 6ul siRNA-IFI44 small interfering RNA and add 50ul Opti-MEM serum-free medium to dilute; S503: Add the diluent in step S501 to step S502, mix well, let stand for 10-15 minutes, and then transfect into cells; S504: After transfection, centrifuge at low speed for 30 min at 1000 rpm and place in a cell culture incubator for culture; 24 hours after transfection, the expression of IFI44 gene was detected by RT-qPCR method. The target gene IFI44 primers used were: the sequence of the upstream primer was shown in SEQ ID NO.3; the sequence of the downstream primer was shown in SEQ ID NO.4; the internal reference gene GAPDH primers used were: the upstream primer was shown in SEQ ID NO.5; the downstream primer was shown in SEQ ID NO.6.

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