Methods, devices, electronic equipment, and storage media for screening key immune-related genes in calves.

Abstract

This invention relates to the field of bioengineering, specifically to a method, apparatus, electronic device, and storage medium for screening key immune-related genes in calves. The screening method includes: acquiring transcriptome data from peripheral blood samples before and after colostrum in different calf samples; screening differentially expressed gene data based on the transcriptome data from the pre-colostrum and post-colostrum peripheral blood samples, and performing enrichment analysis to obtain gene sets related to specific biological processes, functions, or pathways; and then using weighted co-expression network (WCGNA) analysis to obtain key genes. This invention studies the gene expression regulation patterns and metabolite expression changes in calves before and after colostrum, providing a basis and theoretical foundation for further understanding the establishment and response mechanisms of the calf immune system, when the passive immune system terminates, and how the autoimmune system is regulated at the molecular level.

Description

Technical Field

[0001] This invention relates to the field of bioengineering, specifically to methods, apparatus, electronic devices, and storage media for screening key immune-related genes in calves. Background Technology

[0002] Screening of key immune-related genes in calves is an important scientific field, impacting calf health, growth performance, and the economic benefits of animal husbandry. By screening and studying these key genes, we can better understand the operational mechanisms of the calf immune system, gain a deeper understanding of the molecular regulatory mechanisms of the animal immune system, fill gaps in calf research, further improve the theoretical framework of animal immunobiology, and provide important guidance for animal reproduction and the development of animal husbandry. This research can lead to the development of more effective prevention and treatment measures, improving calf health and growth performance, thereby enhancing the economic benefits and sustainable development of animal husbandry. Therefore, this invention provides a method, apparatus, electronic device, and storage medium for screening key immune-related genes in calves. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a method, device, electronic device, and storage medium for screening key immune-related genes in calves. The aim is to provide a basis and theoretical foundation for further understanding the establishment process and response mechanism of the calf's immune system, when the passive immune system terminates, and how the autoimmune system is regulated at the molecular level by studying the gene expression regulation patterns and metabolite expression changes in calves before and after colostrum.

[0004] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: Firstly, a method for screening key immune-related genes in calves, the screening method comprising: Transcriptome data of peripheral blood before colostrum and after colostrum were obtained from different calf samples. Differential gene data were screened based on the transcriptome data of peripheral blood before colostrum and after colostrum, and enrichment analysis was performed to obtain gene sets related to specific biological processes, functions or pathways. Based on gene sets related to specific biological processes, functions, or pathways, WCGNA was analyzed using weighted gene co-expression network to identify key genes.

[0005] Based on the above technical solution, the present invention can be further improved as follows.

[0006] Furthermore, the calf samples include at least two different breeds of calves.

[0007] Furthermore, the transcriptome data of peripheral blood after colostrum includes transcriptome data of peripheral blood from 5 to 8 days after birth and transcriptome data of peripheral blood from 12 days after birth.

[0008] Furthermore, differentially expressed gene data were screened based on transcriptome data from peripheral blood before and after colostrum, with the following screening criteria: |log2FoldChange|>= 1 and FDR<0.05.

[0009] Further enrichment analyses were performed, including GO enrichment analysis and KEGG enrichment analysis of differentially expressed genes.

[0010] Furthermore, based on a gene set related to a specific biological process, function, or pathway, the key genes are obtained by using weighted gene co-expression network analysis (WCGNA). This involves the following specific steps: Based on a gene set related to a specific biological process, function, or pathway, weighted gene co-expression network analysis (WCGNA) is used; connectivity values ​​are added to the gene list of each module obtained from the weighted gene co-expression network analysis (WCGNA); and key genes are obtained based on these connectivity values.

[0011] The connectivity value refers to the degree to which a gene is connected to other genes (usually calculated only within a module), often referred to as connectivity or degree, or represented by the number k. Generally speaking, in a module, genes with the highest connectivity (k value) can be considered hub genes. Furthermore, the key genes include CEBPD, SPI1, FANCD2, BCLAF3, IFIH1, LOC123465206, novel.604, GRB7, LOC102397654, LOC102414909, CCL26, PNPLA8, COL1A2, CA8, CCNT1, SMC2, novel.166, CANX, PALM, MOB1A, AK2, FKBP10, LOC102408646, LOC102410605, LOC102397015, PACC1, CCL5, and NFRKB.

[0012] Secondly, a screening device for key immune-related genes in calves, the device comprising: Acquisition module: used to acquire transcriptome data of peripheral blood before colostrum and peripheral blood after colostrum from different calf samples; based on the transcriptome data of peripheral blood before colostrum and peripheral blood after colostrum, differential gene data are screened and enrichment analysis is performed to obtain gene sets related to specific biological processes, functions or pathways. Key gene acquisition module: Used to obtain key genes by weighted gene co-expression network analysis of WCGNA based on gene sets related to specific biological processes, functions or pathways.

[0013] Thirdly, an electronic device includes: a memory and a processor, the memory and the processor being connected; the memory being used to store a program; the processor calling the program stored in the memory to execute the screening method for immune-related key genes in calves.

[0014] Fourthly, a storage medium storing a computer program, which is executed by a computer to perform the method for screening key immune-related genes in calves.

[0015] The beneficial effects of this invention are: by studying the gene expression regulation pattern and metabolite expression change pattern of calves before and after colostrum, this invention provides a basis and theoretical foundation for further understanding the establishment process and response mechanism of the calf immune system, when the function of the calf passive immune system terminates, and how the autoimmune system is regulated at the molecular level. Attached Figure Description

[0016] Figure 1 This is a heatmap of the expression levels of 2724 genes in this invention; Figure 2 This is a graph showing the number of DEGs in different groups of this invention. Figure 3 Volcano plots for different groups of DEGs; where A: ML_2 vs ML_1, B: ML_2 vs ML_1, C: ML_2 vs ML_1, D: ML_2 vs ML_1, E: ML_2 vs ML_1, F: ML_2 vs ML_1; Figure 4 This is a GO enrichment analysis chart of different groups of DEGs in this invention; Figure 5 This is a diagram of the KEGG signal pathways enriched by different groups of DEGs in this invention; Figure 6 This is a schematic diagram of the soft threshold selection of the present invention; wherein, the left is a square graph of the correlation coefficient in the network, and the right is the average connectivity corresponding to different power values; Figure 7 This is a hierarchical clustering tree diagram of the modules in this invention; Figure 8 This is a heatmap of gene clustering for the modules of this invention; Figure 9 This is a heatmap showing the correlation between modules in this invention. Figure 10 This is a heatmap showing the correlation between the samples and modules of this invention. Figure 11 This is a flowchart illustrating the screening method for key immune-related genes in calves according to the present invention. Detailed Implementation

[0017] The principles and features of this invention are described below. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. Where specific techniques or conditions are not specified in the embodiments, they should be performed according to the techniques or conditions described in the literature in this field, or according to the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0018] This embodiment relates to a method for screening key immune-related genes in calves, the screening method including ( Figure 11 ): Transcriptome data of peripheral blood before colostrum and after colostrum were obtained from different calf samples. Differential gene data were screened based on the transcriptome data of peripheral blood before colostrum and after colostrum, and enrichment analysis was performed to obtain gene sets related to specific biological processes, functions or pathways. Based on gene sets related to specific biological processes, functions, or pathways, WCGNA was analyzed using weighted gene co-expression network to identify key genes.

[0019] Preferably, the calf samples described in this embodiment include at least two different breeds of calves. The transcriptome data of peripheral blood after colostrum includes transcriptome data of peripheral blood from 5-8 days after birth and transcriptome data of peripheral blood from 12 days after birth. Preferably, in this embodiment, the screening criteria for differentially expressed gene data based on the transcriptome data of peripheral blood before and after colostrum are: |log2FoldChange|>= 1 and FDR<0.05.

[0020] Preferably, the enrichment analysis performed in this embodiment includes GO enrichment analysis and KEGG analysis of differentially expressed genes.

[0021] Preferably, in this embodiment, the key genes are obtained by using weighted gene co-expression network analysis (WCGNA) based on a gene set related to a specific biological process, function, or pathway. The specific steps are as follows: based on a gene set related to a specific biological process, function, or pathway, weighted gene co-expression network analysis (WCGNA) is used; connectivity values ​​are added to the gene list of each module obtained by weighted gene co-expression network analysis (WCGNA); and key genes are obtained based on the connectivity values.

[0022] The specific steps for analyzing WCGNA using a weighted gene co-expression network are as follows: (1) Building the network: ① Choosing a soft threshold: WGCNA analysis uses correlation coefficient weighting, which involves raising the gene correlation coefficient to the power of N. This approach strengthens strong correlations and weakens weak or negative correlations, making the connections between genes in the network follow a scale-free network distribution, thus enhancing its biological significance. The optimal power value is calculated using the `pickSoftThreshold` function from the WGCNA package in R (with RsquaredCut set to 0.85). If a suitable power value cannot be calculated, a power value can be selected from the table on the WGCNA official website that corresponds to the number of samples and the corresponding power values ​​for subsequent data analysis.

[0023] ② Module-level clustering: Based on a selected soft threshold, a clustering tree is constructed according to the correlation of gene expression levels, and clustering modules are divided. The similarity between genes is calculated and displayed, especially by calculating the TOM (Topological Overlap Matrix) to reveal the relationship between gene modules, and these similarities are visualized using heatmaps. Modules are further merged or split based on their similarity and size. (2) Module characteristics and expression patterns: Calculate the correlation between modules and cluster the modules according to their eigengene values, outputting a heatmap of correlation between modules; perform correlation analysis between module eigengene values ​​and samples. If a module has a high positive or negative eigengene expression in a sample, it indicates that the module is closely related to the sample.

[0024] (3) Key gene screening: Connectivity values, expression levels, and annotations from seven major databases were added to the gene list of each module obtained from the WGCNA analysis. Connectivity refers to the degree to which a gene is connected to other genes (usually calculated only within a module), often referred to as connectivity or degree, or represented by the number k. Generally speaking, genes with the highest connectivity (k value) in a module can be considered hub genes.

[0025] Preferably, the key genes described in this embodiment include CEBPD, SPI1, FANCD2, BCLAF3, IFIH1, LOC123465206, novel.604, GRB7, LOC102397654, LOC102414909, CCL26, PNPLA8, COL1A2, CA8, CCNT1, SMC2, novel.166, CANX, PALM, MOB1A, AK2, FKBP10, LOC102408646, LOC102410605, LOC102397015, PACC1, CCL5, and NFRKB.

[0026] This implementation also involves a device for screening key immune-related genes in calves, the device comprising: Acquisition module: used to acquire transcriptome data of peripheral blood before colostrum and peripheral blood after colostrum from different calf samples; based on the transcriptome data of peripheral blood before colostrum and peripheral blood after colostrum, differential gene data are screened and enrichment analysis is performed to obtain gene sets related to specific biological processes, functions or pathways. Key gene acquisition module: Used to obtain key genes by weighted gene co-expression network analysis of WCGNA based on gene sets related to specific biological processes, functions or pathways.

[0027] This embodiment also relates to an electronic device, including: a memory and a processor, the memory and the processor being connected; the memory being used to store a program; the processor calling the program stored in the memory to execute the screening method for immune-related key genes in calves.

[0028] This embodiment also relates to a storage medium storing a computer program that, when executed by a computer, performs the method for screening key immune-related genes in calves.

[0029] The following describes specific examples: Examples A method for screening key immune-related genes in calves, the screening method comprising: (1) Experimental Groups: When calves are not fed colostrum, it is difficult to find lymph nodes in their bodies from birth or even for several days after birth. However, after colostrum is administered, maternal cells can be detected in the circulatory system within 4 hours, reach a peak after 24 hours, and are basically undetectable after 36 hours. Within two to three weeks after colostrum administration, labeled lymphocytes can be detected in secondary lymphocytes, and the number of lymphocytes and lymph nodes increases. This may be due to the combined effects of maternal antibodies, maternal cells, maternal cytokines and growth factors, and the stimulation of external bacterial invasion and proliferation, which promote the increase in the number of lymphocytes and lymph nodes. It can be seen that timely and adequate colostrum administration after birth is one of the reasons for the successful establishment of the calf's immune system, which may affect the proliferation of specific cells and the stable expression of specific genes.

[0030] This study collected peripheral blood samples from calves at three time points (within 2 hours of birth, 5-8 days after birth, and 12 days after birth) for transcriptome analysis. The samples included three Mora buffalo and one Nili-Raffi buffalo, and were ML_1 (within 2 hours of birth), ML_2 (5-8 days after birth), ML_3 (12 days after birth), NL_1 (within 2 hours of birth), NL_2 (5-8 days after birth), and NL_3 (12 days after birth), for a total of 12 samples.

[0031] (2) Transcriptome analysis and screening of differentially expressed genes in different groups: Transcriptome analysis was performed on 12 samples, and the expression levels of a total of 2724 genes were detected. Figure 1 Using |log2FoldChange|>= 1 and FDR<0.05 as the screening criteria for differentially expressed genes (DEGs), the results showed that: ML_2 compared to ML_1 found 572 DEGs, of which 401 genes were upregulated and 171 genes were downregulated; ML_3 compared to ML_1 found 1406 DEGs, of which 681 genes were upregulated and 725 genes were downregulated; ML_3 compared to ML_2 found 16 DEGs, of which 13 genes were upregulated and 3 genes were downregulated; NL_1 compared to ML_1 found 632 DEGs, of which 225 genes were upregulated and 407 genes were downregulated; NL_2 compared to ML_2 found 34 DEGs, of which 8 genes were upregulated and 26 genes were downregulated; NL_3 compared to ML_3 found 1253 DEGs, of which 301 genes were upregulated and 952 genes were downregulated. Figure 2 , Figure 3 ).

[0032] (3) Differential gene function annotation and enrichment analysis: ① GO enrichment analysis of differentially expressed genes: GO functional enrichment analysis was performed on DEGs from different groups, including three parts: molecular function, biological process, and cellular component.The results showed that the DEGs of ML_2 compared to ML_1 were mainly enriched in chromosome separation, nuclear chromosome separation, nuclear division, mitotic nuclear division, sister chromatid separation, mitotic sister chromatid separation, regulation of chromosome separation, mitotic cell cycle phase transition, chromosome regions, chromosome separation, chromosome centromere regions, regulation of mitotic cell cycle, DNA replication, regulation of chromosome separation, and positive regulation of cell cycle processes. The DEGs of ML_3 compared to ML_1 were mainly enriched in chromosome separation, nuclear division, mitotic nuclear division, nuclear chromosome separation, sister chromatid separation, mitotic sister chromatid separation, mitotic cell cycle phase transition, and mitotic cell cycle. The regulation of phase transitions, regulation of the mitotic cell cycle, positive regulation of cell cycle processes, immunoglobulin complexes, negative regulation of phase transitions in the mitotic cell cycle, regulation of phase transitions in the cell cycle, regulation of chromosome segregation, and positive regulation of the cell cycle; ML_3 compared to ML_2's DEGs are mainly enriched in the immunoglobulin complex, circulating immunoglobulin complex, immunoglobulin receptor binding, antigen binding, B cell activation regulation, phagocytosis recognition, classical complement activation pathway, B cell receptor signaling pathway, circulating immunoglobulin-mediated humoral immune response, hypersensitivity response regulation, phagocytosis encapsulation, hypersensitivity response, complement activation, plasma membrane invagination, and positive regulation of B cell activation; NL_1 compared to ML_ DEGs of NL_2 are mainly enriched in positive regulation of cytokine production, protein folding, protein folding molecular chaperones, unfolded protein binding, positive regulation of immune response, heat shock protein binding, hemostasis, platelet activation, interleukin-1 production, regulation of interleukin-1 production, ATP-dependent protein folding molecular chaperones, molecular chaperone-mediated protein folding, blood coagulation, and activation of immune response. In contrast, DEGs of NL_2 are mainly enriched in sialic acid binding, L-ornithine transmembrane transport activity, L-lysine transmembrane transport activity, L-arginine transmembrane transport activity, basic amino acid transmembrane transport activity, tertiary granules, secretory granule membranes, immunoglobulin complexes, and ficolipid-rich areas. n-1 granular membrane, outer plasma membrane, tertiary granular membrane, L-lysine transmembrane import, L-ornithine transmembrane import, L-lysine transport, L-lysine transmembrane transport function; NL_3 compared to ML_3 DEGs are mainly enriched in immune response regulation signaling pathways, positive regulation of cytokine production, positive regulation of immune response, outer plasma membrane, regulation of immune effector processes, pattern recognition receptor signaling pathway, interleukin-1 production, regulation of interleukin-1 production, positive regulation of immune effector processes, regulation of adaptive immune response based on somatic cell recombination, regulation of lymphocyte activation, adaptive immune response based on somatic cell recombination, detection of biostimuli, leukocyte-mediated immunity, and cell activation function in immune response. Figure 4 ).

[0033] ML2 vs ML1 are mainly enriched in cell proliferation-related functions such as chromosome segregation, nuclear division, sister chromatid separation, cell cycle phase transition, and DNA replication. ML3 vs ML1, in addition to these, shows the presence of immunoglobulin complexes and negative cell cycle regulation, suggesting that proliferation and immune regulation are equally important. ML3 vs ML2 focuses on immunoglobulins, B cell activation, complement activation, phagocytosis, and hypersensitivity reactions, exhibiting obvious immune-inflammatory characteristics. NL1 vs ML1 highlights protein folding, heat shock proteins, coagulation, platelet activation, and IL-1-mediated hemostasis-initiation. NL2 vs ML2 is mainly characterized by sialic acid binding, basic amino acid transmembrane transport, and secretory granule membranes, reflecting changes in metabolic and secretory activities. NL3 vs ML3 is comprehensively enriched in cytokine production, pattern recognition receptor signaling, adaptive immunity, lymphocyte activation, and leukocyte-mediated immunity, showing strong immune response activation.

[0034] ② KEGG analysis of differentially expressed genes: KEGG analysis of DEGs from different groups showed that DEGs in ML2 compared to ML1 were mainly enriched in cell cycle, DNA replication, oocyte meiosis, primary immunodeficiency, progesterone-mediated oocyte maturation, viral protein-cytokine and cytokine receptor interactions, base excision repair, influenza A, microRNAs in cancer, cellular senescence, hepatitis C, African trypanosomiasis, p53 signaling pathway, amebiasis, and mismatch repair signaling pathways. DEGs in ML3 compared to ML1 were mainly enriched in cell cycle, African trypanosomiasis, primary immunodeficiency, DNA replication, hematopoietic lineage, and amebiasis. Extracellular matrix-receptor interactions, transcriptional dysregulation in cancer, viral protein-cytokine and cytokine receptor interactions, leishmaniasis, cellular senescence, cytokine-cytokine receptor interactions, cancer pathways, platelet activation, and signaling pathways in pathogenic E. coli infection; DEGs of ML3 compared to ML2 are mainly enriched in hematopoietic lineages, African trypanosomiasis, primary immunodeficiency, FcγR signaling pathway, B cell receptor signaling pathway, amebiasis, natural killer cell-mediated cytotoxicity, pathogenic E. coli infection, leishmaniasis, intestinal IgA-producing immune network, FcγR-mediated phagocytosis, and dilated cardiomyopathy. Rheumatoid arthritis, phospholipase D signaling pathway, and coronavirus disease-COVID-19 signaling pathway; DEGs of NL_1 compared to ML_1 are mainly enriched in hematopoietic lineage, cancer pathway, lipids and atherosclerosis, Legionnaires' disease, complement and coagulation cascade, estrogen signaling pathway, osteoclast differentiation, platelet activation, parathyroid hormone synthesis, secretion and action, necrosis and apoptosis, Salmonella infection, IL-17 signaling pathway, endoplasmic reticulum protein processing, MAPK signaling pathway, and NOD-like receptor signaling pathway; DEGs of NL2 compared to ML_2 are mainly enriched in hematopoietic lineage, FcγR-mediated phagocytosis, and tight Close junctions, phagosomes, phototransduction, vitamin digestion and absorption, thyroid cancer, cocaine addiction, ribosomes, endometrial cancer, basal cell carcinoma, non-small cell lung cancer, glioma, acute myeloid leukemia, and the p53 signaling pathway; DEGs of NL_3 compared to ML_3 are mainly enriched in osteoclast differentiation, parathyroid hormone synthesis, secretion and action, tuberculosis, cytokine-cytokine receptor interaction, hematopoietic lineage, FcγR-mediated phagocytosis, phagosomes, Legionnaires' disease, influenza A, acute myeloid leukemia, cancer pathways, lipids and atherosclerosis, glutamatergic synapses, chemokine signaling pathways, and C-type lectin receptor signaling pathways. Figure 5 ).

[0035] KEGG analysis showed that the core pathways of ML2 vs ML1 were concentrated in cell cycle, DNA replication, oocyte meiosis, and p53 signaling pathway, which are related to cell proliferation and DNA homeostasis. ML3 vs ML1, on the other hand, added hematopoietic lineage, cytokine-cytokine receptor interaction, platelet activation, and multiple pathogen infection (African trypanosomiasis, leishmaniasis, Escherichia coli) pathways, suggesting that proliferation and immunity-inflammation are equally important. ML3 vs ML2, on the other hand, shifted entirely to immune defense, significantly enriched hematopoietic lineage, FcεRI / FcγR-mediated phagocytosis, B cell receptor signaling, natural killer cytotoxicity, intestinal IgA network, and coronavirus-COVID-19 pathway, showing strong adaptive immune activation. In contrast, Nili-Raffi buffalo (NL) exhibited more significant immune-metabolic integration at each stage: NL_1 vs ML_1 was dominated by hematopoietic lineage, complement-coagulation cascade, lipid-atherosclerosis, IL-17, MAPK and NOD-like receptor signaling pathways, highlighting inflammation-lipid metabolism interactions; NL_2 vs ML_2 was enriched in FcγR-mediated phagocytosis, tight junctions, phagosomes and multiple tumor-related pathways (thyroid cancer, non-small cell lung cancer, acute myeloid leukemia, etc.), suggesting active immune surveillance and tumor suppression mechanisms; NL_3 vs ML_3 further focused on osteoclast differentiation, cytokine-cytokine receptor interactions, chemokine and C-type lectin receptor signaling, while also superimposed with tuberculosis, Legionnaires' disease, lipid and atherosclerosis and glutamatergic synaptic pathways.

[0036] (4) WCGNA analysis: ① Soft threshold selection: WGCNA analysis employs correlation coefficient weighting, which involves raising the gene correlation coefficient to the Nth power. This approach strengthens strong correlations and weakens weak or negative correlations, resulting in a scale-free network distribution within the gene network, thus enhancing its biological significance. The optimal power value is calculated using the `pickSoftThreshold` function from the WGCNA package in R (with RsquaredCut set to 0.85). If a suitable power value cannot be calculated, it can be selected from the table on the WGCNA website that corresponds to sample size and power value for subsequent data analysis. In this experiment, a soft threshold of 18 was calculated.

[0037] Figure 6The horizontal axis in the graph represents the weight parameter "power," also known as the soft threshold. The vertical axis in the left graph represents the square of the correlation coefficient in the corresponding network. Sometimes negative values ​​appear because they are multiplied by the negative direction of the slope column value; only positive values ​​are relevant. The higher the square of the correlation coefficient, the closer the network approximates the distribution of a scale-free network. The right graph shows the average connectivity corresponding to different power values.

[0038] ② 3.2 Module Hierarchical Clustering: The `blockwiseModules()` function was used to construct a clustering tree based on the correlation of gene expression levels and divide the data into modules. In the diagram, each color represents a gene in the corresponding clustering tree belonging to the same module. If certain genes consistently exhibit similar expression changes in a physiological process or different tissues, these genes may be functionally related and can be defined as a module. This experiment resulted in 28 clustering modules.

[0039] Figure 7 Each color in the tree represents a color corresponding to each gene in the clustering tree belonging to the same module. If certain genes always have similar expression changes in a physiological process or different tissues, then these genes may be functionally related and can be defined as a module. For the upper part of the tree diagram, the vertical distance represents the distance between two nodes (between genes), while the horizontal distance is meaningless.

[0040] Calculate and visualize the similarity between genes, especially by calculating the TOM (Topological Overlap Matrix) to reveal the relationships between gene modules, and visualize these similarities using heatmaps. Figure 8 ).

[0041] Figure 8 Each branch in the tree diagram represents a gene, and the darker the color of each point (white → yellow → red), the stronger the correlation between the two genes corresponding to the row and column.

[0042] ③ Module characteristics and expression patterns: Calculate the correlation between modules and cluster the modules according to their eigenvalues ​​(eigengenes), outputting a heatmap of the correlation between modules. Figure 9 The correlation coefficient between samples can be calculated using the `cor()` function in R. Figure 9 The graph can be divided into two parts. The upper part clusters the modules based on their eigenvalues. The vertical axis represents the dissimilarity of the nodes. In the lower part, each row and column represents a module. The darker (redder) the square, the stronger the correlation; the lighter the square, the weaker the correlation.

[0043] Correlation analysis was performed between the module's eigenvalues ​​and the sample. If the module's eigenvalues ​​showed high positive or negative expression in the sample, it indicated that the module was closely related to the sample. Figure 10 ). Figure 10 The horizontal axis represents the sample name, and the vertical axis represents a module for each color. The darker the color (dark red or dark blue), the stronger the correlation between the sample and the module; the lighter the color, the weaker the correlation.

[0044] ④ Hub gene screening: For each module gene list obtained from the WGCNA analysis, connectivity values, expression levels, and annotations from seven major databases were added. Connectivity refers to the degree to which a gene is connected to other genes (usually calculated only within a module), often referred to as connectivity or degree, or represented by the number k. Generally, genes with the highest connectivity (k value) in a module can be considered hub genes. The hub genes screened from the 28 modules in this experiment are shown in Table 1.

[0045] Table 1. Hub genes of 28 clustering modules In summary, this invention, by studying the gene expression regulation patterns and metabolite expression changes in calves before and after colostrum, provides a basis and theoretical foundation for further understanding the establishment process and response mechanism of the calf immune system, when the passive immune system function of calves terminates, and how the autoimmune system is regulated at the molecular level.

[0046] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for screening key immune-related genes in calves, characterized in that, The screening method includes: Transcriptome data of peripheral blood before colostrum and after colostrum were obtained from different calf samples. Differential gene data were screened based on the transcriptome data of peripheral blood before colostrum and after colostrum, and enrichment analysis was performed to obtain gene sets related to specific biological processes, functions or pathways. Based on gene sets related to specific biological processes, functions, or pathways, WCGNA was analyzed using weighted gene co-expression network to identify key genes.

2. The method for screening key immune-related genes in calves according to claim 1, characterized in that, The calf samples included at least two different breeds of calves.

3. The method for screening key immune-related genes in calves according to claim 1, characterized in that, The transcriptome data of peripheral blood after colostrum includes transcriptome data of peripheral blood from 5 to 8 days after birth and transcriptome data of peripheral blood from 12 days after birth.

4. The method for screening key immune-related genes in calves according to claim 1, characterized in that, Differentially expressed gene data were screened based on transcriptomic data from peripheral blood before and after colostrum. The screening criteria were: |log2FoldChange| >= 1 and FDR < 0.

05.

5. The method for screening immune-related key genes in calves according to any one of claims 1 to 4, characterized in that, The enrichment analysis included GO enrichment analysis and KEGG enrichment analysis of differentially expressed genes.

6. The method for screening immune-related key genes in calves according to any one of claims 1 to 4, characterized in that, The key genes obtained by using weighted gene co-expression network analysis (WCGNA) based on a gene set related to a specific biological process, function, or pathway include the following specific steps: using WCGNA based on a gene set related to a specific biological process, function, or pathway; adding connectivity values ​​to the gene list of each module obtained by WCGNA; and obtaining the key genes based on the connectivity values.

7. The method for screening key immune-related genes in calves according to claim 6, characterized in that, The key genes include CEBPD, SPI1, FANCD2, BCLAF3, IFIH1, LOC123465206, novel.604, GRB7, LOC102397654, LOC102414909, CCL26, PNPLA8, COL1A2, CA8, CCNT1, SMC2, novel.166, CANX, PALM, MOB1A, AK2, FKBP10, LOC102408646, LOC102410605, LOC102397015, PACC1, CCL5, and NFRKB.

8. A device for screening key immune-related genes in calves, characterized in that, The device includes: Acquisition module: used to acquire transcriptome data of peripheral blood before colostrum and peripheral blood after colostrum from different calf samples; based on the transcriptome data of peripheral blood before colostrum and peripheral blood after colostrum, differential gene data are screened and enrichment analysis is performed to obtain gene sets related to specific biological processes, functions or pathways. Key gene modules: Gene sets related to specific biological processes, functions or pathways are obtained by weighted gene co-expression network analysis (WCGNA).

9. An electronic device, characterized in that, include: A memory and a processor, wherein the memory and the processor are connected; The memory is used to store programs; The processor invokes a program stored in the memory to execute the screening method for immune-related key genes in calves as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, It contains a computer program that, when executed by a computer, performs the screening method for immune-related key genes in calves as described in any one of claims 1 to 7.

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