Construction method of microRNA regulation and control network for influencing calf immunity establishment

Abstract

The invention relates to the technical field of immune functions, in particular to a construction method of a microRNA regulation and control network for influencing calf immunity establishment. According to the method, the expression profiles of microRNA in colostrum and finished milk are obtained, the microRNA with high expression and obvious difference in colostrum compared with the finished milk is found, target gene prediction is carried out on the differentially expressed microRNA, gene annotation and enrichment analysis are carried out on the obtained target gene, the target gene enriched by the MAPK pathway related to immunoregulation in the enrichment analysis result is screened, and the immunoregulation related MAPK pathway related to the immunoregulation is screened to obtain the immunoregulation related MAPK pathway related to the immunoregulation related to the immunoregulation related to the immunoregulation related to the immunoregulation related to the immunoregulation related to the immunoregulation related to the immunoregulation. And a network regulation relation graph of the immune-related target gene and the microRNA regulated by the immune-related target gene is obtained, so that the key node target gene regulated by the microRNA in milk in the MAPK pathway and the correlation between the key node target gene and the microRNA are screened out, and a technical support is provided for further research and development of feed additives or regulating agents for industrially improving the immunity of the calves.

Description

Technical Field

[0001] The invention relates to the technical field of immune function, and in particular to a method for constructing a microRNA regulatory network that affects calf immune establishment. Background Art

[0002] Breast milk is essential for the growth and development of mammalian infants. A large number of studies have confirmed that colostrum contains rich nutrients and immune antibodies, which can help newborn infants quickly build their own immune systems and improve their environmental adaptability and disease resistance. However, the specific components of colostrum and the mechanism of how it helps infants build their immune systems are still unclear and remain a hot topic of research.

[0003] MicroRNA (miRNA) is a type of non-coding RNA with a length of 19 to 24 nucleotides. In the post-translational stage, it can regulate the expression of protein-coding genes by binding to the 3' region of its target gene, thereby playing a biological function of regulating protein synthesis. By determining the regulatory network of microRNA in milk and its target genes, microRNA in milk can be associated with target genes in calf immune-related pathways, and the correlation between microRNA in milk and calf immunity establishment can be established, so as to study its network regulatory effect and explain the vital role of milk in the establishment of calf immunity from a genetic level. At present, there is still a gap in the research on the regulatory network at the post-transcriptional level of microRNA formed by microRNA in milk and immune-related target genes in the establishment of calf immunity. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method for constructing a microRNA regulatory network that affects calf immunity. The construction method provided by the present invention screens key node genes by establishing a gene network regulatory graph, thereby screening out key node genes regulated by milk microRNA in the immune-related signaling pathway MAPK, establishing the correlation between milk microRNA and calf immunity, and providing technical support for the subsequent industrialization of further research and development of feed additives or regulators that improve calf immunity.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] The present invention provides a method for constructing a microRNA regulatory network that affects calf immunity establishment, comprising the following steps:

[0007] 1) Perform miRNA sequencing on colostrum and milk of dairy cows respectively to obtain colostrum miRNA dataset and milk miRNA dataset;

[0008] 2) performing differential analysis on the miRNA data in the colostrum miRNA dataset and the lactation miRNA dataset, using |log2(FC)|≥1, P<0.05 and high abundance expression as screening threshold criteria to obtain differential miRNAs; the high abundance expression criteria is: the expression level of the high abundance expression miRNA is higher than the average expression level of each miRNA in the corresponding dataset;

[0009] 3) Using the software TargetScan and miRanda respectively to predict the target genes of the differential miRNAs, the target genes predicted by the two softwares were screened according to the scoring criteria of each software, and the intersection of the screening results of the two softwares was taken to obtain a differential miRNA-target gene dataset consisting of differential miRNAs and their corresponding target genes;

[0010] 4) performing GO enrichment analysis and KEGG enrichment analysis on the target genes in the differential miRNA-target gene data set, retaining the target genes related to immune regulation and their corresponding differential miRNAs, and obtaining an immune miRNA-target gene data set;

[0011] 5) Using software to process the immune miRNA-target gene data set and draw a miRNA-gene-KEGG network diagram.

[0012] Preferably, the drawing method described in step 5) includes: taking the kegg_pathway of the target gene corresponding to the differential miRNA in the immune miRNA-target gene data set as node information, taking the MAPK signaling pathway and the Endocytosis signaling pathway with the highest pathway enrichment significance as information subsets, sorting out the miRNAs and their target genes with extremely significant differences and high abundance contained in the two pathways, and drawing a miRNA-gene-KEGG network diagram; the extremely significant difference means that the |log2(FC)| of the miRNA in colostrum and mature milk is ≥1 and P <0.05; the high abundance expression means that the expression level of the miRNA is higher than the average expression level of each miRNA in the corresponding data set.

[0013] Preferably, the software in step 5) includes Cytoscape.

[0014] Preferably, the scoring criteria in step 3) are: removing target genes with a context score percentile less than 50 in TargetScan, and removing target genes with a maximum free energy greater than -10 in miRanda.

[0015] Preferably, before performing differential analysis in step 2), the miRNA data in the colostrum miRNA dataset and the lactation miRNA dataset are preprocessed respectively to obtain the expression spectrum of colostrum miRNA and the expression spectrum of lactation miRNA; the expression spectrum of colostrum miRNA and the expression spectrum of lactation miRNA are differentially analyzed; the preprocessing includes: aligning the miRNA data to the published bovine reference genome data, identifying miRNA by aligning with miRBase, and obtaining alignment data; normalizing the alignment data to obtain the expression spectrum of miRNA.

[0016] Preferably, the colostrum and lactation of the cow are subjected to miRNA sequencing respectively and then screening processing is performed to obtain the colostrum miRNA dataset and lactation miRNA dataset; the screening processing includes: removing the sequence of the 3' linker and junk sequences, retaining the sequence with a base length of 18 to 26 nt; comparing the retained sequence with a public database, and removing non-miRNA sequences and repeat associate sRNA.

[0017] Preferably, the public databases include the Rfam database and the Repbase database.

[0018] The present invention provides miRNA related to the immune function of bovine colostrum obtained by screening using the construction method described in the above technical solution.

[0019] Preferably, the miRNA includes one or more of bta-mir-2904-2-p3, bta-miR-2284x_R+1, bta-miR-23b-3p_R-1, bta-miR-22-3p_R+1, bta-miR-193a-5p, mmu-miR-200b-5p, bta-miR-342_R-2, bta-miR-885, mmu-miR-203-3p and bta-miR-363_L+1.

[0020] Beneficial effects:

[0021] The present invention provides a method for constructing a microRNA regulatory network that affects calf immunity. The present invention obtains the expression profile of microRNA in colostrum and mature milk, finds microRNAs with high expression abundance and significant differences in mature milk and colostrum according to the expression abundance and difference of microRNA in the expression profile, and then predicts the target gene of the differentially expressed microRNA, annotates and enriches the obtained target gene, screens the genes enriched in the MAPK pathway related to immune regulation in the KEGG enrichment analysis result, constructs a microRNA-gene-KEGG network diagram, thereby forming a network regulation relationship diagram of immune-related target genes and their regulating microRNAs, and screens key node genes through the network regulation diagram, thereby screening out the key node genes in the MAPK pathway that are regulated by microRNA in milk and their correlation with microRNA, and provides technical support for the further research and development of feed additives or regulators that improve the immunity of calves in industrialization. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the embodiments are briefly introduced below.

[0023] Figure 1 The bar graphs are the statistical results of the differentially expressed miRNAs in colostrum and mature milk;

[0024] Figure 2 Figure 2 is the interaction relationship diagram of differential miRNAs and their target genes; the blue miRNAs are: bta-mir-2904-2-p3, mmu-miR-200b-5p, bta-miR-363_L+1, bta-miR-885, bta-miR-342_R-2, bta-miR-22-3p_R+1, mmu-miR-203-3p and bta-miR-23b-3p_R-1;

[0025] Figure 3 This is the miRNA-gene-KEGG regulatory network diagram related to bovine milk immunity. DETAILED DESCRIPTION

[0026] The present invention provides a method for constructing a microRNA regulatory network that affects calf immunity establishment, comprising the following steps:

[0027] 1) Perform miRNA sequencing on colostrum and milk of dairy cows respectively to obtain colostrum miRNA dataset and milk miRNA dataset;

[0028] 2) performing differential analysis on the miRNA data in the colostrum miRNA dataset and the lactation miRNA dataset, using |log2(FC)|≥1, P<0.05 and high abundance expression as screening threshold criteria to obtain differential miRNAs; the high abundance expression criteria is: the expression level of the high abundance expression miRNA is higher than the average expression level of each miRNA in the corresponding dataset;

[0029] 3) Using the software TargetScan and miRanda respectively to predict the target genes of the differential miRNAs, the target genes predicted by the two softwares were screened according to the scoring criteria of each software, and the intersection of the screening results of the two softwares was taken to obtain a differential miRNA-target gene dataset consisting of differential miRNAs and their corresponding target genes;

[0030] 4) performing GO enrichment analysis and KEGG enrichment analysis on the target genes in the differential miRNA-target gene data set, retaining the target genes related to immune regulation and their corresponding differential miRNAs, and obtaining an immune miRNA-target gene data set;

[0031] 5) Using software to process the immune miRNA-target gene data set and draw a miRNA-gene-KEGG network diagram.

[0032] The present invention performs miRNA sequencing on colostrum and mature milk of dairy cows respectively to obtain colostrum miRNA dataset and mature milk miRNA dataset. As an embodiment, the sequencing is performed using Illumina HiSeq2000 or Illumina HiSeq2500 platform, and the library construction and sequencing before microRNA sequencing are performed according to relevant experimental steps.

[0033] As an embodiment, the present invention removes the 3' adapter sequence and junk sequence in the sequencing data, retains the sequence with a base length of 18 to 26 nt, and obtains the processed sequencing data. As an embodiment, the junk sequence includes any one of 1) to 4), specifically: 1) reads with a ratio of more than 10% of base information that cannot be determined; 2) reads contaminated by 5' adapters; 3) reads without 3' adapter sequences and inserted fragments; 4) reads of polyA / T / G / C.

[0034] As an embodiment, the present invention compares the processed sequencing data with a public database, removes non-miRNA sequences and repeat associate sRNA, retains sequences with a base length of 18 to 26 nt, colostrum miRNA data sets and lactation miRNA data sets. As an embodiment, the public databases are Rfam database and Repbase database. By comparing the Rfam database, the repetitive sequences of non-miRNA sequences such as rRNA, small nucleolar RNA (snoRNA), small nuclear (snRNA), tRNA, etc. in Clean Data are found and removed; the Repbase database (http: / / www.girinst.org / education / index.html) is used to annotate the small RNA sequences obtained by sequencing, and the repeat associate sRNA therein is found and removed.

[0035] After obtaining the colostrum miRNA dataset and the lactation miRNA dataset, the present invention performs differential analysis on the miRNA data in the colostrum miRNA dataset and the lactation miRNA dataset, and uses |log2(FC)|≥1, P<0.05 and high abundance expression as screening threshold standards to obtain differential miRNA; the standard for high abundance expression is: the expression level of the high abundance expression miRNA is higher than the average expression level of each miRNA in the corresponding dataset, that is, the expression level of the high abundance expression miRNA in the colostrum miRNA dataset is higher than the average expression level of each miRNA in the colostrum miRNA dataset, and the expression level of the high abundance expression miRNA in the lactation miRNA dataset is higher than the average expression level of each miRNA in the lactation miRNA dataset. As an implementation mode, the miRNA data is compared with the bovine reference genome data published on miRBase using the software bowtie, and miRNA identification is performed by comparing with miRBase to obtain comparison data; the comparison data is combined and processed with the expression spectrum of colostrum miRNA and the expression spectrum of lactation miRNA using a normalization method, and the expression spectrum of colostrum miRNA and the expression spectrum of lactation miRNA are differentially analyzed.

[0036] The log2(FC) described in the present invention is the abbreviation of Log2(fold change), which is a screening expression method for the expression difference fold of two genes in the transcriptome sequencing gene expression spectrum. The logarithmic operation result with base 2 is used as the expression difference relationship between the comparison genes, and log2(fold change) is the difference fold with base log2.

[0037] After obtaining the differential miRNA, the present invention uses the software TargetScan and miRanda to predict the target gene of the differential miRNA, respectively, and the target genes predicted by the two software are screened according to the scoring criteria of each software, and the intersection of the screening results of the two software is taken to obtain a differential miRNA-target gene data set composed of differential miRNA and its corresponding target gene. As an embodiment, the scoring criteria are: in TargetScan, target genes with a context score percentile less than 50 are removed, and in miRanda, target genes with a maximum free energy greater than -10 are removed.

[0038] After obtaining the differential miRNA-target gene data set, the present invention performs GO enrichment analysis and KEGG enrichment analysis on the target genes in the differential miRNA-target gene data set, retains the target genes related to immune regulation and their corresponding differential miRNAs, and obtains an immune miRNA-target gene data set. As an embodiment, the method of performing GO enrichment analysis and KEGG enrichment analysis includes: annotating the biological function of the target gene, and obtaining miRNAs and their target genes related to immune regulation by selecting target genes annotated to immune-related metabolic pathways and target genes with immune function annotations.

[0039] In order to clearly see the regulatory pathways involved in potential key miRNAs, the present invention uses software to process the immune miRNA-target gene data set and draw a miRNA-gene-KEGG network diagram. As an embodiment, the drawing method includes: taking the kegg_pathway of the target gene corresponding to the differential miRNA in the immune miRNA-target gene data set as the node information, taking the MAPK signaling pathway and the Endocytosis signaling pathway with the highest significance of Pathway enrichment as the information subset, sorting out the miRNAs and their target genes with extremely significant differences and high abundance in the two pathways, and drawing a miRNA-gene-KEGG network diagram; the extremely significant difference is that the miRNA in colostrum and milk|log2(FC)|≥1 and P<0.05; the high abundance expression is that the expression of the miRNA is higher than the average expression of each miRNA in the corresponding data set. As an embodiment, the software can be cytoscape.

[0040] The present invention provides miRNA related to immune function in bovine colostrum obtained by screening using the construction method described in the above technical solution.

[0041] As one embodiment, the miRNA includes one or more of bta-mir-2904-2-p3, bta-miR-2284x_R+1, bta-miR-23b-3p_R-1, bta-miR-22-3p_R+1, bta-miR-193a-5p, mmu-miR-200b-5p, bta-miR-342_R-2, bta-miR-885, mmu-miR-203-3p and bta-miR-363_L+1.

[0042] The present invention targets the microRNA carried in milk, and promotes the establishment of the calf's immune system through the gene regulatory network formed by the post-transcriptional regulatory effect of its microRNA. Since milk is the first source of nutrition ingested by mammalian infants after birth, the immune regulatory effect of microRNA in milk can quickly help the infants promote the establishment of the immune system at the gene regulatory level, and milk can be industrially produced into nutrients such as milk replacer and additives, so as to be used as a feed additive to improve the immunity and disease resistance of the infants. The present invention can enrich the physiological and biochemical effects of milk on the calf's body from the gene regulatory level by establishing a correlation between the regulatory effect of microRNA in milk and the calf's immune ability and obtaining a regulatory association mechanism, and provide technical support and research and development direction for the subsequent production of functional feed additives and milk replacers through artificial synthesis coating technology, and for the further research and development of feed additives or regulators for industrializing the improvement of calf immunity.

[0043] To further illustrate the present invention, a method for screening miRNA related to immune function in bovine colostrum provided by the present invention is described in detail below in conjunction with the accompanying drawings and examples, but they should not be construed as limiting the scope of protection of the present invention.

[0044] Example 1

[0045] 1. Sample collection and RNA extraction: The experimental animals were 5 healthy Holstein cows provided by the cattle farm of Kefeng Agriculture and Animal Husbandry Co., Ltd., Hangjinhou Banner, Bayannur City, Inner Mongolia. They were 3 to 4 years old, weighed between 450 and 500 kg, gave birth for the first time, and had the same diet and feeding conditions. Colostrum was collected within 48 hours after the calf was born, and adult milk was collected when the calf was 3 months old during the peak lactation period. The collected colostrum and adult milk samples were transported in liquid nitrogen and stored in an ultra-low temperature refrigerator at -80°C. Total RNA was extracted from colostrum milk samples and adult milk samples, respectively, and then microRNA (miRNA) sequencing was performed.

[0046] 2. Sequencing library construction and microRNA sequencing: The small RNA sequencing library was prepared using TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, USA). After the library was prepared, the library was sequenced using Illumina HiSeq2500, with a single-end read length of 50 bp.

[0047] 3. Bioinformatics analysis process: MicroRNA sequencing and offline data analysis are entrusted to Lianchuan Biotechnology Co., Ltd., including the following steps:

[0048] 1) Remove the 3' linker sequence and junk sequence to obtain the original data (clean data), retain the sequence with base length between 18 and 26 nt; compare with the Rfam database (http: / rfam.xfam.org / ), find and remove the repetitive sequences of non-miRNA sequences such as rRNA, small nucleolar RNA (snoRNA), small nuclear (snRNA), tRNA, etc. in the Clean Data.

[0049] 2) Use the Repbase database (http: / / www.girinst.org / education / index.html) to find and remove possible repeat associate sRNAs.

[0050] 3) The remaining sequences (referred to as miRNA data) were used for known miRNA analysis. Bowtie (https: / / bowtie-bio.sourceforge.net / index.shtml) was used to compare the bovine reference genome data and miRNA identification was performed by comparing with miRBase (http:www.mirbase.org / ). [1-3] The expression profile of miRNA was obtained, and the difference analysis was performed using T-test, with P ≤ 0.05 as the screening standard, to obtain the miRNAs that were differentially expressed in colostrum compared with mature milk.

[0051] 4. MicroRNA target gene prediction and enrichment analysis: TargetScan (v5.0) and miRanda (v3.3a) were used to predict the target genes of the miRNAs with significant differences. The target genes predicted by the two software were screened according to the scoring criteria of each software. In the TargetScan algorithm, target genes with a context score percentile less than 50 were removed, and in the miRanda algorithm, target genes with a maximum free energy (Max Energy) greater than -10 were removed (i.e., the threshold was TargetScan_score ≥ 50, miranda_Energy < -10). Finally, the intersection of these two software was taken as the final target gene of the differential miRNA. The final target genes were subjected to GO and KEGG enrichment analysis, and the biological functions of the target genes were annotated. By selecting target genes annotated to immune-related metabolic pathways and target genes with immune function annotations, miRNAs related to immune regulation (i.e., key miRNAs) and their target genes were screened.

[0052] 5. Construction of miRNA-gene-KEGG network diagram: In order to clearly see the regulatory pathways involved in potential key miRNAs, cytoscape software was used, with the kegg_pathway of the target gene corresponding to the key miRNA as the node information, and the MAPK signaling pathway and the Endocytosis signaling pathway with the highest pathway enrichment significance as the information subsets. The miRNAs and their target genes with extremely significant differences (|log2(FC)|≥1 and P<0.05) and high abundance expression (expression high level) in the two pathways were sorted out to draw the miRNA-gene-KEGG network diagram; the high abundance expression miRNA is the key miRNA whose expression level is above the average expression level of all miRNAs in the corresponding miRNA data (colostrum or lactation).

[0053] 6. Test results.

[0054] A total of 11,987,851 and 12,186,627 original sequences were identified in colostrum and mature milk, respectively. After removing the non-miRNA sequences from the Rfam database, subsequent miRNA sequence screening was performed, of which 863,483 and 1,094,091 non-miRNA sequences were compared with the Rfam database in colostrum and mature milk, respectively.

[0055] The present invention detected 764 and 1433 miRNA precursors, and 849 and 1599 miRNAs in colostrum and mature milk, respectively. When the difference significance P < 0.05 was set, 312 differentially expressed miRNAs in mature milk and colostrum were screened, of which 170 differentially expressed miRNAs in mature milk were higher than those in colostrum (upregulated expression, up), and 142 differentially expressed miRNAs in mature milk were lower than those in colostrum (downregulated expression, down). Under different screening conditions of significant difference P values, the statistical bar graph of differentially expressed miRNAs in colostrum and mature milk is shown in Figure 1 shown.

[0056] Considering the peak expression of miRNA, 219 differentially expressed miRNAs were screened with |log2(FC)|≥1 and P<0.05 as the standard, of which 49 had a difference P value <0.001, and 135 miRNAs were extremely significantly different (P<0.01). Among the 219 differentially expressed miRNAs, 112 miRNAs were expressed at higher levels in lactation than in colostrum, and 107 miRNAs showed a downward trend in expression. There were 10 differentially expressed miRNAs with high abundance (P<0.001), as shown in Table 1.

[0057] Table 1 Highly abundant differentially expressed miRNAs

[0058] miRNA Name miRNA Sequence (5'-3') up / down P-value bta-mir-2904-2-p3 CCTCGGATAGCCGGTCCCCCG up 8.99E-06 bta-miR-2284x_R+1 TGAAAAGTTCGTTCGGGTTTTT up 2.31E-05 bta-miR-23b-3p_R-1 ATCACATTGCCAGGGATTACCA down 7.67E-05 bta-miR-22-3p_R+1 AAGCTGCCAGTTGAAGAACTGT up 7.97E-05 bta-miR-193a-5p TGGGTCTTTGCGGGCGAGATGA up 0.000134632 mmu-miR-200b-5p CATCTTACTGGGCAGCATTGGA up 0.000260741 bta-miR-342_R-2 TCTCACACAGAAATCGCACCCAT down 0.000400312 bta-miR-885 TCCATTACACTACCCTGCCTCT up 0.000412748 mmu-miR-203-3p GTGAAATGTTTAGGACCACTAG down 0.000700817 bta-miR-363_L+1 AATTGCACGGTATCCATCTGCG down 0.000709596

[0059] Note: E in the table is the symbol for scientific notation, and the same applies to the following tables; up means the expression level in mature milk is higher than that in colostrum, and down means the expression level in mature milk is lower than that in colostrum.

[0060] Target gene prediction was performed for the miRNAs listed in Table 1, and a total of 65 target genes were found. Cytoscape was used to plot the relationship between miRNAs and their target genes, as shown in Figure 2. Figure 2 shown.

[0061] Depend on Figure 2As far as we know, bta-miR-23b-3p_R-1, mmu-miR-203-3p and bta-miR-22-3p_R+1 have a common target gene ASAP1; bta-miR-342_R-2, bta-miR-885 and bta-miR-363_L+1 have a common target gene DUSP10; bta-miR-342_R-2, bta-miR-885 and bta-miR-22-3p_R+1 have a common target gene CDC42. DUSP10 and CDC42 are both down-regulated bta-miR-342_R-2 target genes and up-regulated bta-miR-885 target genes. The target genes marked with purple nodes, such as RAS1, PLA2G4A, FASLG, PAK1, CACNG6, RPS6KA1, FCF2, MAP2K2, MAP3K20, FCF5, PRKCA, MAP2K7, MAPK8, TGFBR2 and WIPF1, are the target genes of two significantly differentially expressed miRNAs.

[0062] Functional analysis of target genes included GO functional annotation and KEGG Pathway functional annotation. GO annotation was analyzed according to three categories: Molecular Function, Biological Process, and Celluar Component. Significantly differential miRNA target genes were enriched in 15,310 GO entries, and more genes were enriched in the cell component (Celluar Component). The top three gene numbers were related to membrane, cytoplasm, cell part, and nucleus. KEGG metabolic pathway analysis found that significantly differentially differential miRNA target genes were enriched in 320 signaling pathways. The top three signaling pathways with the highest KEGG Pathway enrichment were MAPK signaling pathway, endocytosis, and fluid shear stress and atherosclerosis, as shown in Table 2.

[0063] Table 2 KEGG pathway enrichment analysis results of the top 10 significant differentially expressed miRNA target genes

[0064] KEGG Pathway P-value MAPK signaling pathway 1.29533E-13 Endocytosis 7.22858E-12 Fluid shear stress and atherosclerosis 4.72307E-11 Human T-cell leukemia virus 1 infection 1.94669E-10 Pathways in cancer 1.56841E-09 Rheumatoid arthritis 4.06066E-09 Cellular senescence 6.90704E-09 Th17 cell differentiation 2.27075E-08 Proteoglycans in cancer 3.24201E-08 Lysosome 4.43506E-08

[0065] Through KEGG enrichment analysis of predicted target genes of differentially expressed microRNA genes, the number of target genes annotated to the MAPK signaling pathway was 246 (246 / 5164), and the number of all target genes annotated to the MAPK pathway was 5164. The number of genes annotated to the MAPK pathway in this species was 287 (287 / 7718), and the total number of genes annotated to MAPK in the genome of this species was 7718, so the hypergeometric test difference of the MAPK pathway was the highest, and the number of target genes annotated to the endocytic signaling pathway was 204 (204 / 5164), and the number of all target genes annotated to the endocytic pathway was 5164. The number of genes annotated to the endocytic pathway in this species was 237 (237 / 7718), and the total number of genes annotated to MAPK in the genome of this species was 7718, so the hypergeometric test difference of the endocytic pathway was the second highest. For the MAPK signaling pathway, 247 differentially expressed (P < 0.05) microRNAs and 246 target genes were screened. For the endocytic signaling pathway, 240 differentially expressed microRNAs and 204 target genes were screened.

[0066] The KEGG signaling pathway of miRNA target genes was used as the information set, and the MAPK signaling pathway and Endocytosis signaling pathway with the highest pathway enrichment significance were used as node information. The miRNAs and their target genes with extremely significant differences and high abundance in the two pathways were sorted out, and the differential miRNA target gene-KEGG network regulation diagram was drawn using cytoscape software. Figure 3 shown.

[0067] Depend on Figure 3 It can be seen that 8 genes, including MRAS, BOLA-DRA, VEGFA, BOLA-DOA, NRAS, MAPK8, ICAM1 and INFN, exist in 3 pathways, and CHEK2, ITGAL, GSTO1, ATP6VOA2, IL12B, MAPK12 and MAPK13 exist in 2 signaling pathways.

[0068] The nutritional value of colostrum is extremely high. Compared with lactation, the role of colostrum in the immune function of young animals is more valued by people. In order to better study the immune function of bovine milk, the present invention screens the differential expression miRNA of bovine colostrum and lactation. The bioinformatics analysis of differential miRNA found that the expression pattern of colostrum and lactation miRNA has changed significantly, and some of the miRNAs with extremely significant differences are highly expressed, such as bta-mir-2904-2-p3, bta-miR-2284x_R+1, bta-miR-23b-3p_R-1, bta-miR-22-3p_R+1, bta-miR-193a-5p, mmu-miR-200b-5p, bta-miR-342_R-2, bta-miR-885, mmu-miR-203-3p and bta-miR-363_L+1, etc. Most of these miRNAs have been reported to be related to immunity and have been studied in tumors. In a study on bovine viral diarrhea, it was found that miR-2904 regulates apoptosis and autophagy by regulating related immune proteins and autophagy genes. [4] miR-2284x and miRNA miR-2284b can inhibit the synthesis of αs1-casein in mammary epithelial cells of dairy goats, affecting the quality of milk [5] . miR-23b-3p has obvious anti-inflammatory activity and can inhibit the release of inflammatory factors, thereby reducing inflammation in the body. [6] In the study of human diseases, it was found that the reduction of miR-22-3p would lead to the increase of some inflammatory factors, and its expression level in lung adenocarcinoma tended to be downregulated. [7] miR-193a-5p negatively regulates the expression of NCX2 by recognizing the 3'-untranslated region of NCX2 mRNA, thus exerting its function in osteosarcoma tissues. [8] miR-200b-5p inhibits tumor progression of salivary gland adenoid cystic carcinoma by targeting BTBD1 [9] In a study on the autoimmune disease cutaneous lupus erythematosus, miR-885-5p was found to be a potential therapeutic target. Downregulated miR-885-5p led to epidermal inflammation by enhancing NF-κB activity, PSMB5 proliferation, and TRAF1 immune renewal.

[10] miR-203-3p inhibits high glucose-induced oxidative stress, inflammatory response and apoptosis in mouse podocytes by regulating the expression of Sema3A

[11] The latest research on miR-363 found that miR-363 can be used as a potential therapeutic target and non-invasive diagnostic marker for cancer patients

[12] Therefore, it is speculated that these miRNAs may be the key miRNAs involved in the immune response of bovine colostrum.

[0069] A large number of studies have shown that miRNA regulates innate immunity, adaptive immunity, and cellular response to bacterial infection by mediating target genes. In target gene prediction, ASAP1, DUSP10, and CDC42 were found to be the common target genes of three key miRNAs. ASAP1, also known as centaurinβ4, is a GTPase activating protein of ADP-ribosylation factor and a negative regulator of Arf. It mainly affects the body's immune process against pathogen invasion by affecting the migration of immune cells.

[13] In hepatocellular carcinoma, ASAP1 can be considered as a prognostic biomarker for hepatocellular carcinoma patients

[14] Dual specificity phosphatase 10 (DUSP10), also known as mitogen-activated protein kinase phosphatase 5 (MPK-5), affects cell proliferation, differentiation, and death by mediating the MAPK signaling pathway, and plays an important role in the occurrence and development of tumors. DUSP10 plays an important role in the proliferation and migration of glioma cells

[15] DUSP10 can also activate tumor necrosis factor α in the NF-κB signaling pathway

[16] CDC42 is a potential target for cancer therapy. Enhanced CDC42 signaling promotes Ras-mediated cell transformation, tumorigenesis, and metastasis, and has been effectively used in Ras-related cancer lines derived from breast cancer, colon cancer, lung cancer, and pancreatic cancer.

[17] The down-regulated bta-miR-342_R-2 and up-regulated bta-miR-885 have common target genes DUSP10 and CDC42; there is no relevant report on whether DUSP10 and CDC42 are co-expressed and interact with each other during bovine colostrum immunity.

[0070] In addition to the three key target genes ASAP1, DUSP10 and CDC42, the target genes marked with blue nodes, such as RAS1, PLA2G4A, FASLG, PAK1, CACNG6, RPS6KA1, FCF2, MAP2K2, MAP3K20, FCF5, PRKCA, MAP2K7, MAPK8, TGFBR2 and WIPF1, are also key immune-related target genes. They are regulated by related miRNAs and are critical in the process of bovine milk exerting its immune function, but the specific mechanism needs further study.

[0071] MAPK signaling pathway and endocytosis play an important regulatory role in immunity. Studies on the pharmacological effects of drugs have found that drugs can have anti-inflammatory and antioxidant effects when they inhibit the MAPK signaling pathway.

[18] Most viruses enter cells through endocytosis, and viruses that rely on endocytosis can cause a delay in the immune system. The functions of key targets in the signaling pathway, such as MRAS, BOLA-DRA, VEGFA, BOLA-DOA, NRAS, MAPK8, ICAM1, and INFN, are very important for us to understand the mechanism of action of the entire signaling pathway.

[0072] The present invention collects and sequences bovine colostrum and milk miRNA, and jointly analyzes the miRNA data obtained by sequencing and the predicted target gene mRNA data to find that bta-mir-2904-2-p3, bta-miR-2284x_R+1, bta-miR-23b-3p_R-1, bta-miR-22-3p_R+1, bta-miR-193a-5p, mmu-miR-200b-5p, bta-miR-342_R-2, bta-miR-885, mmu-miR-203-3p and bta-miR-363_L+1 are key miRNAs related to the immune function of bovine colostrum. By constructing a network diagram, the correlation between microRNA and its target gene and its MAPK signaling pathway is obtained, and by referring to a large number of research documents, it is determined that the screened microRNA in milk has a correlation with the establishment of calf immunity. By constructing a miRNA-gene-KEGG network diagram, we can preliminarily analyze how key miRNAs play a role in immune function, providing technical support for the subsequent production and invention of functional feed additives and milk replacers.

[0073] References:

[0074] [1]Pollen Development in Autotetraploid and Diploid Rice.Int J MolSci.2016 Apr 12;17(4):499.

[0075] [2] Cer, RZ; Herrera-Galeano, JE; Anderson, JJ; Bishop-Lilly, KA; Mokashi, VPmiRNA Temporal Analyzer (miRNATA): A bioinformatics tool for identifying differentially expressed microRNAs in temporal studies using normal quantile transformation. Gigascience, 2014, 3, 20.

[0076] [3] Love MI, Huber W, Anders S.Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[J]. Genome Biol, 2014, 15(12):550.

[0077] [4]Yang N, Hu N, Zhang J, Yi J, Wang Z, Wang Y, Wu P, Chen C.bta-miR-2904inhibits bovine viral diarrhea virus replication by targeting viral-infection-induced autophagy via ATG13.Arch Virol.2022 Dec 28;168(1):11.doi:10.1007 / s00705-022-05630-4.PMID:36576583.

[0078] [5] Li Wenfei. Study on the regulation of αs1-casein synthesis in mammary epithelial cells of dairy goats by miR-2284b[D]. Northwest Agriculture and Forestry University. 2021.

[0079] [6] Jia Yanwei, Zhou Xiao. Correlation between peripheral blood miR-23b-3p, miR-223 and the efficacy and prognosis of bronchoscopic alveolar lavage in the treatment of severe pneumonia[J]. Liaoning Medical Journal, 2024, 38(01): 12-14.

[0080] [7]Ma DJ, Zhou XY, Qin YZ, Tian ZH, Liu HS, Li SQ. MiR-22-3p Expression is down-regulated in lung adenocarcinoma. Acta Biochim Pol. 2021 Jul 8;68(4):667-672.

[0081] [8]Chen R, Ning Y, Zeng G, Zhou H, Zhou L, Xiao P, Li Z, Zhou J. The miR-193a-5p / NCX2 / AKT axis promotes invasion and metastasis of osteosarcoma. JCancer. 2021 Aug8; 12(19):5903-5913.

[0082] [9]Tang Y,Zhu Q,Yang L,Meng Y,Zhang G,Zhou T,Wang C,Song X,Su YX,YeJ.MiR-200b-5p inhibits tumor progression in salivary adenoid cystic carcinoma via targeting BTBD1.Cell Signal.2023Sep:109:110748.doi:10.1016 / j.cellsig.2023.110748.Epub 2023 Jun 7.PMID:37290676.

[0083]

[10] Solé C,Domingo S,Penzo E,Moliné T,Porres L,Aparicio G,Ferrer B,Cortés-Hernandez J.Downregulation of miR-885-5p Promotes NF-κB PathwayActivation and Immune Recruitment in Cutaneous Lupus Erythematosus.J InvestDermatol.2023 Feb143(2):209-219.

[0084]

[11] Chen J,Xu Q,Zhang W,Zhen Y,Cheng F,Hua G,Lan J,Tu C.MiR-203-3pin inhibits the oxidative stress,inflammatory responses and apoptosis of micepodocytes induced by high glucose through regulating Sema3A expression 22:15(1):939-9

[0085]

[12] Nasimi Shad A,Akhlaghipour I,Alshakarchi HI,Saburi E,MoghbeliM.Role of microRNA-363 during tumor progression and invasion.J PhysiolBiochem.2024 May 1.doi:10.1007 / s13105-024-01022-1.Epub ahead of print.PMID:38691273.

[0086]

[13] Waltl S.Regulator of dendritic cell migration,ASAP1is associatedwith increased susceptibility to tuberculosis[J].Clin Genet,2015,88(6):530-531.

[0087]

[14] Bang S,Jee S,Son H,Cha H,Sim J,Kim Y,Park H,Myung J,Kim H,PaikS.Clinicopathological Implications of ASAP1 Expression in HepatocellularCarcinoma.Pathol Oncol Res.2022 Aug 30;28:1610635.

[0088]

[15] Zhou Q,Wang Y,Xin C,Wei X,Yao Y,Xia L.Identification of telomere-associated gene signatures to predict prognosis and drug sensitivity inglioma.Comput Biol Med.2024 Jan;168:107750.doi:10.1016 / j.compbiomed.2023.107750.Epub 2023 Nov 24.PMID:38029531.

[0089]

[16] Luo LJ, Liu F, Wang XY, et al. An essential function for MKP5 in the formation of oxidized low density lipid-induced foam cells[J]. CellularSignalling, 2012, 24(10):1889-1898.

[0090]

[17] Signalling,2012,24(10):1889-1898Aguilar BJ,Zhou H,Lu Q.Cdc42Signaling Pathway Inhibition as a Therapeutic Target in Ras-RelatedCancers.Curr Med Chem.2017;24(32):3485-3507.

[0091]

[18] BANIAHMAD B, SAFAEIAN L, VASEGHI G, et al. Cardioprotective effect of vanillic acid against doxorubicin-induced cardiotoxicity in rat[J]. ResPharmSci, 2020, 15(1):87-96.

[0092] Although the above embodiment describes the present invention in detail, it is only a part of the embodiments of the present invention, not all of the embodiments. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all fall within the protection scope of the present invention.

Claims

1. A method for constructing a microRNA regulatory network that affects calf immunity establishment, characterized in that: The following steps are involved: 1) Perform miRNA sequencing on colostrum and milk of dairy cows respectively to obtain colostrum miRNA dataset and milk miRNA dataset; 2) performing differential analysis on the miRNA data in the colostrum miRNA dataset and the lactation miRNA dataset, using |log2(FC)|≥1, P<0.05 and high abundance expression as screening threshold criteria to obtain differential miRNAs; the high abundance expression criteria is: the expression level of the high abundance expression miRNA is higher than the average expression level of each miRNA in the corresponding dataset; 3) Using the software TargetScan and miRanda respectively to predict the target genes of the differential miRNAs, the target genes predicted by the two softwares were screened according to the scoring criteria of each software, and the intersection of the screening results of the two softwares was taken to obtain a differential miRNA-target gene dataset consisting of differential miRNAs and their corresponding target genes; 4) performing GO enrichment analysis and KEGG enrichment analysis on the target genes in the differential miRNA-target gene data set, retaining the target genes related to immune regulation and their corresponding differential miRNAs, and obtaining an immune miRNA-target gene data set; 5) Using software to process the immune miRNA-target gene data set and draw a miRNA-gene-KEGG network diagram.

2. The construction method according to claim 1, characterized in that: The drawing method described in step 5) includes: taking the kegg_pathway of the target gene corresponding to the differential miRNA in the immune miRNA-target gene dataset as node information, taking the MAPK signaling pathway and the Endocytosis signaling pathway with the highest pathway enrichment significance as information subsets, sorting out the miRNAs and their target genes with extremely significant differences and high abundance in the two pathways, and drawing a miRNA-gene-KEGG network diagram; the extremely significant difference means that the |log2(FC)| of the miRNA in colostrum and mature milk is ≥1 and P < 0.05; the high abundance expression means that the expression level of the miRNA is higher than the average expression level of each miRNA in the corresponding dataset.

3. The construction method according to claim 1 or 2, characterized in that: The software in step 5) includes cytoscape.

4. The construction method according to claim 1, characterized in that: The scoring criteria in step 3) are: in TargetScan, target genes with a context score percentile less than 50 are removed; in miRanda, target genes with a maximum free energy greater than -10 are removed.

5. The construction method according to claim 1, characterized in that: Before performing differential analysis in step 2), the miRNA data in the colostrum miRNA dataset and the lactation miRNA dataset are preprocessed to obtain the expression profile of colostrum miRNA and the expression profile of lactation miRNA; The expression profiles of the colostrum miRNA and the milk miRNA are differentially analyzed; the preprocessing includes: aligning the miRNA data to the published bovine reference genome data, identifying the miRNA by aligning with miRBase, and obtaining alignment data; The alignment data are normalized to obtain the expression profile of miRNA.

6. The construction method according to claim 1, characterized in that: The colostrum and lactation of dairy cows are subjected to miRNA sequencing and then screening to obtain the colostrum miRNA dataset and lactation miRNA dataset; the screening includes: removing 3' linker sequences and junk sequences, retaining sequences with base lengths of 18 to 26 nt; comparing the retained sequences with a public database, and removing non-miRNA sequences and repeat associate sRNAs.

7. The construction method according to claim 1, characterized in that: The public databases include the Rfam database and the Repbase database.

8. The miRNA related to the immune function of bovine colostrum obtained by screening using the construction method according to any one of claims 1 to 7.

9. The miRNA according to claim 8, characterized in that The miRNA includes one or more of bta-mir-2904-2-p3, bta-miR-2284x_R+1, bta-miR-23b-3p_R-1, bta-miR-22-3p_R+1, bta-miR-193a-5p, mmu-miR-200b-5p, bta-miR-342_R-2, bta-miR-885, mmu-miR-203-3p and bta-miR-363_L+1.

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