A meta-analysis of differentially expressed microRNA during mastitis disease in dairy cattle

A meta-analysis of differentially expressed microRNA during mastitis disease in dairy cattle | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A meta-analysis of differentially expressed microRNA during mastitis disease in dairy cattle bahman Panahi, karim hasanpour, nooshin ghahramani, abbas rafat, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3510780/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Bovine mastitis is an important inflammation disease that affects the mammary gland and causing adverse effects on the quality and quantity of the produced milk, leads to a major economic lost in dairy industry. Streptococcus uberis is one of the bacteria commonly responsible for inducing mastitis in dairy cattle. Susceptibility to develop mastitis is a complex multifactorial phenotype and the improvement of the miRNAs and their target genes has not been comprehensively illustrated. Methods and Results: The purpose of this investigation was to perform a meta-analysis of the miRNAs expression profiling datasets to detect the key miRNAs, targets, and regulatory networks associated with mastitis. To this, publicly available miRNA datasets belong to three experiments on dairy cattle which challenged with S. uberis were included in our meta-analyzed. The identified differentially expressed miRNAs were used in TargetScan to identify their target genes. The functional impacts of the meta-miRNAs were further analyzed using Gene ontology and Protein-Protein Interaction network analysis. Three meta-miRNAs, namely bta-miR-98, bta-miR-138 and bta-miR-193a-3p, were obtained to be associated with the progress of the immune system and cell differentiation of the mammary gland during the mastitis. A total of 2061 target genes were identified that which bta-miR-98, bta-miR-138 and bta-miR-193a-3p were regulated 1121, 268 and 672 target genes respectively. Gene ontology analysis results were represented 237 biological process, 41 molecular function, 54 cellular component roles and nine KEGG pathways in mastitis disease. A total of 319, 113 and 124 target genes for bta-miR-98, bta-miR-193a-3p and bta-miR-138, respectively were inputted to cytoscape. The resulted network analysis showed that bta-miR-98 and bta-miR-138 have nine, bta-miR-138 and bta-miR-193a-3p have six, and bta-miR-193a-3p and bta-miR-98 have four common target genes. Twenty-one common genes were revealed by combing 360 common meta-genes in our previous research and 2061 meta-miRNA target genes. The procedure reported in this research offers a comprehensive scheme for the identification of the key miRNAs and target genes in mastitis disease by using global transcriptome data, meta-analysis, gene ontology, enrichment analysis and protein protein interaction. Conclusion: The findings of the current work suggest miRNAs are crucial amplifiers of inflammatory response by controlling metabolic pathway and inhibitors of several biological processes during S. uberis infection. Mastitis Complex genetics Innate immunity miRNA Meta-analysis Molecular network Figures Figure 1 Figure 2 Figure 3 Figure 4 BACKGROUND Attention to animal health and welfare has become an important issue because of the increasing demand for healthy products [ 1 , 2 ]. Bovine mastitis is an inflammation condition of the udder, which can associate with significant economic expenses to dairy industry [ 3 – 5 ]. Mastitis in the dairy cattle reduces the milk yield and its quality, influence the reproductive performance, and increase culling of the cows from the herd. Additionally, side effects of antibiotics on the cow body or their residues on milk for human health are other concerns of the mastitis [ 6 ]. In recent years, numerous studies have been shown that bovine mammary epithelial cells (BMECs) respond to the invasion of bacteria by altering the expression levels of several genes involved in inflammation and immunity [ 7 , 8 ]. Streptococcus uberis is among the four most prevalent species of mastitis causing pathogens [ 9 ]. Infection with this bacterium can occur with very few if any clinical signs, but can also result in severe inflammation of the udder culminating in clinical mastitis [ 10 ]. Monocytes are released from the bone marrow and control the pathogen spread and resolve the disease [ 11 , 12 ]. Typically, chemokines, interleukins (ILs), and tumor necrosis factor (TNF)-α, cell differentiation and apoptosis was reported as a main pathway during inflammation [ 13 ]. The methods of detecting bovine mastitis have been intensively developed over the years [ 14 ]. Immune cells and somatic cells are still exposed in the milk of animals, and the count of such somatic cells is an indicator of mastitis. Improving the dairy cattle resistance against the udder diseases, the risk of mastitis could be reduced [ 15 ]. Therefore, the identification of specific genes related to resistance to mastitis can provide a new way to control mastitis through genetic selection [ 16 , 17 ]. Recently the most relevant development in the transcriptomics technologies, especially the next-generation sequencing, as well as further advances in bioinformatics tools, have paved the way of comprehensive investigation of both messenger RNAs (mRNA) and non-coding RNAs (ncRNAs). These technologies have provided great opportunities for studying of miRNAs involved in husbandry animal diseases [ 18 ]. miRNAs are endogenous, small, noncoding RNA molecules of 22–24 nucleotides in length that are generated by various cell types, also they regulate post-transcriptional gene expression [ 19 , 20 ]. miRNAs prevent the translation initiation or elongation and control the expression of protein-coding genes and induce translational protein degradation and termination of translation [ 21 ]. Recently researches have confirmed the presence of various miRNAs in cow genome and attempted to characterize theirs structures [ 22 , 23 ]. miRNAs can be detected in raw or processed milk [ 24 , 25 ]. Studies involving E. coli and aureus showed alterations in the expression of various miRNAs [ 26 ]. In a challenge study of primary bovine mammary epithelial cells with S. uberis, MIR29B and MIR24-2 were detected in the infected group [ 27 ]. lately recognized miR-378 and miR-185 as candidate biomarkers that regulate the host response to S. aureus via different target genes and pathways of milk infected [ 28 ]. miR-181a detected by qPCR to examine regulatory function on Fc-gammaR-mediated phagocytosis, toll-like receptor signaling, antigen processing and presentation pathways during intramammary infections with S. uberis [ 29 ], related to innate immunity pathways using NGS approach 21 miRNAs identified as significantly differential expression post-infection [ 30 ]. [ 31 ] miR-320a and miR-320b identified during S. uberis mastitis due to their roles in enhance immune system. In another investigation, miR-144-5p and miR-130b-5p were significantly down-regulated and up-regulated respectively, in mastitis animals [ 32 ]. Network analysis of differential expression genes indicated that TNF pathway had positive relationships with genes involved with immune system function [ 33 ]. Meta-analysis has proven to be an efficient way to renew already published data by creating new empirical models, allowing progress in both understanding and prediction aspects [ 34 ]. Because independent experiences have limitations in statistical power, reliability of the results and sample size [ 35 ], meta-analysis is a tool to evaluate the efficacy of an intervention using all available information [ 36 ]. This approach can be used to distinguish effects related to mastitis disease through published study results and to detect factors that may influence those effects [ 37 ]. In first and second hypothesis using meta-analysis approach, the goal is to find DE genes that have non-zero effect sizes in, all studies or "one or more" studies respectively [ 38 ]. In several reviews the functional roles of miRNAs in husbandry animal disease have been reported [ 18 , 39 , 40 ], although they only focused on changes in miRNA expression profiles during mastitis disease. Construction of miRNA-mRNA network revealed crucial role of the miRNAs and target genes associated with specific traits [ 41 ]. Moreover, currently protein-protein interaction has been harnessed to identifying key miRNAs related to bovine mastitis [ 14 ]. In our current efforts, we have incorporated meta-analysis method to detect miRNAs from dairy cattle that have been infected with uberis . To investigate potential interaction networks and regulatory impact of miRNAs, we have constructed a S. uberis bovine mastitis model. MATERIALS AND METHODS Data Collection To conduct the study, we searched the National Center for Biotechnology Information’s Gene Expression Omnibus (GEO) ( https://www.ncbi.nlm.nih.gov/gds/ ) repository using following keywords such as " Bos taurus ", "mastitis", " Streptococcus uberis ", and "miRNAs". We manually selected miRNAs datasets belonging to transcriptome profiling of bovine mammary epithelial cells using small RNA sequencing platform (Table 1 ). The GSE59794 contained three miRNAs’ libraries constructed from infected bovine breast milk at 2 days post-infection, with corresponding control libraries from uninfected milk. They were named control (1, 2 and 3) and infection (1, 2 and 3), respectively. The GSE41278 contained four miRNAs’ libraries constructed from infected bovine after infection during 1, 2, 4 & 6 hours. The GSE51858 dataset consisted of five samples infected with the mastitis-causing pathogen S. uberis , at various time points after infection including 0, 12, 24, 36, and 48 hours post treatment. Table 1 miRNAs datasets list was chosen for the purpose of conducting a meta-analysis on mastitis disease Accession number Specie Bacteria Platform Sampling period Reference GSE59794 Bos-Tausus uberis Illumina 2 days post-infection (Sun et al., 2017) GSE41278 Bos-taurus uberis Illumina 1, 2, 4 & 6 hours [ 27 ] GSE51858 Bos-taurus uberis Illumina 0, 12, 24, 36 & 48 hrs post-infection [ 30 ] Pre-processing of miRNAs datasets and identification of DEGs The miRNA datasets were pre-processed and analyzed to identify the differentially expressed genes between healthy and infected MECs using Galaxy and R software [ 42 ]. The data was quality controlled using FastQC tool [ 43 , 44 ], and reads with average Phred quality less than 20 were removed. The adapter sequences were trimmed out using Trim Galore [ 45 ], and then, the trimmed reads smaller than 18 N bases or longer than 30 N bases were excluded. Bowtie2 software [ 46 ] was applied to build an index reference genome, and clean reads were mapped onto the B. taurus reference genome using Mapper tool of miRDeep2 software [ 47 ]. In the next step, the MiRDeep2 Quantifier tool was used to evaluate the expression count matrix [ 48 ]. Finally, the Bioconductor DESeq2 package was used to determine the DEGs. A normalization step was carried out to remove the non-biological variances, and appropriate scales were used for next steps [ 49 ]. The group comparison was carried out using moderated t-test, and the Benjamini-Hochberg method for adjusted P-value calculation [ 50 ]. DEGs were determined a significant when the false discovery rate (FDR) using the Benjamini Hochberg method was P-value ± 0.5 [ 51 ]. Meta-Analysis of miRNA Datasets The metaRNASeq package was utilized for the meta-analysis [ 52 , 53 ]. Fisher and invorm P-value combining methods were harnessed to identify the meta-miRNAs as described by [ 54 , 55 ]. Using the following formula this method combines the p-values for each gene from individual studies [ 56 , 57 ]. The Benjamini–Hochberg false discovery rate (FDR) was adjusted, and a corrected P-value < 0.05 was considered as statistically significant [ 51 ]. Genes that revealed FDR of < 0.05 and an average fold change of ≥ 2 were considered as meta-miRNAs. $${x}^{2}=-2{\sum }_{i=1}^{k}\text{l}\text{n}\left(pi\right)$$ Prediction of target genes of the identified miRNAs Computational prediction of miRNA targets is a critical initial step in identifying miRNAs, because one miRNA can target multiple genes transcripts. miRNAs are considered important mechanism for regulating gene expression and mRNA translation [ 58 ]. Target genes of the meta-miRNAs were predicted using TargetScan software [ 59 ]. Functional analysis of the target genes For analysis miRNA target genes functional impacts, enrichment analysis using gene ontology was performed using STRING database [ 60 ]. Gene ontology is commonly used to define the cellular location, molecular function and biological processes in which model organism genes participate [ 61 ]. Regulatory Network Construction According to the miRNA and candidate target genes, we established a link between miRNAs and candidate target genes to identify a potential miRNA-mRNA regulatory network [ 62 ]. Constructed regulatory networks were further visualized using Cytoscape software [ 63 ]. RESULTS Differentially expressed miRNAs The three selected studies included 18 miRNA expression profiles. A total of 13 differentially expressed miRNAs were reported in mastitis cases vs. healthy controls in GSE41278 study. There were 8 up-regulated and 5 down-regulated miRNAs. Table 2 lists the up and down-regulated miRNAs with their P-value in mastitis disease. Table 2 List of differentially expressed miRNAs between the healthy and infected cows miRNA name Fold change P-value bta-let-7b 0.457 0.0001 bta-miR-98 0.358 0.0017 bta-miR-193a-3p -0.354 0.0024 bta-miR-29b.1 -0.314 0.0122 bta-miR-29b -0.313 0.0122 bta-miR-193a-5p -0.270 0.0233 bta-let-7i 0.229 0.0279 bta-let-7c 0.353 0.0279 bta-miR-130a -0.273 0.0298 bta-miR-708 0.443 0.0374 bta-let-7a-5p 0.272 0.0401 bta-let-7a-5p.2 0.273 0.0401 bta-let-7a-5p.1 0.273 0.0401 Meta-analysis of the miRNAs We executed a meta-analysis approach using data from miRNA experiments. Three miRNA datasets containing 18 samples in dairy cattle studies were selected for the meta-analysis. According to meta-analysis three meta-miRNAs including bta-miR-98, bta-miR-138, and bta-miR-193a-3p in response to uberis were resulted. miRNA target genes Biological targets of miRNAs predicted using TargetScan [ 59 , 64 ]. As such, the counts of the target genes per each of the three meta-miRNAs are shown in Table 3 . Table 3 Identifying transcriptional target genes of miRNAs miRNA Target genes Gene regulatory network bta-miR-98 1121 319 bta-miR-193a-3p 268 113 bta-miR-138 672 124 Gene ontology of the target genes The STRING database was applied to GO enrichment analysis of 2061 target genes to base on biological process (BP), molecular function (MF), cellular component (CC) and KEGG pathway. The results showed that the 237 BP, 41 MF, 54 CC terms and nine KEGG pathways were significant (FIGURE 1 ). Network analysis of the target genes A total of 319, 113 and 124 target genes were found for bta-miR-98, bta-miR-193a-3p and bta-miR-138, respectively. As shown in FIGURE 2 , there were nine common genes between the targets of bta-miR-98 and bta-miR-138, six common genes between the targets of bta-miR-98 and bta-miR-193a-3p, and four common genes between the targets of the bta-miR-138 and bta-miR-193a-3p. Identification of common genes by combining miRNA and RNA-Seq & microarray results Common meta genes that were identified in our previous research [ 13 ] is combined with miRNA target genes. As shown in FIGURE 3 , twenty-one genes were common between the meta genes of mRNA and small RNA sequence (target of miRNA) based meta-analysis. In order to network analyze of combined genes in two studies, twenty-one genes were inputted to STRING database. FIGURE 4 represents the gene network visualization of common genes. DISCUSSION Mastitis is a disease that leads to acute inflammation of the udder tissue and mammary gland in dairy cattle. To better comprehend the role of meta-miRNAs and their target genes in response to S.uberis mastitis, we executed a meta-analysis of miRNA transcriptome data. Due to the limitations in statistical power and reproducibility of individual studies, various small impact genes remain unidentified. Therefore, meta-analysis has been proposed as a practical solution for this problem, allowing us to identify multiple genes that interact [ 13 , 65 ]. Studies have indicated that bta-miR-98 present in extracellular vesicles (EV) can act as a potential miRNA to regulate the maternal immune system during cattle's peri-implantation period. This miRNA negatively regulates various genes related to the immune system, including CTSC, IL6, CASP4, and IKBKE [ 66 ]. bta-miR-98 has been identified as a significant regulator in the process of apoptosis, also have shown that this particular miRNA plays a crucial role in fertilization and early embryonic loss in dairy cattle [ 67 ]. In the regulatory network of miRNA, bta-miR-138 has been identified as a primary miRNA during the immune response in bovine mastitis [ 68 ]. Through the comparison of miRNA expression in bovine mammary glands bta-miR-138 has been identified as a master regulator of biological processes [ 69 , 70 ]. Bta-miR-193a-3p was found to be the most abundant mature bovine miRNA in E. coli after 6 hours and S. aureus after 48 hours infection. Additionally, the expression of bta-miR-193a-3p in control cells was confirmed through qRT-PCR analysis [ 71 ]. Bta-miR-193a-3p was discovered to exhibit differential expression in intramuscular fat (IMF) tissues and residual feed intake (RFI) [ 72 , 73 ]. Based on our findings, we suggest that bta-miR-98, bta-miR-193a-3p and bta-miR-138 may have the potential to regulate immune system-related genes during mastitis caused by S. uberis infection. The regulation of RNA metabolic process has been described as BPs in porcine oocytes matured by microarray approach [ 74 ]. Protein lysine acetylation has emerged as a key post-translational modification in regulation of cellular metabolism [ 75 ]. Macrophage polarization and function on the evolving roles of coordinated regulation of cellular signaling pathways was reported [ 76 ]. Nitric oxide and mitochondrial biogenesis were identified as key to long-term regulation of cellular metabolism [ 77 ]. miRNAs could be involved in the regulation of gene expression in the mammary gland for milk production and mastitis [ 78 ]. Bovine milk transcriptome analysis revealed regulation of cellular macromolecule biosynthetic process, regulation of nitrogen compound metabolic process, regulation of nucleobase-containing compound metabolic process, regulation of rna metabolic process and regulation of metabolic process that involved in mastitis [ 79 ]. Axon guidance pathway of differentially expressed genes associated with S. aureus mastitis in dairy goats [ 80 ]. Cellular senescence includes a loss of proliferation, change in cell shape, irreversible cell cycle arrest was reported in various diseases, including sepsis, mastitis and enteritis [ 81 ]. Wnt signaling pathway was related to bacterial infection and immune system during mastitis [ 82 ]. The ErbB signaling pathway closely related to progression of bovine S. aureus mastitis [ 79 ]. The PPI networks set up using Cytoscape exposed the top target genes. USP47 is involved in the process of inflammatory responses, myocardial infarction, epithelial to mesenchymal transition and neuronal development [ 83 ]. Additionally, TNIK has been recognized as a significant stimulator of the Wnt pathway and cellular division [ 84 ]. PAPPA is highly expressed in pregnancy and also promoted breast cancer progression [ 85 ]. TRAM2 is introduced one of the gene that effect of postpartum inflammatory diseases on stage of developmental biology and fertility in lactating dairy cows [ 86 ]. TGFB was introduced as a putative biomarkers for early detection of mastitis in cattle [ 87 ], and sheep milk [ 88 ] that plays a key role in activation and inhibition of T and B cells [ 89 ]. HIC2 gene is revealed that had co-associated with milk composition in cows [ 90 ]. Blood transcriptome analysis in Chinese Holstein encoded BRWD3 gene that was significantly down-regulated in supernumerary teats [ 91 ]. OSMR gene was discovered as a candidate gene in innate immunity in dairy sheep [ 92 ], also the potential role of this gene was determined in resistance to udder infection in dairy cattle [ 93 ]. The DYRK2 gene was associated with milk traits in cattle [ 94 ]. Single nucleotide polymorphisms (SNPs) studies in crossbred Bos indicus–Bos taurus cows indicated that this gene has been documented with some role in mammary development [ 95 ]. In the lactating goat mammary gland let-7c-5p and miR-223-3p have ANKFY1 gene as target gene [ 96 ]. PPARGC1A gene was discovered as the most plausible comparative functional candidate gene in milk fat yield [ 97 ], also polymorphism of this gene in Jersey cows was reported [ 98 ]. Expression of CADM1 gene was down-regulated in leukocyte immune response in Holstein cows [ 99 ] and up-regulated in tumor cell apoptosis and inhibits malignant proliferation [ 100 ]. UBP1 was presented in reproduction processes and embryonic development [ 101 ]. PLAU gene mediates cell proliferation and migration through the intracellular signal transduction and chemokine activation in dairy cows infected with S. uberis, also FABP3 gene involved in mammary gland lipid metabolism are down-regulated during mastitis [ 33 , 102 ]. RBPJ has been investigated as a potential candidate for mastitis phenotypes due to role in the mastitis infection based on GWAS, DEGs, QTL and proteome studies [ 103 , 104 ]. Function analysis indicated that ZFP36L2 and HYI genes were clustered into inflammatory responses and with a role in innate immunity and inflammation in dairy cows [ 105 ]. DLG3 gene involved in inflammation and immune response during S. aureus in mammary glands [ 2 ]. FABP3 has been reported as positively related to sheep, goat and cow milk quality, being involved in lipid droplet synthesis [ 106 , 107 ]. In the study, twenty-one genes were identified as being associated with mastitis disease through gene network analysis using the STRING database. The overexpression of bta-miR-199a-3p was found to inhibit inflammation in bMECs by targeting the CD2AP gene of mastitis [ 108 ]. ST3GAL6 and CD47 genes was showed significant differential expression in lactating mammary tissue [ 109 , 110 ]. The previous studies confirmed the effect of the KAT6B gene on bovine mastitis resistance through the identification of quantitative trait loci [ 111 ]. Ghahramani et al. in 2021 used different attribute weighting algorithms to confirm the involvement of TANC2 and MAPK6 genes in mastitis disease [ 13 ]. An investigation into the inflammatory gene expression profiles of bovine peripheral blood mononuclear cells following LPS Challenge, which led to the identification of the GSR gene [ 112 ]. Wang et al. in 2019 found that the MARCKS gene was related to the deregulation of lipid metabolism during mastitis at the early stage [ 113 ]. A direct link between feed intake, deiodinase activity and temperature regulation in response to heat through the TRPC5 gene was discovered [ 114 ]. Association between SNPs in the bovine genome and novel SCC phenotypes through the CELSR2 gene during uberis experimental challenge was reported [ 115 ]. Lastly an examination of miRNAs and their target genes connectivity during S. uberis mastitis concluded that candidate genes involved in the development, proliferation, differentiation of cells in the mammary gland, and immune system improvement. CONCLUSION This research delved into miRNAs in bovine mastitis that have been linked to the development of mastitis disease. Given the intricacy of this disease in dairy cattle, there is a need for more pertinent studies to uncover miRNAs and their related target genes associated with mastitis. By utilizing differential expression genes, meta-analysis, protein-protein interaction, and gene ontology approaches, our study was able to make significant strides in identifying the valuable target genes for S. uberis mastitis. These findings may pave the way for a more powerful biosignature and present as reliable biomarker candidates for future research on this topic. Declarations Acknowledgements We are thankful to Mohammad Ghahramani for his kindly help. Funding No funding to declare. Author information Nooshin Ghahramani 1 *, Jalil Shodja 1 , Seyed Abbas Rafat 1 , Bahman Panahi 2 and Karim Hasanpur 1 1 Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran, 2 Department of Genomics, Branch for Northwest & West Region, Agricultural Biotechnology Research Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran Contributions NG: research concept and design, data analysis and interpretation, wrote the article, and final approval of the article. JS, SR and KH: wrote the article. BP: data analysis, interpretation, wrote the article and final approval of the article. Corresponding author Correspondence to * [email protected] Ethics declarations Conflict of interest We declare no competing interests. Ethics statement Consent of all the participants were taken for the study. Consent for publication All the authors read the manuscript and given their consent for submission of the article. 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Liang M-y, Hou X-m, Qu B, Zhang N, Li N, Cui Y-j, et al. Functional analysis of FABP3 in the milk fat synthesis signaling pathway of dairy cow mammary epithelial cells. In Vitro Cellular & Developmental Biology-Animal. 2014;50:865-73. Moioli B, D’Andrea M, Pilla F. Candidate genes affecting sheep and goat milk quality. Small Ruminant Research. 2007;68:179-92. Li Y, Ren Q, Wang X, Luoreng Z, Wei D. Bta-miR-199a-3p Inhibits LPS-Induced Inflammation in Bovine Mammary Epithelial Cells via the PI3K/AKT/NF-κB Signaling Pathway. Cells. 2022;11:3518. Liu Z, Wang T, Pryce JE, MacLeod IM, Hayes BJ, Chamberlain AJ, et al. Fine-mapping sequence mutations with a major effect on oligosaccharide content in bovine milk. Scientific reports. 2019;9:2137. Suchyta SP, Sipkovsky S, Halgren RG, Kruska R, Elftman M, Weber-Nielsen M, et al. Bovine mammary gene expression profiling using a cDNA microarray enhanced for mammary-specific transcripts. Physiological genomics. 2003;16:8-18. Kurz JP. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3510780","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":244409581,"identity":"d0a6a473-2740-42bf-ad53-9313e81a267c","order_by":0,"name":"bahman Panahi","email":"","orcid":"","institution":"University of Tabriz","correspondingAuthor":false,"prefix":"","firstName":"bahman","middleName":"","lastName":"Panahi","suffix":""},{"id":244409582,"identity":"23d209fd-4248-4053-8a0e-9ce1b5bc8a6e","order_by":1,"name":"karim hasanpour","email":"","orcid":"","institution":"University of 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KEGG pathways enrichment analysis of meta miRNA targets related to mastitis disease\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3510780/v1/9e7e526a8b6590ad65c7ae82.png"},{"id":46030307,"identity":"d8a416a9-8061-45de-a920-b6778851bc7b","added_by":"auto","created_at":"2023-11-07 17:53:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":300788,"visible":true,"origin":"","legend":"\u003cp\u003eProtein–protein interaction network for the target genes using cytoscape\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3510780/v1/b015c48be3fc67a17cce40fb.png"},{"id":46030308,"identity":"5e33d0a4-f174-4eb5-8d24-e06dfb11e168","added_by":"auto","created_at":"2023-11-07 17:53:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41771,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram results by combining the target genes and common genes\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3510780/v1/79d7697e7862b5710582a872.png"},{"id":46030967,"identity":"c2b1fe22-e5d5-4015-802b-ade0bfa3d544","added_by":"auto","created_at":"2023-11-07 18:01:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":199107,"visible":true,"origin":"","legend":"\u003cp\u003eGene network analysis of twenty-one using STRING\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3510780/v1/d57807f560a1fa19563ea661.png"},{"id":46335716,"identity":"aa7bfebf-a8b6-4c1f-9517-e0c7672bcb2f","added_by":"auto","created_at":"2023-11-13 11:14:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1349103,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3510780/v1/9b36df4c-3230-4ad1-8abe-a1dae10d6ea6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A meta-analysis of differentially expressed microRNA during mastitis disease in dairy cattle","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAttention to animal health and welfare has become an important issue because of the increasing demand for healthy products [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Bovine mastitis is an inflammation condition of the udder, which can associate with significant economic expenses to dairy industry [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Mastitis in the dairy cattle reduces the milk yield and its quality, influence the reproductive performance, and increase culling of the cows from the herd. Additionally, side effects of antibiotics on the cow body or their residues on milk for human health are other concerns of the mastitis [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In recent years, numerous studies have been shown that bovine mammary epithelial cells (BMECs) respond to the invasion of bacteria by altering the expression levels of several genes involved in inflammation and immunity [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cem\u003eStreptococcus uberis\u003c/em\u003e is among the four most prevalent species of mastitis causing pathogens [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Infection with this bacterium can occur with very few if any clinical signs, but can also result in severe inflammation of the udder culminating in clinical mastitis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Monocytes are released from the bone marrow and control the pathogen spread and resolve the disease [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Typically, chemokines, interleukins (ILs), and tumor necrosis factor (TNF)-α, cell differentiation and apoptosis was reported as a main pathway during inflammation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The methods of detecting bovine mastitis have been intensively developed over the years [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Immune cells and somatic cells are still exposed in the milk of animals, and the count of such somatic cells is an indicator of mastitis. Improving the dairy cattle resistance against the udder diseases, the risk of mastitis could be reduced [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, the identification of specific genes related to resistance to mastitis can provide a new way to control mastitis through genetic selection [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Recently the most relevant development in the transcriptomics technologies, especially the next-generation sequencing, as well as further advances in bioinformatics tools, have paved the way of comprehensive investigation of both messenger RNAs (mRNA) and non-coding RNAs (ncRNAs). These technologies have provided great opportunities for studying of miRNAs involved in husbandry animal diseases [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. miRNAs are endogenous, small, noncoding RNA molecules of 22\u0026ndash;24 nucleotides in length that are generated by various cell types, also they regulate post-transcriptional gene expression [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. miRNAs prevent the translation initiation or elongation and control the expression of protein-coding genes and induce translational protein degradation and termination of translation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Recently researches have confirmed the presence of various miRNAs in cow genome and attempted to characterize theirs structures [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. miRNAs can be detected in raw or processed milk [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Studies involving \u003cem\u003eE. coli\u003c/em\u003e and aureus showed alterations in the expression of various miRNAs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In a challenge study of primary bovine mammary epithelial cells with \u003cem\u003eS. uberis, MIR29B\u003c/em\u003e and \u003cem\u003eMIR24-2\u003c/em\u003e were detected in the infected group [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. lately recognized miR-378 and miR-185 as candidate biomarkers that regulate the host response to \u003cem\u003eS. aureus\u003c/em\u003e via different target genes and pathways of milk infected [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. miR-181a detected by qPCR to examine regulatory function on Fc-gammaR-mediated phagocytosis, toll-like receptor signaling, antigen processing and presentation pathways during intramammary infections with \u003cem\u003eS. uberis\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], related to innate immunity pathways using NGS approach 21 miRNAs identified as significantly differential expression post-infection [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] miR-320a and miR-320b identified during S. uberis mastitis due to their roles in enhance immune system. In another investigation, miR-144-5p and miR-130b-5p were significantly down-regulated and up-regulated respectively, in mastitis animals [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Network analysis of differential expression genes indicated that TNF pathway had positive relationships with genes involved with immune system function [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Meta-analysis has proven to be an efficient way to renew already published data by creating new empirical models, allowing progress in both understanding and prediction aspects [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Because independent experiences have limitations in statistical power, reliability of the results and sample size [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], meta-analysis is a tool to evaluate the efficacy of an intervention using all available information [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This approach can be used to distinguish effects related to mastitis disease through published study results and to detect factors that may influence those effects [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In first and second hypothesis using meta-analysis approach, the goal is to find DE genes that have non-zero effect sizes in, all studies or \"one or more\" studies respectively [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In several reviews the functional roles of miRNAs in husbandry animal disease have been reported [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], although they only focused on changes in miRNA expression profiles during mastitis disease. Construction of miRNA-mRNA network revealed crucial role of the miRNAs and target genes associated with specific traits [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Moreover, currently protein-protein interaction has been harnessed to identifying key miRNAs related to bovine mastitis [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In our current efforts, we have incorporated meta-analysis method to detect miRNAs from dairy cattle that have been infected with \u003cem\u003euberis\u003c/em\u003e. To investigate potential interaction networks and regulatory impact of miRNAs, we have constructed a \u003cem\u003eS. uberis\u003c/em\u003e bovine mastitis model.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection\u003c/h2\u003e \u003cp\u003eTo conduct the study, we searched the National Center for Biotechnology Information\u0026rsquo;s Gene Expression Omnibus (GEO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/gds/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/gds/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) repository using following keywords such as \"\u003cem\u003eBos taurus\u003c/em\u003e\", \"mastitis\", \"\u003cem\u003eStreptococcus uberis\u003c/em\u003e\", and \"miRNAs\". We manually selected miRNAs datasets belonging to transcriptome profiling of bovine mammary epithelial cells using small RNA sequencing platform (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The GSE59794 contained three miRNAs\u0026rsquo; libraries constructed from infected bovine breast milk at 2 days post-infection, with corresponding control libraries from uninfected milk. They were named control (1, 2 and 3) and infection (1, 2 and 3), respectively. The GSE41278 contained four miRNAs\u0026rsquo; libraries constructed from infected bovine after infection during 1, 2, 4 \u0026amp; 6 hours. The GSE51858 dataset consisted of five samples infected with the mastitis-causing pathogen \u003cem\u003eS. uberis\u003c/em\u003e, at various time points after infection including 0, 12, 24, 36, and 48 hours post treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003emiRNAs datasets list was chosen for the purpose of conducting a meta-analysis on mastitis disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccession number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpecie\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBacteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePlatform\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSampling period\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE59794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBos-Tausus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003euberis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllumina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 days post-infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Sun et al., 2017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE41278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBos-taurus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003euberis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllumina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1, 2, 4 \u0026amp; 6 hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGSE51858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBos-taurus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003euberis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIllumina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0, 12, 24, 36 \u0026amp; 48 hrs post-infection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePre-processing of miRNAs datasets and identification of DEGs\u003c/h2\u003e \u003cp\u003eThe miRNA datasets were pre-processed and analyzed to identify the differentially expressed genes between healthy and infected MECs using Galaxy and R software [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The data was quality controlled using FastQC tool [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], and reads with average Phred quality less than 20 were removed. The adapter sequences were trimmed out using Trim Galore [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and then, the trimmed reads smaller than 18 N bases or longer than 30 N bases were excluded. Bowtie2 software [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] was applied to build an index reference genome, and clean reads were mapped onto the B. taurus reference genome using Mapper tool of miRDeep2 software [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In the next step, the MiRDeep2 Quantifier tool was used to evaluate the expression count matrix [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Finally, the Bioconductor DESeq2 package was used to determine the DEGs. A normalization step was carried out to remove the non-biological variances, and appropriate scales were used for next steps [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The group comparison was carried out using moderated t-test, and the Benjamini-Hochberg method for adjusted P-value calculation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. DEGs were determined a significant when the false discovery rate (FDR) using the Benjamini Hochberg method was P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and the logarithm of fold change\u0026thinsp;\u0026gt;\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeta-Analysis of miRNA Datasets\u003c/h2\u003e \u003cp\u003eThe metaRNASeq package was utilized for the meta-analysis [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Fisher and invorm P-value combining methods were harnessed to identify the meta-miRNAs as described by [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Using the following formula this method combines the p-values for each gene from individual studies [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. The Benjamini\u0026ndash;Hochberg false discovery rate (FDR) was adjusted, and a corrected P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as statistically significant [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Genes that revealed FDR of \u0026lt;\u0026thinsp;0.05 and an average fold change of \u0026ge;\u0026thinsp;2 were considered as meta-miRNAs.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${x}^{2}=-2{\\sum }_{i=1}^{k}\\text{l}\\text{n}\\left(pi\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of target genes of the identified miRNAs\u003c/h2\u003e \u003cp\u003eComputational prediction of miRNA targets is a critical initial step in identifying miRNAs, because one miRNA can target multiple genes transcripts. miRNAs are considered important mechanism for regulating gene expression and mRNA translation [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Target genes of the meta-miRNAs were predicted using TargetScan software [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis of the target genes\u003c/h2\u003e \u003cp\u003eFor analysis miRNA target genes functional impacts, enrichment analysis using gene ontology was performed using STRING database [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Gene ontology is commonly used to define the cellular location, molecular function and biological processes in which model organism genes participate [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRegulatory Network Construction\u003c/h2\u003e \u003cp\u003eAccording to the miRNA and candidate target genes, we established a link between miRNAs and candidate target genes to identify a potential miRNA-mRNA regulatory network [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Constructed regulatory networks were further visualized using Cytoscape software [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed miRNAs\u003c/h2\u003e \u003cp\u003eThe three selected studies included 18 miRNA expression profiles. A total of 13 differentially expressed miRNAs were reported in mastitis cases vs. healthy controls in GSE41278 study. There were 8 up-regulated and 5 down-regulated miRNAs. Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e lists the up and down-regulated miRNAs with their P-value in mastitis disease.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of differentially expressed miRNAs between the healthy and infected cows\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNA name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFold change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-let-7b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-193a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-29b.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-29b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-193a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-let-7i\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-let-7c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-130a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0298\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-let-7a-5p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-let-7a-5p.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-let-7a-5p.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeta-analysis of the miRNAs\u003c/h2\u003e \u003cp\u003eWe executed a meta-analysis approach using data from miRNA experiments. Three miRNA datasets containing 18 samples in dairy cattle studies were selected for the meta-analysis. According to meta-analysis three meta-miRNAs including bta-miR-98, bta-miR-138, and bta-miR-193a-3p in response to uberis were resulted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003emiRNA target genes\u003c/h2\u003e \u003cp\u003eBiological targets of miRNAs predicted using TargetScan [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. As such, the counts of the target genes per each of the three meta-miRNAs are shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIdentifying transcriptional target genes of miRNAs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGene regulatory network\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e319\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-193a-3p\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebta-miR-138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGene ontology of the target genes\u003c/h2\u003e \u003cp\u003eThe STRING database was applied to GO enrichment analysis of 2061 target genes to base on biological process (BP), molecular function (MF), cellular component (CC) and KEGG pathway. The results showed that the 237 BP, 41 MF, 54 CC terms and nine KEGG pathways were significant (FIGURE \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNetwork analysis of the target genes\u003c/h2\u003e \u003cp\u003eA total of 319, 113 and 124 target genes were found for bta-miR-98, bta-miR-193a-3p and bta-miR-138, respectively. As shown in FIGURE \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, there were nine common genes between the targets of bta-miR-98 and bta-miR-138, six common genes between the targets of bta-miR-98 and bta-miR-193a-3p, and four common genes between the targets of the bta-miR-138 and bta-miR-193a-3p.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of common genes by combining miRNA and RNA-Seq \u0026amp; microarray results\u003c/h2\u003e \u003cp\u003eCommon meta genes that were identified in our previous research [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] is combined with miRNA target genes. As shown in FIGURE \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, twenty-one genes were common between the meta genes of mRNA and small RNA sequence (target of miRNA) based meta-analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn order to network analyze of combined genes in two studies, twenty-one genes were inputted to STRING database. FIGURE \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents the gene network visualization of common genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eMastitis is a disease that leads to acute inflammation of the udder tissue and mammary gland in dairy cattle. To better comprehend the role of meta-miRNAs and their target genes in response to S.uberis mastitis, we executed a meta-analysis of miRNA transcriptome data. Due to the limitations in statistical power and reproducibility of individual studies, various small impact genes remain unidentified. Therefore, meta-analysis has been proposed as a practical solution for this problem, allowing us to identify multiple genes that interact [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies have indicated that bta-miR-98 present in extracellular vesicles (EV) can act as a potential miRNA to regulate the maternal immune system during cattle's peri-implantation period. This miRNA negatively regulates various genes related to the immune system, including CTSC, IL6, CASP4, and IKBKE [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. bta-miR-98 has been identified as a significant regulator in the process of apoptosis, also have shown that this particular miRNA plays a crucial role in fertilization and early embryonic loss in dairy cattle [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. In the regulatory network of miRNA, bta-miR-138 has been identified as a primary miRNA during the immune response in bovine mastitis [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Through the comparison of miRNA expression in bovine mammary glands bta-miR-138 has been identified as a master regulator of biological processes [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Bta-miR-193a-3p was found to be the most abundant mature bovine miRNA in E. coli after 6 hours and S. aureus after 48 hours infection. Additionally, the expression of bta-miR-193a-3p in control cells was confirmed through qRT-PCR analysis [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Bta-miR-193a-3p was discovered to exhibit differential expression in intramuscular fat (IMF) tissues and residual feed intake (RFI) [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Based on our findings, we suggest that bta-miR-98, bta-miR-193a-3p and bta-miR-138 may have the potential to regulate immune system-related genes during mastitis caused by S. uberis infection.\u003c/p\u003e \u003cp\u003eThe regulation of RNA metabolic process has been described as BPs in porcine oocytes matured by microarray approach [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Protein lysine acetylation has emerged as a key post-translational modification in regulation of cellular metabolism [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. Macrophage polarization and function on the evolving roles of coordinated regulation of cellular signaling pathways was reported [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Nitric oxide and mitochondrial biogenesis were identified as key to long-term regulation of cellular metabolism [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. miRNAs could be involved in the regulation of gene expression in the mammary gland for milk production and mastitis [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Bovine milk transcriptome analysis revealed regulation of cellular macromolecule biosynthetic process, regulation of nitrogen compound metabolic process, regulation of nucleobase-containing compound metabolic process, regulation of rna metabolic process and regulation of metabolic process that involved in mastitis [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Axon guidance pathway of differentially expressed genes associated with S. aureus mastitis in dairy goats [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Cellular senescence includes a loss of proliferation, change in cell shape, irreversible cell cycle arrest was reported in various diseases, including sepsis, mastitis and enteritis [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Wnt signaling pathway was related to bacterial infection and immune system during mastitis [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. The ErbB signaling pathway closely related to progression of bovine S. aureus mastitis [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe PPI networks set up using Cytoscape exposed the top target genes. USP47 is involved in the process of inflammatory responses, myocardial infarction, epithelial to mesenchymal transition and neuronal development [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. Additionally, TNIK has been recognized as a significant stimulator of the Wnt pathway and cellular division [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. PAPPA is highly expressed in pregnancy and also promoted breast cancer progression [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. TRAM2 is introduced one of the gene that effect of postpartum inflammatory diseases on stage of developmental biology and fertility in lactating dairy cows [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. TGFB was introduced as a putative biomarkers for early detection of mastitis in cattle [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], and sheep milk [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e] that plays a key role in activation and inhibition of T and B cells [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. HIC2 gene is revealed that had co-associated with milk composition in cows [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Blood transcriptome analysis in Chinese Holstein encoded BRWD3 gene that was significantly down-regulated in supernumerary teats [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. OSMR gene was discovered as a candidate gene in innate immunity in dairy sheep [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], also the potential role of this gene was determined in resistance to udder infection in dairy cattle [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. The DYRK2 gene was associated with milk traits in cattle [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. Single nucleotide polymorphisms (SNPs) studies in crossbred Bos indicus\u0026ndash;Bos taurus cows indicated that this gene has been documented with some role in mammary development [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. In the lactating goat mammary gland let-7c-5p and miR-223-3p have ANKFY1 gene as target gene [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. PPARGC1A gene was discovered as the most plausible comparative functional candidate gene in milk fat yield [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], also polymorphism of this gene in Jersey cows was reported [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. Expression of CADM1 gene was down-regulated in leukocyte immune response in Holstein cows [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e] and up-regulated in tumor cell apoptosis and inhibits malignant proliferation [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. UBP1 was presented in reproduction processes and embryonic development [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. PLAU gene mediates cell proliferation and migration through the intracellular signal transduction and chemokine activation in dairy cows infected with S. uberis, also FABP3 gene involved in mammary gland lipid metabolism are down-regulated during mastitis [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. RBPJ has been investigated as a potential candidate for mastitis phenotypes due to role in the mastitis infection based on GWAS, DEGs, QTL and proteome studies [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. Function analysis indicated that ZFP36L2 and HYI genes were clustered into inflammatory responses and with a role in innate immunity and inflammation in dairy cows [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. DLG3 gene involved in inflammation and immune response during S. aureus in mammary glands [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. FABP3 has been reported as positively related to sheep, goat and cow milk quality, being involved in lipid droplet synthesis [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the study, twenty-one genes were identified as being associated with mastitis disease through gene network analysis using the STRING database. The overexpression of bta-miR-199a-3p was found to inhibit inflammation in bMECs by targeting the CD2AP gene of mastitis [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e]. ST3GAL6 and CD47 genes was showed significant differential expression in lactating mammary tissue [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. The previous studies confirmed the effect of the KAT6B gene on bovine mastitis resistance through the identification of quantitative trait loci [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. Ghahramani et al. in 2021 used different attribute weighting algorithms to confirm the involvement of TANC2 and MAPK6 genes in mastitis disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. An investigation into the inflammatory gene expression profiles of bovine peripheral blood mononuclear cells following LPS Challenge, which led to the identification of the GSR gene [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. Wang et al. in 2019 found that the MARCKS gene was related to the deregulation of lipid metabolism during mastitis at the early stage [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. A direct link between feed intake, deiodinase activity and temperature regulation in response to heat through the TRPC5 gene was discovered [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. Association between SNPs in the bovine genome and novel SCC phenotypes through the CELSR2 gene during uberis experimental challenge was reported [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Lastly an examination of miRNAs and their target genes connectivity during S. uberis mastitis concluded that candidate genes involved in the development, proliferation, differentiation of cells in the mammary gland, and immune system improvement.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis research delved into miRNAs in bovine mastitis that have been linked to the development of mastitis disease. Given the intricacy of this disease in dairy cattle, there is a need for more pertinent studies to uncover miRNAs and their related target genes associated with mastitis. By utilizing differential expression genes, meta-analysis, protein-protein interaction, and gene ontology approaches, our study was able to make significant strides in identifying the valuable target genes for S. uberis mastitis. These findings may pave the way for a more powerful biosignature and present as reliable biomarker candidates for future research on this topic.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are thankful to Mohammad Ghahramani\u0026nbsp;for his kindly help.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding to declare.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNooshin Ghahramani\u003csup\u003e1\u003c/sup\u003e *, Jalil Shodja\u003csup\u003e1\u003c/sup\u003e, Seyed Abbas Rafat\u003csup\u003e1\u003c/sup\u003e, Bahman Panahi\u003csup\u003e2\u003c/sup\u003e and Karim Hasanpur\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Department of Animal Science, Faculty of Agriculture, University of Tabriz, Tabriz, Iran,\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Department of Genomics, Branch for Northwest \u0026amp; West Region, Agricultural Biotechnology Research \u0026nbsp; \u0026nbsp;Institute of Iran (ABRII), Agricultural Research, Education and Extension Organization (AREEO), Tabriz, Iran\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNG: research concept and design, data analysis and interpretation, wrote the article, and final approval of the article. JS, SR and KH: wrote the article. BP: data analysis, interpretation, wrote the article and final approval of the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to *[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent of all the participants were taken for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors read the manuscript and given their consent for submission of the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePalii A, Kovalchuk Y, Boyko Y, Bondaruk Y, Diachuk P, Duka T, et al. 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Identifying genome associations with unique mastitis phenotypes in response to intramammary Streptococcus uberis challenge. 2017.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mastitis, Complex genetics, Innate immunity, miRNA, Meta-analysis, Molecular network","lastPublishedDoi":"10.21203/rs.3.rs-3510780/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3510780/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Bovine mastitis is an important inflammation disease that affects the mammary gland and causing adverse effects on the quality and quantity of the produced milk, leads to a major economic lost in dairy industry. \u003cem\u003eStreptococcus uberis\u003c/em\u003eis one of the bacteria commonly responsible for inducing mastitis in dairy cattle. Susceptibility to develop mastitis is a complex multifactorial phenotype and the improvement of the miRNAs and their target genes has not been comprehensively illustrated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods and Results:\u003c/strong\u003eThe purpose of this investigation was to perform a meta-analysis of the miRNAs expression profiling datasets to detect the key miRNAs, targets, and regulatory networks associated with mastitis. To this, publicly available miRNA datasets belong to three experiments on dairy cattle which challenged with \u003cem\u003eS. uberis\u003c/em\u003ewere included in our meta-analyzed. The identified differentially expressed miRNAs were used in TargetScan to identify their target genes. The functional impacts of the meta-miRNAs were further analyzed using Gene ontology and Protein-Protein Interaction network analysis. Three meta-miRNAs, namely bta-miR-98, bta-miR-138 and bta-miR-193a-3p, were obtained to be associated with the progress of the immune system and cell differentiation of the mammary gland during the mastitis. A total of 2061 target genes were identified that which bta-miR-98, bta-miR-138 and bta-miR-193a-3p were regulated 1121, 268 and 672 target genes respectively. Gene ontology analysis results were represented 237 biological process, 41 molecular function, 54 cellular component roles and nine KEGG pathways in mastitis disease. A total of 319, 113 and 124 target genes for bta-miR-98, bta-miR-193a-3p and bta-miR-138, respectively were inputted to cytoscape. The resulted network analysis showed that bta-miR-98 and bta-miR-138 have nine, bta-miR-138 and bta-miR-193a-3p have six, and bta-miR-193a-3p and bta-miR-98 have four common target genes. Twenty-one common genes were revealed by combing 360 common meta-genes in our previous research and 2061 meta-miRNA target genes. The procedure reported in this research offers a comprehensive scheme for the identification of the key miRNAs and target genes in mastitis disease by using global transcriptome data, meta-analysis, gene ontology, enrichment analysis and protein protein interaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The findings of the current work suggest miRNAs are crucial amplifiers of inflammatory response by controlling metabolic pathway and inhibitors of several biological processes during \u003cem\u003eS. uberis\u003c/em\u003e infection.\u003c/p\u003e","manuscriptTitle":"A meta-analysis of differentially expressed microRNA during mastitis disease in dairy cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-07 17:53:18","doi":"10.21203/rs.3.rs-3510780/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ee4ca07-f99b-403b-b4d7-69872d2885c2","owner":[],"postedDate":"November 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-13T11:14:12+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-07 17:53:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3510780","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3510780","identity":"rs-3510780","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}