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However, transcriptomic studies of bovine immune responses often show considerable variability due to differences in tissue type, pathogen, time point, and experimental design, limiting the generalizability. Meta\u2010analysis integrates multiple transcriptomic studies to identify consistent gene expression patterns and enhance statistical power. We integrated bovine RNA\u2010seq datasets using immune\u2010response specific keywords, species constraints, and high\u2010throughput sequencing filters to prioritize biologically comparable and meta\u2010analysis\u2010ready studies. Specifically, in this study, we performed a meta\u2010analysis of four bovine transcriptomic datasets to identify immune\u2010related differentially expressed genes (DEGs) in\n                    <jats:styled-content style=\"fixed-case\">\n                      <jats:italic>Bos taurus<\/jats:italic>\n                    <\/jats:styled-content>\n                    . These datasets showed consistent results across analyses and represent immune responses related to mycobacterial infections (\n                    <jats:styled-content style=\"fixed-case\">\n                      <jats:italic>Mycobacterium bovis<\/jats:italic>\n                    <\/jats:styled-content>\n                    and\n                    <jats:styled-content style=\"fixed-case\">\n                      <jats:italic>Mycobacterium avium<\/jats:italic>\n                    <\/jats:styled-content>\n                    subsp.\n                    <jats:italic>paratuberculosis<\/jats:italic>\n                    ), making them suitable for combined analysis. Our pipeline included FastQC, Trimmomatic, Bowtie2, SAMtools, FeatureCounts, DESeq2, and MetaRNASeq, identifying 28 DEGs (12 upregulated and 16 downregulated). We identified key immune\u2010related genes (\n                    <jats:italic>IL1A<\/jats:italic>\n                    ,\n                    <jats:italic>RGS2<\/jats:italic>\n                    ,\n                    <jats:italic>RCAN1<\/jats:italic>\n                    ,\n                    <jats:italic>ZBP1<\/jats:italic>\n                    ,\n                    <jats:italic>TIMD4<\/jats:italic>\n                    ,\n                    <jats:italic>PPARG<\/jats:italic>\n                    ,\n                    <jats:italic>TLR10<\/jats:italic>\n                    , and\n                    <jats:italic>ACP5<\/jats:italic>\n                    ) with known regulatory roles in immunity. KEGG enrichment analysis revealed involvement in necroptosis, osteoclast differentiation, oxytocin signaling, and cGMP\u2013PKG signaling pathways, associated with inflammatory cell death, cytokine signaling, and immune cell differentiation. Using reproducible transcriptomic signals across systematically selected bovine immune datasets rather than relying on single\u2010experiment analyses, we provide a robust meta\u2010analytic framework. This meta\u2010analysis enhances our understanding of conserved immune signaling mechanisms in cattle for identifying conserved immune mechanisms with broader biological and translational relevance.\n                  <\/jats:p>","DOI":"10.1002\/age.70117","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T02:30:51Z","timestamp":1778466651000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Meta\u2010Analysis of Transcriptomic Datasets Reveals Key Immune Gene Profiles and Signaling Pathways in\n                    <i>Bos taurus<\/i>"],"prefix":"10.1002","volume":"57","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6931-420X","authenticated-orcid":false,"given":"Vennila Kanchana Devi","family":"Marimuthu","sequence":"first","affiliation":[{"name":"Department of Data Science School of Arts, Science, Humanities and Education, SASTRA Deemed To Be University  Thanjavur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0358-0496","authenticated-orcid":false,"given":"Kishore","family":"Matheswaran","sequence":"additional","affiliation":[{"name":"Department of Data Science School of Arts, Science, Humanities and Education, SASTRA Deemed To Be University  Thanjavur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4306-826X","authenticated-orcid":false,"given":"Menaka","family":"Thambiraja","sequence":"additional","affiliation":[{"name":"Department of Bioinformatics, School of Chemical and Biotechnology SASTRA Deemed to be University  Thanjavur India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2834-314X","authenticated-orcid":false,"given":"Suneel Kumar","family":"Onteru","sequence":"additional","affiliation":[{"name":"Animal Biochemistry Division ICAR\u2010National Dairy Research Institute  Karnal India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3327-1582","authenticated-orcid":false,"given":"Ragothaman M.","family":"Yennamalli","sequence":"additional","affiliation":[{"name":"School of Computational and Integrative Science Jawaharlal Nehru University  New Delhi India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/genes12070976"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg3891"},{"key":"e_1_2_11_4_1","unstructured":"Andrews S.2010.\u201cFastQC: A Quality Control Tool for High Throughput Sequence Data.\u201dhttp:\/\/www.bioinformatics.babraham.ac.uk\/projects\/fastqc."},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0038508"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1083\/jcb.201012017"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/357128a0"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517\u20106161.1995.tb02031.x"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1434308100"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.physiol.67.040103.152647"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.ppat.1003077"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btu170"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598\u2010020\u201075104\u20107"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrg.2016.10"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2015.00023"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12889\u2010019\u20106634\u20103"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.coi.2023.102347"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1074\/jbc.m609872200"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/ni.3503"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/emboj\/cdg613"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2012.00266"},{"key":"e_1_2_11_22_1","unstructured":"Drake J.2021.\u201cGlobal Vulnerability to Emerging Diseases of Livestock.\u201dhttps:\/\/www.forbes.com\/sites\/johndrake\/2021\/10\/12\/global\u2010vulnerability\u2010to\u2010emerging\u2010diseases\u2010of\u2010livestock\/."},{"key":"e_1_2_11_23_1","unstructured":"FAO (Food and Agriculture Organization of the United Nations).2025.\u201cIndia at a Glance. 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