Meta-Analysis of Transcriptomic Datasets Reveals Key Immune Gene Profiles and Signaling Pathways in Bos taurus

Abstract

Understanding immune mechanisms in cattle is vital for improving disease resistance through informed breeding and vaccine development. Meta-analysis enables the integration of multiple transcriptomic studies to identify consistent gene expression patterns and enhance statistical power. In this study, we performed a meta-analysis of four bovine transcriptomic datasets (GSE45439, GSE62048, GSE125964, and GSE247921) to identify immune-related differentially expressed genes (DEGs) in Bos taurus. These datasets covered infections with Mycobacterium bovis and Mycobacterium avium subsp. paratuberculosis, comparing diseased and healthy cattle. Our pipeline included FastQC, Trimmomatic, Bowtie2, SAMtools, FeatureCounts, DESeq2, and MetaRNASeq, leading to the discovery of 28 significant DEGs (12 upregulated and 16 downregulated). Comparison with an innate immune gene database identified five key immune-related genes--IL1A, RGS2, RCAN1, and ZBP1--with known regulatory roles in immunity. KEGG enrichment analysis revealed involvement in Necroptosis, Osteoclast Differentiation, Oxytocin Signaling, and cGMP-PKG Signaling pathways, associated with inflammatory cell death, cytokine signaling, and immune cell differentiation. This meta-analysis enhances understanding of conserved immune signaling mechanisms in cattle and highlights genes that may serve as biomarkers for immune competence, disease susceptibility, and vaccine responsiveness, offering valuable insights for future bovine immunogenomics research.