Prediction of potential molecular markers of bovine mastitis by meta-analysis of differentially expressed genes using combined p value and robust rank aggregation.

Bovine mastitis causes significant economic loss to the dairy industry by affecting milk quality and quantity. Escherichia coli and Staphylococcus aureus are the two common mastitis-causing bacteria among the consortia of mastitis pathogens, wherein E. coli is an opportunistic environmental pathogen, and S. aureus is a contagious pathogen. This study was designed to predict molecular markers of bovine mastitis by meta-analysis of differentially expressed genes (DEG) in E. coli- or S. aureus-infected mammary epithelial cells (MECs) using p value combination and robust rank aggregation (RRA) methods. High-throughput transcriptome of bovine MECs, infected with E. coli or S. aureus, were analyzed, and correlation of z-scores were computed for the expression datasets to identify the lineage profile and functional ontology of DEGs. Key pathways enriched in infected MECs were deciphered by Gene Set Enrichment Analysis (GSEA), following which combined p value and RRA were used to perform DEG meta-analysis to limit type I error in the analysis. The miRNA-gene networks were then built to uncover potential molecular markers of mastitis. Lineage profiling of MECs showed that the gene expression levels were associated with mammary tissue lineage. The up-regulated genes were enriched in immune-related pathways, whereas down-regulated genes influenced the cellular processes. GSEA analysis of DEGs deciphered the involvement of Toll-like receptor (TLR), and NF-kappa B signaling pathway during infection. Comparison after meta-analysis yielded with genes ZC3H12A, RND1, and MAP3K8 having significant expression levels in both E. coli and S. aureus dataset, and on evaluating miRNA-gene network, 7 pairs were common to both sets identifying them as potential molecular markers.