Identification of Key Candidate Genes in Dairy Cow in Response to Mastitis by Bioinformatical Analysis.

At present, bovine mastitis is one of the most costly diseases affecting animal health and welfare. () is considered to be one of the main pathogens causing mastitis with clinical signs in dairy cattle. However, the cure rate of mastitis is low, and the pathogenesis of mastitis is not completely known. In order to develop new strategies for the rapid detection of mastitis, a comprehensive molecular investigation of mastitis is necessary. Hence, this study integrated three microarray data sets to identify the potential key candidate genes in dairy cow in response to mastitis. Differentially expressed genes (DEGs) were screened in mammary gland tissues with live infection. Furthermore, the pathways enrichment of DEGs were analyzed, and the protein-protein interaction (PPI) network was performed. In total, 105 shared DEGs were identified from the three data sets. The DEGs were significantly enriched in biological processes mainly involved in immunity. The PPI network of DEGs was constructed with 102 nodes and 546 edges. The module with the highest score through MCODE analysis was filtered from PPI; 18 central node genes were identified. However, in addition to immune-related pathways, some of the 18 DEGs were involved in signaling pathways triggered by other diseases. Considering the specificity of biomarkers for rapid detection, , and were identified as the most potential biomarker for mastitis. In conclusion, the novel DEGs and pathways identified in this study can help to improve the diagnosis and treatment strategies for mastitis in cattle.