Gene network analysis to identify hub genes and biological pathways related to mastitis in dairy cows based on bioinformatics analysis

Mastitis, a significant infectious ailment affecting dairy cows during both dry and lactation periods, has substantial impacts on animal health, economic profitability, and the dairy industry each year. This study seeks to utilize bioinformatics analysis not only to identify hub genes and biological pathways linked to mastitis in dairy cows but also to gain a deeper understanding of its implications.

21 microarray data samples were obtained from the GEO database (accession number GSE24217), comprising 9 healthy cow tissue samples and 12 E. coli infected tissue samples. These samples were assessed using GEO2R. Differentially expressed genes were identified based on two criteria: adjP-value > 0.05 and |LogFC| ≥ 1. Hub genes were identified using the CytoHubba plugin in Cytoscape software and topological analysis methods (DMNC, MCC, MNC, DEGREE). Signaling pathways were identified using the STRING tool based on an FDR > 0.05 index.

Out of 631 differentially expressed genes, 136 had low expression, and 495 had high expression in infected breast tissue compared to healthy ones. Among these, 205 genes were co-expressed in the network. 12 hub genes, including CCL19, ALB, GAPDH, PTPRC, ICAM1, IL6, IL1B, IL18, CXCL8, CCL20, CXCL16, and CCL3, were identified. The analysis of biological pathways revealed that the studied genes are involved in 66 biological pathways, with NOD-like receptor signaling pathway, TNF signaling pathway, and IL-17 signaling pathway playing important functional roles in dairy cow mastitis disease.

The discovered genes and pathways have the potential to enhance our knowledge of the complex molecular mechanisms involved in mastitis development. These discoveries could shape a thorough reform initiative focused on preventing, diagnosing, and treating mastitis. These insights are expected to pave the way for the creation of precise and dependable biomarkers for early detection and prevention of bovine E. coli mastitis, ultimately facilitating personalized treatment approaches.