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

Abstract

At present, bovine mastitis is one of the most costly diseases affecting animal health and welfare. Escherichia coli (E. coli) is considered to be one of the main pathogens causing mastitis with clinical signs in dairy cattle. However, the cure rate of E. coli mastitis is low, and the pathogenesis of E. coli mastitis is not completely known. In order to develop new strategies for the rapid detection of E. coli mastitis, a comprehensive molecular investigation of E. coli mastitis is necessary. Hence, this study integrated three microarray data sets to identify the potential key candidate genes in dairy cow in response to E. coli mastitis. Differentially expressed genes (DEGs) were screened in mammary gland tissues with live E. coli 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 immunerelated pathways, some of the 18 DEGs were involved in signaling pathways triggered by other diseases. Considering the specificity of biomarkers for rapid detection, IL8RB, CXCL6, and MMP9 were identified as the most potential biomarker for E. coli mastitis. In conclusion, the novel DEGs and pathways identified in this study can help to improve the diagnosis and treatment strategies for E. coli mastitis in cattle.

Integrative Network Analysis of Predicted miRNA-Targets Regulating Expression of Immune Response Genes in Bovine Coronavirus Infection

“…Recognition of miRNA targets in conjunction with network analysis creates an extensive visualization of the identified genes' biological processes. Gene interaction networks have previously been used to identify important genes associated with diseases such as bovine mastitis, trypanosomosis, tuberculosis, and human cancer (Aranday-Cortes et al, 2012;Al-Aamri et al, 2019;Li et al, 2019;Morenikeji et al, 2019). Certain genes were revealed to be central in the gene interaction network of this study.…”

Identification of potential crucial genes and molecular mechanisms in glioblastoma multiforme by bioinformatics analysis

“…Module analysis of the PPI network. Generally, biological networks contain several functional modules and they may interact in various biological process (31). in the present study, the top two clusters were identified from the PPI network using MCODE analysis.…”

Integrative Systems Biology Analysis Elucidates Mastitis Disease Underlying Functional Modules in Dairy Cattle

“…Several genes, including LOC407171, MT2A, LPCAT2, CXCL3, SFN, IDH1, and ABCG2, were confirmed as essential genes based on the outcome of the attribute weighting algorithm. The LOC407171 gene is associated with the innate immune response in beef cattle and has been identified as an up-regulated gene in a dairy cow with E. coli mastitis (Li et al, 2019). MT2A plays a role in stimulus response in the pathogenesis of bovine E. coli in early lactation cows (Cheng et al, 2021).…”

“…On the other hand, identifying disease biomarkers can aid breeders in optimizing their genetic programs for dairy animals (Kulkarni and Kaliwal, 2013;Duarte et al, 2015;Lai et al, 2017). Previous research identified TNF-and SAA3 (Swanson et al, 2009), STAT3, MAPK14, TNF (Gorji et al, 2019), IL8RB, CXCL6, MMP9 (Li et al, 2019), IRF9, CCL (Buitenhuis et al, 2011), S100A12, MT2A, SOD2 (Mitterhuemer et al, 2010), CXCL8, CXCL2, S100A9 (Sharifi et al, 2018), PSMA6, HCK, and STAT1 (Bakhtiarizadeh et al, 2020) as potential biomarkers for mastitis disease.…”