GEO dataset mining analysis reveals novel virulence gene regulatory networks and diagnostic targets in mice.

infection is a serious, worldwide health concern, particularly in many communities and hospitals. Understanding the pathogenetic regulatory network will provide significant insights into diagnostic target screening to improve clinical treatment of diseases caused by . We screened differentially expressed genes between normal mice and -infected mice. We used the Gene Expression Omnibus (GEO) DataSets database for functional analysis (GO-analysis) and the DAVID and KEGG databases for signaling pathway analyses. We next integrated the gene and pathway analyses with Transcriptional Regulatory Element Database (TRED) to build an antimicrobial resistance gene regulatory network of . We performed association analysis of network genes and diseases using DAVID online annotation tools. We identified a total of 437 virulence genes and 15 transcription factors (TFs), as well as 444 corresponding target genes, in the TF regulatory network. We screened seven key network nodes (, , , , , , and ), four key transcription factors (, , , and ) and an important signaling pathway (TNF). We hypothesized that the cytokine activity and growth factor activity of are combinatorically cross-regulated by , , , , T, , and genes, the TFs , , , and , as well as the immune response, cellular response to lipopolysaccharide, and inflammatory response. Our study provides information and reference values for the molecular understanding of the pathogenetic gene regulatory network.