Identification of Transcriptional Modules and Key Genes in Chickens Infected with Salmonella enterica Serovar Pullorum Using Integrated Coexpression Analyses.
Bao-Hong LiuJian-Ping CaiPublished in: BioMed research international (2017)
Salmonella enterica Pullorum is one of the leading causes of mortality in poultry. Understanding the molecular response in chickens in response to the infection by S. enterica is important in revealing the mechanisms of pathogenesis and disease progress. There have been studies on identifying genes associated with Salmonella infection by differential expression analysis, but the relationships among regulated genes have not been investigated. In this study, we employed weighted gene coexpression network analysis (WGCNA) and differential coexpression analysis (DCEA) to identify coexpression modules by exploring microarray data derived from chicken splenic tissues in response to the S. enterica infection. A total of 19 modules from 13,538 genes were associated with the Jak-STAT signaling pathway, the extracellular matrix, cytoskeleton organization, the regulation of the actin cytoskeleton, G-protein coupled receptor activity, Toll-like receptor signaling pathways, and immune system processes; among them, 14 differentially coexpressed modules (DCMs) and 2,856 differentially coexpressed genes (DCGs) were identified. The global expression of module genes between infected and uninfected chickens showed slight differences but considerable changes for global coexpression. Furthermore, DCGs were consistently linked to the hubs of the modules. These results will help prioritize candidate genes for future studies of Salmonella infection.
Keyphrases
- network analysis
- genome wide identification
- bioinformatics analysis
- genome wide
- toll like receptor
- signaling pathway
- transcription factor
- listeria monocytogenes
- extracellular matrix
- escherichia coli
- genome wide analysis
- gene expression
- immune response
- poor prognosis
- hiv infected
- type diabetes
- nuclear factor
- risk factors
- induced apoptosis
- long non coding rna
- case control
- heat shock
- heat shock protein
- high resolution
- deep learning