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Melioidosis is an infectious disease caused by  Burkholderia pseudomallei (Bp) , a gram-negative bacillus. Sepsis is the most prevalent type of melioidosis. Due to factors such as lack of precision and slow presentation of bacterial culture tests, the misdiagnosis rate could exceed 100 per cent. Therefore, more reliable, and adaptable diagnostic methods are urgently needed. Weighted gene co-expression network analysis (WGCNA) was employed to screen the featured modules specially expressed in sepsis patients caused by  Bp.  Two representative co-expression modules were selected to perform gene ontology(GO) and KEGG analysis using  ClusterProfiler  package based on R language. We found that antigen processing and presentation of exogenous peptide antigen  via  MHC class I pathway, cytosol to ER transport and cell killing related pathways enriched in darkmagenta module which significantly correlated with the sepsis caused by  Bp . Eventually, a diagnostic 6-mRNA signature consisting of ASPHD2, LAP3, SEPT4, FAM26F, WARS and LGALS3BP was identified, which could discern the sepsis caused by  Bp  compared with other organisms. This will provide a new insight in screening markers for early detection of sepsis caused by  Bp , and the interaction between pathogens and hosts. This should shed light on the early detection of  Bp -caused infectious diseases.

Identification of candidate blood biomarkers for the diagnosis of septicaemic melioidosis based on WGCNA