Melioidosis is a severe infectious disease caused by Burkholderia pseudomallei, a gram-negative bacillus classified by the NIAID as a category B priority agent. Septicemia is the most common presentation of the disease with 40% mortality rate even with appropriate treatments. Faster diagnostic procedures are required to improve therapeutic response and survival rates. We have used microarray technology to generate genome-wide transcriptional profiles (>48,000 transcripts) of whole blood obtained from patients with septicemic melioidosis (n=32), patients with sepsis caused by other pathogens (n=31), and uninfected controls (n=29). Unsupervised analyses demonstrated the existence of a whole blood transcriptional signature distinguishing patients with sepsis from control subjects. The majority of changes observed were common to both septicemic melioidosis and sepsis caused by other infections, including genes related to inflammation, interferon-related genes, neutrophils, cytotoxic cells, and T cells. Finally, class prediction analysis identified a 37 transcript candidate diagnostic signature that distinguished melioidosis from sepsis caused by other organisms with 100% and 78% accuracy in training and independent test sets, respectively. This finding was confirmed by the independent validation set, which showed 80% prediction accuracy. This signature was highly enriched in genes coding for products involved in the MHC Class II antigen processing and presentation pathway. Transcriptional patterns of whole blood RNA distinguish patients with septicemic melioidosis from patients with sepsis caused by other pathogens. Once confirmed in a large scale trial this diagnostic signature might constitute the basis of a differential diagnostic assay.

Whole blood obtained from patients with sepsis caused by B.pseudomallei or other pathogens were collected. mRNA Expression level of each patient was determined and compared to mRNA expression level from uninfected control including patient who had recovered from melioidosis, patient diagnosed type 2 diabetes, and healthy donors suing microarray analysis.

The data were analysed using BeadStudio version 1.5.1.3 using the average chip normalization method.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13015