Genomic transcriptional profiling identifies a candidate blood biomarker signature for the diagnosis of septicemic melioidosis
1 Department of Clinical Immunology, Centre for Research and Development of Medical Diagnostic Laboratories, Faculty of Associated Medical Sciences, Khon Kaen University, 123 Mittraparp Road, Khon Kaen, 40002, Thailand
2 Baylor-National Institute of Allergy and Infectious Diseases (NIAID), Cooperative Center for Translational Research on Human Immunology and Biodefense, Baylor Institute for Immunology Research and Baylor Research Institute, 3434 Live Oak St, Dallas, Texas, 75204, USA
3 Division of Immunoregulation, National Institute for Medical Research, The Ridgeway, Mill Hill, London, NW7 1AA, UK
4 Institute for Health Care Research and Improvement, Baylor Health Care System, 8080 N. Central Expressway Suite 500, Dallas, Texas, 75206, USA
5 Department of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, Keppel St, London, WC1E 7HT, UK
Genome Biology 2009, 10:R127 doi:10.1186/gb-2009-10-11-r127Published: 10 November 2009
Melioidosis is a severe infectious disease caused by Burkholderia pseudomallei, a Gram-negative bacillus classified by the National Institute of Allergy and Infectious Diseases (NIAID) as a category B priority agent. Septicemia is the most common presentation of the disease with a 40% mortality rate even with appropriate treatments. Better diagnostic tests are therefore needed to improve therapeutic efficacy and survival rates.
We have used microarray technology to generate genome-wide transcriptional profiles (>48,000 transcripts) from the whole blood of 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% accuracy in a training set. This finding was confirmed in 2 independent validation sets, which gave high prediction accuracies of 78% and 80%, respectively. This signature was significantly enriched in genes coding for products involved in the MHC class II antigen processing and presentation pathway.
Blood transcriptional patterns 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.