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Title: Adapting microarray gene expression signatures for early melioidosis diagnosis
Authors: Ornuma Sangwichian
Toni Whistler
Arnone Nithichanon
Chidchamai Kewcharoenwong
Myint Myint Sein
Chawitar Arayanuphum
Narisara Chantratita
Ganjana Lertmemongkolchai
Centers for Disease Control and Prevention
Khon Kaen University
Thailand Ministry of Public Health
Mahidol University
Keywords: Medicine
Issue Date: 1-Jul-2020
Citation: Journal of Clinical Microbiology. Vol.58, No.7 (2020)
Abstract: Copyright © 2020 American Society for Microbiology. All Rights Reserved. Melioidosis is caused by Burkholderia pseudomallei and is predominantly seen in tropical regions. The clinical signs and symptoms of the disease are nonspecific and often result in misdiagnosis, failure of treatment, and poor clinical outcome. Septicemia with septic shock is the most common cause of death, with mortality rates above 40%. Bacterial culture is the gold standard for diagnosis, but it has low sensitivity and takes days to produce definitive results. Early laboratory diagnosis can help guide physicians to provide treatment specific to B. pseudomallei. In our study, we adapted host gene expression signatures obtained from microarray data of B. pseudomallei-infected cases to develop a real-time PCR diagnostic test using two differentially expressed genes, AIM2 (absent in melanoma 2) and FAM26F (family with sequence similarity 26, member F). We tested blood from 33 patients with B. pseudomallei infections and 29 patients with other bacterial infections to validate the test and determine cutoff values for use in a cascading diagnostic algorithm. Differentiation of septicemic melioidosis from other sepsis cases had a sensitivity of 82%, specificity of 93%, and negative and positive predictive values (NPV and PPV) of 82% and 93%, respectively. Separation of cases likely to be melioidosis from those unlikely to be melioidosis in nonbacteremic situations showed a sensitivity of 40%, specificity of 54%, and NPV and PPV of 44% and 50%, respectively. We suggest that our AIM2 and FAM26F expression combination algorithm could be beneficial for early melioidosis diagnosis, offering a result within 24 h of admission.
ISSN: 1098660X
Appears in Collections:Scopus 2020

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