Diagnostic host gene signature to accurately distinguish enteric fever from other febrile diseases

Journal article

Misdiagnosis of enteric fever is a major global health problem resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine-learning algorithm to host gene expression profiles, we identified a diagnostic signature which could accurately distinguish culture-confirmed enteric fever cases from other febrile illnesses (AUROC<95%). Applying this signature to a culture-negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data-driven approaches to identify host-response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR highlighting their utility as PCR-based diagnostic for use in endemic settings.

Authors: Blohmke, CJ; Muller, J; Gibani, MM; Dobinson, H; Shrestha, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: EMBO Press
• Journal: EMBO Molecular Medicine
• Volume: 11
• Issue: 10
• Article number: e10431
• Publication date: 2019-08-30
• Acceptance date: 2019-08-09
• DOI: 10.15252/emmm.201910431
• EISSN: 1757-4684
• ISSN: 1757-4676
• Language: English
• Keywords: transcriptomics; biomarker; FFR; enteric fever; machine learning