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

Christoph J. Blohmke; Julius Müller; Malick M. Gibani; Hazel Dobinson; Sonu Shrestha; Soumya Perinparajah; Celina Jin; Harri Hughes; Luke Blackwell; Sabina Dongol; Abhilasha Karkey; Fernanda Schreiber; Derek Pickard; Buddha Basnyat; Gordon Dougan; Stephen G. Baker; Andrew John Pollard; Thomas C. Darton

doi:10.15252/emmm.201910431

Molecular BiologyEMBO Molecular MedicineAug 30, 2019

Peer-reviewed journal article

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

Christoph J. Blohmke iD, Julius Müller iD, Malick M. Gibani iD, Hazel Dobinson iD, Sonu Shrestha, Soumya Perinparajah, Celina Jin iD, Harri Hughes, Luke Blackwell, Sabina Dongol iD, Abhilasha Karkey iD, Fernanda Schreiber, Derek Pickard, Buddha Basnyat iD, Gordon Dougan iD, Stephen G. Baker iD, Andrew John Pollard iD, Thomas C. Darton iD

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Abstract

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.