Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases. Issue 10 (30th August 2019)

• Record Type: Journal Article
• Authors: Blohmke, Christoph J; Muller, Julius; Gibani, Malick M; Dobinson, Hazel; Shrestha, Sonu; Perinparajah, Soumya; Jin, Celina; Hughes, Harri; Blackwell, Luke; Dongol, Sabina; Karkey, Abhilasha; Schreiber, Fernanda; Pickard, Derek; Basnyat, Buddha; Dougan, Gordon; Baker, Stephen; Pollard, Andrew J; Darton, Thomas C
• Is Part Of: EMBO molecular medicine. Volume 11:Issue 10(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-08-30
• Subjects: biomarker -- enteric fever -- machine learning -- transcriptomics; Molecular biology -- Periodicals; Medical genetics -- Periodicals; Pathology, Molecular -- Periodicals; 616.04205
• DOI: 10.15252/emmm.201910431
• Languages: English
• ISSNs: 1757-4676

Abstract

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 distinguish culture‐confirmed enteric fever cases from other febrile illnesses (area under receiver operating characteristic curve > 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 diagnostics for use in endemic settings. Synopsis: Data from controlled human challenge models and publicly available repositories of human molecular immunology response were analyzed and used to identify specific patterns able to differentiate between enteric fever caused by Salmonella or other causes of undifferentiated febrile illnesses. Large transcriptional datasets were generated in this study and re‐purposed from the public domain. A supervised learning algorithm was applied to identify a small gene signature able to detect enteric fever cases. Abstract : Data from controlled human challenge models and publicly available repositories of human molecular immunology response were analyzed and used to identify specific patterns able to differentiate between enteric fever caused by Salmonella or other causes of undifferentiated febrile illnesses.