Diagnostic host gene signature for distinguishing enteric fever from other febrile diseases.

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

doi:10.15252/emmm.201910431

Christoph J Blohmke, Stephen Baker + Show 16 more

Open Access

https://doi.org/10.15252/emmm.201910431

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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 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.

Highlights

Enteric fever, a disease caused by systemic infection with Salmonella enterica serovars Typhi or Paratyphi A, accounts for 13.5– 26.9 million illness episodes worldwide each year (Buckle et al, 2012; Mogasale et al, 2014)
We recently described the molecular response profile of acute enteric fever in individuals participating in the typhoid controlled human infection models (CHIMs) (Data ref: Blohmke et al, 2016b), which was characterized by innate immunity, inflammatory and interferon signalling patterns (Blohmke et al, 2016a)
To compare responses to enteric fever occurring during natural infection in an endemic area, we generated transcriptional profiles in samples collected from culture-confirmed enteric fever patients

Summary

Introduction

A disease caused by systemic infection with Salmonella enterica serovars Typhi or Paratyphi A, accounts for 13.5– 26.9 million illness episodes worldwide each year (Buckle et al, 2012; Mogasale et al, 2014). In resource-limited tropical settings, these infections are endemic and the accurate diagnosis of patients presenting with undifferentiated fever is challenging. Diagnostic tests for enteric fever rely on microbiological culture or detection of a serological response to infection, and are often unavailable or insufficiently sensitive and specific (Parry et al, 2011). Due to the lack of reliable diagnostics, this culminates in substantial overtreatment of enteric fever with unnecessary antibiotics (Andrews et al, 2018). These reports highlight the urgent need of new diagnostic approaches to enable the accurate detection of enteric fever cases in endemic settings, to guide management of febrile patients and appropriate use of antimicrobials, and to identify populations likely to benefit from vaccine implementation

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