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The host-pathogen interactions induced by Salmonella Typhi and Salmonella Paratyphi A during enteric fever are poorly understood. This knowledge gap, and the human restricted nature of these bacteria, limit our understanding of the disease and impede the development of new diagnostic approaches. To investigate metabolite signals associated with enteric fever we performed two dimensional gas chromatography with time-of-flight mass spectrometry (GCxGC/TOFMS) on plasma from patients with S. Typhi and S. Paratyphi A infections and asymptomatic controls, identifying 695 individual metabolite peaks. Applying supervised pattern recognition, we found highly significant and reproducible metabolite profiles separating S. Typhi cases, S. Paratyphi A cases, and controls, calculating that a combination of six metabolites could accurately define the etiological agent. For the first time we show that reproducible and serovar specific systemic biomarkers can be detected during enteric fever. Our work defines several biologically plausible metabolites that can be used to detect enteric fever, and unlocks the potential of this method in diagnosing other systemic bacterial infections.

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Salmonella Paratyphi A, Salmonella Typhi, enteric fever, epidemiology, global health, human, infectious disease, mass spectrometry, metabolites, microbiology, typhoid, Area Under Curve, Bacterial Proteins, Biomarkers, Case-Control Studies, Fluoroquinolones, Gas Chromatography-Mass Spectrometry, Gatifloxacin, Humans, Metabolome, Metabolomics, Multivariate Analysis, Nepal, Ofloxacin, Pattern Recognition, Automated, Principal Component Analysis, ROC Curve, Randomized Controlled Trials as Topic, Salmonella paratyphi A, Salmonella typhi, Typhoid Fever