Abstract
Current diagnostic tests for typhoid fever, the disease caused by Salmonella Typhi, are poor. We aimed to identify serodiagnostic signatures of typhoid fever by assessing microarray signals to 4,445 S. Typhi antigens in sera from 41 participants challenged with oral S. Typhi. We found broad, heterogeneous antibody responses with increasing IgM/IgA signals at diagnosis. In down-selected 250-antigen arrays we validated responses in a second challenge cohort (n = 30), and selected diagnostic signatures using machine learning and multivariable modeling. In four models containing responses to antigens including flagellin, OmpA, HlyE, sipC, and LPS, multi-antigen signatures discriminated typhoid (n = 100) from other febrile bacteremia (n = 52) in Nepal. These models contained combinatorial IgM, IgA, and IgG responses to 5 antigens (ROC AUC, 0.67 and 0.71) or 3 antigens (0.87), although IgA responses to LPS also performed well (0.88). Using a novel systematic approach we have identified and validated optimal serological diagnostic signatures of typhoid fever.