We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis.
- Authors
Saha, Anushka; Chakraborty, Trirupa; Rahimikollu, Javad; Xiao, Hanxi; de Oliveira, Lorena B. Pereira; Hand, Timothy W.; Handali, Sukwan; Secor, W. Evan; A. O. Fraga, Lucia; Fairley, Jessica K.; Das, Jishnu; Sarkar, Aniruddh
- Abstract
Schistosomiasis, a highly prevalent parasitic disease, affects more than 200 million people worldwide. Current diagnostics based on parasite egg detection in stool detect infection only at a late stage, and current antibody-based tests cannot distinguish past from current infection. Here, we developed and used a multiplexed antibody profiling platform to obtain a comprehensive repertoire of antihelminth humoral profiles including isotype, subclass, Fc receptor (FcR) binding, and glycosylation profiles of antigen-specific antibodies. Using Essential Regression (ER) and SLIDE, interpretable machine learning methods, we identified latent factors (context-specific groups) that move beyond biomarkers and provide insights into the pathophysiology of different stages of schistosome infection. By comparing profiles of infected and healthy individuals, we identified modules with unique humoral signatures of active disease, including hallmark signatures of parasitic infection such as elevated immunoglobulin G4 (IgG4). However, we also captured previously uncharacterized humoral responses including elevated FcR binding and specific antibody glycoforms in patients with active infection, helping distinguish them from those without active infection but with equivalent antibody titers. This signature was validated in an independent cohort. Our approach also uncovered two distinct endotypes, nonpatent infection and prior infection, in those who were not actively infected. Higher amounts of IgG1 and FcR1/FcR3A binding were also found to be likely protective of the transition from nonpatent to active infection. Overall, we unveiled markers for antibody-based diagnostics and latent factors underlying the pathogenesis of schistosome infection. Our results suggest that selective antigen targeting could be useful in early detection, thus controlling infection severity. Editor's summary: Schistosomiasis is a parasitic flatworm infection, and diagnosis is based on egg detection in stool. Current tests cannot distinguish between active and past infections. Saha and Chakraborty et al. developed a multiplexed antibody approach that used interpretable machine learning to distinguish different stages of disease. Comparing healthy and infected individuals across two human cohorts from Brazil and Kenya revealed previously uncharacterized signatures of active disease, including increased Fc receptor binding and specific antibody glycoforms. These signatures helped to separate individuals with active infection from those with past infection as well as to establish two distinct endotypes, those with low intensity or early infection and those with cleared prior infections, in those without active infection. These data suggest that profiling of humoral immune responses could be used to diagnose cases of schistosomiasis with higher precision. —Brandon Berry
- Subjects
FC receptors; ANTIBODY titer; PARASITIC diseases; MACHINE learning; SCHISTOSOMIASIS
- Publication
Science Translational Medicine, 2024, Vol 16, Issue 765, p1
- ISSN
1946-6234
- Publication type
Article
- DOI
10.1126/scitranslmed.adk7832