Anushka Saha, Trirupa Chakraborty, Javad Rahimikollu, Hanxi Xiao, Lorena B. Pereira de Oliveira, Timothy W. Hand, Sukwan Handali, W. Evan Secor, Lucia A. O. Fraga, Jessica K. Fairley, Jishnu Das, Aniruddh Sarkar
{"title":"深度体液分析与可解释的机器学习相结合,揭示血吸虫病的诊断标记和病理生理学","authors":"Anushka Saha, Trirupa Chakraborty, Javad Rahimikollu, Hanxi Xiao, Lorena B. Pereira de Oliveira, Timothy W. Hand, Sukwan Handali, W. Evan Secor, Lucia A. O. Fraga, Jessica K. Fairley, Jishnu Das, Aniruddh Sarkar","doi":"10.1126/scitranslmed.adk7832","DOIUrl":null,"url":null,"abstract":"<div >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.</div>","PeriodicalId":21580,"journal":{"name":"Science Translational Medicine","volume":"16 765","pages":""},"PeriodicalIF":15.8000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis\",\"authors\":\"Anushka Saha, Trirupa Chakraborty, Javad Rahimikollu, Hanxi Xiao, Lorena B. Pereira de Oliveira, Timothy W. Hand, Sukwan Handali, W. Evan Secor, Lucia A. O. Fraga, Jessica K. Fairley, Jishnu Das, Aniruddh Sarkar\",\"doi\":\"10.1126/scitranslmed.adk7832\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div >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. 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Deep humoral profiling coupled to interpretable machine learning unveils diagnostic markers and pathophysiology of schistosomiasis
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.
期刊介绍:
Science Translational Medicine is an online journal that focuses on publishing research at the intersection of science, engineering, and medicine. The goal of the journal is to promote human health by providing a platform for researchers from various disciplines to communicate their latest advancements in biomedical, translational, and clinical research.
The journal aims to address the slow translation of scientific knowledge into effective treatments and health measures. It publishes articles that fill the knowledge gaps between preclinical research and medical applications, with a focus on accelerating the translation of knowledge into new ways of preventing, diagnosing, and treating human diseases.
The scope of Science Translational Medicine includes various areas such as cardiovascular disease, immunology/vaccines, metabolism/diabetes/obesity, neuroscience/neurology/psychiatry, cancer, infectious diseases, policy, behavior, bioengineering, chemical genomics/drug discovery, imaging, applied physical sciences, medical nanotechnology, drug delivery, biomarkers, gene therapy/regenerative medicine, toxicology and pharmacokinetics, data mining, cell culture, animal and human studies, medical informatics, and other interdisciplinary approaches to medicine.
The target audience of the journal includes researchers and management in academia, government, and the biotechnology and pharmaceutical industries. It is also relevant to physician scientists, regulators, policy makers, investors, business developers, and funding agencies.