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

IF 15.8 1区 医学 Q1 CELL BIOLOGY Science Translational Medicine Pub Date : 2024-09-18 DOI:10.1126/scitranslmed.adk7832
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
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引用次数: 0

摘要

血吸虫病是一种高度流行的寄生虫病,影响着全球 2 亿多人。目前基于粪便中寄生虫卵检测的诊断方法只能检测到晚期感染,而目前基于抗体的检测方法无法区分过去和现在的感染。在这里,我们开发并使用了一个多重抗体图谱平台,以获得全面的抗蠕虫体液图谱,包括抗原特异性抗体的同型、亚类、Fc受体(FcR)结合和糖基化图谱。利用基本回归(ER)和可解释的机器学习方法 SLIDE,我们确定了超越生物标志物的潜伏因素(特定环境组),并深入了解了血吸虫感染不同阶段的病理生理学。通过比较感染者和健康人的特征,我们确定了具有活动性疾病独特体液特征的模块,包括寄生虫感染的标志性特征,如免疫球蛋白 G4 (IgG4) 升高。不过,我们也捕捉到了以前未曾描述过的体液反应,包括活动性感染患者中升高的 FcR 结合力和特异性抗体糖形,这有助于将他们与没有活动性感染但抗体滴度相当的患者区分开来。这一特征在一个独立的队列中得到了验证。我们的方法还在非活动性感染者中发现了两种不同的内型,即非专利感染和既往感染。我们还发现,较高的 IgG1 和 FcR1/FcR3A 结合量可能对从非专利感染到活动性感染的转变具有保护作用。总之,我们揭示了基于抗体的诊断标记和血吸虫感染发病机制的潜在因素。我们的研究结果表明,选择性抗原靶向可用于早期检测,从而控制感染的严重程度。
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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.
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来源期刊
Science Translational Medicine
Science Translational Medicine CELL BIOLOGY-MEDICINE, RESEARCH & EXPERIMENTAL
CiteScore
26.70
自引率
1.20%
发文量
309
审稿时长
1.7 months
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.
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