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.

中文翻译：

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深度体液分析与可解释机器学习相结合，揭示了血吸虫病的诊断标志物和病理生理学


血吸虫病是一种非常普遍的寄生虫病，影响着全世界超过 2 亿人。目前基于粪便中寄生虫卵检测的诊断仅在晚期检测到感染，而目前基于抗体的检测无法区分过去和当前的感染。在这里，我们开发并使用了一个多重抗体分析平台来获得全面的驱虫体液谱，包括抗原特异性抗体的同种型、亚类、Fc 受体 （FcR） 结合和糖基化谱。使用基本回归 （ER） 和 SLIDE 这种可解释的机器学习方法，我们确定了超越生物标志物的潜在因素（上下文特定组），并提供了对血吸虫感染不同阶段的病理生理学的见解。通过比较感染个体和健康个体的概况，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，例如免疫球蛋白 G4 （IgG4） 升高。然而，我们还捕获了以前未表征的体液反应，包括活动性感染患者的 FcR 结合和特异性抗体糖型升高，有助于将它们与没有活动性感染但抗体滴度相同的患者区分开来。此签名在独立队列中进行了验证。我们的方法还在未主动感染的患者中发现了两种不同的内型，即非专利感染和既往感染。还发现较高水平的 IgG1 和 FcR1/FcR3A 结合可能保护从非专利感染到活动性感染的转变。总体而言，我们揭示了基于抗体的诊断标志物和血吸虫感染发病机制的潜在因素。 我们的结果表明，选择性抗原靶向可能有助于早期检测，从而控制感染的严重程度。