Liu Yiyun, Juma Saleh, Xue Qingkai, He Mingzhen, Ali Said Mohammed, Khamis Khamis Seif, Suleiman Mchanga Mohd, Hamad Mayda Salim, Khamis Mgeni Abdalla, Zhao Hongxia, Dong Xin, Yang Kun, Huang Yuzheng
National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, Jiangsu Institute of Parasitic Diseases, Wuxi, Jiangsu, China.
School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
PLoS Negl Trop Dis. 2025 Aug 25;19(8):e0013429. doi: 10.1371/journal.pntd.0013429. eCollection 2025 Aug.
Schistosomiasis is a major public health challenge and a globally neglected tropical disease. Schistosoma haematobium, the causative agent of urogenital schistosomiasis, is endemic in African countries; with school-aged children ages 7-15 years being the most vulnerable population. Current diagnostic methods rely on microscopy to identify parasite eggs in urine; which is labor-intensive, requires specialized skills, and often lacks sensitivity, especially in mild infections. To address these limitations, we explored host disease-related biomarkers as a promising avenue for advancing diagnosis and detection. We recruited 135 children ages 7-15 years from Zanzibar, a known transmission hotspot, and used data-independent acquisition (DIA) proteomics combined with machine learning to identify potential host protein biomarkers in urine samples from individuals infected with Schistosoma haematobium. Proteomic analysis identified 823 common host proteins in urine samples from the infected group. Machine learning algorithms highlighted candidate discriminative proteins; which were validated using enzyme-linked immunosorbent assays (ELISA). Machine learning emphasized SYNPO2, CD276, α2M, LCAT, and hnRNPM as the most discriminating biomarkers for Schistosoma haematobium infection. ELISA validation confirmed the differential expression trends of these proteins, while machine learning further validated LCAT and α2M, underscoring their diagnostic potential. Our study focused on host-derived proteins and identified key urinary protein biomarkers associated with Schistosoma haematobium infection, and offers new insights into host-parasite interactions and potential tools for non-invasive diagnostics. While validated in African pediatric populations from transmission hotspots, this host-protein approach inherently overcomes geographic limitations of parasite-based diagnostics; which is a critical advantage for surveillance in non-endemic regions where imported cases threaten gains toward elimination. These findings lay the groundwork for developing novel diagnostic approaches that could significantly improve the detection and surveillance of schistosomiasis, particularly in high-risk populations.
血吸虫病是一项重大的公共卫生挑战，也是一种全球被忽视的热带病。埃及血吸虫是泌尿生殖系统血吸虫病的病原体，在非洲国家呈地方性流行；7至15岁的学龄儿童是最脆弱的人群。目前的诊断方法依靠显微镜检查来识别尿液中的寄生虫卵；这一方法劳动强度大，需要专业技能，而且往往缺乏敏感性，尤其是在轻度感染的情况下。为了解决这些局限性，我们探索了宿主疾病相关生物标志物，将其作为推进诊断和检测的一条有前景的途径。我们从已知的传播热点桑给巴尔招募了135名7至15岁的儿童，并使用数据非依赖采集（DIA）蛋白质组学结合机器学习，以识别感染埃及血吸虫个体尿液样本中的潜在宿主蛋白质生物标志物。蛋白质组学分析在感染组的尿液样本中鉴定出823种常见的宿主蛋白质。机器学习算法突出了候选鉴别蛋白质；这些蛋白质通过酶联免疫吸附测定（ELISA）进行了验证。机器学习强调SYNPO2、CD276、α2M、LCAT和hnRNPM是埃及血吸虫感染最具鉴别力的生物标志物。ELISA验证证实了这些蛋白质的差异表达趋势，而机器学习进一步验证了LCAT和α2M，突出了它们的诊断潜力。我们的研究聚焦于宿主衍生蛋白质，识别出了与埃及血吸虫感染相关的关键尿液蛋白质生物标志物，并为宿主-寄生虫相互作用以及非侵入性诊断的潜在工具提供了新的见解。虽然在来自传播热点的非洲儿科人群中得到了验证，但这种基于宿主蛋白质的方法本质上克服了基于寄生虫诊断的地理局限性；这对于在输入性病例威胁消除成果的非流行地区进行监测来说是一个关键优势。这些发现为开发新的诊断方法奠定了基础，这些方法可以显著改善血吸虫病的检测和监测，特别是在高危人群中。
