Shared urinary metabolomic signatures as potential biomarkers for early intestinal Schistosoma infection across multiple species

Schistosomiasis is a parasitic disease with a high global burden. Early diagnosis of intestinal schistosomiasis remains challenging because the current diagnostic methods do not detect early-stage infection. The application of metabolomics is a promising strategy for identifying biomarkers of host-parasite interactions. In this study, we applied untargeted metabolomic profiling to investigate potential urinary biomarkers of early infection with three intestinal species of Schistosoma using a mouse model. Urine samples were collected at multiple time-points post-infection and analyzed using mass spectrometry-based metabolomic methods. More than 23,000 metabolic features were detected. Differential metabolite screening was performed using statistical and machine-learning approaches, including fold-change, adjusted P -value, orthogonal partial least squares discriminant, Random Forest, and receiver operating characteristic analyses. Through this integrated workflow, 71 significant features were identified. Two putative compounds (2-hydroxyglutaric acid and NCGC00380820-01) demonstrated strong discriminatory performance during the early stages of infection. The level of 2-hydroxyglutaric acid showed a declining trend from one week post-infection and was significantly reduced from two weeks onward. This pattern aligned with linear mixed-effects modeling, which accounted for inter-individual variability. In contrast, NCGC00380820-01 exhibited an increasing trend following infection and was significantly different in the infected mice compared to the control mice from four weeks post-infection. Our findings indicate that 2-hydroxyglutaric acid and NCGC00380820-01 are promising biomarkers of early-stage intestinal schistosomiasis and provide a foundation for the development of non-invasive diagnostic tools for the early detection of this neglected disease.