The feasibility of MS and advanced data processing for monitoring Schistosoma mansoni infection

Abstract

Purpose: Sensitive diagnosis, monitoring of disease progression and the evaluation of chemotherapeutic interventions are of prime importance for the improvement of control and prevention strategies for Schistosomiasis. The aim of the present study was to identify novel markers of Schistosoma mansoni infection and disease using urine samples from a large cohort from an area endemic for S. mansoni.

Experimental design: Urine samples were collected and processed on an automated sample clean-up and fractionation system combining strong cation exchange and reversed phase, and analyzed by MS (MALDI ToF MS). The ClinPro Tools(™) (CPT) software and the Discrete Wavelet Transformation-Support Vector Machine (DWT-SVM) procedure were used for classification and statistical analysis.

Results: We observed a large difference in urinary peptide profiles between children and adults but classification based on infection was possible only for children. Here, in the external validation data set, 93% of the infected children were classified correctly with DWT-SVM (versus 76% for CPT). In addition 91% of low-infected children were classified correctly using DWT-SVM (versus 85% for CPT). The discriminating peptides were identified as fragments of collagen 1A1 and 1A3, and uromodulin.

Conclusions and clinical relevance: In conclusion, we provide the usefulness of a peptidomics profiling approach combined with DWT-SVM in the monitoring of S. mansoni infection.