Identification ofSchistosoma haematobiumandSchistosoma mansonilinear B-cell epitopes with diagnostic potential usingin silicoimmunoinformatic tools and peptide microarray technology
Authors: Vengesai Arthur , Manuwa Marble , Midzi Herald , Mandeya Masimba , Muleya Victor , Mujeni Keith , Chipako Isaac , Goldring Dean , Mduluza Takafira 
Published: December 2023

AbstractIntroductionImmunoinformatic tools can be used to predict schistosome-specific B-cell epitopes with little sequence identity to human proteins and antigens other than the target. This study reports an approach for identifying schistosome peptides mimicking linear B-cell epitopes using in-silico tools and peptide microarray immunoassays validation.MethodFirstly, a comprehensive literature search was conducted to obtain published schistosome-specific peptides and recombinant proteins with the best overall diagnostic performances. For novel peptides, linear B-cell epitopes were predicted from target recombinant proteins using ABCpred, Bcepred and BepiPred 2.0in-silicotools. Together with the published peptides, predicted peptides with the highest probability of being B-cell epitopes and the lowest sequence identity with proteins from human and other pathogens were selected. Antibodies against the peptides were measured in sera, using peptide microarray immunoassays. Area under the ROC curve was calculated to assess the overall diagnostic performances of the peptides.ResultsPeptide AA81008-19-30 had excellent and acceptable diagnostic performances for discriminatingS. mansoniandS. haematobiumpositives from healthy controls with AUC values of 0.8043 and 0.7326 respectively for IgG. Peptides MS3_10186-123-131, MS3_10385-339-354, SmSPI-177-193, SmSPI-379-388, MS3-10186-40-49 and SmS-197-214 had acceptable diagnostic performances for discriminatingS. mansonipositives from healthy controls with AUC values ranging from 0.7098 to 0.7763 for IgG. Peptides SmSPI-359-372, Smp126160-438-452 and MS3 10186-25-41 had acceptable diagnostic performances for discriminatingS. mansonipositives fromS. mansoninegatives with AUC values of 0.7124, 0.7156 and 0.7115 respectively for IgG. Peptide MS3-10186-40-49 had an acceptable diagnostic performance for discriminatingS. mansonipositives from healthy controls with an AUC value of 0.7413 for IgM.ConclusionOne peptide with a good diagnostic performance and 9 peptides with acceptable diagnostic performances were identified using the immunoinformatic approach and peptide microarray validation. There is need for evaluation with true negatives and a good reference.1Author summarySchistosomiasis commonly known as bilharzia is the third most significant tropical disease after malaria and soil-transmitted helminthiases. Like other neglected tropical diseases common in Zimbabwe, schistosomiasis remains mostly undiagnosed or undetected. This is partly due to the fact that reliable identification of parasites requires expertise for specimen preparation, and microscopic examination which are largely unavailable in most rural clinics. This limitation is further compounded by the fact that the recommended microscopy-based methods for schistosomiasis diagnosis lack sensitivity, especially in infections of low intensity. To overcome some of the caveats associated with microscopy-based methods, highly sensitive serological tests have been utilized. Unfortunately, currently available serological tests have low specificity and show cross-reactivity with other helminthic infections. One way to mitigate the cross-reactivity challenge and increase the specificity, is to use immunoinformatic tools and immunoassays to identify schistosomiasis species-specific immunogenic peptides mimicking B-cell epitopes (short amino acid sequences of the antigen that reacts with antibodies). Utilizing immunoinformatic tools coupled with peptide microarray immunoassay validation approach several peptides that can be used to develop diagnostic tools for showing exposure to infection for people living in non-endemic or low-transmission areas were identified in the current study.