Identification of Schistosoma haematobium and Schistosoma mansoni linear B-cell epitopes with diagnostic potential using in silico immunoinformatic tools and peptide microarray technology

TL;DR

This study identifies potential B-cell epitopes from Schistosoma haematobium and Schistosoma mansoni using computational tools and peptide microarray validation, aiming to improve schistosomiasis diagnostics.

Contribution

The study introduces a novel immunoinformatic approach to identify schistosome-specific peptides with high diagnostic potential and low cross-reactivity.

Findings

Peptide AA81008-19-30 showed strong diagnostic performance for S. haematobium and S. mansoni infections.

Nine peptides demonstrated acceptable diagnostic performance for schistosomiasis detection.

The approach combines in silico predictions with peptide microarray validation for epitope identification.

Abstract

Immunoinformatic 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 immunoassay validation.

Firstly, 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 2.0 in-silico tools. 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.

Peptide AA81008-19-30 had excellent and acceptable diagnostic performances for discriminating S. mansoni and S. haematobium positives 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 discriminating S. mansoni positives 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 discriminating S. mansoni positives from S. mansoni negatives 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 discriminating S. mansoni positives from healthy controls, with an AUC value of 0.7413 for IgM.

One peptide with a good diagnostic performance and nine peptides with acceptable diagnostic performances were identified using the immunoinformatic approach and peptide microarray validation. There is need for evaluation of the peptides with true negatives and a good standard positive reference.

Schistosomiasis, 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. Unfortunately, this level of expertise is 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 problems 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 cross-reactivity 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.

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