Immunoinformatics Design of Multi-Epitope Peptide-Based Vaccine Against Schistosoma mansoni Using Transmembrane Proteins as a Target.

Schistosomiasis remains a serious health issue nowadays for an estimated one billion people in 79 countries around the world. Great efforts have been made to identify good vaccine candidates during the last decades, but only three molecules reached clinical trials so far. The reverse vaccinology approach has become an attractive option for vaccine design, especially regarding parasites like Schistosoma spp. that present limitations for culture maintenance. This strategy also has prompted the construction of multi-epitope based vaccines, with great immunological foreseen properties as well as being less prone to contamination, autoimmunity, and allergenic responses. Therefore, in this study we applied a robust immunoinformatics approach, targeting S. mansoni transmembrane proteins, in order to construct a chimeric antigen. Initially, the search for all hypothetical transmembrane proteins in GeneDB provided a total of 584 sequences. Using the PSORT II and CCTOP servers we reduced this to 37 plasma membrane proteins, from which extracellular domains were used for epitope prediction. Nineteen common MHC-I and MHC-II binding epitopes, from eight proteins, comprised the final multi-epitope construct, along with suitable adjuvants. The final chimeric multi-epitope vaccine was predicted as prone to induce B-cell and IFN-γ based immunity, as well as presented itself as stable and non-allergenic molecule. Finally, molecular docking and molecular dynamics foresee stable interactions between the putative antigen and the immune receptor TLR 4. Our results indicate that the multi-epitope vaccine might stimulate humoral and cellular immune responses and could be a potential vaccine candidate against schistosomiasis.

Selecting targets for the diagnosis of Schistosoma mansoni infection: An integrative approach using multi-omic and immunoinformatics data.

In order to effectively control and monitor schistosomiasis, new diagnostic methods are essential. Taking advantage of computational approaches provided by immunoinformatics and considering the availability of Schistosoma mansoni predicted proteome information, candidate antigens of schistosomiasis were selected and used in immunodiagnosis tests based on Enzime-linked Immunosorbent Assay (ELISA). The computational selection strategy was based on signal peptide prediction; low similarity to human proteins; B- and T-cell epitope prediction; location and expression in different parasite life stages within definitive host. Results of the above-mentioned analysis were parsed to extract meaningful biological information and loaded into a relational database developed to integrate them. In the end, seven proteins were selected and one B-cell linear epitope from each one of them was selected using B-cell epitope score and the presence of intrinsically disordered regions (IDRs). These predicted epitopes generated synthetic peptides that were used in ELISA assays to validate the rational strategy of in silico selection. ELISA was performed using sera from residents of areas of low endemicity for S. mansoni infection and also from healthy donors (HD), not living in an endemic area for schistosomiasis. Discrimination of negative (NEG) and positive (INF) individuals from endemic areas was performed using parasitological and molecular methods. All infected individuals were treated with praziquantel, and serum samples were obtained from them 30 and 180 days post-treatment (30DPT and 180DPT). Results revealed higher IgG levels in INF group than in HD and NEG groups when peptides 1, 3, 4, 5 and 7 were used. Moreover, using peptide 5, ELISA achieved the best performance, since it could discriminate between individuals living in an endemic area that were actively infected from those that were not (NEG, 30DPT, 180DPT groups). Our experimental results also indicate that the computational prediction approach developed is feasible for identifying promising candidates for the diagnosis of schistosomiasis and other diseases.

Epitopes rationally selected through computational analyses induce T-cell proliferation in mice and are recognized by serum from individuals infected with Schistosoma mansoni.

Schistosomiasis is the second leading cause of death due to parasitic diseases in the world. Seeking an alternative for the control of disease, the World Health Organization funded the genome sequencing of the major species related to schistosomiasis to identify potential vaccines and therapeutic targets. Therefore, the aim of this work was to select T and B-cell epitopes from Schistosoma mansoni through computational analyses and evaluate the immunological potential of epitopes in vitro. Extracellular regions of membrane proteins from the Schistosoma mansoni were used to predict promiscuous epitopes with affinity to different human Major Histocompatibility Class II (MHCII) molecules by bioinformatics analysis. The three-dimensional structure of selected epitopes was constructed and used in molecular docking to verify the interaction with murine MHCII H2-IAb . In this process, four epitopes were selected and synthesized to assess their ability to stimulate proliferation of CD4+ T lymphocytes in mice splenocyte cultures. The results showed that Sm041370 and Sm168240 epitopes induced significant cell proliferation. Additionally, the four epitopes were used as antigens in the Indirect Enzyme-Linked Immunosorbent Assay (ELISA) to assess the recognition by serum from individuals infected with Schistosoma mansoni. Sm140560, Sm168240, and Sm041370 epitopes were recognized by infected individuals IgG antibodies. Therefore, Sm041370 and Sm168240 epitopes that stood out in in silico and in vitro analyses could be promising antigens in schistosomiasis vaccine development or diagnostic kits. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:804-814, 2017.

The Use of Reverse Vaccinology and Molecular Modeling Associated with Cell Proliferation Stimulation Approach to Select Promiscuous Epitopes from Schistosoma mansoni.

Schistosomiasis remains an important parasitic disease that affects millions of individuals worldwide. Despite the availability of chemotherapy, the occurrence of constant reinfection demonstrates the need for additional forms of intervention and the development of a vaccine represents a relevant strategy to control this disease. With the advent of genomics and bioinformatics, new strategies to search for vaccine targets have been proposed, as the reverse vaccinology. In this work, computational analyses of Schistosoma mansoni membrane proteins were performed to predict epitopes with high affinity for different human leukocyte antigen (HLA)-DRB1. Ten epitopes were selected and along with murine major histocompatibility complex (MHC) class II molecule had their three-dimensional structures optimized. Epitope interactions were evaluated against murine MHC class II molecule through molecular docking, electrostatic potential, and molecular volume. The epitope Sm141290 and Sm050890 stood out in most of the molecular modeling analyses. Cellular proliferation assay was performed to evaluate the ability of these epitopes to bind to murine MHC II molecules and stimulate CD4+ T cells showing that the same epitopes were able to significantly stimulate cell proliferation. This work showed an important strategy of peptide selection for epitope-based vaccine design, achieved by in silico analyses that can precede in vivo and in vitro experiments, avoiding excessive experimentation.