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Immunoinformatics Features Linked to Leishmania Vaccine Development: Data Integration of Experimental and In Silico Studies.

ABSTRACT: Leishmaniasis is a wide-spectrum disease caused by parasites from Leishmania genus. There is no human vaccine available and it is considered by many studies as apotential effective tool for disease control. To discover novel antigens, computational programs have been used in reverse vaccinology strategies. In this work, we developed a validation antigen approach that integrates prediction of B and T cell epitopes, analysis of Protein-Protein Interaction (PPI) networks and metabolic pathways. We selected twenty candidate proteins from Leishmania tested in murine model, with experimental outcome published in the literature. The predictions for CD4? and CD8? T cell epitopes were correlated with protection in experimental outcomes. We also mapped immunogenic proteins on PPI networks in order to find Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with them. Our results suggest that non-protective antigens have lowest frequency of predicted T CD4? and T CD8? epitopes, compared with protective ones. T CD4? and T CD8? cells are more related to leishmaniasis protection in experimental outcomes than B cell predicted epitopes. Considering KEGG analysis, the proteins considered protective are connected to nodes with few pathways, including those associated with ribosome biosynthesis and purine metabolism.

SUBMITTER: Brito RC

PROVIDER: S-EPMC5343906 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Immunoinformatics Features Linked to Leishmania Vaccine Development: Data Integration of Experimental and In Silico Studies.

Brito Rory C F RC Guimarães Frederico G FG Velloso João P L JP Corrêa-Oliveira Rodrigo R Ruiz Jeronimo C JC Reis Alexandre B AB Resende Daniela M DM

International journal of molecular sciences 20170210 2

Leishmaniasis is a wide-spectrum disease caused by parasites from <i>Leishmania</i> genus. There is no human vaccine available and it is considered by many studies as apotential effective tool for disease control. To discover novel antigens, computational programs have been used in reverse vaccinology strategies. In this work, we developed a validation antigen approach that integrates prediction of B and T cell epitopes, analysis of Protein-Protein Interaction (PPI) networks and metabolic pathwa ...[more]

PMID: 28208616

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Project description:Zika virus (ZIKV) is an arbovirus from the Flaviviridae family and Flavivirus genus. Neurological events have been associated with ZIKV-infected individuals, such as Guillain-Barré syndrome, an autoimmune acute neuropathy that causes nerve demyelination and can induce paralysis. With the increase of ZIKV infection incidence in 2015, malformation and microcephaly cases in newborns have grown considerably, which suggested congenital transmission. Therefore, the development of an effective vaccine against ZIKV became an urgent need. Live attenuated vaccines present some theoretical risks for administration in pregnant women. Thus, we developed an in silico multiepitope vaccine against ZIKV. All structural and non-structural proteins were investigated using immunoinformatics tools designed for the prediction of CD4 + and CD8 + T cell epitopes. We selected 13 CD8 + and 12 CD4 + T cell epitopes considering parameters such as binding affinity to HLA class I and II molecules, promiscuity based on the number of different HLA alleles that bind to the epitopes, and immunogenicity. ZIKV Envelope protein domain III (EDIII) was added to the vaccine construct, creating a hybrid protein domain-multiepitope vaccine. Three high scoring continuous and two discontinuous B cell epitopes were found in EDIII. Aiming to increase the candidate vaccine antigenicity even further, we tested secondary and tertiary structures and physicochemical parameters of the vaccine conjugated to four different protein adjuvants: flagellin, 50S ribosomal protein L7/L12, heparin-binding hemagglutinin, or RS09 synthetic peptide. The addition of the flagellin adjuvant increased the vaccine's predicted antigenicity. In silico predictions revealed that the protein is a probable antigen, non-allergenic and predicted to be stable. The vaccine's average population coverage is estimated to be 87.86%, which indicates it can be administered worldwide. Peripheral Blood Mononuclear Cells (PBMC) of individuals with previous ZIKV infection were tested for cytokine production in response to the pool of CD4 and CD8 ZIKV peptide selected. CD4 + and CD8 + T cells showed significant production of IFN-γ upon stimulation and IL-2 production was also detected by CD8 + T cells, which indicated the potential of our peptides to be recognized by specific T cells and induce immune response. In conclusion, we developed an in silico universal vaccine predicted to induce broad and high-coverage cellular and humoral immune responses against ZIKV, which can be a good candidate for posterior in vivo validation.

Project description:Visceral leishmaniasis (VL), also known as kala-azar, is the most dangerous form of leishmaniasis. Currently no effective vaccine is available for clinical use. Since the pathogenicity of different Leishmania strains is inconsistent, the differentially expressed proteins in Leishmania strains may play an important role as virulence factors in pathogenesis. Therefore, effective vaccine candidate targets may exist in the differentially expressed proteins. In this study, we used differential proteomics analysis to find the differentially expressed proteins in two Leishmania donovani strains, and combined with immunoinformatics analysis to find new vaccine candidates. The differentially expressed proteins from L. DD8 (low virulent) and L. 9044 (virulent) strains were analyzed by LC-MS/MS, and preliminarily screened by antigenicity, allergenicity and homology evaluation. The binding peptides of MHC II, IFN-γ and MHC I from differentially expressed proteins were then predicted and calculated for the second screening. IFN-γ/IL-10 ratios and conserved domain prediction were performed to choose more desirable differentially expressed proteins. Finally, the 3D structures of three vaccine candidate proteins were produced and submitted for molecular dynamics simulation and molecular docking interaction with TLR4/MD2. The results showed that 396 differentially expressed proteins were identified by LC-MS/MS, and 155 differentially expressed proteins were selected through antigenicity, allergenicity and homology evaluation. Finally, 16 proteins whose percentages of MHC II, IFN-γ and MHC I binding peptides were greater than those of control groups (TSA, LmSTI1, LeIF, Leish-111f) were considered to be suitable vaccine candidates. Among the 16 candidates, amino acid permease, amastin-like protein and the hypothetical protein (XP_003865405.1) simultaneously had the large ratios of IFN-γ/IL-10 and high percentages of MHC II, IFN-γ and MHC I, which should be focused on. In conclusion, our comprehensive work provided a methodological basis to screen new vaccine candidates for a better intervention against VL and associated diseases.

Dataset Information

Immunoinformatics Features Linked to Leishmania Vaccine Development: Data Integration of Experimental and In Silico Studies.

Publications

Immunoinformatics Features Linked to Leishmania Vaccine Development: Data Integration of Experimental and In Silico Studies.

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