Dataset Information

Chagas disease-specific antigens: characterization of epitopes in CRA/FRA by synthetic peptide mapping and evaluation by ELISA-peptide assay.

ABSTRACT: BACKGROUND: The identification of epitopes in proteins recognized by medically relevant antibodies is useful for the development of peptide-based diagnostics and vaccines. In this study, epitopes in the cytoplasmic repetitive antigen (CRA) and flagellar repetitive antigen (FRA) proteins from Trypanosoma cruzi were identified using synthetic peptide techniques and pooled sera from Chagasic patients. The epitopes were further assayed with an ELISA assay based on synthetic peptides. METHODS: Twenty-two overlapping synthetic peptides representing the coding sequence of the T. cruzi CRA and FRA proteins were assessed by a Spot-synthesis array analysis using sera donated by patients with Chagas disease. Shorter peptides were selected that represented the determined epitopes and synthesized by solid phase synthesis to evaluate the patterns of cross-reactivities and discrimination through an ELISA-diagnostic assay. RESULTS: The peptide Spot-synthesis array successfully identified two IgG antigenic determinants in the CRA protein and four in FRA. Bioinformatics suggested that the CRA antigens were unique to T. cruzi while the FRA antigen showed similarity with sequences present within various proteins from Leishmania sp. Subsequently, shorter peptides representing the CRA-1, CRA-2 and FRA-1 epitopes were synthesized by solid phase synthesis and assayed by an ELISA-diagnostic assay. The CRA antigens gave a high discrimination between Chagasic, Leishmaniasis and T. cruzi-uninfected serum. A sensitivity and specificity of 100% was calculated for CRA. While the FRA antigen showed a slightly lower sensitivity (91.6%), its specificity was only 60%. CONCLUSIONS: The epitopes recognized by human anti-T. cruzi antibodies have been precisely located in two biomarkers of T. cruzi, CRA and FRA. The results from screening a panel of patient sera through an ELISA assay based on peptides representing these epitopes strongly suggest that the sequences from CRA would be useful for the development of diagnostic reagents that could improve upon the sensitivity and specificity of currently available diagnostic tests. Overall, the results provide further evidence of the usefulness of identifying specific linear B-cell epitopes for improving diagnostic tools.

SUBMITTER: Bottino CG

PROVIDER: S-EPMC3890492 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Chagas disease-specific antigens: characterization of epitopes in CRA/FRA by synthetic peptide mapping and evaluation by ELISA-peptide assay.

Bottino Carolina G CG Gomes Luciano P LP Pereira José B JB Coura José R JR Provance David William DW De-Simone Salvatore G SG

BMC infectious diseases 20131203

<h4>Background</h4>The identification of epitopes in proteins recognized by medically relevant antibodies is useful for the development of peptide-based diagnostics and vaccines. In this study, epitopes in the cytoplasmic repetitive antigen (CRA) and flagellar repetitive antigen (FRA) proteins from Trypanosoma cruzi were identified using synthetic peptide techniques and pooled sera from Chagasic patients. The epitopes were further assayed with an ELISA assay based on synthetic peptides.<h4>Metho ...[more]

PMID: 24299278

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Project description:The problemAnte-mortem diagnosis of Johne's disease, caused by Mycobacterium avium subsp. paratuberculosis (MAP), is normally achieved through faecal culture, PCR, or serological tests, but agreement as to which samples are positive for Johne's disease is often poor and sensitivities are low, particularly in early-stage infections. The potential solution: Mycobacterial cells contain very complex characteristic mixtures of mycolic acid derivatives that elicit antibodies during infection; this has been used to detect infections in humans. Here, we explore its application in providing an assay differentiating infected from vaccinated animals (DIVA assay) for Johne's disease in cattle.MethodAntibody responses to different classes of mycolic acid derivatives were measured using ELISA for serum from cattle positive for MAP by both faecal PCR and commercial serum ELISA, or just by PCR, and from animals from herds with no history of Johne's disease, bovine tuberculosis reactors, BCG-vaccinated, BCG-vaccinated and M. bovis-infected, and Gudair-vaccinated animals.ResultsThe best-performing antigens, ZAM295 and ST123-the latter a molecule present in the cells of MAP but not of Mycobacterium bovis-achieved a sensitivity of 75% and 62.5%, respectively, for serum from animals positive by both faecal PCR and a commercial MAP serum ELISA, at a specificity of 94% compared to 80 no-history negatives. Combining the results of separate assays with two antigens (ST123 and JRRR121) increased the sensitivity/specificity to 75/97.5%. At the same cut-offs, animals vaccinated with Gudair or BCG vaccines and bTB reactors showed a similar specificity. The specificity in BCG-vaccinated but M. bovis-infected animals dropped to 85%. Combining the results of two antigens gave a sensitivity/specificity of 37.5/97.5% for the full set of 80 PCR-positive samples, detecting 30 positives compared 16 for IDEXX.ConclusionSerum ELISA using synthetic lipids distinguishes effectively between MAP-negative cattle samples and those positive by both PCR and a commercial MAP serodiagnostic, without interference by Gudair or BCG vaccination. It identified almost twice as many PCR positives as the commercial serodiagnostic, offering the possibility of earlier detection of infection.

Project description:A number of machine learning-based predictors have been developed for identifying immunogenic T-cell epitopes based on major histocompatibility complex (MHC) class I and II binding affinities. Rationally selecting the most appropriate tool has been complicated by the evolving training data and machine learning methods. Despite the recent advances made in generating high-quality MHC-eluted, naturally processed ligandome, the reliability of new predictors on these epitopes has yet to be evaluated. This study reports the latest benchmarking on an extensive set of MHC-binding predictors by using newly available, untested data of both synthetic and naturally processed epitopes. 32 human leukocyte antigen (HLA) class I and 24 HLA class II alleles are included in the blind test set. Artificial neural network (ANN)-based approaches demonstrated better performance than regression-based machine learning and structural modeling. Among the 18 predictors benchmarked, ANN-based mhcflurry and nn_align perform the best for MHC class I 9-mer and class II 15-mer predictions, respectively, on binding/non-binding classification (Area Under Curves = 0.911). NetMHCpan4 also demonstrated comparable predictive power. Our customization of mhcflurry to a pan-HLA predictor has achieved similar accuracy to NetMHCpan. The overall accuracy of these methods are comparable between 9-mer and 10-mer testing data. However, the top methods deliver low correlations between the predicted versus the experimental affinities for strong MHC binders. When used on naturally processed MHC-ligands, tools that have been trained on elution data (NetMHCpan4 and MixMHCpred) shows better accuracy than pure binding affinity predictor. The variability of false prediction rate is considerable among HLA types and datasets. Finally, structure-based predictor of Rosetta FlexPepDock is less optimal compared to the machine learning approaches. With our benchmarking of MHC-binding and MHC-elution predictors using a comprehensive metrics, a unbiased view for establishing best practice of T-cell epitope predictions is presented, facilitating future development of methods in immunogenomics.

Dataset Information

Chagas disease-specific antigens: characterization of epitopes in CRA/FRA by synthetic peptide mapping and evaluation by ELISA-peptide assay.

Publications

Chagas disease-specific antigens: characterization of epitopes in CRA/FRA by synthetic peptide mapping and evaluation by ELISA-peptide assay.

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