Dataset Information

Proteome-Wide Zika Virus CD4 T Cell Epitope and HLA Restriction Determination.

ABSTRACT: Zika virus (ZIKV) is a mosquito-borne pathogen that caused an epidemic in 2015-2016. ZIKV-specific T cell responses are functional in animal infection models, and helper CD4 T cells promote avid Abs in the vaccine context. The small volumes of blood available from field research limit the determination of T cell epitopes for complex microbes such as ZIKV. The goal of this project was efficient determination of human ZIKV CD4 T cell epitopes at the whole proteome scale, including validation of reactivity to whole pathogen, using small blood samples from convalescent time points when T cell response magnitude may have waned. Polyclonal enrichment of candidate ZIKV-specific CD4 T cells used cell-associated virus, documenting that T cells in downstream peptide analyses also recognize whole virus after Ag processing. Sequential query of bulk ZIKV-reactive CD4 T cells with pooled/single ZIKV peptides and molecularly defined APC allowed precision epitope and HLA restriction assignments across the ZIKV proteome and enabled discovery of numerous novel ZIKV CD4 T cell epitopes. The research workflow is useful for the study of emerging infectious diseases with a very limited human blood sample availability.

SUBMITTER: Campbell VL

PROVIDER: S-EPMC7839664 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Proteome-Wide Zika Virus CD4 T Cell Epitope and HLA Restriction Determination.

Campbell Victoria L VL Nguyen LeAnn L Snoey Elise E McClurkan Christopher L CL Laing Kerry J KJ Dong Lichun L Sette Alessandro A Lindestam Arlehamn Cecilia S CS Altmann Danny M DM Boyton Rosemary J RJ Roby Justin A JA Gale Michael M Stone Mars M Busch Michael P MP Norris Phillip J PJ Koelle David M DM

ImmunoHorizons 20200804 8

Zika virus (ZIKV) is a mosquito-borne pathogen that caused an epidemic in 2015-2016. ZIKV-specific T cell responses are functional in animal infection models, and helper CD4 T cells promote avid Abs in the vaccine context. The small volumes of blood available from field research limit the determination of T cell epitopes for complex microbes such as ZIKV. The goal of this project was efficient determination of human ZIKV CD4 T cell epitopes at the whole proteome scale, including validation of re ...[more]

PMID: 32753403

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Project description:Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

Project description:Background: There is an urgent need to understand the complex relationship between cross-reactive anti-viral immunity, disease susceptibility, and severity in the face of differential exposure to related, circulating Flaviviruses. Co-exposure to Dengue virus and Zika virus in Brazil is a case in point. A devastating aspect of the 2015-2016 South American Zika outbreak was the dramatic increase in numbers of infants born with microcephaly to mothers exposed to Zika virus during pregnancy. It has been proposed that this is more likely to ensue from Zika infection in women lacking cross-protective Dengue immunity. In this case series we measure the prevalence of Dengue immunity in a cohort of mothers exposed to Zika virus during pregnancy in the 2015-2016 Zika outbreak that gave birth to an infant affected by microcephaly and explore their adaptive immunity to Zika virus. Results: Fifty women from Sergipe, Brazil who gave birth to infants with microcephaly following Zika virus exposure during the 2015-16 outbreak were tested for serological evidence of Dengue exposure and IFN? ELISpot spot forming cell (SFC) response to Zika virus. The majority (46/50) demonstrated Dengue immunity. IFN? ELISpot responses to Zika virus antigens showed the following hierarchy: Env>NS1>NS3>C protein. Twenty T cell epitopes from Zika virus Env were identified. Responses to Zika virus antigens Env and NS1 were polyfunctional with cells making IFN?, TNF?, IL-4, IL-13, and IL-10. In contrast, responses to NS5 only produced the immune regulatory TGF?1 cytokine. There were SFC responses against Zika virus Env (1-20) and variant peptide sequences from West Nile virus, Dengue virus 1-4 and Yellow Fever virus. Conclusion: Almost all the women in our study showed serological evidence of Dengue immunity, suggesting that microcephaly can occur in DENV immune mothers. T cell immunity to Zika virus showed a multifunctional response to the antigens Env and NS1 and immune regulatory responses to NS5 and C protein. Our data support an argument that different viral products may skew the antiviral response to a more pro or anti-inflammatory outcome, with an associated impact on immunopathogenesis.

Dataset Information

Proteome-Wide Zika Virus CD4 T Cell Epitope and HLA Restriction Determination.

Publications

Proteome-Wide Zika Virus CD4 T Cell Epitope and HLA Restriction Determination.

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