Identification and retrospective validation of T-cell epitopes in the hepatitis C virus genotype 4 proteome

Abdel-Hady, Karim M.; Gutiérrez, Andres H.; Terry, Frances; Desrosiers, Joe; Groot, Anne S. De; Azzazy, Hassan M.E.

doi:10.4161/hv.29177

Cited by 7 publications

(7 citation statements)

References 63 publications

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“…Indeed, a previous study described CD8 + T‐cell responses against overlapping peptides in patients with HCV genotype 4 infection but did not determine the optimal minimal peptide sequences and HLA restriction . Another study performed in silico prediction of HCV genotype 4‐specific CD8 + T‐cell epitopes but did not confirm targeting of these epitopes in patients with HCV genotype 4 infection . The only HCV‐specific CD8 + T‐cell epitope that has been analysed for cross‐reactivity between genotypes 1 and 4 is the immunodominant HLA‐A*02‐restricted epitope NS3 1073‐1081 (genotype 1 sequence: CINGVCWTV; genotype 4 sequence: AV NGV M WTV); CD8 + T‐cell lines generated with the genotype 1‐specific peptide displayed varying degrees of cross‐recognition of the genotype 4 variant; however, patients with HCV genotype 4 infection were not analysed in the study .…”

Section: Discussionmentioning

confidence: 99%

“…26 Another study performed in silico prediction of HCV genotype 4-specific CD8 + T-cell epitopes but did not confirm targeting of these epitopes in patients with HCV genotype 4 infection. 27 The only HCV-specific CD8 + T-cell epitope that has been analysed for cross-reactivity between genotypes 1 and 4 is the immunodominant HLA-A*02-restricted epitope NS3 1073-1081 (genotype 1 sequence:…”

Section: Discussionmentioning

confidence: 99%

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Differential virus‐specific CD8⁺ T‐cell epitope repertoire in hepatitis C virus genotype 1 versus 4

Luxenburger

Graß

Baermann

et al. 2018

Journal of Viral Hepatitis

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Virus-specific CD8 T-cell responses play an important role in the outcome of hepatitis C virus (HCV) infection. To date, most HCV-specific CD8 T-cell epitopes have been defined in HCV genotype 1 infection. In contrast, the HCV genotype 4-specific CD8 T-cell response is poorly defined. Here, we analysed whether known HCV-specific CD8 T-cell epitopes are also recognized in HCV genotype 4-infected patients and set out to identify the first HCV genotype 4-specific CD8 T-cell epitopes. We studied patients chronically infected with HCV genotype 1 (n = 20) or 4 (n = 21) using 91 well-described HCV-specific epitope peptides. In addition, we analysed 24 genotype 4-infected patients using 40 epitope candidates predicted using an in silico approach. HCV-specific CD8 T-cell responses targeting previously described epitopes were detectable in the majority of genotype 1-infected patients (11 of 20). In contrast, patients infected with HCV genotype 4 rarely targeted these epitopes (4 of 21; P = .0247). Importantly, we were able to identify eight novel HCV genotype 4-specific CD8 T-cell epitopes. Only one of these epitopes was shared between genotype 1 and genotype 4. These results indicate that there is little overlap between CD8 T-cell repertoires targeting HCV genotype 1 and 4. Prophylactic vaccination studies based on HCV genotype 1 are currently underway. However, in countries with the highest prevalence of HCV infection, such as Egypt, most patients are infected with HCV genotype 4. Thus, prophylactic vaccination strategies need to be adapted to HCV genotype 4 before their application to regions where HCV genotype 4 is endemic.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Differential virus‐specific CD8⁺ T‐cell epitope repertoire in hepatitis C virus genotype 1 versus 4

Luxenburger

Graß

Baermann

et al. 2018

Journal of Viral Hepatitis

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“…Our analysis uses a list of 15 reference alleles and treats each of them with equal importance following a prediction strategy that is considered to be state of the art (Iwai et al, 2003; Tangri et al, 2005; Koren et al, 2007; Cantor et al, 2011; Abdel-Hady et al, 2014; King et al, 2014; Li et al, 2014; Salvat et al, 2014; Salvat et al, 2015); however, it may introduce a bias because those alleles actually have different frequencies in the population. For example, if a peptide is predicted positive to DRB1*0701 (which is the most frequent in the world population), but negative to DRB1*0401 (which is much less frequent in the world population (Gonzalez-Galarza et al, 2011)), those predictions are considered equally important in the in silico analysis.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, previous restriction assays we have done for epitope 1 (peptide 15) using 10 donors that had a positive response to that epitope showed that this epitope is restricted by DR alone (Mazor et al, 2012). Moreover, focus on DR binding prediction is considered highly common in the art of immunogenicity prediction (Iwai et al, 2003; Tangri et al, 2005; Koren et al, 2007; Cantor et al, 2011; Abdel-Hady et al, 2014; King et al, 2014; Li et al, 2014; Salvat et al, 2014; Salvat et al, 2015) and it has been recently shown that prediction of DP and DQ had very low correlation with experimental data. (Paul et al, 2015) Therefore, we chose to focus only on DR.…”

Section: Discussionmentioning

confidence: 99%

Poor correlation between T-cell activation assays and HLA-DR binding prediction algorithms in an immunogenic fragment of Pseudomonas exotoxin A

Mazor

Tai

Lee

et al. 2015

Journal of Immunological Methods

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The ability to identify immunogenic determinants that activate T-cells is important for the development of new vaccines, allergy therapy and protein therapeutics. In silico MHC-II binding prediction algorithms are often used for T-cell epitope identification. To understand how well those programs predict immunogenicity, we computed HLA binding to peptides spanning the sequence of PE38, a fragment of an anti-cancer immunotoxin, and compared the predicted and experimentally identified T-cell epitopes. We found that the prediction for individual donors did not correlate well with the experimental data. Furthermore, prediction of T-cell epitopes in an HLA heterogenic population revealed that the two strongest epitopes were predicted at multiple cutoffs but the third epitope was predicted negative at all cutoffs and overall 4/9 epitopes were missed at several cutoffs. We conclude that MHC class-II binding predictions are not sufficient to predict the T-cell epitopes in PE38 and should be supplemented by experimental work.

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“…Significant advancements in immunome-derived, epitopedriven vaccines designed using the iVAX toolkit have been made in recent years; in particular, basic research studies have demonstrated the ability of the tools to select epitopes for Burkholderia species, 29 H. pylori 30 and HCV, [31][32][33] and validated the importance of the presence of T cell epitopes in subunit vaccines. Recently, the collaborating groups at EpiVax and iCubed discovered epitopes that activate T regulatory cells (Tregs) in viral pathogens using the JanusMatrix tool.…”

Section: Computational Vaccinologymentioning

confidence: 99%

iVAX: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines

Moise

Gutiérrez

Kibria

et al. 2015

Human Vaccines & Immunotherapeutics

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Computational vaccine design, also known as computational vaccinology, encompasses epitope mapping, antigen selection and immunogen design using computational tools. The iVAX toolkit is an integrated set of tools that has been in development since 1998 by De Groot and Martin. It comprises a suite of immunoinformatics algorithms for triaging candidate antigens, selecting immunogenic and conserved T cell epitopes, eliminating regulatory T cell epitopes, and optimizing antigens for immunogenicity and protection against disease. iVAX has been applied to vaccine development programs for emerging infectious diseases, cancer antigens and biodefense targets. Several iVAX vaccine design projects have had success in pre-clinical studies in animal models and are progressing toward clinical studies. The toolkit now incorporates a range of immunoinformatics tools for infectious disease and cancer immunotherapy vaccine design. This article will provide a guide to the iVAX approach to computational vaccinology.

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Identification and retrospective validation of T-cell epitopes in the hepatitis C virus genotype 4 proteome

Cited by 7 publications

References 63 publications

Differential virus‐specific CD8⁺ T‐cell epitope repertoire in hepatitis C virus genotype 1 versus 4

Differential virus‐specific CD8⁺ T‐cell epitope repertoire in hepatitis C virus genotype 1 versus 4

Poor correlation between T-cell activation assays and HLA-DR binding prediction algorithms in an immunogenic fragment of Pseudomonas exotoxin A

iVAX: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines

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Identification and retrospective validation of T-cell epitopes in the hepatitis C virus genotype 4 proteome

Cited by 7 publications

References 63 publications

Differential virus‐specific CD8+ T‐cell epitope repertoire in hepatitis C virus genotype 1 versus 4

Differential virus‐specific CD8+ T‐cell epitope repertoire in hepatitis C virus genotype 1 versus 4

Poor correlation between T-cell activation assays and HLA-DR binding prediction algorithms in an immunogenic fragment of Pseudomonas exotoxin A

iVAX: An integrated toolkit for the selection and optimization of antigens and the design of epitope-driven vaccines

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Differential virus‐specific CD8⁺ T‐cell epitope repertoire in hepatitis C virus genotype 1 versus 4

Differential virus‐specific CD8⁺ T‐cell epitope repertoire in hepatitis C virus genotype 1 versus 4