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Record W2003928321 · doi:10.1128/jvi.01911-14

Recombinant Hepatitis C Virus Envelope Glycoprotein Vaccine Elicits Antibodies Targeting Multiple Epitopes on the Envelope Glycoproteins Associated with Broad Cross-Neutralization

2014· article· en· W2003928321 on OpenAlexafffund
Jason Wong, Rakesh Bhat, Darren Hockman, Michael Logan, Chao Chen, Aviad Levin, Sharon E. Frey, Robert B. Belshe, D. Lorne Tyrrell, John Lok Man Law, Michael Houghton

Bibliographic record

VenueJournal of Virology · 2014
Typearticle
Languageen
FieldMedicine
TopicHepatitis C virus research
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsVirologyEpitopeBiologyNeutralizationMonoclonal antibodyGlycoproteinAntibodyAntiserumNeutralizing antibodyRecombinant DNAHepatitis C virusImmunizationVirusEpitope mappingImmunologyMolecular biologyGeneticsGene

Abstract

fetched live from OpenAlex

UNLABELLED: Although effective hepatitis C virus (HCV) antivirals are on the horizon, a global prophylactic vaccine for HCV remains elusive. The diversity of the virus is a major concern for vaccine development; there are 7 major genotypes of HCV found globally. Therefore, a successful vaccine will need to protect against HCV infection by all genotypes. Despite the diversity, many monoclonal antibodies (MAbs) with broadly cross-neutralizing activity have been described, suggesting the presence of conserved epitopes that can be targeted to prevent infection. Similarly, a vaccine comprising recombinant envelope glycoproteins (rE1E2) derived from the genotype 1a HCV-1 strain has been shown to be capable of eliciting cross-neutralizing antibodies in guinea pigs, chimpanzees, and healthy human volunteers. In order to investigate the basis for this cross-neutralization, epitope mapping of anti-E1E2 antibodies present within antisera from goats and humans immunized with HCV-1 rE1E2 was conducted through peptide mapping and competition studies with a panel of cross-neutralizing MAbs targeting various epitopes within E1E2. The immunized-goat antiserum was shown to compete with the binding of all MAbs tested (AP33, HC33.4, HC84.26, 1:7, AR3B, AR4A, AR5A, IGH526, and A4). Antisera showed the best competition against HC84.26 and AR3B and the weakest competition against AR4A. Furthermore, antisera from five immunized human vaccinees were shown to compete with five preselected MAbs (AP33, AR3B, AR4A, AR5A, and IGH526). These data show that immunization with HCV-1 rE1E2 elicits antibodies targeting multiple cross-neutralizing epitopes. Our results further support the use of such a vaccine antigen to induce cross-genotype neutralization. IMPORTANCE: An effective prophylactic vaccine for HCV is needed for optimal control of the disease burden. The high diversity of HCV has posed a challenge for developing vaccines that elicit neutralizing antibodies for protection against infection. Despite this, we have previously shown that a vaccine comprising recombinant envelope glycoproteins derived from a single genotype 1a strain was capable of eliciting a cross-neutralizing antibody response in human volunteers. Here, we have used competition binding assays and peptide binding assays to show that antibodies present in the antisera from vaccinated goats and humans bind epitopes overlapping with those of a variety of well-characterized cross-neutralizing monoclonal antibodies. This provides a mechanism for the cross-neutralizing human antisera: antibodies present in the antisera bind to conserved regions associated with cross-neutralization. Importantly, this work provides further support for a vaccine comprising recombinant envelope glycoproteins, perhaps in a formulation with a vaccine component eliciting strong anti-HCV CD4(+) and CD8(+) T cell responses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.296
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations65
Published2014
Admission routes2
Has abstractyes

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