A panel of hepatitis C virus glycoproteins for the characterization of antibody responses using antibodies with diverse recognition and neutralization patterns
Bibliographic record
Abstract
A vaccine against Hepatitis C virus (HCV) is urgently needed to limit the spread of HCV. The large antigenic diversity of the HCV glycoprotein E1E2 makes it difficult to design a vaccine but also to fully understand the antibody response after infection or vaccination. Here we designed a panel of HCV pseudoparticles (HCVpps) that cover a wide range of genetically and antigenically diverse E1E2s. We validate our panel using neutralization and a binding antibody multiplex assay (BAMA). The panel of HCVpps includes E1E2 glycoproteins from acute and chronically infected cases in the Netherlands, as well as E1E2 glycoproteins from previously reported HCVs. Using eight monoclonal antibodies targeting multiple antigenic regions on E1E2, we could categorize four groups of neutralization sensitive viruses with viruses showing neutralization titers over a 100-fold range. One HCVpp (AMS0230) was extremely neutralization resistant and only neutralized by AR4-targeting antibodies. In addition, using binding antibody multiplex competition assay, we delineated mAb epitopes and their interactions. The binding and neutralization sensitivity of the HCVpps were confirmed using patient sera. At the end, eleven HCVpps with unique antibody binding and neutralization profiles were selected as the final panel for standardized HCV antibody assessments. In conclusion, this HCVpp panel can be used to evaluate antibody binding and neutralization breadth and potency as well as delineate the epitopes targeted in sera from patients or candidate vaccine trials. The HCVpp panel in combination with the established antibody competition assay present highly valuable tools for HCV vaccine development and evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".