Hepatitis C virus E1 and modified E2 delivered from an mRNA vaccine induces protective immunity
Bibliographic record
Abstract
Abstract Hepatitis C virus (HCV) is characterized by a high number of chronic cases due to an impairment of protective innate and adaptive immune responses. Here, we examined the contribution of the individual ectodomains of E1, E2, or a modified E2 with reduced CD81 binding and an inserted N-linked glycosylation site in combination as vaccine antigen mRNA-lipid nanoparticles (LNPs). The induction of a protective immune response to surrogate recombinant vaccinia virus (VV) expressing homologous HCV glycoprotein(s) challenge infection in a BALB/c mouse model was observed. Vaccination with a mRNA-LNP expressing soluble E1 (sE1) significantly reduced vv/HCV titer in the mouse ovary. However, the addition of sE2 mRNA-LNP for immunization impaired the efficacy of the sE1 construct. Further analysis showed that Th1 related cytokine responses to the sE1 mRNA-LNP were significantly altered in the presence of sE2 following co-immunization. Evaluation of immunogenicity revealed that the use of modified sE2 F442NYT nucleoside mRNA-LNP vaccine results in an improved cellular immune response, IgG2a isotype switching, enhanced total IgG, and an increase in the neutralizing antibody response against HCV pseudotype virus. HCV cross genotype specific reactivity to peptides representing conserved E2 specific linear epitopes were enhanced in modified E2 vaccinated animal sera. In the absence of a suitable immunocompetent small animal model for HCV infection, protection from surrogate HCV vaccinia challenge infection model was observed in the immunized mice as compared to sE1 alone or an unmodified sE2 mRNA-LNP vaccine. Inclusion of sE1 with modified sE2 F442NYT as mRNA-LNP vaccine candidate appeared to be beneficial for protection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".