A Trivalent HSV-2 gC2, gD2, gE2 Nucleoside-Modified mRNA-LNP Vaccine Provides Outstanding Protection in Mice against Genital and Non-Genital HSV-1 Infection, Comparable to the Same Antigens Derived from HSV-1
Bibliographic record
Abstract
HSV-1 disease is a significant public health burden causing orofacial, genital, cornea, and brain infection. We previously reported that a trivalent HSV-2 gC2, gD2, gE2 nucleoside-modified mRNA-lipid nanoparticle (LNP) vaccine provides excellent protection against vaginal HSV-1 infection in mice. Here, we evaluated whether this HSV-2 gC2, gD2, gE2 vaccine is as effective as a similar HSV-1 mRNA LNP vaccine containing gC1, gD1, and gE1 in the murine lip and genital infection models. Mice were immunized twice with a total mRNA dose of 1 or 10 µg. The two vaccines produced comparable HSV-1 neutralizing antibody titers, and surprisingly, the HSV-2 vaccine stimulated more potent CD8+ T-cell responses to gE1 peptides than the HSV-1 vaccine. Both vaccines provided complete protection from clinical disease in the lip model, while in the genital model, both vaccines prevented death and genital disease, but the HSV-1 vaccine reduced day two vaginal titers slightly better at the 1 µg dose. Both vaccines prevented HSV-1 DNA from reaching the trigeminal or dorsal root ganglia to a similar extent. We conclude that the trivalent HSV-2 mRNA vaccine provides outstanding protection against HSV-1 challenge at two sites and may serve as a universal prophylactic vaccine for HSV-1 and HSV-2.
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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) | 0.001 | 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 | 0.001 |
| 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".