Alphaviral backbone of self-amplifying RNA enhances protein expression and immunogenicity against SARS-CoV-2 antigen
Bibliographic record
Abstract
Self-amplifying RNA (saRNA) vectors are a next-generation RNA technology that extends the expression of heterologous genes. Clinical trials have shown the dose-sparing capacity of saRNA vectors in a vaccine context compared with conventional messenger RNA. However, saRNA vectors have historically been based on a limited number of alphaviruses, and only the Venezuelan equine encephalitis virus-based saRNA vaccines have been used clinically. Here, we designed genotypically distinct alphaviral saRNA vectors and characterized their performance in mammalian cell lines, human skin explants and mice. Five of the 12 vectors had substantial luciferase expression in mice with variable pharmacokinetics, enabling modulation of both the magnitude and duration of protein expression. Additionally, we demonstrated that the alphaviral genotype of the saRNA significantly impacts the immunogenicity of saRNA vaccines, including the humoral and cellular responses in mice. Given the differences in RNA reactogenicity and expression between mice and humans, we assessed the saRNA vectors in human skin explants obtained from patients and observed high transgene expression. saRNA bioluminescence and immunogenicity in different mice strains were highly correlative, while minimal correlation was observed when compared with human explants and mammalian cell lines. This work demonstrates that efficacious saRNA vaccines and therapies can be produced by adapting genetically diverse alphaviruses into vectors.
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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 | 0.000 | 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) | 0.000 | 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".