A Novel Self‐Amplifying mRNA with Decreased Cytotoxicity and Enhanced Protein Expression by Macrodomain Mutations
Bibliographic record
Abstract
The efficacy and safety of self-amplifying mRNA (saRNA) have been demonstrated in COVID-19 vaccine applications. Unlike conventional non-replicating mRNA (nrmRNA), saRNA offers a key advantage: its self-replication mechanism fosters efficient expression of the encoded protein, leading to substantial dose savings during administration. Consequently, there is a growing interest in further optimizing the expression efficiency of saRNA. In this study, in vitro adaptive passaging of saRNA is conducted under exogenous interferon pressure, which revealed several mutations in the nonstructural protein (NSP). Notably, two stable mutations, Q48P and I113F, situated in the NSP3 macrodomain (MD), attenuated its mono adenosine diphosphate ribose (MAR) hydrolysis activity and exhibited decreased replication but increased payload expression compared to wild-type saRNA (wt saRNA). Transcriptome sequencing analysis unveils diminished activation of the double-stranded RNA (dsRNA) sensor and, consequently, a significantly reduced innate immune response compared to wt saRNA. Furthermore, the mutant saRNA demonstrated less translation inhibition and cell apoptosis than wt saRNA, culminating in higher protein expression both in vitro and in vivo. These findings underscore the potential of reducing saRNA replication-dependent dsRNA-induced innate immune responses through genetic modification as a valuable strategy for optimizing saRNA, enhancing payload translation efficiency, and mitigating saRNA cytotoxicity.
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 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".