Effect of in vitro transcription conditions on yield of high quality messenger and self-amplifying RNA
Bibliographic record
Abstract
Messenger RNA (mRNA) and self-amplifying RNA (saRNA) vaccines against SARS-CoV-2 produced using in vitro transcription (IVT) were clinically approved in 2020 and 2022, respectively. While the industrial production of mRNA using IVT has been extensively optimized, the optimal conditions for saRNA have been explored to a lesser extent. Most T7 polymerase IVT protocols have been specifically optimized for mRNA which is ∼5–10-fold smaller than saRNA and may have profound effects on both the quality and yield of longer transcripts. Here, we optimized IVT conditions for simultaneously increasing the yield of full-length transcripts and reducing dsRNA formation through Design of Experiments. Using a definitive screening approach, we found that the key parameters are temperature and magnesium in the outcome of RNA quality (% full length transcript) and yield in small scale synthesis. The most important parameter for reducing dsRNA formation for both mRNA and saRNA was Mg2+ concentration (10 mM). We observed that a lower temperature was vital for production of high quality saRNA transcripts. mRNA quality was optimal at higher Mg2+ concentration (>40 mM). High quality transcripts correspond to significantly reduced product yield for saRNA, but not for mRNA. The differences between mRNA and saRNA requirements for high quality product and the relationship between high quality large saRNA molecules and low temperature synthesis have not been reported previously. These findings are key for informing future IVT parameters design and optimization for smaller and larger RNA transcripts.
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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 | 0.000 | 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".