Sequence-engineered mRNA Without Chemical Nucleoside Modifications Enables an Effective Protein Therapy in Large Animals
Bibliographic record
Abstract
Being a transient carrier of genetic information, mRNA could be a versatile, flexible, and safe means for protein therapies. While recent findings highlight the enormous therapeutic potential of mRNA, evidence that mRNA-based protein therapies are feasible beyond small animals such as mice is still lacking. Previous studies imply that mRNA therapeutics require chemical nucleoside modifications to obtain sufficient protein expression and avoid activation of the innate immune system. Here we show that chemically unmodified mRNA can achieve those goals as well by applying sequence-engineered molecules. Using erythropoietin (EPO) driven production of red blood cells as the biological model, engineered Epo mRNA elicited meaningful physiological responses from mice to nonhuman primates. Even in pigs of about 20 kg in weight, a single adequate dose of engineered mRNA encapsulated in lipid nanoparticles (LNPs) induced high systemic Epo levels and strong physiological effects. Our results demonstrate that sequence-engineered mRNA has the potential to revolutionize human protein therapies.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".