Developing a multi-modular assembled prime editing (mPE) system improved precise multi-base insertion efficiency in dicots
Bibliographic record
Abstract
INTRODUCTION: The Prime Editing (PE) system is a precise and versatile genome editing tool with great potential in plant breeding and plant synthetic biology. However, low PE efficiency severely restricts its application, especially in dicots. PE can introduce small tags to trace target protein or cis-element to regulate gene transcription which is an expertise superior to other gene editing tools. Owing to low efficiency, PE adaption in stably transformed Arabidopsis is lacking. OBJECTIVES: This study aimed to investigate the issue of low PE efficiency in dicots and develop systematic solutions to improve it. Currently, PE in dicots is undetectable and inconsistent, and this study seeks to address it. Split PE into several parts showed better performance in some target sites in mammal cells. We plan to discover the optimal split PE combination in dicot. METHODS: We conducted large-scale transformation experiments in dicot model plants Arabidopsis thaliana (At) and Nicotiana benthamiana (Nb) by Agrobacterium-mediated transformation with deep amplicon sequencing (0.2-0.5 million clean total reads). RESULTS: The editing efficiency decreased upon using a fused reverse transcriptase (RT) or an extended pegRNA separately and further decreased dramatically when these were used together. With the help of the pol II strategy to express PE gRNA (pegRNA), we named the most effective split PE combination as a multi-modular assembled prime editing system (mPE). mPE exhibited improved precise editing efficiency on most gene sites with various editing types, ranging from 1.3-fold to 1288.5-fold and achieved PE on some sites that could not be edited by original PE2. Especially, mPE showed superiority for multi-base insertion with an average improvement of 197.9-fold. CONCLUSION: The original PE architecture strongly inhibited the cleavage activity of Cas9. Split PE improved PE efficiency extensively and was in favor of introducing small insertions in dicot plants, indicating that different PE variants might have their own expertise.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".