High-efficiency retron-mediated single-stranded DNA production in plants
Bibliographic record
Abstract
ABSTRACT Background: Retrons are a class of retroelements that produce multicopy single-stranded DNA (msDNA) and participate in anti-phage defenses in bacteria. Retrons have been harnessed for the over-production of single-stranded DNA (ssDNA), genome engineering, and directed evolution in bacteria, yeast, and mammalian cells. However, no studies have shown retron-mediated ssDNA production in plants, which could unlock potential applications in plant biotechnology. For example, ssDNA can be used as a template for homology-directed repair (HDR) in several organisms. However, current gene editing technologies rely on the physical delivery of synthetic ssDNA, which limits their applications. Main methods and major results: Here, we demonstrated retron-mediated over-production of ssDNA in Nicotiana benthamiana. Additionally, we tested different retron architectures for improved ssDNA production and identified a new retron architecture that resulted in greater ssDNA abundance. Furthermore, co-expression of the gene encoding the ssDNA-protecting protein VirE2 from Agrobacterium tumefaciens with the retron systems resulted in a 10.7-fold increase in ssDNA production in vivo. We also demonstrated CRISPR-retron-coupled ssDNA over-production and targeted HDR in N. benthamiana. Conclusion: We present an efficient approach for in vivo ssDNA production in plants, which can be harnessed for biotechnological applications.
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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) | 0.001 | 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 | 0.001 |
| 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".