Application of the OsRPS5 promoter for CRISPR/Cas9-mediated genome editing in rice
Bibliographic record
Abstract
Abstract Constitutive promoters such as CaMV 35S and ubiquitin are commonly utilized in crop genome editing. However, their ectopic overexpression patterns may lead to off-target effects. To address this limitation, tissue-specific or developmentally regulated promoters offer promising alternatives. The RIBOSOMAL PROTEIN S5A (RPS5A) promoter has demonstrated superior editing efficiency compared to the 35S and ubiquitin promoters in dicotyledonous species, yet its potential application in monocots remains unexplored. In this study, we identified and functionally characterized the Oryza sativa RPS5 (OsRPS5) promoters and evaluated their utility in CRISPR/Cas9-mediated genome editing. The activities of the OsRPS5 promoters were assessed through GFP reporter expression in rice protoplasts, and their genome editing capability was validated by targeting two endogenous genes, OsPDS and OsBADH2. Genome editing driven by the OsRPS5 promoter targeting OsPDS resulted in albino phenotypes in approximately 50% of the transgenic lines, with insertion/deletion mutations confirmed through sequencing analysis. Notably, the genome editing efficiency driven by the OsRPS5 promoter was comparable to that of the widely used constitutive promoters in monocots. These findings suggest the OsRPS5 promoter as a potentially more precise and efficient alternative to constitutive promoters for genome editing applications in monocot crops.
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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 | 0.000 | 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".