Rational design of promoter editing confers multipathogen resistance in rice
Bibliographic record
Abstract
BACKGROUND: Fine-tuned gene expression rather than alterations in the protein-coding region of a gene is responsible for the optimal performance conferred by many elite alleles in crops. Lesion mimic mutants (LMMs), a type of plant mutants with hyperactivated immune responses, often show enhanced resistance but with yield penalties. To fine-tune the expression level of LMM genes using promoter editing is of considerable interest in crop disease control. RESULTS: Here we demonstrate the power of predictive promoter editing in optimizing expression of the rice LMM gene RBL1, encoding a CDP-DAG synthase in phospholipid metabolism, by breaking immunity-growth trade-offs. Through bioinformatic analyses of open chromatin accessibility, we identify key cis-regulatory regions in the RBL1 promoter. Guided by these predictions, we efficiently assess the regulatory role of different cis-regulatory regions in the rice protoplast system and then generate multiple promoter-edited rice lines with varied RBL1 expression levels. Notably, Pro1, an edited line with a 71.0% reduction in gene expression and altered levels of multiple phospholipids, shows broad-spectrum resistance to rice blast without compromising yield in field trials. Similarly, we generate a phenotype-copied PRO1 allele for enhanced disease resistance in another rice cultivar. CONCLUSIONS: Our study has generated an edited promoter of RBL1 that confers multipathogen resistance with no yield penalty. Our study demonstrates a framework for predictable promoter engineering in balancing agronomic traits, especially through optimizing LMMs for crop improvement.
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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 | 0.000 | 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) | 0.000 | 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".