RNA-Seq Identification of Peanut Callus-Specific Promoters and Evaluation of Base-Editing Efficiency.
Bibliographic record
Abstract
Prolonged expression of gene-editing components in CRISPR-modified plants can interfere with phenotypic analysis of target traits, increase the risk of off-target mutations, and lead to unnecessary metabolic burden. To mitigate these issues in peanut (Arachis hypogaea L.), callus-specific promoters were screened to restrict Cas9 expression to the callus stage, minimizing its activity in regenerated plants. In this study, six callus-specific genes in peanut were identified by mining RNA sequencing datasets and validating their expression profiles using quantitative reverse transcriptase PCR. The promoters of Arahy.H0FE8D, Arahy.WT3AEF, Arahy.I20Q6X, Arahy.ELJ55T, and Arahy.N9CMH4 were cloned and assessed for their expression activity. Beta-glucuronidase (GUS) histochemical staining confirmed that all five promoters were functional in peanut callus. Further investigation revealed their ability to drive cytosine base editing via a deaminase-nCas9 fusion protein, with all promoters successfully inducing precise base substitutions in peanut. Notably, PAh-H0FE8D, PAh-WT3AEF, PAh-ELJ55T, and PAh-N9CMH4 exhibited comparable or higher editing efficiencies than the commonly used cauliflower mosaic virus 35S promoter. These findings provide valuable tools for improving the biosafety of CRISPR-based genome editing in peanut breeding programs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".