The interplay between miR156/SPL13 and DFR/WD40–1 regulate drought tolerance in alfalfa
Bibliographic record
Abstract
BACKGROUND: Developing Medicago sativa L. (alfalfa) cultivars tolerant to drought is critical for the crop's sustainable production. miR156 regulates various plant biological functions by silencing SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE (SPL) transcription factors. RESULTS: To understand the mechanism of miR156-modulated drought stress tolerance in alfalfa we used genotypes with altered expression levels of miR156, miR156-regulated SPL13, and DIHYDROFLAVONOL-4-REDUCTASE (DFR) regulating WD40-1. Previously we reported the involvement of miR156 in drought tolerance, but the mechanism and downstream genes involved in this process were not fully studied. Here we illustrate the interplay between miR156/SPL13 and WD40-1/DFR to regulate drought stress by coordinating gene expression with metabolite and physiological strategies. Low to moderate levels of miR156 overexpression suppressed SPL13 and increased WD40-1 to fine-tune DFR expression for enhanced anthocyanin biosynthesis. This, in combination with other accumulated stress mitigating metabolites and physiological responses, improved drought tolerance. We also demonstrated that SPL13 binds in vivo to the DFR promoter to regulate its expression. CONCLUSIONS: Taken together, our results reveal that moderate relative miR156 transcript levels are sufficient to enhance drought resilience in alfalfa by silencing SPL13 and increasing WD40-1 expression, whereas higher miR156 overexpression results in drought susceptibility.
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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".