Involvement of the miR156/SPL module in flooding response in Medicago sativa
Bibliographic record
Abstract
The highly conserved plant microRNA, miR156, affects plant development, metabolite composition, and stress response. Our previous research revealed the role of miR156 in abiotic stress response in Medicago sativa exerted by downregulating SQUAMOSA-PROMOTER BINDING PROTEIN-LIKE transcription factors. Here we investigated the involvement and possible mechanism of action of the miR156/SPL module in flooding tolerance in alfalfa. For that, we used miR156 overexpressing, SPL13RNAi, flood-tolerant (AAC-Trueman) and -sensitive (AC-Caribou) alfalfa cultivars exposed to flooding. We also used Arabidopsis ABA insensitive (abi1-2, abi5-8) mutants and transgenic lines with either overexpressed (KIN10-OX1, KIN10-OX2) or silenced (KIN10RNAi-1, KIN10RNAi-2) catalytic subunit of SnRK1 to investigate a possible role of ABA and SnRK1 in regulating miR156 expression under flooding. Physiological analysis, hormone profiling and global transcriptome changes revealed a role for miR156/SPL module in flooding tolerance. We also identified nine novel alfalfa SPLs (SPL1, SPL1a, SPL2a, SPL7, SPL7a, SPL8, SPL13a, SPL14, SPL16) responsive to flooding. Our results also showed a possible ABA-dependent SnRK1 upregulation to enhance miR156 expression, resulting in downregulation of SPL4, SPL7a, SPL8, SPL9, SPL13, and SPL13a. We conclude that these effects induce flooding adaptive responses in alfalfa and modulate stress physiology by affecting the transcriptome, ABA metabolites and secondary metabolism.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".