The down‐regulation of <i>MsWOX13‐2</i> promotes enhanced waterlogging resilience in alfalfa
Bibliographic record
Abstract
SUMMARY Soil waterlogging events are predicted to escalate globally as a result of climate change, threatening the sustainability of alfalfa ( Medicago sativa L.) and livestock production in the future. WUSCHEL‐related homeobox (WOX) transcription factors are known to play a role in numerous developmental processes and abiotic stress responses; however, their function in waterlogging resilience has not been investigated as of yet. In the present study, we functionally characterized the alfalfa MsWOX13‐2 gene, which we found to be differentially expressed in response to waterlogging. Although the RNAi‐mediated silencing of MsWOX13‐2 in alfalfa did not affect growth or morphology under normally watered conditions, MsWOX13‐2 RNAi plants exhibited higher chlorophyll retention and maximum quantum efficiency of photosystem II, as well as greater survivability, compared to empty vector genotypes under waterlogging. Subsequent analyses indicated that MsWOX13‐2 RNAi leaves accumulated less H 2 O 2 and displayed a greater increase in superoxide dismutase activity under waterlogging, resulting in reduced oxidative damage, which may have contributed to the enhanced waterlogging tolerance in these genotypes. RNA‐Seq analysis confirmed alterations in the transcript levels of genes related to antioxidants, as well as those involved in photosynthesis, anaerobic fermentation, phytohormone‐related pathways, and transcriptional regulation in the leaves of WOX13‐2 RNAi genotypes compared to wild type following waterlogging stress. Bi‐allelic mutation of MsWOX13‐2 in alfalfa using CRISPR/Cas9 confirmed its function in waterlogging response. Overall, our findings suggest that MsWOX13‐2 acts as a negative regulator of waterlogging response in alfalfa, providing a novel candidate for downstream breeding endeavors in this important species.
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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 | 0.001 |
| 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".