Transcriptional Down-Regulation of Various Genes in Alfalfa Enhances Tolerance to Abiotic Stresses
Bibliographic record
Abstract
Alfalfa (Medicago sativa L.) is a perennial legume esteemed for its yield, adaptability and superior nutritional quality as a forage crop. However, alfalfa production is often impacted by various environmental challenges such as drought and poor drainage throughout the growing season, which lead to a decline in farmers’ profitability. These factors are anticipated to become more problematic in the coming years due to global warming scenarios, and as such, there is a need for the development of alfalfa cultivars with enhanced abiotic stress resilience. In this study, five gene homologs (CBF2, ACBP3, TAC1, FAO3 and HB2) negatively regulating various abiotic stresses in other closely related crop species were identified in alfalfa, and RNAi genotypes exhibiting down-regulation of each gene, respectively, were generated. The RNAi genotypes were subjected to drought and flooding treatments, respectively, to assess their responses to abiotic stresses. Preliminary results demonstrated that alfalfa genotypes with reduced expression of TAC1 exhibited increased tolerance to drought, while the down-regulation of ACBP3 and HB2 in alfalfa led to enhanced tolerance to flooding. Further experiments are underway to unravel the mechanisms driving increased abiotic stress tolerance in these genotypes. Our aim is to use the knowledge gained in this study to produce transgene-free highly adaptable alfalfa germplasm using advanced molecular breeding platforms such as genome editing via CRISPR/Cas, which could reduce production costs and enhance biomass production by minimizing forage crop losses under extreme weather conditions.
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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".