An Insight into microRNA156 Role in Salinity Stress Responses of Alfalfa
Bibliographic record
Abstract
Salinity is one of the major abiotic stresses affecting alfalfa productivity. Developing salinity tolerant alfalfa genotypes could contribute to sustainable crop production. The functions of microRNA156 (miR156) have been investigated in several plant species, but so far, no studies have been published that explore the role of miR156 in alfalfa response to salinity stress. In this work, we studied the role of miR156 in modulating commercially important traits of alfalfa under salinity stress. Our results revealed that overexpression of miR156 increased biomass, number of branches and time to complete growth stages, while it reduced plant height under control and salinity stress conditions. We observed a miR156-related reduction in neutral detergent fiber (NDF) under non-stress, and acid detergent fibre (ADF) under mild salinity stress conditions. In addition, enhanced total Kjeldahl nitrogen (TKN) content was recorded in miR156 overexpressing genotypes under severe salinity stress. Furthermore, alfalfa genotypes overexpressing miR156 exhibited an altered ion homeostasis under salinity conditions. Under severe salinity stress, miR156 downregulated SPL transcription factor family genes, modified expression of other important transcription factors, and downstream salt stress responsive genes. Taken together, our results reveal that miR156 plays a role in mediating physiological and transcriptional responses of alfalfa to salinity stress.
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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".