Alfalfa transcriptome profiling provides insight into miR156-mediated molecular mechanisms of heat stress tolerance
Bibliographic record
Abstract
Heat is one of the major environmental stressors that negatively affects alfalfa production. Previously, we reported the role of microRNA156 (miR156) in heat tolerance; however, the mechanism and downstream genes involved in this process were not fully studied. To provide further insight, we compared an empty vector control and miR156-overexpressing alfalfa plants (miR156+) after exposing them to heat stress (40 °C) for 24 h. We collected leaf samples for transcriptome analysis to illustrate the miR156-regulated molecular mechanisms underlying the heat stress response. A total of 3579 differentially expressed genes (DEGs) were detected exclusively in miR156+ plants under heat stress using the Medicago sativa genome as a reference. GO and KEGG analysis indicated that these DEGs were mainly involved in “polysaccharide metabolism”, “response to chemical”, “secondary metabolism”, “carbon metabolism”, and “cell cycle”. Transcription factors predicted in miR156+ plants belonged to the TCP family, MYB, ABA response element-binding factor, WRKY, and heat shock transcription factor. We also identified two new SQUAMOSA PROMOTER BINDING PROTEIN-LIKE ( SPL) family gene members ( SPL8a and SPL12a), putatively regulated by miR156. The present study provided a comprehensive transcriptome profile of alfalfa, identified a number of genes and pathways, and revealed an miR156-regulated network of mechanisms at the gene expression level to modulate heat responses in alfalfa.
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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".