The Rice R2R3-MYB Transcription Factor OsMYB55 Is Involved in the Tolerance to High Temperature and Modulates Amino Acid Metabolism
Bibliographic record
Abstract
Temperatures higher than the optimum negatively affects plant growth and development. Tolerance to high temperature is a complex process that involves several pathways. Understanding this process, especially in crops such as rice, is essential to prepare for predicted climate changes due to global warming. Here, we show that OsMYB55 is induced by high temperature and overexpression of OsMYB55 resulted in improved plant growth under high temperature and decreased the negative effect of high temperature on grain yield. Transcriptome analysis revealed an increase in expression of several genes involved in amino acids metabolism. We demonstrate that OsMYB55 binds to the promoter regions of target genes and directly activates expression of some of those genes including glutamine synthetase (OsGS1;2) glutamine amidotransferase (GAT1) and glutamate decarboxylase 3 (GAD3). OsMYB55 overexpression resulted in an increase in total amino acid content and of the individual amino acids produced by the activation of the above mentioned genes and known for their roles in stress tolerance, namely L-glutamic acid, GABA and arginine especially under high temperature condition. In conclusion, overexpression of OsMYB55 improves rice plant tolerance to high temperature, and this high tolerance is associated with enhanced amino acid metabolism through transcription activation.
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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) | 0.001 | 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 | 0.001 |
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".