Regulation of Heat Shock Factor Pathways by γ-aminobutyric Acid (GABA) Associated with Thermotolerance of Creeping Bentgrass
Bibliographic record
Abstract
Activation and enhancement of heat shock factor (HSF) pathways are important adaptive responses to heat stress in plants. The γ-aminobutyric acid (GABA) plays an important role in regulating heat tolerance, but it is unclear whether GABA-induced thermotolerance is associated with activation of HSF pathways in plants. In this study, the changes of endogenous GABA level affecting physiological responses and genes involved in HSF pathways were investigated in creeping bentgrass during heat stress. The increase in endogenous GABA content induced by exogenous application of GABA effectively alleviated heat damage, as reflected by higher leaf relative water content, cell membrane stability, photosynthesis, and lower oxidative damage. Contrarily, the inhibition of GABA accumulation by the application of GABA biosynthesis inhibitor further aggravated heat damage. Transcriptional analyses showed that exogenous GABA could significantly upregulate transcript levels of genes encoding heat shock factor HSFs (HSFA-6a, HSFA-2c, and HSFB-2b), heat shock proteins (HSP17.8, HSP26.7, HSP70, and HSP90.1-b1), and ascorbate peroxidase 3 (APX3), whereas the inhibition of GABA biosynthesis depressed these genes expression under heat stress. Our results indicate GABA regulates thermotolerance associated with activation and enhancement of HSF pathways in creeping bentgrass.
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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) | 0.000 | 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".