GABA is a key player regulating the TCA cycle and polyamine metabolism under combined heat-drought stress in tea plants
Bibliographic record
Abstract
Throughout their development, plants experience a range of abiotic stresses, typically not solitary occurrences. For example, drought stress (DS) and heat stress (HS) often co-occur due to a high-temperature environment being accompanied by drought. Differing from single stress, plants have unique responses to the stress combination, with secondary metabolism holding a pivotal position in the process of plant response. Under combined stresses, plants specifically induce the accumulation of secondary metabolites to resist damage. We found that the metabolic responses of tea plants ( Camellia sinensis ) to DS or HS differed from those to a combination of HS and DS (HS-DS). Metabolic analysis showed that combined HS-DS led to the up-regulation and down-regulation of abundance of key metabolites in the tricarboxylic acid (TCA) cycle and polyamine metabolism pathways. Among the metabolites accumulated under combined HS-DS was γ-aminobutyric acid (GABA). Exogenous spraying of 1 mM GABA and silencing the GABA-synthesis-related gene [glutamate decarboxylase 1 ( GAD1 )] showed that GABA played a crucial part in the resistance of tea plants to combined HS-DS. This study reveals the function of GABA in regulating the response of tea plant to HS-DS, which provides a theoretical basis for the subsequent research on heat and drought resistance for plants.
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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".