Plant resistance inducer AMHA enhances antioxidant capacities to promote cold tolerance by regulating the upgrade of glutathione S-transferase in tea plant
Bibliographic record
Abstract
Abstract Plant resistance inducers represent an alternative strategy that mitigate stress-induced damage in plants. Previously, 2-amino-3-methylhexanoic acid (AMHA), a novel natural plant resistance inducer, was shown to significantly bolster cold tolerance, thermotolerance, and pathogen resistance in plants. However, the intricate mechanisms underlying AMHA’s response to cold stress remain elusive. Thus, we investigated the physiological and transcriptomic analyses of AMHA pretreatment on tea plant to determine its substantial role of AMHA under cold stress. The results showed that pretreatment with 100 nM AMHA effectively mitigated the detrimental effects of cold stress on photosynthesis and growth. Furthermore, differentially expressed genes were identified through RNA-seq during pretreatment, cold stress, and 2 days of recovery. These genes were mainly enriched in pathways related to flavonoid/anthocyanin, carotenoid, and ascorbic acid-glutathione (AsA-GSH) cycle, including GST (encoding glutathione S-transferase). Potential regulatory relationships between the identified genes and transcription factors were also established. Antisense oligodeoxynucleotide-silencing and overexpression experiments revealed that CsGSTU7 enhances cold resistance by maintaining redox homeostasis. In conclusion, our study suggests that antioxidant-related signaling molecules play a critical role in the signaling cascades and transcriptional regulation mediating AMHA-induced cold-stress resistance in tea plant.
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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 | 0.001 |
| 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".