Metabolic analyses reveal different mechanisms of leaf color change in two purple-leaf tea plant (Camellia sinensis L.) cultivars
Bibliographic record
Abstract
Purple-leaf tea plants, as anthocyanin-rich cultivars, are valuable materials for manufacturing teas with unique colors or flavors. In this study, a new purple-leaf cultivar "Zixin" ("ZX") was examined, and its biochemical variation and mechanism of leaf color change were elucidated. The metabolomes of leaves of "ZX" at completely purple, intermediately purple, and completely green stages were analyzed using ultra-performance liquid chromatography quadrupole time of flight mass spectrometry (UPLC-QTOF-MS). Metabolites in the flavonoid biosynthetic pathway remained at high levels in purple leaves, whereas intermediates of porphyrin and chlorophyll metabolism and carotenoid biosynthesis exhibited high levels in green leaves. In addition, fatty acid metabolism was more active in purple leaves, and steroids maintained higher levels in green leaves. Saponin, alcohol, organic acid, and terpenoid-related metabolites also changed significantly during the leaf color change process. Furthermore, the substance changes between "ZX" and "Zijuan" (a thoroughly studied purple-leaf cultivar) were also compared. The leaf color change in "Zijuan" was mainly caused by a decrease in flavonoids/anthocyanins. However, a decrease in flavonoids/anthocyanins, an enhancement of porphyrin, chlorophyll metabolism, carotenoid biosynthesis, and steroids, and a decrease in fatty acids synergistically caused the leaf color change in "ZX". These findings will facilitate comprehensive research on the regulatory mechanisms of leaf color change in purple-leaf tea cultivars.
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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".