CATIONIC AMINO ACID TRANSPORTER 1 modulates amino acid distribution between stem and leaf in new shoots: A case study of theanine distribution in tea plants (<i>Camellia sinensis</i>)
Bibliographic record
Abstract
Theanine, a tea plant (Camellia sinensis)-specific non-proteinogenic amino acid, is one of the most important components conferring the taste quality and health benefits of tea. It is primarily synthesized in roots of tea plants and transported to new shoots, where it is mainly distributed to the young stem; however, tea is predominantly produced from young leaves. To promote more theanine allocation to young leaves, the molecular mechanism underlying theanine distribution between stems and leaves requires elucidation. In this study, we found the ratios of stem-to-leaf theanine content in the new shoots of 11 tea plant cultivars ranged from 3.8 to 8.8. Analyses on transcriptome and gene expression demonstrated that the expression of CATIONIC AMINO ACID TRANSPORTER1 (CsCAT1), an amino acid transporter-encoding gene, was highly correlated with the ratios of theanine content in the stem and leaf (r = 0.97, P < 0.0001). Further analyses indicated that CsCAT1 localizes in the plasma membrane and has theanine transport activity. Moreover, CsCAT1 was predominantly expressed in the vascular ray cells in the stem. Finally, we found that repression of CsCAT1 increased theanine content in young leaves and the ratio of leaf-to-stem theanine content. These results indicate that CsCAT1 modulates theanine distribution between stem and leaf and provides a target for increasing theanine content in young leaves of tea plants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".