Implementation of <i>CsLIS/NES</i> in linalool biosynthesis involves transcript splicing regulation in <scp><i>Camellia sinensis</i></scp>
Bibliographic record
Abstract
Volatile terpenoids produced in tea plants (Camellia sinensis) are airborne signals interacting against other ecosystem members, but also pleasant odorants of tea products. Transcription regulation (including transcript processing) is pivotal for plant volatile terpenoid production. In this study, a terpene synthase gene CsLIS/NES was recovered from tea plants (C. sinensis cv. "Long-Men Xiang"). CsLIS/NES transcription regulation resulted in 2 splicing forms: CsLIS/NES-1 and CsLIS/NES-2 lacking a 305 bp-fragment at N-terminus, both producing (E)-nerolidol and linalool in vitro. Transgenic tobacco studies and a gene-specific antisense oligo-deoxynucleotide suppression applied in tea leaves indicated that CsLIS/NES-1, localized in chloroplasts, acted as linalool synthase, whereas CsLIS/NES-2 localized in cytosol, functioned as a potential nerolidol synthase, but not linalool synthase. Expression patterns of the 2 transcript isoforms in tea were distinctly different and responded differentially to the application of stress signal molecule methyl jasmonate. Leaf expression of CsLIS/NES-1, but not CsLIS/NES-2, was significantly induced by methyl jasmonate. Our data indicated that distinct transcript splicing regulation patterns, together with subcellular compartmentation of CsLIS/NE-1 and CsLIS/NE-2 implemented the linalool biosynthesis regulation in tea plants in responding to endogenous and exogenous regulatory factors.
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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".