Expression of Tryptophan Decarboxylase and Tyrosine Decarboxylase Genes in Tobacco Results in Altered Biochemical and Physiological Phenotypes
Bibliographic record
Abstract
The substrate specificity of tryptophan (Trp) decarboxylase (TDC) for Trp and tyrosine (Tyr) decarboxylase (TYDC) for Tyr was used to modify the in vivo pools of these amino acids in transgenic tobacco. Expression of TDC and TYDC was shown to deplete the levels of Trp and Tyr, respectively, during seedling development. The creation of artificial metabolic sinks for Trp and Tyr also drastically affected the levels of phenylalanine, as well as those of the non-aromatic amino acids methionine, valine, and leucine. Transgenic seedlings also displayed a root-curling phenotype that directly correlated with the depletion of the Trp pool. Non-transformed control seedlings could be induced to display this phenotype after treatment with inhibitors of auxin translocation such as 2,3,5-triiodobenzoic acid or N-1-naphthylphthalamic acid. The depletion of aromatic amino acids was also correlated with increases in the activities of the shikimate and phenylpropanoid pathways in older, light-treated transgenic seedlings expressing TDC, TYDC, or both. These results provide in vivo confirmation that aromatic amino acids exert regulatory feedback control over carbon flux through the shikimate pathway, as well as affecting pathways outside of aromatic amino acid biosynthesis.
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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".