Inhibition of Phenylpropanoid Biosynthesis in Artemisia annua L.: A Novel Approach to Reduce Oxidative Browning in Plant Tissue Culture
Bibliographic record
Abstract
Oxidative browning is a common and often severe problem in plant tissue culture systems caused by the accumulation and oxidation of phenolic compounds. The current study was conducted to investigate a novel preventative approach to address this problem by inhibiting the activity of the phenylalanine ammonia lyase enzyme (PAL), thereby reducing the biosynthesis of phenolic compounds. This was accomplished by incorporating 2-aminoindane-2-phosphonic acid (AIP), a competitive PAL inhibitor, into culture media of Artemisia annua as a model system. Addition of AIP into culture media resulted in significant reductions in visual tissue browning, a reduction in total phenol content, as well as absorbance and autoflourescence of tissue extracts. Reduced tissue browning was accompanied with a significant increase in growth on cytokinin based medium. Microscopic observations demonstrated that phenolic compounds accumulated in discrete cells and that these cells were more prevalent in brown tissue. These cells were highly plasmolyzed and often ruptured during examination, demonstrating a mechanism in which phenolics are released into media in this system. These data indicate that inhibiting phenylpropanoid biosynthesis with AIP is an effective approach to reduce tissue browning in A. annua. Additional experiments with Ulmus americana and Acer saccharum indicate this approach is effective in many species and it could have a wide application in systems where oxidative browning restricts the development of biotechnologies.
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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) | 0.000 | 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".