Photosynthetic Redox Imbalance Influences Flavonoid Biosynthesis in<i>Lemna gibba</i>
Bibliographic record
Abstract
Plants accumulate flavonoids in response to a myriad of environmental challenges, especially when exposed to ultraviolet (UV) radiation or situations causing oxidative stress. However, the origin and nature of the signal triggering their accumulation remain obscure. In this study, a group of flavonoids belonging to the flavone class was identified in Lemna gibba (duckweed). These flavones accumulated upon exposure to UV radiation, low temperature, copper and the photosynthetic electron transport (PET) inhibitors 2,5-dibromo-3-methyl-6-isopropyl-p-benzoquinone (DBMIB) and 1,2-dihydroxyanthraquinone (DHATQ). All of these stressors were also shown to promote PET chain (PETC) reduction; however, in the co-presence of 3-(3,4-dichlorophenyl)-1,1-dimethyl urea (DCMU) or a light regime that oxidized the PETC, flavonoid accumulation ceased. Chloroplast-derived reactive oxygen species (ROS) were not associated with all of the stress conditions that promoted both PETC reduction and flavonoid synthesis, indicating that ROS were not a strict requisite for flavonoid accumulation. Transcripts for the flavonoid biosynthetic genes, chalcone synthase (CHS) and chalcone isomerase, were similarly responsive to the PETC redox state, as were a panel of transcripts revealed by differential display PCR. Collectively, these results provide evidence that PETC redox status is one of the factors affecting flavonoid 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) | 0.002 | 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".