MYB134-RNAi poplar plants show reduced tannin synthesis in leaves but not roots, and increased susceptibility to oxidative stress
Bibliographic record
Abstract
The importance of the poplar MYB134 gene in controlling condensed tannin (CT) biosynthesis was tested by suppressing its expression using RNA interference (RNAi). MYB134-RNAi plants grew normally but showed reduced accumulation of stress-induced CTs in leaves. RNA-seq analysis indicated that flavonoid- and CT-related genes, as well as additional CT regulators, were strongly and specifically down-regulated by MYB134 suppression. This confirmed that the primary MYB134 target is the leaf flavonoid and CT pathway. Root CT accumulation was not impacted by MYB suppression, suggesting that additional CT regulators are active in roots and emphasizing the complexity of the regulation of CTs in poplar. To test the effect of CT down-regulation on oxidative stress resistance, leaves of MYB134-RNAi and control plants were exposed to the reactive oxygen species generator methyl viologen. MYB134-RNAi leaves sustained significantly more photosystem II damage, as seen in reduced chlorophyll fluorescence, compared with wild-type leaves. MYB134-RNAi leaves also contained more hydrogen peroxide, a reactive oxygen species, compared with the wild type. Our data thus corroborate the hypothesis that CT can act as an antioxidant in vivo and protect against oxidative stress. Overall, MYB134 was shown to be a central player in the regulation of CT synthesis in leaves.
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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) | 0.002 | 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".