Flavan-3-ols Are an Effective Chemical Defense against Rust Infection
Bibliographic record
Abstract
Phenolic secondary metabolites are often thought to protect plants against attack by microbes, but their role in defense against pathogen infection in woody plants has not been investigated comprehensively. We studied the biosynthesis, occurrence, and antifungal activity of flavan-3-ols in black poplar (Populus nigra), which include both monomers, such as catechin, and oligomers, known as proanthocyanidins (PAs). We identified and biochemically characterized three leucoanthocyanidin reductases and two anthocyanidin reductases from P. nigra involved in catalyzing the last steps of flavan-3-ol biosynthesis, leading to the formation of catechin [2,3-trans-(+)-flavan-3-ol] and epicatechin [2,3-cis-(−)-flavan-3-ol], respectively. Poplar trees that were inoculated with the biotrophic rust fungus (Melampsora larici-populina) accumulated higher amounts of catechin and PAs than uninfected trees. The de novo-synthesized catechin and PAs in the rust-infected poplar leaves accumulated significantly at the site of fungal infection in the lower epidermis. In planta concentrations of these compounds strongly inhibited rust spore germination and reduced hyphal growth. Poplar genotypes with constitutively higher levels of catechin and PAs as well as hybrid aspen (Populus tremula × Populus alba) overexpressing the MYB134 transcription factor were more resistant to rust infection. Silencing PnMYB134, on the other hand, decreased flavan-3-ol biosynthesis and increased susceptibility to rust infection. Taken together, our data indicate that catechin and PAs are effective antifungal defenses in poplar against foliar rust infection.
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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) | 0.000 | 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".