Forest tree extracts induce resistance to <i>Pseudomonas syringae</i> pv. <i>tomato</i> in Arabidopsis
Bibliographic record
Abstract
Abstract Background The widespread use of conventional pesticides to control plant fungal and bacterial pathogens poses significant risks to human health and the environment, and there is an urgent need for safer and more sustainable alternatives in agricultural management. Studies have shown that plant extracts can be effective in controlling plant diseases either by directly targeting the pathogens or by reinforcing the host plant’s own defenses. Here, we examined the potential of ethanolic extracts from forest tree species eastern hemlock, English oak, eastern red cedar and red pine for their antibacterial activity against Pseudomonas syringae pv. tomato ( Pst ) strain DC3000 and the ability of these forestry by-products to trigger effective defense responses in the model plant Arabidopsis thaliana . Results The four tree extracts exhibited direct toxic effects against Pst DC3000, as notably observed for the English oak extract inhibiting bacterial growth and showing bactericidal activity at relatively low concentrations. Using an Arabidopsis line expressing reporter protein ß-glucuronidase under the control of a salicylic acid-inducible pathogenesis-related protein gene promoter, the extracts were shown also to induce defense-related genes expression in leaf tissue. RT-qPCR assays with DNA primers for different gene markers further confirmed this conclusion and highlighted gene-inducing effects for the tree extracts triggering, at different rates, the expression of salicylic acid- and oxidative stress-responsive genes. The extracts direct antibacterial effects, combined with their defense gene-inducing effects in planta , resulted in a strong host plant-protecting effect against Pst DC3000 associated with bacterial growth rates reduced by ∼75 to 98% seven days post-infection, depending on the extract. Conclusions These findings show the effectiveness of tree extracts as eventual plant protectants against the plant bacterial pathogen Pst . In a broader perspective, they suggest the potential of these forestry by-products as a source of bioactive compounds useful in plant protection and as a sustainable, eco-friendly alternative to conventional synthetic pesticides for the management of economically important plant pathogens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".