A high‐throughput chemical screen for resistance to <i>Pseudomonas syringae</i> in Arabidopsis
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Bibliographic record
Abstract
The study of plant pathogenesis and the development of effective treatments to protect plants from diseases could be greatly facilitated by a high-throughput pathosystem to evaluate small-molecule libraries for inhibitors of pathogen virulence. The interaction between the Gram-negative bacterium Pseudomonas syringae and Arabidopsis thaliana is a model for plant pathogenesis. However, a robust high-throughput assay to score the outcome of this interaction is currently lacking. We demonstrate that Arabidopsis seedlings incubated with P. syringae in liquid culture display a macroscopically visible 'bleaching' symptom within 5 days of infection. Bleaching is associated with a loss of chlorophyll from cotyledonary tissues, and is correlated with bacterial virulence. Gene-for-gene resistance is absent in the liquid environment, possibly because of the suppression of the hypersensitive response under these conditions. Importantly, bleaching can be prevented by treating seedlings with known inducers of plant defence, such as salicylic acid (SA) or a basal defence-inducing peptide of bacterial flagellin (flg22) prior to inoculation. Based on these observations, we have devised a high-throughput liquid assay using standard 96-well plates to investigate the P. syringae-Arabidopsis interaction. An initial screen of small molecules active on Arabidopsis revealed a family of sulfanilamide compounds that afford protection against the bleaching symptom. The most active compound, sulfamethoxazole, also reduced in planta bacterial growth when applied to mature soil-grown plants. The whole-organism liquid assay provides a novel approach to probe chemical libraries in a high-throughput manner for compounds that reduce bacterial virulence in plants.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it