Found in Translation: High-Throughput Chemical Screening in <i>Arabidopsis thaliana</i> Identifies Small Molecules That Reduce Fusarium Head Blight Disease in Wheat
Bibliographic record
Abstract
Despite the tremendous economic impact of cereal crop pathogens such as the fungus Fusarium graminearum, the development of strategies for enhanced crop protection is hampered by complex host genetics and difficulties in performing high-throughput analyses. To bypass these challenges, we have developed an assay in which the interaction between F. graminearum and the model plant Arabidopsis thaliana is monitored in liquid media in 96-well plates. In this assay, fungal infection is associated with the development of dark lesion-like spots on the cotyledons of Arabidopsis seedlings by 4 days postinoculation. These symptoms can be alleviated by the application of known defense-activating small molecules and in previously described resistant host genetic backgrounds. Based on this infection phenotype, we conducted a small-scale chemical screen to identify small molecules that protect Arabidopsis seedlings from infection by F. graminearum. We identified sulfamethoxazole and the indole alkaloid gramine as compounds with strong protective activity in the liquid assay. Remarkably, these two chemicals also significantly reduced the severity of F. graminearum infection in wheat. As such, the Arabidopsis-based liquid assay represents a biologically relevant surrogate system for high-throughput studies of agriculturally important plant-pathogen interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.024 |
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".