Anti-Virulence Strategy against the Honey Bee Pathogenic Bacterium Paenibacillus larvae via Small Molecule Inhibitors of the Bacterial Toxin Plx2A
Bibliographic record
Abstract
American Foulbrood, caused by Paenibacillus larvae, is the most devastating bacterial honey bee brood disease. Finding a treatment against American Foulbrood would be a huge breakthrough in the battle against the disease. Recently, small molecule inhibitors against virulence factors have been suggested as candidates for the development of anti-virulence strategies against bacterial infections. We therefore screened an in-house library of synthetic small molecules and a library of flavonoid natural products, identifying the synthetic compound M3 and two natural, plant-derived small molecules, Acacetin and Baicalein, as putative inhibitors of the recently identified P. larvae toxin Plx2A. All three inhibitors were potent in in vitro enzyme activity assays and two compounds were shown to protect insect cells against Plx2A intoxication. However, when tested in exposure bioassays with honey bee larvae, no effect on mortality could be observed for the synthetic or the plant-derived inhibitors, thus suggesting that the pathogenesis strategies of P. larvae are likely to be too complex to be disarmed in an anti-virulence strategy aimed at a single virulence factor. Our study also underscores the importance of not only testing substances in in vitro or cell culture assays, but also testing the compounds in P. larvae-infected honey bee larvae.
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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) | 0.001 | 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".