Chemical entrapment and killing of insects by bacteria
Bibliographic record
Abstract
Abstract Actinobacteria such as the filamentous streptomycetes are widely known for their ability to produce specialized metabolites that include antibacterial and antifungal compounds. In addition, a growing body of work demonstrates that many insects harbour actinobacteria on their bodies and in their nests. The result of these mutualistic relationships is the protection of their offspring or food sources by virtue of the bacterially encoded specialized metabolites. However, some actinobacteria produce molecules that are toxic to insects and the relevance of this toxicity in nature is unknown. We have explored interactions between streptomycetes and the fruit fly Drosophila. We find that many streptomycetes produce specialized metabolites that have potent larvicidal effects against the fly. Larvae that ingest spores of the species that produce these toxic molecules die as a result. Strikingly, the mechanism of toxicity is specific to the bacterium’s chemical arsenal: cosmomycin D producing cells induce a relatively slow-acting cell death-like response in the larval digestive tract and avermectin producing cells induce rapid onset, whole-body paralysis. We further show that fruit flies are attracted to the volatile terpene 2-methylisoborneol that is produced by most streptomycetes. This interaction can influence their food choice and egg-laying destination such that they preferentially deposit their eggs on contaminated food sources. As a result, the larvae that hatch in these toxic environments are subsequently killed. This phenomena of terpene-mediated attraction and specialized metabolite toxicity must pose a significant risk to insects in nature.
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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".