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Record W3085598139 · doi:10.1038/s41467-020-18462-0

Chemical entrapment and killing of insects by bacteria

2020· article· en· W3085598139 on OpenAlexafffund
Louis Ho, Martin Daniel-Ivad, Swathi P. Jeedigunta, Jing Li, Konstantin G. Iliadi, Gabrielle L. Boulianne, Thomas R. Hurd, Craig A. Smibert, Justin R. Nodwell

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsEntrapmentBacteriaBiologyMicrobiologyMedicineGenetics

Abstract

fetched live from OpenAlex

Actinobacteria produce antibacterial and antifungal specialized metabolites. Many insects harbour actinobacteria on their bodies or in their nests and use these metabolites for protection. However, some actinobacteria produce metabolites that are toxic to insects and the evolutionary relevance of this toxicity is unknown. Here we explore chemical interactions between streptomycetes and the fruit fly Drosophila melanogaster. We find that many streptomycetes produce specialized metabolites that have potent larvicidal effects against the fly; larvae that ingest spores of these species die. The mechanism of toxicity is specific to the bacterium's chemical arsenal: cosmomycin D producing bacteria induce a cell death-like response in the larval digestive tract; avermectin producing bacteria induce paralysis. Furthermore, low concentrations of volatile terpenes like 2-methylisoborneol that are produced by streptomycetes attract fruit flies such that they preferentially deposit their eggs on contaminated food sources. The resulting larvae are killed during growth and development. The phenomenon of volatile-mediated attraction and specialized metabolite toxicity suggests that some streptomycetes pose an evolutionary risk to insects in nature.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score0.136

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.280
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations31
Published2020
Admission routes2
Has abstractyes

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