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Record W2789904388 · doi:10.1128/aem.02820-17

Microbiota-Mediated Modulation of Organophosphate Insecticide Toxicity by Species-Dependent Interactions with Lactobacilli in a Drosophila melanogaster Insect Model

2018· article· en· W2789904388 on OpenAlexafffund
Brendan A. Daisley, Mark Trinder, Tim McDowell, Stephanie L. Collins, Mark W. Sumarah, Gregor Reid

Bibliographic record

VenueApplied and Environmental Microbiology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsSt Joseph's Health CareAgriculture and Agri-Food CanadaLawson Health Research InstituteWestern University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsBiologyLactobacillus plantarumDrosophila melanogasterProbioticLactobacillus rhamnosusMelanogasterOrganophosphateMicrobiologyMetaboliteGut floraBacteriaFood scienceBiochemistryPesticideLactic acidGeneticsEcology

Abstract

fetched live from OpenAlex

ABSTRACT Despite the benefits to the global food supply and agricultural economies, pesticides are believed to pose a threat to the health of both humans and wildlife. Chlorpyrifos (CP), a commonly used organophosphate insecticide, has poor target specificity and causes acute neurotoxicity in a wide range of species via the suppression of acetylcholinesterase. This effect is exacerbated 10- to 100-fold by chlorpyrifos oxon (CPO), a principal metabolite of CP. Since many animal-associated symbiont microorganisms are known to hydrolyze CP into CPO, we used a Drosophila melanogaster insect model to investigate the hypothesis that indigenous and probiotic bacteria could affect CP metabolism and toxicity. Antibiotic-treated and germfree D. melanogaster insects lived significantly longer than their conventionally reared counterparts when exposed to 10 μM CP. Drosophila melanogaster gut-derived Lactobacillus plantarum , but not Acetobacter indonesiensis , was shown to metabolize CP. Liquid chromatography tandem-mass spectrometry confirmed that the L. plantarum isolate preferentially metabolized CP into CPO when grown in CP-spiked culture medium. Further experiments showed that monoassociating germfree D. melanogaster with the L. plantarum isolate could reestablish a conventional-like sensitivity to CP. Interestingly, supplementation with the human probiotic Lactobacillus rhamnosus GG (a strain that binds but does not metabolize CP) significantly increased the survival of the CP-exposed germfree D. melanogaster . This suggests strain-specific differences in CP metabolism may exist among lactobacilli and emphasizes the need for further investigation. In summary, these results suggest that (i) CPO formation by the gut microbiota can have biologically relevant consequences for the host, and (ii) probiotic lactobacilli may be beneficial in reducing in vivo CP toxicity. IMPORTANCE An understudied area of research is how the microbiota (microorganisms living in/on an animal) affects the metabolism and toxic outcomes of environmental pollutants such as pesticides. This study focused specifically on how the microbial biotransformation of chlorpyrifos (CP; a common organophosphate insecticide) affected host exposure and toxicity parameters in a Drosophila melanogaster insect model. Our results demonstrate that the biotransformation of CP by the gut microbiota had biologically relevant and toxic consequences on host health and that certain probiotic lactobacilli may be beneficial in reducing CP toxicity. Since inadvertent pesticide exposure is suspected to negatively impact the health of off-target species, these findings may provide useful information for wildlife conservation and environmental sustainability planning. Furthermore, the results highlight the need to consider microbiota composition differences between beneficial and pest insects in future insecticide designs. More broadly, this study supports the use of beneficial microorganisms to modulate the microbiota-mediated biotransformation of xenobiotics.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.648

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)0.0010.000

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 head0.013
GPT teacher head0.190
Teacher spread0.177 · 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

Citations94
Published2018
Admission routes2
Has abstractyes

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