Gut Microbiota Mediate Insecticide Resistance in the Diamondback Moth, Plutella xylostella (L.)
Bibliographic record
Abstract
The development of insecticide resistance in insect pests is a worldwide concern and elucidating the underlying mechanisms is critical for effective crop protection. Recent studies have indicated potential links between insect gut microbiota and insecticide resistance and these may apply to the Diamondback moth, Plutella xylostella (L.), a globally and economically important pest of cruciferous crops. We isolated Enterococcus sp. (Firmicutes), Enterobacter sp. (Proteobacteria), and Serratia sp. (Proteobacteria) from the guts of P. xylostella and analyzed the effects on, and underlying mechanisms of insecticide resistance. Enterococcus sp. enhanced resistance to the widely used insecticide, chlorpyrifos, in P. xylostella, while in contrast, Serratia sp. decreased resistance and Enterobacter sp. and all strains of heat-killed bacteria had no effect. Importantly, the direct degradation of chlorpyrifos in vitro was consistent among the three strains of bacteria. We found that Enterococcus sp., vitamin C, and acetylsalicylic acid enhanced insecticide resistance in P. xylostella and had similar effects on expression of P. xylostella antimicrobial peptides. Expression of cecropin was down-regulated by the two compounds, while gloverin was up-regulated. Bacteria that were not associated with insecticide resistance induced contrasting gene expression profiles to Enterococcus sp. and the compounds. Our studies confirmed that gut bacteria play an important role in P. xylostella insecticide resistance, but the main mechanism is not direct detoxification of insecticides by gut bacteria. We also suggest that the influence of gut bacteria on insecticide resistance may depend on effects on the immune system. Our work advances understanding of the evolution of insecticide resistance in this key pest and highlights directions for research into insecticide resistance in other insect pest species.
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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 | 0.001 |
| 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".