Enhanced capacity of a leaf beetle to combat dual stress from entomopathogens and herbicides mediated by associated microbiota
Bibliographic record
Abstract
Herbicides have demonstrated their impact on insect fitness by affecting their associated microbiota or altering the virulence of entomopathogenic fungi toward insects. However, limited research has explored the implications of herbicide stress on the intricate tripartite interaction among insects, associated bacterial communities, and entomopathogens. In this study, we initially demonstrated that associated bacteria confer a leaf beetle, Plagiodera versicolora, with the capability to resist the entomopathogenic fungus Aspergillus nomius infection, a capability sustained even under herbicide glyphosate stress. Further analysis of the associated microbiota revealed a significant alteration in abundance and composition due to glyphosate treatment. The dominant bacterium, post A. nomius infection or following a combination of glyphosate treatments, exhibited strong suppressive effects on fungal growth. Additionally, glyphosate markedly inhibited the pathogenic associated bacterium Pseudomonas though it inhibited P. versicolora's immunity, ultimately enhancing the beetle's tolerance to A. nomius. In summary, our findings suggest that the leaf beetle's associated microbiota bestow an augmented resilience against the dual stressors of both the entomopathogen and glyphosate. These results provide insight into the effects of herbicide residues on interactions among insects, associated bacteria, and entomopathogenic fungi, holding significant implications for pest control and ecosystem assessment.
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 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".