Searching beyond the streetlight: Neonicotinoid exposure alters the neurogenomic state of worker honey bees
Bibliographic record
Abstract
Abstract Neonicotinoid insecticides have been implicated in honey bee declines, with many studies showing that sublethal exposure impacts bee behaviors such as foraging, learning, and memory. Despite the large number of ecotoxicological studies carried out to date, most focus on a handful of worker phenotypes leading to a “streetlight effect” where the a priori choice of phenotypes to measure may influence the results and conclusions arising from the studies. This bias can be overcome with the use of toxicological transcriptomics, where changes in gene expression can provide a more objective view of how pesticides alter animal traits. Here, we used RNA sequencing to examine the changes in neurogenomic states of nurse and forager honey bees that were naturally exposed to neonicotinoids in the field and artificially exposed to neonicotinoids in a controlled experiment. We found that neonicotinoid exposure influenced the neurogenomic state of foragers and nurses in different ways; foragers experienced shifts in expression of genes involved in cognition and development, while nurses experienced shifts in expression of genes involved in metabolism. Our study suggests that neonicotinoids influence nurse and forager bees in a different manner. We also found no to minimal overlap in the differentially expressed genes in our study and in previously published studies, which might help reconcile the seemingly contradictory results often reported in the neonicotinoid literature.
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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".