Systemic Effects of Pesticides on Insectivorous Bats: A Proteomics Approach
Bibliographic record
Abstract
Bats play a critical role controlling agricultural pests, yet foraging in croplands exposes them to hazardous pesticides. These chemicals pose significant risks for bats by impairing immune function, locomotion, and cognition even at low doses, jeopardizing their survival and ecological role. Here, we employed proteomics-a powerful, yet underused, tool in ecotoxicology-to examine the systemic effects of chlorpyrifos (CPF), a commonly used insecticide, on big brown bats (Eptesicus fuscus). We exposed bats through their diet to an environmentally relevant concentration of CPF for three or seven consecutive days and took plasma samples before and after exposure for non-targeted proteomics. We identified over 100 proteins with significant abundance changes before and after exposure to the pesticide. Exposure to CPF altered a wide range of molecular processes, including cell communication, cell metabolism, and DNA maintenance. Remarkably, we found changes in key proteins involved in immune response, T cell activation, and inflammation. These effects could reduce a bat's immune response, increasing their susceptibility to viral infections, and intensifying the risk of shedding and transmitting pathogens to other species. Our results provide new insights into the toxicity of pesticides and highlight the utility of proteomics for assessing toxicant effects in understudied and vulnerable species such as bats. Considering a One Health approach and the role of bats as reservoirs for numerous zoonotic pathogens, our work has broad implications for bat and human health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".