Omics Insights Into the Effects of Highbush Blueberry and Cranberry Crop Agroecosystems on Honey Bee Health and Physiology
Bibliographic record
Abstract
Honey bees (Apis mellifera) are vital pollinators in fruit-producing agroecosystems like highbush blueberry (HBB) and cranberry (CRA). However, their health is threatened by multiple interacting stressors, including pesticides, pathogens, and nutritional changes. We tested the hypothesis that distinct agricultural ecosystems-with different combinations of agrochemical exposure, pathogen loads, and floral resources-elicit ecosystem-specific, tissue-level molecular responses in honey bees. We conducted an integrated multi-omics analysis using RNA-sequencing (RNA-seq), proteomics, and gut microbiome profiling across three key tissue types (head, abdomen, and gut) of honey bees collected from two agroecosystems over two field seasons. Quantification was performed for pesticide residues, pathogen loads (Nosema spp., Varroa destructor, and multiple viruses), and gut microbiota. Weighted gene co-expression network analysis (WGCNA) revealed tissue-specific protein modules with ecosystem-associated patterns, which differed from RNA co-expression networks. Microbiome composition also varied, with key genera like Gilliamella, Snodgrassella, and Bartonella correlating with metabolic modules. These findings underscore the complex, environment-dependent impacts of agroecosystem conditions on bee health. Our study provides a system-level understanding of how combined pesticide, pathogen, and parasitic stressors, mediated by diet and microbiome, shape molecular phenotypes in honey bees-informing strategies for pollinator protection in managed landscapes. SUMMARY: This study provides a comprehensive multi-omics analysis of honey bees foraging in blueberry and cranberry agroecosystems, offering novel insights into the molecular mechanisms underlying pollinator health in managed crop environments. By integrating transcriptomic, proteomic, and microbiome profiling across key tissues-head, abdomen, and gut-we reveal how environmental stressors, including pesticide exposure, pathogen infections, and parasitic infestations (e.g., Varroa destructor), differentially impact bee physiology and microbiome composition. Our findings highlight tissue-specific responses to these stressors, with distinct metabolic pathway alterations observed in each tissue. Proteomic and transcriptomic analyses uncovered dysregulated pathways linked to oxidative phosphorylation and protein synthesis, while microbiome analysis revealed crop-dependent shifts in gut bacterial communities, suggesting potential roles in pesticide detoxification and immune modulation. Notably, we identified key molecular biomarkers associated with stress adaptation, which may serve as early indicators of colony health deterioration. This research underscores the need for a system-level approach to understanding pollinator stress in agricultural landscapes. By elucidating the interactions between diet, pesticide residues, pathogen loads, and molecular stress responses, our study provides a foundation for targeted conservation strategies aimed at mitigating environmental risks and improving pollination sustainability in agroecosystems.
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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) | 0.000 | 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) | 0.000 | 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".