Synergistic Parasite-Pathogen Interactions Mediated by Host Immunity Can Drive the Collapse of Honeybee Colonies
Bibliographic record
Abstract
The health of the honeybee and, indirectly, global crop production are threatened by several biotic and abiotic factors, which play a poorly defined role in the induction of widespread colony losses. Recent descriptive studies suggest that colony losses are often related to the interaction between pathogens and other stress factors, including parasites. Through an integrated analysis of the population and molecular changes associated with the collapse of honeybee colonies infested by the parasitic mite Varroa destructor, we show that this parasite can de-stabilise the within-host dynamics of Deformed wing virus (DWV), transforming a cryptic and vertically transmitted virus into a rapidly replicating killer, which attains lethal levels late in the season. The de-stabilisation of DWV infection is associated with an immunosuppression syndrome, characterized by a strong down-regulation of the transcription factor NF-κB. The centrality of NF-κB in host responses to a range of environmental challenges suggests that this transcription factor can act as a common currency underlying colony collapse that may be triggered by different causes. Our results offer an integrated account for the multifactorial origin of honeybee losses and a new framework for assessing, and possibly mitigating, the impact of environmental challenges on honeybee health.
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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".