The innate immune and systemic response in honey bees to a bacterial pathogen, Paenibacillus larvae
Bibliographic record
Abstract
BACKGROUND: There is a major paradox in our understanding of honey bee immunity: the high population density in a bee colony implies a high rate of disease transmission among individuals, yet bees are predicted to express only two-thirds as many immunity genes as solitary insects, e.g., mosquito or fruit fly. This suggests that the immune response in bees is subdued in favor of social immunity, yet some specific immune factors are up-regulated in response to infection. To explore the response to infection more broadly, we employ mass spectrometry-based proteomics in a quantitative analysis of honey bee larvae infected with the bacterium Paenibacillus larvae. Newly-eclosed bee larvae, in the second stage of their life cycle, are susceptible to this infection, but become progressively more resistant with age. We used this host-pathogen system to probe not only the role of the immune system in responding to a highly evolved infection, but also what other mechanisms might be employed in response to infection. RESULTS: Using quantitative proteomics, we compared the hemolymph (insect blood) of five-day old healthy and infected honey bee larvae and found a strong up-regulation of some metabolic enzymes and chaperones, while royal jelly (food) and energy storage proteins were down-regulated. We also observed increased levels of the immune factors prophenoloxidase (proPO), lysozyme and the antimicrobial peptide hymenoptaecin. Furthermore, mass spectrometry evidence suggests that healthy larvae have significant levels of catalytically inactive proPO in the hemolymph that is proteolytically activated upon infection. Phenoloxidase (PO) enzyme activity was undetectable in one or two-day-old larvae and increased dramatically thereafter, paralleling very closely the age-related ability of larvae to resist infection. CONCLUSION: We propose a model for the host response to infection where energy stores and metabolic enzymes are regulated in concert with direct defensive measures, such as the massive enhancement of PO activity.
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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) | 0.000 | 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 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".