Changes in protein expression during honey bee larval development
Bibliographic record
Abstract
BACKGROUND: The honey bee (Apis mellifera), besides its role in pollination and honey production, serves as a model for studying the biochemistry of development, metabolism, and immunity in a social organism. Here we use mass spectrometry-based quantitative proteomics to quantify nearly 800 proteins during the 5- to 6-day larval developmental stage, tracking their expression profiles. RESULTS: We report that honey bee larval growth is marked by an age-correlated increase of protein transporters and receptors, as well as protein nutrient stores, while opposite trends in protein translation activity and turnover were observed. Levels of the immunity factors prophenoloxidase and apismin are positively correlated with development, while others surprisingly were not significantly age-regulated, suggesting a molecular explanation for why bees are susceptible to major age-associated bee bacterial infections such as American Foulbrood or fungal diseases such as chalkbrood. Previously unreported findings include the reduction of antioxidant and G proteins in aging larvae. CONCLUSION: These data have allowed us to integrate disparate findings in previous studies to build a model of metabolism and maturity of the immune system during larval development. This publicly accessible resource for protein expression trends will help generate new hypotheses in the increasingly important field of honey bee research.
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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".