Genome‐wide analysis reveals differences in brain gene expression patterns associated with caste and reproductive status in honey bees (<i>Apis mellifera</i>)
Bibliographic record
Abstract
A key characteristic of eusocial species is reproductive division of labour. Honey bee colonies typically have a single reproductive queen and thousands of sterile workers. Adult queens differ dramatically from workers in anatomy, physiology, behaviour and lifespan. Young female workers can activate their ovaries and initiate egg laying; these 'reproductive' workers differ from sterile workers in anatomy, physiology, and behaviour. These differences, however, are on a much smaller scale than those observed between the queen and worker castes. Here, we use microarrays to monitor expression patterns of several thousand genes in the brains of same-aged virgin queens, sterile workers, and reproductive workers. We found large differences in expression between queens and both worker groups (~2000 genes), and much smaller differences between sterile and reproductive workers (221 genes). The expression patterns of these 221 genes in reproductive workers are more queen-like, and may represent a core group of genes associated with reproductive physiology. Furthermore, queens and reproductive workers preferentially up-regulate genes associated with the nurse bee behavioural state, which supports the hypothesis of an evolutionary link between worker division of labour and molecular pathways related to reproduction. Finally, several functional groups of genes associated with longevity in other species are significantly up-regulated in queens. Identifying the genes that underlie the differences between queens, sterile workers, and reproductive workers will allow us to begin to characterize the molecular mechanisms underlying the evolution of social behaviour and large-scale remodelling of gene networks associated with polyphenisms.
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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".