Major royal jelly proteins influence the neurobiological regulation of the division of labor among honey bee workers
Bibliographic record
Abstract
Age-based division of labor among workers is a fundamental life-history trait of many social insects, including the Western honey bee, Apis mellifera L. Extensive studies of the causation of the most pronounced transition from performing tasks in the nest to outside foraging indicate hormonal regulation of complex physiological changes. However, the proximate neurobiological mechanisms that cause the behavioral repertoire to change are still not understood and require novel approaches to be fully characterized. Thus, we established the first comprehensive monoclonal antibody microarray in honey bees with 16,320 antibodies to directly identify proteins in the brain that regulate the transition to foraging. Major royal jelly protein (MRJP) 1 and MRJP3 were identified as potential protein effectors and further investigated. A series of experimental manipulations of the workers' behavioral transition led to changes in MRJP1 and MRJP3 quantities in accordance with their presumed functional role. Injection of MRJPs into the brain resulted in increased task-reversal from foraging to nursing and decreased task-progression from nursing to foraging, while the latter was increased by injection with MRJP antibodies. Finally, down-regulation of MRJP1 and MRJP3 expression via RNAi injection into the brain increased the transition from in-hive nursing to outside foraging, confirming a causal role of these two proteins in the proximate regulation of behavior and life-history of honey bee workers. Interaction partners of MRJP1 and MRJP3 in the honey bee brain included other regulators of honey bee behavior and life history. Thus, our transformative methodological advancement of proteome analysis in honey bees reveals novel regulators of honey bee behavior, extends our understanding of the functional pleiotropy of MRJPs, and supports a general nutrition-based model of the regulation of the age-based division of labor in honey bees.
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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".