Mechanisms of stable lipid loss in a social insect
Bibliographic record
Abstract
Worker honey bees undergo a socially regulated, highly stable lipid loss as part of their behavioral maturation. We used large-scale transcriptomic and proteomic experiments, physiological experiments and RNA interference to explore the mechanistic basis for this lipid loss. Lipid loss was associated with thousands of gene expression changes in abdominal fat bodies. Many of these genes were also regulated in young bees by nutrition during an initial period of lipid gain. Surprisingly, in older bees, which is when maximum lipid loss occurs, diet played less of a role in regulating fat body gene expression for components of evolutionarily conserved nutrition-related endocrine systems involving insulin and juvenile hormone signaling. By contrast, fat body gene expression in older bees was regulated more strongly by evolutionarily novel regulatory factors, queen mandibular pheromone (a honey bee-specific social signal) and vitellogenin (a conserved yolk protein that has evolved novel, maturation-related functions in the bee), independent of nutrition. These results demonstrate that conserved molecular pathways can be manipulated to achieve stable lipid loss through evolutionarily novel regulatory processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".