Insulin-producing cells and their regulation in physiology and behavior of<i>Drosophila</i><sup>1</sup>This review is part of a virtual symposium on recent advances in understanding a variety of complex regulatory processes in insect physiology and endocrinology, including development, metabolism, cold hardiness, food intake and digestion, and diuresis, through the use of omics technologies in the postgenomic era.
Bibliographic record
Abstract
Insulin-like peptide signaling regulates development, growth, reproduction, metabolism, stress resistance, and life span in a wide spectrum of animals. Not only the peptides, but also their tyrosine kinase receptors and the downstream signaling pathways are conserved over evolution. This review summarizes roles of insulin-like peptides (DILPs) in physiology and behavior of Drosophila melanogaster Meigen, 1830. Seven DILPs (DILP1–7) and one receptor (dInR) have been identified in Drosophila. These DILPs display cell and stage specific expression patterns. In the adult, DILP2, 3, and 5 are expressed in insulin-producing cells (IPCs) among the median neurosecretory cells of the brain, DILP7 in 20 neurons of the abdominal ganglion, and DILP6 in the fat body. The DILPs of the IPCs regulate starvation resistance, responses to oxidative and temperature stress, and carbohydrate and lipid metabolism. Furthermore, the IPCs seem to regulate feeding, locomotor activity, sleep and ethanol sensitivity, but the mechanisms are not elucidated. Insulin also alters the sensitivity in the olfactory system that affects food search behavior, and regulates peptidergic neurons that control aspects of feeding behavior. Finally, the control of insulin production and release by humoral and neuronal factors is discussed. This includes a fat body derived factor and the neurotransmitters GABA, serotonin, octopamine, and two neuropeptides.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".