Reviewer #2 (Public review): Nutritional state-dependent modulation of Insulin-Producing Cells in Drosophila
Bibliographic record
Abstract
Insulin plays a key role in metabolic homeostasis across vertebrate and invertebrate species. Drosophila Insulin-Producing Cells (IPCs) are functional analogues to mammalian pancreatic beta cells and release insulin directly into circulation. IPC activity is modulated by nutrient availability, circadian time, and the behavioral state. To investigate the in vivo dynamics of IPC activity in the context of metabolic homeostasis, we quantified effects of nutritional and internal state changes on IPCs using electrophysiological recordings. We found that the nutritional state strongly modulates IPC activity. IPCs became less active with increasing periods of starvation. Refeeding starved flies with glucose or fructose, two nutritive sugars, significantly increased IPC activity, whereas non-nutritive sugar and protein had no effect. In contrast to feeding, glucose perfusion did not affect IPC activity. This was reminiscent of the mammalian incretin effect, in which ingestion of glucose drives higher insulin release than intravenous glucose application. Contrary to IPCs, Diuretic hormone 44-expressing neurons in the pars intercerebralis (DH44PINs), which are anatomically similar to IPCs, responded to glucose perfusion. Functional connectivity experiments demonstrated that glucose-sensing DH44PINs do not affect IPC activity, while other DH44Ns inhibit IPCs. This suggests that populations of autonomously and systemically sugar-sensing neurons work in parallel to maintain metabolic homeostasis. Ultimately, metabolic state changes affect behavior. For example, hungry flies increase their locomotor activity in search of food. In support of this idea, activating IPCs had a small, satiety-like effect on starved flies, resulting in reduced walking activity, whereas activating DH44Ns strongly increased walking activity. Taken together, we show that IPCs and DH44Ns are an integral part of a sophisticated modulatory network that orchestrates glucose homeostasis and adaptive behavior in response to shifts in the metabolic state.
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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 | 0.019 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.022 | 0.009 |
| Insufficient payload (model declined to judge) | 0.065 | 0.033 |
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".