Manipulation of components that control feeding behavior in Drosophila melanogaster increases sensitivity to amino acid starvation
Bibliographic record
Abstract
Feeding is a complex behavior that is regulated by several internal mechanisms. Neuropeptides are able to survey quantities of stored energy and inform the organism if nutrient intake is required. In addition to this homeostatic regulation, a post-feeding reward system positively reinforces feeding. Slight adjustments to either system can tilt the balance to affect the energy reserves and survivorship in times of nutrient adversity. Neuropeptide F (NPF), a homolog of the mammalian neuropeptide Y, acts to induce feeding within the homeostatic regulation of this behavior. Drosophila and other insects bear a shorter form of NPF known as short NPF (sNPF) that can influence feeding. A neural hormone regulator, the dopamine transporter (DAT), works to clear dopamine from the synapses. This action may manipulate the post-feeding reward circuit in that lowered dopamine levels depress feeding, and excess dopamine levels encourage feeding. Here, we have overexpressed and impaired the activities of NPF, sNPF, and DAT in Drosophila, and we examined their ability to survive during conditions of amino acid starvation. Too much or too little NPF or sNPF, which are key players in homeostatic feeding regulation, leads to increased sensitivity to amino acid starvation and diminished survivorship when compared to controls. When DAT, a member of the post-feeding reward system, is either overexpressed or reduced via mutation, Drosophila has increased sensitivity to amino acid starvation. Taken together, these results indicate that subtle variation in the expression of key components of these systems impacts survivorship during adverse nutrient conditions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".