The foraging gene coordinates brain and heart networks to modulate socially cued interval timing in Drosophila
Bibliographic record
Abstract
The foraging gene (for) regulates behavioral plasticity and decision-making, influencing adaptive behaviors such as foraging, learning, and memory. In Drosophila melanogaster, we explore its role in interval timing behaviors, particularly mating duration. Two allelic variants, rover (forR) and sitter (forS), exhibit distinct effects: forR disrupts shorter mating duration (SMD) but not longer mating duration (LMD), while forS impairs LMD but not SMD. Transheterozygotes (forR/forS) disrupt both behaviors, revealing complex allelic interactions. Using single-cell RNA sequencing and knockdown experiments, we identify foraging expression in Pdfr-positive neurons and fru-positive heart cells as critical for LMD. While the gene is expressed in memory-related brain regions, its impact on LMD is mediated through peptidergic signaling and calcium dynamics in the heart. Social context-dependent calcium fluctuations, observed via CaLexA signals, are disrupted by foraging or Pdfr knockdown, impairing LMD. These findings highlight the foraging gene's role in integrating social cues with physiological states. This study demonstrates the foraging gene's pleiotropic roles in regulating interval timing through neural and non-neural mechanisms, offering insights into the genetic and environmental interplay underlying adaptive behaviors.
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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".