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Record W4412107109 · doi:10.1371/journal.pgen.1011752

The foraging gene coordinates brain and heart networks to modulate socially cued interval timing in Drosophila

2025· article· en· W4412107109 on OpenAlexfundno aff
Hongyu Miao, Wengjing Li, Yongwen Huang, Woo Jae Kim

Bibliographic record

VenuePLoS Genetics · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsnot available
FundersHarbin Institute of TechnologyUniversity of OttawaNatural Sciences and Engineering Research Council of CanadaMitacsBurroughs Wellcome Fund
KeywordsBiologyForagingDrosophila melanogasterGene knockdownMatingNeuroscienceContext (archaeology)Evolutionary biologyMushroom bodiesGeneticsGeneEcology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.169
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.312
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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