Evidence map›Paper›PMID 40897720›Full record

ArticleNature communications2025

Feeding state-dependent neuropeptidergic modulation of reciprocally interconnected inhibitory neurons biases sensorimotor decisions in Drosophila.

Eloïse de Tredern, Dylan Manceau, Alexandre Blanc, Abhijit Parameswaran, Panagiotis Sakagiannis, Chloe Barre, Victoria Sus, Francesca Viscido, Perla Akiki, Md Amit Hasan and 5 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Eloïse de Tredern *Université Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France.
Dylan Manceau *Université Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France.
Alexandre BlancInstitut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, Paris, France.ORCID http://orcid.org/0000-0001-9148-5223
Abhijit ParameswaranUniversité Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France.
Panagiotis SakagiannisComputational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0002-1033-5387
Chloe BarreInstitut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, Paris, France.
Victoria SusUniversité Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France.
Francesca ViscidoUniversité Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France.ORCID http://orcid.org/0000-0001-5734-4435
Perla AkikiUniversité Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France.
Md Amit HasanUniversité Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France.
Sandra AutranUniversité Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France.
François LaurentInstitut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, Paris, France.ORCID http://orcid.org/0000-0002-1615-6400
Martin Paul NawrotComputational Systems Neuroscience, Institute of Zoology, University of Cologne, Cologne, Germany.ORCID http://orcid.org/0000-0003-4133-6419
Jean-Baptiste MassonInstitut Pasteur, Université Paris Cité, CNRS UMR 3571, Decision and Bayesian Computation, Paris, France.ORCID http://orcid.org/0000-0002-5484-9056
Tihana JovanicUniversité Paris-Saclay, CNRS, Institut des neurosciences Paris-Saclay, Saclay, France. tihana.jovanic@cnrs.fr.ORCID http://orcid.org/0000-0002-0525-8620

Funding

Agence Nationale de la Recherche (French National Research Agency) ANR-22-CE37-0027Agence Nationale de la Recherche (French National Research Agency) ANR-NEUC-0002-01Fondation pour la Recherche Médicale (Foundation for Medical Research in France) Équipe FRM EQU202303016317
6 · The paper itself

Abstract

An animal's feeding state changes its behavioral priorities and thus influences even nonfeeding-related decisions. How the feeding state information is transmitted to nonfeeding-related circuits and what circuit mechanisms are involved in biasing nonfeeding-related decisions remain open questions. By combining calcium imaging, neuronal manipulations, behavioral analysis and computational modeling, we determined that the competition between different aversive responses to mechanical cues is biased by changes in the feeding state. We found that this effect is achieved by the differential modulation of two different types of reciprocally connected inhibitory neurons promoting opposing actions. This modulation results in a more frequent active type of response and, less frequently, a protective type of response if larvae are fed sugar than when they are fed a balanced diet. Information about the internal state is conveyed to inhibitory neurons through homologs of the vertebrate neuropeptide Y, which is known to be involved in regulating feeding behavior.

Indexed as

Drosophila melanogasterFeeding BehaviorNeuronsNeuropeptidesAnimalsCalciumDrosophila ProteinsLarvaNeuropeptide YCalciumDrosophila ProteinsNeuropeptidesNeuropeptide Y

Identifiers

PMID40897720
PMCPMC12405588

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.