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

E Eloïse de Tredern D Dylan Manceau A Alexandre Blanc A Abhijit Parameswaran P Panagiotis Sakagiannis C Chloe Barre V Victoria Sus F Francesca Viscido P Perla Akiki M Md Amit Hasan S Sandra Autran F François Laurent (Decision and Bayesian Computation, Institut Pasteur, Université Paris Cité, CNRS UMR3751) M Martin Paul Nawrot J Jean-Baptiste Masson (Decision and Bayesian Computation, Institut Pasteur, Université Paris Cité, CNRS UMR3751) T Tihana Jovanic

Abstract

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.

Article Details

Volume / Issue Vol. 16, Issue 1
Published September 02, 2025
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (15)

E

Eloïse de Tredern

D

Dylan Manceau

A

Alexandre Blanc

A

Abhijit Parameswaran

P

Panagiotis Sakagiannis

C

Chloe Barre

V

Victoria Sus

F

Francesca Viscido

P

Perla Akiki

M

Md Amit Hasan

S

Sandra Autran

F

François Laurent

Decision and Bayesian Computation, Institut Pasteur, Université Paris Cité, CNRS UMR3751

M

Martin Paul Nawrot

J

Jean-Baptiste Masson

Decision and Bayesian Computation, Institut Pasteur, Université Paris Cité, CNRS UMR3751

T

Tihana Jovanic