# A Neuropeptidergic Neural Network Integrates Taste with Internal State to Modulate Feeding

> **NIH NIH R01** · BROWN UNIVERSITY · 2024 · $452,854

## Abstract

Feeding is a fundamental behavior that is tightly regulated to precisely meet the metabolic needs of the animal.
The primary decision that an animal must make regarding food is whether to ingest it or reject it. Substances
with high nutritional value are ingested, while toxins and harmful substances are rejected. To make this decision,
the animal relies on its sense of taste to evaluate the quality of the food. Most animals respond to sweet and
bitter tastants with different stereotyped behaviors: sweet substances, often calorie rich, are appetitive and
accepted, while bitter compounds, usually harmful, are rejected and avoided. Another important part of the
decision whether to ingest or reject potential food is the metabolic need of the animal that is manifested by the
balance between hunger and satiety. This aspect of the internal state of the animals is evaluated through an
intricate balance between various hormones and neuromodulators, some signal hunger while the others signal
satiety. How the concerted action of the various hormones and neuromodulators affects feeding is an area of
significant interest as dysregulation of feeding behavior results in obesity or in malnutrition and their associated
morbidities.
In this proposal, we focus on one network of neuromodulatory neurons in Drosophila in which a neuromodulator
termed leucokinin is secreted by certain subsets of neurons within the network and detected by others. We
propose to test the hypothesis that the leucokinin network of neuromodulatory neurons integrates information
about taste quality and the internal state of the fly to modulates feeding behavior. In this network, one set of
neurons receives inputs from two others: taste information from one, and information about the internal state of
the animal from the other. The recipient neurons integrate the two streams of information and in turn modulate
feeding behavior by secreting other neuropeptides that regulate feeding. To test this hypothesis, we use a
multipronged approach that includes anatomical, functional and behavioral analyses. In our proposed study, we
use state of the art techniques to label neuronal connectivity and to manipulate the activity of selective subsets
of neurons to examine the behavioral and functional effects of these manipulations. We also develop a new
technique for studying sites of neuromodulation. This technique enables selective, unbiased, brain-wide
examination of sites of neuromodulation by specific modulators with cellular resolution. Thus, our studies will
deepen our understanding of the regulation of feeding and provide new tools to study neuromodulation, a
research area that will increase in importance as neural connectivity maps of more model organisms become
available.

## Key facts

- **NIH application ID:** 10862852
- **Project number:** 5R01DC020703-02
- **Recipient organization:** BROWN UNIVERSITY
- **Principal Investigator:** Gilad Barnea
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $452,854
- **Award type:** 5
- **Project period:** 2023-06-08 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862852

## Citation

> US National Institutes of Health, RePORTER application 10862852, A Neuropeptidergic Neural Network Integrates Taste with Internal State to Modulate Feeding (5R01DC020703-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10862852. Licensed CC0.

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