# Gut-brain communication of nutrient information

> **NIH NIH R01** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2021 · $355,500

## Abstract

PROJECT SUMMARY/ABSTRACT
The long-term goal of this project is to uncover how the gut-brain axis regulates appetite in a nutrient-specific
manner. In mammals, enteroendocrine cells in the gastrointestinal tract release a repertoire neuropeptides to
regulate food intake. However, it is not clear how nutrients are represented by gut neuropeptides. The
research outlined here takes advantage of the anatomical simplicity and the powerful genetic toolkit of the
Drosophila to address the questions of how macronutrients — carbohydrates, amino acids and fatty acids —
are transformed into a neuropeptide code and how nutritional information is processed to regulate appetite.
The proposed study focuses on gut neuropeptide — what macronutrients they represent (Aim 1) and whether
they are anorexigenic hormones (Aim 2). In Aim 3, we will test the hypothesis that the level of an anorexigenic
hormone represents not only the quantity but also the quality of amino acids. Results from these studies are
expected to establish a neuropeptide code for macronutrients providing mechanistic insights into how food
intake is regulated in a nutrient-specific manner. These insights are highly relevant to human health as
reducing food intake by designed diets could provide a new avenue to fight the obesity epidemic

## Key facts

- **NIH application ID:** 10264909
- **Project number:** 5R01DK127516-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Jing W Wang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $355,500
- **Award type:** 5
- **Project period:** 2020-09-16 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10264909

## Citation

> US National Institutes of Health, RePORTER application 10264909, Gut-brain communication of nutrient information (5R01DK127516-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10264909. Licensed CC0.

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