Enteroendocrine cell types that drive food reward and aversion
- University of California, San Francisco, United States
- Howard Hughes Medical Institute, University of California, San Francisco, United States
Abstract
Animals must learn through experience which foods are nutritious and should be consumed, and which are toxic and should be avoided. Enteroendocrine cells (EECs) are the principal chemosensors in the GI tract, but investigation of their role in behavior has been limited by the difficulty of selectively targeting these cells in vivo. Here we describe an intersectional genetic approach for manipulating EEC subtypes in behaving mice. We show that multiple EEC subtypes inhibit food intake but have different effects on learning. Conditioned flavor preference is driven by release of cholecystokinin whereas conditioned taste aversion is mediated by serotonin and substance P. These positive and negative valence signals are transmitted by vagal and spinal afferents, respectively. These findings establish a cellular basis for how chemosensing in the gut drives learning about food.
Data availability
Source data is included in the manuscript. RNA-seq data is available from the Gene Expression Omnibus (GSE203200). Villin-Flp mice have been deposited at Jackson laboratory.
-
Enteroendocrine cell types that drive food reward and aversionNCBI Gene Expression Omnibus, GSE203200.
-
Identification of functional enteroendocrine regulators by real-time single-cell differentiation mappingNCBI Gene Expression Omnibus, GSE113561.
Article and author information
Author details
Sheyda Mesgarzadeh
Funding
National Institutes of Health (R01-DK106399)
- Zachary A Knight
National Institutes of Health (RF1-NS116626)
- Zachary A Knight
Howard Hughes Medical Institute (Investigator)
- Zachary A Knight
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All experimental protocols were approved by the University of California, San Francisco IACUC (protocol #AN179674) following the National Institutes of Health guidelines for the Care and Use of Laboratory Animals.
Copyright
© 2022, Bai et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 5,922
- views
-
- 1,252
- downloads
-
- 27
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Enteroendocrine cell types that drive food reward and aversion
eLife 11:e74964.
https://doi.org/10.7554/eLife.74964
Further reading
-
- Biochemistry and Chemical Biology
- Neuroscience
The buildup of knot-like RNA structures in brain cells may be the key to understanding how uncontrolled protein aggregation drives Alzheimer’s disease.
-
- Neuroscience
Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.
