Food intake behavior is regulated by a network of appetite-inducing and appetite-suppressing neuronal populations throughout the brain. The parasubthalamic nucleus (PSTN), a relatively unexplored population of neurons in the posterior hypothalamus, has been hypothesized to regulate appetite due to its connectivity with other anorexigenic neuronal populations and because these neurons express Fos, a marker of neuronal activation, following a meal. However, the individual cell types that make up the PSTN are not well characterized, nor are their functional roles in food intake behavior. Here we identify and distinguish between two discrete PSTN subpopulations, those that express tachykinin-1 (PSTNTac1 neurons) and those that express corticotropin-releasing hormone (PSTNCRH neurons), and use a panel of genetically encoded tools in mice to show that PSTNTac1 neurons play an important role in appetite suppression. Both subpopulations increase activity following a meal and in response to administration of the anorexigenic hormones amylin, cholecystokinin (CCK), and peptide YY (PYY). Interestingly, chemogenetic inhibition of PSTNTac1, but not PSTNCRH neurons, reduces the appetite-suppressing effects of these hormones. Consistently, optogenetic and chemogenetic stimulation of PSTNTac1 neurons, but not PSTNCRH neurons, reduces food intake in hungry mice. PSTNTac1 and PSTNCRH neurons project to distinct downstream brain regions, and stimulation of PSTNTac1 projections to individual anorexigenic populations reduces food consumption. Taken together, these results reveal the functional properties and projection patterns of distinct PSTN cell types and demonstrate an anorexigenic role for PSTNTac1 neurons in the hormonal and central regulation of appetite.
Source Data files have been provided for Figures 2-6 and 8 (Figures 1 and 7 do not contain quantitative data). These files contain the numerical data used to generate figures and analyze data. Supplementary File 1 contains a complete description of all statistical tests used, methods of multiple comparisons, and critical values for n, p, and degrees of freedom. All MatLab scripts used to analyze fiber photometry data are freely available at https://github.com/MattCarter-WilliamsCollege/FiberPhotometryCode.git.
- Matthew E Carter
- Matthew E Carter
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Animal experimentation: All experiments in this study were approved by the Institutional Animal Care and Use Committee (IACUC) at Williams College (protocol #CM-A-19). All experiments were performed in strict accordance with the guidelines described in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All surgery was performed under isoflurane anesthesia, and every effort was made to minimize suffering and animal distress.
- Joel K Elmquist, University of Texas Southwestern Medical Center, United States
This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Artificial neural networks could pave the way for efficiently simulating large-scale models of neuronal networks in the nervous system.
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