A discrete parasubthalamic nucleus subpopulation plays a critical role in appetite suppression

Abstract

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.

Data availability

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.

Article and author information

Author details

  1. Jessica H Kim

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Grace H Kromm

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0847-5468
  3. Olivia K Barnhill

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Jacob Sperber

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Lauren B Heuer

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Sierra Loomis

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Matthew C Newman

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Kenneth Han

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Faris F Gulamali

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2973-6594
  10. Theresa B Legan

    Department of Biology, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Katharine E Jensen

    Department of Physics, Williams College, Williamstown, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1862-8026
  12. Samuel C Funderburk

    Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5146-2035
  13. Michael J Krashes

    3Diabetes, Endocrinology, and Obesity Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0966-3401
  14. Matthew E Carter

    Department of Biology, Williams College, Williamstown, United States
    For correspondence
    mc10@williams.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1802-090X

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (R15 DK105510)

  • Matthew E Carter

National Science Foundation (IOS 1652060)

  • Matthew E Carter

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 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.

Copyright

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.

Metrics

  • 3,616
    views
  • 588
    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)

  1. Jessica H Kim
  2. Grace H Kromm
  3. Olivia K Barnhill
  4. Jacob Sperber
  5. Lauren B Heuer
  6. Sierra Loomis
  7. Matthew C Newman
  8. Kenneth Han
  9. Faris F Gulamali
  10. Theresa B Legan
  11. Katharine E Jensen
  12. Samuel C Funderburk
  13. Michael J Krashes
  14. Matthew E Carter
(2022)
A discrete parasubthalamic nucleus subpopulation plays a critical role in appetite suppression
eLife 11:e75470.
https://doi.org/10.7554/eLife.75470

Share this article

https://doi.org/10.7554/eLife.75470

Further reading

    1. Neuroscience
    Sven Ohl, Martin Rolfs
    Research Article

    Detecting causal relations structures our perception of events in the world. Here, we determined for visual interactions whether generalized (i.e. feature-invariant) or specialized (i.e. feature-selective) visual routines underlie the perception of causality. To this end, we applied a visual adaptation protocol to assess the adaptability of specific features in classical launching events of simple geometric shapes. We asked observers to report whether they observed a launch or a pass in ambiguous test events (i.e. the overlap between two discs varied from trial to trial). After prolonged exposure to causal launch events (the adaptor) defined by a particular set of features (i.e. a particular motion direction, motion speed, or feature conjunction), observers were less likely to see causal launches in subsequent ambiguous test events than before adaptation. Crucially, adaptation was contingent on the causal impression in launches as demonstrated by a lack of adaptation in non-causal control events. We assessed whether this negative aftereffect transfers to test events with a new set of feature values that were not presented during adaptation. Processing in specialized (as opposed to generalized) visual routines predicts that the transfer of visual adaptation depends on the feature similarity of the adaptor and the test event. We show that the negative aftereffects do not transfer to unadapted launch directions but do transfer to launch events of different speeds. Finally, we used colored discs to assign distinct feature-based identities to the launching and the launched stimulus. We found that the adaptation transferred across colors if the test event had the same motion direction as the adaptor. In summary, visual adaptation allowed us to carve out a visual feature space underlying the perception of causality and revealed specialized visual routines that are tuned to a launch’s motion direction.

    1. Neuroscience
    Gergely F Turi, Sasa Teng ... Yueqing Peng
    Research Article

    Synchronous neuronal activity is organized into neuronal oscillations with various frequency and time domains across different brain areas and brain states. For example, hippocampal theta, gamma, and sharp wave oscillations are critical for memory formation and communication between hippocampal subareas and the cortex. In this study, we investigated the neuronal activity of the dentate gyrus (DG) with optical imaging tools during sleep-wake cycles in mice. We found that the activity of major glutamatergic cell populations in the DG is organized into infraslow oscillations (0.01–0.03 Hz) during NREM sleep. Although the DG is considered a sparsely active network during wakefulness, we found that 50% of granule cells and about 25% of mossy cells exhibit increased activity during NREM sleep, compared to that during wakefulness. Further experiments revealed that the infraslow oscillation in the DG was correlated with rhythmic serotonin release during sleep, which oscillates at the same frequency but in an opposite phase. Genetic manipulation of 5-HT receptors revealed that this neuromodulatory regulation is mediated by Htr1a receptors and the knockdown of these receptors leads to memory impairment. Together, our results provide novel mechanistic insights into how the 5-HT system can influence hippocampal activity patterns during sleep.