Obesity causes selective and long-lasting desensitization of AgRP neurons to dietary fat

  1. Lisa R Beutler
  2. Timothy V Corpuz
  3. Jamie S Ahn
  4. Seher Kosar
  5. Weimin Song
  6. Yiming Chen
  7. Zachary A Knight  Is a corresponding author
  1. University of California, San Francisco, United States
  2. Northwestern University, United States

Abstract

Body weight is regulated by interoceptive neural circuits that track energy need, but how the activity of these circuits is altered in obesity remains poorly understood. Here we describe the in vivo dynamics of hunger-promoting AgRP neurons during the development of diet-induced obesity in mice. We show that high-fat diet attenuates the response of AgRP neurons to an array of nutritionally-relevant stimuli including food cues, intragastric nutrients, cholecystokinin and ghrelin. These alterations are are specific to dietary fat but not carbohydrate or protein. Subsequent weight loss restores the responsiveness of AgRP neurons to exterosensory cues but fails to rescue their sensitivity to gastrointestinal hormones or nutrients. These findings reveal that obesity triggers broad dysregulation of hypothalamic hunger neurons that is incompletely reversed by weight loss and may contribute to the difficulty of maintaining a reduced weight.

Data availability

All data generated or analysed during this study are included in the manuscript and supporting file

Article and author information

Author details

  1. Lisa R Beutler

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Timothy V Corpuz

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Jamie S Ahn

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Seher Kosar

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Weimin Song

    Feinberg School of Medicine, Northwestern University, Chicago, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Yiming Chen

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Zachary A Knight

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    For correspondence
    zachary.knight@ucsf.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7621-1478

Funding

National Institutes of Health (R01DK106399)

  • Zachary A Knight

National Institutes of Health (R01NS094781)

  • Zachary A Knight

National Institutes of Health (DP2DK021153)

  • Zachary A Knight

Howard Hughes Medical Institute (Investigator)

  • Zachary A Knight

American Diabetes Association (Pathway Award)

  • Zachary A Knight

New York Stem Cell Foundation (Robertson Investigator Award)

  • Zachary A Knight

Rita Allen Foundation (Scholar Award)

  • Zachary A Knight

National Institutes of Health (K08DK118188)

  • Lisa R Beutler

National Institutes of Health (P30 DK063720)

  • Lisa R Beutler

The funders played no role in the design or interpretation of the work.

Reviewing Editor

  1. Joel K Elmquist, University of Texas Southwestern Medical Center, United States

Ethics

Animal experimentation: Experimental protocols were approved by the University of California, San Francisco IACUC following the National Institutes of Health guidelines for the Care and Use of Laboratory Animals. (protocol# AN179674)

Version history

  1. Received: February 10, 2020
  2. Accepted: July 20, 2020
  3. Accepted Manuscript published: July 28, 2020 (version 1)
  4. Version of Record published: August 3, 2020 (version 2)
  5. Version of Record updated: September 10, 2020 (version 3)

Copyright

© 2020, Beutler 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

  • 7,304
    views
  • 981
    downloads
  • 75
    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. Lisa R Beutler
  2. Timothy V Corpuz
  3. Jamie S Ahn
  4. Seher Kosar
  5. Weimin Song
  6. Yiming Chen
  7. Zachary A Knight
(2020)
Obesity causes selective and long-lasting desensitization of AgRP neurons to dietary fat
eLife 9:e55909.
https://doi.org/10.7554/eLife.55909

Share this article

https://doi.org/10.7554/eLife.55909

Further reading

    1. Neuroscience
    Zilu Liang, Simeng Wu ... Chao Liu
    Research Article

    People form impressions about others during daily social encounters and infer personality traits from others' behaviors. Such trait inference is thought to rely on two universal dimensions: competence and warmth. These two dimensions can be used to construct a ‘social cognitive map’ organizing massive information obtained from social encounters efficiently. Originating from spatial cognition, the neural codes supporting the representation and navigation of spatial cognitive maps have been widely studied. Recent studies suggest similar neural mechanism subserves the map-like architecture in social cognition as well. Here we investigated how spatial codes operate beyond the physical environment and support the representation and navigation of social cognitive map. We designed a social value space defined by two dimensions of competence and warmth. Behaviorally, participants were able to navigate to a learned location from random starting locations in this abstract social space. At the neural level, we identified the representation of distance in the precuneus, fusiform gyrus, and middle occipital gyrus. We also found partial evidence of grid-like representation patterns in the medial prefrontal cortex and entorhinal cortex. Moreover, the intensity of grid-like response scaled with the performance of navigating in social space and social avoidance trait scores. Our findings suggest a neurocognitive mechanism by which social information can be organized into a structured representation, namely cognitive map and its relevance to social well-being.

    1. Neuroscience
    Alina Tetereva, Narun Pat
    Research Article

    One well-known biomarker candidate that supposedly helps capture fluid cognition is Brain Age, or a predicted value based on machine-learning models built to predict chronological age from brain MRI. To formally evaluate the utility of Brain Age for capturing fluid cognition, we built 26 age-prediction models for Brain Age based on different combinations of MRI modalities, using the Human Connectome Project in Aging (n=504, 36–100 years old). First, based on commonality analyses, we found a large overlap between Brain Age and chronological age: Brain Age could uniquely add only around 1.6% in explaining variation in fluid cognition over and above chronological age. Second, the age-prediction models that performed better at predicting chronological age did NOT necessarily create better Brain Age for capturing fluid cognition over and above chronological age. Instead, better-performing age-prediction models created Brain Age that overlapped larger with chronological age, up to around 29% out of 32%, in explaining fluid cognition. Third, Brain Age missed around 11% of the total variation in fluid cognition that could have been explained by the brain variation. That is, directly predicting fluid cognition from brain MRI data (instead of relying on Brain Age and chronological age) could lead to around a 1/3-time improvement of the total variation explained. Accordingly, we demonstrated the limited utility of Brain Age as a biomarker for fluid cognition and made some suggestions to ensure the utility of Brain Age in explaining fluid cognition and other phenotypes of interest.