Cell type-specific transcriptomics of hypothalamic energy-sensing neuron responses to weight-loss
Abstract
Molecular and cellular processes in neurons are critical for sensing and responding to energy deficit states, such as during weight-loss. AGRP neurons are a key hypothalamic population that is activated during energy deficit and increases appetite and weight-gain. Cell type-specific transcriptomics can be used to identify pathways that counteract weight-loss, and here we report high-quality gene expression profiles of AGRP neurons from well-fed and food-deprived young adult mice. For comparison, we also analyzed POMC neurons, an intermingled population that suppresses appetite and body weight. We find that AGRP neurons are considerably more sensitive to energy deficit than POMC neurons. Furthermore, we identify cell type-specific pathways involving endoplasmic reticulum-stress, circadian signaling, ion channels, neuropeptides, and receptors. Combined with methods to validate and manipulate these pathways, this resource greatly expands molecular insight into neuronal regulation of body weight, and may be useful for devising therapeutic strategies for obesity and eating disorders.
Article and author information
Author details
Reviewing Editor
- Joel K Elmquist, University of Texas Southwestern Medical Center, United States
Ethics
Animal experimentation: All experimental protocols were conducted according to U.S. National Institutes of Health guidelines for animal research and approved by the Institutional Animal Care and Use Committee at Janelia Research Campus under protocol number 13-92. Experiments conducted in the UK were licensed (PPL 70/7652) under the UK Animals (Scientific Procedures) Act of 1986 following local ethical approval. All surgery was performed under isoflurance anesthesia to minimize suffering.
Version history
- Received: July 1, 2015
- Accepted: September 2, 2015
- Accepted Manuscript published: September 2, 2015 (version 1)
- Version of Record published: October 8, 2015 (version 2)
Copyright
© 2015, Henry 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
-
- 9,381
- views
-
- 2,549
- downloads
-
- 187
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
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)
Further reading
-
- Computational and Systems Biology
- Neuroscience
Normal aging leads to myelin alterations in the rhesus monkey dorsolateral prefrontal cortex (dlPFC), which are positively correlated with degree of cognitive impairment. It is hypothesized that remyelination with shorter and thinner myelin sheaths partially compensates for myelin degradation, but computational modeling has not yet explored these two phenomena together systematically. Here, we used a two-pronged modeling approach to determine how age-related myelin changes affect a core cognitive function: spatial working memory. First, we built a multicompartment pyramidal neuron model fit to monkey dlPFC empirical data, with an axon including myelinated segments having paranodes, juxtaparanodes, internodes, and tight junctions. This model was used to quantify conduction velocity (CV) changes and action potential (AP) failures after demyelination and subsequent remyelination. Next, we incorporated the single neuron results into a spiking neural network model of working memory. While complete remyelination nearly recovered axonal transmission and network function to unperturbed levels, our models predict that biologically plausible levels of myelin dystrophy, if uncompensated by other factors, can account for substantial working memory impairment with aging. The present computational study unites empirical data from ultrastructure up to behavior during normal aging, and has broader implications for many demyelinating conditions, such as multiple sclerosis or schizophrenia.
-
- Neuroscience
How is new information organized in memory? According to latent state theories, this is determined by the level of surprise, or prediction error, generated by the new information: a small prediction error leads to the updating of existing memory, large prediction error leads to encoding of a new memory. We tested this idea using a protocol in which rats were first conditioned to fear a stimulus paired with shock. The stimulus was then gradually extinguished by progressively reducing the shock intensity until the stimulus was presented alone. Consistent with latent state theories, this gradual extinction protocol (small prediction errors) was better than standard extinction (large prediction errors) in producing long-term suppression of fear responses, and the benefit of gradual extinction was due to updating of the conditioning memory with information about extinction. Thus, prediction error determines how new information is organized in memory, and latent state theories adequately describe the ways in which this occurs.