Somatostatin-expressing parafacial neurons are CO2/H+ sensitive and regulate baseline breathing

  1. Colin M Cleary
  2. Brenda M Milla
  3. Fu-Shan Kuo
  4. Shaun James
  5. William F Flynn
  6. Paul Robson
  7. Daniel K Mulkey  Is a corresponding author
  1. University of Connecticut, United States
  2. The Jackson Laboratory, United States

Abstract

Glutamatergic neurons in the retrotrapezoid nucleus (RTN) function as respiratory chemoreceptors by regulating breathing in response to tissue CO2/H+. The RTN and greater parafacial region may also function as a chemosensing network composed of CO2/H+-sensitive excitatory and inhibitory synaptic interactions. In the context of disease, we showed that loss of inhibitory neural activity in a mouse model of Dravet syndrome disinhibited RTN chemoreceptors and destabilized breathing (Kuo et. al., 2019; 25). Despite this, contributions of parafacial inhibitory neurons to control of breathing are unknown, and synaptic properties of RTN neurons have not been characterized. Here, we show the parafacial region contains a limited diversity of inhibitory neurons including somatostatin (Sst)-, parvalbumin (Pvalb)- and cholecystokinin (Cck)-expressing neurons. Of these, Sst-expressing interneurons appear uniquely inhibited by CO2/H+. We also show RTN chemoreceptors receive inhibitory input that is withdrawn in a CO2/H+-dependent manner, and chemogenetic suppression of Sst+ parafacial neurons, but not Pvalb+ or Cck+ neurons, increases baseline breathing. These results suggest Sst-expressing parafacial neurons contribute to RTN chemoreception and respiratory activity.

Data availability

Raw and processed scRNA-seq data are available through the Gene Expression Omnibus (accession GSE153172) and analysis code is available on GitHub. Analysis of FISH, electrophysiology, and respiratory activity data was done using standard software and no custom code was written.

The following data sets were generated

Article and author information

Author details

  1. Colin M Cleary

    Department of Physiology and Neurobiology, University of Connecticut, Storrs, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0305-1324
  2. Brenda M Milla

    Department of Physiology and Neurobiology, University of Connecticut, Storrs, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Fu-Shan Kuo

    Department of Physiology and Neurobiology, University of Connecticut, Storrs, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Shaun James

    Department of Physiology and Neurobiology, University of Connecticut, Storrs, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. William F Flynn

    Computational Sciences, The Jackson Laboratory, Farmington, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6533-0340
  6. Paul Robson

    Computational Sciences, The Jackson Laboratory, Farmington, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0191-3958
  7. Daniel K Mulkey

    Department of Physiology and Neurobiology, University of Connecticut, Storrs, United States
    For correspondence
    daniel.mulkey@uconn.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7040-3927

Funding

National Institutes of Health (HL104101)

  • Daniel K Mulkey

National Institutes of Health (HL137094)

  • Daniel K Mulkey

National Institutes of Health (NS099887)

  • Daniel K Mulkey

National Institutes of Health (HL142227)

  • Colin M Cleary

National Institutes of Health (F31NS120467)

  • Brenda M Milla

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 procedures were performed in accordance with National Institutes of Health and University of Connecticut Animal Care and Use Guidelines (protocols A19-048 and A20-016).

Copyright

© 2021, Cleary 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

  • 1,503
    views
  • 191
    downloads
  • 12
    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. Colin M Cleary
  2. Brenda M Milla
  3. Fu-Shan Kuo
  4. Shaun James
  5. William F Flynn
  6. Paul Robson
  7. Daniel K Mulkey
(2021)
Somatostatin-expressing parafacial neurons are CO2/H+ sensitive and regulate baseline breathing
eLife 10:e60317.
https://doi.org/10.7554/eLife.60317

Share this article

https://doi.org/10.7554/eLife.60317

Further reading

    1. Medicine
    2. Neuroscience
    Sophie Leclercq, Hany Ahmed ... Nathalie Delzenne
    Research Article

    Background:

    Alcohol use disorder (AUD) is a global health problem with limited therapeutic options. The biochemical mechanisms that lead to this disorder are not yet fully understood, and in this respect, metabolomics represents a promising approach to decipher metabolic events related to AUD. The plasma metabolome contains a plethora of bioactive molecules that reflects the functional changes in host metabolism but also the impact of the gut microbiome and nutritional habits.

    Methods:

    In this study, we investigated the impact of severe AUD (sAUD), and of a 3-week period of alcohol abstinence, on the blood metabolome (non-targeted LC-MS metabolomics analysis) in 96 sAUD patients hospitalized for alcohol withdrawal.

    Results:

    We found that the plasma levels of different lipids ((lyso)phosphatidylcholines, long-chain fatty acids), short-chain fatty acids (i.e. 3-hydroxyvaleric acid) and bile acids were altered in sAUD patients. In addition, several microbial metabolites, including indole-3-propionic acid, p-cresol sulfate, hippuric acid, pyrocatechol sulfate, and metabolites belonging to xanthine class (paraxanthine, theobromine and theophylline) were sensitive to alcohol exposure and alcohol withdrawal. 3-Hydroxyvaleric acid, caffeine metabolites (theobromine, paraxanthine, and theophylline) and microbial metabolites (hippuric acid and pyrocatechol sulfate) were correlated with anxiety, depression and alcohol craving. Metabolomics analysis in postmortem samples of frontal cortex and cerebrospinal fluid of those consuming a high level of alcohol revealed that those metabolites can be found also in brain tissue.

    Conclusions:

    Our data allow the identification of neuroactive metabolites, from interactions between food components and microbiota, which may represent new targets arising in the management of neuropsychiatric diseases such as sAUD.

    Funding:

    Gut2Behave project was initiated from ERA-NET NEURON network (Joint Transnational Call 2019) and was financed by Academy of Finland, French National Research Agency (ANR-19-NEUR-0003-03) and the Fonds de la Recherche Scientifique (FRS-FNRS; PINT-MULTI R.8013.19, Belgium). Metabolomics analysis of the TSDS samples was supported by grant from the Finnish Foundation for Alcohol Studies.

    1. Neuroscience
    Masahiro Takigawa, Marta Huelin Gorriz ... Daniel Bendor
    Research Article

    During rest and sleep, memory traces replay in the brain. The dialogue between brain regions during replay is thought to stabilize labile memory traces for long-term storage. However, because replay is an internally-driven, spontaneous phenomenon, it does not have a ground truth - an external reference that can validate whether a memory has truly been replayed. Instead, replay detection is based on the similarity between the sequential neural activity comprising the replay event and the corresponding template of neural activity generated during active locomotion. If the statistical likelihood of observing such a match by chance is sufficiently low, the candidate replay event is inferred to be replaying that specific memory. However, without the ability to evaluate whether replay detection methods are successfully detecting true events and correctly rejecting non-events, the evaluation and comparison of different replay methods is challenging. To circumvent this problem, we present a new framework for evaluating replay, tested using hippocampal neural recordings from rats exploring two novel linear tracks. Using this two-track paradigm, our framework selects replay events based on their temporal fidelity (sequence-based detection), and evaluates the detection performance using each event's track discriminability, where sequenceless decoding across both tracks is used to quantify whether the track replaying is also the most likely track being reactivated.