Corticohippocampal circuit dysfunction in a mouse model of Dravet syndrome

  1. Joanna H Mattis
  2. Ala Somarowthu
  3. Kevin M Goff
  4. Evan Jiang
  5. Jina Yom
  6. Nathaniel P Sotuyo
  7. Laura M Mcgarry
  8. Huijie Feng
  9. Keisuke Kaneko
  10. Ethan Michael Goldberg  Is a corresponding author
  1. The University of Pennsylvania School of Medicine, United States
  2. The Children's Hospital of Philadelphia, United States
  3. Children's Hospital of Philadelphia, United States
  4. The University of Pennsylvania, United States

Abstract

Dravet syndrome (DS) is a neurodevelopmental disorder due to pathogenic variants in SCN1A encoding the Nav1.1 sodium channel subunit, characterized by treatment-resistant epilepsy, temperature-sensitive seizures, developmental delay/intellectual disability with features of autism spectrum disorder, and increased risk of sudden death. Convergent data suggest hippocampal dentate gyrus (DG) pathology in DS (Scn1a+/-) mice. We performed two-photon calcium imaging in brain slice to uncover a profound dysfunction of filtering of perforant path input by DG in young adult Scn1a+/- mice. This was not due to dysfunction of DG parvalbumin inhibitory interneurons (PV-INs), which were only mildly impaired at this timepoint; however, we identified enhanced excitatory input to granule cells, suggesting that circuit dysfunction is due to excessive excitation rather than impaired inhibition. We confirmed that both optogenetic stimulation of entorhinal cortex and selective chemogenetic inhibition of DG PV-INs lowered seizure threshold in vivo in young adult Scn1a+/- mice. Optogenetic activation of PV-INs, on the other hand, normalized evoked responses in granule cells in vitro. These results establish the corticohippocampal circuit as a key locus of pathology in Scn1a+/- mice and suggest that PV-INs retain powerful inhibitory function and may be harnessed as a potential therapeutic approach towards seizure modulation.

Data availability

All data generated or analyzed during this study are included in the manuscript and supporting files. Source data files for all Figures (1-8) and Tables (1-2) have been included and are also available via G-Node at:https://gin.g-node.org/GoldbergNeuroLab/Mattis-et-al-2022.Source code has been made available here:https://github.com/GoldbergNeuroLab/Mattis-et-al.-2022

The following data sets were generated

Article and author information

Author details

  1. Joanna H Mattis

    Department of Neurology, The University of Pennsylvania School of Medicine, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0341-1270
  2. Ala Somarowthu

    Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Kevin M Goff

    Neuroscience Graduate Group, The University of Pennsylvania School of Medicine, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5862-0219
  4. Evan Jiang

    Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Jina Yom

    College of Arts and Sciences, The University of Pennsylvania, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Nathaniel P Sotuyo

    Neuroscience Graduate Group, The University of Pennsylvania School of Medicine, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Laura M Mcgarry

    Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Huijie Feng

    Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Keisuke Kaneko

    Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5071-0057
  10. Ethan Michael Goldberg

    College of Arts and Sciences, The University of Pennsylvania, Philadelphia, United States
    For correspondence
    goldberge@chop.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7404-735X

Funding

National Institute of Neurological Disorders and Stroke (R25 NS065745)

  • Joanna H Mattis

National Institute of Neurological Disorders and Stroke (K08 NS097633)

  • Ethan Michael Goldberg

National Institute of Neurological Disorders and Stroke (R01 NS110869)

  • Ethan Michael Goldberg

Dana Foundation (David Mahoney Neuroimaging Grant)

  • Ethan Michael Goldberg

Burroughs Wellcome Fund (Career Award for Medical Scientists)

  • Ethan Michael Goldberg

National Institute of Neurological Disorders and Stroke (K08 NS121464)

  • Joanna H Mattis

Institute for Translational Medicine and Therapeutics (Translational Biomedical Imaging Center (TBIC))

  • Joanna H Mattis
  • Ethan Michael Goldberg

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocol 21-001152 of The Children's Hospital of Philadelphia. All surgery was performed under isoflurane anesthesia, and every effort was made to minimize suffering.

Copyright

© 2022, Mattis 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

  • 2,720
    views
  • 392
    downloads
  • 34
    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. Joanna H Mattis
  2. Ala Somarowthu
  3. Kevin M Goff
  4. Evan Jiang
  5. Jina Yom
  6. Nathaniel P Sotuyo
  7. Laura M Mcgarry
  8. Huijie Feng
  9. Keisuke Kaneko
  10. Ethan Michael Goldberg
(2022)
Corticohippocampal circuit dysfunction in a mouse model of Dravet syndrome
eLife 11:e69293.
https://doi.org/10.7554/eLife.69293

Share this article

https://doi.org/10.7554/eLife.69293

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics
    Eric V Strobl, Eric Gamazon
    Research Article

    Root causal gene expression levels – or root causal genes for short – correspond to the initial changes to gene expression that generate patient symptoms as a downstream effect. Identifying root causal genes is critical towards developing treatments that modify disease near its onset, but no existing algorithms attempt to identify root causal genes from data. RNA-sequencing (RNA-seq) data introduces challenges such as measurement error, high dimensionality and non-linearity that compromise accurate estimation of root causal effects even with state-of-the-art approaches. We therefore instead leverage Perturb-seq, or high-throughput perturbations with single-cell RNA-seq readout, to learn the causal order between the genes. We then transfer the causal order to bulk RNA-seq and identify root causal genes specific to a given patient for the first time using a novel statistic. Experiments demonstrate large improvements in performance. Applications to macular degeneration and multiple sclerosis also reveal root causal genes that lie on known pathogenic pathways, delineate patient subgroups and implicate a newly defined omnigenic root causal model.

    1. Chromosomes and Gene Expression
    2. Genetics and Genomics
    Steven Henikoff, David L Levens
    Insight

    A new method for mapping torsion provides insights into the ways that the genome responds to the torsion generated by RNA polymerase II.