Gain of channel function and modified gating properties in TRPM3 mutants causing intellectual disability and epilepsy

Abstract

Developmental and epileptic encephalopathies (DEE) are a heterogeneous group of disorders characterized by epilepsy with comorbid intellectual disability. Recently, two de novo heterozygous mutations in the gene encoding TRPM3, a calcium permeable ion channel, were identified as the cause of DEE in eight probands, but the functional consequences of the mutations remained elusive. Here we demonstrate that both mutations (V990M and P1090Q) have distinct effects on TRPM3 gating, including increased basal activity, higher sensitivity to stimulation by the endogenous neurosteroid pregnenolone sulphate (PS) and heat, and altered response to ligand modulation. Most strikingly, the V990M mutation affected the gating of the non-canonical pore of TRPM3, resulting in large inward cation currents via the voltage sensor domain in response to PS stimulation. Taken together, these data indicate that the two DEE mutations in TRPM3 result in a profound gain of channel function, which may lie at the basis of epileptic activity and neurodevelopmental symptoms in the patients.

Data availability

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

Article and author information

Author details

  1. Evelien Van Hoeymissen

    Department of Development and Regeneration, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3897-8998
  2. Katharina Held

    Department of Development and Regeneration, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1727-9517
  3. Ana Cristina Nogueira Freitas

    VIB-KU Leuven Center for Brain and Disease Research, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  4. Annelies Janssens

    VIB-KU Leuven Center for Brain and Disease Research, KU Leuven, Leuven, Belgium
    Competing interests
    The authors declare that no competing interests exist.
  5. Thomas Voets

    Laboratory of Ion Channel Research (LICR), VIB-KU Leuven Centre for Brain & Disease Research and Department of Cellular and Molecular Medicine, KU Leuven, KU Leuven, Leuven, Belgium
    For correspondence
    thomas.voets@kuleuven.vib.be
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5526-5821
  6. Joris Vriens

    Department of Development and Regeneration, KU Leuven, Leuven, Belgium
    For correspondence
    Joris.Vriens@kuleuven.be
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2502-0409

Funding

Fonds Wetenschappelijk Onderzoek (G.084515N)

  • Joris Vriens

Fonds Wetenschappelijk Onderzoek (G.0B1819N)

  • Joris Vriens

Fonds Wetenschappelijk Onderzoek (G.0565.07)

  • Thomas Voets
  • Joris Vriens

Fonds Wetenschappelijk Onderzoek (G.0825.11)

  • Thomas Voets
  • Joris Vriens

KU Leuven (C1-TRPLe)

  • Thomas Voets

Fonds Wetenschappelijk Onderzoek (POST DOC)

  • Katharina Held

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

Copyright

© 2020, Van Hoeymissen 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

  • 2,065
    views
  • 320
    downloads
  • 40
    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. Evelien Van Hoeymissen
  2. Katharina Held
  3. Ana Cristina Nogueira Freitas
  4. Annelies Janssens
  5. Thomas Voets
  6. Joris Vriens
(2020)
Gain of channel function and modified gating properties in TRPM3 mutants causing intellectual disability and epilepsy
eLife 9:e57190.
https://doi.org/10.7554/eLife.57190

Share this article

https://doi.org/10.7554/eLife.57190

Further reading

    1. Computational and Systems Biology
    2. Neuroscience
    Anna Cattani, Don B Arnold ... Nancy Kopell
    Research Article

    The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta (~3–6 Hz), high theta (~6–12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.

    1. Neuroscience
    Maëliss Jallais, Marco Palombo
    Research Article

    This work proposes µGUIDE: a general Bayesian framework to estimate posterior distributions of tissue microstructure parameters from any given biophysical model or signal representation, with exemplar demonstration in diffusion-weighted magnetic resonance imaging. Harnessing a new deep learning architecture for automatic signal feature selection combined with simulation-based inference and efficient sampling of the posterior distributions, µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches and does not rely on acquisition constraints to define model-specific summary statistics. The obtained posterior distributions allow to highlight degeneracies present in the model definition and quantify the uncertainty and ambiguity of the estimated parameters.