YAP/TAZ initiate and maintain Schwann cell myelination

Abstract

Nuclear exclusion of the transcriptional regulators and potent oncoproteins, YAP/TAZ, is considered necessary for adult tissue homeostasis. Here we show that nuclear YAP/TAZ are essential regulators of peripheral nerve development and maintenance. To proliferate, developing Schwann cells (SCs) require YAP/TAZ to enter S-phase and, without them, fail to generate sufficient SCs for timely axon sorting. To differentiate, SCs require YAP/TAZ to upregulate Krox20 and, without them, completely fail to myelinate, resulting in severe peripheral neuropathy. Remarkably, in adulthood, nuclear YAP/TAZ are selectively expressed by myelinating SCs, and conditional ablation results in severe peripheral demyelination and mouse death. YAP/TAZ regulate both developmental and adult myelination by driving TEAD1 to activate Krox20. Therefore, YAP/TAZ are crucial for SCs to myelinate developing nerve and to maintain myelinated nerve in adulthood. Our study also provides a new insight into the role of nuclear YAP/TAZ in homeostatic maintenance of an adult tissue.

Article and author information

Author details

  1. Matthew Grove

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Hyukmin Kim

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Maryline Santerre

    FELS Institute for Cancer Research and Molecular Biology, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Alexander J Krupka

    Department of Bioengineering, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  5. Seung Baek Han

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Jinbin Zhai

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  7. Jennifer Y Cho

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  8. Raehee Park

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Michele Harris

    Department of Anatomy and Cell Biology, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  10. Seonhee Kim

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  11. Bassel E Sawaya

    FELS Institute for Cancer Research and Molecular Biology, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Shin H Kang

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  13. Mary F Barbe

    Department of Anatomy and Cell Biology, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  14. Seo-Hee Cho

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Michel A Lemay

    Department of Bioengineering, Temple University, Philadelphia, United States
    Competing interests
    The authors declare that no competing interests exist.
  16. Young-Jin Son

    Shriners Hospitals Pediatric Research Center, Lewis Katz School of Medicine, Temple University, Philadelphia, United States
    For correspondence
    yson@temple.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5725-9775

Funding

National Institute of Neurological Disorders and Stroke (NS079631)

  • Young-Jin Son

Shriners Hospitals for Children (research grant,86600)

  • Young-Jin Son

National Institute of Neurological Disorders and Stroke (NS076401)

  • Bassel E Sawaya

National Institute of Mental Health (MH093331)

  • Bassel E Sawaya

National Institute of Neurological Disorders and Stroke (NS095070)

  • Young-Jin Son

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) protocols (#4254, #4255) of the Temple University.

Reviewing Editor

  1. Klaus-Armin Nave, Max Planck Institute for Experimental Medicine,, Germany

Publication history

  1. Received: August 26, 2016
  2. Accepted: January 22, 2017
  3. Accepted Manuscript published: January 26, 2017 (version 1)
  4. Version of Record published: February 1, 2017 (version 2)

Copyright

© 2017, Grove 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

  • 3,334
    Page views
  • 735
    Downloads
  • 47
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, Scopus, PubMed Central.

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. Matthew Grove
  2. Hyukmin Kim
  3. Maryline Santerre
  4. Alexander J Krupka
  5. Seung Baek Han
  6. Jinbin Zhai
  7. Jennifer Y Cho
  8. Raehee Park
  9. Michele Harris
  10. Seonhee Kim
  11. Bassel E Sawaya
  12. Shin H Kang
  13. Mary F Barbe
  14. Seo-Hee Cho
  15. Michel A Lemay
  16. Young-Jin Son
(2017)
YAP/TAZ initiate and maintain Schwann cell myelination
eLife 6:e20982.
https://doi.org/10.7554/eLife.20982

Further reading

    1. Neuroscience
    Jan Boelts et al.
    Research Advance Updated

    Inferring parameters of computational models that capture experimental data are a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate the likelihood of the model—however, for many models of interest in cognitive neuroscience, the associated likelihoods cannot be computed efficiently. Simulation-based inference (SBI) offers a solution to this problem by only requiring access to simulations produced by the model. Previously, Fengler et al. introduced likelihood approximation networks (LANs, Fengler et al., 2021) which make it possible to apply SBI to models of decision-making, but require billions of simulations for training. Here, we provide a new SBI method that is substantially more simulation efficient. Our approach, mixed neural likelihood estimation (MNLE), trains neural density estimators on model simulations to emulate the simulator, and is designed to capture both the continuous (e.g., reaction times) and discrete (choices) data of decision-making models. The likelihoods of the emulator can then be used to perform Bayesian parameter inference on experimental data using standard approximate inference methods like Markov Chain Monte Carlo sampling. We demonstrate MNLE on two variants of the drift-diffusion model and show that it is substantially more efficient than LANs: MNLE achieves similar likelihood accuracy with six orders of magnitude fewer training simulations, and is significantly more accurate than LANs when both are trained with the same budget. Our approach enables researchers to perform SBI on custom-tailored models of decision-making, leading to fast iteration of model design for scientific discovery.

    1. Computational and Systems Biology
    2. Neuroscience
    Vasileios Dimakopoulos et al.
    Research Article

    The maintenance of items in working memory (WM) relies on a widespread network of cortical areas and hippocampus where synchronization between electrophysiological recordings reflects functional coupling. We investigated the direction of information flow between auditory cortex and hippocampus while participants heard and then mentally replayed strings of letters in WM by activating their phonological loop. We recorded local field potentials from the hippocampus, reconstructed beamforming sources of scalp EEG, and – additionally in four participants – recorded from subdural cortical electrodes. When analyzing Granger causality, the information flow was from auditory cortex to hippocampus with a peak in the [4 8] Hz range while participants heard the letters. This flow was subsequently reversed during maintenance while participants maintained the letters in memory. The functional interaction between hippocampus and the cortex and the reversal of information flow provide a physiological basis for the encoding of memory items and their active replay during maintenance.