Kv1.1 channels regulate early postnatal neurogenesis in mouse hippocampus via the TrkB signaling pathway

  1. Shu-Min Chou
  2. Ke-Xin Li
  3. Ming-Yueh Huang
  4. Chao Chen
  5. Yuan-Hung Lin King
  6. Grant Guangnan Li
  7. Wei Zhou
  8. Chin Fen Teo
  9. Yuh Nung Jan
  10. Lily Yeh Jan  Is a corresponding author
  11. Shi-Bing Yang  Is a corresponding author
  1. Academia Sinica, Taiwan
  2. University of California, San Francisco, United States
  3. Nkarta Therapeutics Inc, United States

Abstract

In the postnatal brain, neurogenesis occurs only within a few regions, such as the hippocampal sub-granular zone (SGZ). Postnatal neurogenesis is tightly regulated by factors that balance stem cell renewal with differentiation, and it gives rise to neurons that participate in learning and memory formation (Anacker and Hen, 2017; Bond et al., 2015; Toda et al., 2019). The Kv1.1 channel, a voltage-gated potassium channel, was previously shown to suppress postnatal neurogenesis in the SGZ in a cell-autonomous manner. In this study, we clarified the physiological and molecular mechanisms underlying Kv1.1-dependent postnatal neurogenesis. First, we discovered that the membrane potential of neural progenitor cells is highly dynamic during development. We further established a multinomial logistic regression model for cell type classification based on the biophysical characteristics and corresponding cell markers. We found that loss of Kv1.1 channel activity causes significant depolarization of type 2b neural progenitor cells. This depolarization is associated with increased tropomyosin receptor kinase B (TrkB) signaling and proliferation of neural progenitor cells; suppressing TrkB signaling reduces the extent of postnatal neurogenesis. Thus, our study defines the role of the Kv1.1 potassium channel in regulating the proliferation of postnatal neural progenitor cells in the mouse hippocampus.

Data availability

Most of our results are presented as scatterplots with the intention to show the distribution of our raw data. The variables for the multinomial logistic regression model (fig 5) can be found in the methods section.

Article and author information

Author details

  1. Shu-Min Chou

    Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
    Competing interests
    No competing interests declared.
  2. Ke-Xin Li

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3879-294X
  3. Ming-Yueh Huang

    Institute of Statistics, Academia Sinica, Taipei, Taiwan
    Competing interests
    No competing interests declared.
  4. Chao Chen

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    No competing interests declared.
  5. Yuan-Hung Lin King

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    No competing interests declared.
  6. Grant Guangnan Li

    Nkarta Therapeutics Inc, Nkarta Therapeutics Inc, South San Francisco, United States
    Competing interests
    Grant Guangnan Li, Grant Guangnan Li is affiliated with Nkarta Therapeutics Inc. The author has no financial interests to declare..
  7. Wei Zhou

    Department of Anesthesiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    No competing interests declared.
  8. Chin Fen Teo

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    No competing interests declared.
  9. Yuh Nung Jan

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1367-6299
  10. Lily Yeh Jan

    Department of Physiology, University of California, San Francisco, San Francisco, United States
    For correspondence
    Lily.Jan@ucsf.edu
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3938-8498
  11. Shi-Bing Yang

    Institute of Biomedical Sciences, Academia Sinica, Taipei, Taiwan
    For correspondence
    sbyang@ibms.sinica.edu.tw
    Competing interests
    No competing interests declared.

Funding

Ministry of Science and Technology, Taiwan (106-2320-B-001-013)

  • Shi-Bing Yang

Ministry of Science and Technology, Taiwan (107-2320-B-001-026-MY3)

  • Shi-Bing Yang

NIH Blueprint for Neuroscience Research (R01MH065334)

  • Lily Yeh Jan

Howard Hughes Medical Institute (no number)

  • Yuh Nung Jan

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 carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health, and used protocol approved by the Institutional Animal Care and Use Committee of Academia Sinica (protocol#:15-01-813) and the University of California, San Francisco. Mice (3-5 per cage) housed in the animal facility were fed with regular chow diet and subjected to a standard 12-h light/12-h dark cycle. At least 3 animals were used for every single experiment. Mice were first anesthetized with isoflurane followed by decapitation for electrophysiological recordings and immunostaining.

Copyright

© 2021, Chou 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.

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https://doi.org/10.7554/eLife.58779

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