1. Neuroscience
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Gap junction networks in mushroom bodies participate in visual learning and memory in Drosophila

  1. Qingqing Liu
  2. Xing Yang
  3. Jingsong Tian
  4. Zhongbao Gao
  5. Meng Wang
  6. Yan Li
  7. Aike Guo  Is a corresponding author
  1. Chinese Academy of Sciences, China
  2. Shanghai Institutes for Biological Sciences, China
Research Article
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Cite this article as: eLife 2016;5:e13238 doi: 10.7554/eLife.13238

Abstract

Gap junctions are widely distributed in the brains across species and play essential roles in neural information processing. However, the role of gap junctions in insect cognition remains poorly understood. Using a flight simulator paradigm and genetic tools, we found that gap junctions are present in Drosophila Kenyon cells (KCs), the major neurons of the mushroom bodies (MBs), and showed that they play an important role in visual learning and memory. Using a dye coupling approach, we determined the distribution of gap junctions in KCs. Furthermore, we identified a single pair of MB output neurons (MBONs) that possess a gap junction connection to KCs, and provide strong evidence that this connection is also required for visual learning and memory. Together, our results reveal gap junction networks in KCs and the KC-MBON circuit, and bring new insight into the synaptic network underlying fly's visual learning and memory.

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Author details

  1. Qingqing Liu

    State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  2. Xing Yang

    Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Shanghai, China
    Competing interests
    The authors declare that no competing interests exist.

  3. Jingsong Tian

    State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  4. Zhongbao Gao

    State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  5. Meng Wang

    State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  6. Yan Li

    State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    Competing interests
    The authors declare that no competing interests exist.

  7. Aike Guo

    State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
    For correspondence
    akguo@ion.ac.cn
    Competing interests
    The authors declare that no competing interests exist.

Reviewing Editor

  1. K VijayRaghavan, Tata Institute of Fundamental Research, India

Publication history

  1. Received: November 20, 2015
  2. Accepted: May 20, 2016
  3. Accepted Manuscript published: May 24, 2016 (version 1)
  4. Version of Record published: June 15, 2016 (version 2)
  5. Version of Record updated: August 16, 2017 (version 3)

Copyright

© 2016, Liu 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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