APP modulates KCC2 expression and function in hippocampal GABAergic inhibition

  1. Ming Chen
  2. Jinzhao Wang
  3. Jinxiang Jiang
  4. Xingzhi Zheng
  5. Nicholas J Justice
  6. Kun Wang
  7. Yi Li
  8. Xiangqian Ran
  9. Qingwei Huo
  10. Jiajia Zhang
  11. Hongmei Li
  12. Nannan Lu
  13. Ying Wang
  14. Hui Zheng
  15. Cheng Long
  16. Li Yang  Is a corresponding author
  1. South China Normal University, China
  2. University of Texas Health Sciences Center, United States
  3. South China Normal University, United States
  4. Baylor College of Medicine, United States

Abstract

Amyloid precursor protein (APP) is enriched at the synapse, but its synaptic function is still poorly understood. We previously showed that GABAergic short-term plasticity is impaired in App knock-out (App-/-) animals, but the precise mechanism by which APP regulates GABAergic synaptic transmission has remained elusive. Using electrophysiological, biochemical, moleculobiological, and pharmacological analysis, here we show that APP can physically interact with KCC2, a neuron-specific K+-Cl- cotransporter that is essential for Cl- homeostasis and fast GABAergic inhibition. APP deficiency results in significant reductions in both total and membrane KCC2 levels, leading to a depolarizing shift in the GABA reversal potential (EGABA). Simultaneous measurement of presynaptic action potentials and inhibitory postsynaptic currents (IPSCs) in hippocampal neurons reveals impaired unitary IPSC amplitudes attributable to a reduction in α1 subunit levels of GABAAR. Importantly, restoration of normal KCC2 expression and function in App-/- mice rescues EGABA, GABAAR α1 levels and GABAAR mediated phasic inhibition. We show that APP functions to limit tyrosine-phosphorylation and ubiquitination and thus subsequent degradation of KCC2, providing a mechanism by which APP influences KCC2 abundance. Together, these experiments elucidate a novel molecular pathway in which APP regulates, via protein-protein interaction with KCC2, GABAAR mediated inhibition in the hippocampus.

Article and author information

Author details

  1. Ming Chen

    School of Life Sciences, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  2. Jinzhao Wang

    School of Life Sciences, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  3. Jinxiang Jiang

    School of Psychology, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  4. Xingzhi Zheng

    School of Life Sciences, South China Normal University, Guang Zhou, China
    Competing interests
    The authors declare that no competing interests exist.
  5. Nicholas J Justice

    Institute of Molecular Medicine, University of Texas Health Sciences Center, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. Kun Wang

    School of Life Sciences, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  7. Yi Li

    School of Life Sciences, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  8. Xiangqian Ran

    School of Life Sciences, South China Normal University, Guangzhou, United States
    Competing interests
    The authors declare that no competing interests exist.
  9. Qingwei Huo

    School of Life Sciences, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  10. Jiajia Zhang

    School of Life Sciences, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  11. Hongmei Li

    Huffington Center on Aging, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  12. Nannan Lu

    School of Life Sciences, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  13. Ying Wang

    School of Life Sciences, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  14. Hui Zheng

    Huffington Center on Aging, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  15. Cheng Long

    School of Life Sciences, South China Normal University, Guangzhou, China
    Competing interests
    The authors declare that no competing interests exist.
  16. Li Yang

    School of Psychology, South China Normal University, Guangzhou, China
    For correspondence
    yang_li@m.scnu.edu.cn
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7448-8588

Funding

National Natural Science Foundation of China (31171018,31171355)

  • Cheng Long
  • Li Yang

Natural Science Foundation of Guangdong Province (2014A030313418,2014A030313440)

  • Cheng Long
  • Li Yang

The Science and Technology Division of Guangdong Province (2013KJCX0054,201607010320)

  • Li Yang

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 conducted along the guidelines described in "The Guide for the Care and Use of Laboratory Animals" (Eighth Edition). All of the animals were handled according to approved institutional animal care and use committee (IACUC) protocols (#0312-11) of the South China Normal University. Every effort was made to minimize suffering.

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 3,770
    views
  • 796
    downloads
  • 80
    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. Ming Chen
  2. Jinzhao Wang
  3. Jinxiang Jiang
  4. Xingzhi Zheng
  5. Nicholas J Justice
  6. Kun Wang
  7. Yi Li
  8. Xiangqian Ran
  9. Qingwei Huo
  10. Jiajia Zhang
  11. Hongmei Li
  12. Nannan Lu
  13. Ying Wang
  14. Hui Zheng
  15. Cheng Long
  16. Li Yang
(2017)
APP modulates KCC2 expression and function in hippocampal GABAergic inhibition
eLife 6:e20142.
https://doi.org/10.7554/eLife.20142

Share this article

https://doi.org/10.7554/eLife.20142

Further reading

    1. Neuroscience
    Qing Zhao, Yanjing Zhu ... Ning Xie
    Research Article

    Astrocytes derive from different lineages and play a critical role in neuropathic pain after spinal cord injury (SCI). Whether selectively eliminating these main origins of astrocytes in lumbar enlargement could attenuate SCI-induced neuropathic pain remains unclear. Through transgenic mice injected with an adeno-associated virus vector and diphtheria toxin, astrocytes in lumbar enlargement were lineage traced, targeted, and selectively eliminated. Pain-related behaviors were measured with an electronic von Frey apparatus and a cold/hot plate after SCI. RNA sequencing, bioinformatics analysis, molecular experiment, and immunohistochemistry were used to explore the potential mechanisms after astrocyte elimination. Lineage tracing revealed that the resident astrocytes but not ependymal cells were the main origins of astrocytes-induced neuropathic pain. SCI-induced mice to obtain significant pain symptoms and astrocyte activation in lumbar enlargement. Selective resident astrocyte elimination in lumbar enlargement could attenuate neuropathic pain and activate microglia. Interestingly, the type I interferons (IFNs) signal was significantly activated after astrocytes elimination, and the most activated Gene Ontology terms and pathways were associated with the type I IFNs signal which was mainly activated in microglia and further verified in vitro and in vivo. Furthermore, different concentrations of interferon and Stimulator of interferon genes (STING) agonist could activate the type I IFNs signal in microglia. These results elucidate that selectively eliminating resident astrocytes attenuated neuropathic pain associated with type I IFNs signal activation in microglia. Targeting type I IFNs signals is proven to be an effective strategy for neuropathic pain treatment after SCI.

    1. Neuroscience
    Jinxin Liu, Haoyue He ... Yongbing Deng
    Research Article

    Background:

    Post-stroke epilepsy (PSE) is a critical complication that worsens both prognosis and quality of life in patients with ischemic stroke. An interpretable machine learning model was developed to predict PSE using medical records from four hospitals in Chongqing.

    Methods:

    Medical records, imaging reports, and laboratory test results from 21,459 ischemic stroke patients were collected and analyzed. Univariable and multivariable statistical analyses identified key predictive factors. The dataset was split into a 70% training set and a 30% testing set. To address the class imbalance, the Synthetic Minority Oversampling Technique combined with Edited Nearest Neighbors was employed. Nine widely used machine learning algorithms were evaluated using relevant prediction metrics, with SHAP (SHapley Additive exPlanations) used to interpret the model and assess the contributions of different features.

    Results:

    Regression analyses revealed that complications such as hydrocephalus, cerebral hernia, and deep vein thrombosis, as well as specific brain regions (frontal, parietal, and temporal lobes), significantly contributed to PSE. Factors such as age, gender, NIH Stroke Scale (NIHSS) scores, and laboratory results like WBC count and D-dimer levels were associated with increased PSE risk. Tree-based methods like Random Forest, XGBoost, and LightGBM showed strong predictive performance, achieving an AUC of 0.99.

    Conclusions:

    The model accurately predicts PSE risk, with tree-based models demonstrating superior performance. NIHSS score, WBC count, and D-dimer were identified as the most crucial predictors.

    Funding:

    The research is funded by Central University basic research young teachers and students research ability promotion sub-projec t(2023CDJYGRH-ZD06), and by Emergency Medicine Chongqing Key Laboratory Talent Innovation and development joint fund project (2024RCCX10).