APP modulates KCC2 expression and function in hippocampal GABAergic inhibition
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
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
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)
Further reading
-
- Neuroscience
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.
-
- Neuroscience
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).