Alzheimer's disease risk gene BIN1 induces Tau-dependent network hyperexcitability
- University of Alabama at Birmingham, United States
Abstract
Genome-wide association studies identified the BIN1 locus as a leading modulator of genetic risk in Alzheimer's disease (AD). One limitation in understanding BIN1's contribution to AD is its unknown function in the brain. AD-associated BIN1 variants are generally noncoding and likely change expression. Here, we determined the effects of increasing expression of the major neuronal isoform of human BIN1 in cultured rat hippocampal neurons. Higher BIN1 induced network hyperexcitability on multielectrode arrays, increased frequency of synaptic transmission, and elevated calcium transients, indicating that increasing BIN1 drives greater neuronal activity. In exploring the mechanism of these effects on neuronal physiology, we found that BIN1 interacted with L-type voltage-gated calcium channels (LVGCCs) and that BIN1–LVGCC interactions were modulated by Tau in rat hippocampal neurons and mouse brain. Finally, Tau reduction prevented BIN1-induced network hyperexcitability. These data shed light on BIN1's neuronal function and suggest that it may contribute to Tau-dependent hyperexcitability in AD.
Data availability
All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 6: high throughput raw electrophysiologic recordings of neuronal activity using Axion Biosciences Maesto are deposited at: https://uab.box.com/s/rdjp74ba7stgb2dfrxgbyj507b94tjhn.Brief Analysis used is described in the methods section, in-depth analysis description is publicly available at: https://www.axionbiosystems.com/products/axis-software.
Article and author information
Author details
Funding
National Institutes of Health (RF1AG059405)
- Erik D Roberson
National Institutes of Health (R01NS075487)
- Erik D Roberson
National Institutes of Health (R01MH114990)
- Jeremy J Day
National Institutes of Health (T32NS095775)
- Yuliya Voskobiynyk
National Institutes of Health (T32NS061788)
- Jonathan R Roth
Alzheimer's Association
- Erik D Roberson
Weston Brain Institute
- Jonathan R Roth
- Travis Rush
- Erik D Roberson
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- John D. Fryer, Mayo Clinic, United States
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 (#20450) of the University of Alabama at Birmingham. The protocol was approved by the Committee on the Ethics of Animal Experiments of the University of Alabama at Birmingham.
Version history
- Received: March 29, 2020
- Accepted: July 12, 2020
- Accepted Manuscript published: July 13, 2020 (version 1)
- Version of Record published: July 30, 2020 (version 2)
Copyright
© 2020, Voskobiynyk 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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Alzheimer's disease risk gene BIN1 induces Tau-dependent network hyperexcitability
eLife 9:e57354.
https://doi.org/10.7554/eLife.57354
Further reading
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Probing memory of a complex visual image within a few hundred milliseconds after its disappearance reveals significantly greater fidelity of recall than if the probe is delayed by as little as a second. Classically interpreted, the former taps into a detailed but rapidly decaying visual sensory or ‘iconic’ memory (IM), while the latter relies on capacity-limited but comparatively stable visual working memory (VWM). While iconic decay and VWM capacity have been extensively studied independently, currently no single framework quantitatively accounts for the dynamics of memory fidelity over these time scales. Here, we extend a stationary neural population model of VWM with a temporal dimension, incorporating rapid sensory-driven accumulation of activity encoding each visual feature in memory, and a slower accumulation of internal error that causes memorized features to randomly drift over time. Instead of facilitating read-out from an independent sensory store, an early cue benefits recall by lifting the effective limit on VWM signal strength imposed when multiple items compete for representation, allowing memory for the cued item to be supplemented with information from the decaying sensory trace. Empirical measurements of human recall dynamics validate these predictions while excluding alternative model architectures. A key conclusion is that differences in capacity classically thought to distinguish IM and VWM are in fact contingent upon a single resource-limited WM store.
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Our ability to recall details from a remembered image depends on a single mechanism that is engaged from the very moment the image disappears from view.
