1. Neuroscience
  2. Structural Biology and Molecular Biophysics
Download icon

Ca2+-dependent release of Synaptotagmin-1 from the SNARE complex on phosphatidylinositol 4,5-bisphosphate-containing membranes

  1. Rashmi Voleti
  2. Klaudia Jaczynska
  3. Josep Rizo  Is a corresponding author
  1. University of Texas Southwestern Medical Center, United States
Research Article
  • Cited 1
  • Views 1,287
  • Annotations
Cite this article as: eLife 2020;9:e57154 doi: 10.7554/eLife.57154
Voice your concerns about research culture and research communication: Have your say in our 7th annual survey.

Abstract

The Ca2+ sensor synaptotagmin-1 and the SNARE complex cooperate to trigger neurotransmitter release. Structural studies elucidated three distinct synaptotagmin-1-SNARE complex binding modes involving polybasic, primary and tripartite interfaces of synaptotagmin-1. We investigated these interactions using NMR and fluorescence spectroscopy. Synaptotagmin-1 binds to the SNARE complex through the polybasic and primary interfaces in solution. Ca2+-free synaptotagmin-1 binds to SNARE complexes anchored on PIP2-containing nanodiscs. R398Q/R399Q and E295A/Y338W mutations at the primary interface, which strongly impair neurotransmitter release, disrupt and enhance synaptotagmin-1-SNARE complex binding, respectively. Ca2+ induces tight binding of synaptotagmin-1 to PIP2-containing nanodiscs, disrupting synaptotagmin-1-SNARE interactions. Specific effects of mutations in the polybasic region on Ca2+-dependent synaptotagmin-1-PIP2-membrane interactions correlate with their effects on release. Our data suggest that synaptotagmin-1 binds to the SNARE complex through the primary interface and that Ca2+ releases this interaction, inducing PIP2/membrane binding and allowing cooperation between synaptotagmin-1 and the SNAREs in membrane fusion to trigger release.

Data availability

The NMR data corresponding to Figs. 1-4 and corresponding figure supplements are publicly available at https://doi.org/10.5061/dryad.0zpc866vt. Source data are provided for all the FRET experiments shown in Figs. 5-9 and corresponding figure supplements.

The following data sets were generated

Article and author information

Author details

  1. Rashmi Voleti

    Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, United States
    Competing interests
    The authors declare that no competing interests exist.
  2. Klaudia Jaczynska

    Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Josep Rizo

    Department of Biophysics, University of Texas Southwestern Medical Center, Dallas, United States
    For correspondence
    Jose.Rizo-Rey@UTSouthwestern.edu
    Competing interests
    The authors declare that no competing interests exist.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1773-8311

Funding

National Institute of Neurological Disorders and Stroke (R35 NS097333)

  • Josep Rizo

Welch Foundation (I-1304)

  • Josep Rizo

Howard Hughes Medical Institute

  • Rashmi Voleti

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Reviewing Editor

  1. Nils Brose, Max Planck Institute of Experimental Medicine, Germany

Publication history

  1. Received: March 23, 2020
  2. Accepted: August 12, 2020
  3. Accepted Manuscript published: August 18, 2020 (version 1)
  4. Version of Record published: September 17, 2020 (version 2)
  5. Version of Record updated: September 18, 2020 (version 3)

Copyright

© 2020, Voleti 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.

Metrics

  • 1,287
    Page views
  • 219
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Neuroscience
    Eun Ju Shin et al.
    Research Article Updated

    Studies in rats, monkeys, and humans have found action-value signals in multiple regions of the brain. These findings suggest that action-value signals encoded in these brain structures bias choices toward higher expected rewards. However, previous estimates of action-value signals might have been inflated by serial correlations in neural activity and also by activity related to other decision variables. Here, we applied several statistical tests based on permutation and surrogate data to analyze neural activity recorded from the striatum, frontal cortex, and hippocampus. The results show that previously identified action-value signals in these brain areas cannot be entirely accounted for by concurrent serial correlations in neural activity and action value. We also found that neural activity related to action value is intermixed with signals related to other decision variables. Our findings provide strong evidence for broadly distributed neural signals related to action value throughout the brain.

    1. Neuroscience
    Gonçalo Lopes et al.
    Research Article Updated

    Real-time rendering of closed-loop visual environments is important for next-generation understanding of brain function and behaviour, but is often prohibitively difficult for non-experts to implement and is limited to few laboratories worldwide. We developed BonVision as an easy-to-use open-source software for the display of virtual or augmented reality, as well as standard visual stimuli. BonVision has been tested on humans and mice, and is capable of supporting new experimental designs in other animal models of vision. As the architecture is based on the open-source Bonsai graphical programming language, BonVision benefits from native integration with experimental hardware. BonVision therefore enables easy implementation of closed-loop experiments, including real-time interaction with deep neural networks, and communication with behavioural and physiological measurement and manipulation devices.