AMPAR/TARP stoichiometry differentially modulates channel properties
Abstract
AMPARs control fast synaptic communication between neurons and their function relies on auxiliary subunits, which importantly modulate channel properties. Although it has been suggested that AMPARs can bind to TARPs with variable stoichiometry, little is known about the effect that this stoichiometry exerts on certain AMPAR properties. Here we have found that AMPARs show a clear stoichiometry-dependent modulation by the prototypical TARP γ2 although the receptor still needs to be fully saturated with γ2 to show some typical TARP-induced characteristics (i.e. an increase in channel conductance). We also uncovered important differences in the stoichiometric modulation between calcium-permeable and calcium-impermeable AMPARs. Moreover, in heteromeric AMPARs, γ2 positioning in the complex is important to exert certain TARP-dependent features. Finally, by comparing data from recombinant receptors with endogenous AMPAR currents from mouse cerebellar granule cells, we have determined a likely presence of two γ2 molecules at somatic receptors in this cell type.
Data availability
All data generated or analyzed during this study are included in the manuscript and supporting files. Source data files have been provided for all Figures.
Article and author information
Author details
Funding
Ministerio de Economía y Competitividad (BFU2017-83317-P)
- David Soto
Instituto de Salud Carlos III (RD16/0008/0014)
- Xavier Gasull
Generalitat de Catalunya (2017SGR737)
- Xavier Gasull
Instituto de Salud Carlos III (FIS-PI17/00296)
- Xavier Gasull
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: The authors state that the animals used in this study were sacrificed following the guidelines of CEEA-UB (Ethical Committee for Animal Research) from University of Barcelona with the license number OB117/16, of which Dr. David Soto is the responsible principal investigator.
Reviewing Editor
- Inna Slutsky, Tel Aviv University, Israel
Publication history
- Received: November 25, 2019
- Accepted: May 24, 2020
- Accepted Manuscript published: May 26, 2020 (version 1)
- Accepted Manuscript updated: May 27, 2020 (version 2)
- Version of Record published: June 17, 2020 (version 3)
- Version of Record updated: July 27, 2020 (version 4)
Copyright
© 2020, Miguez-Cabello 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
-
- 1,320
- Page views
-
- 218
- Downloads
-
- 5
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
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
Understanding object representations requires a broad, comprehensive sampling of the objects in our visual world with dense measurements of brain activity and behavior. Here, we present THINGS-data, a multimodal collection of large-scale neuroimaging and behavioral datasets in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1,854 object concepts. THINGS-data is unique in its breadth of richly annotated objects, allowing for testing countless hypotheses at scale while assessing the reproducibility of previous findings. Beyond the unique insights promised by each individual dataset, the multimodality of THINGS-data allows combining datasets for a much broader view into object processing than previously possible. Our analyses demonstrate the high quality of the datasets and provide five examples of hypothesis-driven and data-driven applications. THINGS-data constitutes the core public release of the THINGS initiative (https://things-initiative.org) for bridging the gap between disciplines and the advancement of cognitive neuroscience.
-
- Neuroscience
Sensory neurons previously shown to optimize speed and balance in fish by providing information about the curvature of the spine show similar morphology and connectivity in mice.