Synaptic weight set by Munc13-1 supramolecular assemblies

The weight of synaptic connections, which is controlled not only postsynaptically but also presynaptically, is a key determinant in neuronal network dynamics. The mechanisms controlling synaptic weight, especially on the presynaptic side, remain elusive. Using single-synapse imaging of the neurotran...

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Veröffentlicht in:Nature neuroscience 2018-01, Vol.21 (1), p.41-49
Hauptverfasser: Sakamoto, Hirokazu, Ariyoshi, Tetsuroh, Kimpara, Naoya, Sugao, Kohtaroh, Taiko, Isamu, Takikawa, Kenji, Asanuma, Daisuke, Namiki, Shigeyuki, Hirose, Kenzo
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Sprache:eng
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Zusammenfassung:The weight of synaptic connections, which is controlled not only postsynaptically but also presynaptically, is a key determinant in neuronal network dynamics. The mechanisms controlling synaptic weight, especially on the presynaptic side, remain elusive. Using single-synapse imaging of the neurotransmitter glutamate combined with super-resolution imaging of presynaptic proteins, we identify a presynaptic mechanism for setting weight in central glutamatergic synapses. In the presynaptic terminal, Munc13-1 molecules form multiple and discrete supramolecular self-assemblies that serve as independent vesicular release sites by recruiting syntaxin-1, a soluble N -ethylmaleimide-sensitive-factor attachment receptor (SNARE) protein essential for synaptic vesicle exocytosis. The multiplicity of these Munc13-1 assemblies affords multiple stable states conferring presynaptic weight, potentially encoding several bits of information at individual synapses. Supramolecular assembling enables a stable synaptic weight, which confers robustness of synaptic computation on neuronal circuits and may be a general mechanism by which biological processes operate despite the presence of molecular noise. The authors show that Munc13-1 molecules form multiple supramolecular self-assemblies that serve as vesicular release sites. Having multiple Munc13-1 assemblies affords a stable synaptic weight, which confers robustness of synaptic computation.
ISSN:1097-6256
1546-1726
DOI:10.1038/s41593-017-0041-9