Regulation of synaptic nanodomain by liquid–liquid phase separation: A novel mechanism of synaptic plasticity

Liu, Pin-Wu; Hosokawa, T.; Hayashi, Yasunori

doi:10.1016/j.conb.2021.02.004

Cited by 17 publications

(9 citation statements)

References 68 publications

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“…The protein-protein interactions and LLPS of DRACC1 might be involved in the effect of DRACC1 on neuronal activity (Fig. 7C, 7D), as it has been proposed that synaptic activities are modulated through protein-protein interaction and LLPS in PSD (Bayer et al, 2001; Hosokawa et al, 2021; Liu et al, 2021; Saneyoshi, 2021; Saneyoshi et al, 2019). The heterogeneous expression of DRACC1 across brain regions (Fig, S3C, S4) might be involved in brain region-dependent differences in synaptic strength.…”

Section: Discussionmentioning

confidence: 95%

DRACC1, a major postsynaptic protein, regulates the condensation of postsynaptic proteins via liquid-liquid phase separation

Kaizuka

Hirouchi

Saneyoshi

et al. 2023

Preprint

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Numerous proteome analyses have been conducted on the postsynaptic density (PSD), a protein condensate beneath the postsynaptic membrane of excitatory synapses. Each has identified several hundred to thousands of proteins. While proteins with predictable functions have been well studied, functionally uncharacterized proteins are mostly overlooked. In this study, we perform a meta-analysis of the 35 PSD proteome datasets, including 5,869 proteins, identifying 97 uncharacterized proteins that appeared in multiple datasets. We focus on the top-ranked protein, FAM81A, renamed DRACC1. DRACC1 is expressed in forebrain neurons and enriched at the synapse. DRACC1 interacts with PSD proteins, including PSD-95, SynGAP, and NMDA receptors, and promotes liquid-liquid phase separation of those proteins. Consistently, the downregulation of DRACC1 in neurons causes a decrease in the size of PSD-95 puncta and the frequency of neuronal firing. Our results characterize DRACC1 as a novel synaptic protein facilitating the assembly of proteins within PSD. It also indicates the effectiveness of a meta-analytic approach of existing proteome datasets in identifying uncharacterized proteins.

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Section: Discussionmentioning

confidence: 95%

DRACC1, a major postsynaptic protein, regulates the condensation of postsynaptic proteins via liquid-liquid phase separation

Kaizuka

Hirouchi

Saneyoshi

et al. 2023

Preprint

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“…5), which is an extension of the PSD centric model of LTP (2,40). It incorporates the recent proposal of nanocolumns (29,39,(41)(42)(43)(44)(45) in which AMPARs are clustered opposite to release sites by transsynaptic adhesion complexes, including ADAM22/LGI1 (27,29). We focus on PSD-95 since it is the most abundant MAGUK.…”

Section: Discussionmentioning

confidence: 99%

MAGUKs are essential, but redundant, in long-term potentiation

Chen

Fukata

2021

Proc. Natl. Acad. Sci. U.S.A.

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This study presents evidence that the MAGUK family of synaptic scaffolding proteins plays an essential, but redundant, role in long-term potentiation (LTP). The action of PSD-95, but not that of SAP102, requires the binding to the transsynaptic adhesion protein ADAM22, which is required for nanocolumn stabilization. Based on these and previous results, we propose a two-step process in the recruitment of AMPARs during LTP. First, AMPARs, via TARPs, bind to exposed PSD-95 in the PSD. This alone is not adequate to enhance synaptic transmission. Second, the AMPAR/TARP/PSD-95 complex is stabilized in the nanocolumn by binding to ADAM22. A second, ADAM22-independent pathway is proposed for SAP102.

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“…This NMDAR cycling process can continue until the balance of slow and fast receptors is found, where glutamate and voltage gate conductances peak at the same time, for an optimal τ Glu . The different nanodomain organization of slow and fast NMDARs in the postsynaptic density could also provide more dynamic cycling and replacement of NMDARs 55 , 56 , such that unstable slow NMDARs with high mismatch would be more likely replaced with fast NMDARs, and vice versa. Moreover, SITDL provides potential mechanisms for new learning rules in ANNs, which currently employ primarily synaptic weight changes.…”

Section: Discussionmentioning

confidence: 99%

Role of NMDAR plasticity in a computational model of synaptic memory

Gribkova

Gillette

2021

Sci Rep

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A largely unexplored question in neuronal plasticity is whether synapses are capable of encoding and learning the timing of synaptic inputs. We address this question in a computational model of synaptic input time difference learning (SITDL), where N‐methyl‐d‐aspartate receptor (NMDAR) isoform expression in silent synapses is affected by time differences between glutamate and voltage signals. We suggest that differences between NMDARs’ glutamate and voltage gate conductances induce modifications of the synapse’s NMDAR isoform population, consequently changing the timing of synaptic response. NMDAR expression at individual synapses can encode the precise time difference between signals. Thus, SITDL enables the learning and reconstruction of signals across multiple synapses of a single neuron. In addition to plausibly predicting the roles of NMDARs in synaptic plasticity, SITDL can be usefully applied in artificial neural network models.

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Regulation of synaptic nanodomain by liquid–liquid phase separation: A novel mechanism of synaptic plasticity

Cited by 17 publications

References 68 publications

DRACC1, a major postsynaptic protein, regulates the condensation of postsynaptic proteins via liquid-liquid phase separation

DRACC1, a major postsynaptic protein, regulates the condensation of postsynaptic proteins via liquid-liquid phase separation

MAGUKs are essential, but redundant, in long-term potentiation

Role of NMDAR plasticity in a computational model of synaptic memory

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