Dataset Information

Bayesian Inference of Synaptic Quantal Parameters from Correlated Vesicle Release.

ABSTRACT: Synaptic transmission is both history-dependent and stochastic, resulting in varying responses to presentations of the same presynaptic stimulus. This complicates attempts to infer synaptic parameters and has led to the proposal of a number of different strategies for their quantification. Recently Bayesian approaches have been applied to make more efficient use of the data collected in paired intracellular recordings. Methods have been developed that either provide a complete model of the distribution of amplitudes for isolated responses or approximate the amplitude distributions of a train of post-synaptic potentials, with correct short-term synaptic dynamics but neglecting correlations. In both cases the methods provided significantly improved inference of model parameters as compared to existing mean-variance fitting approaches. However, for synapses with high release probability, low vesicle number or relatively low restock rate and for data in which only one or few repeats of the same pattern are available, correlations between serial events can allow for the extraction of significantly more information from experiment: a more complete Bayesian approach would take this into account also. This has not been possible previously because of the technical difficulty in calculating the likelihood of amplitudes seen in correlated post-synaptic potential trains; however, recent theoretical advances have now rendered the likelihood calculation tractable for a broad class of synaptic dynamics models. Here we present a compact mathematical form for the likelihood in terms of a matrix product and demonstrate how marginals of the posterior provide information on covariance of parameter distributions. The associated computer code for Bayesian parameter inference for a variety of models of synaptic dynamics is provided in the Supplementary Material allowing for quantal and dynamical parameters to be readily inferred from experimental data sets.

SUBMITTER: Bird AD

PROVIDER: S-EPMC5122579 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Bayesian Inference of Synaptic Quantal Parameters from Correlated Vesicle Release.

Bird Alex D AD Wall Mark J MJ Richardson Magnus J E MJ

Frontiers in computational neuroscience 20161125

Synaptic transmission is both history-dependent and stochastic, resulting in varying responses to presentations of the same presynaptic stimulus. This complicates attempts to infer synaptic parameters and has led to the proposal of a number of different strategies for their quantification. Recently Bayesian approaches have been applied to make more efficient use of the data collected in paired intracellular recordings. Methods have been developed that either provide a complete model of the distr ...[more]

PMID: 27932970

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Project description:Proteolytic processing of synaptic adhesion components can accommodate the function of synapses to activity-dependent changes. The adhesion system formed by neurexins (Nrxns) and neuroligins (Nlgns) bidirectionally orchestrate the function of presynaptic and postsynaptic terminals. Previous studies have shown that presenilins (PS), components of the gamma-secretase complex frequently mutated in familial Alzheimer's disease, clear from glutamatergic terminals the accumulation of Nrxn C-terminal fragments (Nrxn-CTF) generated by ectodomain shedding. Here, we characterized the synaptic consequences of the proteolytic processing of Nrxns in cultured hippocampal neurons from mice and rats of both sexes. We show that activation of presynaptic Nrxns with postsynaptic Nlgn1 or inhibition of ectodomain shedding in axonal Nrxn1-β increases presynaptic release at individual terminals, likely reflecting an increase in the number of functional release sites. Importantly, inactivation of PS inhibits presynaptic release downstream of Nrxn activation, leaving synaptic vesicle recruitment unaltered. Glutamate-receptor signaling initiates the activity-dependent generation of Nrxn-CTF, which accumulate at presynaptic terminals lacking PS function. The sole expression of Nrxn-CTF decreases presynaptic release and calcium flux, recapitulating the deficits due to loss of PS function. Our data indicate that inhibition of Nrxn processing by PS is deleterious to glutamatergic function.SIGNIFICANCE STATEMENT To gain insight into the role of presenilins (PS) in excitatory synaptic function, we address the relevance of the proteolytic processing of presynaptic neurexins (Nrxns) in glutamatergic differentiation. Using synaptic fluorescence probes in cultured hippocampal neurons, we report that trans-synaptic activation of Nrxns produces a robust increase in presynaptic calcium levels and neurotransmitter release at individual glutamatergic terminals by a mechanism that depends on normal PS activity. Abnormal accumulation of Nrxn C-terminal fragments resulting from impaired PS activity inhibits presynaptic calcium signal and neurotransmitter release, assigning synaptic defects to Nrxns as a specific PS substrate. These data may provide links into how loss of PS activity inhibits glutamatergic synaptic function in Alzheimer's disease patients.

Dataset Information

Bayesian Inference of Synaptic Quantal Parameters from Correlated Vesicle Release.

Publications

Bayesian Inference of Synaptic Quantal Parameters from Correlated Vesicle Release.

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