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Ziv N.E.,Rappaport Institute | Ziv N.E.,Network Biology Research Laboratories
Neuromethods | Year: 2014

Activity-induced modification of synaptic connections ("synaptic plasticity") is widely believed to represent a major mechanism for modifying the functional properties of neuronal networks, possibly providing the basis for phenomena collectively referred to as "learning and memory." This belief has an important corollary: It implies that synapses, when not driven to change their characteristics by physiologically relevant stimuli, should retain these characteristics over time. Recent studies, however, have shown that synaptic molecules, organelles, and even patches of synaptic specializations continuously move in, out, and between synapses at significant rates. Given these intense dynamics, the ability of synapses to retain their individual characteristics over behaviorally relevant time scales is not at all obvious. This chapter focuses on techniques used to study the cellular and molecular dynamics of synaptic components and on quantitative measures of synaptic tenacity - the capacity of synapses to maintain their characteristics over time. These include fluorescence recovery after photobleaching (FRAP), fluorescence recovery after photoactivation (FRAPA), and several analytical tools used to quantify the (in)stability of individual synapses and of synaptic configurations. © 2014 Springer Science+Business Media New York. Source

Cohen L.D.,Lorry Lokey Center for Life science and Engineering | Cohen L.D.,Network Biology Research Laboratories | Zuchman R.,Smoler Proteomics Center | Sorokina O.,University of Edinburgh | And 8 more authors.
PLoS ONE | Year: 2013

Chemical synapses contain multitudes of proteins, which in common with all proteins, have finite lifetimes and therefore need to be continuously replaced. Given the huge numbers of synaptic connections typical neurons form, the demand to maintain the protein contents of these connections might be expected to place considerable metabolic demands on each neuron. Moreover, synaptic proteostasis might differ according to distance from global protein synthesis sites, the availability of distributed protein synthesis facilities, trafficking rates and synaptic protein dynamics. To date, the turnover kinetics of synaptic proteins have not been studied or analyzed systematically, and thus metabolic demands or the aforementioned relationships remain largely unknown. In the current study we used dynamic Stable Isotope Labeling with Amino acids in Cell culture (SILAC), mass spectrometry (MS), Fluorescent Non-Canonical Amino acid Tagging (FUNCAT), quantitative immunohistochemistry and bioinformatics to systematically measure the metabolic half-lives of hundreds of synaptic proteins, examine how these depend on their pre/postsynaptic affiliation or their association with particular molecular complexes, and assess the metabolic load of synaptic proteostasis. We found that nearly all synaptic proteins identified here exhibited half-lifetimes in the range of 2-5 days. Unexpectedly, metabolic turnover rates were not significantly different for presynaptic and postsynaptic proteins, or for proteins for which mRNAs are consistently found in dendrites. Some functionally or structurally related proteins exhibited very similar turnover rates, indicating that their biogenesis and degradation might be coupled, a possibility further supported by bioinformatics-based analyses. The relatively low turnover rates measured here (∼0.7% of synaptic protein content per hour) are in good agreement with imaging-based studies of synaptic protein trafficking, yet indicate that the metabolic load synaptic protein turnover places on individual neurons is very substantial. © 2013 Cohen et al. Source

Statman A.,Technion - Israel Institute of Technology | Statman A.,Network Biology Research Laboratories | Kaufman M.,Network Biology Research Laboratories | Kaufman M.,Technion - Israel Institute of Technology | And 6 more authors.
PLoS Computational Biology | Year: 2014

Long-term, repeated measurements of individual synaptic properties have revealed that synapses can undergo significant directed and spontaneous changes over time scales of minutes to weeks. These changes are presumably driven by a large number of activity-dependent and independent molecular processes, yet how these processes integrate to determine the totality of synaptic size remains unknown. Here we propose, as an alternative to detailed, mechanistic descriptions, a statistical approach to synaptic size dynamics. The basic premise of this approach is that the integrated outcome of the myriad of processes that drive synaptic size dynamics are effectively described as a combination of multiplicative and additive processes, both of which are stochastic and taken from distributions parametrically affected by physiological signals. We show that this seemingly simple model, known in probability theory as the Kesten process, can generate rich dynamics which are qualitatively similar to the dynamics of individual glutamatergic synapses recorded in long-term time-lapse experiments in ex-vivo cortical networks. Moreover, we show that this stochastic model, which is insensitive to many of its underlying details, quantitatively captures the distributions of synaptic sizes measured in these experiments, the long-term stability of such distributions and their scaling in response to pharmacological manipulations. Finally, we show that the average kinetics of new postsynaptic density formation measured in such experiments is also faithfully captured by the same model. The model thus provides a useful framework for characterizing synapse size dynamics at steady state, during initial formation of such steady states, and during their convergence to new steady states following perturbations. These findings show the strength of a simple low dimensional statistical model to quantitatively describe synapse size dynamics as the integrated result of many underlying complex processes. © 2014 Statman et al. Source

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