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{{PageHead|[[Malinow]]|[[ | {{PageHead|[[Malinow]]|[[ReDiClus]]|[[Quantum Dots]]|[[Choquet]]|[[AMPAR]]}} | ||
[[:Category:Malinow]] | [[:Category:Malinow]][[:Category:ReDiClus]] | ||
==Experiment Notes== | |||
{{ExpandBox|experimental notes and highlighted findings| | {{ExpandBox|experimental notes and highlighted findings| | ||
==Experiment Idea Notes== | |||
<big>Project idea</big><br> | |||
GluR1 is dependent on NMDAR activiation to be driven into synapses, whereas GluR2 will readily enter synapses without NMDAR activation {{#info:For an interesting take on this see: [http://www.ncbi.nlm.nih.gov/pubmed/20980546 Lu, Gray, Granger, During, Nicoll (2011)] Potentiation of Synaptic AMPA Receptors Induced by the Deletion of NMDA Receptors Requires the GluA2 Subunit. J Neurophysiol 105: 923–928, 2011}}. Nicoll (2011) showed that in NMDAR knock-outs, GluR2 can rescue potentiation but not GluR1. But how long-lived is this scenario in WT neurons? Glutamate is known to cause bouts of AMPAR scatter/internalization (particularly GluR2-containing AMPARs {{#info:Tardin, Cognet, Bats, Lounis, [[Choquet]] (2003) Direct imaging of lateral movements of AMPA (GluR2) receptors inside synapses. EMBO}}), and in a model where synapses compete for AMPAR expression, the absence of NMDARs could result in rapid shifts synaptic AMPAR expression. But that would not be a good way to maintain potentiation at important synapses. The synapses become important because they are activated coincidentally input elsewhere on the dendrite. This is how neural networks form associations. NMDARs detect this coincidental synaptic activity and 'tag' the synapses involved. This 'tag' allows these synapses to set up a molecular environment that allows them to not only increase potentiation, but to sustain their potentiated state. This synapse is now faced with the challenge of retaining potentiation, even when input doesn't activate the local NMDARs. Again, glutamate alone is enough to depolarize the neuron, but it will also cause AMPAR scatter/internalization; however, synapses that express GluR1 homomerics will have a moderate calcium influx, even when NMDARs are not activated. So while this moderate calcium influx may not be enough to drive large potentiation cascades like NMDARs, it could be enough to sustain the potentiated state of the synapse when the neuron is bombarded with non-NMDAR-activating signals. | |||
<big> | |||
<big>Background Narrative</big><br> | |||
Overall, we should focus on the key differences in GluR1 vs GluR2 expression in terms of how they are trafficked and their unique roles in potentiation. We know that GluR1 is Ca<sup>2+</sup> permeable and GluR2 isn't, likewise GluR1 is inward rectifying, GluR2 is not. It should be safe to assume these differences are very meaningful in LTP. Aside from those ion channel differences, GluR1 has a longer C-tail producing differences in trafficking, mobility, and intracellular protein association. Other key LTP-related pathways and mechanisms include: NMDAR-associated calcium influx activates CaMKII, which is rapidly translocated to the active synaptic terminal {{#info: [[ChoquetCaMKII|Choquet 2010]]: NMDAR activation promoted the rapid translocation of aCaMKII::GFP to synaptic sites (marked by Homer1C::DsRed), after which AMPARs were completely immobilized during the 1 min posttranslocation recording period}}. Once situated, CaMKII can increase AMPAR activity at synapses {{#info: [[ChoquetCaMKII|Choquet 2010]]: tCaMKII (constitutively active) promotes immobilization of endogenous GluR1 (containing) AMPARs (both synaptic and extrasynaptic), and to a much lesser extent GluA2 (containing) AMPARs, but CaMKII direct phosphorylation of AMPARs unnecessary for synaptic trapping.}}, but direct phosphorylation is not required for AMPAR expression at synapses {{#info: [[Malinow2000|Malinow (2000)]]: Although such phosphorylaton may enhance the function of synaptic receptors, this phosphorylation does not seem to be required for receptor delivery. tCaMKII can deliver mutated GluR1 (S831A - mutated CaMKII p-site) to the synapse, indicating that some protein(s) other than GluR1 must be substrate(s) of CaMKII (we know Stargazin is one of them)}}. CaMKII perhaps influences AMPAR expression via its PDZ binding domain {{#info: [[Malinow2000|Malinow (2000)]] found that co(over)expression of tCaMKII and mutate GluR1 (GluR1::T887A::GFP) at its PDZ binding domain completely blocked synaptic response amplitude and rectification, and in fact depressed transmission in hippocampal slice neurons}} {{#info:[[ChoquetCaMKII|Choquet 2010]] found that the GluA1 - SAP97 interaction unnecessary for CaMKII-dependent synaptic trapping}} {{#info:[[ChoquetStargazin|Choquet 2007]] found that GluR2 surface expression was reduced by half when its PDZ binding site was deleted, but lateral diffusion of GluR2 was not changed by this PDZ mutation. Suggesting the PDZ-Binding site of GluR2 controls surface expression, not lateral mobility}} resulting surface expression at synaptic slots; at active synapses CaMKII can also complex with NMDARs, resulting in a sustained increase in activity of both proteins. There strong evidence to suggest that CaMKII works through Stargazin {{#info:[[ChoquetStargazin|Choquet 2007]] found that tCaMKII caused a robust immobilization of Stargazin, but not a mutated version of Stargazin (S9A) lacking the CaMKII phosphorylation site, and went on to show the critical importance of Stargazin binding to AMPARs in synaptic trapping}} to increase synaptic trapping of AMPARs {{#info:[[ChoquetStargazin|Choquet 2007]] used Stargazin and PSD-95 compensetory mutants to show that the synaptic targeting of Stargazin is dependent on the presence of synaptic PSD-95, and this interaction helped immobilize both GluR1 and GluR2 receptors at synapses}}. | Overall, we should focus on the key differences in GluR1 vs GluR2 expression in terms of how they are trafficked and their unique roles in potentiation. We know that GluR1 is Ca<sup>2+</sup> permeable and GluR2 isn't, likewise GluR1 is inward rectifying, GluR2 is not. It should be safe to assume these differences are very meaningful in LTP. Aside from those ion channel differences, GluR1 has a longer C-tail producing differences in trafficking, mobility, and intracellular protein association. Other key LTP-related pathways and mechanisms include: NMDAR-associated calcium influx activates CaMKII, which is rapidly translocated to the active synaptic terminal {{#info: [[ChoquetCaMKII|Choquet 2010]]: NMDAR activation promoted the rapid translocation of aCaMKII::GFP to synaptic sites (marked by Homer1C::DsRed), after which AMPARs were completely immobilized during the 1 min posttranslocation recording period}}. Once situated, CaMKII can increase AMPAR activity at synapses {{#info: [[ChoquetCaMKII|Choquet 2010]]: tCaMKII (constitutively active) promotes immobilization of endogenous GluR1 (containing) AMPARs (both synaptic and extrasynaptic), and to a much lesser extent GluA2 (containing) AMPARs, but CaMKII direct phosphorylation of AMPARs unnecessary for synaptic trapping.}}, but direct phosphorylation is not required for AMPAR expression at synapses {{#info: [[Malinow2000|Malinow (2000)]]: Although such phosphorylaton may enhance the function of synaptic receptors, this phosphorylation does not seem to be required for receptor delivery. tCaMKII can deliver mutated GluR1 (S831A - mutated CaMKII p-site) to the synapse, indicating that some protein(s) other than GluR1 must be substrate(s) of CaMKII (we know Stargazin is one of them)}}. CaMKII perhaps influences AMPAR expression via its PDZ binding domain {{#info: [[Malinow2000|Malinow (2000)]] found that co(over)expression of tCaMKII and mutate GluR1 (GluR1::T887A::GFP) at its PDZ binding domain completely blocked synaptic response amplitude and rectification, and in fact depressed transmission in hippocampal slice neurons}} {{#info:[[ChoquetCaMKII|Choquet 2010]] found that the GluA1 - SAP97 interaction unnecessary for CaMKII-dependent synaptic trapping}} {{#info:[[ChoquetStargazin|Choquet 2007]] found that GluR2 surface expression was reduced by half when its PDZ binding site was deleted, but lateral diffusion of GluR2 was not changed by this PDZ mutation. Suggesting the PDZ-Binding site of GluR2 controls surface expression, not lateral mobility}} resulting surface expression at synaptic slots; at active synapses CaMKII can also complex with NMDARs, resulting in a sustained increase in activity of both proteins. There strong evidence to suggest that CaMKII works through Stargazin {{#info:[[ChoquetStargazin|Choquet 2007]] found that tCaMKII caused a robust immobilization of Stargazin, but not a mutated version of Stargazin (S9A) lacking the CaMKII phosphorylation site, and went on to show the critical importance of Stargazin binding to AMPARs in synaptic trapping}} to increase synaptic trapping of AMPARs {{#info:[[ChoquetStargazin|Choquet 2007]] used Stargazin and PSD-95 compensetory mutants to show that the synaptic targeting of Stargazin is dependent on the presence of synaptic PSD-95, and this interaction helped immobilize both GluR1 and GluR2 receptors at synapses}}. | ||
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==2009== | ==2009== | ||
{{Article|Makino Malinow|2009|Cell - [http://www.ncbi.nlm.nih.gov/pubmed/19914186 PDF]|19914186|AMPA receptor incorporation into synapses during LTP: the role of lateral movement and exocytosis}}{{ExpandBox|Expand to view experiment summary| | |||
;Abstract | |||
The regulated trafficking of AMPA receptors (AMPARs) to synapses is thought to underlie the enhanced transmission in long-term potentiation (LTP), a cellular model of memory. However, there is controversy regarding the nonsynaptic site, either on the surface or intracellularly, from which AMPARs move into synapses during LTP. Using recombinant surface-fluorescent receptors in organotypic rat hippocampal slices, we show that the majority of AMPARs incorporated into synapses during LTP is from lateral diffusion of spine surface receptors containing GluR1, anAMPARsubunit. Following synaptic potentiation, AMPARs in intracellular pools containing GluR1 are driven to the surface primarily on dendrites. These exocytosed receptors likely serve to replenish the local extrasynaptic pool available for subsequent bouts of plasticity. These results clarify the role of intracellular and surface AMPARs during synaptic plasticity. | |||
}}<!-- END ARTICLE --> | |||
{{Article|Kessels, Kopec, Klein, Malinow|2009|Nat Neurosci. - [http://www.nature.com/neuro/journal/v12/n7/pdf/nn.2340.pdf PDF]|19543281|Roles of stargazin and phosphorylation in the control of AMPA receptor subcellular distribution}}{{ExpandBox|Expand to view experiment summary| | {{Article|Kessels, Kopec, Klein, Malinow|2009|Nat Neurosci. - [http://www.nature.com/neuro/journal/v12/n7/pdf/nn.2340.pdf PDF]|19543281|Roles of stargazin and phosphorylation in the control of AMPA receptor subcellular distribution}}{{ExpandBox|Expand to view experiment summary| | ||
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* As has been previously shown, AMPA-induced currents were substantially increased in neurons overexpressing STG34. | * As has been previously shown, AMPA-induced currents were substantially increased in neurons overexpressing STG34. | ||
* To determine the proportion of these increased currents that results from preventing AMPAR channel desensitization by STG we repeated these experiments in the presence of drugs that block desensitization for both the flip (cyclothiazide) and flop (PEPA) AMPAR isoforms (the flop isoform is dominant in CA1 neurons). | * To determine the proportion of these increased currents that results from preventing AMPAR channel desensitization by STG we repeated these experiments in the presence of drugs that block desensitization for both the flip (cyclothiazide) and flop (PEPA) AMPAR isoforms (the flop isoform is dominant in CA1 neurons). | ||
* In the presence of these drugs, AMPA-evoked currents in infected cells were very similar to those in neighboring uninfected cells, confirming that STG primarily enhances bath-applied AMPA responses in hippocampal neurons by reducing AMPAR desensitization | * In the presence of these drugs, AMPA-evoked currents in infected cells were very similar to those in neighboring uninfected cells, confirming that STG primarily enhances bath-applied AMPA responses in hippocampal neurons by reducing AMPAR desensitization {{Fig|[[File:Malinow2009.png]]|Simultaneous whole-cell recordings of infected (black) and neighboring uninfected (gray) cells showed that overexpression of STG or GFP-GluR1 and STG markedly increased responses to bath-applied AMPA. (f) Neurons expressing STG or GFP-GluR1 and STG did not show changes in their responses to bath-applied AMPA in the presence of the desensitization blockers cyclothiazide and PEPA}} | ||
* Furthermore, AMPA-evoked responses were also left unchanged when we overexpressed both GFP-GluR1 and STG, suggesting that even with an excess of GluR1, the STG interaction is not sufficient to drive GluR1 to the surface. | * Furthermore, AMPA-evoked responses were also left unchanged when we overexpressed both GFP-GluR1 and STG, suggesting that even with an excess of GluR1, the STG interaction is not sufficient to drive GluR1 to the surface. | ||
* These data indicate that enlarging the dendritic intracellular GluR1 pool does not lead to an enhancement of surface GluR1. | * These data indicate that enlarging the dendritic intracellular GluR1 pool does not lead to an enhancement of surface GluR1. | ||
Simultaneous whole-cell recordings of infected (black) and neighboring uninfected (gray) cells showed that overexpression of STG or GFP-GluR1 and STG markedly increased responses to bath-applied AMPA. (f) Neurons expressing STG or GFP-GluR1 and STG did not show changes in their responses to bath-applied AMPA in the presence of the desensitization blockers cyclothiazide and PEPA | |||
Simultaneous whole-cell recordings of infected (black) and neighboring uninfected (gray) cells showed that overexpression of STG or GFP-GluR1 and STG markedly increased responses to bath-applied AMPA. (f) Neurons expressing STG or GFP-GluR1 and STG did not show changes in their responses to bath-applied AMPA in the presence of the desensitization blockers cyclothiazide and PEPA | |||
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{{Article| | |||
==Other Studies== | |||
{{Article|Shouval HZ|2005|PNAS - [http://www.pnas.org/content/102/40/14440.full.pdf PDF]|16189022|Clusters of interacting receptors can stabilize synaptic efficacies}} | |||
{{ExpandBox|Expand to view experiment summary| | {{ExpandBox|Expand to view experiment summary| | ||
}}<!-- END | |||
</ | ;Other papers by Shouval | ||
* [http://www.ncbi.nlm.nih.gov/pubmed/20830512 A network of spiking neurons that can represent interval timing] | |||
* [http://www.ncbi.nlm.nih.gov/pubmed/19536207 Translational switch for long-term maintenance of synaptic plasticity] | |||
<big>Abstract</big><br> | |||
{{Quote|Synaptic weights store memories that can last a lifetime. Yet, memory depends on synaptic protein receptors that are recycled in and out of the membrane at a fast rate, possibly several times an hour. Several proposals to bridge this vast gap in time scales between memory and its molecular substrate have relied on bistable molecular switches. Here, we propose an alternative to this approach based on clusters of interacting receptors in the synaptic membrane. We show that such clusters can be metastable and that the lifetime of such clusters can be many orders of magnitude larger than the lifetime of the receptors of which they are composed. We also demonstrate how bidirectional synaptic plasticity can be implemented in this framework.}} | |||
<big>Introduction</big><br> | |||
* In this paper, I propose a theory in which the stability of synaptic efficacies is based on local interactions between receptors within a single synapse. Specifically, I propose that interactions between receptors within a cluster can alter the trafficking of receptors in and out of the synaptic membrane, thereby creating a metastable synaptic state that significantly increases the stability of synaptic efficacy without changing the mean dwell time of receptors in the synaptic membrane. | |||
* Statistical fluctuations in the number of receptors are a signature of this model that might be used to distinguish it from other synaptic models. | |||
<big>Mathematical Methods</big><br> | |||
The variable {{Button|S<sub>ij</sub>}} is an occupation variable of the lattice site denoted by indices i and j. If the site is occupied, {{Button|S<sub>ij</sub> {{=}} 1}}; otherwise, {{Button|S<sub>ij</sub> {{=}} 0}}. Insertion of a new receptor into the membrane can occur at any unoccupied site in the lattice, and '''internalization of a receptor can occur only at occupied sites'''. In this formulation, internalization occurs at a fixed rate, independent of interaction with other receptors. I used a '''fixed internalization rate''' {{Button|μ {{=}} 1 ⁄ τ<sub>in</sub> }} in per unit time, which implies that the '''probability of internalizing''' a receptor at site {{Button|S<sub>''i, j''</sub>}} in a small time step {{Button|Δ''t''}} is: | |||
:'''Internalization Probability''' | |||
:<big>{{Button|PTB=.6em| {{math|P<sup>in</sup>( <var>i,j, t:t</var> + Δ<sub><var>t</var></sub> ) {{=}} S<sub><var>ij</var></sub>μΔ<sub><var>t</var></sub>}} }}</big> | |||
:*This equation is simply saying that the probability {{Button|{{math|P<sup>in</sup>}}}} of a receptor internalizing from site {{Button|'''''S'''''}} with coordinates {{Button|'''''i,j'''''}} at time-point {{Button|'''''t:t'''''}} plus the elapsed time {{Button|Δ<sub>t</sub>}} <code>is equal to</code> the occupation state of that lattice site {{Button|S<sub>ij</sub>}} (1 or 0) × an internalization rate constant {{Button|μ {{=}} 1⁄τ<sub>in</sub> }} × a small time step {{Button|Δ<sub>t</sub>}} | |||
:* In a nutshell the probability of a receptor internalizing is equal to the internalization rate constant per unit time. Since ''S'' and μ {{=}} 1 and the step size for Δ<sub>time</sub> {{=}} .01 then a receptor internalizes once every 100 steps | |||
{{Cell|'''Parameter''' {{Em}} '''Typical Values''' {{Em}} '''Description'''}} | |||
{{Cell| {{Button|S<sub>i,j</sub>}} {{Em}}{{Em}} 1,0 {{Em}}{{Em}}{{Em}} Lattice State }} | |||
{{Cell| {{Button|μ}} {{Em}}{{Em}} 1 {{Em}}{{Em}} μ {{=}} 1 ⁄ τ<sub>in</sub> {{Em}} Internalization Constant }} | |||
{{Cell|{{Button|τ<sub>in</sub>}} {{Em}}{{Em}} 1 {{Em}}{{Em}} <code>τ<sub>in</sub> {{=}} 1 ⁄ μ</code> {{Em}} Dwell Time }} | |||
{{Cell| {{Button|Δt}} {{Em}}{{Em}} .01 {{Em}}{{Em}}{{Em}}{{Em}} Time Step}} | |||
{{Cell| {{Button|h<sub>k</sub>}} {{Em}}{{Em}} 0,1,2,3,4 {{Em}}{{Em}}{{Em}} Field }} | |||
{{Cell| {{Button|L<sub>1</sub>}} {{Em}}{{Em}} 1.5 {{Em}}{{Em}}{{Em}} Lattice Repulsion}} | |||
{{Cell| {{Button|β}} {{Em}}{{Em}} 50 {{Em}}{{Em}}{{Em}} Slope Constant }} | |||
{{Cell| {{Button|ρ}} {{Em}}{{Em}} 0.95 {{Em}}{{Em}}{{Em}} Position Probability }} | |||
{{Cell| {{Button|'''''r'''''}} {{Em}}{{Em}} 10 {{Em}}{{Em}} {{Em}} Transition Rate}} | |||
* Throughout this paper, we use μ {{=}} 1, which implies that the mean dwell time of a receptor in the membrane is 1 unit of time. Typically, we use a time step δt less than 0.01, which is significantly smaller than the other time constants in this system. | |||
* Inserting a new receptor into an unoccupied site depends on the occupation in the vicinity of the unoccupied site. I calculate a "field" h<sub>k</sub>(i, j) at each unoccupied site i,j that measures the number of membrane-embedded receptors in the local neighborhood. | |||
* The field h<sub>k</sub> will determine the conditional probability of inserting a new receptor into an unoccupied site. A typical parameter used in our simulations is L1 equals 1.5; however, similar stability is obtained for the range of L1 equals 1.2 to 2.0. The lattice repulsion constant used for the second population is L2 equals 0.9 | |||
:* <code>'''''L'''''</code> {{=}} lattice repulsion constant | |||
* To determine insertion probability we use | |||
:'''Insertion Probability''' | |||
:<big>{{Button|PTB=.6em|{{math|P<sub>k</sub>(i,j) {{=}} 1 / ( 1 + exp( -βh<sub>k</sub>(i,j) ) )}}}}</big> | |||
:* This equation is saying the probability of insertion varies smoothly from 0 to 1 as a function of the field ''h'' | |||
:* {{math|(1 / (1 - e(-50×1.5))) × 100<sub>steps</sub> {{=}} .5}} | |||
:* {{math|(1 / (1 - e(-50×2.5))) × 100<sub>steps</sub> {{=}} .3}} | |||
:* {{Fig|[[File:Shouval1.png]]|(A) Receptors in the synapse are internalized stochastically at a constant rate, and their probability of staying in the synapse decays exponentially with a time constant of 1.}} {{Fig|[[File:Shouval2.png]]|(B) The rate of insertion depends of the number of nearest neighbors. Given the occupation state (Left), a field is calculated (Right). The probability of inserting a new receptor is proportional to this field. The field can be computed from convolving the nearest-neighbor function (Center) with the state. The field is higher within the cluster and close to its boundaries than outside the cluster or near the isolated receptor.}} | |||
* which varies smoothly from 0 to 1 as a function of hk(i, j). The constant β is the slope of this function. Stability increases for larger β. I typically use β equals 50. However, values of β greater than 25 are sufficient for stability of up to about 1,000 time steps, with 49 receptors in the initial state. This stability depends on other parameters, such as L1. The probability of inserting a receptor in an unoccupied site in a very small time step Δt is then | |||
:<big>{{Button|PTB=.6em|{{math|P<sub>k</sub><sup>ex</sup>(i,j) {{=}} (1 - ''S''<sub>ij</sub>)(ρ<sub>k</sub> '''r''' Δt P<sub>k</sub>(i,j))}}}}</big> | |||
:* <code>.95 * 10 * .01 * P(1) ≈ .1</code> | |||
* where ρ<sub>k</sub> is the probability that a receptor of type k is present in a position near the empty site, and r is the rate of transition into the empty site. Typically, we use ρ<sub>1</sub> equals 0.95 and r equals 10, which implies that for P<sub>k</sub> about 1, the average time for inserting a receptor into a vacant site with a high h<sub>k</sub> is about 0.1 units of time, significantly faster than the internalization rate and slower than the typical time step used. | |||
* The key to stability is not the identity of specific parameters, such as '''Pk''' and '''r''', but their consequence that the characteristic time for insertion into an empty site in a cluster is much shorter than the characteristic time of removing a receptor from a cluster. To reduce run time, we use parallel dynamics. The use of parallel dynamics is not a problem because we use small time steps in which a very small number of events occur across the whole lattice. I ran a few random sequential simulations and obtained indistinguishable results. | |||
<big>Results</big> | |||
*The cluster theory of synaptic stability is based on several assumptions: | |||
:* (i) Synaptic efficacy is proportional to the number of postsynaptic AMPA receptors. | |||
:* (ii) Receptors in the postsynaptic density are clustered. | |||
:* (iii) The insertion rate of a receptor in the vicinity of other receptors in the cluster is much higher than for an isolated receptor. | |||
:*(iv) The rate of receptor removal from the cluster is independent of interactions with other receptors in the cluster. | |||
* Assumptions i–iii are essential assumptions of this model, whereas assumption iv could be altered while preserving the main features of the model. It is important to note that the insertion rate (on rate) and removal rate (off rate) are controlled independently and not governed by a single parameter, which is important for the robust functioning of the model and makes it formally distinct from an Ising spin model of statistical physics | |||
{{ExpandBox|width=80%|USEFUL PROBABILITY EQUATIONS| | |||
{{ProbabilityEquations}} | |||
}}<!-- END PROBABILITY EQUATIONS --> | |||
{{ExpandBox|width=80%|Scholarpedia Entry| | |||
* [http://www.scholarpedia.org/article/Maintenance_of_synaptic_plasticity Link to Article] | |||
One possible mechanism that can stabilize synaptic efficacies is a bi-stable molecular switch. Suppose we have a kinase that is active when phosphorylated, and can auto-phosphorylate itself ( <figref>AutoPhospho.jpg</figref>a). If these kinases interact with neighboring kinase (cooperativity), the resultant protein complex can exhibit an interesting property.This kinase complex, when brought into a fully activated (phosphorylated) state by a strong synaptic stimulus, can sustain its active state via cooperative interaction. If one of the kinase subunit is dephosphorylated by phosphatase or is replaced by an unphosphorylated kinase, the neighboring active kinase in the complex will quickly turn this subunit into an active state. Thus, the protein complex can sustain its active state (memory). If, on the other hand, the incoming signal is weak, phosphatase quickly brings the protein complex back to the basal state. Thus, this protein complex has two states (“bi-stable”). | |||
[[File:Shouval3.jpg|thumb|500px|right|F1| a. Schematic diagram of an autophosphorylation loop. Here it is assumed that phosphorylation activates the kinase, this active kinase in turn phosphorylates more of the yet unphosphorylated kinase. This kinase can be dephosphorylated by a phosphatase. b. One candidate molecule for an autophosphorylation loop is CaMKII. Twelve CaMKII molecules assemble into a holoenzyme composed of two hexameric rings of CaMKII subunits. Autophosphorylation of CaMKII takes place in a cooperative manner when two Ca2+-CaM molecules are bound to two neighboring subunits.]] | |||
One candidate of such a postsynaptic kinase complex is CaMKII (Ca<sup>2+</sup>/CaM dependent protein kinase II). In fact, CaMKII is the key regulator of LTP induction (E-LTP) and appears to have a capacity to fulfill the requirement for bi-stable memory molecule (Lisman and Goldring, 1988). Twelve CaMKII subunits assemble into a holoenzyme structure composed of two hexameric rings of subunits (Lisman et al., 2002, Bradshaw et al. 2003). CaMKII molecules are activated by binding Ca<sup>2+</sup>-CaM. Autophosphorylation of CaMKII takes place in a cooperative manner when two Ca<sup>2+</sup>-CaM molecules are bound to two neighboring subunits in the CaMKII holoenzyme. For this reason, CaMKII requires a non-zero level of signal (Ca<sup>2+</sup>-CaM) to maintain its activity. | |||
Several modeling and experimental studies were conducted to test if CaMKII can serve in fact as a bi-stable switch. Some mathematical models exhibit bi-stability (Lisman and Zhabotinsky, 2001; Miller et al., 2005), while other modeling study shows no bi-stability (Kubota and Bower, 2003). Some of the kinetic parameters of the CaMKII system are still unknown and the topology of kinetic pathways in these two modeling studies differs significantly. Further computational and theoretical works are required to determine if CaMKII autophosphorylation in fact allows bi-stability or it is robustly monostable (regardless of parameter values). | |||
On the experimental side, a systematic study was conducted by Bradshaw and co-workers to test the bistability of CaMKII-protein phosphatase system. This in vitro study, however, shows no sign of bi-stability but the CaMKII-protein phosphatase system responds sharply to Ca<sup>2+</sup> signals (ultra-sensitivity) (Bradshaw et al. 2003). It is extremely important to note that switching ''on'' (phosphorylation) and ''off'' (dephosphorylation) of CaMKII must NEVER be confused with ''bi-stability''. Although an ''in vitro'' assay differs significantly from the intra-cellular environment, the bi-stability of CaMKII in vivo has not yet been demonstrated either. | |||
Another potential problem of CaMKII hypothesis is, while the importance of CaMKII for the induction of LTP is well-established (Lisman et al., 2002), its role in the maintenance of L-LTP or long-term memory is still unclear. Several experimental studies (Malinow et al., 1989, Otmakhov et al., 1997) show that blocking CaMKII activity during the maintenance phase did not influence L-LTP. In addition, the putative target of CaMKII, AMPA receptor, seems only transiently phosphorylated after protocols that induce long-term memory (Whitlock et al., 2006). | |||
===Protein synthesis and bi-stability of a translational switch=== | |||
[[File:Shouval3.jpg|thumb|300px|right|F2|A bi-stable translational switch. A schematic diagram of a positive feedback loop between translation and a translation factor X. Such positive feedback might result in bi-stability.]] | |||
A detailed model for the protein synthesis dependent induction of L-LTP has been proposed by Smolen et al. (2006). However, this model does not propose a mechanism as to how L-LTP can persist beyond the characteristic time scales of the different molecules within the network. | |||
An intriguing possibility is that a biochemical network involving protein synthesis may serve as a bi-stable switch and contribute to synaptic stability (Blitzer et al., 2005). In fact, the molecular machinery needed for protein translation resides in dendrites (even in spines) and an increasing number of experimental studies support the notion that dendritic protein synthesis is required for many forms of long-term plasticity (Sutton and Schuman, 2006). This localized protein translation could then account for the synapse specificity of plasticity. <figref>TranslationLoop_simp.jpg</figref> shows a (hypothetical) positive feedback loop involving translation factor(s) and a kinase protein X. In this diagram, the translation factor(s) regulates the translation of a specific kinase X, which in turn up-regulates the activity of the same translation factor(s). A biochemical network with such a positive feedback loop could exhibit bi-stability. | |||
A potential problem for this hypothesis is that inhibitors of translation inhibit L-LTP (and L-LTD) only when applied before or shortly after stimulation. A widely accepted interpretation of this finding is that protein synthesis is important only during the induction (or consolidation) phase of plasticity. However, a bi-stable feedback loop of a translation factor and a protein kinase (if it exists) may respond differentially to the inhibitors during induction phase and during maintenance phase. | |||
===The cluster model=== | |||
[[File:Shouval3.jpg|thumb|500px|right|F3|The cluster theory of synaptic stability. a. Receptors in the PSD tend to cluster. When a receptor is removed due to turn over or trafficking, it is rapidly replaced by another receptor. b. A simulation of a 7x7 cluster of receptors shown at times t=0, 50 and 100 (one time unit corresponds to the average dwell-time of a receptor). While receptors are rapidly removed and replaced, the cluster remains stable. c. The number of receptors as a function of time in two independent simulations.]] | |||
The cluster theory of synaptic stability (Shouval, 2005) offers an alternative account for the synaptic stability. This theory is based on several assumptions: (1) Synaptic efficacy is proportional to the number of postsynaptic receptors, for example AMPA receptors. (2) Receptors in the postsynaptic density are clustered. (3) The insertion rate of a receptor in the vicinity of other receptors in the cluster is much higher than for an isolated receptor. (4) The rate of receptor removal from the cluster is independent of interactions with other receptors in the cluster. Assumptions 1-3 are essential assumptions of this model, while assumption 4 could be altered while preserving the main features of the model. | |||
Simulations of such networks ( <figref>Cluster_Fig_tiff.jpg</figref>b, c) show that although individual receptors within a cluster are rapidly removed or replaced, the cluster itself remains stable for an extended period of time. The exact instantaneous number of receptors in a cluster fluctuates; however, the mean number of receptors remains stable for a time period much longer than the dwell-time of a single receptor. These receptor clusters are not in a stable steady state; instead they are in a metastable state and will eventually decay. | |||
In fact, the life-time of clusters depends linearly on the dwell-time of receptors and increases steeply with the initial size of the cluster. For example, if we assume a receptor dwell-time of 20 minutes then the median life-time for a 9x9 cluster of receptors can be more than one year. As in other theoretical models, the experimental test of this hypothesis is necessary. | |||
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==Other Notes== | ==Other Notes== | ||
{{Article|Lu W, Gray JA, Granger AJ, During MJ, Nicoll RA.|2011|J Neurophysiol - [http://www.ncbi.nlm.nih.gov/pubmed/20980546 PDF]|20980546|Potentiation of synaptic AMPA receptors induced by the deletion of NMDA receptors requires the GluA2 subunit}} | {{Article|Lu W, Gray JA, Granger AJ, During MJ, Nicoll RA.|2011|J Neurophysiol - [http://www.ncbi.nlm.nih.gov/pubmed/20980546 PDF]|20980546|Potentiation of synaptic AMPA receptors induced by the deletion of NMDA receptors requires the GluA2 subunit}}{{ExpandBox|expand to view study notes| | ||
{{ExpandBox|expand to view study notes| | |||
We thus tested whether GluA2 is required for the potentiation of AMPAR EPSCs following the loss of NMDARs. In the GRIA2fl/fl mice, deleting GluA2 caused about a 50% reduction in the AMPAR EPSC (Fig. 3A ) with no change in the glutamate-evoked AMPAR-mediated outside-out patch currents (B ). Interestingly, in GRIA2fl/flGRIN1fl/fl mice the loss of NMDARs failed to enhance AMPAR EPSCs. In fact, there was a significant further decrease (Fig. 3A ). Neither genetic manipulation changes the PPR. In addition, as expected, deletion of GluA2 caused strong inward rectification. Finally, no difference was found in glutamate evoked AMPAR-mediated currents in outside-out patches (Fig. 3B ). Therefore in contrast to GluA1, the GluA2 subunit is critical for synaptic potentiation of AMPARs following NMDAR deletion. | We thus tested whether GluA2 is required for the potentiation of AMPAR EPSCs following the loss of NMDARs. In the GRIA2fl/fl mice, deleting GluA2 caused about a 50% reduction in the AMPAR EPSC (Fig. 3A ) with no change in the glutamate-evoked AMPAR-mediated outside-out patch currents (B ). Interestingly, in GRIA2fl/flGRIN1fl/fl mice the loss of NMDARs failed to enhance AMPAR EPSCs. In fact, there was a significant further decrease (Fig. 3A ). Neither genetic manipulation changes the PPR. In addition, as expected, deletion of GluA2 caused strong inward rectification. Finally, no difference was found in glutamate evoked AMPAR-mediated currents in outside-out patches (Fig. 3B ). Therefore in contrast to GluA1, the GluA2 subunit is critical for synaptic potentiation of AMPARs following NMDAR deletion. | ||
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<pre> | |||
{{Article|AUTHORS|YEAR|JOURNAL - [http://domain.com/linktofile.pdf PDF]|PMID|TITLE}} | |||
{{ExpandBox|Expand to view experiment summary| | |||
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</pre> | |||
[[Category:Malinow]] | [[Category:Malinow]] [[Category:ReDiClus]] | ||
__NOTOC__ | __NOTOC__ |
Latest revision as of 17:35, 25 August 2013
Malinow | ReDiClus | Quantum Dots | Choquet | AMPAR |
Category:MalinowCategory:ReDiClus
Experiment Notes
experimental notes and highlighted findings
Experiments
2000
Hayashi, Shi, Esteban, Piccini, Poncer, Malinow • 2000 • Science - PDF
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2009
Makino Malinow • 2009 • Cell - PDF
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Kessels, Kopec, Klein, Malinow • 2009 • Nat Neurosci. - PDF
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2007
Kopec, Real, Kessels, Malinow • 2007 • J Neuro - PDF
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Other Studies
Shouval HZ • 2005 • PNAS - PDF
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Other Notes
Lu W, Gray JA, Granger AJ, During MJ, Nicoll RA. • 2011 • J Neurophysiol - PDF
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RANDOM NOTES
{{Article|AUTHORS|YEAR|JOURNAL - [http://domain.com/linktofile.pdf PDF]|PMID|TITLE}} {{ExpandBox|Expand to view experiment summary| }}<!-- END ARTICLE -->