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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> | <big>Project idea</big><br> | ||
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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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==Other Studies== | ==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}} | {{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| | ||
;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> | <big>Abstract</big><br> | ||
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<big>Mathematical Methods</big><br> | <big>Mathematical Methods</big><br> | ||
The variable {{ | 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''' | :'''Internalization Probability''' | ||
:<big>{{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> | :<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 | :*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 | :* 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}} | |||
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:'''Insertion Probability''' | :'''Insertion Probability''' | ||
:<big>{{math|P<sub>k</sub>(i,j) {{=}} 1 / ( 1 + exp( -βh<sub>k</sub>(i,j) ) )}}</big> | |||
:<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'' | :* This equation is saying the probability of insertion varies smoothly from 0 to 1 as a function of the field ''h'' | ||
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:<big>{{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> | :<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> | :* <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. | * 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. | * 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. | ||
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{{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. | |||
}}<!-- END SCHOLARPEDIA --> | |||
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[[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
Expand to view experiment summary
2009
Makino Malinow • 2009 • Cell - PDF
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Kessels, Kopec, Klein, Malinow • 2009 • Nat Neurosci. - PDF
Expand to view experiment summary
2007
Kopec, Real, Kessels, Malinow • 2007 • J Neuro - PDF
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Other Studies
Shouval HZ • 2005 • PNAS - PDF
Expand to view experiment summary
Other Notes
Lu W, Gray JA, Granger AJ, During MJ, Nicoll RA. • 2011 • J Neurophysiol - PDF
expand to view study notes
RANDOM NOTES
{{Article|AUTHORS|YEAR|JOURNAL - [http://domain.com/linktofile.pdf PDF]|PMID|TITLE}} {{ExpandBox|Expand to view experiment summary| }}<!-- END ARTICLE -->