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Molecular Constraints on Synaptic Tagging and Maintenance of Long-Term Potentiation: A Predictive Model

Overview of attention for article published in PLoS Computational Biology, August 2012
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Title
Molecular Constraints on Synaptic Tagging and Maintenance of Long-Term Potentiation: A Predictive Model
Published in
PLoS Computational Biology, August 2012
DOI 10.1371/journal.pcbi.1002620
Pubmed ID
Authors

Paul Smolen, Douglas A. Baxter, John H. Byrne

Abstract

Protein synthesis-dependent, late long-term potentiation (LTP) and depression (LTD) at glutamatergic hippocampal synapses are well characterized examples of long-term synaptic plasticity. Persistent increased activity of protein kinase M ζ (PKMζ) is thought essential for maintaining LTP. Additional spatial and temporal features that govern LTP and LTD induction are embodied in the synaptic tagging and capture (STC) and cross capture hypotheses. Only synapses that have been "tagged" by a stimulus sufficient for LTP and learning can "capture" PKMζ. A model was developed to simulate the dynamics of key molecules required for LTP and LTD. The model concisely represents relationships between tagging, capture, LTD, and LTP maintenance. The model successfully simulated LTP maintained by persistent synaptic PKMζ, STC, LTD, and cross capture, and makes testable predictions concerning the dynamics of PKMζ. The maintenance of LTP, and consequently of at least some forms of long-term memory, is predicted to require continual positive feedback in which PKMζ enhances its own synthesis only at potentiated synapses. This feedback underlies bistability in the activity of PKMζ. Second, cross capture requires the induction of LTD to induce dendritic PKMζ synthesis, although this may require tagging of a nearby synapse for LTP. The model also simulates the effects of PKMζ inhibition, and makes additional predictions for the dynamics of CaM kinases. Experiments testing the above predictions would significantly advance the understanding of memory maintenance.

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Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 2 3%
Switzerland 1 1%
Netherlands 1 1%
Ireland 1 1%
United Kingdom 1 1%
Belgium 1 1%
Greece 1 1%
Unknown 59 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 22%
Student > Ph. D. Student 12 18%
Student > Master 8 12%
Student > Bachelor 7 10%
Other 4 6%
Other 8 12%
Unknown 13 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 27%
Neuroscience 9 13%
Computer Science 7 10%
Medicine and Dentistry 6 9%
Psychology 6 9%
Other 6 9%
Unknown 15 22%