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

TL;DR

This paper presents a computational model of synaptic plasticity that predicts how molecular mechanisms like PKM sustain long-term potentiation and memory through feedback loops, and suggests new experimental directions.

Contribution

The model integrates synaptic tagging, capture, and PKM dynamics to explain LTP maintenance and makes novel testable predictions about molecular feedback and memory persistence.

Findings

PKM activity exhibits bistability due to positive feedback.

Cross capture involves LTD-induced dendritic PKM synthesis.

Model predicts effects of PKM inhibition on LTP maintenance.

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 the enzyme protein kinase M (PKM) 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 an stimulus sufficient for LTP and learning can "capture" PKM. 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 PKM, STC, LTD, and cross capture, and makes testable predictions concerning the dynamics of PKM. The maintenance of LTP, and consequently of at least some forms of long-term memory, is predicted to require continual positive feedback in which PKM enhances its own synthesis only at potentiated synapses. This feedback underlies bistability in the activity of PKM. Second, cross capture requires the induction of LTD to induce dendritic PKM synthesis, although this may require tagging of a nearby synapse for LTP. The model also simulates the effects of PKM inhibition, and makes additional predictions for the dynamics of CaM kinases. Experiments testing the above predictions would significantly advance the understanding of memory maintenance.