LTP-induced changes in spine geometry and actin dynamics on the timescale of the synaptic tag
Mitha Thomas*1, Cristian Alexandru Bogaciu2, Silvio Rizzoli2, Michael Fauth1
1Third Physics Institute, Georg-August University, Goettingen, Germany
2Department of Neuro- and Sensory Physiology, University Medical Center, Goettingen, Germany
*Email: mitha.thomas@phys.uni-goettingen.de
Introduction
Long-term potentiation of synapses can occur in two phases: an early phase which constitutes a transient increase in synaptic strength, and a late phase which sustains this increase for a longer duration. According to the synaptic tagging and capture hypothesis [1,2], a necessary condition for the late phase is the formation of a transient memory of the stimulation event - the ‘synaptic tag’ - which enables the synapse to capture newly synthesized proteins later on. What implements this transient memory on the timescale of hours remains elusive [2,3]. We follow the hypothesis that it is implemented by actin dynamics in interaction with spine geometry and test this using computational modelling and FRAP experiments.
Methods
Actin forms filaments in the spine which belong to distinct pools (dynamic and stable) with different turnover rates. To study the relation of actin pools with synaptic tagging, we derived a computational model of the interactions between the spine membrane and the actin pools undergoing plasticity. Dynamic actin is modelled as a Markov chain that considers several processes related to actin binding proteins, e.g., branching, capping and severing, which are modulated upon LTP [4, 5]. Stable actin is modelled as a low-pass filter of the dynamic pool with filter coefficients following binding and unbinding of crosslinking proteins. The spine membrane deforms according to the balance between the actin-generated force and the forces resulting from the physical properties of the membrane (Fig 1A-C).
Results
We first test whether can support memory on a timescale of hours without stable actin. At the onset of LTP, there is a rapid increase in dynamic actin, which increases the outward-directed force and, consequently, also the spine volume. However, these changes only last as long as the actin dynamics is modulated. When we introduce the stable pool, it exhibits an overshoot that persists on the timescale of hours, and hence, the synaptic tag. As more stable actin significantly increases the actin-generated force, this also transfers to a long-lasting spine volume increases (Fig 1D-H). To validate these model predictions experimentally, we perform chemical LTP on hippocampal spines and use FRAP to assess stable actin content after LTP. Also here, stable actin shows a significant increase agreeing with the overshoot in the model (Fig 1H).
Discussion
Using a combination of experiments and simulations, we have demonstrated that the dynamics of the stable actin pool after LTP-inducing stimulation leads to a long lasting alteration of actin dynamics and spine geometry. These dynamics fulfil the fundamental criteria for the tag, that is synapse specificity, independence from protein synthesis, and decay within hours. Thus, we present evidence the biophysical implementation of the synaptic tag may be based on the complex interaction of actin with spine membrane.
Figure 1. Actin-spine membrane interactions. A-B: membrane deformation from imbalance between actin-generated force and membrane counter force. C: time evolution of actin dynamics with several associated processes/proteins. D: simulated spine at different time instants. t0: time of stimulation. E: Control and LTP spine volumes. F: amount of dynamic actin. G: amount of stable actin. H: stable actin fraction
Acknowledgements
This work was funded by the German Science Foundation under CRC1286 ”Quantitative Synaptology”, projects C03 and A03. We would like to thank Simon Dannenberg, Stefan Klumpp, Jannik Luboeinski, Francesco Negri, Christian Tetzlaff and Florentin Woergoetter for fruitful discussions on the project.
References
1.https://doi.org/10.1038/385533a0
2.https://doi.org/10.1038/nrn2963
3.https://doi.org/10.1002/iub.2261
4.https://doi.org/10.1016/j.neuron.2014.03.021
5.https://doi.org/10.3389/fnsyn.2020.00009
