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Metaplasticity of synaptic tagging and capture and its implications for maintaining long-term memory in normal and diseased neural networks

Subject Area Molecular Biology and Physiology of Neurons and Glial Cells
Term from 2008 to 2019
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 112578143
 
Final Report Year 2022

Final Report Abstract

‘Synaptic tagging and capture’ (STC) is one of the prominent models of associative memory formation at the cellular level. In line with this STC hypothesis, weak memory formation creates time-dependent ‘synaptic tags/marks’ that might capture memory related proteins available due to a parallel strong memory input in a nearby synaptic network. This process results in the consolidation of the respective weak memory. One of us was involved in the identification of some of the key mechanisms and molecules in STC. However, it is still unclear how memory consolidated through STC erases with time. We propose a new mechanism for the erasure of memory in neural networks: ‘synaptic competition / distribution phase of memory’. According to our hypothesis, a neuronal population will compete for memory proteins if the availability of these proteins is scarce, and the competing partners exhibit nearly similar plasticity thresholds. In consequence, consolidated memories are erased over the time. On the other hand, our model predicts that a synaptic population, which is able to survive synaptic competition, will have a better chance to code long-term memory without disruption. Understanding the cellular mechanisms of synaptic competition is important for finding appropriate therapeutic agents for preventing memory loss. In our studies, we will use ryanodine receptor (RYR) or metabotropic receptor mediated metaplasticity and analyze the role of metaplasticity induced protein PKMzeta for preventing synaptic competition. Understanding the phase and properties of competition of memory will provide new insight into the onset of age related dementia and the puzzling question of why some people have sharp memory irrespective of age and why others have not leading to Alzheimer’s disease (AD). In addition, we will explore the possibility of using metaplasticity as a therapeutic strategy for improving memory by targeting the neural network level in Alzheimer’s disease. Taken together, metaplasticity tunes the synapses to undergo changes that are necessary prerequisites for memory storage under physiological and pathological conditions. Following this hypothesis, we discovered that in APP/PS1 mice, a prominent mouse model of Alzheimer’s disease (AD), late long-term potentiation (L- LTP) and its associative plasticity mechanisms such as synaptic tagging and capture (STC) were impaired already in presymptomatic mice (published in Li et al., PNAS 2017). Interestingly, late long-term depression (L-LTD) was not compromised but the positive associative interaction of LTP and LTD, cross-capture, was altered in these mice. Metaplastic activation of ryanodine receptors (RyR) in these neurons reestablished L-LTP and STC. We propose that RyR mediated metaplastic mechanisms can be considered as a possible therapeutic target for counteracting synaptic impairments in the neuronal networks during the progression of AD. In addition we could show that a balance of protein synthesis and degradation is critical for the dynamic regulation and implementation of long-term memory storage. The role of the ubiquitin-proteasome system (UPS) in regulating the plasticity at potentiated synapses is well studied, but its roles in depressed synaptic populations remain elusive. In this study, we probed the possibility of regulating the UPS by inhibiting the proteasome function during the induction of protein synthesis-independent form of hippocampal long-term depression (early-LTD), an important component of synaptic plasticity. Here, we show that protein degradation is involved in early-LTD induction and interfering with this process facilitates early-LTD to late-LTD.We provide evidence here that under the circumstances of proteasome inhibition brain-derived neurotrophic factor is accumulated as plasticity-related protein and it drives the weakly depressed or potentiated synapses to associativity. Thus, UPS inhibition promotes LTD and establishes associativity betweenweakly depressed or potentiated synapses through the mechanisms of synaptic tagging/capture or crosscapture.

Publications

  • (2011). Metaplasticity governs compartmentalization of synaptic tagging and capture through BDNF and PKMzeta (PKMζ). PNAS. 108(6), 2551-2556
    Sajikumar, S., Korte, M.
    (See online at https://doi.org/10.1073/pnas.1016849108)
  • (2012). Dopamine induces LTP differentially in apical and basal dendrites through BDNF and voltage-dependent calcium channels. Learning & Memory. 19(7), 294-299
    Navakkode, S., Sajikumar, S., Korte, M., Soong, T.W.
    (See online at https://doi.org/10.1101/lm.026203.112)
  • (2014). Competition between recently potentiated synaptic inputs reveals a winner-take-all phase of synaptic tagging and capture. PNAS. 111(33), 12217-12221
    Sajikumar, S., Morris, R.G., Korte, M.
    (See online at https://doi.org/10.1073/pnas.1403643111)
  • (2014). Making synapses strong: metaplasticity prolongs associativity of long-term memory by switching synaptic tag mechanisms. Cerebral Cortex. 24(2), 353-363
    Li, Q., Rothkegal, M., Xiao, Z.C., Abraham, W.C., Korte, M., Sajikumar, S.
    (See online at https://doi.org/10.1093/cercor/bhs315)
  • (2011). Different compartments of apical CA1 dendrites have different plasticity thresholds for expressing synaptic tagging and capture. Learning & Memory. 18(5), 327-331
    Sajikumar, S., Korte, M.
    (See online at https://doi.org/10.1101/lm.2095811)
  • (2015). Ubiquitin-proteasome system inhibition promotes long-term depression and synaptic tagging /capture. Cerebral Cortex. 26(6), 2541-2548
    Li, Q., Korte, M., Sajikumar, S.
    (See online at https://doi.org/10.1093/cercor/bhv084)
  • (2017). Metaplasticity mechanisms restore plasticity and associativity in an animal model of Alzheimer's disease. PNAS. 114(21), 5527-5532
    Li, Q., Navakkode, S., Rothkegel, M., Soong, T.W., Sajikumar, S., Korte, M.
    (See online at https://doi.org/10.1073/pnas.1613700114)
 
 

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