Stochastic Induction of Long-Term Potentiation and Long-Term Depression

Lurcher Mouse as a Model of Cerebellar Syndromes

Cerebellar extinction lesions can manifest themselves with cerebellar motor and cerebellar cognitive affective syndromes. For investigation of the functions of the cerebellum and the pathogenesis of cerebellar diseases, particularly hereditary neurodegenerative cerebellar ataxias, various cerebellar mutant mice are used. The Lurcher mouse is a model of selective olivocerebellar degeneration with early onset and rapid progress. These mice show both motor deficits as well as cognitive and behavioral changes i.e., pathological phenotype in the functional domains affected in cerebellar patients. Therefore, Lurcher mice might be considered as a tool to investigate the mechanisms of functional impairments caused by cerebellar degenerative diseases. There are, however, limitations due to the particular features of the neurodegenerative process and a lack of possibilities to examine some processes in mice. The main advantage of Lurcher mice would be the expected absence of significant neuropathologies outside the olivocerebellar system that modify the complex behavioral phenotype in less selective models. However, detailed examinations and further thorough validation of the model are needed to verify this assumption.

Molecular Role of Protein Phosphatases in Alzheimer's and Other Neurodegenerative Diseases

Alzheimer's disease (AD) is distinguished by the gradual loss of cognitive function, which is associated with neuronal loss and death. Accumulating evidence supports that protein phosphatases (PPs; PP1, PP2A, PP2B, PP4, PP5, PP6, and PP7) are directly linked with amyloid beta (Aβ) as well as the formation of the neurofibrillary tangles (NFTs) causing AD. Published data reported lower PP1 and PP2A activity in both gray and white matters in AD brains than in the controls, which clearly shows that dysfunctional phosphatases play a significant role in AD. Moreover, PP2A is also a major causing factor of AD through the deregulation of the tau protein. Here, we review recent advances on the role of protein phosphatases in the pathology of AD and other neurodegenerative diseases. A better understanding of this problem may lead to the development of phosphatase-targeted therapies for neurodegenerative disorders in the near future.

PDI augments kainic acid-induced seizure activity and neuronal death by inhibiting PP2A-GluA2-PICK1-mediated AMPA receptor internalization in the mouse hippocampus

Protein disulfide isomerase (PDI) is a redox-active enzyme and also serves as a nitric oxide donor causing S-nitrosylation of cysteine residues in various proteins. Although PDI knockdown reduces α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid receptor (AMPAR)-mediated neuronal activity, the underlying mechanisms are largely unknown. In the present study, we found that under physiological condition PDI knockdown increased CaMKII activity (phosphorylation) in the mouse hippocampus. However, PDI siRNA inhibited protein phosphatase (PP) 2A-mediated GluA2 S880 dephosphorylation by increasing PP2A oxidation, independent of S-nitrosylation. PDI siRNA also enhanced glutamate ionotropic receptor AMPA type subunit 1 (GluA1) S831 and GluA2 S880, but not GluA1 S845 and GluA2 Y869/Y873/Y876 phosphorylations, concomitant with the enhanced protein interacting with C kinase 1 (PICK1)-mediated AMPAR internalization. Furthermore, PDI knockdown attenuated seizure activity and neuronal damage in response to kainic acid (a non-desensitizing agonist of AMPAR). Therefore, these findings suggest that PDI may regulate surface AMPAR expression through PP2A-GluA2-PICK1 signaling pathway, and that PDI may be one of the therapeutic targets for epilepsy via AMPAR internalization without altering basal neurotransmission.

A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics

Discovering the rules of synaptic plasticity is an important step for understanding brain learning. Existing plasticity models are either (1) top-down and interpretable, but not flexible enough to account for experimental data, or (2) bottom-up and biologically realistic, but too intricate to interpret and hard to fit to data. To avoid the shortcomings of these approaches, we present a new plasticity rule based on a geometrical readout mechanism that flexibly maps synaptic enzyme dynamics to predict plasticity outcomes. We apply this readout to a multi-timescale model of hippocampal synaptic plasticity induction that includes electrical dynamics, calcium, CaMKII and calcineurin, and accurate representation of intrinsic noise sources. Using a single set of model parameters, we demonstrate the robustness of this plasticity rule by reproducing nine published ex vivo experiments covering various spike-timing and frequency-dependent plasticity induction protocols, animal ages, and experimental conditions. Our model also predicts that in vivo-like spike timing irregularity strongly shapes plasticity outcome. This geometrical readout modelling approach can be readily applied to other excitatory or inhibitory synapses to discover their synaptic plasticity rules.

Muscarinic acetylcholine receptor-dependent and NMDA receptor-dependent LTP and LTD share the common AMPAR trafficking pathway

The forebrain cholinergic system promotes higher brain function in part by signaling through the M₁ muscarinic acetylcholine receptor (mAChR). Long-term potentiation (LTP) and long-term depression (LTD) of excitatory synaptic transmission in the hippocampus are also induced by mAChR. An AMPA receptor (AMPAR) trafficking model for hippocampal neurons has been proposed to simulate N-methyl-D-aspartate receptor (NMDAR)-dependent synaptic plasticity in the early phase. In this study, we demonstrated the validity of the hypothesis that the mAChR-dependent LTP/LTD shares a common AMPAR trafficking pathway associated with NMDAR-dependent LTP/LTD. However, unlike NMDAR, Ca²⁺ influx into the spine cytosol occurs owing to the Ca²⁺ stored inside the ER and is induced via the activation of inositol 1,4,5-trisphosphate (IP3) receptors during M1 mAChR activation. Moreover, the AMPAR trafficking model implies that alterations in LTP and LTD observed in Alzheimer's disease could be attributed to age-dependent reductions in AMPAR expression levels.

Comparison of rule- and ordinary differential equation-based dynamic model of DARPP-32 signalling network

Dynamic modelling has considerably improved our understanding of complex molecular mechanisms. Ordinary differential equations (ODEs) are the most detailed and popular approach to modelling the dynamics of molecular systems. However, their application in signalling networks, characterised by multi-state molecular complexes, can be prohibitive. Contemporary modelling methods, such as rule- based (RB) modelling, have addressed these issues. The advantages of RB modelling over ODEs have been presented and discussed in numerous reviews. In this study, we conduct a direct comparison of the time courses of a molecular system founded on the same reaction network but encoded in the two frameworks. To make such a comparison, a set of reactions that underlie an ODE model was manually encoded in the Kappa language, one of the RB implementations. A comparison of the models was performed at the level of model specification and dynamics, acquired through model simulations. In line with previous reports, we confirm that the Kappa model recapitulates the general dynamics of its ODE counterpart with minor differences. These occur when molecules have multiple sites binding the same interactor. Furthermore, activation of these molecules in the RB model is slower than in the ODE one. As reported for other molecular systems, we find that, also for the DARPP-32 reaction network, the RB representation offers a more expressive and flexible syntax that facilitates access to fine details of the model, easing model reuse. In parallel with these analyses, we report a refactored model of the DARPP-32 interaction network that can serve as a canvas for the development of more complex dynamic models to study this important molecular system.

Stochastic reaction-diffusion modeling of calcium dynamics in 3D dendritic spines of Purkinje cells

Calcium (Ca²⁺) is a second messenger assumed to control changes in synaptic strength in the form of both long-term depression and long-term potentiation at Purkinje cell dendritic spine synapses via inositol trisphosphate (IP₃)-induced Ca²⁺ release. These Ca²⁺ transients happen in response to stimuli from parallel fibers (PFs) from granule cells and climbing fibers (CFs) from the inferior olivary nucleus. These events occur at low numbers of free Ca²⁺, requiring stochastic single-particle methods when modeling them. We use the stochastic particle simulation program MCell to simulate Ca²⁺ transients within a three-dimensional Purkinje cell dendritic spine. The model spine includes the endoplasmic reticulum, several Ca²⁺ transporters, and endogenous buffer molecules. Our simulations successfully reproduce properties of Ca²⁺ transients in different dynamical situations. We test two different models of the IP₃ receptor (IP₃R). The model with nonlinear concentration response of binding of activating Ca²⁺ reproduces experimental results better than the model with linear response because of the filtering of noise. Our results also suggest that Ca²⁺-dependent inhibition of the IP₃R needs to be slow to reproduce experimental results. Simulations suggest the experimentally observed optimal timing window of CF stimuli arises from the relative timing of CF influx of Ca²⁺ and IP₃ production sensitizing IP₃R for Ca²⁺-induced Ca²⁺ release. We also model ataxia, a loss of fine motor control assumed to be the result of malfunctioning information transmission at the granule to Purkinje cell synapse, resulting in a decrease or loss of Ca²⁺ transients. Finally, we propose possible ways of recovering Ca²⁺ transients under ataxia.

Functional repertoire of protein kinases and phosphatases in synaptic plasticity and associated neurological disorders

Protein phosphorylation and dephosphorylation are two essential and vital cellular mechanisms that regulate many receptors and enzymes through kinases and phosphatases. Ca²⁺- dependent kinases and phosphatases are responsible for controlling neuronal processing; balance is achieved through opposition. During molecular mechanisms of learning and memory, kinases generally modulate positively while phosphatases modulate negatively. This review outlines some of the critical physiological and structural aspects of kinases and phosphatases involved in maintaining postsynaptic structural plasticity. It also explores the link between neuronal disorders and the deregulation of phosphatases and kinases.

Mechanism underlying hippocampal long-term potentiation and depression based on competition between endocytosis and exocytosis of AMPA receptors

N-methyl-D-aspartate (NMDA) receptor-dependent long-term potentiation (LTP) and long-term depression (LTD) of signal transmission form neural circuits and thus are thought to underlie learning and memory. These mechanisms are mediated by AMPA receptor (AMPAR) trafficking in postsynaptic neurons. However, the regulatory mechanism of bidirectional plasticity at excitatory synapses remains unclear. We present a network model of AMPAR trafficking for adult hippocampal pyramidal neurons, which reproduces both LTP and LTD. We show that the induction of both LTP and LTD is regulated by the competition between exocytosis and endocytosis of AMPARs, which are mediated by the calcium-sensors synaptotagmin 1/7 (Syt1/7) and protein interacting with C-kinase 1 (PICK1), respectively. Our result indicates that recycling endosomes containing AMPAR are always ready for Syt1/7-dependent exocytosis of AMPAR at peri-synaptic/synaptic membranes. This is because molecular motor myosin V_b constitutively transports the recycling endosome toward the membrane in a Ca²⁺-independent manner.

Insulin Modulates Excitatory Synaptic Transmission and Synaptic Plasticity in the Mouse Hippocampus

The administration of exogenous insulin into the hippocampus has the potential to enhance cognitive function and exert other beneficial effects. Elucidating the neurobiological substrates of insulin action and its underlying physiological mechanisms may further improve treatment efficacy. Previous work has shown that insulin affects synaptic plasticity, however there are discrepancies and contradictory conclusions between studies. Here, we used extracellular field recordings in mouse hippocampal slices to investigate how insulin acutely modulates synaptic transmission and synaptic plasticity, both of which are correlated with learning and memory processes. Our data demonstrate that insulin application inhibited basal excitatory synaptic transmission and promoted long-term potentiation (LTP) induction at hippocampal Schaffer collateral-CA1 synapses. Under similar conditions, insulin strongly activated the PI3K/AKT pathway, but had only a weak effect on the MAPK/ERK pathway. Although insulin-induced inhibition of field excitatory post-synaptic potentials (fEPSPs) was previously termed insulin-long-term depression (insulin-LTD), insulin application potentiated recovery from classically induced LTD. Further analysis suggests suppression of presynaptic neurotransmitter release contributed to the insulin-LTD. At low concentrations, insulin primarily inhibited fEPSPs; however, at high concentration, its effects were of mixed inhibition and enhancement in different recordings. Moreover, a broad spectrum protein kinase C blocker, cannabinoid receptor type 1 activator, or a high glucose concentration inhibited fEPSPs per se, and disturbed insulin's effect on fEPSP. Insulin also caused depotentiation during LTP expression and triggered depression during LTD recovery. Given the essential roles of dynamic synaptic transmission and plasticity in learning and memory, our data provide more evidence that insulin application may actively modulate hippocampal-dependent cognitive events.

A Theoretical Framework to Derive Simple, Firing-Rate-Dependent Mathematical Models of Synaptic Plasticity

Synaptic plasticity serves as an essential mechanism underlying cognitive processes as learning and memory. For a better understanding detailed theoretical models combine experimental underpinnings of synaptic plasticity and match experimental results. However, these models are mathematically complex impeding the comprehensive investigation of their link to cognitive processes generally executed on the neuronal network level. Here, we derive a mathematical framework enabling the simplification of such detailed models of synaptic plasticity facilitating further mathematical analyses. By this framework we obtain a compact, firing-rate-dependent mathematical formulation, which includes the essential dynamics of the detailed model and, thus, of experimentally verified properties of synaptic plasticity. Amongst others, by testing our framework by abstracting the dynamics of two well-established calcium-dependent synaptic plasticity models, we derived that the synaptic changes depend on the square of the presynaptic firing rate, which is in contrast to previous assumptions. Thus, the here-presented framework enables the derivation of biologically plausible but simple mathematical models of synaptic plasticity allowing to analyze the underlying dependencies of synaptic dynamics from neuronal properties such as the firing rate and to investigate their implications in complex neuronal networks.

AMPA receptor trafficking and its role in heterosynaptic plasticity

Historically, long-term potentiation (LTP) and long-term depression (LTD), the best-characterized forms of long-term synaptic plasticity, are viewed as experience-dependent and input-specific processes. However, cumulative experimental and theoretical data have demonstrated that LTP and LTD can promote compensatory alterations in non-stimulated synapses. In this work, we have developed a computational model of a tridimensional spiny dendritic segment to investigate the role of AMPA receptor (AMPAR) trafficking during synaptic plasticity at specific synapses and its consequences for the populations of AMPAR at nearby synapses. Our results demonstrated that the mechanisms of AMPAR trafficking involved with LTP and LTD can promote heterosynaptic plasticity at non-stimulated synapses. These alterations are compensatory and arise from molecular competition. Moreover, the heterosynaptic changes observed in our model can modulate further activity-driven inductions of synaptic plasticity.

Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder

Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons. This is a proof of concept that pairs of neocortical neurons associating sensory inputs to motor outputs could operate like mirror neurons. In addition, we used the mirror neuron model to investigate whether channelopathies associated with autism spectrum disorder could impair the modeled mirror function. Our simulation results showed that impaired hyperpolarization-activated cationic currents (Ih) affected the mirror function between the pairs of neocortical neurons coupled by STDP.

Molecular mechanisms of detection and discrimination of dynamic signals

Many molecules decode not only the concentration of cellular signals, but also their temporal dynamics. However, little is known about the mechanisms that underlie the detection and discrimination of dynamic signals. We used computational modelling of the interaction of a ligand with multiple targets to investigate how kinetic and thermodynamic parameters regulate their capabilities to respond to dynamic signals. Our results demonstrated that the detection and discrimination of temporal features of signal inputs occur for reactions proceeding outside mass-action equilibrium. For these reactions, thermodynamic parameters such as affinity do not predict their outcomes. Additionally, we showed that, at non-equilibrium, the association rate constants determine the amount of product formed in reversible reactions. In contrast, the dissociation rate constants regulate the time interval required for reversible reactions to achieve equilibrium and, consequently, control their ability to detect and discriminate dynamic features of cellular signals.