Bistable switches for synaptic plasticity

Localization of long-term synaptic plasticity defects in cerebellar circuits using optokinetic reflex learning profile

Objective.Functional maps of the central nervous system attribute the coordination and control of many body movements directly or indirectly to the cerebellum. Despite this general picture, there is little information on the function of cerebellar neural components at the circuit level. The presence of multiple synaptic junctions and the synergistic action of different types of plasticity make it virtually difficult to determine the distinct contribution of cerebellar neural processes to behavioral manifestations. In this study, investigating the effect of long-term synaptic changes on cerebellar motor learning, we intend to provide quantitative criteria for localizing defects in the major forms of synaptic plasticity in the cerebellum.Approach.To this end, we develop a firing rate model of the cerebellar circuits to simulate learning of optokinetic reflex (OKR), one of the most well-known cerebellar-dependent motor tasks. In the following, by comparing the simulated OKR learning profile for normal and pathosynaptic conditions, we extract the learning features affected by long-term plasticity disorders. Next, conducting simulation with different massed (continuous with no rest) and spaced (interleaved with rest periods) learning paradigms, we estimate the detrimental impact of plasticity defects at corticonuclear synapses on short- and long-term motor memory.Main results.Our computational approach predicts a correlation between location and grade of the defect with some learning factors such as the rate of formation and retention of motor memory, baseline performance, and even cerebellar motor reserve capacity. Further, spacing analysis reveal the dependence of learning paradigm efficiency on the spatiotemporal characteristic of defect in the network. Indeed, defects in cortical memory formation and nuclear memory consolidation mainly harm massed and spaced learning, respectively. This result is used to design a differential assay for identifying the faulty phases of cerebellar learning.Significance.The proposed computational framework can help develop neural-screening systems and prepare meso-scale functional maps of the cerebellar circuits.

Computational analysis of memory consolidation following inhibitory avoidance (IA) training in adult and infant rats: Critical roles of CaMKIIα and MeCP2

Key features of long-term memory (LTM), such as its stability and persistence, are acquired during processes collectively referred to as consolidation. The dynamics of biological changes during consolidation are complex. In adult rodents, consolidation exhibits distinct periods during which the engram is more or less resistant to disruption. Moreover, the ability to consolidate memories differs during developmental periods. Although the molecular mechanisms underlying consolidation are poorly understood, the initial stages rely on interacting signaling pathways that regulate gene expression, including brain-derived neurotrophic factor (BDNF) and Ca2+/calmodulin-dependent protein kinase II α (CaMKIIα) dependent feedback loops. We investigated the ways in which these pathways may contribute to developmental and dynamical features of consolidation. A computational model of molecular processes underlying consolidation following inhibitory avoidance (IA) training in rats was developed. Differential equations described the actions of CaMKIIα, multiple feedback loops regulating BDNF expression, and several transcription factors including methyl-CpG binding protein 2 (MeCP2), histone deacetylase 2 (HDAC2), and SIN3 transcription regulator family member A (Sin3a). This model provides novel explanations for the (apparent) rapid forgetting of infantile memory and the temporal progression of memory consolidation in adults. Simulations predict that dual effects of MeCP2 on the expression of bdnf, and interaction between MeCP2 and CaMKIIα, play critical roles in the rapid forgetting of infantile memory and the progress of memory resistance to disruptions. These insights suggest new potential targets of therapy for memory impairment.

Postsynaptic Stability and Variability Described by a Stochastic Model of Endosomal Trafficking

Neurons undergo dynamic processes of constitutive AMPA-type glutamate receptor (AMPAR) trafficking, such as the insertion and internalization of AMPARs by exocytosis and endocytosis, while stably maintaining synaptic efficacy. Studies using advanced imaging techniques have suggested that the frequency of these constitutive trafficking processes, as well as the number of AMPARs that are involved in a particular event highly fluctuate. In addition, mechanisms that trigger some forms of synaptic plasticity have been shown to include not only these processes but also additional fluctuating processes, such as the sorting of AMPARs to late endosomes (LEs). Thus, the regulation of postsynaptic AMPARs by the endosomal trafficking system appears to have superficially conflicting properties between the stability or organized control of plasticity and highly fluctuating or stochastic processes. However, it is not clear how the endosomal trafficking system reconciles and utilizes such conflicting properties. Although deterministic models have been effective to describe the stable maintenance of synaptic AMPAR numbers by constitutive recycling, as well as the involvement of endosomal trafficking in synaptic plasticity, they do not take stochasticity into account. In this study, we introduced the stochasticity into the model of each crucial machinery of the endosomal trafficking system. The specific questions we solved by our improved model are whether stability is accomplished even with a combination of fluctuating processes, and how overall variability occurs while controlling long-term synaptic depression (LTD). Our new stochastic model indeed demonstrated the stable regulation of postsynaptic AMPAR numbers at the basal state and during LTD maintenance, despite fast fluctuations in AMPAR numbers as well as high variability in the time course and amounts of LTD. In addition, our analysis suggested that the high variability arising from this stochasticity is beneficial for reproducing the relatively constant timing of LE sorting for LTD. We therefore propose that the coexistence of stability and stochasticity in the endosomal trafficking system is suitable for stable synaptic transmission and the reliable induction of synaptic plasticity, with variable properties that have been observed experimentally.

Timely regulated sorting from early to late endosomes is required to maintain cerebellar long-term depression

An important feature of long-term synaptic plasticity is the prolonged maintenance of plastic changes in synaptic transmission. The trafficking of AMPA-type glutamate receptors (AMPARs) is involved in the expression of many forms of synaptic plasticity, yet the subsequent events accomplishing the maintenance of plastic changes in synaptic AMPAR numbers are not fully understood. Here, we find that maintenance of cerebellar long-term depression results from a reduction in the number of AMPARs residing within endocytic recycling pathways. We then develop a genetically encoded, photosensitive inhibitor of late endosome sorting and use this to discover that initial maintenance of long-term depression relies on timely regulated late endosome sorting, which exhibits a threshold as well as switch-like behavior. Thus, our results indicate that recycling AMPAR numbers are reduced by a switching machinery of transient late endosome sorting, and that this process enables the transition from basal synaptic transmission to long-term depression maintenance.

Long term depression (LTD) of the cerebellum is known to be mediated by postsynaptic trafficking of glutamate receptor AMPAR. Here, Kim and colleagues show that early- to late-endosomal sorting of AMPAR represents the switch from expression to maintenance phase of cerebellar LTD.

Computational model of a positive BDNF feedback loop in hippocampal neurons following inhibitory avoidance training

Inhibitory avoidance (IA) training in rodents initiates a molecular cascade within hippocampal neurons. This cascade contributes to the transition of short- to long-term memory (i.e., consolidation). Here, a differential equation-based model was developed to describe a positive feedback loop within this molecular cascade. The feedback loop begins with an IA-induced release of brain-derived neurotrophic factor (BDNF), which in turn leads to rapid phosphorylation of the cAMP response element-binding protein (pCREB), and a subsequent increase in the level of the β isoform of the CCAAT/enhancer binding protein (C/EBPβ). Increased levels of C/EBPβ lead to increased bdnf expression. Simulations predicted that an empirically observed delay in the BDNF-pCREB-C/EBPβ feedback loop has a profound effect on the dynamics of consolidation. The model also predicted that at least two independent self-sustaining signaling pathways downstream from the BDNF-pCREB-C/EBPβ feedback loop contribute to consolidation. Currently, the nature of these downstream pathways is unknown.

Mechanisms producing time course of cerebellar long-term depression

Cerebellar long-term depression (LTD) is induced by short-lasting synaptic activities, progressively expressed, and then maintained for hours or longer. Short-lasting events, such as calcium transients, are activated and required for the induction of LTD. Further, a positive-feedback kinase loop was shown to follow the transient events and to aid the transition between LTD induction and prolonged synaptic depression. Yet, it is not entirely clear as to how LTD is maintained and how the maintenance mechanisms are activated, mainly because of a lack of experimental studies regarding this topic, while an idea has been theoretically proposed. A new analysis of the experimental results suggests that early maintenance mechanisms display a threshold behavior and that they may be of stochastic nature. This suggestion is conceptually consistent with an idea from a computational study, which postulates that other bistable switch systems are required for LTD maintenance. We thus propose that cellular mechanisms showing a threshold behavior and a stochastic nature maintain LTD, and that future experimental studies in search of such mechanisms would be an important step toward fully understanding the time course of LTD.

Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control

The biophysical models of spike-timing-dependent plasticity have explored dynamics with molecular basis for such computational concepts as coincidence detection, synaptic eligibility trace, and Hebbian learning. They overall support different learning algorithms in different brain areas, especially supervised learning in the cerebellum. Because a single spine is physically very small, chemical reactions at it are essentially stochastic, and thus sensitivity-longevity dilemma exists in the synaptic memory. Here, the cascade of excitable and bistable dynamics is proposed to overcome this difficulty. All kinds of learning algorithms in different brain regions confront with difficult generalization problems. For resolution of this issue, the control of the degrees-of-freedom can be realized by changing synchronicity of neural firing. Especially, for cerebellar supervised learning, the triangle closed-loop circuit consisting of Purkinje cells, the inferior olive nucleus, and the cerebellar nucleus is proposed as a circuit to optimally control synchronous firing and degrees-of-freedom in learning.

The protein kinase Mζ network as a bistable switch to store neuronal memory

Background

Protein kinase Mζ (PKMζ), the brain-specific, atypical protein kinase C isoform, plays a key role in long-term maintenance of memory. This molecule is essential for long-term potentiation of the neuron and various modalities of learning such as spatial memory and fear conditioning. It is unknown, however, how PKMζ stores information for long periods of time despite molecular turnover.

Results

We hypothesized that PKMζ forms a bistable switch because it appears to constitute a positive feedback loop (PKMζ induces its local synthesis) part of which is ultrasensitive (PKMζ stimulates its synthesis through dual pathways). To examine this hypothesis, we modeled the biochemical network of PKMζ with realistic kinetic parameters. Bifurcation analyses of the model showed that the system maintains either the up state or the down state according to previous inputs. Furthermore, the model was able to reproduce a variety of previous experimental results regarding synaptic plasticity and learning, which suggested that it captures the essential mechanism for neuronal memory. We proposed in vitro and in vivo experiments that would critically examine the validity of the model and illuminate the pivotal role of PKMζ in synaptic plasticity and learning.

Conclusions

This study revealed bistability of the PKMζ network and supported its pivotal role in long-term storage of memory.

The sensitivity of memory consolidation and reconsolidation to inhibitors of protein synthesis and kinases: computational analysis

Memory consolidation and reconsolidation require kinase activation and protein synthesis. Blocking either process during or shortly after training or recall disrupts memory stabilization, which suggests the existence of a critical time window during which these processes are necessary. Using a computational model of kinase synthesis and activation, we investigated the ways in which the dynamics of molecular positive-feedback loops may contribute to the time window for memory stabilization and memory maintenance. In the models, training triggered a transition in the amount of kinase between two stable states, which represented consolidation. Simulating protein synthesis inhibition (PSI) from before to 40 min after training blocked or delayed consolidation. Beyond 40 min, substantial (>95%) PSI had little effect despite the fact that the elevated amount of kinase was maintained by increased protein synthesis. However, PSI made established memories labile to perturbations. Simulations of kinase inhibition produced similar results. In addition, similar properties were found in several other models that also included positive-feedback loops. Even though our models are based on simplifications of the actual mechanisms of molecular consolidation, they illustrate the practical difficulty of empirically measuring "time windows" for consolidation. This is particularly true when consolidation and reconsolidation of memory depends, in part, on the dynamics of molecular positive-feedback loops.

CaMKII "autonomy" is required for initiating but not for maintaining neuronal long-term information storage

Ca(2+)/calmodulin (CaM)-dependent protein kinase II (CaMKII) "autonomy" (T286-autophosphorylation-induced Ca(2+)-independent activity) is required for long-term potentiation (LTP) and for learning and memory, as demonstrated by CaMKII T286A mutant mice. The >20-year-old hypothesis that CaMKII stimulation is required for LTP induction, while CaMKII autonomy is required for LTP maintenance was recently supported using the cell-penetrating fusion-peptide inhibitor antCN27. However, we demonstrate here that ant/penetratin fusion to CN27 compromised CaMKII-selectivity, by enhancing a previously unnoticed direct binding of CaM to ant/penetratin. In contrast to antCN27, the improved cell-penetrating inhibitor tatCN21 (5 mum) showed neither CaM binding nor inhibition of basal synaptic transmission. In vitro, tatCN21 inhibited stimulated and autonomous CaMKII activity with equal potency. In rat hippocampal slices, tatCN21 inhibited LTP induction, but not LTP maintenance. Correspondingly, tatCN21 also inhibited learning, but not memory storage or retrieval in a mouse in vivo model. Thus, CaMKII autonomy provides a short-term molecular memory that is important in the signal computation leading to memory formation, but is not required as long-term memory store.

Electroconvulsive seizure increases phosphorylation of PKC substrates, including GAP-43, MARCKS, and neurogranin, in rat brain

Protein kinase C (PKC) has been suggested as a molecular target related to the pathogenetic and therapeutic mechanisms of mood disorders in which electroconvulsive seizure (ECS) is effective. However, the reports concerning the effects of ECS on PKC are anecdotal and need further clarification. In this study, we examined the effects of ECS treatment on the phosphorylation of PKC substrates, including GAP-43, MARCKS, and neurogranin. Immunoblot using anti-p-PKC substrate antibodies revealed that a single ECS treatment induced temporal changes in the phosphorylation level of PKC substrates in rat brain, reflecting the effects on PKC activity. Phosphorylation of GAP-43 and MARCKS, representative PKC substrates related to synaptic remodeling, increased from 5 to 30 min, after a transient decrease at 0 min immediately after ECS, and returned to basal levels at 60 min in rat frontal cortex, hippocampus, and cerebellum. Phosphorylation of neurogranin, another PKC substrate, showed a similar pattern of temporal changes in the frontal cortex and hippocampus. Immunohistochemical analysis revealed that p-GAP-43 and p-MARCKS were densely stained throughout the neuronal cells of the prefrontal cortex and hippocampus, and the Purkinje cells of cerebellum, after ECS treatment. Brief and transient activation of PKC may be translated into long-term biochemical changes, resulting in synaptic plasticity. Taken together, the acute effects of ECS on PKC activity, which could be an underpinning of long-term biochemical changes induced by ECS, may contribute to understand the molecular mechanism of ECS.

Making cellular memories

The induction of a protracted response to a brief stimulus is a form of cellular memory. Here we describe the role of transcriptional regulation in both natural and synthetic memory networks and discuss the potential applications of engineered memory networks in medicine and industrial biotechnology.