Synaptic plasticity in vitro and in silico: insights into an intracellular signaling maze

Synapse diversity and synaptome architecture in human genetic disorders

Over 130 brain diseases are caused by mutations that disrupt genes encoding the proteome of excitatory synapses. These include neurological and psychiatric disorders with early and late onset such as autism, schizophrenia and depression and many other rarer conditions. The proteome of synapses is highly complex with over 1000 conserved proteins which are differentially expressed generating a vast, potentially unlimited, number of synapse types. The diversity of synapses and their location in the brain are described by the synaptome. A recent study has mapped the synaptome across the mouse brain, revealing that synapse diversity is distributed into an anatomical architecture observed at scales from individual dendrites to the whole systems level. The synaptome architecture is built from the hierarchical expression and assembly of proteins into complexes and supercomplexes which are distributed into different synapses. Mutations in synapse proteins change the synaptome architecture leading to behavioral phenotypes. Mutations in the mechanisms regulating the hierarchical assembly of the synaptome, including transcription and proteostasis, may also change synapse diversity and synaptome architecture. The logic of synaptome hierarchical assembly provides a mechanistic framework that explains how diverse genetic disorders can converge on synapses in different brain circuits to produce behavioral phenotypes.

Challenges in Reproducibility, Replicability, and Comparability of Computational Models and Tools for Neuronal and Glial Networks, Cells, and Subcellular Structures

The possibility to replicate and reproduce published research results is one of the biggest challenges in all areas of science. In computational neuroscience, there are thousands of models available. However, it is rarely possible to reimplement the models based on the information in the original publication, let alone rerun the models just because the model implementations have not been made publicly available. We evaluate and discuss the comparability of a versatile choice of simulation tools: tools for biochemical reactions and spiking neuronal networks, and relatively new tools for growth in cell cultures. The replicability and reproducibility issues are considered for computational models that are equally diverse, including the models for intracellular signal transduction of neurons and glial cells, in addition to single glial cells, neuron-glia interactions, and selected examples of spiking neuronal networks. We also address the comparability of the simulation results with one another to comprehend if the studied models can be used to answer similar research questions. In addition to presenting the challenges in reproducibility and replicability of published results in computational neuroscience, we highlight the need for developing recommendations and good practices for publishing simulation tools and computational models. Model validation and flexible model description must be an integral part of the tool used to simulate and develop computational models. Constant improvement on experimental techniques and recording protocols leads to increasing knowledge about the biophysical mechanisms in neural systems. This poses new challenges for computational neuroscience: extended or completely new computational methods and models may be required. Careful evaluation and categorization of the existing models and tools provide a foundation for these future needs, for constructing multiscale models or extending the models to incorporate additional or more detailed biophysical mechanisms. Improving the quality of publications in computational neuroscience, enabling progressive building of advanced computational models and tools, can be achieved only through adopting publishing standards which underline replicability and reproducibility of research results.

Postsynaptic signal transduction models for long-term potentiation and depression

More than a hundred biochemical species, activated by neurotransmitters binding to transmembrane receptors, are important in long-term potentiation (LTP) and long-term depression (LTD). To investigate which species and interactions are critical for synaptic plasticity, many computational postsynaptic signal transduction models have been developed. The models range from simple models with a single reversible reaction to detailed models with several hundred kinetic reactions. In this study, more than a hundred models are reviewed, and their features are compared and contrasted so that similarities and differences are more readily apparent. The models are classified according to the type of synaptic plasticity that is modeled (LTP or LTD) and whether they include diffusion or electrophysiological phenomena. Other characteristics that discriminate the models include the phase of synaptic plasticity modeled (induction, expression, or maintenance) and the simulation method used (deterministic or stochastic). We find that models are becoming increasingly sophisticated, by including stochastic properties, integrating with electrophysiological properties of entire neurons, or incorporating diffusion of signaling molecules. Simpler models continue to be developed because they are computationally efficient and allow theoretical analysis. The more complex models permit investigation of mechanisms underlying specific properties and experimental verification of model predictions. Nonetheless, it is difficult to fully comprehend the evolution of these models because (1) several models are not described in detail in the publications, (2) only a few models are provided in existing model databases, and (3) comparison to previous models is lacking. We conclude that the value of these models for understanding molecular mechanisms of synaptic plasticity is increasing and will be enhanced further with more complete descriptions and sharing of the published models.

The h current is a candidate mechanism for regulating the sliding modification threshold in a BCM-like synaptic learning rule

Hebbian synaptic plasticity acts as a positive feedback mechanism and can destabilize a neuronal network unless concomitant homeostatic processes that counterbalance this instability are activated. Within a Bienenstock-Cooper-Munro (BCM)-like plasticity framework, such compensation is achieved through a modification threshold that slides in an activity-dependent fashion. Although the BCM-like plasticity framework has been a useful formulation to understand synaptic plasticity and metaplasticity, a mechanism for the activity-dependent regulation of this modification threshold has remained an open question. In this simulation study based on CA1 pyramidal cells, we use a modification of the calcium-dependent hypothesis proposed elsewhere and show that a change in the hyperpolarization-activated, nonspecific-cation h current is capable of shifting the modification threshold. Based on the direction of such a shift in relation to changes in the h current, and supported by previous experimental results, we argue that the h current fits the requirements for an activity-dependent regulator of this modification threshold. Additionally, using the same framework, we show that multiple voltage- and ligand-gated ion channels present in a neuronal compartment can regulate the modification threshold through complex interactions among themselves. Our results underscore the heavy mutual interdependence of synaptic and intrinsic properties/plasticity in regulating learning and homeostasis in single neurons and their networks under both physiological and pathological brain states.

Corticolimbic expression of TRPC4 and TRPC5 channels in the rodent brain

The canonical transient receptor potential (TRPC) channels are a family of non-selective cation channels that are activated by increases in intracellular Ca(2+) and G(q)/phospholipase C-coupled receptors. We used quantitative real-time PCR, in situ hybridization, immunoblots and patch-clamp recording from several brain regions to examine the expression of the predominant TRPC channels in the rodent brain. Quantitative real-time PCR of the seven TRPC channels in the rodent brain revealed that TRPC4 and TRPC5 channels were the predominant TRPC subtypes in the adult rat brain. In situ hybridization histochemistry and immunoblotting further resolved a dense corticolimbic expression of the TRPC4 and TRPC5 channels. Total protein expression of HIP TRPC4 and 5 proteins increased throughout development and peaked late in adulthood (6-9 weeks). In adults, TRPC4 expression was high throughout the frontal cortex, lateral septum (LS), pyramidal cell layer of the hippocampus (HIP), dentate gyrus (DG), and ventral subiculum (vSUB). TRPC5 was highly expressed in the frontal cortex, pyramidal cell layer of the HIP, DG, and hypothalamus. Detailed examination of frontal cortical layer mRNA expression indicated TRPC4 mRNA is distributed throughout layers 2-6 of the prefrontal cortex (PFC), motor cortex (MCx), and somatosensory cortex (SCx). TRPC5 mRNA expression was concentrated specifically in the deep layers 5/6 and superficial layers 2/3 of the PFC and anterior cingulate. Patch-clamp recording indicated a strong metabotropic glutamate-activated cation current-mediated depolarization that was dependent on intracellular Ca(2+)and inhibited by protein kinase C in brain regions associated with dense TRPC4 or 5 expression and absent in regions lacking TRPC4 and 5 expression. Overall, the dense corticolimbic expression pattern suggests that these Gq/PLC coupled nonselective cation channels may be involved in learning, memory, and goal-directed behaviors.