Hyperexcitability and translational phenotypes in a preclinical mouse model of SYNGAP1-Related Intellectual Disability.

Disruption of SYNGAP1 directly causes a genetically identifiable neurodevelopmental disorder (NDD) called SYNGAP1-related intellectual disability (SRID). Without functional SynGAP1 protein, individuals are developmentally delayed and have prominent features of intellectual disability, motor impairments, and epilepsy. Over the past two decades, there have been numerous discoveries indicting the critical role of Syngap1. Several rodent models with a loss of Syngap1 have been engineered identifying precise roles in neuronal structure and function, as well as key biochemical pathways key for synapse integrity. Homozygous loss of SYNGAP1/Syngap1 is lethal. Heterozygous mutations of Syngap1 result in a broad range of behavioral phenotypes. Our in vivo functional data, using the original mouse model from the Huganir laboratory, corroborated behaviors including robust hyperactivity and deficits in learning and memory in young adults. Furthermore, we described impairments in the domain of sleep, characterized using neurophysiological data collected with wireless, telemetric electroencephalography (EEG). Syngap1+/- mice exhibited elevated spiking events and spike trains, in addition to elevated power, most notably in the delta power frequency. For the first time, we illustrated primary neurons from Syngap1+/- mice displayed increased network firing activity, greater bursts, and shorter inter-burst intervals between peaks by employing high density microelectrode arrays (HD-MEA). Our work bridges in-vitro electrophysiological neuronal activity and function with in vivo neurophysiological brain activity and function. These data elucidate quantitative, translational biomarkers in vivo and in vitro that can be utilized for the development and efficacy assessment of targeted treatments for SRID.

Complex biophysical changes and reduced neuronal firing in an SCN8A variant associated with developmental delay and epilepsy.

Mutations in the SCN8A gene, encoding the voltage-gated sodium channel NaV1.6, are associated with a range of neurodevelopmental syndromes. The p.(Gly1625Arg) (G1625R) mutation was identified in a patient diagnosed with developmental epileptic encephalopathy (DEE). While most of the characterized DEE-associated SCN8A mutations were shown to cause a gain-of-channel function, we show that the G1625R variant, positioned within the S4 segment of domain IV, results in complex effects. Voltage-clamp analyses of NaV1.6G1625R demonstrated a mixture of gain- and loss-of-function properties, including reduced current amplitudes, increased time constant of fast voltage-dependent inactivation, a depolarizing shift in the voltage dependence of activation and inactivation, and increased channel availability with high-frequency repeated depolarization. Current-clamp analyses in transfected cultured neurons revealed that these biophysical properties caused a marked reduction in the number of action potentials when firing was driven by the transfected mutant NaV1.6. Accordingly, computational modeling of mature cortical neurons demonstrated a mild decrease in neuronal firing when mimicking the patients' heterozygous SCN8A expression. Structural modeling of NaV1.6G1625R suggested the formation of a cation-π interaction between R1625 and F1588 within domain IV. Double-mutant cycle analysis revealed that this interaction affects the voltage dependence of inactivation in NaV1.6G1625R. Together, our studies demonstrate that the G1625R variant leads to a complex combination of gain and loss of function biophysical changes that result in an overall mild reduction in neuronal firing, related to the perturbed interaction network within the voltage sensor domain, necessitating personalized multi-tiered analysis for SCN8A mutations for optimal treatment selection.

Hyperexcitability and translational phenotypes in a preclinical model of SYNGAP1 mutations.

SYNGAP1 is a critical gene for neuronal development, synaptic structure, and function. Although rare, the disruption of SYNGAP1 directly causes a genetically identifiable neurodevelopmental disorder (NDD) called SYNGAP1 -related intellectual disability. Without functional SynGAP1 protein, patients present with intellectual disability, motor impairments, and epilepsy. Previous work using mouse models with a variety of germline and conditional mutations has helped delineate SynGAP1's critical roles in neuronal structure and function, as well as key biochemical signaling pathways essential to synapse integrity. Homozygous loss of SYNGAP1 is embryonically lethal. Heterozygous mutations of SynGAP1 result in a broad range of phenotypes including increased locomotor activity, impaired working spatial memory, impaired cued fear memory, and increased stereotypic behavior. Our in vivo functional data, using the original germline mutation mouse line from the Huganir laboratory, corroborated robust hyperactivity and learning and memory deficits. Here, we describe impairments in the translational biomarker domain of sleep, characterized using neurophysiological data collected with wireless telemetric electroencephalography (EEG). We discovered Syngap1+/- mice exhibited elevated spike trains in both number and duration, in addition to elevated power, most notably in the delta power band. Primary neurons from Syngap1+/- mice displayed increased network firing activity, greater spikes per burst, and shorter inter-burst intervals between peaks using high density micro-electrode arrays (HD-MEA). This work is translational, innovative, and highly significant as it outlines functional impairments in Syngap1 mutant mice. Simultaneously, the work utilized untethered, wireless neurophysiology that can discover potential biomarkers of Syngap1 RI-D, for clinical trials, as it has done with other NDDs. Our work is substantial forward progress toward translational work for SynGAP1R-ID as it bridges in-vitro electrophysiological neuronal activity and function with in vivo neurophysiological brain activity and function. These data elucidate multiple quantitative, translational biomarkers in vivo and in vitro for the development of treatments for SYNGAP1-related intellectual disability.

Hyperexcitability and translational phenotypes in a preclinical model of SYNGAP1 mutations.

SYNGAP1 is a critical gene for neuronal development, synaptic structure, and function. Although rare, the disruption of SYNGAP1 directly causes a genetically identifiable neurodevelopmental disorder (NDD) called SYNGAP1-related intellectual disability. Without functional SynGAP1 protein, patients present with intellectual disability, motor impairments, and epilepsy. Previous work using mouse models with a variety of germline and conditional mutations has helped delineate SynGAP1's critical roles in neuronal structure and function, as well as key biochemical signaling pathways essential to synapse integrity. Homozygous loss of SYNGAP1 is embryonically lethal. Heterozygous mutations of SynGAP1 result in a broad range of phenotypes including increased locomotor activity, impaired working spatial memory, impaired cued fear memory, and increased stereotypic behavior. Our in vivo functional data, using the original germline mutation mouse line from the Huganir laboratory, corroborated robust hyperactivity and learning and memory deficits. Here, we describe impairments in the translational biomarker domain of sleep, characterized using neurophysiological data collected with wireless telemetric electroencephalography (EEG). We discovered Syngap1 +/- mice exhibited elevated spike trains in both number and duration, in addition to elevated power, most notably in the delta power band. Primary neurons from Syngap1 +/- mice displayed increased network firing activity, greater spikes per burst, and shorter inter-burst intervals between peaks using high density micro-electrode arrays (HD-MEA). This work is translational, innovative, and highly significant as it outlines functional impairments in Syngap1 mutant mice. Simultaneously, the work utilized untethered, wireless neurophysiology that can discover potential biomarkers of Syngap1R-ID, for clinical trials, as it has done with other NDDs. Our work is substantial forward progress toward translational work for SynGAP1R-ID as it bridges in-vitro electrophysiological neuronal activity and function with in vivo neurophysiological brain activity and function. These data elucidate multiple quantitative, translational biomarkers in vivo and in vitro for the development of treatments for SYNGAP1-related intellectual disability.

Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models.

Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.

Cellular and behavioral effects of altered NaV1.2 sodium channel ion permeability in Scn2aK1422E mice.

Genetic variants in SCN2A, encoding the NaV1.2 voltage-gated sodium channel, are associated with a range of neurodevelopmental disorders with overlapping phenotypes. Some variants fit into a framework wherein gain-of-function missense variants that increase neuronal excitability lead to developmental and epileptic encephalopathy, while loss-of-function variants that reduce neuronal excitability lead to intellectual disability and/or autism spectrum disorder (ASD) with or without co-morbid seizures. One unique case less easily classified using this framework is the de novo missense variant SCN2A-p.K1422E, associated with infant-onset developmental delay, infantile spasms and features of ASD. Prior structure-function studies demonstrated that K1422E substitution alters ion selectivity of NaV1.2, conferring Ca2+ permeability, lowering overall conductance and conferring resistance to tetrodotoxin (TTX). Based on heterologous expression of K1422E, we developed a compartmental neuron model incorporating variant channels that predicted reductions in peak action potential (AP) speed. We generated Scn2aK1422E mice and characterized effects on neurons and neurological/neurobehavioral phenotypes. Cultured cortical neurons from heterozygous Scn2aK1422E/+ mice exhibited lower current density with a TTX-resistant component and reversal potential consistent with mixed ion permeation. Recordings from Scn2aK1442E/+ cortical slices demonstrated impaired AP initiation and larger Ca2+ transients at the axon initial segment during the rising phase of the AP, suggesting complex effects on channel function. Scn2aK1422E/+ mice exhibited rare spontaneous seizures, interictal electroencephalogram abnormalities, altered induced seizure thresholds, reduced anxiety-like behavior and alterations in olfactory-guided social behavior. Overall, Scn2aK1422E/+ mice present with phenotypes similar yet distinct from other Scn2a models, consistent with complex effects of K1422E on NaV1.2 channel function.

NeuroGPU: Accelerating multi-compartment, biophysically detailed neuron simulations on GPUs.

BACKGROUND: The membrane potential of individual neurons depends on a large number of interacting biophysical processes operating on spatial-temporal scales spanning several orders of magnitude. The multi-scale nature of these processes dictates that accurate prediction of membrane potentials in specific neurons requires the utilization of detailed simulations. Unfortunately, constraining parameters within biologically detailed neuron models can be difficult, leading to poor model fits. This obstacle can be overcome partially by numerical optimization or detailed exploration of parameter space. However, these processes, which currently rely on central processing unit (CPU) computation, often incur orders of magnitude increases in computing time for marginal improvements in model behavior. As a result, model quality is often compromised to accommodate compute resources. NEW METHOD: Here, we present a simulation environment, NeuroGPU, that takes advantage of the inherent parallelized structure of the graphics processing unit (GPU) to accelerate neuronal simulation. RESULTS & COMPARISON WITH EXISTING METHODS: NeuroGPU can simulate most biologically detailed models 10-200 times faster than NEURON simulation running on a single core and 5 times faster than GPU simulators (CoreNEURON). NeuroGPU is designed for model parameter tuning and best performs when the GPU is fully utilized by running multiple (> 100) instances of the same model with different parameters. When using multiple GPUs, NeuroGPU can reach to a speed-up of 800 fold compared to single core simulations, especially when simulating the same model morphology with different parameters. We demonstrate the power of NeuoGPU through large-scale parameter exploration to reveal the response landscape of a neuron. Finally, we accelerate numerical optimization of biophysically detailed neuron models to achieve highly accurate fitting of models to simulation and experimental data. CONCLUSIONS: Thus, NeuroGPU is the fastest available platform that enables rapid simulation of multi-compartment, biophysically detailed neuron models on commonly used computing systems accessible by many scientists.

Paradoxical hyperexcitability from NaV1.2 sodium channel loss in neocortical pyramidal cells.

Loss-of-function variants in the gene SCN2A, which encodes the sodium channel NaV1.2, are strongly associated with autism spectrum disorder and intellectual disability. An estimated 20%-30% of children with these variants also suffer from epilepsy, with altered neuronal activity originating in neocortex, a region where NaV1.2 channels are expressed predominantly in excitatory pyramidal cells. This is paradoxical, as sodium channel loss in excitatory cells would be expected to dampen neocortical activity rather than promote seizure. Here, we examined pyramidal neurons lacking NaV1.2 channels and found that they were intrinsically hyperexcitable, firing high-frequency bursts of action potentials (APs) despite decrements in AP size and speed. Compartmental modeling and dynamic-clamp recordings revealed that NaV1.2 loss prevented potassium channels from properly repolarizing neurons between APs, increasing overall excitability by allowing neurons to reach threshold for subsequent APs more rapidly. This cell-intrinsic mechanism may, therefore, account for why SCN2A loss-of-function can paradoxically promote seizure.

Alternative splicing potentiates dysfunction of early-onset epileptic encephalopathy SCN2A variants.

Epileptic encephalopathies are severe forms of infantile-onset epilepsy often complicated by severe neurodevelopmental impairments. Some forms of early-onset epileptic encephalopathy (EOEE) have been associated with variants in SCN2A, which encodes the brain voltage-gated sodium channel NaV1.2. Many voltage-gated sodium channel genes, including SCN2A, undergo developmentally regulated mRNA splicing. The early onset of these disorders suggests that developmentally regulated alternative splicing of NaV1.2 may be an important consideration when elucidating the pathophysiological consequences of epilepsy-associated variants. We hypothesized that EOEE-associated NaV1.2 variants would exhibit greater dysfunction in a splice isoform that is prominently expressed during early development. We engineered five EOEE-associated NaV1.2 variants (T236S, E999K, S1336Y, T1623N, and R1882Q) into the adult and neonatal splice isoforms of NaV1.2 and performed whole-cell voltage clamp to elucidate their functional properties. All variants exhibited functional defects that could enhance neuronal excitability. Three of the five variants (T236S, E999K, and S1336Y) exhibited greater dysfunction in the neonatal isoform compared with those observed in the adult isoform. Computational modeling of a developing cortical pyramidal neuron indicated that T236S, E999K, S1336Y, and R1882Q showed hyperexcitability preferentially in immature neurons. These results suggest that both splice isoform and neuronal developmental stage influence how EOEE-associated NaV1.2 variants affect neuronal excitability.

The Autism-Associated Gene Scn2a Contributes to Dendritic Excitability and Synaptic Function in the Prefrontal Cortex.

Autism spectrum disorder (ASD) is strongly associated with de novo gene mutations. One of the most commonly affected genes is SCN2A. ASD-associated SCN2A mutations impair the encoded protein NaV1.2, a sodium channel important for action potential initiation and propagation in developing excitatory cortical neurons. The link between an axonal sodium channel and ASD, a disorder typically attributed to synaptic or transcriptional dysfunction, is unclear. Here we show that NaV1.2 is unexpectedly critical for dendritic excitability and synaptic function in mature pyramidal neurons in addition to regulating early developmental axonal excitability. NaV1.2 loss reduced action potential backpropagation into dendrites, impairing synaptic plasticity and synaptic strength, even when NaV1.2 expression was disrupted in a cell-autonomous fashion late in development. These results reveal a novel dendritic function for NaV1.2, providing insight into cellular mechanisms probably underlying circuit and behavioral dysfunction in ASD.

Serotonin enhances excitability and gamma frequency temporal integration in mouse prefrontal fast-spiking interneurons.

The medial prefrontal cortex plays a key role in higher order cognitive functions like decision making and social cognition. These complex behaviors emerge from the coordinated firing of prefrontal neurons. Fast-spiking interneurons (FSIs) control the timing of excitatory neuron firing via somatic inhibition and generate gamma (30-100 Hz) oscillations. Therefore, factors that regulate how FSIs respond to gamma-frequency input could affect both prefrontal circuit activity and behavior. Here, we show that serotonin (5HT), which is known to regulate gamma power, acts via 5HT2A receptors to suppress an inward-rectifying potassium conductance in FSIs. This leads to depolarization, increased input resistance, enhanced spiking, and slowed decay of excitatory post-synaptic potentials (EPSPs). Notably, we found that slowed EPSP decay preferentially enhanced temporal summation and firing elicited by gamma frequency inputs. These findings show how changes in passive membrane properties can affect not only neuronal excitability but also the temporal filtering of synaptic inputs.

Opposing Effects on NaV1.2 Function Underlie Differences Between SCN2A Variants Observed in Individuals With Autism Spectrum Disorder or Infantile Seizures.

BACKGROUND: Variants in the SCN2A gene that disrupt the encoded neuronal sodium channel NaV1.2 are important risk factors for autism spectrum disorder (ASD), developmental delay, and infantile seizures. Variants observed in infantile seizures are predominantly missense, leading to a gain of function and increased neuronal excitability. How variants associated with ASD affect NaV1.2 function and neuronal excitability are unclear. METHODS: We examined the properties of 11 ASD-associated SCN2A variants in heterologous expression systems using whole-cell voltage-clamp electrophysiology and immunohistochemistry. Resultant data were incorporated into computational models of developing and mature cortical pyramidal cells that express NaV1.2. RESULTS: In contrast to gain of function variants that contribute to seizure, we found that all ASD-associated variants dampened or eliminated channel function. Incorporating these electrophysiological results into a compartmental model of developing excitatory neurons demonstrated that all ASD variants, regardless of their mechanism of action, resulted in deficits in neuronal excitability. Corresponding analysis of mature neurons predicted minimal change in neuronal excitability. CONCLUSIONS: This functional characterization thus identifies SCN2A mutation and NaV1.2 dysfunction as the most frequently observed ASD risk factor detectable by exome sequencing and suggests that associated changes in neuronal excitability, particularly in developing neurons, may contribute to ASD etiology.

ß-Arrestin-Dependent Dopaminergic Regulation of Calcium Channel Activity in the Axon Initial Segment.

G-protein-coupled receptors (GPCRs) initiate a variety of signaling cascades, depending on effector coupling. ß-arrestins, which were initially characterized by their ability to "arrest" GPCR signaling by uncoupling receptor and G protein, have recently emerged as important signaling effectors for GPCRs. ß-arrestins engage signaling pathways that are distinct from those mediated by G protein. As such, arrestin-dependent signaling can play a unique role in regulating cell function, but whether neuromodulatory GPCRs utilize ß-arrestin-dependent signaling to regulate neuronal excitability remains unclear. Here, we find that D3 dopamine receptors (D3R) regulate axon initial segment (AIS) excitability through ß-arrestin-dependent signaling, modifying CaV3 voltage dependence to suppress high-frequency action potential generation. This non-canonical D3R signaling thereby gates AIS excitability via pathways distinct from classical GPCR signaling pathways.

Accelerating compartmental modeling on a graphical processing unit.

Compartmental modeling is a widely used tool in neurophysiology but the detail and scope of such models is frequently limited by lack of computational resources. Here we implement compartmental modeling on low cost Graphical Processing Units (GPUs), which significantly increases simulation speed compared to NEURON. Testing two methods for solving the current diffusion equation system revealed which method is more useful for specific neuron morphologies. Regions of applicability were investigated using a range of simulations from a single membrane potential trace simulated in a simple fork morphology to multiple traces on multiple realistic cells. A runtime peak 150-fold faster than the CPU was achieved. This application can be used for statistical analysis and data fitting optimizations of compartmental models and may be used for simultaneously simulating large populations of neurons. Since GPUs are forging ahead and proving to be more cost-effective than CPUs, this may significantly decrease the cost of computation power and open new computational possibilities for laboratories with limited budgets.

Optimizing ion channel models using a parallel genetic algorithm on graphical processors.

We have recently shown that we can semi-automatically constrain models of voltage-gated ion channels by combining a stochastic search algorithm with ionic currents measured using multiple voltage-clamp protocols. Although numerically successful, this approach is highly demanding computationally, with optimization on a high performance Linux cluster typically lasting several days. To solve this computational bottleneck we converted our optimization algorithm for work on a graphical processing unit (GPU) using NVIDIA's CUDA. Parallelizing the process on a Fermi graphic computing engine from NVIDIA increased the speed ∼180 times over an application running on an 80 node Linux cluster, considerably reducing simulation times. This application allows users to optimize models for ion channel kinetics on a single, inexpensive, desktop "super computer," greatly reducing the time and cost of building models relevant to neuronal physiology. We also demonstrate that the point of algorithm parallelization is crucial to its performance. We substantially reduced computing time by solving the ODEs (Ordinary Differential Equations) so as to massively reduce memory transfers to and from the GPU. This approach may be applied to speed up other data intensive applications requiring iterative solutions of ODEs.