Characterization of seizure-like events recorded in vivo in a mouse model of Rett syndrome

Panx1 channels promote both anti- and pro-seizure-like activities in the zebrafish via p2rx7 receptors and ATP signaling

The molecular mechanisms of excitation/inhibition imbalances promoting seizure generation in epilepsy patients are not fully understood. Evidence suggests that Pannexin1 (Panx1), an ATP release channel, modulates the excitability of the brain. In this report, we performed electrophysiological, behavioral, and molecular phenotyping experiments on zebrafish larvae bearing genetic or pharmacological knockouts of Panx1a and Panx1b channels, each homologous to human PANX1. When Panx1a function is lost, or both channels are under pharmacological blockade, seizures with ictal-like events and seizure-like locomotion are reduced in the presence of pentylenetetrazol. Transcriptome profiling by RNA-seq demonstrates a spectrum of distinct metabolic and cell signaling states which correlate with the loss of Panx1a. Furthermore, the pro- and anticonvulsant activities of both Panx1 channels affect ATP release and involve the purinergic receptor P2rx7. Our findings suggest a subfunctionalization of Panx1 enabling dual roles in seizures, providing a unique and comprehensive perspective to understanding seizure mechanisms in the context of this channel.

Group I metabotropic glutamate receptor-mediated long term depression is disrupted in the hippocampus of WAG/Rij rats modelling absence epilepsy

Absence epilepsy is frequently associated with cognitive dysfunction, although the underlying mechanisms are not well understood. Here we report that some forms of hippocampal synaptic plasticity are abnormal in symptomatic Wistar Albino Glaxo/Rijswijk (WAG/Rij) rats. Metabotropic Glu 1/5 receptor-mediated long term depression (LTD) at Schaffer collateral CA1 synapses is significantly reduced in symptomatic, 5-6 months old WAG/Rij rats compared to age-matched non epileptic control rats. There were no significant changes in mGlu1/5-dependent LTD in pre-symptomatic, 4-6 weeks old WAG/Rij rats compared to age matched controls. The changes in LTD found in symptomatic WAG/Rij forms are not indicative of general deficits in all forms of synaptic plasticity as long term potentiation (LTP) was unchanged. Immunoblot analysis of hippocampal tissue showed a significant reduction in mGlu5 receptor expression, a trend to an increase in pan Homer protein levels and a decrease in GluA1 receptor expression in the hippocampus of symptomatic WAG/Rij rats vs non-epileptic control rats. There were no changes in mGlu1α receptor or GluA2 protein levels. These findings suggest that abnormalities in hippocampal mGlu5 receptor-dependent synaptic plasticity are associated with the pathological phenotype of WAG/Rij rats. This lays the groundwork for the study of mGlu5 receptors as a candidate drug target for the treatment of cognitive dysfunction linked to absence epilepsy.

Deletion of a non-canonical regulatory sequence causes loss of Scn1a expression and epileptic phenotypes in mice

BACKGROUND

Genes with multiple co-active promoters appear common in brain, yet little is known about functional requirements for these potentially redundant genomic regulatory elements. SCN1A, which encodes the NaV1.1 sodium channel alpha subunit, is one such gene with two co-active promoters. Mutations in SCN1A are associated with epilepsy, including Dravet syndrome (DS). The majority of DS patients harbor coding mutations causing SCN1A haploinsufficiency; however, putative causal non-coding promoter mutations have been identified.

METHODS

To determine the functional role of one of these potentially redundant Scn1a promoters, we focused on the non-coding Scn1a 1b regulatory region, previously described as a non-canonical alternative transcriptional start site. We generated a transgenic mouse line with deletion of the extended evolutionarily conserved 1b non-coding interval and characterized changes in gene and protein expression, and assessed seizure activity and alterations in behavior.

RESULTS

Mice harboring a deletion of the 1b non-coding interval exhibited surprisingly severe reductions of Scn1a and NaV1.1 expression throughout the brain. This was accompanied by electroencephalographic and thermal-evoked seizures, and behavioral deficits.

CONCLUSIONS

This work contributes to functional dissection of the regulatory wiring of a major epilepsy risk gene, SCN1A. We identified the 1b region as a critical disease-relevant regulatory element and provide evidence that non-canonical and seemingly redundant promoters can have essential function.

Transitions between neocortical seizure and non-seizure-like states and their association with presynaptic glutamate release

The transition between seizure and non-seizure states in neocortical epileptic networks is governed by distinct underlying dynamical processes. Based on the gamma distribution of seizure and inter-seizure durations, over time, seizures are highly likely to self-terminate; whereas, inter-seizure durations have a low chance of transitioning back into a seizure state. Yet, the chance of a state transition could be formed by multiple overlapping, unknown synaptic mechanisms. To identify the relationship between the underlying synaptic mechanisms and the chance of seizure-state transitions, we analyzed the skewed histograms of seizure durations in human intracranial EEG and seizure-like events (SLEs) in local field potential activity from mouse neocortical slices, using an objective method for seizure state classification. While seizures and SLE durations were demonstrated to have a unimodal distribution (gamma distribution shape parameter >1), suggesting a high likelihood of terminating, inter-SLE intervals were shown to have an asymptotic exponential distribution (gamma distribution shape parameter <1), suggesting lower probability of cessation. Then, to test cellular mechanisms for these distributions, we studied the modulation of synaptic neurotransmission during, and between, the in vitro SLEs. Using simultaneous local field potential and whole-cell voltage clamp recordings, we found a suppression of presynaptic glutamate release at SLE termination, as demonstrated by electrically- and optogenetically-evoked excitatory postsynaptic currents (EPSCs), and focal hypertonic sucrose application. Adenosine A1 receptor blockade interfered with the suppression of this release, changing the inter-SLE shape parameter from asymptotic exponential to unimodal, altering the chance of state transition occurrence with time. These findings reveal a critical role for presynaptic glutamate release in determining the chance of neocortical seizure state transitions.

Dosage-sensitive genes in autism spectrum disorders: From neurobiology to therapy

Autism spectrum disorders (ASDs) are a group of heterogenous neurodevelopmental disorders affecting 1 in 59 children. Syndromic ASDs are commonly associated with chromosomal rearrangements or dosage imbalance involving a single gene. Many of these genes are dosage-sensitive and regulate transcription, protein homeostasis, and synaptic function in the brain. Despite vastly different molecular perturbations, syndromic ASDs share core symptoms including social dysfunction and repetitive behavior. However, each ASD subtype has a unique pathogenic mechanism and combination of comorbidities that require individual attention. We have learned a great deal about how these dosage-sensitive genes control brain development and behaviors from genetically-engineered mice. Here we describe the clinical features of eight monogenic neurodevelopmental disorders caused by dosage imbalance of four genes, as well as recent advances in using genetic mouse models to understand their pathogenic mechanisms and develop intervention strategies. We propose that applying newly developed quantitative molecular and neuroscience technologies will advance our understanding of the unique neurobiology of each disorder and enable the development of personalized therapy.

Neuro-Glial Network Model Of Postictal Generalized EEG Suppression (PGES)

Over the past couple of decades, glial cells have been highlighted as active agents in hyperexcitability of neuronal networks, specifically playing key roles in seizure onset and termination. In particular, microglia have been suggested to have both neuroprotective and neurotoxic effects on the brain. Investigation into seizure termination is of particular interest, as it is sometimes followed by a postictal generalized EEG suppression (PGES) - a low activity state that is potentially associated with sudden unexpected death in epilepsy. In this study, we attempt to link glial effects - synaptic pruning and astrocytic potassium clearance - to the duration of spontaneous epileptiform discharges (SEDs) as well as interSED intervals (iSEDs). We build upon an earlier model of a neuroglial network by translating it into the cortical paradigm and including microglial units. Preliminary findings of our model demonstrated that the duration of SEDs is largely determined by the astrocytic potassium clearance, whereas iSEDs significantly increased with microglial-driven synaptic pruning. In our model, astrocytic potassium clearance itself did not bring a PGES-like state, whereas microglial effects did, which suggests a potential biomarker for PGES phenomena.

Phase Coherent Currents Underlying Neocortical Seizure-Like State Transitions

In the epileptic brain, phase amplitude cross-frequency coupling (CFC) features have been used to objectively classify seizure-related states, and the inter-seizure state has been demonstrated as being random, in contrast to the seizure state being predictable; however, the excitatory and inhibitory networks underlying their dynamics remain unclear. Therefore, the objectives of this study are to classify the dynamics of seizure sub-states labeling seizure-like event (SLE) onset and termination intervals using CFC features and to obtain their underlying excitatory/inhibitory cellular correlates. SLEs were induced in mouse neocortical brain slices using a low-magnesium perfusate, and were recorded in Layer II/III using simultaneous local field potential (LFP) and whole-cell voltage clamp electrodes. Classification of onset and termination of SLE transitions was investigated using CFC features in conjunction with an unsupervised two-state hidden Markov model (HMM). γ-Distributions of their durations indicated that both are predictable. Furthermore, omitting 4 Hz from the HMM classifier switched both SLE sub-states from statistically deterministic to random without changing the dynamics of the SLE state. These results were generalized to 4-aminopyridine (4-AP)-induced SLEs and human seizure traces. Only during these sub-states, both excitatory and inhibitory currents coupled with the field. Where excitatory currents phase locked to a broad range of frequencies between 1 and 12 Hz, inhibitory currents dominantly phase locked at 4 Hz. We conclude that inhibition underlies the predictability of neocortical CFC-defined SLE transition sub-states.

Coupled Oscillators Model of Hyperexcitable Neuroglial Networks

Glial populations within neuronal networks of the brain have recently gained much interest in the context of hyperexcitability and epilepsy. In this paper, we present an oscillator-based neuroglial model capable of generating Spontaneous Electrical Discharges (SEDs) in hyperexcitable conditions. The network is composed of 16 coupled Cognitive Rhythm Generators (CRGs), which are oscillator-based mathematical constructs previously described by our research team. CRGs are well-suited for modeling assemblies of excitable cells, and in this network, each represents one of the following populations: excitatory pyramidal cells, inhibitory interneurons, astrocytes, and microglia. We investigated various pathways leading to hyperexcitability, and our results suggest an important role for astrocytes and microglia in the generation of SEDs of various durations. Analysis of the resultant SEDs revealed two underlying duration distributions with differing properties. Particularly, short and long SEDs are associated with deterministic and random underlying processes, respectively. The mesoscale of this model makes it well-suited for (a) the elucidation of glia-related hypotheses in hyperexcitable conditions, (b) use as a testing platform for neuromodulation purposes, and (c) a hardware implementation for closed-loop neuromodulation.

New functions of Semaphorin 3E and its receptor PlexinD1 during developing and adult hippocampal formation

The development and maturation of cortical circuits relies on the coordinated actions of long and short range axonal guidance cues. In this regard, the class 3 semaphorins and their receptors have been seen to be involved in the development and maturation of the hippocampal connections. However, although the role of most of their family members have been described, very few data about the participation of Semaphorin 3E (Sema3E) and its receptor PlexinD1 during the development and maturation of the entorhino-hippocampal (EH) connection are available. In the present study, we focused on determining their roles both during development and in adulthood. We determined a relevant role for Sema3E/PlexinD1 in the layer-specific development of the EH connection. Indeed, mice lacking Sema3E/PlexinD1 signalling showed aberrant layering of entorhinal axons in the hippocampus during embryonic and perinatal stages. In addition, absence of Sema3E/PlexinD1 signalling results in further changes in postnatal and adult hippocampal formation, such as numerous misrouted ectopic mossy fibers. More relevantly, we describe how subgranular cells express PlexinD1 and how the absence of Sema3E induces a dysregulation of the proliferation of dentate gyrus progenitors leading to the presence of ectopic cells in the molecular layer. Lastly, Sema3E mutant mice displayed increased network excitability both in the dentate gyrus and the hippocampus proper.

Low-to-High Cross-Frequency Coupling in the Electrical Rhythms as Biomarker for Hyperexcitable Neuroglial Networks of the Brain

OBJECTIVE

One of the features used in the study of hyperexcitablility is high-frequency oscillations (HFOs, >80 Hz). HFOs have been reported in the electrical rhythms of the brain's neuroglial networks under physiological and pathological conditions. Cross-frequency coupling (CFC) of HFOs with low-frequency rhythms was used to identify pathologic HFOs in the epileptogenic zones of epileptic patients and as a biomarker for the severity of seizure-like events in genetically modified rodent models. We describe a model to replicate reported CFC features extracted from recorded local field potentials (LFPs) representing network properties.

METHODS

This study deals with a four-unit neuroglial cellular network model where each unit incorporates pyramidal cells, interneurons, and astrocytes. Three different pathways of hyperexcitability generation-Na - ATPase pump, glial potassium clearance, and potassium afterhyperpolarization channel-were used to generate LFPs. Changes in excitability, average spontaneous electrical discharge (SED) duration, and CFC were then measured and analyzed.

RESULTS

Each parameter caused an increase in network excitability and the consequent lengthening of the SED duration. Short SEDs showed CFC between HFOs and theta oscillations (4-8 Hz), but in longer SEDs the low frequency changed to the delta range (1-4 Hz).

CONCLUSION

Longer duration SEDs exhibit CFC features similar to those reported by our team.

SIGNIFICANCE

First, Identifying the exponential relationship between network excitability and SED durations; second, highlighting the importance of glia in hyperexcitability (as they relate to extracellular potassium); and third, elucidation of the biophysical basis for CFC coupling features.

Dynamics of convulsive seizure termination and postictal generalized EEG suppression

It is not fully understood how seizures terminate and why some seizures are followed by a period of complete brain activity suppression, postictal generalized EEG suppression. This is clinically relevant as there is a potential association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden death following a seizure. We combined human encephalographic seizure data with data of a computational model of seizures to elucidate the neuronal network dynamics underlying seizure termination and the postictal generalized EEG suppression state. A multi-unit computational neural mass model of epileptic seizure termination and postictal recovery was developed. The model provided three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subjects with refractory focal epilepsy (20 females, age range 15-61 years). The duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative of a deterministic process (shape parameter 2.6 and 1.5, respectively) as predicted by the model. In the model and in humans, the time between two clonic bursts increased exponentially from the start of the clonic phase of the seizure. The terminal interclonic interval, calculated using the projected terminal value of the log-linear fit of the clonic frequency decrease was correlated with the presence and duration of postictal suppression. The projected terminal interclonic interval explained 41% of the variation in postictal generalized EEG suppression duration (P < 0.02). Conversely, postictal generalized EEG suppression duration explained 34% of the variation in the last interclonic interval duration. Our findings suggest that postictal generalized EEG suppression is a separate brain state and that seizure termination is a plastic and autonomous process, reflected in increased duration of interclonic intervals that determine the duration of postictal generalized EEG suppression.

Prediction of antiepileptic drug treatment outcomes using machine learning

OBJECTIVE

Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs.

APPROACH

Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (I _CFC) of the delta (2-5 Hz) rhythm with the fast ripple (400-600 Hz) rhythm in epileptiform discharges. Using the I _CFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes.

MAIN RESULTS

(a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) I _CFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably.

SIGNIFICANCE

Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Methyl-CpG binding-protein 2 function in cholinergic neurons mediates cardiac arrhythmogenesis

Sudden unexpected death occurs in one quarter of deaths in Rett Syndrome (RTT), a neurodevelopmental disorder caused by mutations in Methyl-CpG-binding protein 2 (MECP2). People with RTT show a variety of autonomic nervous system (ANS) abnormalities and mouse models show similar problems including QTc interval prolongation and hypothermia. To explore the role of cardiac problems in sudden death in RTT, we characterized cardiac rhythm in mice lacking Mecp2 function. Male and female mutant mice exhibited spontaneous cardiac rhythm abnormalities including bradycardic events, sinus pauses, atrioventricular block, premature ventricular contractions, non-sustained ventricular arrhythmias, and increased heart rate variability. Death was associated with spontaneous cardiac arrhythmias and complete conduction block. Atropine treatment reduced cardiac arrhythmias in mutant mice, implicating overactive parasympathetic tone. To explore the role of MeCP2 within the parasympathetic neurons, we selectively removed MeCP2 function from cholinergic neurons (MeCP2 ChAT KO), which recapitulated the cardiac rhythm abnormalities, hypothermia, and early death seen in RTT male mice. Conversely, restoring MeCP2 only in cholinergic neurons rescued these phenotypes. Thus, MeCP2 in cholinergic neurons is necessary and sufficient for autonomic cardiac control, thermoregulation, and survival, and targeting the overactive parasympathetic system may be a useful therapeutic strategy to prevent sudden unexpected death in RTT.

Support vector machines using EEG features of cross-frequency coupling can predict treatment outcome in Mecp2-deficient mice

Anti-convulsive drug treatments of epilepsy typically produce varied outcomes from one patient to the next, often necessitating patients to go through several anticonvulsive drug trials until an appropriate treatment is found. The focus of this study is to predict treatment outcome using a priori electroencephalogram (EEG) features for a rare genetic model of epilepsy seen in patients with Rett Syndrome. Previous work on Mecp2-deficient mice, exhibiting the symptoms of Rett syndrome, have revealed EEG-based biomarkers that track the pathology well. Specifically the presence of cross-frequency coupling of the delta-like (3-6 Hz) frequency range phase with the fast ripple (400 - 600 Hz) frequency range amplitude in long duration discharges was found to track seizure pathology. Support Vector Machines (SVM) were trained with features generated from phase-amplitude comodulograms and tested on (n=6) Mecp2-deficient mice to predict treatment outcome to Midazolam, a commonly used anti-convulsive drug. Using SVMs it was shown that it is possible to generate a likelihood score to predict treatment outcomes on all of the animal subjects. Identifying the most appropriate treatment a priori would potentially lead to improved treatment outcomes.

Epilepsy in Rett syndrome--lessons from the Rett networked database

OBJECTIVE

Rett syndrome is an X-linked dominant neurodevelopmental disorder caused by mutations in the MECP2 gene, and characterized by cognitive and communicative regression, loss of hand use, and midline hand stereotypies. Epilepsy is a core symptom, but literature is controversial regarding genotype-phenotype correlation. Analysis of data from a large cohort should overcome this shortcoming.

METHODS

Data from the Rett Syndrome Networked Database on 1,248 female patients were included. Data on phenotypic and genotypic parameters, age of onset, severity of epilepsy, and type of seizures were collected. Statistical analysis was done using the IBM SPSS Version 21 software, logistic regression, and Kaplan-Meier survival curves.

RESULTS

Epilepsy was present in 68.1% of the patients, with uncontrolled seizures in 32.6% of the patients with epilepsy. Mean age of onset of epilepsy was 4.68 ± (standard deviation) 3.5 years. Younger age of onset was correlated to severity of epilepsy (Spearman correlation r = 0.668, p < 0.01). Patients with late truncating deletions had lower prevalence of epilepsy. Compared to them, the p.R133C mutation, associated with a milder Rett phenotype, increased the risk for epilepsy (odds ratio [OR] 2.46, confidence interval [CI] 95% 1.3-4.66), but not for severe epilepsy. The p.R255X mutation conferred an increased risk for epilepsy (OR 2.07, CI 95% 1.2-3.59) as well as for severe epilepsy (OR 3.4, CI 95% 1.6-7.3). The p.T158M and p.C306C mutations relatively increased the risk for severe epilepsy (OR 3.09 and 2.69, CI 95% 1.48-6.4 and 1.19-6.05, respectively), but not for epilepsy occurrence.

SIGNIFICANCE

Various mutations in the MECP2 gene have a different influence on epilepsy, unrelated to the severity of the general Rett phenotype. This might suggest a site-specific effect of MeCp2 on epileptic pathways. Further investigation of these mechanisms should promote better understanding of epileptogenesis in Rett syndrome.

Reduced synaptic activity in neuronal networks derived from embryonic stem cells of murine Rett syndrome model

Neurodevelopmental diseases such as the Rett syndrome (RTT) have received renewed attention, since the mechanisms involved may underlie a broad range of neuropsychiatric disorders such as schizophrenia and autism. In vertebrates early stages in the functional development of neurons and neuronal networks are difficult to study. Embryonic stem cell-derived neurons provide an easily accessible tool to investigate neuronal differentiation and early network formation. We used in vitro cultures of neurons derived from murine embryonic stem cells missing the methyl-CpG-binding protein 2 (MECP2) gene (MeCP2-/y) and from wild type cells of the corresponding background. Cultures were assessed using whole-cell patch-clamp electrophysiology and immunofluorescence. We studied the functional maturation of developing neurons and the activity of the synaptic connections they formed. Neurons exhibited minor differences in the developmental patterns for their intrinsic parameters, such as resting membrane potential and excitability; with the MeCP2-/y cells showing a slightly accelerated development, with shorter action potential half-widths at early stages. There was no difference in the early phase of synapse development, but as the cultures matured, significant deficits became apparent, particularly for inhibitory synaptic activity. MeCP2-/y embryonic stem cell-derived neuronal cultures show clear developmental deficits that match phenotypes observed in slice preparations and thus provide a compelling tool to further investigate the mechanisms behind RTT pathophysiology.