Rare and common epilepsies converge on a shared gene regulatory network providing opportunities for novel antiepileptic drug discovery

Generation of iPSC lines (KAIMRCi003A, KAIMRCi003B) from a Saudi patient with Dravet syndrome carrying homozygous mutation in the CPLX1 gene and heterozygous mutation in SCN9A

The most prevalent form of epileptic encephalopathy is Dravet syndrome (DRVT), which is triggered by the pathogenic variant SCN1A in 80% of cases. iPSCs with different SCN1A mutations have been constructed by several groups to model DRVT syndrome. However, no studies involving DRVT-iPSCs with rare genetic variants have been conducted. Here, we established two DRVT-iPSC lines harboring a homozygous mutation in the CPLX1 gene and heterozygous mutation in SCN9A gene. Therefore, the derivation of these iPSC lines provides a unique cellular platform to dissect the molecular mechanisms underlying the cellular dysfunctions consequent to CPLX1 and SCN9A mutations.

Impacted spike frequency adaptation associated with reduction of KCNQ2/3 exacerbates seizure activity in temporal lobe epilepsy

Numerous epilepsy-related genes have been identified in recent decades by unbiased genome-wide screens. However, the available druggable targets for temporal lobe epilepsy (TLE) remain limited. Furthermore, a substantial pool of candidate genes potentially applicable to TLE therapy awaits further validation. In this study, we reveal the significant role of KCNQ2 and KCNQ3, two M-type potassium channel genes, in the onset of seizures in TLE. Our investigation began with a quantitative analysis of two publicly available TLE patient databases to establish a correlation between seizure onset and the downregulated expression of KCNQ2/3. We then replicated these pathological changes in a pilocarpine seizure mouse model and observed a decrease in spike frequency adaptation due to the affected M-currents in dentate gyrus granule neurons. In addition, we performed a small-scale simulation of the dentate gyrus network and confirmed that the impaired spike frequency adaptation of granule cells facilitated epileptiform activity throughout the network. This, in turn, resulted in prolonged seizure duration and reduced interictal intervals. Our findings shed light on an underlying mechanism contributing to ictogenesis in the TLE hippocampus and suggest a promising target for the development of antiepileptic drugs.

Identifying Novel Drug Targets for Epilepsy Through a Brain Transcriptome-Wide Association Study and Protein-Wide Association Study with Chemical-Gene-Interaction Analysis

Epilepsy is a severe neurological condition affecting 50-65 million individuals worldwide that can lead to brain damage. Nevertheless, the etiology of epilepsy remains poorly understood. Meta-analyses of genome-wide association studies involving 15,212 epilepsy cases and 29,677 controls of the ILAE Consortium cohort were used to conduct transcriptome-wide association studies (TWAS) and protein-wide association studies (PWAS). Furthermore, a protein-protein interaction (PPI) network was generated using the STRING database, and significant epilepsy-susceptible genes were verified using chip data. Chemical-related gene set enrichment analysis (CGSEA) was performed to determine novel drug targets for epilepsy. TWAS analysis identified 21,170 genes, of which 58 were significant (TWASfdr < 0.05) in ten brain regions, and 16 differentially expressed genes were verified based on mRNA expression profiles. The PWAS identified 2249 genes, of which 2 were significant (PWASfdr < 0.05). Through chemical-gene set enrichment analysis, 287 environmental chemicals associated with epilepsy were identified. We identified five significant genes (WIPF1, IQSEC1, JAM2, ICAM3, and ZNF143) that had causal relationships with epilepsy. CGSEA identified 159 chemicals that were significantly correlated with epilepsy (Pcgsea < 0.05), such as pentobarbital, ketone bodies, and polychlorinated biphenyl. In summary, we performed TWAS, PWAS (for genetic factors), and CGSEA (for environmental factors) analyses and identified several epilepsy-associated genes and chemicals. The results of this study will contribute to our understanding of genetic and environmental factors for epilepsy and may predict novel drug targets.

Precision medicine for genetic epilepsy on the horizon: Recent advances, present challenges, and suggestions for continued progress

The genetic basis of many epilepsies is increasingly understood, giving rise to the possibility of precision treatments tailored to specific genetic etiologies. Despite this, current medical therapy for most epilepsies remains imprecise, aimed primarily at empirical seizure reduction rather than targeting specific disease processes. Intellectual and technological leaps in diagnosis over the past 10 years have not yet translated to routine changes in clinical practice. However, the epilepsy community is poised to make impressive gains in precision therapy, with continued innovation in gene discovery, diagnostic ability, and bioinformatics; increased access to genetic testing and counseling; fuller understanding of natural histories; agility and rigor in preclinical research, including strategic use of emerging model systems; and engagement of an evolving group of stakeholders (including patient advocates, governmental resources, and clinicians and scientists in academia and industry). In each of these areas, we highlight notable examples of recent progress, new or persistent challenges, and future directions. The future of precision medicine for genetic epilepsy looks bright if key opportunities on the horizon can be pursued with strategic and coordinated effort.

Interactome overlap between risk genes of epilepsy and targets of anti-epileptic drugs

Aanti-epileptic drugs have been used for treating epilepsy for decades, meanwhile, more than one hundred genes have been identified to be associated with risk of epilepsy; however, the interaction mechanism between anti-epileptic drugs and risk genes of epilepsy was still not clearly understood. In this study, we systematically explored the interaction of epilepsy risk genes and anti-epileptic drug targets through a network-based approach. Our results revealed that anti-epileptic drug targets were significantly over-represented in risk genes of epilepsy with 17 overlapping genes and P-value = 2.2 ×10 −16. We identified a significantly localized PPI network with 55 epileptic risk genes and 94 anti-epileptic drug target genes, and network overlap analysis showed significant interactome overlap between risk genes and drug targets with P-value = 0.04. Besides, genes from PPI network were significantly enriched in the co-expression network of epilepsy with 22 enriched genes and P-value = 1.3 ×10 −15; meanwhile, cell type enrichment analysis indicated genes in this network were significantly enriched in 4 brain cell types (Interneuron, Medium Spiny Neuron, CA1 pyramidal Neuron, and Somatosensory pyramidal Neuron). These results provide evidence for significant interactions between epilepsy risk genes and anti-epileptic drug targets from the perspective of network biology.

Transcriptome meta-analysis of valproic acid exposure in human embryonic stem cells

Valproic acid (VPA) is a widely used antiepileptic drug not recommended in pregnancy because it is teratogenic. Many assays have assessed the impact of the VPA exposure on the transcriptome of human embryonic stem-cells (hESC), but the molecular perturbations that VPA exerts in neurodevelopment are not completely understood. This study aimed to perform a transcriptome meta-analysis of VPA-exposed hESC to elucidate the main biological mechanisms altered by VPA effects on the gene expression. Publicly available microarray and RNA-seq transcriptomes were selected in the Gene Expression Omnibus (GEO) repository. Samples were processed according to the standard pipelines for each technology in the Galaxy server and R. Meta-analysis was performed using the Fisher-P method. Overrepresented genes were obtained by evaluating ontologies, pathways, and phenotypes' databases. The meta-analysis performed in seven datasets resulted in 61 perturbed genes, 54 upregulated. Ontology and pathway enrichments suggested neurodevelopment and neuroinflammatory effects; phenotype overrepresentation included epilepsy-related genes, such as SCN1A and GABRB2. The NDNF gene upregulation was also identified; this gene is involved in neuron migration and survival during development. Sub-network analysis proposed TGFβ and BMP pathways activation. These results suggest VPA exerts effects in epilepsy-related genes even in embryonic cells. Neurodevelopmental genes, such as NDNF were upregulated and VPA might also disturb several development pathways. These mechanisms might help to explain the spectrum of VPA-induced congenital anomalies and the molecular effects on neurodevelopment.

Doublecortin mutation leads to persistent defects in the Golgi apparatus and mitochondria in adult hippocampal pyramidal cells

Human doublecortin (DCX) mutations are associated with severe brain malformations leading to aberrant neuron positioning (heterotopia), intellectual disability and epilepsy. DCX is a microtubule-associated protein which plays a key role during neurodevelopment in neuronal migration and differentiation. Dcx knockout (KO) mice show disorganized hippocampal pyramidal neurons. The CA2/CA3 pyramidal cell layer is present as two abnormal layers and disorganized CA3 KO pyramidal neurons are also more excitable than wild-type (WT) cells. To further identify abnormalities, we characterized Dcx KO hippocampal neurons at subcellular, molecular and ultrastructural levels. Severe defects were observed in mitochondria, affecting number and distribution. Also, the Golgi apparatus was visibly abnormal, increased in volume and abnormally organized. Transcriptome analyses from laser microdissected hippocampal tissue at postnatal day 60 (P60) highlighted organelle abnormalities. Ultrastructural studies of CA3 cells performed in P60 (young adult) and > 9 months (mature) tissue showed that organelle defects are persistent throughout life. Locomotor activity and fear memory of young and mature adults were also abnormal: Dcx KO mice consistently performed less well than WT littermates, with defects becoming more severe with age. Thus, we show that disruption of a neurodevelopmentally-regulated gene can lead to permanent organelle anomalies contributing to abnormal adult behavior.

Identification of potentially anti-COVID-19 active drugs using the connectivity MAP

Drug repurposing can be an interesting strategy for an emergency response to the severe acute respiratory syndrome-coronavirus-2, (SARS-COV-2), the causing agent of the coronavirus disease-19 (COVID-19) pandemic. For this, we applied the Connectivity Map (CMap) bioinformatic resource to identify drugs that generate, in the CMap database, gene expression profiles (GEP) that negatively correlate with a SARS-COV-2 GEP, anticipating that these drugs could antagonize the deleterious effects of the virus at cell, tissue or organism levels. We identified several anti-cancer compounds that target MDM2 in the p53 pathway or signaling proteins: Ras, PKBβ, Nitric Oxide synthase, Rho kinase, all involved in the transmission of proliferative and growth signals. We hypothesized that these drugs could interfere with the high rate of biomass synthesis in infected cells, a feature shared with cancer cells. Other compounds including etomoxir, triacsin-c, PTB1-IN-3, are known to modulate lipid metabolism or to favor catabolic reactions by activating AMPK. Four different anti-inflammatory molecules, including dexamethasone, fluorometholone and cytosporone-b, targeting the glucocorticoid receptor, cyclooxygenase, or NUR77 also came out of the analysis. These results represent a first step in the characterization of potential repositioning strategies to treat SARS-COV-2.

A systems-level analysis highlights microglial activation as a modifying factor in common epilepsies

AIMS

The causes of distinct patterns of reduced cortical thickness in the common human epilepsies, detectable on neuroimaging and with important clinical consequences, are unknown. We investigated the underlying mechanisms of cortical thinning using a systems-level analysis.

METHODS

Imaging-based cortical structural maps from a large-scale epilepsy neuroimaging study were overlaid with highly spatially resolved human brain gene expression data from the Allen Human Brain Atlas. Cell-type deconvolution, differential expression analysis and cell-type enrichment analyses were used to identify differences in cell-type distribution. These differences were followed up in post-mortem brain tissue from humans with epilepsy using Iba1 immunolabelling. Furthermore, to investigate a causal effect in cortical thinning, cell-type-specific depletion was used in a murine model of acquired epilepsy.

RESULTS

We identified elevated fractions of microglia and endothelial cells in regions of reduced cortical thickness. Differentially expressed genes showed enrichment for microglial markers and, in particular, activated microglial states. Analysis of post-mortem brain tissue from humans with epilepsy confirmed excess activated microglia. In the murine model, transient depletion of activated microglia during the early phase of the disease development prevented cortical thinning and neuronal cell loss in the temporal cortex. Although the development of chronic seizures was unaffected, the epileptic mice with early depletion of activated microglia did not develop deficits in a non-spatial memory test seen in epileptic mice not depleted of microglia.

CONCLUSIONS

These convergent data strongly implicate activated microglia in cortical thinning, representing a new dimension for concern and disease modification in the epilepsies, potentially distinct from seizure control.

Using common genetic variants to find drugs for common epilepsies

Better drugs are needed for common epilepsies. Drug repurposing offers the potential of significant savings in the time and cost of developing new treatments. In order to select the best candidate drug(s) to repurpose for a disease, it is desirable to predict the relative clinical efficacy that drugs will have against the disease. Common epilepsy can be divided into different types and syndromes. Different antiseizure medications are most effective for different types and syndromes of common epilepsy. For predictions of antiepileptic efficacy to be clinically translatable, it is essential that the predictions are specific to each form of common epilepsy, and reflect the patterns of drug efficacy observed in clinical studies and practice. These requirements are not fulfilled by previously published drug predictions for epilepsy. We developed a novel method for predicting the relative efficacy of drugs against any common epilepsy, by using its Genome-Wide Association Study summary statistics and drugs' activity data. The methodological advancement in our technique is that the drug predictions for a disease are based upon drugs' effects on the function and abundance of proteins, and the magnitude and direction of those effects, relative to the importance, degree and direction of the proteins' dysregulation in the disease. We used this method to predict the relative efficacy of all drugs, licensed for any condition, against each of the major types and syndromes of common epilepsy. Our predictions are concordant with findings from real-world experience and randomized clinical trials. Our method predicts the efficacy of existing antiseizure medications against common epilepsies; in this prediction, our method outperforms the best alternative existing method: area under receiver operating characteristic curve (mean ± standard deviation) 0.83 ± 0.03 and 0.63 ± 0.04, respectively. Importantly, our method predicts which antiseizure medications are amongst the more efficacious in clinical practice, and which antiseizure medications are amongst the less efficacious in clinical practice, for each of the main syndromes of common epilepsy, and it predicts the distinct order of efficacy of individual antiseizure medications in clinical trials of different common epilepsies. We identify promising candidate drugs for each of the major syndromes of common epilepsy. We screen five promising predicted drugs in an animal model: each exerts a significant dose-dependent effect upon seizures. Our predictions are a novel resource for selecting suitable candidate drugs that could potentially be repurposed for each of the major syndromes of common epilepsy. Our method is potentially generalizable to other complex diseases.

Real-life survey of pitfalls and successes of precision medicine in genetic epilepsies

OBJECTIVE

The term 'precision medicine' describes a rational treatment strategy tailored to one person that reverses or modifies the disease pathophysiology. In epilepsy, single case and small cohort reports document nascent precision medicine strategies in specific genetic epilepsies. The aim of this multicentre observational study was to investigate the deeper complexity of precision medicine in epilepsy.

METHODS

A systematic survey of patients with epilepsy with a molecular genetic diagnosis was conducted in six tertiary epilepsy centres including children and adults. A standardised questionnaire was used for data collection, including genetic findings and impact on clinical and therapeutic management.

RESULTS

We included 293 patients with genetic epilepsies, 137 children and 156 adults, 162 females and 131 males. Treatment changes were undertaken because of the genetic findings in 94 patients (32%), including rational precision medicine treatment and/or a treatment change prompted by the genetic diagnosis, but not directly related to known pathophysiological mechanisms. There was a rational precision medicine treatment for 56 patients (19%), and this was tried in 33/56 (59%) and was successful (ie, >50% seizure reduction) in 10/33 (30%) patients. In 73/293 (25%) patients there was a treatment change prompted by the genetic diagnosis, but not directly related to known pathophysiological mechanisms, and this was successful in 24/73 (33%).

SIGNIFICANCE

Our survey of clinical practice in specialised epilepsy centres shows high variability of clinical outcomes following the identification of a genetic cause for an epilepsy. Meaningful change in the treatment paradigm after genetic testing is not yet possible for many people with epilepsy. This systematic survey provides an overview of the current application of precision medicine in the epilepsies, and suggests the adoption of a more considered approach.

Distinct gene-set burden patterns underlie common generalized and focal epilepsies

Background

Analyses of few gene-sets in epilepsy showed a potential to unravel key disease associations. We set out to investigate the burden of ultra-rare variants (URVs) in a comprehensive range of biologically informed gene-sets presumed to be implicated in epileptogenesis.

Methods

The burden of 12 URV types in 92 gene-sets was compared between cases and controls using whole exome sequencing data from individuals of European descent with developmental and epileptic encephalopathies (DEE, n = 1,003), genetic generalized epilepsy (GGE, n = 3,064), or non-acquired focal epilepsy (NAFE, n = 3,522), collected by the Epi25 Collaborative, compared to 3,962 ancestry-matched controls.

Findings

Missense URVs in highly constrained regions were enriched in neuron-specific and developmental genes, whereas genes not expressed in brain were not affected. GGE featured a higher burden in gene-sets derived from inhibitory vs. excitatory neurons or associated receptors, whereas the opposite was found for NAFE, and DEE featured a burden in both. Top-ranked susceptibility genes from recent genome-wide association studies (GWAS) and gene-sets derived from generalized vs. focal epilepsies revealed specific enrichment patterns of URVs in GGE vs. NAFE.

Interpretation

Missense URVs affecting highly constrained sites differentially impact genes expressed in inhibitory vs. excitatory pathways in generalized vs. focal epilepsies. The excess of URVs in top-ranked GWAS risk-genes suggests a convergence of rare deleterious and common risk-variants in the pathogenesis of generalized and focal epilepsies.

Funding

DFG Research Unit FOR-2715 (Germany), FNR (Luxembourg), NHGRI (US), NHLBI (US), DAAD (Germany).

Gene networks and microRNAs: Promises and challenges for treating epilepsies and their comorbidities

Neurobiology research has used an essentially reductionist approach for many years, dissecting out the brain in more simple elements. Recent technical advances, like systems biology, have made now possible to embrace a more holistic vision and try to tackle the complexity of the system. In this short review, we describe how these approaches, in particular analyses or gene networks and of microRNAs, may be useful for epilepsy research. We will describe and discuss recent studies that illustrate how these research approaches can lead to the identification of therapeutic targets and pharmacological strategies to prevent or treat some forms of epilepsy. We aim to show that studying epilepsy and its comorbidities within a complex system framework is a promising integration to the traditional reductionist approaches, and that it will become more and more important in the future for developing new therapies. This article is part of the Special Issue "NEWroscience 2018."

Resting-state EEG theta activity reflects degree of genetic determination of the major epilepsy syndromes

OBJECTIVE

To explore relationship between EEG theta activity and clinical data that imply the degree of genetic determination of epilepsy.

METHODS

Clinical data of interest were epilepsy diagnosis and positive / negative family history of epilepsy. Study groups were: idiopathic generalized epilepsy (IGE), focal epilepsy (FE); FE of unknown etiology (FEUNK), FE of postnatal-acquired etiology (FEPA); all patients with positive / negative family history of epilepsy (FAPALL, FANALL, respectively), disregarding of the syndrome; FAP patients with 1st degree affected relative (FAP1) and those with 2nd degree epileptic relative only (FAP2). Quantitative EEG analysis assessed amount of theta (3.5-7.0 Hz) activity in 180 seconds of artifact-free waking EEG background activity for each patient and group. Group comparison was carried out by nonparametric statistics.

RESULTS

Differences of theta activity were: FAPALL > FANALL (p = 0.01); FAP1 > FAP2 (p = 0.2752). IGE > FE (p = 0.02); FEUNK > FEPA (p = 0.07).

CONCLUSIONS

This was the first attempt to explore and quantitatively ascertain relationship between an EEG variable and clinical data that imply greater or lesser degree of genetic determination in epilepsy.

SIGNIFICANCE

Theta activity is endophenotype that bridges the gap between epilepsy susceptibility genes and clinical phenotypes. Amount of theta activity is indicative of degree of genetic determination of the epilepsies.

Gene Editing and Modulation: the Holy Grail for the Genetic Epilepsies?

Epilepsy is a complex neurological disorder for which there are a large number of monogenic subtypes. Monogenic epilepsies are often severe and disabling, featuring drug-resistant seizures and significant developmental comorbidities. These disorders are potentially amenable to a precision medicine approach, of which genome editing using CRISPR/Cas represents the holy grail. Here we consider mutations in some of the most 'common' rare epilepsy genes and discuss the different CRISPR/Cas approaches that could be taken to cure these disorders. We consider scenarios where CRISPR-mediated gene modulation could serve as an effective therapeutic strategy and discuss whether a single gene corrective approach could hold therapeutic potential in the context of homeostatic compensation in the developing, highly dynamic brain. Despite an incomplete understanding of the mechanisms of the genetic epilepsies and current limitations of gene editing tools, CRISPR-mediated approaches have game-changing potential in the treatment of genetic epilepsy over the next decade.

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood-brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug-gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.

Modelling epilepsy in the mouse: challenges and solutions

In most mouse models of disease, the outward manifestation of a disorder can be measured easily, can be assessed with a trivial test such as hind limb clasping, or can even be observed simply by comparing the gross morphological characteristics of mutant and wild-type littermates. But what if we are trying to model a disorder with a phenotype that appears only sporadically and briefly, like epileptic seizures? The purpose of this Review is to highlight the challenges of modelling epilepsy, in which the most obvious manifestation of the disorder, seizures, occurs only intermittently, possibly very rarely and often at times when the mice are not under direct observation. Over time, researchers have developed a number of ways in which to overcome these challenges, each with their own advantages and disadvantages. In this Review, we describe the genetics of epilepsy and the ways in which genetically altered mouse models have been used. We also discuss the use of induced models in which seizures are brought about by artificial stimulation to the brain of wild-type animals, and conclude with the ways these different approaches could be used to develop a wider range of anti-seizure medications that could benefit larger patient populations.

Summary: This Review discusses the challenges of modelling epilepsy in mice, a condition in which the outward manifestation of the disorder appears only sporadically, and reviews possible solutions encompassing both genetic and induced models.

A Transcriptome-Based Drug Discovery Paradigm for Neurodevelopmental Disorders

Advances in genetic discoveries have created substantial opportunities for precision medicine in neurodevelopmental disorders. Many of the genes implicated in these diseases encode proteins that regulate gene expression, such as chromatin-associated proteins, transcription factors, and RNA-binding proteins. The identification of targeted therapeutics for individuals carrying mutations in these genes remains a challenge, as the encoded proteins can theoretically regulate thousands of downstream targets in a considerable number of cell types. Here, we propose the application of a drug discovery approach originally developed for cancer called "transcriptome reversal" for these neurodevelopmental disorders. This approach attempts to identify compounds that reverse gene-expression signatures associated with disease states. ANN NEUROL 2021;89:199-211.

Investigating rare and ultrarare epilepsy syndromes with Drosophila models

One in three epilepsy cases is drug resistant, and seizures often begin in infancy, when they are life-threatening and when therapeutic options are highly limited. An important tool for prioritizing and validating genes associated with epileptic conditions, which is suitable for large-scale screening, is disease modeling in Drosophila. Approximately two-thirds of disease genes are conserved in Drosophila, and gene-specific fly models exhibit behavioral changes that are related to symptoms of epilepsy. Models are based on behavior readouts, seizure-like attacks and paralysis following stimulation, and neuronal, cell-biological readouts that are in the majority based on changes in nerve cell activity or morphology. In this review, we focus on behavioral phenotypes. Importantly, Drosophila modeling is independent of, and complementary to, other approaches that are computational and based on systems analysis. The large number of known epilepsy-associated gene variants indicates a need for efficient research strategies. We will discuss the status quo of epilepsy disease modelling in Drosophila and describe promising steps towards the development of new drugs to reduce seizure rates and alleviate other epileptic symptoms.

"Omics" in traumatic brain injury: novel approaches to a complex disease

BACKGROUND

To date, there is neither any pharmacological treatment with efficacy in traumatic brain injury (TBI) nor any method to halt the disease progress. This is due to an incomplete understanding of the vast complexity of the biological cascades and failure to appreciate the diversity of secondary injury mechanisms in TBI. In recent years, techniques for high-throughput characterization and quantification of biological molecules that include genomics, proteomics, and metabolomics have evolved and referred to as omics.

METHODS

In this narrative review, we highlight how omics technology can be applied to potentiate diagnostics and prognostication as well as to advance our understanding of injury mechanisms in TBI.

RESULTS

The omics platforms provide possibilities to study function, dynamics, and alterations of molecular pathways of normal and TBI disease states. Through advanced bioinformatics, large datasets of molecular information from small biological samples can be analyzed in detail and provide valuable knowledge of pathophysiological mechanisms, to include in prognostic modeling when connected to clinically relevant data. In such a complex disease as TBI, omics enables broad categories of studies from gene compositions associated with susceptibility to secondary injury or poor outcome, to potential alterations in metabolites following TBI.

CONCLUSION

The field of omics in TBI research is rapidly evolving. The recent data and novel methods reviewed herein may form the basis for improved precision medicine approaches, development of pharmacological approaches, and individualization of therapeutic efforts by implementing mathematical "big data" predictive modeling in the near future.

Unexpected Transcriptional Programs Contribute to Hippocampal Memory Deficits and Neuronal Stunting after Early-Life Adversity

Early-life adversity (ELA) is associated with lifelong memory deficits, yet the responsible mechanisms remain unclear. We impose ELA by rearing rat pups in simulated poverty, assess hippocampal memory, and probe changes in gene expression, their transcriptional regulation, and the consequent changes in hippocampal neuronal structure. ELA rats have poor hippocampal memory and stunted hippocampal pyramidal neurons associated with ~140 differentially expressed genes. Upstream regulators of the altered genes include glucocorticoid receptor and, unexpectedly, the transcription factor neuron-restrictive silencer factor (NRSF/REST). NRSF contributes critically to the memory deficits because blocking its function transiently following ELA rescues spatial memory and restores the dendritic arborization of hippocampal pyramidal neurons in ELA rats. Blocking NRSF function in vitro augments dendritic complexity of developing hippocampal neurons, suggesting that NRSF represses genes involved in neuronal maturation. These findings establish important, surprising contributions of NRSF to ELA-induced transcriptional programming that disrupts hippocampal maturation and memory function.

Underlying Mechanisms and Candidate Drugs for COVID-19 Based on the Connectivity Map Database

Background

The coronavirus disease 2019 (COVID-19) has become a worldwide public health crisis. At present, there are no effective antiviral drugs to treat COVID-19. Although some vaccines have been developed, late-stage clinical trials that allow licensure by regulatory agencies are still needed. Previous reports have indicated that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and SARS-CoV are highly homologous and both use angiotensin-converting enzyme 2 (ACE2) as the receptor to enter cells, and SARS-CoV infection reduces the ACE2 expression in the lung. Therefore, the analysis of genes co-expressed with ACE2 in the lung may uncover the underlying mechanism of COVID-19. Finally, we used the Connectivity map (Cmap) database to search for candidate drugs using transcriptome profiles of patients with COVID-19.

Method

Based on the differentially expressed genes (DEGs), indicated by the expression of RNAs isolated from bronchoalveolar lavage fluid (BALF) cells of patients with COVID-19, we performed functional enrichment analysis and hub gene cluster analysis. Furthermore, we identified genes co-expressed with ACE2 in healthy lung samples and analyzed the significant genes. Additionally, to identify several candidate drugs for the treatment of COVID-19, we queried Cmap using DEGs and genes co-expressed with ACE2.

Results and Conclusion

The up-regulated genes in the BALF cells of patients with COVID-19 are related to viral mRNA translation. The down-regulated genes are related to immune response functions. Genes positively correlated with ACE2 are related to immune defense and those negatively correlated are enriched in synaptic transmission functions. The results reflected prosperous viral proliferation and immune dysfunction in patients. Furthermore, ACE2 may not only mediate viral entrance, but also play an important role in immune defense. By using Cmap with transcriptome profiles of patients with COVID-19, we identified candidate drugs for the treatment of COVID-19, such as amantadine and acyclovir.

Parallel metabolomics and lipidomics enables the comprehensive study of mouse brain regional metabolite and lipid patterns

Global profiling of the metabolome and lipidome of specific brain regions is essential to understanding the cellular and molecular mechanisms regulating brain activity. Given the limited amount of starting material, conventional mouse studies comparing brain regions have mainly targeted a set of known metabolites in large brain regions (e.g., cerebrum, cortex). In this work, we developed a multimodal analytical pipeline enabling parallel analyses of metabolomic and lipidomic profiles from anatomically distinct mouse brain regions starting with less than 0.2 mg of protein content. This analytical pipeline is composed of (1) sonication-based tissue homogenization, (2) parallel metabolite and lipid extraction, (3) BCA-based sample normalization, (4) ultrahigh performance liquid chromatography-mass spectrometry-based multimodal metabolome and lipidome profiling, (5) streamlined data processing, and (6) chord plot-based data visualization. We applied this pipeline to the study of four brain regions in males including the amygdala, dorsal hippocampus, nucleus accumbens and ventral tegmental area. With this novel approach, we detected over 5000 metabolic and 6000 lipid features, among which 134 metabolites and 479 lipids were directly confirmed via automated MS² spectral matching. Interestingly, our analysis identified unique metabolic and lipid profiles in each brain regions. Furthermore, we identified functional relationships amongst metabolic and lipid subclasses, potentially underlying cellular and functional differences across all four brain regions. Overall, our novel workflow generates comprehensive region-specific metabolomic and lipidomic profiles using very low amount of brain sub-regional tissue sample, which could be readily integrated with region-specific genomic, transcriptomic, and proteomic data to reveal novel insights into the molecular mechanisms underlying the activity of distinct brain regions.

Machine learning applications in drug development

Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.

Identifying Common Genes, Cell Types and Brain Regions Between Diseases of the Nervous System

Background: Diseases of the nervous system are widely considered to be caused by genetic mutations, and they have been shown to share pathogenic genes. Discovering the shared mechanisms of these diseases is useful for designing common treatments.

Method: In this study, by reviewing 518 articles published after 2007 on 20 diseases of the nervous system, we compiled data on 1607 mutations occurring in 365 genes, totals that are 1.9 and 3.2 times larger than those collected in the Clinvar database, respectively. A combination with the Clinvar data gives 2434 pathogenic mutations and 424 genes. Using this information, we measured the genetic similarities between the diseases according to the number of genes causing two diseases simultaneously. Further detection was carried out on the similarity between diseases in terms of cell types. Disease-related cell types were defined as those with disease-related gene enrichment among the marker genes of cells, as ascertained by analyzing single-cell sequencing data. Enrichment profiles of the disease-related genes over 25 cell types were constructed. The disease similarity in terms of cell types was obtained by calculating the distances between the enrichment profiles of these genes. The same strategy was applied to measure the disease similarity in terms of brain regions by analyzing the gene expression data from 10 brain regions.

Results: The disease similarity was first measured in terms of genes. The result indicated that the proportions of overlapped genes between diseases were significantly correlated to the DMN scores (phenotypic similarity), with a Pearson correlation coefficient of 0.40 and P-value = 6.0×10^-3. The disease similarity analysis for cell types identified that the distances between enrichment profiles of the disease-related genes were negatively correlated to the DMN scores, with Spearman correlation coefficient = -0.26 (P-value = 1.5 × 10^-2). However, the brain region enrichment profile distances of the disease-related genes were not significantly correlated with the DMN score. Besides the similarity of diseases, this study identified novel relationships between diseases and cell types.

Conclusion: We manually constructed the most comprehensive dataset to date for genes with mutations related to 20 nervous system diseases. By using this dataset, the similarities between diseases in terms of genes and cell types were found to be significantly correlated to their phenotypic similarity. However, the disease similarities in terms of brain regions were not significantly correlated with the phenotypic similarities. Thus, the phenotypic similarity between the diseases is more likely to be caused by dysfunctions of the same genes or the same types of neurons rather than the same brain regions. The data are collected into the database NeurodisM, which is available at http://biomed-ai.org/neurodism.

A systems-level framework for anti-epilepsy drug discovery

Modern anti-seizure drug development yielded benefits in terms of improved pharmacokinetics, safety and tolerability profiles, but offered no advances in efficacy compared to previous older generations of anti-seizure drugs. Despite significant advances in our understanding of the genetic bases to epilepsy, and a welcome renewed interest on the severe monogenic epilepsies, modern genetics has yet to directly inform more effective or disease-modifying anti-seizure drugs. Here, we describe a new approach to the identification of novel disease modifying anti-epilepsy drugs. The systems genetics approach aims to first identify pathophysiological mechanisms by integrating polygenic risk with cellular gene expression profiles and then to relate these molecular mechanisms to druggable targets using a gene regulatory (regulome) framework. The approach offers an exciting and flexible framework for future drug discovery in epilepsy, and is applicable to any disease for which appropriate cell-type and disease-context specific data exist. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

In Vitro and In Vivo Pipeline for Validation of Disease-Modifying Effects of Systems Biology-Derived Network Treatments for Traumatic Brain Injury-Lessons Learned

We developed a pipeline for the discovery of transcriptomics-derived disease-modifying therapies and used it to validate treatments in vitro and in vivo that could be repurposed for TBI treatment. Desmethylclomipramine, ionomycin, sirolimus and trimipramine, identified by in silico LINCS analysis as candidate treatments modulating the TBI-induced transcriptomics networks, were tested in neuron-BV2 microglial co-cultures, using tumour necrosis factor α as a monitoring biomarker for neuroinflammation, nitrite for nitric oxide-mediated neurotoxicity and microtubule associated protein 2-based immunostaining for neuronal survival. Based on (a) therapeutic time window in silico, (b) blood-brain barrier penetration and water solubility, (c) anti-inflammatory and neuroprotective effects in vitro (p < 0.05) and (d) target engagement of Nrf2 target genes (p < 0.05), desmethylclomipramine was validated in a lateral fluid-percussion model of TBI in rats. Despite the favourable in silico and in vitro outcomes, in vivo assessment of clomipramine, which metabolizes to desmethylclomipramine, failed to demonstrate favourable effects on motor and memory tests. In fact, clomipramine treatment worsened the composite neuroscore (p < 0.05). Weight loss (p < 0.05) and prolonged upregulation of plasma cytokines (p < 0.05) may have contributed to the worsened somatomotor outcome. Our pipeline provides a rational stepwise procedure for evaluating favourable and unfavourable effects of systems-biology discovered compounds that modulate post-TBI transcriptomics.

Transcriptomes of Dravet syndrome iPSC derived GABAergic cells reveal dysregulated pathways for chromatin remodeling and neurodevelopment

Dravet syndrome (DS) is an early onset refractory epilepsy typically caused by de novo heterozygous variants in SCN1A encoding the α-subunit of the neuronal sodium channel Na_v1.1. The syndrome is characterized by age-related progression of seizures, cognitive decline and movement disorders. We hypothesized that the distinct neurodevelopmental features in DS are caused by the disruption of molecular pathways in Na_v1.1 haploinsufficient cells resulting in perturbed neural differentiation and maturation. Here, we established DS-patient and control induced pluripotent stem cell derived neural progenitor cells (iPSC NPC) and GABAergic inter-neuronal (iPSC GABA) cells. The DS-patient iPSC GABA cells showed a shift in sodium current activation and a perturbed response to induced oxidative stress. Transcriptome analysis revealed specific dysregulations of genes for chromatin structure, mitotic progression, neural plasticity and excitability in DS-patient iPSC NPCs and DS-patient iPSC GABA cells versus controls. The transcription factors FOXM1 and E2F1, positive regulators of the disrupted pathways for histone modification and cell cycle regulation, were markedly up-regulated in DS-iPSC GABA lines. Our study highlights transcriptional changes and disrupted pathways of chromatin remodeling in Na_v1.1 haploinsufficient GABAergic cells, providing a molecular framework that overlaps with that of neurodevelopmental disorders and other epilepsies.

Integrated systems-genetic analyses reveal a network target for delaying glioma progression

Objective

To identify a convergent, multitarget proliferation characteristic for astrocytoma transformation that could be targeted for therapy discovery.

Methods

Using an integrated functional genomics approach, we prioritized networks associated with astrocytoma progression using the following criteria: differential co‐expression between grade II and grade III IDH1‐mutated and 1p/19q euploid astrocytomas, preferential enrichment for genetic risk to cancer, association with patient survival and sample‐level genomic features. Drugs targeting the identified multitarget network characteristic for astrocytoma transformation were computationally predicted using drug transcriptional perturbation data and validated using primary human astrocytoma cells.

Results

A single network, M2, consisting of 177 genes, was associated with glioma progression on the basis of the above criteria. Functionally, M2 encoded physically interacting proteins regulating cell cycle processes and analysis of genome‐wide gene‐regulatory interactions using mutual information and DNA–protein interactions revealed the known regulators of cell cycle processes FoxM1, B‐Myb, and E2F2 as key regulators of M2. These results suggest functional disruption of M2 via gene mutation or altered expression as a convergent pathway regulating astrocytoma transformation. By considering M2 as a multitarget drug target regulating astrocytoma transformation, we identified several drugs that are predicted to restore M2 expression in anaplastic astrocytoma toward its low‐grade profile and of these, we validated the known antiproliferative drug resveratrol as down‐regulating multiple nodes of M2 including at nanomolar concentrations achievable in human cerebrospinal fluid by oral dosing.

Interpretation

Our results identify M2 as a multitarget network characteristic for astrocytoma progression and encourage M2‐based drug screening to identify new compounds for preventing glioma transformation.

The path from scientific discovery to cures for epilepsy

The epilepsies are a complex group of disorders that can be caused by a myriad of genetic and acquired factors. As such, identifying interventions that will prevent development of epilepsy, as well as cure the disorder once established, will require a multifaceted approach. Here we discuss the progress in scientific discovery propelling us towards this goal, including identification of genetic risk factors and big data approaches that integrate clinical and molecular 'omics' datasets to identify common pathophysiological signatures and biomarkers. We discuss the many animal and cellular models of epilepsy, what they have taught us about pathophysiology, and the cutting edge cellular, optogenetic, chemogenetic and anti-seizure drug screening approaches that are being used to find new cures in these models. Finally, we reflect on the work that still needs to be done towards identify at-risk individuals early, targeting and stopping epileptogenesis, and optimizing promising treatment approaches. Ultimately, developing and implementing cures for epilepsy will require a coordinated and immense effort from clinicians and basic scientists, as well as industry, and should always be guided by the needs of individuals affected by epilepsy and their families. This article is part of the special issue entitled 'New Epilepsy Therapies for the 21st Century - From Antiseizure Drugs to Prevention, Modification and Cure of Epilepsy'.

Mitochondrial Regulation of the Hippocampal Firing Rate Set Point and Seizure Susceptibility

Maintaining average activity within a set-point range constitutes a fundamental property of central neural circuits. However, whether and how activity set points are regulated remains unknown. Integrating genome-scale metabolic modeling and experimental study of neuronal homeostasis, we identified mitochondrial dihydroorotate dehydrogenase (DHODH) as a regulator of activity set points in hippocampal networks. The DHODH inhibitor teriflunomide stably suppressed mean firing rates via synaptic and intrinsic excitability mechanisms by modulating mitochondrial Ca²⁺ buffering and spare respiratory capacity. Bi-directional activity perturbations under DHODH blockade triggered firing rate compensation, while stabilizing firing to the lower level, indicating a change in the firing rate set point. In vivo, teriflunomide decreased CA3-CA1 synaptic transmission and CA1 mean firing rate and attenuated susceptibility to seizures, even in the intractable Dravet syndrome epilepsy model. Our results uncover mitochondria as a key regulator of activity set points, demonstrate the differential regulation of set points and compensatory mechanisms, and propose a new strategy to treat epilepsy.

Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies

The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.

Epilepsies are common brain disorders and are classified based on clinical phenotyping, imaging and genetics. Here, the authors perform genome-wide association studies for 3 broad and 7 subtypes of epilepsy and identify 16 loci - 11 novel - that are further annotated by eQTL and partitioned heritability analyses.

Juvenile myoclonic epilepsy has hyper dynamic functional connectivity in the dorsolateral frontal cortex

PURPOSE

Characterize the static and dynamic functional connectivity for subjects with juvenile myoclonic epilepsy (JME) using a quantitative data-driven analysis approach.

METHODS

Whole-brain resting-state functional MRI data were acquired on a 3 T whole-body clinical MRI scanner from 18 subjects clinically diagnosed with JME and 25 healthy control subjects. 2-min sliding-window approach was incorporated in the quantitative data-driven data analysis framework to assess both the dynamic and static functional connectivity in the resting brains. Two-sample t-tests were performed voxel-wise to detect the differences in functional connectivity metrics based on connectivity strength and density.

RESULTS

The static functional connectivity metrics based on quantitative data-driven analysis of the entire 10-min acquisition window of resting-state functional MRI data revealed significantly enhanced functional connectivity in JME patients in bilateral dorsolateral prefrontal cortex, dorsal striatum, precentral and middle temporal gyri. The dynamic functional connectivity metrics derived by incorporating a 2-min sliding window into quantitative data-driven analysis demonstrated significant hyper dynamic functional connectivity in the dorsolateral prefrontal cortex, middle temporal gyrus and dorsal striatum. Connectivity strength metrics (both static and dynamic) can detect more extensive functional connectivity abnormalities in the resting-state functional networks (RFNs) and depict also larger overlap between static and dynamic functional connectivity results.

CONCLUSION

Incorporating a 2-min sliding window into quantitative data-driven analysis of resting-state functional MRI data can reveal additional information on the temporally fluctuating RFNs of the human brain, which indicate that RFNs involving dorsolateral prefrontal cortex have temporal varying hyper dynamic characteristics in JME patients. Assessing dynamic along with static functional connectivity may provide further insights into the abnormal function connectivity underlying the pathological brain functioning in JME.

Integrated systems analysis reveals conserved gene networks underlying response to spinal cord injury

Spinal cord injury (SCI) is a devastating neurological condition for which there are currently no effective treatment options to restore function. A major obstacle to the development of new therapies is our fragmentary understanding of the coordinated pathophysiological processes triggered by damage to the human spinal cord. Here, we describe a systems biology approach to integrate decades of small-scale experiments with unbiased, genome-wide gene expression from the human spinal cord, revealing a gene regulatory network signature of the pathophysiological response to SCI. Our integrative analyses converge on an evolutionarily conserved gene subnetwork enriched for genes associated with the response to SCI by small-scale experiments, and whose expression is upregulated in a severity-dependent manner following injury and downregulated in functional recovery. We validate the severity-dependent upregulation of this subnetwork in rodents in primary transcriptomic and proteomic studies. Our analysis provides systems-level view of the coordinated molecular processes activated in response to SCI.

A systems-level framework for drug discovery identifies Csf1R as an anti-epileptic drug target

The identification of drug targets is highly challenging, particularly for diseases of the brain. To address this problem, we developed and experimentally validated a general computational framework for drug target discovery that combines gene regulatory information with causal reasoning (“Causal Reasoning Analytical Framework for Target discovery”—CRAFT). Using a systems genetics approach and starting from gene expression data from the target tissue, CRAFT provides a predictive framework for identifying cell membrane receptors with a direction-specified influence over disease-related gene expression profiles. As proof of concept, we applied CRAFT to epilepsy and predicted the tyrosine kinase receptor Csf1R as a potential therapeutic target. The predicted effect of Csf1R blockade in attenuating epilepsy seizures was validated in three pre-clinical models of epilepsy. These results highlight CRAFT as a systems-level framework for target discovery and suggest Csf1R blockade as a novel therapeutic strategy in epilepsy. CRAFT is applicable to disease settings other than epilepsy.

The identification of new drug targets is highly challenging, particularly for diseases of the brain. This study describes a general computational gene regulatory framework called CRAFT for drug target discovery, and the authors use CRAFT to identify the microglial membrane receptor Csf1R as a potential therapeutic target for epilepsy.

WONOEP APPRAISAL: The many facets of epilepsy networks

The brain is a complex system composed of networks of interacting elements, from genes to circuits, whose function (and dysfunction) is not derivable from the superposition of individual components. Epilepsy is frequently described as a network disease, but to date, there is no standardized framework within which network concepts applicable to all levels from genes to whole brain can be used to generate deeper insights into the pathogenesis of seizures or the associated morbidities. To address this shortcoming, the Neurobiology Commission of the International League Against Epilepsy dedicated a Workshop on Neurobiology of Epilepsy (XIV WONOEP 2017) with the aim of formalizing network concepts as they apply to epilepsy and to critically discuss whether and how such concepts could augment current research endeavors. Here, we review concepts and strategies derived by considering epilepsy as a disease of different network hierarchies that range from genes to clinical phenotypes. We propose that the concept of networks is important for understanding epilepsy and is critical for developing new study designs. These approaches could ultimately facilitate the development of novel diagnostic and therapeutic strategies.

From gene networks to drugs: systems pharmacology approaches for AUD

The alcohol research field has amassed an impressive number of gene expression datasets spanning key brain areas for addiction, species (humans as well as multiple animal models), and stages in the addiction cycle (binge/intoxication, withdrawal/negative effect, and preoccupation/anticipation). These data have improved our understanding of the molecular adaptations that eventually lead to dysregulation of brain function and the chronic, relapsing disorder of addiction. Identification of new medications to treat alcohol use disorder (AUD) will likely benefit from the integration of genetic, genomic, and behavioral information included in these important datasets. Systems pharmacology considers drug effects as the outcome of the complex network of interactions a drug has rather than a single drug-molecule interaction. Computational strategies based on this principle that integrate gene expression signatures of pharmaceuticals and disease states have shown promise for identifying treatments that ameliorate disease symptoms (called in silico gene mapping or connectivity mapping). In this review, we suggest that gene expression profiling for in silico mapping is critical to improve drug repurposing and discovery for AUD and other psychiatric illnesses. We highlight studies that successfully apply gene mapping computational approaches to identify or repurpose pharmaceutical treatments for psychiatric illnesses. Furthermore, we address important challenges that must be overcome to maximize the potential of these strategies to translate to the clinic and improve healthcare outcomes.

Transcriptomic changes following valproic acid treatment promote neurogenesis and minimize secondary brain injury

BACKGROUND

Early treatment with valproic acid (VPA) has demonstrated benefit in preclinical models of traumatic brain injury, including smaller brain lesion size, decreased edema, reduced neurologic disability, and faster recovery. Mechanisms underlying these favorable outcomes are not fully understood. We hypothesized that VPA treatment would upregulate genes involved in cell survival and proliferation and downregulate those associated with cell death and the inflammatory response.

METHODS

Ten female swine were subjected to a protocol of traumatic brain injury and hemorrhagic shock. They were assigned to two groups (n = 5): normal saline (NS; 3× volume of shed blood), or NS + VPA (150 mg/kg). Following 6 hours of observation, brain tissue was harvested to evaluate lesion size and edema. Brain tissue was processed for RNA sequencing. Gene set enrichment and pathway analysis was performed to determine the differential gene expression patterns following injury.

RESULTS

Animals treated with VPA were noted to have a 46% reduction in brain lesion size and a 57% reduction in ipsilateral brain edema. Valproic acid significantly upregulated genes involved in morphology of the nervous system, neuronal development and neuron quantity. The VPA treatment downregulated pathways related to apoptosis, glial cell proliferation, and neuroepithelial cell differentiation. Ingenuity Pathway Analysis identified VPA as the top upstream regulator of activated transcription, supporting it as a direct cause of these transcriptional changes. Master transcriptional regulator NEUROD1 was also significantly upregulated, suggesting that VPA may induce additional transcription factors.

CONCLUSION

Administration of VPA attenuated brain lesion size, reduced brain edema, and induced significant changes in the transcriptome of injured brain within 6 hours. Patterns of differential expression were consistent with the proposed neurogenic and prosurvival effects of VPA treatment.

HMGB1 mediates microglia activation via the TLR4/NF-κB pathway in coriaria lactone induced epilepsy

Epilepsy is a chronic and recurrent disease of the central nervous system, with a complex pathology. Recent studies have demonstrated that the activation of glial cells serve an important role in the development of epilepsy. The objective of the present study was to investigate the role of high‑mobility group box‑1 (HMGB1) in mediating the activation of glial cells through the toll‑like receptor 4 (TLR4)/nuclear factor (NF)‑κB signaling pathway in seizure, and the underlying mechanism. The brain tissue of post‑surgery patients with intractable epilepsy after resection and the normal control brain tissue of patients with craniocerebral trauma induced intracranial hypertension were collected. The expression level and distribution pattern of HMGB1, OX42 and NF‑κB p65 were detected by immunohistochemistry. HMGB1, TLR4, receptor for advanced glycation end products (RAGE), NF‑κB p65 and inducible nitric oxide synthase (iNOS) expression levels were detected by western blotting, and serum cytokine levels of interleukin (IL)‑1, IL‑6, tumor necrosis factor (TNF)‑α, transforming growth factor (TGF)‑β and IL‑10 in patients with epilepsy and craniocerebral trauma were detected by ELISA. And cell model of epilepsy was established by coriaria lactone (CL)‑stimulated HM cell, and the same factors were measured. The potential toxic effect of HMGB1 on HM cells was evaluated by MTT and 5‑ethynyl‑2‑deoxyuridine assays. The results demonstrated that compared with the control group, levels of HMGB1, TLR4, RAGE, NF‑κB p65 and iNOS in the brain of the epilepsy group were significantly increased, and increased cytokine levels of IL‑1, IL‑6, TNF‑α, TGF‑β and IL‑10 in patients with epilepsy were also observed. At the same time, the above results were also observed in HM cells stimulated with CL. Overexpression of HMGB1 enhanced the results, while HMGB1 small interfering RNA blocked the function of CL. There was no significant toxic effect of HMGB1 on HM cells. In conclusion, overexpression of HMGB1 potentially promoted epileptogenesis. CL‑induced activation of glial cells may act via up‑regulation of HMGB1 and TLR4/RAGE receptors, and the downstream transcription factor NF‑κB.

Commonalities in epileptogenic processes from different acute brain insults: Do they translate?

The most common forms of acquired epilepsies arise following acute brain insults such as traumatic brain injury, stroke, or central nervous system infections. Treatment is effective for only 60%-70% of patients and remains symptomatic despite decades of effort to develop epilepsy prevention therapies. Recent preclinical efforts are focused on likely primary drivers of epileptogenesis, namely inflammation, neuron loss, plasticity, and circuit reorganization. This review suggests a path to identify neuronal and molecular targets for clinical testing of specific hypotheses about epileptogenesis and its prevention or modification. Acquired human epilepsies with different etiologies share some features with animal models. We identify these commonalities and discuss their relevance to the development of successful epilepsy prevention or disease modification strategies. Risk factors for developing epilepsy that appear common to multiple acute injury etiologies include intracranial bleeding, disruption of the blood-brain barrier, more severe injury, and early seizures within 1 week of injury. In diverse human epilepsies and animal models, seizures appear to propagate within a limbic or thalamocortical/corticocortical network. Common histopathologic features of epilepsy of diverse and mostly focal origin are microglial activation and astrogliosis, heterotopic neurons in the white matter, loss of neurons, and the presence of inflammatory cellular infiltrates. Astrocytes exhibit smaller K+ conductances and lose gap junction coupling in many animal models as well as in sclerotic hippocampi from temporal lobe epilepsy patients. There is increasing evidence that epilepsy can be prevented or aborted in preclinical animal models of acquired epilepsy by interfering with processes that appear common to multiple acute injury etiologies, for example, in post-status epilepticus models of focal epilepsy by transient treatment with a trkB/PLCγ1 inhibitor, isoflurane, or HMGB1 antibodies and by topical administration of adenosine, in the cortical fluid percussion injury model by focal cooling, and in the albumin posttraumatic epilepsy model by losartan. Preclinical studies further highlight the roles of mTOR1 pathways, JAK-STAT3, IL-1R/TLR4 signaling, and other inflammatory pathways in the genesis or modulation of epilepsy after brain injury. The wealth of commonalities, diversity of molecular targets identified preclinically, and likely multidimensional nature of epileptogenesis argue for a combinatorial strategy in prevention therapy. Going forward, the identification of impending epilepsy biomarkers to allow better patient selection, together with better alignment with multisite preclinical trials in animal models, should guide the clinical testing of new hypotheses for epileptogenesis and its prevention.

Integrated Bayesian analysis of rare exonic variants to identify risk genes for schizophrenia and neurodevelopmental disorders

BACKGROUND

Integrating rare variation from trio family and case-control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DDs), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through the study of rare variation, only two risk genes have been identified.

METHODS

We used hierarchical Bayesian modeling of rare-variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence data for SCZ (1,077 trios, 6,699 cases, and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls).

RESULTS

For SCZ, we estimate there are 1,551 risk genes. There are more risk genes and they have weaker effects than for NDDs. We provide power analyses to predict the number of risk-gene discoveries as more data become available. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs (ρ>0.55), but low between SCZ and the NDDs (ρ<0.3). An in-depth analysis of 288 NDD genes shows there is highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichment, single-cell RNA-seq cell types, and multi-region developmental brain RNA-seq.

CONCLUSIONS

We have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs ( https://github.com/hoangtn/extTADA ). We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.

Integrated network analysis reveals potentially novel molecular mechanisms and therapeutic targets of refractory epilepsies

Epilepsy is a complex neurological disorder and a significant health problem. The pathogenesis of epilepsy remains obscure in a significant number of patients and the current treatment options are not adequate in about a third of individuals which were known as refractory epilepsies (RE). Network medicine provides an effective approach for studying the molecular mechanisms underlying complex diseases. Here we integrated 1876 disease-gene associations of RE and located those genes to human protein-protein interaction (PPI) network to obtain 42 significant RE-associated disease modules. The functional analysis of these disease modules showed novel molecular pathological mechanisms of RE, such as the novel enriched pathways (e.g., "presynaptic nicotinic acetylcholine receptors", "signaling by insulin receptor"). Further analysis on the relationships between current drug targets and the RE-related disease genes showed the rational mechanisms of most antiepileptic drugs. In addition, we detected ten potential novel drug targets (e.g., KCNA1, KCNA4-6, KCNC3, KCND2, KCNMA1, CAMK2G, CACNB4 and GRM1) located in three RE related disease modules, which might provide novel insights into the new drug discovery for RE therapy.