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

ABSTRACT: BACKGROUND:The relationship between monogenic and polygenic forms of epilepsy is poorly understood and the extent to which the genetic and acquired epilepsies share common pathways is unclear. Here, we use an integrated systems-level analysis of brain gene expression data to identify molecular networks disrupted in epilepsy. RESULTS:We identified a co-expression network of 320 genes (M30), which is significantly enriched for non-synonymous de novo mutations ascertained from patients with monogenic epilepsy and for common variants associated with polygenic epilepsy. The genes in the M30 network are expressed widely in the human brain under tight developmental control and encode physically interacting proteins involved in synaptic processes. The most highly connected proteins within the M30 network were preferentially disrupted by deleterious de novo mutations for monogenic epilepsy, in line with the centrality-lethality hypothesis. Analysis of M30 expression revealed consistent downregulation in the epileptic brain in heterogeneous forms of epilepsy including human temporal lobe epilepsy, a mouse model of acquired temporal lobe epilepsy, and a mouse model of monogenic Dravet (SCN1A) disease. These results suggest functional disruption of M30 via gene mutation or altered expression as a convergent mechanism regulating susceptibility to epilepsy broadly. Using the large collection of drug-induced gene expression data from Connectivity Map, several drugs were predicted to preferentially restore the downregulation of M30 in epilepsy toward health, most notably valproic acid, whose effect on M30 expression was replicated in neurons. CONCLUSIONS:Taken together, our results suggest targeting the expression of M30 as a potential new therapeutic strategy in epilepsy.

SUBMITTER: Delahaye-Duriez A

PROVIDER: S-EPMC5154105 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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

Delahaye-Duriez Andree A Srivastava Prashant P Shkura Kirill K Langley Sarah R SR Laaniste Liisi L Moreno-Moral Aida A Danis Bénédicte B Mazzuferi Manuela M Foerch Patrik P Gazina Elena V EV Richards Kay K Petrou Steven S Kaminski Rafal M RM Petretto Enrico E Johnson Michael R MR

Genome biology 20161213 1

<h4>Background</h4>The relationship between monogenic and polygenic forms of epilepsy is poorly understood and the extent to which the genetic and acquired epilepsies share common pathways is unclear. Here, we use an integrated systems-level analysis of brain gene expression data to identify molecular networks disrupted in epilepsy.<h4>Results</h4>We identified a co-expression network of 320 genes (M30), which is significantly enriched for non-synonymous de novo mutations ascertained from patien ...[more]

PMID: 27955713

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Project description:ObjectiveCognitive impairment is prevalent among individuals with epilepsy, and it is possible that genetic factors can underlie this relationship. Here, we investigated the potential shared genetic basis of common epilepsies and general cognitive ability (COG).MethodsWe applied linkage disequilibrium score (LDSC) regression, MiXeR and conjunctional false discovery rate (conjFDR) to analyze different aspects of genetic overlap between COG and epilepsies. We used the largest available genome-wide association study data on COG (n = 269,867) and common epilepsies (n = 27,559 cases, 42,436 controls), including the broad phenotypes 'all epilepsy', focal epilepsies and genetic generalized epilepsies (GGE), and as well as specific subtypes. We functionally annotated the identified loci using a variety of biological resources and validated the results in independent samples.ResultsUsing MiXeR, COG (11.2k variants) was estimated to be almost four times more polygenic than 'all epilepsy', GGE, juvenile myoclonic epilepsy (JME), and childhood absence epilepsy (CAE) (2.5k - 2.9k variants). The other epilepsy phenotypes were insufficiently powered for analysis. We show extensive genetic overlap between COG and epilepsies with significant negative genetic correlations (-0.23 to -0.04). COG was estimated to share 2.9k variants with both GGE and 'all epilepsy', and 2.3k variants with both JME and CAE. Using conjFDR, we identified 66 distinct loci shared between COG and epilepsies, including novel associations for GGE (27), 'all epilepsy' (5), JME (5) and CAE (5). The implicated genes were significantly expressed in multiple brain regions. The results were validated in independent samples (COG: p = 1.0 × 10-14; 'all epilepsy': p = 5.6 × 10-3).SignificanceOur study demonstrates a substantial genetic basis shared between epilepsies and COG and identifies novel overlapping genomic loci. Enhancing our understanding of the relationship between epilepsies and COG may lead to the development of novel comorbidity-targeted epilepsy treatments.

Project description:Psychiatric disorders and common epilepsies are heritable disorders with a high comorbidity and overlapping symptoms. However, the causative mechanisms underlying this relationship are poorly understood. Here we aimed to identify overlapping genetic loci between epilepsy and psychiatric disorders to gain a better understanding of their comorbidity and shared clinical features. We analysed genome-wide association study data for all epilepsies (n = 44 889), genetic generalized epilepsy (n = 33 446), focal epilepsy (n = 39 348), schizophrenia (n = 77 096), bipolar disorder (n = 406 405), depression (n = 500 199), attention deficit hyperactivity disorder (n = 53 293) and autism spectrum disorder (n = 46 350). First, we applied the MiXeR tool to estimate the total number of causal variants influencing the disorders. Next, we used the conjunctional false discovery rate statistical framework to improve power to discover shared genomic loci. Additionally, we assessed the validity of the findings in independent cohorts, and functionally characterized the identified loci. The epilepsy phenotypes were considerably less polygenic (1.0 K to 3.4 K causal variants) than the psychiatric disorders (5.6 K to 13.9 K causal variants), with focal epilepsy being the least polygenic (1.0 K variants), and depression having the highest polygenicity (13.9 K variants). We observed cross-trait genetic enrichment between genetic generalized epilepsy and all psychiatric disorders and between all epilepsies and schizophrenia and depression. Using conjunctional false discovery rate analysis, we identified 40 distinct loci jointly associated with epilepsies and psychiatric disorders at conjunctional false discovery rate <0.05, four of which were associated with all epilepsies and 39 with genetic generalized epilepsy. Most epilepsy risk loci were shared with schizophrenia (n = 31). Among the identified loci, 32 were novel for genetic generalized epilepsy, and two were novel for all epilepsies. There was a mixture of concordant and discordant allelic effects in the shared loci. The sign concordance of the identified variants was highly consistent between the discovery and independent datasets for all disorders, supporting the validity of the findings. Gene-set analysis for the shared loci between schizophrenia and genetic generalized epilepsy implicated biological processes related to cell cycle regulation, protein phosphatase activity, and membrane and vesicle function; the gene-set analyses for the other loci were underpowered. The extensive genetic overlap with mixed effect directions between psychiatric disorders and common epilepsies demonstrates a complex genetic relationship between these disorders, in line with their bi-directional relationship, and indicates that overlapping genetic risk may contribute to shared pathophysiological and clinical features between epilepsy and psychiatric disorders.

Project description: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.

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

Publications

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

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