Drug-resistant epilepsy classified by a phenotyping algorithm associates with NTRK2

Genetic influences on epilepsy outcomes: A whole-exome sequencing and health care records data linkage study

OBJECTIVE

This study was undertaken to develop a novel pathway linking genetic data with routinely collected data for people with epilepsy, and to analyze the influence of rare, deleterious genetic variants on epilepsy outcomes.

METHODS

We linked whole-exome sequencing (WES) data with routinely collected primary and secondary care data and natural language processing (NLP)-derived seizure frequency information for people with epilepsy within the Secure Anonymised Information Linkage Databank. The study participants were adults who had consented to participate in the Swansea Neurology Biobank, Wales, between 2016 and 2018. DNA sequencing was carried out as part of the Epi25 collaboration. For each individual, we calculated the total number and cumulative burden of rare and predicted deleterious genetic variants and the total of rare and deleterious variants in epilepsy and drug metabolism genes. We compared these measures with the following outcomes: (1) no unscheduled hospital admissions versus unscheduled admissions for epilepsy, (2) antiseizure medication (ASM) monotherapy versus polytherapy, and (3) at least 1 year of seizure freedom versus <1 year of seizure freedom.

RESULTS

We linked genetic data for 107 individuals with epilepsy (52% female) to electronic health records. Twenty-six percent had unscheduled hospital admissions, and 70% were prescribed ASM polytherapy. Seizure frequency information was linked for 100 individuals, and 10 were seizure-free. There was no significant difference between the outcome groups in terms of the exome-wide and gene-based burden of rare and deleterious genetic variants.

SIGNIFICANCE

We successfully uploaded, annotated, and linked genetic sequence data and NLP-derived seizure frequency data to anonymized health care records in this proof-of-concept study. We did not detect a genetic influence on real-world epilepsy outcomes, but our study was limited by a small sample size. Future studies will require larger (WES) data to establish genetic variant contribution to epilepsy outcomes.

Functional characterization and potential therapeutic avenues for variants in the NTRK2 gene causing developmental and epileptic encephalopathies

Variants within the Neurotrophic Tyrosine Kinase Receptor Type 2 (NTRK2) gene have been discovered to play a role in developmental and epileptic encephalopathies, a group of debilitating conditions for which little is known about cause or treatment. Here, we determine the functional consequences of two variants: p.Tyr434Cys (Y434C) (located in the transmembrane domain) and p.Thr720Ile (T720I) (located in the catalytic domain). Wild-type and variant cDNAs were constructed and transfected into HEK293 cells. In cell culture, variant Y434C exhibited ligand-independent activation of tropomyosin-related kinase B (TRKB) signaling with an associated abnormal response to brain-derived neurotrophic factor (BDNF) stimulation and increased levels of phosphorylated extracellular signal-regulated kinase (ERK) and ETS like-1 protein (ELK1) activity. Expression of variant T720I resulted in decreased TRKB signaling with reduced mTor activity as determined by decreased levels of phosphorylated S6. With the deleterious mechanisms characterized, we utilized mediKanren (a novel artificial intelligence tool) to identify therapeutics to compensate for the pathological effects. Downregulation of TRKB through inhibition with mediKanren-predicted compound 1NM-PP1 led to decreased MEK activity. Upregulation of TRKB signaling by mediKanren-predicted valproic acid led to subsequent increase of mTor activity. Overall, our results provide further characterization of the pathogenicity of these two variants in the NTRK2 gene. Indeed, Y434C is the first patient-specific NTRK2 variant with demonstrated hypermorphic activity. Furthermore, we observed that variants Y434C and T720I result in distinct functional consequences that require distinct therapeutic strategies. These data suggest the possibility that unique mutations within different regions of the NTRK2 gene results in separate clinical presentations, representing distinct genetic disorders requiring unique therapeutics.

Development and Evaluation of Computable Phenotypes in Pediatric Epilepsy:3 Cases

INTRODUCTION

Computable phenotypes allow identification of well-defined patient cohorts from electronic health record data. Little is known about the accuracy of diagnostic codes for important clinical concepts in pediatric epilepsy, such as (1) risk factors like neonatal hypoxic-ischemic encephalopathy; (2) clinical concepts like treatment resistance; (3) and syndromes like juvenile myoclonic epilepsy. We developed and evaluated the performance of computable phenotypes for these examples using electronic health record data at one center.

METHODS

We identified gold standard cohorts for neonatal hypoxic-ischemic encephalopathy, pediatric treatment-resistant epilepsy, and juvenile myoclonic epilepsy via existing registries and review of clinical notes. From the electronic health record, we extracted diagnostic and procedure codes for all children with a diagnosis of epilepsy and seizures. We used these codes to develop computable phenotypes and evaluated by sensitivity, positive predictive value, and the F-measure.

RESULTS

For neonatal hypoxic-ischemic encephalopathy, the best-performing computable phenotype (HIE ICD-9/10 and [brain magnetic resonance imaging (MRI) or electroencephalography (EEG) within 120 days of life] and absence of commonly miscoded conditions) had high sensitivity (95.7%, 95% confidence interval [CI] 85-99), positive predictive value (100%, 95% CI 95-100), and F measure (0.98). For treatment-resistant epilepsy, the best-performing computable phenotype (3 or more antiseizure medicines in the last 2 years or treatment-resistant ICD-10) had a sensitivity of 86.9% (95% CI 79-93), positive predictive value of 69.6% (95% CI 60-79), and F-measure of 0.77. For juvenile myoclonic epilepsy, the best performing computable phenotype (JME ICD-10) had poor sensitivity (52%, 95% CI 43-60) but high positive predictive value (90.4%, 95% CI 81-96); the F measure was 0.66.

CONCLUSION

The variable accuracy of our computable phenotypes (hypoxic-ischemic encephalopathy high, treatment resistance medium, and juvenile myoclonic epilepsy low) demonstrates the heterogeneity of success using administrative data to identify cohorts important for pediatric epilepsy research.

NTRK2-related developmental and epileptic encephalopathy: Report of 5 new cases

PURPOSE

This study aimed to describe the phenotype of five new cases of NTRK2-related developmental and epileptic encephalopathy (DEE).

METHODS

The clinical features, EEG, neuroimaging and genetics were reviewed for cases with likely pathogenic and pathogenic NTRK2 variants and then summarized.

RESULTS

Five cases of NTRK2-related DEE were identified. Four had a previously described recurrent variant in NTRK2 and one had a novel variant. The phenotype was characterized by early- onset seizures (infantile spasms, later evolving to multifocal seizures), global developmental delay, variable movement disorders, microcephaly and optic nerve hypoplasia.

CONCLUSIONS

This series further expands our knowledge of the phenotype and genotype of NTRK2-related DEE.

Profiling Analysis of Circular RNA and mRNA in Human Temporal Lobe Epilepsy with Hippocampal Sclerosis ILAE Type 1

Hippocampal sclerosis (HS) is the most common surgical pathology associated with temporal lobe epilepsy (TLE). However, the cause of TLE with or without HS remains unknown. Our current study aimed to illustrate the essential molecular mechanism that is potentially involved in the pathogenesis of TLE-HS and to shed light on the transcriptional changes associated with hippocampal sclerosis. Compared to no-HS group, 341 mRNA transcripts and 131 circRNA transcripts were differentially expressed in ILAE type 1 group. The raw sequencing data have been deposited into sequence-read archive (SRA) database under accession number PRJNA699348.Gene Ontology analysis demonstrated that the dysregulated genes were associated with the biological processes of vesicle-mediated transport. Enrichment analysis demonstrated that dysregulated genes were involved mainly in the MAPK signal pathway. Subsequently, A total of 441 known or predicted interactions were formed among DEGs, and the most important module was detected in the PPI network using the MCODE plug-in. There were mainly four functional modules enriched: ER to Golgi transport vesicle membrane, Basal transcription factors, GABA-gated chloride ion channel activity, CENP-A containing nucleosome assembly. A circRNA-mRNA co-expression network was constructed including 5 circRNAs(hsa_circ_0025349, hsa_circ_0002405, hsa_circ_0004805, hsa_circ_0032254, and hsa_circ_0032875) and three mRNAs (FYN, SELENBP1, and GRIPAP1) based on the normalized mRNA signal intensities. This is the first to report the circRNAs and mRNAs expression profile of surgically resected hippocampal tissues from TLE patients of ILAE-1 and no-HS, and these results may provide new insight into the transcriptional changes associated with this pathology.

Personalized medicine in genetic epilepsies - possibilities, challenges, and new frontiers

Identifying the optimal treatment based on specific characteristics of each patient is the main promise of precision medicine. In the field of epilepsy, the identification of more than 100 causative genes provides the enticing possibility of treatments targeted to specific disease etiologies. These conditions include classical examples, such as the use of vitamin B6 in antiquitin deficiency or the ketogenic diet in GLUT1 deficiency, where the disease mechanism can be directly addressed by the selection of a specific therapeutic compound. For epilepsies caused by channelopathies there have been advances in understanding how the selection of existing medications can be targeted to the functional consequences of genetic alterations. We discuss the examples of the use of sodium channel blockers such as phenytoin and oxcarbazepine in the sodium channelopathies, quinidine in KCNT1-related epilepsies, and strategies in GRIN-related epilepsies as examples of epilepsy precision medicine. Assessing the clinical response to targeted treatments of these conditions has been complicated by genetic and phenotypic heterogeneity, as well as by various neurological and non-neurological comorbidities. Moving forward, the development of standardized outcome measures will be critical to successful precision medicine trials in complex and heterogeneous disorders like the epilepsies. Finally, we address new frontiers in epilepsy precision medicine, including the need to match the growing volume of genetic data with high-throughput functional assays to assess the functional consequences of genetic variants and the ability to extract clinical data at large scale from electronic medical records and apply quantitative methods based on standardized phenotyping language.