Semantic Similarity Analysis Reveals Robust Gene-Disease Relationships in Developmental and Epileptic Encephalopathies

Cerebral Palsy Phenotypes in Genetic Epilepsies

BACKGROUND

Although there are established connections between genetic epilepsies and neurodevelopmental disorders like intellectual disability, the presence of cerebral palsy (CP) in genetic epilepsies is undercharacterized. We performed a retrospective chart review evaluating the motor phenotype of patients with genetic epilepsies.

METHODS

Patients were ascertained through a research exome sequencing study to identify genetic causes of epilepsy. We analyzed data from the first 100 individuals with molecular diagnoses. We determined motor phenotype by reviewing medical records for muscle tone and motor function data. We characterized patients according to CP subtypes: spastic diplegic, spastic quadriplegic, spastic hemiplegic, dyskinetic, hypotonic-ataxic.

RESULTS

Of 100 individuals with genetic epilepsies, 14% had evidence of possible CP, including 5% characterized as hypotonic-ataxic CP, 5% spastic quadriplegic CP, 3% spastic diplegic CP, and 1% hemiplegic CP. Presence of CP did not correlate with seizure onset age (P = 0.63) or seizure control (P = 0.07). CP occurred in 11% (n = 3 of 27) with focal epilepsy, 9% (n = 5 of 54) with generalized epilepsy, and 32% (n = 6 of 19) with combined focal/generalized epilepsy (P = 0.06).

CONCLUSIONS

In this retrospective analysis of patients with genetic epilepsies, we identified a substantial portion with CP phenotypes, representing an under-recognized comorbidity. These findings underscore the many neurodevelopmental features associated with neurogenetic conditions, regardless of the feature for which they were ascertained for sequencing. Detailed motor phenotyping is needed to determine the prevalence of CP and its subtypes among genetic epilepsies. These motor phenotypes require clinical management and represent important targeted outcomes in trials for patients with genetic epilepsies.

Advances in big data and omics: Paving the way for discovery in childhood epilepsies

The insights gained from big data and omics approaches have transformed the field of childhood genetic epilepsy. With an increasing number of individuals receiving genetic testing for seizures, we are provided with an opportunity to identify clinically relevant subgroups and extract meaningful observations from this large-scale clinical data. However, the volume of data from electronic medical records and omics (e.g., genomics, transcriptomics) is so vast that standardized methods, such as the Human Phenotype Ontology, are necessary for reliable and comprehensive characterization. Here, we explore the integration of clinical and omics data, highlighting how these approaches pave the way for discovery in childhood epilepsies.

A Longitudinal Exploration of CACNA1A -related Hemiplegic Migraine in Children

Introduction

Since the initial description of CACNA1A- related hemiplegic migraine (HM), the phenotypic spectrum has expanded from mild episodes in neurotypical individuals to potentially life-threatening events frequently seen in individuals with developmental and epileptic encephalopathies. However, the overall longitudinal course throughout childhood remains unknown.

Methods

We analyzed HM and seizure history in individuals with CACNA1A -related HM, delineating frequency and severity of events in monthly increments through a standardized approach. Combining these data with medication prescription information, we assessed the response of HM to different agents.

Results

Our cohort involved 15 individuals between 3 and 29 years (163 patient years) and included 11 unique and two recurrent variants (p.R1349Q and p.V1393M; both n= 2). The age of first confirmed HM ranged from 14 months to 13 years (average 3 years). 25% of all HM events were severe (lasting >3 days) and 73% of individuals had at least 1 severe occurrence. Spacing of HM events ranged from 1 month to 14 years and changes in HM severity over time of showed increases or decreases of >2 severity levels in 12/122 events. Eight individuals had epilepsy, but severity of epilepsy did not correlate with frequency and severity of HM events. While levetiracetam ( n= 6) and acetazolamide ( n= 5) were the most frequently used medications, they did not show efficacy in HM prevention or HM severity reduction. However, verapamil ( n= 3) showed efficacy in preventing HM episodes (OR 2.68, CI 1.39-5.67).

Significance

The longitudinal course of CACNA1A -related HM lacks recognizable patterns for timing and severity of HM events or correlation with seizure patterns. Our data underscores the unpredictability of CACNA1A -related HM, highlighting the need for close surveillance for reoccurring HM events even in individuals with symptom-free periods.

Key points

24% of hemiplegic migraines (HM) in CACNA1A- related disorders are severe, involving cerebral edema and greater than 4 days to recover Timing and severity of HM are unpredictable, with large changes in severity between events, and age of onset ranging from 1-13 yearsEpilepsy occurred in 53% of individuals, with neither the timing nor severity of seizures correlated with HM.

PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies

Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.

STXBP1-Related Disorders: Clinical Presentation, Molecular Function, Treatment, and Future Directions

In recent years, the affordability and availability of genetic testing have led to its increased use in clinical care. The increased frequency of testing has led to STXBP1 variants being identified as one of the more common variants associated with neurological disorders. In this review, we aim to summarize the common clinical phenotypes associated with STXBP1 pathogenic variants, provide an overview of their known natural history, and discuss current research into the genotype to phenotype correlation. We will also provide an overview of the suspected normal function of the STXBP1-encoded Munc18-1 protein, animal models, and experimental techniques that have been developed to study its function and use this information to try to explain the diverse phenotypes associated with STXBP1-related disorders. Finally, we will explore current therapies for STXBP1 disorders, including an overview of treatment goals for STXBP1-related disorders, a discussion of the current evidence for therapies, and future directions of personalized medications for STXBP1-related disorders.

Individualised human phenotype ontology gene panels improve clinical whole exome and genome sequencing analytical efficacy in a cohort of developmental and epileptic encephalopathies

BACKGROUND

The majority of genetic epilepsies remain unsolved in terms of specific genotype. Phenotype-based genomic analyses have shown potential to strengthen genomic analysis in various ways, including improving analytical efficacy.

METHODS

We have tested a standardised phenotyping method termed 'Phenomodels' for integrating deep-phenotyping information with our in-house developed clinical whole exome/genome sequencing analytical pipeline. Phenomodels includes a user-friendly epilepsy phenotyping template and an objective measure for selecting which template terms to include in individualised Human Phenotype Ontology (HPO) gene panels. In a pilot study of 38 previously solved cases of developmental and epileptic encephalopathies, we compared the sensitivity and specificity of the individualised HPO gene panels with the clinical epilepsy gene panel.

RESULTS

The Phenomodels template showed high sensitivity for capturing relevant phenotypic information, where 37/38 individuals' HPO gene panels included the causative gene. The HPO gene panels also had far fewer variants to assess than the epilepsy gene panel.

CONCLUSION

We have demonstrated a viable approach for incorporating standardised phenotype information into clinical genomic analyses, which may enable more efficient analysis.

Enriching representation learning using 53 million patient notes through human phenotype ontology embedding

The Human Phenotype Ontology (HPO) is a dictionary of >15,000 clinical phenotypic terms with defined semantic relationships, developed to standardize phenotypic analysis. Over the last decade, the HPO has been used to accelerate the implementation of precision medicine into clinical practice. In addition, recent research in representation learning, specifically in graph embedding, has led to notable progress in automated prediction via learned features. Here, we present a novel approach to phenotype representation by incorporating phenotypic frequencies based on 53 million full-text health care notes from >1.5 million individuals. We demonstrate the efficacy of our proposed phenotype embedding technique by comparing our work to existing phenotypic similarity-measuring methods. Using phenotype frequencies in our embedding technique, we are able to identify phenotypic similarities that surpass current computational models. Furthermore, our embedding technique exhibits a high degree of agreement with domain experts' judgment. By transforming complex and multidimensional phenotypes from the HPO format into vectors, our proposed method enables efficient representation of these phenotypes for downstream tasks that require deep phenotyping. This is demonstrated in a patient similarity analysis and can further be applied to disease trajectory and risk prediction.

Investigating the genetic contribution in febrile infection-related epilepsy syndrome and refractory status epilepticus

Introduction

Febrile infection-related epilepsy syndrome (FIRES) is a severe childhood epilepsy with refractory status epilepticus after a typically mild febrile infection. The etiology of FIRES is largely unknown, and outcomes in most individuals with FIRES are poor.

Methods

Here, we reviewed the current state-of-the art genetic testing strategies in individuals with FIRES. We performed a systematic computational analysis to identify individuals with FIRES and characterize the clinical landscape using the Electronic Medical Records (EMR). Among 25 individuals with a confirmed FIRES diagnosis over the last decade, we performed a comprehensive review of genetic testing and other diagnostic testing.

Results

Management included use of steroids and intravenous immunoglobulin (IVIG) in most individuals, with an increased use of immunomodulatory agents, including IVIG, plasma exchange (PLEX) and immunosuppressants such as cytokine inhibitors, and the ketogenic diet after 2014. Genetic testing was performed on a clinical basis in almost all individuals and was non-diagnostic in all patients. We compared FIRES with both status epilepticus (SE) and refractory status epilepticus (RSE) as a broader comparison cohort and identified genetic causes in 36% of patients with RSE. The difference in genetic signatures between FIRES and RSE suggest distinct underlying etiologies. In summary, despite the absence of any identifiable etiologies in FIRES, we performed an unbiased analysis of the clinical landscape, identifying a heterogeneous range of treatment strategies and characterized real-world clinical practice.

Discussion

FIRES remains one of the most enigmatic conditions in child neurology without any known etiologies to date despite significant efforts in the field, suggesting a clear need for further studies and novel diagnostic and treatment approaches.

A draft conceptual model of SLC6A1 neurodevelopmental disorder

Introduction

SLC6A1 Neurodevelopmental Disorder (SLC6A1-NDD), first described in 2015, is a rare syndrome caused by a mutation in the SLC6A1 gene which encodes for the GABA Transporter 1 (GAT-1) protein. Epilepsy is one of the most common symptoms in patients and is often the primary treatment target, though the severity of epilepsy is variable. The impact of seizures and other symptoms of SLC6A1-NDD on patients and caregivers is wide-ranging and has not been described in a formal disease concept study.

Methods

A literature search was performed using the simple search term, "SLC6A1." Papers published before 2015, and those which did not describe the human neurodevelopmental disorder were removed from analysis. Open-ended interviews on lived experiences were conducted with two patient advocate key opinion leaders. An analysis of de-identified conversations between families of people with SLC6A1-NDD on social media was performed to quantify topics of concern.

Results

Published literature described symptoms in all of the following domains: neurological, visual, motor, cognitive, communication, behavior, gastrointestinal, sleep, musculo-skeletal, and emotional in addition to epilepsy. Key opinion leaders noted two unpublished features: altered hand use in infants, and developmental regression with onset of epilepsy. Analysis of social media interactions confirmed that the core symptoms of epilepsy and autistic traits were prominent concerns, but also demonstrated that other symptoms have a large impact on family life.

Discussion

For rare diseases, analysis of published literature is important, but may not be as comprehensive as that which can be gleaned from spontaneous interactions between families and through qualitative interviews. This report reflects our current understanding of the lived experience of SLC6A1-NDD. The discrepancy between the domains of disease reported in the literature and those discussed in patient conversations suggests that a formal qualitative interview-based disease concept study of SLC6A1-NDD is warranted.

Precision medicine via the integration of phenotype-genotype information in neonatal genome project

The explosion of next-generation sequencing (NGS) has enabled the widespread use of genomic data in precision medicine. Currently, several neonatal genome projects have emerged to explore the advantages of NGS to diagnose or screen for rare genetic disorders. These projects have made remarkable achievements, but still the genome data could be further explored with the assistance of phenotype collection. In contrast, longitudinal birth cohorts are great examples to record and apply phenotypic information in clinical studies starting at the neonatal period, especially the trajectory analyses for health development or disease progression. It is obvious that efficient integration of genotype and phenotype benefits not only the clinical management of rare genetic disorders but also the risk assessment of complex diseases. Here, we first summarize the recent neonatal genome projects as well as some longitudinal birth cohorts. Then, we propose two simplified strategies by integrating genotypic and phenotypic information in precision medicine based on current studies. Finally, research collaborations, sociological issues, and future perspectives are discussed. How to maximize neonatal genomic information to benefit the pediatric population remains an area in need of more research and effort.

Computational analysis of neurodevelopmental phenotypes: Harmonization empowers clinical discovery

Making a specific diagnosis in neurodevelopmental disorders is traditionally based on recognizing clinical features of a distinct syndrome, which guides testing of its possible genetic etiologies. Scalable frameworks for genomic diagnostics, however, have struggled to integrate meaningful measurements of clinical phenotypic features. While standardization has enabled generation and interpretation of genomic data for clinical diagnostics at unprecedented scale, making the equivalent breakthrough for clinical data has proven challenging. However, increasingly clinical features are being recorded using controlled dictionaries with machine readable formats such as the Human Phenotype Ontology (HPO), which greatly facilitates their use in the diagnostic space. Improving the tractability of large-scale clinical information will present new opportunities to inform genomic research and diagnostics from a clinical perspective. Here, we describe novel approaches for computational phenotyping to harmonize clinical features, improve data translation through revising domain-specific dictionaries, quantify phenotypic features, and determine clinical relatedness. We demonstrate how these concepts can be applied to longitudinal phenotypic information, which represents a critical element of developmental disorders and pediatric conditions. Finally, we expand our discussion to clinical data derived from electronic medical records, a largely untapped resource of deep clinical information with distinct strengths and weaknesses.

Genetic Testing for Malformations of Cortical Development

Background and Objectives

Malformations of cortical development (MCD), though individually rare, constitute a significant burden of disease. The diagnostic yield of next-generation sequencing (NGS) in these patients varies across studies and methods, and novel genes and variants continue to emerge.

Methods

Patients (n = 123) with a definite radiologic or histopathologic diagnosis of MCD, with or without epilepsy were included in this study. They underwent NGS-based targeted gene panel (TGP) testing, whole-exome sequencing (WES), or WES-based virtual panel testing. Selected patients who underwent epilepsy surgery (n = 69) also had somatic gene testing of brain tissue–derived DNA. We analyzed predictors of positive germline genetic finding and diagnostic yield of respective methods.

Results

Pathogenic or likely pathogenic germline genetic variants were detected in 21% of patients (26/123). In the surgical subgroup (69/123), we performed somatic sequencing in 40% of cases (28/69) and detected causal variants in 18% (5/28). Diagnostic yield did not differ between TGP, WES-based virtual gene panel, and open WES (p = 0.69). Diagnosis of focal cortical dysplasia type 2A, epilepsy, and intellectual disability were associated with positive results of germline testing. We report previously unpublished variants in 16/26 patients and 4 cases of MCD with likely pathogenic variants in non-MCD genes.

Discussion

In this study, we are reporting genetic findings of a large cohort of MCD patients with epilepsy or potentially epileptogenic MCD. We determine predictors of successful ascertainment of a genetic diagnosis in real-life setting and report novel, likely pathogenic variants in MCD and non-MCD genes alike.

Risk factors for acute encephalitis and early seizure recurrence in complex febrile seizures

The purpose of this study is to elucidate risk factors for central nervous system infection and early seizure recurrence in children with febrile seizures (FSs) and thus facilitate outpatient management of complex FS. This single-center, retrospective cohort study investigated 688 children (6-60 months old) with FSs in Japan during 2011-2021. We investigated the incidence and clinical manifestations of children with acute encephalitis or bacterial meningitis. Logistic regression modeling was used to examine risk factors for seizure recurrence within 24 h. Among children with recurrent FSs, the distribution of intervals between first and second FS was assessed. Among 145 children with complex FSs, 2 patients (1.4%) had acute viral encephalitis and none had bacterial meningitis. Acute encephalitis was found in 2 of 8 patients (25%) with FSs prolonged ≥30 min and 2 of 3 patients (67%) requiring ≥2 intravenous anticonvulsants to stop seizures. Seizure recurrence within 24 h was observed in 16% of participants and was independently associated with preceding use of diazepam and family history of FS. In 82% of patients with FS recurrence within 24 h, early recurrences occurred within 8 h of the first seizure.      Conclusion: Patients with prolonged or refractory FSs are still indicated for hospital admission due to the risk of acute encephalitis. FS patients with a family history of FS may be managed safely by 8-h observation or single-dose rectal diazepam as prophylaxis against early recurrent seizure. What is Known: • Hospitalization has been recommended for children with complex febrile seizures due to the increased risk of central nervous infections. • Recent studies showed low incidences of bacterial meningitis (<1%) in children with complex febrile seizures in the presence of routine immunization. What is New: • Acute encephalitis was identified in 1.4% of children with complex febrile seizures, characterized by prolonged seizures ≥30 min and refractory seizures. • Early recurrent seizures may be safely managed by prophylactic diazepam or 8-h expectant observation.

PheNominal: an EHR-integrated web application for structured deep phenotyping at the point of care

BACKGROUND

Clinical phenotype information greatly facilitates genetic diagnostic interpretations pipelines in disease. While post-hoc extraction using natural language processing on unstructured clinical notes continues to improve, there is a need to improve point-of-care collection of patient phenotypes. Therefore, we developed "PheNominal", a point-of-care web application, embedded within Epic electronic health record (EHR) workflows, to permit capture of standardized phenotype data.

METHODS

Using bi-directional web services available within commercial EHRs, we developed a lightweight web application that allows users to rapidly browse and identify relevant terms from the Human Phenotype Ontology (HPO). Selected terms are saved discretely within the patient's EHR, permitting reuse both in clinical notes as well as in downstream diagnostic and research pipelines.

RESULTS

In the 16 months since implementation, PheNominal was used to capture discrete phenotype data for over 1500 individuals and 11,000 HPO terms during clinic and inpatient encounters for a genetic diagnostic consultation service within a quaternary-care pediatric academic medical center. An average of 7 HPO terms were captured per patient. Compared to a manual workflow, the average time to enter terms for a patient was reduced from 15 to 5 min per patient, and there were fewer annotation errors.

CONCLUSIONS

Modern EHRs support integration of external applications using application programming interfaces. We describe a practical application of these interfaces to facilitate deep phenotype capture in a discrete, structured format within a busy clinical workflow. Future versions will include a vendor-agnostic implementation using FHIR. We describe pilot efforts to integrate structured phenotyping through controlled dictionaries into diagnostic and research pipelines, reducing manual effort for phenotype documentation and reducing errors in data entry.

Assessing the landscape of STXBP1-related disorders in 534 individuals

Disease-causing variants in STXBP1 are among the most common genetic causes of neurodevelopmental disorders. However, the phenotypic spectrum in STXBP1-related disorders is wide and clear correlations between variant type and clinical features have not been observed so far. Here, we harmonized clinical data across 534 individuals with STXBP1-related disorders and analysed 19 973 derived phenotypic terms, including phenotypes of 253 individuals previously unreported in the scientific literature. The overall phenotypic landscape in STXBP1-related disorders is characterized by neurodevelopmental abnormalities in 95% and seizures in 89% of individuals, including focal-onset seizures as the most common seizure type (47%). More than 88% of individuals with STXBP1-related disorders have seizure onset in the first year of life, including neonatal seizure onset in 47%. Individuals with protein-truncating variants and deletions in STXBP1 (n = 261) were almost twice as likely to present with West syndrome and were more phenotypically similar than expected by chance. Five genetic hotspots with recurrent variants were identified in more than 10 individuals, including p.Arg406Cys/His (n = 40), p.Arg292Cys/His/Leu/Pro (n = 30), p.Arg551Cys/Gly/His/Leu (n = 24), p.Pro139Leu (n = 12), and p.Arg190Trp (n = 11). None of the recurrent variants were significantly associated with distinct electroclinical syndromes, single phenotypic features, or showed overall clinical similarity, indicating that the baseline variability in STXBP1-related disorders is too high for discrete phenotypic subgroups to emerge. We then reconstructed the seizure history in 62 individuals with STXBP1-related disorders in detail, retrospectively assigning seizure type and seizure frequency monthly across 4433 time intervals, and retrieved 251 anti-seizure medication prescriptions from the electronic medical records. We demonstrate a dynamic pattern of seizure control and complex interplay with response to specific medications particularly in the first year of life when seizures in STXBP1-related disorders are the most prominent. Adrenocorticotropic hormone and phenobarbital were more likely to initially reduce seizure frequency in infantile spasms and focal seizures compared to other treatment options, while the ketogenic diet was most effective in maintaining seizure freedom. In summary, we demonstrate how the multidimensional spectrum of phenotypic features in STXBP1-related disorders can be assessed using a computational phenotype framework to facilitate the development of future precision-medicine approaches.

Patient characteristics and antiseizure medication pathways in newly diagnosed epilepsy: Feasibility and pilot results using the common data model in a single-center electronic medical record database

INTRODUCTION

Efforts to characterize variability in epilepsy treatment pathways are limited by the large number of possible antiseizure medication (ASM) regimens and sequences, heterogeneity of patients, and challenges of measuring confounding variables and outcomes across institutions. The Observational Health Data Science and Informatics (OHDSI) collaborative is an international data network representing over 1 billion patient records using common data standards. However, few studies have applied OHDSI's Common Data Model (CDM) to the population with epilepsy and none have validated relevant concepts. The goals of this study were to demonstrate the feasibility of characterizing adult patients with epilepsy and ASM treatment pathways using the CDM in an electronic health record (EHR)-derived database.

METHODS

We validated a phenotype algorithm for epilepsy in adults using the CDM in an EHR-derived database (2001-2020) against source records and a prospectively maintained database of patients with confirmed epilepsy. We obtained the frequency of all antecedent conditions and procedures for patients meeting the epilepsy phenotype criteria and characterized ASM exposure sequences over time and by age and sex.

RESULTS

The phenotype algorithm identified epilepsy with 73.0-85.0% positive predictive value and 86.3% sensitivity. Many patients had neurologic conditions and diagnoses antecedent to meeting epilepsy criteria. Levetiracetam incrementally replaced phenytoin as the most common first-line agent, but significant heterogeneity remained, particularly in second-line and subsequent agents. Drug sequences included up to 8 unique ingredients and a total of 1,235 unique pathways were observed.

CONCLUSIONS

Despite the availability of additional ASMs in the last 2 decades and accumulated guidelines and evidence, ASM use varies significantly in practice, particularly for second-line and subsequent agents. Multi-center OHDSI studies have the potential to better characterize the full extent of variability and support observational comparative effectiveness research, but additional work is needed to validate covariates and outcomes.

Cardiovascular Phenotypes Profiling for L-Transposition of the Great Arteries and Prognosis Analysis

Objectives

Congenitally corrected transposition of the great arteries (ccTGA) is a rare and complex congenital heart disease with the characteristics of double discordance. Enormous co-existed anomalies are the culprit of prognosis evaluation and clinical decision. We aim at delineating a novel ccTGA clustering modality under human phenotype ontology (HPO) instruction and elucidating the relationship between phenotypes and prognosis in patients with ccTGA.

Methods

A retrospective review of 270 patients diagnosed with ccTGA in Fuwai hospital from 2009 to 2020 and cross-sectional follow-up were performed. HPO-instructed clustering method was administered in ccTGA risk stratification. Kaplan-Meier survival, Landmark analysis, and cox regression analysis were used to investigate the difference of outcomes among clusters.

Results

The median follow-up time was 4.29 (2.07–7.37) years. A total of three distinct phenotypic clusters were obtained after HPO-instructed clustering with 21 in cluster 1, 136 in cluster 2, and 113 in cluster 3. Landmark analysis revealed significantly worse mid-term outcomes in all-cause mortality (p = 0.021) and composite endpoints (p = 0.004) of cluster 3 in comparison with cluster 1 and cluster 2. Multivariate analysis indicated that pulmonary arterial hypertension (PAH), atrioventricular septal defect (AVSD), and arrhythmia were risk factors for composite endpoints. Moreover, the surgical treatment was significantly different among the three groups (p < 0.001) and surgical strategies had different effects on the prognosis of the different phenotypic clusters.

Conclusions

Human phenotype ontology-instructed clustering can be a potentially powerful tool for phenotypic risk stratification in patients with complex congenital heart diseases, which may improve prognosis prediction and clinical decision.

From Physiology to Pathology of Cortico-Thalamo-Cortical Oscillations: Astroglia as a Target for Further Research

The electrographic hallmark of childhood absence epilepsy (CAE) and other idiopathic forms of epilepsy are 2.5-4 Hz spike and wave discharges (SWDs) originating from abnormal electrical oscillations of the cortico-thalamo-cortical network. SWDs are generally associated with sudden and brief non-convulsive epileptic events mostly generating impairment of consciousness and correlating with attention and learning as well as cognitive deficits. To date, SWDs are known to arise from locally restricted imbalances of excitation and inhibition in the deep layers of the primary somatosensory cortex. SWDs propagate to the mostly GABAergic nucleus reticularis thalami (NRT) and the somatosensory thalamic nuclei that project back to the cortex, leading to the typical generalized spike and wave oscillations. Given their shared anatomical basis, SWDs have been originally considered the pathological transition of 11-16 Hz bursts of neural oscillatory activity (the so-called sleep spindles) occurring during Non-Rapid Eye Movement (NREM) sleep, but more recent research revealed fundamental functional differences between sleep spindles and SWDs, suggesting the latter could be more closely related to the slow (<1 Hz) oscillations alternating active (Up) and silent (Down) cortical activity and concomitantly occurring during NREM. Indeed, several lines of evidence support the fact that SWDs impair sleep architecture as well as sleep/wake cycles and sleep pressure, which, in turn, affect seizure circadian frequency and distribution. Given the accumulating evidence on the role of astroglia in the field of epilepsy in the modulation of excitation and inhibition in the brain as well as on the development of aberrant synchronous network activity, we aim at pointing at putative contributions of astrocytes to the physiology of slow-wave sleep and to the pathology of SWDs. Particularly, we will address the astroglial functions known to be involved in the control of network excitability and synchronicity and so far mainly addressed in the context of convulsive seizures, namely (i) interstitial fluid homeostasis, (ii) K⁺ clearance and neurotransmitter uptake from the extracellular space and the synaptic cleft, (iii) gap junction mechanical and functional coupling as well as hemichannel function, (iv) gliotransmission, (v) astroglial Ca²⁺ signaling and downstream effectors, (vi) reactive astrogliosis and cytokine release.

Phenotypic homogeneity in childhood epilepsies evolves in gene-specific patterns across 3251 patient-years of clinical data

While genetic studies of epilepsies can be performed in thousands of individuals, phenotyping remains a manual, non-scalable task. A particular challenge is capturing the evolution of complex phenotypes with age. Here, we present a novel approach, applying phenotypic similarity analysis to a total of 3251 patient-years of longitudinal electronic medical record data from a previously reported cohort of 658 individuals with genetic epilepsies. After mapping clinical data to the Human Phenotype Ontology, we determined the phenotypic similarity of individuals sharing each genetic etiology within each 3-month age interval from birth up to a maximum age of 25 years. 140 of 600 (23%) of all 27 genes and 3-month age intervals with sufficient data for calculation of phenotypic similarity were significantly higher than expect by chance. 11 of 27 genetic etiologies had significant overall phenotypic similarity trajectories. These do not simply reflect strong statistical associations with single phenotypic features but appear to emerge from complex clinical constellations of features that may not be strongly associated individually. As an attempt to reconstruct the cognitive framework of syndrome recognition in clinical practice, longitudinal phenotypic similarity analysis extends the traditional phenotyping approach by utilizing data from electronic medical records at a scale that is far beyond the capabilities of manual phenotyping. Delineation of how the phenotypic homogeneity of genetic epilepsies varies with age could improve the phenotypic classification of these disorders, the accuracy of prognostic counseling, and by providing historical control data, the design and interpretation of precision clinical trials in rare diseases.

Modeling seizures in the Human Phenotype Ontology according to contemporary ILAE concepts makes big phenotypic data tractable

Objective:

The clinical features of epilepsy determine how it is defined, which in turn guides management. Therefore, consideration of the fundamental clinical entities that comprise an epilepsy is essential in the study of causes, trajectories, and treatment responses. The Human Phenotype Ontology (HPO) is used widely in clinical and research genetics for concise communication and modeling of clinical features, allowing extracted data to be harmonized using logical inference. We sought to redesign the HPO seizure subontology to improve its consistency with current epileptological concepts, supporting the use of large clinical data sets in high-throughput clinical and research genomics.

Methods:

We created a new HPO seizure subontology based on the 2017 International League Against Epilepsy (ILAE) Operational Classification of Seizure Types, and integrated concepts of status epilepticus, febrile, reflex, and neonatal seizures at different levels of detail. We compared the HPO seizure subontology prior to, and following, our revision, according to the information that could be inferred about the seizures of 791 individuals from three independent cohorts: 2 previously published and 150 newly recruited individuals. Each cohort’s data were provided in a different format and harmonized using the two versions of the HPO.

Results:

The new seizure subontology increased the number of descriptive concepts for seizures 5-fold. The number of seizure descriptors that could be annotated to the cohort increased by 40% and the total amount of information about individuals’ seizures increased by 38%. The most important qualitative difference was the relationship of focal to bilateral tonic-clonic seizure to generalized-onset and focal-onset seizures.

Computational analysis of 10,860 phenotypic annotations in individuals with SCN2A-related disorders

Purpose

Pathogenic variants in SCN2A cause a wide range of neurodevelopmental phenotypes. Reports of genotype–phenotype correlations are often anecdotal, and the available phenotypic data have not been systematically analyzed.

Methods

We extracted phenotypic information from primary descriptions of SCN2A-related disorders in the literature between 2001 and 2019, which we coded in Human Phenotype Ontology (HPO) terms. With higher-level phenotype terms inferred by the HPO structure, we assessed the frequencies of clinical features and investigated the association of these features with variant classes and locations within the Na_V1.2 protein.

Results

We identified 413 unrelated individuals and derived a total of 10,860 HPO terms with 562 unique terms. Protein-truncating variants were associated with autism and behavioral abnormalities. Missense variants were associated with neonatal onset, epileptic spasms, and seizures, regardless of type. Phenotypic similarity was identified in 8/62 recurrent SCN2A variants. Three independent principal components accounted for 33% of the phenotypic variance, allowing for separation of gain-of-function versus loss-of-function variants with good performance.

Conclusion

Our work shows that translating clinical features into a computable format using a standardized language allows for quantitative phenotype analysis, mapping the phenotypic landscape of SCN2A-related disorders in unprecedented detail and revealing genotype–phenotype correlations along a multidimensional spectrum.

The Human Phenotype Ontology in 2021

The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for phenotype exchange. The HPO has grown steadily since its inception due to considerable contributions from clinical experts and researchers from a diverse range of disciplines. Here, we present recent major extensions of the HPO for neurology, nephrology, immunology, pulmonology, newborn screening, and other areas. For example, the seizure subontology now reflects the International League Against Epilepsy (ILAE) guidelines and these enhancements have already shown clinical validity. We present new efforts to harmonize computational definitions of phenotypic abnormalities across the HPO and multiple phenotype ontologies used for animal models of disease. These efforts will benefit software such as Exomiser by improving the accuracy and scope of cross-species phenotype matching. The computational modeling strategy used by the HPO to define disease entities and phenotypic features and distinguish between them is explained in detail.We also report on recent efforts to translate the HPO into indigenous languages. Finally, we summarize recent advances in the use of HPO in electronic health record systems.