A recurrent de novo splice site variant involving DNM1 exon 10a causes developmental and epileptic encephalopathy through a dominant-negative mechanism.

Heterozygous pathogenic variants in DNM1 cause developmental and epileptic encephalopathy (DEE) as a result of a dominant-negative mechanism impeding vesicular fission. Thus far, pathogenic variants in DNM1 have been studied with a canonical transcript that includes the alternatively spliced exon 10b. However, after performing RNA sequencing in 39 pediatric brain samples, we find the primary transcript expressed in the brain includes the downstream exon 10a instead. Using this information, we evaluated genotype-phenotype correlations of variants affecting exon 10a and identified a cohort of eleven previously unreported individuals. Eight individuals harbor a recurrent de novo splice site variant, c.1197-8G>A (GenBank: NM_001288739.1), which affects exon 10a and leads to DEE consistent with the classical DNM1 phenotype. We find this splice site variant leads to disease through an unexpected dominant-negative mechanism. Functional testing reveals an in-frame upstream splice acceptor causing insertion of two amino acids predicted to impair oligomerization-dependent activity. This is supported by neuropathological samples showing accumulation of enlarged synaptic vesicles adherent to the plasma membrane consistent with impaired vesicular fission. Two additional individuals with missense variants affecting exon 10a, p.Arg399Trp and p.Gly401Asp, had a similar DEE phenotype. In contrast, one individual with a missense variant affecting exon 10b, p.Pro405Leu, which is less expressed in the brain, had a correspondingly less severe presentation. Thus, we implicate variants affecting exon 10a as causing the severe DEE typically associated with DNM1-related disorders. We highlight the importance of considering relevant isoforms for disease-causing variants as well as the possibility of splice site variants acting through a dominant-negative mechanism.

The clinical and genetic spectrum of paediatric speech and language disorders.

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52 143 individuals, reconstructing clinical histories using a large-scale data-mining approach of the electronic medical records from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of 26 broad speech and language diagnoses. We used natural language processing to assess the degree to which clinical diagnoses in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be retrieved easily through ICD-10 diagnosis codes, whereas stuttering as a speech phenotype was coded in only 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and, to a lesser degree, with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our analysis of electronic medical records were STXBP1 (n = 21), PTEN (n = 20) and CACNA1A (n = 18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P = 8.57 × 10-7, 95% confidence interval = 18.62-130.39) and MYO7A with speech and language development delay attributable to hearing loss (P = 1.24 × 10-5, 95% confidence interval = 17.46-infinity). Finally, in a sub-cohort of 726 individuals with whole-exome sequencing data, we identified an enrichment of rare variants in neuronal receptor pathways, in addition to associations of UQCRC1 and KIF17 with expressive aphasia, MROH8 and BCHE with poor speech, and USP37, SLC22A9 and UMODL1 with aphasia. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype-phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies.

Clinical signatures of genetic epilepsies precede diagnosis in electronic medical records of 32,000 individuals.

PURPOSE: An early genetic diagnosis can guide the time-sensitive treatment of individuals with genetic epilepsies. However, most genetic diagnoses occur long after disease onset. We aimed to identify early clinical features suggestive of genetic diagnoses in individuals with epilepsy through large-scale analysis of full-text electronic medical records. METHODS: We extracted 89 million time-stamped standardized clinical annotations using Natural Language Processing from 4,572,783 clinical notes from 32,112 individuals with childhood epilepsy, including 1925 individuals with known or presumed genetic epilepsies. We applied these features to train random forest models to predict SCN1A-related disorders and any genetic diagnosis. RESULTS: We identified 47,774 age-dependent associations of clinical features with genetic etiologies a median of 3.6 years before molecular diagnosis. Across all 710 genetic etiologies identified in our cohort, neurodevelopmental differences between 6 to 9 months increased the likelihood of a later molecular diagnosis 5-fold (P < .0001, 95% CI = 3.55-7.42). A later diagnosis of SCN1A-related disorders (area under the curve [AUC] = 0.91) or an overall positive genetic diagnosis (AUC = 0.82) could be reliably predicted using random forest models. CONCLUSION: Clinical features predictive of genetic epilepsies precede molecular diagnoses by up to several years in conditions with known precision treatments. An earlier diagnosis facilitated by automated electronic medical records analysis has the potential for earlier targeted therapeutic strategies in the genetic epilepsies.

Early life seizures and epileptic spasms in STXBP1-related disorders.

OBJECTIVE: Individuals with disease-causing variants in STXBP1 frequently have epilepsy onset in the first year of life with a variety of seizure types, including epileptic spasms. However, the impact of early onset seizures and antiseizure medication (ASM) on the risk of developing epileptic spasms and impact on their trajectory are poorly understood, limiting informed and anticipatory treatment, as well as trial design. METHODS: We retrospectively reconstructed seizure and medication histories in weekly intervals for individuals with STXBP1 developmental and epileptic encephalopathy (DEE) with epilepsy onset in the first year of life and quantitatively analyzed longitudinal seizure histories and medication response. RESULTS: We included 61 individuals with early onset seizures, 29 of whom had epileptic spasms. Individuals with neonatal seizures were likely to have continued seizures after the neonatal period (25/26). The risk of developing epileptic spasms was not increased in individuals with neonatal seizures or early infantile seizures (21/41 vs. 8/16, odds ratio [OR] = 1, 95% confidence interval [CI] = .3-3.9, p = 1). We did not find any ASM associated with the development of epileptic spasms following prior seizures. Individuals with prior seizures (n = 16/21, 76%) had a higher risk of developing refractory epileptic spasms (n = 5/8, 63%, OR = 1.9, 95% CI = .2-14.6, p = .6). Individuals with refractory epileptic spasms had a later onset of epileptic spasms (n = 20, median = 20 weeks) compared to individuals with nonrefractory epileptic spasms (n = 8, median = 13 weeks, p = .08). SIGNIFICANCE: We provide a comprehensive assessment of early onset seizures in STXBP1-DEE and show that the risk of epileptic spasms is not increased following a prior history of early life seizures, nor by certain ASMs. Our study provides baseline information for targeted treatment and prognostication in early life seizures in STXBP1-DEE.

SCN1A gain-of-function mutation causing an early onset epileptic encephalopathy.

OBJECTIVE: Loss-of-function variants in SCN1A cause Dravet syndrome, the most common genetic developmental and epileptic encephalopathy (DEE). However, emerging evidence suggests separate entities of SCN1A-related disorders due to gain-of-function variants. Here, we aim to refine the clinical, genetic, and functional electrophysiological features of a recurrent p.R1636Q gain-of-function variant, identified in four individuals at a single center. METHODS: Individuals carrying the recurrent SCN1A p.R1636Q variant were identified through diagnostic testing. Whole cell voltage-clamp electrophysiological recording in HEK-293 T cells was performed to compare the properties of sodium channels containing wild-type Nav 1.1 or Nav 1.1-R1636Q along with both Nav ß1 and Nav ß2 subunits, including response to oxcarbazepine. To delineate differences from other SCN1A-related epilepsies, we analyzed electronic medical records. RESULTS: All four individuals had an early onset DEE characterized by focal tonic seizures and additional seizure types starting in the first few weeks of life. Electrophysiological analysis showed a mixed gain-of-function effect with normal current density, a leftward (hyperpolarized) shift of steady-state inactivation, and slower inactivation kinetics leading to a prominent late sodium current. The observed functional changes closely paralleled effects of pathogenic variants in SCN3A and SCN8A at corresponding positions. Both wild type and variant exhibited sensitivity to block by oxcarbazepine, partially correcting electrophysiological abnormalities of the SCN1A p.R1636Q variant. Clinically, a single individual responded to treatment with oxcarbazepine. Across 51 individuals with SCN1A-related epilepsies, those with the recurrent p.R1636Q variants had the earliest ages at onset. SIGNIFICANCE: The recurrent SCN1A p.R1636Q variant causes a clinical entity with a wider clinical spectrum than previously reported, characterized by neonatal onset epilepsy and absence of prominent movement disorder. Functional consequences of this variant lead to mixed loss and gain of function that is partially corrected by oxcarbazepine. The recurrent p.R1636Q variant represents one of the most common causes of early onset SCN1A-related epilepsies with separate treatment and prognosis implications.

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.

Child neurology telemedicine: Analyzing 14 820 patient encounters during the first year of the COVID-19 pandemic.

AIM: To determine the long-term impact of telemedicine in child neurology care during the COVID-19 pandemic and with the reopening of outpatient clinics. METHOD: We performed an observational cohort study of 34 837 in-person visits and 14 820 telemedicine outpatient visits across 26 399 individuals. We assessed differences in care across visit types, time-period observed, time between follow-ups, patient portal activation rates, and demographic factors. RESULTS: We observed a higher proportion of telemedicine for epilepsy (International Classification of Diseases, 10th Revision G40: odds ratio [OR] 1.4, 95% confidence interval [CI] 1.3-1.5) and a lower proportion for movement disorders (G25: OR 0.7, 95% CI 0.6-0.8; R25: OR 0.7, 95% CI 0.6-0.9) relative to in-person visits. Infants were more likely to be seen in-person after reopening clinics than by telemedicine (OR 1.6, 95% CI 1.5-1.8) as were individuals with neuromuscular disorders (OR 1.6, 95% CI 1.5-1.7). Self-reported racial and ethnic minority populations and those with highest social vulnerability had lower telemedicine participation rates (OR 0.8, 95% CI 0.8-0.8; OR 0.7, 95% CI 0.7-0.8). INTERPRETATION: Telemedicine continued to be utilized even once in-person clinics were available. Pediatric epilepsy care can often be performed using telemedicine while young patients with neuromuscular disorders often require in-person assessment. Prominent barriers for socially vulnerable families and racial and ethnic minorities persist.

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.

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 NaV1.2 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.

Mortality in tuberous sclerosis complex.

We studied mortality in tuberous sclerosis complex (TSC) by analyzing data from the Tuberous Sclerosis Alliance Natural History Database of 2233 patients from 18 United States TSC centers. Among 31 decedents with data; mean age of death was 29 years. Cause of death could be determined in 26 cases: 11 definitely related to TSC, 14 possibly related to TSC, and 1 unrelated to TSC. Causes of death included SUDEP in 11 (35.5%; Definite (5), Probable (4), Possible (2)), respiratory conditions in 6 (23.1%; lymphangiomyelomatosis in one), tumors in 3 (11.5%), suicide in 2 (7.7%), cardiopulmonary in 2 (7.7%), shunt malfunction in one, and drowning in one. For SUDEP cases, mean age of epilepsy onset was 7 months and 10/11 were treated with multiple anti-seizure medications (ASMs) at death; 7 had intractable epilepsy and 3 were controlled with ASMs. Patients with TSC and their families should be counseled about ASM adherence and lifestyle factors, and the potential role of nocturnal supervision or seizure detection devices to prevent SUDEP.

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.

The clinical and genetic spectrum of paediatric speech and language disorders in 52,143 individuals.

Speech and language disorders are known to have a substantial genetic contribution. Although frequently examined as components of other conditions, research on the genetic basis of linguistic differences as separate phenotypic subgroups has been limited so far. Here, we performed an in-depth characterization of speech and language disorders in 52,143 individuals, reconstructing clinical histories using a large-scale data mining approach of the Electronic Medical Records (EMR) from an entire large paediatric healthcare network. The reported frequency of these disorders was the highest between 2 and 5 years old and spanned a spectrum of twenty-six broad speech and language diagnoses. We used Natural Language Processing to assess to which degree clinical diagnosis in full-text notes were reflected in ICD-10 diagnosis codes. We found that aphasia and speech apraxia could be easily retrieved through ICD-10 diagnosis codes, while stuttering as a speech phenotype was only coded in 12% of individuals through appropriate ICD-10 codes. We found significant comorbidity of speech and language disorders in neurodevelopmental conditions (30.31%) and to a lesser degree with epilepsies (6.07%) and movement disorders (2.05%). The most common genetic disorders retrievable in our EMR analysis were STXBP1 (n=21), PTEN (n=20), and CACNA1A (n=18). When assessing associations of genetic diagnoses with specific linguistic phenotypes, we observed associations of STXBP1 and aphasia (P=8.57 × 10-7, CI=18.62-130.39) and MYO7A with speech and language development delay due to hearing loss (P=1.24 × 10-5, CI=17.46-Inf). Finally, in a sub-cohort of 726 individuals with whole exome sequencing data, we identified an enrichment of rare variants in synaptic protein and neuronal receptor pathways and associations of UQCRC1 with expressive aphasia and WASHC4 with abnormality of speech or vocalization. In summary, our study outlines the landscape of paediatric speech and language disorders, confirming the phenotypic complexity of linguistic traits and novel genotype-phenotype associations. Subgroups of paediatric speech and language disorders differ significantly with respect to the composition of monogenic aetiologies.

Concomitant Calcium Channelopathies Involving CACNA1A and CACNA1F: A Case Report and Review of the Literature.

Calcium channels are an integral component in maintaining cellular function. Alterations may lead to channelopathies, primarily manifested in the central nervous system. This study describes the clinical and genetic features of a unique 12-year-old boy harboring two congenital calcium channelopathies, involving the CACNA1A and CACNA1F genes, and provides an unadulterated view of the natural history of sporadic hemiplegic migraine type 1 (SHM1) due to the patient's inability to tolerate any preventative medication. The patient presents with episodes of vomiting, hemiplegia, cerebral edema, seizure, fever, transient blindness, and encephalopathy. He is nonverbal, nonambulatory, and forced to have a very limited diet due to abnormal immune responses. The SHM1 manifestations apparent in the subject are consistent with the phenotype described in the 48 patients identified as part of a systematic literature review. The ocular symptoms of CACNA1F align with the family history of the subject. The presence of multiple pathogenic variants make it difficult to identify a clear phenotype-genotype correlation in the present case. Moreover, the detailed case description and natural history along with the comprehensive review of the literature contribute to the understanding of this complex disorder and point to the need for comprehensive clinical assessments of SHM1.

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.

Genome-wide identification and phenotypic characterization of seizure-associated copy number variations in 741,075 individuals.

Copy number variants (CNV) are established risk factors for neurodevelopmental disorders with seizures or epilepsy. With the hypothesis that seizure disorders share genetic risk factors, we pooled CNV data from 10,590 individuals with seizure disorders, 16,109 individuals with clinically validated epilepsy, and 492,324 population controls and identified 25 genome-wide significant loci, 22 of which are novel for seizure disorders, such as deletions at 1p36.33, 1q44, 2p21-p16.3, 3q29, 8p23.3-p23.2, 9p24.3, 10q26.3, 15q11.2, 15q12-q13.1, 16p12.2, 17q21.31, duplications at 2q13, 9q34.3, 16p13.3, 17q12, 19p13.3, 20q13.33, and reciprocal CNVs at 16p11.2, and 22q11.21. Using genetic data from additional 248,751 individuals with 23 neuropsychiatric phenotypes, we explored the pleiotropy of these 25 loci. Finally, in a subset of individuals with epilepsy and detailed clinical data available, we performed phenome-wide association analyses between individual CNVs and clinical annotations categorized through the Human Phenotype Ontology (HPO). For six CNVs, we identified 19 significant associations with specific HPO terms and generated, for all CNVs, phenotype signatures across 17 clinical categories relevant for epileptologists. This is the most comprehensive investigation of CNVs in epilepsy and related seizure disorders, with potential implications for clinical practice.

Early life seizures and epileptic spasms in STXBP1-related disorders.

Background and Objectives: Individuals with disease-causing variants in STXBP1 frequently have epilepsy onset in the first year of life with a variety of seizure types, including epileptic spasms. However, the impact of early-onset seizures and anti-seizure medication (ASM) on the risk of developing epileptic spasms and impact on their trajectory is poorly understood, limiting informed and anticipatory treatment, as well as trial design. Methods: We retrospectively reconstructed seizure and medication histories in weekly intervals for individuals with STXBP1-related disorders with epilepsy onset in the first year of life and quantitatively analyzed longitudinal seizure histories and medication response. Results: We included 61 individuals with early onset seizures, 29 of whom had epileptic spasms. Individuals with neonatal seizures were likely to have continued seizures after the neonatal period (25/26). The risk of developing epileptic spasms was not increased in individuals with neonatal seizures or early infantile seizures (21/41 vs. 8/16; OR 1, 95% CI 0.3-3.9, p = 1). We did not find any ASM associated with the development of epileptic spasms following prior seizures. Individuals with prior seizures (n = 16/21, 76%) had a higher risk to develop refractory epileptic spasms (n = 5/8, 63%, OR =1.9, 95% CI 0.2-14.6, p = 0.6). Individuals with refractory epileptic spasms had a later onset of epileptic spasms (n = 20, median 20 weeks) compared to individuals with non-refractory epileptic spasms (n = 8, median 13 weeks; p = 0.08). When assessing treatment response, we found that clonazepam (n = 3, OR 12.6, 95% CI 2.2-509.4; p < 0.01), clobazam (n=7, OR 3, 95% CI 1.6-6.2; p < 0.01), topiramate (n=9, OR 2.3, 95% CI 1.4-3.9; p < 0.01), and levetiracetam (n=16, OR 1.7, 95% CI 1.2-2.4; p < 0.01) were more likely to reduce seizure frequency and/or to maintain seizure freedom with regards to epileptic spasms than other medications. Discussion: We provide a comprehensive assessment of early-onset seizures in STXBP1-related disorders and show that the risk of epileptic spasms is not increased following a prior history of early-life seizures, nor by certain ASM. Our study provides baseline information for targeted treatment and prognostication in early-life seizures in STXBP1-related disorders.

Predicting the functional effects of voltage-gated potassium channel missense variants with multi-task learning.

BACKGROUND: Variants in genes encoding voltage-gated potassium channels are associated with a broad spectrum of neurological diseases including epilepsy, ataxia, and intellectual disability. Knowledge of the resulting functional changes, characterized as overall ion channel gain- or loss-of-function, is essential to guide clinical management including precision medicine therapies. However, for an increasing number of variants, little to no experimental data is available. New tools are needed to evaluate variant functional effects. METHODS: We catalogued a comprehensive dataset of 959 functional experiments across 19 voltage-gated potassium channels, leveraging data from 782 unique disease-associated and synthetic variants. We used these data to train a taxonomy-based multi-task learning support vector machine (MTL-SVM), and compared performance to several baseline methods. FINDINGS: MTL-SVM maintains channel family structure during model training, improving overall predictive performance (mean balanced accuracy 0·718 ± 0·041, AU-ROC 0·761 ± 0·063) over baseline (mean balanced accuracy 0·620 ± 0·045, AU-ROC 0·711 ± 0·022). We can obtain meaningful predictions even for channels with few known variants (KCNC1, KCNQ5). INTERPRETATION: Our model enables functional variant prediction for voltage-gated potassium channels. It may assist in tailoring current and future precision therapies for the increasing number of patients with ion channel disorders. FUNDING: This work was supported by intramural funding of the Medical Faculty, University of Tuebingen (PATE F.1315137.1), the Federal Ministry for Education and Research (Treat-ION, 01GM1907A/B/G/H) and the German Research Foundation (FOR-2715, Le1030/16-2, He8155/1-2).

Staphylococcus lugdunensis: Review of Epidemiology, Complications, and Treatment.

Staphylococcus lugdunensis is a species of coagulase-negative staphylococci (CNS) that induces a variety of infectious diseases, including skin and soft tissue infection (SSTI), infective endocarditis (IE), and bone and PJI. This review article underscores the important points in the literature about S. lugdunensis infections, including its epidemiology, diagnosis, and treatment, as well as specific types of infections it can cause. Anatomical and age-related distributions of S. lugdunensis SSTIs have been noted, though they most commonly occur as abscesses. S. lugdunensis can also manifest as an aggressive form of IE presenting with valve destruction and abscess formation, frequently requiring surgery and with a high mortality rate. Bone and joint infections caused by S. lugdunensis are also more invasive than infections by other species of CNS. The clinical presentation of S. lugdunensis infection in SSTI, IE, and bone/joint infection is frequently more similar to that of S. aureus infection than that of other CNS infections, necessitating species-level differentiation of CNS for proper diagnosis. Though historically, this depended upon biochemical tests that were neither routine nor reliable, the implementation of matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) in clinical laboratories has made identification of CNS species such as S. lugdunensis more practical. Imaging modalities, especially the fluorodeoxyglucose (FDG) with positron emission tomography (PET), are another important emerging trend in the diagnosis of infectious diseases such as S. lugdunensis infection. S. lugdunensis remains highly susceptible to a wide gamut of antibacterial therapies, which is uncharacteristic of other CNS. Infections can usually be treated by antibiotics traditionally used for CNS such as oxacillin. The breakpoints for S. lugdunensis are higher than those of other CNS and similar to S. aureus breakpoints. In the case of aggressive IE or bone/joint infection by S. lugdunensis, it is recommended to treat with a ß-lactam agent. Further study is needed to understand the diversity, virulence, and population structure of this species, as well as its role in other infections, such as urinary tract infections (UTIs), respiratory infections, peritonitis, and bacteremia.