Search

Focal epilepsy (FE) is clinically highly heterogeneous. It has been shown recently that not only rare but also a subset of common genetic variants confer risk for FE. The relatively modest power of genetic studies in FE suggests a high genetic heterogeneity of FE when grouped as one disorder. We hypothesize that the clinical heterogeneity of FE is correlated with genetic heterogeneity on a common risk variant level. To test the hypothesis, we used an FE polygenic risk score "FE-PRS" that combines small effect sizes of thousands of common variants from the largest FE-GWAS (genome-wide association study) into a single measure. We grouped 414 individuals with FE according to common clinical features into subgroups, either by one feature at a time or by all features combined in a cluster analysis. We examined their association with FE-PRS compared to 20 435 matched population controls and observed heterogeneous FE-PRS burden among the subgroups. The highest phenotypic variance explained by FE-PRS was identified in a cluster analysis-defined FE subgroup where all individuals had unknown etiologies and psychiatric comorbidities, and the majority had early onset seizures. Our results indicate that genetic factors associated with FE have differential burden among FE subtypes. Future studies using better-powered FE-PRS might have clinical utility., (© 2020 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Individuals with Down syndrome (DS) develop Alzheimer's disease (AD)-related neuropathology, characterized by amyloid plaques with amyloid β (Aβ) and neurofibrillary tangles with tau accumulation. Peripheral inflammation and the innate immune response are elevated in DS. Triggering receptor expressed in myeloid cells 2 ( TREM2 ) genetic variants are risk factors for AD and other neurodegenerative diseases. Soluble TREM2 (sTREM2), a soluble cleavage product of TREM2, is elevated in AD cerebrospinal fluid and positively correlates with cognitive decline. There is relatively little information about TREM2 in DS. Our objective was to examine the relationship between sTREM2 and inflammatory markers in young adults with DS, prior to the development of dementia symptoms. Because TREM2 plays a role in the innate immune response and has been associated with dementia, the hypothesis of this exploratory study was that young adults with DS predementia ( n = 15, mean age = 29.5 y) would exhibit a different relationship between sTREM2 and inflammatory markers in plasma, compared with neurotypical, age-matched controls ( n = 16, mean age = 29.6 y). Indeed, young adults with DS had significantly elevated plasma sTREM2 and inflammatory markers. Additionally, in young adults with DS, sTREM2 correlated positively with 24 of the measured cytokines, whereas there were no significant correlations in the control group. Hierarchical clustering of sTREM2 and cytokine concentrations also differed between the groups, supporting the hypothesis that its function is altered in people with DS predementia. This preliminary report of human plasma provides a basis for future studies investigating the relationship between TREM2 and the broader immune response predementia., (Copyright © 2020 by The American Association of Immunologists, Inc.)

By accumulating ever greater amounts of genomic data, scientists have identified >100 genes associated with Mendelian forms of epilepsy and neurodevelopmental disorders with seizures. For most of the identified genes a wide range of genetic variants have been identified and affected patients are clinically heterogeneous. It is not clear to which degree the clinical heterogeneity can be attributed to the disease causing variant alone. We need to improve our current understanding of biophysical effects of variants on protein function and the role of polygenic background in modifying the clinical representation. In addition, longitudinal clinical data need to be recorded using standardized methods and shared across research centers to build large virtual cohorts for each single gene disorder. Without large, comprehensive, longitudinal datasets, studying the interplay of environmental factors and genetic factors will be challenging. As a community, we must work together to set the foundation for biorepositories and the collection and sharing of 'big data' in order to allow genetic-phenotypic characterization of the epilepsies and to fully utilize the potential for drug discovery, and patient-specific tailored management., (Copyright © 2019 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved.)

Clinical genetic testing has exponentially expanded in recent years, leading to an overwhelming amount of patient variants with high variability in pathogenicity and heterogeneous phenotypes. A large part of the variant level data is aggregated in public databases such as ClinVar. However, the ability to explore this rich resource and answer general questions such as 'How many genes inside ClinVar are associated with a specific disease? or 'In which part of the protein are patient variants located?' is limited and requires advanced bioinformatics processing. Here, we present Simple ClinVar (http://simple-clinvar.broadinstitute.org/) a web server application that is able to provide variant, gene and disease level summary statistics based on the entire ClinVar database in a dynamic and user-friendly web-interface. Overall, our web application is able to interactively answer basic questions regarding genetic variation and its known relationships to disease. By typing a disease term of interest, the user can identify in seconds the genes and phenotypes most frequently reported to ClinVar. Subsets of variants can then be further explored, filtered or mapped and visualized in the corresponding protein sequences. Our website will follow ClinVar monthly releases and provide easy access to ClinVar resources to a broader audience including basic and clinical scientists., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Objective: To characterize the phenotypic spectrum associated with GNAO1 variants and establish genotype-protein structure-phenotype relationships., Methods: We evaluated the phenotypes of 14 patients with GNAO1 variants, analyzed their variants for potential pathogenicity, and mapped them, along with those in the literature, on a three-dimensional structural protein model., Results: The 14 patients in our cohort, including one sibling pair, had 13 distinct, heterozygous GNAO1 variants classified as pathogenic or likely pathogenic. We attributed the same variant in two siblings to parental mosaicism. Patients initially presented with seizures beginning in the first 3 months of life (8/14), developmental delay (4/14), hypotonia (1/14), or movement disorder (1/14). All patients had hypotonia and developmental delay ranging from mild to severe. Nine had epilepsy, and nine had movement disorders, including dystonia, ataxia, chorea, and dyskinesia. The 13 GNAO1 variants in our patients are predicted to result in amino acid substitutions or deletions in the GNAO1 guanosine triphosphate (GTP)-binding region, analogous to those in previous publications. Patients with variants affecting amino acids 207-221 had only movement disorder and hypotonia. Patients with variants affecting the C-terminal region had the mildest phenotypes., Significance: GNAO1 encephalopathy most frequently presents with seizures beginning in the first 3 months of life. Concurrent movement disorders are also a prominent feature in the spectrum of GNAO1 encephalopathy. All variants affected the GTP-binding domain of GNAO1, highlighting the importance of this region for G-protein signaling and neurodevelopment., (Wiley Periodicals, Inc. © 2019 International League Against Epilepsy.)

Human protein-coding genes are often accompanied by divergently transcribed non-coding RNAs whose functions, especially in cell fate decisions, are poorly understood. Using an hESC-based cardiac differentiation model, we define a class of divergent lncRNAs, termed yin yang lncRNAs (yylncRNAs), that mirror the cell-type-specific expression pattern of their protein-coding counterparts. yylncRNAs are preferentially encoded from the genomic loci of key developmental cell fate regulators. Most yylncRNAs are spliced polyadenylated transcripts showing comparable expression patterns in vivo in mouse and in human embryos. Signifying their developmental function, the key mesoderm specifier BRACHYURY (T) is accompanied by yylncT, which localizes to the active T locus during mesoderm commitment. yylncT binds the de novo DNA methyltransferase DNMT3B, and its transcript is required for activation of the T locus, with yylncT depletion specifically abolishing mesodermal commitment. Collectively, we report a lncRNA-mediated regulatory layer safeguarding embryonic cell fate transitions., (Copyright © 2018 Elsevier Inc. All rights reserved.)

We described physical activity measures and hourly patterns in patients with chronic obstructive pulmonary disease (COPD) after stratification for generic and COPD-specific characteristics and, based on multiple physical activity measures, we identified clusters of patients. In total, 1001 patients with COPD (65% men; age, 67 years; forced expiratory volume in the first second [FEV 1 ], 49% predicted) were studied cross-sectionally. Demographics, anthropometrics, lung function and clinical data were assessed. Daily physical activity measures and hourly patterns were analysed based on data from a multisensor armband. Principal component analysis (PCA) and cluster analysis were applied to physical activity measures to identify clusters. Age, body mass index (BMI), dyspnoea grade and ADO index (including age, dyspnoea and airflow obstruction) were associated with physical activity measures and hourly patterns. Five clusters were identified based on three PCA components, which accounted for 60% of variance of the data. Importantly, couch potatoes (i.e. the most inactive cluster) were characterised by higher BMI, lower FEV 1 , worse dyspnoea and higher ADO index compared to other clusters ( p < 0.05 for all). Daily physical activity measures and hourly patterns are heterogeneous in COPD. Clusters of patients were identified solely based on physical activity data. These findings may be useful to develop interventions aiming to promote physical activity in COPD.

Background: Sleep disturbances are common in patients with chronic obstructive pulmonary disease (COPD) with a considerable negative impact on their quality of life. However, factors associated with measures of sleep in daily life have not been investigated before nor has the association between sleep and the ability to engage in physical activity on a day-to-day basis been studied., Aims: To provide insight into the relationship between actigraphic sleep measures and disease severity, exertional dyspnoea, gender and parts of the week; and to investigate the association between sleep measures and next day physical activity., Methods: Data were analysed from 932 patients with COPD (66% male, 66.4±8.3 years, FEV 1 % predicted=50.8±20.5). Participants had sleep and physical activity continuously monitored using a multisensor activity monitor for a median of 6 days. Linear mixed effects models were applied to investigate the factors associated with sleep impairment and the association between nocturnal sleep and patients' subsequent daytime physical activity., Results: Actigraphic estimates of sleep impairment were greater in patients with worse airflow limitation and worse exertional dyspnoea. Patients with better sleep measures (ie, non-fragmented sleep, sleeping bouts ≥225 min, sleep efficiency ≥91% and time spent awake after sleep onset <57 min) spent significantly more time in light (p<0.01) and moderate-to-vigorous physical activity (p<0.01)., Conclusions: There is a relationship between measures of sleep in patients with COPD and the amount of activity they undertake during the waking day. Identifying groups with specific sleep characteristics may be useful information when designing physical activity-enhancing interventions., Competing Interests: Competing interests: None declared., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

Background: Physical activity (PA) is associated with disease severity in idiopathic pulmonary fibrosis (IPF), but longitudinal studies evaluating its prognostic value and changes over time are lacking., Methods: We measured PA (steps per day, SPD) in a cohort of 46 IPF-patients (mean age, 67 years; mean FVC, 76.1%pred.) by accelerometry at baseline, recorded survival status during 3 years follow-up and repeated measurements in survivors. We compared the prognostic value of PA to established mortality predictors including lung function (FVC, DLCO) and 6-min walking-distance (6MWD)., Results: During follow-up (median 34 months) 20 patients (43%) died. SPD and FVC best identified non-survivors (AUROC-curve 0.79, p < 0.01). After adjustment for confounders (sex, age, therapy), a standardized increase (i.e. one SD) in SPD, FVC%pred. or DLCO%pred. was associated with a more than halved risk of death (HR < 0.50; p < 0.01). Compared to baseline, SPD, FVC, and 6MWD annually declined in survivors by 973 SPD, 130 ml and 9 m, resulting in relative declines of 48.3% (p < 0.001), 13.3% (p < 0.001) and 7.8% (p = 0.055), respectively., Conclusion: While PA predicts mortality of IPF patients similar to established functional measures, longitudinal decline of PA seems to be disproportionally large. Our data suggest that the clinical impact of disease progression could be underestimated by established functional measures.

Background: Little is known about the consequences of idiopathic pulmonary fibrosis (IPF) for physical activity (PA)., Objectives: We aimed to investigate levels of PA in IPF and to study associations of PA with lung function, exercise capacity, symptoms, and quality of life., Methods: In stable patients with IPF we measured PA (steps per day, SPD; physical activity level, PAL; minutes of moderate activity, MMA) by accelerometry (SenseWear Armband) for 1 week. We also assessed lung function (forced vital capacity, FVC; diffusing capacity for carbon monoxide, DLCO); exercise capacity (6-minute walking distance, 6MWD); dyspnea (modified Medical Research Council, mMRC); fatigue (Multidimensional Fatigue Inventory, MFI-20), and generic (12-Item Short Form Survey, SF-12) and health-related quality of life (St. George's Respiratory Questionnaire) as further clinical variables., Results: We investigated 48 patients with IPF in two centers (mean age, 67 years; 75% male; 23% on long-term oxygen therapy; mean FVC 75%pred.; mean DLCO 43%pred.; mean 6MWD 355 ± 140 m; mean SPD 5,017 ± 3,360). On a bivariate level, all clinical variables were significantly associated with SPD (p < 0.05). The associations of mMRC, MFI-20, SF-12 (physical health), and 6MWD with SPD were independent of impaired lung function (p < 0.05). At multivariate analyses, either 6MWD (total explained variance of the model, total R2: 42%) or MFI-20 (total R2: 39%) were the strongest independent predictors of SPD., Conclusion: Fatigue and exercise capacity are strong and independent predictors of PA in patients with IPF, which suggests that both measures should be assessed when the consequences of IPF for PA in daily life are studied., (© 2016 S. Karger AG, Basel.)

Using a dataset of 274 male Thoroughbred racehorses in the United States, we study the effect of age on racing performance. Beyer speed figures, which are uniform measures of racing performance across distance and racing surface, are utilized in this study. A system of equations is estimated to determine quadratic improvement and decline in racing performance. We find that a typical horse's peak racing age is 4.45 years. The rate of improvement from age 2 to 4 1/2 is greater than the rate of decline after age 4 1/2. A typical horse will improve by 10 (horse) lengths in sprints (less than 1 mile) and 15 lengths in routes (one mile or greater) from age 2 to 4 1/2. Over the next five years the typical decline is 6 lengths for sprints and 9 1/2 lengths for routes.