Data-Driven Phenotyping of Central Disorders of Hypersomnolence With Unsupervised Clustering

Background and ObjectivesRecent studies fueled doubts as to whether all currently defined central disorders of hypersomnolence are stable entities, especially narcolepsy type 2 and idiopathic hypersomnia. New reliable biomarkers are needed, and the question arises of whether current diagnostic criteria of hypersomnolence disorders should be reassessed. The main aim of this data-driven observational study was to see whether data-driven algorithms would segregate narcolepsy type 1 and identify more reliable subgrouping of individuals without cataplexy with new clinical biomarkers.MethodsWe used agglomerative hierarchical clustering, an unsupervised machine learning algorithm, to identify distinct hypersomnolence clusters in the large-scale European Narcolepsy Network database. We included 97 variables, covering all aspects of central hypersomnolence disorders such as symptoms, demographics, objective and subjective sleep measures, and laboratory biomarkers. We specifically focused on subgrouping of patients without cataplexy. The number of clusters was chosen to be the minimal number for which patients without cataplexy were put in distinct groups.ResultsWe included 1,078 unmedicated adolescents and adults. Seven clusters were identified, of which 4 clusters included predominantly individuals with cataplexy. The 2 most distinct clusters consisted of 158 and 157 patients, were dominated by those without cataplexy, and among other variables, significantly differed in presence of sleep drunkenness, subjective difficulty awakening, and weekend-week sleep length difference. Patients formally diagnosed as having narcolepsy type 2 and idiopathic hypersomnia were evenly mixed in these 2 clusters.DiscussionUsing a data-driven approach in the largest study on central disorders of hypersomnolence to date, our study identified distinct patient subgroups within the central disorders of hypersomnolence population. Our results contest inclusion of sleep-onset REM periods in diagnostic criteria for people without cataplexy and provide promising new variables for reliable diagnostic categories that better resemble different patient phenotypes. Cluster-guided classification will result in a more solid hypersomnolence classification system that is less vulnerable to instability of single features

Increased TIMP-3 expression alters the cellular secretome through dual inhibition of the metalloprotease ADAM10 and ligand-binding of the LRP-1 receptor

The tissue inhibitor of metalloproteinases-3 (TIMP-3) is a major regulator of extracellular matrix turnover and protein shedding by inhibiting different classes of metalloproteinases, including disintegrin metalloproteinases (ADAMs). Tissue bioavailability of TIMP-3 is regulated by the endocytic receptor low-density-lipoprotein receptor-related protein-1 (LRP-1). TIMP-3 plays protective roles in disease. Thus, different approaches have been developed aiming to increase TIMP-3 bioavailability, yet overall effects of increased TIMP-3 in vivo have not been investigated. Herein, by using unbiased mass-spectrometry we demonstrate that TIMP-3-overexpression in HEK293 cells has a dual effect on shedding of transmembrane proteins and turnover of soluble proteins. Several membrane proteins showing reduced shedding are known as ADAM10 substrates, suggesting that exogenous TIMP-3 preferentially inhibits ADAM10 in HEK293 cells. Additionally identified shed membrane proteins may be novel ADAM10 substrate candidates. TIMP-3-overexpression also increased extracellular levels of several soluble proteins, including TIMP-1, MIF and SPARC. Levels of these proteins similarly increased upon LRP-1 inactivation, suggesting that TIMP-3 increases soluble protein levels by competing for their binding to LRP-1 and their subsequent internalization. In conclusion, our study reveals that increased levels of TIMP-3 induce substantial modifications in the cellular secretome and that TIMP-3-based therapies may potentially provoke undesired, dysregulated functions of ADAM10 and LRP-1

Identification of novel risk loci for restless legs syndrome in genome-wide association studies in individuals of European ancestry : a meta-analysis

Background Restless legs syndrome is a prevalent chronic neurological disorder with potentially severe mental and physical health consequences. Clearer understanding of the underlying pathophysiology is needed to improve treatment options. We did a meta-analysis of genome-wide association studies (GWASs) to identify potential molecular targets. Methods In the discovery stage, we combined three GWAS datasets (EU-RLS GENE, INTERVAL, and 23andMe) with diagnosis data collected from 2003 to 2017, in face-to-face interviews or via questionnaires, and involving 15126 cases and 95 725 controls of European ancestry. We identified common variants by fixed-effect inverse-variance meta-analysis. Significant genome-wide signals (p Findings We identified and replicated 13 new risk loci for restless legs syndrome and confirmed the previously identified six risk loci. MEIS1 was confirmed as the strongest genetic risk factor for restless legs syndrome (odds ratio 1.92, 95% CI 1 85-1.99). Gene prioritisation, enrichment, and genetic correlation analyses showed that identified pathways were related to neurodevelopment and highlighted genes linked to axon guidance (associated with SEMA6D), synapse formation (NTNG1), and neuronal specification (HOXB cluster family and MYT1). Interpretation Identification of new candidate genes and associated pathways will inform future functional research. Advances in understanding of the molecular mechanisms that underlie restless legs syndrome could lead to new treatment options. We focused on common variants; thus, additional studies are needed to dissect the roles of rare and structural variations.Peer reviewe

Recognition and diagnosis of sleep disorders in Parkinson's disease

Contains fulltext : 109296.pdf (publisher's version ) (Open Access)Sleep disturbances are among the most frequent and incapacitating non-motor symptoms of Parkinson's disease (PD), and are increasingly recognized as an important determinant of impaired quality of life. Here we review several recent developments regarding the recognition and diagnosis of sleep disorders in PD. In addition, we provide a practical and easily applicable approach to the diagnostic process as a basis for tailored therapeutic interventions. This includes a stepwise scheme that guides the clinical interview and subsequent ancillary investigations. In this scheme, the various possible sleep disorders are arranged not in order of prevalence, but in a 'differential diagnostic' order. We also provide recommendations for the use of sleep registrations such as polysomnography. Furthermore, we point out when a sleep specialist could be consulted to provide additional diagnostic and therapeutic input. This structured approach facilitates early detection of sleep disturbances in PD, so treatment can be initiated promptly

Genome-wide association study of REM sleep behavior disorder identifies polygenic risk and brain expression effects

Rapid-eye movement (REM) sleep behavior disorder (RBD), enactment of dreams during REM sleep, is an early clinical symptom of alpha-synucleinopathies and defines a more severe subtype. The genetic background of RBD and its underlying mechanisms are not well understood. Here, we perform a genome-wide association study of RBD, identifying five RBD risk loci near SNCA, GBA, TMEM175, INPP5F, and SCARB2. Expression analyses highlight SNCA-AS1 and potentially SCARB2 differential expression in different brain regions in RBD, with SNCA-AS1 further supported by colocalization analyses. Polygenic risk score, pathway analysis, and genetic correlations provide further insights into RBD genetics, highlighting RBD as a unique alpha-synucleinopathy subpopulation that will allow future early intervention

Robotic injection of zebrafish embryos for high-throughput screening in disease models

The increasing use of zebrafish larvae for biomedical research applications is resulting in versatile models for a variety of human diseases. These models exploit the optical transparency of zebrafish larvae and the availability of a large genetic tool box. Here we present detailed protocols for the robotic injection of zebrafish embryos at very high accuracy with a speed of up to 2000 embryos per hour. These protocols are benchmarked for several applications: (1) the injection of DNA for obtaining transgenic animals, (2) the injection of antisense morpholinos that can be used for gene knock-down, (3) the injection of microbes for studying infectious disease, and (4) the injection of human cancer cells as a model for tumor progression. We show examples of how the injected embryos can be screened at high-throughput level using fluorescence analysis. Our methods open up new avenues for the use of zebrafish larvae for large compound screens in the search for new medicines

Determination of the Proteolytic Cleavage Sites of the Amyloid Precursor-Like Protein 2 by the Proteases ADAM10, BACE1 and γ-Secretase

Regulated intramembrane proteolysis of the amyloid precursor protein (APP) by the protease activities α-, β- and γ-secretase controls the generation of the neurotoxic amyloid β peptide. APLP2, the amyloid precursor-like protein 2, is a homolog of APP, which shows functional overlap with APP, but lacks an amyloid β domain. Compared to APP, less is known about the proteolytic processing of APLP2, in particular in neurons, and the cleavage sites have not yet been determined. APLP2 is cleaved by the β-secretase BACE1 and additionally by an α-secretase activity. The two metalloproteases ADAM10 and ADAM17 have been suggested as candidate APLP2 α-secretases in cell lines. Here, we used RNA interference and found that ADAM10, but not ADAM17, is required for the constitutive α-secretase cleavage of APLP2 in HEK293 and SH-SY5Y cells. Likewise, in primary murine neurons knock-down of ADAM10 suppressed APLP2 α-secretase cleavage. Using mass spectrometry we determined the proteolytic cleavage sites in the APLP2 sequence. ADAM10 was found to cleave APLP2 after arginine 670, whereas BACE1 cleaves after leucine 659. Both cleavage sites are located in close proximity to the membrane. γ-secretase cleavage was found to occur at different peptide bonds between alanine 694 and valine 700, which is close to the N-terminus of the predicted APLP2 transmembrane domain. Determination of the APLP2 cleavage sites enables functional studies of the different APLP2 ectodomain fragments and the production of cleavage-site specific antibodies for APLP2, which may be used for biomarker development