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      Clinical, genetic, and functional characterization of the glycine receptor β-subunit A455P variant in a family affected by hyperekplexia syndrome

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          Abstract

          Hyperekplexia is a rare neurological disorder characterized by exaggerated startle responses affecting newborns with the hallmark characteristics of hypertonia, apnea, and noise or touch-induced nonepileptic seizures. The genetic causes of the disease can vary, and several associated genes and mutations have been reported to affect glycine receptors (GlyRs); however, the mechanistic links between GlyRs and hyperekplexia are not yet understood. Here, we describe a patient with hyperekplexia from a consanguineous family. Extensive genetic screening using exome sequencing coupled with autozygome analysis and iterative filtering supplemented by in silico prediction identified that the patient carries the homozygous missense mutation A455P in GLRB, which encodes the GlyR β-subunit. To unravel the physiological and molecular effects of A455P on GlyRs, we used electrophysiology in a heterologous system as well as immunocytochemistry, confocal microscopy, and cellular biochemistry. We found a reduction in glycine-evoked currents in N2A cells expressing the mutation compared to WT cells. Western blot analysis also revealed a reduced amount of GlyR β protein both in cell lysates and isolated membrane fractions. In line with the above observations, coimmunoprecipitation assays suggested that the GlyR α 1-subunit retained coassembly with β A455P to form membrane-bound heteromeric receptors. Finally, structural modeling showed that the A455P mutation affected the interaction between the GlyR β-subunit transmembrane domain 4 and the other helices of the subunit. Taken together, our study identifies and validates a novel loss-of-function mutation in GlyRs whose pathogenicity is likely to cause hyperekplexia in the affected individual.

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          The Protein Data Bank.

          The Protein Data Bank (PDB; http://www.rcsb.org/pdb/ ) is the single worldwide archive of structural data of biological macromolecules. This paper describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information, and near-term plans for the future development of the resource.
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            A method and server for predicting damaging missense mutations

            To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
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              CADD: predicting the deleteriousness of variants throughout the human genome

              Abstract Combined Annotation-Dependent Depletion (CADD) is a widely used measure of variant deleteriousness that can effectively prioritize causal variants in genetic analyses, particularly highly penetrant contributors to severe Mendelian disorders. CADD is an integrative annotation built from more than 60 genomic features, and can score human single nucleotide variants and short insertion and deletions anywhere in the reference assembly. CADD uses a machine learning model trained on a binary distinction between simulated de novo variants and variants that have arisen and become fixed in human populations since the split between humans and chimpanzees; the former are free of selective pressure and may thus include both neutral and deleterious alleles, while the latter are overwhelmingly neutral (or, at most, weakly deleterious) by virtue of having survived millions of years of purifying selection. Here we review the latest updates to CADD, including the most recent version, 1.4, which supports the human genome build GRCh38. We also present updates to our website that include simplified variant lookup, extended documentation, an Application Program Interface and improved mechanisms for integrating CADD scores into other tools or applications. CADD scores, software and documentation are available at https://cadd.gs.washington.edu.
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                Author and article information

                Contributors
                Journal
                J Biol Chem
                J Biol Chem
                The Journal of Biological Chemistry
                American Society for Biochemistry and Molecular Biology
                0021-9258
                1083-351X
                06 May 2022
                July 2022
                06 May 2022
                : 298
                : 7
                : 102018
                Affiliations
                [1 ]Department of Translational Genomics, Center for Genomic Medicine, King Faisal Specialist Hospital and Research Centre, Riyadh, Kingdom of Saudi Arabia
                [2 ]Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi Arabia
                [3 ]Department of Pharmacology, The School of Pharmacy, University College London, London, United Kingdom
                [4 ]Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Kingdom of Saudi Arabia
                [5 ]Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia
                [6 ]Department of Pediatrics, College of Medicine, King Saud University, Riyadh, Kingdom of Saudi Arabia
                [7 ]Department of Pediatrics, Security Forces Hospital, Riyadh, Kingdom of Saudi Arabia
                [8 ]Department of Cell Biology, King Faisal Specialist Hospital and Research Centre, Riyadh, Kingdom of Saudi Arabia
                [9 ]Department of Molecular Oncology, King Faisal Specialist Hospital and Research Centre, Riyadh, Kingdom of Saudi Arabia
                [10 ]School of Health and Behavioural Sciences, University of the Sunshine Coast, Maroochydore, Queensland, Australia
                [11 ]Sunshine Coast Health Institute, Birtinya, Queensland, Australia
                [12 ]Centre de Biologie Structurale, CNRS, INSERM, Université de Montpellier, Montpellier, France
                Author notes
                []For correspondence: Arnaud J. Ruiz; Namik Kaya nkaya@ 123456kfshrc.edu.sa a.ruiz@ 123456ucl.ac.uk
                [‡]

                These senior authors contributed equally to this work.

                Article
                S0021-9258(22)00458-6 102018
                10.1016/j.jbc.2022.102018
                9241032
                35526563
                376017ce-cf69-4c84-ba53-92b65af5be8c
                © 2022 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 19 November 2021
                : 21 April 2022
                Categories
                Research Article

                Biochemistry
                autozygosity mapping,coimmunoprecipitation,confocal imaging,exome sequencing,glrb,immunohistochemistry,patch clamp,startle disease,glyr, glycine receptor,ngs, next-generation sequencing,tm, transmembrane

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