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      PIGO‐CDG: A case study with a new genotype, expansion of the phenotype, literature review, and nosological considerations

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          Abstract

          The phosphatidylinositol glycan anchor biosynthesis class O protein (PIGO) enzyme is an important step in the biosynthesis of glycosylphosphatidylinositol (GPI), which is essential for the membrane anchoring of several proteins. Bi‐allelic pathogenic variants in PIGO lead to a congenital disorder of glycosylation (CDG) characterized by global developmental delay, an increase in serum alkaline phosphatase levels, congenital anomalies including anorectal, genitourinary, and limb malformations in most patients; this phenotype has been alternately called “Mabry syndrome” or “hyperphosphatasia with impaired intellectual development syndrome 2.” We report a 22‐month‐old female with PIGO deficiency caused by novel PIGO variants. In addition to the Mabry syndrome phenotype, our patient's clinical picture was complicated by intermittent hypoglycemia with signs of functional hyperinsulinism, severe secretory diarrhea, and osteopenia with a pathological fracture, thus, potentially expanding the known phenotype of this disorder, although more studies are necessary to confirm these associations. We also provide an updated review of the literature, and propose unifying the nomenclature of PIGO deficiency as “PIGO‐CDG,” which reflects its pathophysiology and position in the broad scope of metabolic disorders and congenital disorders of glycosylation.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

            The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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              The mutational constraint spectrum quantified from variation in 141,456 humans

              Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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                Author and article information

                Contributors
                rodrigo.starosta@cuanschutz.edu
                Journal
                JIMD Rep
                JIMD Rep
                10.1002/(ISSN)2192-8312
                JMD2
                JIMD Reports
                John Wiley & Sons, Inc. (Hoboken, USA )
                2192-8304
                2192-8312
                20 September 2023
                November 2023
                : 64
                : 6 ( doiID: 10.1002/jmd2.v64.6 )
                : 424-433
                Affiliations
                [ 1 ] Division of Genetics and Genomic Medicine, Department of Pediatrics Washington University in St. Louis Clayton Missouri USA
                [ 2 ] Division of Pediatric Neurology, Department of Neurology Washington University in St. Louis Clayton Missouri USA
                [ 3 ] Division of Academic Pediatrics, Department of Pediatrics Washington University in St. Louis Clayton Missouri USA
                [ 4 ] Department of Laboratory Medicine and Pathology Mayo Clinic Rochester Minnesota USA
                [ 5 ] Department of Clinical Genomics Mayo Clinic Rochester Minnesota USA
                [ 6 ] Division of Laboratory and Genomic Medicine, Department of Pathology and Immunology Washington University in St. Louis Clayton Missouri USA
                Author notes
                [*] [* ] Correspondence

                Rodrigo Tzovenos Starosta. 660 S Euclid Ave, 9th floor, St. Louis, MO 63110, USA.

                Email: rodrigo.starosta@ 123456cuanschutz.edu

                Author information
                https://orcid.org/0000-0002-2350-7192
                https://orcid.org/0000-0001-7425-6322
                Article
                JMD212396
                10.1002/jmd2.12396
                10623102
                13148afb-4bd3-4420-97bc-26eca48a76fc
                © 2023 The Authors. JIMD Reports published by John Wiley & Sons Ltd on behalf of SSIEM.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 August 2023
                : 06 July 2023
                : 06 September 2023
                Page count
                Figures: 4, Tables: 2, Pages: 10, Words: 5587
                Funding
                Funded by: National Center for Advancing Translational Sciences , doi 10.13039/100006108;
                Funded by: National Institute of Neurological Disorders and Stroke , doi 10.13039/100000065;
                Award ID: 1U54NS115198‐01
                Funded by: Rare Disorders Consortium Disease Network
                Categories
                Case Report
                Case Reports
                Custom metadata
                2.0
                November 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.4 mode:remove_FC converted:03.11.2023

                congenital disorder of glycosylation,diarrhea,glycophosphatidylinositol,hyperphosphatasia,hypoglycemia,mabry syndrome

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