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      Is Open Access

      A restricted spectrum of missense KMT2D variants cause a multiple malformations disorder distinct from Kabuki syndrome

      research-article
      , PhD 1 , 2 , , MD PhD 3 , 4 , , BSc 5 , , MSc 6 , , PhD 7 , , MD 8 , , MD 9 , 10 , , MD PhD 1 , 11 , , MD 12 , , MD 13 , , MD PhD 12 , , PhD 5 , , MD 14 , , PhD 5 , , PhD 15 , , PhD 5 , , MSc 6 , , MD 16 , , MD 16 , , PhD 17 , 18 , , MSc 19 , , MD PhD 18 , , MD PhD 20 , , PhD 19 , , PhD 21 , , PhD 22 , , FRCPath PhD 22 , 23 , Genomics England Research Consortium 24 , , MD PhD 7 , , PhD 2 , , MD PhD 3 , 4 , , PhD 19 , , PhD 6 , , PhD 3 , , MD PhD 1 , 25 ,
      Genetics in Medicine
      Nature Publishing Group US
      multiple congenital anomaly, Kabuki syndrome, KMT2D, histone 3 lysine 4 methyltransferase, intrinsically disordered region

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Purpose

          To investigate if specific exon 38 or 39 KMT2D missense variants (MVs) cause a condition distinct from Kabuki syndrome type 1 (KS1).

          Methods

          Multiple individuals, with MVs in exons 38 or 39 of KMT2D that encode a highly conserved region of 54 amino acids flanked by Val3527 and Lys3583, were identified and phenotyped. Functional tests were performed to study their pathogenicity and understand the disease mechanism.

          Results

          The consistent clinical features of the affected individuals, from seven unrelated families, included choanal atresia, athelia or hypoplastic nipples, branchial sinus abnormalities, neck pits, lacrimal duct anomalies, hearing loss, external ear malformations, and thyroid abnormalities. None of the individuals had intellectual disability. The frequency of clinical features, objective software-based facial analysis metrics, and genome-wide peripheral blood DNA methylation patterns in these patients were significantly different from that of KS1. Circular dichroism spectroscopy indicated that these MVs perturb KMT2D secondary structure through an increased disordered to ɑ-helical transition.

          Conclusion

          KMT2D MVs located in a specific region spanning exons 38 and 39 and affecting highly conserved residues cause a novel multiple malformations syndrome distinct from KS1. Unlike KMT2D haploinsufficiency in KS1, these MVs likely result in disease through a dominant negative mechanism.

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          Most cited references24

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          The COMPASS family of histone H3K4 methylases: mechanisms of regulation in development and disease pathogenesis.

          The Saccharomyces cerevisiae Set1/COMPASS was the first histone H3 lysine 4 (H3K4) methylase identified over 10 years ago. Since then, it has been demonstrated that Set1/COMPASS and its enzymatic product, H3K4 methylation, is highly conserved across the evolutionary tree. Although there is only one COMPASS in yeast, Drosophila possesses three and humans bear six COMPASS family members, each capable of methylating H3K4 with nonredundant functions. In yeast, the histone H2B monoubiquitinase Rad6/Bre1 is required for proper H3K4 and H3K79 trimethylations. The machineries involved in this process are also highly conserved from yeast to human. In this review, the process of histone H2B monoubiquitination-dependent and -independent histone H3K4 methylation as a mark of active transcription, enhancer signatures, and developmentally poised genes is discussed. The misregulation of histone H2B monoubiquitination and H3K4 methylation result in the pathogenesis of human diseases, including cancer. Recent findings in this regard are also examined.
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            Bump hunting to identify differentially methylated regions in epigenetic epidemiology studies.

            During the past 5 years, high-throughput technologies have been successfully used by epidemiology studies, but almost all have focused on sequence variation through genome-wide association studies (GWAS). Today, the study of other genomic events is becoming more common in large-scale epidemiological studies. Many of these, unlike the single-nucleotide polymorphism studied in GWAS, are continuous measures. In this context, the exercise of searching for regions of interest for disease is akin to the problems described in the statistical 'bump hunting' literature. New statistical challenges arise when the measurements are continuous rather than categorical, when they are measured with uncertainty, and when both biological signal, and measurement errors are characterized by spatial correlation along the genome. Perhaps the most challenging complication is that continuous genomic data from large studies are measured throughout long periods, making them susceptible to 'batch effects'. An example that combines all three characteristics is genome-wide DNA methylation measurements. Here, we present a data analysis pipeline that effectively models measurement error, removes batch effects, detects regions of interest and attaches statistical uncertainty to identified regions. We illustrate the usefulness of our approach by detecting genomic regions of DNA methylation associated with a continuous trait in a well-characterized population of newborns. Additionally, we show that addressing unexplained heterogeneity like batch effects reduces the number of false-positive regions. Our framework offers a comprehensive yet flexible approach for identifying genomic regions of biological interest in large epidemiological studies using quantitative high-throughput methods.
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              An HMM model for coiled-coil domains and a comparison with PSSM-based predictions.

              Large-scale sequence data require methods for the automated annotation of protein domains. Many of the predictive methods are based either on a Position Specific Scoring Matrix (PSSM) of fixed length or on a window-less Hidden Markov Model (HMM). The performance of the two approaches is tested for Coiled-Coil Domains (CCDs). The prediction of CCDs is used frequently, and its optimization seems worthwhile. We have conceived MARCOIL, an HMM for the recognition of proteins with a CCD on a genomic scale. A cross-validated study suggests that MARCOIL improves predictions compared to the traditional PSSM algorithm, especially for some protein families and for short CCDs. The study was designed to reveal differences inherent in the two methods. Potential confounding factors such as differences in the dimension of parameter space and in the parameter values were avoided by using the same amino acid propensities and by keeping the transition probabilities of the HMM constant during cross-validation. The prediction program and the databases are available at http://www.wehi.edu.au/bioweb/Mauro/Marcoil
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                Author and article information

                Contributors
                Siddharth.Banka@manchester.ac.uk
                Journal
                Genet Med
                Genet. Med
                Genetics in Medicine
                Nature Publishing Group US (New York )
                1098-3600
                1530-0366
                17 January 2020
                17 January 2020
                2020
                : 22
                : 5
                : 867-877
                Affiliations
                [1 ]ISNI 0000000121662407, GRID grid.5379.8, Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, , Medicine, and Health, The University of Manchester, ; Manchester, UK
                [2 ]ISNI 0000000121662407, GRID grid.5379.8, Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology, , Medicine, and Health, The University of Manchester, ; Manchester, UK
                [3 ]ISNI 0000 0004 1936 8403, GRID grid.9909.9, Leeds Institute of Medical Research, Faculty of Medicine and Health, , The University of Leeds, ; Leeds, UK
                [4 ]ISNI 0000 0004 0426 1312, GRID grid.413818.7, Department of Clinical Genetics, , Chapel Allerton Hospital, Leeds Teaching Hospitals Trust, ; Leeds, UK
                [5 ]ISNI 0000 0004 1936 8403, GRID grid.9909.9, Astbury Centre for Structural Molecular Biology, Faculty of Biological Sciences, , The University of Leeds, ; Leeds, UK
                [6 ]ISNI 0000000121662407, GRID grid.5379.8, Division of Developmental Biology & Medicine, School of Biological Sciences, Faculty of Biology, Medicine, and Health, , The University of Manchester, ; Manchester, UK
                [7 ]ISNI 0000000121662407, GRID grid.5379.8, Centre of Genetics & Genomics Versus Arthritis, Manchester Academic Health Sciences Centre, , The University of Manchester, ; Manchester, UK
                [8 ]Department of Paediatrics, College of Medicine & Health Sciences, United Arab University, Al-Ain, UAE
                [9 ]ISNI 0000 0004 0421 1251, GRID grid.419317.9, Liverpool Centre for Genomic Medicine, , Liverpool Women’s NHS Foundation Trust, ; Liverpool, UK
                [10 ]ISNI 0000 0004 0398 9627, GRID grid.416568.8, North West Thames Regional Genetics Service, , Northwick Park Hospital, ; Harrow, UK
                [11 ]ISNI 0000 0004 0385 4466, GRID grid.443909.3, Laboratorio de Genética y Enfermedades Metabólicas, , Instituto de Nutrición y Tecnología de los Alimentos, Universidad de Chile, ; Santiago, Chile
                [12 ]GRID grid.420545.2, Department of Clinical Genetics, , Guy’s & St Thomas NHS Foundation Trust, ; London, UK
                [13 ]ISNI 0000 0004 1771 6937, GRID grid.416924.c, Department of Paediatrics, , Tawam Hospital, ; Al-Ain, UAE
                [14 ]ISNI 0000 0004 0514 6607, GRID grid.412459.f, Temple street Children’s University Hospital, ; Dublin, Ireland
                [15 ]ISNI 0000 0004 0606 5382, GRID grid.10306.34, Wellcome Trust Sanger Institute, ; Cambridge, UK
                [16 ]ISNI 0000 0004 1757 1758, GRID grid.6292.f, Medical Genetics Unit, St. Orsola-Malpighi, University of Bologna, ; Bologna, Italy
                [17 ]ISNI 0000 0000 8546 682X, GRID grid.264200.2, Molecular and Clinical Sciences Research Institute, , St George’s University of London, ; London, UK
                [18 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Department of Medical & Molecular Genetics, , King’s College London, ; London, UK
                [19 ]Division of Medical Genetics, Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Foggia, Italy
                [20 ]ISNI 0000 0001 2171 1133, GRID grid.4868.2, Queen Mary University of London, ; London, UK
                [21 ]GRID grid.500208.f, Clinical Psychology Department, Royal Manchester Children’s Hospital, , Manchester University Foundation NHS Trust, Health Innovation Manchester, ; Manchester, UK
                [22 ]ISNI 0000 0004 0495 6261, GRID grid.419309.6, Molecular Genetics Department, , Royal Devon and Exeter NHS Foundation Trust, ; Exeter, UK
                [23 ]ISNI 0000 0004 1936 8024, GRID grid.8391.3, Institute of Biomedical and Clinical Science, , University of Exeter Medical School, ; Exeter, UK
                [24 ]GRID grid.498322.6, Genomics England, ; London, UK
                [25 ]ISNI 0000 0004 0641 2620, GRID grid.416523.7, Manchester Centre for Genomic Medicine, , St. Mary’s Hospital, Manchester University Foundation NHS Trust, Health Innovation Manchester, ; Manchester, UK
                Author information
                http://orcid.org/0000-0002-8527-2210
                Article
                743
                10.1038/s41436-019-0743-3
                7200597
                31949313
                3b76d7fa-493d-48cb-a479-59005b914c45
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 October 2019
                : 24 December 2019
                Funding
                Funded by: FundRef https://doi.org/10.13039/http://dx.doi.org/10.13039/501100000871, Newlife – The Charity for Disabled Children;
                Award ID: 16-17/10
                Award ID: 16-17/10
                Award ID: 16-17/10
                Award ID: 16-17/10
                Award ID: 16-17/10
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/, Chile’s National Commission for Scientific and Technological Research;
                Award ID: 72160007
                Award Recipient :
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                © American College of Medical Genetics and Genomics 2020

                Genetics
                multiple congenital anomaly,kabuki syndrome,kmt2d,histone 3 lysine 4 methyltransferase,intrinsically disordered region

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