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      Loss-of-function variants affecting the STAGA complex component SUPT7L cause a developmental disorder with generalized lipodystrophy

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          Abstract

          Generalized lipodystrophy is a feature of various hereditary disorders, often leading to a progeroid appearance. In the present study we identified a missense and a frameshift variant in a compound heterozygous state in SUPT7L in a boy with intrauterine growth retardation, generalized lipodystrophy, and additional progeroid features. SUPT7L encodes a component of the transcriptional coactivator complex STAGA. By transcriptome sequencing, we showed the predicted missense variant to cause aberrant splicing, leading to exon truncation and thereby to a complete absence of SUPT7L in dermal fibroblasts. In addition, we found altered expression of genes encoding DNA repair pathway components. This pathway was further investigated and an increased rate of DNA damage was detected in proband-derived fibroblasts and genome-edited HeLa cells. Finally, we performed transient overexpression of wildtype SUPT7L in both cellular systems, which normalizes the number of DNA damage events. Our findings suggest SUPT7L as a novel disease gene and underline the link between genome instability and progeroid phenotypes.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00439-024-02669-y.

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          Most cited references52

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                bjoern.fischer@charite.de
                Journal
                Hum Genet
                Hum Genet
                Human Genetics
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0340-6717
                1432-1203
                9 April 2024
                9 April 2024
                2024
                : 143
                : 5
                : 683-694
                Affiliations
                [1 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Institute of Medical Genetics and Human Genetics, , Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, ; 13353 Berlin, Germany
                [2 ]Max Planck Institute for Molecular Genetics, FG Development and Disease, ( https://ror.org/03ate3e03) Berlin, Germany
                [3 ]Institute of Chemistry and Biochemistry, Department of Biology, Chemistry and Pharmacy, Freie Universität Berlin, ( https://ror.org/046ak2485) Berlin, Germany
                [4 ]Exploratory Diagnostic Sciences, Berlin Institute of Health, Charité – Universitätsmedizin Berlin, ( https://ror.org/001w7jn25) Berlin, Germany
                [5 ]Core Unit Bioinformatics (CUBI), Berlin Institute of Health, Charité – Universitätsmedizin Berlin, ( https://ror.org/001w7jn25) Berlin, Germany
                [6 ]GRID grid.411154.4, ISNI 0000 0001 2175 0984, Service de Génétique Moléculaire et Génomique, CHU, ; Rennes, F-35033 France
                [7 ]Univercity Rennes, CNRS, INSERM, IGDR, UMR 6290, ERL U1305, ( https://ror.org/02vjkv261) Rennes, F-35000 France
                [8 ]GRID grid.414271.5, Service de Cytogénétique et Biologie cellulaire, , Hôpital Pontchaillou - CHU Rennes, ; 2 rue Henri Le Guilloux – Rennes cedex 9, France, Rennes, F-35033 France
                [9 ]GRID grid.6363.0, ISNI 0000 0001 2218 4662, Department of Endocrinology and Metabolism, , Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, ; 10117 Berlin, Germany
                [10 ]German Centre for Cardiovascular Research, partner site Berlin, ( https://ror.org/031t5w623) Berlin, Germany
                [11 ]Berlin Institute of Health, Charité – Universitätsmedizin Berlin, ( https://ror.org/001w7jn25) Berlin, Germany
                [12 ]GRID grid.411154.4, ISNI 0000 0001 2175 0984, Service de Génétique Clinique, Centre Référence Déficiences Intellectuelles CRDI, , Hôpital Sud - CHU Rennes, ; 16 boulevard de Bulgarie - BP 90347, Rennes cedex 2, Rennes, F-35203 France
                [13 ]Service de Génétique, CH Saint Brieuc, St Brieuc, 22000 France
                [14 ]Institute of Human Genetics, University Medical Center Göttingen, ( https://ror.org/021ft0n22) Göttingen, Germany
                Author information
                http://orcid.org/0000-0002-0391-1497
                http://orcid.org/0000-0003-4943-198X
                http://orcid.org/0000-0002-5590-3835
                http://orcid.org/0009-0008-2628-2890
                http://orcid.org/0000-0002-3051-1763
                http://orcid.org/0000-0002-4824-1925
                http://orcid.org/0000-0003-1345-4522
                http://orcid.org/0000-0003-1546-7319
                http://orcid.org/0000-0002-6336-3939
                http://orcid.org/0000-0002-9788-3166
                http://orcid.org/0000-0003-1449-9909
                http://orcid.org/0000-0002-9746-4412
                http://orcid.org/0000-0002-2535-2198
                http://orcid.org/0000-0002-4582-9838
                http://orcid.org/0000-0002-1075-7571
                Article
                2669
                10.1007/s00439-024-02669-y
                11098864
                38592547
                71acbedb-3a6e-4bfc-b409-209881ef6610
                © The Author(s) 2024

                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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 November 2023
                : 11 March 2024
                Funding
                Funded by: Charité - Universitätsmedizin Berlin (3093)
                Categories
                Original Investigation
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2024

                Genetics
                lipodystrophy,wiedemann-rautenstrauch syndrome,supt7l,aberrant splicing,progeroid disorder,staga complex

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