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      Antagonism among DUX family members evolved from an ancestral toxic single homeodomain protein

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          Summary

          Double homeobox (DUX) genes are unique to eutherian mammals, expressed transiently during zygotic genome activation (ZGA) and involved in facioscapulohumeral muscular dystrophy (FSHD) and cancer when misexpressed. We evaluate the 3 human DUX genes and the ancestral single homeobox gene sDUX from the non-eutherian mammal, platypus, and find that DUX4 cytotoxicity is not shared with DUXA or DUXB, but surprisingly is shared with platypus sDUX, which binds DNA as a homodimer and activates numerous ZGA genes and long terminal repeat (LTR) elements. DUXA, although transcriptionally inactive, has DNA binding overlap with DUX4, and DUXA-VP64 activates DUX4 targets and is cytotoxic. DUXA competition antagonizes the activity of DUX4 on its target genes, including in FSHD patient cells. Since DUXA is a DUX4 target gene, this competition potentiates feedback inhibition, constraining the window of DUX4 activity. The DUX gene family therefore comprises antagonistic members of opposing function, with implications for their roles in ZGA, FSHD, and cancer.

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          Highlights

          • •

            Platypus sDUX is toxic and inhibits myogenic differentiation

          • •

            DUXA targets overlap substantially with those of DUX4

          • •

            DUXA fused to a synthetic transactivation domain acquires DUX4-like toxicity

          • •

            DUXA behaves as a competitive inhibitor of DUX4

          Abstract

          Developmental genetics; Molecular interaction; Molecular toxicology; Evolutionary developmental biology

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          Most cited references49

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

          Michael Love, Wolfgang Huber, Simon Anders (2014)
          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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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)
            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              STAR: ultrafast universal RNA-seq aligner.

              Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)
              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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                Author and article information

                Contributors
                Michael Kyba
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 02 September 2023
                Publication date Collection: 20 October 2023
                Publication date (Electronic): 02 September 2023
                Volume: 26
                Issue: 10
                Electronic Location Identifier: 107823
                Affiliations
                [1 ]Lillehei Heart Institute, University of Minnesota, Minneapolis, MN 55455, USA
                [2 ]Department of Pediatrics, University of Minnesota, Minneapolis, MN 55455, USA
                [3 ]Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN 55455, USA
                [4 ]Department of Biochemistry, Molecular Biology, and Biophysics, University of Minnesota, Minneapolis, MN 55455, USA
                [5 ]Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
                [6 ]Department of Cardiology, University of Minnesota, Minneapolis, MN 55455, USA
                [7 ]The Center for Cardiovascular Regeneration and Immunology at TRON – Translational Oncology, Johannes Gutenberg-University Mainz, Mainz, Germany
                Author notes
                [∗ ]Corresponding author kyba@ 123456umn.edu
                [8]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(23)01900-4 Publisher ID: 107823
                DOI: 10.1016/j.isci.2023.107823
                PMC ID: 10514451
                PubMed ID: 37744032
                SO-VID: 30a93c86-5b92-4122-9a35-8581a945b594
                Copyright © © 2023 The Authors
                License:

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

                History
                Date received : 6 February 2023
                Date revision received : 7 July 2023
                Date accepted : 31 August 2023
                Categories
                Subject: Article

                Keywords: developmental genetics,molecular interaction,molecular toxicology,evolutionary developmental biology
                Data availability:
                Keywords: developmental genetics, molecular interaction, molecular toxicology, evolutionary developmental biology

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