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      The U2AF1 S34F mutation induces lineage-specific splicing alterations in myelodysplastic syndromes

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          Abstract

          Mutations of the splicing factor–encoding gene U2AF1 are frequent in the myelodysplastic syndromes (MDS), a myeloid malignancy, and other cancers. Patients with MDS suffer from peripheral blood cytopenias, including anemia, and an increasing percentage of bone marrow myeloblasts. We studied the impact of the common U2AF1 S34F mutation on cellular function and mRNA splicing in the main cell lineages affected in MDS. We demonstrated that U2AF1 S34F expression in human hematopoietic progenitors impairs erythroid differentiation and skews granulomonocytic differentiation toward granulocytes. RNA sequencing of erythroid and granulomonocytic colonies revealed that U2AF1 S34F induced a higher number of cassette exon splicing events in granulomonocytic cells than in erythroid cells. U2AF1 S34F altered mRNA splicing of many transcripts that were expressed in both cell types in a lineage-specific manner. In hematopoietic progenitors, the introduction of isoform changes identified in the U2AF1 S34F target genes H2AFY, encoding an H2A histone variant, and STRAP, encoding serine/threonine kinase receptor–associated protein, recapitulated phenotypes associated with U2AF1 S34F expression in erythroid and granulomonocytic cells, suggesting a causal link. Furthermore, we showed that isoform modulation of H2AFY and STRAP rescues the erythroid differentiation defect in U2AF1 S34F MDS cells, suggesting that splicing modulators could be used therapeutically. These data have critical implications for understanding MDS phenotypic heterogeneity and support the development of therapies targeting splicing abnormalities.

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          Revised international prognostic scoring system for myelodysplastic syndromes.

          The International Prognostic Scoring System (IPSS) is an important standard for assessing prognosis of primary untreated adult patients with myelodysplastic syndromes (MDS). To refine the IPSS, MDS patient databases from international institutions were coalesced to assemble a much larger combined database (Revised-IPSS [IPSS-R], n = 7012, IPSS, n = 816) for analysis. Multiple statistically weighted clinical features were used to generate a prognostic categorization model. Bone marrow cytogenetics, marrow blast percentage, and cytopenias remained the basis of the new system. Novel components of the current analysis included: 5 rather than 3 cytogenetic prognostic subgroups with specific and new classifications of a number of less common cytogenetic subsets, splitting the low marrow blast percentage value, and depth of cytopenias. This model defined 5 rather than the 4 major prognostic categories that are present in the IPSS. Patient age, performance status, serum ferritin, and lactate dehydrogenase were significant additive features for survival but not for acute myeloid leukemia transformation. This system comprehensively integrated the numerous known clinical features into a method analyzing MDS patient prognosis more precisely than the initial IPSS. As such, this IPSS-R should prove beneficial for predicting the clinical outcomes of untreated MDS patients and aiding design and analysis of clinical trials in this disease.
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            featureCounts: An efficient general-purpose program for assigning sequence reads to genomic features

            , , (2013)
            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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              Xist RNA is a potent suppressor of hematologic cancer in mice.

              X chromosome aneuploidies have long been associated with human cancers, but causality has not been established. In mammals, X chromosome inactivation (XCI) is triggered by Xist RNA to equalize gene expression between the sexes. Here we delete Xist in the blood compartment of mice and demonstrate that mutant females develop a highly aggressive myeloproliferative neoplasm and myelodysplastic syndrome (mixed MPN/MDS) with 100% penetrance. Significant disease components include primary myelofibrosis, leukemia, histiocytic sarcoma, and vasculitis. Xist-deficient hematopoietic stem cells (HSCs) show aberrant maturation and age-dependent loss. Reconstitution experiments indicate that MPN/MDS and myelofibrosis are of hematopoietic rather than stromal origin. We propose that Xist loss results in X reactivation and consequent genome-wide changes that lead to cancer, thereby causally linking the X chromosome to cancer in mice. Thus, Xist RNA not only is required to maintain XCI but also suppresses cancer in vivo. Copyright © 2013 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                Journal
                J Clin Invest
                J. Clin. Invest
                J Clin Invest
                The Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                24 April 2017
                1 June 2017
                1 June 2017
                : 127
                : 6
                : 2206-2221
                Affiliations
                [1 ]Bloodwise Molecular Haematology Unit, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, and BRC Blood Theme, National Institute for Health Research (NIHR) Oxford Biomedical Centre, Oxford University Hospital, Oxford, United Kingdom.
                [2 ]The Computational Biology Research Group, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom.
                [3 ]Department of Biochemistry, Downing Site, University of Cambridge, Cambridge, United Kingdom.
                [4 ]Albert Einstein College of Medicine, Bronx, New York, USA.
                [5 ]Medical Research Council, Molecular Hematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, and Department of Hematology, Oxford University Hospital National Health Service Trust, Oxford, United Kingdom.
                [6 ]Faculty of Medicine, University of Southampton, Southampton, and National Genetics Reference Laboratory (Wessex), Salisbury, United Kingdom.
                [7 ]Fondazione IRCCS Policlinico San Matteo and University of Pavia, Pavia, Italy.
                [8 ]Center for Hematology and Regenerative Medicine, Karolinska University Hospital Huddinge, Stockholm, Sweden.
                [9 ]Department of Pathology and Tumor Biology, Kyoto University, Kyoto, Japan.
                Author notes
                Address correspondence to: Jacqueline Boultwood, Bloodwise Molecular Haematology Unit, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, United Kingdom. Phone: 44.1865.220480; E-mail: jacqueline.boultwood@ 123456ndcls.ox.ac.uk .

                Authorship note: A. Pellagatti and J. Boultwood contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-5468-460X
                http://orcid.org/0000-0001-8085-0731
                http://orcid.org/0000-0002-4724-1720
                http://orcid.org/0000-0002-0908-770X
                http://orcid.org/0000-0003-3931-0914
                http://orcid.org/0000-0002-1460-1611
                http://orcid.org/0000-0001-6984-8817
                http://orcid.org/0000-0002-7778-5374
                http://orcid.org/0000-0002-6122-0221
                http://orcid.org/0000-0002-4330-2928
                Article
                91363
                10.1172/JCI91363
                5451246
                28436936
                58f780b2-2f53-4aa4-836c-6474e99eb854
                Copyright © 2017 Yip et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 21 October 2016
                : 21 February 2017
                Categories
                Research Article

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