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      H3K79me2/3 controls enhancer–promoter interactions and activation of the pan-cancer stem cell marker PROM1/CD133 in MLL-AF4 leukemia cells

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          Abstract

          MLL gene rearrangements (MLLr) are a common cause of aggressive, incurable acute lymphoblastic leukemias (ALL) in infants and children, most of which originate in utero. The most common MLLr produces an MLL-AF4 fusion protein. MLL-AF4 promotes leukemogenesis by activating key target genes, mainly through recruitment of DOT1L and increased histone H3 lysine-79 methylation (H3K79me2/3). One key MLL-AF4 target gene is PROM1, which encodes CD133 (Prominin-1). CD133 is a pentaspan transmembrane glycoprotein that represents a potential pan-cancer target as it is found on multiple cancer stem cells. Here we demonstrate that aberrant PROM1/CD133 expression is essential for leukemic cell growth, mediated by direct binding of MLL-AF4. Activation is controlled by an intragenic H3K79me2/3 enhancer element (KEE) leading to increased enhancer–promoter interactions between PROM1 and the nearby gene TAPT1. This dual locus regulation is reflected in a strong correlation of expression in leukemia. We find that in PROM1/CD133 non-expressing cells, the PROM1 locus is repressed by polycomb repressive complex 2 (PRC2) binding, associated with reduced expression of TAPT1, partially due to loss of interactions with the PROM1 locus. Together, these results provide the first detailed analysis of PROM1/CD133 regulation that explains CD133 expression in MLLr ALL.

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          The Sequence Alignment/Map format and SAMtools

          Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk
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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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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                anindita.roy@paediatrics.ox.ac.uk
                thomas.milne@imm.ox.ac.uk
                Journal
                Leukemia
                Leukemia
                Leukemia
                Nature Publishing Group UK (London )
                0887-6924
                1476-5551
                2 April 2020
                2 April 2020
                2021
                : 35
                : 1
                : 90-106
                Affiliations
                [1 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, MRC Molecular Haematology Unit, MRC Weatherall Institute of Molecular Medicine, NIHR Oxford Biomedical Research Centre Haematology Theme, Radcliffe Department of Medicine, , University of Oxford, ; Oxford, UK
                [2 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Paediatrics, , University of Oxford, ; Oxford, UK
                [3 ]GRID grid.420468.c, Great Ormond Street Hospital for Children, ; London, UK
                [4 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, MRC Weatherall Institute of Molecular Medicine, Radcliffe Department of Medicine, , University of Oxford, ; Oxford, UK
                [5 ]Princess Maxima Centrum for Pediatric Oncology, Utrecht, The Netherlands
                [6 ]GRID grid.1006.7, ISNI 0000 0001 0462 7212, Wolfson Childhood Cancer Research Centre, , Newcastle University, ; Newcastle upon Tyne, UK
                [7 ]GRID grid.410556.3, ISNI 0000 0001 0440 1440, Department of Haematology, , Oxford University Hospitals NHS Foundation Trust, ; Oxford, UK
                [8 ]Josep Carreras Leukemia Research Institute, Barcelona, Spain
                [9 ]GRID grid.425902.8, ISNI 0000 0000 9601 989X, Institucio Catalana of Recerca i Estudis Avançats (ICREA), ; Barcelona, Spain
                [10 ]Centro de Investigación Biomédica en Red en cancer (CIBERONC)-ISCIII, Barcelona, Spain
                [11 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Laboratory Medicine, , University of California, San Francisco, ; San Francisco, CA 94143 USA
                Author information
                http://orcid.org/0000-0001-9610-6763
                http://orcid.org/0000-0001-5404-6483
                http://orcid.org/0000-0003-3931-0914
                http://orcid.org/0000-0002-0413-4271
                Article
                808
                10.1038/s41375-020-0808-y
                7787973
                32242051
                a334b044-4beb-4aea-b618-c12484c27f81
                © 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
                : 15 August 2019
                : 12 March 2020
                : 18 March 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100009187, RCUK | MRC | Medical Research Foundation;
                Award ID: MC_UU_12009/6
                Award ID: MC_UU_12009/6
                Award Recipient :
                Funded by: Department of Paediatrics and Alexander Thatte Fund, University of Oxford
                Funded by: FundRef https://doi.org/10.13039/501100000265, RCUK | Medical Research Council (MRC);
                Award ID: MC_UU_12009/6
                Award ID: MR/M003221/1
                Award ID: MC_UU_12009/6
                Award ID: MC_UU_12009/6
                Award ID: MC_UU_12009/6
                Award ID: MC_UU_12009
                Award ID: MC_UU_12009/14
                Award ID: MC_UU_12009/6
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100007903, Bloodwise;
                Funded by: I.R. is supported by the NIHR Oxford BRC and by a Bloodwise Program Grant (13001)
                Funded by: FundRef https://doi.org/10.13039/100012117, Lady Tata Memorial Trust;
                Funded by: A.R. was supported by a Bloodwise Clinician Scientist Fellowship (grants: 14041 and 17001)
                Categories
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                Custom metadata
                © The Author(s), under exclusive licence to Springer Nature Limited 2021

                Oncology & Radiotherapy
                acute lymphocytic leukaemia,cancer genomics
                Oncology & Radiotherapy
                acute lymphocytic leukaemia, cancer genomics

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