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      Base-editing-mediated dissection of a γ-globin cis-regulatory element for the therapeutic reactivation of fetal hemoglobin expression

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          Abstract

          Sickle cell disease and β-thalassemia affect the production of the adult β-hemoglobin chain. The clinical severity is lessened by mutations that cause fetal γ-globin expression in adult life (i.e., the hereditary persistence of fetal hemoglobin). Mutations clustering ~200 nucleotides upstream of the HBG transcriptional start sites either reduce binding of the LRF repressor or recruit the KLF1 activator. Here, we use base editing to generate a variety of mutations in the −200 region of the HBG promoters, including potent combinations of four to eight γ-globin-inducing mutations. Editing of patient hematopoietic stem/progenitor cells is safe, leads to fetal hemoglobin reactivation and rescues the pathological phenotype. Creation of a KLF1 activator binding site is the most potent strategy – even in long-term repopulating hematopoietic stem/progenitor cells. Compared with a Cas9-nuclease approach, base editing avoids the generation of insertions, deletions and large genomic rearrangements and results in higher γ-globin levels. Our results demonstrate that base editing of HBG promoters is a safe, universal strategy for treating β-hemoglobinopathies.

          Abstract

          Antoniou and colleagues used base editing to generate a variety of mutations inducing γ-globin and rescue the β-hemoglobinopathy phenotype. This strategy was safe and effective in long-term repopulating hematopoietic stem/progenitor cells.

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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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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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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
                annarita.miccio@institutimagine.org
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                4 November 2022
                4 November 2022
                2022
                : 13
                : 6618
                Affiliations
                [1 ]GRID grid.462336.6, Université Paris Cité, Imagine Institute, Laboratory of chromatin and gene regulation during development, INSERM UMR 1163, ; 75015 Paris, France
                [2 ]GRID grid.462336.6, Université Paris Cité, Imagine Institute, Laboratory of Human Lymphohematopoiesis, INSERM UMR 1163, ; 75015 Paris, France
                [3 ]Biotherapy Department and Clinical Investigation Center, Assistance Publique Hopitaux de Paris, INSERM, 75015 Paris, France
                [4 ]GRID grid.462336.6, Bioinformatics Platform, Imagine Institute, ; 75015 Paris, France
                [5 ]GRID grid.11696.39, ISNI 0000 0004 1937 0351, CIBIO, University of Trento, ; 38100 Trento, Italy
                [6 ]GRID grid.418241.a, ISNI 0000 0000 9373 1902, Sorbonne Université, INSERM, CNRS, Institut de la Vision, ; 75015 Paris, France
                [7 ]GRID grid.503191.f, ISNI 0000 0001 0143 5055, INSERM U1154, CNRS UMR7196, Museum National d’Histoire Naturelle, ; Paris, France
                [8 ]GRID grid.462336.6, Université Paris Cité, Imagine Institute, Laboratory of genome dynamics in the immune system, INSERM UMR 1163, ; 75015 Paris, France
                [9 ]GRID grid.443947.9, ISNI 0000 0000 9751 7639, Établissement Français du Sang, UMR 7268, ; 13005 Marseille, France
                [10 ]GRID grid.484422.c, Laboratoire d’Excellence GR-Ex, ; 75015 Paris, France
                [11 ]GRID grid.419946.7, ISNI 0000 0004 0641 2700, Genethon, ; 91000 Evry, France
                [12 ]GRID grid.8390.2, ISNI 0000 0001 2180 5818, Université Paris-Saclay, Univ Evry, Inserm, Genethon, Integrare research unit UMR_S951, ; 91000 Evry, France
                [13 ]GRID grid.508487.6, ISNI 0000 0004 7885 7602, Université Paris Cité, ; 75015 Paris, France
                [14 ]GRID grid.462336.6, Imagine Institute, ; 75015 Paris, France
                [15 ]GRID grid.7548.e, ISNI 0000000121697570, Department of Life Sciences, , University of Modena and Reggio Emilia, ; 41125 Modena, Italy
                Author information
                http://orcid.org/0000-0003-3404-1401
                http://orcid.org/0000-0002-7770-5688
                http://orcid.org/0000-0003-3935-6971
                http://orcid.org/0000-0001-5987-0463
                http://orcid.org/0000-0001-8924-4316
                http://orcid.org/0000-0001-8551-2846
                http://orcid.org/0000-0001-8184-427X
                http://orcid.org/0000-0002-1188-8856
                http://orcid.org/0000-0002-0264-0891
                http://orcid.org/0000-0003-4453-2597
                http://orcid.org/0000-0002-3483-1115
                http://orcid.org/0000-0002-3409-9665
                Article
                34493
                10.1038/s41467-022-34493-1
                9636226
                36333351
                b38655fe-bf45-48d5-99fc-111643981d89
                © The Author(s) 2022

                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
                : 6 January 2022
                : 20 October 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001665, Agence Nationale de la Recherche (French National Research Agency);
                Award ID: ANR-10-IAHU-01
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100007492, Fondation Bettencourt Schueller (Bettencourt Schueller Foundation);
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 865797
                Award Recipient :
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                © The Author(s) 2022

                Uncategorized
                gene therapy,haematopoietic stem cells
                Uncategorized
                gene therapy, haematopoietic stem cells

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