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      DDX41 is needed for pre- and postnatal hematopoietic stem cell differentiation in mice

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          Summary

          DDX41 is a tumor suppressor frequently mutated in human myeloid neoplasms, but whether it affects hematopoiesis is unknown. Using a knockout mouse, we demonstrate that DDX41 is required for mouse hematopoietic stem and progenitor cell (HSPC) survival and differentiation, particularly of myeloid lineage cells. Transplantation of Ddx41 knockout fetal liver and adult bone marrow (BM) cells was unable to rescue mice from lethal irradiation, and knockout stem cells were also defective in colony formation assays. RNA-seq analysis of Lin /cKit +/Sca1 + Ddx41 knockout cells from fetal liver demonstrated that the expression of many genes associated with hematopoietic differentiation were altered. Furthermore, differential splicing of genes involved in key biological processes was observed. Our data reveal a critical role for DDX41 in HSPC differentiation and myeloid progenitor development, likely through regulating gene expression programs and splicing.

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          Highlights

          • DDX41 knockout in HSCs results in neonatal death

          • DDX41 is required for HSC differentiation, particularly in the myeloid lineage

          • HSCs lacking DDX41 fail to repopulate the bone marrow

          • DDX41-deficient fetal HSCs lack expression of genes associated with HSC differentiation

          Abstract

          Ma and colleagues show using knockout mice that DDX41 is critical for the development of hematopoietic stem cells and particularly affects differentiation of myeloid lineage cells. This finding suggest that previous work showing that loss of DDX41 leads to hyper-proliferation in stem cells might be the result of decreased or aberrant protein expression, rather than total loss of activity

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          Most cited references50

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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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              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
                Journal
                Stem Cell Reports
                Stem Cell Reports
                Stem Cell Reports
                Elsevier
                2213-6711
                17 March 2022
                12 April 2022
                17 March 2022
                : 17
                : 4
                : 879-893
                Affiliations
                [1 ]Department of Microbiology and Immunology, University of Illinois at Chicago College of Medicine, 835 South Wolcott Avenue, E705 MSB (MC 790), Chicago, IL 60612, USA
                [2 ]Department of Medicine, University of Illinois at Chicago College of Medicine, Chicago, IL, USA
                [3 ]Department of Pathology, University of Illinois at Chicago College of Medicine, Chicago, IL, USA
                Author notes
                []Corresponding author srross@ 123456uic.edu
                Article
                S2213-6711(22)00099-6
                10.1016/j.stemcr.2022.02.010
                9023775
                35303436
                4bd263a8-4ba1-4e4b-a626-cf32dc761a07
                © 2022 The Author(s)

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

                History
                : 18 August 2021
                : 15 February 2022
                : 16 February 2022
                Categories
                Article

                hsc differentiation,myeloid development,ddx41,rna helicase,mds/aml

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