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      Interactions between lineage‐associated transcription factors govern haematopoietic progenitor states

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          Abstract

          Recent advances in molecular profiling provide descriptive datasets of complex differentiation landscapes including the haematopoietic system, but the molecular mechanisms defining progenitor states and lineage choice remain ill‐defined. Here, we employed a cellular model of murine multipotent haematopoietic progenitors (Hoxb8‐ FL) to knock out 39 transcription factors ( TFs) followed by RNA‐Seq analysis, to functionally define a regulatory network of 16,992 regulator/target gene links. Focussed analysis of the subnetworks regulated by the B‐lymphoid TF Ebf1 and T‐lymphoid TF Gata3 revealed a surprising role in common activation of an early myeloid programme. Moreover, Gata3‐mediated repression of Pax5 emerges as a mechanism to prevent precocious B‐lymphoid differentiation, while Hox‐mediated activation of Meis1 suppresses myeloid differentiation. To aid interpretation of large transcriptomics datasets, we also report a new method that visualises likely transitions that a progenitor will undergo following regulatory network perturbations. Taken together, this study reveals how molecular network wiring helps to establish a multipotent progenitor state, with experimental approaches and analysis tools applicable to dissecting a broad range of both normal and perturbed cellular differentiation landscapes.

          Abstract

          Functional definition of a transcription factor network reveals regulatory interdependencies during early blood development.

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          Most cited references104

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

          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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            BEDTools: a flexible suite of utilities for comparing genomic features

            Motivation: Testing for correlations between different sets of genomic features is a fundamental task in genomics research. However, searching for overlaps between features with existing web-based methods is complicated by the massive datasets that are routinely produced with current sequencing technologies. Fast and flexible tools are therefore required to ask complex questions of these data in an efficient manner. Results: This article introduces a new software suite for the comparison, manipulation and annotation of genomic features in Browser Extensible Data (BED) and General Feature Format (GFF) format. BEDTools also supports the comparison of sequence alignments in BAM format to both BED and GFF features. The tools are extremely efficient and allow the user to compare large datasets (e.g. next-generation sequencing data) with both public and custom genome annotation tracks. BEDTools can be combined with one another as well as with standard UNIX commands, thus facilitating routine genomics tasks as well as pipelines that can quickly answer intricate questions of large genomic datasets. Availability and implementation: BEDTools was written in C++. Source code and a comprehensive user manual are freely available at http://code.google.com/p/bedtools Contact: aaronquinlan@gmail.com; imh4y@virginia.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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              Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities.

              Genome-scale studies have revealed extensive, cell type-specific colocalization of transcription factors, but the mechanisms underlying this phenomenon remain poorly understood. Here, we demonstrate in macrophages and B cells that collaborative interactions of the common factor PU.1 with small sets of macrophage- or B cell lineage-determining transcription factors establish cell-specific binding sites that are associated with the majority of promoter-distal H3K4me1-marked genomic regions. PU.1 binding initiates nucleosome remodeling, followed by H3K4 monomethylation at large numbers of genomic regions associated with both broadly and specifically expressed genes. These locations serve as beacons for additional factors, exemplified by liver X receptors, which drive both cell-specific gene expression and signal-dependent responses. Together with analyses of transcription factor binding and H3K4me1 patterns in other cell types, these studies suggest that simple combinations of lineage-determining transcription factors can specify the genomic sites ultimately responsible for both cell identity and cell type-specific responses to diverse signaling inputs. Copyright 2010 Elsevier Inc. All rights reserved.
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                Author and article information

                Contributors
                bg200@cam.ac.uk
                Journal
                EMBO J
                EMBO J
                10.1002/(ISSN)1460-2075
                EMBJ
                embojnl
                The EMBO Journal
                John Wiley and Sons Inc. (Hoboken )
                0261-4189
                1460-2075
                26 October 2020
                15 December 2020
                26 October 2020
                : 39
                : 24 ( doiID: 10.1002/embj.v39.24 )
                : e104983
                Affiliations
                [ 1 ] Wellcome–MRC Cambridge Stem Cell Institute Department of Haematology Jeffrey Cheah Biomedical Centre University of Cambridge Cambridge UK
                [ 2 ] Department of Cellular and Molecular Immunology Max Planck Institute of Immunobiology and Epigenetics Freiburg Germany
                [ 3 ] International Max Planck Research School for Molecular and Cellular Biology Max Planck Institute of Immunobiology and Epigenetics Freiburg Germany
                Author notes
                [*] [* ]Corresponding author. Tel: +44 1223 336829; E‐mail: bg200@ 123456cam.ac.uk
                Author information
                https://orcid.org/0000-0002-9385-0359
                https://orcid.org/0000-0002-0058-1250
                https://orcid.org/0000-0001-6302-5705
                Article
                EMBJ2020104983
                10.15252/embj.2020104983
                7737608
                33103827
                c41904dd-4c89-4fa6-ba59-43789339c3a6
                © 2020 The Authors Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 12 March 2020
                : 07 September 2020
                : 08 September 2020
                Page count
                Figures: 14, Tables: 0, Pages: 23, Words: 17805
                Funding
                Funded by: HHS|National Institutes of Health (NIH)
                Award ID: NIDDK DK106766
                Funded by: Wellcome Trust (WT)
                Award ID: 206328/Z/17/Z
                Funded by: Blood Cancer UK , open-funder-registry 10.13039/501100015570;
                Award ID: 18002
                Funded by: Cancer Research UK (CRUK)
                Award ID: C1163/A21762
                Funded by: UKRI|Medical Research Council (MRC)
                Award ID: MR/S036113/1
                Funded by: Cancer Research UK (CRUK)
                Award ID: C49940/A25117
                Funded by: Wellcome Trust (WT)
                Award ID: 203151/Z/16/Z
                Funded by: UKRI|Medical Research Council (MRC)
                Award ID: 203151/Z/16/Z
                Categories
                Article
                Articles
                Custom metadata
                2.0
                15 December 2020
                Converter:WILEY_ML3GV2_TO_JATSPMC version:5.9.5 mode:remove_FC converted:15.12.2020

                Molecular biology
                haematopoiesis,network,progenitors,scrna‐seq,transcription factor,chromatin, epigenetics, genomics & functional genomics,development & differentiation,haematology

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