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      PAXIP1 and STAG2 converge to maintain 3D genome architecture and facilitate promoter/enhancer contacts to enable stress hormone-dependent transcription

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          Abstract

          How steroid hormone receptors (SHRs) regulate transcriptional activity remains partly understood. Upon activation, SHRs bind the genome together with a co-regulator repertoire, crucial to induce gene expression. However, it remains unknown which components of the SHR-recruited co-regulator complex are essential to drive transcription following hormonal stimuli. Through a FACS-based genome-wide CRISPR screen, we functionally dissected the Glucocorticoid Receptor (GR) complex. We describe a functional cross-talk between PAXIP1 and the cohesin subunit STAG2, critical for regulation of gene expression by GR. Without altering the GR cistrome, PAXIP1 and STAG2 depletion alter the GR transcriptome, by impairing the recruitment of 3D-genome organization proteins to the GR complex. Importantly, we demonstrate that PAXIP1 is required for stability of cohesin on chromatin, its localization to GR-occupied sites, and maintenance of enhancer-promoter interactions. In lung cancer, where GR acts as tumor suppressor, PAXIP1/STAG2 loss enhances GR-mediated tumor suppressor activity by modifying local chromatin interactions. All together, we introduce PAXIP1 and STAG2 as novel co-regulators of GR, required to maintain 3D-genome architecture and drive the GR transcriptional programme following hormonal stimuli.

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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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            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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              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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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                13 October 2023
                18 April 2023
                18 April 2023
                : 51
                : 18
                : 9576-9593
                Affiliations
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Erasmus Optical Imaging Center, Erasmus University Medical Center Rotterdam , Rotterdam, The Netherland
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Erasmus Optical Imaging Center, Erasmus University Medical Center Rotterdam , Rotterdam, The Netherland
                Department of Pathology, Erasmus University Medical Center Rotterdam , Rotterdam, The Netherlands
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Flow Cytometry Facility, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Proteomics Facility, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Division of Molecular Carcinogenesis, The NKI Robotics and Screening Centre, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Proteomics Facility, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, Utrecht Institute for Pharmaceutical Sciences, Utrecht University and Netherlands Proteomics Centre , Utrecht, The Netherlands
                Division of Molecular Carcinogenesis, The NKI Robotics and Screening Centre, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Erasmus Optical Imaging Center, Erasmus University Medical Center Rotterdam , Rotterdam, The Netherland
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Center for Molecular Medicine, University Medical Center Utrecht , Utrecht, The Netherlands
                Division of Oncogenomics, Oncode Institute, The Netherlands Cancer Institute , Amsterdam, The Netherlands
                Laboratory of Chemical Biology and Institute for Complex Molecular Systems, Department of Biomedical Engineering , Eindhoven University of Technology, Eindhoven, The Netherlands
                Author notes
                To whom correspondence should be addressed: Tel: +31 205122101; Fax: +31 205122029; Email: w.zwart@ 123456nki.nl
                Correspondence may also be addressed to Stefan Prekovic. s.prekovic@ 123456umcutrecht.nl

                These authors contributed equally to this work as second authors.

                Author information
                https://orcid.org/0000-0001-9209-5403
                https://orcid.org/0000-0003-4816-4154
                https://orcid.org/0000-0002-9823-7289
                Article
                gkad267
                10.1093/nar/gkad267
                10570044
                37070193
                21eb048b-2859-48ec-8d47-ebd57e5c5409
                © The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@ 123456oup.com

                History
                : 12 April 2023
                : 03 March 2023
                : 23 January 2023
                Page count
                Pages: 18
                Funding
                Funded by: KWF Kankerbestrijding/Alpe d’Huzes;
                Funded by: Oncode Institute, DOI 10.13039/501100021821;
                Funded by: Dutch NWO X-omics Initiative;
                Funded by: NWO Building Blocks;
                Award ID: 737.016.014
                Categories
                AcademicSubjects/SCI00010
                Gene regulation, Chromatin and Epigenetics

                Genetics
                Genetics

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