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      The HTLV-1 viral oncoproteins Tax and HBZ reprogram the cellular mRNA splicing landscape

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          Abstract

          Viral infections are known to hijack the transcription and translation of the host cell. However, the extent to which viral proteins coordinate these perturbations remains unclear. Here we used a model system, the human T-cell leukemia virus type 1 (HTLV-1), and systematically analyzed the transcriptome and interactome of key effectors oncoviral proteins Tax and HBZ. We showed that Tax and HBZ target distinct but also common transcription factors. Unexpectedly, we also uncovered a large set of interactions with RNA-binding proteins, including the U2 auxiliary factor large subunit (U2AF2), a key cellular regulator of pre-mRNA splicing. We discovered that Tax and HBZ perturb the splicing landscape by altering cassette exons in opposing manners, with Tax inducing exon inclusion while HBZ induces exon exclusion. Among Tax- and HBZ-dependent splicing changes, we identify events that are also altered in Adult T cell leukemia/lymphoma (ATLL) samples from two independent patient cohorts, and in well-known cancer census genes. Our interactome mapping approach, applicable to other viral oncogenes, has identified spliceosome perturbation as a novel mechanism coordinated by Tax and HBZ to reprogram the transcriptome.

          Author summary

          Tax and HBZ are two viral regulatory proteins encoded by the human T-cell leukemia virus type 1 (HTLV-1) via sense and antisense transcripts, respectively. Both proteins are known to drive oncogenic processes that culminate in a T-cell neoplasm, known as Adult T cell leukemia/lymphoma (ATLL). We measured the effects of Tax and HBZ on host gene expression pathway by analyzing the interactome with cellular transcriptional and post-transcriptional regulators, and the transcriptome and mRNA splicing of cell lines expressing either Tax or HBZ. We compared our results with data obtained from independent cohorts of Japanese and Afro-Caribbean patients, and identified common splicing changes that might represent clinically useful biomarkers for ATLL. Finally, we provide evidence that the viral protein Tax can reprogram initial steps of the T-cell transcriptome diversification by hijacking the U2AF complex, a key cellular regulator of pre-mRNA splicing.

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          Most cited references91

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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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            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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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Contributors
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: SoftwareRole: Writing – review & editing
                Role: Formal analysisRole: InvestigationRole: Methodology
                Role: InvestigationRole: Methodology
                Role: InvestigationRole: Methodology
                Role: Methodology
                Role: Methodology
                Role: Data curationRole: Formal analysis
                Role: VisualizationRole: Writing – review & editing
                Role: Formal analysisRole: Methodology
                Role: Data curationRole: Resources
                Role: Data curationRole: Resources
                Role: Resources
                Role: Resources
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Visualization
                Role: Formal analysisRole: MethodologyRole: ResourcesRole: Supervision
                Role: Supervision
                Role: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Resources
                Role: Formal analysisRole: InvestigationRole: Methodology
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: SupervisionRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: Writing – review & editing
                Role: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: Funding acquisitionRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                PLoS Pathogens
                Public Library of Science (San Francisco, CA USA )
                1553-7366
                1553-7374
                20 September 2021
                September 2021
                : 17
                : 9
                : e1009919
                Affiliations
                [1 ] Laboratory of Viral Interactomes, GIGA Institute, University of Liege, Liege, Belgium
                [2 ] Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
                [3 ] Laboratory of Gene Expression and Cancer, GIGA Institute, University of Liege, Liege, Belgium
                [4 ] Unit of Animal Genomics, GIGA, Université de Liège (ULiège), Liège, Belgium
                [5 ] Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, Massachusetts, United States of America
                [6 ] Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, United States of America
                [7 ] Laboratory of Biology and Modeling of the Cell, CNRS UMR 5239, INSERM U1210, University of Lyon, Lyon, France
                [8 ] Department of Pathology and Tumor Biology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
                [9 ] Service Hématologie Adultes, Assistance Publique-Hôpitaux de Paris, Hôpital Necker Enfants Malades, Université de Paris, Laboratoire d’onco-hématologie, Institut Necker-Enfants Malades, INSERM U1151, Université de Paris, Paris, France
                [10 ] Unit of Cell and Tissue Biology, GIGA Institute, University of Liege, Liege, Belgium
                [11 ] Laboratory of Clinical and Epidemiological Virology, Rega Institute for Medical Research, Department of Microbiology, Immunology and Transplantation, Catholic University of Leuven, Leuven, Belgium
                [12 ] Université Montpellier, IRIM CNRS UMR 9004 Montpellier, France
                [13 ] Department of Human Genetics, CHU of Liege, University of Liege, Liege, Belgium
                [14 ] Laboratory of Experimental Hematology, Institut Jules Bordet, Université Libre de Bruxelles (ULB), Brussels, Belgium
                Imperial College London, UNITED KINGDOM
                Author notes
                Author information
                https://orcid.org/0000-0001-5026-9567
                https://orcid.org/0000-0002-1283-0293
                https://orcid.org/0000-0001-5113-1877
                https://orcid.org/0000-0001-7025-6106
                https://orcid.org/0000-0003-4115-5546
                https://orcid.org/0000-0002-2975-8822
                https://orcid.org/0000-0002-8263-9902
                https://orcid.org/0000-0002-6844-7920
                https://orcid.org/0000-0002-4944-584X
                https://orcid.org/0000-0002-4840-021X
                https://orcid.org/0000-0001-6475-1418
                https://orcid.org/0000-0003-3234-8426
                https://orcid.org/0000-0002-9677-9703
                https://orcid.org/0000-0003-4282-9688
                https://orcid.org/0000-0001-5192-0921
                https://orcid.org/0000-0002-8683-705X
                Article
                PPATHOGENS-D-21-00613
                10.1371/journal.ppat.1009919
                8483338
                34543356
                26bd1410-fb2f-48f9-b003-f37abdc62817
                © 2021 Vandermeulen et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 March 2021
                : 27 August 2021
                Page count
                Figures: 8, Tables: 1, Pages: 29
                Funding
                Funded by: Fonds de la Recherche Scientifique
                Award ID: 2.5020.11
                Funded by: Walloon Region
                Funded by: funder-id http://dx.doi.org/10.13039/501100002661, Fonds De La Recherche Scientifique - FNRS;
                Award ID: PDR 14461191 and Televie 30823819
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100003134, Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture;
                Award ID: 24343558 and 29315509
                Award Recipient :
                Funded by: Flanders Research Foundation
                Award ID: G0D6817N
                Award Recipient :
                Funded by: KU Leuven grant
                Award ID: Vaast Leysen Leerstoel
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: P50HG004233
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: U41HG001715
                Award Recipient :
                Computational resources have been provided by the Consortium des Équipements de Calcul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique (FRS-FNRS, Belgium) under Grant No. 2.5020.11 and by the Walloon Region. This work was primarily supported by the FRS-FNRS grants PDR 14461191 and Televie 30823819 to J-C.T; Fund for Research Training in Industry and Agriculture grants 24343558 and 29315509 to C.V.; Flanders Research Foundation grant # G0D6817N and KU Leuven grant (“Vaast Leysen Leerstoel”) to J.V.W; and National Institutes of Health grants P50HG004233 to M.V. and U41HG001715 to M.V., D.E.H., and M.A.C.; M.V. and F.D. are Chercheurs Qualifiés Honoraires, and J.C.T. a Maitre de Recherche of the F.R.S.-FNRS. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and life sciences
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                Custom metadata
                vor-update-to-uncorrected-proof
                2021-09-30
                RNA-sequencing data have been deposited in NCBI's Gene Expression Omnibus (Edgar, 2002) and are accessible through GEO accession number GSE146210 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE146210).

                Infectious disease & Microbiology
                Infectious disease & Microbiology

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