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      Epstein-Barr virus BORF2 inhibits cellular APOBEC3B to preserve viral genome integrity

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          Abstract

          The APOBEC family of single-stranded (ss)DNA cytosine deaminases provides innate immunity against virus and transposon replication 14 . A well-studied mechanism is APOBEC3G restriction of HIV-1, which is counteracted by a virus-encoded degradation mechanism 14 . Accordingly, most work has focused on retroviruses with obligate ssDNA replication intermediates and it is unclear whether large double-stranded (ds)DNA viruses may be similarly susceptible to restriction. Here, we show that the large dsDNA herpesvirus Epstein-Barr virus (EBV), which is the causative agent of infectious mononucleosis and multiple cancers 5 , utilizes a two-pronged approach to counteract restriction by APOBEC3B. The large subunit of the EBV ribonucleotide reductase, BORF2 6, 7 , bound to APOBEC3B in proteomics studies and immunoprecipitation experiments. Mutagenesis mapped the interaction to the APOBEC3B catalytic domain, and biochemical studies demonstrated that BORF2 stoichiometrically inhibits APOBEC3B DNA cytosine deaminase activity. BORF2 also caused a dramatic relocalization of nuclear APOBEC3B to perinuclear bodies. Upon lytic reactivation, BORF2-null viruses were susceptible to APOBEC3B-mediated deamination as evidenced by lower viral titers, lower infectivity, and hypermutation. The Kaposi’s sarcoma herpesvirus (KSHV) homolog, ORF61, also bound APOBEC3B and mediated relocalization. These data support a model in which the genomic integrity of human γ-herpesviruses is maintained by active neutralization of the antiviral enzyme APOBEC3B.

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          Most cited references42

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          A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.

          Heng Li (2011)
          Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. http://samtools.sourceforge.net. hengli@broadinstitute.org.
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            A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data

            (2013)
            Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e.g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty. Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors. Availability: http://samtools.sourceforge.net. Contact: hengli@broadinstitute.org.
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              Molecular mechanisms of antibody somatic hypermutation.

              Functional antibody genes are assembled by V-D-J joining and then diversified by somatic hypermutation. This hypermutation results from stepwise incorporation of single nucleotide substitutions into the V gene, underpinning much of antibody diversity and affinity maturation. Hypermutation is triggered by activation-induced deaminase (AID), an enzyme which catalyzes targeted deamination of deoxycytidine residues in DNA. The pathways used for processing the AID-generated U:G lesions determine the variety of base substitutions observed during somatic hypermutation. Thus, DNA replication across the uracil yields transition mutations at C:G pairs, whereas uracil excision by UNG uracil-DNA glycosylase creates abasic sites that can also yield transversions. Recognition of the U:G mismatch by MSH2/MSH6 triggers a mutagenic patch repair in which polymerase eta plays a major role and leads to mutations at A:T pairs. AID-triggered DNA deamination also underpins immunoglobulin variable (IgV) gene conversion, isotype class switching, and some oncogenic translocations in B cell tumors.
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                Author and article information

                Journal
                101674869
                44774
                Nat Microbiol
                Nat Microbiol
                Nature microbiology
                2058-5276
                18 October 2018
                12 November 2018
                January 2019
                12 May 2019
                : 4
                : 1
                : 78-88
                Affiliations
                [1 ]Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, Minnesota, USA, 55455.
                [2 ]Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota, USA, 55455.
                [3 ]Institute for Molecular Virology, University of Minnesota, Minneapolis, Minnesota, USA, 55455.
                [4 ]Center for Genome Engineering, University of Minnesota, Minneapolis, Minnesota, USA, 55455.
                [5 ]Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada, M5S 1A8.
                [6 ]Ontario Institute for Cancer Research, MaRS Centre, South Tower, 101 College Street, Suite 800, Toronto, Ontario, Canada, M5G 0A3.
                [7 ]Donnelly Centre, University of Toronto, Toronto, Ontario, Canada, M5S 1A8.
                [8 ]Howard Hughes Medical Institute, University of Minnesota, Minneapolis, Minnesota, USA, 55455.
                Author notes

                Author Contributions AZC, JY-M, LF, and RSH conceived and designed the studies. AZC and JY-M performed the bulk of experimental work. NM-So, EM, and JG did AP-MS analyses. MCJ, MAC, JLM, NMSh, and WLB provided technical training and advice. JLM helped validate the BORF2-A3B interaction and MAC performed UDG experiments. AZC, IB, MCJ, and DE conducted bioinformatics analyses. AZC, JY-M, LF, and RSH drafted the manuscript, and all authors contributed to revisions.

                Correspondence and requests for materials should be addressed to LF ( lori.frappier@ 123456utoronto.ca ) or RSH ( rsh@ 123456umn.edu ).
                Article
                NIHMS1509127
                10.1038/s41564-018-0284-6
                6294688
                30420783
                60954178-d3d3-4502-93eb-bf8776b95152

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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