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      Gene-Specific Substitution Profiles Describe the Types and Frequencies of Amino Acid Changes during Antibody Somatic Hypermutation

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          Abstract

          Somatic hypermutation (SHM) plays a critical role in the maturation of antibodies, optimizing recognition initiated by recombination of V(D)J genes. Previous studies have shown that the propensity to mutate is modulated by the context of surrounding nucleotides and that SHM machinery generates biased substitutions. To investigate the intrinsic mutation frequency and substitution bias of SHMs at the amino acid level, we analyzed functional human antibody repertoires and developed mGSSP (method for gene-specific substitution profile), a method to construct amino acid substitution profiles from next-generation sequencing-determined B cell transcripts. We demonstrated that these gene-specific substitution profiles (GSSPs) are unique to each V gene and highly consistent between donors. We also showed that the GSSPs constructed from functional antibody repertoires are highly similar to those constructed from antibody sequences amplified from non-productively rearranged passenger alleles, which do not undergo functional selection. This suggests the types and frequencies, or mutational space, of a majority of amino acid changes sampled by the SHM machinery to be well captured by GSSPs. We further observed the rates of mutational exchange between some amino acids to be both asymmetric and context dependent and to correlate weakly with their biochemical properties. GSSPs provide an improved, position-dependent alternative to standard substitution matrices, and can be utilized to developing software for accurately modeling the SHM process. GSSPs can also be used for predicting the amino acid mutational space available for antigen-driven selection and for understanding factors modulating the maturation pathways of antibody lineages in a gene-specific context. The mGSSP method can be used to build, compare, and plot GSSPs 1 ; we report the GSSPs constructed for 69 common human V genes (DOI: 10.6084/m9.figshare.3511083) and provide high-resolution logo plots for each (DOI: 10.6084/m9.figshare.3511085).

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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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            Two levels of protection for the B cell genome during somatic hypermutation.

            Somatic hypermutation introduces point mutations into immunoglobulin genes in germinal centre B cells during an immune response. The reaction is initiated by cytosine deamination by the activation-induced deaminase (AID) and completed by error-prone processing of the resulting uracils by mismatch and base excision repair factors. Somatic hypermutation represents a threat to genome integrity and it is not known how the B cell genome is protected from the mutagenic effects of somatic hypermutation nor how often these protective mechanisms fail. Here we show, by extensive sequencing of murine B cell genes, that the genome is protected by two distinct mechanisms: selective targeting of AID and gene-specific, high-fidelity repair of AID-generated uracils. Numerous genes linked to B cell tumorigenesis, including Myc, Pim1, Pax5, Ocab (also called Pou2af1), H2afx, Rhoh and Ebf1, are deaminated by AID but escape acquisition of most mutations through the combined action of mismatch and base excision repair. However, approximately 25% of expressed genes analysed were not fully protected by either mechanism and accumulated mutations in germinal centre B cells. Our results demonstrate that AID acts broadly on the genome, with the ultimate distribution of mutations determined by a balance between high-fidelity and error-prone DNA repair.
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              Denoising DNA deep sequencing data—high-throughput sequencing errors and their correction

              Characterizing the errors generated by common high-throughput sequencing platforms and telling true genetic variation from technical artefacts are two interdependent steps, essential to many analyses such as single nucleotide variant calling, haplotype inference, sequence assembly and evolutionary studies. Both random and systematic errors can show a specific occurrence profile for each of the six prominent sequencing platforms surveyed here: 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Illumina sequencing by synthesis, Ion Torrent semiconductor sequencing, Pacific Biosciences single-molecule real-time sequencing and Oxford Nanopore sequencing. There is a large variety of programs available for error removal in sequencing read data, which differ in the error models and statistical techniques they use, the features of the data they analyse, the parameters they determine from them and the data structures and algorithms they use. We highlight the assumptions they make and for which data types these hold, providing guidance which tools to consider for benchmarking with regard to the data properties. While no benchmarking results are included here, such specific benchmarks would greatly inform tool choices and future software development. The development of stand-alone error correctors, as well as single nucleotide variant and haplotype callers, could also benefit from using more of the knowledge about error profiles and from (re)combining ideas from the existing approaches presented here.
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                10 May 2017
                2017
                : 8
                : 537
                Affiliations
                [1] 1Department of Biochemistry and Molecular Biophysics, Columbia University , New York, NY, United States
                [2] 2Department of Systems Biology, Columbia University , New York, NY, United States
                [3] 3Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health , Bethesda, MD, United States
                [4] 4NIH Intramural Sequencing Center, National Human Genome Research Institute, National Institutes of Health , Bethesda, MD, United States
                Author notes

                Edited by: Deborah K. Dunn-Walters, University of Surrey, UK

                Reviewed by: Patricia Johanna Gearhart, National Institutes of Health, United States; Frederick Matsen, Fred Hutchinson Cancer Research Center, United States; Almudena R. Ramiro, Spanish National Centre for Cardiovascular Research, Spain

                *Correspondence: Zizhang Sheng, zs2248@ 123456c2b2.columbia.edu ; Lawrence Shapiro, shapiro@ 123456convex.hhmi.columbia.edu

                These authors have contributed equally to this work.

                Specialty section: This article was submitted to B Cell Biology, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2017.00537
                5424261
                28539926
                a940b73e-3751-4976-bd1a-cddbbf10fa98
                Copyright © 2017 Sheng, Schramm, Kong, NISC Comparative Sequencing Program, Mullikin, Mascola, Kwong and Shapiro.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 23 February 2017
                : 21 April 2017
                Page count
                Figures: 6, Tables: 0, Equations: 4, References: 48, Pages: 14, Words: 10893
                Funding
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: AI104722-3, R01 AI104387-3, S10OD012351, S10OD021764
                Categories
                Immunology
                Methods

                Immunology
                antibodyomics,b cell ontogeny,broadly neutralizing antibody,mutation frequency,repertoire diversity

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