Literature DB >> 27707999

A Model of Somatic Hypermutation Targeting in Mice Based on High-Throughput Ig Sequencing Data.

Ang Cui1, Roberto Di Niro2, Jason A Vander Heiden1, Adrian W Briggs3, Kris Adams3, Tamara Gilbert3, Kevin C O'Connor4,5, Francois Vigneault3, Mark J Shlomchik2, Steven H Kleinstein6,5,7.   

Abstract

Analyses of somatic hypermutation (SHM) patterns in B cell Ig sequences have important basic science and clinical applications, but they are often confounded by the intrinsic biases of SHM targeting on specific DNA motifs (i.e., hot and cold spots). Modeling these biases has been hindered by the difficulty in identifying mutated Ig sequences in vivo in the absence of selection pressures, which skew the observed mutation patterns. To generate a large number of unselected mutations, we immunized B1-8 H chain transgenic mice with nitrophenyl to stimulate nitrophenyl-specific λ+ germinal center B cells and sequenced the unexpressed κ L chains using next-generation methods. Most of these κ sequences had out-of-frame junctions and were presumably uninfluenced by selection. Despite being nonfunctionally rearranged, they were targeted by SHM and displayed a higher mutation frequency than functional sequences. We used 39,173 mutations to construct a quantitative SHM targeting model. The model showed targeting biases that were consistent with classic hot and cold spots, yet revealed additional highly mutable motifs. We observed comparable targeting for functional and nonfunctional sequences, suggesting similar biological processes operate at both loci. However, we observed species- and chain-specific targeting patterns, demonstrating the need for multiple SHM targeting models. Interestingly, the targeting of C/G bases and the frequency of transition mutations at C/G bases was higher in mice compared with humans, suggesting lower levels of DNA repair activity in mice. Our models of SHM targeting provide insights into the SHM process and support future analyses of mutation patterns.
Copyright © 2016 by The American Association of Immunologists, Inc.

Entities:  

Mesh:

Substances:

Year:  2016        PMID: 27707999      PMCID: PMC5161250          DOI: 10.4049/jimmunol.1502263

Source DB:  PubMed          Journal:  J Immunol        ISSN: 0022-1767            Impact factor:   5.422


  32 in total

1.  Double producers of kappa and lambda define a subset of B cells in mouse plasmacytomas.

Authors:  L Diaw; D Siwarski; W DuBois; G Jones; K Huppi
Journal:  Mol Immunol       Date:  2000 Aug-Sep       Impact factor: 4.407

Review 2.  Rep-Seq: uncovering the immunological repertoire through next-generation sequencing.

Authors:  Jennifer Benichou; Rotem Ben-Hamo; Yoram Louzoun; Sol Efroni
Journal:  Immunology       Date:  2012-03       Impact factor: 7.397

3.  Sequence-specific targeting of two bases on both DNA strands by the somatic hypermutation mechanism.

Authors:  Gary S Shapiro; Misoo C Ellison; Lawrence J Wysocki
Journal:  Mol Immunol       Date:  2003-09       Impact factor: 4.407

4.  Change-O: a toolkit for analyzing large-scale B cell immunoglobulin repertoire sequencing data.

Authors:  Namita T Gupta; Jason A Vander Heiden; Mohamed Uduman; Daniel Gadala-Maria; Gur Yaari; Steven H Kleinstein
Journal:  Bioinformatics       Date:  2015-06-10       Impact factor: 6.937

5.  Elements regulating somatic hypermutation of an immunoglobulin kappa gene: critical role for the intron enhancer/matrix attachment region.

Authors:  A G Betz; C Milstein; A González-Fernández; R Pannell; T Larson; M S Neuberger
Journal:  Cell       Date:  1994-04-22       Impact factor: 41.582

6.  Di- and trinucleotide target preferences of somatic mutagenesis in normal and autoreactive B cells.

Authors:  D S Smith; G Creadon; P K Jena; J P Portanova; B L Kotzin; L J Wysocki
Journal:  J Immunol       Date:  1996-04-01       Impact factor: 5.422

7.  B cells populating the multiple sclerosis brain mature in the draining cervical lymph nodes.

Authors:  Joel N H Stern; Gur Yaari; Jason A Vander Heiden; George Church; William F Donahue; Rogier Q Hintzen; Anita J Huttner; Jon D Laman; Rashed M Nagra; Alyssa Nylander; David Pitt; Sriram Ramanan; Bilal A Siddiqui; Francois Vigneault; Steven H Kleinstein; David A Hafler; Kevin C O'Connor
Journal:  Sci Transl Med       Date:  2014-08-06       Impact factor: 17.956

Review 8.  The biochemistry of somatic hypermutation.

Authors:  Jonathan U Peled; Fei Li Kuang; Maria D Iglesias-Ussel; Sergio Roa; Susan L Kalis; Myron F Goodman; Matthew D Scharff
Journal:  Annu Rev Immunol       Date:  2008       Impact factor: 28.527

9.  Quantifying selection in high-throughput Immunoglobulin sequencing data sets.

Authors:  Gur Yaari; Mohamed Uduman; Steven H Kleinstein
Journal:  Nucleic Acids Res       Date:  2012-05-27       Impact factor: 16.971

10.  Sequence-Intrinsic Mechanisms that Target AID Mutational Outcomes on Antibody Genes.

Authors:  Leng-Siew Yeap; Joyce K Hwang; Zhou Du; Robin M Meyers; Fei-Long Meng; Agnė Jakubauskaitė; Mengyuan Liu; Vinidhra Mani; Donna Neuberg; Thomas B Kepler; Jing H Wang; Frederick W Alt
Journal:  Cell       Date:  2015-11-12       Impact factor: 41.582

View more
  24 in total

1.  High-affinity, neutralizing antibodies to SARS-CoV-2 can be made without T follicular helper cells.

Authors:  Jennifer S Chen; Ryan D Chow; Eric Song; Tianyang Mao; Benjamin Israelow; Kathy Kamath; Joel Bozekowski; Winston A Haynes; Renata B Filler; Bridget L Menasche; Jin Wei; Mia Madel Alfajaro; Wenzhi Song; Lei Peng; Lauren Carter; Jason S Weinstein; Uthaman Gowthaman; Sidi Chen; Joe Craft; John C Shon; Akiko Iwasaki; Craig B Wilen; Stephanie C Eisenbarth
Journal:  Sci Immunol       Date:  2022-02-04

2.  Position-Dependent Differential Targeting of Somatic Hypermutation.

Authors:  Julian Q Zhou; Steven H Kleinstein
Journal:  J Immunol       Date:  2020-11-13       Impact factor: 5.422

3.  Sex-Biased Aging Effects on Ig Somatic Hypermutation Targeting.

Authors:  Ang Cui; Daniel G Chawla; Steven H Kleinstein
Journal:  J Immunol       Date:  2020-12-07       Impact factor: 5.422

4.  Landscape of variable domain of heavy-chain-only antibody repertoire from alpaca.

Authors:  Zhui Tu; Xiaoqiang Huang; Jinheng Fu; Na Hu; Wei Zheng; Yanping Li; Yang Zhang
Journal:  Immunology       Date:  2020-06-30       Impact factor: 7.397

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

Authors:  Zizhang Sheng; Chaim A Schramm; Rui Kong; James C Mullikin; John R Mascola; Peter D Kwong; Lawrence Shapiro
Journal:  Front Immunol       Date:  2017-05-10       Impact factor: 7.561

6.  High-throughput immune repertoire analysis with IGoR.

Authors:  Quentin Marcou; Thierry Mora; Aleksandra M Walczak
Journal:  Nat Commun       Date:  2018-02-08       Impact factor: 14.919

Review 7.  Computational Strategies for Dissecting the High-Dimensional Complexity of Adaptive Immune Repertoires.

Authors:  Enkelejda Miho; Alexander Yermanos; Cédric R Weber; Christoph T Berger; Sai T Reddy; Victor Greiff
Journal:  Front Immunol       Date:  2018-02-21       Impact factor: 7.561

8.  Selection and Neutral Mutations Drive Pervasive Mutability Losses in Long-Lived Anti-HIV B-Cell Lineages.

Authors:  Marcos C Vieira; Daniel Zinder; Sarah Cobey
Journal:  Mol Biol Evol       Date:  2018-05-01       Impact factor: 16.240

9.  Cigarette smoke exposure attenuates the induction of antigen-specific IgA in the murine upper respiratory tract.

Authors:  Joshua J C McGrath; Danya Thayaparan; Steven P Cass; Jonathan P Mapletoft; Peter Y F Zeng; Joshua F E Koenig; Matthew F Fantauzzi; Puja Bagri; Bruce Ly; Rachel Heo; L Patrick Schenck; Pamela Shen; Matthew S Miller; Martin R Stämpfli
Journal:  Mucosal Immunol       Date:  2021-06-09       Impact factor: 7.313

10.  Predicting B cell receptor substitution profiles using public repertoire data.

Authors:  Amrit Dhar; Kristian Davidsen; Frederick A Matsen; Vladimir N Minin
Journal:  PLoS Comput Biol       Date:  2018-10-17       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.