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Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data

Somatic hypermutations of immunoglobulin (Ig) genes occurring during affinity maturation drive B-cell receptors’ ability to evolve strong binding to their antigenic targets. The landscape of these mutations is highly heterogeneous, with certain regions of the Ig gene being preferentially targeted. H...

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Autores principales: Spisak, Natanael, Walczak, Aleksandra M, Mora, Thierry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641750/
https://www.ncbi.nlm.nih.gov/pubmed/33035336
http://dx.doi.org/10.1093/nar/gkaa825
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author Spisak, Natanael
Walczak, Aleksandra M
Mora, Thierry
author_facet Spisak, Natanael
Walczak, Aleksandra M
Mora, Thierry
author_sort Spisak, Natanael
collection PubMed
description Somatic hypermutations of immunoglobulin (Ig) genes occurring during affinity maturation drive B-cell receptors’ ability to evolve strong binding to their antigenic targets. The landscape of these mutations is highly heterogeneous, with certain regions of the Ig gene being preferentially targeted. However, a rigorous quantification of this bias has been difficult because of phylogenetic correlations between sequences and the interference of selective forces. Here, we present an approach that corrects for these issues, and use it to learn a model of hypermutation preferences from a recently published large IgH repertoire dataset. The obtained model predicts mutation profiles accurately and in a reproducible way, including in the previously uncharacterized Complementarity Determining Region 3, revealing that both the sequence context of the mutation and its absolute position along the gene are important. In addition, we show that hypermutations occurring concomittantly along B-cell lineages tend to co-localize, suggesting a possible mechanism for accelerating affinity maturation.
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spelling pubmed-76417502020-11-10 Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data Spisak, Natanael Walczak, Aleksandra M Mora, Thierry Nucleic Acids Res Computational Biology Somatic hypermutations of immunoglobulin (Ig) genes occurring during affinity maturation drive B-cell receptors’ ability to evolve strong binding to their antigenic targets. The landscape of these mutations is highly heterogeneous, with certain regions of the Ig gene being preferentially targeted. However, a rigorous quantification of this bias has been difficult because of phylogenetic correlations between sequences and the interference of selective forces. Here, we present an approach that corrects for these issues, and use it to learn a model of hypermutation preferences from a recently published large IgH repertoire dataset. The obtained model predicts mutation profiles accurately and in a reproducible way, including in the previously uncharacterized Complementarity Determining Region 3, revealing that both the sequence context of the mutation and its absolute position along the gene are important. In addition, we show that hypermutations occurring concomittantly along B-cell lineages tend to co-localize, suggesting a possible mechanism for accelerating affinity maturation. Oxford University Press 2020-10-09 /pmc/articles/PMC7641750/ /pubmed/33035336 http://dx.doi.org/10.1093/nar/gkaa825 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
Spisak, Natanael
Walczak, Aleksandra M
Mora, Thierry
Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data
title Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data
title_full Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data
title_fullStr Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data
title_full_unstemmed Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data
title_short Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data
title_sort learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641750/
https://www.ncbi.nlm.nih.gov/pubmed/33035336
http://dx.doi.org/10.1093/nar/gkaa825
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