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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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. |
format | Online Article Text |
id | pubmed-7641750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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