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

TL;DR

This study develops a model to accurately quantify the heterogeneous landscape of immunoglobulin hypermutations, revealing positional and sequence context biases, and suggests co-localization of mutations may accelerate affinity maturation.

Contribution

The paper introduces a novel method to correct for phylogenetic and selective biases, enabling precise modeling of hypermutation preferences from high-throughput Ig repertoire data.

Findings

Model predicts mutation profiles accurately across Ig regions.

Both sequence context and position influence hypermutation bias.

Hypermutations tend to co-localize along B-cell lineages.

Abstract

Somatic hypermutations of immunoglobulin (Ig) genes occuring 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.