论文标题
从高通量曲目数据中学习免疫球蛋白的异质性超突变景观
Learning the heterogeneous hypermutation landscape of immunoglobulins from high-throughput repertoire data
论文作者
论文摘要
在亲和力成熟期间发生的免疫球蛋白（Ig）基因的体细胞过度驱动B细胞受体与其抗原靶标有强结合的能力。这些突变的景观是高度异质的，IG基因的某些区域优先针对靶向。但是，由于序列与选择性力的干扰之间的系统发育相关性，因此对这种偏见的严格定量很困难。在这里，我们提出了一种可以纠正这些问题的方法，并利用它从最近发表的大型IGH曲目数据集中学习了超称偏好模型。所获得的模型以可重复的方式准确预测突变曲线，包括在先前未表征的互补性确定区域3中，表明突变的序列上下文及其沿基因的绝对位置都很重要。此外，我们表明，沿B细胞谱系同时发生的高温倾向于共定位，这表明可能是加速亲和力成熟的可能机制。
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.
