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B 细胞库的分支随机进化模型。

A branching stochastic evolutionary model of the B-cell repertoire.

机构信息

Biostatistics, Bioinformatics, Epidemiology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.

Department of Operations Research, Probability and Statistics, Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria.

出版信息

J Math Biol. 2024 Jun 7;89(1):10. doi: 10.1007/s00285-024-02102-y.

Abstract

We propose a stochastic framework to describe the evolution of the B-cell repertoire during germinal center (GC) reactions. Our model is formulated as a multitype age-dependent branching process with time-varying immigration. The immigration process captures the mechanism by which founder B cells initiate clones by gradually seeding GC over time, while the branching process describes the temporal evolution of the composition of these clones. The model assigns a type to each cell to represent attributes of interest. Examples of attributes include the binding affinity class of the B cells, their clonal family, or the nucleotide sequence of the heavy and light chains of their receptors. The process is generally non-Markovian. We present its properties, including as when the process is supercritical, the most relevant case to study expansion of GC B cells. We introduce temporal alpha and beta diversity indices for multitype branching processes. We focus on the dynamics of clonal dominance, highlighting its non-stationarity, and the accumulation of somatic hypermutations in the context of sequential immunization. We evaluate the impact of the ongoing seeding of GC by founder B cells on the dynamics of the B-cell repertoire, and quantify the effect of precursor frequency and antigen availability on the timing of GC entry. An application of the model illustrates how it may help with interpretation of BCR sequencing data.

摘要

我们提出了一个随机框架来描述生发中心 (GC) 反应期间 B 细胞受体库的演变。我们的模型被构造成一个具有时变移民的多类型时变分支过程。移民过程捕捉了创始 B 细胞通过逐渐在 GC 中播种来启动克隆的机制,而分支过程描述了这些克隆组成的时间演变。该模型为每个细胞分配一个类型,以表示感兴趣的属性。属性的示例包括 B 细胞的结合亲和力类别、它们的克隆家族,或其受体的重链和轻链的核苷酸序列。该过程通常是非马尔可夫过程。我们介绍了其性质,包括当过程是超临界时,这是研究 GC B 细胞扩张的最相关情况。我们为多类型分支过程引入了时间 alpha 和 beta 多样性指数。我们专注于克隆优势的动态,突出其非平稳性,以及在连续免疫接种背景下体细胞超突变的积累。我们评估了创始 B 细胞不断播种 GC 对 B 细胞受体库动态的影响,并量化了前体频率和抗原可用性对 GC 进入时间的影响。模型的一个应用说明了它如何帮助解释 BCR 测序数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1be7/11161549/7d05a3d94b03/285_2024_2102_Fig1_HTML.jpg

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