bioRxivImmunology
Repertoire-wide phylogenetic models of B cell molecular evolution reveal evolutionary signatures of aging and vaccination
K. B. Hoehn, J. A. Vander Heiden, J. Q. Zhou, G. Lunter, O. G. Pybus, S. H. Kleinstein
Abstract
In order to produce effective antibodies, B cells undergo rapid somatic hypermutation (SHM) and selection for binding affinity to antigen via a process called affinity maturation. The similarities between this process and evolution by natural selection have led many groups to use evolutionary and phylogenetic methods to characterize the development of immunological memory, vaccination, and other processes that depend on affinity maturation. However, these applications are limited by several features of affinity maturation that violate assumptions in standard phylogenetic models. Further, most phylogenetic models are designed to be applied to individual lineages comprising genetically diverse sequences, while B cell repertoires often consist of hundreds to thousands of separate low-diversity lineages. Here, we introduce a hierarchical phylogenetic framework that incorporates the unique features of SHM, and integrates information from all lineages in a repertoire to more precisely estimate model parameters. We demonstrate the power of this approach by characterizing previously un-described phenomena in affinity maturation. First, we find evidence consistent with age related changes in SHM hot-and cold-spot motifs. Second, we identify a consistent relationship between increased tree length and signs of increased negative selection, apparent in the repertoires of both healthy subjects and those undergoing active immune responses. This suggests that B cell lineages shift towards negative selection over time as a general feature of affinity maturation. Our study provides a framework for undertaking repertoire-wide phylogenetic testing of SHM hypotheses, and provides a new means of charactering the process of mutation and selection during affinity maturation.
- organism: human
- assay: scRNA-seq
- research focus: chromatin & epigenetic regulation
- approach: deep learning
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