bioRxivEvolutionary Biology
Selection and neutral mutations drive pervasive mutability losses in long-lived B cell lineages
Abstract
High-affinity antibodies arise within weeks of infection from the evolution of B cell receptors under selection to improve antigen recognition. This rapid adaptation is enabled by the frequency and distribution of highly mutable \"hotspotx\" motifs in B cell receptor genes. High mutability in antigen binding regions (CDRs) creates variation in binding affinity, whereas low mutability in structurally important regions (FRs) may reduce the frequency of destabilizing mutations. During the response, the loss of mutational hotspots and changes in their distribution across CDRs and FRs are predicted to compromise the adaptability of B cell receptors, yet the contributions of different mechanisms to gains and losses of hotspots remain unclear. We reconstructed changes in anti-HIV B cell receptor sequences and show that mutability losses were about 60% more frequent than gains in both CDRs and FRs, with the higher relative mutability of CDRs maintained throughout the response. At least 34% of the mutability losses were caused by synonymous mutations. However, non-synonymous substitutions caused most of the mutability loss in CDRs. Because CDRs also show strong positive selection, this result suggests positive selection contributed to as much as 66% of the mutability loss in those regions. Although recurrent adaptation to the evolving virus could indirectly select for high mutation rates, we found no evidence of indirect selection to increase or retain hotspots. Our results suggest mutability losses are intrinsic to the neutral and adaptive evolution of B cell populations and might constrain their adaptation to rapidly evolving pathogens such as HIV and influenza.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
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