A Bayesian phylogenetic hidden Markov model for B cell receptor sequence analysis

The human body generates a diverse set of high affinity antibodies, the soluble form of B cell receptors (BCRs), that bind to and neutralize invading pathogens. The natural development of BCRs must be understood in order to design vaccines for highly mutable pathogens such as influenza and HIV. BCR diversity is induced by naturally occurring combinatorial “V(D)J” rearrangement, mutation, and selection processes. Most current methods for BCR sequence analysis focus on separately modeling the above processes. Statistical phylogenetic methods are often used to model the mutational dynamics of BCR sequence data, but these techniques do not consider all the complexities associated with B cell diversification such as the V(D)J rearrangement process. In particular, standard phylogenetic approaches assume the DNA bases of the progenitor (or “naive”) sequence arise independently and according to the same distribution, ignoring the complexities of V(D)J rearrangement. In this paper, we introduce a novel approach to Bayesian phylogenetic inference for BCR sequences that is based on a phylogenetic hidden Markov model (phylo-HMM). This technique not only integrates a naive rearrangement model with a phylogenetic model for BCR sequence evolution but also naturally accounts for uncertainty in all unobserved variables, including the phylogenetic tree, via posterior distribution sampling.Author summary: Rational vaccine design efforts depend on accurate inference of full evolutionary paths from a given naive sequence to the corresponding mature B cell receptor sequences in a germinal center. Before one can perform ancestral sequence inference for clonal sequences that result from the same naive rearrangement event, one must first obtain an estimate of the clonal phylogenetic tree. Currently, standard phylogenetic inference techniques are used to model the process of sequence evolution along the tree; however, these methods do not account for all the complexities associated with this evolutionary process. In this paper, we propose a Bayesian approach to phylogenetic inference for clonal sequences that is based on a phylogenetic hidden Markov model. Our phylo-HMM models both the naive rearrangement and somatic hypermutation processes and this Bayesian framework allows us to naturally account for uncertainty in all unobserved variables, including a phylogenetic tree, via posterior distribution sampling. We perform simulation-based experiments to show that naive sequence and phylogenetic inference performed jointly provides higher-quality estimates than those obtained by considering these inferences separately. Our application to real data reveals significant uncertainty in naive and ancestral sequences, confirming the importance of a Bayesian approach.