Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data

Abstract

Analyses of somatic hypermutation (SHM) patterns in B cell immunoglobulin (Ig) sequences contribute to our basic understanding of adaptive immunity, and have broad applications not only for understanding the immune response to pathogens, but also to determining the role of SHM in autoimmunity and B cell cancers. Although stochastic, SHM displays intrinsic biases that can confound statistical analysis, especially when combined with the particular codon usage and base composition in Ig sequences. Analysis of B cell clonal expansion, diversification, and selection processes thus critically depends on an accurate background model for SHM micro-sequence targeting (i.e., hot/cold-spots) and nucleotide substitution. Existing models are based on small numbers of sequences/mutations, in part because they depend on data from non-coding regions or non-functional sequences to remove the confounding influences of selection. Here, we combine high-throughput Ig sequencing with new computational analysis methods to produce improved models of SHM targeting and substitution that are based only on synonymous mutations, and are thus independent of selection. The resulting “S5F” models are based on 806,860 Synonymous mutations in 5-mer motifs from 1,145,182 Functional sequences and account for dependencies on the adjacent four nucleotides (two bases upstream and downstream of the mutation). The estimated profiles can explain almost half of the variance in observed mutation patterns, and clearly show that both mutation targeting and substitution are significantly influenced by neighboring bases. While mutability and substitution profiles were highly conserved across individuals, the variability across motifs was found to be much larger than previously estimated. The model and method source code are made available at

Automated analysis of high-throughput B-cell sequencing data reveals a high frequency of novel immunoglobulin V gene segment alleles

“…In this case, six positions were mutated in >30% of the sequences. Four of these positions occurred at classic WRC/GYW mutation hotspots, and there was a mild overall correlation between the predicted mutability [according to the S5F targeting model (25)] and the observed mutation frequency (R = 0.551) (Fig. 2, Right).…”

“…Samples coincide with those used by a previous study (25), and were originally collected and sequenced as part of two other studies (28,29). Sequencing results were preprocessed to remove low-quality reads, annotate and mask primers, assemble paired-end reads, and remove duplicate sequences using the Repertoire Sequencing Toolkit (pRESTO) (30) (clip.med.yale.edu/presto) as previously described (25).…”

“…Using the experimental data from subject hu420143, S m was defined as the number of sequences with an IGHV mutation count of m. For each m ∈ {0,1,. . .,10}, simulations were used to generate S m sequences, each carrying m mutations, based on the method described in previous work (35) with the "S5F" SHM targeting and substitution models (25). To investigate the mutation pattern in a homozygous polymorphic allele, the starting sequence for the simulations was a novel allele (IGHV1-2*02_T163C) that was created by introducing a single nucleotide substitution (T → C at position 163) into a known germline IGHV gene segment (IGHV1-2*02).…”

“…Although application of such filtering-based methods have successfully identified novel alleles (19), the resulting predictions require manual curation, and their sensitivity and specificity have not been evaluated. It is unclear whether this approach can distinguish polymorphic positions from SHM hot-spots, which can be mutated in >40% of sequences (25,26). Here we present a Tool for Immunoglobulin Genotype Elucidation via Rep-Seq (TIg-GER).…”

Antigen Receptor Galaxy: A User-Friendly, Web-Based Tool for Analysis and Visualization of T and B Cell Receptor Repertoire Data

“…Therefore, two sequences with the same nucleotide sequence but different subclasses will be both included in the analysis. This option can be used to differentiate between true SHM and sequencing errors as the likelihood of a sequencing error occurring twice in the exact same location in the same sequence is really small (27). When selecting the "keep unique" option, all duplicates are removed based on the combination of the aboveselected region and the subclass.…”