Critical steps for computational inference of the 3′-end of novel alleles of immunoglobulin heavy chain variable genes - illustrated by an allele of IGHV3-7

Abstract

Sequencing of immunoglobulin germline gene loci is a challenging process, e.g. due to their repetitiveness and complexity, hence limiting the insight in the germline gene repertoire of humans and other species. Through next generation sequencing technology, it is possible to generate immunoglobulin transcript data sets large enough to computationally infer the germline genes from which the transcripts originate. Multiple tools for such inference have been developed and they can be used for construction of individual germline gene databases, and for discovery of new immunoglobulin germline genes and alleles. However, there are challenges associated with these methods, many of them related to the biological process through which immunoglobulin coding genes are generated. The junctional diversity introduced during rearrangement of the immunoglobulin heavy chain variable (IGHV), diversity and joining genes specifically complicates the inference of the junction regions, with implications for inference of the 3'-end of IGHV genes. With the aim of coping with such diversity, an inference software package may not be able to identify novel alleles harbouring a difference in these regions compared to their closest relatives in the starting database. In this study, we were able to computationally infer one such previously uncharacterized allele, IGHV3-7*02 A318G. However, this was possible only if a strategy was used in which different variants of IGHV3-7*02 were included in the inference-initiating database. Importantly, the presence of the novel allele, but not the standard IGHV3-7*02 sequence, in the genotype was strongly supported by the actual sequences that were assigned to the allele. We thus showed that the starting database used will impact the germline gene inference process, and that difference in the 3'-end of IGHV genes may remain undetected unless specific, non-standard procedures are used to address this matter. We suggest that inferred genes/alleles should be confirmed e.g. by examination of the nucleotide composition of the 3'-bases of the inference-supporting sequence reads.

Polymorphisms in immunoglobulin heavy chain variable genes and their upstream regions

“…We managed to validate a number of novel alleles by targeted amplification of genomic DNA of the same individuals. In addition, we report the presence of G at position 318 instead of A in the gDNA sequence of IGHV3-7*02 , which supports the findings of previous studies 31, 32 .…”

“…Genomic validation of IGHV3-7*03_G144A revealed a further polymorphism in the intron. During validation of this allele, we also managed to amplify the genomic sequence of IGHV3-7*02 , which carried the previously reported polymorphism A318G 31 . This polymorphism was not inferred from the AIRR-seq data in our study, since the default parameters of the inference tools are set to detect polymorphisms up to position 312.…”

A comparison of immunoglobulin IGHV, IGHD and IGHJ genes in wild‐derived and classical inbred mouse strains

“…We cannot rule out the possibility that this analysis failed to identify SNVs in either the terminal 5′ or 3′ nucleotides of the IGHD sequences, though we believe this is unlikely. It has been shown that the inference process is often unable to accurately identify terminal nucleotides in adaptive immune receptor repertoire‐sequence (AIRR‐seq) data . By contrast, the antibody repertoire of the mouse is strongly shaped by joining at sequences of short homology and there may be strong evolutionary pressure maintaining, for example, the identity between the 3′ ends of the IGHD2 gene family and the 5′ ends of some IGHJ sequences, thereby preventing the development of variation in the terminal IGHD gene nucleotides.…”

Automated analysis of immunosequencing datasets reveals novel immunoglobulin D genes across diverse species

“…Safonova and Pevzner [28] recently developed the IgScout algorithm for de novo inference of D genes using immunosequencing data. Unlike algorithms for de novo inference of V and J genes [23,24], it does not rely on alignments against closest germline genes that might lead to erroneous inferences [29,30]. Instead, IgScout uses the observation that the most abundant k-mers in CDR3s arise from D genes (a k-mer refers to a string of length k).…”