T cell receptor beta germline variability is revealed by inference from repertoire data

Abstract

Background T and B cell receptor (TCR, BCR) repertoires constitute the foundation of adaptive immunity. Adaptive immune receptor repertoire sequencing (AIRR-seq) is a common approach to study immune system dynamics. Understanding the genetic factors influencing the composition and dynamics of these repertoires is of major scientific and clinical importance. The chromosomal loci encoding for the variable regions of TCRs and BCRs are challenging to decipher due to repetitive elements and undocumented structural variants. Methods To confront this challenge, AIRR-seq-based methods have recently been developed for B cells, enabling genotype and haplotype inference and discovery of undocumented alleles. However, this approach relies on complete coverage of the receptors’ variable regions, whereas most T cell studies sequence a small fraction of that region. Here, we adapted a B cell pipeline for undocumented alleles, genotype, and haplotype inference for full and partial AIRR-seq TCR data sets. The pipeline also deals with gene assignment ambiguities, which is especially important in the analysis of data sets of partial sequences. Results From the full and partial AIRR-seq TCR data sets, we identified 39 undocumented polymorphisms in T cell receptor Beta V (TRBV) and 31 undocumented 5 ′ UTR sequences. A subset of these inferences was also observed using independent genomic approaches. We found that a single nucleotide polymorphism differentiating between the two documented T cell receptor Beta D2 (TRBD2) alleles is strongly associated with dramatic changes in the expressed repertoire. Conclusions We reveal a rich picture of germline variability and demonstrate how a single nucleotide polymorphism dramatically affects the composition of the whole repertoire. Our findings provide a basis for annotation of TCR repertoires for future basic and clinical studies.

Targeted long-read sequencing facilitates phased diploid assembly and genotyping of the human T cell receptor alpha, delta and beta loci

“…The fact that we identified 79 undocumented (non-IMGT) alleles in only 9 unrelated individuals highlights the severe deficits that currently exist in germline databases. Considering our findings alongside other recent efforts underlines the work that remains to be done to fully catalog TR gene diversity in the human population (9,26). At present, the impact that these missing germline genes and alleles has on the analysis and interpretation of TCR repertoire sequencing studies is not clear, but would be expected to have profound effects on germline gene/allele assignment efforts, similar to what has been noted for IG repertoires.…”

“…A critical step in analyzing TCR repertoire sequencing data is the alignment of reads to a TCR germline gene database in order to identify the V, D and J allele present in a given read. Therefore, it is important to utilize a complete and accurate allele database (9). To determine the extent to which targeted long-read capture sequencing can help complete TCR germline databases, we first genotyped the alleles in both trios to measure the genotyping accuracy and then genotyped the remaining samples (Figure 5; Supplementary Table 1).…”

“…Specifically in the IG heavy chain locus (IGH), we have previously demonstrated that the use of NGS results in high rates of false positive and negative variants (17). Because of these technical barriers we have not characterized the full extent of genetic diversity in these immune receptor loci (9), limiting our understanding of the role of germline variation in TCR function.…”

Combining genotypes and T cell receptor distributions to infer genetic loci determining V(D)J recombination probabilities

“…Previous work has suggested that polymorphisms within the immunoglobulin V-gene region are not completely captured by existing SNP array technology, and have been underrepresented in previous genome-wide association studies ( Watson and Breden, 2012 ). SNP coverage of the TCRβ locus is thought to be even sparser ( Omer et al, 2022 ), and thus, much of the actual TCRβ variation present within our data cohort is likely not captured by the SNP dataset used here (which contains 7,304 SNPs within the TRB locus, hg19:chr7:141950000–142550000). Lastly, we have used the recombination statistics from non-productive rearrangements here as a means of studying the V(D)J recombination generation process in the absence of selection; however, we acknowledge that the repertoire of non-productive rearrangements may be an imperfect proxy for a pre-selection TCR repertoire.…”

“…The TCRA and TCRB regions have been historically difficult to reliably map using short read sequencing due to their repetitive and complex nature. While recent work has identified many new TRBV alleles, many more undocumented TRBV alleles likely remain to be discovered ( Omer et al, 2022 ). As such, the incomplete characterization of the TCRB locus limited our ability to infer the correct V(D)J -gene allele for each final nucleotide sequence.…”