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The human naive T-cell receptor (TCR) repertoire is extremely diverse and accurately estimating its distribution is challenging. We address this challenge by combining a quantitative sequencing protocol of TCRA and TCRB sequences with computational modelling. We observed the vast majority of TCR chains only once in our samples, confirming the enormous diversity of the naive repertoire. However, a substantial number of sequences were observed multiple times within samples, and we demonstrated that this is due to expression by many cells in the naive pool. We reason that α and β chains are frequently observed due to a combination of selective processes and summation over multiple clones expressing these chains. We test the contribution of both mechanisms by predicting samples from phenomenological and mechanistically modelled repertoire distributions. By comparing these with sequencing data, we show that frequently observed chains are likely to be derived from multiple clones. Still, a neutral model of T-cell homeostasis cannot account for the observed distributions. We conclude that the data are only compatible with distributions of many small clones in combination with a sufficient number of very large naive T-cell clones, the latter most likely as a result of peripheral selection.

Motivation: High Throughput Sequencing (HTS) has enabled researchers to probe the human T cell receptor (TCR) repertoire, which consists of many rare sequences. Distinguishing between true but rare TCR sequences and variants generated by polymerase chain reaction (PCR) and sequencing errors remains a formidable challenge. The conventional approach to handle errors is to remove low quality reads, and/or rare TCR sequences. Such filtering discards a large number of true and often rare TCR sequences. However, accurate identification and quantification of rare TCR sequences is essential for repertoire diversity estimation. Results: We devised a pipeline, called Recover TCR (RTCR), that accurately recovers TCR sequences, including rare TCR sequences, from HTS data (including barcoded data) even at low coverage. RTCR employs a data-driven statistical model to rectify PCR and sequencing errors in an adaptive manner. Using simulations, we demonstrate that RTCR can easily adapt to the error profiles of different types of sequencers and exhibits consistently high recall and high precision even at low coverages where other pipelines perform poorly. Using published real data, we show that RTCR accurately resolves sequencing errors and outperforms all other pipelines. Availability and Implementation: The RTCR pipeline is implemented in Python (v2.7) and C and is freely available at http://uubram.github.io/RTCR/along with documentation and examples of typical usage. Contact: b.gerritsen@uu.nl

Immunodominance describes a phenomenon whereby the immune system consistently targets only a fraction of the available Ag pool derived from a given pathogen. In the case of CD8+ T cells, these constrained epitope-targeting patterns are linked to HLA class I expression and determine disease progression. Despite the biological importance of these predetermined response hierarchies, little is known about the factors that control immunodominance in vivo. In this study, we conducted an extensive analysis of CD8+ T cell responses restricted by a single HLA class I molecule to evaluate the mechanisms that contribute to epitope-targeting frequency and antiviral efficacy in HIV-1 infection. A clear immunodominance hierarchy was observed across 20 epitopes restricted by HLA-B*42:01, which is highly prevalent in populations of African origin. Moreover, in line with previous studies, Gag-specific responses and targeting breadth were associated with lower viral load set-points. However, peptide–HLA-B*42:01 binding affinity and stability were not significantly linked with targeting frequencies. Instead, immunodominance correlated with epitope-specific usage of public TCRs, defined as amino acid residue–identical TRB sequences that occur in multiple individuals. Collectively, these results provide important insights into a potential link between shared TCR recruitment, immunodominance, and antiviral efficacy in a major human infection.

MAIT cells can discriminate between pathogen-derived ligands in a clonotype-dependent manner, and the TCR repertoire is distinct within individuals, indicating that the MAIT cell repertoire is shaped by prior microbial exposure., Mucosal-associated invariant T (MAIT) cells express a semi-invariant T cell receptor (TCR) that detects microbial metabolites presented by the nonpolymorphic major histocompatibility complex (MHC)–like molecule MR1. The highly conserved nature of MR1 in conjunction with biased MAIT TCRα chain usage is widely thought to indicate limited ligand presentation and discrimination within a pattern-like recognition system. Here, we evaluated the TCR repertoire of MAIT cells responsive to three classes of microbes. Substantial diversity and heterogeneity were apparent across the functional MAIT cell repertoire as a whole, especially for TCRβ chain sequences. Moreover, different pathogen-specific responses were characterized by distinct TCR usage, both between and within individuals, suggesting that MAIT cell adaptation was a direct consequence of exposure to various exogenous MR1-restricted epitopes. In line with this interpretation, MAIT cell clones with distinct TCRs responded differentially to a riboflavin metabolite. These results suggest that MAIT cells can discriminate between pathogen-derived ligands in a clonotype-dependent manner, providing a basis for adaptive memory via recruitment of specific repertoires shaped by microbial exposure.