Exploring the pre-immune landscape of antigen-specific T cells

Abstract

BackgroundAdaptive immune responses to newly encountered pathogens depend on the mobilization of antigen-specific clonotypes from a vastly diverse pool of naive T cells. Using recent advances in immune repertoire sequencing technologies, models of the immune receptor rearrangement process, and a database of annotated T cell receptor (TCR) sequences with known specificities, we explored the baseline frequencies of T cells specific for defined human leukocyte antigen (HLA) class I-restricted epitopes in healthy individuals.MethodsWe used a database of TCR sequences with known antigen specificities and a probabilistic TCR rearrangement model to estimate the baseline frequencies of TCRs specific to distinct antigens epitopespecificT-cells. We verified our estimates using a publicly available collection of TCR repertoires from healthy individuals. We also interrogated a database of immunogenic and non-immunogenic peptides is used to link baseline T-cell frequencies with epitope immunogenicity.ResultsOur findings revealed a high degree of variability in the prevalence of T cells specific for different antigens that could be explained by the physicochemical properties of the corresponding HLA class I-bound peptides. The occurrence of certain rearrangements was influenced by ancestry and HLA class I restriction, and umbilical cord blood samples contained higher frequencies of common pathogen-specific TCRs. We also identified a quantitative link between specific T cell frequencies and the immunogenicity of cognate epitopes presented by defined HLA class I molecules.ConclusionsOur results suggest that the population frequencies of specific T cells are strikingly non-uniform across epitopes that are known to elicit immune responses. This inference leads to a new definition of epitope immunogenicity based on specific TCR frequencies, which can be estimated with a high degree of accuracy in silico, thereby providing a novel framework to integrate computational and experimental genomics with basic and translational research efforts in the field of T cell immunology.Electronic supplementary materialThe online version of this article (10.1186/s13073-018-0577-7) contains supplementary material, which is available to authorized users.

A framework for annotation of antigen specificities in high-throughput T-cell repertoire sequencing studies

“…There are three assumptions that make it possible to detect a set of homologous TCRs that are involved in an ongoing antigen-specific response from AIRR-seq data: 1a) TCR rearrangement process follows probabilistic model of Murugan et al [23]. This assumption allows to compute the expected incidence rate of a given TCR, which is in a good agreement with observed results [15] in case a single amino acid substitution is allowed in the CDR3 sequence. 1b) As it follows from [23], average TCR sequence is extremely rare: median rearrangement probability across all TCRs is ~10 -12 .…”

“…Finally, the features of the unperturbed TCR repertoire structure itself should be considered, as the repertoire is heavily shaped by the process of VDJ rearrangement. The fact that the VDJ rearrangement process can be described by a relatively simple probabilistic model makes it possible to accurately predict population frequencies of specific TCRs [15]. However, huge differences in epitope-specific TCR frequencies can lead to the detection of potentially irrelevant high-frequency (public) TCRs in case the cohort size is relatively small as illustrated in [22].…”

“…Existing computational methods for TCR repertoire annotation allow both matching against a database of known antigen specificities [12], [13] and clustering of TCR sequences for de novo motif detection [4], [14]. Annotation of a large number of TCR repertoires from healthy donors [15], [16] demonstrates both high variance of frequencies of epitope-specific T-cells and the imprint of past and ongoing pathogen encounters. Thus, de novo discovery of Tcells associated with antigens of interest or certain disease appears to be a hard problem, complicated by the biases in the structure of the naive (unperturbed) TCR repertoire [17], presence of existing clonal expansions specific to unrelated pathogens and high number of false positives that result from the extremely high diversity of the TCR repertoire.…”

Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs

“…In general, TCR-sequencing have been commonly limited to the TCR β chain due to its greater combinatorial and junctional diversity (8) and to the fact that a single TCRβ chain can be paired with multiple TCRα chains (32). Pogorelyy et al (33) have shown concordance between TCRα and TCRβ chain frequencies specific for a given epitope and suggested this justifies the exclusive use of TCRβ sequences in analyzing the antigen-specific landscape of heterodimeric TCRs. Currently, most proposed classifiers have used only CDR3 beta chains (4, 34) but some attempts have been made to include alpha chains (16).…”

CDR3 and V- genes show distinct reconstitution patterns in T-cell repertoire post allogeneic bone marrow transplantation

“…Repertoire next-generation immune-sequencing (often denoted rep-seq) (15) supplies relatively detailed information on T cell repertoires (16)(17)(18)(19)(20) and has been applied to tracking T-cell 3 reconstitution after allo-BMT (2,3,7,(21)(22)(23). Current analytical approaches for the comparative analysis of TCR repertoires are mainly based on statistical inference and include repertoire diversity, repertoire overlap, V-gene and J-gene segments usage similarity, and amino acid composition of the CDR3 (24,25).…”