Quantifying selection in immune receptor repertoires

Abstract

The efficient recognition of pathogens by the adaptive immune system relies on the diversity of receptors displayed at the surface of immune cells. T-cell receptor diversity results from an initial random DNA editing process, called VDJ recombination, followed by functional selection of cells according to the interaction of their surface receptors with self and foreign antigenic peptides. Using high-throughput sequence data from the β-chain of human T-cell receptors, we infer factors that quantify the overall effect of selection on the elements of receptor sequence composition: the V and J gene choice and the length and amino acid composition of the variable region. We find a significant correlation between biases induced by VDJ recombination and our inferred selection factors together with a reduction of diversity during selection. Both effects suggest that natural selection acting on the recombination process has anticipated the selection pressures experienced during somatic evolution. The inferred selection factors differ little between donors or between naive and memory repertoires. The number of sequences shared between donors is well-predicted by our model, indicating a stochastic origin of such public sequences. Our approach is based on a probabilistic maximum likelihood method, which is necessary to disentangle the effects of selection from biases inherent in the recombination process.T he T-cell response of the adaptive immune system begins when receptor proteins on the surface of these cells recognize a pathogen peptide displayed by an antigen-presenting cell. The immune cell repertoire of a given individual is comprised of many clones, each with a distinct surface receptor. This diversity, which is central to the ability of the immune system to defeat pathogens, is initially created by a stochastic process of germline DNA editing (called VDJ recombination) that gives each new immune cell a unique surface receptor gene. This initial repertoire is subsequently modified by selective forces, including nonpathogen-related thymic selection against excessive (or insufficient) recognition of self proteins, which are also stochastic in nature. Because of this stochasticity and the large T-cell diversity, these repertoires are best described by probability distributions. In this paper, we apply a probabilistic approach to sequence data to obtain quantitative measures of the overall (not necessarily pathogenic) selection pressures that shape T-cell receptor repertoires.New receptor genes are formed by randomly choosing alleles from a set of genomic templates for the subregions (V, D, and J) of the complete gene. Insertion and deletion of nucleotides in the junctional regions between the V and D and D and J genes greatly enhance diversity beyond pure VDJ combinatorics (1). The most variable region of the gene is between the last amino acids of the V segment and the beginning of the J segment; it codes for the Complementarity Determining Region 3 (CDR3) loop of the receptor protein, a region known to be functional...

Predicting the spectrum of TCR repertoire sharing with a data‐driven model of recombination

“…This effect is reproduced in detail by the model calculations. This result generalizes previous observations that the number of shared TCRs between a pair of individuals should scale approximately with the product of the numbers of unique TCRs in each sample20, 21, 26, 43 to arbitrary sharing numbers.…”

“…We have found that an overall selection factor is needed to predict sharing numbers correctly, but this simple and effective model is sequence independent. Previous work20 inferred a sequence‐specific selection process by comparing generation model results to observed sequences. In principle, such a model could be combined within our framework to yield refined sharing predictions.…”

“…The data sequences, however, have passed thymic and possibly other kinds of peripheral selection, affecting their statistics. The sequence‐dependent nature of this effect was characterized and quantified in a previous work,20 with the general finding that selection favors sequences with high generation probability. This is qualitatively consistent with the positive sign of the shift (solid lines vs dotted lines) we see in Figure 5.…”

“…Our sharing calculations ignore any possible sequence dependence of selection, and instead selects TCRs at random (with probability q ), regardless of their sequence identity. The model prediction could in principle be improved by adding sequence‐dependent selection factors to match the distributions as was done previously 20. However, unlike the recombination model, such factors are expected to be specific to each individual, owing to their unique HLA type which is involved in thymic selection.…”

“…In this hypothesis, shared TCRs are simply those that have a higher‐than‐average generation probability and are thus more abundant in the unselected repertoire 13. The advent of high‐throughput sequencing of TCR repertoires14, 15, 16, 17 has largely confirmed this view through the analysis of shared TCR sequences between unrelated humans,18, 19, 20 monozygous human twins,21, 22 and mice 23. However, despite recent efforts to characterize the landscape of public TCRs,24 the contributions of V(D)J generation biases and convergent recombination relative to convergent selection remain to be elucidated and quantified.…”