How heterogeneous thymic output and homeostatic proliferation shape naive T cell receptor clone abundance distributions

Abstract

The set of T cells that express the same T cell receptor (TCR) sequence represent a T cell clone. The number of different naive T cell clones in an organism reflects the number of different T cell receptors (TCRs) arising from recombination of the V(D)J gene segments during T cell development in the thymus. TCR diversity and more specifically, the clone abundance distribution is an important factor in immune function. Specific recombination patterns occur more frequently than others while subsequent interactions between TCRs and self-antigens are known to trigger proliferation and sustain naive T cell survival. These processes are TCR-dependent, leading to clone-dependent thymic export and naive T cell proliferation rates. Using a mean-field approximation to the solution of a regulated birth-death-immigration model, we systematically quantify how TCR-dependent heterogeneities in immigration and proliferation rates affect the shape of clone abundance distributions (the number of different clones that are represented by a specific number of cells). By comparing predicted clone abundances derived from our heterogeneous birth-death-immigration model with experimentally sampled clone abundances, we quantify the heterogeneity necessary to generate the observed abundances. Our findings indicate that heterogeneity in proliferation rates is more likely the mechanism underlying the observed clone abundance distributions than heterogeneity in immigration rates. Author SummaryThe abundance distribution of different T cell receptors (TCRs) expressed on naive T cells depends on their rates of thymic output, homeostatic proliferation, and death. However, measured TCR count distributions do not match, even qualitatively, those predicted from a multiclone birth death-immigration process when constant birth, death, and immigration rates are used (a neutral model). We show how non-neutrality in the birth-death-immigration process, where naive T cells with different TCRs are produced and proliferate with a distribution of rates shape the predicted sampled clone abundance distributions (the clone counts). Using physiological parameters, we find that heterogeneity in proliferation rates, and not in thymic output rates, is the main determinant in generating the observed clone counts. These findings are consistent with proliferation-driven maintenance of the T cell population in humans.

Diversity in biology: definitions, quantification and models

“…Proliferation in the periphery depends on interactions between self-peptides with T cell receptors and is thus clone-dependent. Recently, it has been shown that TCR-dependent thymic output and proliferation rates (a nonneutral BDI model) influence the measured clone count patterns [155]. These processes form and maintain a diverse T cell receptor repertoire, which is usually characterized by its richness.…”

Early life imprints the hierarchy of T cell clone sizes

“…However, while the TCR locus provides a natural barcode for clonal lineages due to its large diversity, this same diversity also makes inferring past clonal dynamics a challenging inverse problem, in particular given practical limitations on sequencing depth and temporal resolution in longitudinal studies. Mathematical modeling can help address this challenge by solving the forward problem of linking clonal dynamics to emergent statistical patterns ( Desponds et al, 2016 ; Lythe et al, 2016 ; Dessalles et al, 2019 ; Altan-Bonnet et al, 2020 ; de Greef et al, 2020 ). Comparing patterns to data can provide insights about dynamics from static snapshots of repertoire organization in different individuals.…”

Understanding Adaptive Immune System as Reinforcement Learning

“…This coincidence implies that a simple mechanism underlies the generation of power-law like distributions irrespective of the details of the dynamics. In previous theoretical analyses, the symmetric variation in the fitness of clones was proposed as the mechanism of the power-law distribution [54,55]. Similar symmetric fitness variations The dynamics of the rank-abundance distributions along the learning trial, which were calculated from the trajectories of n in (A).…”