Chromatin conformation governs T-cell receptor Jβ gene segment usage

Abstract

T cells play fundamental roles in adaptive immunity, relying on a diverse repertoire of T-cell receptor (TCR) α and β chains. Diversity of the TCR β chain is generated in part by a random yet intrinsically biased combinatorial rearrangement of variable (Vβ), diversity (Dβ), and joining (Jβ) gene segments. The mechanisms that determine biases in gene segment use remain unclear. Here we show, using a high-throughput TCR sequencing approach, that a physical model of chromatin conformation at the DJβ genomic locus explains more than 80% of the biases in Jβ use that we measured in murine T cells. This model also predicts correctly how differences in intersegment genomic distances between humans and mice translate into differences in Jβ bias between TCR repertoires of these two species. As a consequence of these structural and other biases, TCR sequences are produced with different a priori frequencies, thus affecting their probability of becoming public TCRs that are shared among individuals. Surprisingly, we find that many more TCR sequences are shared among all five mice we studied than among only subgroups of three or four mice. We derive a necessary mathematical condition explaining this finding, which indicates that the TCR repertoire contains a core set of receptor sequences that are highly abundant among individuals, if their a priori probability of being produced by the recombination process is higher than a defined threshold. Our results provide evidence for an expanded role of chromatin conformation in VDJ rearrangement, from control of gene accessibility to precise determination of gene segment use.lymphocyte receptor repertoires | public T-cell clones | VDJ recombination | epigenetics | next generation sequencing

The Hayflick Limit May Determine the Effective Clonal Diversity of Naive T Cells

“…The results are consistent with experimental data (9) showing that naive CD4 T cell proliferation rate doubles after the age of 70 y coincident with a sharp decline of T cell diversity. These results, which were obtained by assuming that there is a strong bias in the production frequencies of different T cell clones (i.e., w = 0.01) consistent with empirical data (46,47,60), are similar to results obtained without such bias (Supplemental Fig. 1).…”

“…We adapted our published bioinformatics pipeline (46) to analyze a large, previously published TCR sequence database (16) (dbGap accession number phs000787.v1.p1). The data were obtained via high-throughput TCR sequencing of samples of naive CD4 and CD8 T cells isolated from the blood of nine individuals aged 20-83 y.…”

“…Simulations predict sharp decline of effective clonal diversity. We simulated the population dynamics of human naive T cells as described in the text and Materials and Methods, based on experimental estimates of the Hayflick limit (22,29), the bias in thymic production of different T-cell clones (46,47), and the rates of thymopoeisis (9,18,19,21), T cell turnover (42,43,45), and telomere attrition (31,48). We set the bias in thymic production of different T cell clones to w = 0.01.…”

“…We sample individual clones from the distribution G(i), where i, i = 1,..., S, is a clonal index, and G(i) is the expected frequency of clone i. Empirical data indicate that different clones have very different a priori production frequencies (60), with the fold difference between the highest and lowest frequencies being on the order of 10 4 (46,47). This implies that G(i) must be highly biased.…”

“…We propose that over time this dynamic will cause a chain reaction of cell loss, which will suddenly drive most T cell clones to extinction. In the following, we will demonstrate, using stochastic numerical simulations based on experimental data (18,21,22,28,29,31,38,(42)(43)(44)(45)(46)(47)(48), that this model provides a plausible explanation for the sharp decline of effective clonal diversity observed in elderly humans.…”