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Proc Natl Acad Sci U S A. 2014 Jul 8;111(27):9875-80. doi: 10.1073/pnas.1409572111. Epub 2014 Jun 18.

Quantifying selection in immune receptor repertoires.

Author information

1
Laboratoire de Physique Théorique, Unité Mixte de Recherche 8549 and.
2
Department of Applied Physics, Stanford University, Stanford, CA 94305; and.
3
Joseph Henry Laboratories, Princeton University, Princeton, NJ 08544 ccallan@princeton.edu.
4
Laboratoire de Physique Statistique, Unité Mixte de Recherche 8550, Centre National de la Recherche Scientifique and École Normale Supérieure, 75005 Paris, France;

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.

KEYWORDS:

T cell; public repertoire; statistical inference; thymic selection

PMID:
24941953
PMCID:
PMC4103359
DOI:
10.1073/pnas.1409572111
[Indexed for MEDLINE]
Free PMC Article

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