Statistical inference of the generation probability of T-cell receptors from sequence repertoires

TL;DR

This paper develops a statistical inference method to quantify the probability of generating specific T-cell receptor sequences, revealing insights into immune diversity and the underlying biochemical mechanisms across individuals.

Contribution

It introduces a maximum likelihood inference approach to deduce the biochemical event distributions of T-cell receptor generation from sequence data, separating molecular mechanisms from selection effects.

Findings

The generative process is consistent across individuals.

The inferred distribution predicts sequence generation probabilities.

Shared sequences between individuals can be explained by generation probabilities.

Abstract

Stochastic rearrangement of germline DNA by VDJ recombination is at the origin of immune system diversity. This process is implemented via a series of stochastic molecular events involving gene choices and random nucleotide insertions between, and deletions from, genes. We use large sequence repertoires of the variable CDR3 region of human CD4+ T-cell receptor beta chains to infer the statistical properties of these basic biochemical events. Since any given CDR3 sequence can be produced in multiple ways, the probability distribution of hidden recombination events cannot be inferred directly from the observed sequences; we therefore develop a maximum likelihood inference method to achieve this end. To separate the properties of the molecular rearrangement mechanism from the effects of selection, we focus on non-productive CDR3 sequences in T-cell DNA. We infer the joint distribution of the various generative events that occur when a new T-cell receptor gene is created. We find a rich picture of correlation (and absence thereof), providing insight into the molecular mechanisms involved. The generative event statistics are consistent between individuals, suggesting a universal biochemical process. Our distribution predicts the generation probability of any specific CDR3 sequence by the primitive recombination process, allowing us to quantify the potential diversity of the T-cell repertoire and to understand why some sequences are shared between individuals. We argue that the use of formal statistical inference methods, of the kind presented in this paper, will be essential for quantitative understanding of the generation and evolution of diversity in the adaptive immune system.