Tracking global changes induced in the CD4 T cell receptor repertoire by immunisation with a complex antigen using local sequence features of CDR3 protein sequence

Abstract

Motivation: The clonal theory of adaptive immunity proposes that immunological responses are encoded by increases in the frequency of lymphocytes carrying antigen-specific receptors. In this study, we measure the frequency of different T-cell receptors (TcR) in CD4 + T cell populations of mice immunized with a complex antigen, killed Mycobacterium tuberculosis, using high throughput parallel sequencing of the TcR chain. Our initial hypothesis that immunization would induce repertoire convergence proved to be incorrect, and therefore an alternative approach was developed that allows accurate stratification of TcR repertoires and provides novel insights into the nature of CD4 + T-cell receptor recognition. Results: To track the changes induced by immunization within this heterogeneous repertoire, the sequence data were classified by counting the frequency of different clusters of short (3 or 4) continuous stretches of amino acids within the antigen binding complementarity determining region 3 (CDR3) repertoire of different mice. Both unsupervised (hierarchical clustering) and supervised (support vector machine) analyses of these different distributions of sequence clusters differentiated between immunized and unimmunized mice with 100% efficiency. The CD4 + TcR repertoires of mice 5 and 14 days postimmunization were clearly different from that of unimmunized mice but were not distinguishable from each other. However, the repertoires of mice 60 days postimmunization were distinct both from naive mice and the day 5/14 animals. Our results reinforce the remarkable diversity of the TcR repertoire, resulting in many diverse private TcRs contributing to the T-cell response even in genetically identical mice responding to the same antigen. However, specific motifs defined by short stretches of amino acids within the CDR3 region may determine TcR specificity and define a new approach to TcR sequence classification. Availability and implementation: The analysis was implemented in R and Python, and source code can be found in Supplementary Data.

A compact vocabulary of paratope-epitope interactions enables predictability of antibody-antigen binding

“…Adaptive immune receptor repertoires represent a major target area for the application of machine learning in the hope that it may fast-track the in silico discovery and development of immunereceptor based immunotherapies and immunodiagnostics (Brown et al, 2019;Greiff et al, 2012;Mason et al, 2018Mason et al, , 2019Miho et al, 2018). The complexity of sequence dependencies that determine antigen binding (Dash et al, 2017;Glanville et al, 2017), immune receptor publicity (Greiff et al, 2017b) and immune status (immunodiagnostics) (Ostmeyer et al, 2019;Thomas et al, 2014) represent a perfect application ground for machine learning analysis (Arora et al, 2019;Cinelli et al, 2017;Greiff et al, 2017b;Liu et al, 2019;Mason et al, 2019;Sidhom et al, 2018;Sun et al, 2017). As discussed extensively in a recent literature review by us (Brown et al, 2019) the development of ML approaches for immune receptor datasets was and is still hampered by the lack of ground truth datasets.…”

TCRβ repertoire of CD4+ and CD8+ T cells is distinct in richness, distribution, and CDR3 amino acid composition

“…One hundred rounds of bootstrap were performed to generate a confidence interval. This methodology is derived from the methods first used by Thomas et al [27].…”

“…To determine further if there are characteristics to distinguish CDR3s between CD4 + and CD8 + T cells, we developed a machine-learning approach inspired by the methodology used by Thomas et al [27]. We first converted the amino acid sequences to numerical arrays made of Atchley factors, which are 5 unique numbers encoding the most important chemical and physical properties regarding each amino acid [25].…”

Some deterministic and stochastic mathematical models of naïve T‐cell homeostasis

“…77,84 Once the total number of T cells, or of a subset defined by naïve or memory surface markers, is established, we may ask how many TCR clonotypes there are, in each subset, in humans, mice and other mammals. [85][86][87][88] Estimates of the number of different TCRs that could, in principle, be produced by VDJ gene rearrangement in the thymus [89][90][91][92] far exceed the total number of T cells in a mouse or human body. 75,93 With the latest developments in sequencing techniques, is a direct count of the number of clonotypes in a mammal feasible?…”