Abstract
MotivationSomatic DNA recombination, the hallmark of vertebrate adaptive immunity, has the potential to generate a vast diversity of antigen receptor sequences. How this diversity captures antigen specificity remains incompletely understood. In this study we use high throughput sequencing to compare the global changes in T cell receptor β chain complementarity determining region 3 (CDR3β) sequences following immunization with ovalbumin administered with complete Freund’s adjuvant (CFA) or CFA alone.ResultsThe CDR3β sequences were deconstructed into short stretches of overlapping contiguous amino acids. The motifs were ranked according to a one-dimensional Bayesian classifier score comparing their frequency in the repertoires of the two immunization classes. The top ranking motifs were selected and used to create feature vectors which were used to train a support vector machine. The support vector machine achieved high classification scores in a leave-one-out validation test reaching >90% in some cases.SummaryThe study describes a novel two-stage classification strategy combining a one-dimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund’s Adjuvant.Availability and implementationThe sequence data is available at www.ncbi.nlm.nih.gov/sra/?term¼SRP075893. The Decombinator package is available at github.com/innate2adaptive/Decombinator. The R package e1071 is available at the CRAN repository https://cran.r-project.org/web/packages/e1071/index.html.Supplementary information Supplementary data are available at Bioinformatics online.
Highlights
We have previously used short read parallel high-throughput sequencing (HTS) to estimate T cell receptor b transcript frequencies and sharing (Madi et al, 2014; Ndifon et al, 2012), and to explore the global changes in the CD4þ T cell receptor repertoire following immunization of mice (Thomas et al, 2014)
Summary: The study describes a novel two-stage classification strategy combining a onedimensional Bayesian classifier with a support vector machine. Using this approach we demonstrate that the frequency of a small number of linear motifs three amino acids in length can accurately identify a CD4 T cell response to ovalbumin against a background response to the complex mixture of antigens which characterize Complete Freund’s Adjuvant
We mapped the sets of TCRb CDR3 sequences from each animal to a lower dimensional feature space indexed by short stretches of contiguous amino acids
Summary
We have previously used short read parallel high-throughput sequencing (HTS) to estimate T cell receptor b transcript frequencies and sharing (Madi et al, 2014; Ndifon et al, 2012), and to explore the global changes in the CD4þ T cell receptor repertoire following immunization of mice (Thomas et al, 2014). We mapped the sets of TCRb CDR3 sequences from each animal to a lower dimensional feature space indexed by short stretches of contiguous amino acids (typically triplets). Classical regularized machine learning algorithms (e.g. Support Vector Machines) were able to distinguish between TCR repertoires of unimmunized mice and mice immunized with an extract of Mycobacterium tuberculosis (Complete Freund’s Adjuvant, CFA) within the lower dimensional transformed feature space. These studies suggested that short amino acid motifs within the TCRb CDR3 region might contribute to defining TCR specificity
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