Dataset Information

Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires.

ABSTRACT: High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining.be.

SUBMITTER: Gielis S

PROVIDER: S-EPMC6896208 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires.

Gielis Sofie S Moris Pieter P Bittremieux Wout W De Neuter Nicolas N Ogunjimi Benson B Laukens Kris K Meysman Pieter P

Frontiers in immunology 20191129

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual's TCR repertoire and directly queries their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires with their epitope specificities. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. Th ...[more]

PMID: 31849987

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Project description:Inter-sample comparisons of T-cell receptor (TCR) repertoires are crucial for gaining a better understanding of the immunological states determined by different collections of T cells from different donor sites, cell types, and genetic and pathological backgrounds. For quantitative comparison, most previous studies utilized conventional methods in ecology, which focus on TCR sequences that overlap between pairwise samples. Some recent studies attempted another approach that is categorized into Poisson abundance models using the abundance distribution of observed TCR sequences. However, these methods ignore the details of the measured sequences and are consequently unable to identify sub-repertoires that might have important contributions to the observed inter-sample differences. Moreover, the sparsity of sequence data due to the huge diversity of repertoires hampers the performance of these methods, especially when few overlapping sequences exist. In this paper, we propose a new approach for REpertoire COmparison in Low Dimensions (RECOLD) based on TCR sequence information, which can estimate the low-dimensional structure by embedding the pairwise sequence dissimilarities in high-dimensional sequence space. The inter-sample differences between repertoires are then quantified by information-theoretic measures among the distributions of data estimated in the embedded space. Using datasets of mouse and human TCR repertoires, we demonstrate that RECOLD can accurately identify the inter-sample hierarchical structures, which have a good correspondence with our intuitive understanding about sample conditions. Moreover, for the dataset of transgenic mice that have strong restrictions on the diversity of their repertoires, our estimated inter-sample structure was consistent with the structure estimated by previous methods based on abundance or overlapping sequence information. For the dataset of human healthy donors and Sézary syndrome patients, our method also showed robust estimation performance even under the condition of high sparsity in TCR sequences, while previous studies failed to estimate the structure. In addition, we identified the sequences that contribute to the pairwise-sample differences between the repertoires with the different genetic backgrounds of mice. Such identification of the sequences contributing to variation in immune cell repertoires may provide substantial insight for the development of new immunotherapies and vaccines.

Project description:BACKGROUND:Melon (Cucumis melo), an economically important vegetable crop, belongs to the Cucurbitaceae family which includes several other important crops such as watermelon, cucumber, and pumpkin. It has served as a model system for sex determination and vascular biology studies. However, genomic resources currently available for melon are limited. RESULT:We constructed eleven full-length enriched and four standard cDNA libraries from fruits, flowers, leaves, roots, cotyledons, and calluses of four different melon genotypes, and generated 71,577 and 22,179 ESTs from full-length enriched and standard cDNA libraries, respectively. These ESTs, together with ~35,000 ESTs available in public domains, were assembled into 24,444 unigenes, which were extensively annotated by comparing their sequences to different protein and functional domain databases, assigning them Gene Ontology (GO) terms, and mapping them onto metabolic pathways. Comparative analysis of melon unigenes and other plant genomes revealed that 75% to 85% of melon unigenes had homologs in other dicot plants, while approximately 70% had homologs in monocot plants. The analysis also identified 6,972 gene families that were conserved across dicot and monocot plants, and 181, 1,192, and 220 gene families specific to fleshy fruit-bearing plants, the Cucurbitaceae family, and melon, respectively. Digital expression analysis identified a total of 175 tissue-specific genes, which provides a valuable gene sequence resource for future genomics and functional studies. Furthermore, we identified 4,068 simple sequence repeats (SSRs) and 3,073 single nucleotide polymorphisms (SNPs) in the melon EST collection. Finally, we obtained a total of 1,382 melon full-length transcripts through the analysis of full-length enriched cDNA clones that were sequenced from both ends. Analysis of these full-length transcripts indicated that sizes of melon 5' and 3' UTRs were similar to those of tomato, but longer than many other dicot plants. Codon usages of melon full-length transcripts were largely similar to those of Arabidopsis coding sequences. CONCLUSION:The collection of melon ESTs generated from full-length enriched and standard cDNA libraries is expected to play significant roles in annotating the melon genome. The ESTs and associated analysis results will be useful resources for gene discovery, functional analysis, marker-assisted breeding of melon and closely related species, comparative genomic studies and for gaining insights into gene expression patterns.

Dataset Information

Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires.

Publications

Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires.

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