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Background Deep immune receptor sequencing, RepSeq, provides unprecedented opportunities for identifying and studying condition-associated T-cell clonotypes, represented by T-cell receptor (TCR) CDR3 sequences. However, due to the immense diversity of the immune repertoire, identification of condition relevant TCR CDR3s from total repertoires has mostly been limited to either “public” CDR3 sequences or to comparisons of CDR3 frequencies observed in a single individual. A methodology for the identification of condition-associated TCR CDR3s by direct population level comparison of RepSeq samples is currently lacking. Results We present a method for direct population level comparison of RepSeq samples using immune repertoire sub-units (or sub-repertoires) that are shared across individuals. The method first performs unsupervised clustering of CDR3s within each sample. It then finds matching clusters across samples, called immune sub-repertoires, and performs statistical differential abundance testing at the level of the identified sub-repertoires. It finally ranks CDR3s in differentially abundant sub-repertoires for relevance to the condition. We applied the method on total TCR CDR3β RepSeq datasets of celiac disease patients, as well as on public datasets of yellow fever vaccination. The method successfully identified celiac disease associated CDR3β sequences, as evidenced by considerable agreement of TRBV-gene and positional amino acid usage patterns in the detected CDR3β sequences with previously known CDR3βs specific to gluten in celiac disease. It also successfully recovered significantly high numbers of previously known CDR3β sequences relevant to each condition than would be expected by chance. Conclusion We conclude that immune sub-repertoires of similar immuno-genomic features shared across unrelated individuals can serve as viable units of immune repertoire comparison, serving as proxy for identification of condition-associated CDR3s.

The tumor immune response is dependent on the interaction between tumor cells and the T-cell subset expressing the T-cell receptor (TCR) repertoire that infiltrates into the tumor microenvironment. The present study explored the diversity and shared TCR repertoires expressed on the surface of locoregional T cells and identified the T lymphocyte subsets infiltrating into esophageal squamous cell carcinoma (ESCC), in order to provide insight into the efficiency of immunotherapy and the development of a novel immune-oriented therapeutic strategy. A total of 53 patients with ESCC were enrolled in the present study, and immunohistochemical analysis of CD3, CD8, CD45RO, FOXP3, CD274, HLA class I and AE1/AE3 was performed. Digital pathological assessment was performed to evaluate the expression level of each marker. The clinicopathological significance of the immuno relation high (IR-Hi) group was assessed. Adaptor ligation PCR and next-generation sequencing were performed to explore the diversity of the TCR repertoire and to investigate the shared TCR repertoire in the IR-Hi group. Repertoire dissimilarity index (RDI) analysis was performed to assess the diversity of TCR, and the existence of shared TCRα and TCRβ was also investigated. Further stratification was performed according to the expression of markers of different T-cell subsets. Patients were stratified into IR-Hi and immuno relation low (IR-Lo) groups. Cancer-specific survival and recurrence-free survival rates were significantly improved in the IR-Hi group compared with in the IT-Lo group. The diversity of the TCR repertoire was significantly higher in the IR-Hi group. TCR repertoire analysis revealed 27 combinations of TCRα and 23 combinations of TCRβ VJ regions that were shared among the IR-Hi group. The IR-Hi group was divided into three clusters. Overall, the current findings revealed that the IR-Hi group maintained the diversity of TCR, and a portion of the IR-Hi cases held the T cells with shared TCR repertoires, implying recognition of shared antigens. The prognosis of patients with ESCC was affected by the existence of immune response cells and may possibly be stratified by the T-cell subsets.

Predicting the binding specificity of T Cell Receptors (TCR) to MHC-peptide complexes (pMHCs) is essential for the development of repertoire-based biomarkers. This affinity may be affected by different components of the TCR, the peptide, and the MHC allele. Historically, the main element used in TCR-peptide binding prediction was the Complementarity Determining Region 3 (CDR3) of the beta chain. However, recently the contribution of other components, such as the alpha chain and the other V gene CDRs has been suggested. We use a highly accurate novel deep learning-based TCR-peptide binding predictor to assess the contribution of each component to the binding. We have previously developed ERGO-I (pEptide tcR matchinG predictiOn), a sequence-based T-cell receptor (TCR)-peptide binding predictor that employs natural language processing (NLP) -based methods. We improved it to create ERGO-II by adding the CDR3 alpha segment, the MHC typing, V and J genes, and T cell type (CD4+ or CD8+) as to the predictor. We then estimate the contribution of each component to the prediction. ERGO-II provides for the first time high accuracy prediction of TCR-peptide for previously unseen peptides. For most tested peptides and all measures of binding prediction accuracy, the main contribution was from the beta chain CDR3 sequence, followed by the beta chain V and J and the alpha chain, in that order. The MHC allele was the least contributing component. ERGO-II is accessible as a webserver at http://tcr2.cs.biu.ac.il/ and as a standalone code at https://github.com/IdoSpringer/ERGO-II.

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.

Crohn's disease (CD) is a clinical subset of inflammatory bowel disease that is characterized by patchy transmural inflammation across the gastrointestinal tract. Although the exact etiology remains unknown, recent findings suggest that it is a complex multifactorial disease with contributions from the host genetics and environmental factors such as the microbiome. We have previously shown that the T cell repertoire of individuals with CD harbors a group of highly expanded T cells which hints toward an antigen-mediated pathology. We simultaneously profiled the αβ and γδ T cell repertoire in addition to the B cell repertoire of both the blood and the colonic mucosa of 27 treatment-naïve individuals with CD and 27 age-matched symptomatic controls. Regardless of disease status, we observed multiple physiological differences between the immune repertoire of blood and colonic mucosa. Additionally, by comparing the repertoire of individuals with CD relative to controls, we observed different alterations that were only detected in the blood or colonic mucosa. These include a depletion of mucosal-associated invariant T (MAIT) cells and an expansion of clonotypes in the blood repertoire of individuals with CD. Also, a significant depletion of multiple and clonotypes in the blood and gut IGH repertoire of individuals with CD. Our findings highlight the importance of studying the immune repertoire in a tissue-specific manner and the need to profile the T and B cell immune repertoire of gut tissues as not all disease-induced alterations will be detected in the blood.

The adaptive immune system in vertebrates has evolved to recognize non-self antigens, such as proteins expressed by infectious agents and mutated cancer cells. T cells play an important role in antigen recognition by expressing a diverse repertoire of antigen-specific receptors, which bind epitopes to mount targeted immune responses. Recent advances in high-throughput sequencing have enabled the routine generation of T-cell receptor (TCR) repertoire data. Identifying the specific epitopes targeted by different TCRs in these data would be valuable. To accomplish that, we took advantage of the ever-increasing number of TCRs with known epitope specificity curated in the Immune Epitope Database (IEDB) since 2004. We compared seven metrics of sequence similarity to determine their power to predict if two TCRs have the same epitope specificity. We found that a comprehensive k -mer matching approach produced the best results, which we have implemented into TCRMatch, an openly accessible tool ( http://tools.iedb.org/tcrmatch/ ) that takes TCR β-chain CDR3 sequences as an input, identifies TCRs with a match in the IEDB, and reports the specificity of each match. We anticipate that this tool will provide new insights into T cell responses captured in receptor repertoire and single cell sequencing experiments and will facilitate the development of new strategies for monitoring and treatment of infectious, allergic, and autoimmune diseases, as well as cancer.

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO reaches similar results to state of the art methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO. ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/.

Background The Immunoglobulins (IG) and the T cell receptors (TR) play the key role in antigen recognition during the adaptive immune response. Recent progress in next-generation sequencing technologies has provided an opportunity for the deep T cell receptor repertoire profiling. However, a specialised software is required for the rational analysis of massive data generated by next-generation sequencing. Results Here we introduce tcR, a new R package, representing a platform for the advanced analysis of T cell receptor repertoires, which includes diversity measures, shared T cell receptor sequences identification, gene usage statistics computation and other widely used methods. The tool has proven its utility in recent research studies. Conclusions tcR is an R package for the advanced analysis of T cell receptor repertoires after primary TR sequences extraction from raw sequencing reads. The stable version can be directly installed from The Comprehensive R Archive Network ( http://cran.r-project.org/mirrors.html ). The source code and development version are available at tcR GitHub ( http://imminfo.github.io/tcr/ ) along with the full documentation and typical usage examples.

B cells play a crucial role in the immune response and contribute to various autoimmune diseases. Recent studies have revealed abnormalities in the B cell receptor (BCR) repertoire of patients with autoimmune diseases, with distinct features observed among different diseases and B cell subsets. Classically, BCR repertoire was used as an identifier of distinct antigen-specific clonotypes, but the recent advancement of analyzing large-scale repertoire has enabled us to use it as a tool for characterizing cellular biology. In this review, we provide an overview of the BCR repertoire in autoimmune diseases incorporating insights from our latest research findings. In systemic lupus erythematosus (SLE), we observed a significant skew in the usage of VDJ genes, particularly in CD27 + IgD + unswitched memory B cells and plasmablasts. Notably, autoreactive clones within unswitched memory B cells were found to be increased and strongly associated with disease activity, underscoring the clinical significance of this subset. Similarly, various abnormalities in the BCR repertoire have been reported in other autoimmune diseases such as rheumatoid arthritis. Thus, BCR repertoire analysis holds potential for enhancing our understanding of the underlying mechanisms involved in autoimmune diseases. Moreover, it has the potential to predict treatment effects and identify therapeutic targets in autoimmune diseases.