Biology subjects

Shugay, M.

Publications and source records attributed to Shugay, M..

14 recordsLinked to original sources

bioRxiv2025

Comprehensive analysis of αβT-cell receptor repertoires reveals signatures of thymic selection

Thymic selection is a multi-stage process that establishes a T-cell immunity that is efficient in fighting diverse foreign pathogens, while not being self-reactive. During the process, T-cell receptor (TCR) and {beta} chains are rearranged to form a highly diverse set of heterodimers that are selected based on their affinity to peptides presented by major histocompatibility molecules (MHC) in the thymus. Here we employ high-throughput TCR sequencing data, theoretical model of TCR rearrangement and dedicated statistical methods to infer how the selection process affects human TCR repertoire on different scales. On a global scale, our results indicate differences in V(D)J gene usage, complementarity determining region 3 (CDR3) amino acid composition, CDR3 physicochemical properties, k-mer composition and differences in loop structure induced by the selection process. On a local scale, we were able to determine enriched TCR motifs and "holes" in repertoire induced by positive and negative selection and characterize their features. Finally, we demonstrated how TCR sequence composition affects lineage commitment via thymic selection and highlighted the effect of individual MHC haplotype. Our results can aid in identification of potentially self-reactive TCRs in donor repertoires in autoimmunity and immunotherapy studies. Graphical abstract (Figure 0)Study overview. A. Datasets used in the study and the way TCR sequences were analyzed. B. Main directions in which thymic selection shapes TCR repertoire. Selection results in TCR-CDR3 losing positively charged and large amino-acids while increasing its flexibility. Unlike CDR3 of the TCR chain, CDR3{beta} hydrophobicity is also increased by selection. CDR3s carrying Cysteines and glycosylation sites are unlikely to pass the selection. In contrast, CDR3 carrying poly-Glycine regions are more likely to be selected for both chains. T-cells committed to CD8+ lineage were more likely to feature bulged CDR3s compared to CD4+. C. CDR3s enriched after thymic selection guide lineage commitment according to single-cell RNA sequencing data analysis, e.g. the MAIT cells and CD8+ phenotypes. D. Enrichment and depletion of certain TCR motifs pinpoint fine-structure of post-selection repertoires, as inferred by sequence neighborhood analysis. E. Comparative analysis of enriched and depleted TCR clusters in monozygotic twins revealed that the selection process is shaped by HLA haplotype. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/635277v2_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@148f9f1org.highwire.dtl.DTLVardef@117858org.highwire.dtl.DTLVardef@f3f837org.highwire.dtl.DTLVardef@12cf2c1_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Tracking antigen-specific T cell response to cancer immunotherapy

In each human or model animal, T cell receptor (TCR) of each effector/memory T cell clone recognizes from one to several cognate peptide-MHC complexes (pMHC). Limited knowledge on TCR repertoire specificities restricts our capacity to rationally interpret this information, both diagnostically and in preclinical research. Here we 1) develop cost-efficient wet lab and computational pipeline to identify mouse TCRs specific to particular peptides, 2) produce dataset of helper T cells (Th) TCR beta CDR3s specific to B16 melanoma neoantigens in the I-Ab pMHCII context, available in VDJdb, and 3) apply this dataset to track tumor-specific T cell response to the CTLA4 blocking immunotherapy on orthotopic B16 melanoma model. We show that tumor promotes clonally independent Tregs carrying tumor-specific TCRs characteristic for Th cells. We further show that CTLA4 blockade promotes Th clonal expansion and induces general, non-tumor-specific Th-to-Treg plasticity. Altogether, we provide a universal pipeline for the investigation of mouse T cell responses at the antigen-specific level, facilitating development and validation of immunotherapeutic and vaccination approaches.

immunology↗

bioRxiv2023

Convergence, plasticity, and tissue residence of regulatory T cell response via TCR repertoire prism

Suppressive function of regulatory T cells (Treg) is dependent on signaling of their antigen receptors triggered by cognate self, dietary or microbial antigens in the form of peptide-MHC class II complexes. However, it remains largely unknown whether distinct or shared repertoires of Treg TCRs are mobilized in response to different challenges in the same tissue or the same challenge in different tissues. Here we used a fixed TCR{beta} chain FoxP3-GFP mouse model to analyze conventional (eCD4) and regulatory (eTreg) effector TCR repertoires in response to six distinct antigenic challenges to the lung and skin. This model showed highly "digital" repertoire behavior, allowing for easy-to-track challenge-specific TCR CDR3 clusters. For both studied subsets, we observed challenge-specific clonal expansion yielding homologous TCR clusters within and across animals and exposure sites, which were also reflected in the draining lymph nodes but not systemically. Some clusters were shared across cancer challenges, suggesting response to common tumor-associated antigens. For most challenges, eCD4 and eTreg clonal response did not overlap, indicating the distinct origin of the two cell subsets. At the same time, we observed such overlap at the sites of certain tumor challenges. The overlaps included dominant responding TCR motif and characteristic iNKT TCR, suggesting the tumor-induced eCD4-eTreg plasticity. Additionally, our TCR repertoire analysis demonstrated that distinct antigenic specificities are characteristic for eTreg cells residing in particular lymphatic tissues, regardless of the challenge, revealing the homing-specific, antigen-specific resident Treg populations. Altogether, our study highlights both challenge-specific and tissue-specific responses of Treg cells associated with distinct clonal expansions. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/544726v4_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@5a8353org.highwire.dtl.DTLVardef@63fef3org.highwire.dtl.DTLVardef@f7390forg.highwire.dtl.DTLVardef@c347e1_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2023

Large-scale template-based structural modeling of T-cell receptors with known antigen specificity reveals complementarity features.

T-cell receptor (TCR) recognition of foreign peptides presented by the major histocompatibility complex (MHC) initiates the adaptive immune response against pathogens. A large number of TCR sequences specific to different antigens are known to date, however, the structural data describing the conformation and contacting residues for TCR:antigen:MHC complexes is relatively limited. In the present study we aim to extend and analyze the set of available structures by performing highly accurate template-based modeling of TCR:antigen:MHC complexes using TCR sequences with known specificity. Using the set of 29 complex templates (including a template with SARS-CoV-2 antigen) and 732 specificity records, we built a database of 1585 model structures carrying substitutions in either TCR or TCR{beta} chains with some models representing the result of different mutation pathways for the same final structure. This database allowed us to analyze features of amino acid contacts in TCR:antigen interfaces that govern antigen recognition preferences and interpret these interactions in terms of physicochemical properties of interacting residues. Our results provide a methodology for creating high-quality TCR:antigen:MHC models for antigens of interest that can be utilized to predict TCR specificity.

bioinformatics↗

bioRxiv2022

Predicting TCR-peptide recognition based on residue-level pairwise statistical potential

Prediction of TCR-peptide interactions has great importance for therapy of cancer, infectious and autoimmune diseases, but remains a major challenge, particularly for unseen epitopes. We present a structure-based method that enables scoring of TCR-peptide interactions using an energy potential (TCRen) derived from statistics of TCR-peptide contacts in existing crystal structures. We show that TCRen has high performance in discriminating cognate/unrelated peptides and can facilitate the identification of cancer neoepitopes recognized by tumor-infiltrating lymphocytes.

immunology↗

bioRxiv2022

Memory persistence and differentiation into antibody-secreting cells accompanied by positive selection in longitudinal BCR repertoires

The stability and plasticity of B cell-mediated immune memory ensures the ability to respond to the repeated challenges. We have analyzed the longitudinal dynamics of immunoglobulin heavy chain repertoires from memory B cells, plasmablasts, and plasma cells from the peripheral blood of generally healthy volunteers. We reveal a high degree of clonal persistence in individual memory B cell subsets, with inter-individual convergence in memory and antibody-secreting cells (ASCs). ASC clonotypes demonstrate clonal relatedness to memory B cells, and are transient in peripheral blood. We identify two clusters of expanded clonal lineages with differing prevalence of memory B cells, isotypes, and persistence. Phylogenetic analysis revealed signs of reactivation of persisting memory B cell-enriched clonal lineages, accompanied by new rounds of affinity maturation during proliferation and differentiation into ASCs. Negative selection contributes to both persisting and reactivated lineages, preserving the functionality and specificity of BCRs to protect against current and future pathogens.

immunology↗

bioRxiv2021

HLA binding of self-peptides is biased towards proteins with specific molecular functions

Human leukocyte antigen (HLA) is highly polymorphic and plays a key role in guiding adaptive immune responses by presenting foreign and self peptides to T cells. Each HLA variant selects a minor fraction of peptides that match a certain motif required for optimal interaction with the peptide-binding groove. These restriction rules define the landscape of peptides presented to T cells. Given these limitations, one might suggest that the choice of peptides presented by HLA is non-random and there is preferential presentation of an array of peptides that is optimal for distinguishing self and foreign proteins. In this study we explore these preferences with a comparative analysis of self peptides enriched and depleted in HLA ligands. We show that HLAs exhibit preferences towards presenting peptides from certain proteins while disfavoring others with specific functions, and highlight differences between various HLA genes and alleles in those preferences. We link those differences to HLA anchor residue propensities and amino acid composition of preferentially presented proteins. The set of proteins that peptides presented by a given HLA are most likely to be derived from can be used to distinguish between class I and class II HLAs and HLA alleles. Our observations can be extrapolated to explain the protective effect of certain HLA alleles in infectious diseases, and we hypothesize that they can also explain susceptibility to certain autoimmune diseases and cancers. We demonstrate that these differences lead to differential presentation of HIV, influenza virus, SARS-CoV-1 and SARS-CoV-2 proteins by various HLA alleles. Finally, we show that the reported self peptidome preferences of distinct HLA variants can be compensated by combinations of HLA-A/HLA-B and HLA-A/HLA-C alleles in frequent haplotypes.

bioinformatics↗

bioRxiv2020

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

T cell recognition of a cognate peptide-MHC complex (pMHC) presented on the surface of infected or malignant cells, is of utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T Cell Receptors (TCR) would greatly facilitate identification of vaccine targets for both pathogenic diseases as well as personalized cancer immunotherapies. Predicting immunogenic peptides therefore has been at the centre of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes in the context of human pathology, has not been measured and compared. In this study, we evaluated the performance of several publicly available models, in identifying immunogenic CD8+ T cell targets in the context of pathogens and cancers. We found that for predicting immunogenic peptides from an emerging virus such as SARS-CoV-2, none of the models perform substantially better than random or offer considerable improvement beyond HLA ligand prediction. We also observed suboptimal performance for predicting cancer neoantigens. Through investigation of potential factors associated with ill performance of models, we highlight several data- and model-associated issues. In particular, we observed that cross-HLA variation in the distribution of immunogenic and non-immunogenic peptides in training data of the models seem to substantially confound the predictions. We additionally compared key parameters associated with immunogenicity between pathogenic peptides and cancer neoantigens and observed evidence for differences in the thresholds of binding affinity and stability, which suggested the need to modulate different features in identifying immunogenic pathogen vs. cancer peptides. Overall, we demonstrate that accurate and reliable prediction of immunogenic CD8+ T cell targets remains unsolved, thus we hope our work will guide users and model developers regarding potential pitfalls and unsettled questions in existing immunogenicity predictors.

immunology↗

bioRxiv2020

Human thymopoiesis selects unconventional CD8+ α/β T cells that respond to multiple viruses.

T cell receptors (TCRs) are formed by stochastic gene rearrangements, theoretically generating >1019 sequences. They are selected during thymopoiesis, which releases a repertoire of about 108 unique TCRs per individual. How evolution shaped a process that produces TCRs that can effectively handle a countless and evolving set of infectious agents is a central question of immunology. The paradigm is that a diverse enough repertoire of TCRs should always provide a proper, though rare, specificity for any given need. Expansion of such rare T cells would provide enough fighters for an effective immune response and enough antigen-experienced cells for memory. We show here that human thymopoiesis releases a large population of CD8+ T cells harboring /{beta} paired TCRs that (i) have high generation probabilities and (ii) a preferential usage of some V and J genes, (iii) are shared between individuals and (iv) can each recognize and be activated by multiple unrelated viral peptides, notably from EBV, CMV and influenza. These polyspecific T cells may represent a first line of defense that is mobilized in response to infections before a more specific response subsequently ensures viral elimination. Our results support an evolutionary selection of polyspecific /{beta} TCRs for broad antiviral responses and heterologous immunity.

immunology↗

bioRxiv2019

sumrep: a summary statistic framework for immune receptor repertoire comparison and model validation

The adaptive immune system generates an incredible diversity of antigen receptors for B and T cells to keep dangerous pathogens at bay. The DNA sequences coding for these receptors arise by a complex recombination process followed by a series of productivity-based filters, as well as affinity maturation for B cells, giving considerable diversity to the circulating pool of receptor sequences. Although these datasets hold considerable promise for medical and public health applications, the complex structure of the resulting adaptive immune receptor repertoire sequencing (AIRR-seq) datasets makes analysis difficult. In this paper we introduce sumrep, an R package that efficiently performs a wide variety of repertoire summaries and comparisons, and show how sumrep can be used to perform model validation. We find that summaries vary in their ability to differentiate between datasets, although many are able to distinguish between covariates such as donor, timepoint, and cell type for BCR and TCR repertoires. We show that deletion and insertion lengths resulting from V(D)J recombination tend to be more discriminative characterizations of a repertoire than summaries that describe the amino acid composition of the CDR3 region. We also find that state-of-the-art generative models excel at recapitulating gene usage and recombination statistics in a given experimental repertoire, but struggle to capture many physiochemical properties of real repertoires.

immunology↗

bioRxiv2019

Comprehensive analysis of structural and sequencing data reveals almost unconstrained chain pairing in TCRαβ complex

Antigen recognition by T-cells is guided by the T-cell receptor (TCR) heterodimer formed by and {beta} chains. A huge diversity of TCR sequences should be maintained by the immune system in order to be able to mount an effective response towards foreign pathogens, so, due to cooperative binding of and {beta} chains to the pathogen, any constraints on chain pairing can have a profound effect on immune repertoire structure, diversity and antigen specificity. By integrating available structural data and paired chain sequencing results we were able to show that there are almost no constraints on pairing in TCR{beta} complexes, allowing naive T-cell repertoire to reach the highest possible diversity. Additional analysis reveals that the specific choice of contacting amino acids can still have a profound effect on complex conformation. Moreover, antigen-driven selection can distort the uniform landscape of chain pairing, while small, yet significant, differences in the pairing can be attributed to various specialized T-cell subsets such as MAIT and iNKT T-cells, as well as other putative invariant TCRs.

bioinformatics↗

bioRxiv2019

A framework for annotation of antigen specificities in high-throughput T-cell repertoire sequencing studies

Recently developed molecular methods allow large-scale profiling of T-cell receptor (TCR) sequences that encode for antigen specificity and immunological memory of these cells. However, it is well known, that the even unperturbed TCR repertoire structure is extremely complex due to the high diversity of TCR rearrangements and multiple biases imprinted by VDJ rearrangement process. The latter gives rise to the phenomenon of \"public\" TCR clonotypes that can be shared across multiple individuals and non-trivial structure of the TCR similarity network. Here we outline a framework for TCR sequencing data analysis that can control for these biases in order to infer TCRs that are involved in response to antigens of interest. Using an example dataset of donors with known HLA haplotype and CMV status we demonstrate that by applying HLA restriction rules and matching against a database of TCRs with known antigen specificity it is possible to robustly detect motifs of an epitope-specific responses in individual repertoires. We also highlight potential shortcomings of TCR clustering methods and demonstrate that highly expanded TCRs should be individually assessed to get the full picture of antigen-specific response.

immunology↗

bioRxiv2018

Detecting T-cell receptors involved in immune responses from single repertoire snapshots

Hypervariable T-cell receptors (TCR) play a key role in adaptive immunity, recognising a vast diversity of pathogen-derived antigens. High throughput sequencing of TCR repertoires (RepSeq) produces huge datasets of T-cell receptor sequences from blood and tissue samples [1, 2]. However, our ability to extract clinically relevant information from RepSeq data is limited, mainly because little is known about TCR-disease associations. Here we present a statistical approach called ALICE (Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences) that identifies TCR sequences that are actively involved in the current immune response from a single RepSeq sample, and apply it to repertoires of patients with a variety of disorders -- autoimmune disease (ankylosing spondylitis [3]), patients under cancer immunotherapy [4, 5], or subject to an acute infection (live yellow fever vaccine [6]). The methods robustness is demonstrated by the agreement of its predictions with independent assays, and is supported by its ability to selectively detect responding TCR in the memory but not in the naive subset. ALICE requires no longitudinal data collection [6, 7] nor large cohorts [8-10], and is thus directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with a wide variety of diseases and conditions, which can be used for diagnostics, rational vaccine design and evaluation of the adaptive immune system state.

immunology↗

bioRxiv2018

T cell immunity does not age in a long-lived rodent species

Numerous studies have demonstrated that the percentage of naive T cells and diversity of T cell receptor (TCR) repertoire decrease with age, with some findings likewise suggesting that increased repertoire diversity may be associated with longer lifespan and healthy aging. In this work, we have analyzed peripheral TCR diversity from humans, mice, and blind mole-rats (Spalax spp.)--long-lived, hypoxia- and cancer-tolerant rodents. We employed a quantitative approach to TCR repertoire profiling based on 5RACE with unique molecular identifiers (UMI) to achieve accurate comparison of repertoire diversity, which also required development of specific wet lab protocol and TCR gene reference for Spalax. Our direct comparison reveals a striking phenomenon. Whereas TCR diversity of mice and humans decreases with age, resulting primarily from the shrinkage of the naive T cell pool, Spalax TCR diversity remains stable even for the animals that reach extreme old age (15-17 years). This indicates that T cell immunity does not meaningfully age in long-lived rodents, at least in terms of the classical understanding of immunosenescence, which is associated with the accumulation of large numbers of memory clones. We suggest that the extraordinary longevity of Spalax may be attributable at least in part to the distinctive organization of their T cell immunity. Our findings should therefore encourage a close re-examination of the contribution of immunosenescence to life span in mammals.

immunology↗