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

Shcherbinin, Dmitrii S.; Belousov, Vlad A; Shugay, Mikhail

doi:10.1371/journal.pcbi.1007714

Cited by 16 publications

(22 citation statements)

References 18 publications

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“…Moreover, some YLQ-specific TCRs were shared between multiple individuals and others exhibited a high degree of global similarity (Figure S5). No significant correlation in frequency was observed between pairs of TRA and TRB motifs, suggesting that the pairing between motifs of different chains may be entirely random, in line with recent observations (Shcherbinin, Belousov et al 2020). Position-weight matrices of the motifs demonstrate a set of highly dissimilar consensuses (Figure 4e) suggesting that while RLQ- and YLQ-specific TCR repertoires are highly public, they are also diverse.…”

Section: Resultssupporting

confidence: 89%

SARS-CoV-2 epitopes are recognized by a public and diverse repertoire of human T-cell receptors

Shomuradova

Vagida

Sheetikov

et al. 2020

Preprint

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Understanding the hallmarks of the adaptive immune response to SARS-CoV-2 is critical for fighting the COVID-19 pandemic. We assessed the antibody and T-cell reactivity in COVID-19 convalescent patients and healthy donors sampled both prior to and during the pandemic. The numbers of SARS- CoV-2-specific T cells were increased in healthy donors examined during COVID-19. Combined with the absence of symptoms and humoral response across that group, this finding suggests that some individuals might be protected by T-cell cross-reactivity. In convalescent patients we observed public and diverse T-cell response to SARS-CoV-2 epitopes, revealing T-cell receptor motifs with germline- encoded features. Bulk CD4+ and CD8+ T-cell responses to Spike glycoprotein were mediated by groups of homologous T-cell receptors, some of them shared across multiple donors. Overall, our results demonstrate that T-cell response to SARS-CoV-2, including the identified set of specific T-cell receptors, can serve as a useful biomarker for surveying viral exposure and immunity.

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Section: Resultssupporting

confidence: 89%

SARS-CoV-2 epitopes are recognized by a public and diverse repertoire of human T-cell receptors

Shomuradova

Vagida

Sheetikov

et al. 2020

Preprint

Self Cite

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“…Similarly, BCRs and antibodies are made up of a heavy and two major groups (κ and λ) of light chains. Previous work has identified low but consistent correlations between features of αβ-chain pairs in TCRs, with the largest contributions between Vα, V β and Jα, V β (34)(35)(36)(37)(38). In B cells, preferences for receptor features within immunoglobulin heavy (IgH) and light (λ or κ) chains have been studied separately (39,40), but interchain correlations have not been systematically investigated.…”

Section: Resultsmentioning

confidence: 99%

Deep generative selection models of T and B cell receptor repertoires with soNNia

Isacchini

Walczak

Mora

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here, we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically, using only repertoire-level sequence information, we classify CD4+ and CD8+ T cells, find correlations between receptor chains arising during selection, and identify T cell subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.

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“…Similarly, B-cell receptors and antibodies are made up of a heavy and two major groups (κ and λ) of light chains. Previous work has identified low but consistent correlations between features of αβ chain pairs in T-cell receptors, with the largest contributions between V α , V β and J α , V β [35][36][37]. In B-cells, preferences for receptor features within heavy and light chains have been studied separately [38,39] but inter-chain correlations have not been systematically investigated.…”

Section: Intra-and Inter-chain Interactions In Tcrs and Bcrsmentioning

confidence: 99%

Deep generative selection models of T and B cell receptor repertoires with soNNia

Isacchini

Walczak

Mora

et al. 2020

Preprint

View full text Add to dashboard Cite

Subclasses of lymphocytes carry different functional roles to work together to produce an immune response and lasting immunity. Additionally to these functional roles, T and B-cell lymphocytes rely on the diversity of their receptor chains to recognize different pathogens. The lymphocyte subclasses emerge from common ancestors generated with the same diversity of receptors during selection processes. Here we leverage biophysical models of receptor generation with machine learning models of selection to identify specific sequence features characteristic of functional lymphocyte repertoires and subrepertoires. Specifically using only repertoire level sequence information, we classify CD4+ and CD8+ T-cells, find correlations between receptor chains arising during selection and identify T-cells subsets that are targets of pathogenic epitopes. We also show examples of when simple linear classifiers do as well as more complex machine learning methods.

show abstract

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

Cited by 16 publications

References 18 publications

SARS-CoV-2 epitopes are recognized by a public and diverse repertoire of human T-cell receptors

SARS-CoV-2 epitopes are recognized by a public and diverse repertoire of human T-cell receptors

Deep generative selection models of T and B cell receptor repertoires with soNNia

Deep generative selection models of T and B cell receptor repertoires with soNNia

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