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Exploring the pre-immune landscape of antigen-specific T cells

MV. Pogorelyy, AD. Fedorova, JE. McLaren, K. Ladell, DV. Bagaev, AV. Eliseev, AI. Mikelov, AE. Koneva, IV. Zvyagin, DA. Price, DM. Chudakov, M. Shugay,

. 2018 ; 10 (1) : 68. [pub] 20180825

Jazyk angličtina Země Anglie, Velká Británie

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc19000423

BACKGROUND: Adaptive immune responses to newly encountered pathogens depend on the mobilization of antigen-specific clonotypes from a vastly diverse pool of naive T cells. Using recent advances in immune repertoire sequencing technologies, models of the immune receptor rearrangement process, and a database of annotated T cell receptor (TCR) sequences with known specificities, we explored the baseline frequencies of T cells specific for defined human leukocyte antigen (HLA) class I-restricted epitopes in healthy individuals. METHODS: We used a database of TCR sequences with known antigen specificities and a probabilistic TCR rearrangement model to estimate the baseline frequencies of TCRs specific to distinct antigens epitopespecificT-cells. We verified our estimates using a publicly available collection of TCR repertoires from healthy individuals. We also interrogated a database of immunogenic and non-immunogenic peptides is used to link baseline T-cell frequencies with epitope immunogenicity. RESULTS: Our findings revealed a high degree of variability in the prevalence of T cells specific for different antigens that could be explained by the physicochemical properties of the corresponding HLA class I-bound peptides. The occurrence of certain rearrangements was influenced by ancestry and HLA class I restriction, and umbilical cord blood samples contained higher frequencies of common pathogen-specific TCRs. We also identified a quantitative link between specific T cell frequencies and the immunogenicity of cognate epitopes presented by defined HLA class I molecules. CONCLUSIONS: Our results suggest that the population frequencies of specific T cells are strikingly non-uniform across epitopes that are known to elicit immune responses. This inference leads to a new definition of epitope immunogenicity based on specific TCR frequencies, which can be estimated with a high degree of accuracy in silico, thereby providing a novel framework to integrate computational and experimental genomics with basic and translational research efforts in the field of T cell immunology.

Citace poskytuje Crossref.org

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$a BACKGROUND: Adaptive immune responses to newly encountered pathogens depend on the mobilization of antigen-specific clonotypes from a vastly diverse pool of naive T cells. Using recent advances in immune repertoire sequencing technologies, models of the immune receptor rearrangement process, and a database of annotated T cell receptor (TCR) sequences with known specificities, we explored the baseline frequencies of T cells specific for defined human leukocyte antigen (HLA) class I-restricted epitopes in healthy individuals. METHODS: We used a database of TCR sequences with known antigen specificities and a probabilistic TCR rearrangement model to estimate the baseline frequencies of TCRs specific to distinct antigens epitopespecificT-cells. We verified our estimates using a publicly available collection of TCR repertoires from healthy individuals. We also interrogated a database of immunogenic and non-immunogenic peptides is used to link baseline T-cell frequencies with epitope immunogenicity. RESULTS: Our findings revealed a high degree of variability in the prevalence of T cells specific for different antigens that could be explained by the physicochemical properties of the corresponding HLA class I-bound peptides. The occurrence of certain rearrangements was influenced by ancestry and HLA class I restriction, and umbilical cord blood samples contained higher frequencies of common pathogen-specific TCRs. We also identified a quantitative link between specific T cell frequencies and the immunogenicity of cognate epitopes presented by defined HLA class I molecules. CONCLUSIONS: Our results suggest that the population frequencies of specific T cells are strikingly non-uniform across epitopes that are known to elicit immune responses. This inference leads to a new definition of epitope immunogenicity based on specific TCR frequencies, which can be estimated with a high degree of accuracy in silico, thereby providing a novel framework to integrate computational and experimental genomics with basic and translational research efforts in the field of T cell immunology.
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$a Fedorova, Alla D $u Department of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
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$a McLaren, James E $u Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK.
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$a Ladell, Kristin $u Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK.
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$a Bagaev, Dmitri V $u Department of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
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$a Eliseev, Alexey V $u Department of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia. Department of Molecular Technologies, Pirogov Russian National Research Medical University, Moscow, Russia.
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$a Mikelov, Artem I $u Department of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia. Center for Data-Intensive Biomedicine and Biotechnology, Skoltech, Moscow, Russia.
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$a Koneva, Anna E $u Department of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia.
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$a Zvyagin, Ivan V $u Department of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia. Department of Molecular Technologies, Pirogov Russian National Research Medical University, Moscow, Russia.
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$a Price, David A $u Division of Infection and Immunity, Cardiff University School of Medicine, Cardiff, UK. Systems Immunity Research Institute, Cardiff University School of Medicine, Cardiff, UK.
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$a Chudakov, Dmitry M $u Department of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia. Department of Molecular Technologies, Pirogov Russian National Research Medical University, Moscow, Russia. Center for Data-Intensive Biomedicine and Biotechnology, Skoltech, Moscow, Russia. Central European Institute of Technology, CEITEC, Brno, Czech Republic.
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$a Shugay, Mikhail $u Department of Genomics of Adaptive Immunity, IBCH RAS, Moscow, Russia. mikhail.shugay@gmail.com. Department of Molecular Technologies, Pirogov Russian National Research Medical University, Moscow, Russia. mikhail.shugay@gmail.com. Center for Data-Intensive Biomedicine and Biotechnology, Skoltech, Moscow, Russia. mikhail.shugay@gmail.com.
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