Detecting T cell receptors involved in immune responses from single repertoire snapshots
Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens. Our ability to extract clinically relevant information from large high-throughput sequencing of TCR repertoires (RepSeq) data is limited, because little is known abo...
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| Vydané v: | PLoS biology Ročník 17; číslo 6; s. e3000314 |
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| Hlavní autori: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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United States
Public Library of Science
13.06.2019
Public Library of Science (PLoS) |
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| ISSN: | 1545-7885, 1544-9173, 1545-7885 |
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| Abstract | Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens. Our ability to extract clinically relevant information from large high-throughput sequencing of TCR repertoires (RepSeq) data is limited, because little is known about TCR-disease associations. We present Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences (ALICE), a statistical approach that identifies TCR sequences actively involved in current immune responses from a single RepSeq sample and apply it to repertoires of patients with a variety of disorders - patients with autoimmune disease (ankylosing spondylitis [AS]), under cancer immunotherapy, or subject to an acute infection (live yellow fever [YF] vaccine). We validate the method with independent assays. ALICE requires no longitudinal data collection nor large cohorts, and it is directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design. |
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| AbstractList | Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens. Our ability to extract clinically relevant information from large high-throughput sequencing of TCR repertoires (RepSeq) data is limited, because little is known about TCR–disease associations. We present Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences (ALICE), a statistical approach that identifies TCR sequences actively involved in current immune responses from a single RepSeq sample and apply it to repertoires of patients with a variety of disorders — patients with autoimmune disease (ankylosing spondylitis [AS]), under cancer immunotherapy, or subject to an acute infection (live yellow fever [YF] vaccine). We validate the method with independent assays. ALICE requires no longitudinal data collection nor large cohorts, and it is directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design. Hypervariable T-cell receptors (TCRs) recognise a vast diversity of pathogen-derived antigens; this study shows that TCRs responding to ongoing immune challenges can be identified using a statistical null model based on sequence similarity. Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens. Our ability to extract clinically relevant information from large high-throughput sequencing of TCR repertoires (RepSeq) data is limited, because little is known about TCR-disease associations. We present Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences (ALICE), a statistical approach that identifies TCR sequences actively involved in current immune responses from a single RepSeq sample and apply it to repertoires of patients with a variety of disorders - patients with autoimmune disease (ankylosing spondylitis [AS]), under cancer immunotherapy, or subject to an acute infection (live yellow fever [YF] vaccine). We validate the method with independent assays. ALICE requires no longitudinal data collection nor large cohorts, and it is directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design.Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens. Our ability to extract clinically relevant information from large high-throughput sequencing of TCR repertoires (RepSeq) data is limited, because little is known about TCR-disease associations. We present Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences (ALICE), a statistical approach that identifies TCR sequences actively involved in current immune responses from a single RepSeq sample and apply it to repertoires of patients with a variety of disorders - patients with autoimmune disease (ankylosing spondylitis [AS]), under cancer immunotherapy, or subject to an acute infection (live yellow fever [YF] vaccine). We validate the method with independent assays. ALICE requires no longitudinal data collection nor large cohorts, and it is directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design. Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens. Our ability to extract clinically relevant information from large high-throughput sequencing of TCR repertoires (RepSeq) data is limited, because little is known about TCR–disease associations. We present Antigen-specific Lymphocyte Identification by Clustering of Expanded sequences (ALICE), a statistical approach that identifies TCR sequences actively involved in current immune responses from a single RepSeq sample and apply it to repertoires of patients with a variety of disorders — patients with autoimmune disease (ankylosing spondylitis [AS]), under cancer immunotherapy, or subject to an acute infection (live yellow fever [YF] vaccine). We validate the method with independent assays. ALICE requires no longitudinal data collection nor large cohorts, and it is directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design. Hypervariable T cell receptors (TCRs) play a key role in adaptive immunity, recognizing a vast diversity of pathogen-derived antigens. Our ability to extract clinically relevant information from large high-throughput sequencing of TCR repertoires (RepSeq) data is limited, because little is known about TCR-disease associations. We present Antigen-specific Lym-phocyte Identification by Clustering of Expanded sequences (ALICE), a statistical approach that identifies TCR sequences actively involved in current immune responses from a single RepSeq sample and apply it to repertoires of patients with a variety of disorders-patients with autoimmune disease (ankylosing spondylitis [AS]), under cancer immunotherapy, or subject to an acute infection (live yellow fever [YF] vaccine). We validate the method with independent assays. ALICE requires no longitudinal data collection nor large cohorts, and it is directly applicable to most RepSeq datasets. Its results facilitate the identification of TCR variants associated with diseases and conditions, which can be used for diagnostics and rational vaccine design. |
| Audience | Academic |
| Author | Chudakov, Dmitriy M. Walczak, Aleksandra M. Shugay, Mikhail Mora, Thierry Pogorelyy, Mikhail V. Lebedev, Yuri B. Minervina, Anastasia A. |
| AuthorAffiliation | 3 Privolzhsky Research Medical University, Nizhny Novgorod, Russia 4 Center of Life Sciences, Skoltech, Moscow, Russia 5 Masaryk University, Central European Institute of Technology, Brno, Czech Republic 1 Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia 7 Laboratoire de physique statistique, CNRS, Sorbonne Université, Université Paris-Diderot, and École normale supérieure (PSL University), Paris, France University of Edinburgh, UNITED KINGDOM 2 Pirogov Russian National Research Medical University, Moscow, Russia 8 Laboratoire de physique théorique, CNRS, Sorbonne Université, Université Paris-Diderot, and École normale supérieure (PSL University), Paris, France 6 Moscow State University, Moscow, Russia |
| AuthorAffiliation_xml | – name: 6 Moscow State University, Moscow, Russia – name: 2 Pirogov Russian National Research Medical University, Moscow, Russia – name: 4 Center of Life Sciences, Skoltech, Moscow, Russia – name: 5 Masaryk University, Central European Institute of Technology, Brno, Czech Republic – name: 1 Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia – name: University of Edinburgh, UNITED KINGDOM – name: 8 Laboratoire de physique théorique, CNRS, Sorbonne Université, Université Paris-Diderot, and École normale supérieure (PSL University), Paris, France – name: 7 Laboratoire de physique statistique, CNRS, Sorbonne Université, Université Paris-Diderot, and École normale supérieure (PSL University), Paris, France – name: 3 Privolzhsky Research Medical University, Nizhny Novgorod, Russia |
| Author_xml | – sequence: 1 givenname: Mikhail V. surname: Pogorelyy fullname: Pogorelyy, Mikhail V. – sequence: 2 givenname: Anastasia A. surname: Minervina fullname: Minervina, Anastasia A. – sequence: 3 givenname: Mikhail orcidid: 0000-0001-7826-7942 surname: Shugay fullname: Shugay, Mikhail – sequence: 4 givenname: Dmitriy M. orcidid: 0000-0003-0430-790X surname: Chudakov fullname: Chudakov, Dmitriy M. – sequence: 5 givenname: Yuri B. orcidid: 0000-0003-4554-4733 surname: Lebedev fullname: Lebedev, Yuri B. – sequence: 6 givenname: Thierry orcidid: 0000-0002-5456-9361 surname: Mora fullname: Mora, Thierry – sequence: 7 givenname: Aleksandra M. orcidid: 0000-0002-2686-5702 surname: Walczak fullname: Walczak, Aleksandra M. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31194732$$D View this record in MEDLINE/PubMed https://hal.sorbonne-universite.fr/hal-02271874$$DView record in HAL |
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| ContentType | Journal Article |
| Copyright | COPYRIGHT 2019 Public Library of Science 2019 Pogorelyy et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Distributed under a Creative Commons Attribution 4.0 International License 2019 Pogorelyy et al 2019 Pogorelyy et al |
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| DOI | 10.1371/journal.pbio.3000314 |
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| Keywords | Immune response Lymphocytes Protein sequencing T cell receptors Nucleotide sequencing Cancer immunotherapy Sequence motif analysis Vaccination and immunization |
| Language | English |
| License | Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Creative Commons Attribution License |
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| SubjectTerms | Adaptive immunity Adaptive Immunity - genetics Adaptive immunology Amino acids Ankylosing spondylitis Antigen receptors, T cell Antigens Antigens, Viral Arthritis Autoimmune diseases Biology and Life Sciences Cancer immunotherapy Cluster Analysis Clustering Complementarity Determining Regions - genetics Complementarity Determining Regions - physiology Data collection Disease control Funding High-Throughput Nucleotide Sequencing - methods Humans Immune response Immunity Immunology Immunotherapy Information processing Life Sciences Lymphocytes Lymphocytes T Medical research Medicine and Health Sciences Next-generation sequencing Observations Physiological aspects Receptors Receptors, Antigen, T-Cell - immunology Receptors, Antigen, T-Cell - metabolism Receptors, Antigen, T-Cell - physiology Research and Analysis Methods Sequence Analysis, DNA - methods Software Spondylitis Supervision T cell receptors T cells T-cell receptor Vaccines Vector-borne diseases Yellow fever |
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