Einzeltreffer — DigiBib

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.

Competing Interests: The authors declare no competing interest.

Titel:	Deep generative selection models of T and B cell receptor repertoires with soNNia.
Autor/in / Beteiligte Person:	Isacchini, G ; Walczak, AM ; Mora, T ; Nourmohammad, A
Link:	Full Text Finder (Volltext)
Zeitschrift:	Proceedings of the National Academy of Sciences of the United States of America, Jg. 118 (2021-04-06), Heft 14
Veröffentlichung:	Washington, DC : National Academy of Sciences, 2021
Medientyp:	academicJournal
ISSN:	1091-6490 (electronic)
DOI:	10.1073/pnas.2023141118
Schlagwort:	Epitopes chemistry Epitopes immunology Humans Receptors, Immunologic classification Receptors, Immunologic immunology B-Lymphocytes immunology Machine Learning Receptors, Immunologic chemistry T-Lymphocytes immunology
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article; Research Support, Non-U.S. Gov't Language: English [Proc Natl Acad Sci U S A] 2021 Apr 06; Vol. 118 (14). MeSH Terms: Machine Learning* ; B-Lymphocytes / immunology ; Receptors, Immunologic / chemistry ; T-Lymphocytes / *immunology ; Epitopes / chemistry ; Epitopes / immunology ; Humans ; Receptors, Immunologic / classification ; Receptors, Immunologic / immunology References: PLoS One. 2015 Oct 28;10(10):e0141561. (PMID: 26509579) ; Nature. 1988 Aug 4;334(6181):395-402. (PMID: 3043226) ; Nat Immunol. 2010 Jan;11(1):7-13. (PMID: 20016504) ; PLoS Comput Biol. 2016 Jan 11;12(1):e1004409. (PMID: 26751373) ; JCI Insight. 2016 Dec 8;1(20):e88242. (PMID: 27942583) ; Nat Med. 2015 Jan;21(1):86-91. (PMID: 25501908) ; Proc Natl Acad Sci U S A. 2009 Dec 1;106(48):20216-21. (PMID: 19875695) ; Nat Biotechnol. 2014 Feb;32(2):158-68. (PMID: 24441474) ; Philos Trans R Soc Lond B Biol Sci. 2015 Sep 5;370(1676):. (PMID: 26194757) ; Nature. 2017 Jul 6;547(7661):89-93. (PMID: 28636592) ; Elife. 2020 Feb 21;9:. (PMID: 32081129) ; PLoS Comput Biol. 2021 Sep 2;17(9):e1009297. (PMID: 34473697) ; Nat Protoc. 2016 Mar;11(3):429-42. (PMID: 26844430) ; Bioinformatics. 2019 Sep 1;35(17):2974-2981. (PMID: 30657870) ; Front Immunol. 2020 Aug 25;11:1803. (PMID: 32983088) ; Immunogenetics. 2018 Mar;70(3):159-168. (PMID: 28779185) ; Phys Rev E. 2020 Jun;101(6-1):062414. (PMID: 32688532) ; Front Immunol. 2014 Feb 04;5:13. (PMID: 24550908) ; Nature. 1983 Apr 14;302(5909):575-81. (PMID: 6300689) ; Nat Rev Immunol. 2014 Jun;14(6):377-91. (PMID: 24830344) ; J Immunol Methods. 2013 May 31;391(1-2):14-21. (PMID: 23428915) ; Genome Med. 2015 Nov 23;7:123. (PMID: 26596423) ; J Leukoc Biol. 2016 Mar;99(3):505-13. (PMID: 26394815) ; Nature. 2017 Jul 6;547(7661):94-98. (PMID: 28636589) ; Proc Natl Acad Sci U S A. 2014 Jul 8;111(27):9875-80. (PMID: 24941953) ; Elife. 2019 Sep 05;8:. (PMID: 31487240) ; Bioinformatics. 2010 Apr 1;26(7):867-72. (PMID: 20147303) ; Nat Commun. 2018 Feb 8;9(1):561. (PMID: 29422654) ; Nucleic Acids Res. 2020 Jan 8;48(D1):D1057-D1062. (PMID: 31588507) ; PLoS Comput Biol. 2019 Mar 4;15(3):e1006874. (PMID: 30830899) ; Nat Methods. 2015 May;12(5):380-1. (PMID: 25924071) ; Nucleic Acids Res. 2018 Jan 4;46(D1):D419-D427. (PMID: 28977646) ; Nat Commun. 2019 Mar 21;10(1):1321. (PMID: 30899025) ; Curr Protoc Immunol. 2010 Feb;Chapter 18:18.17.1-18.17.13. (PMID: 20143317) ; PLoS Comput Biol. 2020 Dec 9;16(12):e1008394. (PMID: 33296360) ; Proc Natl Acad Sci U S A. 2016 May 10;113(19):E2636-45. (PMID: 27114511) ; Genes Immun. 2016 Apr;17(3):153-64. (PMID: 26963138) ; Proc Natl Acad Sci U S A. 2005 Dec 20;102(51):18538-43. (PMID: 16344461) ; Eur J Immunol. 2013 Sep;43(9):2507-15. (PMID: 23696157) ; Proc Natl Acad Sci U S A. 2020 Jan 7;117(1):532-540. (PMID: 31879353) ; Proc Natl Acad Sci U S A. 2012 Sep 25;109(39):15865-70. (PMID: 22984176) ; J Immunol. 2012 Sep 15;189(6):3221-30. (PMID: 22865917) ; Mol Syst Biol. 2020 Aug;16(8):e9416. (PMID: 32779888) ; PLoS Comput Biol. 2020 Mar 12;16(3):e1007714. (PMID: 32163410) ; Nat Genet. 2017 May;49(5):659-665. (PMID: 28369038) ; Proc Natl Acad Sci U S A. 2012 Oct 2;109(40):16161-6. (PMID: 22988065) ; J Immunol. 2017 Oct 15;199(8):2985-2997. (PMID: 28924003) ; Nat Biotechnol. 2013 Feb;31(2):166-9. (PMID: 23334449) ; Front Immunol. 2019 Nov 29;10:2820. (PMID: 31849987) ; Front Immunol. 2019 Jul 31;10:1516. (PMID: 31417541) ; Nat Rev Immunol. 2017 May;17(5):281-294. (PMID: 28368006) Contributed Indexing: Keywords: adaptive immune repertoires; central tolerance; deep neural networks; statistical inference; thymic selection Substance Nomenclature: 0 (Epitopes) ; 0 (Receptors, Immunologic) Entry Date(s): Date Created: 20210402 Date Completed: 20211119 Latest Revision: 20240331 Update Code: 20240331 PubMed Central ID: PMC8040596

Klicken Sie ein Format an und speichern Sie dann die Daten oder geben Sie eine Empfänger-Adresse ein und lassen Sie sich per Email zusenden.

BibTeX Citavi, JabRef, u.a.
(Literaturverwaltung)

PDF kein Volltext!
(Merkzettel, Notizen)

RIS Endnote, Citavi u.a.
(Literaturverwaltung)

MODS
(XML zur Weiterverarbeitung)

oder

Wählen Sie das für Sie passende Zitationsformat und kopieren Sie es dann in die Zwischenablage, lassen es sich per Mail zusenden oder speichern es als PDF-Datei.

Gewünschter Zitations-Stil:

oder

Bitte prüfen Sie, ob die Zitation formal korrekt ist, bevor Sie sie in einer Arbeit verwenden. Benutzen Sie gegebenenfalls den "Exportieren"-Dialog, wenn Sie ein Literaturverwaltungsprogramm verwenden und die Zitat-Angaben selbst formatieren wollen.

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