Learning the high-dimensional immunogenomic features that predict public and private antibody repertoires
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Authors
Weber, Cédric R.
Palme, Johannes
Bodenhofer, Ulrich
Menzel, Ulrike
Reddy, Sai T.
Author (Corporation)
Publication date
15.10.2017
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01A - Journal article
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Parent work
Journal of Immunology
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Volume
199
Issue / Number
8
Pages / Duration
2985-2997
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Publisher / Publishing institution
American Association of Immunologists
Place of publication / Event location
Rockville
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Abstract
Recent studies have revealed that immune repertoires contain a substantial fraction of public clones, which may be defined as Ab or TCR clonal sequences shared across individuals. It has remained unclear whether public clones possess predictable sequence features that differentiate them from private clones, which are believed to be generated largely stochastically. This knowledge gap represents a lack of insight into the shaping of immune repertoire diversity. Leveraging a machine learning approach capable of capturing the high-dimensional compositional information of each clonal sequence (defined by CDR3), we detected predictive public clone and private clone–specific immunogenomic differences concentrated in CDR3’s N1–D–N2 region, which allowed the prediction of public and private status with 80% accuracy in humans and mice. Our results unexpectedly demonstrate that public, as well as private, clones possess predictable high-dimensional immunogenomic features. Our support vector machine model could be trained effectively on large published datasets (3 million clonal sequences) and was sufficiently robust for public clone prediction across individuals and studies prepared with different library preparation and high-throughput sequencing protocols. In summary, we have uncovered the existence of high-dimensional immunogenomic rules that shape immune repertoire diversity in a predictable fashion. Our approach may pave the way for the construction of a comprehensive atlas of public mouse and human immune repertoires with potential applications in rational vaccine design and immunotherapeutics.
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Subject (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
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ISSN
0022-1767
1550-6606
1550-6606
Language
English
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No
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Publication status
Published
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Peer review of the complete publication
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Closed
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Citation
GREIFF, Victor, Cédric R. WEBER, Johannes PALME, Ulrich BODENHOFER, Enkelejda MIHO, Ulrike MENZEL und Sai T. REDDY, 2017. Learning the high-dimensional immunogenomic features that predict public and private antibody repertoires. Journal of Immunology. 15 Oktober 2017. Bd. 199, Nr. 8, S. 2985–2997. DOI 10.4049/jimmunol.1700594. Verfügbar unter: https://irf.fhnw.ch/handle/11654/46932