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Learning the High-Dimensional Immunogenomic Features That Predict Public and Private Antibody Repertoires.
Greiff, Victor; Weber, Cédric R; Palme, Johannes; Bodenhofer, Ulrich; Miho, Enkelejda; Menzel, Ulrike; Reddy, Sai T.
Afiliación
  • Greiff V; Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology Zurich, CH-4058 Basel, Switzerland.
  • Weber CR; Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology Zurich, CH-4058 Basel, Switzerland.
  • Palme J; Institute of Bioinformatics, Johannes Kepler University, 4040 Linz, Austria; and.
  • Bodenhofer U; Health and Environment Department, Austrian Institute of Technology, 1220 Vienna, Austria.
  • Miho E; Institute of Bioinformatics, Johannes Kepler University, 4040 Linz, Austria; and.
  • Menzel U; Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology Zurich, CH-4058 Basel, Switzerland.
  • Reddy ST; Department of Biosystems Science and Engineering, Swiss Federal Institute of Technology Zurich, CH-4058 Basel, Switzerland.
J Immunol ; 199(8): 2985-2997, 2017 10 15.
Article en En | MEDLINE | ID: mdl-28924003
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Vacunas / Linfocitos B / Receptores de Antígenos de Linfocitos B / Receptores de Antígenos de Linfocitos T / Linfocitos T / Regiones Determinantes de Complementariedad / Inmunoterapia Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals / Humans Idioma: En Revista: J Immunol Año: 2017 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Vacunas / Linfocitos B / Receptores de Antígenos de Linfocitos B / Receptores de Antígenos de Linfocitos T / Linfocitos T / Regiones Determinantes de Complementariedad / Inmunoterapia Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals / Humans Idioma: En Revista: J Immunol Año: 2017 Tipo del documento: Article País de afiliación: Suiza Pais de publicación: Estados Unidos