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Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses.
Bakker, Olivier B; Aguirre-Gamboa, Raul; Sanna, Serena; Oosting, Marije; Smeekens, Sanne P; Jaeger, Martin; Zorro, Maria; Võsa, Urmo; Withoff, Sebo; Netea-Maier, Romana T; Koenen, Hans J P M; Joosten, Irma; Xavier, Ramnik J; Franke, Lude; Joosten, Leo A B; Kumar, Vinod; Wijmenga, Cisca; Netea, Mihai G; Li, Yang.
  • Bakker OB; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Aguirre-Gamboa R; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Sanna S; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Oosting M; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Smeekens SP; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Jaeger M; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Zorro M; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Võsa U; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Withoff S; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Netea-Maier RT; Department of Internal Medicine, Division of Endocrinology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Koenen HJPM; Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Joosten I; Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Xavier RJ; Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.
  • Franke L; Broad Institute of MIT and Harvard University, Cambridge, MA, USA.
  • Joosten LAB; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Kumar V; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Wijmenga C; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Netea MG; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.
  • Li Y; Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. c.wijmenga@umcg.nl.
Nat Immunol ; 19(7): 776-786, 2018 07.
Article en En | MEDLINE | ID: mdl-29784908
ABSTRACT
The immune response to pathogens varies substantially among people. Whereas both genetic and nongenetic factors contribute to interperson variation, their relative contributions and potential predictive power have remained largely unknown. By systematically correlating host factors in 534 healthy volunteers, including baseline immunological parameters and molecular profiles (genome, metabolome and gut microbiome), with cytokine production after stimulation with 20 pathogens, we identified distinct patterns of co-regulation. Among the 91 different cytokine-stimulus pairs, 11 categories of host factors together explained up to 67% of interindividual variation in cytokine production induced by stimulation. A computational model based on genetic data predicted the genetic component of stimulus-induced cytokine production (correlation 0.28-0.89), and nongenetic factors influenced cytokine production as well.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Citocinas Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Año: 2018 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Citocinas Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Año: 2018 Tipo del documento: Article