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Nat Immunol. 2018 Jul;19(7):776-786. doi: 10.1038/s41590-018-0121-3. Epub 2018 May 21.

Integration of multi-omics data and deep phenotyping enables prediction of cytokine responses.

Author information

1
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
2
Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands.
3
Department of Internal Medicine, Division of Endocrinology, Radboud University Medical Center, Nijmegen, the Netherlands.
4
Department of Laboratory Medicine, Laboratory for Medical Immunology, Radboud University Medical Center, Nijmegen, the Netherlands.
5
Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA.
6
Broad Institute of MIT and Harvard University, Cambridge, MA, USA.
7
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. c.wijmenga@umcg.nl.
8
Department of Immunology, University of Oslo, Oslo University Hospital, Rikshospitalet, Oslo, Norway. c.wijmenga@umcg.nl.
9
Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands. mihai.netea@radboudumc.nl.
10
Department of Genomics & Immunoregulation, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany. mihai.netea@radboudumc.nl.
11
Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. y.li01@umcg.nl.

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.

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