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Nat Commun. 2018 Oct 24;9(1):4418. doi: 10.1038/s41467-018-06735-8.

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection.

Author information

1
Department of Pathology, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA.
2
Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520, Turku, Finland.
3
Department of Future Technologies, University of Turku, FI-20014 Turku, Finland.
4
Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, Portland, OR, 97239, USA.
5
Laboratory of Evolutionary Genetics, Institute of Ecology and Evolution, University of Oregon, Eugene, OR, 97403, USA.
6
Duke Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, NC, 27710, USA.
7
Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA.
8
Sage Bionetworks, Seattle, WA, 98121, USA.
9
Department of Computer Engineering, Abdullah Gul University, Kayseri, 38080, Turkey.
10
School of Engineering and Technology, University of Washington Tacoma, Tacoma, WA, 98402, USA.
11
Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
12
Origent Data Sciences, Inc., Vienna, VA, 22182, USA.
13
Department of Mechanical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan.
14
Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, Gifu, 501-1193, Japan.
15
Department of Computer Science, University of West Georgia, Carrolton, GA, 30116, USA.
16
IBM T.J. Watson Research Center, Yorktown Heights, NY, 10598, USA.
17
Section of Infectious Diseases and Immunity, Imperial College London, London, W12 0NN, UK.
18
Medical Service, Durham VA Health Care System, Durham, NC, 27705, USA.
19
Department of Medicine, Duke University School of Medicine, Durham, NC, 27710, USA.
20
Emergency Medicine Service, Durham VA Health Care System, Durham, NC, 27705, USA.
21
Sage Bionetworks, Seattle, WA, 98121, USA. lara.mangravite@sagebase.org.
22
Sage Bionetworks, Seattle, WA, 98121, USA. solly.sieberts@sagebase.org.

Abstract

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses.

PMID:
30356117
PMCID:
PMC6200745
DOI:
10.1038/s41467-018-06735-8
[Indexed for MEDLINE]
Free PMC Article

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