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

Slim Fourati, Aarthi Talla, Mehrad Mahmoudian, Joshua G. Burkhart, Riku Klén, Ricardo Henao, Thomas Yu, Zafer Aydın, Ka Yee Yeung, Mehmet Eren Ahsen, Reem Almugbel, Samad Jahandideh, Xiao Liang, Torbjörn E. M. Nordling, Motoki Shiga, Ana Stanescu, Robert Vogel, Gaurav Pandey, Christopher Chiu, Micah T. McClain, Christopher W. Woods, Geoffrey S. Ginsburg, Laura L. Elo, Ephraim L. Tsalik, Lara M. Mangravite () and Solveig K. Sieberts ()
Additional contact information
Slim Fourati: Case Western Reserve University
Aarthi Talla: Case Western Reserve University
Mehrad Mahmoudian: University of Turku and Åbo Akademi University
Joshua G. Burkhart: Oregon Health & Science University
Riku Klén: University of Turku and Åbo Akademi University
Ricardo Henao: Duke University School of Medicine
Thomas Yu: Sage Bionetworks
Zafer Aydın: Abdullah Gul University
Ka Yee Yeung: University of Washington Tacoma
Mehmet Eren Ahsen: Icahn School of Medicine at Mount Sinai
Reem Almugbel: University of Washington Tacoma
Samad Jahandideh: Origent Data Sciences, Inc.
Xiao Liang: University of Washington Tacoma
Torbjörn E. M. Nordling: National Cheng Kung University
Motoki Shiga: Gifu University
Ana Stanescu: Icahn School of Medicine at Mount Sinai
Robert Vogel: Icahn School of Medicine at Mount Sinai
Gaurav Pandey: Icahn School of Medicine at Mount Sinai
Christopher Chiu: Imperial College London
Micah T. McClain: Duke University School of Medicine
Christopher W. Woods: Duke University School of Medicine
Geoffrey S. Ginsburg: Duke University School of Medicine
Laura L. Elo: University of Turku and Åbo Akademi University
Ephraim L. Tsalik: Duke University School of Medicine
Lara M. Mangravite: Sage Bionetworks
Solveig K. Sieberts: Sage Bionetworks

Nature Communications, 2018, vol. 9, issue 1, 1-11

Abstract: 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-06735-8

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DOI: 10.1038/s41467-018-06735-8

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