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

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Fourati, Slim
Taa, Aarthi
Mahmoudian, Mehrad
Burkhart, Joshua G
Klen, Riku
Henao, Ricardo
Yu, Thomas
Aydin, Zafer
Yeung, Ka Yee
Ahsen, Mehmet Eren
Almugbel, Reem
Jahandideh, Samad
Liang, Xiao
Nordling, Torbjorn EM
Shiga, Motoki
Stanescu, Ana
Vogel, Robert
Pandey, Gaurav
Chiu, Christopher
McClain, Micah T
Woods, Christopher W
Ginsburg, Geoffrey S
Elo, Laura L
Tsalik, Ephraim L
Mangravite, Lara M
Sieberts, Solveig K
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2018
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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.

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Nature Communications

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9

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© The Author(s) 2018. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Clinical sciences not elsewhere classified

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