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

Slim Fourati 1 Aarthi Talla 1 Mehrad Mahmoudian 2, 3 Joshua Burkhart 2, 4 Riku Klén 2 Ricardo Henao 5 Thomas yu 6 Zafer Aydin 7 Ka yee yeung 8 Mehmet Eren Ahsen 9 Reem Almugbel 8 Samad Jahandideh 10 Xiao Liang 8 Torbjörn Nordling 11 Motoki Shiga 12 Ana Stanescu 9, 13 Robert Vogel 9, 14 Gaurav Pandey 9 Christopher Chiu 15 Micah Mcclain 15, 5 Christopher Woods 5, 15 Geoffrey Ginsburg 5 Laura Elo 2 Ephraim Tsalik 5 Lara Mangravite 6, * Solveig Sieberts 6, * 
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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Slim Fourati, Aarthi Talla, Mehrad Mahmoudian, Joshua Burkhart, Riku Klén, et al.. A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nature Communications, Nature Publishing Group, 2018, 9 (1), pp.4418. ⟨10.1038/s41467-018-06735-8⟩. ⟨hal-01960254⟩



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