Journal article

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 EM Nordling, Motoki Shiga, Ana Stanescu, Robert Vogel, undefined Respiratory Viral DREAM Challenge Consortium, Gaurav Pandey, Christopher Chiu Show all

Nat Commun | Published : 2018

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 f..

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University of Melbourne Researchers

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Awarded by NIGMS NIH HHS


Awarded by DOD | Defense Advanced Research Projects Agency (DARPA)


Awarded by NHLBI NIH HHS


Awarded by U.S. Department of Health & Human Services | National Institutes of Health (NIH)


Awarded by NLM NIH HHS


Awarded by NIH HHS


Awarded by Medical Research Council