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

Published online

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Fourati, S; Talla, A; Mahmoudian, M; Burkhart, JG; Klén, R; Henao, R; Yu, T; Aydın, Z; Yeung, KY; Ahsen, ME; Almugbel, R; Jahandideh, S; Liang, X; Nordling, TEM; Shiga, M; Stanescu, A; Vogel, R; Respiratory Viral DREAM Challenge Consortium, ; Pandey, G; Chiu, C; McClain, MT; Woods, CW; Ginsburg, GS; Elo, LL; Tsalik, EL; Mangravite, LM; Sieberts, SK

Published Date

  • October 24, 2018

Published In

Volume / Issue

  • 9 / 1

Start / End Page

  • 4418 -

PubMed ID

  • 30356117

Pubmed Central ID

  • 30356117

Electronic International Standard Serial Number (EISSN)

  • 2041-1723

Digital Object Identifier (DOI)

  • 10.1038/s41467-018-06735-8

Language

  • eng

Conference Location

  • England