Skip to main content

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

Publication ,  Journal Article
Fourati, S; Talla, A; Mahmoudian, M; Burkhart, JG; Klén, R; Henao, R; Yu, T; Aydın, Z; Yeung, KY; Ahsen, ME; Almugbel, R; Jahandideh, S ...
Published in: Nat Commun
October 24, 2018

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.

Duke Scholars

Altmetric Attention Stats
Dimensions Citation Stats

Published In

Nat Commun

DOI

EISSN

2041-1723

Publication Date

October 24, 2018

Volume

9

Issue

1

Start / End Page

4418

Location

England

Related Subject Headings

  • Rhinovirus
  • Respiratory Syncytial Viruses
  • Influenza A Virus, H3N2 Subtype
  • Influenza A Virus, H1N2 Subtype
  • Humans
  • Heme
  • Healthy Volunteers
  • Gene Expression
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Fourati, S., Talla, A., Mahmoudian, M., Burkhart, J. G., Klén, R., Henao, R., … Sieberts, S. K. (2018). A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nat Commun, 9(1), 4418. https://doi.org/10.1038/s41467-018-06735-8
Fourati, Slim, Aarthi Talla, Mehrad Mahmoudian, Joshua G. Burkhart, Riku Klén, Ricardo Henao, Thomas Yu, et al. “A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection.Nat Commun 9, no. 1 (October 24, 2018): 4418. https://doi.org/10.1038/s41467-018-06735-8.
Fourati S, Talla A, Mahmoudian M, Burkhart JG, Klén R, Henao R, et al. A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nat Commun. 2018 Oct 24;9(1):4418.
Fourati, Slim, et al. “A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection.Nat Commun, vol. 9, no. 1, Oct. 2018, p. 4418. Pubmed, doi:10.1038/s41467-018-06735-8.
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. A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nat Commun. 2018 Oct 24;9(1):4418.

Published In

Nat Commun

DOI

EISSN

2041-1723

Publication Date

October 24, 2018

Volume

9

Issue

1

Start / End Page

4418

Location

England

Related Subject Headings

  • Rhinovirus
  • Respiratory Syncytial Viruses
  • Influenza A Virus, H3N2 Subtype
  • Influenza A Virus, H1N2 Subtype
  • Humans
  • Heme
  • Healthy Volunteers
  • Gene Expression