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

Fourati, Slim

Talla, Aarthi

Mahmoudian, Mehrad

Burkhart, Joshua G

Klén, Riku

Henao, Ricardo

Yu, Thomas

Aydın, Zafer

Yeung, Ka Yee

Ahsen, Mehmet Eren

Almugbel, Reem

Jahandideh, Samad

Liang, Xiao

Nordling, Torbjörn EM

Shiga, Motoki

Stanescu, Ana

Vogel, Robert

Respiratory Viral DREAM Challenge Consortium

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

2020-11-01T14:39:02Z

2020-11-01T14:39:02Z

2018-10-24

2020-11-01T14:39:00Z

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.

10.1038/s41467-018-06735-8

2041-1723

2041-1723

https://hdl.handle.net/10161/21658

eng

Springer Science and Business Media LLC

Nature communications

10.1038/s41467-018-06735-8

Respiratory Viral DREAM Challenge Consortium

Humans

Respiratory Syncytial Viruses

Rhinovirus

Heme

Gene Expression

Influenza A Virus, H3N2 Subtype

Influenza A Virus, H1N2 Subtype

Healthy Volunteers

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

Journal article

Henao, Ricardo|0000-0003-4980-845X

Woods, Christopher W|0000-0001-7240-2453

Ginsburg, Geoffrey S|0000-0003-4739-9808

Tsalik, Ephraim L|0000-0002-6417-2042

4418

1

School of Medicine

Nursing

Duke Cancer Institute

Pathology

Medicine, Cardiology

Duke

School of Nursing

Institutes and Centers

Clinical Science Departments

Medicine

Electrical and Computer Engineering

Duke Clinical Research Institute

Biostatistics & Bioinformatics

Pratt School of Engineering

Basic Science Departments

Medicine, Infectious Diseases

Duke Global Health Institute

University Institutes and Centers

Institutes and Provost's Academic Units

Molecular Genetics and Microbiology

Published

9