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

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A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. / The Respiratory Viral DREAM Challenge Consortium; Sieberts, Solveig K.

I: Nature Communications, Bind 9, Nr. 1, 4418, 01.12.2018, s. 1-11.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

The Respiratory Viral DREAM Challenge Consortium & Sieberts, SK 2018, 'A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection', Nature Communications, bind 9, nr. 1, 4418, s. 1-11. https://doi.org/10.1038/s41467-018-06735-8

APA

The Respiratory Viral DREAM Challenge Consortium, & Sieberts, S. K. (2018). A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nature Communications, 9(1), 1-11. [4418]. https://doi.org/10.1038/s41467-018-06735-8

Vancouver

The Respiratory Viral DREAM Challenge Consortium, Sieberts SK. A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nature Communications. 2018 dec. 1;9(1):1-11. 4418. https://doi.org/10.1038/s41467-018-06735-8

Author

The Respiratory Viral DREAM Challenge Consortium ; Sieberts, Solveig K. / A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. I: Nature Communications. 2018 ; Bind 9, Nr. 1. s. 1-11.

Bibtex

@article{9323182aa9d54eccbb8a47c3f41bbf6e,
title = "A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection",
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.",
author = "Slim Fourati and Aarthi Talla and {The Respiratory Viral DREAM Challenge Consortium} and Chengzhe Tian and Sieberts, {Solveig K.}",
year = "2018",
month = dec,
day = "1",
doi = "10.1038/s41467-018-06735-8",
language = "English",
volume = "9",
pages = "1--11",
journal = "Nature Communications",
issn = "2041-1723",
publisher = "nature publishing group",
number = "1",

}

RIS

TY - JOUR

T1 - A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

AU - Fourati, Slim

AU - Talla, Aarthi

AU - The Respiratory Viral DREAM Challenge Consortium

AU - Tian, Chengzhe

AU - Sieberts, Solveig K.

PY - 2018/12/1

Y1 - 2018/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85055459624&partnerID=8YFLogxK

U2 - 10.1038/s41467-018-06735-8

DO - 10.1038/s41467-018-06735-8

M3 - Journal article

C2 - 30356117

AN - SCOPUS:85055459624

VL - 9

SP - 1

EP - 11

JO - Nature Communications

JF - Nature Communications

SN - 2041-1723

IS - 1

M1 - 4418

ER -

ID: 222250569