TY - JOUR
T1 - A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection
AU - The Respiratory Viral DREAM Challenge Consortium
AU - Fourati, Slim
AU - Talla, Aarthi
AU - Mahmoudian, Mehrad
AU - Burkhart, Joshua G.
AU - Klén, Riku
AU - Henao, Ricardo
AU - Yu, Thomas
AU - Aydın, Zafer
AU - Yeung, Ka Yee
AU - Ahsen, Mehmet Eren
AU - Almugbel, Reem
AU - Jahandideh, Samad
AU - Liang, Xiao
AU - Nordling, Torbjörn E.M.
AU - Shiga, Motoki
AU - Stanescu, Ana
AU - Vogel, Robert
AU - Abdallah, Emna Ben
AU - Aghababazadeh, Farnoosh Abbas
AU - Amadoz, Alicia
AU - Bhalla, Sherry
AU - Bleakley, Kevin
AU - Bongen, Erika
AU - Borzacchielo, Domenico
AU - Bucher, Philipp
AU - Carbonell-Caballero, Jose
AU - Chaudhary, Kumardeep
AU - Chinesta, Francisco
AU - Chodavarapu, Prasad
AU - Chow, Ryan D.
AU - Cokelaer, Thomas
AU - Cubuk, Cankut
AU - Dhanda, Sandeep Kumar
AU - Dopazo, Joaquin
AU - Faux, Thomas
AU - Feng, Yang
AU - Flinta, Christofer
AU - Guziolowski, Carito
AU - He, Di
AU - Hidalgo, Marta R.
AU - Hou, Jiayi
AU - Inoue, Katsumi
AU - Jaakkola, Maria K.
AU - Ji, Jiadong
AU - Kumar, Ritesh
AU - Kumar, Sunil
AU - Kursa, Miron Bartosz
AU - Li, Qian
AU - Łopuszyński, Michał
AU - Lu, Pengcheng
N1 - Funding Information:
This work was supported by Defense Advanced Research Projects Agency and the Army Research Office through Grant W911NF-15-1-0107. The views expressed are those of the authors and do not reflect the official policy or position of the Department of Defense or the U.S. Government. J.G.B. was supported by a training grant from the National Institutes of Health, USA (NIH grant 4T15LM007088-25). G.P. and A.S.’s work was supported by NIH grant # R01GM114434 and an IBM faculty award to G.P. T.E.M.N. was supported by the Ministry of Science and Technology of Taiwan grants MOST 105-2218-E-006-016-MY2 and 107-2634-F-006-009. K.Y.Y. was supported by NIH grants U54 HL127624 and R01GM126019. M.S. was supported by Grants-in-Aid for Scientific Research JP16H02866 from the Japan Society for the Promotion of Science. We wish to thank the DARPA Biochronicity program and its program manager, Dr. Jim Gimlett, for generously offering to share gene expression data generated as part of that program and Rafick P. Sekaly (Case Western Reserve University) for his critical feedback during the writing process.
Publisher Copyright:
© 2018, The Author(s).
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.
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U2 - 10.1038/s41467-018-06735-8
DO - 10.1038/s41467-018-06735-8
M3 - Article
C2 - 30356117
AN - SCOPUS:85055459624
SN - 2041-1723
VL - 9
JO - Nature communications
JF - Nature communications
IS - 1
M1 - 4418
ER -