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

Slim Fourati; Aarthi Talla; Mehrad Mahmoudian; Joshua G. Burkhart; Riku Klén; Ricardo Henao; Thomas Yu; Zafer Aydın; Ka Yee Yeung; Mehmet Eren Ahsen; Reem Almugbel; Samad Jahandideh; Xiao Liang; Torbjörn E.M. Nordling; Motoki Shiga; Ana Stanescu; Robert Vogel; Emna Ben Abdallah; Farnoosh Abbas Aghababazadeh; Alicia Amadoz; Sherry Bhalla; Kevin Bleakley; Erika Bongen; Domenico Borzacchielo; Philipp Bucher; Jose Carbonell-Caballero; Kumardeep Chaudhary; Francisco Chinesta; Prasad Chodavarapu; Ryan D. Chow; Thomas Cokelaer; Cankut Cubuk; Sandeep Kumar Dhanda; Joaquin Dopazo; Thomas Faux; Yang Feng; Christofer Flinta; Carito Guziolowski; Di He; Marta R. Hidalgo; Jiayi Hou; Katsumi Inoue; Maria K. Jaakkola; Jiadong Ji; Ritesh Kumar; Sunil Kumar; Miron Bartosz Kursa; Qian Li; Michał Łopuszyński; Pengcheng Lu; Morgan Magnin; Weiguang Mao; Bertrand Miannay; Iryna Nikolayeva; Zoran Obradovic; Chi Pak; Mohammad M. Rahman; Misbah Razzaq; Tony Ribeiro; Olivier Roux; Ehsan Saghapour; Harsh Saini; Shamim Sarhadi; Hiroki Sato; Benno Schwikowski; Alok Sharma; Ronesh Sharma; Deepak Singla; Ivan Stojkovic; Tomi Suomi; Maria Suprun; Chengzhe Tian; Lewis E. Tomalin; Lei Xie; Xiang Yu; Gaurav Pandey; Christopher Chiu; Micah T. McClain; Christopher W. Woods; Geoffrey S. Ginsburg; Laura L. Elo; Ephraim L. Tsalik; Lara M. Mangravite; Solveig K. Sieberts

doi:10.1038/s41467-018-06735-8

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

Slim Fourati, Aarthi Talla, Mehrad Mahmoudian, Joshua G. Burkhart, Riku Klén, Ricardo Henao, Thomas Yu, Zafer Aydın, Ka Yee Yeung, Mehmet Eren Ahsen, Reem Almugbel, Samad Jahandideh, Xiao Liang, Torbjörn E.M. Nordling, Motoki Shiga, Ana Stanescu, Robert Vogel, Emna Ben Abdallah, Farnoosh Abbas Aghababazadeh, Alicia AmadozSherry Bhalla, Kevin Bleakley, Erika Bongen, Domenico Borzacchielo, Philipp Bucher, Jose Carbonell-Caballero, Kumardeep Chaudhary, Francisco Chinesta, Prasad Chodavarapu, Ryan D. Chow, Thomas Cokelaer, Cankut Cubuk, Sandeep Kumar Dhanda, Joaquin Dopazo, Thomas Faux, Yang Feng, Christofer Flinta, Carito Guziolowski, Di He, Marta R. Hidalgo, Jiayi Hou, Katsumi Inoue, Maria K. Jaakkola, Jiadong Ji, Ritesh Kumar, Sunil Kumar, Miron Bartosz Kursa, Qian Li, Michał Łopuszyński, Pengcheng Lu, Morgan Magnin, Weiguang Mao, Bertrand Miannay, Iryna Nikolayeva, Zoran Obradovic, Chi Pak, Mohammad M. Rahman, Misbah Razzaq, Tony Ribeiro, Olivier Roux, Ehsan Saghapour, Harsh Saini, Shamim Sarhadi, Hiroki Sato, Benno Schwikowski, Alok Sharma, Ronesh Sharma, Deepak Singla, Ivan Stojkovic, Tomi Suomi, Maria Suprun, Chengzhe Tian, Lewis E. Tomalin, Lei Xie, Xiang Yu, Gaurav Pandey, Christopher Chiu, Micah T. McClain, Christopher W. Woods, Geoffrey S. Ginsburg, Laura L. Elo, Ephraim L. Tsalik, Lara M. Mangravite, Solveig K. Sieberts

Research output: Contribution to journal › Article › peer-review

11 Scopus citations

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.

Original language	English (US)
Journal	Nature Communications
Volume	9
Issue number	1
DOIs	https://doi.org/10.1038/s41467-018-06735-8
State	Published - Dec 1 2018
Externally published	Yes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-02-15

ASJC Scopus subject areas

General Biochemistry, Genetics and Molecular Biology
General Chemistry
General Physics and Astronomy

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10.1038/s41467-018-06735-8

Cite this

Fourati, S., Talla, A., Mahmoudian, M., Burkhart, J. G., Klén, R., Henao, R., Yu, T., Aydın, Z., Yeung, K. Y., Ahsen, M. E., Almugbel, R., Jahandideh, S., Liang, X., Nordling, T. E. M., Shiga, M., Stanescu, A., Vogel, R., Abdallah, E. B., Aghababazadeh, F. A., ... Sieberts, S. K. (2018). A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection. Nature Communications, 9(1). https://doi.org/10.1038/s41467-018-06735-8

@article{b6fc4fe403da461ca4d3492a58a2f507,

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 Mehrad Mahmoudian and Burkhart, {Joshua G.} and Riku Kl{\'e}n and Ricardo Henao and Thomas Yu and Zafer Aydın and Yeung, {Ka Yee} and Ahsen, {Mehmet Eren} and Reem Almugbel and Samad Jahandideh and Xiao Liang and Nordling, {Torbj{\"o}rn E.M.} and Motoki Shiga and Ana Stanescu and Robert Vogel and Abdallah, {Emna Ben} and Aghababazadeh, {Farnoosh Abbas} and Alicia Amadoz and Sherry Bhalla and Kevin Bleakley and Erika Bongen and Domenico Borzacchielo and Philipp Bucher and Jose Carbonell-Caballero and Kumardeep Chaudhary and Francisco Chinesta and Prasad Chodavarapu and Chow, {Ryan D.} and Thomas Cokelaer and Cankut Cubuk and Dhanda, {Sandeep Kumar} and Joaquin Dopazo and Thomas Faux and Yang Feng and Christofer Flinta and Carito Guziolowski and Di He and Hidalgo, {Marta R.} and Jiayi Hou and Katsumi Inoue and Jaakkola, {Maria K.} and Jiadong Ji and Ritesh Kumar and Sunil Kumar and Kursa, {Miron Bartosz} and Qian Li and Micha{\l} {\L}opuszy{\'n}ski and Pengcheng Lu and Morgan Magnin and Weiguang Mao and Bertrand Miannay and Iryna Nikolayeva and Zoran Obradovic and Chi Pak and Rahman, {Mohammad M.} and Misbah Razzaq and Tony Ribeiro and Olivier Roux and Ehsan Saghapour and Harsh Saini and Shamim Sarhadi and Hiroki Sato and Benno Schwikowski and Alok Sharma and Ronesh Sharma and Deepak Singla and Ivan Stojkovic and Tomi Suomi and Maria Suprun and Chengzhe Tian and Tomalin, {Lewis E.} and Lei Xie and Xiang Yu and Gaurav Pandey and Christopher Chiu and McClain, {Micah T.} and Woods, {Christopher W.} and Ginsburg, {Geoffrey S.} and Elo, {Laura L.} and Tsalik, {Ephraim L.} and Mangravite, {Lara M.} and Sieberts, {Solveig K.}",

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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, Abdallah, EB, Aghababazadeh, FA, Amadoz, A, Bhalla, S, Bleakley, K, Bongen, E, Borzacchielo, D, Bucher, P, Carbonell-Caballero, J, Chaudhary, K, Chinesta, F, Chodavarapu, P, Chow, RD, Cokelaer, T, Cubuk, C, Dhanda, SK, Dopazo, J, Faux, T, Feng, Y, Flinta, C, Guziolowski, C, He, D, Hidalgo, MR, Hou, J, Inoue, K, Jaakkola, MK, Ji, J, Kumar, R, Kumar, S, Kursa, MB, Li, Q, Łopuszyński, M, Lu, P, Magnin, M, Mao, W, Miannay, B, Nikolayeva, I, Obradovic, Z, Pak, C, Rahman, MM, Razzaq, M, Ribeiro, T, Roux, O, Saghapour, E, Saini, H, Sarhadi, S, Sato, H, Schwikowski, B, Sharma, A, Sharma, R, Singla, D, Stojkovic, I, Suomi, T, Suprun, M, Tian, C, Tomalin, LE, Xie, L, Yu, X, Pandey, G, Chiu, C, McClain, MT, Woods, CW, Ginsburg, GS, Elo, LL, Tsalik, EL, Mangravite, LM & Sieberts, SK 2018, 'A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection', Nature Communications, vol. 9, no. 1. https://doi.org/10.1038/s41467-018-06735-8

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 - 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

AU - Magnin, Morgan

AU - Mao, Weiguang

AU - Miannay, Bertrand

AU - Nikolayeva, Iryna

AU - Obradovic, Zoran

AU - Pak, Chi

AU - Rahman, Mohammad M.

AU - Razzaq, Misbah

AU - Ribeiro, Tony

AU - Roux, Olivier

AU - Saghapour, Ehsan

AU - Saini, Harsh

AU - Sarhadi, Shamim

AU - Sato, Hiroki

AU - Schwikowski, Benno

AU - Sharma, Alok

AU - Sharma, Ronesh

AU - Singla, Deepak

AU - Stojkovic, Ivan

AU - Suomi, Tomi

AU - Suprun, Maria

AU - Tian, Chengzhe

AU - Tomalin, Lewis E.

AU - Xie, Lei

AU - Yu, Xiang

AU - Pandey, Gaurav

AU - Chiu, Christopher

AU - McClain, Micah T.

AU - Woods, Christopher W.

AU - Ginsburg, Geoffrey S.

AU - Elo, Laura L.

AU - Tsalik, Ephraim L.

AU - Mangravite, Lara M.

AU - Sieberts, Solveig K.

N1 - Generated from Scopus record by KAUST IRTS on 2023-02-15

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 - https://www.nature.com/articles/s41467-018-06735-8

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U2 - 10.1038/s41467-018-06735-8

DO - 10.1038/s41467-018-06735-8

M3 - Article

SN - 2041-1723

VL - 9

JO - Nature Communications

JF - Nature Communications

IS - 1

ER -

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

Abstract

Bibliographical note

ASJC Scopus subject areas

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