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

• Published in: Nature communications Vol. 9; no. 1; pp. 4418 - 11
• Main Authors: 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 E. M., Shiga, Motoki, Stanescu, Ana, Vogel, Robert, 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.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.10.2018; Nature Publishing Group; Nature Portfolio
• Subjects: 38; 38/39; 631/208/199; 631/250/255/2514; 692/53/2423; Crowdsourcing; Exposure; Gene expression; Gene Expression - genetics; Healthy Volunteers; Heme; Heme - metabolism; Humanities and Social Sciences; Humans; Infections; Influenza A Virus, H1N2 Subtype - immunology; Influenza A Virus, H1N2 Subtype - pathogenicity; Influenza A Virus, H3N2 Subtype - immunology; Influenza A Virus, H3N2 Subtype - pathogenicity; Machine Learning; Mathematical models; Metabolism; multidisciplinary; Peripheral blood; Respiratory syncytial virus; Respiratory Syncytial Viruses - immunology; Respiratory Syncytial Viruses - pathogenicity; Rhinovirus; Rhinovirus - immunology; Rhinovirus - pathogenicity; Science; Science (multidisciplinary); Statistics; Viral infections; Viruses
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-018-06735-8

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. The response to respiratory virus exposure can currently not be predicted by pre- or early post-exposure molecular signatures. Here, the authors conduct a community-based analysis of blood gene expression from healthy individuals exposed to respiratory viruses and provide predictive models and biological insight into the physiological response.