A crowdsourced analysis to identifyab initiomolecular signatures predictive of susceptibility to viral infection
| Autor: | Ephraim L. Tsalik, Ana Stanescu, Xiao Liang, Micah T. McClain, Aarthi Talla, Lara M. Mangravite, Slim Fourati, Robert Vogel, Samad Jahandideh, Reem Almugbel, Torbjörn E. M. Nordling, Solveig K. Sieberts, Riku Klén, Zafer Aydin, Gaurav Pandey, Laura L. Elo, Joshua Burkhart, Ricardo Henao, Geoffrey S. Ginsburg, Mehmet Eren Ahsen, Ka Yee Yeung, Christopher Chiu, Christopher W. Woods, Motoki Shiga, Mehrad Mahmoudian |
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| Rok vydání: | 2018 |
| Předmět: |
0303 health sciences
viruses Late stage Healthy subjects Biology medicine.disease_cause Viral infection Peripheral blood 3. Good health 03 medical and health sciences 0302 clinical medicine Gene expression Immunology medicine Rhinovirus Respiratory system Gene 030217 neurology & neurosurgery 030304 developmental biology |
| Popis: | Respiratory viruses are highly infectious; however, the variation of individuals’ physiologic responses to viral exposure is poorly understood. Most studies examining molecular predictors of response focus on late stage predictors, typically near the time of peak symptoms. To determine whether pre- or early post-exposure factors could predict response, we conducted a community-based analysis to identify predictors of resilience or susceptibility to several respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV) using peripheral blood gene expression profiles collected from healthy subjects prior to viral exposure, as well as up to 24 hours following exposure. This analysis revealed that it is possible to construct models predictive of symptoms using profiles even prior to viral exposure. Analysis of predictive gene features revealed little overlap among models; however, in aggregate, these genes were enriched for common pathways. Heme Metabolism, the most significantly enriched pathway, was associated with higher risk of developing symptoms following viral exposure. |
| Databáze: | OpenAIRE |
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