Influenza is a major public health concern around the world and determining the prognosis of an infected patient who was otherwise healthy is often a major challenge. In 2009, infections with the H1N1 strain resulted in 274,000 hospitalizations and 12,470 deaths. Risk factors for morbidity and mortality include age, co-morbid illness, such as diabetes meNitus, and lower respiratory tract disease. Viral infection is initiated in the upper ainway and, in severe cases, followed by progression to lower tract disease. In both human studies and pre-clinical animal models, several biomarkers have been associated with more severe disease, including TNF-a, IL-6, and IL-17. Host response to influenza infection is a complex trait that involves entire host-pathogen interaction networks of RNA transcripts, proteins and metabolites impacting cellular, tissue and whole organism behaviors that ultimately define both the risk and severity of infection. The complex array of these interacting factors affect entire network states that in turn increase or decrease the risk of infection or the severity of response to infection. The focus of our project is to integrate multi-scale data collected over the course of influenza infections-including system-wide transcriptomics and meta- transcriptomics, immunological response and physiological markers, along with viral diversity-in order to perform network analyses and develop computational models that predict severe disease outcome. Our goal is to leverage the power of high-dimensional, large-scale Omics data and mathematical modeling to identify risk-stratifying prognostic biomarkers that could be used in the development of point-of-care testing applicable to clinical respiratory samples to identify patients at risk for severe influenza disease. To achieve this goal, we will build predictive models from molecular interaction networks, translated to specific severity outcomes. We propose to use an age-dependent animal model (neonatal, adult and aged ferrets) and clinical human samples to collect biological measurements at multiple scales of host-virus interaction.

Public Health Relevance

Agency
National Institute of Health (NIH)
Institute
National Institute of Allergy and Infectious Diseases (NIAID)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01AI111598-05
Application #
9124711
Study Section
Special Emphasis Panel (ZAI1)
Program Officer
Degrace, Marciela M
Project Start
2013-09-01
Project End
2018-08-31
Budget Start
2016-09-01
Budget End
2017-08-31
Support Year
5
Fiscal Year
2016
Total Cost
Indirect Cost
Name
New York University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
041968306
City
New York
State
NY
Country
United States
Zip Code
10012
Wong, Madeline Y; Chen, Kenny; Antonopoulos, Aristotelis et al. (2018) XBP1s activation can globally remodel N-glycan structure distribution patterns. Proc Natl Acad Sci U S A 115:E10089-E10098
Zhou, Xianxiao; Wang, Minghui; Katsyv, Igor et al. (2018) EMUDRA: Ensemble of Multiple Drug Repositioning Approaches to improve prediction accuracy. Bioinformatics 34:3151-3159
McKenzie, Andrew T; Moyon, Sarah; Wang, Minghui et al. (2017) Multiscale network modeling of oligodendrocytes reveals molecular components of myelin dysregulation in Alzheimer's disease. Mol Neurodegener 12:82
Wonderlich, Elizabeth R; Swan, Zachary D; Bissel, Stephanie J et al. (2017) Widespread Virus Replication in Alveoli Drives Acute Respiratory Distress Syndrome in Aerosolized H5N1 Influenza Infection of Macaques. J Immunol 198:1616-1626
Sobel Leonard, Ashley; Weissman, Daniel B; Greenbaum, Benjamin et al. (2017) Transmission Bottleneck Size Estimation from Pathogen Deep-Sequencing Data, with an Application to Human Influenza A Virus. J Virol 91:
Chimelli, Leila; Melo, Adriana S O; Avvad-Portari, Elyzabeth et al. (2017) The spectrum of neuropathological changes associated with congenital Zika virus infection. Acta Neuropathol 133:983-999
Forst, Christian V; Zhou, Bin; Wang, Minghui et al. (2017) Integrative gene network analysis identifies key signatures, intrinsic networks and host factors for influenza virus A infections. NPJ Syst Biol Appl 3:35
McKenzie, Andrew T; Katsyv, Igor; Song, Won-Min et al. (2016) DGCA: A comprehensive R package for Differential Gene Correlation Analysis. BMC Syst Biol 10:106
Zhao, Yongzhong; Forst, Christian V; Sayegh, Camil E et al. (2016) Molecular and genetic inflammation networks in major human diseases. Mol Biosyst 12:2318-41
Katsyv, Igor; Wang, Minghui; Song, Won Min et al. (2016) EPRS is a critical regulator of cell proliferation and estrogen signaling in ER+ breast cancer. Oncotarget 7:69592-69605

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