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Omics-Based Predictive Modeling of Age-Dependent Outcome to Influenza Infection

基于组学的流感感染年龄依赖性结果预测模型

基本信息

批准号：
9331417
负责人：
Elodie Ghedin
金额：
$ 271.45万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9331417
关键词：
Adult Affect Age Animal Model Bedside Testings Behavior Binding Biological Biological Markers Cessation of life Clinical Complex Computer Simulation Data Development Diabetes Mellitus Disease Disease Outcome Ferrets Goals Hospitalization Human Immune response Infection Influenza Influenza A Virus, H1N1 Subtype Instruction Interleukin-17 Interleukin-6 Lower respiratory tract structure Measurement Molecular Models Morbidity - disease rate Neonatal Outcome Pathway Analysis Patients Physiological Prognostic Marker Public Health RNA Risk Risk Factors Sampling Severities System TNF gene Tissues Transcript Translating Viral Virus Diseases Whole Organism age related aged base data modeling high dimensionality mathematical model mortality outcome forecast pathogen pre-clinical predictive modeling protein metabolite respiratory response trait transcriptomics virus host interaction

项目摘要

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. RELEVANCE (See instructions):

流感是世界各地的一个主要公共卫生问题，并决定着感染者的预后

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Genetic diversity of the 2013-14 human isolates of influenza H7N9 in China.

DOI：
10.1186/s12879-015-0829-8
发表时间：
2015-02-28
期刊：
BMC infectious diseases
影响因子：
3.7
作者：
Farooqui A;Leon AJ;Huang L;Wu S;Cai Y;Lin P;Chen W;Fang X;Zeng T;Liu Y;Zhang L;Su T;Chen W;Ghedin E;Zhu H;Guan Y;Kelvin DJ
通讯作者：
Kelvin DJ

Pathogenic influenza B virus in the ferret model establishes lower respiratory tract infection.

DOI：
10.1099/vir.0.064352-0
发表时间：
2014-10
期刊：
The Journal of general virology
影响因子：
0
作者：
Huang SSH;Banner D;Paquette SG;Leon AJ;Kelvin AA;Kelvin DJ
通讯作者：
Kelvin DJ

Influenza Transmission in the Mother-Infant Dyad Leads to Severe Disease, Mammary Gland Infection, and Pathogenesis by Regulating Host Responses.

DOI：
10.1371/journal.ppat.1005173
发表时间：
2015-10
期刊：
PLoS pathogens
影响因子：
6.7
作者：
Paquette SG;Banner D;Huang SS;Almansa R;Leon A;Xu L;Bartoszko J;Kelvin DJ;Kelvin AA
通讯作者：
Kelvin AA

Emerging Infections of CNS: Avian Influenza A Virus, Rift Valley Fever Virus and Human Parechovirus.