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

流感是世界各地一个主要的公共卫生问题，并决定着感染者的预后。 在其他方面健康的患者通常是一个主要的挑战。2009年，感染H1N1病毒株 导致274,000人住院，12,470人死亡。发病率和死亡率的危险因素包括年龄、 并存疾病，如糖尿病和下呼吸道疾病。病毒感染是在 在严重的情况下，继而发展为下呼吸道疾病。在这两项人体研究中 和临床前的动物模型，一些生物标记物与更严重的疾病有关， 包括肿瘤坏死因子-α、白介素6和白介素17。宿主对流感感染的反应是一个复杂的特征，涉及 影响细胞、组织的RNA转录物、蛋白质和代谢物的宿主-病原体相互作用网络 以及最终决定感染风险和严重程度的整体行为。情结 这些相互作用的因素会影响整个网络状态，进而增加或降低 感染或对感染的反应的严重程度。我们项目的重点是集成多尺度数据 在流感感染过程中收集的--包括全系统转录和Meta- 转录、免疫反应和生理标记，以及病毒多样性--以便 执行网络分析并开发预测严重疾病后果的计算模型。我们的目标 是利用高维、大规模的Omics数据和数学建模的力量来识别 可用于开发护理点检测的风险分层预后生物标记物 适用于临床呼吸道样本，以识别有严重流感疾病风险的患者。要实现 为了实现这一目标，我们将从分子相互作用网络中建立预测模型，转化为特定的严重性 结果。我们建议使用年龄相关的动物模型(新生、成年和老年雪貂)和 临床人类样本收集宿主-病毒相互作用的多个尺度的生物测量。 相关性(请参阅说明)：

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):