Mathematical Modeling of Influenza Severity in Outbred Mice

项目摘要 在美国，肺流感感染每年发生在5-20%的人口中，死亡率为20%。 3万人死亡的范围。最近的2009年H1N1流感大流行表明， 感染率，据报告在某些年龄组高达45%。2017-18流感季节 儿童死亡率是2009年流感大流行以来最高的。已知流感感染会导致广泛的 尽管严重肺炎相对罕见，但它在人类中具有疾病表型谱。尽管如此，严重 这种疾病往往需要对年轻人，包括以前健康的儿童，提供先进的支持性护理。主机 决定流感感染结果的因素尚不清楚， 严重疾病的风险更高。第一次接触流感也被认为决定了终身免疫力。小 年轻和性别对流感反应和严重程度的影响。这突出表明 了解儿科人群中流感发病机制的重要性。流感的发病机制很可能是 在很大程度上由肺中旺盛的炎症宿主反应介导。很可能预测可溶性 炎症介质存在于严重感染中。此外，预测性生物标志物或数学模型 流感肺炎的严重程度将提高临床决策和病人护理。我们建议 宿主免疫终点的机器学习和数学建模将定义一个分子 青少年严重流感肺炎的指纹。这一假设将在两个目标中得到检验。要求1 将重点关注与幼年动物流感严重程度相关的特征分子途径， 小鼠我们将利用机器学习和新的数学方法进行途径和生物标志物的选择。 目标2将测试流感发病机理的数学模型，以阐明驱动肺的新机制， 损伤这项研究的总体目标是确定新的生物标志物和机制模型， 流感肺炎的严重程度，可适用于儿童。为了做到这一点，我们将使用广泛的， 探索性的、公正的方法。候选生物标志物和途径将在未来进行评估 在小鼠和人类中的机制和转化研究。

PROJECT SUMMARY In the United States, pulmonary influenza infection occurs annually in 5-20% of the population with mortality in the range of 30,000 deaths. The recent 2009 influenza H1N1 pandemic illustrated the potential for higher infection rates, which were reported to be as high as 45% in certain age groups. The 2017-18 influenza season had the highest pediatric mortalities since the 2009 pandemic. Influenza infection is known to result in a broad spectrum of disease phenotypes in humans, although severe pneumonia is relatively rare. Despite this, severe disease often requires advanced supportive care in the young, including previously healthy children. Host factors involved in determining the outcome of influenza infection are unclear and children are known to be at higher risk of severe disease. First life exposure to influenza is also thought to dictate life-long immunity. Little is known about the effects of young age and gender on influenza responses and severity. This underscores the importance of understanding influenza pathogenesis in a pediatric population. Influenza pathogenesis is likely mediated in large part by exuberant inflammatory host responses in the lung. It is likely that predictive soluble inflammatory mediators are present in severe infection. Further, predictive biomarkers or mathematical models of influenza pneumonia severity would enhance clinical decision making and patient care. We propose that machine learning and mathematical modeling of host immune endpoints will define a molecular fingerprint of severe influenza pneumonia in juveniles. This hypothesis will be tested in two Aims. Aim 1 will focus on characteristic molecular pathways related to influenza severity in juvenile animals, using outbred mice. We will utilize machine learning and new mathematical approaches for pathway and biomarker selection. Aim 2 will test mathematical models of influenza pathogenesis to elucidate new mechanisms that drive lung injury. The overall goal of the proposed study is to identify novel biomarkers and mechanistic models of influenza pneumonia severity that can be applied to children. To accomplish this we will use a broad, exploratory, and unbiased approach. Candidate biomarkers and pathways would then be evaluated in future mechanistic and translational studies in mice and humans.