Deciphering the Heterogeneous Response to Influenza by a Multi-Scale Systems Approach

项目摘要 由甲型和乙型流感病毒引起的季节性流感疫情导致300-500万例严重病例和 全球每年有300,000-500,000人死亡--特别是在高危人群中，如幼儿、孕妇 妇女、肥胖者、免疫系统受损的人和土著人口。这个 流感的负担在不同季节可能会有很大的不同，部分原因是流行病毒的特点， 人群中现有的免疫力，以及季节性流感疫苗对 流行的病毒株。当一种新的流感病毒株再次出现或 跳过宿主，变得能够感染人类。在这种情况下，没有(或最少)预先存在 在人群水平上对新病毒株的抗体介导的免疫，导致数百万人感染和 病毒在全球的快速传播。在没有抗体的情况下，疾病的严重性可以得到改善。 通过广泛的交叉反应细胞免疫。然而，免疫细胞如何调节的确切机制 一些人的复苏还很不明朗，但另一些人则并非如此。NIAID在以下方面进行了重大投资 生成数据以提高我们对传染病及其进展、风险和严重性的了解 以及治疗和预防。不仅是特定项目的主题，如CEIRS(卓越中心 用于流感研究和监测)和公民领域正在进行的努力(合作流感疫苗 创新中心)，但特别是与组学相关的计划已经产生了高通量基因组， 并向科学界提供了其他相关资源，以 推进传染病基础研究和应用研究。我们将利用这些开放访问数据集 以及可通过本应用程序中的生物信息学资源中心(BRCs)获得的资源。特别是，我们 将利用来自流感研究的免疫表位、病毒序列和抗病毒药物信息 数据库(IRD)，并将这些数据与来自人类感染队列研究的其他公共信息结合起来 与流感病毒有关。单元格数据将提供足够的单元格细节，并将在 只有批量数据可用的情况下。在我们看来，对这些复杂情况建立一个全面和真正具有预测性的模型 只有通过系统的、综合的和多维的OMICS方法才能实现关系 这是我们提供的。宿主对疫苗接种和流感感染的反应是复杂特征的结果，这些特征包括 宿主因子以及转录本、蛋白质、多糖和代谢物的整个网络的组合。 这些反应共同影响细胞、组织和整个生物体的行为。因此，主机对 疫苗接种和感染是分子网络的一种新特性。该集成系统的目标是 生物学的方法是通过确定对流感的异质性反应的机制 高危人群和低危人群生物成分之间的相互作用进行了比较。是这样的 这些发现将大大改进与这些具有威胁性的传染病作斗争的治疗选择。 通过该项目开发的所有模型和软件工具将与社区共享。

Project Summary Seasonal influenza epidemics, caused by influenza A and B viruses, result in 3–5 million severe cases and 300,000–500,000 deaths globally each year - especially in high-risk groups such as young children, pregnant women, obese individuals, individuals with a compromised immune system, and indigenous populations. The burden of influenza can vary widely between seasons, in part due to characteristics of the circulating viruses, the existing immunity in the population, and the effectiveness of seasonal influenza vaccines against the circulating virus strains. Disease morbidity and mortality increase when a new influenza strain reasserts or jumps the host and becomes capable of infecting humans. In this case, there is no (or minimal) pre-existing antibody-mediated immunity to the new viral strain at the population level, leading to millions of infections and a rapid global spread of the virus. In the absence of antibodies, the severity of the disease can be ameliorated by broadly cross-reactive cellular immunity. However, the precise mechanism of how immune cells mediate recovery in some individuals, but not others is far from clear. NIAID has made significant investments in the generation of data to improve our understanding of infectious diseases, their progression, risk, and severity as well as treatment and prevention. Not only subject of specific programs, such as CEIRS (Centers of Excellence for Influenza Research and Surveillance) and the ongoing efforts in CIVICs (Collaborative Influenza Vaccine Innovation Centers), but in particular, omics-related programs have generated high-throughput genomic, proteomic, and integrated "omic" data sets, and provided other related resources to the scientific community to promote basic and applied research in infectious diseases. We will make use of these open access datasets and resources available via the Bioinformatics Resource Centers (BRCs) in this application. In particular, we will utilize immune epitope, viral sequence and antiviral drug information from the Influenza Research Database (IRD) and combine these data with other public information from studies of human cohorts infected with the influenza virus. Single-cell data will provide sufficient cellular detail and will serve as “scaffold” in the case that only bulk data is available. In our view, a comprehensive and truly predictive model of these complex relationships can only be achieved through the systematic, integrative, and multi-dimensional OMICS approach that we offer. Host response to vaccination and to influenza infection is the result of complex traits that involve a combination of host factors along with entire networks of transcripts, proteins, glycans and metabolites. Together these responses impact cellular, tissue, and whole organism behaviors. Thus, the host responses to vaccination and infection are an emergent property of molecular networks. The goal of this integrated systems biology approach is to understand mechanisms of heterogeneous response to Influenza by determining how the interactions among biological components compare between high-risk and lower risk populations. Such findings will significantly improve therapeutic options in the fight against these threatening infectious diseases. All the models and the software tools developed through this project will be shared with the community.