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

通过多尺度系统方法解读对流感的异质反应

• 批准号: 10665770
• 负责人: CHRISTIAN FORST
• 金额: $ 58.94万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-14 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10665770
• 关键词: Algorithms; Antibodies; Antibody-mediated protection; Antiviral Agents; Applied Research; Automobile Driving; Basic Science; Behavior; Biological; Biology; Cells; Cellular Immunity; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Cessation of life; Characteristics; Child; Communicable Diseases; Communities; Complex; Data; Data Set; Databases; Demographic Factors; Dimensions; Disease; Effectiveness; Epitopes; Essential Genes; Gene Expression; Generations; Genomics; Goals; Human; Immune; Immune response; Immune system; Immunity; Immunologics; In Vitro; Individual; Infection; Influenza; Influenza A virus; Influenza B Virus; Influenza vaccination; Integration Host Factors; Investments; Lead; Life; Linear Models; Machine Learning; Mediating; Modeling; Molecular; Morbidity - disease rate; Multiomic Data; Mus; National Institute of Allergy and Infectious Disease; Native-Born; Network-based; Pathogenesis; Pathology; Pathway Analysis; Pathway interactions; Physiological; Polysaccharides; Population; Pregnant Women; Prevention; Process; Property; Proteins; Proteomics; Recovery; Research; Resources; Risk; Risk Factors; Seasons; Serology; Severities; Severity of illness; Software Tools; System; Systems Biology; Testing; Therapeutic; Tissues; Transcript; Vaccination; Viral; Virus; Whole Organism; bioinformatics resource; cohort; cross reactivity; cross-species transmission; data access; data resource; experimental study; fighting; genetic signature; high risk; high risk population; improved; in silico; in vivo; influenza epidemic; influenza infection; influenza virus strain; influenza virus vaccine; innovation; molecular scale; mortality; multiple omics; novel; obese person; predictive modeling; programs; protein expression; response; scaffold; seasonal influenza; trait; transcriptome sequencing; transcriptomics

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.

项目摘要 季节性影响因影响A和B病毒引起的发作，导致3-50万例严重病例和 每年全球300,000–500,000人死亡 - 尤其是在高风险群体中，例如幼儿，怀孕 妇女，肥胖个体，具有损害免疫系统和土著人口的人。这 在季节之间，影响力的负担可能会有所不同，部分原因是循环病毒的特征， 人口中的现有免疫力以及季节性影响的有效性 循环病毒菌株。当新的影响力菌株缓解或 跳跃宿主，变得有能力感染人类。在这种情况下，没有（或最少）先前存在的 抗体介导的对种群水平新病毒株的免疫力，导致数百万感染和 病毒的全球迅速传播。在没有抗体的情况下，疾病的严重程度可以改善 通过广泛的交叉反应性细胞免疫史。但是，免疫史细胞如何介导的精确机制 某些人的康复，而其他人则远非清楚。 Niaid对 生成数据以提高我们对传染病的理解，其进展，风险和严重程度为 以及治疗和预防。不仅是特定计划的主题，例如CEIR（卓越中心） 用于流感研究和监视）以及公民的持续努力（合作流感疫苗 创新中心），但特别是，与OMICS相关的程序产生了高通量基因组， 蛋白质组学和集成的“ OMIC”数据集，并为科学界提供其他相关资源 促进传染病的基本和应用研究。我们将利用这些开放访问数据集 以及通过本应用程序中的生物信息学资源中心（BRC）提供的资源。特别是我们 将利用流感研究中的免疫发作，病毒序列和抗病毒药物信息 数据库（IRD）并将这些数据与其他公共信息结合起来，来自人类人群的研究 有影响力病毒。单细胞数据将提供足够的蜂窝细节，并将作为“脚手架” 只有大量数据可用的情况。在我们看来，这些复杂的全面且真正的预测模型 关系只能通过系统，综合和多维的OMICS方法来实现 我们提供的。宿主对疫苗接种和影响力感染的反应是涉及复杂性状的结果 宿主因素以及整个转录本，蛋白质，聚糖和代谢产物的组合。 这些反应共同影响细胞，组织和整个生物体行为。那，主机对 疫苗接种和感染是分子网络的紧急特性。这个集成系统的目标 生物学方法是通过确定如何确定如何理解对流感的异质反应的机制 生物学成分之间的相互作用比较了高风险和较低风险人群之间的相比。这样的 在与这些威胁性传染病的斗争中，发现将显着改善治疗选择。 通过该项目开发的所有模型和软件工具将与社区共享。