Multi-scale analysis of single cell sequencing data to dissect the complexity of influenza infections

项目摘要 SARS-CoV-2和新型流感病毒株等新出现的病毒感染给患者和社会带来了巨大的健康和经济负担。动物宿主和人类种群之间的病毒循环每年导致数百万人住院和数千人死亡，特别是在高危群体中，如老年人、孕妇、免疫系统受损的肥胖者和土著人口。在一种新的病毒株跨物种传播后，疾病发病率和死亡率增加，并能够感染人类。在这种情况下，在人口层面上没有(或最低限度)预先存在的对新病毒株的抗体介导的免疫，导致数百万人感染，并最终导致大流行。在没有抗体的情况下，疾病的严重性可以通过广泛的交叉反应细胞免疫来改善。然而，免疫细胞如何在某些个体中调节康复的确切机制，而不是另一些个体，还远不清楚。幸运的是，可以利用丰富多样的公开数据集来深入研究病毒感染和宿主反应的特定分子机制。例如，来自GEO/SRA的人类队列和动物研究的基因表达谱，Immport或病毒株数据的免疫学谱，以及与NIAID生物信息学资源中心(BRC)的流感研究数据库(IRD)或病毒病原体数据库和分析资源(ViPR)中的免疫表位的相互作用。特别是，高分辨率的单细胞RNA-SEQ数据使我们能够更详细地研究流感和SARS-CoV-2感染期间的相关过程。我们提出的工作的总体假设是，病毒株、遗传免疫表位和反应免疫细胞的多样性导致了病毒感染的异质性结果。通过整合SARS-CoV-2和流感感染中所有现有的大规模单细胞和大量转录数据，我们的目标是利用整合的多尺度网络生物学方法识别病毒感染的决定因素和潜在的病毒复制和免疫反应的关键过程。这项拟议的研究强调了在单细胞分辨率下确定相关关键免疫过程的重要性，这些过程可以控制感染并限制炎症损害的程度。这些发现将极大地改进抗击这些具有威胁性的传染病的治疗选择。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 Emergent viral infections, such as SARS-CoV-2 and new influenza strains, pose an enormous health and economic burden on patients and the society. Viral cycles between the animal reservoir and the human population cause millions of hospitalizations and thousands of deaths each year, especially in high-risk groups, such as elderly, pregnant women, obese individuals with a compromised immune system, and indigenous populations. Disease morbidity and mortality increase after interspecies transmission of a new viral strain, 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, ultimately, a pandemic. 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. Fortunately, a diverse and rich collection of publically available datasets can be leveraged to thoroughly investigate the specific molecular mechanisms of viral infection and host response. Gene expression profiles from human cohorts and animal studies in GEO/SRA, immunological profiles in ImmPort or viral strain data, and interaction with immune epitopes in the Influenza Research Database (IRD) or the Virus Pathogen Database and Analysis Resource (ViPR), both Bioinformatics Resource Centers (BRC) of NIAID, are examples of such resources. In particular, high-resolution single-cell RNA-seq data enables us to study relevant processes during influenza and SARS-CoV-2 infections in greater detail. The overarching hypothesis of our proposed work is that diversity in virus strains, genetic immune epitopes, and responding immune cells contributes to heterogeneous outcomes of viral infection. By integrating all existing large-scale single-cell and bulk transcriptomic data in SARS-CoV-2 and influenza infections, we aim to identify determinants of viral infections and key processes underlying viral replication, and immune response using integrative multi-scale network biology approaches. The proposed research highlights the importance of identifying relevant key-immune processes at a single-cell resolution that control the infection and limit the extent of inflammatory damage. 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.