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

单细胞测序数据的多尺度分析以剖析流感感染的复杂性

• 批准号: 10214529
• 负责人: CHRISTIAN FORST
• 金额: $ 21.19万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-10 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10214529
• 关键词: 2019-nCoV; Affect; Animals; Antibodies; Antibody-mediated protection; Antiviral Agents; Applied Research; Basic Science; Biology; COVID-19 pandemic; Cell Communication; Cells; Cellular Immunity; Cessation of life; Child; Collection; Communicable Diseases; Communities; Data; Data Set; Databases; Diagnostic; Disease; Economic Burden; Elderly; Epitopes; Gene Expression; Generations; Genetic; Genomics; Goals; Hospitalization; Human; Immune; Immune response; Immune system; Immunologics; In Vitro; Individual; Infection; Infection Control; Inflammatory; Influenza; Investments; Lead; Life; Maps; Mediating; Modeling; Molecular; Morbidity - disease rate; National Institute of Allergy and Infectious Disease; Native-Born; Outcome; Pathway Analysis; Pathway interactions; Patients; Physiological; Population; Pregnant Women; Prevention; Process; Proteomics; Public Health; Recovery; Research; Resolution; Resources; Risk; SARS-CoV-2 infection; Sampling; Severities; Severity of illness; Signal Pathway; Societies; Software Tools; Therapeutic; Validation; Viral; Viral Proteins; Virulent; Virus; Virus Diseases; Virus Replication; Work; bioinformatics resource; cohort; cross reactivity; cross-species transmission; fight against; health economics; high dimensionality; high risk population; improved; in silico; influenza epidemic; influenza infection; influenza virus strain; influenzavirus; mortality; multiple omics; obese person; pandemic disease; pathogenic virus; personalized medicine; programs; response; scaffold; single cell analysis; single cell sequencing; single-cell RNA sequencing; targeted treatment; therapeutic target; transcriptomics; virus infection mechanism

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.

项目摘要 紧急病毒感染，如SARS-CoV-2和新流感病毒株，对患者和社会造成巨大的健康和经济负担。动物宿主和人群之间的病毒循环每年导致数百万人住院治疗和数千人死亡，特别是在高危人群中，如老年人，孕妇，免疫系统受损的肥胖个体和土著人群。在新病毒株的种间传播后，疾病发病率和死亡率增加，并且能够感染人类。在这种情况下，在人群水平上对新病毒株没有（或只有最低限度的）预先存在的抗体介导的免疫力，导致数百万人感染，最终导致大流行。在缺乏抗体的情况下，疾病的严重程度可以通过广泛的交叉反应性细胞免疫来改善。然而，免疫细胞如何在某些个体中介导恢复而不是其他个体的确切机制尚不清楚。幸运的是，可以利用多样化和丰富的实验可用数据集来彻底研究病毒感染和宿主反应的特定分子机制。来自GEO/SRA中的人类队列和动物研究的基因表达谱、ImmPort中的免疫学谱或病毒株数据以及与流感研究数据库（IRD）或病毒病原体数据库和分析资源（ViPR）（两者均为NIAID的生物信息学资源中心（BRC））中的免疫表位的相互作用是此类资源的示例。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，遗传免疫表位和应答免疫细胞的多样性有助于病毒感染的异质性结果。通过整合所有现有的大规模单细胞和批量转录组学数据在SARS-CoV-2和流感感染，我们的目标是确定病毒感染的决定因素和病毒复制的关键过程，免疫反应使用综合多尺度网络生物学方法。这项研究强调了以单细胞分辨率识别相关关键免疫过程的重要性，这些免疫过程控制感染并限制炎症损伤的程度。这些发现将大大改善与这些威胁性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。

项目成果

Vaccination History, Body Mass Index, Age, and Baseline Gene Expression Predict Influenza Vaccination Outcomes.

• DOI: 10.3390/v14112446
• 发表时间: 2022-11-04
• 期刊: Viruses
• 作者: Forst CV;Chung M;Hockman M;Lashua L;Adney E;Hickey A;Carlock M;Ross T;Ghedin E;Gresham D
• 通讯作者: Gresham D

Multiscale network analysis identifies potential receptors for SARS-CoV-2 and reveals their tissue-specific and age-dependent expression.

• DOI: 10.1002/1873-3468.14613
• 发表时间: 2023-05
• 期刊: FEBS LETTERS
• 影响因子: 3.5
• 作者: Forst, Christian V.;Zeng, Lu;Wang, Qian;Zhou, Xianxiao;Vatansever, Sezen;Xu, Peng;Song, Won-Min;Tu, Zhidong;Zhang, Bin
• 通讯作者: Zhang, Bin