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 SARS-COV-2和新的流感菌株等新兴病毒感染对患者和社会造成了巨大的健康和经济负担。动物储层与人口之间的病毒周期每年导致数百万个住院和数千人死亡，尤其是在高风险群体中，例如老年人，孕妇，肥胖的免疫系统受损和本地人群。种间传播新病毒菌株后，疾病的发病率和死亡率增加，并能够感染人类。在这种情况下，没有（或最少）先前存在的抗体介导的抗体介导的对新病毒菌株的免疫力，导致数百万感染，最终导致大流行。在没有抗体的情况下，可以通过广泛的交叉反应性细胞免疫来改善该疾病的严重程度。但是，免疫细胞如何介导某些个体（而不是其他人）如何介导恢复的确切机制远非清晰。幸运的是，可以利用多种公开可用的数据集收集，以彻底研究病毒感染和宿主反应的特定分子机制。来自人类人群和动物研究的基因表达谱图，Immport或病毒株数据中的免疫学特征以及在流感研究数据库（IRD）中与免疫表位的相互作用，或者是生物信息信息中心（BRC）的基因研究数据库（IRD）或病毒病原体数据库和分析资源（VIPR）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，病毒菌株，遗传免疫表位和反应免疫细胞的多样性有助于病毒感染的异质结果。通过在SARS-COV-2和流感感染中整合所有现有的大规模单细胞和批量转录组数据，我们旨在确定病毒感染的决定因素和病毒复制的关键过程的决定因素，并使用整合性多尺度网络生物学方法进行免疫反应。拟议的研究强调了以单细胞分辨率来控制感染并限制炎症性损害程度的单细胞分辨率的相关钥匙免疫过程的重要性。这种发现将显着改善与这些威胁性传染病的斗争中的治疗选择。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