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.

项目总结

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