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
基本信息
- 批准号:10214529
- 负责人:
- 金额:$ 21.19万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:2019-nCoVAffectAnimalsAntibodiesAntibody-mediated protectionAntiviral AgentsApplied ResearchBasic ScienceBiologyCOVID-19 pandemicCell CommunicationCellsCellular ImmunityCessation of lifeChildCollectionCommunicable DiseasesCommunitiesDataData SetDatabasesDiagnosticDiseaseEconomic BurdenElderlyEpitopesGene ExpressionGenerationsGeneticGenomicsGoalsHospitalizationHumanImmuneImmune responseImmune systemImmunologicsIn VitroIndividualInfectionInfection ControlInflammatoryInfluenzaInvestmentsLeadLifeMapsMediatingModelingMolecularMorbidity - disease rateNational Institute of Allergy and Infectious DiseaseNative-BornOutcomePathway AnalysisPathway interactionsPatientsPhysiologicalPopulationPregnant WomenPreventionProcessProteomicsPublic HealthRecoveryResearchResolutionResourcesRiskSARS-CoV-2 infectionSamplingSeveritiesSeverity of illnessSignal PathwaySocietiesSoftware ToolsTherapeuticValidationViralViral ProteinsVirulentVirusVirus DiseasesVirus ReplicationWorkbioinformatics resourcecohortcross reactivitycross-species transmissionfight againsthealth economicshigh dimensionalityhigh risk populationimprovedin silicoinfluenza epidemicinfluenza infectioninfluenza virus straininfluenzavirusmortalitymultiple omicsobese personpandemic diseasepathogenic viruspersonalized medicineprogramsresponsescaffoldsingle cell analysissingle cell sequencingsingle-cell RNA sequencingtargeted treatmenttherapeutic targettranscriptomicsvirus infection mechanism
项目摘要
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
期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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:
- 影响因子:0
- 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:
- 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
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CHRISTIAN FORST其他文献
CHRISTIAN FORST的其他文献
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{{ truncateString('CHRISTIAN FORST', 18)}}的其他基金
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 - 财政年份:2022
- 资助金额:
$ 21.19万 - 项目类别:
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:
10057816 - 财政年份:2020
- 资助金额:
$ 21.19万 - 项目类别:
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