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
基本信息
- 批准号:10665770
- 负责人:
- 金额:$ 58.94万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-07-14 至 2027-06-30
- 项目状态:未结题
- 来源:
- 关键词:AlgorithmsAntibodiesAntibody-mediated protectionAntiviral AgentsApplied ResearchAutomobile DrivingBasic ScienceBehaviorBiologicalBiologyCellsCellular ImmunityCellular Indexing of Transcriptomes and Epitopes by SequencingCessation of lifeCharacteristicsChildCommunicable DiseasesCommunitiesComplexDataData SetDatabasesDemographic FactorsDimensionsDiseaseEffectivenessEpitopesEssential GenesGene ExpressionGenerationsGenomicsGoalsHumanImmuneImmune responseImmune systemImmunityImmunologicsIn VitroIndividualInfectionInfluenzaInfluenza A virusInfluenza B VirusInfluenza vaccinationIntegration Host FactorsInvestmentsLeadLifeLinear ModelsMachine LearningMediatingModelingMolecularMorbidity - disease rateMultiomic DataMusNational Institute of Allergy and Infectious DiseaseNative-BornNetwork-basedPathogenesisPathologyPathway AnalysisPathway interactionsPhysiologicalPolysaccharidesPopulationPregnant WomenPreventionProcessPropertyProteinsProteomicsRecoveryResearchResourcesRiskRisk FactorsSeasonsSerologySeveritiesSeverity of illnessSoftware ToolsSystemSystems BiologyTestingTherapeuticTissuesTranscriptVaccinationViralVirusWhole Organismbioinformatics resourcecohortcross reactivitycross-species transmissiondata accessdata resourceexperimental studyfightinggenetic signaturehigh riskhigh risk populationimprovedin silicoin vivoinfluenza epidemicinfluenza infectioninfluenza virus straininfluenza virus vaccineinnovationmolecular scalemortalitymultiple omicsnovelobese personpredictive modelingprogramsprotein expressionresponsescaffoldseasonal influenzatraittranscriptome sequencingtranscriptomics
项目摘要
Project Summary
Seasonal influenza epidemics, caused by influenza A and B viruses, result in 3–5 million severe cases and
300,000–500,000 deaths globally each year - especially in high-risk groups such as young children, pregnant
women, obese individuals, individuals with a compromised immune system, and indigenous populations. The
burden of influenza can vary widely between seasons, in part due to characteristics of the circulating viruses,
the existing immunity in the population, and the effectiveness of seasonal influenza vaccines against the
circulating virus strains. Disease morbidity and mortality increase when a new influenza strain reasserts or
jumps the host 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
a rapid global spread of the virus. 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. NIAID has made significant investments in the
generation of data to improve our understanding of infectious diseases, their progression, risk, and severity as
well as treatment and prevention. Not only subject of specific programs, such as CEIRS (Centers of Excellence
for Influenza Research and Surveillance) and the ongoing efforts in CIVICs (Collaborative Influenza Vaccine
Innovation Centers), but in particular, omics-related programs have generated high-throughput genomic,
proteomic, and integrated "omic" data sets, and provided other related resources to the scientific community to
promote basic and applied research in infectious diseases. We will make use of these open access datasets
and resources available via the Bioinformatics Resource Centers (BRCs) in this application. In particular, we
will utilize immune epitope, viral sequence and antiviral drug information from the Influenza Research
Database (IRD) and combine these data with other public information from studies of human cohorts infected
with the influenza virus. Single-cell data will provide sufficient cellular detail and will serve as “scaffold” in the
case that only bulk data is available. In our view, a comprehensive and truly predictive model of these complex
relationships can only be achieved through the systematic, integrative, and multi-dimensional OMICS approach
that we offer. Host response to vaccination and to influenza infection is the result of complex traits that involve
a combination of host factors along with entire networks of transcripts, proteins, glycans and metabolites.
Together these responses impact cellular, tissue, and whole organism behaviors. Thus, the host responses to
vaccination and infection are an emergent property of molecular networks. The goal of this integrated systems
biology approach is to understand mechanisms of heterogeneous response to Influenza by determining how
the interactions among biological components compare between high-risk and lower risk populations. 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
由甲型和B型流感病毒引起的季节性流感流行导致3-5百万严重病例,
全球每年有30万至50万人死亡,特别是在幼儿、孕妇和孕妇等高危人群中。
女性、肥胖个体、免疫系统受损的个体和土著人群。的
流感的负担在不同季节之间变化很大,部分是由于流行病毒的特征,
人口的现有免疫力,以及季节性流感疫苗对预防
循环病毒株。当一种新的流感病毒株重新出现或
跳跃到宿主身上并能感染人类在这种情况下,没有(或最小)预先存在
抗体介导的对新病毒株的免疫在人群水平上,导致数百万人感染,
病毒在全球迅速蔓延在没有抗体的情况下,疾病的严重程度可以得到改善
广泛交叉反应的细胞免疫。然而,免疫细胞如何介导
一些人的康复情况,而另一些人的康复情况还远不清楚。NIAID在以下方面进行了大量投资:
生成数据,以提高我们对传染病,其进展,风险和严重性的理解,
以及治疗和预防。不仅是具体方案的主题,如CEIRS(卓越中心
流感研究和监测)和CIVIC(合作流感疫苗)正在进行的努力
创新中心),但特别是,组学相关的计划已经产生了高通量的基因组,
蛋白质组学和综合“组学”数据集,并向科学界提供其他相关资源,
促进传染病的基础和应用研究。我们将利用这些开放获取的数据集
和可通过本申请中的生物信息学资源中心(BRC)获得的资源。我们尤其
将利用来自流感研究的免疫表位、病毒序列和抗病毒药物信息
数据库(IRD)和联合收割机将这些数据与来自感染人群研究的其他公共信息相结合
与流感病毒有关。单细胞数据将提供足够的细胞细节,并将作为“支架”,
只有批量数据可用的情况。在我们看来,一个全面的和真正的预测模型,这些复杂的
只有通过系统的、综合的和多维度的OMICS方法才能实现这些关系
我们提供的。宿主对疫苗接种和流感感染的反应是复杂性状的结果,
宿主因子沿着转录物、蛋白质、聚糖和代谢物的整个网络的组合。
这些反应共同影响细胞、组织和整个生物体的行为。因此,主机响应于
疫苗接种和感染是分子网络的一个紧急特性。这个集成系统的目标是
生物学方法是通过确定如何理解对流感的异质反应机制,
生物成分之间的相互作用在高风险和低风险人群之间进行比较。等
研究结果将大大改善治疗选择,以对抗这些威胁性的传染病。
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
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('CHRISTIAN FORST', 18)}}的其他基金
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 - 财政年份:2020
- 资助金额:
$ 58.94万 - 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:
$ 58.94万 - 项目类别:
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