Multiscale analysis of immune profiling data from dengue and chikungunya virus infections in humans

对人类登革热和基孔肯雅病毒感染的免疫分析数据进行多尺度分析

• 批准号: 9333951
• 负责人: Theodore Robertson Pak
• 金额: $ 4.12万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-06-13 至 2020-06-12
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9333951
• 关键词: Acute; Adult; Antibodies; Antiviral Agents; Antiviral Therapy; Automobile Driving; Bayesian Analysis; Behavior; Big Data; Binding; Biochemical Pathway; Bioinformatics; Biological; Biological Assay; Biological Markers; Blood specimen; Cell model; Cells; Cessation of life; Chikungunya virus; Childhood; Chronic; Clinical; Communicable Diseases; Complex; Computer Simulation; Culicidae; Data; Data Analyses; Data Sources; Databases; Dendritic Cells; Dengue; Dengue Infection; Dengue Vaccine; Dengue Virus; Dependence; Development; Diagnostic tests; Disease; Enrollment; Fatigue; Flowers; Funding; Future; Genes; Genetic; Genetic Transcription; Genets; Genome; Genomics; Global Change; Human; Immune; Immune response; Individual; Infection; Innate Immune System; Interferon Type I; Interferons; Lead; Longitudinal cohort; Maps; Measurement; Measures; Methods; Modeling; Molecular; Monitor; Multivariate Analysis; Nicaragua; Noise; Outcome; Pathogenesis; Pathogenicity; Pathway Analysis; Pathway interactions; Patients; Persons; Polyarthritides; Population; Predisposition; Process; Proteomics; Public Health; Quantitative Trait Loci; Recording of previous events; Regulator Genes; Reporting; Research; Resolution; Risk; Role; Running; Serotyping; Serum; Shock; Side; Signal Transduction; Signaling Protein; Small Interfering RNA; Standardization; Subgroup; Symptoms; Syndrome; System; Systems Biology; Techniques; Technology; Testing; Therapeutic; Time; Tissues; Transcript; Uncertainty; Vaccines; Validation; Variant; Viral; Viral Hemorrhagic Fevers; Viral Proteins; Virus; Virus Diseases; Whole Organism; biomarker selection; cell type; chikungunya; cytokine; diagnostic assay; disabling symptom; experimental study; genetic variant; global health; infectious disease model; insight; member; model design; monocyte; network models; next generation sequencing; novel; pathogen; predictive marker; respiratory; screening; transcriptome sequencing; transcriptomics; translational diagnostics; transmission process; vaccine discovery; vector mosquito; viral transmission; virus host interaction; virus pathogenesis

PROJECT SUMMARY Dengue virus (DENV) is a significant threat to public health, transmitting via mosquitos and infecting hundreds of millions around the world each year, of which roughly one hundred million become symptomatic and one half-million develop severe complications such as hemorrhagic fever, shock syndrome, or death. Despite many efforts to interpret antibody and serum cytokine measurements, only once has a diagnostic test been able to differentiate patients that are susceptible to heterotypic infections that associate with greater risk for severe complications. The uncertainty of the dynamics of the immune response to DENV has repeatedly thwarted efforts to create a vaccine protecting against all prevalent serotypes for over 80 years. A perhaps even more urgent problem is the blossoming spread of chikungunya virus (CHIKV), another mosquito-borne virus characterized by higher transmissibility and infection rates much higher than those of DENV, with roughly three quarters of infected persons developing symptoms that can include chronic polyarthritis and fatigue. Sharing the same urban mosquito vectors as DENV, the transmission of CHIKV within the Western hemisphere was reported for the first time two years ago. Like DENV, there are no approved specific treatments or vaccines. So far, very little is known about the molecular interactions necessary for CHIKV to enter human cells and effectively counter the innate immune system. Next generation sequencing and immune profiling technologies such as RNA-seq, Luminex, proteomics, and CyTOF have the ability to generate a wealth of data that can be used to help illuminate global biomolecular changes driving viral infections in humans, but only if signal can be separated from noise to identify useful signatures and key pathways. The pathogenesis of an infection within a host is a complex process, involving interactions among networks of biomolecules, cell types, tissues, and host individuals. Such complexity necessitates a multiscale, integrative approach, since characterizing one network or phenomenon in isolation is unlikely to sufficiently explain changes occurring across the entire system. This study proposes analyses that will identify robust biomarkers derived from immune profiling of a longitudinal cohort of pediatric DENV and CHIKV infections in Nicaragua, as part of a multi-institutional consortium (DHIPC). Furthermore, we will integrate these data into causal network models of the host- pathogen interaction, which will reveal key driver genes for pathways that associate with changes in the host immune response and facilitate antiviral and vaccine discovery. Given the anticipated data, this proposal maximizes the impact of the modeling approach for DENV and CHIKV on future biological discovery and advances the state of the art in holistic, data-driven modeling of infectious disease.

项目摘要 登革热病毒（DENV）是对公共卫生的重大威胁，通过蚊子传播并感染数百人 每年全球有数百万人，其中大约一亿人出现症状， 50万人出现严重并发症，如出血热、休克综合征或死亡。尽管有许多 尽管我们一直在努力解释抗体和血清细胞因子的测量结果，但只有一次诊断测试能够 区分易受异型感染的患者，这些异型感染与严重 并发症对登革病毒免疫反应动力学的不确定性一再阻碍了 80多年来，人们一直在努力创造一种针对所有流行血清型的疫苗。 一个也许更为紧迫的问题是基孔肯雅病毒（CHIKV）的迅速蔓延， 蚊媒病毒的特点是传播性强，感染率远高于 DENV，大约四分之三的感染者出现症状，包括慢性 多发性关节炎和疲劳。CHIKV与DENV共享相同的城市蚊子媒介， 两年前首次报道了西半球的情况。像DENV一样，没有批准的 特殊治疗或疫苗。到目前为止，人们对分子间的相互作用知之甚少， CHIKV能够进入人体细胞并有效对抗先天免疫系统。 下一代测序和免疫分析技术，如RNA-seq，Luminex， 蛋白质组学和CyTOF有能力产生丰富的数据，可用于帮助阐明全球 生物分子的变化驱动人类的病毒感染，但只有当信号可以从噪音中分离出来， 确定有用的特征和关键途径。宿主内感染的发病机制是复杂的， 过程，涉及生物分子、细胞类型、组织和宿主个体网络之间的相互作用。等 复杂性需要多尺度的综合方法，因为表征一个网络或现象 孤立地说，不可能充分解释整个系统发生的变化。 这项研究提出的分析，将确定强大的生物标志物来源于免疫概况的一个 尼加拉瓜儿童DENV和CHIKV感染的纵向队列，作为多机构 财团（DHIPC）。此外，我们将把这些数据整合到宿主的因果网络模型中- 病原体相互作用，这将揭示与宿主变化相关的途径的关键驱动基因 免疫应答和促进抗病毒和疫苗发现。根据预期数据，该提案 最大化DENV和CHIKV建模方法对未来生物学发现的影响， 推进了传染病的整体数据驱动建模的最新技术水平。