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: 9191028
• 负责人: Theodore Robertson Pak
• 金额: $ 4.34万
• 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/9191028
• 关键词: Acute; Adult; Antibodies; Antiviral Agents; Antiviral Therapy; Automobile Driving; Bayesian Analysis; Behavior; Big Data; Binding; Bioinformatics; Biological; Biological Assay; Biological Markers; Biology; 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 Virus; Dependence; Development; Diagnostic; Diagnostic tests; Disease; Enrollment; Fatigue; Flowers; Funding; Future; Genes; Genetic; Genets; Genome; Genomics; Global Change; Human; Immune; Immune response; Immune system; Individual; Infection; Interferon Type I; Interferons; Lead; Maps; Measurement; Measures; Metabolic; Methods; Modeling; Molecular; Monitor; Multivariate Analysis; Nicaragua; Noise; Outcome; Pathogenesis; 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; Subgroup; Symptoms; Syndrome; System; Techniques; Technology; Testing; Therapeutic; Time; Tissues; Transcript; Uncertainty; Vaccines; Validation; Variant; Viral; Viral Hemorrhagic Fevers; Viral Proteins; Virus; Virus Diseases; Weight; Whole Organism; biomarker selection; cell type; chikungunya; cohort; cytokine; diagnostic assay; disabling symptom; genetic variant; global health; improved; infectious disease model; insight; member; model design; monocyte; network models; next generation sequencing; novel; pathogen; receptor; research study; respiratory; screening; transcriptome sequencing; transcriptomics; transmission process; 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.

项目总结