Integrative network-based analysis of multi-omics data to elucidate the molecular connection between asthma and COPD

基于多组学数据的综合网络分析，阐明哮喘和慢性阻塞性肺病之间的分子联系

• 批准号: 10434711
• 负责人: Arda Halu
• 金额: $ 18.9万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-18 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10434711
• 关键词: Address; Adult; Advisory Committees; Algorithms; Area; Asthma; Award; Big Data; Bioinformatics; Biological; Biology; Blood; Blood specimen; CD4 Positive T Lymphocytes; Catalogs; Childhood; Childhood Asthma; Chronic Obstructive Pulmonary Disease; Complex; Computing Methodologies; Confidence Intervals; DNA Methylation; Data; Development; Disease; Disease Progression; Disease model; Educational workshop; Environment; Epidemiology; Epigenetic Process; Etiology; Fetal Lung; Gene Expression; Genes; Genetic; Genetic Risk; Genetic Transcription; Genome; Genomics; Impairment; Life; Link; Lung; Lung diseases; Mediating; Mediator of activation protein; Medical Informatics; Medicine; Mentors; Messenger RNA; Methodology; Methods; Methylation; MicroRNAs; Modeling; Molecular; Multiomic Data; Network-based; Pathway interactions; Pattern; Peripheral Blood Mononuclear Cell; Phenotype; Physics; Play; Population; Proteome; Public Health; Research; Role; SNP genotyping; Sampling; Signal Transduction; Site; Smoke; Standardization; Structure of parenchyma of lung; System; Systems Biology; Teaching Hospitals; Testing; Time; Tissue Microarray; Tissues; Training; Training Programs; Transcription Process; Whole Blood; airway obstruction; asthmatic; base; chronic airflow obstruction; clinical translation; cohort; computing resources; epigenetic regulation; epigenomics; fetal smoke exposure; high risk; human data; improved; in utero; insight; lung development; medical schools; meetings; member; precision medicine; prenatal; risk variant; skills; transcriptomics

Project Summary/Abstract Growing evidence suggests an etiological link between asthma and chronic obstructive pulmonary disease (COPD) wherein mild-to-moderate persistent asthmatics are susceptible to persistent airflow obstruction, putting them at a higher risk for developing COPD. Furthermore, epigenetic modulation due to in utero smoke (IUS) exposure during fetal lung development may play a role in asthma and COPD. These and other data suggest a lifelong trajectory of lung impairment from prenatal lung development to childhood asthma to COPD in adulthood. Longitudinal and carefully phenotyped multi-omic data offer the opportunity to understand the molecular determinants of this disease trajectory. Systems and network biology methods hold the potential to effectively integrate, analyze and interpret multi-omics data. Multilayer networks, in particular, offer a feasible first step to model disease perturbations jointly across multiple molecular levels, from the genome to the proteome. In this application, we will simultaneously analyze the rich multi-omics data (SNP genotyping, DNA methylation, mRNA and miRNA expression) collected as part of long-standing asthma and COPD cohorts using multilayer network methods. We will first integrate IUS exposure, asthma and COPD multi-omics data into networks and develop their statistical framework to facilitate subsequent bioinformatics analyses. We will then track the developmental origins of asthma by the integrated temporal analysis of fetal lung and childhood asthma multi-omics data. Finally, we will identify key molecules and pathways of the phenotypic transition from asthma in early life to COPD in adulthood using multilayer networks. Dr. Halu’s training in statistical physics and complex networks has prepared him well for his proposed research. However, understanding the molecular basis connecting complex lung diseases such as asthma and COPD through the analysis of multi-omics data is a formidable task that will require further training in specific areas. Dr. Halu will leverage the excellent intellectual environment of Harvard Medical School (HMS) and its teaching hospitals, and will have access to extensive computational resources through the Channing Division of Network Medicine and HMS. Through formal coursework and workshops, and with the help of a mentoring and advisory team with complementary expertise, Dr. Halu will immerse himself in a training program focusing on statistical genetics, epigenetics, and omics integration, big data in medical informatics, and the biology of pulmonary diseases and clinical translation. Dr. Halu will also participate in regular meetings with his mentors and advisory board members, which will allow him to share his progress. Altogether, Dr. Halu’s training and research plan will enable him to expand his current skill set to include the ability to address the challenges of analyzing the complex genomic and epigenomic data of large epidemiological cohorts, identify open questions in the systems biology of asthma and COPD, and ultimately contribute to the precision medicine of lung disease.

项目总结/摘要 越来越多的证据表明哮喘和慢性阻塞性肺疾病之间存在病因学联系 慢性阻塞性肺病（COPD），其中轻度至中度持续性哮喘患者易受持续性气流阻塞的影响， 他们患COPD的风险更高。此外，子宫内吸烟（IUS）引起的表观遗传调节 胎儿肺发育期间的暴露可能在哮喘和COPD中起作用。这些和其他数据表明， 从产前肺发育到儿童期哮喘再到成年期COPD的肺损伤终身轨迹。 纵向和仔细的表型多组学数据提供了了解分子生物学的机会。 这一疾病轨迹的决定因素。系统和网络生物学方法有可能有效地 整合、分析和解释多组学数据。特别是多层网络，提供了可行的第一步， 从基因组到蛋白质组，在多个分子水平上共同模拟疾病扰动。在这 应用程序，我们将同时分析丰富的多组学数据（SNP基因分型，DNA甲基化，mRNA 和miRNA表达）作为长期哮喘和COPD队列的一部分收集， 方法.我们将首先将IUS暴露、哮喘和COPD多组学数据整合到网络中， 他们的统计框架，以促进随后的生物信息学分析。然后我们将跟踪 通过对胎儿肺和儿童哮喘多组学数据的整合时间分析，最后， 我们将确定从早期哮喘到慢性阻塞性肺病表型转变的关键分子和途径， 使用多层网络的成年期。Halu博士在统计物理学和复杂网络方面的培训为他的研究做好了准备。 他对自己提出的研究很感兴趣。然而，了解连接复杂肺的分子基础， 通过多组学数据分析哮喘和COPD等疾病是一项艰巨的任务， 具体领域的进一步培训。Halu博士将充分利用哈佛医学院优秀的学术环境 学校（HMS）及其教学医院，并将通过 Channing Division of Network Medicine and HMS.通过正式的课程和研讨会， 一个具有互补专业知识的指导和咨询团队，Halu博士将沉浸在培训中， 计划侧重于统计遗传学，表观遗传学和组学整合，医学信息学中的大数据，以及 肺部疾病的生物学和临床翻译。哈鲁博士还将参加定期会议， 他的导师和咨询委员会成员，这将使他能够分享他的进步。总的来说，哈鲁博士 培训和研究计划将使他能够扩大他目前的技能，包括解决 分析大型流行病学队列的复杂基因组和表观基因组数据的挑战， 哮喘和COPD系统生物学中的开放问题，并最终有助于精准医学 肺部疾病。