Statistical physics and network-based approaches for elucidating molecular biomarkers of COPD

阐明 COPD 分子生物标志物的统计物理学和基于网络的方法

• 批准号: 10559835
• 负责人: Xuwen Wang
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10559835
• 关键词: Address; Advisory Committees; Airway Disease; Area; Behavior; Biology; Cessation of life; Chronic Obstructive Pulmonary Disease; Committee Members; Complex; DNA Methylation; Data; Detection; Dimensions; Disease; Drug Targeting; Educational workshop; Environment; Epidemiology; Epigenetic Process; Feedback; Gene Combinations; Genes; Genetic; Genomics; Genotype; Goals; Health; Human; Intervention; Ising model; Knowledge; Literature; Lung; Lung diseases; Maps; Mathematics; Medicine; Mentors; Messenger RNA; Methods; MicroRNAs; Mission; Modeling; Molecular; Morbidity - disease rate; Multiomic Data; Nature; Neighborhoods; Network-based; Organism; Outcome; Pathogenesis; Pathway interactions; Performance; Phenotype; Physics; Process; Proteins; Public Health; Reporting; Research; Respiratory Disease; SNP genotyping; Science; Statistical Data Interpretation; Structure of parenchyma of lung; Systems Biology; Teaching Hospitals; Testing; Therapeutic; Tissues; Training; Training Programs; Translating; United States National Institutes of Health; Weight; Whole Blood; airway obstruction; chronic inflammatory lung disease; cohort; computing resources; deep learning; deep reinforcement learning; detection method; disease phenotype; epigenomics; gene product; human disease; human interactome; improved; insight; learning strategy; mRNA Expression; medical schools; meetings; molecular marker; mortality; multiple omics; novel; precision medicine; prevent; protein protein interaction; skills; tool; vector

PROJECT SUMMARY Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. As a common complex disease, COPD has high global morbidity and mortality. Indeed, deaths due to respiratory disease numbered nearly four million, which was mostly contributed by COPD. There is a clear demand to improve our understanding of COPD pathogenesis and develop interventions to prevent and treat COPD. Yet, a complex disease phenotype is usually determined by various pathobiological processes that interact in a network, rather than induced by the abnormality in a single effector gene product. Extensive evidence implies that disease-associated proteins have distinct interactions within the human protein-protein interaction (PPI) network (a.k.a. the human interactome), and the pathobiological processes of a complex disease are associated with perturbation within specific disease neighborhoods of the interactome, often referred to as the disease module. Comprehensive understanding of the COPD pathogenesis and predicting disease genes to inform therapeutic treatment require advanced tools to identify its disease module. Although many disease module detection methods have been reported in the literature, they all have fundamental limitations. More importantly, existing methods do not fully leverage the advantage of multi-omics data. In this application, a statistical physics and network-based framework will be developed to detect disease modules for complex human diseases using multi-omics data. This framework will be systematically validated with synthetic data. Then it will be applied to the rich multi-omics data (SNP genotyping, DNA methylation, mRNA and miRNA expression) in two large COPD cohorts. Dr. Wang’s training in statistical physics, network science and deep learning have prepared him well for his proposed research. However, understanding and interpreting the molecular basis of complex diseases and the statistical analysis of multi-omics data are still arduous tasks that will require further training in specific areas. Dr. Wang will leverage the excellent intellectual environment of Harvard Medical School and its teaching hospitals and will have access to extensive computational resources through the Channing Division of Network Medicine and Harvard Medical School. Through the guidance of a mentoring and advisory team with complementary expertise, together with formal coursework and workshops, Dr. Wang will immerse himself in a training program focusing on statistical genetics, epigenetics, multi-omics integration, and the biology of pulmonary diseases. Dr. Wang will also participate in regular meetings with his mentors and advisory committee members, allowing him to share his progress and receive timely feedback. Altogether, Dr. Wang’s training and research plan will enable him to expand his current skillset 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 COPD, and ultimately contribute to the precision medicine of lung diseases.

项目概要 慢性阻塞性肺疾病（COPD）是一种慢性炎症性肺部疾病，会导致阻塞性肺部疾病 作为一种常见的复杂疾病，慢性阻塞性肺病确实具有很高的全球发病率和死亡率。 因呼吸系统疾病死亡的人数接近 400 万人，其中大部分是慢性阻塞性肺病。 明确要求我们提高对 COPD 发病机制的了解并制定干预措施来预防 然而，复杂的疾病表型通常是由多种病理生物学过程决定的。 它们在网络中相互作用，而不是由单个效应基因产物的异常引起的。 有证据表明，与疾病相关的蛋白质在人类蛋白质-蛋白质之间具有独特的相互作用 相互作用（PPI）网络（又名人类相互作用组），以及复杂的病理生物学过程 疾病与相互作用组的特定疾病邻域内的扰动有关，通常被称为 作为疾病模块。全面了解 COPD 发病机制并预测疾病。 为治疗提供信息的基因需要先进的工具来识别其疾病模块。 文献报道了疾病模块检测方法，但它们都有根本性的局限性。 更重要的是，现有方法没有充分利用多组学数据的优势。 将开发统计物理和基于网络的框架来检测复杂的疾病模块 使用多组学数据的人类疾病将通过合成数据进行系统验证。 然后将其应用于丰富的多组学数据（SNP基因分型、DNA甲基化、mRNA和miRNA 表达）在两个大型慢性阻塞性肺病队列中，王博士接受过统计物理学、网络科学和深度学习的培训。 学习为他提出的研究做好了充分的准备。 复杂疾病的分子基础和多组学数据的统计分析仍然是艰巨的任务 需要在特定领域进行进一步的培训，王博士将利用良好的智力环境。 哈佛医学院及其教学医院将获得广泛的计算资源 通过网络医学钱宁分部和哈佛医学院的指导。 具有互补专业知识的指导和咨询团队，以及正式的课程和研讨会， 王博士将沉浸在统计遗传学、表观遗传学、多组学等方面的培训项目 王博士也将参加他的定期会议。 导师和咨询委员会成员，让他分享自己的进步并获得及时的反馈。 总而言之，王博士的培训和研究计划将使他能够扩展他目前的技能，包括 能够应对分析大型流行病学的复杂基因组和表观基因组数据的挑战 队列，确定 COPD 系统生物学中的悬而未决的问题，并最终有助于精确度 肺部疾病的医学。