Using Integrative Networks to Explore Heterogeneous Phenotypes in COPD

使用综合网络探索 COPD 的异质表型

• 批准号: 9164450
• 负责人: Kimberly Renee Glass
• 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: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9164450
• 关键词: American; Area; Automobile Driving; Award; Benchmarking; Biological; Biological Process; Biology; Blood; Cancer Center; Cells; ChIP-seq; Characteristics; Chest; Chronic Obstructive Airway Disease; Clinical; Clinical Data; Complex; Computer Architectures; Data; Data Analyses; Data Science; Data Set; Databases; Disease; Educational workshop; Environment; Epigenetic Process; Faculty; Funding; Gene Expression Regulation; Genes; Genetic; Genome; Genomic approach; Genomics; Genotype-Tissue Expression Project; Glass; Goals; Grant; Heterogeneity; Histocompatibility Testing; Hospitals; Image; Individual; Information Networks; Institutes; Institution; Knowledge; Link; Lung; Lung diseases; Massachusetts; Measures; Mediating; Medical; Medicine; Mentors; Methods; MicroRNAs; Modeling; Nature; Pathogenesis; Pathway Analysis; Pathway interactions; Patients; Phenotype; Physics; Play; Population; Population Heterogeneity; Process; Property; Proteome; Public Health Schools; Qualifying; Regulator Genes; Research; Research Personnel; Research Training; Resources; Role; Sampling; Science; Societies; Source; Statistical Models; Structure; Structure of parenchyma of lung; System; Technology; Tissues; Training; Training Programs; Translations; Universities; Washington; Woman; Work; Writing; abstracting; career; catalyst; clinical application; computer science; computing resources; data integration; disease heterogeneity; disease phenotype; effective therapy; epigenome; experience; genomic data; innovation; insight; medical schools; meetings; member; metabolome; method development; network models; new technology; phenotypic data; precision medicine; reconstruction; respiratory; skills; statistics; symposium; targeted treatment; tool; transcriptome; translational medicine; treatment response; treatment strategy

Project Summary/Abstract Rapidly evolving genomic technologies are providing unprecedented amounts of data with the potential to yield new insights into the processes driving lung disease, including Chronic Obstructive Pulmonary Disease (COPD). These data have already allowed us to develop a more unified understanding of how multiple biological mechanisms work together to influence COPD. We now appreciate that in most cases a single gene or pathway does not fully characterize the disease or alterations in disease-state. Rather, disease-related changes often involve simultaneous alterations to the genome, epigenome, transcriptome, metabolome, and proteome of the cell and can be represented by complex networks whose structures are altered as the disease develops. Importantly, many of these changes are associated with complex shifts in the regulatory networks from the normal to a diseased state. Modeling these changes can inform us about the processes that drive COPD and suggest potential targeted therapies. In this proposal we develop and expand methods for integrating emerging multi-omic data to reconstruct comprehensive regulatory networks in COPD. We then develop approaches for analyzing these networks and for effectively linking regulatory alterations with disease mechanisms within different observed COPD phenotypes. We begin by developing quantitative approaches for inferring, analyzing, decomposing and comparing networks. These methods will allow us to discover new features about the nature of lung disease, to understand the complex regulatory processes at work across patients, and ultimately have the power suggest ways to more effectively treat COPD. Executing on this plan will require a unique set of skills that span biology, network science, computer science, translational medicine and lung disease. Dr. Glass’ background is in physics, complex systems and genomic data analysis. Although her previous experiences have prepared her well for the proposed research, she recognizes that there are new challenges that need to be overcome when applying networks and genomics approaches to study COPD. Therefore, Dr. Glass has selected a mentored research environment and crafted a training program that will allow her to obtain the interdisciplinary skills necessary to accomplish the goals of this project. In support of her proposed research, Dr. Glass will make use of the many high-quality computational resources available to her through the Channing Division of Network Medicine (CDNM) at Brigham and Women’s Hospital (BWH), the Dana-Farber/Harvard Cancer Center, Harvard Medical School, and the Harvard School of Public Health and well as additional resources directly provided by her mentors and advisory board members. Along these lines, Dr. Glass has assembled a diverse and well-qualified mentoring team to oversee and advise her research efforts. Her primary mentor, Dr. Quackenbush, and advisory board member Dr. Yuan both have extensive and complementary experience in analyzing and interpreting many types of genomic data. Advisory board member Dr. Kepner has deep knowledge of scalable computer architecture and will support Dr. Glass by providing computational resources such as access to the MIT SuperCloud. After constructing regulatory networks in COPD, interpreting them in the context of relevant biological questions will be essential. Advisory board member Dr. Onnela is an expert in developing methods for network quantification and will play an important role in helping Dr. Glass to create objective measures of network structural differences. Finally, co-mentor Dr. Silverman is a leading expert in COPD and network medicine, and will provide important guidance to Dr. Glass as she determines how to relate network measures to patient data, including relevant clinical features of COPD. Dr. Glass will supplement her hands-on training with formal coursework and specific mentored exploration focused in three main areas: 1) Lung disease, translational medicine and clinical applications, with training through courses offered through the Harvard Catalyst and Harvard School of Public Health, attending the annual American Thoracic Society meeting, and working closely with Dr. Silverman and the Respiratory Medicine faculty at the CDNM/BWH; 2) Biomedical data analysis and computation, with training from taking online classes offered by the University of Washington and Massachusetts Institute of Technology, attending local workshops and working closely with Drs. Quackenbush, Yuan and Kepner; and 3) Statistics and network analysis methods development, with training from taking courses offered by the Harvard School of Public Health, attending national conferences, and working closely with Drs. Quackenbush and Onnela. Finally, Dr. Glass will actively participate in and receive training on the grant writing process throughout the award period, so as to be well-prepared to apply for independent funding at the conclusion of the project. Dr. Glass’s career goal is to become an independent investigator studying non-neoplastic lung disease at an academic institution. Through the proposed research and training plan, she will be able to hone the computational abilities she has already developed and collect a variety of additional skills that will be essential to becoming an independent investigator capable of leveraging biomedical data to perform computational research and network analysis that has translational applications in COPD and lung disease.

项目摘要/摘要 快速发展的基因组技术正在提供前所未有的海量数据，有可能 对包括慢性阻塞性肺疾病在内的肺部疾病的驱动过程有了新的见解 (COPD)。这些数据已经使我们能够更统一地理解 生物机制共同作用影响COPD。我们现在意识到，在大多数情况下，单个基因 或途径不能完全描述疾病或疾病状态的改变。更确切地说，与疾病有关 变化通常涉及基因组、表观基因组、转录组、代谢组和 细胞的蛋白质组，可由复杂的网络表示，其结构随着疾病的变化而改变 发展起来。重要的是，这些变化中的许多与监管网络的复杂变化有关 从正常状态到疾病状态。对这些变化进行建模可以让我们了解驱动这些变化的过程 并建议潜在的靶向治疗。 在这个建议中，我们开发和扩展了集成新兴的多组数据以重建的方法 全面的慢性阻塞性肺病监管网络。然后，我们开发分析这些网络的方法，并 在不同观察到的COPD中有效地将调节变化与疾病机制联系起来 表型。我们首先开发定量的方法来推断、分析、分解和 比较网络。这些方法将使我们能够发现关于肺部疾病本质的新特征， 了解患者工作中复杂的监管流程，并最终有能力建议 如何更有效地治疗慢性阻塞性肺病。 执行这一计划将需要一套独特的技能，涵盖生物学、网络科学、计算机 科学、转化医学和肺部疾病。格拉斯博士的背景是物理学、复杂系统和 基因组数据分析。尽管她之前的经验为她提出的研究做好了准备， 她认识到在应用网络和网络时需要克服新的挑战 研究慢性阻塞性肺疾病的基因组学方法。因此，格拉斯博士选择了一个有指导的研究环境 并制定了一项培训计划，使她能够获得完成任务所需的跨学科技能 这个项目的目标。 为了支持她提出的研究，格拉斯博士将利用许多高质量的计算 她可通过布里格姆大学钱宁网络医学部(CDNM)获得的资源 妇女医院(BWH)、达纳-法伯/哈佛癌症中心、哈佛医学院和哈佛大学 公共卫生学院，以及她的导师和顾问委员会直接提供的额外资源 会员。按照这些思路，格拉斯博士组建了一支多样化的、资质良好的指导团队，负责监督 并为她的研究工作提供建议。她的主要导师奎肯布什博士和顾问委员会成员袁博士 两者在分析和解释多种类型的基因组数据方面都有广泛和互补的经验。 顾问委员会成员Kepner博士对可伸缩计算机体系结构有深入的了解，并将为Dr。 玻璃通过提供计算资源，如访问麻省理工学院超级云。施工后 关于慢性阻塞性肺病的监管网络，在相关生物学问题的背景下对其进行解释将是至关重要的。 顾问委员会成员Onnela博士是开发网络量化方法的专家，他将在 在帮助格拉斯博士创建网络结构差异的客观衡量标准方面发挥了重要作用。最后， 共同导师Silverman博士是COPD和网络医学方面的领先专家，将为 为Glass博士提供指导，以确定如何将网络测量与患者数据相关联，包括相关 慢性阻塞性肺疾病的临床特点。 格拉斯博士将用正式的课程和有指导的具体探索来补充她的实践训练 侧重于三个主要领域：1)肺部疾病、转化医学和临床应用，并接受培训 通过哈佛大学促进会和哈佛公共卫生学院提供的课程，参加 美国胸科学会年度会议，并与西尔弗曼博士和呼吸科密切合作 CDNM/BWH的医学系；2)生物医学数据分析和计算，通过 华盛顿大学和麻省理工学院提供的在线课程，参加 当地讲习班，并与QuackenBush、袁和Kepner博士密切合作；3)统计和网络 分析方法的发展，通过参加哈佛公共学院提供的课程进行培训 健康，参加国家会议，并与夸肯布什博士和昂内拉博士密切合作。最后，戴维斯博士说。 在整个授权期内，格拉斯将积极参与和接受赠款编写过程的培训， 以便做好准备，在项目结束时申请独立资金。 格拉斯博士的职业目标是成为一名研究非肿瘤性肺部疾病的独立研究员 一个学术机构。通过建议的研究和培训计划，她将能够磨练 她已经开发了计算能力，并收集了各种必要的额外技能 成为能够利用生物医学数据进行计算的独立调查者 在慢性阻塞性肺疾病和肺部疾病中有翻译应用的研究和网络分析。