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数据，有可能 对驱动肺部疾病的过程产生新的见解，包括慢性阻塞性肺疾病 （COPD）。这些数据已经使我们能够对多个多重 生物学机制共同影响COPD。我们现在感谢在大多数情况下一个基因 或途径不能完全表征疾病或疾病状态的改变。相反，与疾病有关 变化通常涉及对基因组，表观基因组，转录组，代谢组和 细胞的蛋白质组，可以由复杂网络表示，其结构被改变为疾病 发展。重要的是，其中许多变化与监管网络中的复杂变化有关 从正常状态到否认状态。对这些更改进行建模可以告知我们驱动的过程 COPD并建议潜在的靶向疗法。 在此提案中，我们开发和扩展了整合新兴的多族数据以重建的方法 COPD中的全面监管网络。然后，我们开发了分析这些网络的方法 有效地将调节性改变与不同观察到的COPD中的疾病机制联系起来 表型。我们首先开发定量方法来推断，分析，分解和 比较网络。这些方法将使我们能够发现有关肺部疾病性质的新特征 了解患者工作的复杂监管过程，并最终具有力量 更有效地治疗COPD的方法。 执行该计划将需要一套独特的技能，这些技能涵盖生物学，网络科学，计算机 科学，转化医学和肺部病。 Glass博士的背景在物理，复杂系统和 基因组数据分析。尽管她以前的经历为拟议的研究做好了准备，但 她认识到，应用网络时需要克服一些新的挑战 基因组学研究COPD的方法。因此，格拉斯博士选择了一个心理化的研究环境 并制定了一个培训计划，该计划将使她获得完成所需的跨学科技能 该项目的目标。 为了支持她拟议的研究，格拉斯博士将利用许多高质量的计算 她通过Brigham的Channing Network Medicine（CDNM）和 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其他资源 成员。沿着这些线路，格拉斯博士将一个转移和合格的指导团队汇集到海外 并建议她的研究工作。她的主要导师Quackenbush博士和顾问委员会成员Yuan博士 两者在分析和解释多种类型的基因组数据方面都有丰富而完整的经验。 顾问委员会成员Kepner博士对可扩展的计算机架构有深刻的了解，并将支持博士。 通过提供计算资源，例如访问MIT超级云的玻璃。构造后 COPD中的监管网络在相关生物学问题的背景下解释它们至关重要。 顾问委员会成员Onnela博士是开发网络量化方法的专家，并将发挥作用 帮助格拉斯博士创建网络结构差异的客观测量的重要作用。最后， Co-Mentor Silverman博士是COPD和网络医学领域的领先专家，将提供重要的 Glass博士确定如何将网络措施与患者数据联系起来，包括相关的指导 COPD的临床特征。 格拉斯博士将通过正式的课程和特定的《探索》来补充她的动手培训 专注于三个主要领域：1）肺部疾病，翻译医学和临床应用，并进行培训 通过哈佛大学催化剂和哈佛公共卫生学院提供的课程，参加 年度美国胸腔协会会议，并与Silverman博士和呼吸道紧密合作 CDNM/BWH的医学系； 2）生物医学数据分析和计算，并进行培训 华盛顿大学和马萨诸塞州理工学院提供的在线课程 当地讲习班，并与Drs紧密合作。 Quackenbush，Yuan和Kepner； 3）统计和网络 分析方法开发，并接受哈佛公立学院提供的课程的培训 健康，参加民族会议，并与博士紧密合作。 Quackenbush和Onnela。最后，博士 Glass将在整个奖励期内积极参与并接受有关赠款写作过程的培训， 为了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研究非肿瘤肺病的独立调查员 一个学术机构。通过拟议的研究和培训计划，她将能够磨练 她已经开发并收集了各种其他技能，这将是必不可少的 成为一个独立的研究者，能够利用生物医学数据执行计算 研究和网络分析在COPD和肺部疾病中具有转化应用。