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.

Public Health Relevance

New technologies are rapidly increasing our ability to generate large data sets that contain information on the biological underpinnings of lung disease. However, we lack methods to take full advantage of the information they contain or to uncover the complex interactions that are represented in this data. In this application, we propose to develop new Data Science methods to infer, analyze, decompose, and compare networks in order to enhance our understanding of Chronic Obstructive Pulmonary Disease (COPD). These new methods will allow us to discover new features about the nature of lung disease, to better understand the complex regulatory processes mediating disease features, and ultimately to suggest ways to develop more effective treatment strategies for COPD.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Mentored Quantitative Research Career Development Award (K25)
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Special Emphasis Panel (MPOR (MA))
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Tigno, Xenia
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Brigham and Women's Hospital
Independent Hospitals
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Morrow, Jarrett D; Cho, Michael H; Platig, John et al. (2018) Ensemble genomic analysis in human lung tissue identifies novel genes for chronic obstructive pulmonary disease. Hum Genomics 12:1
Lopes-Ramos, Camila M; Kuijjer, Marieke L; Ogino, Shuji et al. (2018) Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism. Cancer Res 78:5538-5547
Morrow, Jarrett D; Glass, Kimberly; Cho, Michael H et al. (2018) Human Lung DNA Methylation Quantitative Trait Loci Colocalize with Chronic Obstructive Pulmonary Disease Genome-Wide Association Loci. Am J Respir Crit Care Med 197:1275-1284
Qiu, Weiliang; Guo, Feng; Glass, Kimberly et al. (2018) Differential connectivity of gene regulatory networks distinguishes corticosteroid response in asthma. J Allergy Clin Immunol 141:1250-1258
Lopes-Ramos, Camila M; Paulson, Joseph N; Chen, Cho-Yi et al. (2017) Regulatory network changes between cell lines and their tissues of origin. BMC Genomics 18:723
Fagny, Maud; Paulson, Joseph N; Kuijjer, Marieke L et al. (2017) Exploring regulation in tissues with eQTL networks. Proc Natl Acad Sci U S A 114:E7841-E7850
Schlauch, Daniel; Glass, Kimberly; Hersh, Craig P et al. (2017) Estimating drivers of cell state transitions using gene regulatory network models. BMC Syst Biol 11:139
Schlauch, Daniel; Paulson, Joseph N; Young, Albert et al. (2017) Estimating gene regulatory networks with pandaR. Bioinformatics 33:2232-2234
Sonawane, Abhijeet Rajendra; Platig, John; Fagny, Maud et al. (2017) Understanding Tissue-Specific Gene Regulation. Cell Rep 21:1077-1088
Marco, Eugenio; Meuleman, Wouter; Huang, Jialiang et al. (2017) Multi-scale chromatin state annotation using a hierarchical hidden Markov model. Nat Commun 8:15011

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