The over-arching hypothesis of this proposal is that inter-individual differences in asthma control result from the complex interplay of both environmental, genomic, and socioeconomic factors organized in discrete, scale-free molecular networks. Though strict patient compliance with asthma controller therapy and avoidance of environmental triggers are important strategies for the prevention of asthma exacerbation, failure to maintain control is the most common health-related cause of lost school and workdays. Therefore, better understanding of the molecular underpinnings and the role of environmental factors that lead to poor asthma control is needed. Using the Asthma BioRepository for Integrative Genomic Exploration (Asthma BRIDGE), we will perform a series of systems-level genomic analyses that integrate clinical, environmental and various forms of "omic" data (genetics, genomics, and epigenetics) to better understand how molecular processes interact with critical environmental factors to impair asthma control. This proposal consists three Specific Aims, each consisting of three investigational phases: (i) an initial computational discovery phase to define specific molecular networks using the Asthma BRIDGE datasets, followed by two validation phases - (ii) a computational validation phase using an independent clinical cohort, and (iii) an experimental phase to validate critical molecular edges (gene-gene interactions) that emerge from the defined molecular network.
In Specific Aim 1, we will use the Asthma BRIDGE datasets to define interactome sub-module perturbed in poor asthma control;the regulatory variants that modulate this asthma-control module;and to develop a predictive model of asthma control.
In Specific Aim 2, we will study the effects exposure to air pollution and environmental tobacco smoke on modulating the asthma control networks, testing for environment-dependent alterations in network dynamics.
In Specific Aim 3, we will study the impact of inhaled corticosteroids (ICS - the most efficacious asthma-controller medication) on network dynamics of the asthma-control sub-module by comparing network topologies of acute asthma control between subjects taking ICS to those not on ICS. For our experimental validations, we will assess relevant gene-gene interactions by shRNA studies bronchial epithelial and Jurkat T- cell lines. Experimental validations of findings from Aim 2 will be performed by co-treating cells with either cigarette smoke extract (CSE) or ozone. Similar studies will be performed with co-treatment using dexamethasone to validate findings from Aim 2. From the totality of these studies, we will gain new insights into the pathobiology of poor asthma control, and define targets for biomarker development and therapeutic targeting.

Public Health Relevance

Failure to maintain tight asthma symptom control is a major health-related cause of lost school and workdays. This project aims to use novel statistical network-modeling approaches to model the molecular basis of poor asthma control in a well-characterized cohort of asthmatic patients with available genetic, gene expression, and DNA methylation data. Using this data, we will define an asthma-control gene network, and the genetic, epigenetic, and environmental factors that determine inter-individual differences in asthma control.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1-GGG-L (50))
Program Officer
Gan, Weiniu
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Brigham and Women's Hospital
United States
Zip Code
Basler, Georg; Nikoloski, Zoran; Larhlimi, Abdelhalim et al. (2016) Control of fluxes in metabolic networks. Genome Res 26:956-68
Zhang, Pingye; Lewinger, Juan Pablo; Conti, David et al. (2016) Detecting Gene-Environment Interactions for a Quantitative Trait in a Genome-Wide Association Study. Genet Epidemiol 40:394-403
Vinayagam, Arunachalam; Gibson, Travis E; Lee, Ho-Joon et al. (2016) Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets. Proc Natl Acad Sci U S A 113:4976-81
Yan, Xiting; Chu, Jen-Hwa; Gomez, Jose et al. (2015) Noninvasive analysis of the sputum transcriptome discriminates clinical phenotypes of asthma. Am J Respir Crit Care Med 191:1116-25
Sharma, Amitabh; Menche, Jörg; Huang, C Chris et al. (2015) A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. Hum Mol Genet 24:3005-20
Croteau-Chonka, Damien C; Rogers, Angela J; Raj, Towfique et al. (2015) Expression Quantitative Trait Loci Information Improves Predictive Modeling of Disease Relevance of Non-Coding Genetic Variation. PLoS One 10:e0140758
Howrylak, Judie A; Fuhlbrigge, Anne L; Strunk, Robert C et al. (2014) Classification of childhood asthma phenotypes and long-term clinical responses to inhaled anti-inflammatory medications. J Allergy Clin Immunol 133:1289-300, 1300.e1-12
Breton, Carrie V; Siegmund, Kimberly D; Joubert, Bonnie R et al. (2014) Prenatal tobacco smoke exposure is associated with childhood DNA CpG methylation. PLoS One 9:e99716