The overarching goal of the lab is to use biomedical informatics approaches to better understand complex respiratory diseases. We are currently focused on asthma as a disease model, but our approaches and findings will be extended to other diseases. Some ongoing projects are:


Identify Gene Expression Patterns that are Associated with Asthma and Asthma Medications

For pulmonary disease research it is very helpful to have cell type-specific information, rather than that based on whole lung as is available in most online databases. We have used RNA-Seq to study a prominent asthma-specific tissue: the human airway smooth muscle. Specifically, we have measured airway smooth muscle response to glucocorticoids, beta-agonists, vitamin D, and to identify differences between fatal asthma patients vs. individuals without asthma. The gene-specific results for these studies and other asthma-relevant datasets that are publicly available can be retrieved with the Lung Cell Transcriptome Explorer.


Integrate Diverse Genomic Datasets To Model Cellular Transcriptome Changes Involved In Disease

Two current challenges in biomedical research are that 1) diverse genomic datasets are being generated at a pace greater than they can be analyzed and integrated, and that 2) when disease-focused genome-scale datasets are analyzed, it is often the case that only single genes/loci are functionally validated. Mining publicly available genomic data that contain valuable and unused information has the potential to improve our understanding of complex diseases through the careful integration of datasets to obtain more comprehensive views of genomic relationships and help to prioritize genes for functional validation studies. Using asthma as a disease model, we are integrating results from individual expression experiments in which specific tissues, with intact genomes and in the context of knocked-down or overexpressed genes, were stimulated with pro-inflammatory cytokines, environmental pollutants, allergens, and/or asthma medications. Subsequent generation of transcriptome networks of asthma-related tissues where nodes represent genes and treatment conditions, and edges represent statistically significant differential expression changes will be used to identify modules of genes related to similar biological processes as well as to prioritize genes for functional validation studies by virtue of being key and/or uncharacterized network nodes.


Characterize Demographic and Socioeconomic Determinants of Asthma Prevalence and Exacerbations

Understanding asthma-associated factors among US adults remains an important topic to address asthma disparities that are known to occur by sex, race/ethnicity and socioeconomic status. We have used data on over 1.5 million respondents of the Behavioral Risk Factors Surveillance System (BRFSS) for the years 2002-2012 to better understand factors associated with current and lifetime asthma. AsthmaMaps shows county-level results from these analyses.


Use Geospatial Environmental Data to Better Understand Factors that Contribute to Asthma Prevalence and Exacerbation

Asthma is a prototypical complex disease for which studying genetic and environmental factors simultaneously may lead to greater breakthroughs in understanding of pathophysiology than studying genetics or the environment in isolation. However, there have been few attempts to simultaneously and comprehensively address the influence of genetics and the environment on asthma. While gene-by-environment studies in asthma have been performed, most are statistically underpowered to detect associations, and the heterogeneity of environmental and phenotype measures obtained across studies has limited the potential to conduct meta-analyses. Rather than using genetic variation data, we are focused on identifying environmental factors that are associated with asthma in specific geographic locations, understanding the relationship among these factors and demographic variables, and subsequently, conducting gene expression and variation studies that consider environmental factors in detail. A natural extension of these efforts will be to conduct expansive environmental genomics studies that take advantage of the increasing availability of health information technologies, including smartphone and inhaler medication sensors, to collect large amounts of real-time environmental and medication use data that can inform genetic and genomics studies.


Collaborative Projects

We are using RNA-Seq to characterize gene and transcript expression differences in other diseases including acute respiratory distress syndrome (ARDS), sepsis, lymphangioleiomyomatosis (LAM), and pulmonary arterial hypertension. We have also analyzed CRISPR libraries to identify genes involved in arsenic toxicity and functional characteristics of human airway smooth muscle.



Current awards that make our work possible are NIH R01-HL133433 and R01-HL141992, and a pilot award from the Center of Excellence in Environmental Toxicology (P30 ES013508).