Jessica Lasky-Su - Metabolomics in the multi-omic era: the key component for clinical translation in asthma
Dr. Lasky-Su is an Associate Professor in Medicine and associate statistician at Harvard Medical School and Brigham and Women’s Hospital. Over the last 20 years, Dr. Lasky-Su has focused on the analysis of genetics, genomics, and metabolomics data of various complex diseases with a primary focus on respiratory disease over the last 15 years. The accumulation of these efforts has resulted in a productive track record of over 130 original research articles. Through the funding of several metabolomics-related grants, Dr. Lasky-Su has developed a “metabolomics epidemiology” research program at the Channing Division of Network Medicine that has been highly successful and synergistic in nature, and has developed into one of the largest and most impactful groups of metabolomic epidemiologists with a strong national and international presence and comprehensive publication record. In addition to using metabolomics to study the etiology of several complex diseases, including body mass index, asthma, allergies, autism, bacteremia, and macular degeneration, Dr. Lasky-Su has also focused on using metabolomics data in conjunction with other omics data to study disease etiology using several approaches to integrative omics. I also serve in national and international leadership capacities, including the acting chairman of the Consortium of METabolomic Studies (COMETS), a board member of the International Metabolomics Society, and a scientific advisor to the “Metabolomics Workbench.”
The rapid advance in scientific technology has resulted in a multi-omic era, where multiple omics data types are available for single, large population-based cohorts. For the first time, epidemiologists have the ability to study complex diseases biology through the use of multiple omics data types simultaneously, in conjunction with relevant clinical and environmental information. While multi-omic integration has the potential to provide optimal insight into disease pathogenesis, the best approaches to multi-omic integration are not clear. In this talk we describe several approaches to multi-omic data integration that can be utilized to study asthma eitiology. From a reductionist approach that relies on previous biologic and scientific knowledge to guide the analyses to a systems biology approach that integrates genomic, epigenomic, and metabolomic networks together, we review examples of each from our research focusing on asthma, allergy, and obesity phenotypes. Highlights include 1) Using 2 birth cohorts with >1,000 children to demonstrate the interplay between genetics, metabolomics, and environmental exposures in pregnancy and early life that lead to childhood asthma and allergies; 2) Applying a systems approach to integrate gene expression, methylation, and metabolomics data to identify multiomic influences on asthma phenotypes; 3) Analyzing metabolomic data in 2 large population-based cohorts totaling >12,000 to study prevalent asthma and to examine the impact of inhaled steroid use; and 4) Demonstrating the feasibility of large-scale metabolomic integration through a meta-analysis of metabolomic data in >85,000 people from multiple cohorts throughout the world. We examine these approaches and discuss the advantages and weaknesses of each and under what conditions each approach is most relevant. The talk concludes with a discussion of why metabolomics as an essential component of omics research and the scientific challenges that are imperative to address in order to continue making progress towards utilizing multi-omics in a personalized medicine framework.