- 1 Short Biography
- 2 Expert Opinion
- 2.1 When and why did you start using metabolomics in your investigations?
- 2.2 What have you been working on recently?
- 2.3 What are the advantages of applying multi-omics in precision medicine?
- 2.4 What learning materials would you recommend for early career researchers regarding integration of multi-omic data?
- 2.5 You have previously discussed analytical approaches that can be applied to multi-omic data; a reductionist approach and a systems biology approach. Could you tell our readers about advantages of utilizing these approaches in your studies?
- 2.6 Could you give us an example as to when your research has been translated from basic research into clinical use?
- 2.7 You are a part of the Consortium of METabolomic Studies (COMETS); what are the challenges of utilizing metabolomics in large population-based cohorts?
- 3 See also
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 150 original research articles. Through the funding of multiple metabolomics-related grants, Dr. Lasky-Su has developed a “metabolomic 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. She serves 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 governing board member of the “Metabolomics Workbench.”
When and why did you start using metabolomics in your investigations?
I was speaking with one of my colleagues who mentioned to me that I should look into metabolomics and stressed his belief that there was great potential here for complex diseases and precision medicine.
What have you been working on recently?
A tremendous amount of exciting projects so it is difficult to summarize. Some of the more exciting projects that I am currently working on are using metabolomics in conjunction with other omics and electronic medical records to help understand disease outcomes.
What are the advantages of applying multi-omics in precision medicine?
I think that this is a fundamentally new way to classify individuals that may help inform their potential to respond to different medical treatments. Using one “omic” data type by itself is often underpowered to identify robust differences in disease endotypes, which is where multi-omic data may be more informative. While multi-omics are super important and interesting, I do think that is it important to appreciate the potentially ground breaking information that we could use for precision medicine efforts with metabolomics alone.
What learning materials would you recommend for early career researchers regarding integration of multi-omic data?
This is a fast-moving area and there are not set standards of approaches to integrate multi-omic data together. I would start by looking up some free teaching resources online and then look at some general statistic approaches to multi-omic integration (e.g. met-GWA, the use of various biological networks, some machine learning approaches, multi-omic pathway integration). Beyond these more general approaches, looking up some of the popular basic integrative strategies through PUBMED searches is always a good approach!
You have previously discussed analytical approaches that can be applied to multi-omic data; a reductionist approach and a systems biology approach. Could you tell our readers about advantages of utilizing these approaches in your studies?
A reductionist approach focuses on using apriori hypothesis to motivate your integrative strategy. So if you know that particular metabolites are associated with your disease outcome, then you can identify the important genes, metabolites, and other omic variants that are known to operate within the given pathway. The advantage of this is it limits the multi-omics variants to be integrated together and it also often provides important clues about the best approach towards integrating the data together A systems biology approach starts with no apriori hypothesis and uses all available multiomic data to identify interrelationships and associations with the disease of interest. The advantage here is the potential to identify novel relationships that may have otherwise gone unnoticed.
Could you give us an example as to when your research has been translated from basic research into clinical use?
I want to be clear the that path to clinical translation is a long one that takes several steps after the discovery of an “translatable finding.” Often to be used in the clinical, there are subsequent steps to develop a clinical test that is “CLIA” approved. I currently have 2 two projects where we have direct clinical translations with potential for broad impact. If you ask me in 1 year, I am happy to talk about either of them more!
You are a part of the Consortium of METabolomic Studies (COMETS); what are the challenges of utilizing metabolomics in large population-based cohorts?
There are many challenges with utilizing metabolomics in large population-based cohorts and really the goal of COMETS is to help address these issues. I want to highlight some of the tremendous progress that COMETS has made in these areas, in particular the development of a metabolite harmonization nomenclature across multiple metabolomics laboratories, and the ability to meta-analyze metabolomics data on a large scale that has now been implemented for age and BMI phenotypes in nearly 100,000 individuals. This is truly tremendous progress that is likely to identify many important, robust associations!