Research Interests and Projects
Exposomics and Metabolomics
The focus of my research is to use metabolomics to explore the interaction between our environment and metabolism. This includes air, water, natural health products, food and food intolerances, and drugs. By using a diverse set of analytical tools, including air monitoring, breath metabolomics, and bodily fluid metabolomics, I am exploring our interactions with our envrionment.
Healthy Baby Brains
One of my main projects is centered around investigating the interaction of cannabis use and its affect on human metabolism. Specifically, the affects that cannabis use during pregnancy has on mothers and baby's metabolism and development. With the 2018 legalization of cannabis there is growing concern around the fetal and infant cannabis exposures and the lack of research around the health implications. The long-term outcomes of cannabis exposure are still unknown, however some teens who use cannabis have permanent brain changes and increased risk of mental health problems. Much less is known about second-hand cannabis exposure and whether this exposes children to biologically significant levels of cannabis. Cannabis has hundreds of biologically relevant compounds, with the affect of the majority of these compounds on human physiology still unknown. I am developing comprehensive two-dimensional gas chromatography time of flight mass spectrometry (GCxGC-TOFMS) methods for untargeted metabolomics analysis of bodily fluids, including urine and breastmilk, to detect cannabis related changes in metabolism. I am working in collaboration with a number of top interdisciplinary researchers at UofA in the Faculty of Medicine on this project. To learn more about this project, visit the Healthy Baby Brains research clusters website.
Data processing and chemometrics
I am also interested on using machine learning and artifical intelligence to develop workflows to automate the processing of chemical data. I am currently working on the development of fully automated software for the analysis of homogenous GCxGC-TOFMS datasets. GCxGC-TOFMS data is an incredibly challenging problem as there is retention drift in two dimensions, as well as highly fragmented mass spectrometry data due to the nature of electron impact ionization. Therefore, we need complex computational tools to handle GCxGC-TOFMS data. Specifically, I am working on the development of a region of interest selection tool to isolate regions of interest in GCxGC-TOFMS chromatograms which can then be processed further. My work on region of interest selection for 1D GC-MS data was published in the Journal of Chromatography A. My GCxGC ROI selection algorithm was recently published in the Journal of Chromatography Open.
In addition to working with processing chromatographic data, I am using machine learning algorithms and chemometrics to process NMR data. Check out my recent paper in the Journal of Food Composition and Analysis. Additionally I have a preprint out on ChemRxiv comparing classification models trained on 60 and 400 MHz NMR data.
In recent years online metabolomics resources and data bases have become increasingly popular. However, no tool exists yet which can putatively identify plant hormones from untargeted liquid chromatography mass spectrometry (LC-MS) datasets. Plant hormones are small molecule hormones which drive physiological processes such as reproduction, defense, growth, development, day-night cycles, and stress responses. We set out to catalogue all known small molecule hormones into a webtool that can be queued for these plant hormones. Since plant hormones often exist as "conjugates", where they are modified with various chemical moieties to change their chemical and physical properties. Detecting plant hormones in their native state only tells half the story, we need to be able to detect these conjugates as well. Therefore, we developed a webtool, dubbed HormonomicsDB, and an accompanying hormonomics method for the untargeted LC-MS analysis of plant hormones. I developed HormonomicsDB in the R environment, using R-shiny to develop the user interface. HormonomicsDB not only searches for these plant hormones in their native state, but also for these conjugates by employing a synthetic biotransformation approach. Unlike similar database search tools, HormonomicsDB allows users to upload a whole peak table, then an annotated peak table, preserving ion intensity information, is returned to the user. HormonomicsDB also uses machine learning predicted retention times to give the user information about the approximate elution order of their analytes. I am fortunate to have collaborated with Drs. Susan Murch and Lauren Erland on this project. The manuscript for this tool was recently published in F1000Research! Please see the HormonomicsDB website to try the tool!
It is often said that plants are the best chemists on Earth. Plants can't run away from their problems like we can, so they have developed a strategic arsenal of potent chemicals through evolution to control their physiology and mortally wound any would be attackers. Plants have acquired their unique ability to produce some of the most bizarre chemicals known to man through millions of years of evolution. Since the dawn of civilization humans have taken advantage of these potent plant chemicals to treat ailments, get high, or even kill. In fact, even in the 21st century over 90% of our pharmaceuticals are inspired by plant chemicals. While at UBC Okanagan in Dr. Susan Murch's group, I spent a lot of time implementing metabolomics strategies to study the metabolism of plants, to see what these unique chemists are really up to. I employed metabolomics to study how evolution has shaped plant's ability to learn new chemical tricks through the comparison of the "living fossil" Wollemi pine (Wollemi nobilis) to its closest living relative Norfolk Island pine (Araucaria heterophylla). Check out our recent publication in Botany about Wollemi pine here. I also used metabolomics strategies to compare varieties of the recreational drug Kratom (Mitragyna speciosa) to better understand the chemistry of different ecotypes and detect potentially harmful adulterants. While in Dr. Susan Murch's laboratory I was incredibly fortunate to collaborate on many plant metabolomics projects with post doctoral fellow Dr. Lauren Erland and assistant professor Dr. Thuy Dang.