Lung cancer biomarkers based on integrated metabolomics (DOD W91ZSQ0142N614)
Lung cancer is one of the diseases that require very early detection in order to provide efficient treatments. Unlike genetic markers or mRNA transcripts, changes in metabolite levels have not been studied to a great extent with respect of insights into cellular dysregulation of lung cancer cells or use of metabolic profiles to diagnose and predict lung cancer early diagnostics using patient blood plasma samples. The Fiehn laboratory contributes to a 3-year project focusing on this disease by delivering standardized metabolic readouts and extension of our current databases. In addition, we will work towards structural annotation of biomarkers using advanced query tools.
Identification of Muscle-Specific Biomarkers of Fatty Acid beta-Oxidation (NIH R01 DK078328)
Elevated fat levels within skeletal muscle cells (intramyocellular lipids) are highly correlated with muscle and whole-body insulin resistance, and more prevalent in obesity. Reduced muscle mitochondrial fatty acid beta-oxidation is more prevalent among insulin-resistant/diabetic persons. Led by Principal Investigator Sean Adams of the USDA-Western Health Nutrition Research Center in Davis, this project will identify specific biomarkers of muscle fatty acid beta- oxidation using multiple metabolomic analytical platforms to compare metabolite profiles in isolated mitochondrial organelles, in muscle cells that are catabolizing tatty acids at different rates, in a transgenic animal model, and in human subjects harboring a truncation polymorphism. This project will further determine whether metabolomic profiles reflective of muscle fat combustion predict metabolic health changes following diet and exercise intervention in obese subjects.
Exogenous and endogenous biomarkers of CYP2D6 variability in pediatrics (NIH R01 HD058556)
This study investigates the contributions of development and genetics in determining the CYP2D6 phenotype in children. If successful, these studies will facilitate the custom design of nontoxic and effective pediatric drug therapy regimens since CYP2D6 is responsible for the metabolism and clearance of many drugs commonly used by children.
Chlamydomonas metabolism (NSF-JST program)
We utilize the unicellular green algae Chlamydomonas reinhardtii to study fundamental problems in metabolism of photoautotrophic organisms. Causal connectivity in metabolic networks is investigated by a different degrees of nutritional deprivation in a time-dependent manner. We are integrating metabolomic and proteomic analyses to study biochemical responses upon environmental stimuli or genetic differences.
Informatics and Technology Research
Two databases developed in the Fiehn laboratory steer laboratory workflows, process mass spectrometry raw data and disseminate result data. SetupX hosts study design metadata that define the biological objects, treatments and time points of a study as well as experimental metadata such as sample preparation and mass spectrometry methods. Laboratory assistants use SetupX to download and randomize sequence tables for the mass spectrometers. After data acquisition, a relational database system (BinBase) is employed for automated metabolite annotation using a multi-tiered filtering system to retain only high quality and consistent metabolite peaks. Results are exported back to SetupX for downloads. Most studies are still not publicly available, mostly because the results have not yet been published in peer-reviewed scientific journals.
Mass spectral libraries of identified compounds (sponsored by mass spectrometry vendors)
The Fiehn laboratory has analyzed 1,000 commercially available pure reference compounds by GC/MS under standardized conditions with C08-C30 FAMEs as retention index markers using two different instruments, a quadrupole and a time-of-flight mass spectrometer. Spectra and retention indices together are the primary source to identify metabolite spectra stored in BinBase. These compounds were compiled by querying the KEGG pathway database, LipidMaps and the Dictionary of Natural Compounds using a cut-off at molecular masses < 700 Da. The libraries are licensed out to the mass spectrometry vendors from whom the spectra will soon be commercially available. We currently investigate these libraries for substructure characterization of unknown metabolic peaks.
Annotation of unknown metabolites by mass spectrometry and database queries
(initiated by a DuPont donation, now part as sub-aim in multiple grants)
Identification of unknown metabolic peaks presents a challenge to metabolomics. The Fiehn laboratory pursues automatic algorithms to annotate mass spectra by matching experimental data to calculated properties from databases. The first step for annotation of mass spectra has been completed, the calculation of molecular formulas using the 'Seven Golden Rules' . Specifically, we have investigated the impact of accurate isotopic patterns for compound identification using mass spectrometry.