Gary Patti

Gary Patti

​Associate Professor of Chemistry and of Genetics and Medicine (School of Medicine)
Michael and Tana Powell Associate Professor of Chemistry
research interests:
  • Metabolomics
  • Cancer
  • Chronic Pain
  • Aging

contact info:

mailing address:

    CB 1134
    ST. LOUIS, MO 63130-4899

​Professor Patti’s research is focused in the rapidly evolving field of metabolomics. He is developing new mass spectrometry and nuclear magnetic resonance (NMR) technologies.

Our laboratory is interested in the biochemical reactions that underlie fundamental physiological processes and associated metabolic derangements that cause disease. By using state-of-the-art mass spectrometers coupled with cutting-edge metabolomic technologies, we take a systems-level approach to study comprehensive metabolism and identify specific pathways that are altered in connection with particular phenotypes. We strive to translate our metabolomic findings into a physiological context through the use of classical biochemical tools and animal models as well as with the development of new technologies such as mass spectrometry-based metabolite imaging and whole-cell NMR. Our specific interests are outlined below.

Metabolomics: Advancing Technology for Biological Discovery

Modern day mass spectrometers enable the detection of thousands of compounds in the metabolic extract of biological samples with unprecedented sensitivity. The goal of metabolomics is to compare these data across different sample types to gain insight into the metabolic programs that govern distinct biological phenotypes. A major challenge in the field, however, has been the translation of mass spectrometric peaks into metabolic structures and pathways. Indeed, the masses of more than half of the peaks routinely detected from biological samples in our laboratory return no hits when searched in currently available metabolomic databases. A major effort of our research program is to develop new metabolomic technologies that improve the throughput of structural identifications as well as our ability to characterize the pathway, physiological function, and anatomical localization of metabolites that do not fit into canonical metabolic reaction maps. To accomplish these goals, we rely heavily on bioinformatic strategies and modified LC/MS/MS experimental methodologies. Additionally, our strategies include mass spectrometry-based metabolite imaging, whole-cell NMR, and integration with sequencing data.


It is well established that most cancer cells take up an increased amount of glucose relative to that taken up by normal differentiated cells. This phenomenon, known as the Warburg effect, is also observed in other rapidly dividing cells. It is speculated that Warburg metabolism in proliferating cells supports the metabolic demands of cellular growth. However, the fates of glucose and other nutrients taken up by cancer cells have not been comprehensively mapped. We are interested in using untargeted metabolomic technologies to quantitatively determine how cancer cells metabolize nutrients differently than normal differentiated cells. In addition to examining the well-studied pathways of central carbon metabolism, we also seek to study peripheral metabolic pathways and pathways involving unknown compounds that have yet to be characterized.