Dr. Gary Patti Presents Rossiter Lecture Series
Posted: Mar 23, 2018
Professor Gary Patti in the Department of Genetics and Medicine from Washington University in St. Louis, will be presenting the 2018 memorial Rossiter lecture series. During his graduate studies, Dr. Patti focused on applying NMR to study metabolic pathways in bacteria that could be targeted in antibiotic-resistant bugs. Dr. Patti completed his postdoctoral studies at the Scripps Research Institute with Professor Siuzdak, who is a pioneer of metabolomics and known for establishing key resources in the field such as the METLIN metabolite database. At Scripps, Gary contributed to the development of mass spectrometry-based metabolomic technologies such as the XCMS software. Gary joined the faculty at Washington University in 2011, with appointments in the departments of chemistry, medicine, and genetics. He has received numerous awards including the Sloan Fellowship, the Camille Dreyfus Teacher-Scholar Award, the Mallinckrodt Scholar Award, the Pew Scholar Award, the Agilent Early Career Award, and an inaugural NIH RIVER award for revolutionizing, innovative, and visionary research. Professor Patti's lab at Washington University works on developing new metabolomic technologies and applying them to understand physiological regulation in mammalian systems. A major focus of his lab is using isotope-based metabolomic approaches in samples ranging from cell culture to human patients.
Professor Patti will be giving two talks: His public lecture will be on the 27 March at 4:00 in W140, and his technical lecture will be on the 28 March at the same time and location.
Location, location, location: compartmentalized metabolism creates therapeutic vulnerabilities in cancer
An increasing number of untargeted metabolomic studies have focused on characterizing “dark matter”. This effort aims to identify the chemical structures of naturally occurring metabolites that had not been previously described. To modernize our perspective of cellular metabolism, we argue that it is also important to complement this work with experiments that can discover new reaction arrows in metabolism maps. In this talk, we will describe an experimental platform using NMR and mass spectrometry-based metabolomic technologies to achieve the latter and, in some cases, to localize reaction arrows within cellular compartments. Significant attention will be dedicated to tracking the comprehensive fates of lactate and 2-hydroxyglutarate without bias. We will show that lactate, often recognized as an excreted waste product of fermentation, can be used as a metabolic precursor to synthesize lipids in cancer cells. Using stable isotopes, we will demonstrate that lactate is directly imported into the mitochondria where it is subsequently oxidized. A model will be presented in which fermentation and mitochondrial lactate transport constitute an electron shuttle that promotes lipogenesis. Surprisingly, in contrast to lactate, we find that 2-hydroxyglutarate (the so-called "oncometabolite") is minimally metabolized in mammalian cells. Instead we will focus on its synthesis, which consumes NADPH. We will show that consuming NADPH for 2-hydroxyglutarate alters the redox balance of cancer cells, and we will illustrate how this can be therapeutically exploited in the clinic.
Untargeted metabolomics: a last resort or the next frontier?
It has become relatively routine to acquire mass spectrometry-based metabolomic data, either in one’s own laboratory or using one of the many service facilities around the world. Despite this progress, however, interpreting metabolomic results continues to be a major challenge for many researchers that severely limits potential applications of the technology. Indeed, out of the thousands of metabolomic signals that are typically detected from a biological specimen, only a small fraction are commonly identified. Although the challenge of interpreting metabolomic data may seem to be purely informatic in nature, we will discuss how the problem fundamentally starts with poorly designed experiments that adversely affect data quality and unnecessarily complicate results. Opportunities to optimize metabolomic workflows with respect to extraction, chromatography, mass spectrometry, and informatics will be reviewed. We will present solutions to identify artifacts, contaminants, and signal redundancies (such as adducts, fragments, and isotopes) within the data. Using one dataset as a representative example, we will illustrate that there can be an order of magnitude more metabolomic signals than unique metabolites. The implications of these findings for various applications of untargeted metabolomics will be discussed and exciting biochemical problems that are best suited for the technology highlighted.
Photograph courtesy of Washington University in St. Louis
Writer: Taelin Wilford