Thursday, October 6, 2022 12pm to 1pm
Speaker: José E. Manautou, Professor of Toxicology, Department of Pharmaceutical Sciences at the University Of Connecticut School Of Pharmacy.
Short Bio: José E. Manautou is the newly appointed Boehringer-Ingelheim Pharmaceuticals, Inc. Chair in Mechanistic Toxicology, Department Head of Pharmaceutical Sciences. His long-term research interests are on biochemical and molecular determinants of xenobiotic-induced hepatotoxicity and defining compensatory responses to liver injury that enhance tissue resistance to toxicant re-exposure. Manautou has published over 200 original research articles, abstracts, commentaries and other reports. He is also a Fellow of the Academy of Toxicological Scienes. He has earned national and international recognition as a toxicology scholar, educator and for his service to the global toxicology community. He is Co-Editor-in-Chief of the journal Current Opinion in Toxicology and the President of the International Union of Toxicology. Manautou obtained his BS in pharmacy from the University of Puerto Rico, Ph.D. in pharmacology and toxicology from Purdue University, and postdoctoral training at the University of Connecticut. He also conducted sabbatical training at the Academic Medical Center in Amsterdam.
Research: Exposures are believed to initiate diseases or adverse health outcomes through their influence on metabolites, proteins, and genes. With the development of omics, measurements of thousands of compounds from biological samples can be performed at the same time. However, there are still gaps between measurements and meaningful information linking exposures and diseases or adverse health outcomes. In metabolomics studies, such gaps include massive numbers of molecular signals with redundant information, limited identification of metabolites, and using pathway analysis with known metabolites on known pathways instead of performing discovery. To fill such gaps, I developed the GlobalStd algorithm to remove redundant information in metabolomics features data and built the PMDDA workflow to perform exhaustive MS2 data collection for annotation. I also developed the concept of “reactomics” to mine chemical relationships in HRMS data and the “gatekeeper discovery” workflow as tools for interrogating the functional exposome. Based on those studies, "Active Metabolites Network (AMN)" can be constructed to refine the metabolites based on correlation clusters and/or paired mass distances among metabolites. Coupling with the PMDDA workflow, such AMN can be used for discovery of metabolites and biological pathways highly influenced by multiple exposures which can then be linked to health conditions.
We will serve lunch. So I respectfully ask you to RSVP only if you plan to attend in person.