Guest Speaker – Analytical (CHEM 741) – March 27, 2023

Bio
Dr. Nadja B. Cech is Patricia A. Sullivan Professor of Chemistry at the University of North Carolina Greensboro. A mass spectrometrist by training, she supervises a dynamic research group engaged in developing novel mass spectrometry metabolomics approaches to solve challenging problems in natural products research. Dr. Cech is the recipient of the 2011 Jack L. Beal Award from the Journal of Natural Products and the 2017 Thomas Norwood Award for Undergraduate Research Mentorship. She is a Principal Investigator for the NCCIH- and ODS funded Center for High Content Functional Annotation of Natural Products, Co-Director of the Analytical Core for the Center of Excellence for Natural Product Drug Interactions, and Co-Director of the Medicinal Chemistry Collaborative.

Abstract
A common goal in analytical research is to identify biologically active compounds in complex mixtures. The gold standard approach towards accomplishing this task is assay-guided fractionation, wherein the mixture is subjected to successive stages of purification and biological evaluation until active compounds are identified. Increasingly, scientists conducting assay-guided fractionation rely on integrating metabolomics datasets with assay data to predict active mixture components. What happens, though, when the activity of a mixture is not due to a single compound, but to a mixture of compounds, which could act together synergistically, additively, or antagonistically? Classical metabolomics data analysis approaches assume a linear relationship between analyte concentration and assay response. This is a limitation in scenarios (such as synergy) where the relationship is not linear. Our laboratory has recently developed “interaction metabolomics” to overcome this limitation. The innovation of interaction metabolomics is the inclusion of compound interaction terms in the data matrix. Interaction terms are calculated as the product of the intensities of each pair of features (detected ions). Herein, we tested the utility of interaction metabolomics by spiking known concentrations of an antimicrobial compound (berberine) and a synergist (piperine) into a set of inactive matrices. We measured the antimicrobial activity for each of the resulting mixtures against Staphylococcus aureus and analyzed the mixtures with liquid chromatography coupled to high resolution mass spectrometry (LC-MS). When the dataset was processed without compound interaction terms (classical metabolomics), statistical analysis yielded a pattern of false positives. Interaction metabolomics correctly identified berberine and piperine as the compounds responsible for synergistic activity. Our results demonstrate the utility of a conceptually new approach for identifying synergists in mixtures that may be useful for many applications that require comprehensive mixture analysis.