Browsing by Author "Sobreira, Tiago J. P."

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    Multiple Reaction Monitoring Profiling (MRM-Profiling) of Lipids To Distinguish Strain-Level Differences in Microbial Resistance in Escherichia coli
    (ACS, 2019-09) Xie, Zhuoer; Gonzalez, L. Edwin; Ferreira, Christina R.; Vorsilak, Anna; Frabutt, Dylan; Sobreira, Tiago J. P.; Pugia, Michael; Cooks, R. Graham; Chemistry and Chemical Biology, School of Science
    The worldwide increase in antimicrobial resistance is due to antibiotic overuse in agriculture and overprescription in medicine. For appropriate and timely patient support, faster diagnosis of antimicrobial resistance is required. Current methods for bacterial identification rely on genomics and proteomics and use comparisons with databases of known strains, but the diagnostic value of metabolites and lipids has not been explored significantly. Standard mass spectrometry/chromatography methods involve multiple dilutions during sample preparation and separation. To increase the amount of chemical information acquired and the speed of analysis of lipids, multiple reaction monitoring profiling (MRM-Profiling) has been applied. The MRM-Profiling workflow includes a discovery stage and a screening stage. The discovery stage employs precursor (PREC) ion and neutral loss (NL) scans to screen representative pooled samples for functional groups associated with particular lipid classes. The information from the first stage is organized in precursor/product ion pairs, or MRMs, and the screening stage rapidly interrogates individual samples for these MRMs. In this study, we performed MRM-Profiling of lipid extracts from four different strains of Escherichia coli cultured with amoxicillin or amoxicillin/clavulanate, a β-lactam and β-lactamase inhibitor, respectively. t tests, analysis of variance and receiver operating characteristic (ROC) curves were used to determine the significance of each MRM. Principal component analysis was applied to distinguish different strains cultured under conditions that allowed or disallowed development of bacterial resistance. The results demonstrate that MRM-Profiling distinguishes the lipid profiles of resistant and nonresistant E. coli strains.
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    Suspect Screening of Exogenous Compounds Using Multiple Reaction Screening (MRM) Profiling in Human Urine Samples
    (Elsevier, 2022) Marasco, César A., Jr.; Edwards, Madison E.; Lamarca, Rafaela S.; Sobreira, Tiago J. P.; Caterino, Jeffrey M.; Hains, David S.; Schwaderer, Andrew L.; de Lima Gomes, Paulo C. F.; Ferreira, Christina R.; Pediatrics, School of Medicine
    Thousands of chemical compounds produced by industry are dispersed in the human environment widely enough to reach the world population, and the introduction of new chemicals constantly occurs. As new synthetic molecules emerge, rapid analytical workflows for screening possible presence of exogenous compounds in biofluids can be useful as a first pass analysis to detect chemical exposure and guide the development and application of more elaborate LC-MS/MS methods for quantification. In this study, a suspect screening workflow using the multiple reaction monitoring (MRM) profiling method is proposed as a first pass exploratory technique to survey selected exogenous molecules in human urine samples. The workflow was applied to investigate 12 human urine samples using 310 MRMs related to the chemical functionalities of 87 exogenous compounds present in the METLIN database and reported in the literature. A total of 11 MRMs associated with five different compounds were detected in the samples. Product ion scans for the precursor ions of the selected MRMs were acquired as a further identification step for these chemicals. The suspect screening results suggested the presence of five exogenous compounds in the human urine samples analyzed, namely metformin, metoprolol, acetaminophen, paraxanthine and acrylamide. LC-MS/MS was applied as a last step to confirm these results, and the presence of four out of the five targets selected by MRM profiling were corroborated, indicating that this workflow can support the selection of suspect compounds to screen complex samples and guide more time-consuming and specific quantification analyses.