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3-Methoxytyrosine as an indicator of dopaminergic manipulation in equine plasma. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1220:123652. [PMID: 36933516 DOI: 10.1016/j.jchromb.2023.123652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/09/2023]
Abstract
The use of catechol-O-methyltransferase inhibitors may mask doping agents, primarily levodopa, administered to racehorses and prolong the stimulating effects of dopaminergic compounds such as dopamine. It is known that 3-methoxytyramine is a metabolite of dopamine and 3-methoxytyrosine is a metabolite of levodopa thus these compounds are proposed to be potential biomarkers of interest. Previous research established a urinary threshold of 4,000 ng/mL for 3-methoxytyramine to monitor misuse of dopaminergic agents. However, there is no equivalent biomarker in plasma. To address this deficiency a rapid protein precipitation method was developed and validated to isolate target compounds from 100 µL equine plasma. A liquid chromatography-high resolution accurate mass (LC-HRAM) method using an IMTAKT Intrada amino acid column provided quantitative analysis of 3-methoxytyrosine (3-MTyr) with lower limit of quantification of 5 ng/mL. Reference population profiling (n = 1129) investigated the expected basal concentrations for raceday samples from equine athletes and showed a right-skewed distribution (skewness = 2.39, kurtosis = 10.65) which resulted from large variation (RSD = 71%) within the data. Logarithmic transformation of the data provided a normal distribution (skewness = 0.26, kurtosis = 3.23) resulting in the proposal of a conservative threshold for plasma 3-MTyr of 1,000 ng/mL at a 99.995% confidence level. A 12-horse administration study of Stalevo® (800 mg L-DOPA, 200 mg carbidopa, 1600 mg entacapone) revealed elevated 3-MTyr concentrations for 24-hours post-administration.
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Analytical Platforms for Mass Spectrometry-Based Metabolomics of Polar and Ionizable Metabolites. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1336:215-242. [PMID: 34628634 DOI: 10.1007/978-3-030-77252-9_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
Metabolomics studies rely on the availability of suitable analytical platforms to determine a vast collection of chemically diverse metabolites in complex biospecimens. Liquid chromatography-mass spectrometry operated under reversed-phase conditions is the most commonly used platform in metabolomics, which offers extensive coverage for nonpolar and moderately polar compounds. However, complementary techniques are required to obtain adequate separation of polar and ionic metabolites, which are involved in several fundamental metabolic pathways. This chapter focuses on the main mass-spectrometry-based analytical platforms used to determine polar and/or ionizable compounds in metabolomics (GC-MS, HILIC-MS, CE-MS, IPC-MS, and IC-MS). Rather than comprehensively describing recent applications related to GC-MS, HILIC-MS, and CE-MS, which have been covered in a regular basis in the literature, a brief discussion focused on basic principles, main strengths, limitations, as well as future trends is presented in this chapter, and only key applications with the purpose of illustrating important analytical aspects of each platform are highlighted. On the other hand, due to the relative novelty of IPC-MS and IC-MS in the metabolomics field, a thorough compilation of applications for these two techniques is presented here.
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Segers K, Zhang W, Aourz N, Bongaerts J, Declerck S, Mangelings D, Hankemeier T, De Bundel D, Vander Heyden Y, Smolders I, Ramautar R, Van Eeckhaut A. CE-MS metabolic profiling of volume-restricted plasma samples from an acute mouse model for epileptic seizures to discover potentially involved metabolomic features. Talanta 2020; 217:121107. [PMID: 32498853 DOI: 10.1016/j.talanta.2020.121107] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 04/27/2020] [Accepted: 04/30/2020] [Indexed: 01/07/2023]
Abstract
Currently, a high variety of analytical techniques to perform metabolomics is available. One of these techniques is capillary electrophoresis coupled to mass spectrometry (CE-MS), which has emerged as a rather strong analytical technique for profiling polar and charged compounds. This work aims to discover with CE-MS potential metabolic consequences of evoked seizures in plasma by using a 6Hz acute corneal seizure mouse model. CE-MS is an appealing technique because of its capability to handle very small sample volumes, such as the 10 μL plasma samples obtained using capillary microsampling in this study. After liquid-liquid extraction, the samples were analyzed with CE-MS using low-pH separation conditions, followed by data analysis and biomarker identification. Both electrically induced seizures showed decreased values of methionine, lysine, glycine, phenylalanine, citrulline, 3-methyladenine and histidine in mice plasma. However, a second provoked seizure, 13 days later, showed a less pronounced decrease of the mean concentrations of these plasma metabolites, demonstrated by higher fold change ratios. Other obtained markers that can be related to seizure activities based on literature data, are isoleucine, serine, proline, tryptophan, alanine, arginine, valine and asparagine. Most amino acids showed relatively stable plasma concentrations between the basal levels (Time point 1) and after the 13-day wash-out period (Time point 3), which suggests its effectiveness. Overall, this work clearly demonstrated the possibility of profiling metabolite consequences related to seizure activities of an intrinsically low amount of body fluid using CE-MS. It would be useful to investigate and validate, in the future, the known and unknown metabolites in different animal models as well as in humans.
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Affiliation(s)
- Karen Segers
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium; Department of Pharmaceutical Chemistry, Drug Analysis and Drug Information, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Wei Zhang
- Biomedical Microscale Analytics, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Einsteinweg 55, 2333, CC Leiden, the Netherlands.
| | - Najat Aourz
- Department of Pharmaceutical Chemistry, Drug Analysis and Drug Information, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium
| | - Jana Bongaerts
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium; Department of Pharmaceutical Chemistry, Drug Analysis and Drug Information, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium
| | - Sven Declerck
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium
| | - Debby Mangelings
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium.
| | - Thomas Hankemeier
- Biomedical Microscale Analytics, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Einsteinweg 55, 2333, CC Leiden, the Netherlands.
| | - Dimitri De Bundel
- Department of Pharmaceutical Chemistry, Drug Analysis and Drug Information, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Yvan Vander Heyden
- Department of Analytical Chemistry, Applied Chemometrics and Molecular Modelling, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium.
| | - Ilse Smolders
- Department of Pharmaceutical Chemistry, Drug Analysis and Drug Information, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.
| | - Rawi Ramautar
- Biomedical Microscale Analytics, Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research (LACDR), Leiden University, Einsteinweg 55, 2333, CC Leiden, the Netherlands.
| | - Ann Van Eeckhaut
- Department of Pharmaceutical Chemistry, Drug Analysis and Drug Information, Center for Neurosciences (C4N), Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium.
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Guarino VA, Oldham WM, Loscalzo J, Zhang YY. Reaction rate of pyruvate and hydrogen peroxide: assessing antioxidant capacity of pyruvate under biological conditions. Sci Rep 2019; 9:19568. [PMID: 31862934 PMCID: PMC6925109 DOI: 10.1038/s41598-019-55951-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 11/21/2019] [Indexed: 11/09/2022] Open
Abstract
Pyruvate, a pivotal glucose metabolite, is an α-ketoacid that reacts with hydrogen peroxide (H2O2). Its pharmacological precursor, ethyl pyruvate, has shown anti-inflammatory/anti-tissue injury effects in various animal models of disease, but failed in a multicenter clinical trial. Since rodents, but not humans, can convert ethyl pyruvate to pyruvate in blood plasma, this additional source of extracellular pyruvate may have contributed to the discrepancy between the species. To examine this possibility, we investigated the kinetics of the reaction under biological conditions and determined the second order rate constant k as 2.360 ± 0.198 M−1 s−1. We then calculated the time required for H2O2 elimination by pyruvate. The results show that, with an average intracellular concentration of pyruvate (150 µM), elimination of 95% H2O2 at normal to pathological concentrations (0.01–50 µM) requires 141–185 min (2.4–3 hour). With 1,000 µM pyruvate, a concentration that can only exist extracellularly or in cell culture media, 95% elimination of H2O2 at 5–200 µM requires 21–25 min. We conclude that intracellular pyruvate, or other α-ketoacids, whose endogenous concentration is controlled by metabolism, have little role in H2O2 clearance. An increased extracellular concentration of pyruvate, however, does have remarkable peroxide scavenging effects, considering minimal peroxidase activity in this space.
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Affiliation(s)
- Victoria A Guarino
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - William M Oldham
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Joseph Loscalzo
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Ying-Yi Zhang
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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Zhang W, Segers K, Mangelings D, Van Eeckhaut A, Hankemeier T, Vander Heyden Y, Ramautar R. Assessing the suitability of capillary electrophoresis-mass spectrometry for biomarker discovery in plasma-based metabolomics. Electrophoresis 2019; 40:2309-2320. [PMID: 31025710 PMCID: PMC6767474 DOI: 10.1002/elps.201900126] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 01/20/2023]
Abstract
The actual utility of capillary electrophoresis-mass spectrometry (CE-MS) for biomarker discovery using metabolomics still needs to be assessed. Therefore, a simulated comparative metabolic profiling study for biomarker discovery by CE-MS was performed, using pooled human plasma samples with spiked biomarkers. Two studies have been carried out in this work. Focus of study I was on comparing two sets of plasma samples, in which one set (class I) was spiked with five isotope-labeled compounds, whereas another set (class II) was spiked with six different isotope-labeled compounds. In study II, focus was also on comparing two sets of plasma samples, however, the isotope-labeled compounds were spiked to both class I and class II samples but with concentrations which differ by a factor two between both classes (with one compound absent in each class). The aim was to determine whether CEMS-based metabolomics could reveal the spiked biomarkers as the main classifiers, applying two different data analysis software tools (MetaboAnalyst and Matlab). Unsupervised analysis of the recorded metabolic profiles revealed a clear distinction between class I and class II plasma samples in both studies. This classification was mainly attributed to the spiked isotope-labeled compounds, thereby emphasizing the utility of CE-MS for biomarker discovery.
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Affiliation(s)
- Wei Zhang
- Biomedical Microscale AnalyticsDivision of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityThe Netherlands
| | - Karen Segers
- Biomedical Microscale AnalyticsDivision of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityThe Netherlands
- Department of Analytical ChemistryApplied Chemometrics and Molecular ModellingVrije Universiteit BrusselBrusselBelgium
- Department of Pharmaceutical ChemistryDrug Analysis and Drug InformationCenter for NeurosciencesVrije Universiteit BrusselBrusselBelgium
| | - Debby Mangelings
- Department of Analytical ChemistryApplied Chemometrics and Molecular ModellingVrije Universiteit BrusselBrusselBelgium
| | - Ann Van Eeckhaut
- Department of Pharmaceutical ChemistryDrug Analysis and Drug InformationCenter for NeurosciencesVrije Universiteit BrusselBrusselBelgium
| | - Thomas Hankemeier
- Biomedical Microscale AnalyticsDivision of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityThe Netherlands
| | - Yvan Vander Heyden
- Department of Analytical ChemistryApplied Chemometrics and Molecular ModellingVrije Universiteit BrusselBrusselBelgium
| | - Rawi Ramautar
- Biomedical Microscale AnalyticsDivision of Systems Biomedicine and PharmacologyLeiden Academic Centre for Drug ResearchLeiden UniversityThe Netherlands
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Liu Y, Song Q, Zheng J, Li J, Zhao Y, Li C, Song Y, Tu P. Sensitive profiling of phenols, bile acids, sterols, and eicosanoids in mammalian urine by large volume direct injection-online solid phase extraction-ultra high performance liquid chromatography-polarity switching tandem mass spectrometry. RSC Adv 2016. [DOI: 10.1039/c6ra13272c] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
A new sensitive LVDI-online SPE-UHPLC-psMS/MS method was developed and applied for simultaneous and direct determination of 28 constituents, including 10 BAs, 5 sterols, 8 eicosanoids and 8 phenolic compounds in mammalian urinary samples.
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Affiliation(s)
- Yao Liu
- Modern Research Center for Traditional Chinese Medicine
- Beijing University of Chinese Medicine
- Beijing 100029
- China
- School of Chinese Materia Medica
| | - Qingqing Song
- Modern Research Center for Traditional Chinese Medicine
- Beijing University of Chinese Medicine
- Beijing 100029
- China
- School of Chinese Materia Medica
| | - Jiao Zheng
- Modern Research Center for Traditional Chinese Medicine
- Beijing University of Chinese Medicine
- Beijing 100029
- China
| | - Jun Li
- Modern Research Center for Traditional Chinese Medicine
- Beijing University of Chinese Medicine
- Beijing 100029
- China
| | - Yunfang Zhao
- Modern Research Center for Traditional Chinese Medicine
- Beijing University of Chinese Medicine
- Beijing 100029
- China
| | - Chun Li
- Modern Research Center for Traditional Chinese Medicine
- Beijing University of Chinese Medicine
- Beijing 100029
- China
| | - Yuelin Song
- Modern Research Center for Traditional Chinese Medicine
- Beijing University of Chinese Medicine
- Beijing 100029
- China
| | - Pengfei Tu
- Modern Research Center for Traditional Chinese Medicine
- Beijing University of Chinese Medicine
- Beijing 100029
- China
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