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Evaluation of amino acid profile by targeted metabolomics in the eukaryotic model under exposure of benzo[a]pyrene as the exclusive stressor.
- Source :
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Talanta [Talanta] 2023 Dec 01; Vol. 265, pp. 124859. Date of Electronic Publication: 2023 Jun 21. - Publication Year :
- 2023
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Abstract
- Amino acids (AAs) are a class of important metabolites in metabolomics methodology that investigates metabolite changes in a cell, tissue, or organism for early diagnosis of diseases. Benzo[a]pyrene (BaP) is considered a priority contaminant by different environmental control agencies because it is a proven carcinogenic compound for humans. Therefore, it is important to evaluate the BaP interference in the metabolism of amino acids. In this work, a new amino acid extraction procedure (derivatized with propyl chloroformate/propanol) using functionalized magnetic carbon nanotubes was developed and optimized. A hybrid nanotube was used followed by desorption without heating, and excellent extraction of analytes was obtained. After exposure of Saccharomyces cerevisiae, the BaP concentration of 25.0 μmol L <superscript>-1</superscript> caused changes in cell viability, indicating metabolic changes. A fast and efficient GC/MS method using a Phenomenex ZB-AAA column was optimized, enabling the determination of 16 AAs in yeasts exposed or not to BaP. A comparison of AA concentrations obtained in the two experimental groups showed that glycine (Gly), serine (Ser), phenylalanine (Phe), proline (Pro), asparagine (Asn), aspartic acid (Asp), glutamic acid (Glu), tyrosine (Tyr), and leucine (Leu) statistically differentiated, after subsequent application of ANOVA with Bonferroni post-hoc test, with a confidence level of 95%. This amino acid pathway analysis confirmed previous studies that revealed the potential of these AAs as toxicity biomarker candidates.<br />Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br /> (Copyright © 2023 Elsevier B.V. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1873-3573
- Volume :
- 265
- Database :
- MEDLINE
- Journal :
- Talanta
- Publication Type :
- Academic Journal
- Accession number :
- 37393711
- Full Text :
- https://doi.org/10.1016/j.talanta.2023.124859