Aplicação de cromatografia a gás e extração por mNTC em estudo metabolômico de contaminação ambiental por poluentes emergentes

Abstract

Metabolomics investigates changes in metabolites in a cell, tissue or organism. Amino acids represent a class of important metabolites in studies aimed at the early diagnosis of diseases, including cancer, whose main experimental cellular model is the yeast Saccharomyces cerevisiae. Benzo[a]pyrene (BaP) is considered a priority contaminant for 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 order to find cancer biomarkers. In this work, a new amino acid extraction procedure (derivatized with propyl chloroformate/propanol) using functionalized magnetic carbon nanotubes was developed and optimized. It was possible to conclude that the hybrid nanotube A15E15 and desorption without heating promoted the best analytes extraction. In studies of exposure of Saccharomyces cerevisiae, it was found, through cell growth curves, that the BaP concentration of 25.0 µmol/L caused changes in cell viability, indicating metabolic changes. A gas chromatography method with mass spectrometry detector using a Phenomenex ZB-AAA column was optimized, enabling the determination of 16 amino acids (AA) in yeasts exposed and not exposed to BaP. The study of some parameters of merit showed that the method showed good selectivity and linearity in the range of 0.004 to 50.00 mg/L (R2 > 0.9800). The limits of detection (LOD) were in the range of 0.002 to 0.31 mg/L and the limits of quantification (LOQ) in the range of 0.004 to 0.41 mg/L. The AA determination in cells exposed and not exposed to BaP showed, through ANOVA with Bonferroni post-hoc test, with a confidence interval of 95%, that the amino acids glycine (Gly), serine (Ser), phenylalanine (Phe), proline (Pro), asparagine (Asn), aspartic acid (Asp), glutamic acid (Glu), tyrosine (Tyr) and leucine (Leu) statistically differentiated the two groups of samples. This result confirmed previous studies that revealed the potential of these AAs as cancer biomarkers candidates.