Pharmaceutical Metabolite Identification in Lettuce (Lactuca sativa) and Earthworms (Eisenia fetida) Using Liquid Chromatography Coupled to High-Resolution Mass Spectrometry and In Silico Spectral Library

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dc.contributor.authorFučík, Jancs
dc.contributor.authorFučík, Stanislavcs
dc.contributor.authorRexroth, Saschacs
dc.contributor.authorSedlář, Mariancs
dc.contributor.authorZlámalová Gargošová, Helenacs
dc.contributor.authorMravcová, Ludmilacs
dc.coverage.issue28cs
dc.coverage.volume416cs
dc.date.accessioned2025-03-28T16:15:47Z
dc.date.available2025-03-28T16:15:47Z
dc.date.issued2024-09-10cs
dc.description.abstractPharmaceuticals released into the aquatic and soil environments can be absorbed by plants and soil organisms, potentially leading to the formation of unknown metabolites that may negatively affect these organisms or contaminate the food chain. The aim of this study was to identify pharmaceutical metabolites through a triplet approach for metabolite structure prediction (software-based predictions, literature review, and known common metabolic pathways), followed by generating in silico mass spectral libraries and applying various mass spectrometry modes for untargeted LC-qTOF analysis. Therefore, Eisenia fetida and Lactuca sativa were exposed to a pharmaceutical mixture (atenolol, enrofloxacin, erythromycin, ketoprofen, sulfametoxazole, tetracycline) under hydroponic and soil conditions at environmentally relevant concentrations. Samples collected at different time points were extracted using QuEChERS and analyzed with LC-qTOF in data-dependent (DDA) and data-independent (DIA) acquisition modes, applying both positive and negative electrospray ionization. The triplet approach for metabolite structure prediction yielded in a total of 3,762 pharmaceutical metabolites, and in silico mass spectral library was created based on these predicted metabolites. This approach resulted in the identification of 26 statistically significant metabolites (p<0.05), with DDA+ and DDA- outperforming DIA modes by successfully detecting 56/67 sample type:metabolite combinations. Lettuce roots had the highest metabolite count (26), followed by leaves (6) and earthworms (2). Despite the lower metabolite count, earthworms showed the highest peak intensities, closely followed by roots, with leaves displaying the lowest intensities. Common metabolic reactions observed included hydroxylation, decarboxylation, acetylation, and glucosidation, with ketoprofen-related metabolites being the most prevalent, totaling 12 distinct metabolites. In conclusion, we developed a high-throughput workflow combining open-source software with LC-HRMS for identifying unknown metabolites across various sample types.en
dc.formattextcs
dc.format.extent6291-6306cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationAnalytical and Bioanalytical Chemistry. 2024, vol. 416, issue 28, p. 6291-6306.en
dc.identifier.doi10.1007/s00216-024-05515-2cs
dc.identifier.issn1618-2650cs
dc.identifier.orcid0000-0002-3408-4383cs
dc.identifier.orcid0000-0003-4536-3983cs
dc.identifier.orcid0000-0001-8309-8012cs
dc.identifier.orcid0000-0003-1011-6616cs
dc.identifier.other189394cs
dc.identifier.researcheridG-9690-2018cs
dc.identifier.scopus25621742800cs
dc.identifier.urihttps://hdl.handle.net/11012/250693
dc.language.isoencs
dc.publisherSpringer-Verlagcs
dc.relation.ispartofAnalytical and Bioanalytical Chemistrycs
dc.relation.urihttps://link.springer.com/article/10.1007/s00216-024-05515-2cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1618-2650/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectpharmaceuticalsen
dc.subjectsoftware predictionen
dc.subjectmetabolite identification in Eisenia fetida and Lactuca sativaen
dc.subjectliquid chromatographyen
dc.subjecthigh resolution mass spectrometryen
dc.subjectin silico spectral libraryen
dc.titlePharmaceutical Metabolite Identification in Lettuce (Lactuca sativa) and Earthworms (Eisenia fetida) Using Liquid Chromatography Coupled to High-Resolution Mass Spectrometry and In Silico Spectral Libraryen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-189394en
sync.item.dbtypeVAVen
sync.item.insts2025.03.28 17:15:46en
sync.item.modts2025.03.28 14:32:01en
thesis.grantorVysoké učení technické v Brně. Fakulta chemická. Ústav chemie a technologie ochrany životního prostředícs
thesis.grantorVysoké učení technické v Brně. Středoevropský technologický institut VUT. Pokročilé biomateriálycs
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