Publication detail

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

FUČÍK, J. FUČÍK, S. REXROTH, S. SEDLÁŘ, M. ZLÁMALOVÁ GARGOŠOVÁ, H. MRAVCOVÁ, L.

Original Title

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

Type

journal article in Web of Science

Language

English

Original Abstract

Pharmaceuticals 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.

Keywords

pharmaceuticals; software prediction; metabolite identification in Eisenia fetida and Lactuca sativa; liquid chromatography; high resolution mass spectrometry; in silico spectral library

Authors

FUČÍK, J.; FUČÍK, S.; REXROTH, S.; SEDLÁŘ, M.; ZLÁMALOVÁ GARGOŠOVÁ, H.; MRAVCOVÁ, L.

Released

10. 9. 2024

Publisher

Springer-Verlag

Location

Germany

ISBN

1618-2650

Periodical

Analytical and Bioanalytical Chemistry

Year of study

416

Number

28

State

Federal Republic of Germany

Pages from

6291

Pages to

6306

Pages count

16

URL

https://link.springer.com/article/10.1007/s00216-024-05515-2

BibTex

@article{BUT189394,
  author="Jan {Fučík} and Stanislav {Fučík} and Sascha {Rexroth} and Marian {Sedlář} and Helena {Zlámalová Gargošová} and Ludmila {Mravcová}",
  title="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",
  journal="Analytical and Bioanalytical Chemistry",
  year="2024",
  volume="416",
  number="28",
  pages="16",
  doi="10.1007/s00216-024-05515-2",
  issn="1618-2650",
  url="https://link.springer.com/article/10.1007/s00216-024-05515-2"
}