Innovative Integration of High-Resolution Mass Spectrometry and In-Silico Libraries for Pharmacetical Metabolite Identification in Lettuce (Lactuca sativa)

abstract

English

Lettuce, as one of many types of edible vegetables, is often grown hydroponically to maximize yield, reduce soil-related issues, and save water. Given that lettuce is composed of 95% water, it is particularly well-suited for hydroponic cultivation. This method frequently utilizes wastewater, which is cost-effective and sustainable. However, such water can be contaminated with pharmaceuticals, in some cases as up to 90% of ingested medication is excreted in its original form. Additionally, wastewater may contain metabolites and degradation products of these pharmaceuticals. All of these substances can be absorbed by the crops, particularly through their roots. This can lead to the translocation of these compounds into the leafy edible parts of the lettuce. Furthermore, the presence of pharmaceuticals in the water can lead to the formation of further metabolites. This contamination not only fosters the emergence of antimicrobial resistance but also presents health risk due to prolonged exposure to these residues. However, current monitoring practices often focus solely on quantifying parent drugs, leading to an underestimation of health risks associated with the consumption of grown vegetables. Therefore, the novelty/benefit of this study lies in the development of a novel high-throughput workflow for identifying pharmaceutical metabolites (Met-ID) using LC-HRMS. Unlike commercially available software, our approach utilizes our own python script and open-source software with accessible algorithms, allowing for scientific scrutiny and validation of the process. Specifically, software tools were utilized for: 1) Metabolite structure prediction, 2) In Silico spectral library prediction, 3) Data processing, and 4) Statistical data evaluation. Additionally, LC-qTOF measurements were conducted in both ESI+ and ESI- modes, employing both Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) modes to assess their efficacy in Met-ID.

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

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

13. 9. 2024

Vysoké učení technické v Brně, Fakulta chemická

Brno

65

65

1

https://www.fch.vut.cz/chl/konference/program/bookofabstracts-pdf-p269547