A Refined Nontarget Workflow for the Investigation of Metabolites through the Prioritization by in Silico Prediction Tools
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/33596
comunitat-uji-handle3:10234/33597
comunitat-uji-handle4:
INVESTIGACIONAquest recurs és restringit
https://doi.org/10.1021/acs.analchem.9b01218 |
Metadades
Títol
A Refined Nontarget Workflow for the Investigation of Metabolites through the Prioritization by in Silico Prediction ToolsData de publicació
2019-04-11Editor
American Chemical SocietyISSN
0003-2700; 1520-6882Cita bibliogràfica
BIJLSMA, L.; BERNTSSEN, M. H. G.; MEREL, S. A Refined Nontarget Workflow for the Investigation of Metabolites through the Prioritization by in Silico Prediction Tools. Analytical chemistry, 2019, vol. 91, no 9, p. 6321-6328Tipus de document
info:eu-repo/semantics/articleVersió de l'editorial
https://pubs.acs.org/doi/10.1021/acs.analchem.9b01218Versió
info:eu-repo/semantics/publishedVersionParaules clau / Matèries
Resum
The application of nontargeted strategies based on high-resolution mass spectrometry (HRMS) directed toward the discovery of metabolites of known contaminants in fish is an interesting alternative to true nontarget ... [+]
The application of nontargeted strategies based on high-resolution mass spectrometry (HRMS) directed toward the discovery of metabolites of known contaminants in fish is an interesting alternative to true nontarget screening. To reduce prolonged and costly laboratory experiments, recent advances in computing power have permitted the development of comprehensive knowledge-based software to predict the metabolic fate of chemicals. In addition, machine-based learning tools allow the prediction of chromatographic retention times (RT) or collision cross section (CCS) values when using ion mobility spectrometry (IMS). These tools can ease data evaluation and strengthen the confidence in the identification of compounds. The current work explores the capabilities of in silico prediction tools, refined by the use of RT and CCS prediction, to prioritize and facilitate nontarget liquid chromatography (LC)–IMS–HRMS data processing. The fate of the insecticide pirimiphos-methyl (PM) in farmed Atlantic salmon exposed to contaminated feed was used as a case study. The theoretical prediction of 60 potentially relevant biological PM metabolites permitted the prioritization of screening in different salmon tissues (liver, kidney, bile, muscle, and fat) of known and unknown PM metabolites. An average of 43 potential positives was found in the sample matrixes based on the accurate mass of protonated molecules (mass error ≤5 ppm). The application of different tolerance filters for RT (Δ ≤ 2 min) and CCS (Δ ≤ 6%) based on predicted values permitted us to reduce this number up to 66% of the features. Finally, five PM metabolites could be identified; two known metabolites (2-DAMP and N-desethyl PM) were confirmed with a standard, whereas three previously unknown metabolites (2-DAMP glucuronide, didesethyl PM, and hydroxy-2-DAMP glucuronide) were tentatively identified in different matrixes, allowing the first proposition of a metabolic pathway in fish. [-]
Publicat a
Analytical chemistry, 2019, vol. 91, no 9Proyecto de investigación
Norwegian Research council project "AQUASAFE": 254807Drets d'accés
Copyright © American Chemical Society
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
http://rightsstatements.org/vocab/InC/1.0/
info:eu-repo/semantics/restrictedAccess
Apareix a les col.leccions
- IUPA_Articles [310]