scholarly journals Large scale enzyme based xenobiotic identification for exposomics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ken H. Liu ◽  
Choon M. Lee ◽  
Grant Singer ◽  
Preeti Bais ◽  
Francisco Castellanos ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Retention Time ◽  
Human Disease ◽  
Large Scale ◽  
High Resolution Mass Spectrometry ◽  
Chemical Identification ◽  
Mass Spectrometry Identification ◽  
Level Of Understanding ◽  
Resolution Mass

AbstractAdvances in genomics have revealed many of the genetic underpinnings of human disease, but exposomics methods are currently inadequate to obtain a similar level of understanding of environmental contributions to human disease. Exposomics methods are limited by low abundance of xenobiotic metabolites and lack of authentic standards, which precludes identification using solely mass spectrometry-based criteria. Here, we develop and validate a method for enzymatic generation of xenobiotic metabolites for use with high-resolution mass spectrometry (HRMS) for chemical identification. Generated xenobiotic metabolites were used to confirm identities of respective metabolites in mice and human samples based upon accurate mass, retention time and co-occurrence with related xenobiotic metabolites. The results establish a generally applicable enzyme-based identification (EBI) for mass spectrometry identification of xenobiotic metabolites and could complement existing criteria for chemical identification.

scholarly journals Large-scale, enzyme-based xenobiotic identification for exposomics

2020 ◽  
Author(s):  
Ken Liu ◽  
Choon Lee ◽  
Grant Singer ◽  
Michael Woodworth ◽  
Thomas Ziegler ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Retention Time ◽  
Human Disease ◽  
Large Scale ◽  
High Resolution Mass Spectrometry ◽  
Chemical Identification ◽  
Mass Spectrometry Identification ◽  
Level Of Understanding ◽  
Resolution Mass

Abstract Advances in genomics have revealed many of the genetic underpinnings of human disease, but exposomics methods are currently inadequate to obtain a similar level of understanding of environmental contributions to human disease. Exposomics methods are limited by low abundance of xenobiotic metabolites and lack of authentic standards, which precludes identification using solely mass spectrometry-based criteria. Here, we develop and validate a method for enzymatic generation of xenobiotic metabolites for use with high-resolution mass spectrometry (HRMS) for chemical identification. Generated xenobiotic metabolites were used to confirm identities of respective metabolites in mice and human samples based upon accurate mass, retention time and co-occurrence with related xenobiotic metabolites. The results establish a generally applicable enzyme-based identification (EBI) for mass spectrometry identification of xenobiotic metabolites.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Gabriel L. Streun ◽  
Andrea E. Steuer ◽  
Lars C. Ebert ◽  
Akos Dobay ◽  
Thomas Kraemer
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
High Resolution ◽  
Retention Time ◽  
Drug Testing ◽  
High Resolution Mass Spectrometry ◽  
Urine Samples ◽  
Machine Learning Model ◽  
Resolution Mass

Abstract Objectives Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. Methods Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. Results Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. Conclusions With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

Compositional analysis of manuka honeys by high-resolution mass spectrometry: Identification of a manuka-enriched archetypal molecule

2012 ◽  
Vol 132 (2) ◽  
pp. 948-953 ◽  
Author(s):  
Liam Fearnley ◽  
David R. Greenwood ◽  
Michael Schmitz ◽  
Jonathan M. Stephens ◽  
Ralf C. Schlothauer ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
High Resolution Mass Spectrometry ◽  
Compositional Analysis ◽  
High Resolution Mass ◽  
Mass Spectrometry Identification ◽  
Resolution Mass

scholarly journals High-Resolution Mass Spectrometry Identification of Secondary Metabolites in Four Red Grape Varieties Potentially Useful as Traceability Markers of Wines

Beverages ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. 74 ◽  
Author(s):  
Christine Mayr ◽  
Mirko De Rosso ◽  
Antonio Dalla Vedova ◽  
Riccardo Flamini
Keyword(s):  
Mass Spectrometry ◽  
High Resolution ◽  
Secondary Metabolites ◽  
High Resolution Mass Spectrometry ◽  
High Resolution Mass ◽  
Mass Spectrometry Identification ◽  
Red Grape ◽  
Grape Varieties ◽  
Acyl Derivatives ◽  
Resolution Mass

Liquid chromatography coupled to high-resolution mass spectrometry (LC-Q/TOF) is a powerful tool to perform chemotaxonomic studies through identification of grape secondary metabolites. In the present work, the metabolomes of four autochthonous Italian red grape varieties including the chemical classes of anthocyanins, flavonols/flavanols/flavanones, and terpenol glycosides, were studied. By using this information, the metabolites that can potentially be used as chemical markers for the traceability of the corresponding wines were proposed. In Raboso wines, relatively high abundance of both anthocyanic and non-anthocyanic acyl derivatives, is expected. Potentially, Primitivo wines are characterized by high tri-substituted flavonoids, while Corvina wines are characterized by higher di-substituted compounds and lower acyl derivatives. Negro Amaro wine’s volatile fraction is characterized by free monoterpenes, such as α-terpineol, linalool, geraniol, and Ho-diendiol I. A similar approach can be applied for the traceability of other high-quality wines.

scholarly journals Characterization of Phase I and Glucuronide Phase II Metabolites of 17 Mycotoxins Using Liquid Chromatography—High-Resolution Mass Spectrometry

Toxins ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 433 ◽  
Author(s):  
Irina Slobodchikova ◽  
Reajean Sivakumar ◽  
Md Samiur Rahman ◽  
Dajana Vuckovic
Keyword(s):  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
High Resolution ◽  
Phase I ◽  
Phase Ii ◽  
Large Scale ◽  
High Resolution Mass Spectrometry ◽  
High Resolution Mass ◽  
Phase Ii Metabolites ◽  
Resolution Mass

Routine mycotoxin biomonitoring methods do not include many mycotoxin phase I and phase II metabolites, which may significantly underestimate mycotoxin exposure especially for heavily metabolized mycotoxins. Additional research efforts are also needed to measure metabolites in vivo after exposure and to establish which mycotoxin metabolites should be prioritized for the inclusion during large-scale biomonitoring efforts. The objective of this study was to perform human in vitro microsomal incubations of 17 mycotoxins and systematically characterize all resulting metabolites using liquid chromatography–high-resolution mass spectrometry (LC-HRMS). The results obtained were then used to build a comprehensive LC-MS library and expand a validated 17-mycotoxin method for exposure monitoring to screening of additional 188 metabolites, including 100 metabolites reported for the first time. The final method represents one of the most comprehensive LC-HRMS methods for mycotoxin biomonitoring or metabolism/fate studies.

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