In Silico Prediction and Automatic LC–MSn Annotation of Green Tea Metabolites in Urine

Research output: Contribution to journalArticleAcademicpeer-review

25 Citations (Scopus)

Abstract

The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC-MSn), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption. Reaction rules for intestinal degradation and human biotransformation were systematically applied to chemical structures of 75 green tea components, resulting in a virtual library of 27¿245 potential metabolites. All matching precursor ions in the urine LC–MSn data sets, as well as the corresponding fragment ions, were automatically annotated by in silico generated (sub)structures. The results were evaluated based on 74 previously identified urinary metabolites and lead to the putative identification of 26 additional green tea-derived metabolites. A total of 77% of all annotated metabolites were not present in the Pubchem database, demonstrating the benefit of in silico metabolite prediction for the automatic annotation of yet unknown metabolites in LC–MSn data from nutritional metabolite profiling experiments.
Original languageEnglish
Pages (from-to)4767-4774
JournalAnalytical Chemistry
Volume86
Issue number10
DOIs
Publication statusPublished - 2014

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Metabolites
Tea
Ions
Liquid chromatography
Mass spectrometry
Plasmas
Degradation
Molecules

Keywords

  • human fecal microbiota
  • mass-spectrometry
  • structural elucidation
  • human plasma
  • phenolic-compounds
  • spectral trees
  • polyphenols
  • identification
  • absorption
  • metabolomics

Cite this

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title = "In Silico Prediction and Automatic LC–MSn Annotation of Green Tea Metabolites in Urine",
abstract = "The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC-MSn), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption. Reaction rules for intestinal degradation and human biotransformation were systematically applied to chemical structures of 75 green tea components, resulting in a virtual library of 27¿245 potential metabolites. All matching precursor ions in the urine LC–MSn data sets, as well as the corresponding fragment ions, were automatically annotated by in silico generated (sub)structures. The results were evaluated based on 74 previously identified urinary metabolites and lead to the putative identification of 26 additional green tea-derived metabolites. A total of 77{\%} of all annotated metabolites were not present in the Pubchem database, demonstrating the benefit of in silico metabolite prediction for the automatic annotation of yet unknown metabolites in LC–MSn data from nutritional metabolite profiling experiments.",
keywords = "human fecal microbiota, mass-spectrometry, structural elucidation, human plasma, phenolic-compounds, spectral trees, polyphenols, identification, absorption, metabolomics",
author = "L.O. Ridder and {van der Hooft}, J.J.J. and S. Verhoeven and {de Vos}, R.C.H. and J.J.M. Vervoort and R.J. Bino",
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In Silico Prediction and Automatic LC–MSn Annotation of Green Tea Metabolites in Urine. / Ridder, L.O.; van der Hooft, J.J.J.; Verhoeven, S.; de Vos, R.C.H.; Vervoort, J.J.M.; Bino, R.J.

In: Analytical Chemistry, Vol. 86, No. 10, 2014, p. 4767-4774.

Research output: Contribution to journalArticleAcademicpeer-review

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T1 - In Silico Prediction and Automatic LC–MSn Annotation of Green Tea Metabolites in Urine

AU - Ridder, L.O.

AU - van der Hooft, J.J.J.

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AU - de Vos, R.C.H.

AU - Vervoort, J.J.M.

AU - Bino, R.J.

PY - 2014

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AB - The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC-MSn), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption. Reaction rules for intestinal degradation and human biotransformation were systematically applied to chemical structures of 75 green tea components, resulting in a virtual library of 27¿245 potential metabolites. All matching precursor ions in the urine LC–MSn data sets, as well as the corresponding fragment ions, were automatically annotated by in silico generated (sub)structures. The results were evaluated based on 74 previously identified urinary metabolites and lead to the putative identification of 26 additional green tea-derived metabolites. A total of 77% of all annotated metabolites were not present in the Pubchem database, demonstrating the benefit of in silico metabolite prediction for the automatic annotation of yet unknown metabolites in LC–MSn data from nutritional metabolite profiling experiments.

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KW - phenolic-compounds

KW - spectral trees

KW - polyphenols

KW - identification

KW - absorption

KW - metabolomics

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