Computer assisted identification of green tea metabolites in human urine. Lars Ridder ICCS, Noordwijkerhout 2014

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1 Computer assisted identification of green tea metabolites in human urine Lars Ridder ICCS, Noordwijkerhout 2014

2 The food metabolome? Nutrients Vitamins Other bioactive compounds Microbiota Human biotransformations Bioactivation Biomarkers Many unknowns

3 Untargeted mass spectrometry ( big data ) LC-MS rapidly develops: more sensitive more accurate faster -> more fragmentation, e.g. MS n Van der Hooft et al. (2012) J. Agric. Food Chem. 60: 8841 Van der Hooft et al. (2012) Anal. Chem. 84,

4 Metabolite annotation with LC-MS n Manual approach Recognize fragment ions Reconstruct molecules Rationalize biochemistry Computational approach Explain fragment ions Match data & molecules Model biochemistry

bonds Select matching fragments MS 2 spectrum Candidate score = p 1 I p p

5 Explaining fragments ions with substructures Buspirone Break up to 4 bonds >2000 substructures Substructure score = h b p b b disconn. bonds Select matching fragments MS 2 spectrum Candidate score = p 1 I p p matched peaks I p min LSS p, MSP + I p MSP p missed peaks Rank 1 among 15 alternatives

6 Extension to hierarchical MS n data MS 1 MS 2 MS 3 MS 4 Consistent matching of substructures over multistage MS spectra Ridder et al. (2012) Rapid Commun. Mass Spectrom. 26: 2461

7 Example annotation of online MS n H O H O O H O H O O H O O O H O H H O H O O H O H O O H O O O H O H H O H O O H O H O O H O O O H O H O H O O H H O H O O H O O O H O H

8 MAGMa (MS annotation based on in silico generated metabolites) LC-MS n experiment PubChem Database Datafile, e.g. mzxml Candidate molecules Extract MS n spectral data Generate substructures Assign substructures to fragment ions Score and rank candidate molecules User interface

Application to green tea Ridder et al. (2013) Anal. Chem. 85: 6033-6040 89 Previously annotated molecules: By Van der Hooft et al. (2012) J. Agric. Food Chem.

9 Application to green tea Ridder et al. (2013) Anal. Chem. 85: Previously annotated molecules: By Van der Hooft et al. (2012) J. Agric. Food Chem. 60: were top-ranked (partly tied with alternative candidates) Fractional rank: median = 3.5 3rd quart. = poorly ranked molecules chemical rearrangements in MS/MS!

10 Ranking of isobaric molecules MS 2 spectrum quercetin 3-O-p-coumaroylhexoside (T0084) MS 2 spectrum prodelphinidin (T0081) m/z= (1) (1) (2) (6) (5) (5) (10) (2) (3) (3)

11 New assignments caffeoylglucose di-o-galloylglucose tri-o-galloyl- di-o-p-coumaroyl- (epi)gallocatechin- (epi)gallocatechinglucose quinic acid 3-O-p-coumarate 3-O-ferulate vitexin-2 -sulfate vitexin-2''-rhamnoside mollupentin- saponarin trichocarpin 2 -O-rhamnoside (epi)gallocatechin- procyanidin C1 theasinensin A myricetin-3-o- theasaponin C1 methyl(epi)gallocatechin galloylhexoside Chemically diverse molecules Some not earlier reported in tea

12 Metabolite identification with LC-MSn Manual approach Recognize fragment ions Reconstruct molecules Rationalize biochemistry Proposed computational approach Explain fragment ions Match data & molecules Model biochemistry

13 Metabolomics: urine of a tea-drinker

14 Conversion rules Conversions of flavonoids by gut microbiota Hydrolytic reactions in the gut Flavan-3-ols Protease + Esterase + Flavones Glycosidase + Flavonols Phase II conjugations Glucuronidation Sulfation α-oxidation Dehydroxylation Methylation β-oxidation Decarboxylation Glycination

15 Example: trihydroxyphenyl-valerolactone A Metabolites of (epi)gallocatechin Detection in LC-MS n urine profile B Matching of in silico fragmentation Ridder et al. (2014) Anal. Chem. 86: MS 2 MS 3 C

Application of the rules, matches in urine samples 109 tea components 60 120 Glycosidase Gut (microbial) rules

16 Application of the rules, matches in urine samples 109 tea components Glycosidase Gut (microbial) rules Phase 2 Biotransformation rules (3 steps) potential metabolites m/z matches 97 assigned molecules

17 The in silico network Green tea components In silico metabolites Confirmed urine metabolites

18 The in silico network Green tea components In silico metabolites Confirmed urine metabolites

19 Pathway hypotheses Hydrolysis Hydrolysis Valerolacton formation Reduction Phase II conjugation β- oxidation Valeric acid formation

20 Conclusions MAGMa (MS Annotation using in silico Generated Metabolites) algorithm for automatic annotation of LC-MS n datasets Multistage data Fast: annotation of hundreds of compounds (ten thousands of candidates) in one run Annotation of green tea based on PubChem candidates Good ranking of known compounds New putative assignments Annotation of urine after tea consumption Automatic generation of candidates using metabolism rules (not present in PubChem database) Good ranking of known compounds New putative assignments of completely novel metabolites Future applications Biomarker research Identification of novel bioactive compounds from natural resources Identification of peptides / glycosylated proteins

21 Acknowledgements Wageningen UR/NMC Justin van der Hooft Jacques Vervoort Ric de Vos John van Duynhoven Raoul Bino NL escience Center Stefan Verhoeven René van Schaik Jacob de Vlieg

Metabolite identification in metabolomics: Database and interpretation of MSMS spectra

Metabolite identification in metabolomics: Database and interpretation of MSMS spectra Jeevan K. Prasain, PhD Department of Pharmacology and Toxicology, UAB jprasain@uab.edu utline Introduction Putative