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The Food metabolome
- 1. Glasgow July 2013 THE FOOD METABOLOMETHE FOOD METABOLOME C. Manach Human Nutrition Unit, INRA Clermont‐Ferrand, France Review paper in preparation
- 2. COMPLEXITY OF NUTRITIONAL EXPOSURESCOMPLEXITY OF NUTRITIONAL EXPOSURES Nutrients Non nutrients Natural Non-nutrients DIET Non nutrients Man-made Non-nutrients Contaminants, Additives, Agrochemicals, … Polyphenols, Carotenoids, Phytosterols, … Proteins, Carbohydrates, Lipids, Vitamins Minerals EXPOSOME Dietary habits (food choices, shopping places, cooking habits…) Lifestyle, Environment Glasgow, July 2013 « We eat other metabolomes » (D. Wishart)
- 3. Genetics, Epigenetics, Age, Gender, Microbiota, Physiological status, Medical history Digestion Xenobiotic metabolism Microbial metabolism Elimination Storage INDIVIDUAL METABOLIC CAPACITYINDIVIDUAL METABOLIC CAPACITY Glasgow, July 2013
- 4. Food Metabolome All the metabolites that derive from the digestion and metabolism of food components Dietary habits Metabolic capacity THE FOOD METABOLOME DEFINITIONTHE FOOD METABOLOME DEFINITION Health outcomes Clinical trials Clinical trials Cohorts Glasgow, July 2013 Metabolomes of foods = Food metabolome ?« Food chemicalome »?
- 5. FOOD METABOLOME APPLICATIONSFOOD METABOLOME APPLICATIONS Age, sex,BMI, Lifestyle,exercice… Genotype Enterotype Food metabolome analysis Glasgow, July 2013 Segmentation of Poor/High absorbers & metabolizers New metabolites New potential food bioactives Public health Research Diet-genotype-health relationships Monitoring impact of recommendations or policies Medicine Personalized nutrition Food intake Nutritional exposures Dietary questionnaires Dietary assessment
- 6. FOOD METABOLOME APPLICATIONSFOOD METABOLOME APPLICATIONS Food intake Nutritional exposures Dietary questionnaires Food metabolome analysis Glasgow, July 2013 Biomarkers of compliance for intervention studies Validation of dietary questionnaires with biomarkers for a few representative foods Subject stratification in dietary patterns Assessment of recent or long-term consumption of a range of foods Comprehensive and detailed assessment of individual nutritional exposures Dietary assessment
- 7. Usually analyzed using a range of distinct targeted methods (GC‐MS, LC‐UV, LC‐MS in pos or neg mode, NMR, …) FOOD METABOLOME: AN ANALYTICAL CHALLENGEFOOD METABOLOME: AN ANALYTICAL CHALLENGE Food metabolome = at least 25,000 compounds Carbohydrates Proteins Lipids Vitamins Minerals Flavonoids Phenolic acids Carotenoids Phytosterols Chlorophylls Alkaloids Artificial colors Flavoring additives Maillard reaction products Food contaminants … Largerangeofconcentrations mM nM
- 8. FOOD METABOLOME: AN ANALYTICAL CHALLENGEFOOD METABOLOME: AN ANALYTICAL CHALLENGE Food metabolome = at least 25,000 compounds Carbohydrates Proteins Lipids Vitamins Minerals Flavonoids Phenolic acids Carotenoids Phytosterols Chlorophylls Alkaloids Artificial colors Flavoring additives Maillard reaction products Food contaminants … Hydrolysis, Oxidation, Reduction, Methylation, Dehydrogenation, Sulfation, Glucuronidation, Acetylation, Glutathione conjugation, … Host and microbial biotransformations Many unknowns Non‐targeted metabolomics (LC‐MS, GC‐MS, NMR, …) Largerangeofconcentrations mM nM
- 9. Saito et al., Annu Rev Plant Biol, 2010 TOWARD A MULTIPLATFORM UNTARGETED ANALYSIS OF THE FOOD METABOLOMETOWARD A MULTIPLATFORM UNTARGETED ANALYSIS OF THE FOOD METABOLOME Glasgow, July 2013
- 10. Saito et al., Annu Rev Plant Biol, 2010 TOWARD A MULTIPLATFORM UNTARGETED ANALYSIS OF THE FOOD METABOLOMETOWARD A MULTIPLATFORM UNTARGETED ANALYSIS OF THE FOOD METABOLOME Glasgow, July 2013 Same approach for the food metabolome analysis 1‐ Map the analytical coverage of Food Metabolome chemical space by various platforms 2‐ Optimize methods with wide and complementary coverages & Define SOPs
- 11. FIRST STUDIES TO DISCOVER NEW BIOMARKERS OF FOOD INTAKE FIRST STUDIES TO DISCOVER NEW BIOMARKERS OF FOOD INTAKE Glasgow, July 2013
- 12. Nootkatone-diol Limonene-diolProline betaine DISCOVERY OF BIOMARKERS OF FOOD INTAKEDISCOVERY OF BIOMARKERS OF FOOD INTAKE m/z 312.21 m/z 144.06CO group OJ group m/z 232.09 0200040006000 020004000600080001000012000050010001500200025003000 020040060080010001200 010020030040050050010001500 050010001500 050100150200250300 CO OR CO ORCO OR CO OR CO ORCO OR CO OR CO OR One month controlled intervention study with orange juice 12 volunteers 500 ml/d Orange juice / Control drink Usual diet Cross-over study, 24h urine D30 LC-ESI-Qtof in positive mode & 105 significant ions (ANOVA BH) Score plot PLSDA Pujos‐Guillot et al., J Proteome Res, 2013 Glasgow, July 2013 Hesperetin Naringenin HCA heatmap
- 13. DISCOVERY OF BIOMARKERS OF FOOD INTAKEDISCOVERY OF BIOMARKERS OF FOOD INTAKE Short‐term intervention studies Citrus Cruciferous vegetables Cocoa drink Almonds Coffee Nuts Red wine Grape juice Whole rye grain Black tea Green tea Milk Soy Salmon Rapsberry Tomato 125 candidate biomarkers (75%= phytochemical metabolites) 16 foods studied Mostly controlled intervention studies (4-61 subjects) 60% acute / 40% medium-term studies (4 days-12 weeks) >90% used urine samples (Spots, 24hr urines, or kinetics) NMR (8 studies), LC-MS (13 studies) or GC-MS (4 studies), including multiplatform analyses (5 studies) Glasgow, July 2013 Scalbert et al., in preparation
- 14. WHAT DID WE LEARN FROM THE FIRST STUDIES?WHAT DID WE LEARN FROM THE FIRST STUDIES? Urine metabolome well reflects recent food intake, plasma may better reflect long-term dietary habits Dozens of metabolite had increased level in urine after acute food challenge But many remain unidentified Phytochemical metabolites are key discriminants for plant food intake More putative biomarkers are detected with LC-MS compared to GC-MS or NMR A small number of subjects (8-20) seems sufficient for biomarker discovery A standardized diet before the food challenge limits unwanted variation in acute studies and help detecting metabolic changes Glasgow, July 2013
- 15. NEW BIOMARKERS REVEALED BY METABOLOMICSNEW BIOMARKERS REVEALED BY METABOLOMICS Proline betaine for Citrus Many candidates require further validation Glasgow, July 2013 Common to many organisms, Not specific for a given food? Some exceptions May not be systematically found in the target food, but only in certain populations and/or geographic locations Host met. Microbiota metabolites Host met. Microbiota metabolites Host met.Host met. Microbiota metabolites Microbiota metabolites The natural non‐nutrients and their host metabolites are more likely to constitute specific biomarkers of food intake
- 16. FOOD METABOLOMICS FOR DISCOVERY OF PLANT FOOD INTAKE BIOMARKERSFOOD METABOLOMICS FOR DISCOVERY OF PLANT FOOD INTAKE BIOMARKERS Six 24h recalls (1994-2002) +FFQ 2007-2009 Selection of low and high consumers for 20 plant foods or food groups PhenoMeNEp ALIA 2011‐2013 CorrelationsDistribution of food consumption Coll. S. Hercberg, P. Galan, M. Touvier UREN, Inserm/INRA/CNAM/Paris 13 SU.VI.MAX2 sub‐cohort (210 subjects) UPLC‐ESI‐Qtof‐MS (mode pos & neg)Morning spot urines Good discrimination for most foods, especially those consumed frequently & rich in phytochemicals Caffeine metabolites Trigonelline Hippuric acid Atractyligenin gluc Cyclo‐(Isoleu‐Pro) … Cohort studies Glasgow, July 2013 Fillâtre et al., in preparation
- 17. FOOD METABOLOMICS FOR DISCOVERY OF PLANT FOOD INTAKE BIOMARKERSFOOD METABOLOMICS FOR DISCOVERY OF PLANT FOOD INTAKE BIOMARKERS Six 24h recalls (1994-2002) +FFQ 2007-2009 Selection of low and high consumers for 20 plant foods or food groups PhenoMeNEp ALIA 2011‐2013 CorrelationsDistribution of food consumption Coll. S. Hercberg, P. Galan, M. Touvier UREN, Inserm/INRA/CNAM/Paris 13 SU.VI.MAX2 sub‐cohort (210 subjects) UPLC‐ESI‐Qtof‐MS (mode pos & neg)Morning spot urines Good discrimination for most foods, especially those consumed frequently & rich in phytochemicals Caffeine metabolites Trigonelline Hippuric acid Atractyligenin gluc Cyclo‐(Isoleu‐Pro) … Cohort studies 68 subjects from the GrainMark study, stratified for consumption of 38 food groups / 4 FFQs over 3 months Same conclusion in Lloyd et al., AJCN 2013 Glasgow, July 2013 Conduct similar studies in various populations with different dietary habits Fillâtre et al., in preparation
- 18. CO OR GR 6-12h 12h-night 0-6h 1st urine d0 6-12h 12h-night 0-6h 1st urine d0 1st urine d1 1st urine d1 -25 -20 -15 -10 -5 0 5 10 15 20 25 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 t[2] t[1] 6 F4 20 F5 6 N F3 0 F5 6 N20 F5 F2 F420 F5 F2 F5 F4 F3 F1 F2 F4 F3 F1 F5 F2 F4 F3 F1 F5 F2 F4 F3 F1 F5 F2 F4 F3 F1 F5 F2 F4 F3 F5 F2 F4 F3 F1 F5 F2 F4 F3 F1 F5 F1 -20 -18 -16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 t[2] t[1] 12816H 13214H 13245H 13280H 17774H 13374H 13413H 13435H 13457H 13862H 13890H 13934H 13950H 15244H 15445H 15817H 15836H 15893H 15935H 16355H 16375H 16405H 16472H 16518H 16701H 16725H 16772H16774H 16895H 17159H 17190H 17209H 13911H 17316H 17328H 17469H17477H 17544H 17580H 17870H 11864L12521L 12585L 12675L 12756L 13144L 13150L 13204L 15230L13483L 13735L 13766L 13910L 13962L 14274L 14517L 15398L 15420L 15554L 15656L 15884L 16387L 16467L 16543L 16550L 16751L 16886L16947L 16987L 17006L 17049L 17291L 17396L 17472L 17536L 17735L 17753L 17877L 17934L A ACUTE CONTROLLED INTERVENTION STUDY COHORT STUDY Number of discriminant ions Level of control of the diet 1-MO INTERVENTION STUDY COMPARISON OF STUDY DESIGNSCOMPARISON OF STUDY DESIGNS 603 significant ions 105 significant ions 19 significant ions Metabolite StabilityPharmacokinetics Lack of specificity Heterogeneity of the population Risk of false discovery (Correlations between foods) Validation in intervention study Glasgow, July 2013 Pujos‐Guillot et al., J Proteome Res, 2013
- 19. Biomarkers of intake usable in epidemiology Comprehensive phenotyping of nutritional exposures COHORT STUDYMEDIUM-TERM STUDY COMPARISON OF STUDY DESIGNSCOMPARISON OF STUDY DESIGNS Biomarkers of compliance ACUTE CONTROLLED INTERVENTION STUDY Glasgow, July 2013 DISCOVERY PHASE Different validations ? ControlledControlled interventions studies Cohort studies
- 20. 3‐days weighed food diaries K‐means Cluster Analysis (33 food groups) 160 Irish subjects O’Sullivan et al., AJCN 2011 BIOMARKERS OF DIETARY PATTERNSBIOMARKERS OF DIETARY PATTERNS PLS-DA of 1H-NMR urine data of dietary cluster 1 ( ) compared with cluster 3 ( ) Glasgow, July 2013 It is more difficult to find biomarkers of dietary patterns than biomarkers of food intake
- 21. Questionnaire data (Times/wk) Biomarker Concentration SCORING OF FOOD INTAKE BIOMARKERS TO DETERMINE DIETARY PATTERNS SCORING OF FOOD INTAKE BIOMARKERS TO DETERMINE DIETARY PATTERNS Fish TMAO +? Meat 1-Methyl-Histidine + Anserine Milk Cheese Citrus Proline Betaine +? Berries Apple Phloretin +? Cruciferous veg. S-Methyl-L-cysteine sulfoxide +? Tomato Lycopene +? Potato Rice and pasta White Bread Whole bread Alkylresorcinols + ? Chocolate Theobromine + ? Confectionaries Red wine Resveratrol metab. + ? Coffee Atractyligenin+1-methylxanthine Tea 4-O-Methylgallic acid +? Priority list to be defined with epidemiologists Glasgow, July 2013 Kits for dietary pattern determination? List completed in a few years time if we work in a concerted action?
- 22. FOOD METABOLOME DATA REPOSITORYFOOD METABOLOME DATA REPOSITORY Food metabolome studies Controlled study B Cohort study A Controlled study C Cohort study D Food Metabolome Data repository Study Metadata Method description Identified markers Annotated raw data Non-identified markers Glasgow, July 2013 Candidate biomarkers identified in Study A Correlation with coffee intake in all available studies? dbNP? Metabolights? Reporting standards Data formats Fiehn et al. Metabolomics 2007 Metabolomic standards Initiative
- 23. BIOMARKER VALIDATION: PROLINE BETAINE AS AN EXAMPLEBIOMARKER VALIDATION: PROLINE BETAINE AS AN EXAMPLE Heinzmann et al., 2010, Lloyd et al., 2011&2013, Pujos‐Guillot et al., 2013, May et al., 2013 Glasgow, July 2013 Heinzmann et al., 2010; de Zwart et al., 2003; Slow et al., 2005 Found almost exclusively in citrus fruits, with dominance in orange Associated with citrus intake in 3 acute studies, 2 medium-term interventions , 3 cohort studies Detected with NMR, LC-QTof, FIE-MS In morning spot urines, 24hr urine & post-prandial urine kinetics 250 ml orange juice challenge Heinzmann et al., AJCN 2010 Pharmacokinetics data Training set n=220 Validation set n=279 « Excellent biomarker » ROC curve Heinzmann et al., AJCN 2010 Validation in INTERMAP-UK cohort
- 24. BIOMARKER VALIDATIONBIOMARKER VALIDATION Glasgow, July 2013 Define a procedure /workflow for validation of biomarkers of intake Define a validation mark? Identify the factors affecting the biomarker concentration in biofluids & the content of its precursor in the food source D. Newly discovered C. With analytical validation including kinetics and dose-response relationship in the sample type of interest B. Confirmed in a controlled dietary intervention as well as in cross- sectional studies The number of validating studies could be indicated in a code: Ex: Proline betaine = B8 ? (found in 3 cohorts and 5 intervention studies) A. Confirmed to be in accordance with other marker(s) for the same food(s) Adapted from Lars Dragsted’s poster
- 26. IDENTIFICATION OF UNKNOWNS, THE MAIN BOTTLENECKIDENTIFICATION OF UNKNOWNS, THE MAIN BOTTLENECK Identification workflow (LC-MS) Find the molecular ion and its related fragments & adducts (MSClust, Camera, MZedDB, …) Get exact mass with high accuracy (Orbitrap, FT-ICR…) Elemental formula (Golden rules) Query compound databases to obtain hypotheses Analyze standard or compare mass fragmentation in librairies of spectra or literature Glasgow, July 2013 HMDB Compound databases HMDB Librairies of spectra In‐house librairies Definitive or tentative identification
- 27. IDENTIFICATION OF UNKNOWNS, THE MAIN BOTTLENECKIDENTIFICATION OF UNKNOWNS, THE MAIN BOTTLENECK Identification workflow (LC-MS) Find the molecular ion and its related fragments & adducts (MSClust, Camera, MZedDB, …) Get exact mass with high accuracy (Orbitrap, FT-ICR…) Elemental formula (Golden rules) Query compound databases to obtain hypotheses Analyze standard or compare mass fragmentation in librairies of spectra or literature Glasgow, July 2013 Definitive or tentative identification Why? Host & microbial metabolites of non-nutrient compounds : Unknown or not yet included in databases Their standards are lacking Their mass fragmentation spectra are unknown It often does not work!!!!
- 28. ENRICH DATABASES TO FACILITATE IDENTIFICATIONENRICH DATABASES TO FACILITATE IDENTIFICATION Food composition databases 30,000 natural food components & additives 7,500 compounds 28,000 compounds, 888 foods 500 polyphenols 100 food components 8,500 phytochemicals Quantitative data on food contents HMDB Use in silico prediction tools when no information is available on the metabolic fate of a given compound Literature Glasgow, July 2013 Add the known metabolites on non-nutrients in compound databases Rothwell et al., Database, 2012 HMDB
- 29. IN SILICO PREDICTION OF METABOLISMIN SILICO PREDICTION OF METABOLISM Developed for the pharmaceutical industry. Validation required for dietary compounds No tool for prediction of microbial metabolism Meteor Nexus (Lhasa Ltd) is probably the most powerful tool (477 biotransformations), but costs 5,000€/year To enrich online and in-house databases with predicted metabolitesTo enrich online and in-house databases with predicted metabolites To support putative identifications from spectral dataTo support putative identifications from spectral data META, Metabolexpert, Metabolizer, MetaPrint2D-React, MetaSite, Meteor nexus, SyGMa, TIMES T’jollyn et al., 2011, Piechota et al., 2013 Tools Glasgow, July 2013
- 30. IN SILICO PREDICTION OF METABOLISM: METEOR NEXUS (LHASA LTD)IN SILICO PREDICTION OF METABOLISM: METEOR NEXUS (LHASA LTD) Glasgow, July 2013
- 31. IN SILICO PREDICTION OF METABOLISM: METEOR NEXUS (LHASA LTD)IN SILICO PREDICTION OF METABOLISM: METEOR NEXUS (LHASA LTD) Glasgow, July 2013 Tendency to overpredict, Good sensitivity / known metabolites Good prediction for polyphenols (>80%) Currrently tested for alkaloids and terpenes Can be used to built in‐house databases for selected foods from knowledge of their composition Pujos‐Guillot et al., 2013 Rothwell et al., subm. 2013 Helpful for identification of candidate biomarkers for citrus and coffee intake Kahweol oxide glucuronide Limonene 8,9‐diol glucuronide Nootkatone 13,14‐diol glucuronide
- 32. PHYTOHUBPHYTOHUB An online database for dietary phytochemicals and their human metabolites Glasgow, July 2013 (www.phytohub.eu) Dietary sources Known metabolites Predicted metabolites Spectral data Physico-chemical data Links to other databases 1,000 dietary phytochemicals Literature Literature expert knowledge on biotransformations Literature Experimental data Structure developed by INRA, website in collaboration with Giacomoni et al., in preparation Should be launched by the end of 2013
- 33. An online database for dietary phytochemicals and their human metabolites Glasgow, July 2013 Dietary sources Known metabolites Predicted metabolites Spectral data Physico-chemical data Links to other databases 1,000 dietary phytochemicals What are the phytochemical precursors & metabolites matching with a monoisotopic mass ? What are the phytochemical metabolites expected in biological fluids after consumption of a given food? Open for collaborations for filling and curating the database PHYTOHUBPHYTOHUB (www.phytohub.eu)
- 34. UV spectra Enzymatic reactions (hydrolysis of conjugates,…) H/D exchange experiments MSn spectral trees In silico fragmentation Peak collection & preconcentration + NMR, GC-MS… All the new tools proposed by the metabolomics community MetFrag, Metfusion, MetiTree, HighChem Mass frontier, mzCloud … Glasgow, July 2013 Experimental structural elucidation strategies using: EFFICIENT TOOLS & METHODS FOR STRUCTURAL ELUCIDATIONEFFICIENT TOOLS & METHODS FOR STRUCTURAL ELUCIDATION Develop projects to synthetize and distribute standards for non commercially available metabolites Expand in-house libraries of spectra
- 35. CONCLUSION: NETWORKING IS ESSENTIAL NOWCONCLUSION: NETWORKING IS ESSENTIAL NOW To provide rapid access and training to new tools and methodologies To define current good practices from ring-tests on shared datasets & develop shared pipeline for dietary studies, data analysis and compound identification To develop of a metabolism prediction tool customized for food compounds To organize data sharing with a Food metabolome data repository To avoid redundancy in research and work at commonly defined priorities To develop a concerted action for biomarker validation Glasgow, July 2013