Glasgow July 2013

THE FOOD METABOLOMETHE FOOD METABOLOME

C. ManachHuman Nutrition Unit, INRA Clermont‐Ferrand, France

Review paper in preparation

COMPLEXITY OF NUTRITIONAL EXPOSURESCOMPLEXITY OF NUTRITIONAL EXPOSURES

Nutrients Non nutrientsNatural

Non-nutrients

DIET

Non nutrientsMan-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)

Genetics, Epigenetics,Age, Gender,Microbiota, Physiological status, Medical history

Digestion

Xenobioticmetabolism

Microbialmetabolism

Elimination

Storage

INDIVIDUAL METABOLIC CAPACITYINDIVIDUAL METABOLIC CAPACITY

Glasgow, July 2013

Food MetabolomeAll the metabolites that derive from

the digestion and metabolism of food components

Dietary habits

Metaboliccapacity

THE FOOD METABOLOME DEFINITIONTHE FOOD METABOLOME DEFINITION

Health outcomesClinicaltrialsClinicaltrials Cohorts

Glasgow, July 2013

Metabolomes of foods =Food metabolome ?« Food chemicalome »?

FOOD METABOLOME APPLICATIONSFOOD METABOLOME APPLICATIONS

Age, sex,BMI,Lifestyle,exercice…

GenotypeEnterotype

Food metabolome analysis

Glasgow, July 2013

Segmentation of Poor/High absorbers& metabolizers

New metabolitesNew potentialfood bioactives

Public healthResearch Diet-genotype-health relationships

Monitoring impact of recommendations or policiesMedicine Personalized nutrition

Food intakeNutritional exposures

Dietaryquestionnaires

Dietary assessment

FOOD METABOLOME APPLICATIONSFOOD METABOLOME APPLICATIONS

Food intakeNutritional exposures

Dietaryquestionnaires

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

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 

CarbohydratesProteinsLipidsVitaminsMineralsFlavonoidsPhenolic acidsCarotenoidsPhytosterolsChlorophyllsAlkaloidsArtificial colorsFlavoring additives Maillard reaction productsFood contaminants…

Larg

e ra

nge

of c

once

ntra

tions

mM

nM

FOOD METABOLOME: AN ANALYTICAL CHALLENGEFOOD METABOLOME: AN ANALYTICAL CHALLENGE

Food metabolome = at least 25,000 compounds 

CarbohydratesProteinsLipidsVitaminsMineralsFlavonoidsPhenolic acidsCarotenoidsPhytosterolsChlorophyllsAlkaloidsArtificial colorsFlavoring additives Maillard reaction productsFood 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, …)

Larg

e ra

nge

of c

once

ntra

tions

mM

nM

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

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 foodmetabolome analysis

1‐Map the analytical coverage of Food Metabolome chemical space

by various platforms

2‐ Optimize methods with wideand complementary coverages

& Define SOPs

FIRST STUDIES TO DISCOVERNEW BIOMARKERS OF FOOD INTAKE

FIRST STUDIES TO DISCOVERNEW BIOMARKERS OF FOOD INTAKE

Glasgow, July 2013

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

020

0040

0060

00

020

0040

0060

0080

0010

000

1200

00

500

1000

1500

2000

2500

3000

020

040

060

080

010

0012

00

010

020

030

040

050

050

010

0015

00

050

010

0015

00

050

100

150

200

250

300

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 drinkUsual dietCross-over study, 24h urine D30LC-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

DISCOVERY OF BIOMARKERS OF FOOD INTAKEDISCOVERY OF BIOMARKERS OF FOOD INTAKEShort‐term intervention studies

Citrus Cruciferous vegetablesCocoa 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

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 challengeBut 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

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 besystematically found in the target food, but onlyin certain populations and/or geographiclocations

Host met.Microbiota metabolites

Host met.Microbiota metabolites

Host met.Host met.Microbiota metabolitesMicrobiota metabolites

The natural non‐nutrients and their host metabolitesare more likely to constitute specific

biomarkers of food intake

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 foodconsumption

Coll. S. Hercberg, P. Galan, M. TouvierUREN, 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 consumedfrequently & rich in phytochemicals

Caffeine metabolitesTrigonellineHippuric acidAtractyligenin glucCyclo‐(Isoleu‐Pro)…

Cohort studies

Glasgow, July 2013Fillâtre et al., in preparation

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 foodconsumption

Coll. S. Hercberg, P. Galan, M. TouvierUREN, 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 consumedfrequently & rich in phytochemicals

Caffeine metabolitesTrigonellineHippuric acidAtractyligenin glucCyclo‐(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 populationswith different dietary habits

Fillâtre et al., in preparation

COORGR

6-12h

12h-night

0-6h

1st urine d0

6-12h

12h-night

0-6h1st 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

6N

F30F5

6N20F5

F2 F420

F5

F2

F5

F4

F3

F1

F2

F4

F3

F1 F5

F2

F4

F3

F1 F5

F2

F4

F3

F1F5

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

13413H13435H

13457H

13862H13890H

13934H

13950H

15244H

15445H

15817H

15836H

15893H

15935H

16355H

16375H

16405H

16472H

16518H16701H

16725H

16772H16774H

16895H

17159H

17190H

17209H

13911H

17316H

17328H

17469H17477H

17544H17580H

17870H

11864L12521L

12585L

12675L

12756L

13144L

13150L

13204L

15230L13483L

13735L

13766L

13910L

13962L

14274L

14517L

15398L

15420L

15554L15656L

15884L

16387L16467L16543L

16550L

16751L

16886L16947L

16987L

17006L

17049L

17291L

17396L

17472L

17536L

17735L17753L

17877L

17934L

A

ACUTE CONTROLLEDINTERVENTION 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

MetaboliteStabilityPharmacokinetics

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

Biomarkers of intakeusable in epidemiology

Comprehensive phenotyping of nutritional exposures

COHORT STUDYMEDIUM-TERM STUDY

COMPARISON OF STUDY DESIGNSCOMPARISON OF STUDY DESIGNS

Biomarkers of compliance

ACUTE CONTROLLEDINTERVENTION STUDY

Glasgow, July 2013

DISCOVERY PHASE

Different validations ?ControlledControlled

interventions studies

Cohort studies

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

Questionnaire data (Times/wk)

BiomarkerConcentration

SCORING OF FOOD INTAKE BIOMARKERS TO DETERMINE DIETARY PATTERNS SCORING OF FOOD INTAKE BIOMARKERS TO DETERMINE DIETARY PATTERNS 

Fish TMAO +?Meat 1-Methyl-Histidine + AnserineMilkCheeseCitrus Proline Betaine +?BerriesApple Phloretin +?Cruciferous veg. S-Methyl-L-cysteine sulfoxide +?Tomato Lycopene +?PotatoRice and pastaWhite BreadWhole bread Alkylresorcinols + ?Chocolate Theobromine + ?ConfectionariesRed wine Resveratrol metab. + ?Coffee Atractyligenin+1-methylxanthineTea 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?

FOOD METABOLOME DATA REPOSITORYFOOD METABOLOME DATA REPOSITORY

Food metabolome studies

Controlled study B

Cohort study A

Controlled study C

Cohort study D

Food Metabolome

Datarepository

Study MetadataMethod descriptionIdentified markers

Annotated raw dataNon-identified markers

Glasgow, July 2013

Candidate biomarkersidentified in Study A 

Correlation with coffee intake in all available

studies?

dbNP?Metabolights?

Reporting standardsData formats

Fiehn et al. Metabolomics 2007Metabolomic standards Initiative

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-MSIn morning spot urines, 24hr urine & post-prandial urine kinetics

250 ml orange juice challengeHeinzmann et al., AJCN 2010

Pharmacokinetics data

Training set n=220Validation set n=279

« Excellent biomarker »

ROC curve

Heinzmann et al., AJCN 2010

Validation in INTERMAP-UK cohort

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 discoveredC. With analytical validation including kinetics and dose-response

relationship in the sample type of interestB. Confirmed in a controlled dietary intervention as well as in cross-

sectional studiesThe 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

IDENTIFICATION OF UNKNOWNS IN LC‐MS, THE MAIN BOTTLENECK

IDENTIFICATION OF UNKNOWNS IN LC‐MS, THE MAIN BOTTLENECK

Glasgow, July 2013

IDENTIFICATION OF UNKNOWNS, THE MAIN BOTTLENECKIDENTIFICATION OF UNKNOWNS, THE MAIN BOTTLENECK

Identification workflow (LC-MS)

Find the molecular ion and itsrelated 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

IDENTIFICATION OF UNKNOWNS, THE MAIN BOTTLENECKIDENTIFICATION OF UNKNOWNS, THE MAIN BOTTLENECK

Identification workflow (LC-MS)

Find the molecular ion and itsrelated 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 2013Definitive or tentative identification

Why?

Host & microbial metabolitesof non-nutrient compounds :

Unknown or not yetincluded in databases

Their standards are lacking

Their mass fragmentation spectra are unknown

It often does not work!!!!

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 polyphenols100 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

IN SILICO PREDICTION OF METABOLISMIN SILICO PREDICTION OF METABOLISM

Developed for the pharmaceutical industry. Validation required for dietary compoundsNo 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

IN SILICO PREDICTION OF METABOLISM: METEOR NEXUS (LHASA LTD)IN SILICO PREDICTION OF METABOLISM: METEOR NEXUS (LHASA LTD)

Glasgow, July 2013

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 oxideglucuronide

Limonene 8,9‐diol glucuronideNootkatone 13,14‐diol glucuronide

PHYTOHUBPHYTOHUBAn 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

LiteratureExperimental data

Structure developed by INRA, website in collaboration with

Giacomoni et al., in preparation

Should be launched by the end of 2013

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)

UV spectraEnzymatic reactions (hydrolysis of conjugates,…)H/D exchange experimentsMSn spectral treesIn silico fragmentation Peak collection & preconcentration + NMR, GC-MS…

All the new tools proposed by the metabolomics communityMetFrag, 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

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

Yoann FILLATREJoe ROTHWELLMercedes QUINTANAMathieu RAMBEAU Christine MORANDDragan MILENKOVICBlandine COMTE

JRU1019‐ Human Nutrition Unit

Mathilde TOUVIERLeopold FEZEUNathalie ARNAULTPilar GALANSerge HERCBERG

UREN, Inserm/INRA/CNAM/Paris 13

Charlotte JOLYBernard LYANJean‐François MARTINFrank GIACOMONIEstelle PUJOS‐GUILLOT

THANK YOU VERY MUCH FOR YOUR ATTENTIONTHANK YOU VERY MUCH FOR YOUR ATTENTION

Craig KNOXRoman EISNER

Glasgow, July 2013