This document discusses using metabolomics to analyze the food metabolome - all metabolites derived from digestion and metabolism of food components. It notes the complexity of nutritional exposures from foods, which contain nutrients, non-nutrients from natural and man-made sources, and contaminants. Individual metabolic capacity is also influenced by genetics, microbiota, age, and other factors. The food metabolome is defined and applications are described, including discovering new food bioactives, understanding diet-health relationships, and personalized nutrition. Challenges in analyzing the large and diverse food metabolome are also outlined. Initial studies using targeted and untargeted metabolomics are discovering new biomarkers of food intake from various foods. Larger cohort studies and controlled interventions are needed
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 »?
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
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
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
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
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