Embed Size (px)
Citation preview
DRF/JOLIOT/DMTS/SPI
Apport de l'analyse métabolomique par
spectrométrie de masse à la détection sans a
priori de xénobiotiques dans les matrices
alimentaires et environnementales
Christophe Junot
Service de Pharmacologie et Immunoanalyse
CEA-INRA UMR 0496
DRF/JOLIOT/DMTS/SPI, CEA-Saclay
Université Paris Saclay
MetaboHUB
DRF/JOLIOT/DMTS/SPI
Current regulatory analyses of pollutants
Advantages :
➢ Very sensitive
➢ Specific
➢ Fast and easy interpretation
Limitations :
Time consuming development➢
Focused on targeted and known ➢
molecules
• GC/MS analyses
• LC/ESI-MS/MS analyses in MRM mode (Multiple ReactionMonitoring) on triple quadrupole spectrometers
Reference methods to date
DRF/JOLIOT/DMTS/SPI
Food safety: unknown or unexpected molecules?
❖ Known but unexpected contaminants :
– The case of milk adulteration with melamine :
Melamine
Production of unknown or untargeted xenometabolites with questionable toxicity
❖ Metabolism of pollutants :
In the environment In water treatment plants By man or animals
4 deaths and thousands of intoxications
DRF/JOLIOT/DMTS/SPI
Improvements provided by High Resolution Mass Spectrometry (HRMS)
FT-ICR
TOF
Orbitrap
➢ Access to large quantity of information➢ High measurement precision : elemental composition
▪ Aconitic acid(m/z 173.009163)
▪ Shikimic acid(m/z 173.045548)
▪ Suberic acid(m/z 173.081933)
R = m/ m
1 ppm: 100,0001
CxHyNzOw …
DRF/JOLIOT/DMTS/SPI
The metabolomic workflow
Sample
preparation
1 Metabolome
analysis
GC/MS
LC/MS
RMN
2
Data processing
Automatic detection of
signals
Selection of
relevant features
Annotation :
Databases
public / internal
3
Statistical
analyses
Multivariate
Univariate
4Metabolite
identification
- To confirm annotations
- To characterize unknowns
5Data visualization
6
Krebs cycle
α KG Succinyl CoA
Succinic acid
Fumaric acid
Malic acidOAA
Citric acid
Isocitric acid
NH4+
Gln
AcetylCoA
Pyruvic
acid
Acetylated
amino acids
Acylcarnitines
β-Oxidation
PDH PC
Ala
α KG
Asp
Glu Saccharopine
Lys
Glu-20
-10
0
10
20
30
40
-35 -30 -25 -20 -15 -10 -5 0 5 10 15 20 25 30 35 40
t[2]
t[1]
RPpos_ACP_Fig.M4 (PCA-X), ACP RP pos UV sans GM5
t[Comp. 1]/t[Comp. 2]
Colored according to Obs ID (Obs. Sec. ID:3)
R2X[1] = 0.198219 R2X[2] = 0.0908155 Ellipse: Hotelling T2 (0.95)
0
Ehtraite
shunt PC
Ehm
1
LK4AG6AN4BF8
BL2BM1BS61CC5CE3CM0DK4
DQ0
GB6HJ9HK3JB9
JM8LT0
MJ8
MM7MR61PD2PJ4PO7SV9VP2YB2
MP11 RM70PV2SP3
MHG9
BG9NQ1AZ0BC8
MP01DA8
ZG8
CJ3
BM2
SIMCA-P+ 12.0.1 - 2016-04-18 23:01:38 (UTC+1)
HECTL
HE is a neurological complication of acute or chronic liver disease.
60 to 80 % of cirrhotic patients exhibit cognitive disorders potentially related to minimal HE.
The aim of the study: to highlight altered metabolicpathways in HE patients by using CSF metabolomics.➢ patient stratification➢ pharmacological targets
~ 500 signaux LC/MS annotés, 120 identifiés et sélectionnés, 73 métabolites dont les concentrations
sont modifiées par la pathologie
- Altération du métabolisme énergétique cérébral: potentielles nouvelles cibles pharmacologiques
- Possibilité de stratifier les patients selon la gravité des atteintes hépatique et neurologique
DRF/JOLIOT/DMTS/SPI
High intensity features related to drugs and metaboliteshave been detected in 7 out of the 14 HE patients
RT: 0.16 - 11.11
1 2 3 4 5 6 7 8 9 10 11
Time (min)
0
20
40
60
80
100
0
20
40
60
80
100
Re
lative
Ab
un
da
nce
0
20
40
60
80
1000.86
1.08
1.46 6.221.58
1.63 3.422.87 3.986.022.05 4.85 6.51 6.955.40 7.19 11.058.74 10.298.97
5.65
0.84 1.066.230.76 2.241.99 3.45 6.372.89 7.214.17 8.764.58 10.347.83 10.11
5.65
0.851.07
3.446.24
1.57 8.762.89 4.171.93 6.96 7.855.35 10.2410.09
NL:4.35E7
TIC F: FTMS {1;1} - p ESI Full ms [95.00-1000.00] MS CJ3neg
NL:1.60E8
TIC F: FTMS {1;1} - p ESI Full ms [95.00-1000.00] MS mhg9neg
NL:1.06E8
TIC F: FTMS {1;1} - p ESI Full ms [95.00-1000.00] MS mq1neg
CJ3
MHG9
NQ1
mhg9neg #277-281 RT: 5.62-5.68 AV: 5 NL: 2.28E7T: FTMS {1;1} - p ESI Full ms [95.00-1000.00]
180 200 220 240 260 280 300 320 340 360
m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lative
Ab
un
da
nce
351.10211
341.07347
305.09677
191.06853
225.06145197.80774 329.03063
317.06319
298.76626278.10371238.00086 265.07056
[M-H]-
[M+Cl]-
[M+HCOOH-H]-
[M-C6H4F2H]-
Fluconazole
m/z 191.0685
Putative annotation HMDB Metlin KEGG Samples
Levetiracetam HMDB15333 66750 C07841 BM2
Metronidazole HMDB15052 573 C07203 BG9
Fluconazole HMDB14342 2708 MGH9, NQ1, ZG8, AZ0
diazepam HMDB14967 3521 C06948 BC8, MHG9, MP01, NQ1, ZG8
N-Desmethyldiazepam HMDB60538 896 C07486 BC8, MHG9, MP01, NQ1, ZG8
Tazobactam HMDB15544 581 D00660 BG9, NQ1, ZG8
Piperacillin HMDB14464 1964 D08380 BG9, NQ1, ZG8
Could explain PCA outliersDrug induced HE???
DRF/JOLIOT/DMTS/SPI
Foodomics
(Herrero M., Mass Spectrom. Rev., 2012)
The study of food and nutrition through advanced omics technologies to improve consumer’s well-being, health and confidence.
DRF/JOLIOT/DMTS/SPI
Metabolomic based approach▪ Unknown or untargeted contaminants
▪ Adulteration markers
HRMS: all in one approachRT: 0.00 - 29.95
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
Time (min)
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Re
lative
Ab
un
da
nce
28.38
0.88
15.18
9.01
12.6310.66
10.49
28.308.196.04 7.20 16.5113.06
5.1722.31 27.2322.6222.144.83 26.8216.94 18.31 21.164.653.841.24
2.62
NL:1.34E10
TIC MS 20131018_pool_803_mlc_HSS-T3_pH2-5_100ngmL_pos_045
Targeted approach▪ Based on a large spectral
database of hundreds of pollutants
FT-ICR
TOF
Orbitrap
Advantages :✓
High resolution➢
High versatility ➢
Retrospective analyses➢
Structural analysis➢
✓ Limitations :➢ Data complexity➢ Sensitivity➢ Time consuming
interpretation
DRF/JOLIOT/DMTS/SPI
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
1985 1990 1995 2000 2005 2010 2015
Ton
s
Year
Production and consumption of honey in France
Consumption
Production
❖ Decline of bees population: exposition to pesticides
❖ Significant imports of honey: increased risks of adulteration
❖ Presence of antibiotics: human health concern
Adapted from Planetoscope, 2009
Proof of concept study on honey
Evaluation of the quality of honey
DRF/JOLIOT/DMTS/SPI
Material and Methods
▪ 76 honeys regrouping 6 floral origins :▪ Multi flower▪ Acacia▪ Orange tree
▪ Lavender▪ Mountain▪ Eucalyptus
❖ Pollutants:
▪ 55 pesticides
▪ 28 antibiotics83 targeted pollutants
❖ Samples:
❖ Spectral database:
Injection of individual standardImplementation of pertinent information
DRF/JOLIOT/DMTS/SPI
Material and Methods❖ Sample preparation: liquid-liquid extraction
❖ Honeys samples analyses:
5ghoney
Samples spiked with reference
compounds
QuECheRSprotocol
Nexera(Shimadzu)
Q-Exactive (Thermo Fisher scientifics)
POSR = 50 000
< 5ppm
▪ Semi-quantification▪ 80% < extraction yields < 100%▪ 3 replicates of each condition▪ All molecules spiked at 10ng/g = MRLs
C18 column
DRF/JOLIOT/DMTS/SPI
Metabolomic based approach
First data miningRT: 0.00 - 29.95
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
Time (min)
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Re
lative
Ab
un
da
nce
28.38
0.88
15.18
9.01
12.6310.66
10.49
28.308.196.04 7.20 16.5113.06
5.1722.31 27.2322.6222.144.83 26.8216.94 18.31 21.164.653.841.24
2.62
NL:1.34E10
TIC MS 20131018_pool_803_mlc_HSS-T3_pH2-5_100ngmL_pos_045
Targeted approach▪ 83 molecules in the
chemical library
DRF/JOLIOT/DMTS/SPI
AmitrazCarbendazim
Antibiotics / Pesticides9 / 27
❖ 74 out of 76 honey samples are polluted by at least one molecule❖ 36 out of the 83 molecules analyzed were detected in samples
Occ
urr
ence
in h
on
ey
Targeted analysis results
Pollutants (Cotton et al., J. Agricultural Food Chem., 2014)
DRF/JOLIOT/DMTS/SPI
Metabolomic based approach
Second data miningRT: 0.00 - 29.95
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
Time (min)
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Re
lative
Ab
un
da
nce
28.38
0.88
15.18
9.01
12.6310.66
10.49
28.308.196.04 7.20 16.5113.06
5.1722.31 27.2322.6222.144.83 26.8216.94 18.31 21.164.653.841.24
2.62
NL:1.34E10
TIC MS 20131018_pool_803_mlc_HSS-T3_pH2-5_100ngmL_pos_045
Targeted approach
DRF/JOLIOT/DMTS/SPI
1. Identification of new xenobiotics
2. Chemical characterization of honey samples
XCMS
Automatic peak detection and alignment
Peaktable
Samples
Featu
res
(rt-
m/z
)
RT: 0.00 - 29.95
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
Time (min)
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Re
lative
Ab
un
da
nce
28.38
0.88
15.18
9.01
12.6310.66
10.49
28.308.196.04 7.20 16.5113.06
5.1722.31 27.2322.6222.144.83 26.8216.94 18.31 21.164.653.841.24
2.62
NL:1.34E10
TIC MS 20131018_pool_803_mlc_HSS-T3_pH2-5_100ngmL_pos_045
Statistical analyses and data mining
LC/HRMS fingerprint
DRF/JOLIOT/DMTS/SPI
Detection of new xenobiotics
Use of chlorine isotope contribution: ➢35Cl and 37Cl
Implementation of an algorithm for the automatic detection of chlorinated ➢
compounds from XCMS peaktables
Δm/z (37Cl – 35Cl) = 1.9970
Δm/z (37Cl – 35Cl) = 1.9970Same RT
0.25 < Intensity ratio < 1.1
Min intensity
(Cotton et al., J. Agricultural Food Chem., 2014)
DRF/JOLIOT/DMTS/SPI
✓ Validation:
▪ All chlorinated pollutants detected previously were found
✓ Formal identification of 2,6-dichlorobenzamide
▪ Metabolite of dichlobenil, an herbicide used in lavender culture▪ Only found in lavender honey▪ Banned in France in 2010
✓ Characterization of 10 unknown xenobiotics (present in 1 to 70 honey samples):
▪ Elemental composition (MS/MS experiments often not informative)▪ Not found in databases (HMDB, KEGG, METLIN)▪ Unknown metabolites or abiotic degradation products ?
(Cotton et al., J. Agricultural Food Chem., 2014)
DRF/JOLIOT/DMTS/SPI
Acacia
Orange trees
LavenderMulti flowers
Mountain
Discrimination of honeys according to their floral origin
Hypoxanthine Naringin Phenylalanine
DRF/JOLIOT/DMTS/SPI
Future safety analyses?
✓ Screening of hundreds of contaminants(regulatory assessments)
✓Warnings based on chemical profiledatabases
DRF/JOLIOT/DMTS/SPI
The main challenges: Xenobiotic detection and identification
There is no universal method to detect, identify and quantifymetabolites and xenobiotics
Global approaches are less sensitive than targeted ones
(Saric J., Anal.Chem., 2012)
mM
Concentration range
µM
nM
pM
NMR
GC/MS
LC/MS
LC/Fluo
(Adapted from Sumner L.W. et al., 2003)
DRF/JOLIOT/DMTS/SPI
RT: 0.00 - 60.00 SM: 7G
0 10 20 30 40 50
Time (min)
0
10
20
30
40
50
60
70
80
90
100
0
10
20
30
40
50
60
70
80
90
100
Rel
ativ
e A
bund
ance
0
10
20
30
40
50
60
70
80
90
10016.69
22.57
5.40 23.4015.67 30.84 53.6845.0239.26
16.52
18.35 31.12 44.1234.74 56.093.19 6.65
16.53
22.54 23.365.01 15.62 38.84 43.29 52.83
NL: 1.03E7
Base Peak m/z= 220.10669-220.12871 F: FTMS + c ESI Full ms [75.00-1000.00] MS 070829-pos-05-urines
NL: 2.43E7
Base Peak m/z= 220.10669-220.12871 F: FTMS + c ESI Full ms [75.00-1000.00] MS 070829-POS-04-melstd
NL: 2.17E7
Base Peak m/z= 220.10669-220.12871 F: FTMS + c ESI Full ms [75.00-1000.00] MS 070829-pos-06-urinesurch
50 100 150 200
m/z
0
20
40
60
80
100
0
20
40
60
80
100
Rela
tive A
bundance
184.09719
142.08651156.10193
116.0342790.05494
201.8544672.04433
184.09723
142.08658156.10229
116.0344290.05491
201.8620672.04417
NL: 1.76E6
070829-pos-05-urines#817-877 RT: 16.67-16.75 AV: 3 F: FTMS + c ESI d w Full ms2 [email protected] [50.00-235.00]
NL: 3.70E6
070829-POS-04-melstd#793-844 RT: 16.10-16.73 AV: 17 F: FTMS + c ESI d w Full ms2 [email protected] [50.00-235.00]
x5
IdentificationU
STD
Putative annotation
Metabolite identification
As only few authentic references are available, most of the compounds willbe putatively annotated
DRF/JOLIOT/DMTS/SPI
The main challenges: automatic detectionand alignment of signals
Samples
Va
ria
ble
s (
Rt-
ma
ss
e)
n chromatograms
80% of features are detected
Many artifacts are generated ( 400 for 100 reliable features)
Data matrices have to be filtered/cleaned
(Tautenhahn R, 2008)
DRF/JOLIOT/DMTS/SPI
The main challenges: How to select signalsof interest among thousands of features?
Control matrix
d:\010140\...\bio raw\mhg9neg 19/04/2013 11:27:34
RT: 0.00 - 8.00
0 1 2 3 4 5 6 7 8
Time (min)
0
20
40
60
80
100
0
20
40
60
80
100
Re
lative
Ab
un
da
nce
0.86
1.08
1.46 6.221.58
1.630.74 3.422.87 3.981.95 6.024.854.58 5.19 6.51 6.95 7.195.46 7.82
0.49
5.65
0.84 1.066.230.76 2.241.99 3.45 6.372.89 6.944.17 4.58 5.41 7.83
NL: 4.35E7
m/z= 95.00000-1000.00000 F: FTMS {1;1} - p ESI Full ms [95.00-1000.00] MS cj3neg
NL: 1.60E8
m/z= 95.00000-1000.00000 F: FTMS {1;1} - p ESI Full ms [95.00-1000.00] MS mhg9neg
Which kinds of statistical tools?
A huge variability is observedin the chemical composition of matrices. «Control/normal matrices» have to beextensively characterized
Polluted matrix
DRF/JOLIOT/DMTS/SPI
The main challenges: how to builddatabases?
Samples
Va
ria
ble
s (
Rt-
ma
ss
)?
?
?
?
?
?
?
?
?
D:\439010\...\ara 0uM CdCl2 n1.1 12/07/02 15:50:20
RT: 0,00 - 150,02
0 20 40 60 80 100 120 140
Time (min)
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
NL:
1,63E7
Base Peak F: + c
ESI Full ms [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
D:\439010\...\ara 0uM CdCl2 n1.1 12/07/02 15:50:20
RT: 0,00 - 150,02
0 20 40 60 80 100 120 140
Time (min)
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
NL:
1,63E7
Base Peak F: + c
ESI Full ms [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1D:\439010\...\ara 0uM CdCl2 n1.1 12/07/02 15:50:20
RT: 0,00 - 150,02
0 20 40 60 80 100 120 140
Time (min)
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
NL:
1,63E7
Base Peak F: + c
ESI Full ms [
100,00-1000,00]
MS ara 0uM
CdCl2 n1.1
URINE30DIL4_CID20_endogènes #68 RT: 1.22 AV: 1 NL: 4.17E5F: FTMS + p ESI Full ms2 [email protected] [50.00-800.00]
60 80 100 120 140 160 180
m/z
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
80
85
90
95
100
Rel
ativ
e A
bund
ance
116.07041
173.09190
127.08652
155.0813680.4945870.06497
169.6742686.06400184.8621861.03990 102.71753 143.04167
-H2O
[M+H]+-HCOOH
-C2H3ON
Spectral DBESI/MS: annotation MS/MS: identification
1018
_55
1043
_31
1047
_127
1048
_95
1050
_71
1052
_46
1057
.A_8
8
1057
.B_1
33
1058
_107
1061
_32
1067
_74
48 469
XC
96
LM8
1073
_29
1199
_121
1074
_80
1086
_114
1087
_27
1113
_28
1114
_50
1124
_67
1134
_43
1135
_131
1136
_39
1138
_89
1140
_68
1142
_125
1148
_77
1157
_79
1161
_90
1166
_117
sugar acid N-acetylneuraminic acid 0.9 0.5 1.0 0.5 1.1 1.4 0.3 1.0 1.2 1.0 0.9 2.4 5.9 7.7 7.8 4.4 5.0 1.5 0.8 0.6 1.2 0.5 0.8 0.9 0.7 1.2 0.7 0.8 0.7 1.2 0.7 0.8 1.2
Deoxyribose 0.6 0.4 0.5 0.5 0.6 1.5 0.3 10.2 1.0 0.8 0.7 2.3 9.1 6.3 8.1 4.0 5.0 0.9 0.7 0.5 1.0 0.4 0.7 0.5 0.7 1.7 0.5 0.6 0.7 1.3 0.5 0.5 0.7
Pentose 1.4 0.7 0.6 0.4 0.6 3.9 0.3 1.1 1.7 1.0 0.9 2.0 8.3 2.2 1.1 4.1 2.0 3.0 0.7 0.6 2.1 1.0 1.0 0.6 0.6 1.2 0.5 0.6 0.8 1.2 0.7 0.6 0.7
Mannitol or isomers 0.9 0.6 1.1 0.4 0.8 1.8 0.6 1.3 1.1 0.8 0.7 1.6 2.2 4.4 3.3 2.5 8.1 1.3 0.8 0.6 0.9 0.6 1.0 0.6 0.7 1.0 0.5 0.4 1.5 1.7 0.6 0.6 1.3
Threonic acid 0.8 1.1 0.7 0.6 0.9 2.5 0.5 1.1 1.3 1.0 0.8 2.3 1.2 1.9 1.6 3.3 3.3 1.1 0.7 1.0 1.7 0.8 0.8 0.7 0.6 1.0 0.5 0.9 1.1 1.2 1.4 0.6 1.1
Quinic acid 3.4 0.3 0.6 0.0 0.0 4.7 0.0 0.9 5.0 1.6 0.8 1.8 4.8 4.0 1.5 2.3 3.2 5.3 1.1 0.4 9.3 2.3 3.1 0.1 1.1 0.3 0.0 0.6 1.2 0.7 0.0 1.1 0.0
nucleoside derivative Succinyladenosine 0.8 0.6 0.7 0.5 0.8 1.7 0.4 1.3 1.2 1.0 1.1 1.4 8.0 3.9 6.2 4.1 2.3 1.5 0.9 0.7 1.3 0.6 1.0 0.7 0.8 1.2 0.8 0.9 0.7 1.4 0.6 0.6 1.3
Proline Betaine 0.7 0.5 2.5 0.5 0.8 2.1 0.4 1.7 3.0 2.3 0.1 6.0 0.1 5.7 0.1 4.1 0.8 0.1 0.9 1.9 4.3 0.6 1.0 0.9 0.2 0.9 0.1 2.3 0.8 0.7 3.2 0.3 2.1
Glutamic acid0.6 0.5 0.9 0.5 0.9 1.5 0.4 1.2 1.1 0.8 0.8
2.7 3.60.7
3.42.3 2.8 1.8 0.9 0.8 1.2 0.7 0.9 0.7 0.8 1.1 0.8 0.9 0.7 1.2 0.8 0.9 1.1
Aminoadipic acid0.8 0.6 0.8 0.6 0.9 1.4 0.4 1.1 1.2 0.8 0.8
2.2 2.21.8
3.22.6 2.9 1.7 0.9 0.8 1.2 0.7 0.9 0.7 0.9 1.2 0.6 1.0 0.8 1.5 0.7 0.8 1.1
N-acetyl-L-glutamic acid 0.5 0.5 0.7 0.5 1.0 1.4 1.0 2.8 1.2 0.8 0.8 1.1 48.6 0.6 11.9 0.7 2.0 0.8 1.2 1.4 1.0 0.4 0.9 0.7 1.6 1.3 1.3 0.8 0.4 0.8 1.3 1.3 1.2
N-Acetyl-D-allo-isoleucine0.6 0.5 1.0 0.7 1.0 1.5 0.5 1.4 1.2 1.0 1.1
2.1 2.8 1.3 2.31.2 2.0 1.7 0.9 1.2 1.2 0.6 0.7 0.7 1.0 1.1 0.8 0.8 0.8 1.1 0.9 1.0 1.4
Pyrimidine derivated Dihydroorotic acid 0.9 0.7 0.8 0.4 0.9 2.2 0.4 1.3 0.9 0.8 1.0 2.1 1.6 5.5 2.7 2.2 5.0 1.3 0.5 0.7 1.1 0.8 1.0 0.7 0.5 1.4 0.6 0.7 1.1 1.4 0.5 0.6 1.3
Acetyl-carnitine 1.8 0.7 0.6 1.1 0.5 0.9 0.6 0.9 2.7 1.0 1.5 2.7 1.2 2.1 1.5 2.0 1.1 1.5 1.0 0.4 1.5 1.2 0.9 0.6 1.5 0.9 0.9 0.3 0.7 1.5 0.8 0.8 0.8
Propionyl-carnitine 1.9 0.5 0.5 1.0 0.5 1.6 0.8 0.8 2.3 1.3 1.5 2.5 0.8 3.7 1.2 2.1 0.8 0.8 1.0 0.7 1.3 1.2 0.8 0.6 1.9 0.8 1.0 0.2 0.4 1.8 1.1 0.9 0.9
Butyryl-carnitine 0.9 0.5 0.5 0.7 0.8 1.6 0.6 0.6 1.4 1.6 1.5 2.4 1.0 3.3 1.3 2.2 1.3 0.6 0.8 0.8 1.2 1.1 0.9 0.6 1.2 1.0 0.9 0.3 0.6 1.6 0.7 0.9 1.1
Methylbutyroyl-carnitine 1.3 0.4 0.6 0.9 0.8 1.9 0.5 0.9 1.5 1.4 1.3 3.0 1.0 2.5 1.1 2.4 0.7 0.7 0.8 1.0 1.3 0.9 0.7 0.6 1.1 0.8 0.9 0.3 0.4 1.7 0.9 0.8 1.0
Glycochenodeoxycholic acid4.4 0.1 1.0 0.2 0.2 2.6 0.6 0.1 1.0 0.9 0.0
0.0 0.0 11.0 0.0
11.1 6.7 7.4 0.3 0.0 0.6 2.8 1.3 0.0 1.2 1.3 0.1 0.0 0.0 0.6 0.1 0.7 0.2
Glycocholic acid 1.7 0.0 1.0 0.2 0.2 5.5 0.1 0.0 0.6 0.6 0.00.0 0.0 16.0 0.0
9.6 4.8 6.2 0.1 0.4 0.4 1.9 2.2 0.1 0.5 1.8 0.2 0.5 0.0 0.2 0.2 1.2 0.9
Bile acids
Patients with unexplained encephalopathy
Carbohydrates
Hydroxy acids
Aminoacid and derivatives
Acylcarnitines
Biochemical/chemical DB
annotation
Chemical/metabolic profile DB
DRF/JOLIOT/DMTS/SPI
Conclusion : Analyses beyond regulatoryconcerns for better food safety
❖ High Resolution Mass Spectrometry can be successfully appliedto different food matrices for large screening of xenobiotics.
❖ Unexpected pollutant determination or quality assessment canbe simultaneously performed on the same food samples bycomparison with reference spectral fingerprint databases
❖ Implementation of global approaches for highlightingunexpected contaminants in food matrices will require advancedstatistical tools and the development of spectral and chemicalprofile databases.
DRF/JOLIOT/DMTS/SPI
Acknowledgement
CEA/SPI/LEMMBenoit ColschFrançois FenailleFlorence CastelliMarie-Françoise OlivierLydie OliveiraSandrine Aros-CaltPierre Barbier Saint-HilaireMikail BerdiUlli Hohenester
And thank you for your attention!!!
UPMCJean-Claude TabetAnna Warnet
CEA/LISTEtienne ThévenotPierrick RogerNatacha LenuzzaAlexis Delabrière
PROFILOMICCéline DucruixAlexandre SeyerJérôme CottonStéphanie OurselFanny LerouxMarion PoirelSimon BroudinBruno Corman
CEA/SPISandrine LebloisLaurie MénezEmmanuel FavryFlorence Vizet
