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 [email protected]
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Apport de l'analyse métabolomique par spectrométrie de ...DRF/JOLIOT/DMTS/SPI Apport de l'analyse métabolomique par spectrométrie de masse à la détection sans a priori de xénobiotiques
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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
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?
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.