Download Recherche et identification de candidats biomarqueurs

nc
ol
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iq
ue
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O
DSV/iBiTec-S/SPI/LEMM
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C
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Recherche et identification de candidats
biomarqueurs par analyse métabolomique
ac
ol
Christophe Junot
G
ro
up
e
de
Ph
a
rm
CEA/Laboratoire d’Etude du Métabolisme des Médicaments
CEA-Saclay (iBiTec-S)
[email protected]
nc
ol
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iq
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DSV/iBiTec-S/SPI/LEMM
O
Laboratoire d’Etude du Métabolisme des
Médicaments (LEMM)
ue
MASS SPECTROMETRY FOR BIOLOGICAL MEDIA
Metabolomics
Quantification of small
molecules (A. Pruvost)
C
lin
iq
(C. Junot, B. Colsch, F. Fenaille,
F. Castelli, A. Damont)
Lipidomics
og
ie
(B. Colsch)
Glycomics
ol
12 LC/MS instruments,
1 MALDI-FOF/TOF
ac
(F. Fenaille)
Quantification of
proteins (F. Bécher)
Structural analysis of
proteins (F. Fenaille)
ANTIBODY ENGINEERING
(A. Mabondzo)
(D. Boquet)
Ph
a
rm
CELLULAR PHARMACOLOGY
de
Neurovascular pharmacology
Toxicity of nanomaterials
G
ro
up
e
30-35 people
GLP (ANSM, since 1997)
ISO 9001 (LRQA, since December 2014)
Pharmacology, Diagnosis
nc
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og
iq
ue
DSV/iBiTec-S/SPI/LEMM
O
MetaboHUB:
Infrastructure nationale de métabolomique
ue
Pharmacology &
Clinical diagnostic
Nutrition, Health
& Environment
Marc Ferrara
G
ro
up
e
de
Ph
a
rm
ac
ol
og
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C
lin
iq
Christophe Junot
Plant Biology &
Biotechnology
Annick Moing
Microbiology,
Biotechnology &
Toxicology
JC Portais
nc
ol
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iq
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DSV/iBiTec-S/SPI/LEMM
ue
CEA/DM2I/LADIS, CEA-Saclay,
Dr. Etienne Thévenot
Bioinformatics and Biostatistics
C
lin
iq
LCSOB (Université Paris 6)
Pr. Richard Cole, Pr. Jean-Claude
Tabet)
Mass spectrometry,
Interpretation of mass spectra
ac
ol
og
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LEMM, DSV, CEA-Saclay,
Dr. Christophe Junot
Analytical chemistry,
metabolomics
O
MetaboHUB-Paris:
Mass Spectrometry based Metabolomics
rm
Activities
Ph
a
1. Development and validation of MS methods for metabolomics and targeted
metabolite profiling: biomarkers (medicine, toxicology), microbiology
de
2. Metabolite identification (MS/MS and MSn experiments)
up
e
3. Bioinformatics and biostatistics
G
ro
4. Metabolite database building
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
Analyse métabolomique par spectrométrie de masse
Developpement et validation de
méthodes de profilage métabolique
dans des milieux biologiques
ue
90
80
RelativeAbundance
70
60
50
40
30
20
10
0
0
20
40
60
80
Tim e (m in)
G
ro
Darghouth D. et al. Blood, 2011
30 novembre
2015
Darghouth
D. et al.
Hematologica, 2011
120
140
Coll. Dr. F. Sedel
G.H. Pitié-Salpétrière
1.1
0.8
0 .7
1.6
2.2
4 .4
3 .3
2 .5
8 .1
Thre onic 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
nucleos ide derivative
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
Succinylade nos ine
0 .8
0.6
0 .7
0.5 0.8
1.7
1.3
1.2
1.0
1.1
1.4
8.0
3 .9
6 .2
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
0 .6
0 .5 0 .9
0.5 0.9
1.5
1.2
1.1
0.8
0 .8
Proline Be taine
Glutam ic acid
Aminoacid and derivatives
Aminoadipic acid
N-acetyl-L-glutam ic acid
N-Ace tyl-D-allo-isole ucine
Pyrim idine derivate d
Acylcarnitines
0 .4
0 .4
0 .8
0.6
0 .8
0 .6
0.9
1.4
0 .4
1.1
1.2
0.8
0 .8
0.5
0 .5
0 .7
0.5
1.0
1.4
1.0
2.8
1.2
0.8
0 .8
0 .6
0 .5
1.0
0.7
1.0
1.5
0 .5
1.4
1.2
1.0
1.1
0 .4
0.9
9 .1
7.7
6 .3
5.7
1.5
0.8
0 .6
0 .5 0 .6
0.7
1.3
0.5
0 .7
1.2
0 .5 0 .6
0 .8
1.2
0 .7 0 .6
0 .7
0 .7
1.0
0 .5 0 .4
1.5
1.7
0 .6
0 .6
1.3
0 .8
0 .7 0 .6
1.0
0 .5 0 .9
1.1
1.2
1.4
0 .6
1.1
3 .1
0 .1
1.1
0 .3
0.0
0 .6
1.2
0 .7 0 .0
1.0
0 .7 0 .8
1.2
0.8
0 .9
0.7
1.4
0.9
3 .3
1.1
0 .7
1.0
1.7
0.8
3 .2
5.3
1.1
0 .4
9 .3
2.3
4 .1 2 .3
1.5
0.9
0 .7
1.3
0.6
1.0
1.1
48 .6
0 .6
11.9
2.1
2.8
1.3
2 .3
1.2
1.7
0 .6
0.6
0 .9
0 .1 4 .1 0 .8
0 .7 0 .8
0 .7
0.6
1.0
0 .6
2.2
1.2
0 .5
1.0
2.1
0.8
3.6
0.7
0 .7
1.0
0.6
0 .7 0 .6
1.3
2.7
0 .7 0 .8
0.9
0.4
0 .9
3 .0
2 .2
1.2
0 .5 0 .8
1.0
5.0
5.0
4 .1 2 .0
0 .5
0 .7
1.2
7.8 4.4
8.1 4.0
0 .7
1166_117
1.3
1161_90
0 .6
1157_79
1.8
1148_77
0.8
1142_125
0 .4
1140_68
1.1
1138_89
1.1
0.6
1136_39
2 .2
0 .9
1135_131
8.3
Mannitol or isom ers
5.9
1134_43
2 .0
1124_67
0 .9
1114_50
1.0
1113_28
0 .7 2 .3
1.7
1087_27
2 .4
0.8
1.1
1086_114
0 .9
1.0
0 .3
1.4
1074_80
1.0
10 .2
3 .9
1.1
1199_121
1.2
0 .3
0.6
0.5 0.6
1073_29
1.0
1.5
0 .4
LM8
0 .3
0 .5
0 .7 0 .6
XC96
48
1.0
0.4
1.4
469
1067_74
0 .5
0 .6
0.5
1058_107
1061_32
Hydroxy acids
1057.B_133
0 .9
Pe ntose
1052_46
N-acetylneuram inic acid
De oxyribos e
1050_71
1047_127
Carbohydrates
1048_95
1043_31
sugar acid
1057.A_88
Patients with unexplained encephalopathy
1018_55
Ph
a
up
e
de
Coll. Dr. P.-H. Roméo (INSERM)
Pr. Galactéros (AP-HP),
Dr. Y. Colin (INTS)
100
Stratification de patients présentant des
encéphalopathies inexpliquées
rm
ac
Maladies génétiques du globule rouge
(drépanocytose, stomatocytose)
ol
og
Roux A. et al., Anal. Chem., 2012
Boudah S. et al., soumis
100
iq
Attribution
[(M+H)-(NH3)-(C4H6)-(NH3)]+
[(M+H)-(NH3)-(C3H7N)]+
[(M+H)-(NH3)-(C4H6)]+
[(M+H)-2(NH3)]+
[(M+H)-2(NH3)]+ (13C)
[(M+H)-(NH3)]+
[(M+H)-(NH3)]+ (13C)
[M+H]+
[M+H]+ (13C)
[M+H]+ (13C2)
C
lin
Composition
C3 H8 N
C4 H10 N
C3 H11 N2
C7 H14 N
C6 [13]C H14 N
C7 H17 N2
C6 [13]C H17 N2
C7 H20 N3
C6 [13]C H20 N3
C5 [13]C2 H20 N3
ie
SPERMIDINE
SPECTRUM - MS
N83_spermidine_fia_pos.RAW
FTMS + p ESI Full ms [50.00-1000.00]
Scan #: 28-36
RT: 0.55-0.70
AV: 3
m/z
Intensity
Relative
Theo. Mass Delta (ppm) RDB equiv.
58.06492
1973821.3
3.28
58.06513
-3.58
0.5
72.08064
5000802.5
8.31
72.08078
-1.94
0.5
75.09137
630972
1.05
75.09167
-4.03
-0.5
112.11171
3586580.3
5.96
112.11208
-3.22
1.5
113.11506
164880.4
0.27
113.11543
-3.271
129.13825
4447115
7.39
129.13863
-2.91
0.5
130.14163
185404.8
0.31
130.14198
-2.689
146.1648
60633172
100
146.16517
-2.53
-0.5
147.16799
4654796.5
7.68
147.16853
-3.669
148.17134
68268.2
0.11
148.17188
-3.644
O
Bases de données spectrales
0 .5
0 .6
1.1
0.0
0 .6
1.3
2 .1
0.1 0.9
1.9
4 .3
0.6
0 .2
0 .9
0 .1 2 .3
0 .8
0 .7 3 .2
0 .3
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
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
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
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
1.1
3 .4
0 .7
2.3
2 .8
2.6
1.1
3 .2
1.8
1.2
2 .0
Dihydroorotic acid
0 .9
0 .7 0 .8
2 .2
0 .4
1.3
0 .9
0.8
1.0
2.1
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
1.4
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
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
1.6
1.4
1.5
2 .4
0 .6
0.8
0 .8
0.6
0 .6
Me thylbutyroyl-carnitine
0 .5
0 .5
0 .5
0.7 0.8
0 .6
0.6
1.3
0.4
0 .6
0 .9
0.8
1.9
0 .5
0.9
1.5
1.4
1.3
4 .4
0 .1
1.0
0 .2
0.2
2 .6
0 .6
0 .1
1.0
0.9
1.6
0 .0
1.7
0.0
1.0
0 .2
0.2
5.5
0 .1
0.0
0 .6
0.6
0 .0
Glycoche nodeoxycholic acid
Bile acids
Glycocholic acid
1.6
1.0
3 .0
1.0
0 .0
0.0
0 .0
0.0
5.5
3 .3
2 .5
11.0
16.0
2 .7
1.3 2.2
1.3
1.1 2.4
1.2
1.1
0 .9
1.2
1.0
0.9
0 .3
1.6
0 .7 0 .9
1.1
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
11.1 6 .7
0 .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
9.6
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 .0
0 .0
4 .8
0.9
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
MS à haute résolution: détecter plus de
métabolites et les identifier plus facilement
0.9885
R=1400
ue
1
O
Résolution en masse
0.8
iq
0.6
0.4
0
999.00
m/z
FWHM: R = (m /z)/( ∆ m/z)
1002.0
1.0000
R=3000
ie
0.8
1001.0
∆m/z
og
0.6
0.4
0.2
0
999.00
m/z
1001.0
1002.0
ac
1000.0
ol
1
1000.0
C
lin
0.2
Ph
a
rm
Précision en masse
de
C10H15O4 (0.1 ppm)
G
ro
up
e
Databases
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
MS à haute résolution: détecter plus de
métabolites et les identifier plus facilement
Mais attention aux isomères!!
0.9885
R=1400
ue
1
O
Résolution en masse
0.8
iq
0.6
0.4
0
999.00
m/z
FWHM: R = (m /z)/( ∆ m/z)
1002.0
1.0000
R=3000
ie
0.8
1001.0
∆m/z
og
0.6
0.4
0.2
0
999.00
m/z
1001.0
1002.0
ac
1000.0
ol
1
1000.0
Urines humaines
C
lin
0.2
Ph
a
rm
Précision en masse
de
C10H15O4 (0.1 ppm)
G
ro
up
e
Databases
38 métabolites détectés en conditions C18
correspondent à 83 métabolites en
conditions PFPP
(Roux et al., Anal. Chem., 2012)
nc
ol
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iq
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DSV/iBiTec-S/SPI/LEMM
RP-C8
iq
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O
To discriminate between
isomer species
C
lin
Isoleucine
PFPP
G
ro
up
e
de
ac
Ph
a
rm
Norleucine
ol
og
ie
β Leucine
ZICpHILIC
nc
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iq
ue
DSV/iBiTec-S/SPI/LEMM
Valine ?
Betaine ?
Both ?
ue
O
Alkyl RP vs PFPP
Multiplatform strategy: toward a comprehensive
assessment of metabolomic profiles
iq
**
***
e
de
Ph
a
rm
*
ac
ol
og
ie
C
lin
Extracted Ion Chromatogram
of m/z= 118.086 obtained in
RP-LC
G
ro
up
Proteinogenic
amino acids
Detoxification
reaction
:
liver, kidney
Betaine
Valine
Extracted Ion Chromatogram
of m/z= 118.086 obtained in
PFPP-LC
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
O
Annotation of peak lists is required
to help for metabolite identification
iq
C
lin
RT: 0,00 - 150,02
100
90
RT: 0,00 - 150,02
80
100
90
60
80
50
20
40
80
Time (min)
60
100
80
Time (min)
120
100
140
120
140
30
20
10
0
20
40
60
80
Time (min)
100
120
140
ie
Ph
a
0
og
60
ol
40
ac
20
10
50
0
40
0
rm
30
70
20
0
60
de
Few thousands of
variables…
…Few hundreds of
metabolites ??
Chemical and biochemical databases: KEGG (www.genome.jp/kegg),
Metlin (www.metlin.scripps.edu), HMDB (www.hmdb.ca)
e
0
up
10
50
90
40
80
spectral databases
ro
20
G
30
60 0,00 - 150,02
RT:
100
Variables (Rt-mass)
70
Relative Abundance
40
Relative Abundance
Relative Abundance
70
?
?
?
?
?
?
?
?
?
ue
Samples
nc
ol
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iq
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DSV/iBiTec-S/SPI/LEMM
The relevance of a spectral database
C
lin
iq
ue
O
One molecule = several ions
ie
Automated detection of ions, list of annotated features
Compound Attribution
5.28 C11H10NO2
Tryptophan
[(M+H)-(NH3)]+
Tryptophan
Tryptophan
[(M+H)-(NH3)]+ (13C)
[(M+H)-(NH3)]+ (13C2)
RT
Formula
ol
M/Z
og
Pic of interest
ac
188.0709
rm
189.0757
190.0787
5.28 C11H13N2O2
206.1010
5.28 C10[13C]H13N2O2 Tryptophan
[(M+H)]+ (13C)
207.1051
5.28 C9[13C]2H13N2O2 Tryptophan
[(M+H)]+ (13C2)
409.1902
5.28 C22H25N4O4
[(2M+H)]+
410.1938
5.28 C21[13C]H25N4O4 Tryptophan
Ph
a
205.0975
Tryptophan
Tryptophan
[(M+H)]+
G
ro
up
e
de
5.28 C10[13C]H10NO2
5.28 C9[13C]2H10NO2
[(2M+H)]+ (13C)
Annotations
(HMDB, KEGG, METLIN)
Deethylatrazine
3-amino-2-naphthoic acid
Indoleacrylic acid
Ethyl Oxalacetate
Tryptophan
ethotoin
Vasicinol
Idazoxan
Nirvanol
N-Acetyl-D-fucosamine
N-Acetyl-D-quinovosamine
Gly Trp Phe (and isomers)
Lys Met Met (and isomers)
Tyr Leu Asp (and isomers)
Ile Tyr Asp (and isomers)
Val Tyr Glu (and isomers)
(Roux et al., PhD work, 2008-2011, Roux et al., Anal. Chem. 2012)
1 6.6 9
100
x5
90
70
10 0
U
Relative Abundance
Metabolite identification
80
60
50
40
20
10
1 5.6 7
1 6 .5 2
Formal Identification
70
C
lin
60
20
40
30
ie
e
de
Ph
a
rm
ac
ol
og
0
up
1 16. 03 42 7
72 .0 44 33
2 01.85 44 6
184 .0 97 23
80
60
STD
142.0 865 8
156 .1 02 29
40
9 0.05 49 1
20
1 16. 03 44 2
72 .0 44 17
10
ro
9 0.05 49 4
50
20
G
142.0 865 1
156 .1 01 93
40
0
10 0
iq
80
2 2 .57
23 .40
184 .0 97 19
60
ue
5 .4 0
90
Relative Abundance
80
O
30
0
100
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
3 .1 9
6 .6 5
18 .3 5
1 6 .5 3
2 01 .86 20 6
3 1.1 2
0
50
10 0
1 50
20 0
m /z
???
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
Annotation of human biological matrices
Plasma
ol
og
ie
C
lin
iq
ue
O
Urine
Red blood cells
Ph
a
rm
ac
236 identified metabolites
74 putatively identified metabolites
27% of isomers
G
ro
up
e
de
205 annotated or identified metabolites
(RP, PFPP, HILIC)
Cerebrospinal fluid
146 annotated or identified metabolites
(RP, HILIC)
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
C
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iq
ue
O
Cerebrospinal fluid metabolomics highlights alterations
of multiple metabolic pathways in patients with hepatic
encephalopathy.
ol
og
ie
Nicolas Weiss1, Benoit Colsch2, Foucault Isnard1,2, Suleiman Attala3, Frédéric Sedel3,
Dominique Thabut1, Christophe Junot2
1Assistance
rm
ac
Publique - Hôpitaux de Paris, Brain Liver Pitié-Salpêtrière (B-LIPS) study
group, Groupement Hospitalier Pitié-Salpêtrière-Charles Foix, Paris, France.
2CEA,
de
Ph
a
iBiTec-S, Service de Pharmacologie et d’Immunoanalyse, Laboratoire d’Etude du
Métabolisme des Médicaments, MetaboHUB-Paris, 91191 Gif-sur-Yvette cedex,
France.
3
G
ro
up
e
Medday Pharmaceuticals, ICM-Brain and Spine Institute-iPEPS, Groupe Hospitalier
Pitie Salpetriere-Charles Foix, 83 Boulevard de l'Hopital, 75013, Paris, France
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
ue
HE is a neurological complication of acute or chronic liver
disease.
O
Hepatic encephalopathy (HE)
C
lin
iq
The proportion of cirrhotic patients developing HE is about
40 to 60%.
og
ie
60 to 80 % of cirrhotic patients exhibit cognitive disorders
potentially related to minimal HE.
de
Ph
a
rm
ac
ol
However, the pathophysiological mechanism of HE remains
poorly understood:
- Hyperammonemia
- Inflammation
- Altered permeability of blood-brain barrier
G
ro
up
e
The aim of the study: to highlight altered metabolic
pathways in HE patients by using CSF metabolomics.
patient stratification
pharmacological targets
(Morgan and Stubbs, CML Gastroenterology)
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
O
The CSF metabolome analyzed by
LC/MS
ue
~500 annotated MS features
C
lin
ie
120 metabolites identified
og
100 µl CSF + 300 µl MeOH
Centrifugation
Evaporation to dryness
Resuspension in 100 µl H2O, HCOOH 0.1%
5 µl of an internal standard mixture are added
(10 labeled metabolites and xenobiotics)
iq
Sample preparation:
UHPLC/MS:
Others; 3
Ph
a
10 µl of sample are injected
rm
ac
ol
1.Hypersil C8 2.1×
×150 mm, 1.9 µm, run time: 30’
2.ZIC-p-HILIC 2.1×
×150 mm, 5 µm, run time: 40’
Nucleosides and
conjugates; 12
G
ro
up
e
de
Orbitrap-Exactive and Q-Exactive Plus:
Positive and negative ESI
Scanning from m/z 75 to m/z 1000
Mass resolution: 100 000 FWHM
Carbohydrates
and conjugates; 6
Steroids and bile
acids; 4
Amino acids and
Amino Acid
conjugates; 36
Ketones; 3
Amines; 4
Alcohols ; 3
Organic acids; 28
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
ue
O
Patients
C
lin
iq
CSF samples collected from patients of Pitié-Salpétrière Hospital:
og
11 HE patients with cirrhosis
ie
27 control patients without any proven neurological disease
ol
3 HE patient without cirrhosis
ac
• 1 patient with status epilepticus
rm
• 1 liver transplanted patient that developed a minimal HE
G
ro
up
e
de
Ph
a
• 1 patient with hepatoportal sclerosis
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
LC/MS based metabolomics for CSF
analysis
90
10
50
40
30
0
20
0
20
10
80
10 0
70
90
60
80
50
40
30
0
20
0
20
100
90
60
80
50
70
100
40
30
20
0
0
60
60
80
Time (min)
100
12 0
140
40
20
20
10
30
20
80
70
100
70
60
50
4040
40
60
80
Ti me (m in)
100
1 20
20
20
40
140
10
0
20
40
60
10
80
Time (min)
100
60
80
Ti me (m in)
12 0
80
50
50
20
100
60
120
80
Tim e (m in)
40
10
140
100
120
30
0
0
0
90
60
70
80
40
Tim e (m in)
60
30
60
0
0
40
0
90
80
30
50
10
100
90
60
50
30
10
70
40
80
70
RelativeAbundance
RelativeAbundance
20
90
60
RelativeAbundance
30
RelativeAbundance
1 00
70
40
RelativeAbundance
RelativeAbundance
80
50
RelativeAbundance
1. Analytical chemistry
90
60
RelativeAbundance
10 0
70
RelativeAbundance
80
20
140
20
40
0
20
60
80
40
Tim e (m in)
60
10
0
0
0
20
40
100
1 20
140
0
CONTROL
20
40
60
100
ue
100
90
O
UHPLC / Exactive
1 00
140
80
Tim e (m in)
80
Tim e (m in)
120
100
100
140
120
120
140
140
iq
DISEASE
Filtration according to:
- Correlation between dilution
factor of QC and area. r²>0.7
- Mean QC/ mean BL>3
- CV (QC) < 30%
20
t[2]
3. Statistical analyses
og
CONTROL
3
rm
10
ac
ol
2. Data pre-treatment
XCMS R package
ie
Variables
(RT, m/z)
C
lin
Samples
0
Multivariate statistics
13
4
27
56
16 15
9
11
18 10
(PCA, PLSDA)
14
Ph
a
-10
-20
de
DISEASE
12
-20
-10
0
10
20
t[1]
x100
x20
x20
247.0721
G
ro
up
e
4. Identification
Relative Abundance
100
204.0667
80
176.0720
218.0333
60
161.0612
20
0
198.8961
133.0664
40
60.9031 77.0776 85.4235
60
80
100.6693 114.1821 126.9277 137.0212 149.5655
100
120
140
179.1933
242.8735
191.3928
165.7842
160
m/z
220.7238
180
229.1446
214.9476
200
220
257.4338
240
260
Univariate statistics
(Mann-Whitney test)
Feature annotation using
public databases,
informatic tools (CAMERA
R package) and a spectral
database (ESI-MS and HCD)
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
HE and control patients have different metabotypes
CTL patients
HE patients
O
40
BM2
ue
HE with status epilepticus
C
lin
iq
30
20
mEH after transplantation
ie
10
SP3
MP01
GB6 PV2DA8
CC5
LT0
DK4
SV9
HK3
BL2
MJ8
RM70
HJ9
VP2
MR61
BF8
MM7
PJ4
AG6
BM1
AZ0
MP11
JM8
AN4
CM0
PD2
CE3
YB2
BS61
LK4
JB9
PO7
DQ0
BG9
NQ1
BC8 CJ3
og
t[2]
Hepatoportal sclerosis
ac
ol
0
rm
-10
-30
-25
-20
e
de
-35
Ph
a
-20
up
R2X[1] = 0.19505
-15
-10
ZG8
GM5MHG9
-5
0
5
10
15
20
25
30
35
t[1]
R2X[2] = 0.0923833
Ellipse: Hotelling T2 (0.95)
ro
PCA score plot of a XCMS filtered peaktable (RP conditions and ESI positive mode)
G
40
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
C06948
Samples
BM2
BG9
MGH9, NQ1, ZG8, AZ0
BC8, MHG9, MP01, NQ1, ZG8
896
581
1964
C07486
D00660
D08380
BC8, MHG9, MP01, NQ1, ZG8
BG9, NQ1, ZG8
BG9, NQ1, ZG8
N-Desmethyldiazepam HMDB60538
Tazobactam
HMDB15544
Piperacillin
HMDB14464
KEGG
C07841
C07203
Could explain PCA outliers
Drug induced HE???
ue
Metlin
66750
573
2708
3521
iq
HMDB
HMDB15333
HMDB15052
HMDB14342
HMDB14967
C
lin
Putative annotation
Levetiracetam
Metronidazole
Fluconazole
diazepam
O
High intensity features related to drugs and metabolites
have been detected in 7 out of the 14 HE patients
[M+HCOOH-H]-
T: FTMS {1;1} - p ESI Full ms [95.00-1000.00]
100
og
CJ3
1.08
80
90
2.05
2.87 3.42 3.98
4.85
rm
5.65
MHG9
Ph
a
60
40
20
0.84 1.06
0.76
1.99 2.24 2.89 3.45
0
100
1.93
G
1
2
2.89
3
[M-H]-
70
305.09677
60
Fluconazole
50
40
[M-C6H4F2H]-
20
191.06853
10
3.44
1.57
317.06319
6.24
4.17
4
341.07347
80
30
1.07
ro
0
0.85
up
20
6.37 7.21 7.83
e
NQ1
60
40
6.23
4.17 4.58
5.65
80
[M+Cl]-
m/z 191.0685
6.51 6.95 7.19
de
Relative Abundance
0
100
6.02
5.40
ac
1.63
20
6.22
R e la tiv e A b u n d a n c e
1.46 1.58
40
ol
60
80
351.10211
ie
RT: 0.16 - 11.11
0.86
100
5.35
5
6
Time (min)
6.96
7
7.85
8
197.80774
0
180
200
225.06145 238.00086 265.07056 278.10371 298.76626
220
240
260
280
m/z
300
329.03063
320
340
360
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
The metabolic signature of HE in CSF samples
72 out of 120 CSF metabolites have altered concentrations in HE samples
2.4
2.5
2.4
2.1
6.1
7.7
2.2
7.6
1.9
1.3
9.0
og
ol
0.6
4.0E-07
phenylalanine
1.0
3.2
0.8
4.0E-07
Tyrosine
1.0
5.5
0.6
2.5E-04
1.0
6.4
0.8
4.0E-07
kynurenine
1.0
6.3
0.9
4.7E-07
Hydroxytryptophan
1.0
4.2
1.1
1.1E-06
Tryptophan
1.0
3.8
0.6
5.4E-06
Aminomuconic acid
1.0
1.6
1.0
5.0E-05
5-hydroxyindoleacetic acid
1.0
2.7
0.8
8.2E-03
Taurocholic acid
ILD
Glycocholic acid
1.0
3E+5
259.0
1828
0.6
Glycoursodeoxycholic /
Glycodeoxycholic acid
1.0
100.5
1.5
Ph
a
de
e
up
G
ro
Bile Acids
9.9E-07
2.5E-04
1.2E-03
8.2E-03
2.5E-04
4.0E-07
6.3E-07
2.5E-04
1.8E-02
3.1E-02
4.0E-07
9.4
Indolelactic acid
Tryptophan metabolism
1.1
0.9
0.8
0.3
0.7
0.2
0.7
0.6
1.1
0.8
0.4
1.0
ac
Glutamylphenylalanine
rm
Phenylalanine metabolism
p-values
O
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
MP11
ue
HE
ie
Pyroglutamic acid
Glutamine
Glutamate/Glutamine metabolism Glutamic acid
Phenylacetyl-L-glutamine
glutamyl-glutamine
Formylmethionine
Methionine sulfoxide
Methionine metabolism
Methionine
S-Adenosylmethioninamine
Methylthioadenosine
p-hydroxyphenyllactic acid
CTL
C
lin
Metabolite ID
iq
Biochemical pathway or chemical
class
e
e
ND
4.0E-07
4.0E-07
MP11: patient with
hepatoportal
sclerosis
fold change > 3 σ
2 σ < fold change < 3 σ
σ < fold change < 2 σ
- 2 σ < fold change < - 1 σ
fold change < - 2 σ
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
HE
MP11
p-values
1.0
iq
1.8
0.3
2.7E-03
1.0
0.7
0.9
4.4E-02
1.0
4.3
0.9
1.8E-05
1.0
2.4
0.9
5.0E-04
Propionylcarnitine
1.0
2.6
0.7
7.7E-04
3-Hydroxyisovalerylcarnitine
1.0
5.8
0.8
4.5E-03
2-Hydroxyvaleric acid
1.0
4.1
0.6
8.1E-05
3-Hydroxy-2-methylbutanoic acid 1.0
2.6
0.7
3.4E-02
carnitine
1.9
0.7
4.9E-03
Metabolite ID
Pyruvic acid
Creatine
ie
Octanoylcarnitine
C
lin
Carbohydrate/Amino-acids
metabolism
Energy metabolism
CTL
og
Biochemical pathway or chemical
class
Acetyl-L-carnitine
de
Ph
a
MP11: patient with
hepatoportal sclerosis
rm
ac
ol
Fatty acid metabolism
ue
O
Alteration of energy metabolism in HE patients
1.0
fold change > 3 σ
2 σ < fold change < 3 σ
σ < fold change < 2 σ
- 2 σ < fold change < - 1 σ
fold change < - 2 σ
up
e
Krebs cycle activity reduced in animal model (Shorey, Gastroenterology, 1967)
Reduced brain glucose utilization in rat with portocaval shunting
G
ro
(Mans, J. Neurochem., 1994)
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
Metabolite ID
Acetylated compounds
Acetyl-methionine
Acetyl-tyrosine
Acetyl-glucosamine
Acetyl-alanine
Acetyl-valine /
acetylnorvaline
N-Acetyl-L-phenylalanine
N-acetyl-isoleucine / N-acetylLeucine
N-Acetyl-L-histidine
O-Acetyl-L-homoserine
Acetyl-serine
4-Acetamidobutanoic acid
N4-Acetylcytidine
og
ol
ac
20
rm
HE/CTL
30
Ph
a
e
up
ro
G
ce
ty
l
A
de
ce
ty
- g A c l- M
lu e t e t
co yl
s -ty
A c am r
e in
A c t yl e
N- e -a
A t y la
ce l v
A c t y l- a l
P
e h
4A t y l- e
A
c
ce
A ety Leu
ta
c
m A e t l- H
id c e y l i s
ob ty -H
u l- S S e
A t an er r
c e o i in
ty c a e
lc c
y t id
id
in
e
0
A
ie
C
lin
iq
ue
Biochemical pathway or
chemical class
40
10
O
Concentrations of acetylated compounds
are increased in CSF samples of HE patients
CTL
HE
MP11
p-values
1.0
1.0
1.0
1.0
11.5
7.3
2.3
2.4
0.2
1.0
1.1
0.7
4.0E-07
9.8E-07
2.0E-06
2.7E-06
1.0
2.1
0.8
8.1E-06
1.0
4.9
1.6
1.0E-05
1.0
2.3
0.6
1.2E-05
1.0
1.0
1.0
1.0
1.0
3.5
1.9
1.5
3.0
7.4
1.1
1.0
0.8
0.8
0.5
6.2E-04
1.7E-03
1.8E-02
1.8E-05
5.0E-05
MP11: patient with
hepatoportal sclerosis
fold change > 3 σ
2 σ < fold change < 3 σ
σ < fold change < 2 σ
- 2 σ < fold change < - 1 σ
fold change < - 2 σ
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
Altered energy metabolism pathways in CSF
of EH patients
O
Cytosol
Mitochondrion
Gln
Glu
ue
+
Not detected
iq
NH4
Saccharopine
C
lin
Lys
Succinyl CoA
α KG
Increased
Succinic acid
ie
Isocitric acid
FFA
ol
Citric acid
AcylCoA
rm
Acylcarnitines
ac
AcetylCoA
β-Oxidation
Acylcarnitines
α KG
Malic acid
de
Asp
Asp
Malic acid
α KG
G
ro
up
e
Lys
OAA
PC
Pyruvic
acid
Ala
Trimethyllysine
PDH
Fumaric acid
Glu
Ph
a
Carnitine
decreased
Krebs cycle
og
Acetylated
amino acids
Unchanged
Gln
Glu
NH4+
OAA
Krebs cycle activity reduced in
animal model (Shorey,
Gastroenterology, 1967)
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
30 of the significant metabolites out of 72 correlate
with clinical scores
60000000
Ph
a
rm
ac
Pyrimidine metabolism
Peptides
Alcohols and polyols
Steroid
Vitamin B6 metabolism
Severity of cirrhosis:
MELD score: Model for End-Stage Liver Disease (1 to 40)
Child Pugh score (1 to 15)
West-Haven criteria (1 to 4). WH 2-4: overt HE
e
de
Severity of HE:
G
ro
up
Stratification of HE patient?
Severity prediction?
30
p=0.004
8000000.0
6000000.0
4000000.0
2000000.0
0.0
24
-0.193
-0.263
-0.175
0.77
-0.338
-0.143
-0.315
0.046
0.161
20
MELD
W
H
0.722
0.535
0.802
0.327
0.691
0.553
-0.803
0.622
0.512
10
1
0.653
0.662
0.612
0.434
0.626
0.644
-0.72
0.233
0.805
0
0-
-0.278
[Trp], AU
0.47
ol
Fatty acid metabolism
0.703
20000000
W
H
Bile acids
40000000
0
Carnitine (AU)
Tryptophan metabolism
-0.152
-0.029
-0.187
0.037
0.037
0.246
-0.137
-0.117
iq
Phenylalanine metabolism
0.682
0.608
0.364
0.396
0.618
0.507
0.516
0.521
C
lin
Methionine metabolism
West-Haven
MELD
0.598
0.452
0.689
0.484
0.708
0.671
0.671
0.612
ie
Acetyl-methionine
4-Acetamidobutanoic acid
Methionine
phenylalanine
p-hydroxyphenyllactic acid
Tryptophan
Indolelactic acid
Glycocholic acid
Glycoursodeoxycholic /
Glycodeoxycholic acid
Taurocholic acid
Octanoylcarnitine
2-Hydroxyvaleric acid
carnitine
Dihydrothymine
Leu-Ala
Quinic acid
Cortisol
Pyridoxic acid
Acetylated compounds
Child Pugh
ue
b
Metabolite ID
og
Biochemical pathway or chemical class
O
rs
40
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
O
Conclusion
C
lin
iq
ue
Metabotypes of HE patients: alterations of amino-acid, acylcarnitine,
bile acid and energy metabolism pathways.
rm
ac
ol
og
ie
Accumulations of acetylated compounds is reported for the first time.
It could be due to a dysregulation of the Krebs cycle in HE patients:
G
ro
up
e
de
Ph
a
Metabolomics could be used to stratify HE patients
Energy metabolism as a pharmacological target for HE??
O
Perspectives
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
ue
Validation of the HE metabotype:
iq
In patients:
og
ie
C
lin
Confirmatory cohort (CSF? Plasma?)
Specificity of the signature: To include a group of patients with cirrhosis and
without HE (plasma samples)
G
ro
up
e
de
Ph
a
rm
ac
ol
Animal models:
Hyperammonemia, drug induced HE models…
Mechanistic studies
nc
ol
og
iq
ue
DSV/iBiTec-S/SPI/LEMM
Acknowledgement
ue
iq
C
lin
ie
og
Ph
a
rm
ac
ol
CEA/SPI
Christophe Créminon
Sandrine Leblois
Laurie Ménez
Nicolas Caudy
Florence Vizet
CEA/LIST
Etienne Thévenot
Pierrick Roger
Natacha Lenuzza
Alexis Delabrière
PROFILOMIC
Céline Ducruix
Alexandre Seyer
Jérôme Cotton
Stéphanie Oursel
Fanny Leroux
Marion Poirel
Simon Broudin
Bruno Corman
G
ro
up
e
de
UPMC
Jean-Claude Tabet
Anna Warnet
Farid Ichou
Sandra Alves
Estelle Paris
Richard Cole
O
CEA/SPI/LEMM
Benoit Colsch
François Fenaille
Florence Castelli
Marie-Françoise Olivier
Lydie Oliveira
Sandrine Aros-Calt
Pierre Barbier Saint-Hilaire
Mikail Berdi
Ulli Hohenester
And thank you for your attention!!!