Apport de l’analyse métabolomique à la recherche biomédicale · Laboratoire d’Etude du...

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Christophe Junot

CEA/Laboratoire d’Etude du Métabolisme des Médicaments

CEA-Saclay (iBiTec-S)

christophe.junot@cea.fr

Apport de l’analyse métabolomique

à la recherche biomédicale

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Les approches globales «omiques»

Les gènes

Le génome

génomique

polymorphisme

Les ARN messagers

Le transcriptome

transcritomique

Les métabolites

Le métabolome

métabolomique

Les protéines

Le protéome

protéomique

GENOTYPE PHENOTYPE

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Métabolites et métabolome

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Pathologie

Environnement :

xénobiotiques

(polluants,

médicaments…)

Flore intestinale

Métabolisme «central»

Métabolites primaires Métabolites secondaires Xénobiotiques

Empreinte métabolique

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Déroulement d’une analyse métabolomique

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t[2]O

t[1]P

Data Tri 22-5 NVol Q=1.M13 (OPLS), M1-par

t[Comp. 1]/t[Comp. 2]

Colored according to Obs ID (Primary)

R2X[1] = 0.279651 R2X[2] = 0.313673

Ellipse: Hotelling T2 (0.95)

G1

G3

SIMCA-P 11 - 11/06/2008 10:05:39

TEMOINS MALADES

PREPARATION DES

ECHANTILONS

ACQUISITION DES EMPREINTES TRAITEMENT DES DONNEES

IDENTIFICATION DES BIOMARQUEURS CONFIRMATION ET

QUANTIFICATION

ECH.

VA

RIA

BL

ES

(RT

-MA

SS

ES

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EXTRACTION

DILUTION…

URINE30DIL4_CID20_endogènes #68 RT: 1.22 AV: 1 NL: 4.17E5F: FTMS + p ESI Full ms2 173.09@cid20.00 [50.00-800.00]

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173.09190

127.08652

155.0813680.4945870.06497

169.6742686.06400184.8621861.03990 102.71753 143.04167

-H2O

[M+H]+ -HCOOH

-C2H3ON

BASES DES DONNEES, MS/MS …

DETECTION AUTOMATIQUE

DES SIGNAUX

DETECTION AUTOMATIQUE

DES SIGNAUX

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Metabolites occur at a wide concentration

range

(Adapted from Sumner L.W. et al., 2003)

mM

Concentration

range

µM

nM

pM

Glucose

Citric acid

Amino-acids

Hormones

Neurotransmitters

Prostanoids

NMR

GC/MS

LC/MS

LC/Fluo

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Acquisition des empreintes métaboliques

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RMN GC-EI-MS - Simple, non invasif

- Rapide

- Applicable à des biomatériaux

intacts et aux grandes séries

Mais :

- Sensibilité limitée

- Difficulté d’identification de

composés inconnus

- Sensible

- Reproductible

- Bibliothèques de spectres de

masse

Mais :

- Modification chimique requise

pour les composés non volatils

- Composés non

thermosensibles

LC-API-MS - Accès à la masse moléculaire

(identification)

- Analyse des molécules

thermolabiles

- sensible

Mais:

peu reproductible

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

La spectrométrie de masse:

détecter et identifier des molécules par mesure

de leur masse

Source Analyseur(s) Détecteur Traitement du signal

Production d’ions en phase gazeuse

Séparation des ions selon leur rapport m/z

Conversion d’un courant ionique en courant électrique

080805-03 #7-29 RT: 0.10-0.46 AV: 23 NL: 1.91E7T: FTMS + p ESI Full ms [80.00-800.00]

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288.2531

179.1068

147.1126114.0912

135.0910

159.0676119.0831

234.0970167.0830196.6413

183.0779

265.1116227.1752 244.2270

281.0650

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Ionisation à pression atmosphérique et analyseurs

à haute résolution

Résolution en masse

C10H15O4 (0.1 ppm)

Databases

Précision en masse

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

nM

µM

pM

Metabolome

1-10 10-100 100-500

Targeted

metabolites

Metabolic pathways

Chemical families

Number of analyzed compounds

High

Resolution MS

Triple quadrupole

instruments

(MRM mode)

Relative quantification

Absolute quantification

D:\439010\...\ara 0uM CdCl2 n1.1 12/07/02 15:50:20

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Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Few thousands of

variables…

…Few hundreds of

metabolites ??

Annotation of peak lists is required

to help for metabolite identification

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Samples

Va

ria

ble

s (

Rt-

ma

ss

)

?

?

?

?

?

?

?

?

?

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Chemical and biochemical databases: KEGG (www.genome.jp/kegg),

Metlin (www.metlin.scripps.edu), HMDB (www.hmdb.ca)

spectral databases

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

var mzmed rtmed mziT nom annotation

331 94.1035879 41.2395458 93.0963079

164 75.091789 41.4078375 74.084509

1,3-Propanediamine METLIN 3216 109-76-2@1,3-

Diaminopropane HMDB HMDB00002 109-76-2@1,3-

Diaminopropane KEGG C00986 109-76-2@ [(M+H)-(NH3)-(C4H6)]+ spermidine

121 72.0808764 41.4121375 71.0735964 3-Buten-1-amine KEGG C12244 IND@ [(M+H)-(NH3)-(C3H7N)]+ spermidine

401 102.115188 41.4677549 101.107908

595 129.139029 41.4682607 128.131749 [(M+H)-(NH3)]+ spermidine

404 102.616829 41.5832821 101.609549

152 74.0879546 41.5832821 73.0806746 [(M+2H)]2+ (13C) spermidine

34 58.0649479 41.6414273 57.0576679 Cyclopropylamine KEGG C14150 765-30-0@ [(M+H)-(NH3)-(C4H6)-(NH3)]+ spermidine

327 93.6019358 41.9281549 92.5946558

775 149.03607 42.3813985 148.02879

144 73.586346 42.3914349 72.579066 [(M+2H)]2+ spermidine

494 113.115813 42.7321078 112.108533 [(M+H)-2(NH3)]+ (13C) spermidine

834 159.368708 42.7372617 158.361428

884 164.864699 42.7393498 163.857419

81 65.072954 42.8461541 64.065674

848 161.719771 43.2554375 160.712491

762 147.169448 43.2633375 146.162168 [M+H]+ (13C)spermidine

485 112.112241 43.3123549 111.104961 [(M+H)-2(NH3)]+ spermidine

870 162.444745 43.3125665 161.437465

872 162.672941 43.3140799 161.665661

871 162.582679 43.6324198 161.575399

235 85.0888656 43.6602673 84.0815856 1-Methylpyrrolinium KEGG C06178 IND@

866 162.299377 43.661028 161.292097

858 162.081633 43.6615902 161.074353

371 97.9689677 43.6634508 96.9616877

756 146.165593 43.6718352 145.158313

Spermidine METLIN 254 124-20-9@Spermidine HMDB

HMDB01257 124-20-9@Spermidine KEGG C00315 124-20-

9@ [M+H]+ spermidine

857 162.050661 43.7169657 161.043381

679 133.561403 43.7169657 132.554123

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Formal identification of metabolites often requires several

complementary analytical tools

Mass spectrometry NMR spectroscopy

To discriminate between isomers

C:\Documents and Settings\...\MTA-CID1 12/02/2007 18:17:20 5 ug/ml

eau/MeCM, HCOOH 0.1%, col 15%

MTA-CID1 #1 RT: 0.01 AV: 1 NL: 3.22E7

T: + p Full ms2 298.00@cid-15.00 [50.00-300.00]

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136.0

297.9

163.1145.1

97.1 119.261.1 238.4178.493.875.1 208.2 280.1256.9221.2159.6 191.5

m/z Molecular mass

061017-CIDPB-PT-UPLC #2447-2474 RT: 44.30-44.76 AV: 10 NL: 2.60E7F: FTMS - c ESI d Full ms2 247.07@30.00 [ 55.00-260.00]

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247.0721

204.0667

161.0612

176.0720

218.0333

133.0664

191.3928137.0212100.6693 229.144685.4235 165.784260.9031 77.0776 214.9476126.9277 149.5655114.1821

198.8961

179.1933

257.4338

242.8735

220.7238

x100 x20 x20

MS/MS, MSn

Structural information

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Identification of pantothenic acid in rat urine RT: 0.00 - 60.00 SM: 7G

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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

U

STD

US

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m/z

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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 220.12@cid35.00 [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 220.12@cid35.00 [50.00-235.00]

x5

U

STD

MS²

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Characterization of unknown metabolites using MSn

Proposed by CAS Proposed (MSn, H/D)

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100139.11337

87.04586

155.10819113.06150

201.11358

71.05096101.06151 165.09146

196.98576

139.11337

201.11352157.1237874.02549 111.08214 194.63205

125.26109

139.11240

112.98559

201.1121168.99611 157.1227385.80583 119.15112

NL: 1.43E6

070829-urinepbpd_070829152643#1167-1206 RT: 21.82-21.88 AV: 3 F: FTMS - c ESI d w Full ms2 201.02@cid35.00 [50.00-215.00]

NL: 2.12E6

070829-urinepbpd_070829152643#1487-1534 RT: 26.41-26.50 AV: 3 F: FTMS - c ESI d w Full ms2 201.02@cid35.00 [50.00-215.00]

NL: 5.41E5

070829-mel10standards#697-723 RT: 26.86-27.02 AV: 4 F: FTMS - c ESI d w Full ms2 201.11@cid35.00 [50.00-215.00]

(Werner E. et al., 2008), Collaboration Pr. J.-C. Tabet, UPMC

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Quantitative metabolite profiling for large

scale studies

Normalized metabolic profiles Absolute quantification of metabolites

A/A

ISConcentration

Molarity unit

Samples Biological

matrix

Internal

standards (ISs)Calibrants

Calibration curve

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

GC/MS ESI/MS LC/MS MALDI-TOF

NMR

1960 2010 2000 1990 1980 1970

Imaging MS

In situ analyses

Metabolomics

Metabonomics: The metabolic response

of living systems to physiopathological

stimuli via multivariate statistical

analysis of biological NMR spectra. (Nicholson et al., 1999)

Intermap study (NMR)

4630 participants (Holmes E., Nature, 2008)

«Metabolome Wide Association Studies» (Holmes E., Cell, 2008)

(Gieger C, Plos Genetics, 2008)

Sarcosine as a biomarker of

aggressive prostate cancers (Skreekumar, Nature 2009)

Microbiome interaction

Chronobiology, epigenetics

Biomarker discovery (cohort size<100)

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Glu-Phe

×

× ×

×

×

× ×

×

×

×

×

× ×

×

Glutamate

CSF metabotype of a Dihydropteridine

reductase (DHPR) deficient patient Autosomal recessive genetic disorder.

Hyperphenylalaninemia due to tetrahydrobiopterine

deficiency (malfunctioning Tyr and Trp

hydroxylases)

no change

not detected ×

increased concentrations

Phenylalanine metabolism

Tryptophan

metabolism

Medication

Miscellaneous

(Coll. Dr. F. Sedel, G.H. Pitié-Salpétrière)

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

1018_55

1043_31

1047_127

1048_95

1050_71

1052_46

1057.A

_88

1057.B

_133

1058_107

1061_32

1067_74

469

XC

96

LM

8

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 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 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 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 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 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 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 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 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

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

2.21.8

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 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

2.8 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 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 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 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 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 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 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 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

CAFSA

Kearns-Sayre syndrome

Values are fold changes

related to control value

value >mean+2SD

Mean+SD<value <mean+2SD

(Coll. Dr. F. Sedel, G.H. Pitié-Salpétrière)

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Faciliter l’échange

des données

Identification des métabolites

Informatique et

bioinformatique

Améliorer et

standardiser la détection

des métabolites Traitement des données

Visualisation des données

Formats d’échange des données

Annotation des données

Bases de données spectrales

Le futur?

Le profilage métabolique à haut débit pour

l’épidémiologie et la médecine personnalisée

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Le futur?

Le profilage métabolique pour l’anatomo-pathologie?

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Remerciements CEA/LEMM Jean-François Heilier

Alexandra Lafaye

Céline Ducruix

Erwan Werner

Geoffrey Madalinski

Emmanuel Godat

Jérôme Cotton

Ying Xu

Aurélie Roux

Eric Ezan

Benoit Colsch

Samia Boudah

CEA/iRCM Paul-Henri Roméo

Dhouha Darghouth

Bérengère Koehl

Marie-Françoise Olivier

G.H. Pitié-Salpétrière Frédéric Sedel

Maria del Mar Amador

Fanny Mochel

Foudil Lamari

Laboratoire de Chimie

Structurale Organique et

Biologique (Paris 6, CNRS) Jean-Claude Tabet

Denis Lesage

Sandra Alves

Groupe de Chimie Analytique de Paris

Sud,EA 4041 (Université Paris 11) Pierre Chaminade

CEA/DRT/LIST/DETECS Olivier Gal, Antoine Souloumiac,

Etienne Thévenot, Jean-Pierre Both

CEA/Programme Transversal de

Technologies pour la santé Jacques Grassi, Pierre-Noël Lirsac,

Bruno Corman

CEA/iBiTec Jean Labarre, Franck et Corinne

Chauvat, Michel Toledano

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

(2008)

Polymorphism in the FADS1 (fatty acid delta 5 desaturase) gene

«Genetically determined metabotypes» Quantitative measurement of363

metabolites in 284 human serum samples

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Dendrogram from hierarchical cluster analysis

Samesample, twoexperiments

Thimainetransporter and

glucose transporter deficiencies Leukodystrophies

Same patient, two samples

(2009 and 2011)

Myoclonicataxia

MitochondriopathiesPsychiatricdisorders

Cerebellar ataxia

Same sample, twoexperiments

(Coll. Dr. F. Sedel, G.H. Pitié-Salpétrière)

Laboratoire d’Etude du Métabolisme des Médicaments, DSV/iBiTec-S/SPI

Sample preparation

Sophisticated sample preparation improves detection sensitivity

at the expense of metabolome coverage

Solid phase

extraction

Liquid-liquid

extraction Solvent/acid

precipitation

To clean-up samples and/or to concentrate metabolites

Sample preparation depends on the kind of biological medium investigated and

on the kind of metabolites detected.

Cell extracts, tissue extracts, biofluids

Quenching issue