Liquid Chromatography-Mass Spectrometry (LC/MS)-based parallel metabolic profiling of human and mouse model serum reveals putative biomarkers associated with the progression of non-alcoholic fatty liver disease. Jonathan Barr, Mercedes Vázquez-Chantada, Cristina Alonso, Miriam Pérez-Cormenzana, Rebeca Mayo, Asier Galán, Juan Caballería, Antonio Martín-Duce, Albert Tran, Conrad Wagner, Zigmund Luka, Shelly C. Lu, Azucena Castro, Yannick Le Marchand-Brustel , M. Luz Martínez-Chantar, Nicolas Veyrie, Karine Clément, Joan Tordjman, Philippe Gual, José M. Mato. ** * *** *** *** SM 36:3 SM 36:2 † d18:2/16:0 ** *** ** *** *** ** ** d18:2/14:0 d18:1/18:0 † d18:1/16:0 † d18:1/15:0 d18:1/14:0 d18:1/12:0 † -40 -30 -20 -10 0 10 % Deviation (GNMTKO-WT) -40 -20 0 20 40 60 % Deviation (NAFLD-Healthy) * d18:0/16:0 Synopsis: This article describes a parallel animal model / human NAFLD exploratory metabolomics study, using ultra performance liquid chromatography-mass spectrometry (UPLC ® -MS) to analyze 42 serum samples collected from non-diabetic, morbidly obese, biopsy-proven NAFLD patients, and 17 animals belonging to the glycine N- methyltransferase knockout (GNMT-KO) NAFLD mouse model. Many of the altered metabolites observed could be methyltransferase knockout (GNMT KO) NAFLD mouse model. Many of the altered metabolites observed could be associated with biochemical perturbations associated with liver dysfunction (e.g. reduced Creatine) and inflammation (e.g. eicosanoid signaling).
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Liquid Chromatography-Mass Spectrometry (LC/MS)-based parallel metabolic profiling of human and mouse model serum reveals putative biomarkers associated with the
Galán, Juan Caballería, Antonio Martín-Duce, Albert Tran, Conrad Wagner, Zigmund Luka, Shelly C. Lu, Azucena Castro, Yannick Le Marchand-Brustel , M. Luz Martínez-Chantar, Nicolas Veyrie, Karine Clément, Joan Tordjman,
Philippe Gual, José M. Mato.
**
*
***
***
***
SM 36:3
SM 36:2 †
d18:2/16:0
**
***
** ***
***
**
**
d18:2/14:0
d18:1/18:0 †
d18:1/16:0 †
d18:1/15:0
d18:1/14:0
d18:1/12:0 †
-40 -30 -20 -10 0 10
% Deviation (GNMTKO-WT)
-40 -20 0 20 40 60
% Deviation (NAFLD-Healthy)
*d18:0/16:0
Synopsis: This article describes a parallel animal model / human NAFLD exploratory metabolomics study, usingultra performance liquid chromatography-mass spectrometry (UPLC®-MS) to analyze 42 serum samples collectedfrom non-diabetic, morbidly obese, biopsy-proven NAFLD patients, and 17 animals belonging to the glycine N-methyltransferase knockout (GNMT-KO) NAFLD mouse model. Many of the altered metabolites observed could bemethyltransferase knockout (GNMT KO) NAFLD mouse model. Many of the altered metabolites observed could beassociated with biochemical perturbations associated with liver dysfunction (e.g. reduced Creatine) and inflammation(e.g. eicosanoid signaling).
J.B., Y.L.M.B., P.G., K.C., J.T. and N.V.), the FLIP UP consortium (K.C., J.T., N.V.) the
Institut National de la Santé et de la Recherche Médicale (France), the University of
Nice, the Programme Hospitalier de Recherche Clinique (CHU of Nice), Assistance-
Publique Hôpitaux de Paris, Hospitalier de Recherche Clinique, Paris region lle de
France, and charities (ALFEDIAM and AFEF/Schering-Plough to P.G.). Y.L.M.B. and
P.G. are the recipients of an Interface Grant from CHU of Nice. N.V. is supported by
Fondation pour la Recherche Médicale (FRM). Ciberehd is funded by the Instituto de
Salud Carlos III.
The contribution to this work from the technicians Ziortza Ispizua, Jessica Arribas,
Mónica Martínez and Stephanie Bounnafous is gratefully acknowledged.
Supporting Information Available
Raw data mean values and standard deviations within the different subgroups shown in
Figures 2 and 3 are included in supplementary Tables 1 and 2. This information is
available free of charge via the Internet at http://pubs.acs.org/.
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Figure 1. PCA scores plots discriminating GNMT-KO mice fromtheir WT littermates [Upper plot was obtained from negative ionUPLC™-MS data (t[1]: R2X = 0.28, Q2 = 0.20; t[2]: R2X = 0.09,Q2 = 0.03), lower plot from positive ion data (t[1]: R2X = 0.23, Q2
= 0.12; t[2]: R2X = 0.10, Q2 = 0.007) ]: 4 month old WT (n = 6),open squares; 6 5 month old WT (n = 4) open triangles; 4open squares; 6.5 month old WT (n 4), open triangles; 4month old GNMT-KO (n = 4), squares; 6.5 month old GNMT-KO(n = 3), triangles. Duplicate sample injection data are shown inthe plots.
Figure 2
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A. B. *
***
22:6n-3†
22:5n-6†
22:5n-3†
22:4n-6†
20:5n-3†***
20:4
20:5
22:4
22:5
22:6
*********
****
***
**
***
*********
***
*******
†
†
†
20:4n-6†
20:3n-3 † + n-6 † + n-9†
20:2n-6†
20:1n-9†
18:3n-3 † + n-6†
18:2n-6†
18:1n-9†
16:1n-7†***
18:0
20:0
16:1
18:1
18:2
20:1
20:2
20:3
-100 -50 0 50 100
% Deviation (GNMTKO-WT) -20 -10 0 10 20
% Deviation (NAFLD-Healthy)
****
C
†
†18:0†
16:0†
14:0†
†
-100 -50 0 50
14:0
15:0
16:0
% Deviation (GNMTKO-WT)
-30 -20 -10 0 10 20
% Deviation (NAFLD-Healthy))
Taurodeoxycholic†
Taurochenodeoxycholic†
**
**
*
***
***
***
***
***
***
***
D.C.SM 36:3
SM 36:2 †
d18:2/16:0
d18:2/14:0
d18 1/18 0 †
Deoxycholic†
Taurocholic†
Cholic†
**
***
***
**
***
** ***d18:1/18:0 †
d18:1/16:0 †
d18:1/15:0
d18:1/14:0
d18:1/12:0 †
Figure 2. Mean percent changes of (a) free fatty acids, (b) sn-1 monoacylglycerophosphocholine, (c)phosphosphingolipids, (d) bile acids in human NAFLD (S0 vs. S1, S2, S3, S3+NASH - right) and GNMT mice(GNMT-WT vs. GNMT-KO - left) sera. Positive and negative percentages indicate higher levels of metabolites in
-40 -30 -20 -10 0 10
% Deviation (GNMTKO-WT)
-40 -20 0 20 40 60
% Deviation (NAFLD-Healthy)
*
-100 0 100 200 300
% Deviation (NAFLD-Healthy)
-100 -50 0 50 100 150
% Deviation (GNMTKO-WT)
d18:0/16:0
(GNMT WT vs. GNMT KO left) sera. Positive and negative percentages indicate higher levels of metabolites inNAFLD (GNMT-KO) and healthy (GNMT-WT) sera, respectively. Unpaired Student’s t-test p-values are indicated whereappropriate: *p < 0.15, **p < 0.1, ***p < 0.05. †Metabolite identifications performed by comparison of mass spectra andchromatographic retention times with those obtained using commercially available standards. All other identificationswere performed by accurate mass database searching with fragment ion analysis. Lipid nomenclature follows the LIPIDMAPS convention (www.lipidmaps.org). Raw data mean values and standard deviations within the different subgroupsare detailed in supplementary tables 1 and 2.
Figure 3. Mean percent changes of diacylglycerophosphocholine in human NAFLD (S0 vs. S1, S2, S3, S3+NASH -right) and GNMT mice (GNMT-WT vs. GNMT-KO - left) sera. Positive and negative percentages indicate higherlevels of metabolites in NAFLD (GNMT-KO) and healthy (GNMT-WT) sera, respectively. Unpaired Student’s t-test p-values are indicated where appropriate: *p < 0.15, **p < 0.1, ***p < 0.05. †Metabolite identifications performed bycomparison of mass spectra and chromatographic retention times with those obtained using commercially availablestandards. All other identifications were performed by accurate mass database searching with fragment ion analysis.Lipid nomenclature follows the LIPID MAPS convention (www.lipidmaps.org). Raw data mean values and standarddeviations within the different subgroups are detailed in supplementary tables 1 and 2.
Table 1: Clinicopathological characteristics of the human patients included in the study. NAFLDdiagnoses were established histologically21. Values are given as mean ± 1 standard error of themean. ALT, a known biomarker of liver damage, is the only parameter found significantly altered(p < 0.05) between the groups of patients under comparison.
PC (16:0/20:4)† -0.16 (0.03)PC (18:2/0:0) 0.26 (0.06)PC (20:0/0:0)† 0.14 (0.01)
PC (18:2/18:2)† 0.14 (0.03)( ) ( )
Table 2: Principal variable loadings p[1] in theGNMTWT/KO PCA model (negative ion data). Thestandard error of the loading p[1]cvSE generated fromthe cross-validation rounds is shown in parenthesis.†M t b lit id tifi ti f d b i f†Metabolite identifications performed by comparison ofmass spectra and chromatographic retention times withthose obtained using commercially available standards.All other identifications were performed by accuratemass database searching with fragment ion analysis (allmass spectra are available on request). Lipidnomenclature follows the LIPID MAPS conventionnomenclature follows the LIPID MAPS convention(www.lipidmaps.org).
Table 3
Metabolite % Change(NASH – S3)
p-value(NASH – S3)(NASH S3) (NASH S3)
PC (14:0/20:4) 73.6 0.033PC (16:0/20:3) 14.4 0.086PC (18:1/0:0)† 60.8 0.028
Table 3: Biomarker metabolites found in human sera.Mean percentage changes are provided, comparing theS3 + NASH and S3 sample groups. Positive andnegative percentages indicate higher levels ofmetabolites in S3 + NASH and S3 sera, respectively., p yStatistical p-value calculated using the unpairedStudent’s t-test. †Metabolite identifications performed bycomparison of mass spectra and chromatographicretention times with those obtained using commerciallyavailable standards. All other identifications wereperformed by accurate mass database searching withf t i l i ( ll t il blfragment ion analysis (all mass spectra are available onrequest). Lipid nomenclature follows the LIPID MAPSconvention (www.lipidmaps.org).