Metabolomics (2015) 11:920–938 DOI 10.1007/s11306-014-0750-y
ORIGINAL ARTICLE
Metabolomics reveals differential metabolic adjustments of normal and overweight subjects during overfeeding Beatrice Morio • Blandine Comte • Jean-Franc¸ois Martin • Emilie Chanseaume • Maud Alligier • Christophe Junot • Bernard Lyan • Yves Boirie • Hubert Vidal • Martine Laville • Estelle Pujos-Guillot • Jean-Louis Se´be´dio
Received: 6 June 2014 / Accepted: 8 November 2014 / Published online: 22 November 2014 Ó Springer Science+Business Media New York 2014
Abstract Changes in eating habits, food composition and processing are involved in the ‘‘nutritional transition’’ that accompanied the obesity pandemic and the burst of metabolic diseases. This study is one of the first to describe the metabolic trajectories that differentiate the responses of overweight (OW) from lean individuals during weight gain. Nineteen lean and 19 OW male volunteers were submitted to moderate weight gain using a lipid-enriched overfeeding protocol designed to add about 3,300 kJ per day in excess to their usual diet. Metabolic explorations in combination with plasma and urine metabolomic profiles using liquid chromatography coupled with mass
Beatrice Morio and Blandine Comte have contributed equally to the study.
Electronic supplementary material The online version of this article (doi:10.1007/s11306-014-0750-y) contains supplementary material, which is available to authorized users. B. Morio B. Comte J.-F. Martin E. Chanseaume B. Lyan Y. Boirie E. Pujos-Guillot J.-L. Se´be´dio (&) INRA, UMR 1019, UNH, CRNH Auvergne, 63000 Clermont-Ferrand, France e-mail:
[email protected] B. Morio B. Comte J.-F. Martin E. Chanseaume B. Lyan Y. Boirie E. Pujos-Guillot J.-L. Se´be´dio Clermont Universite´, Universite´ d’Auvergne, Unite´ de Nutrition Humaine, BP 10448, 63000 Clermont-Ferrand, France J.-F. Martin B. Lyan E. Pujos-Guillot J.-L. Se´be´dio INRA, UMR 1019, Plateforme d’Exploration du Me´tabolisme, UNH, 63000 Clermont-Ferrand, France M. Alligier H. Vidal M. Laville Institut National de la Sante´ et de la Recherche Me´dicale Unit 1060, CarMeN Laboratory and Centre Europe´en Nutrition Sante´, Lyon 1 University, 69600 Oullins, France
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spectrometry were determined along 8 weeks to compare metabolic trajectories and determine early changes in metabolic processes after identification of specific early responding markers. Urinary metabolomic profiles during overfeeding evidenced differences in metabolic trajectories between groups, characterized by an increase over time of short-, medium-chain acylcarnitines, and bile acids in overweight subjects. For most of the anthropometric, metabolic parameters and plasma metabolomics data, the time-course evolution of all subjects was similar with distinction between groups. Plasma abundances of unsaturated lysophosphosphatidylcholine (22:6) decreased over time more importantly in normal weight subjects while most of those of the saturated species increased in both groups. These findings not evidenced with classical parameters, indicate a differential response to overfeeding in urine metabolomes of subjects, suggesting different nutrient metabolic fate with weight status. Subtle plasma and urine metabolic changes, mostly related to differences M. Alligier H. Vidal M. Laville Centre de Recherche en Nutrition Humaine (CRNH) RhoˆneAlpes, Centre Hospitalier Lyon-Sud, 69310 Pierre Be´nite, France C. Junot CEA-LEMM, 91191 Gif sur Yvette cedex, France J.-L. Se´be´dio INRA, UMR 1019, Clermont University, Research Centre of Clermont-Ferrand/Theix, 63122 Saint-Gene`s-Champanelle, France
Metabolomics and metabolic changes in weight gain
in the adaptation of b-oxidation and inflammation indicate a lower metabolic flexibility of OW subjects facing weight gain induced by overfeeding. Keywords Nutritional metabolomics UPLC–MS Urinary metabolic trajectories Weight gain Lipid-enriched overnutrition
1 Introduction Shifts in dietary pattern, food composition and processing as well as a modification in physical activity are collectively known as ‘‘nutritional transition’’. In the context of deleterious behavior, this transition has led to an energy imbalance not only in the Western world but also in emerging countries with an increasing prevalence of overweight (OW) and obesity (Popkin 2011). Obesity is often accompanied by a cluster of metabolic abnormalities, termed the plurimetabolic syndrome, which markers are related to inflammation, oxidative stress, lipid and glucose metabolism or energy imbalance (Kaess et al. 2012). The complexity of the disease brings challenges for an integrative understanding of the metabolic pathways that contribute to the development of these disorders. The development of high-throughput technologies (transcriptomics, proteomics, and metabolomics) which permit to generate large-scale analyses can now offer the possibility of characterizing global alterations associated with disease conditions or nutritional exposition and transition (Popkin 2011). Among these technologies, application of metabolomics to nutrition is still relatively new although this topic constitutes a growing domain of interest (Gibney et al. 2005). Metabolomics can be described as a global analysis of small molecules present in a biofluid, which are produced or modified as a result of a stimulus (nutritional intervention, environmental stressors, drugs etc.) (Nicholson et al. 1999). This field has been driven by major advances in analytical tools such as nuclear magnetic resonance (NMR) (Eisenreich and Bacher 2007) and mass spectrometry (MS) (Dettmer et al. 2007; Pan and Raftery 2007) and in chemometrics and bioinformatics (Idle and Gonzalez 2007; Moco et al. 2007; Wishart 2007). The production and utilization of metabolites are directly connected to the phenotype exhibited by an organism. Metabolomics provides an integrated view of the biological status at a certain time and thus allows defining and characterizing metabolic changes over time, identified as metabolic trajectories that are representative of coordinated and contingent metabolic adjustments according to deep homeostatic constraints. Several studies on obese and lean subjects have been recently carried out to compare metabolic profiles of biofluids using metabolomics (Kim et al. 2010; Mihalik et al.
921
2012; Newgard et al. 2009; Pietilainen et al. 2007; Wang et al. 2011; Zeng et al. 2010). Urinary and plasma metabolomics profiles allowed discriminating OW/obese versus lean phenotypes with evidence of branched-chain amino acids (BCAA) and long-chain acylcarnitines. These two sets of biomarkers indirectly reflect insulin resistance and defective lipid oxidation, respectively. Yet, the differential metabolic responses of OW/obese versus lean individuals associated with a body weight gain are still unknown. Moreover, whether such differential responses have a predictive value for the predisposition to metabolic perturbations is still to be determined. The purpose of the present work was therefore to compare early changes in metabolic signatures followed by male free living non-obese but OW and lean subjects during a moderate weight gain induced by a lipid-enriched supplementation of the usual diet (Alligier et al. 2012), to allow determining metabolic trajectories. Thirty eight volunteers (19 OW and 19 lean healthy men) were recruited and involved in an overfeeding protocol for 8 weeks. In addition to conventional anthropometric and metabolic parameters follow-up (Alligier et al. 2013; Alligier et al. 2012), plasma and urine were collected at different time points for an untargeted MS metabolomic approach. While plasma classical biochemical parameters and metabolomic data reveal similar evolution for both groups, our study evidenced contrasted urinary metabolic trajectories between lean and OW subjects. Changes mostly related to modifications in fatty acid (FA) metabolism suggest differences in metabolic flexibility, defined as the capability to modify fuel oxidation in response to variations in nutrient availability (Galgani et al. 2008), and inflammation between the lean and OW individuals.
2 Materials and methods 2.1 Research design and metabolic assessments 2.1.1 Subjects and design of the overfeeding protocol Over the 44 male subjects recruited within the initial bicentric study (Alligier et al. 2012) (n = 22 normal weight (NW) and 22 OW), 2 OW subjects discontinued the intervention and biological samples were missing for one individual, and 3 NW subjects were removed in order to compare two homogenous groups for age (n = 38 total; see flow chart supplemental data). Half of the subjects had a BMI between 20 and 24.9 kg m2 (NW), whereas the other half had a BMI ranging between 25 and 29.9 kg m2 (OW). Considering the distribution of BMI of the two groups of subjects along the experiment (Fig. S1), it appears that they belong to two distinct populations. Therefore the
123
123 –
81.4 ± 5.4 52.7 ± 4.3 16.1 ± 3.5 5.2 ± 1.7 10.7 ± 2.9
Waist circumference (cm)
Fat free mass (kg)
Fat mass (kg)
Trunk fat mass (kg)
Intrahepatic lipid content (%)
294 ± 150 0.79 ± 0.29 – 38.3 ± 30.2 – 0.86 ± 0.06
2.30 ± 1.52 6.5 ± 2.8 5.8 ± 2.2 0.86 ± 1.23 1.73 ± 0.26 417 ± 192 0.80 ± 0.23 4.36 ± 0.80 32.8 ± 24.1 6.67 ± 0.76 126.1 ± 12.3 0.84 ± 0.06 182 ± 83 78 ± 29
HOMA
Leptin (lg L-1)
Adiponectin (mg L-1)
hsCRP (mg L-1) IL6 (g L-1)
Free fatty acids (lM)
Triacylglycerols (mM)
Total Cholesterol (mM)
Glycerol (lM)
Basal metabolic rate (MJ 24 h-1)
Basal metabolic rate (kJ kg FFM-1 24 h-1)
Respiratory quotient
Carbohydrate oxidation (g 24 h-1)
Lipid oxidation (g 24 h-1)
69 ± 29
216 ± 103
6.88 ± 0.72
2.02 ± 4.12 1.91 ± 0.81
7.3 ± 4.2
74 ± 28
210 ± 100
0.86 ± 0.06
129.1 ± 8.8
7.00 ± 0.59
29.6 ± 22.8
4.41 ± 0.75
0.84 ± 0.29
323 ± 140
1.17 ± 1.38 1.94 ± 0.63
7.1 ± 3.3
7.7 ± 2.7
2.47 ± 1.34
10.4 ± 5.3
5.3 ± 0.5
12.9 ± 4.4
5.9 ± 1.6
16.8 ± 3.3
54.0 ± 4.2
84.8 ± 5.6
23.2 ± 1.7
71.8 ± 6.8
19 31.5 ± 7.2
100 ± 20
170 ± 55
0.82 ± 0.03
119.5 ± 13.1
7.38 ± 0.60
54.9 ± 40.8
4.88 ± 0.94
1.39 ± 0.63
413 ± 132
1.70 ± 1.00 1.94 ± 0.37
6.5 ± 3.6
12.2 ± 4.1
2.44 ± 0.49
10.6 ± 2.0
5.2 ± 0.3
21.8 ± 111.8
10.3 ± 2.2
22.8 ± 3.9
63.0 ± 6.4
97.3 ± 6.0
28.2 ± 1.2
89.6 ± 6.7
19 35.4 ± 10.3
78 ± 27
246 ± 82
0.87 ± 0.04
–
7.70 ± 0.79
48.9 ± 33.0
–
1.39 ± 0.55
313 ± 134
2.66 ± 4.16 2.47 ± 2.37
6.5 ± 4.7
16.2 ± 7.7
2.93 ± 0.96
12.8 ± 3.5
5.1 ± 0.6
–
–
–
–
–
28.4 ± 1.2
90.3 ± 7.0
19 35.4 ± 10.3
97 ± 24
193 ± 98
0.83 ± 0.05
121.9 ± 13.1
7.69 ± 0.78
44.9 ± 11.6
4.92 ± 0.80
1.39 ± 0.61
364 ± 97
2.08 ± 1.70 1.96 ± 0.89
6.5 ± 4.5
16.2 ± 6.8
2.58 ± 0.60
11.1 ± 2.3
5.2 ± 0.3
23.6 ± 12.5
11.0 ± 2.4
23.2 ± 4.0
64.4 ± 6.7
99.8 ± 6.4
28.9 ± 1.3
91.9 ± 7.2
19 35.4 ± 10.3
D56
NS
p \ 0.001
NS
NS
p \ 0.01
p \ 0.001
p \ 0.01
NS
p \ 0.0001
NS p \ 0.01
p \ 0.05
NS NS
p \ 0.0001 P = 0.051
p \ 0.001
NS NS
NS
p \ 0.001
p \ 0.05
p \ 0.001
NS
NS NS
NS
p \ 0.0001
NS
NS
NS
p \ 0.0001 p \ 0.001
p \ 0.0001 p \ 0.01 NS
p \ 0.0001 p \ 0.0001
p \ 0.0001 p \ 0.0001
p \ 0.0001 p \ 0.0001
p \ 0.0001
p \ 0.0001
Time
p \ 0.0001
p \ 0.0001
NS
Phenotype
Significance
Values are mean ± SD. Comparisons are based on repeated-measures ANOVA with post hoc Bonferroni multiple comparison tests. NS: no statistical difference (p [ 0.05). No interaction (phenotype 9 time) was found significant. Carbohydrate and lipid oxidation were evaluated from data of indirect calorimetry measurements and expressed per day, in relation with body weight
a
2.43 ± 1.37
9.8 ± 6.0 7.6 ± 2.5
10.8 ± 6.4
5.1 ± 0.4
Glucose (mM)
5.1 ± 0.6
–
–
–
Insulin (mIU L-1)
Fasting plasma metabolic phenotypinga
–
22.4 ± 1.7
22.6 ± 1.8
69.2 ± 6.8
70.0 ± 6.8
19 31.5 ± 7.2
Body weight (kg)
19 31.5 ± 7.2
Body mass index (kg m-2)
Anthropometric phenotypinga
N Age (years)
D14
D0
D56
D0
D14
Overweight subjects
Normal weight subjects
Table 1 Anthropometric and metabolic phenotyping of the subjects (n = 38) at baseline and after 14 and 56 days of overfeeding
922 B. Morio et al.
Metabolomics and metabolic changes in weight gain
923
NW
A
OW
22
D14 D56
20 18
D56
16
D14
14
D00
12 10
D14 D56
D00 8
D56 D56
D56 6
D14
D14 D14
2
t[2]
D00 D00 D14 D56D00 D56D56
D14D14
0
-4
D14 D14
-6
D00
D14
D14 D00
D00 D56 D00
D56 D14D00
D00
D00 D00 D00
D00
D56 D56 D56 D00
D00 D00D14D00 D56 D56 D56
D14
-8 -10
D14 D56
D14 D14 D14
D14 D00 D56 D00
-2
D56
D14
D00
4
D14
D00 D14 D14
D14
D56
B
D56 D00 D00 D00
D00 D14 D56 D14 D14 D56
D00 D56 T56
D56 D14 D56 D56
-12
D14 D00
D00
-14
D56
-16
D56
D56
-18
D56
-20 -22 -28
-26
-24
-22
-20
-18
-16
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
t[1]
Fig. 1 Partial least squares discriminant analysis with orthogonal signal correction on mixed model significant plasma ions for phenotype, time and interaction effects. a Scores plot showing the phenotype effect (R2Y = 0.87, Q2cum = 0.78). b Graphical result of 100 permutations test
phenotype OW versus NW was considered as a factor in this study. For sample size calculation, changes induced by the intervention on muscle mitochondrial oxidative capacity were used as primary endpoint. From previously obtained data, we estimated that a 10 % change in the muscle mitochondrial oxidative capacity could be expected during overnutrition. Based on this, the hypothesis of a common standard deviation of 5 %, and a careful intrapairs correlation coefficient of 0.15, a sample of 10 subjects per group for each center was required. All subjects were in good health without family history of diabetes and with a stable weight during at least the last 3 months. They were non-smokers, with age ranging from 21 to 53 years. Subjects’ characteristics are presented in Table 1. All subjects gave written consent after being informed of nature, purpose and possible risks of the protocol. The protocol was approved by the ethics committees of the two investigation centers, namely Clerrmont-Ferrand and Lyon Sud-Est, according to the French ‘‘Huriet-Serusclat’’ law and the Second Declaration of Helsinki, and is registered at www. clinicaltrials.gov (NCT00905892). The subjects were submitted for 56 days to an overfeeding consisting in adding an excess of 3,300 kJ per day to their usual diet. The overfeeding protocol has been previously described (Meugnier et al. 2007). Briefly,
subjects consumed daily and additionally to their diet 100 g of cheese, 20 g of butter and 40 g of almonds (unsalted), representing about 70 g of lipids mainly composed of saturated (46.3 %) and monounsaturated (44.7 %) FAs, and about 40 g of proteins. The subjects were also asked to maintain their lifestyle, regular level of physical activity and usual eating behavior. Metabolic explorations were performed before the study (D0), after 14 days (D14), 28 days (D28), and at the end (D56) of the nutritional intervention. As an evaluation of compliance, all subjects completed 5-day dietary records, before the study and twice during the overfeeding period (D9-13 and D51-55) (Table S1). The collected data were analyzed by crossreferencing with French databases (GENI Micro6, Villersle`s-Nancy, France). 2.1.2 Metabolic explorations After an overnight fast, subjects were convened to the Clinical Research Centers. Height and waist circumference were carefully recorded. Weight was measured to the nearest 0.1 kg on a SECA balance (SECA, Les Mureaux, France). Fasting blood samples were drawn and immediately frozen at -80 °C for further analyses of hormones, plasma metabolites and lipid profiles, and also for
123
123
10.4
10.4
10.6 11.9
12.1
12.2
13.5
P544.337
P590.323
P570.357 P524.361
P550.388
P466.334
P305.250
12.8
3-Hydroxy-hexadecanoic acid
Arachidonic acid
PC (18:1/20:4) or isomers
LPC (20:1)
LPC (22:5) LPC (18:0)
LPC (22:6)
LPC (20:4)
LPC (18:2)
LPE (20:2)
LPC (14:0)
LPE (16:0)
Bilirubin
Acetylcarnitine
L-Carnitine
Betaine
Creatinine
Identified compounds
C16H32O3
C20H32O2
C46H82NO8P
C28H56NO7P
C30H52NO7P C26H54NO7P
C30H50NO7P
C28H50NO7P
C26H50NO7P
C25H48NO7P
C22H46NO7P
C21H44NO7P
C33H36N4O6
C9H17NO4
C7H15NO3
C5H11NO2
C4H7N3O
Formula
0.540
0.988
0.170
0.0129
0.0128 0.951
0.051
0.205
0.0133
0.0034
0.499
0.982
0.649
0.267
0.818
0.0414
0.575
0.0004
0.0035
0.0065
0.0013
0.193 0.0261
0.0015
0.0346
0.949
0.248
0.0001
0.0001
0.0021
0.0035
0.0271
0.0026
0.0024
0.0353
0.558
0.363
0.115
0.622 0.214
0.0469
0.991
0.877
0.744
0.449
0.940
0.678
0.378
0.078
0.181
0.167
The bold figures reached significance whereas the ones in italics were close to significance
P: identified in the positive mode; N: identified in the negative mode
LPC lysophosphatidylcholine, LPE lysophosphatidylethanolamine
b
a
-0.3
-0.5
-0.1
-0.3
-0.2
OW/NW
Interaction
Phenotype
Time
Log2R
Factor p valuesb
Log2R OW/NW represents the log2 of the ratio between the means ion intensities of the OW and of the NW subjects
N271.238
Putative annotated metabolite
10.3
9.6
P468.307
10.3
9.6
N452.286
N504.297
7.9
P585.273
P520.324
0.5
0.5
0.5
P118.087
P162.111
0.5
P114.067
P204.113
RT (min)
Measureda (m/z)
Table 2 Identifications of plasma significant metabolites
316 469
142
390
98
223
619 26,217
242
9,252
35,105
7,264
2,347
561
722
820
1042
D0
94
323
76
176
543 24,115
169
8,591
34,811
7,019
3,042
648
513
667
1149
417
322
D14
105
321
80
161
547 24,571
161
8,276
34,504
7,527
3,360
769
614
662
882
357
304
D56
NW—Mean ion intensity
332 391
118
392
82
159
432 24,959
149
7,925
31,399
5,635
2,667
558
779
720
1004
D0
104
346
75
153
422 24,691
141
7,406
31,391
5,919
3,128
632
548
653
1,032
335
330
D14
116
293
65
137
376 23,974
129
7,001
31,518
6,362
3,253
747
582
657
1,006
346
293
D56
OW—Mean ion intensity
924 B. Morio et al.
Metabolomics and metabolic changes in weight gain
A
925 OW
NW 50
D00
D07 D07
40
D00
D00
D14 D07 D45 D00 D45D07 D07 D45 D07 D14 D28 D07 D21 D28 D14 D07 D00 D21 D07 D28 D07 D00 D00 D00 D00 D45 D00 D28 D07 D45 D00 D00 D00 D07 D14 D45 D45 D45 D14 D21 D21 D45 D45 D21 D45 D56 D21 D21D00 D00 D45 D56 D45 D45 D14 D14 D45 D00 D21 D14 D28 D21 D21 D14 D00 D00 D21 D45 D07 D14D21 D28 D21 D45 D45 D45 D45 D56 D56 D21D14 D21 D21 D00 D21D00 D00D56 D21 D21 D28 D28 D00 D14 D14 D21 D07 D28 D45 D00 D45 D28 D28 D56 D00 D28 D21 D21D07 D56 D00 D14 D45 D45 D07 D07 D56D07 D56 D00 D00 D56 D14 D28 D14 D14D14 D56 D45 D14 D21 D56 D28 D45 D21 D28 D28 D56 D14 D14 D56 D00 D45 D56 D07 D28 D21 D45 D21 D21 D21 D21 D14 D45D45 D45 D45 D07 D45 D00 D00 D07 D14 D00 D21 D45 D28 D56 D45 D56 D28 D28D56 D56 D00 D14 D28D21D21 D28 D28 D21 D14 D00 D07 D07 D28 D00 D14 D00 D07 D28 D56 D56 D14 D56 D07 D14 D21 D45 D14 D07 D28 D14 D56 D07 D07 D07 D56 D14 D14D07D28 D07 D00 D00 D56 D28 D28 D28 D28 D28 D21D28 D28 D14D14 D21 D07 D56 D56 D56 D56 D56 D56 D45 D56 D28 D00 D28 D14 D14 D56 D56 D21 D14 D56 D00 D56 D07 D56
30
20
10
t[2]
D07
0
-10
-20
-30
D56
-40
B
D28
D14 -50
D28 -60
-50
-40
-30
-20
-10
0
10
20
30
40
50
60
t[1]
Fig. 2 Partial least squares discriminant analysis with orthogonal signal correction on mixed model significant urine ions for phenotype, kinetic and interaction effects. a Scores plot showing phenotype effect (R2Y = 0.73, Q2cum = 0.62). b Graphical result of 100 permutations test
metabolomic analyses. Then, fasting lipid and carbohydrate oxidation rate were assessed for 1 h by indirect calorimetry using a ventilated-hood system (Deltatrac Datex, Helsinki, Finland) (Nazare et al. 2009). At D0 and D56, body composition was measured using dual-energy X-ray absorptiometry (Rimbert et al. 2004) (on a Hologic QDR 4500 X-Ray bone densimeter, Hologic, Waltham, MA, USA in Clermont-Ferrand, and on a Discovery A system, Hologic, Bedford, MA, USA in Lyon). Peripheral insulin sensitivity was assessed in a subgroup of volunteers (NW: n = 13, OW: n = 13) using the hyperinsulinemic-euglycemic clamp method (Alligier et al. 2013). The insulin sensitivity index was calculated as ISI = M/(G 9 DI), in which M is the steady-state glucose infusion rate calculated at 10-min intervals, G the steady-state blood glucose concentration, and DI the difference between fasting and steady-state insulin level concentrations (Katz et al. 2000). 2.1.3 Plasma biological parameters Fasting plasma lipids (triglycerides, glycerol, free FAs, and total cholesterol) were determined using an automated
system (Konelab 20, ThermoElectron Corporation, Waltham, USA). Chemicals were obtained from Randox (Crumlin, UK) for glycerol and free FA tests and from ThermoElectron Corporation (Waltham, USA) for all the other measured parameters. Glycemia (Modular, Roche Diagnostics, Meylan France), insulinemia, leptin, and adiponectin (IRMA kit, Diasource Immunoassays, Nivelles, Belgium; Quantikine leptin, Oxford, UK; and EKMADP, Bu¨hlmann laboratories AG, Switzerland, respectively) were measured using commercial kits following the recommended protocols.
3 Metabolomics 3.1 Chemicals and reagents Methanol and acetonitrile were purchased from Sigma– Aldrich (Saint-Quentin Fallavier, France), ultrapure water (18.2 MX) from Millipore (Molsheim, France) and formic acid from Fluka (Saint-Quentin Fallavier, France). All the chemicals used were of analytical grade. Leucine enkephalin (Sigma-Aldrich) at a concentration of 0.5 ng L-1 (in
123
123
9.3
9.6
9.7
10.1
10.6
11.8
12.4
4.7
8.5
8.2
8.8
13.6
13.7
13.3
P286.191
P330.224
P288.216
P310.197
P344.242
P328.239
P342.264
N218.105
P377.146
P206.046
N216.983
N448.307
N407.28
P291.123
Dihydrotestosterone glucuronide fragment [(M?H)–(glucuronide)]?
Cholic acid
Glycochenodeoxycholic acid
5-Sulfosalicylic acid
Xanthurenic acid
Riboflavin
Pantothenic acid
Dodecenoylcarnitine
Undecenoylcarnitine
Hydroxyundecenoylcarnitine
Decatrienoylcarnitine
Octanoylcarnitine
Hydroxydecenoylcarnitine
2-Octenoylcarnitine
Hydroxyoctanoylcarnitine
Heptenoylcarnitine
Dihydroxynonadienoylcarnitine
Isovalerylcarnitine
P247.13
3.4
Gamma glutamyl valine
Amino acid derivative
Steroid
Bile acid
Bile acid
Organic acid
Organic acid
Vitamin
Vitamin
Carnitine (C12:1)
Carnitine (C11:1)
Carnitine (C11(OH):1)
Carnitine (C10:3)
Carnitine (C8:0)
Carnitine (C10 (OH):1)
Carnitine (C8:1)
Carnitine (C8(OH):0)
Carnitine (C7:1)
Carnitine (C9(OH)2:2)
Carnitine (C5:0)
Carnitine (C5:1)
Amino acid
Tryptophan fragment [(M?H)–(NH3)]?
Tiglylcarnitine
Amino acid derivative
Amino acid derivative
Family
L-Phenylalanyl-L-proline
Aspartylphenylalanine
Identified compounds
Putative annotated compounds
7.0
8.0
P272.182
6.9
P330.19
P304.209
3.3
4.8
P188.056
3.8
6.4
P263.14
P244.154
5.7
P281.114
P246.168
RT (min)
Measured* (m/z)
Table 3 Identifications of urine significant metabolites
C10H18N2O5
Hydroxyoctanoylcarnitine
C24H40O5
C26H43NO5
C7H6O6S
C10H7NO4
C17H20N4O6
C9H16NO5
C19H36NO4
C18H33NO4
C18H33NO5
C17H27NO4
0.835
0.0286
0.0069
0.730
0.075
0.533
0.0332
0.411
0.0431
0.227
0.278
0.504
0.648
0.450
C15H29NO4
0.156
C15H27NO4
0.320
0.622
0.861
0.690
0.874
0.814
0.083
0.976
0.0018
0.0001
0.712
0.244
0.494
0.061
0.136
0.521
0.190
0.799
0.166
0.843
0.788
0.116
0.961
0.0336
0.0288
0.0097
0.076
0.0362
0.268
0.237
0.0001
0.137
0.311
0.583
0.0075
0.374
0.072
0.431
0.0520
0.0003
0.0023
0.0034
0.070
0.0145
0.070
0.0002
0.0387
0.686
0.0287
0.0144
0.0493
0.0282
0.461
0.379
0.4
1.1
0.4
0.7
0.6
0.2
OW/ NW
Interaction
Phenotype
Time
log2R
Factor p valuesb
C17H31NO5
C15H30NO5
C14H25NO4
C15H30NO5
C12H23NO4
C12H21NO4
C11H10NO2
C14H18N2O3
C13H16N2O5
Formula
-0.2
-0.7
0.5
-0.4
0.3
-0.4
-0.1
0.4
-0.1
r Time
r OW
-0.5 0.6
0.1 0.5
-0.8 0.5
-0.9 0.8
-0.9 0.8
-0.6 0.5
-0.7 0.5
-0.6 0.3
-0.4 0.8
-0.6 0.6
-0.8 0.4
-0.9 0.3
-0.6 0.4
-0.3 0.8
-0.3 0.7
r NW
Correlation coefficient
1.8
2.3
2.5
4.3
4.8
2.8
1.6
1.6
2.5
VIP
926 B. Morio et al.
11.8 13.8
P365.232 N431.228
Deoxycholic acid 3-glucuronide
Oxocholenoic acid
Steroid sulfate
Dihydrocortisol Androsterone glucuronide fragment [(M–H)– (H2O)–O]-
Tetrahydroaldosterone-3-glucuronide
Hydroxycyclohexene-5aminobutenoylcarnitinec
3-Methylglutarylcarnitine
Hydroxyhexanoylcarnitine
Indoxylsulfate
Adipic acid
5-Methylthioadenosine
Beta aspartyl leucine
Identified compounds
Bile acid metabolite
Bile acid
Steroid
Steroid Steroid
Steroid
Carnitine
Carnitine (C5:0)
Carnitine (C6(OH):0)
Organic acid
Organic acid
Amino acid derivative Nucleoside
Family
C30H48O10
C24H36O3
C19H28O6S
C21H32O5 C25H38O8
C27H40O11
C17H25N2O5
C13H23NO6
C13H25NO5
C8H7NO4S
C6H10O4
C11H15N5OS
C10H18N2O5
Formula
0.0215
0.0539
0.864
0.196 0.448
0.109
0.0107
0.790
0.143
0.721
0.780
0.360
0.716
0.389
0.832
0.0314
0.0075 0.0019
0.095
0.953
0.0001
0.358
0.359
0.389
0.0315
0.072
0.0154
0.891
0.450
0.949 0.337
0.238
0.152
0.911
0.0389
0.0552
0.0215
0.145
0.0020
0.7
0.8
0.4
OW/ NW
Interaction
Phenotype
Time
log2R
Factor p valuesb
-0.3
-0.3 -0.5
-0.2
-0.5
-0.4
-0.0
r Time
r OW
-0.4 0.8
-0.8 0.6
-0.8 0.4
-0.2 0.7
-0.6 0.5
r NW
Correlation coefficient
1.5
1.6
2.5
VIP
P: identified in the positive mode; N: identified in the negative mode
The bold figures reached significance whereas those in italics were close to significance
Identified from the Orbitrap MS fragmentation pattern. Variable importance on projection (VIP) values were obtained from the OSC-PLS-DA performed with data at only D0 and D56, in regards to the data presented in Fig. 5. Chemical formulae were determined from exact mass measurements
c
b
a
Log2R OW/NW represents the log2 of the ratio between the means ion intensities of the OW and of the NW subjects. Correlation coefficients represent the evolution of the ion intensities during the 7 time point follow-up (r Time for both phenotypes, r NW and r OW for NW and OW respectively when there is a ‘time 9 phenotype’ interaction)
14.0
11.4
N539.246
N567.318
7.3
P337.175
14.6
2.8
P290.159
12.8
2.4
P276.181
N383.152
10.5
N211.992
N373.273
5.2
4.8
P298.101
3.7
P247.174
P147.064
RT (min)
Measured* (m/z)
Table 3 continued
Metabolomics and metabolic changes in weight gain 927
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B. Morio et al. Dihydroxycyclohexene 5aminobutenoylcarnitine (C6:1) m/z 337.175, RT: 7.3 min S4
Intensity
Tiglylcarnitine (C5:1) m/z 244.154, RT: 3.3 min S5
Hydroxy-octanoylcarnitine (C8(OH):0) m/z 304.209, RT: 8.0 min S1 10000
Heptenoylcarnitine (C7:1) m/z 272.182190, RT: 7.0 min S5
5500
4000
17500
4500
3000
15000
3500
2000
12500
8500 7000
2500
1000 0
7
14
21
28
45
56
5500
10000 0
7
14
21
28
45
4000 0
56
7
14
21
28
45
56
0
7
14
21
28
45
56
NW OW
40000
Intensity
Dihydroxynonadienoylcarnitine (C9(OH)2:2) m/z 330.190, RT: 9.6 min S3
2-octenoylcarnitine (C8:1) m/z 286.191, RT: 9.3 min S2
Undecenoylcarnitine (C11:1) m/z 328.239, RT: 11.8 min S5
5500
40000
4500
30000
3500
20000
2500
10000
Dodecenoylcarnitine (C12:1) m/z 342.264, RT: 12.4 min S5 2500
35000 30000
2000 1500
25000
1000
20000 15000 0
7
14
21
28
45
0
56
7
14
Time (days)
21
28
45
500
56
0
7
14
21
28
45
0
7
14
21
28
45
56
Time (days)
Time (days)
Time (days)
Glycochenodeoxycholic acid m/z 448.307, RT: 13.6 min S2
Cholic acid m/z 407.280, RT: 13.7 min S4
800
56
1400
Intensity
1200 600
1000 800
400
600 400
200
200 0
0 0
7
14
21
28
45
Time (days)
56
0
7
14
21
28
45
56
Time (days)
Fig. 3 Kinetics for both phenotypes (NW and OW) on mixed model of some significant carnitine and bile acid urine ions. Abundances (mean ± SEM) of saturated, unsaturated, hydroxylated short- and
medium-chain acylcarnitines, and bile acids in urine are presented for normal (triangle) or overweight (square) subjects over the nutritional intervention. Sx reports to the sub-trees identified in the Fig. 4
acetonitrile/water, 50/50 v/v, with 0.1 % formic acid) was used as reference for mass measurements.
-20 °C before being stored until further analyses. Two hundred and sixty six urine samples (500 lL; 38 subjects with sampling at D0, 7, 14, 21, 28, 45, and D56) were defrosted at room temperature, centrifuged at 7,0009g for 5 min at 4 °C, and then diluted fourfold with distilled water.
3.2 Biological samples collection and preparation Ninety six plasma samples were collected into heparinized tubes from 32 volunteers (sampling at D0, D14 and D56) as blood samples from 6 subjects were not available. A pooled quality control sample was prepared by mixing 20 lL from each of the plasma samples. All solvents were kept at 4 °C prior to their utilization. Plasma (200 lL) was mixed with twice the volume of cold methanol. After protein precipitation, the sample was kept in the freezer at -20 °C for 30 min. The mixture was then centrifuged (Sigma 3-16PK, Fischer Bioblock Scientific) at 13,7009g for 10 min, the supernatant was removed and evaporated under nitrogen. The dry residue was dissolved in 50 lL of a 50/50 (v/v) water/acetonitrile mixture, containing 0.1 % formic acid. First void of urine was collected and immediately frozen at
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3.3 Untargeted LC–MS metabolomic analyses 3.3.1 Plasma metabolomic profiling The chromatography was performed on a Waters Acquity Ultra Performance Liquid Chromatography (UPLC) module (Saint-Quentin en Yvelines, France). Separations were carried out at 30 °C using a 2.1 9 50 mm Acquity UPLC HSS T3 column (Waters), with a particle size of 1.8 lm at a flow rate of 0.4 mL min-1. Deproteinized plasma sample was eluted from the LC column using the following linear gradient:: 0–2 min: 100 % A; 2–15 min: 0–100 % B;
Metabolomics and metabolic changes in weight gain
S1 S2 S3
S4
S5
S6
929
Hydroxydecenoylcarnitine (C10(OH):1) Hydroxyoctanoylcarnitine (C8(OH):0) Hydroxyundecenoylcarnitine (C11(OH):1) Hydroxyhexanoylcarnitine (C6(OH):0) 2-Octenoylcarnitine (C8:1) Decatrienoylcarnitine (C10:3) Glycochenodeoxycholic acid Indoxylsulfate 3-Methylglutarylcarnitine (C5) Aspartylphenylalanine γ−glutamyl valine 5-methylthioadenosine Dihydroxynonadienoylcarnitine (C9(OH2):2) Riboflavin Deoxycholic acid 3-glucuronide Hydroxycyclohexene-5 aminobutenoylcarnitine (C6:1) Cholic acid Oxocholenoic acid Undecenoylcarnitine (C11:1) Dodecenoylcarnitine (C12:1) Dihydrotestosterone glucuronide fragment Androsterone glucuronide fragment Heptenoylcarnitine (C7:1) Tiglylcarnitine (C5:1) Isovalerylcarnitine (C5:1) Octanoylcarnitine (C8:0) β−Aspartyl leucine Adipic acid Tryptophan fragment Xanthurenic acid Tetrahydroaldosterone-3-glucuronide L-phenylalanyl-L-proline 5-Sulfosalicylic acid Pantothenic acid Steroid sulfate Dihydrocortisol
Fig. 4 Heatmap with hierarchical clustering (Euclidian distance, Ward aggregation) of mixed model significant urine ions for at least one of the fixed effects (time, phenotype or phenotype 9 time interaction). Hierarchical clustering was performed on the average
values of each phenotype at each time point. Lowest metabolite intensity is shown in white while maximum intensity is shown in dark blue. In blue: acylcarnitines; in red: bile acids; in italic: putative metabolites
15–22 min: 100 % B. Re-equilibration to 100 % A was achieved in 4 min. Solvent A was H2O and solvent B was acetonitrile, both solvents containing 0.1 % formic acid. Injection volume was set to 6 lL. The UPLC system was coupled to a Waters QToF-Micro mass spectrometer (Saint-Quentin en Yvelines, France) equipped with an electrospray source (ESI) and a lock mass sprayer to ensure accuracy. Experiments were carried out in positive and negative ion modes (ESI?, ESI-) with a scan range from 70 to 1,000 mass-to-charge ratio (m/z). Capillary voltage was set to 3 kV and cone voltage was optimized at 30 V. The scan time and the dwell time were fixed at 1 and 0.1 s, respectively. ESI needle and drying gas temperatures were set at 120 and 450 °C, respectively. The drying and
nebulizing gas flows (nitrogen) were set to 50 and 500 L h-1, respectively (Pereira et al. 2010). 3.3.2 Urine metabolomic profiling Chromatography was performed using the Waters Acquity UPLC module. Six lL of diluted urine samples were injected into a 100 9 2.1 mm 1.7 lm BEH Shield RP18 column (Waters) at 30 °C. Mobile phase components were A: H2O with 1 % formic acid and B: acetonitrile with 1 % formic acid. The column was eluted with a gradient of 100 % A held for 2 min, followed by a decrease to 90 % A over 2–7 min, and then to 5 % A over 7–22 min. The mobile phase was then returned to 100 % A at 22.1 min for
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Fig. 5 Partial least squares discriminant analysis with orthogonal signal correction on mixed model significant urine ions for phenotype, time and interaction effects. a: Scores plot showing the
(phenotype 9 time) interaction effect at baseline (D00) and at end of protocol (D56) (R2Y = 0.76, Q2cum = 0.59). b Graphical result of 100 permutations test of OW-D56 group
a 4-min length re-equilibration. The flow rate was set to 400 lL min-1. The Waters QToF-Micro source temperature was set to 120 °C with a cone gas flow of 40 L h, a desolvation temperature of 330 °C, and a nebulization gas flow of 650 L h-1. The capillary voltage was set at 3,000 V and the cone voltage to 30 V. The MS data were collected in the continuum full-scan mode with a mass-tocharge ratio of 70:1,000 from 0 to 22 min, in positive and negative ion modes. All analyses were acquired by using the lockspray with a frequency of 5 s to ensure accuracy. Leucine enkephalin was used as a lock mass compound. To avoid possible differences between sample batches, a Latin square was carried out to obtain randomized analytical sequences. The stability of the analytical system was monitored using pooled samples injected one time at the beginning of each sequences and then after each set of ten samples.
3.5 Statistical analyses
3.4 Data extraction Raw data files were converted to NetCDF format using the Waters DataBridge software. All LC–MS data were processed using XCMS (Benton et al. 2008; Smith et al. 2006) to yield a data matrix containing retention times, accurate masses and normalized peak intensities.
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For a prior selection of metabolic features, ANOVA was performed on repeated measures to assess the effect of phenotype (OW vs NW), time and the (phenotype 9 time) interaction, this latter being included in the model to assess any difference in kinetics of the studied feature between the two groups. Mixed effect model was achieved using proc mixed procedure of SAS software v9.1 (SA Institute, Cary, NC, USA). Time and phenotype were considered as fixed effects. Batch series of injection and subjects were also included in the model as a ‘‘block’’ and ‘‘subject’’, respectively, both corresponding to a random effect. For any effect studied for every feature, the p value significance threshold was set to 0.05. The log2 of the ratio of the mean ion intensity of OW versus the mean ion intensity of NW subjects was calculated to represent the fold change, when ion was found significant for phenotype. Moreover, Pearson correlation coefficient between time point values and ion intensities were calculated to visualize the evolution of the ion intensities during the 7 time point follow-up (r Time for both phenotypes, r NW and r OW for NW and OW respectively when a ‘time 9 phenotype’ interaction was observed).
Metabolomics and metabolic changes in weight gain
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Fig. 6 Schematic model of potential biological processes during early phases of weight gain induced by overnutrition. Dynamic relationships between oxidative capacity and caloric load related to overnutrition during weight gain: In lean subjects, reserve capacity is large enough to adjust to caloric load by complete oxidative processes; there is a balance between oxidative capacity and reserve capacity. In overweight subjects (OW), reserve capacity is decreased
as well as oxidative capacity. In condition of overnutrition, metabolic flexibility is lower as seen in the release in urine of medium and short chain acyl carnitines (AcCNs) and lipid storage. In obese patients, as it has been reported (Adams et al. 2009), these processes are amplified resulting in chronic insulin resistance, incomplete b-oxidation and accumulation of circulating AcCNs
For metabolomic data, a statistical workflow based on univariate and multivariate analyses was used, as previously described (Kenny et al. 2014; Momken et al. 2011). Even if partial least square methods can use datasets with more variables than objects and also highly correlated variables, a reduction of variables can avoid over fitting and lead to an improved prediction performance. Therefore, ANOVA analyses were performed to preselect variables based on experimental relevance (‘information-rich’ peaks) or noise criteria for removing signal-free baseline noise from acquired spectra. No p-value adjustment for multiple testing was performed to keep the number of probable false negative to a minimum. Indeed, it has been shown that multiple testing correction was not able to remove all false positives yet inducing false negatives (Saccenti et al. 2014), and that several biomarkers could be lost because of the correction (Franceschi et al. 2012). Ions with p value \0.05 for at least one of the fixed effects
(time, phenotype) or their interaction were selected for further multivariate analyses. An orthogonal signal correction (OSC) of data prior to partial least square-based discriminant analyses (PLS-DA) was used in order to filter all the source of variability except the fixed effects described above. From this correction of data, nearly 70 % of the initial variance was kept to performed discriminant analyses, named OSC-PLS-DA hereafter. For all these analyses, unit variance scaling (UV) was applied to all ion intensities. The structure of the dependent Y-vector was made of dummy variables corresponding to the different ‘Time 9 phenotype’ groups. SIMCAP? v12 (Umetrics, Umea˚, Sweden) was used to perform these multivariate analyses. The overall quality of the models was assessed by the cumulative R2 (R2Ycum) criterion, the fraction of the sum of squares of the Y-vector explained by all the components, and by the cumulative Q2 (Q2cum) criterion, the fraction of the total variance of the matrix that can be
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predicted by all the components, as estimated by crossvalidation. Predictive discrimination ability by cumulative Q2 (Q2cum) extracted was obtained according to the sevenfold cross-validation default method of SIMCA-P software. Q2cum is a ratio of predicted residual sum of square (PRESS) versus residual sum of square (RSS). To verify that PLS components could not lead to classifications by chance, a permutation test was carried out (n = 100). Permutation tests involve the random assignment of the y-variable in the data. Permutation testing using 100 random permutations allows studying if the goodness of fit and predictive ability (R2/Q2) of the original models, remain higher than those of the permuted models. Thus it evaluates the degree of possible over-fit. For each test, samples were randomly assigned to each group. A PLS model was carried out and R2Y and Q2cum were computed. Results of this test are displayed on validation plots. These plots show on the X axis the correlation coefficient with the original non permuted sample (value 1) and R2Y and Q2cum values on the Y axis. Logically, permuted samples must lead to poor predictive models with low Q2cum values and therefore the regression line of Q2cum values must have a negative intercept. Variable importance on projection (VIP) values were obtained as indicators of importance of each ion in the PLS-DA model. Univariate methods are useful for explanatory analysis purposes by providing an overview of the pre-processed data. In addition, PLS-DA allows revealing multivariate latent structure in the data which helps assessing the combinatorial predictive ability of the candidate biomarkers. Finally, both PLS and ANOVA results were considered in order to analyze the biological importance of the identified molecules. In order to explore the kinetic differences between OW and NW subjects, significant identified ions for at least one of the fixed effects (time, phenotype, or their interaction) were analyzed by a hierarchical clustering analysis (HCA) using Permutmatrix v1.9 (Caraux and Pinloche 2005). Unit variance scaling was carried out on ion intensity and Euclidian distance and Ward aggregation criterion were used. 3.6 Metabolite identification Identification experiment were performed on a Thermo Scientific LTQ Orbitrap Velos hybrid mass spectrometer (Thermo Fisher Scientific, San Jose´, CA, USA) using high resolution, full MS (m/z 70–1,000) at 60,000 resolving power, with acquisitions carried out for a limited selection of samples of interest. In further experiments, an inclusion list was added to the method for validation of some of the identifications using CID MS2 data acquired on the same samples. Metabolites contributing to the discrimination of
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the different phenotypes were identified on the basis of their exact masses which were compared to those registered in Kyoto Encyclopedia of Genes and Genomes (KEGG) database (http://www.genome.jp/kegg/) or in the Human Metabolome Database (HMDB; www.hmdb.ca) (Wishart 2007). Identification was confirmed using appropriate standards when available, isotopic patterns and mass fragmentation analyses. For unidentified ions the number of plausible elemental compositions may be restricted to a small number (or uniquely identified) with the support of additional chemical information—i.e. the molecular formula of the parent and knowledge of possible metabolic pathways. Metabolites were classified accordingly to Sumner et al. (2007) concerning the levels of confidence in the identification process: identified (confirmed by standard), putatively annotated (based upon physicochemical properties and/or spectral similarity with public/commercial spectral libraries), putatively characterized compound classes.
4 Results 4.1 Anthropometric, clinical and conventional metabolic characterization The volunteers were asked to consume a supplement of about 3,300 kJ d-1, mostly as fat (70 g) and proteins (40 g) in addition to their regular diet. Based on the dietary records, compliance was considered good with a daily energy intake showing overall no significant difference between the NW and OW phenotypes: -1 9,751 ± 1,663 KJ 24 h at baseline. The nutritional intervention significantly increased this value of 3,052 ± 1,974 kJ 24 h-1 at D14 that was maintained at D56: ?3,152 ± 1,756 kJ 24 h-1 versus D0 (p \ 0.001, Tables S1 and S2). Carbohydrate intake was not significantly modified, whereas as expected, lipid intake increased significantly by 65 ± 25 and 60 ± 28 g 24 h-1 at D14 and D56 compared to baseline (?60–66 %; p \ 0.0001, Tables S1, and S2), mostly represented by saturated and monounsaturated FA. Furthermore, overfeeding did not change the relative proportions of FA classes despite a significant higher consumption of saturated and monounsaturated FA in OW volunteers. Protein intake increased significantly by 38 ± 21 and 34 ± 23 g 24 h-1 at D14 and D56 versus D0 (? *38 %; p \ 0.0001, Tables S1 and S2) as a result of proteins contained in the daily 160 g of cheese, butter and almonds. Physical activity measured using RT3 accelerometers did not show significant changes regardless the BMI status (data not shown).
Metabolomics and metabolic changes in weight gain
A summary of the anthropometric and metabolic characteristics of the subjects at baseline, D14, and D56 is given in Tables 1 and S3. At D0, there were no significant differences between the NW and OW groups concerning age while BMIs were significantly different with higher body weight (? *30 %), waist circumference (? *20 %), fat free mass (? *20 %), fat mass (? *61 %), and trunk fat mass (? *98 %), in OW compared to NW subjects. Noteworthy, body weight gain was similar in the two groups over the feeding period (?2.4 ± 1.4 kg at D56 vs D0, p \ 0.0001), as well as the increase in waist circumference (?3.1 ± 1.8 cm at D56 vs D0, p \ 0.0001) and body fat mass (?0.57 ± 1.11 kg.m-2 at D56 vs D0, p \ 0.0001). In agreement with fat mass augmentation, plasma leptin concentration increased after 14 days of overfeeding and was maintained until the end of the protocol. No significant differences in plasma levels of hsCRP and IL6 were observed between NW and OW over the nutritional intervention. Plasma triacylglycerol as well as total cholesterol levels were not significantly modified by the nutritional intervention and not surprisingly, they were significantly different between the NW and OW volunteers (p \ 0.0001 and p = 0.051, respectively). Fasting free FA concentrations were significantly decreased by * 25 % at D14 of the protocol in both groups (p \ 0.001) with a trend to return to initial values at D56. These changes seem to be associated with a significant reduction in whole body lipid oxidation rates measured by indirect calorimetry, at D14 of the protocol in both groups (p \ 0.01) with a return to initial values in the two groups at D56 (D0 vs D56 and D14 vs D56: ns) and positive correlations between these two factors with R2 = 0.128, (p \ 0.01) and R2 = 0.232, (p \ 0.001) for NW and OW, respectively. The phenotype and dietary-induced differences in basal metabolic rates disappeared after adjustment for fat free mass. Fasting glycemia was stable all along the protocol for both groups. The evolution of the insulin resistance index, HOMA, over the 56 days of the intervention was not significantly different for the NW and OW subjects. It did significantly change over time in association with the increase of insulinemia (p \ 0.01): it increased at D14 with a trend at D56 to return to what was observed at D0, especially for the OW subjects. Calculation of the insulin sensitivity index from the insulin clamp values revealed a 75 % higher value in NW compared to OW at D0 (ISI = 13.9 ± 7.3 vs 7.9 ± 2.5 10-4 dL kg-1 min-1 /mIU-1 L-1 respectively, p \ 0.05), but no significant impact of the nutritional intervention in the two groups (data not shown).
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4.2 Metabolic overfeeding-related signatures based on MS untargeted metabolomic pattern analyses 4.2.1 Plasma metabolomic analyses In positive and negative ESI, a total of 623 ions were extracted and statistical analyses have brought out 146 ions that were significantly expressed according to the phenotype (n = 32 ions), time (n = 96 ions) and interaction (n = 18 ions) criteria. The OSC-PLS-DA showed a clear discrimination between the NW and OW subjects at any time of the kinetic with a R2Y of 0.87 and Q2cum of 0.78 (Fig. 1), but no discrimination was obtained between time points of the overfeeding protocol within each group of subjects. Out of these ions (p \ 0.05), 17 metabolites were identified using databases combined with high resolution MS analyses (Table 2). Differences between the two phenotypes can be established from 5 known compounds whereas the time effect can be revealed from 15 known and 1 putative compounds (p \ 0.05). Among these metabolites, lysophosphatidylcholine (LPC) derivatives (saturated and unsaturated) belong to the major class of molecules which abundance is significantly modified over time (5 over 16 metabolites). For phenotype, unsaturated LPC derivatives were the most significantly modified molecules and in all cases, the abundances in the OW subjects were lower than those of the NW. It is interesting to note that when considering the time effect, most of the compounds saw their abundances decreased over the overfeeding period except for lysophosphatidylethanolamine (LPE) (16:0) and LPC(14:0). Only abundances of one identified metabolite, namely LPC (22:6), and one putatively identified compound (3-hydroxy-hexadecanoic acid) were found as significantly modified according to the (phenotype 9 time) interaction. The decrease of plasma abundance of LPC (22:6) along time was bigger for the NW subjects (-33 %) compared to the OW (-13 %) ones. Moreover, the levels of this LPC in the NW subjects at the end of the trial were close to the ones of OW at the beginning, as also observed for the LPC (20:4) and the other metabolite found with the interaction effect, namely 3-hydroxyhexanoic acid. 4.2.2 Urinary metabolic trajectories Univariate statistical methods such as ANOVA for repeated measures using a mixed-effect model highlighted significant differences in urinary metabolomic features according to at least one of the 3 different criteria: phenotype (NW vs OW), time of overfeeding and the
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(phenotype 9 time) interaction. In positive and negative ESI, a total of 2,603 ions were extracted and statistical analyses have brought out 671 ions significantly expressed according to one of these 3 criteria. Among these ions, 114 ions depended on the phenotype, 365 on the overfeeding time and 215 on the interaction. Phenotypes were separated according to the OSC filtered PLS-DA model at any time of the kinetic with a R2Y of 0.73 and a Q2cum of 0.62 (Fig. 2). Out of the significant ions (p \ 0.05) including redundant information (i.e. isotopes, adducts, and in-source fragmentation), parent ions were filtered in order to be utilized in databases for queries. Twenty three metabolites were successfully identified using high resolution MS analyses and 13 putative molecules were found as significant features either for phenotype (NW vs OW), time or (phenotype 9 time) interaction. Data presented in Table 3 shows that differences between the two phenotypes can be identified with 5 known and 2 putative compounds (one being at the limit of significance) whereas the time effect can be revealed with 6 known compounds (p \ 0.05, and 2 at the limit of significance) and 7 putative metabolites (one being at the limit of significance). However, the most interesting features to consider are the metabolites for which there is a significant (phenotype x time) interaction, responsible for distinction between NW and OW timecourse phenotypes (metabolic trajectories) and constituting the metabolic signatures differentiating a normal from an OW metabolic response to overfeeding. Twelve metabolites were identified including 9 acylcarnitines (AcCNs), 1 amino acid, namely tryptophan, and 1 bile acid, namely glycochenodeoxycholic acid (Table 3). Cholic acid was only found significant for phenotype (higher levels in OW subjects compared to the NW, log2 ratio OW/NW = 1.1). Two other bile acids were putatively identified, deoxycholic acid 3-glucuronide as significant variable for the (phenotype 9 time) interaction and oxocholenoic acid (p = 0.054) for the phenotype factor (Table 3). The identified and putative AcCNs are short- and medium-chain species including some odd-chain metabolites resulting from amino acid catabolism. Two AcCNs namely, dodecenoylcarnitine and hydroxycyclohexene 5-aminobutenoylcarnitine, were significantly modified according to phenotype, these being more abundant in the OW group compared to the NW (log2 ratio OW/NW = 0.6 and 0.4, respectively). Five AcCNs (and one being at the limit of significance) were significantly modified during the nutritional intervention. Even though the trend of the kinetics was similar for most of these compounds, the amplitude of variations was different depending on given AcCNs (i.e. undecenoylcarnitine, dodecenoylcarnitine) (Fig. 3). Classification of ions that were preselected as significant for either the effects of phenotype, time or the (phenotype 9 time) interaction at every sampling time was
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achieved thanks to a hierarchical analysis which is helpful to display the main differences detected in metabolic trajectories between the NW and OW groups (Fig. 4). Three sub-trees were identified. The first one included the OW subjects at D0 and D7 and the NW subjects at the end of the protocol (D21 to D56, except D28; in green on the figure). The second sub-tree (blue line) incorporated the NW subjects over the first half of the intervention, while the last one (red line) was composed of the OW subjects at D14 and after. Over the 56 days of the intervention, D14, D21 and D28 could be important time points regarding metabolic adaptation to the diet, as shifts seem to occur in the kinetics of different metabolites for the NW and OW individuals. Changes in metabolic signatures occurring at different time points for the NW volunteers (D21 and D28) are indicative of some metabolic adjustments found in this group, while major changes occurred at D14 for the OW subjects with resulting signatures maintained in the same sub-tree to the end of the protocol. Furthermore, at D21 and at the end of the protocol, metabolic signatures of the NW subjects were close to the one of OW at the beginning. Additionally, when analyzing the different clusters of metabolites, 6 sub-trees can be defined and interestingly most of the AcCNs were found in S1, S5, and S2 whereas the bile acid(s) was(ere) found in S4 and S2. Besides, abundances of metabolites in the S1 and S5 sub-trees were close for the NW subjects at the end of the trial to those found for OW at the beginning whereas those of S4 represented the different metabolic signatures for the NW and OW subjects with a weak (phenotype 9 time) interaction. OSC-PLS-DA was performed with data at D0 and D56 to investigate the global metabolic evolution of both phenotypes. Results (Fig. 5) showed a clear discrimination of the OW at D56 from all other groups (NW-D0 and -D56, OWD0), with a good quality of the models (R2Y = 0.76, Q2cum = 0.59). All these data indicated that metabolic signatures of the OW subjects diverged from the initial point. Inspection of the loadings plot indicated that discrimination of the samples was mainly explained by ions identified as AcCNs, bile acids, tryptophan, and two glucuronide derivatives of steroids, namely dihydrotestosterone and androsterone (Table 3).
5 Discussion The understanding of metabolic processes occurring in nutritional transition characterized by positive energy balance and weight gain has been improved with evidence on abnormal lipid metabolism and altered levels of intermediates such as betaine, carnitine, and acylcarnitines leading to dysregulation of energy metabolism going toward fat accumulation and lipotoxicity (Newgard 2012).
Metabolomics and metabolic changes in weight gain
Nonetheless, kinetic data on the first steps linked to adaptive processes and metabolic flexibility are still lacking. Indeed, the present study allowed detecting early metabolic adaptations setting up to face the overnutrition and maintenance of energy homeostasis. Evaluation of metabolic status associated with OW and obesity is classically performed with well established, limited plasma and/or urine metabolic parameters, indicators of metabolic alteration. The present longitudinal study based on the combination of classical clinical parameters and an untargeted metabolomic approach that allows monitoring a large number of metabolites (thousands) was able to reveal significant metabolic trajectory differences between NW and OW subjects following a two-month lipid-enriched overfeeding protocol, thus suggesting differences in metabolic flexibility. In fact, these differences are well evidenced in urine after univariate (based on the complete phenotype 9 time model) and multivariate (OSC-PLS-DA) statistical analyses, which mainly highlighted significant variations of AcCNs and bile acid levels. Indeed, PLS-DA model allowed predicting sample classification at the end of the protocol. In addition, this approach on plasma showed the effect of the nutritional intervention and/or of the phenotype but plasma metabolite signatures evolve in general similarly in NW and OW subjects. This may suggest that plasma compartment is submitted to a more constraint homeostatic regulation. Moreover, these data advocate toward distinct metabolic homeostatic regulations and metabolic consequences of a comparable weight gain induced by overfeeding, in the two populations considered here. Comparison of the present plasma metabolomics data with those already published is not trivial as to the best of our knowledge our study is the first long-term longitudinal intervention (2 months) while most of literature data deals only with comparison of case versus control. Moreover, metabolite identification is still the limiting step in the generation of mechanistic understanding of the metabolomic data. The estimation of insulin resistance using the HOMA index showed no significant difference at the beginning of the intervention between the NW and OW. Only the ISI revealed that OW subjects were significantly less insulin sensitive than the NW ones without effect of the nutritional intervention. Nonetheless, the metabolomic approach on plasma allowed to identify 2 metabolites in interaction among which is LPC (22:6). LPC species constitute an important class of bioactive molecules which have been shown to be implicated in inflammatory disorders (D’Arrigo and Servi 2010; Hung et al. 2011; Hung et al. 2012; Sevastou et al. 2013). Some unsaturated LPCs especially those containing 20:4 and 22:6 (Huang et al. 2010; Hung et al. 2009), may have anti-inflammatory
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properties compared to the saturated ones such as LPC (14:0) and LPC (16:0). In the present study, plasma abundances of LPC (22:6) decreased whereas those of saturated LPC (14:0) increased along the trial, which could suggest an augmentation of low grade inflammation induced by the overnutrition. This observation is unlikely to be directly associated with the saturated FA content of the diet, considering that the intake of 14:0 from the overfeeding is much lower than that of 16:0. Increased levels of LPC (14:0), LPC (18:0) as in our study, and lower levels of LPC (18:1) were observed by Kim and coll. (Kim et al. 2010) when investigating the plasma metabolomic profile of insulin resistant (evaluated with HOMA index) OW/obese and lean subjects. In metabolic situations such as obesity, diabetes, where inflammation is a common factor, plasma LPCs seem to be affected (Ha et al. 2012). Interestingly, the low grade inflammation effects observed in our study were not evidenced using the usually used markers of inflammation such as IL6 and hsCRP. In addition, no major changes in the expression of genes related to inflammation have been identified in adipose tissue of the same subjects (Alligier et al. 2012). However, these data should be taken cautiously as heterogeneous results have been published so far concerning the role of PC and LPC metabolites and their modulation by the diets (Hanhineva et al. 2013). Detailed lipidomic profiling would be necessary to gain a deeper insight into the regulation and roles of various lipid species, and in particular PCs and LPCs. Furthermore, increased levels of C3, C5 AcCNs (byproducts of BCAA catabolism), of C4 AcCN (in potential abnormal metabolism of FA oxidation), of two aromatic amino acids, and of FA oxidation and synthesis products were evidenced in OW men, observations which have already been linked with insulin resistance (Mihalik et al. 2010; Newgard 2012). However, these metabolites were not identified here as discriminant molecules in the plasma metabolome of the present volunteers who were not obese or diabetic. In the present study, a release of AcCNs in urine suggests, as it has been shown for inborn metabolic disorders, that FA intermediates are excreted as carnitine esters in conditions of caloric imbalance (Libert et al. 2000) and an increased urine excretion of AcCN is consistent with lower circulating levels of these metabolites. In the context of inborn mitochondrial diseases, acylcarnitine production has been viewed as a detoxifying system that permits mitochondrial efflux of excess of acyl groups (Steiber et al. 2004). Although specific functions of the different carnitines are still not known, methylglutarylcarnitine has been previously related to insulin resistance and plasma levels in diabetic mice are increased (Altmaier et al. 2008). Moreover, the decrease observed over the feeding period in the level of this carnitine is in accordance with
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data of mice fed a high-fat diet (Rubio-Aliaga et al. 2011). Hydroxy-, saturated and unsaturated, odd- but also evenshort and medium-chain AcCNs are identified in the present study. The found C5 AcCNs are byproducts of BCAA and not of lipid catabolism. The development and progression of insulin resistance have been shown to involve a series of specific metabolic shifts in particular in skeletal muscle and liver, leading to changes in plasma and urine metabolic profiles. Variations in plasma levels of AcCNs have been associated with incomplete FA b-oxidation in insulin resistance and diabetes (Adams et al. 2009; Koves et al. 2008). In the present study, the lack of changes in plasma abundances of AcCNs and the increase in urine excretion of several medium-chain AcCNs in OW subjects prompts speculation. The overfeeding protocol in OW subjects may have initiated progressively some imbalanced environment, leading to incomplete b-oxidation and thus intramitochondrial accumulation of acyl-CoAs and their respective AcCNs. Those are nonetheless eliminated through urine excretion as metabolic flexibility seems to still exist. Consistently, there was no indication of important insulin resistance in the OW subjects. One can think that elimination of these molecules still occurs before an accumulation in liver and muscle that takes place with insulin resistance progression and further metabolic-related dysfunctions. Going further towards obesity and fat storage will lead to more complex b-oxidation dysfunction with urinary excretion of long-chain AcCNs (Duranti et al. 2008). This is consistent with our acquired indirect calorimetry data, which measures complete oxidation, suggesting changes in metabolic flexibility related to lipid versus carbohydrate oxidation with changes in fuel partitioning. Indeed, in all subjects, a significant decrease in lipid oxidation was observed at D14, being compensated by an increase in carbohydrate oxidation. In an attempt to summarize these processes, we could speculate, integrating them within the Fig. 6 that proposes a schematic overview of the dynamic changes in metabolic flexibility that could occur during weight gain induced by overnutrition with progression of insulin resistance. Apart from their well-known role in intestinal fat absorption, bile acids have also been shown to affect metabolism, triggering downstream signaling pathways, influencing energy expenditure and glucose homeostasis, both in mouse and human studies (Alnouti et al. 2008; Lefebvre et al. 2009; Nieuwdorp et al. 2014; Taylor et al. 2014). Moreover, it is well established that intestinal microbiota has profound effects on bile acid metabolism but also on glucose metabolism in humans (Joyce and Gahan 2014; Vrieze et al. 2014). In this context, the modifications of abundances of 4 bile acids (2 identified and 2 putative) observed in this study can be of importance
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as, according to literature data, they are increased in OW subjects (Taylor et al. 2014). The excretion of bile acids especially that of deoxycholic acid 3-glucuronide, which abundances were significantly modified according to the (phenotype 9 time) interaction, may be of special interest. Deoxycholic acid is a major secondary bile acid synthesized by gut microbiota and a complex relationship between bile acid metabolism and gut microbiota has been documented (Kootte et al. 2012). Therefore, targeted analyses of gut microbiota and of urine bile acid compositions would be of great interest to analyze the implication of microbiota in the differential response of NW and OW subjects to overfeeding within a validation in a larger study.
6 Conclusion Our study demonstrates that metabolomics, as a more comprehensive characterization of the biological response, allows detecting subtle changes in metabolism adjustments that were not evidenced by a classical approach, between OW or NW subjects challenged with the same high fatenriched overfeeding along 8 weeks. This approach provides an opportunity to generate new hypotheses on metabolic pathway perturbations and one could hypothesize that some of the significant identified molecules (specific lysophospholipids/LPCs or acylcarnitine esters) may function as signaling molecules to modulate and/or participate in processes such as low grade inflammation and altered FA metabolism as well as differential mitochondrial function. In comparison to the NW, the OW subjects have shown different metabolic trajectories, linked to difference in metabolic plasticity, and ended-up with different urinary metabolomic profiles, revealing maladjustment to overnutrition. Over time increase of short-, medium-chain acylcarnitines in urine may be linked to early incomplete boxidation. Excretion of bile acids may also deserve a special attention to evaluate the implication of gut microbiota in the differential response of NW and OW subjects to overfeeding. This longitudinal study also allowed identifying the effects of mild weight gain, before any clinical sign of obesity or metabolic disorders and therefore opened the door for development of predictive candidate biomarkers. When duly validated they may later become comprehensive and sensitive biomarkers of metabolic dysregulation. Acknowledgments This research was supported by the Agence Nationale pour la Recherche (Project, PNRA-007, 2007-2010), Danone (18 months of a post-doctorate position), the Actions Incitatives from the Hospices Civils de Lyon and the Programme Hospitalier de Recherche Clinique Inter-re´gional. We would also like to thank H. Pereira for LC–MS analyses of plasma samples.
Metabolomics and metabolic changes in weight gain Conflict of interest None of the authors have a conflict of interest relevant to this work. Ethical Standards All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2000. Informed consent was obtained from all participants for being included in the study.
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