Metabolomic Analysis of Livers and Serum from High-Fat Diet Induced Obese Mice Hyun-Jin Kim, Jin Hee Kim, Siwon Noh, Haeng Jeon Hur, Mi Jeong Sung, Jin-Taek Hwang, Jae Ho Park, Hye Jeong Yang, Myung-Sunny Kim, Dae Young Kwon,* and Suk Hoo Yoon* Research Division for Emerging Innovation Technology, Korea Food Research Institute, 516 Baekhyun, Bundang, Sungnam, Kyongki 463-746, Republic of Korea Received September 1, 2010
Liver and serum metabolites of obese and lean mice fed on high fat or normal diets were analyzed using ultraperformance liquid chromatography-quadrupole-time-of-flight mass spectrometry, gas chromatography-mass spectrometry, and partial least-squares-discriminant analysis (PLS-DA). Obese and lean groups were clearly discriminated from each other on PLS-DA score plot and major metabolites contributing to the discrimination were assigned as lipid metabolites (fatty acids, phosphatidylcholines (PCs), and lysophosphatidylcholines (lysoPCs)), lipid metabolism intermediates (betaine, carnitine, and acylcarnitines), amino acids, acidic compounds, monosaccharides, and serotonin. A high-fat diet increased lipid metabolites but decreased lipid metabolism intermediates and the NAD/NADH ratio, indicating that abnormal lipid and energy metabolism induced by a high-fat diet resulted in fat accumulation via decreased β-oxidation. In addition, this study revealed that the levels of many metabolites, including serotonin, betaine, pipecolic acid, and uric acid, were positively or negatively related to obesity-associated diseases. On the basis of these metabolites, we proposed a metabolic pathway related to high-fat diet-induced obesity. These metabolites can be used to better understand obesity and related diseases induced by a hyperlipidic diet. Furthermore, the level changes of these metabolites can be used to assess the risk of obesity and the therapeutic effect of obesity management. Keywords: obesity • metabolomics • UPLC-Q-TOF • GC-MS • betaine • carnitine • acylcarnitine • lysophosphatidylcholine • β-oxidation • serotonin • high-fat diet
Introduction Over the past decade, the prevalence of obesity has dramatically increased across all genders and age groups and has reached epidemic proportions in developed and developing countries due to many factors, including genetic, environmental, psychological, and physical causes.1-3 Because obesity is a primary risk factor for many diseases such as noninsulindependent diabetes, cardiovascular disease, arthritis, and cancer,4 it is rapidly becoming a serious public health problem and together with smoking may become the number one cause of death in many countries including the United States where 65% of the adult population is considered overweight or obese.2 Recent studies suggest that obesity is linked to adipocyte hypertrophy and hyperplasia caused by the response to longterm imbalance between energy intake and expenditure5 and may be regulated by controlling adipogenesis.6,7 However, the metabolism of obesity and the organic dysfunction associated with obesity is not clearly understood despite completion of many physiological evidence-based molecular studies. To better understand these biochemical mechanisms, comprehensive investigations of obesity and related dysfunctions using high* To whom correspondence should be addressed. Dr. Suk Hoo Yoon; Dr. Dae Young Kwon; Korea Food Research Institute, 516 Baekhyun, Bundang, Sungnam, Kyongki 463-746, Republic of Korea. Tel: +82-31-780-9124. Fax: +82-31-709-9876. E-mail:
[email protected];
[email protected].
722 Journal of Proteome Research 2011, 10, 722–731 Published on Web 11/04/2010
throughput analysis such as genomics, proteomics, and metabolomics have recently been performed. Metabolomics (also referred to as metabonomics) is defined as the comprehensive quantitative and qualitative analysis of all metabolites in cells, tissues, or biofluids following a genetic modification or physiological stimulus8 and is a newly emerging field in advanced and specialized analytical biochemistry. Although no single instrument platform can analyze all metabolites, it has been shown that metabolomics is a suitable technology for distinguishing different phenotypes and finding potential biomarkers associated with certain phenotypes. Recently, metabolomic profiles of urine and serum from high fat-diet induced obese mice9,10 and the plasma and/or liver from obese Zucker rats11,12 were investigated using nuclear magnetic resonance (NMR) or liquid chromatography-mass spectrometry (LC-MS), and we have also previously investigated the differences in plasma metabolites between overweight/ obese and lean men using ultraperformance liquid chromatography-quadrupole-time-of-flight (UPLC-Q-TOF) MS.13 Several metabolites were found to be potential biomarkers of obesity and related diseases through these human and animal studies, including lysophosphatidylcholines (lysoPCs), fatty acids, and branched-amino acids (BCAAs), but there are limitations to understanding obesity metabolism based on a few identified metabolites. 10.1021/pr100892r
2011 American Chemical Society
Metabolomic Analysis of Livers and Serum Therefore, the identification of more metabolites related to obesity is necessary to further our understanding of obesity metabolism. Metabolite identification will be facilitated by the combination of high-throughput technologies (LC-MS, gas chromatography-mass spectrometry (GC-MS), or NMR), leading to a better understanding of obesity metabolism. The combination of LC-MS and GC-MS is especially appropriate to assess dynamic changes in metabolism of obesity and related diseases because LC-MS is sensitive enough to detect the largest portion of metabolites in tissue and biofluids and GC-MS can successfully analyze some of the components undetectable parts of LC/MS. However, this combined technology has not yet been used to metabolomic analysis of the liver and serum from obese subjects. In addition to these combined technologies, the metabolomic analysis of more than two samples (tissue and biofluid(s)) can be more effective to find metabolites related to obesity than a single sample (blood, urine, or liver). In this study, metabolomic profiling of livers and serum from obese and lean mice on a high-fat and normal diet, respectively, was analyzed by UPLC-Q-TOF MS and GC-MS and the resultant data was subjected to multivariate statistical analysis to find metabolites that contribute to the discrimination between lean and obese groups. In addition, a high-fat diet induced obesity metabolic pathway was proposed on the basis of the assigned metabolites.
Experimental Section Materials and Methods. Cell Culture and Induction of Steatosis. HepG2 hepatocarcinoma cells were purchased from American Type Culture Collection (Manassas, VA). HepG2 cells were cultured in RPMI media (Welgene, Korea) supplemented with 10% fetal bovine serum (FBS, Welgene) and 1% Antibiotic Antimycotic Solution (Welgene) in a CO2 incubator at 37 °C. To induce steatosis, HepG2 cells were seeded onto 10 cm culture dishes, grown to full confluence, and incubated with culture media containing 1 mM oleic acid and 1% bovine serum albumin (Invitrogen, CA). After 18 h, HepG2 cells were washed 2 times with PBS and were frozen by liquid nitrogen and freezedried, and cell metabolites were extracted with 20% aqueous methanol. Animals and Diets. Six-week-old male C57BL/6 mice obtained from Charles River Korea (Seoul, Korea) were housed at the Korea Food Research Institute at a constant temperature (22-26 °C) under light/dark cycles of 12 h per day. Mice had access to autoclaved water and pellet food ad libitum. Mice were fed a high fat D12492 (60% kcal, high fat) or low fat D12450B (10% kcal, low fat) diet (Research Diets, New Brunswick, NJ) for 10 weeks. Body weight was measured weekly. Serum was prepared from blood collected from the eyes of mice just before sacrifice, and the harvested liver and fat were immediately put into liquid nitrogen after weighing. All samples were stored at -70 °C until analysis. Histological Examination. The mouse fat and livers were sectioned in blocks and fixed in 4% paraformaldehyde. The sections were dehydrated in graded concentrations of alcohols, embedded in paraffin, and stained with hematoxylin-eosin. Triglyceride, Cholesterol, and NAD/NADH Ratio Assay. Triglyceride (TG) and cholesterol contents in serum and the liver extracted by a mixture solvent of chloroform/methanol (2:1) was determined by the TG Assay Kit (Biovision Inc., Mountain View, CA) and the Cholesterol/Cholesteryl Ester Quantitation Kit (Biovision Inc.), respectively. The hepatic
research articles NAD/NADH ratio was measured with an NAD/NADH Quantification Kit (Biovision Inc.). Sample Preparation. Freeze-dried liver powder (20 mg) was extracted with 1 mL of cold acetonitrile (ACN) by a homogenizer. After centrifuging, the supernatant was completely dried and the dried sample was dissolved in 40% aqueous methanol for UPLC-Q-TOF analysis. For GC-MS analysis, liver ACN extract containing an internal standard, caprylic acid, was dried and derivatized by the mixture solvent of N,O-bis(trimethylsilyl) trifluoroacetamide (BSTFA) 50 µL, ACN 100 µL, DMSO 100 µL, and pyridine 50 µL at 70 °C for 2 h. After cooling, the derivatized sample was diluted with methanol. Serum protein was precipitated by addition of cold ACN. After centrifuging, the dried supernatant was dissolved in 20% aqueous methanol for UPLC-Q-TOF analysis. For GC-MS analysis, dried plasma sample containing the internal standard was derivatized and diluted under the same conditions described for liver sample preparation. UPLC-Q-TOF MS Analysis of Liver and Serum Extracts. An UPLC system (Waters, Milford, MA) equipped with a binary solvent delivery system and an autosampler detector was used to analyze liver and serum metabolites. Liver and serum extracts were injected into an Acquity UPLC BEH C18 column (2.1 × 50 mm, 1.7 µm; Waters) equipped to the UPLC system and equilibrated with water containing 0.1% trifluoroacetic acid (TFA). Sample was eluted in a gradient with acetonitrile containing 0.1% TFA at a flow rate of 0.35 mL/min for 14.5 min and metabolites separated by C18-UPLC were analyzed and assigned by Q-TOF Premier MS (Waters). The Q-TOF was operated in ESI positive mode. The capillary and sampling cone voltages were set at 2.78 kV and 26 V, respectively. The desolvation flow was set to 700 L/h at a temperature of 300 °C and the source temperature was set to 110 °C. The TOF MS data was collected in the range of m/z 50-1000 with a scan time of 0.2 s and interscan delay time of 0.02 s. All analyses were performed using the lock spray to ensure accuracy and reproducibility; leucine-enkephalin (556.2771 Da in ESI positive mode) was used as the lock mass at a concentration of 200 Fmole and a flow rate of 3 µL/min. The lock spray frequency was set at 10 s. For quality control (QC), a mixture of 5 standard compounds (caffeine, sulfadimethoxine, terfenadine, 4-acetoaminophenol, and reserpine) was injected after each 5 samples. The MS/MS spectra of metabolites was obtained by a collision energy ramp from 10-30 eV. The accurate mass and composition for the precursor ions and the fragment ions were calculated and sequenced by MassLynx (Waters) incorporated in the instrument. All MS data including retention time, m/z, and ion intensity was extracted by MarkerLynx (Waters) incorporated in the instrument and the resulting MS data were assembled into a data matrix. GC/MS Analysis of Liver and Serum Extracts. A GC system (7890A; Agilent technologies, Inc., Santa Clara, CA) equipped with a mass selective detector (MSD; Agilent technologies, Inc.) and a DB-5MS capillary column (30 m × 0.25 mm × 0.25 µm; J&W Scientific, Folsom, CA) was used to analyze derivatized liver and serum samples. The oven temperature was programmed from 60 to 250 at 10 °C/min and held at 60 and 250 °C for 3 and 10 min, respectively. Injector, source, and quadrupole temperatures were 250, 230, and 150 °C, respectively. Detector voltage was 70 eV and the MS spectra were obtained in the mass range of m/z 50-550. The flow rate of the carrier gas, helium, was 1 mL/min and the solvent delay Journal of Proteome Research • Vol. 10, No. 2, 2011 723
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Figure 1. Characteristics of mice fed a normal or high-fat diet. Histological (A) fat and liver sections, (B) body weight, (C) fat weight, (D) triglyceride (TG) content, (E) cholesterol content, (F) blood sugar amount, and (G) NAD/NADH ratio.
time was 7.5 min. The injection volume was 1 µL and the split ratio was 1:20. Data Processing. LC-MS data, including retention time, m/z, and ion intensity were extracted using the MarkerLynx software (Waters) and assembled into a data matrix. LC-MS data of the liver and serum were aligned and normalized by MarkerLynx (Waters). Peaks were collected using a peak width of 5% height of 1 s, a noise elimination of 6, and an intensity threshold of 50. Data were aligned with a mass tolerance of 0.04 Da and a retention time window of 0.15. All spectra were aligned and normalized to total peak intensity. Assignment of metabolites contributing to the observed variance was performed by elemental composition analysis software with calculated mass, mass tolerance (mDa and ppm), double bond equivalent (DBE), and i-Fit algorithm (the likelihood that the isotopic pattern of the elemental composition matches a cluster of peaks in the spectrum) implemented in the MassLynx, by ChemSpider database (www.chemspider.com), and by Human Metabolome Database (www.hmdb.ca). Authentic standards were used to confirm the assignments and for quantitative analysis. GC-MS data were exported to MATLAB (Mathworks, Inc., 2006) and the data were aligned. All spectra were normalized to total peak intensity. Identification of compounds was based on the comparison of mass spectra, retention index (RI), and authentic standards. The mass spectrum of each compound was compared with that of Wiley and NIST mass spectral databases. Statistical Analysis. The mean-centered LC-/MS and GC-MS data sets were analyzed by multivariate statistical analysis of SIMCA-P+ version 12.0.1 (Umetrics, Umeå, Sweden). Partial least-squares discriminant analysis (PLS-DA) and orthogonal 724
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partial least-squares discriminant analysis (OPLS-DA) were used to visually discriminate between obese and lean samples. Hotelling’s T2 test was used to statistically analyze the difference between groups and outlying samples of the ellipse region defined as the 95% confidence interval of the modeled variation were excluded from further analysis. The quality of PLS-DA and OPLS-DA models was assessed by 3 parameters: R2X, R2Y, and Q2Y. The goodness of fit was quantified by R2X and R2Y and the predictive ability was indicated by Q2Y. Tovalidatemodels,a 7-fold validation was applied to PLS-DA and OPLS-DA models and the reliabilities of models were further rigorously validated by a permutation test (n ) 200). To find metabolites that contributed to the discrimination, differences of the metabolite intensities of lean and obese groups were tested by independent t test with the Mann-Whitney U-test. The S-plot showing a combination of covariance p(1) and correlation p(corr) from the OPLS-DA model was used to better visualize the metabolites contributing to the discrimination. In addition, statistical analysis of body and fat weight and the contents of TG, cholesterol, and blood sugar were performed by an independent t test with the Mann-Whitney U test.
Results Animal Characteristics. The characteristics of mice fed a high fat or normal diet are shown in Figure 1. The body weight of mice fed a high-fat diet (33.2 ( 1.8 g) was significantly heavier than the body weight of control mice fed a normal diet (23.7 ( 1.4 g). The fat weight and the size of fat cells were increased and fatty livers were observed histologically in mice
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Metabolomic Analysis of Livers and Serum
Figure 2. Partial least-squares-discriminant analysis (PLS-DA) score plots obtained from ultraperformance liquid chromatographyquadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF MS) data of (A) serum and (B) liver, and gas chromatography (GC)-MS data of liver (C) hydrophilic and (D) hydrophobic extracts. Outlying samples of the ellipse region with the 95% confidence interval were excluded by Hotelling’s T2 test. The score plots showed a significant separation between normal and high-fat diet fed mice (A, P < 0.000; B, P < 0.038; C, P < 0.003; D, P < 0.000 by permutation test). Table 1. Summary of Parameters for Assessment of the Quality of PLS-DA Models
LC-MS GC-MS
models
no.a
R2Xcumb
R2Ycumb
Q2Ycumb
R interceptc
Q interceptc
Pd
Serum Liver Liver-hydrophilic Liver-hydrophobic
2 4 2 2
0.596 0.491 0.934 0.946
0.916 0.973 0.541 0.917
0.850 0.541 0.576 0.907
0.289 0.780 0.037 0.053
-0.273 -0.392 -0.279 -0.245
0.000 0.038 0.003 0.000
a No. is the number of components. b R2Xcum and R2Ycum are the cumulative modeled variation in X and Y matrix, respectively, and Q2Ycum is the cumulative predicted variation in Y matrix. c R and Q intercepts were obtained after permutation test (n ) 200). d P is P value obtained from cross validation ANOVA of OPLS-DA or PLS-DA.
fed a high-fat diet, but the liver weight was not significantly different between the high-fat and normal diet groups. Mice fed a high-fat diet also had higher triglyceride (TG) and cholesterol contents in serum (143.0 ( 12.7 mg/dL and 260.4 ( 79.6 mg/dL, respectively) and the liver (331.5 ( 8.4 mg/g and 518.8 ( 216.8 mg/dg, respectively) than lean mice (116.4 ( 15.8 mg/dL and 295.2 ( 17.0 mg/g of TG and 184.5 ( 89.0 mg/dL and 389.9 ( 180.7 mg/dg of cholesterol in serum and liver, respectively). In addition, the blood sugar content was increased 20% by a high-fat diet. The hepatic NAD/NADH ratio of obese mice (2.1 ( 0.4) was 2-fold lower than that of lean mice (4.8 ( 1.1). These results showed that a high-fat diet resulted in obesity in C57BL/6J mice. Multivariate Statistical Analysis of Serum and Liver Metabolites. The MS data of serum and liver metabolites from lean and obese mice analyzed by UPLC-Q-TOF and of hydrophilic and hydrophobic liver metabolites analyzed by GC-MS were applied to PLS-DA score plot (Figure 2A-D). The first two-component PLS-DA score plots of serum and liver metabolites showed distinct clustering for each group of lean and obese mice. Both groups were clearly discriminated from each other by the primary component t(1) or the secondary component t(2) based on the model with R2X
(cum) and R2 (cum) values of 0.49-0.95 and 0.54-0.97, respectively, indicating the goodness of fit of the data, and with Q2 (cum) values of 0.54-0.91, estimating the predictive ability of the model (Table 1). In addition, the PLS-DA models were validated by a permutation test. R intercept values of all models, with the exception of the liver, analyzed by LC-MS were lower than 0.3 and their Q intercept values were lower than -0.2, indicating that the models were not overfitted. Also, the P-values obtained from 7-fold cross validation showed that all groups fitted by different models were significantly different (Table 1). To identify the metabolites contributing to the discrimination, the S-plots of p(1) and p(corr)(1) were generated using centroid scaling (Figure 3). The S-plots revealed that the metabolites with higher or lower p(corr) values were the more relevant ions for explaining the discrimination between both groups. The levels of the metabolites with positive p(corr)values were decreased by high-fat diet, whereas those with negative values were increased. Inversely, in the model of a hydrophobic liver analyzed by GC-MS, the level of the metabolites with positive p(corr) values were increased by high-fat diet, but those of negative values were decreased. In many metabolites, the Journal of Proteome Research • Vol. 10, No. 2, 2011 725
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Figure 3. S-plots associated with the PLS-DA score plots of plasma and liver metabolite profiles derived from UPLC-Q-TOF MS of (A) serum and (B) liver and from GC-MS of liver (hydrophilic extract, C; hydrophobic extract, D). The numbers for the metabolites are as given in Table 2 [liquid chromatography (LC)-MS) and 3(GC-MS).
number marked metabolites in the S-plots were identified and their fold changes were calculated (Tables 2 and 3). Qualitative and Quantitative Analysis of Serum and Liver Metabolites. The normalized intensities of whole metabolites detected by UPLC-Q-TOF (509 and 728 metabolites in serum and liver, respectively) or by GC-MS (64 and 34 metabolites in hydrophilic and hydrophobic liver extracts, respectively) were statistically analyzed by nonparametric t test. All metabolites, including 206 and 91 metabolites in serum and liver, respectively, and 14 and 3 metabolites in hydrophilic and hydrophobic liver extracts, respectively, were significantly affected by a high-fat diet. However, few metabolites could be identified and the results assigned by UPLC-Q-TOF and GC-MS are shown in Tables 2 and 3, respectively. In serum, the contents of 15 metabolites (arginine, tyrosine, pipecolic acid, benzoic acid, pantothenic acid, uric acid, phenylpyruvic acid, phenylacetamide, serotonin, L-carnitine, stearoylcarnitine, PCs, and 3 lysoPCs with C17:0, C18:0, and C18:3) were significantly increased by high-fat diet, whereas 17 metabolites including 4 acyl-carnitines (with C14:0, C16:1, C18:0, C18:1, and C18:2), 11 lysoPCs (with C14:0, C15:0, C16:0, C16:1, C17:1, C18:1, and C18:2, C19:0, C20:1, and C20:4), and 2 lysoPEs (with C18:2 and C20:4) were decreased. Of these, lysoPCs containing C16:0, C18:0, C18:1, and C18:2 with VIP (variable importance in the projection) values above 2.3, which indicates high relevance to the difference between sample groups, were major serum metabolites that contributed to the discrimination between normal and high-fat diet fed mice on the PLS-DA score plot (Table 2). In liver, 5 metabolites (7-ketodeoxycholic acid, pantothenic acid, PCs, and lysoPCs with C20:4 and C22:6) were increased by high-fat diet and 9 metabolites (valine, betaine, L-carnitine, 3-metylgutarylcarnitine, and lysoPCs with C14:0, C16:0, C16:1, C18:0, and C18:3) were negatively affected. Although some identified metabolites had a high P value (P > 0.1), 7-ketodeoxycholic acid, betaine, L-carnitine, and lysoPCs containing C16:0, C16:1, C18:0, C20:4, and C22:6 with VIP values over 1.2 726
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were the most important hepatic metabolites for evaluating the difference between normal and high-fat diet mice (Table 2). In liver, GC/MS analysis revealed that 7 fatty acids (transpalmitoleic acid, palmitic acid, linoleic acid, oleic acid, and stearic acid, and 2 unidentified fatty acids), 5 saccharides (glucose, melibiose, maltose, and 2 unidentified saccharides), glycerol, and tyrosin were affected by a high-fat diet, whereas only tyrosin, glycerol, glucose, and trans-palmitoleic acid were negatively affected by a high-fat diet. Of these, glucose, two unidentified monosaccharides, trans-palmitoleic acid, palmitic acid, linoleic acid, and oleic acid had VIP values over 1.8 and were the major metabolites that contributed to discrimination between normal and high-fat diet fed mice (Table 3).
Discussion In the present study, we investigated blood and hepatic metabolites of high-fat diet-induced obese mice using UPLC-QTOF and GC-MS, and their metabolic profiles were compared to those of normal lean mice by multivariate statistical analysis. We found that several hepatic and blood metabolites associated with lipid metabolism- and obesity-related diseases were altered by high-fat diet feeding and changes in these metabolic profiles contributed to the difference between lean and obese mice. The number of identified metabolites showing a significant difference (P < 0.05) in the present study was greater than that identified in previous studies.9-12 On the basis of the metabolites identified, a high-fat diet induced obese metabolic pathway was proposed (Figure 4). Some of these metabolites have been well studied, whereas others have not. Obesity was associated with distinct changes in global blood and hepatic lipid profiles, including phospholipids and fatty acids, which were the most abundant metabolites in blood and liver. As compared to lean mice, total PC and fatty acid levels were elevated in obese mice and the change in lysoPC levels depended on fatty acid chains. Several studies suggested that fat accumulation is alleviated by PC treatment.14,15 However, the relationship between PC and obesity is not clear. Our study
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Table 2. Identification of Serum and Hepatic Metabolites from Obese and Lean Mice Analyzed from UPLC-Q-TOF MS and their Fold Change Analysis sample
no.a
identity
Serum
1 2 3 4 5 6 7 8
Valine Arginine Tyrosine Pipecolic acid Benzoic acid Pantothenic acid Uric acid Phenylpyruvic acid/ 4-coumaric acid Phenylacetamide Serotonin L-Carnitine Decanoylcarnitine Myristoylcarnitine Hexadecenoylcarnitine Linoleylcarnitine Vaccenylcarnitine Stearoylcarnitine LysoPC (14:0) LysoPC (15:0) LysoPC (16:0) LysoPC (16:1) LysoPC (17:0) LysoPC (17:1) LysoPC (18:0) LysoPC (18:1) LysoPC (18:2) LysoPC (18:3) LysoPC (19:0) LysoPC (20:1) LysoPC (20:4) LysoPC (20:5) LysoPE (18:2) LysoPE (20:4) Pcse Valine 7-ketodeoxycholic acid Pantothenic acid Betaine L-Carnitine 3-Metylgutarylcarnitine LysoPC (14:0) LysoPC (16:0) LysoPC (16:1) LysoPC (18:0) LysoPC (18:3) LysoPC (20:4) LysoPC (22:6) Pcse
Liver
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 1 35 6 36 11 37 18 20 21 24 27 30 38 34
exact mass (M + H)
actual mass (M + H)
mass error (mDa)
118.0868 175.1195 182.0817 130.0868 123.0434 220.1185 169.0362 165.0552
118.0849 175.1175 182.0791 130.0858 123.0434 220.1167 169.0350 165.0526
1.9 2.0 2.6 1.0 0.0 1.8 1.2 2.6
136.0762 177.1028 162.1130 316.2488 372.3114 398.3270 424.3427 426.3583 428.3740 468.3090 482.3247 496.3403 494.3247 510.3560 508.3767 524.3716 522.3560 520.3403 518.3247 538.3873 550.3873 544.3403 542.3247 478.2934 502.2934
136.0744 177.1010 162.1115 316.2476 372.3083 398.3253 424.3414 426.3560 428.3735 468.3072 482.3232 496.3379 494.3208 510.3529 508.3457 524.3699 522.3550 520.3391 518.3240 538.3848 550.3848 544.3373 542.3232 478.2913 502.2920
1.8 1.8 1.5 1.2 3.1 1.7 1.3 2.3 0.5 1.8 1.5 2.4 3.9 3.1 31.0 1.7 1.0 1.2 0.7 2.5 2.5 3.0 1.5 2.1 1.4
118.0868 407.2797 220.1185 118.0868 162.1130 290.1604 468.3090 496.3403 494.3247 524.3716 518.3247 544.3403 568.3403
118.0848 407.2792 220.1162 118.0847 162.1109 290.1624 468.3100 496.3404 494.3235 524.3724 518.3248 544.3397 568.3398
2.0 0.5 2.3 2.1 2.1 -2.0 -1.0 -0.1 1.2 -0.8 -0.1 0.6 0.5
MS fragments
fold changeb (vs lean)
P-valuec
VIPd
72 130, 116, 72, 70 165, 136, 123, 91 105, 91, 84, 72 95, 79 202, 184, 145, 90 169, 152, 141, 98 147, 123, 119, 95, 77
1.32 1.22 1.22 1.72 1.46 1.43 1.60 1.37
0.091 0.037 0.004 0.001 0.001 0.007 0.009 0.001
0.54 0.04 0.70 0.20 0.27 0.05 0.12 0.94
119, 107, 91 142, 132, 115, 105 102, 85, 60 257, 229, 179, 85 341, 285, 267, 85 341, 239, 144, 85 367, 365, 144, 85 369, 144, 85 311, 114, 85 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 337, 184, 104, 62 361, 203, 104 184 72 353, 159, 109, 93 202, 184, 145, 90 58 85, 60 273, 169, 127, 85 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184, 104, 86 184
1.44 7.50 1.56 0.78 0.61 0.51 0.70 0.51 1.38 0.46 0.73 0.90 0.32 1.20 0.18 1.13 0.72 0.74 1.41 0.53 0.39 0.77 0.75 0.59 0.85 3.07 0.38 2.80 1.29 0.64 0.55 0.07 0.67 0.71 0.48 0.72 0.86 1.66 1.27 2.15
0.001 0.001 0.001 0.091 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.023 0.003 0.001 0.007 0.001 0.001 0.003 0.001 0.001 0.003 0.001 0.001 0.002 0.001 0.013 0.003 0.091 0.069 0.001 0.001 0.134 0.260 0.003 1.000 0.608 0.013 0.379 0.235
0.80 0.10 0.07 0.02 0.21 0.38 0.16 0.58 0.11 0.62 0.20 11.97 0.30 0.50 0.19 5.34 9.68 2.31 0.21 0.68 0.90 0.30 0.07 0.78 0.34 0.21 1.20 0.80 5.39 1.72 0.44 0.15 9.00 2.51 1.47 0.28 7.14 2.51
a No. was the number of metabolites marked in Figure 3A and B. b Fold change was calculated by dividing the mean of normalized intensities of each metabolite from obese mice by the mean intensity of the same metabolite from lean mice. c P-value was analyzed by independent t test with the Mann-Whitney U-test. d VIP is variable importance in the project and its value of above 1.00 showing high relevance for explaining the differences of sample groups. e PCs was not successfully separated by C18-UPLC used in this study, but detected by Q-TOF MS. Thus, the amounts of all detected PCs were combined together.
showed opposite results because hepatic PC induced by a highfat diet was secreted into the plasma and resulted in PC accumulation in the blood and an enhanced PC level was positively correlated with fat accumulation. This is consistent with a study on a high-fat and high-cholesterol diet in male mice.16 In contrast to the stimulation of PCs in blood and liver, the levels of most lysoPCs and lysoPEs were decreased in obese mice. Regarding 15 and 7 lysoPC species in blood and liver, respectively, lysoPCs with acyl groups of C17:0, C18:0, and C18:3 in blood and C20:4 and C22:6 in liver were increased. This result
shared some similarities with previous studies in obese men,13 pigs with high-fat/high-carbohydrate,17 and young adult monozygotic twins.18 The plasma levels of lysoPC 14:0 and 18:0 in obese men and lysoPC 18:0 in obese pigs were significantly increased compared to normal subjects, whereas lysoPC 18:1 levels were decreased in obese men and no difference was observed in the lysoPC 16:0 level in pigs. The monozygotic twin study demonstrated that obesity, independent of genetic influences, was primarily related to increases in lysoPCs. Further studies are required to correlate obesity with lysoPCs; however, many studies have suggested that elevated lysoPC levels are Journal of Proteome Research • Vol. 10, No. 2, 2011 727
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Table 3. Identification of Hepatic Metabolites from Obese and Lean Mice Analyzed Using GC-MS and their Fold Change Analysis sample
Liver
no.a
identity
MS fragments
RI value
fold changeb (vs lean)
P-valuec
VIPd
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Tyrosine Unknown Glycerol-3TMS Glucose-5TMS monosaccharidee monosaccharidee trans-Palmitoeic acid-1TMS Palmitic acid(-1TMS) Linoleic acid-1TMS Oleic acid-1TMS Stearic acid-1TMS Melibiose-8TMS Fatty acide Fatty acide Maltose-8TMS
136, 107, 61 233, 185, 61 205, 147, 73 204, 191, 73 217, 204, 73 217, 204, 73 311, 129, 117, 75 313, 117, 73 337, 129, 75, 67 339, 129, 117, 73 341, 117, 73 281, 204, 191, 73 483, 337, 129, 73 485, 339, 129, 73 361, 204, 191, 73
1130 1283 1276 1934/2017 1968 1993 2035 2056 2221 2227 2252 2722 2725 2730 2767
0.48 0.54 0.50 0.84 2.55 2.87 0.45 1.15 1.08 1.14 1.30 3.53 1.53 1.74 2.70
0.001 0.001 0.001 0.001 0.021 0.026 0.001 0.927 0.563 0.738 0.347 0.067 0.041 0.131 0.001
0.28 1.53 0.58 5.15 2.98 3.49 2.66 2.81 1.87 2.38 0.43 0.09 0.24 0.19 0.32
a No. was the number of metabolites marked in Figure 3C and D. b Fold change was calculated by dividing the mean of normalized intensities of each metabolite from obese mice by the mean intensity of the same metabolite from lean mice. c P-value was analyzed by independent t test with the Mann-Whitney U-test. d VIP is variable importance in the project and its value of above 1.00 showing high relevance for explaining the differences of sample groups. e monosaccharide and fatty acid were not successfully identified, but their mass fragments were the same as general mass fragments of monosaccharide and fatty acid, respectively.
Figure 4. Schematic diagram of the metabolic pathway to obesity induced by a high-fat diet based on serum and liver metabolites analysis. The metabolites analyzed by LC-MS and GC-MS are shown in color; red represents increased metabolites, green represents decreased metabolites, yellow represents no change, and the open circles represent no detected metabolites.
positively associated with endothelial dysfunction,17 oxidative stress,17 inflammation,19 and atherogenesis.20 The levels of most hepatic free fatty acids were increased in obese mice compared to lean mice, but interestingly, the transpalmitoleic acid level was decreased. An epidemiological study 728
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on the relationship between trans-fatty acid consumption and biomarkers of inflammation and endothelial dysfunction in 730 healthy women revealed that trans-fatty acids were positively associated with a higher risk of cardiovascular disease; however, whereas trans-oleic acid, trans, trans-linoleic acid, and cis,
Metabolomic Analysis of Livers and Serum trans-linoleic acid were positively correlated with the biomarker levels, trans-palmitoleic acid was inversely associated with the biomarker levels.21 Thus, this epidemiological result supported our finding of decreased trans-palmitoleic acid levels in obese mice with cardiovascular disease symptoms. Generally, fatty acids are broken down in the mitochondria and/or peroxisomes to produce energy through β-oxidation and excess lipids are stored in adipose tissue as TG. However, the lipid metabolic process was abnormal and the levels of the metabolism intermediates such as betaine, carnitines, and acylcarnitines were altered with high-fat diet intake. Consequently, energy metabolism was dysregulated. Hepatic NAD levels in obese mice were decreased whereas NADH was increased compared to lean mice, resulting in a decreased NAD/NADH ratio, which plays an important role in regulation of energy metabolism.22 Similarly, a study of obese mice fed a high-fat diet with/without resveratrol showed that resveratrol used as an antiobesity agent increased the NAD/NADH ratio in an AMPK-dependent manner compared to high-fat diet fed obese mice without resveratrol.23 The changes in both lipid metabolism intermediates and energy metabolism were directly or indirectly involved in fat accumulation of obese subjects. Betaine is an essential osmolyte obtained from either the diet or choline oxidation that assists in cell volume regulation and plays a role as a methyl donor for the remethylation of the methionine-homocysteine cycle, producing a key factor in fatty acid metabolism related to transportation of long-chain fatty acids into mitochondria, carnitine.24,25 Thus, decreased betaine levels in obese mice presumably have a negative effect on the flow of the methionine-homocysteine cycle, resulting in a decreased hepatic carnitine level. Eventually, the transportation of fatty acids (acylcarnitine forms) for energy production via β-oxidation and the citric acid cycle might be decreased by a high-fat diet and presumably result in the accumulation of transported fatty acids in adipose tissue. Although the activities of enzymesassociated with metabolism were not measured in the present study, many previous enzyme studies in high-fat diet animal models26,27 supported our results. In particular, the activity of carnitine palmitoyltransferase-1 (CPT-1), an enzyme that catalyzes the rate-limiting step of β-oxidation through the transportation of long-chain acyl groups from fatty acids, was significantly reduced in mice with high-fat diet-induced obesity and its mRNA levels were reduced in mice fed the high-fat diet versus regular diet26 despite a report that CPT-1 expression levels were affected by fat deposits.28 Thus, these results suggested that fat accumulation by a high-fat diet is directly affected by abnormal metabolic processes, from betaine to carnitine to β-oxidation. A cell metabolomic study on oleic acid-induced steatosis in HepG2 cells confirmed that betaine, methionine, and carnitine levels were decreased in the cell treated with oleic acid (data not shown). Furthermore, hepatic levels of the bile acid 7-ketodeoxycholic acid that is essential for the absorption of dietary fats and cholesterol homeostasis was increased by a high-fat diet, and this result was supported by an epidemiological study showing that consumption of a low-fat, high-carbohydrate, and highfiber diet leads to reduced fecal concentrations of secondary bile acids.29 In addition to abnormal lipid metabolism, the main biological cause of obesity is disordered appetite regulation. Serotonin (5-hydroxytryptamine), a monoamine neurotransmitter derived from tryptophan, is involved in appetite
research articles regulation. Genetic evidence suggests that serotonin has a suppressive effect on food intake and body weight gain.30,31 Although approximately 80% of the human body’s total serotonin is located in the enterochromaffin cells in the gut and blood platelets, appetite regulation occurs by serotonin in the brain or the central nervous system30,32 rather than by blood serotonin because blood serotonin penetration of the brain is prevented by the blood-brain barrier.33 Interestingly, we found that the serotonin blood level in high-fat diet-fed obese mice was much higher than in lean mice and the food intake of obese mice was decreased approximately10%. A similar result was observed at the onset of the high-fat diet in an Osborne-Mendel rat study.34 Although typically considered a neurotransmitter, recent studies indicate that serotonin plays an important role in the pathogenesis of inflammatory disorders.35 Hence, it was suggested that obesity induced by a high-fat diet was related to inflammation and that blood serotonin could be recognized as an important inflammatory mediator. In addition to causing obesity, hyperlipidic diets lead to various obesity-associated diseases, and we found some metabolites related to such diseases. A decrease in fatty oxidation and lipid accumulation are linked with liver dysfunction associated with peroxisomal disorders. The dysfunction leads to pipecolic acid accumulation in biofluids such as blood and urine and to decreased betaine levels in liver and plasma,24,36 which is consistent with our observations. Additionally, many studies suggest that betaine, an important intermediate of lipid metabolism, was negatively correlated with metabolic syndrome and diabetes24 and helped to reduce potentially toxic levels of homocysteine, which is related to heart disease and stroke.24 We also observed that blood levels of uric acid, a known obesityrelated indicator and the final oxidation product of purine metabolism, were significantly elevated in obese mice compared to lean mice. An elevated level of uric acid is positively correlated with increased body weight, TG, and total cholesterol, and increasing evidence strongly suggests that hyperuricemia is associated with an increased risk of obesity-associated diseases such as hypertension, metabolic syndrome, diabetes, abdominal obesity, endothelial dysfunction, inflammation, subclinical atherosclerosis, and cardiovascular events.37,38 Epidemiological studies also indicated that hyperuricemia is positively related to a risk factor for renal dysfunction in the normal population39 and in patients with hypertension40 and diabetes.41 Regarding branchedchain amino acids (BCAAs) related to diabetes,42 blood valine levels were increased in obese mice whereas levels were decreased in the liver. A metabolomic study of obese versus lean humans13 and an animal study on high-fat diet with/ without BCAA or normal diet42 revealed that blood BCAA levels were significantly higher in obese subjects compared to lean subjects and that the elevated BCAA levels, especially valine, associated with decreased BCAA catabolism contributed to the development of obesity-associated insulin resistance. Elevated insulin resistance by obesity was positively related to increased hepatic levels of mono- and disaccharides in obese mice, as expected, but was not related to the hepatic glucose level. Although genomic and biochemical studies will be required to clearly explain the correlation between metabolites and obesity-associated diseases, and some metabolites affected by high-fat diet were not discussed here, these results strongly suggest that some meJournal of Proteome Research • Vol. 10, No. 2, 2011 729
research articles tabolites altered by high-fat diet are positively or negatively related to diseases and possibly can be used as biomarkers for obesity-associated diseases. We proposed a metabolic pathway related to high-fat dietinduced obesity based on the metabolites we found in this study (Figure 4). Although the pathway was insufficient to clearly explain obesity metabolism, the pathway with the most metabolites found by previous human13 and animal studies9-12 was the abnormal regulation of lipid and energy metabolism and derived metabolites. The proposed pathway will presumably facilitate our understanding of obesity and related diseases symptoms, but more studies including genetic and biochemical studies are needed to confirm the role of this pathway in highfat diet-induced obesity. In summary, a metabolomic study on the liver and blood of obese mice induced by a high-fat diet using UPLC-Q-TOF MS and GC-MS provided important metabolic information for obesity and related diseases. A high-fat diet increased lipid profiles (i.e., PCs, lysoPCs, and fatty acids) and decreased intermediates of lipid metabolism (i.e., betaine, carnitine, and acylcarnitines) and the NAD/NADH ratio, indicating dysregulation of energy metabolism via β-oxidation. Thus, the abnormal lipid and energy metabolism by high-fat diet induced fat accumulation. In addition, this study revealed that many metabolites, including serotonin, betaine, pipecolic acid, uric acid, and valine, were positively or negatively related to obesityassociated diseases. Therefore, these metabolites could be used to better understand obesity and related diseases induced by a hyperlipidic diet, increase the predictability of the obesity risk, and assess the therapeutic effect of antiobesity agents.
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