Identifying Static and Kinetic Lipid Phenotypes by High Resolution

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Identifying Static and Kinetic Lipid Phenotypes by High Resolution UPLC MS: Unraveling Diet-Induced Changes in Lipid Homeostasis by Coupling Metabolomics and Fluxomics Jose M. Castro-Perez,*,†,‡ Thomas P. Roddy,† Vinit Shah,† David G. McLaren,† Sheng-Ping Wang,† Kristian Jensen,† Rob J. Vreeken,‡,§ Thomas Hankemeier,‡,§ Douglas G. Johns,† Stephen F. Previs,† and Brian K. Hubbard† †

Department of Cardiovascular Diseases Atherosclerosis Rahway, Merck Research Laboratories, New Jersey 07065, United States Division of Analytical Biosciences, LACDR, Leiden University, P.O. Box 9502, 2300 RA Leiden, The Netherlands § Netherlands Metabolomics Centre, LACDR, Leiden University, P.O. Box 9502, 2300RA Leiden, The Netherlands ‡

ABSTRACT: A novel method to differentiate diet-induced alterations in plasma lipid phenotypes “static (concentration of lipids) and kinetic (endogenous production, e.g., denovo lipogenesis)” was employed. C57Bl6 mice were randomized into 2 groups and fed either a high-carbohydrate, low-fat (HC) or a carbohydrate-free, high-fat diet (HF) diet for 13 days; D2O was administered via intraperitoneal injection and then adding D2O to the drinking water for 96 h. Principal component analysis (PCA) revealed differences in the plasma lipid content, for example, triglycerides (TG) 50:2, 50:3, and 52:2 were up-regulated in mice fed the HC diet, whereas TG 52:4, 52:1, 54:5, 54:3, 54:4, and 54:2 were higher in animals fed the HF diet. However, although the fractional contribution of synthesis was ∼10-fold lower in HF vs HC fed mice, changes in TG concentration were not entirely mediated by altered de novo lipogenesis. In addition, the ability to couple isotope labeling measurements with PCA analyses revealed cases where there were no differences in the concentration of a compound but its source was substantially altered. In summary, this strategy determined (i) the presence/absence of differences in concentration and (ii) the contribution of different pathways and synthesis that could affect lipid biology in a mouse model respectively. KEYWORDS: deuterium oxide, mass spectrometry, denovo lipogenesis, dyslipidemia, atherosclerosis, metabolomics, multivariate statistical analysis, isotopomer

’ INTRODUCTION It is well recognized that dietary carbohydrate and fat intake can affect plasma lipid profiles. For example, fructose-induced triglyceridemia is readily observed in several models; in addition, overfeeding of glucose will up-regulate denovo lipogenesis (DNL).1,2 We have recently examined the effect of dietary carbohydrate on modulating triglyceride synthesis in adipose tissue.3 Those studies demonstrated an uncoupling of fatty acid synthesis from glyceride synthesis, that is, the absence of dietary carbohydrate was associated with a marked reduction in fatty acid synthesis yet there was no effect on total triglyceride synthesis. A follow-up study demonstrated that fatty acid synthesis/secretion into the plasma compartment could be uncoupled from cholesterol synthesis/secretion.4 Steady state measurements of metabolite concentrations that are intermediates in specific biochemical pathways may only provide a snapshot in time, but this does measure their turnover, and their synthesis rates have to be seen in the context of a larger and more complex network of enzymes. Therefore, steady state measurements on their own may not completely reflect the underlying biochemical processes, failing to fully describe the r 2011 American Chemical Society

complete phenotypic modulation. Metabolic or lipid flux analysis as in the context of this research provides a powerful dynamic portrait of the phenotype because it captures the metabolome and its functional biology interactions mediated by enzymatic actions and in relation to the genome. Therefore, the combination of static and dynamic measurements (metabolomics and fluxomics) is expected to be a very powerful approach to enhance data interpretation in biological systems. In spite of the fact that novel findings have been obtained showing dietary-influences on lipid biosynthesis,5 8 virtually all studies have been performed under a relatively low level of resolution. For example, investigators typically examine the effects on total triglyceride or phospholipids and do not direct attention to specific species within each class. There are numerous permutations to combine fatty acids when assembling a triglyceride or a phospholipid; modern UPLC MS instrumentation is capable of resolving many of these analytes by their distinct molecular weight and/or retention time.9 13 The utilization of high resolution mass spectrometry has allowed researchers Received: May 22, 2011 Published: July 11, 2011 4281

dx.doi.org/10.1021/pr200480g | J. Proteome Res. 2011, 10, 4281–4290

Journal of Proteome Research

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Figure 1. Liver gene expression analysis comparing C57Bl6 mice cohort that were either on a HC or a HF diet. Major genes perturbed in this comparison included stearoyl-coenzyme A desaturase 1 (Scd1) (69 fold, p = 0.0008 vs HF diet), Acetyl-CoA carboxylase 1 (Acaca) (3 fold, p = 0.02 vs HF diet), Acetyl-CoA carboxylase 2 (Acacb) (9 fold, p = 0.002 vs HF diet), Fatty acid synthase (Fasn) (3 fold, p = 0.09 vs HF diet), and Fatty acid desaturase (Fads3) (2 fold, p = 0.05 vs HF diet). Red denotes up-regulation and green denotes down-regulation.

to obtain a high degree of specificity and accuracy when trying to decipher unknown molecules. For example, it is possible to obtain full scan MS and pseudo MS2 data (MSE) from a single chromatographic injection,14,15 therein improving throughput and streamlining the identification step. In addition to this, the use of stable isotopes and mass spectrometry has been of paramount importance in nutrition and cardiovascular research as it has enabled researchers with a unique analytical tool to monitor lipid synthesis in plasma and tissues.16 19 Mass isotopomer distribution analysis (MIDA) has been employed to investigate rate of synthesis of proteins, lipids, and carbohydrates.20 22 This involves measurements of the enrichment (or labeling profile) of an analyte after introduction of a precursor stable isotope label (e.g., 13C or 2H).23 29 D2O has been used extensively to measure anabolic rates,30 33 the advantages being (i) it does not perturb the biological homeostasis, (ii) it enters tissues/ cells evenly, and (iii) it is easy to administer. Metabolomics in combination with multivariate statistical analysis34 43 is an emerging field which provides a powerful insight into underlying molecular mechanism of disease or phenotypic effects to treatment of disease via therapeutic intervention. Differences in treatment or

disease groups are attributed to static changes, and no information regarding synthesis rates are obtained by this approach. Coupling static and dynamic alterations may prove to be a powerful approach to provide a more significant and in-depth understanding of biological perturbations either by diet, genetic modification, or therapeutic intervention. In this report, we have determined whether the presence/ absence of dietary glucose would have an effect(s) on individual glyceride species. Attention was directed toward measuring changes in the concentration and the synthesis of circulating triglycerides and phospholipids using the combination of a twotier metabolomics and fluxomics strategy.

’ MATERIALS AND METHODS Biological Samples

All animal protocols were reviewed and approved by Merck Research Laboratories Institutional Animal Care and Use Committee (Rahway, NJ). Male C57Bl/6 mice from Taconic were acclimated at the animal facility for one week. At an age of 10 weeks old, mice were randomized into two groups (n = 26 per 4282

dx.doi.org/10.1021/pr200480g |J. Proteome Res. 2011, 10, 4281–4290


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Figure 2. Effects of dietary intervention on body weight and caloric intake. (A) Slight increase in body weight in mice fed a HF vs a HF diet. (B) Comparable energy intake after ∼5 days of diet intervention. Data are expressed as mean ( standard error mean (sem), n = 6 per group. ***(p < 0.001, day 13).

Figure 3. Effect of a HF vs a HC diet on changes in plasma lipid composition. Multivariate statistics was used to identify differences in the concentration of circulating lipids, a total of 1463 lipid variables were identified. (A) PCA scores plot. (B) S-plot. In (B), the upper-right box highlights lipids that are up-regulated in mice fed a HF diet vs HC diet whereas the lower-left box highlights lipids that are down-regulated in mice fed a HF diet vs HC diet.

group) and fed either a high carbohydrate (HC) or a high fat (HF) diet (D12450, 10% fat, 70% carbohydrate, and 20% protein

or D12369B, 90% fat, 0% carbohydrate, and 10% protein, respectively, Research Diets, New Brunswick, NJ) for 13 days. 4283



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Table 1. Identification and Quantitation of Differences in Circulating Lipidsa retention time (min)

m/z

ppm error

p[1]P

p(corr)[1]P

lipid

fold change HF vs HC diet

1.43

496.3407

0.8

0.187384

0.937384

LPC 16:0

2.0

5.29

760.5854

0.3

0.263713

0.969972

PC 34:1

2.8

4.71

782.5701

0.1

0.195263

0.942795

PC 36:4

2.0

4.55

806.5698

0.2

0.199245

0.945895

PC 38:6

2.0

8.35

846.7541

1.2

0.121045

0.916528

TG 50:3

3.1

8.57

848.7701

0.7

0.198257

0.888712

TG 50:2

3.3

8.81

876.8012

0.9

0.11628

0.559187

TG 52:2

1.2

8.98

666.6196

1.1

0.079173

0.743351

CE 18:2

+1.3

4.40

756.5544

0.1

0.066602

0.918485

PC 34:3

+3.1

4.99

784.5855

0.1

0.245886

0.96919

PC 36:3

+2.7

4.68

832.5854

0.2

0.076552

0.887637

PC 40:7

+1.9

8.39

872.7693

1.6

0.135296

0.886215

TG 52:4

+1.6

9.03

878.8171

0.7

0.148966

0.846114

TG 52:1

+3.2

8.42

898.7855

1

0.186827

0.837785

TG 54:5

+3.1

8.85 8.65

902.817 900.801

0.8 1.1

0.324792 0.313566

0.960438 0.953305

TG 54:3 TG 54:4

+3.8 +4.4

9.05

904.8322

1.2

0.254099

0.925253

TG 54:2

+4.1

a

The fold-change in concentration is shown as the relative difference in mice fed a HF diet vs. HC diet. A positive sign notes an increase in mice fed a HF diet vs. HC diet. PC = phosphatidylcholines, TG = triglyceride, CE = cholesterol ester, LPC = lysophosphatidyl choline.

All mice were then given an intraperitoneal injection of D2O (20 mL/kg of body weight, 99% 2H) and returned to their cages (n = 6 mice per cage) where they were maintained on 5% 2Hlabeled drinking water for the remainder of the study (96 h); this design was sufficient to maintain a steady-state 2H-labeling of body water. A separate cohort of male C57Bl/6 mice (n = 6 in each group) were fed the same diets as described above but in the absence of D2O dosing, this control cohort was used for static lipid measurements. Mice in each group were fed the respective diets ad libitum, and were sedated on various days after injection (n = 6 per day per group), blood and liver tissue samples were then collected and quick-frozen in liquid nitrogen. Samples were stored at 80 °C until analyzed. 2

H-Labeling of Plasma Water

The 2H-labeling in plasma water was determined as previously described.4 Briefly, 2H present in water was exchanged with hydrogen bound to acetone by incubating samples (e.g., 10 μL of plasma or known standards) in a 2 mL glass screw-top GC vial at room temperature for 4 h with 2 μL 10 N NaOH and 5 μL of acetone. The instrument is programmed to inject 5 μL of headspace gas from the GC vial in a splitless mode. Samples were analyzed using a 0.8 min isothermal run (Agilent 5973 MS coupled to a 6890 GC oven fitted with a DB-17 MS column, 15 m 250 μm 0.15 μm, the oven was set at 170 °C and helium carrier flow was set at 1.0 mL min 1), acetone elutes at ∼0.4 min, and the mass spectrometer was set to perform selected ion monitoring of m/z 58 and 59 (10 ms dwell time per ion) in the electron impact ionization mode. Lipid Profiling by UPLC MS

Plasma (20 μL) was extracted for lipid analysis using a dichloromethane (DCM)/methanol mixture (2:1, v/v) as described by Bligh and Dyer.44,45 The inlet system (Acquity UPLC (Waters, Milford, MA) was coupled to a hybrid quadrupole orthogonal time-of-flight mass spectrometer (SYNAPT G2

Figure 4. Reproducibility of natural background isotope ratios. The natural isotopic labeling was determined in several lipids detected in control plasma from C57Bl/6 mice; data are expressed as the ratio of the M1 to the M0 isotopomer. PC = phosphatidylcholine, TG = triglyceride, LPC = lysophosphatidylcholine, cv = coefficient of variation.

HDMS, Waters, MS Technologies, Manchester, U.K.). Mouse plasma lipid extracts were injected (10 μL) using a 1.8 μm particle 100 2.1 mm id Waters Acquity HSS T3 column (Waters, Milford, MA); the column temperature was maintained at 55 °C. The flow rate used was 0.4 mL/min. A binary gradient system consisting of acetonitrile (Burdick & Jackson, Muskegon, MI) and water with 10 mM ammonium formate (Sigma-Aldrich, St Louis, MO) (40:60, v/v) was used as eluent A. Eluent B, consisted of acetonitrile and isopropanol (Burdick & Jackson, Muskegon, MI) both containing 10 mM ammonium formate (10:90, v/v). The gradient used was a linear gradient (curve 6) over a 15 min total run time; during the initial portion of the gradient, it was held at 60% A and 40% B. For the next 10 min, the gradient was ramped in a linear fashion to 100% B and held at this composition for 2 min; hereafter, the system was switched back to 4284



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Figure 5. Mass isotopomer distribution profile of triglyceride 52:2 (16:0 / 18:1 / 18:1). The plasma water labeling remained constant over the course of the study at ∼2.5% enrichment and there was incorporation of 2H as determined using UPLC MS. There are distinct differences in the abundance of heavy isotopomers in mice fed a HC vs a HF diet, Panel (A, C, and E) vs (B, D and F), respectively. (A and B) Isotope labeling in the parent (intact) molecule, (C and D) isotope distribution profile of the daughter ion containing glycerol, 16:0 and 18:1 (i.e., loss of 18:1), and (E and F) isotope distribution profile of the daughter ion containing glycerol, 18:1 and 18:1 (i.e., loss of 16:0). Data are expressed as the average abundance (n = 5) at a given time after correction for natural background labeling.

60% B and 40% A and equilibrated for an additional 3 min. A single injection was made for each sample, and blanks were utilized throughout the sample list. The mass spectrometer was operated in electrospray (ESI) positive ionization mode, and a capillary voltage of 2 kV and a cone voltage of 30 V were utilized. The inlet LC flow was nebulized using nitrogen gas (700 L/h), and the desolvation temperature was kept at 450 °C. Data was acquired over the mass range of 50 1200 Da for both MS and MSE modes.11,14,15,46 The first acquisition function (full scan MS) was set at 5 eV and collected low energy or unfragmented data, while the second acquisition function (MSE) recorded high energy or

fragmented data using a collision energy ramp from 25 to 35 eV. Argon gas was used for collision induced dissociation (CID). The mass spectral resolution was set to 25K full width half mass (FWHM) and typical mass accuracies were in the order of 0 2 ppm. The system was equipped with an integral LockSpray unit with its own reference sprayer that was controlled automatically by the acquisition software to collect a reference scan every 10 s lasting 0.3 s. The LockSpray internal reference used for these experiments was leucine enkephalin (Sigma-Aldrich, St Louis, MO) at a concentration of 5 ng/μL in 50% acetonitrile/50% H2O + 0.1% formic acid (v/v). The reference internal 4285



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Figure 6. Effect of a HC vs a HF diet on plasma lipid labeling. The plasma water labeling remained constant over the course of the study at ∼2.5% 2Henrichment. Consumption of the HC vs the HF diet led to a greater increase in the isotope labeling, consistent with changes in the contribution of de novo lipogenesis. In all cases, there was an increase in the concentration of the noted analytes in mice fed a HC vs HF diet. Data are expressed as the ratio of M1 to M0 isotopomer for a given analyte;. PC = phosphatidylcholine, TG = triglyceride, LPC = lysophosphatidylcholine.

calibrant was introduced into the lock mass sprayer at a constant flow rate of 50 μL/min using an integrated solvent delivery system. A single lock mass calibration at m/z 556.2771 in positive ion mode was used during analysis. RNA Isolation and Real-Time Quantitative PCR Analysis

Frozen liver tissue (∼20 mg) was homogenized in 600 μL RLT lysis buffer (Qiagen, Valencia, CA) containing 0.1% (v/v) β-mercaptoethanol using a PowerGen 125 homogenizer and 7 65 mm disposable plastic generators (Fisher Scientific). Total RNA was extracted from the homogenized tissue using RNeasy Mini Kit (Qiagen, Valencia, CA) following the manufacturer’s protocol. cDNA was generated from 2 μg of RNA using RT2 First Strand kit (SA Biosciences). Real-time PCR analysis was performed on the 7900HT PCR System (Applied Biosystems, Foster City, CA) with 2 SYBR PCR Master Mix and mouse-specific PCR primers for mouse Scd1, (SABioscienses). Expression levels of stearoyl-CoA desaturase (Scd1) mRNA were normalized to an average of that of mouse beta-actin (Actb), Glyceraldehyde 3-phosphate dehydrogenase (Gapdh), Beta-glucuronidase (Gusb), Hypoxanthine-guanine phosphoribosyltransferase (Hprt1), Peptidylprolyl isomerase A (cyclophilin A) (Ppia) and ribosomal protein 113a (Rp113a) in each sample.

Data Processing and Statistical Analysis

GC MS and UPLC MS data acquired were processed by the manufacturer software (Chemstation and MassLynx, respectively). MarkerlynxXS (Waters Corp, MA) was utilized to deconvolute the UPLC MS data into a table of variables containing exact mass and retention time pairs (EMRT). Principal component analysis (PCA) and orthogonal partial least-squares- discrimination analysis (OPLS-DA) were computed using Simca-P (Umetrics, Umea, Sweden). The data was scaled using the Pareto algorithm, and normalization of the variables was conducted utilizing total peak intensities for all the variables detected. Gene expression data for real-time quantitative PCR was processed using Ingenuity software (Ingenuity systems, Redwood City, CA). For the statistical analysis, all the data are presented as ( standard error mean (SEM). Differences between groups were computed by student’s t test statistical analysis (GraphPad Prism, La Jolla, CA). Post-test analysis for quantifiable variables was conducted using Mann Whitney U nonparametric test with one-tailed p-values. Statistical significance was considered for p < 0.05. Lipid Nomenclature

The lipid nomenclature utilized throughout the manuscript is the same as described by Fahy et al.47 For instance, CE 18:1 4286



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Figure 7. Distinct output from static and kinetic lipid measurements. Panels A and D demonstrate the effect(s) of the respective diets on static concentrations of two lipids, whereas the other panels demonstrate the isotopic labeling. In the case of PC 34:2 (A) there was no change in the concentration between animals fed the different diets yet the HC diet affected the source of the fatty acids, that is, they mainly come from de novo lipogenesis (B and C). In contrast, in the case of TG 54:4 (D) there was a ∼4-fold decrease in the concentration in mice fed the HC vs the HF diet yet the fatty acids mainly come from de novo lipogenesis (E and F). Data are expressed as the ratio of M1 to M0 isotopomers for a given analyte; PC = phosphatidylcholine, TG = triglyceride.**** p < 0.0001.

denotes cholesteryl ester containing 18 carbon atoms, and 1 double bond in the fatty acyl substituent, TG 54:3, translates to a triglyceride containing 54 carbon atoms attached to the glycerol backbone and a total of 3 double bonds in the 3 fatty acyl substituents.

’ RESULTS AND DISCUSSION Gene Expression in HC Diet Showed Increased Modulation of Denovo Lipogenesis

Liver samples were analyzed in a key expression node array as described in the method section where 384 specific hepatic genes involved in glucose, lipid, apoptosis, autophagy, and inflammatory

pathways were monitored. Results from the HC diet (Figure 1) clearly depicted a significant increase in the expression of genes that were responsible for fatty acid biosynthesis (red color showed upregulation while green color showed down-regulation); stearoylcoenzyme A desaturase 1 (Scd1) (69 fold, p = 0.0008 vs HF diet), Acetyl-CoA carboxylase 1 (Acaca) (3 fold, p = 0.02 vs HF diet), Acetyl-CoA carboxylase 2 (Acacb) (9 fold, p = 0.002 vs HF diet), Fatty acid synthase (Fasn) (3 fold, p = 0.09 vs HF diet), and Fatty acid desaturase (Fads3) (2 fold, p = 0.05 vs HF diet). These observations were all consistent with an induction of denovo lipogenesis by the HC diet and it was in good agreement with previous studies.1,48,49 4287


Journal of Proteome Research Multivariate Statistical Analysis Revealed Diet-Specific Lipid Alterations

After 13 days on both diets, there was an increment in body weight for mice that were in the HF diet cohort yet energy intake post-treatment remained fairly constant throughout the course of the study (Figure 2A and B). A lipid profile by UPLC MS was conducted on sample sets obtained from animals that were not dosed with D2O. In this analysis, only electrospray positive ion was utilized as we were mainly interested in the synthesis of triglycerides and phosphatidylcholines. A total of 1463 endogenous metabolite variables were obtained. PCA analysis revealed significant lipid phenotypic differences between the two diets (Figure 3A) and OPLS-DA analysis (Figure 3B) identified dietspecific lipid alterations (Table 1); note that there are a substantial number of TGs with high p (corr) values, ranging from 0.84 to 0.96, that are responsible for the separation between the HF vs HC diets. However, uniform changes are not observed across the entire TG pool, for example, TG 50:3, TG 50:2, and TG 52:2 are decreased in mice fed the HF diet whereas TG 54:4, TG 54:3, and TG 54:2 are increased in mice fed the HF diet. Although these observations demonstrate the ability to obtain specific diet-induced lipid phenotypes, these measurements did not provide any specific insight regarding the pathophysiology, for example, the contribution of newly made lipids. MSE Allowed for Further Analysis of TG Composition and Specific FA Synthesis

Total body water measurement as described in the Material and Methods section resulted in ∼2.5% 2H-labeling at steadystate for the duration of the study. To examine the suitability of UPLC MS-based analyses for detecting 2H-labeling in various lipids, replicate injections of plasma samples were performed to determine the reproducibility of the isotope ratios. For example, we observed a relatively high degree of reproducibility when measuring the natural background labeling (in the M1 isotopomer/M0 isotopomer) of LPC 16:0, PC 34:1, PC 36:4, TG 52:2 and TG 54:5 (Figure 4), the coefficients of variation ranged from ∼1.2 to 2.6% (LPC 16:0 1.2%, PC 34:1 2.6%, PC 36:4 1.8%, TG 52:2 1.4% and TG 54:5 2.1%). Utilization of MSE increased our ability to quantify the positional labeling. For instance, TG 52:2, was increased in mice fed the HC diet, can be used as an example to describe the level of structural information obtained in teasing apart the isotopic enrichment. Figure 5 shows discrete differences in the abundance of heavy isotopomers in mice fed HC vs HF diet. Panel A and B demonstrate the isotope labeling in the parent (intact) molecule, whereas Panel C and D contain the isotope distribution profile of the fragment ion generated by the high energy acquisition in MSE mode corresponding to glycerol, 16:0 and 18:1 (i.e., loss of 18:1) and Panel E and F contain the isotope distribution profile of the daughter ion containing glycerol, 18:1 and 18:1 (i.e., loss of 16:0). In all cases, enhanced fatty acid synthesis was observed in mice fed the HC diet. The differences between the labeling in A and E or B and F can be used to estimate the labeling of palmitate. For example, the de novo synthesis of palmitate incorporates a set number of hydrogens from water, that is, ∼22,50 52 however, the 2H-labeling in stearate and/or oleate can originate from newly made palmitate or from the elongation of pre-existing/dietary-derived palmitate. In the former case, the labeling of stearate/oleate is markedly different than in the latter, that is, ∼25 hydrogens are incorporated when stearate is made from acetyl-CoA whereas only ∼3

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are incorporated when unlabeled palmitate is elongated.53 In addition, the glycerol backbone can incorporate up to 5 hydrogens depending on its source. As we demonstrated3 that value will be relatively constant ranging between ∼3.5 and 5, other data in the literature support our observations and suggest that the hydrogen bound to glycerol will readily equilibrate with that in body water.54,55 Although the interpretation of all glyceride labeling patterns is not immediately obvious, in favorable cases one can determine the positional labeling (Figure 5). D2O Lipid Flux Analysis Resulted in Specific Subclass Glyceride Synthesis as a Result of de novo Lipogenesis

In an attempt to explain the nature of the different lipid profiles observed in mice fed the HC vs the HF diet, we examined the 2H-labeling of several lipids that were identified via PCA analyses (Figure 6). The data are plotted as the M1/M0 ratio observed at a given time minus the natural background M1/M0 ratio. Note that there are increases in the other isotopomers, for example, M2, M3 and M4 (as shown in Figure 5). However, to make these figures manageable for the reader, we have not plotted those data. The observation of greater incorporation of 2 H suggests an enhanced contribution of de novo lipogenesis in specific LPCs, PCs, and TGs that were determined to be in greater relative concentration via PCA analyses. In addition to this, it was also of interest to investigate if there was a disconnection between the pool size of specific lipid classes and the contribution of de novo lipogenesis. For example, comparable levels of phospholipid PC 34:2 were found in animals fed the different diets. Figure 7A compares the normalized peak areas to total ion intensity of variables detected by PCA for m/z 758.5700 with retention time of 4.81 min (PC 34:2) in both diets which showed no change. However, in contrast with this finding, when the isotopic enrichment was determined there was a clear shift in the abundance of the M1, M2 and M3 isotopomers (Figure 7B and C). These observations prompted us to quantify the 2H-labeling of certain glycerides which were determined to be higher in mice fed the HF diet. For example, although the abundance of TG 54:4 (m/z 908.8010) was ∼336% higher (p < 0.0001) in the HF diet than the same triglyceride in the HC diet (Figure 7D) the 2H-labeling was much greater in mice for the HC diet (Figure 7E and F). Hence, showing a clear disconnection between static and kinetic profiles in the different groups. One plausible hypothesis for this finding may be attributed to the fact that in the HC diet de novo lipogenesis is markedly up-regulated by specific genes which resulted in increased fatty acid synthesis (Figure 1). Increased modulation of fatty acid synthesis leads to an enhancement in overall lipid synthesis, moving these fatty acids to other lipid pools such as phospholipids and triglycerides.

’ CONCLUSIONS The purpose of the work described herein was to determine whether it was possible to improve the identification and interpretation of data sets by combining metabolomics with fluxomics. As expected, the ability to use rather extreme diets served an important role in manipulating the contribution of de novo lipogenesis. Our work clearly demonstrates the complexity of alterations in plasma lipid profiles at both the static and kinetic levels. As noted, although we identified 1463 lipid variables using multivariate statistical analysis, a more comprehensive examination was attained when this data output was coupled together with lipid flux measurements. A further advantage of this approach lay in 4288


Journal of Proteome Research the fact that it is possible to measure individual synthesis rates for specific lipids and therefore tracking the kinetics of these lipids following dietary intervention. Finally, the use of gas chromatography mass spectrometry and isotope ratio mass spectrometry typically yields information regarding the synthesis of specific fatty acids. While those analyses are highly informative it is not trivial to obtain information regarding fatty acid flux in specific subclasses let alone individual species. We expect that the approach described herein can be used to complement studies of lipid biology yielding information regarding intact lipid class and subclass information with a minimum of sample preparation. This is essential information to have as it will provide a more in-depth understanding of which metabolic lipid pathways undergo modulation and how is it can be combined with the hepatic transcriptome.

’ AUTHOR INFORMATION Corresponding Author

*Jose Castro-Perez, Department of Atherosclerosis Exploratory Biomarkers, Merck & Co., Inc. Research Laboratories. 126 E. Lincoln Ave, 80Y-2D7, Rahway, NJ 07065, USA. Telephone: +1 732-594-5033, Fax: +1 732-594-1169; e-mail: [email protected].

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