Time-Course Changes in Potential Biomarkers Detected Using a

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Time-Course Changes in Potential Biomarkers Detected Using a Metabonomic Approach in Walker 256 Tumor-Bearing Rats Guoqing Shen,† Yanhua Chen,† Jianghao Sun,† Ruiping Zhang,† Yi Zhang,† Jiuming He,† Yaping Tian,† Yongmei Song,‡ Xiaoguang Chen,† and Zeper Abliz*,† †

Key Laboratory of Bioactive Substances and Resource Utilization of Chinese Herbal Medicine, Ministry of Education, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, P. R. China ‡ State Key Laboratory of Molecular Oncology, Cancer Institute and Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, P. R. China

bS Supporting Information ABSTRACT:

A metabonomic approach based on complementary hydrophilic interaction chromatography and reversed-phase liquid chromatography combined with tandem mass spectrometry and time-course analysis of metabolites was implemented to find more reliable potential biomarkers in urine of Walker 256 tumor-bearing rats. A major challenge in metabonomics is distinguishing reliable biomarkers that are closely associated with the genesis and progression of diseases from those that are unrelated but altered significantly. In this study, these biomarkers were selected according to the change trends of discriminating metabolites during the genesis and progression of cancer. Seven consecutive batches of urine samples from preinoculation to 16 days after were collected and analyzed. Multivariate analysis revealed 87 discriminating metabolites. Time-course analysis of discriminating metabolites was used to select more reliable biomarkers with regular and reasonable change trends. Finally, 47 were found and 15 were identified including 12 carnitine derivatives, 2 amino acid derivatives, 1 nucleoside. On the basis of time-course behaviors of these potential biomarkers, we hypothesize such disruption might result from elevated cell proliferation, reduced β-oxidation of fatty acids, and poor renal tubular reabsorption. These studies demonstrate that this method can help to find more reliable potential biomarkers and provide valuable biochemical insights into metabolic alterations in tumor-bearing biosystems. KEYWORDS: metabonomics, time-course analysis, Walker 256 tumor, biomarkers, reversed-phase liquid chromatography, hydrophilic interaction chromatography

’ INTRODUCTION Metabonomics provides a valuable way to measure the global, dynamic metabolic responses of living organisms to biological stimuli or perturbations of whatever source.1-3 One of the primary goals of metabonomic studies is to find and identify disease biomarkers.4 Developments of metabonomics have been particularly useful in cancer research. In this field, there has been a drive to replace invasive histopathology with noninvasive techniques that can diagnose and monitor cancer progression readily at multiple time points.5 Profiles of steady state levels of metabolites can be considered “static metabolic” data,6 because the metabolite levels cannot provide information about change trends of metabolites. In contrast, metabonomics studies on experimental animal models can provide continuous time-dependent r 2011 American Chemical Society

data that allow dynamic metabonomic analysis.7-9 Metabolic trajectory analysis has been very well established in previous studies, and many methods have been used including nonlinear mapping,10,11 PCA trajectories3,8,11 as well as statistical batch processing.7 In our present work, PCA trajectories were applied in the global alternations of Walker 256 tumor-bearing rats and then the time-course analysis of individual discriminating metabolites were further applied to the discovery of more reliable biomarkers. Currently, LC-MS have been widely used in metabonomics for discovery of cancer biomarkers.12-15 Due to the high throughput and chromatographic resolution, reversed-phase liquid chromatography Received: December 1, 2010 Published: January 28, 2011 1953

dx.doi.org/10.1021/pr101198q | J. Proteome Res. 2011, 10, 1953–1961

Journal of Proteome Research

Figure 1. Workflow of integrating time-course analysis of metabolites into HILIC-MS- and RPLC-MS-based metabonomic study. MVDA = multivariate data analysis.

(RPLC)-MS is now a common metabonomics tool.16 However, urine contains a vast array of high-polar metabolites and high salt content. High-polar metabolites are not retained well on RPLC columns, and salt content can bring more ionization suppression and more contamination to mass spectrometer. The complementary hydrophilic interaction chromatography (HILIC)-MS and RPLC-MS analysis of water (or buffer) elution and methanol elution of solid phase extraction (SPE) is a good choice to analyze this kind of biological samples.17,18 In our study, time-course analysis of metabolites was integrated into metabonomics to find more reliable potential biomarkers for Walker 256 tumor-bearing rats. Walker 256 tumor is a well-known cancer model and cachectic model in rats and has been extensively studied.19-21 HILIC-MS and RPLC-MS were used for metabonomic analysis of water elution and methanol elution of SPE, respectively. The more reliable potential biomarkers were selected by multivariate analysis and time-course behaviors. Furthermore, the biological significance of those potential biomarkers was investigated and explained with the help of time-course analysis of metabolites. The workflow of our metabonomic studies is shown in Figure 1.

’ MATERIALS AND METHODS Chemicals

HPLC grade acetonitrile, methanol and formic acid were purchased from Merck (Darmstadt, Germany). Ammonium acetate (HPLC grade) was purchased from Fluka (Zwijndrecht, The Netherlands). Hexanoylcarnitine, 20 -deoxycytidine, urocanic acid, creatine and L-carnitine were purchased from Sigma-Aldrich (St. Louis, MO). Salicylic acid and rhein were purchased from National Institute for the Control of Pharmaceutical and Biological Products (Beijing, China). Animal Handling and Sample Collection

The animal study was approved by the Animal Care & Welfare Committee, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College (No. 001669), and was performed at the Animal Experimental Centre, Institute of Materia Medica. Thirty male Wistar rats, 6-8 weeks old, 150 ( 10 g, were

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purchased from Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences, housed individually in stainless steel wire mesh cages, and provided with a certified standard rat chow and water ad libitum. Room temperature and relative humidity were regulated at 23 ( 5 °C and 55 ( 5%, respectively. A light cycle of 12 h on and 12 h off was set. Walker 256 tumor cells were obtained from ascitic fluid in Wistar rats, after two cycles of 7 days cell passage by intraperitoneal injection of 107 Walker 256 carcinoma cells. After cell harvesting, the tumor cell suspension of 106 cells/100 μL was obtained by dilution with sterile saline. After 3 days of acclimatization in metabolic cages, rats were randomly divided into two groups: model group (n = 20, labeled M01-M20) and control group (n = 10, labeled C01-C10). The rat model was established by subcutaneous injection of a 200 μL suspension of Walker 256 tumor cells into the right forelimb. An equal volume of sterile saline was injected into the control group. After fasting for 12 h before each sampling, 24-h baseline urine samples from each animal were collected on day 0 (day before inoculation), and on days 4, 6, 9, 11, 14, and 16 after inoculation. Sample collection was ceased because model rats began to die on day 17. All urine samples were centrifuged at 10 000 rpm (7833 g) for 5 min at 4 °C to remove particle contaminants. The resultant supernatants were stored at -80 °C pending sample preparation. Sample Preparation

Creatinine analysis was carried out by the Inspection Department of the Cancer Institute and Hospital of Chinese Academy of Medical Sciences using an enzymatic procedure. SPE was used for sample preparation. The adsorbent was activated, purified and conditioned first with 10 mL methanol and then with 2 mL purified water. An aliquot of rat urine that contained 5.0 μmol creatinine was loaded onto the SPE cartridge (GracePure, C18Max, 100 mg/1 mL). In the washing step, 2.0 mL purified water and 2.0 mL methanol was used for elution. The water eluate was freeze-dried using a vacuum freeze-dryer and the methanol eluate was completely evaporated under ultrahigh purity nitrogen gas. The sample residues of two eluates were dissolved with 500 μL 40% acetonitrile and 500 μL purified water, respectively. LC-MS/MS Analysis

For the LC-MS analysis, an Agilent 1200 series rapid resolution liquid chromatography system (Agilent Technologies, Waldbronn, Germany) was coupled to a Q-TOF LC-MS/MS system (QSTAR Elite, Applied Biosystems/MDS Sciex, Canada). HILIC-MS and RPLC-MS were used to analyze the urinary metabolites in water eluate and methanol eluate of SPE, respectively. For the HILIC-MS analysis, an HILIC column (3.0 μm, 150 2.1 mm; Grace Alltech, Deerfield, IL) with a in-line filter was used, and the column temperature was maintained at 20 °C. The injection volume was 1 μL. Mobile phase A consisted 5 mM ammonium acetate and mobile phase B acetonitrile. The gradient started with 98% B and then linearly decreased to 60% B within 20 min. In the next 0.1 min, B linearly decreased to 45% and remained for 4.9 min. The flow rate was 300 μL/min. For the RPLC-MS analysis, a Zorbax SB-C18 column (1.8 μm, 100 2.1 mm; Agilent Technologies, Santa Clara, CA, USA) with a in-line filter was used, and the column temperature was maintained at 50 °C. The injection volume was 10 μL. Mobile phase A comprised 0.01% formic acid and mobile phase B acetonitrile. The gradient started with 0% B and then linearly increased to 100% B within 20 min and remained for 3 min. The flow rate was 250 μL/min. 1954

dx.doi.org/10.1021/pr101198q |J. Proteome Res. 2011, 10, 1953–1961


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RPLC-MS and HILIC-MS spectra were acquired on a QTOF LC-MS/MS system in both positive and negative ion modes. The scan mode was TOF scan for sample analysis and product ion scan for MS/MS analysis of ions of interest. The source voltage was set at 5.5 kV or -4.5 kV, and the vaporizer temperature at 450 °C. The mass spectrometer was operated with gas settings of 60 arb for nebulizing gas, 60 arb for drying gas, 30 arb for curtain gas, and 5 arb for collision gas. Declustering potential was set at 60 or -60 V. The scan range was from m/z 100 to 1000. Nitrogen gas was used as both nebulizing and drying gas. Data acquisition and processing were performed using Analyst QS 2.0 (Applied Biosystems/MDS Sciex). The quality control sample (QC) which pooled by equal volume of 10 real urine samples was processed as real samples during SPE and LC-MS analysis and placed in the sample queue to monitor the stability of the system.22 Samples from control and model rats were alternated in random order in the analysis batch. Data acquired in positive ion mode were calibrated by autocalibration with background ions (m/z 149.0233 and m/z 279.1591). In negative ion mode, the signals were also acquired with autocalibration using a standard solution of salicylic acid and rhein introduced by postcolumn mixing at a concentration of 100 ng/ mL and a flow rate of 10 μL/min, which generated [M - H]- ions of m/z 137.0244 and m/z 283.0248. Data Handling

Raw LC-MS data files were converted into mzData format using Wiff to mzData utility (Applied Biosystems/MDS Sciex) and directly processed by open-source XCMS23 package under R statistical software (version 2.10.0) to carry out peak discrimination, filtering and alignment. The four resultant 2D matrices, including variable index (paired m/z-retention time), sample names (observations) and peak areas were introduced into Sweden) SIMCA-P version 12.0 software (Umetrics AB, Umea, for multivariate analysis. The pareto variance scaled data were analyzed by principal component analysis (PCA) to visualize general clustering, trends, or outliers among the observations. To avoid overfitting of the models, the partial least-squares-discriminant analysis (PLS-DA) models were carefully validated by an iterative seven-round cross-validation with 1/7 of the samples being excluded from the mode in each round and 100 random permutations testing. OPLS-DA models were constructed and discriminating variables were selected according to variable importance on projection (VIP) values, S-plot, and raw data plot. The structure of the potential biomarkers was elucidated as described in our previous study14 and by searching free databases of HMDB (http://www.hmdb.ca/) and MassBank (http:// www.massbank.jp) using exact mass and MS/MS spectra. Subsequently, the elucidated molecular structures were further confirmed with authentic standards, if commercially available.

’ RESULTS AND DISCUSSION Data Quality Assessment

Both water elutes and methanol elutes of SPE were separated effectively and the typical total ion chromatograms (TICs) of HILIC-MS and RPLC-MS in positive ion mode are shown in Figure 2. To obtain reliable and high-quality data with LC-MS-based metabonomics, technical and analytical errors must be small enough to avoid interrupting multivariate data analysis. The multivariate analysis results of QC samples show that the peak areas deviation of the analytical system was thought to be acceptable (Supporting

Figure 2. Typical TICs of HILIC-MS and RPLC-MS in positive ion mode.

Information Figure S1, S2 and QC data as an Excel files).22,24 Furthermore, samples from control and model rats were alternated in random order in the analysis batch. In addition, the reproducibility of the chromatography was determined by the retention time variation profiles that were generated by XCMS.23 The retention time deviation profiles that were derived from HILIC-(()ESI-MS and RPLC-(()ESI-MS analyses (Supporting Information Figure S3) showed that the retention time deviation was < ( 40 s and < ( 20 s for most HILIC-(()ESI-MS and RPLC-(()ESI-MS analyses, respectively. On the basis of these results of data quality assessment, the differences between the test samples from different individuals were proved more likely to reflect varied metabolite profiles rather than analytical variation. Multivariate Statistical Analysis and Explanation

For RPLC-(()ESI-MS data, 662 peaks of positive ions and 992 peaks of negative ions from 0.9 to 20 min of retention time were obtained (the program used for RPLC-(þ)ESI-MS data is available in Supporting Information). The two resultant 2D matrices were introduced into SIMCA-P and all data were centered and pareto scaled to reduce the impact of noise and artifacts in the models. To investigate the global metabolomic alterations in rats, all observations acquired in both ion modes were integrated and coanalyzed using PCA. Figure 3A is the overview of the entire observations and their relationships of RPLC-(þ)ESI-MS data (The corresponding loading plot, permutation test result of the PLSDA model, and S-plot of the OPLS-DA model are demonstrated in Supporting Information Figure S4). Figure 3A shows four clusters for 1955



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Figure 4. Score plots for OPLS-DA model derived from the (A) RPLC-(þ)ESI-MS data, (B) RPLC-(-)ESI-MS data, (C) HILIC-(þ)ESI-MS data and (D) HILIC-(-)ESI-MS data. (observations of red O controls and blue þ model rats).

Figure 3. (A) Score plot for PCA model of model rats and controls and (B) score plot for OPLS-DA model of all model rats derived from the RPLC-(þ)ESI-MS data (- observations of controls, observations of model rats on red 9 day 0, blue O day 4, green þ day 6, 2 day 9, purple b day 11, red * day 14, and blue 0 day 16).

model rats including day 0, 4, 6, and 9-16. This plot depicts the metabolic variation that consisted of preinoculation and cancer progression. Clearer results can be obtained from the score plot of OPLS-DA (Figure 3B), which shows that the observations of day 4 and 6 deviated considerably from those of day 0, and the observations of day 9-16 had their projections on nearly the same area, which reflected the relatively stable status of tumor growth and deterioration. These are in accordance with the results of tumor observation during sample collection. No palpable tumor was observed on day 6 after inoculation, and on day 7-9, every rat had a palpable tumor except M04 (palpable on day 10). A series of PCA score plots were used to further investigate the metabolomic alterations that characterize cancer progression (Supporting Information Figure S5). Little separation between the two groups was observed in the PCA score plot before inoculation. On day 4, a clear separation between the groups was observed. On day 9-14, the metabolic profiling in tumorbearing rats had changed considerably, which led to further separation between the groups. For the exploration of the difference between the tested groups, OPLS-DA, a supervised method, was applied for stoichiometric analysis as below (Figure 4A). For RPLC-(þ)ESI-MS data, the classification of models and controls resulted in one predictive and two orthogonal (1þ2) components with the cross-validated predictive ability Q2(Y) = 81.5%. A value of 31.0% of the variance in X [R2(X)] was used to account for 87.7% of the variance of Y [R2(Y)], and the variance related to class separation R2p(X) = 12.0%. To investigate further the validation of this model, the random permutation test was performed with the PLS-DA model corresponding to the OPLS-DA model across three components (Supporting Information Figure S4). The validation with the permutation number 100 generated intercepts of R2 = 0.176 and Q2 = -0.214. The second model (model vs control in negative ion mode) was produced with an R2(X) = 22.4%, R2(Y) = 84.3%, and Q2(Y) = 72.4% across one predictive and two orthogonal components (Figure 4B), and

validation intercepts of R2 = 0.339 and Q2 = -0.264 (Supporting Information Figure S6). The relevant R2(Y), Q2(Y) and validation results indicated that the OPLS-DA models derived from the RPLC-(()ESI-MS data were robust and valid.25 HILIC-(()ESI-MS data have also been analyzed by the same approaches (Supporting Information Figure S7 and S8). For HILIC-(þ)ESI-MS data, little separation between the groups was observed with the first two principal components (PCs), but an obvious separation trend was observed in the scores of the third and first PCs (PC3 vs PC1) and PC3 vs PC2 (Supporting Information Figure S7). OPLS-DA was used for further discriminant analysis (Figure 4C and D). Discovery of More Reliable Potential Biomarkers

An S-plot was used for the selection of the discriminating variables. The preferred selection of metabolites that have a high covariance combined with a high correlation14,26 (Supporting Information Figure S4, S6-S8). Moreover, only those variables with a VIP above 1.1 were considered.27 Then the variables were further confirmed by the raw data plots.14 For LC-MS-based metabonomics, each metabolite gives rise to a number of mass signals. R-package CAMERA (available at http:// www.bioconductor.org/packages/release/bioc/) combined with a manual method was used to find discriminating metabolites from discriminating variables (the program used for RPLC-(þ)ESI-MS data is available in Supporting Information). Of the 48 discriminating variables in RPLC-(þ)ESI-MS data, 5 were found as fragment ions, 7 had isotope annotations, and 3 were annotated as adduct ions. The numbers of variables, discriminating variables and discriminating metabolites are listed in Table 1. The change trend of cancer biomarkers should be in accordance with the growth of the tumor or cancer progression. Take three typical discriminating metabolites: A, B and C for example, which represent three change trends, respectively (Figure 5). The figure shows that the peak areas of the three metabolites were extremely low in the control group. The peak area of A in the model group rose significantly on day 6, and reached a maximum around day 11, and fell thereafter. The peak area of B in the model group rose throughout the period from day 6 to day 16. However, the peak area of C in the model group had its maximum value on day 4 (the first sample collection postinoculation) and then dropped rapidly to almost the preinoculation level on day 14. The increase of A could be attributed to the rapid growth of tumor which has a similar pattern. The change trend of A reflects that the tumor cells proliferated quickly around day 11, and 1956



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Table 1. Numbers of Variables, Discriminating Variables, Discriminating Metabolites and More Reliable Potential Biomarkers

Variables

RPLC-(þ)ESI-MS

RPLC-(-)ESI-MS

HILIC-(þ)ESI-MS

HILIC-(-)ESI-MS

662

992

851

757

Discriminating variables

48

30

51

14

Discriminating metabolites

33

24

23

20(11)

14(0)

12(5)

More reliable potential biomarkers (identified)

7 4(1)

Figure 5. Four typical change trends of discriminating metabolites with the peak area as a vertical coordinate and the time of sample collection as the horizontal. The bottom and the top of the box are the 25th and the 75th percentiles, and the blank band near the middle of the box is the median of the peak area of this variable.

then slowed thereafter. The change trend of B could reflect the cancer progression or the physical state of tumor-bearing rats such as the cachectic state.19 The change trend of C deviates from cancer progression and the peak areas of both groups were extremely low on day 0. On the basis of the analysis in the section of Multivariate Statistical Analysis above, it is reasonable to propose that the increase of C could result from the physiological reaction to the acute stimulation of carcinoma cell inoculation. Therefore, the discriminating metabolites that have change trends like C are false biomarkers and should be excluded from biomarker candidates. Besides, the discriminating metabolites that have irregular change trends like D are also eliminated. Only those discriminating metabolites with change trends like A and B were considered as more reliable biomarkers. Among the 33 discriminating metabolites obtained from RPLC-(þ)ESI-MS data, 13 were eliminated similarly as C or D. The numbers of more

reliable potential biomarkers are listed in Table 1. This table shows that less (more reliable) potential biomarkers were found by timecourse analysis. There was one identical biomarker in both ion modes of HILIC-MS and RPLC-MS respectively, and one identical potential biomarker (L-carnitine, m/z 162.1129) was obtained from both RPLC-(þ)ESI-MS and HILIC-(þ)ESI-MS data. Thus, 47 more reliable biomarkers were obtained in all (Supporting Information Table S1). Identification of Potential Biomarkers

The possible elemental compositions of the biomarkers were determined by exact mass weights considering relative intensities of the isotope peaks through the high-resolution MS spectra. The structures of the potential biomarkers were elucidated with the high-resolution MS/MS spectra and by searching various databases. Finally, fifteen potential biomarkers were provisionally identified, including twelve carnitine derivatives (L-carnitine, 1957



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Table 2. Potential Biomarkers and their Identification m/z

RT (min)

p valuea

RSD (%)b

228.0982

9.10, HILIC

1.81 10-7

69.85

20 -Deoxycytidined,e

50.16

Urocanic acidd,e

15.03

Creatined,e

23.64

γ-Butyrobetainee

42.44

d,e L-Carnitine

139.0501 132.0769 146.1179 162.1129 160.0971

10.36, HILIC 17.62, HILIC 23.52, HILIC 21.33, HILIC

-4

1.24 10

-11

3.73 10

-5

2.01 10

-7

9.46 10

-6

1.13, RPLC

1.72 10

2.16, RPLC

9.73 10-13

metabolite identificationc

5.27

-12

9.87

3-Dehydrocarnitinee Hexanoylcarnitine (carnitine C6:0)d,e Heptanoylcarnitine (carnitine C7:0)e

260.1854 274.2013

6.80, RPLC 8.07, RPLC

6.95 10 3.50 10-11

6.52 6.76

286.2014

8.30, RPLC

8.03 10-7

73.64

Octenoylcarnitine (carnitine C8:1)

288.2164

9.17, RPLC

1.73 10-9

8.89

Octanoylcarnitine (carnitine C8:0)e

-14

300.2169

9.32, RPLC

1.07 10

302.2323

10.06, RPLC

3.22 10-11

314.2324

10.11, RPLC

1.80 10-15

5.19

328.2475

10.98, RPLC

7.77 10-13

24.08

Undecenoylcarnitine (carnitine C11:1)

330.2646

11.55, RPLC

1.54 10-8

22.16

Undecanoylcarnitine (carnitine C11:0)e

4.38

Nonenoylcarnitine (carnitine C9:1)

26.66

Nonanoylcarnitine (carnitine C9:0)e Decenoylcarnitine (carnitine C10:1)e

a

p value of independent t-test. b RSDs of peak areas in quality control samples. c All these potential biomarkers are found up-regulated in the urine of Walker 256 tumor-bearing rats. d Metabolites confirmed using standard samples. e Metabolites provisionally identified by database searches and MS fragmentation. Others, proposals based on MS fragmentation and exact mass data.

3-dehydrocarnitine, γ-butyrobetaine and nine acylcarnitines), two amino acid derivatives, and one nucleoside. Detailed information is shown in Table 2. Five of them were further confirmed by comparison with authentic standards, including retention times and MS/MS fragmentation patterns. Some high-polar metabolites such as γ-butyrobetaine and creatine were found as potential biomarkers in the HILIC-MS data, complementary to the biomarkers from the RPLC-MS data. If only one analysis with RPLC-MS were performed, information of several polar potential biomarkers might be lost because of poor separation and strong ion suppression near the dead time of chromatography. More metabolites information, including hydrophobic and hydrophilic metabolites is helpful to understand the metabolic alterations.28 Explanation of Change Trends and Biological Significance of Identified Potential Biomarkers

The change trends of nine acylcarnitines are shown in Figure 6 in a line chart, and the change trends of another six potential biomarkers are shown in Figure 5B and Figure 7 in boxplots (L-carnitine has two change trends derived from RPLC-MS and HILIC-MS data, respectively). These 15 potential biomarkers were all up-regulated in urine of Walker 256 tumor-bearing rats, but the change trends can be classified into two: those that increased throughout the cancer progression as A (L-carnitine, 3-dehydrocarnitine and γ-butyrobetaine, Figure 7E-G) and those that rose first and fell later as B (the other 12 potential biomarkers, Figure 6 and 7H-J). Nine urinary acylcarnitines were found to be significantly higher in tumor-bearing rats compared with controls and they had the similar change trends (Figure 6). The unusual medium-chain (6-12 carbons) acylcarnitines are synthesized at the cytosolic side of the endoplasmic reticulum of liver cells29 with L-carnitine, and the corresponding medium-chain fatty acids, which are mainly generated through incomplete β-oxidation of long-chain fatty acids28 The elevated urinary excretion of medium-chain acylcarnitines was attributed to the deficiency or reduced activity of medium-chain acyl-CoA dehydrogenase,30 and the elevation reflected the reduced ability of βoxidation of long-chain fatty acids. Previous researches on oxygen

Figure 6. Change trends of 9 acylcarnitines from day 0 to day 16 (standard errors are omitted for clarity).

uptake or ketone body formation has proved this conclusion,31,32 but this study drew the conclusion through a distinct aspect. Numerous disorders have been described that lead to disturbances in energy production and intermediary metabolism,30 which are characterized by the excretion of unusual acylcarnitines.33,34 Four identical acylcarnitines (carnitine C8:1, C9:1, C9:0 and C10:1) have been found as potential biomarkers in liver cancer patients.28 However, contrary to our results, these four potential biomarkers were down-regulated. Liver plays a crucial role in lipid metabolism. The synthesis of medium-chain acylcarnitines and β-oxidation of fatty acids in liver cells might be badly impaired because hepatic cells are seriously damaged simultaneously in liver carcinogenesis. L-Carnitine is essential in the transport of long-chain fatty acids from the cytosol to the mitochondria for subsequent β-oxidation. In rats, L-carnitine is biosynthesized in liver and involves five enzymatic reactions with methionine and lysine as early precursors.35 The final reaction is the hydroxylation of γ-butyrobetaine to L-carnitine. 3-dehydrocarnitine is an intermediate in L-carnitine degradation. 1958



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Figure 7. Change trends of another six potential biomarkers from day 0 to day 16.

These three metabolites have similar change trends (Figures 7E-G). Several metabolites in one metabolic pathway are considered to be more reliable biomarkers. L-Carnitine is reabsorbed by more than 95% in the renal proximal tubule.36 A high urinary level may correspond to excess L-carnitine ingestion, excess biosynthesis or poor reabsorption.37 The excess ingestion seems to have little possibility because the dietary intake is significantly reduced for cachectic animals.38 Moreover, the endogenous synthesis of carnitine is reduced in living systems with malignant disorders.38 Thus, the increase of urinary L-carnitine might be attributed to poor reabsorption,39,40 although this needs further investigation. Urinary creatine level was significantly higher in tumor-bearing rats compared to controls (Figure 7H). Creatine is synthesized mainly in the liver and mostly stored in muscle as phosphocreatine. Phosphocreatine has a direct function in cellular energy transport.37 Many researchers have reported elevated creatine level in cancer patients.41-43 It is therefore likely that creatine is elevated in rapidly growing cells,37 such as Walker 256 tumor cells. 20 -Deoxycytidine is the intermediate or end-product of nucleotides and nucleic acids.44 The increase of 20 -deoxycytidine (Figure 7I) could have stemmed from abnormal cell proliferation in tumor tissue and/or from the loss of immunocompetence in Walker 256 tumor-bearing rats.44 Urocanic acid is an intermediate in the conversion of histidine to glutamic acid. The up-regulation of urinary urocanic acid (Figure 7J) could result from histidine metabolism disorder in tumor tissue, and/or glutamic acid metabolism disorder. These 15 potential biomarkers have regular change trends in urine during genesis and progression of Walker 256 tumor. The concentrations of these metabolites may be helpful to monitor cancer progression in Walker 256 tumor-bearing rats with noninvasive techniques.

’ CONCLUSIONS The integration of metabonomics and time-course analysis of metabolites enabled a more thorough interpretation of these

time-dependent data than would have been achieved by traditional metabonomic approaches. The metabolites with irregular and unreasonable change trends can be easily eliminated. The urinary metabolomes of healthy and Walker 256 tumor-bearing rats were significantly different, such as significantly elevated levels of creatine, L-carnitine, 3-dehydrocarnitine, acylcarnitines, γ-butyrobetaine, urocanic acid and 20 -deoxycytidine. The present study analyzed the biological significance of these potential biomarkers mainly in relation to the energy demand and supply in tumor-bearing rats. The results showed that the time-course behaviors of metabolites can provide biochemical insights into metabolic alterations in biosystems and might be helpful to monitor cancer progression. The strategy could provide a valuable reference for the discovery of more reliable biomarkers and the diagnosis of cancer progression in humans. The biomarkers indicated in the current study could be proved useful in further attempts to apply metabonomics to noninvasive monitoring of the cancer progression.

’ ASSOCIATED CONTENT

bS

Supporting Information Supplementary tables and figures. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*E-mail: [email protected].

’ ACKNOWLEDGMENT Many thanks to Ms. Li-jia Dong (Cancer Institute of Chinese Academy of Medical Sciences) for her excellent technical supports in the creatinine analysis and to the financial 1959


Journal of Proteome Research support from the National Natural Science Foundation of China (No. 30873195).

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