ARTICLE pubs.acs.org/jpr
Dynamic Metabolic Transformation in Tumor Invasion and Metastasis in Mice with LM-8 Osteosarcoma Cell Transplantation Yingqi Hua,†,‡,# Yunping Qiu,§,# Aihua Zhao,^,# Xiaoyan Wang,||,# Tianlu Chen,||,# Zhiyu Zhang,z,# Yi Chi,^ Quan Li,‡ Wei Sun,† Guodong Li,† Zhengdong Cai,*,† Zhanxiang Zhou,§ and Wei Jia*,§ †
)
Musculoskeletal Oncology Center, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai 200072, P. R. China ‡ Department of Orthopedics, The Changhai Hospital of Second Military Medical University, Shanghai 200433, P. R. China § Department of Nutrition, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, North Carolina 28081, United States ^ School of Pharmacy, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China z Department of Orthopedics, The 4th Affiliated Hospital, China Medical University, Shenyang, 110032, P. R. China
bS Supporting Information ABSTRACT: While extensive evidence indicates that tumor cells shift their global metabolic programs, the molecular details of the metabolic transformation in tumor invasion, progression, and metastasis remain largely unknown. Characterization of the timedependent metabolic shift during the tumor invasion, development, and metastasis will describe an important aspect of tumor phenotypes and potentially allow us to design therapies that inhibit tumor cell movement. In this study, a metabonomic study was performed to characterize the global metabolic changes during the process of tumor invasion and metastasis to lung in a mouse model with subcutaneous transplantation of murine osteosarcoma cell line (LM8). The serum metabolic profiling revealed that many key metabolites in glycolysis and tricarboxylic acid (TCA) cycle, as well as most of the amino acids were elevated at rapidly growing stage of tumor, presumably resulting from a high energy demand and turnover of anabolic metabolism during the tumor cell proliferation. Serum levels of succinic acid and proline significantly increased (with fold change FC = 10.75 and 4.43, relative to controls) among all the metabolites in the third week. The serum metabolic profile of lung metastasis at week 4 was different from that at week 3, in that most of previously increased serum metabolites were found decreased, except for cholesterol and several free fatty acids, suggesting lowered carbohydrate and amino acids metabolism, but an elevated lipid metabolism associated with tumor metastasis. KEYWORDS: metabonomics, metabolomics, metabolic transformation, osteosarcoma, metastasis, invasion
’ INTRODUCTION Tumor cells exhibit distinct metabolic phenotypes that are essential for them to sustain higher proliferative rates and resist cell death signals.1 These metabolic phenotypes include increased glucose utilization in energy metabolism and alteration in the flux along key metabolic pathways, such as glycolysis and glutaminolysis.2,3 Recent advances in cellular metabolism resulting from metabolic dysregulation and adaptation of cancer cells are providing increasing support for the development of treatments that target tumor metabolic transformation.3 However, the molecular details of the metabolic transformation in tumor invasion and metastasis remain largely unknown. Characterization of the time-dependent metabolic shift during the tumor invasion, development, and metastasis will provide new insights into tumor phenotypes r 2011 American Chemical Society
and potentially allow us to design therapies that inhibit tumor cell growth and migration. Osteosarcoma accounts for about 60% of the common histological subtypes of bone sarcomas in the pediatric age group.4 Despite advances in surgery, radiotherapy, and the development of more effective chemotherapy, long-term survival rates of osteosarcoma have stagnated at approximately 65%,5 primarily due to its metastasis with distant spread mostly to lungs in ∼80% of patients.6 To understand and prevent lung metastasis, several animal models were established with high pulmonary metastastic potential.7,8 LM8 cell line, originated from Dunn osteosarcoma cell line (a murine osteosarcoma cell line),9 is one Received: February 17, 2011 Published: June 10, 2011 3513
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Journal of Proteome Research of the most commonly used cell lines to test antimetastatic compounds for osteosarcoma such as TNP-470 and interlukin2.10,11 After inoculation in the C3H mice, it has a 100% efficiency of metastasis to the lung at about 4 weeks.9,12 Therefore, this model is suitable for the investigation of dynamic metabolic transformation during tumor growth and metastasis. The unbiased profiling of mammalian blood and urine metabolites in metabonomics approach enabled characterization of dynamic metabolic changes as the end points of biochemical dysregulation that drive the progressive change of normal differentiated cells into diverse states of malignancy.13 15 By coupling high-throughput analytical platforms with sophisticated pattern recognition techniques, metabonomics offers a particularly sensitive method to biomarker discovery and understand regulation of metabolic pathways of a biological system.16,17 Recently, with the metabonomic technology, we discriminated osteosarcoma patients from benign bone tumor and healthy controls with serum and urine samples.18 Here, we report a metabonomic study designed to characterize the global metabolic changes during the process of tumor invasion and metastasis to lung in a mouse model with subcutaneous transplantation of murine osteosarcoma cell line (LM8). We also aimed to identify metabolite markers with diagnostic potential for osteosarcoma and its metastasis to the lung.
’ MATERIALS AND METHODS Chemicals
Methoxyamine HCl, bis(trimethylsilyl)-trifluoroacetamide (BSTFA, with 1% trimethylchlorosilane, TMCS), and heptadecanoic acid were purchased from Sigma-Aldrich (St. Louis, MO). L-2-chlorophenylalanine was purchased from Intechem Tech. Co. Ltd. (Shanghai, China). Establishment of Mouse Model and Sample Collection
LM8 cells were grown as monolayers in Eagle’s modified minimum essential medium with nonessential amino acids (DMEM, Sigma Aldrich, St. Louis, MO) containing 10% fetal bovine serum (FBS). The cultures were maintained in a humidified atmosphere with 5% CO2 at 37 C. A total of 55 C3H mice were purchased from Vital River Laboratories (Beijing, China). The animal study was conducted in accordance with Chinese national legislation and local guidelines, and performed at experimental animal facility of Second Military Medical University, P. R. China. The mice were kept in a barrier system with regulated temperature (23 24 C) and humidity (60 ( 10%), on a 12/12-h light/dark cycle (lights on at 08:00 a.m.), and fed certified standard rat chow and tap water ad libitum. Mouse osteosarcoma model with lung metastasis was established as previously reported.9 Briefly, LM8 cells (2 107) suspended in 200 mL of serum-free DMEM were implanted subcutaneously into the dorsal flank of 5-week-old female C3H mice. The inoculated mice were randomly divided into four groups (n = 8 for each group) according to different ending time of experiment. The tumor-bearing mice were sacrificed at 1, 2, 3, and 4 weeks post inoculation. The control mice were also sacrificed at the same time points (n = 6 for each time point, except for week 2, n = 5). The excised lungs were fixed in 5% buffered formalin and subjected to histological analysis. Sera samples were collected at the time of sacrificing and stored in 80 C pending for metabonomic analysis.
ARTICLE
Figure 1. The volume of the subcutaneous tumors at week 1, 2, 3, and 4, after LM8 inoculation. (A) The volume of the subcutaneous tumors after LM8 inoculation; (B) typical microphotograph of LM8 induced subcutaneous primary tumor; (C and D) typical microphotograph of normal lung (C) and metastatic lung tumor (D). The microphotographs were obtained under a magnification of 10 25.
GC TOFMS Spectral Acquisition of Serum Samples and Data Pretreatment
Serum metabolites were subjected to trimethylsilyl derivatization and analyzed by gas chromatography-time-of-flight mass spectrometry (GC TOFMS) according to the procedures outlined in our previously published paper with minor modifications.19 Briefly, two internal standard solutions (10 μL of L-2-chlorophenylalanine in water, 0.3 mg/mL; 10 μL of heptadecanoic acid in methanol, 1 mg/mL) were spiked into a 100-μL aliquot of serum sample. A mixture of methanol/chloroform (3:1) (300 μL) was used to extract the metabolites from the serum. After vortexing and metabolites extracting, the samples were centrifuged at 12 000g for 10 min. An aliquot of the 300-μL supernatant was vacuum-dried at room temperature in a glass vial. The residue was derivatized with 80 μL of methoxyamine (15 mg/mL in pyridine) at 30 C for 90 min, and followed by 80 μL of BSTFA (1% TMCS) at 70 C for 60 min. Each 1-μL of derivatized solution was analyzed by gas chromatography coupled with a Pegasus HT time-of-flight mass spectrometer (Leco Corporation, St. Joseph, MI) at 270 C in splitless mode. A DB-5 ms capillary column (30 m 250 μm i.d., 0.25-μm film thickness; Agilent J&W Scientific, Folsom, CA) was used to separate the metabolites with a constant flow rate of 1.0 mL/min of helium. The GC oven temperature programming was started at 80 C and maintained for 2 min, followed by 10 C/min ramps to 180 C, 5 C/min to 240 C, and 25 C/ min to 290 C, and a final 9 min maintenance at 290 C. The temperature of transfer interface and ion source was set to 260 and 200 C, respectively. The analytes were ionized by electron impaction mode ( 70 eV). After 5 min solvent delay, mass data was collected using Leco ChromaTOF software (v3.30, Leco Co., CA) at full scan mode (m/z 30 600) with an acquisition rate of 20 spectrum/s. All GC/TOFMS files were converted into CDF format via ChromaTOF software and then processed with custom scripts (revised Matlab toolbox HDA, developed by Par Jonsson, et al.20) in MATLAB 7.0 (The MathWorks, Inc.) for data pretreatment procedures, such as baseline correction, denoising, smoothing and alignment, time-window splitting, and peak feature extraction (based on multivariate curve resolution algorithm).20 Three-dimensional 3514
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Figure 2. PCA and PLS-DA scores plots of samples at different time points. (A and B) PCA and PLS-DA scores plots of the samples at week 3. Each red diamond stands for one mouse in the model group, while each blue diamond stands for one mouse from control group. (C and D) PCA and PLS-DA scores plots of the model samples in time point of week 3 and week 4. The red diamonds stand for samples at week 3, and the green triangles stand for samples at week 4. (E and F) The scores plots of two-dimensional PCA and three-dimensional PCA score plots of model mice showed the dynamic changes in the serum metabolites of experimental mice from week 1 4 post LM8 inoculation. Each black box stands for one mouse at week 1, violet dot for week 2, red diamond for week 3, and green triangle for week 4.
output data results were obtained with arbitrary peak index information (window number and 5 most intensity m/z), sample names (observations), and peak intensity (variables). Internal standards and any known artificial peaks, such as peaks caused by noise, column bleed, and BSTFA derivatization procedure, were removed from the data set. The mean centered and unit variance scaled data were analyzed by principal component analysis (PCA) to visualize general clustering, trends, or outliers among the observations in SIMCA-p 12.0 software (Umetrics, Umea, Sweden). Then, partial least-squares-discriminant analysis (PLSDA) was conducted to identify the metabolites differentially expressed in model and control mice. The cutoff value of 1 in the variable importance in the projection (VIP) was set to select differential variables in the PLS-DA models. The selected variables were then confirmed by
Student’s t test with the critical value of 0.05. Compound identification of metabolites was performed by comparing the mass fragments of the significant variables with those present in commercially available mass spectral databases such as NIST, Wiley, NBS, and the library we established with a similarity threshold of 70%. Finally, about 70% of them were verified by reference compounds.
’ RESULTS Histological Result of Lung Metastasis
Small tumors were developed at first week after LM8 inoculation and the tumors continued to grow until the mice were sacrificed (Figure 1A). All the mice in the model group developed lung metastasis at 4 weeks after LM8 inoculation. Histology 3515
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Table 1. Differentially Expressed Serum Metabolites in the Model Group at Week 3 no.
compound name
VIPb
pc
FCd
no.
compound name
VIPb
pc
FCd
27
Nonanoic acid
1.42
6.52 10
3
1.77
3.29
28
Pentanedioic acid
1.14
3.95 10
2
5.25
3
2.73
29
Arachidonic acid a
1.13
4.11 10
2
1.26
6.02 10
3
1.61
30
1-monopalmitin a
1.29
1.62 10
2
1.64
1.75
1.22 10
4
2.68
31
9-octadecenamide
1.33
1.28 10
2
2.73
Proline a
1.74
1.30 10
4
4.43
32
Oleamide a
1.11
4.58 10
2
1.51
6
Threonine a
1.84
1.50 10
5
2.80
7 8
Lysine a Tyrosine a
1.24 1.38
2.26 10 8.70 10
2
1.72 2.48
33 34
Succinic acid a Fumaric acid a
1.31 1.41
1.41 10 7.03 10
2
10.75 2.31
9
Methionine a
2.15
35
Isocitric acid a
Amino Acids 2.02 10
4
1.56
1.74 10
Serine a
1.43
4
Valine a
5
1
Glycine
a
1.72
2
Alanine a
3
3
1.82
2.50 10
5
10
Ornithine
a
1.32
1.37 10
2
2.12
11
Citrulline a
1.35
1.12 10
2
12
Glutamine a
1.70
2.68 10
13
Asparagine a
1.67
14
Phenylalanine a
15 16
5-oxoproline a 4-hydroxy-proline a
17
4-aminobutanoic acid a
18
3-methyl-histidine
TCA and Glycolysis Metaolism 3
1.50
3.25 10
3
1.96
36
Citric acid
a
1.40
7.57 10
3
1.93
1.90
37
Malic acid a
1.30
1.52 10
2
2.30
4
2.53
38
Lactic acid a
1.55
1.90 10
3
1.76
4.60 10
4
2.17
39
Pyruvic acid a
1.56
1.66 10
3
1.50
1.76
1.06 10
4
3.29
1.18 1.34
3.15 10 1.13 10
2
1.91 1.56
40 41
Galactose a Fructose a
1.51 1.45
2.74 10 4.98 10
3
1.67 3.03
1.36
1.02 10
2
1.56
42
Sucrose a
1.21
2.79 10
2
6.66
1.39
8.33 10
3
1.71
43
Myo-inositol
1.17
3.39 10
2
1.57
2
Sugars and Polyols
Organic Acids
3
Others
19
2,4-dihydroxybutanoic acid
1.26
1.99 10
2
1.41
44
Uracil a
1.68
3.78 10
4
6.38
20
2-methyl-3-hydroxy-butanoic acid
1.71
2.47 10
4
4.12
45
Uric acid a
1.42
6.37 10
3
4.92
21
2-piperidinecarboxyliate a
1.31
1.42 10
2
1.96
46
Glycerate a
1.59
1.26 10
3
2.51
22 23
3-methyl-3-hydroxybutanoic acid 2,3-dihydroxybutanoate
1.64 1.49
6.25 10 3.50 10
4
3.73 1.65
47 48
Threonic acid 3-isoxazolidinone
1.23 1.22
2.34 10 2.55 10
2
1.53 2.84
24
2-hydroxybutanoic acid a
1.15
3.75 10
2
1.77
49
Chenodeoxycholic acid
1.22
2.63 10
2
5.32
1.84
50
3-amino-2-piperidone
1.70
2.84 10
4
2.92
2.00
51
4-hydroxyphenylpyruvic acid
1.35
1.07 10
2
2.82
52
4-pyrimidinecarboxylic acid
1.25
2.13 10
2
2.78
3
25
2-hydroxyglutaric acid
1.41
6.97 10
3
26
2-oxo-3-methylvaleric acid
1.10
4.81 10
2
Lipid Metabolism
2
a
Metabolites verified by reference compounds, other were directly obtained from library searching. b Variable importance in the projection (VIP) was obtained from PLS-DA with a threshold of 1.0. c p-value was calculated from student t test. d Fold change was calculated from the arithmetic mean values of each group. Fold change with a value more than 1 indicates a relatively higher concentration present in model mice while a value less than 1 means a relatively lower concentration as compared to the controls 3 weeks after LM8 inoculation.
of the primary tumor, normal lung, and the metastatic lung tumor was shown in Figure 1B D, which confirmed that the tumor model was successfully produced in this experiment. There was no significant difference in total body weight between model mice and controls in week 1 4 of the experiment (Supporting Information (SI) Table 2). Metabonomic Variations Associated with Inoculation of Primary Tumor
A total of 262 aligned individual peaks were obtained from serum samples after the removal of internal standards and any known artificial peaks. There is a separation trend between the model group and controls in the scores plot of the principal component analysis (PCA, see SI Figure 1). No significant outliers were found in the PCA scores plot of all the samples, which suggest that the samples were of high quality. To better characterize tumor-induced metabolic variations, the serum metabolites in the model group were compared with those in the control group at the third week as the osteoscarma tumors became fully developed and stabilized after LM-8 cell inoculation. Clear separations were obtained in the PCA (2 components, R2X = 0.456, Q2 = 0.172) and PLS-DA (3 components,
R2X = 0.493, R2Y = 0.992, Q2 = 0.825) scores plots derived from the GC TOFMS data as shown in Figure 2A,B. Fifty-two differentially expressed serum metabolites (Table 1) were identified in the model mice as compared to the controls using VIP values and Student’s t test as described in the Materials and methods section. These differential metabolites can be classified into amino acids, organic acids, fatty acids, sugars and polyols, and others. Metabonomic Variations Associated with Lung Metastasis
Since all the mice developed lung metastasis at 4 weeks after LM8 inoculation, the serum metabolite expression levels in the model group at week 4 were compared with those at week 3, to identify metabolite changes associated with lung metastasis. The level of each metabolite in the model mice was divided by the average values of the same metabolite in the control group at the same time point, to minimize the physiological variations of experimental mice at different time points. As shown in Figure 2C,D, clear separations were obtained from PCA (7 components, R2X = 0.698, Q2 = 0.241) and PLS-DA (PLS-DA 3 components, R2X = 0.512, R2Y = 0.991, Q2 = 0.934) scores plot. A total of 3516
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Table 2. Differential Serum Metabolites in the Model Group at Week 4 Relative to Those at Week 3 no.
compound name
VIPb
pc
FCd
no.
Amino Acids
compound name
2
1.77
1.00
4.92 10
2
1.35
1-monopalmitin a
1.24
8.12 10
3
0.62
32
9-octadecenamide
1.32
4.01 10
3
0.42
33
Cholesterol a
1.21
1.05 10
2
1.67
0.47
30
Decosahexaenoic acid
1.17
1.41 10
2
0.58
31
Serine a
1.36
2.74 10
3
0.65
4
Valine a
1.64
4.10 10
5
0.43
5
Proline a
1.24
8.07 10
3
0.57
6
Lysine a
1.25
7.46 10
3
0.57
Glycine
1.48
2
Alanine a
3
FCd
1.10 10
Elaidic acida
6.44 10
1
pc
1.20
29 4
a
VIPb
a
TCA and Glycolysis Metaboilism 34
Succinic acid a
1.18
1.29 10
2
0.22
0.42 0.61
35 36
a
Fumaric acid Isocitric acid a
1.00 1.48
4.21 10 6.40 10
2
0.65 0.49
0.75
37
Citric acid a
1.43
1.25 10
3
0.47
a
1.06
2.94 10
2
0.59
7 8
Tyrosine Methionine a
1.32 1.56
3.76 10 1.94 10
3
9
Ornithine a
1.03
3.46 10
2
10
Citrulline
a
1.15
1.54 10
2
0.61
38
Malic acid
11
Glutamine a
1.05
3.19 10
2
0.72
39
Lactic acid a
1.19
1.20 10
2
0.74
12
Asparagine a
1.65
3.67 10
5
0.42
40
Pyruvic acid a
1.41
1.57 10
3
0.74
13
Phenylalanine a
1.66
2.78 10
5
0.36
a
4
4
Sugars and Polyols
14
5-oxoproline a
1.19
1.20 10
2
0.51
41
Galactose a
1.08
2.57 10
2
0.70
15 16
4-hydroxy-proline a 4-aminobutanoic acid a
1.50 1.68
4.50 10 1.69 10
4
0.54 0.41
42 43
Glucose a Fructose a
1.28 1.15
5.84 10 1.55 10
3
0.16 0.50
17
3-methyl-histidine
1.77
1.21 10
6
5
0.27
Organic Acids 18
2,4-dihydroxybutanoic acid
1.12
1.92 10
2
1.24
19
2-methyl-3-hydroxy-butanoic acid
1.59
1.24 10
4
0.33
2
44
Arabitol
1.37
2.30 10
3
0.73
45
Arabinofuranose
1.33
3.61 10
3
0.69
46
Myo-Inositol
1.19
1.18 10
2
0.67
Others
20
2-piperidinecarboxyliate a
1.56
1.83 10
4
2.34
47
Uracil a
1.26
6.87 10
3
0.49
21
3-methyl-3-hydroxybutanoic acid
1.40
1.68 10
3
0.47
48
Uric acid a
1.28
5.87 10
3
0.32
22 23
2,3-dihydroxybutanoate 2-hydroxybutanoic acid a
1.58 1.44
1.44 10 1.08 10
4
0.69 0.43
49 50
Chenodeoxycholic acid 3-amino-2-piperidone
1.13 1.59
1.83 10 1.24 10
2
0.26 0.40
24
2-hydroxyglutaric acid
1.08
2.63 10
2
0.63
51
phosphate
1.18
1.28 10
2
0.46
1.13
1.80 10
2
0.48
52
Glyceric acid
1.49
5.77 10
4
0.46
53
Threonic acid
1.05
3.11 10
2
0.72
25
2-oxo-3-methylvaleric acid
3
Lipid Metabolism
4
26
Nonanoic acid
1.18
1.24 10
2
0.69
54
4-hydroxyphenylpyruvic acid
1.16
1.45 10
2
0.46
27
Palmitic acid a
1.26
6.73 10
3
1.54
55
Cadaverine
1.26
6.86 10
3
0.49
28
Octadecanoic acid a
1.46
8.47 10
4
1.76
56
Creatinine
1.18
1.30 10
2
0.56
a
Metabolites verified by reference compounds, other were directly obtained from library searching. b Variable importance in the projection (VIP) was obtained from PLS-DA with a threshold of 1.0. c p-value was calculated from student t test. d Fold change was calculated from the arithmetic mean values of each group. Fold change with a value more than 1 indicates a relatively higher concentration present in the group of 4 weeks after LM8 inoculation, while a value less than 1 means a relatively lower concentration as compared to the group of 3 weeks after LM8 inoculation.
56 serum metabolites were found significantly altered at week 4 relative to those at week 3. Most of these metabolite markers (in Table 2) were found decreased at week 4, except for the long chain fatty acids such as elaidic acid, octadecanoic acid, and decosahexaenoic acid, which were elevated at week 4. Dynamic Metabonomic Changes Associated with the Tumor Progression and Metastasis
The PCA models were constructed (Figure 2E,F) to demonstrate the dynamic metabolic changes associated with the progression and metastasis of osteosarcoma in the model mice. To eliminate the metabolic variations due to physiological changes over the 4-week experimental period, the ratio of the serum level of each metabolite in the model group to that in the control group was used for the data analysis and model construction. In the two-dimensional PCA scores plot (Figure 2E, PC1 and PC2, 7 components, R2X = 0.698, Q2 = 0.241), samples from week 1 and week 4 clustered into two groups and separated from samples from week 2 and week 3
mice, while samples from week 2 and week 3 also separated from each other with some overlaps. The three-dimensional scores plot of PCA also demonstrated clear separation among the mice at week 1 4 (Figure 2F). The two-dimensional and three-dimensional PLS-DA scores plots showed similar trend to PCA model with better separation among the mice at week 1 4 (SI Figure 3). To better demonstrate the metabolites variations during tumor growth and lung metastasis, PCA and PLS-DA models were carried out between model samples and controls at each time point, respectively (SI Figure 4 for week 1, 2 and 4, Figure 2 for week 3). Differential metabolites accounting for the separations in PLS-DA models were selected from each time point. A total of 78 differential metabolites were identified as listed in SI Table 1. Heat-map of all the differential metabolites and the bar-plots of some typical metabolite markers were shown in Figure 3 to visualize the fluctuations (in fold change) from week 1 to week 4 after LM8 inoculation. The mean values and the individual values of those differential metabolites from model and control group in each time point were provided in the SI data sheet 1 and data sheet 2. 3517
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Figure 3. Heat-map and bar-plot of all differentially expressed metabolites from week 1 to week 4 in the model mice, relative to controls. Fold changes were calculated by the ratio of values in each model mouse to the mean values of controls. Fold change (FC) > 1 indicates that the serum level of the metabolite was elevated, while FC < 1 means that the serum level is lower in the model mouse than the mean value of controls. (A) Heat-map of all the differential metabolites. (B I) Bar-plot of some typical differential metabolites (*, p < 0.05, **, p < 0.01, ***, p < 0.001, p values were calculated by Student’s t test between model samples and corresponding controls).
’ DISCUSSION The distinct metabolic transformation needed for cell proliferation in the tumor microenvironment may result in unique metabolic signature in the biofluids, which can be readily captured by an unbiased metabonomics profiling approach. In this study, we observed dynamic fluctuations in serum metabolome associated with the growth, development, and metastasis of osteosarcoma in mice after LM-8 inoculation. As we can see from the heat-map in Figure 3A, most of the metabolite markers were at lower levels at the first week and also at the fourth week post LM-8 inoculation, showing a relatively lower metabolic activity during the tumor formation. These two similar metabonomic signatures (close to each other in PCA scores plot in Figure 4) at the first week and the fourth week may suggest that similar metabolic transformation mechanisms are implicated in tumor cell inoculation and tumor metastasis. Another possible reason for lowered metabolic activity in the fourth week may be due to the hypoxia environment in the tumor tissues associated with the growth of the solid tumor. As serum metabolites are the end points of the whole body biochemical regulation, it is hard to attribute these confounding metabolic effects to specific causes, such as angiogenesis, hypoxia, and metastasis. The high metabolic activity involving multiple pathways in energy metabolism at the third week, as shown in the heatmap, is indicative of high amount of energy and precursors needed for cell proliferation and tumor growth, as the tumor reached the largest size prior to metastasis.
A total of 78 differentially expressed metabolites in the serum of model mice and controls were identified from week 1 to week 4. These metabolite markers include amino acids, intermediates of tricarboxylic acid (TCA) cycle, free fatty acids, and sugars and polyols. Most of the intermediates involved in glycolysis and TCA cycle, including pyruvic acid, lactic acid, citric acid, fumaric acid, isocitric acid, malic acid, and succinic acid, were significantly elevated in the model group compared with controls at week 3. The increased glycolysis (as indicated by the levels of lactic acid and pyruvic acid) is consistent with a characteristic feature of higher glycolytic rates in malignant tumor cells, known as the Warburg effect.21 The higher levels of intermediates in TCA cycle indicate higher energy supply through aerobic process in mitochondria of rapidly growing tumor cells. Furthermore, we compared the ratios of two metabolites closely related in the metabolic pathways (direct substance and product) at the four time points, which may reveal the alteration of the enzymatic activities. As shown in Figure 4, the ratios of citric acid to isocitric aciod and fumaric acid to malic acid remain consistent from week 1 to week 4. The ratio of isocitric acid to 2-ketoglutaric acid, and the ratio of succinic acid to furamic acid increased from week 1 to week 3 and lowered in week 4. This may suggest that the activities of isocitrate dehydrogenase (IDH) and succinate dehydrogenase (SDH) were down-regulated during the tumor growth and then up-regulated in the metastatic phase. The mutation of IDH was observed during the formation 3518
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Figure 4. The altered levels of several key metabolites in TCA cycle in the fold changes between two adjacent metabolites (direct substance and product). (A) The ratio between citric acid and isocitric acid; (B) the ratio between isocitric acid and 2-ketoglutaric acid; (C) the ratio between succinic acid and furamic acid; (D) the ratio between furamic acid and malic acid.
and malignant progression of gliomas associated with accumulated levels of 2-hydroxyglutaric acid.22 The increased level of 2-hydroxyglutaric acid in the serum of model mice was also observed in the third week. SDH has previously been associated with tumorigenesis.23 The succinic acid was observed with the highest fold change (FC = 10.75, relative to controls) among all the metabolites in the third week. The accumulation of succinate in mitochondria and then in cytosol is believed to help tumor cells resist certain apoptotic signals and/or enhance glycolysis through inhibition of prolyl hydroxylase enzymes.23 The inhibited prolyl hydroxylase and accumulated succinate may overexpress hypoxia-inducible factor (HIF-1R) in solid tumor in association with angiogenesis, and tumor invasion and metastasis.24 The altered glycolysis and TCA cycle metabolism may be a result of the variation in the expression of mammalian target of rapamycin (mTOR), which was reported to impact mitochondrial function and play critical role in tumor cell motility and cancer metastasis.25,26 Therefore, the alterations of metabolites in glycolysis and TCA cycle revealed the high energy demand as well as the altered enzyme activities in association with rapid tumor growth prior to tumor metastasis. Most of the amino acids were observed at significantly increased levels in the model animals at week 3, presumably resulting from higher turnover of anabolic metabolism during the tumor proliferation, which was also observed in the colon cancer carcinoma tissue compared to normal colon mucosa.27 In addition, our previous research also revealed higher levels of amino acids in the urine samples of osteosarcoma patients compared with healthy controls.18 Among all the differently expressed amino acids, proline was noticed with highest fold change (FC = 4.43). A significantly decreased activity of proline oxidase (POX, catalyzing proline catabolism) in the tumor tissues was observed in previous studies.28 Additionally, POX
has been associated with regulating tumor cell survival through p53 gene.29 In our study, a number of small-molecule organic acids, which are believed to be downstream products of amino acids,30 33 such as pipecolic acid (from lysine), 3-methyl-2oxopentanoate (from isoleucine), 3-hydroxy-2-methyl-butanoic acid (from isoleucine), and 2-hydroxybutyric acid (from threonine), were also observed at higher levels in the model mice at week 3. These elevated amino acids may constitute a characteristic metabolic signature of rapid tumor growth prior to metastasis. The serum metabolic profile of lung metastasis at week 4 was different from those in week 2 and 3. Compared with model mice at week 3, most of the metabolites at week 4 were decreased, except for cholesterol and several free fatty acids, such as octadecanoic acid and decosahexaenoic acid, which were found increased. This observation suggests that the tumor metastasis is associated with lowered carbohydrate and amino acids metabolism, but an elevated lipid metabolism. Interestingly, higher level of cholesterol was recently found in metastatic bone tissue derived from prostate cancer compared with adjacent normal bone tissue.34 Previous reports also revealed that inhibition of cholesterol synthesis could lead to a decreased development of metastatic nodules for tumor cells.35 Docosahexaenoic acid (DHA) was proved to be able to inhibit lung metastasis of highly metastatic colon carcinoma due to its activity of suppressing type-IV collagenase.36 In our study, elevated level of arachidonic acid was also observed in the model mice at week 3 and week 4 compared with corresponding controls (see SI Table 1). Previous reports have shown that arachidonic acid and its metabolites affected apoptosis, angiogenesis, the proliferation of cancer cells and metastasis.37 The increased serum level of arachidonic acid may activate cyclooxygenase and lipoxygenase pathways, presumably by the tumor necrosis factor alpha (TNF-alpha) in 3519
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Journal of Proteome Research human osteoblastic osteosarcoma cell line.38 Given these observations, an altered lipid metabolism may play important role in lung metastasis of LM8 induced OS. Several sugars and polyols, such as glucose phosphate, glucose, arabitol, ribofuranose, and gluconolactone, significantly decreased at week 4 (see SI Table 1) compared with controls at week 4 and those of model mice at week 3. It is interesting to notice that these sugars (glucose phosphate, glucose, and gluconolactone) were all involved in the pentose phosphate pathway (PPP). The decreased serum level of PPP intermediates may be due to the increased intracellular utilization of pentose cycle for increased nucleic acid synthesis as metabolic adaptation to the tumor cell invasion.39 Another evidence in our results is that uridine and uracil were observed significantly increased in the model groups at the first and fourth week of the experiment, suggesting increased nucleic acid synthesis in tumor cells. Collectively, these data suggest a characteristic metabolic transformation during the tumor invasion/migration, involving increased nucleic acid synthesis through pentose phosphate pathway as an alternative to glycolysis. Cystine was found in model mice at significantly decreased levels at week 4 as compared to controls. As a dimeric amino acid formed by the oxidation of two cysteine molecules, cystine is correlated with glutathione metabolism. The serum level of 2-hydroxybutanoic acid, a byproduct of the methionine to glutathione pathway,40 was also found significantly lower at week 4 as compared to the model mice at week 3. These findings may indicate a down-regulated glutathione pathway during the lung metastasis in the LM8 induced osteosarcoma tumor. Interestingly, threonic acid (metabolite of ascorbate,41 another cellular antioxidant as glutathione) was also observed down-regulated at week 4 as compared to the model mice at week 3. This may further indicate impaired antioxgen ability in the lung metastatic mice. In addition, arabitol and arabinofuranose were observed significantly lower at week 4 as compared to the model mice at week 3. As these metabolites may come from intestinal microbial degradation of nondigestible carbohydratesare,42 their variations may reveal the gut microbial compositional changes in the model mice during tumor metastasis. In summary, the metabonomic approach is able to acquire time-dependent and characteristic changes in serum metabolic signatures in mice with transplanted osteosarcoma progressing to lung metastasis. Differential metabolites were identified in the stages of tumor cell invasion, tumor growth, and metastasis to the lung, including amino acids, fatty acids, and metabolites involved in carbohydrate metabolism. These findings will contribute to our knowledge of characteristic metabolic changes associated with the metastatic phenotype at tumor growth stage, as well as to the development of potential antimetastatic cancer therapies.
’ ASSOCIATED CONTENT
bS
Supporting Information PCA scores plot of experimental samples collected at all time points; PCA scores plot among all the control samples; the scores plots of two-dimensional (A) and three-dimensional PLS-DA (B) showing the dynamic changes in the serum metabolites of experimental mice at week 1 4 post LM8 inoculation; PCA and PLS-DA scores plots between control and model samples at week 1, week 2, and week 4; all the differentially expressed metabolites; body weight of model mice and controls; mean values of those identified differential metabolites in from model
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and control group in each time point; the individual values of those differential metabolites from model and control group in each time point. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Wei Jia, Department of Nutrition, University of North Carolina at Greensboro, North Carolina Research Campus, Kannapolis, NC 28081, USA. Phone: 1-704-250-5803. Fax: 1-704-250-5809. E-mail:
[email protected]. Zhengdong Cai, Musculoskeletal Oncology Center, Shanghai 10th People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China. Phone: 8621-66307330. Fax: 86-21-66307330. E-mail:
[email protected]. Author Contributions #
These authors contributed equally to this work.
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