JD is employed by a commercial company, JOINN Laboratories, Inc., Beijing, China. There are no patents, products in development or marketed products to declare. This affiliation does not alter PLOS ONE policies on sharing data and materials.
Burkitt lymphoma (BL) is a rare and highly aggressive type of non-Hodgkin lymphoma. The mortality rate of BL patients is very high due to the rapid growth rate and frequent systemic spread of the disease. A better understanding of the pathogenesis, more sensitive diagnostic tools and effective treatment methods for BL are essential. Metabolomics, an important aspect of systems biology, allows the comprehensive analysis of global, dynamic and endogenous biological metabolites based on their nuclear magnetic resonance (NMR) and mass spectrometry (MS). It has already been used to investigate the pathogenesis and discover new biomarkers for disease diagnosis and prognosis. In this study, we analyzed differences of serum metabolites in BL mice and normal mice by NMR-based metabolomics. We found that metabolites associated with energy metabolism, amino acid metabolism, fatty acid metabolism and choline phospholipid metabolism were altered in BL mice. The diagnostic potential of the metabolite differences was investigated in this study. Glutamate, glycerol and choline had a high diagnostic accuracy; in contrast, isoleucine, leucine, pyruvate, lysine, α-ketoglutarate, betaine, glycine, creatine, serine, lactate, tyrosine, phenylalanine, histidine and formate enabled the accurate differentiation of BL mice from normal mice. The discovery of abnormal metabolism and relevant differential metabolites may provide useful clues for developing novel, noninvasive approaches for the diagnosis and prognosis of BL based on these potential biomarkers.
BL is a rare and highly aggressive type of non-Hodgkin lymphoma, mainly from B lymphocytes, that was first discovered by British surgeon Dennis Burkitt [
Metabolomics is an important aspect of systems biology that can comprehensively analyze global, dynamic and endogenous biological metabolites based on NMR or MS [
Metabolomics research using clinical serum samples faces many challenges because the concentrations of metabolites vary frequently due to various genetic and environmental factors. In addition, serum samples from newly diagnosed BL patients may not be readily available. Li Zhang [
Currently, little is known about the metabolomics of BL. The comprehensive pathogenesis of BL is expected to be revealed by metabolomics, which is very important for the diagnosis and treatment of BL. In this study, we analyzed serum metabolomics of BL mouse models, based on NMR techniques. The concentration of some serum metabolites such as glucose, glutamate, and unsaturated lipids was significantly different between BL mice and wild-type mice. Abnormality of metabolism and the relevant different metabolites of BL were discovered. These results may provide useful clues for developing novel noninvasive methods for the diagnosis and prognosis of BL based on these potential biomarkers.
Twenty non-obese diabetic-severe combined immune-deficiency (NOD-SCID) mice (20 to 26 g) aged seven to nine weeks were housed in cages under a regular light cycle (12 h) and fed a sterilized mouse diet and water. Ten mice served as controls, and the others were tumor-bearing. Raji cells (2 × 106 cells / mouse) were injected subcutaneously into the right front axilla of the mice. The samples were collected when the tumor volume had reached approximately 500 to 1000 mm3. This study was performed in strict accordance with the recommendations of the Guidelines for the Care and Use of Laboratory Animals of the National Science Center of China. The protocol was approved by the Committee on the Ethics of Animal Experiments of China. The animals were housed and cared for in accordance with the guidelines established by the National Science Center of China.
All blood samples without anticoagulants were placed at room temperature for 45 minutes and centrifuged at 8,000 g for 10 min at 4°C. The serum samples were collected and stored at -80°C until analysis.
The serum samples were thawed at room temperature and centrifuged at 12,000 g for 10 min at 4°C. Each serum sample (100 μL) was mixed with phosphate buffer (100 μL) (1:1 v/v, mixture of 1 M K2HPO4 and 0.25 M NaH2PO4; pH 7.4, 100% D2O) by vortexing for 1 min and centrifuged at 12,000 g for 10 min at 4°C. Next, 150 μL of the supernatant was transferred to the 3 mm NMR tube.
The serum samples were analyzed by 1H NMR spectroscopy at 600.13 MHz in a Bruker AVANCE 600 spectrometer equipped with a TBO probe. To detect the low molecular weight components of serum, the 1H NMR spectra of all serum samples were acquired at 300 K using standard Carr-Purcell-Meiboom-Gill (CPMG) plus sequence ((RD-90°-(ô-180°-ô) n -acquire)) with a total spin—spin relaxation delay of 40 ms [
To identify the metabolites, two-dimensional NMR spectra were acquired, including 1H-1H correlation spectroscopy (COSY), total correlation spectroscopy (TCOSY), 1H-13C heteronuclear single quantum correlation (HSQC) and 1H-13C heteronuclear multiple bond correlation (HMBC) spectra.
All serum 1H NMR spectra were corrected for phase and baseline using MestReNova (6.1.1-6384-Win). The spectra over the range of δ 0.5–9.5 for CPMG were divided into buckets with equal width of 0.005 ppm, and the regions at δ 4.200–5.200 were excluded for eliminating the interference of the water signals. The spectra over the range of δ 0.5–9.5 for BPP-LED were divided into buckets with equal width of 0.005 ppm, and the regions at δ 4.385–5.075 were excluded for eliminating the interference of the water signals. Buckets were normalized to a constant sum (100) of all spectra intensity to reduce the differences of the concentration between the serum samples [
The data were imported into SIMCA 14 Umetrics for multivariate analysis. The principal component analysis (PCA) was used to discern the presence of inherent similarities of spectral profiles. The partial least squares-discriminant analysis (PLS-DA) or the orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to find the metabolite differences between the control and tumor-bearing mice [
Serum comprises both low molecular weight metabolites and high molecular weight proteins and lipoproteins. To amplify the low weight metabolites in serum samples, the CPMG plus sequence was employed to acquire the spectra. Typical CPMG spectra of serum samples for controls and tumor-bearing mice are shown in
(A) controls (B) tumor-bearing mice. Keys for metabolites: 1, Lipids (mainly LDL); 2, Lipids (mainly VLDL); 3, Isoleucine; 4, Leucine; 5, Valine; 6, 3-Hydroxybutyrate; 7, Unknown; 8, Lactate; 9, Alanine; 10, Citrulline; 11, Arginine; 12, Acetate; 13, Proline; 14, Glutamate; 15, Glutamine; 16, Methionine; 17, Lipid; 18, Pyruvate; 19, Citrate; 20, Polyunsaturated fatty acid; 21, Asparagine; 22, Lysine; 23, α-Ketoglutarate; 24, Creatine; 25, Creatinine; 26, Choline; 27, Phosphocholine (PC) / Glycerophosphocholine (GPC); 28, Glucose; 29, TMAO (Trimethylamine-N-oxide); 30, Betaine; 31, Glycine; 32, Myo-inositol; 33, Glycerol; 34, Serine; 35, β-glucose; 36, α-glucose; 37, Urea; 38, Tyrosine; 39, Histidine; 40, Phenylalanine; 41, Formate.
(A) controls (B) tumor-bearing mice. Keys for metabolites: 42, Cholesterol; 43, Lipids (mainly HDL); 44, Lipids (triglycerides and fatty acids); 45, O-acetyl glycoproteins; 46, Glycerolipids; 47, Phosphatidylcholine; 48, Triglyceride; 49, Unsaturated lipid.
Multivariate analysis of 1H NMR spectra was used to screen the different metabolites between the controls and tumor-bearing mice. At first, unsupervised PCA was used to analyze the 1H NMR CPMG spectra. The score scatter plot of PCA followed by examination of the first two principal components failed to reveal any clear separation between controls and tumor-bearing mice (
(A) The score scatter plot of PCA for controls (black triangle) and tumor-bearing mice (red box). (B) PLS-DA showed a clear separation between controls (black triangle) and tumor-bearing mice (red box) in the score scatter plot. (C) Permutation test results for PLS-DA models (R2 = (0.0, 0.482), Q2 = (0.0, -0.214)). (D) Loading plot corresponding to PLS-DA score scatter plot.
Metabolites | δ1H (ppm) | Multiplicity | p-value | Changes in tumor-bearing mice compared to controls |
---|---|---|---|---|
Isoleucine | 0.94 | t | 0.0089 | ↑ |
Leucine | 0.95 | d | 0.0355 | ↑ |
VLDL | 1.26 | m | 0.0433 | ↓ |
Glutamine | 2.08 | m | <0.0001 | ↑ |
Pyruvate | 2.41 | s | 0.0355 | ↓ |
Citrate | 2.54 | d | 0.0355 | ↑ |
Lysine | 3.01 | m | 0.0433 | ↑ |
α-Ketoglutarate | 3.02 | m | 0.0288 | ↑ |
Glucose | 3.46, 3.47, 3.48 | m | 0.0147 | ↓ |
Glycerol | 3.87 | m | 0.0001 | ↑ |
Phosphocholine (PC)/Glycerophosphocholine (GPC) | 3.23 | s | 0.0115 | ↓ |
Betaine | 3.28 | s | 0.0232 | ↑ |
Glycine | 3.56 | s | 0.0068 | ↑ |
Creatine | 3.93 | s | 0.0288 | ↑ |
Serine | 3.945, 3.95, 3.97 | m | 0.0147 | ↑ |
Choline | 3.66 | m | 0.0007 | ↑ |
Lactate | 4.12 | q | 0.0232 | ↑ |
α-Glucose | 5.24 | d | 0.0039 | ↓ |
Tyrosine | 6.9 | d | 0.0089 | ↑ |
Phenylalanine | 7.33 | m | 0.0147 | ↑ |
Histidine | 7.75 | t | 0.0029 | ↑ |
Formate | 8.46 | s | 0.0147 | ↑ |
Unsaturated lipids | 5.29 | m | 0.0115 | ↓ |
The models of PCA and PLS-DA for BPP-LED spectra of serum could not commendably separate controls and tumor-bearing mice (
(A) The score scatter plot of PCA for controls (black triangle) and tumor-bearing mice (red box). (B) The score scatter plot of PLS-DA for controls (black triangle) and tumor-bearing mice (red box). (C) OPLS-DA showed clear separation between controls (black triangle) and tumor-bearing mice (red box) in the score scatter plot. (D) Permutation test results for PLS-DA models (R2 = (0.0, 0.56), Q2 = (0.0, -0.404)). (E) Loading plot corresponding to PLS-DA score scatter plot.
To fully and intuitively display the relationships and differences between different samples, HCA (
The ROC curves based on the result of area under the curve (AUC) can be conducted to investigate the clinical diagnostic potentials of these significantly different metabolites. The diagnostic accuracy is higher when the value of the AUC is closer to 1. As shown in
Serum metabolomics are thought to be a collective "snapshot" of changes throughout the body's metabolism. The alterations of serum metabolomics may be induced by many factors, such as disease, behavior, gender, drug intake, and environmental factors. However, when these factors are similar, the differences of serum metabolomics between the tumor patients and the healthy controls may be derived from the presence of tumor cells. For instance, DA MacIntyre et al. found that the serum pyruvate and glutamate levels may be a good indicator of chronic lymphocytic leukemia (CLL) patients [
In this study, we analyzed the differences of serum metabolite levels in BL mice and wild-type mice based on NMR-based metabolomics. Although the changes of serum metabolites may be associated with many factors such as behavior, gender and environment, these factors were almost identical in our tumor-bearing mice and control mice. The only difference between the tumor-bearing mice and the controls was the presence of tumor cells. Therefore, we infer that the changes of metabolite levels in serum may be derived from multiple tumor-related metabolic pathways, involving energy metabolism, amino acid metabolism, fatty acid metabolism and choline phospholipid metabolism.
Different levels of serum metabolites between tumor-bearing BL mice and the control wild-type mice can reflect the changes in energy metabolism. Glucose is a starting material of glycolysis and lactate is an end product of glycolysis. In our study, lower serum glucose levels and higher serum lactate levels in tumor-bearing mice suggested that blood glucose may be rapidly consumed by glycolysis for tumor cell proliferation and growth. This phenomenon is consistent with the “Warburg effect” [
Creatine is a nitrogen-containing organic acid, which can provide substrates for energy and protein synthesis to meet the requirements for cancer cell proliferation [
Amino acid metabolism is the basis of life activities. The amino acids in serum can be used to not only feed tricarboxylic acid cycle (TCA cycle) but also provide amino acids for tumor proliferation and growth [
In our study, pyruvate was lower in the serum of tumor-bearing mice. Pyruvate is not only an important product of glycolysis but also a starting material of the TCA cycle. Citrate and α-ketoglutarate are intermediate products of the TCA cycle. In our research, lower pyruvate and higher citrate and α-ketoglutarate levels in BL tumor-bearing mice than in controls suggested that the TCA cycle may be altered to cause the accumulation of intermediate products of the TCA cycle.
We observed increased levels of glutamine in our study. Glutamine is a nitrogen carrier and a major fuel substrate for the proliferation of tumor cells. It can enter the TCA cycle after catabolism as the preferred amino acid to provide ATP for tumor cells and it can also be used in the biosynthesis of nucleotides for the tumor cell proliferation [
The serum levels for isoleucine, leucine, phenylalanine, tyrosine and histidine, which are essential amino acids, were higher in tumor-bearing BL mice than in the control wild-type mice. Among these amino acids, isoleucine and leucine belong to the branched chain amino acids, which are increased because of the interaction between different amino acid pools in the tumor host [
The levels of VLDL and unsaturated lipids are lower in the serum of tumor-bearing mice. According to this result, the fatty acid metabolism is also increased in tumor cells and the oxidation of fatty acids can deliver bioenergy for tumor cell proliferation and tumor growth [
Choline plays important roles in choline-mediated one-carbon metabolism and signaling functions of cell membranes [
We further tested the diagnostic potential of metabolite differences for diagnosing BL using ROC curves (
In summary, the results of this study offer evidence for changes in serum metabolite profiles in a BL mouse model utilizing NMR-based serum metabolomics. Energy metabolism, amino acid metabolism, fatty acid metabolism and choline phospholipid metabolism are altered in BL mice. The diagnostic potential of the metabolite differences was investigated using ROC curves. The results show that glutamate, glycerol and choline had the highest diagnostic accuracy and isoleucine, leucine, pyruvate, lysine, α-ketoglutarate, betaine, glycine, creatine, serine, lactate, tyrosine, phenylalanine, histidine and formate also offered diagnostic accuracy in distinguishing BL mice from wild-type mice. Abnormal metabolism and relevant metabolite differences provide useful clues for developing novel noninvasive methods for the diagnosis and prognosis of BL based on these potential biomarkers.
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This work was supported by the Major National Basic Research Projects (973) (Grant Number 2013CB733701), the General Program of the National Natural Science Foundation of China (Grant Number 21472197), the National Natural Science Foundation of China (Grant Number 21305145) and “Strategic Priority Research Program” of the Chinese Academy of Sciences (Grant Number XDA09030307). Funding for open access charge comes from General Program of the National Natural Science Foundation of China (Grant Number 21472197).