Serum Metabolomics of Burkitt Lymphoma Mouse Models

Abstract

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.

Fig 1. Typical 600 MHz ¹H CPMG spectra of serum samples.

(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.

Fig 2. Typical 600 MHz ¹H BPP-LED spectra of serum samples.

(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.

Fig 3. Multivariate analysis of CPMG spectra of serum samples of control 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.

Fig 4. Multivariate analysis of BPP-LED spectra of serum samples in control 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.

Fig 5. Heat map of the 23 significantly changed serum metabolites in the control and tumor—bearing mice.

Fig 6. ROC curves for distinguishing controls from tumor-bearing mice according to metabolite differences.