Metastatic Melanoma Induced Metabolic Changes in C57BL/6J Mouse Stomach Measured by 1H NMR Spectroscopy

Abstract

Melanoma is a malignant tumor of melanocytes with high capability of invasion and rapid metastasis to other organs. Malignant melanoma is the most common metastatic malignancy found in Gastrointestinal Tract (GI). In this work, the 1H NMR-based metabolomics approach is used to investigate the metabolite profile differences of stomach tissue extracts of metastatic B16-F10 melanoma and control groups in C57BL/6J mouse and to search for specific metabolite biomarker candidates. Principal Component Analysis (PCA), an unsupervised multivariate data analysis method, is used to detect possible outliers, while Orthogonal Projection to Latent Structure (OPLS), a supervised multivariate data analysis method, is employed to evaluate important metabolites responsible for discriminating the control and the melanoma groups. Both PCA and OPLS results reveal that the melanoma group can be well separated from its control group. Among the 50 identified metabolites, it is found that the concentrations of 19 metabolites are significantly changed with the levels of O-phosphocholine and hypoxanthine down-regulated while the levels of isoleucine, leucine, valine, isobutyrate, threonine, cadaverine, alanine, glutamate, glutamine, methionine, citrate, asparagine, tryptophan, glycine, serine, uracil, and formate up-regulated in the melanoma group. These significantly changed metabolites are associated with multiple biological pathways and may be potential biomarkers for metastatic melanoma in stomach.

Keywords: 1H NMR Metabolomics; B16-F10 melanoma; Metabolomics; Multivariate analysis; OPLS; PCA; Stomach.

Figure: 1

800 MHz liquid state ¹H NMR metabolite spectra of the hydrophilic extracts of stomachs excised from the control (black) and the melanoma mice (red). The peak intensities were normalized to per unit weight of tissue before extraction. The dotted regions were vertically expanded 17 times as denoted in the figure. A total of 50 metabolites were identified with metabolite numbers, i.e. the metabolite keys shown in Table 1

Figure: 2

OPLS scores (left) and coefficients-coded loadings plot (right) of the model discriminating the control (blue dots) and the tumor (green dots) groups. (a) Using the hydrophilic metabolites concentrations obtained by spectral deconvolution (only metabolites with correlation coefficients |r| > 0.811 were plotted, correlation coefficients of all metabolites used for OPLS analysis were listed in Table S1 in Supporting Information). (b) Using the data derived from binning results of ¹H NMR spectra from hydrophobic extracts. CV-ANOVA results gave p values of 0.0506 and 0.028 for models (a) and (b), respectively. A total of 2 components were extracted for both model (a) and model (b). R², i.e. fraction of Y variation modeled by X, was 0.93 and 0.961 for model (a) and model (b), respectively. Lipids peak assignments in model (b): 1, CH₃CH₂CH₂C=C; 2, CH₃CH₂; 3, (CH₂)_n, CH₃CH₂(CH₂)_n, CH₂CH₂CH₂CO, CH₂; 4, CH₂CH₂CO; 5, CH₂C=C; 6, CH₂CO; 7, C=CHCH₂CH=C; 8, -CH=CHCH₂CH=CH- (Unsaturated lipid), =CHCH₂CH₂; 9, =CHCH₂CH₂ (Unsaturated lipid). “Asterisk” indicates unassigned lipid contents

Figure: 3

Schematic diagram of potentially disturbed metabolic pathway networks associated with the significantly altered metabolites during melanoma development based on the findings from this work and the diverse metabolic fates depicted in the Small Molecule Pathway Database (SMPDB) (http://www.smpdb.ca/) and literatures. Solid line indicates one-step process, while dash line indicates multi-steps process. Metabolites colored green are not detected. Metabolic pathways under the numbered shadow squares are parts of metabolism networks of particular metabolites: 1 purine metabolism, 2 glycine and serine metabolism, 3 pyrimidine metabolism, 4 pyruvate metabolism, 5 glutamate metabolism, 6 tryptophan metabolism, 7 threonine degradation. Other significantly affected metabolites pathways are valine, leucine, isoleucine degradation, lipids metabolism, methionine metabolism and TCA cycle