| To explore the effects of different altitudes on changes of different metabolites and metabolic pathways the sera and livers of T. belangeri from Dali (high-altitude) and Mongla (low-altitude) were collected. The metabolites were analyzed by using the non-targeted metabonomics gas chromatography-mass spectrometry (GC-MS). The original data were preprocessed by software XCMS (www. bioconductor.org/), converting the original GC-MS data into common data format (CDF) format. The XCMS program was used for peak identification, peak filtering and peak alignment to determine the parameters of XCMS (Fig. 1). A metabolite tree map was constructed based on the euclidean distance between samples, and clustering of samples was performed by a clustering algorithm (Fig. 2). Then the processed data were imported into SIMCA-P software (Umetrics, Umea, Sweden), and multivariate statistical analysis was carried out. Hierarchical cluster analysis (HCA) was used to analyze the metabolite thermograms with the heatmap function in the R package (Fig. 3, 4). The correlation analysis of metabolites was carried out for significance statistical test, and the statistical test method was the COR. TEST function in R language package (Fig. 9, 10). Metabolic pathways were assigned to metabolites based on Kyoto encyclopedia of genes and genomes (KEGG, http://www.genome.jp/kegg/), and Pathway Activity Profiling (PAPi) was used to compare the relative activities of different metabolic pathways in different groups (Appendix 1, 2). All analyses were performed using the R package. Differential metabolites were screened by One-way ANOVA analysis (P < 0.05) and ploidy change Log2 value (fold change > 1.5 or fold change < 0.667) (Fig. 5‐8). The results showed that there were 36 different metabolites in serum of the high-altitude population compared to the low-altitude population (Fig. 3), among which 32 were up-regulated (citric acid, glucose, cholesterol, et al) and 4 were down-regulated (N-acetylglutamic acid, decanoic acid, 4-hdroxybutyric acid, et al.). There were 18 metabolites showing significant difference in the high-altitude population compared to the low-altitude population (Fig. 4), among which 10 were up-regulated (malic acid, ribose, glucose, et al.) and 8 were down-regulated (glutamine, glycolic acid, octadecanoic acid, et al.). Compared with the serum metabolic pathways at low-altitude, there were 76 metabolic pathways with significantly different activity scores in high-altitude population (Appendix 1), among which 69 were up-regulated and 7 were down-regulated. There were 75 metabolic pathways with significantly different activity scores in the high-altitude population compared with the low-altitude population (Appendix 2), among which 43 were up-regulated and 32 were down-regulated. All of the above results suggest that T. belangeri would adjust the metabolites of different metabolic pathways (including tricarboxylic acid cycle, glycolysis, lipid metabolism and amino acid metabolism) in different tissues to adapt to different environments, and serum is more sensitive to environmental changes than the liver. |
