## 3 Results and Discussion
### Multivariate Statistical Analysis  
To better understand the relationship between metabolites and gene expressions determined in the present study, multivariate statistical analysis was performed. As Figure 4 and Figure S2 (Supplementary Materials) show, the partial least-square discriminant analysis (PLS-DA) and correlation analysis were performed with the data concerning soluble sugars, organic acids, anthocyanins, GSVs, and related gene expressions. The first two components accounted for 72.5% of total variance (47.9% and 24.6%, respectively). PLS-DA analysis showed that the four spine grape varieties were distinguished from each other, which indicates that they possessed significant differences in metabolite and gene expression levels. The G2 and G4 groups were located on the positive side of the first and second components, and the distance was close, indicating that the metabolite and gene expression differences between the two varieties were not obvious; however, the BY and XZZ groups were located on the positive and negative sides of the first component, respectively, and were far away, indicating that there was a significant difference in metabolites and gene expressions between these two varieties. To better analyze the differences in metabolites and gene expressions among the four varieties, 15 metabolites or genes with significant differences among the varieties were screened. Interestingly, the anthocyanin content and related gene expression levels were the most significant among the four varieties, especially malvidin-3-acetyl-glucoside and cyanidin-3-glucose. These results further indicate that the characteristic anthocyanins in spine grapes were malvidin-3-acetyl-glucose and cyanidin-3-glucose, which is different from _Vitis vinifera_ grapes [4; 17]. The glucose and fructose content also contributed greatly to the distinction between varieties, and there were no bound volatiles in the screened components. Correlation analysis revealed the relationship between metabolites and related gene expressions. The positive correlations between the expression level of _VdGT9_ and bound volatiles contents, such as eugenol, 1-heptanol, 3-methyl-3-buten-1-ol, and 3-methyl-1-butanol, were detected. The expressions of _VdGT5_ and _VdGT6_ were significantly related to the concentrations of mequinol, benzyl alcohol, 1-hexanol, isogeraniol, citronello, and linalool. Our results were consistent with those of [PERSON] et al. [11], which reported that the expression levels of _GTs_ genes were significantly related to the concentrations of GSVs. In addition, the concentrations of malvidin-3-acetyl-glucose and cyanidin-3-glucose were significantly related to the expressions of anthocyanin biosynthesis genes, especially _VdGST_, _VdGT3H_, _VdOMT_, _VdLDOX_, and _VdUFGT_. These genes play important roles in the biosynthesis and proportion of individual anthocyanins [46]. The difference in anthocyanin components in different spine grape  
Figure 3: Expression levels of _VdGT5_, _VdGT6_, _VdGT7_, _VdGT9_ and _VdPNGer_, which are associated withglycosidally bound volatiles biosynthesis of spine grapes. Values presented are means \(\pm\) SD (\(n=3\)). _VdGAPDH_ was used as reference gene. Different letters indicate significant differences among four spine grapes using Duncan’s test (\(p<0.05\)). G2: Gaoshan #2, G4: Gaoshan #4, XZZ: Xiangzhenzhu, BY: Baiyu. varieties might therefore be caused by the difference in gene expression. We also found that sugar and organic acids were correlated with GBVs and anthocyanin contents. For example, there was a significant positive correlation between glucose and fructose content and mequinol, geraniol, benzyl alcohol, and 2-ethyl-1-hexanol. These results indicate that there may be a close relationship between primary and secondary metabolites [24]. Figure 4: Multivariate statistical analysis. (**A**) Score plot of partial least-square discriminant analysis (PLS-DA) using metabolites and gene expression levels researched in this study.