Widely Targeted Metabolomics Analysis Reveals Key Quality-Related Metabolites in Kernels of Sweet Corn

Abstract

Sweet corn (Zea mays convar. saccharata var. rugosa) is a major economic vegetable crop. Different sweet corn cultivars vary largely in flavor, texture, and nutrition. The present study performed widely targeted metabolomics analysis based on the HPLC-MS/MS technology to analyze the metabolic profiles in three sweet corn cultivars widely grown in China. A total of 568 metabolites in the three sweet corn cultivars were detected, of which 262 differential metabolites significantly changed among cultivars. Carbohydrates, organic acids, and amino acids were the majority detected primary metabolites. Organic acids were mainly concentrated on shikimate, benzoic acids, and quinic acid with aromatic groups. And the essential amino acids for the human body, methionine and threonine, were highly accumulated in the high-quality cultivar. In addition, phenylpropanoids and alkaloids were the most enriched secondary metabolites while terpenes were low-detected in sweet corn kernels. We found that the flavonoids exist in both free form and glycosylated form in sweet corn kernels. PCA and HCA revealed clear separations among the three sweet corn cultivars, suggesting distinctive metabolome profiles among three cultivars. The differential metabolites were mapped into flavonoid biosynthesis, phenylpropanoid biosynthesis, biosynthesis of amino acids, and other pathways according to the KEGG classification. Furthermore, we identified skimmin, N',N^″-diferuloylspermidine, and 3-hydroxyanthranilic acid as the key quality-related metabolites related to grain quality traits in sweet corn. The results suggested variations of metabolic composition among the three cultivars, providing the reference quality-related metabolites for sweet corn breeding.

Figure 1

The sugar content and pericarp thickness in three sweet corn cultivars: (a) sucrose content; (b) glucose content; (c) fructose content; (d) pericarp thickness; (e) images of kernel pericarp in sweet corn using a scanning electron microscope. The results were shown as mean ± standard deviation with triplicate experiments for each sample (P < 0.05); (f) enzyme activity of C4H, CAD, and CHI in three sweet corn cultivars.

Figure 2

Detected metabolite distribution in sweet corn cultivars: (a) primary metabolites; (b) secondary metabolites.

Figure 3

Differential metabolites in pairwise comparison among the sweet corn cultivars: (a) volcanic plots of differential metabolites in JZY vs. CPL; (b) volcanic plots of differential metabolites in JBT vs. CPL; (c) volcanic plots of differential metabolites in JBT vs. JZY; (d) overlap of differentially expressed metabolites in sweet corn kernels (VIP > 1). The number in each subcollection refers to quantity of metabolites in intersection.

Figure 4

The enriched KEGG pathway terms covered by differential metabolites among three sweet corn cultivars. Rich factor is the percentage of the differential metabolite numbers in the corresponding pathway to the total annotated metabolite numbers in this pathway. The dot refers to the JBT_vs_JZY, the triangle represents the JBT_vs_CPL, and the square appoints to the JZY_vs_CPL. The size of the dots indicates the number of differential metabolites in the corresponding pathway.

Figure 5

Ten significantly changed metabolites among all cultivars in the sweet corn's kernels.