Combined transcriptome and metabolome analysis of the resistance mechanism of quinoa seedlings to Spodoptera exigua

Abstract

Quinoa has attracted considerable attention owing to its unique nutritional, economic, and medicinal values. The damage intensity of Spodoptera exigua at the seedling stage of quinoa fluctuates with the crop's biological cycle and the environmental changes throughout the growing season. In this study, we used independently selected quinoa seedling resistant and susceptible cultivars to investigate the difference between insect resistance and insect susceptibility of quinoa at the seedling stage. Samples were collected when Spodoptera exigua 45 days after planting the seedlings, and broad targeted metabolomics studies were conducted using liquid chromatography-mass spectrophotometry combined with transcriptomic co-analysis. The metabolomic and genomic analyses of the insect-resistant and insect-susceptible quinoa groups revealed a total of 159 differential metabolites and were functionally annotated to 2334 differential genes involved in 128 pathways using the Kyoto Encyclopedia of Genes and Genomes analysis. In total, 14 metabolites and 22 genes were identified as key factors for the differential accumulation of insect-resistant metabolites in quinoa seedlings. Among them, gene-LOC110694254, gene-LOC110682669, and gene-LOC110732988 were positively correlated with choline. The expression of gene-LOC110729518 and gene-LOC110723164, which were notably higher in the resistant cultivars than in the susceptible cultivars, and the accumulations of the corresponding metabolites were also significantly higher in insect-resistant cultivars. These results elucidate the regulatory mechanism between insect resistance genes and metabolite accumulation in quinoa seedlings, and can provide a basis for the breeding and identification of new insect-resistant quinoa cultivars as well as for screening potential regulatory metabolites of quinoa insect-resistant target genes.

Keywords: Spodoptera exigua; insect resistance mechanism; metabolome; quinoa seedling stage; transcriptome.

FIGURE 1

(A) Insect-resistant quinoa cultivars (R). (B–D) Insect-susceptible quinoa cultivars (N). (E) Map of cultivars planting plots.

FIGURE 2

(A) Principal component analysis (PCA) score plot. (B) Orthogonal partial least squares discriminant analysis (OPLS-DA) score plot. (C) Heat map of the differential metabolite clustering. (D) Volcano plot of differential metabolites. The percentage of the PCA score plot indicates the explanation rate of this principal component to the data set. The horizontal and vertical coordinates in the OPLS-DA score plot show the gap between and within groups. The left side of the heat map of the differential metabolite clustering represents the differential metabolite clustering tree. The larger the abscissa and ordinate values in the volcano plot, the greater the difference of expression multiples between the two samples, and the more reliable the differentially expressed metabolites are.

FIGURE 3

(A) Histogram of differential metabolite abundance. (B) Differential metabolite Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment graph. (C) Differential metabolite k-means graph. Log2FC and differential metabolites in the abscissa and ordinate in the histogram of differential metabolite abundance are shown. The color bar in (B) shows the range of p-value. The ordinate in the differential metabolite k-means graph represents the normalized relative content of metabolites.

FIGURE 4

(A) Gene principal component analysis (PCA) map. (B) Differential gene clustering heat map. The abscissa and ordinate of differential gene clustering heat map represent the sample name, differential gene, and hierarchical clustering results.

FIGURE 5

(A) Enrichment scatter plot. (B) Differential gene GO enrichment bar graph. The vertical coordinate represents the KEGG pathway. The horizontal coordinate indicates the rich factor, the larger the rich factor, the greater the enrichment. The larger the dot, the greater the number of differential genes enriched in the pathway. The redder the color of the dot, the more significant the enrichment. The abscissa of the column diagram of differential gene GO enrichment bar graph represents the proportion of the genes in the total number of genes annotated, and the ordinate represents the name of the go entry.

FIGURE 6

(A–F) Validation of the transcription levels for selected DEGs via RT-qPCR. (G) Verification of the expression patterns of RNA-seq results using RT-qPCR.

FIGURE 7

(A) Correlation analysis nine quadrant diagram. (B) Correlation coefficient clustering heat map. In the KEGG enrichment analysis, the p-Value histogram shows the enrichment degree of pathways with both differential metabolites and genes. In the correlation analysis, nine-quadrant plot analysis shows the difference multiple of gene metabolites with Pearson correlation coefficient greater than 0.8 in each difference group, which is divided into 9 quadrants from left to right and top to bottom, using black dashed lines. Each dot represents a gene/metabolite, black dots indicate non-differential metabolites and genes, blue dots indicate genes and metabolites that are both significantly different (up or downregulated), red dots indicate genes whose transcriptomes are significantly different but whose metabolomes are not, and green dots indicate metabolites whose metabolomes are significantly different but whose transcriptomes are not. For differential metabolites with correlation coefficient above 0.8, select all the correlation calculation results and draw the correlation coefficient cluster heat map.