Uncovering Pathways Highly Correlated to NUE through a Combined Metabolomics and Transcriptomics Approach in Eggplant

Abstract

Nitrogen (N) fertilization is one of the main inputs to increase crop yield and food production. However, crops utilize only 30-40% of N applied; the remainder is leached into the soil, causing environmental and health damage. In this scenario, the improvement of nitrogen-use efficiency (NUE) will be an essential strategy for sustainable agriculture. Here, we compared two pairs of NUE-contrasting eggplant (Solanum melongena L.) genotypes, employing GC-MS and UPLC-qTOF-MS-based technologies to determine the differential profiles of primary and secondary metabolites in root and shoot tissues, under N starvation as well as at short- and long-term N-limiting resupply. Firstly, differences in the primary metabolism pathways of shoots related to alanine, aspartate and glutamate; starch, sucrose and glycine; serine and threonine; and in secondary metabolites biosynthesis were detected. An integrated analysis between differentially accumulated metabolites and expressed transcripts highlighted a key role of glycine accumulation and the related glyA transcript in the N-use-efficient genotypes to cope with N-limiting stress. Interestingly, a correlation between both sucrose synthase (SUS)- and fructokinase (scrK)-transcript abundances, as well as D-glucose and D-fructose accumulation, appeared useful to distinguish the N-use-efficient genotypes. Furthermore, increased levels of L-aspartate and L-asparagine in the N-use-efficient genotypes at short-term low-N exposure were detected. Granule-bound starch synthase (WAXY) and endoglucanase (E3.2.1.4) downregulation at long-term N stress was observed. Therefore, genes and metabolites related to these pathways could be exploited to improve NUE in eggplant.

Keywords: GC-MS; RNA-seq; Solanum melongena L.; UPLC-qTOF-MS; glycoalkaloids; nitrogen-use efficiency; primary metabolites.

Figure 1

Two-dimensional plot of principal component analysis (PCA) of eggplant metabolites for root (A) and shoot (B). The dots represent accessions with 95% confidence regions as ellipses. (A) In root, PC1 and PC2 explained 41% of total variation; time sampling and accessions are not clearly distinguished. (B) In shoot, PC1 and PC2 explained 35.4% of total variations; AM22 do not respond to N limitation, while the other genotypes are clearly distinguished by treatments (time).

Figure 2

Partial least-squares discriminant analysis (PLS-DA) in shoot. (A) 2D scores plot of PLS-DA; the dots represent accessions with 95% confidence regions as ellipses. (B) The importance measures used in PLS-DA are VIP scores (variable importance in projection). The colored boxes on the right indicate the relative concentrations of the corresponding metabolite in each group.

Figure 3

Heatmap of metabolites significantly differentially abundant among genotypes in root (one-way ANOVA and post hoc with p ≤ 0.05). Each column and row represent a sample and a metabolite, respectively. Comparison among genotypes shows that the main differences are in the secondary metabolites at T₀ (A), T₁ (B), and T₂ (C).

Figure 4

Heatmap of metabolites significantly differentially abundant among genotypes in shoot (one-way ANOVA and post hoc with p ≤ 0.05). Each column and row represent a sample and a metabolite, respectively. Comparison among genotypes shows that the main differences are in the primary metabolites at T₀ (A), T₁ (B), and T₂ (C).