Identification of metabolic and protein markers representative of the impact of mild nitrogen deficit on agronomic performance of maize hybrids

Abstract

Introduction: A better understanding of the physiological response of silage maize to a mild reduction in nitrogen (N) fertilization and the identification of predictive biochemical markers of N utilization efficiency could contribute to limit the detrimental effect of the overuse of N inputs.

Objectives: We integrated phenotypic and biochemical data to interpret the physiology of maize in response to a mild reduction in N fertilization under agronomic conditions and identify predictive leaf metabolic and proteic markers that could be used to pilot and rationalize N fertilization.

Methods: Eco-physiological, developmental and yield-related traits were measured and complemented with metabolomic and proteomic approaches performed on young leaves of a core panel of 29 European genetically diverse dent hybrids cultivated in the field under non-limiting and reduced N fertilization conditions.

Results: Metabolome and proteome data were analyzed either individually or in an integrated manner together with eco-physiological, developmental, phenotypic and yield-related traits. They allowed to identify (i) common N-responsive metabolites and proteins that could be used as predictive markers to monitor N fertilization, (ii) silage maize hybrids that exhibit improved agronomic performance when N fertilization is reduced.

Conclusions: Among the N-responsive metabolites and proteins identified, a cytosolic NADP-dependent malic enzyme and four metabolite signatures stand out as promising markers that could be used for both breeding and agronomic purposes.

Keywords: Maize; Markers; Metabolome; Nitrogen nutrition; Proteome; Silage.

Fig. 1

Network diagram showing the main correlations between the different eco-physiological, developmental and yield-related traits. Traits were measured in the panel of 29 maize hybrids grown under reduced (LN) and non-limiting (HN) fertilization conditions. Network diagrams show significant correlations with FDR correction (P < 0.05) between trait LSmeans (n = 29) based on positive and negative Pearson coefficients in HN A and LN B. Traits with a larger number of significant correlations are represented by larger dots. Traits with a smaller number of significant correlations are represented by smaller dots. Green dots correspond to the vegetative stage, yellow dots to the flowering stage and orange dots to plant maturity at harvest. Lines represent a significant correlation between two traits. Full red lines represent positive correlations. Dashed green lines represent negative correlations. See Table 1 for definitions of abbreviations and Table S2d for Pearson coefficients and corresponding statistical analyses

Fig. 2

Ordination diagram of the PCA analysis for the 29 maize hybrids based on the different eco-physiological, developmental and yield-related traits. Traits were measured in the panel of 29 maize hybrids grown under reduced (LN) and non-limiting (HN) fertilization conditions. Diagrams are defined by the first two PCs of the PCA of the different variables using their LSmeans: PC1 (27% of variance explained) and PC2 (19% of variance explained). LN: reduced N fertilization (blue circles). HN: non-limiting N fertilization (red squares). Black lines between LN and HN represent the Euclidian distance between the three hybrids (B84, FR19 and F618) exhibiting the most contrasted difference with respect to their morphological and physiological responsiveness to N fertilization

Fig. 3

Multiblock sparse PLS-DA of the LSmeans per condition and hybrid combining the eco-physiological, developmental and yield-related traits with metabolome data and proteome data measured in the panel of 29 maize hybrids grown under reduced (LN, blue circles) and non-limiting (HN, red squares) fertilization conditions. A model with two latent variables (LV) was chosen. A Scores plot for the eco-physiological, developmental and yield-related data block. B Scores plot for the metabolome data block. C Scores plot for the proteome data block. Hybrid codes are indicated on each scores plot. D Loadings plot for the eco-physiological, developmental and yield-related traits. E Loadings plot for the metabolome. F Loadings plot for the proteome. On each loadings plot, variables with a loading value higher than 0.15 on at least one latent variable are annotated. See Table S3 for details on metabolite annotations and Table S6 for the highest loading values on LV1

Fig. 4

Correlation network between the variables contributing to separate the two N treatments selected based on the multiblock sparse PLS-DA presented in Fig. 3 and having an absolute loading value higher than 0.1 on LV1. Spearman correlations with an absolute value higher than 0.75 are shown in a network built with Cytoscape. Only the subnetwork comprising variables of the three datasets is shown. Node size is proportional to the number of connections. For edges, a solid line means a positive correlation and a dashed line means a negative correlation. Node symbols are represented by green octagons for eco-physiological, developmental and yield-related traits and red hexagons for metabolites. For proteins, different symbols and colors are used according to Mercator categories

Fig. 5

Hierarchical classification of the 29 maize hybrids based on the susceptibility of key physiological and yield-related traits to reduced N fertilization (LN). The level of susceptibility to LN for each trait was calculated using its LSmean value as follows: [(LS-means (HN)−LS-means (LN)]/LS-means (HN). The different clusters identified for the 29 hybrids were named C1, C2, C3 and C4