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. 2017 May;29(5):919-943.
doi: 10.1105/tpc.16.00613. Epub 2017 Apr 10.

Exploiting the Genetic Diversity of Maize Using a Combined Metabolomic, Enzyme Activity Profiling, and Metabolic Modeling Approach to Link Leaf Physiology to Kernel Yield

Affiliations

Exploiting the Genetic Diversity of Maize Using a Combined Metabolomic, Enzyme Activity Profiling, and Metabolic Modeling Approach to Link Leaf Physiology to Kernel Yield

Rafael A Cañas et al. Plant Cell. 2017 May.

Erratum in

  • CORRECTION.
    [No authors listed] [No authors listed] Plant Cell. 2018 Apr;30(4):946. doi: 10.1105/tpc.18.00273. Epub 2018 Apr 5. Plant Cell. 2018. PMID: 29622566 Free PMC article. No abstract available.

Abstract

A combined metabolomic, biochemical, fluxomic, and metabolic modeling approach was developed using 19 genetically distant maize (Zea mays) lines from Europe and America. Considerable differences were detected between the lines when leaf metabolic profiles and activities of the main enzymes involved in primary metabolism were compared. During grain filling, the leaf metabolic composition appeared to be a reliable marker, allowing a classification matching the genetic diversity of the lines. During the same period, there was a significant correlation between the genetic distance of the lines and the activities of enzymes involved in carbon metabolism, notably glycolysis. Although large differences were observed in terms of leaf metabolic fluxes, these variations were not tightly linked to the genome structure of the lines. Both correlation studies and metabolic network analyses allowed the description of a maize ideotype with a high grain yield potential. Such an ideotype is characterized by low accumulation of soluble amino acids and carbohydrates in the leaves and high activity of enzymes involved in the C4 photosynthetic pathway and in the biosynthesis of amino acids derived from glutamate. Chlorogenates appear to be important markers that can be used to select for maize lines that produce larger kernels.

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Figures

Figure 1.
Figure 1.
Differences in the Metabolite Content, Enzyme Activities, and Biomass-Related Components in the Leaves of 19 Maize Lines Originating from Europe and America at Two Key Stages of Plant Development. The top of the figure shows the coefficients of variations (expressed as percentage) of the biomass-related components in red (biomass components are shown as: C, total carbon; N, total nitrogen), including yield. Enzyme activities are in blue (GS, glutamine synthetase; MDH, NADP-dependent malate dehydrogenase; GOGAT: ferredoxin-dependent glutamate synthase; AspAT, aspartate aminotransferase). Metabolites and classes of metabolites are in black (2-OG, 2-oxoglutarate; Cit, citrate; Pyr, pyruvate; AA, total amino acids; Unknown, unidentified metabolites). An overview representation of the average of the coefficients of variations (from 0 to 80%) for the main classes of metabolites, enzyme activities, and biomass components is shown at the bottom of the figure. Anions correspond to nitrate and phosphate and biomass components to C, N, and water contents.
Figure 2.
Figure 2.
Graphic Representation of the Relationships Existing between the Relative Amounts of Leaf Metabolites in 19 Lines of Maize Originating from Europe and America. The metabolite content was measured by GC-MS analysis of young developing leaves at the vegetative stage (V) and of the leaves below the ear, 15 DAS, during the kernel-filling period. HCA was performed using the metabolome data presented in Supplemental Data Set 1. For each metabolite exhibiting a significant difference between the 19 lines (P ≤ 0.05), the ratio (content for each line/mean value of the 19 lines) was calculated and transformed into a log2 ratio before clustering analysis. The vertical green, blue, orange, red, and yellow boxes represent the five groups of maize lines in different countries of Europe and America (Tropical, orange; European Flint, blue; Northern Flint, red; Maize Belt Dent, green; Stiff Stalk, yellow). At the right of the panel, the silking dates are indicated (J = July, A = August, S = September), along with the grain yield (g−1 plant). Grain yield was determined for the 19 maize lines grown in the field (Supplemental Data Set 1). Compared with the others lines, the Tropical line Argl256 had a very low yield. It was also observed that the kernels of the other Tropical line, CML254, did not reach full maturity at the time of harvest (nm = not measured). Details of the HCA are presented in Supplemental Figure 3.
Figure 3.
Figure 3.
Example of Leaf Metabolic Signatures Representative of the Five Groups of Maize Lines during the Grain Filling (15 DAS) Period. sPLS-DA was used to quantify the relationship between the leaf metabolite content and the five groups of maize lines to detect putative leaf metabolite biomarkers at 15 DAS. (A) Relative content of four main classes of metabolites, including carbohydrates, organic acids, chlorogenates, and amino acids, in the five groups of maize lines. (B) Amount of soluble carbohydrates detected in the five groups of maize lines. From left to right: fructose (dark gray), glucose (pale gray), and sucrose (gray).
Figure 4.
Figure 4.
Relationship between the Genetic Distance of the 19 Lines Originating from Europe and America and the Phenotypic Distance of Enzyme Activities. Heat map showing the standardized level of enzyme activities of the 19 maize lines at 15 DAS. Two HCAs were performed to group the lines and the enzymatic pathways according to their genetic distances based on molecular markers (A_IBD) and according to their Euclidean phenotypic distance based on enzyme activities, respectively. At the left of the HCA, the vertical bar represents the five groups of maize lines (Tropical, orange; European Flint, blue; Northern Flint, red; Corn Belt Dent, green; Stiff Stalk, yellow). At the top of the HCA, colored bars represent the main classes of enzymes (C4 cycle, red; TCA, yellow; glycolysis, pale green; N-assimilation, turquoise; pentose-P, pale blue; secondary metabolism, dark blue; carbohydrate metabolism, purple). The top left scale represents the relative higher (yellow) or lower values (red) for enzyme activities compared with the median.
Figure 5.
Figure 5.
Example of the Differences in 15N Amino Acid Content in the 19 Maize Lines Originating from Europe and America. The 15N-labeled glutamine, glutamate, asparagine, and alanine contents were measured at the end of the labeling period when the isotope 15N was replaced by 14N. The data for the 15N-labeling experiment are presented in Supplemental Data Set 5. Young developing leaves at the vegetative stage of plant development were labeled for 8 h with 15NH4Cl. Values expressed as μmol g−1 FW are the mean of three individual leaves of (±sd bars) each harvested from three different plants grown in the field. Letters a, b, and c represent the result of an ANOVA statistical analysis performed with a Student-Newman-Keuls test and used to identify groups of lines exhibiting a similar pattern of 15N-labeling (P ≤ 0.05).
Figure 6.
Figure 6.
Pearson Correlations between Agronomic and Physiological Traits Representative of the Leaf Physiological and Kernel Physiological Status of the 19 Maize Lines. (A) Heat map showing the significant correlations (adjusted P values <0.05) found between kernel yield traits (GY, KN, and TKW) and key parameters representative of the kernel and leaf physiological status (C = carbon, N = nitrogen, C/N ratio, leaf soluble protein, PEPC protein, and nitrate contents). The negative and positive correlation coefficient values are indicated in each colored box of the heat map using the scale on the left side of the panel. (B) Network diagram illustrating the most significant correlations found between agronomic and physiological traits. Traits with a higher number of correlations are represented by larger and darker red dots. Thicker and red lines represent the highest positive correlations. Thicker and blue lines represent the highest negative correlations.
Figure 7.
Figure 7.
Heat Map and Pearson Correlations with the Modules and Agronomic Traits (GY, KN, and TKW) and Physiological Parameters Representative of the Kernel and Leaf Physiological Status 15 DAS. Correlations with the modules containing the generic physiological traits 15 DAS. The color names correspond to the 12 modules that were obtained from the 15 DAS metabolite and enzyme activity data set network analyses (Supplemental Data Set 10). Heat map and Pearson correlations between modules and kernel yield traits (GY, KN, and TKW) and key parameters representative of the kernel and leaf physiological status (C = carbon, N = nitrogen, C/N ratio, leaf soluble protein, PEPC protein, and nitrate contents). Correlations were considered significant with Bonferroni adjusted P values <0.05. Adjusted P values are shown in parentheses. Only significant negative and positive correlation coefficient values are indicated in each colored box. For clarity, only those with a correlation higher than 0.4 have been considered. The box of the heat map using the scale on the right side of the panel. e = exponent base 10. The same analysis was performed at the V stage (Supplemental Figure 8).
Figure 8.
Figure 8.
Network Diagram for Relationships between Leaf Metabolites and Enzyme Activities 15 DAS. Diamonds represent enzymes, and circles represent metabolites. Colors of the circles and diamonds correspond to the different components within a module. The names of metabolites and enzymes positively or negatively correlated with GY are highlighted in bold black and in red characters, respectively. Only the network connections that have a topological overlap above the threshold of 0.1 are shown. The red circle in the center corresponds to a metabolite of unknown function (unk. Fer-quin 3088.4/249) with a structure similar to a feruloylquinate that exhibits a significant negative correlation with GY. Lines represent a significant correlation between two traits. Thicker lines represent the highest positive or negative correlations. The same analysis was performed at 15 DAS for correlations with TKW (Supplemental Figure 9).

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