Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition

Abstract

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

Figure 1

The natural variation and relationships of PBAA traits measured from the diversity panel. Boxplot showing the PBAA absolute levels (A) and relative compositional distribution (B) in the 279 taxa from the Goodman–Buckler maize association panel; T stands for sum total of 15 PBAA. The bold line in the center of the boxplot represents median while the lower and upper edges represent the 25th and 75th quartiles, respectively. The whiskers extend from the edges to the most extreme data points that are no more than 1.5× the length of the upper and lower quartiles. Pairwise Pearson correlation analysis between the absolute PBAA levels (C) and relative composition (D) using back transformed BLUPs of 279 taxa from the Goodman–Buckler maize association panel. The correlation matrix was visualized in R version 3.4.3 (R Core Team). Each dot represents a significant correlation coefficient (r) at qFDR values <0.05. Blue dots indicate positive correlation, and red dots indicate negative correlations. Asx denotes Asn + Asp; Glx denotes Gln + Glu. Bracketed numbers on (A) and (B) represent groups based on PBAA absolute levels (A) and PBAA/TPBAA ratios (B) where 1 is for PBAA levels >10%; 2 is for PBAA levels between 10% and 2%; and 3 is for PBAA levels <2%.

Figure 2

The genomic distribution of the significant unique SNPs found by GWAS and the functional categorization of the extracted candidate. A, The partition of the significant unique SNPs across the 10 chromosomes in maize. B, Pie chart represents the functional categorization of the 1,399 GWAS candidate genes using MapMan version 3.6. The percentage in parentheses represents the proportion of genes that falls into a functional category. The top four categories are highlighted in dark orange and include protein (metabolism), RNA, signaling, and transport.

Figure 3

Seed PBAA composition dynamics during maturation. A, Heatmap and (B) expression trends of the PBAA relative compositions across 10 seed filling time points of maize inbred B73. The average values of three biological replicates from each time point were scaled and used to create the heatmap (n = 3) using hierarchical clustering. Blue indicates low values for PBAA accumulation, and red indicates high accumulations. The red line in (B) indicates the average expression pattern of individual PBAA accumulation within a cluster.

Figure 4

Relationships among protein coexpression modules and PBAA compositional dynamics during seed maturation. A, Module–trait relationships from the WGCNA analysis. Module names and number of proteins in the module are displayed on the left y-axis (e.g. MEblue denotes module eigen protein for the blue module comprising 356 proteins). The relative PBAA composition traits (e.g. Ala/T, which is the ratio of Ala/Sum total of all PBAA levels) are displayed on the x-axis. Each cell shows the correlation coefficients between modules Eigen protein (ME)-PBAA traits (top number) and the corresponding P-value (bottom number in parentheses). The module–trait relationships are colored based on their correlation: red is a strong positive correlation, and green is a strong negative correlation. B, Expression trend of Eigen protein found in the corresponding modules across the seed development time points. The x-axis is the 10 time-points, in DAP. The y-axis is the expression of module eigen protein using the scaled spectral count of protein (scaled SP).

Figure 5

Comparison between the candidate genes lists generated by WGCNA and GWAS. Venn diagram depicting the 80 genes that overlap with both the GWAS candidate gene list and the relevant proteomic coexpression modules (turquoise, brown, green, black, and blue). We refer to the overlapping genes as HCCGs.

Figure 6

PPI of the 80 HCCG list. A, A PPI of the 80 HCCG was created using STRING version 11.0. HCCG are indicated by nodes labeled with the encoding protein symbol from STRING. Interactions between nodes are indicated by edges. Smooth line edges indicate intracluster interactions, and the dotted edges indicate intercluster interactions (turquoise were based on curated database, pink were based on experimentally determined and black were based on coexpression). Cluster analysis using MCL algorithm resulted in 11 distinct clusters. B, Table representation of cluster numbers, cluster color, gene count within each cluster from STRING, and the bin name/functional category of the clusters from MapMan version 3.6.0.

Figure 7

Protein and gene expression for the HCCG from Cluster 1. A, Heatmap of 19 genes in Cluster 1 (red) obtained from the 80 HCCG PPIs. B, Average protein expression pattern across the 10 seed developmental stages of B73 obtained from shotgun proteomic sequencing from the current study. Y-axis is the scaled data for spectral counts of proteins (scaled SP) and X-axis the DAP. (C) Heatmap of the same 19 genes created using the gene expression data across eight seed developmental stages of B73 obtained from Chen et al. (2014) in the same order as (A). D, Average gene expression patterns of the 19 genes across the eight seed developmental stages of B73 and created using the scaled FPKM gene values. Red indicates high expression, and blue indicates low expression. E, Names and annotations of the 19 proteins/genes in Cluster 1, labeled as rows in (A) and (C).