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

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.

HAPPI GWAS: Holistic Analysis with Pre- and Post-Integration GWAS.

MOTIVATION: Advanced publicly available sequencing data from large populations have enabled informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis, such as outlier removal, data transformation and calculation of Best Linear Unbiased Predictions or Best Linear Unbiased Estimates. In addition, post-GWAS analysis, such as haploblock analysis and candidate gene identification, is lacking. RESULTS: Here, we present Holistic Analysis with Pre- and Post-Integration (HAPPI) GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS and post-GWAS analysis in an automated pipeline using the command-line interface. AVAILABILITY AND IMPLEMENTATION: HAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

mGWAS Uncovers Gln-Glucosinolate Seed-Specific Interaction and its Role in Metabolic Homeostasis.

Gln is a key player in plant metabolism. It is one of the major free amino acids that is transported into the developing seed and is central for nitrogen metabolism. However, Gln natural variation and its regulation and interaction with other metabolic processes in seeds remain poorly understood. To investigate the latter, we performed a metabolic genome-wide association study (mGWAS) of Gln-related traits measured from the dry seeds of the Arabidopsis (Arabidopsis thaliana) diversity panel using all potential ratios between Gln and the other members of the Glu family as traits. This semicombinatorial approach yielded multiple candidate genes that, upon further analysis, revealed an unexpected association between the aliphatic glucosinolates (GLS) and the Gln-related traits. This finding was confirmed by an independent quantitative trait loci mapping and statistical analysis of the relationships between the Gln-related traits and the presence of specific GLS in seeds. Moreover, an analysis of Arabidopsis mutants lacking GLS showed an extensive seed-specific impact on Gln levels and composition that manifested early in seed development. The elimination of GLS in seeds was associated with a large effect on seed nitrogen and sulfur homeostasis, which conceivably led to the Gln response. This finding indicates that both Gln and GLS play key roles in shaping the seed metabolic homeostasis. It also implies that select secondary metabolites might have key functions in primary seed metabolism. Finally, our study shows that an mGWAS performed on dry seeds can uncover key metabolic interactions that occur early in seed development.