Plants produce diverse metabolites to cope with the challenges presented by complex and ever-changing environments. These challenges drive the diversification of specialized metabolites within and between plant species. However, we are just beginning to understand how frequently new alleles arise controlling specialized metabolite diversity and how the geographic distribution of these alleles may be structured by ecological and demographic pressures. Here, we measure the variation in specialized metabolites across a population of 797 natural Arabidopsis thaliana accessions. We show that a combination of geography, environmental parameters, demography and different genetic processes all combine to influence the specific chemotypes and their distribution. This showed that causal loci in specialized metabolism contain frequent independently generated alleles with patterns suggesting potential within-species convergence. This provides a new perspective about the complexity of the selective forces and mechanisms that shape the generation and distribution of allelic variation that may influence local adaptation.

Plants face a variety of challenges within their ever-changing environment. Diverse metabolites are central to the plants ability to overcome these challenges. Understanding the environmental and genetic factors influencing the variation in specialized metabolites is the key to understand how plants survive and develop under changing environments. Here we measure the variation in specialized metabolites across a population of 797 natural Arabidopsis thaliana accessions. We show a combination of geography, environmental parameters, demography, and different genetic processes that creates a specific pattern in their accumulation and distribution. By identifying and tracking causal polymorphisms at multiple loci controlling metabolites variation we show that each locus displays extensive allelic heterogeneity with signatures of both parallel and convergent evolutionary processes. These loci combine epistatically and show differing relationships to environmental parameters leading to different distributions. This provides a detailed perspective about the complexity of the forces and mechanisms that shape the accumulation and distribution of a family of specialized metabolites critical for plant fitness.

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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.

MS performed the experiments, wrote the manuscript, and processed and analyzed data, AY wrote the manuscript and carried out metabolic analysis, CB carried out genotyping experiments, YC analyzed data, VS analyzed data, SH carried out genotyping and metabolic analysis, EK performed GLS measurements, CK peformed initial gtr1/2 experiment, AL verified analytical methods and assisted with statistical aid, H N-E provided gtr1/2 mutants and initial analysis, DK provided all the GLS mutants and GLS related measurements from the population, RA conceived the experimental design, supervised the work, provided funding, and wrote the manuscript. All authors have reviewed the final version of the manuscript and approved it and therefore are equally responsible for the integrity and accuracy of its content.