Linking Metabolic QTLs with Network and cis-eQTLs Controlling Biosynthetic Pathways

Phenotypic variation between individuals of a species is often under quantitative genetic control. Genomic analysis of gene expression polymorphisms between individuals is rapidly gaining popularity as a way to query the underlying mechanistic causes of variation between individuals. However, there is little direct evidence of a linkage between global gene expression polymorphisms and phenotypic consequences. In this report, we have mapped quantitative trait loci (QTLs)–controlling glucosinolate content in a population of 403 Arabidopsis Bay × Sha recombinant inbred lines, 211 of which were previously used to identify expression QTLs controlling the transcript levels of biosynthetic genes. In a comparative study, we have directly tested two plant biosynthetic pathways for association between polymorphisms controlling biosynthetic gene transcripts and the resulting metabolites within the Arabidopsis Bay × Sha recombinant inbred line population. In this analysis, all loci controlling expression variation also affected the accumulation of the resulting metabolites. In addition, epistasis was detected more frequently for metabolic traits compared to transcript traits, even when both traits showed similar distributions. An analysis of candidate genes for QTL-controlling networks of transcripts and metabolites suggested that the controlling factors are a mix of enzymes and regulatory factors. This analysis showed that regulatory connections can feedback from metabolism to transcripts. Surprisingly, the most likely major regulator of both transcript level for nearly the entire pathway and aliphatic glucosinolate accumulation is variation in the last enzyme in the biosynthetic pathway, AOP2. This suggests that natural variation in transcripts may significantly impact phenotypic variation, but that natural variation in metabolites or their enzymatic loci can feed back to affect the transcripts

Genetic Networks Controlling Structural Outcome of Glucosinolate Activation across Development

Most phenotypic variation present in natural populations is under polygenic control, largely determined by genetic variation at quantitative trait loci (QTLs). These genetic loci frequently interact with the environment, development, and each other, yet the importance of these interactions on the underlying genetic architecture of quantitative traits is not well characterized. To better study how epistasis and development may influence quantitative traits, we studied genetic variation in Arabidopsis glucosinolate activation using the moderately sized Bayreuth×Shahdara recombinant inbred population, in terms of number of lines. We identified QTLs for glucosinolate activation at three different developmental stages. Numerous QTLs showed developmental dependency, as well as a large epistatic network, centered on the previously cloned large-effect glucosinolate activation QTL, ESP. Analysis of Heterogeneous Inbred Families validated seven loci and all of the QTL×DPG (days post-germination) interactions tested, but was complicated by the extensive epistasis. A comparison of transcript accumulation data within 211 of these RILs showed an extensive overlap of gene expression QTLs for structural specifiers and their homologs with the identified glucosinolate activation loci. Finally, we were able to show that two of the QTLs are the result of whole-genome duplications of a glucosinolate activation gene cluster. These data reveal complex age-dependent regulation of structural outcomes and suggest that transcriptional regulation is associated with a significant portion of the underlying ontogenic variation and epistatic interactions in glucosinolate activation

Genetic networks controlling structural outcome of glucosinolate activation across development.

Most phenotypic variation present in natural populations is under polygenic control, largely determined by genetic variation at quantitative trait loci (QTLs). These genetic loci frequently interact with the environment, development, and each other, yet the importance of these interactions on the underlying genetic architecture of quantitative traits is not well characterized. To better study how epistasis and development may influence quantitative traits, we studied genetic variation in Arabidopsis glucosinolate activation using the moderately sized Bayreuth x Shahdara recombinant inbred population, in terms of number of lines. We identified QTLs for glucosinolate activation at three different developmental stages. Numerous QTLs showed developmental dependency, as well as a large epistatic network, centered on the previously cloned large-effect glucosinolate activation QTL, ESP. Analysis of Heterogeneous Inbred Families validated seven loci and all of the QTL x DPG (days post-germination) interactions tested, but was complicated by the extensive epistasis. A comparison of transcript accumulation data within 211 of these RILs showed an extensive overlap of gene expression QTLs for structural specifiers and their homologs with the identified glucosinolate activation loci. Finally, we were able to show that two of the QTLs are the result of whole-genome duplications of a glucosinolate activation gene cluster. These data reveal complex age-dependent regulation of structural outcomes and suggest that transcriptional regulation is associated with a significant portion of the underlying ontogenic variation and epistatic interactions in glucosinolate activation

MODIFIED VACUOLE PHENOTYPE1 Is an Arabidopsis Myrosinase-Associated Protein Involved in Endomembrane Protein Trafficking1[W][OA]

We identified an Arabidopsis (Arabidopsis thaliana) ethyl methanesulfonate mutant, modified vacuole phenotype1-1 (mvp1-1), in a fluorescent confocal microscopy screen for plants with mislocalization of a green fluorescent protein-δ tonoplast intrinsic protein fusion. The mvp1-1 mutant displayed static perinuclear aggregates of the reporter protein. mvp1 mutants also exhibited a number of vacuole-related phenotypes, as demonstrated by defects in growth, utilization of stored carbon, gravitropic response, salt sensitivity, and specific susceptibility to the fungal necrotroph Alternaria brassicicola. Similarly, crosses with other endomembrane marker fusions identified mislocalization to aggregate structures, indicating a general defect in protein trafficking. Map-based cloning showed that the mvp1-1 mutation altered a gene encoding a putative myrosinase-associated protein, and glutathione S-transferase pull-down assays demonstrated that MVP1 interacted specifically with the Arabidopsis myrosinase protein, THIOGLUCOSIDE GLUCOHYDROLASE2 (TGG2), but not TGG1. Moreover, the mvp1-1 mutant showed increased nitrile production during glucosinolate hydrolysis, suggesting that MVP1 may play a role in modulation of myrosinase activity. We propose that MVP1 is a myrosinase-associated protein that functions, in part, to correctly localize the myrosinase TGG2 and prevent inappropriate glucosinolate hydrolysis that could generate cytotoxic molecules