Genetics of aliphatic glucosinolates. IV. Side-chain modification in Brassica oleracea

Giamoustaris, A.; Mithen, Richard

doi:10.1007/bf00224105

Cited by 115 publications

(83 citation statements)

References 15 publications

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“…All of the 148 L er ϫ Col RILs with predominantly C 4 glucosinolates accumulated 4-methylsulfinylbutyl glucosinolate, with no lines containing 4-hydroxybutyl glucosinolate instead of the 1:1 mix of 4-methylsulfinylbutyl to 4-hydroxybutyl glucosinolate, as expected. This finding suggests that the enzyme encoded by the GS-OHP locus is unable to catalyze the conversion of 4-methylsulfinylbutyl glucosinolate to 4-hydroxybutyl glucosinolate and has specificity for substrates with side chains of three carbon atoms (Giamoustaris and Mithen, 1996). This further supports the theory that methylsulfinylalkyl accumulation is indicative of a nonfunctional GS-AOP variant.…”

Section: Cosegregation Of Gs-alk Gs-ohp and Gs-aop Nullsupporting

confidence: 69%

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Gene Duplication in the Diversification of Secondary Metabolism: Tandem 2-Oxoglutarate–Dependent Dioxygenases Control Glucosinolate Biosynthesis in Arabidopsis

et al. 2001

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Secondary metabolites are a diverse set of plant compounds believed to have numerous functions in plant-environment interactions. The large chemical diversity of secondary metabolites undoubtedly arises from an equally diverse set of enzymes responsible for their biosynthesis. However, little is known about the evolution of enzymes involved in secondary metabolism. We are studying the biosynthesis of glucosinolates, a large group of secondary metabolites, in Arabidopsis to investigate the evolution of enzymes involved in secondary metabolism. Arabidopsis contains natural variations in the presence of methylsulfinylalkyl, alkenyl, and hydroxyalkyl glucosinolates. In this article, we report the identification of genes encoding two 2-oxoglutarate-dependent dioxygenases that are responsible for this variation. These genes, AOP2 and AOP3 , which map to the same position on chromosome IV, result from an apparent gene duplication and control the conversion of methylsulfinylalkyl glucosinolate to either the alkenyl or the hydroxyalkyl form. By heterologous expression in Escherichia and the correlation of gene expression patterns to the glucosinolate phenotype, we show that AOP2 catalyzes the conversion of methylsulfinylalkyl glucosinolates to alkenyl glucosinolates. Conversely, AOP3 directs the formation of hydroxyalkyl glucosinolates from methylsulfinylalkyl glucosinolates. No ecotype coexpressed both genes. Furthermore, the absence of functional AOP2 and AOP3 leads to the accumulation of the precursor methylsulfinylalkyl glucosinolates. A third member of this gene family, AOP1 , is present in at least two forms and found in all ecotypes examined. However, its catalytic role is still uncertain.

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Section: Cosegregation Of Gs-alk Gs-ohp and Gs-aop Nullsupporting

confidence: 69%

“…These differences have been ascribed to the GS-ALK and GS-OHP loci (Giamoustaris and Mithen, 1996), which are alleles of the same locus or tightly linked loci. Both the GS-ALK and GS-OHP variants map to the top of chromosome IV; thus, we refer to them jointly as GS-AOP .…”

Section: Cosegregation Of Gs-alk Gs-ohp and Gs-aop Nullmentioning

confidence: 99%

Gene Duplication in the Diversification of Secondary Metabolism: Tandem 2-Oxoglutarate–Dependent Dioxygenases Control Glucosinolate Biosynthesis in Arabidopsis

et al. 2001

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“…One can consider blocks in the pathway, so that specific GLSs accumulates as endpoints in the pathway. For example, methylsulphinylalkyl GLS accumulate in broccoli as this botanical variety lacks functional alleles at the GLS-ALK locus [44]. Other forms of B. oleracea with a functional GLS-ALK allele, and all forms of B. rapa and B. napus synthesis alkenyl GLSs, and, potentially hydroxyl -alkenyl GLSs.…”

Section: Breeding For Altered Gls Content In Brassicamentioning

confidence: 99%

Glucosinolates inBrassicavegetables: The influence of the food supply chain on intake, bioavailability and human health

Verkerk¹,

Schreiner

Krumbein

et al. 2009

Molecular Nutrition Food Res

518

424

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Glucosinolates (GLSs) are found in Brassica vegetables. Examples of these sources include cabbage, Brussels sprouts, broccoli, cauliflower and various root vegetables (e.g. radish and turnip). A number of epidemiological studies have identified an inverse association between consumption of these vegetables and the risk of colon and rectal cancer. Animal studies have shown changes in enzyme activities and DNA damage resulting from consumption of Brassica vegetables or isothiocyanates, the breakdown products (BDP) of GLSs in the body. Mechanistic studies have begun to identify the ways in which the compounds may exert their protective action but the relevance of these studies to protective effects in the human alimentary tract is as yet unproven. In vitro studies with a number of specific isothiocyanates have suggested mechanisms that might be the basis of their chemoprotective effects. The concentration and composition of the GLSs in different plants, but also within a plant (e.g. in the seeds, roots or leaves), can vary greatly and also changes during plant development. Furthermore, the effects of various factors in the supply chain of Brassica vegetables including breeding, cultivation, storage and processing on intake and bioavailability of GLSs are extensively discussed in this paper.

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“…Quantitative trait locus mapping with human insight has enabled the characterization and prediction of numerous pathways. And this has lead initiatives to create an automated algorithm which takes the metabolite and genetic data to predict metabolic pathways [45,[54][55][56][57][58]. However, a direct comparison of computational predictions to empirical pathway evidence showed the none of the current algorithms are sufficient and new approaches need to be developed to enable this automated pathway discovery [59].…”

Section: Quantitative Trait Locus Mapping To Reverse Engineer the Shamentioning

confidence: 99%

Synthetic biology of metabolism: using natural variation to reverse engineer systems

Kliebenstein

2014

Current Opinion in Plant Biology

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A goal of metabolic engineering is to take a plant and introduce new or modify existing pathways in a directed and predictable fashion. However, existing data does not provide the necessary level of information to allow for predictive models to be generated. One avenue to reverse engineer the necessary information is to study the genetic control of natural variation in plant primary and secondary metabolism. These studies are showing that any engineering model will have to incorporate information about 1000s of genes in both the nuclear and organellar genome to optimize the function of the introduced pathway. Further, these genes may interact in an unpredictable fashion complicating any engineering approach as it moves from the one or two gene manipulation to higher order stacking efforts. Finally, metabolic engineering may be influenced by a previously unrecognized potential for a plant to measure the metabolites within it. In combination, these observations from natural variation provide a beginning to help improve current efforts at metabolic engineering. Introduction A significant goal of synthetic biology or biological engineering is to take an existant organism and alter it in a directed and predictive fashion to introduce a new phenotype that had not previously existed in that organism. Some of these efforts start with introducing a new metabolic pathway into an organism to provide a better source of a commercially important chemical or to make new chemicals entirely [1][2][3]. Other efforts are focused at taking agronomically important traits such as defensive chemicals, nitrogen fixation or perennial growth from one species and transferring these traits into other species that do not contain them [4 ,5,6]. However these efforts while exciting frequently run into great difficulty and for this review article, I will focus on one the potential of using information from natural variation studies to facilitate these efforts for metabolic engineering.To predictively engineer a new metabolic pathway into a plant with optimal efficiency requires a base level of knowledge that likely does not exist to any full level. To generate full predictive ability we would need much deeper understanding of the following questions. First, how many genes within the plant need to be manipulated to facilitate the integration of the new pathway into the organisms metabolic and regulatory network. How do these genes interact, are they solely additive or is there evidence of more complex epistasis that complicates predictive capacity? Finally, are these genes solely in the nuclear genome or is there causal variation also in the organellar genomes? Most efforts to answer these questions focus largely on forward or single-gene reverse genetics approaches which are highly time consuming and difficult to scale up to the necessary level.An alternative approach to understanding a system is to use the concept of reverse engineering [7,8]. The goal of reverse engineering is to take a functioning system, say an existing metabolic pathway, ...

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Genetics of aliphatic glucosinolates. IV. Side-chain modification in Brassica oleracea

Cited by 115 publications

References 15 publications

Gene Duplication in the Diversification of Secondary Metabolism: Tandem 2-Oxoglutarate–Dependent Dioxygenases Control Glucosinolate Biosynthesis in Arabidopsis

Gene Duplication in the Diversification of Secondary Metabolism: Tandem 2-Oxoglutarate–Dependent Dioxygenases Control Glucosinolate Biosynthesis in Arabidopsis

Glucosinolates inBrassicavegetables: The influence of the food supply chain on intake, bioavailability and human health

Synthetic biology of metabolism: using natural variation to reverse engineer systems

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