A Systems Biology Approach Identifies a R2R3 MYB Gene Subfamily with Distinct and Overlapping Functions in Regulation of Aliphatic Glucosinolates

Sønderby, Ida Elken; Hansen, Bertel; Bjarnholt, Nanna; Ticconi, Carla A.; Halkier, Barbara Ann; Kliebenstein, Daniel J.

doi:10.1371/journal.pone.0001322

Cited by 307 publications

(429 citation statements)

References 61 publications

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“…Reverse-genetic and molecular biological experiments have proved Myb28 to be a key transcription factor that positively regulates Met-derived GSL biosynthesis and Myb29 to be a transcription factor probably involved in methyl jasmonate-mediated induction of GSL biosynthesis (Hirai et al 2007). Concurrently, several groups have independently found that Myb28, Myb29 and Myb76 (At5g07700) are the positive regulators of Met-derived GSL biosynthesis and that Myb51 (At1g18570) and Myb122 (At1g74080), as well as previously-characterized Myb34 (At5g60890), are the positive regulators of Trp-derived GSL biosynthesis (Beekwilder et al 2008;Gigolashvili et al 2007a-c;Sonderby et al 2007;Malitsky et al 2008). These authors have discussed the specific functions of individual Mybs, the mutual regulation among these Mybs and the mutual regulation between Met-and Trp-derived GSL pathways (see other reviews in this issue).…”

Section: Prediction Of the Genes Involved In Glucosinolate Biosynthesmentioning

confidence: 94%

A robust omics-based approach for the identification of glucosinolate biosynthetic genes

Hirai

2008

Phytochem Rev

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Transcriptome coexpression analysis, which is based on a vast amount of transcriptome data obtained by using DNA arrays, has become a routine method for functional genomics studies in Arabidopsis. This analysis enables us to predict the function of genes on the basis of a simple assumption that a set of genes involved in a particular biological process can be coexpressed under the control of a shared regulatory system. Candidate genes involved in glucosinolate biosynthesis were successfully identified by this approach. In this review, the methodology of coexpression analysis is briefly described. The advantages and disadvantages of this analysis are also discussed in the context of its ability to predict gene functions involved in glucosinolate biosynthesis.

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Section: Prediction Of the Genes Involved In Glucosinolate Biosynthesmentioning

confidence: 94%

A robust omics-based approach for the identification of glucosinolate biosynthetic genes

Hirai

2008

Phytochem Rev

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“…Some microbial root colonists are saprophytes, which also colonise splinters of wood inserted into soil (Bulgarelli et al, 2012), but others are selected by exudation of nutrient sources and phytoalexins, such as glucosinolates and avenacins (Sonderby et al, 2007;Bressan et al, 2009;Turner et al, 2013b). Furthermore, by altering the rhizosphere microbiota, plants induce formation of suppressive soil where growth of plant pathogens is inhibited (Lebreton et al, 2007;Kinkel et al, 2011;Mendes et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition

et al. 2015

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We examined succession of the rhizosphere microbiota of three model plants (Arabidopsis, Medicago and Brachypodium) in compost and sand and three crops (Brassica, Pisum and Triticum) in compost alone. We used serial inoculation of 24 independent replicate microcosms over three plant generations for each plant/soil combination. Stochastic variation between replicates was surprisingly weak and by the third generation, replicate microcosms for each plant had communities that were very similar to each other but different to those of other plants or unplanted soil. Microbiota diversity remained high in compost, but declined drastically in sand, with bacterial opportunists and putative autotrophs becoming dominant. These dramatic differences indicate that many microbes cannot thrive on plant exudates alone and presumably also require carbon sources and/or nutrients from soil. Arabidopsis had the weakest influence on its microbiota and in compost replicate microcosms converged on three alternative community compositions rather than a single distinctive community. Organisms selected in rhizospheres can have positive or negative effects. Two abundant bacteria are shown to promote plant growth, but in Brassica the pathogen Olpidium brassicae came to dominate the fungal community. So plants exert strong selection on the rhizosphere microbiota but soil composition is critical to its stability. microbial succession/ plant-microbe interactions/rhizosphere microbiota/selection.

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“…This stunting was associated with significant pleiotropic impacts on primary metabolism [15]. This difference in genotypes response to the transgenes was occurring even thoughLer and Col-0 start off with similar glucosinolate content and the Col-0 transgenic lines accumulated higher glucosinolate [13][14][15][16][17]. This argues that the stunting is not a simple response to the cost of producing more glucosinolate.…”

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confidence: 99%

“…Similarly, the overexpression of a secondary metabolite specific transcription factor in Arabidopsis had dramatically different effects depending upon the genetic background. When MYB28 was introduced into the Col-0 background, it was possible to obtain up to 10Â increases in the content of the endogenous glucosinolates without showing significant impact on growth or development of the plant [13,14]. In contrast, the introduction of a similar MYB28 construct into the Landsberg erecta (Ler) background led to maximally a 3Â induction of glucosinolates and always concomitant with a dramatic stunting of the plants growth and development [15].…”

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confidence: 99%

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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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A Systems Biology Approach Identifies a R2R3 MYB Gene Subfamily with Distinct and Overlapping Functions in Regulation of Aliphatic Glucosinolates

Cited by 307 publications

References 61 publications

A robust omics-based approach for the identification of glucosinolate biosynthetic genes

A robust omics-based approach for the identification of glucosinolate biosynthetic genes

Stability and succession of the rhizosphere microbiota depends upon plant type and soil composition

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

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