Download Use of Secondary Metabolite Variation in Crop Improvement

A quantitative genetics and ecological model system: Understanding the
aliphatic glucosinolate biosynthetic network via QTLs
Daniel J. Kliebenstein1
1
Department of Plant Sciences
University of California, Davis
One Shields Avenue
Davis, CA 95616
USA
Corresponding author: Kliebenstein, D.J. ([email protected])
The author has no conflict of interest to report with regards to this review.
1
Abstract
Key Words: glucosinolate, quantitative trait, QTL, system biology,
maintenance of diversity
Abbreviations
QTL – Quantitative trait locus.
GSL-() – QTL for glucosinolates.
GSL-OX – QTL for conversion of methylthio to methylsulfinyl glucosinolate.
GSL-Elong – QTL for controlling side-chain length of glucosinolates.
GSL-ALK – QTL for production of alkenyl glucosinolates.
GSL-OHP – QTL for production of hydroxalkyl glucosinolates.
GSL-AOP – QTL for production of alkenyl or hydroxyalkyl glucosinolates.
GSL-OH – QTL for production of hydroxylalkenyl glucosinolates.
FMO – flavin monooxygenase
BCAT – branched chain amino transferase
CYP – cytochromes P450
PMSR – peptide methionine sulfoxide reductase
MAM – methylthioalkylmalate synthase
UGT – UDP-glucosyl transferase
ST – Sulfotransferase
2
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Introduction
Plants’ sessile nature forces them to directly cope with environmental changes
rather than escape to more favorable sites. Secondary metabolites are a large
and diverse set of small organic compounds that enable plants to respond to
these environmental challenges, including insect herbivory, pathogen invasion,
UV-B radiation, and drought. The currently known >100,000 secondary
metabolites probably represents a minority of the total (Wink, 1988) with this
large chemical diversity presumably arising from a vast set of biosynthetic
enzymes. While numerous studies have biochemically investigated the enzymes
and regulatory factors controlling biosynthesis of secondary metabolites, little is
known about the genetics or ecology controlling quantitative and qualitative
variation in secondary chemistry in wild plants. The easy detection of numerous
secondary metabolites makes them an optimal model trait to investigate complex
quantitative genetics and the pressures that maintain this variation.
A model system for understanding complex quantitative traits with
potential ecological importance are the glucosinolates, amino acid derived
thioglucosides specific to the order Capparales (Bones and Rossiter, 1996).
Plants utilize a three part biosynthetic pathway to produce glucosinolates from
methionine, phenylalanine and other amino acids (Hogge et al., 1988; Halkier
and Du, 1997). This involves carbon chain elongation of a protein amino acid,
entrance of the amino acid into the core pathway and finally side chain
modification (Kahn et al., 1999; de Quiros et al., 2000; Graser et al., 2000;
Wittstock and Halkier, 2000; Kliebenstein et al., 2001a; Hansen et al., 2007;
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Textor et al., 2007). Tryptophan derived glucosinolates do not go through the
chain elongation component. The final glucosinolates form a bipartite defense
system such that upon tissue disruption, a myrosinase enzyme cleaves the sugar
from the glucosinolate, and a series of toxic compounds are released (Burow and
Wittstock, This Issue). Glucosinolates and their breakdown products alter insect
herbivory (Li et al., 2000; Lambrix et al., 2001; Barth and Jander, 2006; Zhang et
al., 2006; Lankau, 2007), plant-plant interactions (Lankau and Strauss, 2007),
nematode survival (Donkin et al., 1995), and fungal resistance (Mithen et al.,
1986; Tierens et al., 2001).
The diverse biological roles played by glucosinolates are controlled by
variation in the final glucosinolate structure and the amount of glucosinolate. This
variation is controlled by numerous qualitative and quantitative loci for both the
type of glucosinolate produced (Magrath et al., 1994; Parkin et al., 1994b; Mithen
et al., 1995; Giamoustaris and Mithen, 1996; Kliebenstein et al., 2001b; 2001c)
and the amount of glucosinolate produced (Toroser et al., 1995; Kliebenstein et
al., 2001b; Wentzell et al., 2007). This variation exists in multiple species and is
frequently controlled by homologous genes in each species. More importantly, it
is this natural genetic variation in the glucosinolate system that controls variation
in plant/biotic interactions (Mauricio and Rausher, 1997; Li et al., 2000; Lambrix
et al., 2001; Zhang et al., 2006; Lankau, 2007; Lankau and Strauss, 2007). This
genetic diversity suggests that the glucosinolates are important for controlling
variation in crucifer fitness. The recent identification of most biosynthetic pathway
genes and numerous regulators makes the glucosinolate pathway an optimal
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system for studying important quantitative genetics or ecological questions
(Halkier and Gershenzon, 2006; Gigolashvili et al., 2007; Hirai et al., 2007;
Sønderby et al., 2007; Wentzell et al., 2007). These questions include why does
genetic variation in a trait exist, what genes control this variation and does
variation at one locus impact variation at another locus (Mackay, 2001). This
review article will discuss how these questions are being or can be addressed
within the context of the glucosinolate biosynthetic pathway.
Structural genetic variation in glucosinolates
Variation in glucosinolate structure both within and between different
species has long been a noted characteristic of this group of secondary
metabolites. In contrast to phenylpropanoids and isoprenoids that have 10’s of
thousands of structures, glucosinolate structural variation does not appear open
ended. Plants within the order Capparales appear to resample the same set of
glucosinolate structures (Rodman, 1980; Rodman et al., 1981; Daxenbichler et
al., 1991). This resampling is capable of generating a significant level of
secondary metabolite diversity within the crucifers without requiring
glucosinolates to have as many different structures as other plant secondary
metabolites. This biosynthetic diversity of the glucosinolate system is further
expanded upon by structural modifications during glucosinolate activation (Burow
and Wittstock, This Issue).
Glucosinolate structural variation is under genetic control allowing the
causes of this variation to be studied in the Brassica’s using structured mapping
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populations (Magrath et al., 1994; Parkin et al., 1994a; Mithen et al., 1995;
Mithen and Toroser, 1995; Giamoustaris and Mithen, 1996). This generated a
model whereby there are four major biosynthetic loci, GSL-AOP, -ELONG, -OH
and -OX, controlling structural variation in methionine derived glucosinolates
(Figure 1 and 2). These four loci function epistatically to generate a modular
genetic system whereby the four loci allow the plant to generate 14 different
structural profiles (Figure 1). Thus, a plant population can quickly shift between
these profiles via mutation, migration or recombination allowing for rapid
evolution in glucosinolate structural variation with a minimal amount of genetic
polymorphism (Kliebenstein et al., 2001c). However, not all of the 14 different
profiles appear equally fit as some occur more frequently than expected and
others significantly less frequently (Figure 1).
Of ecological and evolutionary importance, the presence and absence of
these four structural loci explains a significant fraction of aliphatic glucosinolate
variation in most major crucifer genera including Streptanthus, Cakile, Brassica
and Arabidopsis (Rodman, 1980; Rodman et al., 1981; Magrath et al., 1993;
Windsor et al., 2005). This raises the interesting question about how the same
apparent biochemical polymorphisms can evolve and exist in widely divergent
plant families. Additionally, why do crucifers frequently resample the same
structural variation rather than generate new structural variation as occurs in
other secondary metabolite pathways? Answering these questions requires the
identification of the causal genes for each polymorphism in each species and
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progress towards these ends will be discussed in the ensuing section of this
review.
GSL-AOP
The GSL-AOP structural locus was first identified as the GSL-ALK locus
controlling the presence or absence of alkenyl glucosinolates within Brassica
mapping populations (Mithen et al., 1995; Mithen and Toroser, 1995;
Giamoustaris and Mithen, 1996; Raybould and Moyes, 2001). Next, a GSL-OHP
locus was identified that controlled the presence or absence of hydroxyalkyl
glucosinolates within Brassica and shown to be an allele of GSL-ALK. As such,
the GSL-AOP locus was defined as a single genetic locus potentially containing
an OHP allele, ALK allele or a null allele whereby the plant accumulates
methylsulfinyl glucosinolates (Kliebenstein et al., 2001b; 2001c). GSL-AOP was
shown to control plant resistance to a number of biotic stresses including multiple
herbivores (Mithen, 1992; Giamoustaris and Mithen, 1995; Kliebenstein et al.,
2002b). GSL-AOP was cloned in Arabidopsis and shown to be two tandem
genes, AOP2 and AOP3, which control all three GSL-AOP allelic states in both
Arabidopsis and Brassica (Kliebenstein et al., 2001a; Li and Quiros, 2003)(Table
1). AOP2 is the enzyme for the production of alkenyl glucosinolates while AOP3
is the enzyme for the production of 3C hydroxyalkyl glucosinolates (Table
1)(Kliebenstein et al., 2001a). In the GSL-ALK allele, the AOP2 gene is
expressed while the AOP3 gene is not expressed in rosette tissue. In contrast,
within the GSL-OHP allele, the AOP3 gene is expressed while the AOP2 gene is
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silent. For the null allele, neither AOP2 nor AOP3 is expressed or functional
leading to the accumulation of the precursor methylsulfinyl glucosinolates (Figure
1)(Wentzell et al., 2007). While the cloned AOP2 and AOP3 enzymes remove the
methylsulfinyl moiety during their reaction, some plants such as Raphanus sativa
contain glucosinolates with both the alkenyl and methylsulfinyl moiety yet it
remains to be determined if these glucosinolates are produced by AOP2/AOP3
homologues or other previously unidentified glucosinolate biosynthesis genes
(Daxenbichler et al., 1991).
One interesting facet of the GSL-AOP locus is that it acts as a single
genetic locus with no published reports of any mapping population having
functional AOP2 and AOP3 both on the same chromatid (Parkin et al., 1994b;
Mithen et al., 1995; Giamoustaris and Mithen, 1996; Mithen and Campos, 1996;
Kliebenstein et al., 2001b; 2002a; 2002b; Pfalz et al., 2007; Wentzell et al.,
2007). The published and unpublished Arabidopsis mapping populations
represent thousands of meiotic events that should have allowed for
recombination between AOP2 and AOP3. This lack of recombination suggests
that there may be a mechanism preventing recombination between these genes.
Sequencing of the GSL-AOP locus in Arabidopsis has shown that the difference
between the GSL-ALK and GSL-OHP alleles is a local inversion involving AOP2
and AOP3 such that the two genes have flipped and there is only one functional
promoter within the locus. This local inversion likely causes a localized
suppression of recombination preventing the two alleles from combining. Another
possibility is that the two enzymes may interfere with each other and as such the
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plant has evolved this complex genetic structure to separate them. However,
heterozygous progeny containing the two alleles on opposite chromatids show no
evidence of inhibition between the two enzymes (Daxenbichler et al., 1991;
Mithen et al., 1995; Mithen and Campos, 1996; Kliebenstein et al., 2001b; Pfalz
et al., 2007). Another fitness related possibility is that the presence of both alleles
in one plant may diminish the defensive capacity of the plant. This agrees with
the observation that no wild collected plants have both hydroxyalkyl and alkenyl
glucosinolates in the same leaf (Daxenbichler et al., 1991). As such, the unique
inversion structure of this locus may be of selective benefit to the plant by forcing
the two genes to act as a single genetic locus. Fitness consequences of the
different GSL-AOP alleles remain to be tested in the field.
GSL-ELONG
The GSL-ELONG locus is responsible for Arabidopsis and Brassica
making either predominantly 3 or 4-Carbon aliphatic glucosinolates (Mithen et al.,
1995). In Streptanthus, GSL-ELONG also appears to have evolved a 5-Carbon
allele and other species have even longer chain glucosinolates that may be
caused by evolution at this locus (Rodman et al., 1981; Daxenbichler et al.,
1991). The causal basis of this locus is a family of methylthioalkylmalate (MAM)
enzymes that are under positive selection potentially for new biochemical
activities (Kroymann et al., 2003; Textor et al., 2004; Benderoth et al., 2006;
Textor et al., 2007; Kroymann et al., This issue). The MAM family is also the
source of polymorphism leading to the GSL-PRO locus that blocks aliphatic
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glucosinolate synthesis in Brassica (Gao et al., 2007). The molecular basis of
natural variation at GSL-ELONG within the Brassicaceae is distinct from the
GSL-AOP in that GSL-AOP is controlled by the presence or absence of specific
genes and GSL-ELONG uses both presence/absence and repeated evolution of
new amino acid sequences and hence biochemical activities. It is possible that
this difference in the molecular pattern of evolution at the two loci is that both
GSL-ALK and GSL-OHP have only two potential states, on or off. In contrast,
GSL-ELONG genes have at least 10 potential biochemical states, adding 0 to 9
different carbons to the entering methionine (Daxenbichler et al., 1991; Textor et
al., 2007). It is possible that the different causal polymorphism patterns may
allow the plants to efficiently sample the maximal potential variation at each
locus.
GSL-OX
The GSL-OX locus is responsible for the oxygenation of a methylthioalkyl
glucosinolate to its methylsulfinylalkyl structure (Figure 1 and Table 1)(Mithen et
al., 1995; Giamoustaris and Mithen, 1996; Kliebenstein et al., 2001c). In some
Arabidopsis populations, there are multiple QTLs controlling GSL-OX and a
forward genetics screen of Arabidopsis mutants also identified multiple modifier
loci complicating genetic approaches to identify the causal basis of this structural
polymorphism (Kliebenstein et al., 2007; Wentzell et al., 2007). The majority of
this appears to be due to regulatory polymorphisms controlling the expression of
the actual GSL-OX enzyme allowing this to act as a single structural locus with
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numerous modifiers (Hansen et al., 2007; Wentzell et al., 2007; West et al.,
2007). Further biochemical identification of the enzyme controlling this reaction
was also highly recalcitrant. The conclusive identification of the GSL-OX
biosynthetic locus required a combination of genomics, enzymology and genetics
(Hansen et al., 2007). In this analysis, fine scale-mapping of natural variation
identified a subset of candidate genes that was further filtered by genomic
analysis to find candidate genes co-expressed with the known aliphatic
glucosinolate biosynthetic genes. This identified a crucifer specific family of
flavin-monooxygenases that are the likely candidates for the GSL-OX QTLs and
reaction (Hansen et al., 2007). One of these enzymes (FMOGS-OX1) was shown to
control the enzymatic conversion of methylthioalkyl to methylsulfinylalkyl
glucosinolate both in vitro and in planta. Similar to AOP2 and AOP3, this gene
also showed naturally variable gene expression that appears to cause a GSL-OX
QTL (Hansen et al., 2007; Wentzell et al., 2007). However, unlike GSL-AOP
absolute loss of FMOGS-OX1 expression does not lead to a complete blockage of
the GSL-OX reaction. This is likely due to the presence of the other FMOGS-OX1
gene homologues whose gene expression is also naturally variable and co-locate
with other known GSL-OX loci (Kliebenstein et al., 2006; Hansen et al., 2007;
Wentzell et al., 2007; West et al., 2007). The function of the flavinmonooxygenases homologous to FMOGS-OX1 remains to be proven but this
system establishes a potential model whereby several QTL for the same
enzymatic reaction may be caused by differential expression of gene family
members.
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Other biosynthetic loci
The GSL-OH locus is polymorphic within a number of species and
between species and controls the introduction of a hydroxyl into 4C or longer
alkenyl glucosinolates. However, it is difficult to predict if inter- and intra-specific
polymorphisms are due to new enzyme evolution as in GSL-ELONG or simple
presence/absence polymorphisms as seen in GSL-AOP and GSL-OX. A
presence/absence polymorphism is supported by the biochemical activity
showing presence or absence variation within the Brassicas and Arabidopsis
thaliana (Parkin et al., 1994a; Kliebenstein et al., 2001b; 2001c). However, the
variation between species is more indicative of new enzyme evolution because
the reactions stereochemistry of ranges from a mixture of R/S in Arabidopsis
thaliana to mainly R in Brassica napus to mainly S in Crambe abyssinica
(Daxenbichler et al., 1991; Parkin et al., 1994a; Daubos et al., 1998; Kliebenstein
et al., 2001c). It is difficult to theorize how polymorphisms can cause a single
enzyme to change its stereochemistry thus, suggesting that the different species
may use different enzymes. However, the explanation of this sterochemical
puzzle requires the identification of the underlying gene/s. The identity of the
GSL-OH gene remains to be validated in part because of its dependency upon
the allelic status at the other three structural loci limiting the potential germplasm
within which this locus can be studied (Figure 1 and Table 1).
While the epistatic network between the four major loci sets up a modular
genetic system that may facilitate evolution, it also generates the potential to
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miss significant QTLs that require specific allelic combinations at the four major
biosynthetic loci (Figure 1 and Table 1). A detailed analysis of a large
Arabidopsis population suggested that a biosynthetic enzyme was the basis of a
QTL only when GSL-AOP and GSL-ELONG are in a single specific allelic
combination (Wentzell et al., 2007). This enzyme, SGT74B1, catalyzes
glycosylation of the characteristic glucosinolate backbone structure and has
differential expression within Arabidopsis accessions likely due to a promoter
polymorphism (Grubb et al., 2004; Wentzell et al., 2007). While we might predict
that variation in the expression of SGT74B1 would influence production of
multiple aliphatic glucosinolates, this locus only controlled one metabolic trait, the
accumulation of but-3-enyl glucosinolate. The SGT74B1 enzyme is not specific
to the synthesis of but-3-enyl glucosinolate, as previous work has demonstrated
that it has broad glucosylation activity (Grubb et al., 2004). This QTL detection
bias is likely because genotypes accumulating but-3-enyl glucosinolate also
exhibit the highest level of total aliphatic glucosinolates (Figures 1 and 2 and
Table 1)(Wentzell et al., 2007). Thus, the SGT74B1 expression polymorphism
may only become limiting when flux across the biosynthetic pathway is above a
certain high threshold. A similar relationship is observed for the SGT7471
enzyme in a different Arabidopsis population (Figure 2)(Gachon et al., 2005).
This potential for interactions between the four major loci to limit the ability to
detect additional biosynthetic QTLs raises the question of how many
enzyme/QTL linkages exist in the aliphatic glucosinolate pathway. If we control
for the effect of the four major structural polymorphisms we may find that the
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majority of genes in the aliphatic glucosinolate pathway control quantitative
variation in the accumulation of the metabolites. Very large populations would be
required to test this hypothesis.
Selection on structural diversity
The repeated evolution of identical interspecific glucosinolate structural
diversity between different crucifer species suggests that there may be selection
pressures favoring the presence of structural diversity (Rodman et al., 1981;
Daxenbichler et al., 1991). Supporting this concept is the observation that the
frequency distribution of glucosinolate structural profiles is not random within
Arabidopsis accessions (Kliebenstein et al., 2001c; Lambrix et al., 2001)(Figure
1). In Arabidopsis, some specific structural profiles are statistically overrepresented at the apparent cost of other structural profiles in comparison to an
expectation of independent assortment. For instance 3-hydroxypropyl accessions
appear to be favored over accession containing 3-methylsulfinyl glucosinolates
(Figure 1). In accessions with the 4C GSL-Elong, this relationship is reversed
such that the 4-methylsulfinyl profile is favored over the hydroxyalkyl profile
(Figure 1). A potential alternative explanation for this distortion is population
structure or migration history but this would have to ignore the differential impacts
of these structural loci on insect herbivory (Kliebenstein et al., 2002b). Further
supporting the concept that there is potential selection for diversity is the
observation that glucosinolate gene expression amongst Arabidopsis accessions
is under positive selection for diversity (Kliebenstein, 2008). In this study, the
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glucosinolate biosynthetic genes were shown to have significantly elevated levels
of gene expression polymorphisms in comparison to the average neutral gene as
would be expected for genes under positive selection for diversity. This
expression diversity shows the most extreme selection at the major structural loci
(Kliebenstein, 2008). Thus, while the four structural loci establish a modular
diversity system that can rapidly sample glucosinolate structural diversity, not all
profiles appear equally fit. Now that nearly all the structural loci have or are
nearly cloned, it will be possible to transgenically re-engineer these profiles and
test their fitness in the field. Given that selection appears to act to maintain
diversity, these field tests would have to be conducted in multiple years in
multiple locations as it is likely fluctuating or balancing selection due to variable
insect populations that is the major driver of glucosinolate variation (Hairston and
Dillon, 1990; Ellner and Hairston, 1994; Tiffin and Rausher, 1999).
Regulatory genetic variation in glucosinolates
In addition to structural variation, there is significant natural genetic
variation both within and between species that generates glucosinolate levels
ranging across nearly two log orders within individual species. While the added
complexity of measuring absolute accumulation has led to less focus on this
topic, numerous QTLs have been identified that control glucosinolate
accumulation both within the Brassicas (Toroser et al., 1995; Uzunova et al.,
1995; Faulkner et al., 1998; Hill et al., 2003; Mithen et al., 2003; Lionneton et al.,
2004) and Arabidopsis thaliana (Kliebenstein et al., 2001b; 2002a; 2002b; Pfalz
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et al., 2007; Wentzell et al., 2007). Like the structural loci, these content QTL
frequently overlap between the different species and within Arabidopsis these
loci frequently overlap between populations (Figure 2). Recent work is beginning
to identify the genes underlying the quantitative glucosinolate accumulation loci
and this work is starting to explain long identified relationships between structure
and content as well as illuminating the aliphatic glucosinolate regulatory genes
regulating. These “regulatory” QTL will be the focus of the next section of this
review.
Structural QTLs and pathway regulation
During the identification of the structural QTL, frequent observations
identified a relationship between the glucosinolate structure and the level
accumulation such that some structures accumulated to different levels (Toroser
et al., 1995; Giamoustaris and Mithen, 1996; Kliebenstein et al., 2001b; Raybould
and Moyes, 2001)(Figure 1). However the mechanism for how structure and
accumulation were co-regulated was unknown. Recent global transcriptomics
QTL approaches have begun to unravel mechanisms relating structure to
potential accumulation. In these transcriptomics QTL experiments, microarrays
are used to measure gene expression for all genes in a mapping population. This
data is then used to map QTLs controlling gene expression for all genes, so
called eQTLs (Brem et al., 2002; Keurentjes et al., 2007; West et al., 2007;
Hansen et al., 2008; Potokina et al., 2008). Given that most of the glucosinolate
biosynthetic genes are known and there is an existing global eQTL analysis
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within Arabidopsis, it became possible to compare eQTLs controlling expression
of the biosynthetic enzymes to the QTLs controlling the metabolites (Figure
2)(West et al., 2006; Wentzell et al., 2007; West et al., 2007).
This comparison identified a number of eQTLs controlling the expression
of most if not all of the biosynthetic genes suggesting that these loci are
regulatory eQTL controlling the aliphatic glucosinolate biosynthetic pathway
(Wentzell et al., 2007). Unexpectedly, the GSL-AOP and GSL-ELONG QTL colocated with two regulatory eQTLs controlling gene expression for the aliphatic
glucosinolate biosynthetic pathway. This suggested that the structural loci may
also be regulatory loci controlling pathway gene expression. This was further
confirmed when introduction of AOP2 into a GSL-AOP null background increased
transcript level for all genes in the aliphatic biosynthetic pathway. This suggests
that in addition to controlling glucosinolate structure, some molecular property of
the AOP2 gene controls glucosinolate accumulation by increasing gene
expression and potentially flux through the aliphatic glucosinolate pathway
(Wentzell et al., 2007). Similar variation in expression and possibly the presence
of the MAM1 and MAM3 genes also associated with altered gene expression for
the entire pathway. This suggests that the previously noted relationship between
glucosinolate content and accumulation may occur because the structural QTL
are also regulatory eQTL (Toroser et al., 1995; Giamoustaris and Mithen, 1996;
Kliebenstein et al., 2001b; Raybould and Moyes, 2001; Wentzell et al., 2007).
The mechanism by which these enzymatic genes may regulate gene
expression for the aliphatic glucosinolate pathway is currently unknown. One
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proposed explanation is that the AOP2 enzymatic reaction releases sulfur and
this leads to increased sulfur availability and corresponding increases in aliphatic
glucosinolate related transcripts under sulfur regulation (Kliebenstein et al.,
2001a; Maruyama-Nakashita et al., 2006). This hypothesis predicts that because
the AOP3 enzymatic reaction also releases sulfur, that AOP3 would also
increase aliphatic glucosinolate related transcript accumulation. However,
AOP2’s transcriptional effect is greater than AOP3 and AOP3 in the absence of
AOP2 does not regulate glucosinolate accumulation (Kliebenstein et al., 2001a;
2001b; 2002a; 2002b). Further introduction of AOP2 does not alter levels of the
sulfur regulatory gene EIL3/SLIM1 arguing against an involvement of sulfur in the
AOP2 mechanism (Maruyama-Nakashita et al., 2006; Wentzell et al., 2007). A
further argument against a sulfur availability explanation is that AOP2 releases
one out of three sulfur molecules while doubling the aliphatic GLS content. As
such, in the absence of AOP2, there is one unit of glucosinolate containing three
sulfurs but in the presence of AOP2 there are two units of glucosinolate
containing two sulfurs. Thus, AOP2 leads to increased sulfur demand and if
sulfur deficiency was the explanation, AOP2 should inhibit gene expression,
which is not the case. A final argument against a sulfur mechanism is that the
GSL-ELONG reaction does not alter sulfur status in the plant but also controls
gene expression. Future experiments are required to identify the molecular
mechanism by which the structural loci control gene expression.
MYBs and QTLs
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In addition to structural enzymes, traditional transcription factors appear
to be the basis of other regulatory eQTLs that control glucosinolate
accumulation. A set of three MYB transcription factors, MYB28, MYB29 and
MYB76, have been found via numerous independent approaches to control
aliphatic glucosinolate accumulation and gene expression for the biosynthetic
enzymes (Gigolashvili et al., 2007; Hirai et al., 2007; Sønderby et al., 2007). The
MYB28 gene co-localizes with one of three major QTLs for aliphatic
glucosinolate content and gene expression within the Arabidopsis Bay × Sha
population (Figure 2 and Table 1)(Wentzell et al., 2007). Several lines of
evidence strongly argue for MYB28 being the basis for this regulatory QTL. First,
MYB28 has differential expression between the Bay and Sha parents that
correlates with glucosinolate content such that the high glucosinolate Sha has
higher MYB28 expression. Additionally, MYB28 underlies a QTL for both
increased aliphatic glucosinolate content and increased gene expression for the
majority of the biosynthetic pathway (Sønderby et al., 2007). Finally, transgenic
manipulation of MYB28’s expression mimics the expression polymorphism
between Bay and Sha for both aliphatic glucosinolate content and transcripts
(Sønderby et al., 2007). Thus, it appears that variation in MYB28 controls this
QTL within Arabidopsis and potentially a co-localized QTL in Brassica juncea
(Lionneton et al., 2004).
MYB29 and MYB76 are tandem MYB transcription factor genes that colocate with a QTL in the Ler × Cvi population of Arabidopsis thaliana (Figure 2
and Table 1)(Kliebenstein et al., 2001b; 2002b). Of the two genes, only MYB76
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shows an expression polymorphism within the Arabidopsis accessions and is
likely the basis of this glucosinolate accumulation QTL (Kliebenstein et al., 2006).
MYB76 has differential expression between Ler and Cvi and this differential
expression is positively correlated with glucosinolate content. Further, there are
no predicted functional polymorphisms in the MYB29 gene using genomic
sequence from Ler and Cvi (Borevitz et al., 2007; Clark et al., 2007). Similar to
the other glucosinolate loci, there is overlap in this genomic region with a QTL
controlling aliphatic glucosinolate content in Brassica juncea (Lionneton et al.,
2004). The identification of these MYBs as the likely cause of two regulatory
QTLs within Arabidopsis and their association with similar QTLs in Brassica
raises the possibility that like the structural diversity, the regulatory diversity of
glucosinolates is also modular and utilizes similar genes in diverse species.
Epistasis and gene regulation
In addition to identifying structural and accumulation QTL, the previous research
on quantitative glucosinolate variation also identified numerous epistatic
interactions between these loci. The most obvious epistatic interactions were
those between the four major structural loci that led to altered profiles of
glucosinolates within the plant (Figure 1)(Magrath et al., 1994; Parkin et al.,
1994a; Mithen et al., 1995; Giamoustaris and Mithen, 1996; Kliebenstein et al.,
2001c). However, there were also numerous epistatic interactions identified that
controlled aliphatic glucosinolate accumulation (Kliebenstein et al., 2001b; Pfalz
et al., 2007; Wentzell et al., 2007). The identification of the causal genes for
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these regulatory QTL and the fact that they control of gene expression is
beginning to generate a regulatory model of how epistasis controls glucosinolate
accumulation.
In the Arabidopsis Ler x Cvi population, there is a three way epistasis
between the GSL-ALK, GSL-ELONG and GSL-MYB2976 loci (Kliebenstein et al.,
2001b). This is likely caused by the AOP2 gene within the GSL-ALK QTL
inducing the MYB76 gene which is polymorphic within the GSL-MYB2976 QTL
and can control the AOP2 gene expression (Sønderby et al., 2007; Wentzell et
al., 2007). This sets up a feedback amplification loop whereby the two
polymorphisms amplify each others phenotypic effect leading to a non-linear
interaction of the two loci. Similarly, the measured MAM genes are induced by
MYB76 and AOP2 and one of the MAM genes likely induces AOP2 and MYB76
(Wentzell et al., 2007). This sets up a network whereby the genes underlying all
three QTL are regulating each other and amplifying each others phenotypic
variation. Thus, the polymorphisms begin to generate an epistatic network due to
their multiplicative effect.
A similar situation exists in the Arabidopsis Bay x Sha population where
there is a three way epistasis between the GSL-ALK, GSL-ELONG and GSLMYB28 loci (Wentzell et al., 2007). In this situation, it is likely that crossregulation by AOP2, MYB28 and the MAMs is leading to the formation of a
epistatic network based on expression (Hirai et al., 2007; Sønderby et al., 2007;
Wentzell et al., 2007). This sets up a highly interconnected regulatory network
whereby variation in gene expression for the transcript regulators MYB28,
22
MYB76, a MAM and AOP2 can generate an epistatic regulatory network and
produce a wide range of aliphatic glucosinolate content, again with a minimal
level of genetic polymorphism (Kliebenstein et al., 2001b; Wentzell et al., 2007).
As the model Arabidopsis accession Col-0 does not have functional AOP2, it
remains to be seen how these feedback amplification loops actually function
within Arabidopsis (Kliebenstein et al., 2001a).
Conclusion
Genetic variation within the aliphatic glucosinolate pathway is rapidly
becoming a model system for understanding the molecular basis of quantitative
genetic traits. The cloning of numerous genes for individual QTLs is allowing
studies into the molecular basis of epistatic interactions within quantitative traits.
This will also greatly facilitate the study of how genotype and environment
interact to control the resulting phenotypic value. Beyond quantitative genetics,
the fact that these QTLs are also the basis of glucosinolate variation in wild
species will allow glucosinolates to become a model system for studying
ecological and evolutionary questions about the relationship between variation in
specific genes/pathways and plant fitness.
Acknowledgements Funding for metabolite QTL analysis was obtained by a
National Science Foundation grants DBI 0642481 to DJK.
23
Tables
Table 1. Identification of glucosinolate QTLs
Arabidopsis
Gene
AGI
Proof
MAM2
NA
Comp.
MAM1
AT5G23010 Comp.
Locus
Elong
Alleles
C3
C4
AOP
ALK
OHP
Null
AOP2
AOP3
AT4G03060 Comp.
AT4G03050 Comp.
Comp.
Yes
OH
Quant
GSL-OH
AT2G25450 Assoc.
Yes
OX
Quant
FMO
FMO
FMO
FMO
AT1G62540
AT1G62560
AT1G62570
AT1G65860
Assoc.
Assoc.
Assoc.
Comp.
Yes
MYB28
Quant
MYB28
AT5G61420 Comp.
Yes
MYB2976
Quant
MYB29
MYB76
AT5G07690 None
AT5G07700 Assoc.
Yes
Brassica
Yes
UGT74B1 Quant
UGT74B1 AT1G24100 Assoc.
?
UGT74C1 Quant
UGT74C1 AT2G31790 Assoc.
?
Locus shows the GSL nomenclature. Allele shows which allele at the specific
locus is being described. For Allele, quant shows that there are likely multiple
alleles at the locus each with a different quantitative value. Gene and AGI
provide gene name information on the cloned Arabidopsis genes. Proof shows
that the QTL to gene linkage is validated at either the complementation or
association level. Brassica shows if there is an overlapping QTL identified in
Brassicas.
24
Figure Legends
Figure 1. Genetic control of aliphatic glucosinolates.
Shown is the impact of the four main structural QTL within Arabidopsis and
Brassica upon the composition of glucosinolates within the plant. Glucosinolate
content shows the average accumulation of total aliphatic glucosinolates in pmol
per 100 µg of tissue in the leaves of Arabidopsis with the corresponding
genotypes. Letters show those genotypes with different total aliphatic
glucosinolate accumulation. Observed genotype frequency shows the number of
Arabidopsis accessions out of 126 that had the given four locus genotype.
Deviation from expected frequency shows the χ2 value comparing the observed
four locus genotype against the expected four locus frequency if the genetically
unlinked loci admixed at random. Black bars show genotypes that occur more
(positive) or less (negative) frequently than would be expected by random
chance. White bars are genotypes occurring in a random fashion. All
glucosinolates are shown with regards to the specific side-chain. Glucosinolate
side-chain names are as follows (1) 3-hydroxypropyl, (2) 3-methylthiopropyl, (3)
3-methylsulfinylpropyl, (4) allyl, (5) 4-methylsulfinylbutyl, (6) 4-methylthiobutyl, (7)
but-3-enyl, (8) 2-hydroxybut-3-enyl. In Arabidopsis, structure 8 occurs as both the
R and S enantiomers and the non-sterospecific structure is shown for simplicity.
Figure 2. Aliphatic glucosinolate QTL to gene linkage.
25
Shown are any QTL detected for any aliphatic glucosinolate in five different QTL
mapping experiments within Arabidopsis. The most likely position of the QTL
is shown for an Ler x Cvi population, (Kliebenstein et al., 2001b) using white
boxes; two different analyses of the Ler x Col population, (Kliebenstein et al.,
2002b) as grey boxes and (Kliebenstein et al., 2002a) as black boxes; a Bay0 x Sha population (Wentzell et al., 2007),as boxes with black circles and a
Da(1)-12 x Ei-2 population (Pfalz et al., 2007),shown as boxes with crosshatches. All known or predicted glucosinolate related genes are shown with
those genes having evidence for significant gene expression polymorphisms
in Arabidopsis accessions shown in bold.
26
References
Barth C, Jander G (2006) Arabidopsis myrosinases TGG1 and TGG2 have redundant
function in glucosinolate breakdown and insect defense. The Plant Journal 46:549-562
Benderoth M, Textor S, Windsor AJ et al (2006) Positive selection driving diversification
in plant secondary metabolism. Proc Natl Acad Sci USA 103:9118-9123
Bones AM, Rossiter JT (1996) The myrosinase-glucosinolate system, its organisation and
biochemistry. Physiol Plant 97:194-208
Borevitz JO, Hazen SP, Michael TP et al (2007) Genome-wide patterns of single-feature
polymorphism in Arabidopsis thaliana. Proc Natl Acad Sci USA 104:12057-12062
Brem RB, Yvert G, Clinton R et al (2002) Genetic dissection of transcriptional regulation
in budding yeast. Science 296:752-755
Burow M, Wittstock U (This Issue) Regulation and function of specifier proteins in
plants. Phytochem Rev
Clark RM, Schweikert G, Toomajian C et al (2007) Common sequence polymorphisms
shaping genetic diversity in Arabidopsis thaliana. Science 317:338-342
Daubos P, Grumel V, Iori R et al (1998) Crambe abyssinica meal as starting material for
the production of enantiomerically pure fine chemicals. Ind Crops Prod 7:187-193
Daxenbichler ME, Spencer GF, Carlson DG et al (1991) Glucosinolate composition of
seeds from 297 species of wild plants. Phytochemistry 30:2623-2638
de Quiros HC, Magrath R, McCallum D et al (2000) α-Keto acid elongation and
glucosinolate biosynthesis in Arabidopsis thaliana. Theor Appl Genet 101:429-437
Donkin SG, Eiteman MA, Williams PL (1995) Toxicity of glucosinolates and their
enzymatic decomposition products to Caenorhabditis elegans. J Nematology 27:258-262
Ellner S, Hairston NG (1994) Role of overlapping generations in maintaining geneticvariation in a fluctuating environment. American Nat 143:403-417
Faulkner K, Mithen R, Williamson G (1998) Selective increase of the potential
anticarcinogen 4-methylsulphinylbutyl glucosinolate in broccoli. Carcinogenesis 19:605609
Gachon CMM, Langlois-Meurinne M, Henry Y et al (2005) Transcriptional co-regulation
of secondary metabolism enzymes in Arabidopsis: functional and evolutionary
implications. Plant Mol Biol 58:229-245
Gao MQ, Li GY, Yang B et al (2007) High-density Brassica oleracea linkage map:
identification of useful new linkages. Theoretical and Applied Genetics 115:277-287
Giamoustaris A, Mithen R (1995) The effect of modifying the glucosinolate content of
leaves of oilseed rape (Brassica napus Ssp oleifera) on its interaction with specialist and
generalist pests. Ann Appl Biol 126:347-363
Giamoustaris A, Mithen R (1996) Genetics of aliphatic glucosinolates .IV. Side-chain
modification in Brassica oleracea. Theor Appl Genet 93:1006-1010
Gigolashvili T, Yatusevich R, Berger B et al (2007) The R2R3-MYB transcription factor
HAG1/MYB28 is a regulator of methionine-derived glucosinolate biosynthesis in
Arabidopsis thaliana. The Plant Journal 51 247–261
Graser G, Schneider B, Oldham NJ et al (2000) The methionine chain elongation
pathway in the biosynthesis of glucosinolates in Eruca sativa (Brassicaceae). Arch
Biochem Biophys 378:411-419
27
Grubb CD, Zipp BJ, Ludwig-Müller J et al (2004) Arabidopsis glucosyltransferase
UGT74B1 functions in glucosinolate biosynthesis and auxin homeostasis. The Plant
Journal 40:893-908
Hairston NG, Dillon TA (1990) Fluctuating selection and response in a population of
fresh-water copepods. Evolution 44:1796-1805
Halkier BA, Du L (1997) The biosynthesis of glucosinolates. Trends Plant Sci 2:425-431
Halkier BA, Gershenzon J (2006) Biology and biochemistry of glucosinolates. Ann Rev
Plant Biol 57:303-333
Hansen BG, Halkier BA, Kliebenstein DJ (2008) Identifying the molecular basis of
QTLs: eQTLs add a new dimension. Trends Plant Sci 13:72-77
Hansen BG, Kliebenstein DJ, Halkier BA (2007) Identification of a flavinmonooxygenase as the S-oxygenating enzyme in aliphatic glucosinolate biosynthesis in
Arabidopsis. The Plant Journal 50:902-910
Hill J, Lethenborg P, Li PW et al (2003) Inheritance of progoitrin and total aliphatic
glucosinolates in oilseed rape (Brassica napus L). Euphytica 134:179-187
Hirai M, Sugiyama K, Sawada Y et al (2007) Omics-based identification of Arabidopsis
Myb transcription factors regulating aliphatic glucosinolate biosynthesis. Proc Natl Acad
Sci USA 104:6478-6483
Hogge LR, Reed DW, Underhill EW et al (1988) HPLC separation of glucosinolates
from leaves and seeds of Arabidopsis thaliana and their identification using thermospary
liquid chromatography-mass spectometry. J Chrom Sci 26:551-556
Kahn RA, Fahrendorf T, Halkier BA et al (1999) Substrate specificity of the cytochrome
P450 enzymes CYP79A1 and CYP71E1 involved in the biosynthesis of the cyanogenic
glucoside dhurrin in Sorghum bicolor (L.) Moench. Arch Biochem Biophys 363:9-18
Keurentjes JJB, Fu JY, Terpstra IR et al (2007) Regulatory network construction in
Arabidopsis by using genome-wide gene expression quantitative trait loci. Proc Natl
Acad Sci USA 104:1708-1713
Kliebenstein D, Lambrix V, Reichelt M et al (2001a) Gene duplication and the
diversification of secondary metabolism: side chain modification of glucosinolates in
Arabidopsis thaliana. Plant Cell 13:681-693
Kliebenstein DJ (2008) A role for gene duplication and natural variation of gene
expression in the evolution of metabolism. PLos ONE 3:e1838
Kliebenstein DJ, D’Auria JC, Behere AS et al (2007) Characterization of seed-specific
benzoyloxyglucosinolate mutations in Arabidopsis thaliana. The Plant Journal 51:10621076
Kliebenstein DJ, Figuth A, Mitchell-Olds T (2002a) Genetic architecture of plastic
methyl jasmonate responses in Arabidopsis thaliana. Genetics 161:1685-1696
Kliebenstein DJ, Gershenzon J, Mitchell-Olds T (2001b) Comparative quantitative trait
loci mapping of aliphatic, indolic and benzylic glucosinolate production in Arabidopsis
thaliana leaves and seeds. Genetics 159:359-370
Kliebenstein DJ, Kroymann J, Brown P et al (2001c) Genetic control of natural variation
in Arabidopsis thaliana glucosinolate accumulation. Plant Physiol 126:811-825
Kliebenstein DJ, Pedersen D, Mitchell-Olds T (2002b) Comparative analysis of insect
resistance QTL and QTL controlling the myrosinase/glucosinolate system in Arabidopsis
thaliana. Genetics 161:325-332
28
Kliebenstein DJ, West MAL, Van Leeuwen H et al (2006) Genomic survey of gene
expression diversity in Arabidopsis thaliana. Genetics 172:1179-1189
Kroymann J, Benderoth M, Pfalz M (This issue) Methylthioalkylmalate synthases:
Genetics, ecology & evolution. Phytochemistry Rev
Kroymann J, Donnerhacke S, Schnabelrauch D et al (2003) Evolutionary dynamics of an
Arabidopsis insect resistance quantitative trait locus. Proc Natl Acad Sci USA
100:14587-14592
Lambrix V, Reichelt M, Mitchell-Olds T et al (2001) The Arabidopsis epithiospecifier
protein promotes the hydrolysis of glucosinolates to nitriles and influences Trichoplusia
ni herbivory. Plant Cell 13:2793-2807
Lankau RA (2007) Specialist and generalist herbivores exert opposing selection on a
chemical defense. New Phytologist 175:176-184
Lankau RA, Strauss SY (2007) Mutual feedbacks maintain both genetic and species
diversity in a plant community. Science 317:1561-1563
Li G, Quiros CF (2003) In planta side-chain glucosinolate modification in Arabidopsis by
introduction of dioxygenase Brassica homolog BoGSL-ALK. Theor Appl Genet
106:1116-1121
Li Q, Eigenbrode SD, Stringham GR et al (2000) Feeding and growth of Plutella
xylostella and Spodoptera eridania on Brassica juncea with varying glucosinolate
concentrations and myrosinase activities. J Chem Ecol 26:2401-2419
Lionneton E, Aubert G, Ochatt S et al (2004) Genetic analysis of agronomic and quality
traits in mustard (Brassica juncea). Theor Appl Genet 109:792-799
Mackay TFC (2001) The genetic architecture of quantitative traits. Ann Rev Genet
35:303-339
Magrath R, Bano F, Morgner M et al (1994) Genetics of aliphatic glucosinolates. I. Side
chain elongation in Brassica napus and Arabidopsis thaliana. Heredity 72:290-299
Magrath R, Herron C, Giamoustaris A et al (1993) The inheritance of aliphatic
glucosinolates in Brassica napus.. Plant Breeding 111:55-72
Maruyama-Nakashita A, Nakamura Y, Tohge T et al (2006) Arabidopsis SLIM1 is a
central transcriptional regulator of plant sulfur response and metabolism. Plant Cell
18:3235-3251
Mauricio R, Rausher MD (1997) Experimental manipulation of putative selective agents
provides evidence for the role of natural enemies in the evolution of plant defense.
Evolution 51:1435-1444
Mithen R (1992) Leaf glucosinolate profiles and their relationships to pest and disease
resistance in oilseed rape. Euphytica 63:71-83
Mithen R, Campos H (1996) Genetic variation of aliphatic glucosinolates in Arabidopsis
thaliana and prospects for map based gene cloning. Entomol Exp App 80:202-205
Mithen R, Clarke J, Lister C et al (1995) Genetics of aliphatic glucosinolates .III. Sidechain structure of aliphatic glucosinolates in Arabidopsis thaliana. Heredity 74:210-215
Mithen R, Faulkner K, Magrath R et al (2003) Development of isothiocyanate-enriched
broccoli, and its enhanced ability to induce phase 2 detoxification enzymes in
mammalian cells. Theor Appl Genet 106:727-734
Mithen R, Toroser D (1995) Biochemical genetics of aliphatic glucosinolates in Brassica
and Arabidopsis. Seminar series 56 (Society for experimental biology):261-275
29
Mithen RF, Lewis BG, Fenwick GR (1986) In vitro activity of glucosinolates and their
products against Leptosphaeria maculans. Trans Br Mycol Soc 87:433-440
Parkin I, Magrath R, Keith D et al (1994a) Genetics of aliphatic glucosinolates .II.
Hydroxylation of alkenyl glucosinolates in Brassica napus. Heredity 72:594-598
Parkin I, Magrath R, Keith D et al (1994b) Genetis of aliphatic glucosinolates. II.
Hydroxylation of alkenyl glucosinolates in Brassica napus. Heredity 72:594-598
Pfalz M, Vogel H, Mitchell-Olds T et al (2007) Mapping of QTL for Resistance against
the Crucifer Specialist Herbivore Pieris brassicae in a New Arabidopsis Inbred Line
Population, Da(1)-12×Ei-2. PLos ONE 2:e578
Potokina E, Druka A, Luo Z et al (2008) Gene expression quantitative trait locus analysis
of 16 000 barley genes reveals a complex pattern of genome-wide transcriptional
regulation. The Plant Journal 53:90-101
Raybould AF, Moyes CL (2001) The ecological genetics of aliphatic glucosinolates.
Heredity 87:383-391
Rodman J (1980) Population variation and hybridization in sea-rockets (Cakile,
Cruciferae): Seed glucosinolate characters. Am J Bot 67:1145-1159
Rodman JE, Kruckeberg AR, Alshehbaz IA (1981) Chemotaxonomic diversity and
complexity in seed glucosinolates of Caulanthus and Streptanthus (Cruciferae). Syst Bot
6:197-222
Sønderby IE, Hansen BG, Bjarnholt N et al (2007) A systems biology approach identifies
a R2R3 MYB gene subfamily with distinct and overlapping functions in regulation of
aliphatic glucosinolates. PLos ONE 2:e1322
Textor S, Bartram S, Kroymann J et al (2004) Biosynthesis of methionine-derived
glucosinolates in Arabidopsis thaliana: recombinant expression and characterization of
methylthioalkylmalate synthase, the condensing enzyme of the chain-elongation cycle.
Planta 218:1026-1035
Textor S, de Kraker JW, Hause BG, J. et al (2007) MAM3 catalyzes the formation of all
aliphatic glucosinolate chain lengths in Arabidopsis. Plant Physiol 144:60-71
Tierens K-J, Thomma B, Brower M et al (2001) Study of the role of antimicrobial
glucosinolate-derived isothiocyanates in resistance of Arabidopsis to microbial
pathogens. Plant Physiol 125:1688-1699
Tiffin P, Rausher MD (1999) Genetic constraints and selection acting on tolerance to
herbivory in the common morning glory Ipomoea purpurea. Am Nat 154:700-716
Toroser D, Thormann C, Osborn T et al (1995) RFLP mapping of quantitative trait loci
controlling seed aliphatic glucosinolate content in oilseed rape (Brassica napus L.).
Theor Appl Genet 91:802-808
Uzunova M, Ecke W, Weissleder K et al (1995) Mapping the genome of rapeseed
(Brassica napus L) .1. Construction of an RFLP linkage map and localization of QTLs
for seed glucosinolate content. Theor Appl Genet 90:194-204
Wentzell AM, Rowe HC, Hansen BG et al (2007) Linking metabolic QTL with network
and cis-eQTL controlling biosynthetic pathways. PLOS Genetics 3:e162
West MAL, Kim K, Kliebenstein DJ et al (2007) Global eQTL mapping reveals the
complex genetic architecture of transcript level variation in Arabidopsis. Genetics
175:1441-1450
30
West MAL, van Leeuwen H, Kozik A et al (2006) High-density haplotyping with
microarray-based expression and single feature polymorphism markers in Arabidopsis.
Genom Res 16:787-795
Windsor AJ, Reichelt M, Figuth A et al (2005) Geographic and evolutionary
diversification of glucosinolates among near relatives of Arabidopsis thaliana
(Brassicaceae). Phytochem 66:1321-1333
Wink M (1988) Plant Breeding: Importance of plant secondary metabolites for protection
against pathogens and herbivores. Theor Appl Genet 75:225-233
Wittstock U, Halkier BA (2000) Cytochrome P450 CYP79A2 from Arabidopsis thaliana
L. catalyzes the conversion of L-phenylalanine to phenylacetaldoxime in the biosynthesis
of the benzylglucosinolate. J Biol Chem 275:14659-14666
Zhang Z-Y, Ober JA, Kliebenstein DJ (2006) The gene controlling the quantitative trait
locus EPITHIOSPECIFIER MODIFIER1 alters glucosinolate hydrolysis and insect
resistance in Arabidopsis. Plant Cell 18:1524-1536
31
Figure 1
Biosynthetic Locus Genotype
S
d
null
O
null
3
Fxnl
4
null
4
Fxnl
1
null
1
methionine
S
cd
S
2
a
ALK
S
2
O
OH
C4
ND
5
ND
e
S
OHP
OH
ND
S
6
ND
O
Fxnl
5
S
d
null
O
null
5
Fxnl
4
S
6
S
e
7
b
null
null
ALK
7
4
2
S
6
ab
S
OH
R
S Glc
Fxnl
4
7
8
null
4
7
8
b
OH
OH
N OSO3-
2
S
6
S
cd
15
O
0
3
cd
S
2
Deviation from
Expected Frequency
-15
Fxnl
OH
30
1
OHP
20
null
c
10
C3
1
Observed
Genotype Frequency
0
0
OH
Fxnl
40
GSL-OX
30
GSL-OH
20
AOP
Glucosinolate Content
10
Elong
Glucosinolate Composition
Figure 2
II
I
III
ATGSTF11
DOF1.1
IQD1
CYP79F1/F2
ATST5C
IV
V
AOP2,3
PMSR1
MYB29 & 76
PMSR2
Aconitase
CYP83A1
MAM1,2,3
BCAT4
UGT74B1
C-S Lyase
IPMDH
GS-OH
UGT74C1
CYP83B1
Aconitase
FMO x3
FMO
AtSt5A,B
Ler x Cvi (162 Lines, 3 reps)
Ler x Col-0 (300 Lines, 3 reps)
Ler x Col-0 (94 Lines, 6 reps)
Bay-0 x Sha (411 Lines, 4 reps)
Da1-1 x Ei-2 (200 Lines, 4 reps)
Aconitase
MYB28