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ISSN 1742-206X

HIGHLIGHT
Hirai and Saito
Analysis of systemic sulfur
metabolism in plants using
integrated ‘-omics’ strategies

PAPER
Gipson et al.
Multi-platform investigation of
the metabolome in a leptin
receptor defective murine modelof type 2 diabetes

1742-206X(2008)4:10;1-T

28/08/2008 15:03:06

HIGHLIGHT

www.rsc.org/molecularbiosystems | Molecular BioSystems

Analysis of systemic sulfur metabolism inplants using integrated ‘-omics’ strategies
Masami Yokota Hiraiab* and Kazuki Saitoac*
DOI: 10.1039/b802911n

Sulfur (S) assimilation by plants plays a key role in the S cycle in nature, and metabolismof the assimilated S provides various compounds that are useful for animals, including
humans. It is important to understand the mechanisms involved in systemic S
metabolism in order to improve crop agronomy and the production of dietary andnutraceutical plants. Recent advances in ‘-omic’ studies have provided comprehensive
insights into S metabolism. These studies may be regarded as important case studies
that provide information on the complicated regulatory mechanisms involved in plant
metabolism.

S-containing metabolites
produced in plants
S metabolism is essential for both plants
and animals because it produces various
S-containing organic compounds that play
pivotal roles in many aspects of life.1,2Inorganic sulfate in soil is absorbed
into plant cells via sulfate transporters
and is metabolized ﬁrst into cysteine andsubsequently into methionine, which is an
essential amino acid in animals. Cysteine
residues are indispensable in proteinsdue to their presence in the active site ofmany enzymes, and due to the extensivea

RIKEN Plant Science Center, Suehiro-cho
1-7-22, Tsurumi-ku, Yokohama, Kanagawa
230-0045, Japanb
Core Research for Evolutional Science andTechnology, Japan Science and Technology
Agency, 4-1-8 Hon-chou, Kawaguchi,
Saitama 332-0012, Japanc
Graduate School of Pharmaceutical Sciences,
Chiba University, Yayoi-cho 1-33, Inage-ku,
Chiba, Chiba 263-8522, Japan

Masami Yokota

functions they perform via the formationof disulﬁde bonds. Further, cysteine residues are incorporated into oligopeptides
such as glutathione (GSH), which plays
an important role along with Fe–S clusters in various redox reactions. GSH also
plays a role in the detoxiﬁcation of xenobiotics. S-Adenosylmethionine (SAM) is
essential as a methyl-group donor for
DNA methylation, 5 0 -capping of mRNA,and almost all other methylation reactions occurring in cells. It also functions
as a precursor of bioactive polyamines.
Many reactions occurring in living
cells require S-containing cofactors suchas biotin, thiamine pyrophosphate andcoenzyme A.
In plants, sulfolipids are essential forthe formation of thylakoid membranes inchloroplasts, the photosynthetic organelles. Plants produce species-speciﬁc secondary metabolites containing S, such as
glucosinolates (GSLs), camalexin (produced by cruciferous plants) and alliins

Masami Yokota Hirai began
her -omics studies in investigations of sulfur metabolism carried out at Chiba University.
Since April, 2005, she has
been a Principal Investigator
at RIKEN Plant Science Center. Her research interests include discovering regulatory
mechanisms of amino acid biosynthesis by omics-based strategies as well as identifying
gene functions involved in secondary metabolism in plants.

c The Royal Society of Chemistry 2008
This journal is 

Kazuki Saito(produced by Allium species), which act as
phytoalexins. Glucosinolates and alliins
are broken down by speciﬁc degradation
enzymes into volatile compounds such as
isothiocyanates (ITCs) and allicin, respectively, which are defence compounds thatpossess antimicrobial activities. Interestingly, these volatile compounds are also
beneﬁcial to humans because they impart
ﬂavour to vegetables such as cabbage,broccoli, mustard (family, Cruciferae),garlic and onion (Allium species). These
compounds have recently been attracting
attention for their health-promoting effects such as their anticancer activities.

Regulation of S metabolism inplantsAs mentioned above, inorganic sulfate isprocessed into various plant metabolites(Fig. 1). Since each S-containing metabolite is essential to the plant, theKazuki Saito has been a full
professor at Chiba University
since 1995. Since 2005, hehas been a group director at
RIKEN Plant Science Center.
His research interests are
metabolome-based functional
genomics, biochemistry, molecular biology and biotechnology of primary and secondary
metabolism in plants. In particular, he is engaged in biosynthetic studies of sulfur
compounds, ﬂavonoids andalkaloids.
Mol. BioSyst., 2008, 4, 967–973 | 967

adequate distribution of S atoms to eachmetabolite is important for plant survival. For such distribution, the availability of S in the environment is monitored
and S metabolism is regulated at various
levels. Biochemical, molecular biologicaland genetic studies conducted over recent years have revealed the following
independent mechanisms for the regulation of S metabolism: induction ofgene expression during S-deﬁciency; Savailability-dependent formation of an
enzyme complex involving serine acetyltransferase and OAS(thiol)-lyase (OAS,
O-acetylserine) leading to modulation of
catalytic activity; and feedback regulation of enzymes involved in S assimilation at both the protein andtranscript levels.1–5 These studies investigated in detail the individual molecularmechanisms governing S homeostasis.
However, their ﬁndings were generally
restricted to the main pathways involvedin S assimilation. Investigation from a
broader perspective is expected to clarify
the details of the complete regulatory
mechanisms.

Transcriptome analyses in
S-deficiency
In the model plants Arabidopsis thaliana
and rice, genomic sequencing was completed in early 2000.6–10 Since then, plantscience has progressed beyond genomics.
In Arabidopsis approximately 27 000
genes and their translation products havebeen targets of -omic studies.
S-deﬁciency is a serious agriculturalproblem that results in a decrease in crop
yield and quality, and the symptoms andresponses of plants to S-deﬁciency havebeen extensively reported in studies onplant physiology. Therefore, early -omic
studies on S metabolism have focused ontranscriptomic changes occurring in
S-starved Arabidopsis plants to provide
insights into the mechanisms of adaptation to S-deﬁciency.11–14 In these studies,11–14 wild-type Arabidopsis plants
were grown under conditions of continuous S-deﬁciency or were transferredfrom the S-suﬃcient control condition
to the S-deﬁciency condition and viceversa. Leaves, roots or whole seedlings

were analyzed using DNA arrays in order to determine the transcript proﬁles of
8000–9000 genes. The status of S in theseplants varied depending on the sulfate
concentration in the culture media, thegrowth stage of the plant and the duration for which the plant was maintained
under the S-deﬁciency condition (summarized in ref. 15). Therefore, the genes
exhibiting the most notable changes intheir expression levels diﬀered amongexperiments. However, by consideringtranscriptomic data along with metabolic maps, metabolic pathways, which
generally respond to S-deﬁciency, could
be elucidated. As expected, the results
conﬁrmed the induction of several genes
involved in the metabolism of sulfate
into cysteine.
The most intriguing discovery is theinvolvement of jasmonate, which is aplant hormone, in metabolic pathways
that are functional under S-deﬁciency
conditions.11,13,14 These studies were theﬁrst to suggest the involvement of jasmonates in S-deﬁciency responses. The biosynthetic pathway of another plant
hormone, namely, auxin, is closely related to the pathway of indole GSL
biosynthesis from tryptophan. In S-deﬁciency, the biosynthesis of tryptophan,indole GSL and auxin is aﬀected.12–14
On the other hand, by performing atranscriptomic analysis of jasmonateregulated metabolic pathways in
Arabidopsis plants, Sasaki-Sekimoto
et al. reported that jasmonates activate
the metabolic pathways of ascorbate andGSH and the biosynthesis of indole
GSL.16 All these studies suggest the involvement of complex metabolic andsignalling networks in S assimilationand in the biosynthesis of GSH, indole
GSL, auxin and jasmonate.

Integrated transcriptome andmetabolome analysis in
S-deficiency

Fig. 1 S metabolism and regulatory mechanisms induced depending on S availability in
Arabidopsis. Sultr, sulfate transporter; APS, ATP sulfurylase; APK, APS kinase; APR,
adenosine 5 0 -phosphosulfate reductase; PAPS, 3 0 -phosphoadenosine 5 0 -phosphosulfate; SIR,
sulﬁte reductase; Serat, serine acetyltransferase; OASTL, OAS(thiol)-lyase; CGS, cystathionine
gamma-synthase; CBL, cystathionine beta-lyase; MetS, methionine synthase; SAMS, S-adenosylmethionine synthetase; GSH1, gamma-glutamylcysteine synthetase; GSH2, glutathione
synthetase.

968 | Mol. BioSyst., 2008, 4, 967–973

The analytical technology of transcriptomics has developed considerably inrecent years; for example, the 22-k
Arabidopsis microarray has become commercially available. In contrast, the technology of metabolomics is still developing
because the targets of this technology arechemically diverse metabolites, of whichmore than 200 000 exist in the plantc The Royal Society of Chemistry 2008
This journal is kingdom. Hence, no single method is
suﬃcient for the simultaneous analysisof all existing metabolites.17 In particular,
the improvement of methods for identifying metabolites is an urgent requirement
to enable the use of metabolomics as atool for investigating the biology of systems. Nevertheless, metabolome analyses
performed using current techniques canprovide useful information on metabolic
systems, particularly if these analyses
are integrated with transcriptomics.18,19
Nikiforova et al.20 and Hirai et al.12
analyzed the metabolome in the sameplant samples that were used for theabove-mentioned transcriptome analyses,
and their studies were the ﬁrst to integrate
metabolomics and transcriptomics.
Hirai et al.12 analyzed the metabolomes of S-starved, nitrogen (N)-starved,
and OAS-treated Arabidopsis plants (seebelow) in a non-targeted manner by performing Fourier transform-ion cyclotron
resonance (FT-ICR) mass spectrometry(MS) to detect approximately 2000 putative metabolites. The N-starved plants
were expected to exhibit the reverse of
the response exhibited by the S-starved
plants because S-deﬁciency is considered
to be a shortage of S in relation to N.
OAS is considered to be a positive regulator of genes responsive to S-deﬁciency,1 and its exogenous applicationinduces responses that mimic several responses observed in S-deﬁciency. Therefore, the OAS-supplemented plants were
expected to exhibit responses similar to
those observed in S-deﬁciency. The treatments administered were divided into 2
categories, namely, short- and long-term
treatments (summarized in Fig. 2A). The
data obtained were subjected to a principal component analysis (PCA) to clarify
the global changes occurring in the metabolome and transcriptome. As shownin Fig. 2B and C, the experiments (samples) were clustered according to theplant organ sampled (leaves or roots),method of plant culture (hydroponic culture or culture on an agar-solidiﬁed plate)and period of stress (long- or short-term
stress). The results indicated the following features of global regulation: (1) longterm S-deﬁciency, N-deﬁciency, andS- and N-deﬁciency had similar eﬀects
on the metabolome and transcriptome;(2) the metabolite and transcript proﬁles
diﬀered considerably between long- andshort-term S-deﬁciency; and (3) the

Fig. 2 Plant growth conditions and PCA performed to elucidate the changes induced in themetabolome and transcriptome by the culture conditions. (A) Plant growth conditions. (B) In
order to classify the samples based on the global changes occurring in the metabolome, the log
ratio of the accumulation level of each metabolite in S-deﬁciency to that in the corresponding
control sample was calculated and was subjected to PCA. Each small globe represents a sample.
The proportions of the ﬁrst, second and third components are 27.6%, 18.8% and 15.8%,
respectively. (C) In order to classify the samples based on the global changes occurring in thetranscriptome, the log ratio of the expression level of each gene in S-deﬁciency to that in
the corresponding control sample was calculated and was subjected to PCA. The proportions of
the ﬁrst, second and third components are 35.8%, 14.6% and 13.3%, respectively. The ﬁgures
have been modiﬁed from ref. 12.

eﬀects of OAS treatment were similar to
those of short-term S-deﬁciency, suggesting that OAS regulates the global
metabolite and transcript proﬁles inshort-term S-deﬁciency. The fact thatsimilar clustering patterns were obtainedby using the transcriptome and metabolome data indicates that the global
transcript and metabolite proﬁles werestrongly related to each other. Unexpectedly, similar changes were observed
during S-deﬁciency and N-deﬁciency,
suggesting the existence of general
responses to nutritional deﬁciency in the
global regulation of the metabolome
and transcriptome. With regard toindividual responses, however, general
responses to both S-deﬁciency andN-deﬁciency as well as speciﬁc responses

c The Royal Society of Chemistry 2008
This journal is 

to either S-deﬁciency or N-deﬁciency
were observed, for example, in the caseof GSL metabolism.12
Nikiforova et al.20 analyzed the metabolome of S-starved Arabidopsis plants
by performing gas chromatography(GC)-MS and liquid chromatography(LC)-MS. These analyses revealed the
response patterns of 6023 peaks of nonredundant ion traces and the relative
concentration levels of 134 non-redundant compounds with known chemical
structures. In order to understand thedetailed response mechanisms, the metabolomic and transcriptomic data wereconsidered in relation to known metabolic pathways. This revealed the interplay among S assimilation, N imbalance,
lipid breakdown, purine metabolism andMol. BioSyst., 2008, 4, 967–973 | 969

enhanced photorespiration associatedwith S-deﬁciency stress.

Analysis of time-series data ofthe transcriptome andmetabolome
Due to the complexity of the regulatory
mechanisms functional at various levels,‘snapshot’ analyses of the transcriptome
and metabolome often merely describe
the patterns of gene expression andmetabolite accumulation. Time-series
analyses of the transcriptome and metabolome provide deeper insights into
gene–gene, metabolite–metabolite andgene–metabolite networks because timeseries data are suitable for calculating thesimilarity distance between genes andmetabolites. Time-series transcriptomic
and metabolomic data obtained for
S-starved Arabidopsis plants were combined into a single matrix and wereanalyzed using 2 diﬀerent algorithms
to elucidate the gene–metabolite networks.21,22 Another study aimed to identify the transition time point during plant
adaptation to S-deﬁciency.23

1 Batch-learning self-organizing
mapping
Hirai et al.21 applied batch-learning selforganizing mapping (BL-SOM)24,25 tocalculate the similarity distance between
S-deﬁciency-responsive genes and metabolites. Based on the time-dependentchanges in their accumulation/expression
patterns in response to S-deﬁciency,21
approximately 1000 putative metabolites
and 10 000 genes were classiﬁed into
clusters within single or adjacent cellson the ‘feature map’ obtained (Fig. 3A).
For example, GSLs and their degradation products ITCs were clustered into
their respective clusters on the featuremap (Fig. 3A). The accumulation patterns of ITCs and GSLs were mirror
images of each other (Fig. 3B), suggesting that GSL metabolism is coordinately
regulated. In Arabidopsis, most of thegenes involved in the synthesis of the
GSL core structure have been identiﬁed.
These genes known to be involved in
GSL biosynthesis were clustered in thesame region on the map (Fig. 3A), and970 | Mol. BioSyst., 2008, 4, 967–973

this supports the notion that GSL metabolism
iscoordinately
regulated(Fig. 3C). This analysis provides an effective method for predicting novel genesthat may be involved in GSL biosynthesis (see below).

2 Network reconstruction basedon the cause–effect relationship
Nikiforova et al.22 used 2 methods
to calculate the correlation distance(Pearson correlation coeﬃcient and mutual information) for the combined timeseries data of the transcriptome (6454
genes) and metabolome (81 chemicalcompounds) of S-starved Arabidopsis
plants.22 Assuming that an altered S levelis the primary cause of systemic stimulation, they centralized S in the graphdepicting the correlation network between S-deﬁciency-responsive genes andmetabolites, and reconstructed the gene–metabolite network based on the cause–eﬀect relationship.22 By using thisreconstructed network, the information
ﬂux from the primary cause (S-deﬁciency) to the response end-point (forexample, anthocyanin accumulation)and the pathways involved in this ﬂux(for example, the ‘S-serine-tryptophan’pathway and hormone-related pathways)
could be elucidated. Recently, Nikiforova
and co-workers analyzed the topology ofthe metabolite–gene network in order to
identify metabolites that regulate gene
expression during adaptive responses toperturbation of the biological system.26
By comparing 3 independent biologicalsystems (S-starved Arabidopsis, herbicide-treated Arabidopsis and ripening tomato), they suggested that metabolites
are less involved in gene expression in the
S-deﬁciency stress response than in the
ripening of tomatoes.

3 Detection of the transition time
point
Morioka et al.23 established a novel method based on a linear dynamical systemmodel to predict the transition time pointof the transcriptome and metabolome
during the process of adaptation. The
transition time point, at which the transcriptome and/or the metabolome change
drastically in response to environmental
changes, is determined based on the time-series data by using the logarithm of the‘likelihood value’, which is the generative
probability of current data based on thecondition of previous data sets. If thelikelihood value is low, then the current
data cannot be adequately explained
based on previous data sets; that is,a transition has occurred. Morioka
et al.23 applied this method to time-seriestranscriptomic and metabolomic data.21
The results revealed that both the metabolome and transcriptome transitioned
between 12 and 24 h after the plants weretransferred to S-deﬁciency conditions.23
Relevant factors such as genes and/or
metabolites related to the transitions
could then be identiﬁed using BL-SOM.
For example, changes in lipid metabolism
were observed around the transition time
points.23

Functional identification ofnovel GSL biosynthesis genes
As mentioned above, known GSL biosynthesis genes were clustered on thefeature map in the BL-SOM analysis(Fig. 3A). This suggested that unknown
genes that clustered along with the
known GSL biosynthesis genes may also
be involved in GSL biosynthesis. Basedon this assumption, Hirai et al.21,27 comprehensively predicted the functions ofthese genes. The predicted functions of 3
sulfotransferase genes and an Myb transcription factor gene (Myb28) were subsequently conﬁrmed by molecularbiological and reverse genetic studies.21,27 Further, the predicted functionsof two other Myb genes (Myb29 and
Myb76) were recently conﬁrmed;28,29 thisindicates the validity of this strategy for
functional genomics.

Proteome analysis of
S-starved seeds
Seed storage proteins (SSPs), which accumulate during seed maturation, arereservoirs of carbon (C), N and S forthe growth of next-generation progenies
during infancy in the absence of nutrient
supply from the environment. SSPs comprise several proteins encoded by various
gene families. The composition of SSPs
varies depending on the availability of S
in the environment; for maintenance of

c The Royal Society of Chemistry 2008
This journal is 

Fig. 3 BL-SOM of time-series metabolomic and transcriptomic data. The metabolome dataobtained by performing FT-ICR-MS, high-pressure liquid chromatography and capillaryelectrophoresis were merged with the transcriptome data. Approximately 1000 metabolites
and 10 000 genes responsive to S-deﬁciency were classiﬁed by BL-SOM based on the timedependent changes in their patterns of accumulation and expression. (A) A feature map based on
the data obtained for the leaf samples. Each cell was coloured based on the relative log ratio
values of the metabolites and genes therein as follows: when all the relative log ratio values of themetabolites and genes in a particular cell were greater or smaller than the average, the cell wascoloured in pink or pale blue, respectively. Red and blue cells indicated that at least one of therelative log ratio values was greater than the average plus standard deviation or smaller than the
average minus standard deviation, respectively. Yellow circles indicate the clusters for GSLs (a),ITCs (b) and genes involved in GSL biosynthesis (c). (B) Changes in the content of GSLs and
ITCs in the leaves. (C) Changes in the expressions of genes involved in GSL biosynthesis.
Abbreviations indicate the side-chain groups of the GSLs and ITCs as follows: 3-msp,
3-methylsulﬁnylpropyl; 4-mtb, 4-methylthiolbutyl; 7-msh, 7-methylsulﬁnylheptyl; 8-mso,
8-methylsulﬁnyloctyl; i-3ym, indol-3-ylmethyl; 4-mi-3-ym, 4-methoxyindol-3-ylmethyl; 4-msb,
4-methylsulﬁnylbutyl; and 5-msp, 5-methysulﬁnylpentyl. The ﬁgures have been modiﬁed fromref. 21.
c The Royal Society of Chemistry 2008
This journal is 

the total protein content, proteins having
a low S content increase in abundance
during S-deﬁciency, while those having ahigh S content decrease in abundance. In
order to elucidate the changes occurringin the composition of SSPs during
S-deﬁciency in detail, Higashi et al.30 conducted proteome analysis on Arabidopsis
seeds. In addition to the expected peptides that included all the amino acidresidues encoded by the genes, many
truncated versions were observed underboth the S-suﬃcient control condition
and the S-deﬁciency condition. The
C- and N-terminal residues of all theobserved SSPs were determined by performing extensive MS analysis, and theresults indicated successive C-terminal
degradation of 12S globulins from particular residues.30 In addition to the expected hyperaccumulation of proteins
having a low S content, the accumulation
proﬁles of these truncated peptides werealtered under the S-deﬁciency condition.
Integration of this analysis with transcriptome analysis suggested that multilevel regulation of SSP gene expression,
particularly post-translational modiﬁcations, enables the plant to respond eﬃciently to S-deﬁciency.
The content of S-containing aminoacids in SSPs determines the quality ofseeds used for feed and food. In order toimprove the S content in seeds, a transgene encoding an S-rich protein under thecontrol of a seed-speciﬁc promoter was
introduced into legumes, canola and cereal plants (ref. 31–34 and the references
therein). In the case of transgenic lupins
and chickpeas, the methionine content inseeds increased concomitantly with theaccumulation of foreign protein.31,33 On
the other hand, transgenic rice grains exhibited little change in the total S aminoacid content regardless of foreign S-richprotein accumulation.32,34 In all cases, achange in the composition of endogenous
SSPs, but not in total S content, wasobserved. This change was assumed tobe mediated by a signal transduction pathway that normally modulates SSP composition in response to environmental
ﬂuctuations in S availability, via bothtranscriptional and post-transcriptional
control of gene expression.32 A proteomics approach enabled detailed analysisof the change in SSP composition in rice.34
These transgenic studies can be considered
as good model cases of integrated -omics
Mol. BioSyst., 2008, 4, 967–973 | 971

designed to reveal the complicated mechanism of S homeostasis, involving
sink/source relationships, in seeds.

Future perspectives andconclusion
In plant science, pioneering studies involving the integration of metabolomics
and transcriptomics have been conducted in an attempt to understand themechanisms of systemic S metabolism
and regulation. Future improvements inmetabolomics technology and metabolite
databases will enable us to identify moremetabolites detected using techniquessuch as mass spectroscopy and nuclear
magnetic resonance, and hence to understand metabolism more clearly by considering metabolomic data along withmetabolic maps. In addition, enhancement of public metabolome databases
will contribute to a systematic and comprehensive understanding of metabolism
as public transcriptome databases havealready done (ref. 19 and the references
therein).
The strategies introduced in this report
may be applicable to other studies not
only on sulfur metabolism in plants but
also on other pathways in various organisms.19 Since metabolism is a basic process occurring in life, plants have evolveda robust metabolic system to protect
themselves against environmental andinternal perturbations. Obtaining a global understanding of systemic metabolism
isessentialforeﬀective
manipulation of plant production.18 Integrative studies on S metabolism will
make it possible to elucidate the regulatory mechanisms involved in the conversion of sulfate to primary and secondary
S-containing metabolites in response toenvironmental changes; this will enable
the production of plants having valuable
functions, such as cancer-preventing vegetables possessing a high GSL content
and highly nutritious grains with higher
S amino acid contents.

References
1 K. Saito, Plant Physiol., 2004, 136,
2443–2450.
2 V. Nikiforova, B. Gakière, S. Kempa, M.
Adamik, L. Willmitzer, H. Hesse and R.
Hoefgen, J. Exp. Bot., 2004, 55,
1861–1870.

972 | Mol. BioSyst., 2008, 4, 967–973

3 L. M. Hicks, R. E. Cahoon, E. R. Bonner,
R. S. Rivard, J. Sheﬃeld and J. M. Jez,
Plant Cell, 2007, 19, 2653–2661.
4 T. Rausch, R. Gromes, V. Liedschulte, I.
Müller, J. Bogs, V. Galovic and A. Wachter,
Plant Biol. (Stuttgart, Ger.), 2007, 9,
565–572.
5 H. Onouchi, Y. Nagami, Y. Haraguchi,
M. Nakamoto, Y. Nishimura, R. Sakurai,
N. Nagao, D. Kawasaki, Y. Kadokura
and S. Naito, Genes Dev., 2005, 19,
1799–1810.
6 The Arabidopsis Initiative, Nature, 2000,
408, 796–815.
7 Q. Feng, Y. Zhang, P. Hao, S. Wang, G.
Fu, Y. Huang, Y. Li, J. Zhu, Y. Liu, X.
Hu, P. Jia, Y. Zhang, Q. Zhao, K. Ying, S.
Yu, Y. Tang, Q. Weng, L. Zhang, Y. Lu, J.
Mu, Y. Lu, L. S. Zhang, Z. Yu, D. Fan, X.
Liu, T. Lu, C. Li, Y. Wu, T. Sun, H. Lei,
T. Li, H. Hu, J. Guan, M. Wu, R. Zhang,
B. Zhou, Z. Chen, L. Chen, Z. Jin,
R. Wang, H. Yin, Z. Cai, S. Ren, G. Lv,
W. Gu, G. Zhu, Y. Tu, J. Jia, Y. Zhang,
J. Chen, H. Kang, X. Chen, C. Shao, Y.
Sun, Q. Hu, X. Zhang, W. Zhang, L.
Wang, C. Ding, H. Sheng, J. Gu, S. Chen,
L. Ni, F. Zhu, W. Chen, L. Lan, Y. Lai,
Z. Cheng, M. Gu, J. Jiang, J. Li, G. Hong,
Y. Xue and B. Han, Nature, 2002, 420,
316–320.
8 T. Sasaki, T. Matsumoto, K. Yamamoto,
K. Sakata, T. Baba, Y. Katayose, J. Wu,
Y. Niimura, Z. Cheng, Y. Nagamura, B.
A. Antonio, H. Kanamori, S. Hosokawa,
M. Masukawa, K. Arikawa, Y. Chiden,
M. Hayashi, M. Okamoto, T. Ando, H.
Aoki, K. Arita, M. Hamada, C. Harada,
S. Hijishita, M. Honda, Y. Ichikawa, A.
Idonuma, M. Iijima, M. Ikeda, M. Ikeno,
S. Ito, T. Ito, Y. Ito, Y. Ito, A. Iwabuchi,
K. Kamiya, W. Karasawa, S. Katagiri, A.
Kikuta, N. Kobayashi, I. Kono, K.
Machita, T. Maehara, H. Mizuno, T. Mizubayashi, Y. Mukai, H. Nagasaki, M.
Nakashima, Y. Nakama, Y. Nakamichi,
M. Nakamura, N. Namiki, M. Negishi, I.
Ohta, N. Ono, S. Saji, K. Sakai, M.
Shibata, T. Shimokawa, A. Shomura, J.
Song, Y. Takazaki, K. Terasawa, K. Tsuji,
K. Waki, H. Yamagata, H. Yamane, S.
Yoshiki, R. Yoshihara, K. Yukawa, H.
Zhong, H. Iwama, T. Endo, H. Ito, J. H.
Hahn, H.-I. Kim, M.-Y. Eun, M. Yano, J.
Jiang and T. Gojobori, Nature, 2002, 420,
312–316.
9 S. A. Goﬀ, D. Ricke, T.-H. Lan, G.
Presting, R. Wang, M. Dunn, J.
Glazebrook, A. Sessions, P. Oeller, H.
Varma, D. Hadley, D. Hutchison, C.
Martin, F. Katagiri, B. M. Lange, T.
Moughamer, Y. Xia, P. Budworth, J.
Zhong, T. Miguel, U. Paszkowski, S.
Zhang, M. Colbert, W.-l. Sun, L. Chen,
B. Cooper, S. Park, T. C. Wood, L. Mao,
P. Quail, R. Wing, R. Dean, Y. Yu, A.
Zharkikh, R. Shen, S. Sahasrabudhe, A.
Thomas, R. Cannings, A. Gutin, D. Pruss,
J. Reid, S. Tavtigian, J. Mitchell, G.
Eldredge, T. Scholl, R. M. Miller, S.
Bhatnagar, N. Adey, T. Rubano, N.
Tusneem, R. Robinson, J. Feldhaus, T.
Macalma, A. Oliphant and S. Briggs,
Science, 2002, 296, 92–100.

10 J. Yu, S. Hu, J. Wang, G. K.-S. Wong, S.
Li, B. Liu, Y. Deng, L. Dai, Y. Zhou, X.
Zhang, M. Cao, J. Liu, J. Sun, J. Tang, Y.
Chen, X. Huang, W. Lin, C. Ye, W. Tong,
L. Cong, J. Geng, Y. Han, L. Li, W. Li, G.
Hu, X. Huang, W. Li, J. Li, Z. Liu, L. Li,
J. Liu, Q. Qi, J. Liu, L. Li, T. Li, X. Wang,
H. Lu, T. Wu, M. Zhu, P. Ni, H. Han, W.
Dong, X. Ren, X. Feng, P. Cui, X. Li, H.
Wang, X. Xu, W. Zhai, Z. Xu, J. Zhang, S.
He, J. Zhang, J. Xu, K. Zhang, X. Zheng,
J. Dong, W. Zeng, L. Tao, J. Ye, J. Tan,
X. Ren, X. Chen, J. He, D. Liu, W. Tian,
C. Tian, H. Xia, Q. Bao, G. Li, H. Gao, T.
Cao, J. Wang, W. Zhao, P. Li, W. Chen,
X. Wang, Y. Zhang, J. Hu, J. Wang, S.
Liu, J. Yang, G. Zhang, Y. Xiong, Z. Li,
L. Mao, C. Zhou, Z. Zhu, R. Chen,
B. Hao, W. Zheng, S. Chen, W. Guo, G.
Li, S. Liu, M. Tao, J. Wang, L. Zhu, L.
Yuan and H. Yang, Science, 2002, 296,
79–92.
11 M. Y. Hirai, T. Fujiwara, M. Awazuhara,
T. Kimura, M. Noji and K. Saito, Plant J.,
2003, 33, 651–663.
12 M. Y. Hirai, M. Yano, D. B. Goodenowe,
S. Kanaya, T. Kimura, M. Awazuhara,
M. Arita, T. Fujiwara and K. Saito, Proc.
Natl. Acad. Sci. U. S. A., 2004, 101,
10205–10210.
13 A. Maruyama-Nakashita, E. Inoue, A.
Watanabe-Takahashi, T. Yamaya and H.
Takahashi, Plant Physiol., 2003, 132,
597–605.
14 V. Nikiforova, J. Freitag, S. Kempa, M.
Adamik, H. Hesse and R. Hoefgen, Plant
J., 2003, 33, 633–650.
15 M. Y. Hirai and K. Saito, J. Exp. Bot.,
2004, 55, 1871–1879.
16 Y. Sasaki-Sekimoto, N. Taki, T. Obayashi,
M. Aono, F. Matsumoto, N. Sakurai, H.
Suzuki, M. Y. Hirai, M. Noji, K. Saito, T.
Masuda, K. Takamiya, D. Shibata and H.
Ohta, Plant J., 2005, 44, 653–668.
17 R. J. Bino, R. D. Hall, O. Fiehn, J. Kopka,
K. Saito, J. Draper, B. J. Nikolau, P.
Mendes, U. Roessner-Tunali, M. H. Beale,
R. N. Trethewey, B. M. Lange, E. S.
Wurtele and L. W. Sumner, Trends Plant
Sci., 2004, 9, 418–425.
18 K. M. Oksman-Caldentey and K. Saito,
Curr. Opin. Biotechnol., 2005, 16, 174–179.
19 K. Saito, M. Y. Hirai and K. YonekuraSakakibara, Trends Plant Sci., 2008, 13,
36–43.
20 V. J. Nikiforova, J. Kopka, V. Tolstikov,
O. Fiehn, L. Hopkins, M. J. Hawkesford,
H. Hesse and R. Hoefgen, Plant Physiol.,
2005, 138, 304–318.
21 M. Y. Hirai, M. Klein, Y. Fujikawa, M.
Yano, D. B. Goodenowe, Y. Yamazaki, S.
Kanaya, Y. Nakamura, M. Kitayama,
H. Suzuki, N. Sakurai, D. Shibata, J.
Tokuhisa, M. Reichelt, J. Gershenzon, J.
Papenbrock and K. Saito, J. Biol. Chem.,
2005, 280, 25590–25595.
22 V. J. Nikiforova, C. O. Daub, H. Hesse, L.
Willmitzer and R. Hoefgen, J. Exp. Bot.,
2005, 56, 1887–1896.
23 R. Morioka, S. Kanaya, M. Y. Hirai, M.
Yano, N. Ogasawara and K. Saito, BMC
Bioinf., 2007, 8, 343.
24 T. Abe, S. Kanaya, M. Kinouchi, Y.
Ichiba, T. Kozuki and T. Ikemura, Genome Res., 2003, 13, 693–702.

c The Royal Society of Chemistry 2008
This journal is 

25 S. Kanaya, M. Kinouchi, T. Abe, Y.
Kudo, Y. Yamada, T. Nishi, H. Mori
and T. Ikemura, Gene, 2001, 276, 89–99.
26 J. Szymanski, M. Bielecka, F. Carrari, A.
R. Fernie, R. Hoefgen and V. J. Nikiforova, Phytochemistry, 2007, 68, 2163–2175.
27 M. Y. Hirai, K. Sugiyama, Y. Sawada, T.
Tohge, T. Obayashi, A. Suzuki, R. Araki,
N. Sakurai, H. Suzuki, K. Aoki, H. Goda,
O. I. Nishizawa, D. Shibata and K. Saito,
Proc. Natl. Acad. Sci. U. S. A., 2007, 104,
6478–6483.

28 T. Gigolashvili, M. Engqvist, R. Yatusevich,
C. Muller and U. I. Flugge, New Phytol.,
2007, 177, 627–642.
29 I. E. Sonderby, B. G. Hansen, N.
Bjarnholt, C. Ticconi, B. A. Halkier and
D. J. Kliebenstein, PLoS One, 2007, 2,
e1322.
30 Y. Higashi, M. Y. Hirai, T. Fujiwara, S.
Naito, M. Noji and K. Saito, Plant J.,
2006, 48, 557–571.
31 L. M. Tabe and M. Droux, Plant Physiol.,
2002, 128, 1137–1148.

c The Royal Society of Chemistry 2008
This journal is 

32 N. D. Hagan, N. Upadhyaya, L. M.
Tabe and T. J. V. Higgins, Plant J., 2003,
34, 1–11.
33 P. Chiaiese, N. Ohkama-Ohtsu, L.
Molvig, R. Godfree, H. Dove, C. Hocart,
T. Fujiwara, T. J. V. Higgins andL. M. Tabe, J. Exp. Bot., 2004, 55,
1889–1901.
34 N. Islam, N. M. Upadhyaya, P. M.
Campbell, R. Akhurst, N. Hagan and T.
J. V. Higgins, Phytochemistry, 2005, 66,
2534–2539.

Mol. BioSyst., 2008, 4, 967–973 | 973