Phytochem Rev (2009) 8:15–23
DOI 10.1007/s11101-008-9114-4

A robust omics-based approach for the identificationof glucosinolate biosynthetic genes
Masami Yokota Hirai

Received: 25 March 2008 / Accepted: 10 October 2008 / Published online: 29 November 2008
Ó The Author(s) 2008. This article is published with open access at Springerlink.com

Abstract Transcriptome coexpression analysis,
which is based on a vast amount of transcriptome
data obtained by using DNA arrays, has become aroutine method for functional genomics studies in
Arabidopsis. This analysis enables us to predict thefunction of genes on the basis of a simple assumptionthat a set of genes involved in a particular biologicalprocess can be coexpressed under the control of ashared regulatory system. Candidate genes involved
in glucosinolate biosynthesis were successfully identified by this approach. In this review, themethodology of coexpression analysis is briefly
described. The advantages and disadvantages of thisanalysis are also discussed in the context of its ability
to predict gene functions involved in glucosinolate
biosynthesis.
Keywords Coexpression  Correlation 
Gene function  Network  Transcriptome Prediction

M. Y. Hirai (&)
RIKEN Plant Science Center, 1-7-22 Suehiro-cho,
Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japane-mail: myhirai@psc.riken.jp
M. Y. Hirai
JST, CREST, 4-1-8 Hon-chou, Kawaguchi, Saitama
332-0012, Japan

Introduction
Over the last few years, transcriptome coexpression
analysis has become a routine method for functional
genomics studies in Arabidopsis. In this analysis we
predict the function of genes on the basis of a simple
assumption that a set of genes involved in aparticular biological process can be coexpressed
under the control of a shared regulatory system. In
other words, if a gene of an unknown function is
coexpressed with a set of genes involved in aparticular biological process, it can be assumed to
be one of the components of the same biologicalprocess. The development of comprehensive methods
to measure mRNA accumulation such as DNA arrayand bioinformatics tools for handling large-scale
datasets has enabled transcriptome coexpression
analysis based on hundreds of transcriptome data.
Individual biologists perform transcriptome analysis
using DNA arrays to find answers to specificquestions, such as how gene expression patternschange in plants under specific conditions of interest.
DNA array data thus obtained are deposited in public
databases. On the other hand, DNA array data has
been systematically acquired by the AtGenExpress(Goda et al. 2008; Kilian et al. 2007; Schmid et al.
2005) and NASCArrays (Craigon et al. 2004) byusing the same analytical platform, i.e., Affymetrix
GeneChip microarray. This led to the development of
secondary databases equipped with web-based coexpression analysis tools that help in calculating and

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storing the information regarding the level of similarity of gene expression patterns, and thisinformation is made available to users.
We have analyzed the transcriptome of nutrientstarved Arabidopsis since 2001. During the datamining of the in-house dataset obtained in our lab,
we found that coexpression analysis is a powerful
technique to identify candidate genes involved inglucosinolate (GSL) biosynthesis. More recently, as
mentioned above, the development of web-based
analytical tools has enhanced the predicting powerof transcriptome coexpression analysis. In thisreview I describe briefly the methodology of coexpression analysis and discuss its advantages anddisadvantages of this analysis in the context of its
ability to predict gene functions involved in GSL
biosynthesis. For a general review of coexpression
analysis and network representation of coexpression
relationship, please refer to other reviews (Aokiet al. 2007; Saito et al. 2008). In order to avoid any
overlap with other reviews in this special issue, I
have not discussed the details of the characterizationof gene functions.

A brief overview of coexpression analyses
In coexpression analysis the degree of similarity ofgene expression patterns across a variety of experimental conditions is evaluated by calculating thesimilarity between pairs of genes using statisticalmeasures such as Pearson’s correlation coefficient(PCC). Both in-house datasets and publicly available
datasets can be utilized for calculation of similarity,
although the results obtained would differ. In-housetranscriptome data are often obtained under specificcondition of interest (e.g. sulfur-starvation condition
in my study), and hence higher similarity of expression pattern indicates that the coexpression
relationship occurs only under the specific conditionof interest (e.g. coexpression under sulfur-starvation),that is, condition-dependent coexpression relationship. In contrast, thousands of transcriptome data
available in the public databases have been obtained
under a wide range of experimental conditions, andhence the higher similarity coefficient calculated on
the basis of publicly available dataset indicates aconstitutive or condition-independent coexpression
relationship; that is, a set of genes with higher

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Phytochem Rev (2009) 8:15–23

similarity are coexpressed across a variety of experimental conditions. Many coexpression analysis tools
have been recently released, such as ATTED-II (theArabidopsis thaliana trans-factor and cis-element
prediction database) (Obayashi et al. 2007), CSB.DB(the Comprehensive Systems-Biology Database)(Steinhauser et al. 2004), BAR (the Botany Array
Resource) (Toufighi et al. 2005), ACT (the Arabidopsis Co-expression Tool) (Jen et al. 2006; Manfield
et al. 2006), Genevestigator (Zimmermann et al.
2005; Zimmermann et al. 2004), PED (Plant Gene
Expression Database) (Horan et al. 2008) and CressExpress (Srinivasasainagendra et al. 2008). Some ofthese provide users the option of calculating thedegree of similarity for a dataset. When a wholedataset (e.g. all data obtained by AtGenExpress) is
selected for analysis, the constitutive coexpression
relationship is elucidated. However, by selecting a
subset of dataset (e.g. developmental-series, stressseries, or hormone-treatment-series data of AtGenExpress), condition-dependent, or context-specificcoexpression relationship can be determined, as is
the case with the coexpression analysis using inhouse datasets.
Coexpression analysis has several advantages in
predicting gene functions: (1) Researchers do notneed to conduct ‘‘wet’’ experiments in order topredict the function of unknown genes of interest.
The coexpression relationship of these genes withgenes of known function, as well as the sequencesimilarity between the 2 sets of genes, will provide
clues to predict gene function. (2) Researchers canidentify the components involved in a particularbiological process. Apparently, however, coexpression analysis does work for this purpose only when acomplete biological process is coordinately regulated
at the level of mRNA accumulation. (3) Even if theknockout of genes belonging to a gene family, themembers of which have unknown biological function, fails to reveal any apparent phenotype, thefunction of these genes can be predicted on the basisof their coexpression relationship with other genes(Rautengarten et al. 2005). (4) Coexpression analysis
can even be conducted using a non-targeted approachwithout any preexisting hypothesis. In other words,
coexpression relationship can often be determinedfrom a set of transcriptome data irrespective of theoriginal purpose of the experiments by which the data
were obtained.

Phytochem Rev (2009) 8:15–23

Prediction of the genes involved in glucosinolate
biosynthesis – a case study of coexpression analysis
In this section I briefly describe the transcriptome
analysis of nutrient-starved Arabidopsis conducted in
our lab. As mentioned above, during the course of ourstudy, we realized that coexpression analysis isconsiderably useful for identifying candidate genes
involved in GSL biosynthesis.
In order to understand the plant’s response to sulfur
deficiency by omics-based approach, we conducted an
integrated analysis of the transcriptome and metabolome of sulfur-starved Arabidopsis (Hirai and Saito
2008; Hirai et al. 2004, 2005). Time-series data for thetranscriptome and the metabolome of leaves and roots
were obtained, and analyzed by batch-learning selforganizing mapping (BL-SOM), a sophisticated formof multivariate analysis (Abe et al. 2003; Kanaya et al.
2001). BL-SOM, along with other clustering algorithms such as k-means and hierarchical clustering,can be used for co-occurrence analysis of genes andmetabolites. When BL-SOM is applied to transcriptome and/or metabolome data, the genes and/or
metabolites can be classified into the cells on a2-dimensional lattice called a feature map on the basisof the similarity of expression and/or accumulation
patterns. In this analysis, we defined a set ofco-occurring genes and/or metabolites as a cluster.
We identified many clusters, for example, a set of thegenes involved in anthocyanin biosynthesis and a setof those involved in sulfate assimilation (Hirai et al.
2005). Several Met- and Trp-derived GSLs wereclassified into a single cluster, suggesting that GSL
metabolism is coordinately regulated under sulfur
deficiency. This idea was supported by the finding thatthe known GSL biosynthetic genes—the MAM (methylthioalkylmalate synthase), CYP79 and CYP83
families, SUR1 and AOP2—were classified intoanother single cluster. This indicated that GSL
biosynthetic genes are coexpressed under sulfur deficiency probably via a shared regulatory mechanism.
On the basis of the coexpression relationship with thepreviously-characterized genes mentioned above, we
identified the following genes as candidates involvedin GSL biosynthesis: three putative sulfotransferase
genes (AtSOT16/At1g74100, AtSOT17/At1g18590,
and AtSOT18/At1g74090), an S-glucosyltransferase
gene (UGT74B1/At1g24100), a putative Tyr aminotransferase gene (At5g36160), and two putative

17

glutathione S-transferase (GST) genes (GSTF11/
At3g03190 and GSTU20/At1g78370) (Hirai et al.
2005). To date, some of these candidate genes havebeen characterized experimentally. The predictedfunctions of the AtSOTs and UGT74B1 have been
confirmed by concurrent studies (Hirai et al. 2005;
Piotrowski et al. 2004; Douglas Grubb et al. 2004).
In the same analysis using in-house dataset, we
identified several genes encoding transcription factors, including Myb28 (At5g61420) and Myb29(At5g07690), as the candidate positive regulators of
GSL biosynthesis. We also analyzed constitutive
coexpression relationship by ATTED-II (Obayashi
et al. 2007) using a whole dataset of AtGenExpress(1,388 ATH1 arrays), and found that Myb28 andMyb29 were coexpressed only with the genes
involved in Met-derived GSL biosynthesis. The
known Met-derived GSL genes were highly coexpressed with Myb28, but to a lesser extent with
Myb29. This analysis suggested that Myb28 andMyb29 may be transcription factors positively regulating Met-derived GSL biosynthesis, but not Trpderived GSL biosynthesis. Reverse-genetic andmolecular 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 inmethyl 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 ofMet-derived GSL biosynthesis and that Myb51(At1g18570) and Myb122 (At1g74080), as well as
previously-characterized Myb34 (At5g60890), are thepositive regulators of Trp-derived GSL biosynthesis(Beekwilder et al. 2008; Gigolashvili et al. 2007a-c;
Sonderby et al. 2007; Malitsky et al. 2008). Theseauthors have discussed the specific functions ofindividual Mybs, the mutual regulation among these
Mybs and the mutual regulation between Met- andTrp-derived GSL pathways (see other reviews in thisissue).
In our analysis, AtBCAT-3 (At3g49680) and AtBCAT-4 (At3g19710) were also coexpressed with theMet-derived GSL biosynthetic genes of known function (Hirai et al. 2007), suggesting the involvement ofthese genes in Met side-chain elongation. The
function of these genes has recently been confirmed,
and AtBCAT-3 was shown to function in both GSL

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and amino acid biosynthesis (Knill et al. 2008;
Schuster et al. 2006). We also identified othercandidate genes involved in Met-derived GSL biosynthesis, although these predicted functions remain
to be confirmed: AtGSTU20, AtGSTF11, PMSR2(At5g07460), and the homologs of bacterial Leu
biosynthetic genes named AtLeuC1 (At4g13430),
AtLeuD1 (At2g43100), AtLeuD2 (At3g58990), and
AtIMD1 (At5g14200). With regard to the GST genes,
it has been suggested that GST-type enzymes may becomponents of an enzyme complex formed by
CYP83s and C-S lyase (Mikkelsen et al. 2004). The
PMSR2 gene encodes a cytosolic peptide methionine
sulfoxide reductase. Because a null mutation in thisgene resulted in reduced growth in Arabidopsis undershort-day conditions, it was hypothesized that the roleof PMSR2 is to repair oxidized proteins in a short-day
photoperiod (Bechtold et al. 2004). We speculate that
the PMSR2 protein can recognize the methylsulfinyl
moiety of methylsulfinylalkyl GSL as well as that of
peptide methionine sulfoxide, and that hence, thisenzyme may have some function in the side-chain
conversion of Met-GSLs, although FMOGS-OX has
been shown to be responsible for the conversion of
methylthioalkyl GSLs to methylsulfinylalkyl GSLs
(Hansen et al. 2007). We assumed that the homologsof Leu biosynthetic genes are involved in Met sidechain elongation for the following reason. The
reactions involved in Met side-chain elongation are
similar to those involved in Leu biosynthesis; moreover, the enzymes involved in Met side-chain
elongation and Leu biosynthesis are presumably
encoded by homologous genes belonging to the samegene families. In fact, MAM genes and IPMS
(isopropylmalate synthase) genes, which are responsible for Met side-chain elongation and Leu
synthesis, respectively, share sequence similarity
with each other and with bacterial IPMS (de Kraker
et al. 2007; Field et al. 2004; Kroymann et al. 2001).
All of the above-mentioned candidate genes areunder the transcriptional regulation involving Myb28(Hirai et al. 2007). In addition, UGT74C1(At2g31790), which is assumed to be involved in
Met-derived GSL biosynthesis on the basis of thecoexpression analysis (Gachon et al. 2005), is positively regulated by Myb28 (Hirai et al. 2007). On theother hand, a putative Tyr aminotransferase gene
mentioned above is not regulated by Myb28 (Hirai
et al. 2007), suggesting that it may encode a C-S

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Phytochem Rev (2009) 8:15–23

lyase involved only in Trp-/Phe-derived GSL biosynthesis. The reason for this assumption was that the
C-S lyase gene SUR1 had been originally misannotated as a Tyr aminotransferase. Another possibility isthat this gene may encode a Phe aminotransferase.
Arabidopsis ecotype Columbia contains 2-phenylethyl GSL derived from homoPhe. If homoPhe isformed from Phe via a reaction mechanism similar tothat involved in the formation of homoMet from Met,
Phe must be transaminated by an aminotransferase
prior to condensation with acetyl-CoA for the side
chain to extend.

The advantages and limitations of coexpression
analysis for glucosinolate biosynthetic genes
As described above, the coexpression analysis could
predict many, although not all, of the genes involvedin the biosynthesis of GSLs, especially Met-derived
GSLs. This implies that the genes responsible for
Met-derived GSL biosynthetic pathway (side-chain
elongation, core structure formation, and side-chain
modification) may be coordinately controlled by alimited number of regulatory components including
Myb28, Myb29, and Myb76, at the mRNA accumulation level. Coexpression analysis could also
effectively predict the candidate genes involved in
the other secondary pathways, such as flavonoid andanthocyanin biosynthesis (Tohge et al. 2005; Vanderauwera et al. 2005; Yonekura-Sakakibara et al.
2007).
Quantitative trait locus (QTL) analysis is a powerful tool for identifying candidate genes involved in
GSL biosynthesis as well as those involved inhydrolysis, for example, ESM1 (Epithiospecifier modifier 1, At3g14210; Zhang et al. 2006). ATTED-II
analysis using a whole dataset showed weak correlation between ESM1 and ESP (Epithiospecifier protein,At1g54040) (data not shown). MAM genes that encode
one of the Met side-chain elongation enzymes werealso identified and characterized on the basis of the
QTL analysis (Field et al. 2004; Textor et al. 2004;
Kroymann et al. 2001, 2003). To my knowledge,
however, some other genes that are involved in Met
side-chain elongation process, namely, MAM-I (coding for methylthioalkylmalate isomerase) and MAM-D(coding for methylthioalkylmalate dehydrogenase)have not been identified by QTL analysis, presumablyPhytochem Rev (2009) 8:15–23

19transcriptional regulator involved in down-regulationof GSL biosynthetic genes under sulfur deficiency,
could never be identified by coexpression analysis,
because SLIM1 itself is not regulated at mRNA
accumulation level under sulfur deficiency (Maruyama-Nakashita et al. 2006). Presumably, SLIM1 may
be post-transcriptionally regulated in response tosulfur deficiency. Among Myb28, Myb29, andMyb34, at least Myb34 was shown to be downregulated via a SLIM1-dependent mechanism in theroots of sulfur-starved Arabidopsis (Maruyama-Nakashita et al. 2006). The other regulators of GSL
metabolism, IQD1 (At3g09710) (Levy et al. 2005),
TFL2 (At5g17690) (Kim et al. 2004), and OBP2
(At1g07640) (Skirycz et al. 2006) did not show any
obvious correlation with the known GSL biosynthetic
genes in an ATTED-II analysis performed using awhole dataset (data not shown).
Figure 1 is a graph (so-called network) thatindicates the coexpression relationship between thecharacterized and candidate GSL biosynthetic genes,
which has been calculated using a whole AtGenExpress dataset. Among 35 query genes (see figure

because natural variation of these genes does not result
in metabolic natural variation. However, coexpression
analysis could distinguish candidate genes i.e., MAM-I
and MAM-D, from the putative Leu biosynthetic genes
among the members of the same gene families(AtLeuCs, AtLeuDs, and AtIMDs) (Hirai et al. 2007).
However, coexpression analysis requires previouslycharacterized genes such as MAMs as ‘‘guide genes’’(Lisso et al. 2005), with which genes of unknown
function are associated depending on whether coexpression relationship occurs. A combination of QTL
analysis and coexpression analysis led to the identification of a flavin-monooxygenase (FMO) gene,
FMOGS-OX, which is responsible for the side-chain
modification of Met-derived GSLs (Hansen et al.
2007).
Although several Myb transcription factors controlling GSL biosynthesis could be predicted bycoexpression analysis, this methodology is not sufficiently versatile to identify all regulatory genes.
While the functions of at least three Mybs–Myb28,
Myb29, and Myb34 could be predicted by coexpression analysis (see Fig. 1), SLIM1, which codes for a

MYB29

PMSR2

AOP2

MYB28

AtBCAT-4
FMOGS-OXCYP79F1/F2AtGSTF11
Myb34

MAM1
AtSOT17

CYP83B1MAM3AtSOT18

CYP79B2

SUR1
AtLeuD2AtSOT16
CY79B3

CYP83A1

UGT74B1

AtLeuD1
AtGSTU20

UGT74C1AtBCAT-3

Fig. 1 A correlation network comprising the known andcandidate GSL biosynthetic genes. Coexpression relationshipwas analyzed by using Correlated Gene Search in PRIMe(Platform for RIKEN Metabolomics, http://prime.psc.riken.jp/)(Akiyama et al. 2008) using the following 35 genes as queries:
Myb28, Myb29, Myb76, AtBCAT-4, AtBCAT-3, MAM1, MAM3,
AtLeuC1, AtLeuD1, AtLeuD2, AtIMD1, CYP79F1, CYP79F2,
CYP83A1, AtGSTU20, AtGSTF11, SUR1, UGT74B1,
UGT74C1, AtSOT17, AtSOT18, FMOGS-OX, AOP2, PMSR2,
MYB34, MYB51, MYB122, CYP79B2, CYP79B3, CYP83B1,
AtGSTU8, AtGSTU3, AtSOT16, putative Tyr aminotransferase,
CYP79A2. We did not include the genes responsible for Met
and Trp biosynthesis into the queries, although some of them

AtLeuC1

are regulated by some Mybs described here. AtGSTU8 and
AtGSTU3 were coexpressed with known GSL biosynthetic
genes under sulfur deficiency (Hirai et al. in press). Parametersetting was as follows: Matrix, All data sets v.3 (1,388 data of
AtGenExpress); Method, interconnection of sets. The correlation data used in PRIMe have been released by ATTED-II. The
gene pairs with PCC greater than 0.50 were selected, and the
network was visualized by BioLayoutJava (Goldovsky et al.
2005). Transcripts from CYP79F1 and CYP79F2 were crosshybridized to the same probe sets on a GeneChip microarrayand hence are indistinguishable. The lengths of the lines
depicted in this type of graph do not have any values

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legend), the pairs of coexpressed genes (threshold
PCC [ 0.5) have been connected by lines. The graphrepresents 2 partially-overlapping modules. The
larger and smaller modules consist mainly of Metand Trp-derived GSL genes, respectively. The genes
specifically involved in Met-derived GSL biosynthesis are not connected directly with those specificallyinvolved in Trp-derived GSL biosynthesis, and viceversa. SUR1 and UGT74B1, the genes involved inboth Met- and Trp-derived GSL biosynthesis (Mikkelsen et al. 2004; Douglas Grubb et al. 2004), are in
the boundary region of two modules. It has beenreported that the preferable substrates of the AtSOT17
product are Met- and Phe-derived GSLs (Klein et al.
2006; Piotrowski et al. 2004). Although graph structure depends on the dataset and the measure ofsimilarity used, it may possibly suggest the functional
relationship of the genes. Myb51 and Myb122,transcriptional regulators of Trp-derived GSL biosynthetic genes (Gigolashvili et al. 2007a), were notconnected to any genes in this analysis (Fig. 1).
However, Myb51 and Myb122 may form a conditiondependent network that can be drawn on the basis of
the calculation using a sub dataset such as stressseries data, because at least Myb51 exhibits an
expression pattern different from that of Myb34 withregards to tissue specificity and response to mechanical stimuli (Gigolashvili et al. 2007a).
Coexpression analysis can be applied to nonmodel Brassicaceae plants by analyzing their transcript profiles using comprehensive techniques suchas cDNA-amplified fragment length polymorphism.
In such a study, only a few previously-characterized
GSL biosynthetic genes are expected, and hence,parallel analysis of their metabolic profile will help
predict candidate genes involved in GSL biosynthesis. Integrated analysis of the transcriptome and themetabolome has led to the elucidation of functions ofvarious other genes in many non-model plants (Saito
et al. 2008).
Conclusions and perspectives
As described in this review, coexpression analysis has
become an easy-to-use tool for functional genomics
studies of Arabidopsis. There is certainly a possibilityof selecting false positives as candidates, which is the
drawback with other genome-wide large-scale

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analyses. To overcome this problem, novel algorithms for coexpression analysis have been reported
in a number of bioinformatics articles and thesealgorithms have been validated by statistical analysis.
However, large-scale analyses only provide clues that
help in forming a hypothesis.. Hence, biologists who
predict gene function by coexpression analysis should
confirm the predicted function by performing wet lab
experiments, regardless of the algorithm used.
In our studies, we identified candidate genes on
the basis of coexpression relationships, and thenselected some genes for further analysis fromamong the candidate genes on the basis of functional annotation. If a gene that is coexpressed withknown GSL biosynthetic genes has a functionalannotation, which is not expected on the basis ofa priori knowledge of the GSL metabolic pathway,
this gene may not be selected for further analysis
since there may be a risk of false-positive results
due to a coexpression relationship without any
functional relationship. However, such a gene might
be a novel, unexpected component of GSL metabolic pathway. I believe that new insights into abiological process can be provided by a nontargeted approach that is independent of a priori
biological knowledge. An interesting study has
recently been reported by Horan et al. (2008), in
which 1,541 genes encoding proteins of unknown
function were systematically associated with functional annotations of tightly coexpressed genes
coding for proteins of known function. This typeof genome-wide non-targeted approach will lead tothe formation of a novel, data-driven hypothesis. In
future, we should utilize large-scale biologicalmethods for understanding a biological processcompletely, while taking into consideration the
drawbacks of the methods (Aoki et al. 2007; Saito
et al. 2008).
Acknowledgements I am grateful to the coauthors of mypapers cited here. This research was supported in part by
CREST of Japan Science and Technology Agency and Grantsin-Aid for Scientific Research from the Japan Society for the
Promotion of Science.
Open Access This article is distributed under the terms of theCreative Commons Attribution Noncommercial License which
permits any noncommercial use, distribution, and reproduction
in any medium, provided the original author(s) and source arecredited.

Phytochem Rev (2009) 8:15–23

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