Phytochemical genomics—a new trend

COPLBI-1064; NO. OF PAGES 8

Phytochemical genomics — a new trendKazuki Saito1,2

Available online at www.sciencedirect.com

Phytochemical genomics is a recently emerging field, which

investigates the genomic basis of the synthesis and function of

phytochemicals (plant metabolites), particularly based on

advanced metabolomics. The chemical diversity of the model

plant Arabidopsis thaliana is larger than previously expected,

and the gene-to-metabolite correlations have been elucidated

mostly by an integrated analysis of transcriptomes and

metabolomes. For example, most genes involved in the

biosynthesis of flavonoids in Arabidopsis have been

characterized by this method. A similar approach has been

applied to the functional genomics for production of

phytochemicals in crops and medicinal plants. Great promise is

seen in metabolic quantitative loci analysis in major crops such

as rice and tomato, and identification of novel genes involved in

the biosynthesis of bioactive specialized metabolites in

medicinal plants.

Addresses1 RIKEN Center for Sustainable Resource Science, 1-7-22, Suehiro-cho,

Tsurumi-ku, Yokohama 230-0045, Japan2 Graduate School of Pharmaceutical Sciences, Chiba University, 1-8-1,

Inohana, Chuo-ku, Chiba 260-8675, Japan

Corresponding author: Saito, Kazuki ([email protected],

[email protected])

Current Opinion in Plant Biology 2013, 16:xx–yy

This review comes from a themed issue on Physiology and

metabolism

Edited by John Browse and Ted Farmer

1369-5266/$ – see front matter, Published by Elsevier Ltd.

http://dx.doi.org/10.1016/j.pbi.2013.04.001

IntroductionAlong with the development of high-throughput massive

DNA sequencing technology and metabolic profiling

technology (metabolomics), a new area called ‘phyto-

chemical genomics’ has emerged. In this new field, the

biosynthetic mechanism and regulation, function and

evolution of plant metabolites (phytochemicals) are

investigated by the systematic integration of genomics

and related ‘-omics’ such as transcriptomics, proteomics

and metabolomics. Testable hypotheses can be generated

by this integrated systems analysis. Subsequently the

hypotheses must be validated by reverse genetics/bio-

chemistry/chemistry for further application by biotech-

nology (Figure 1). The secrets of the origin of huge

chemical diversity of plants, that is, 200 000 to one million

metabolites estimated [1�], can be unveiled by these

Please cite this article in press as: Saito K. Phytochemical genomics — a new trend, Curr Opin P

www.sciencedirect.com

studies. Furthermore, knowledge obtained through the

studies would be the basis for further application of

plants’ function to agriculture, medicine and chemical

industries. Obviously this study was initiated with a few

model plants like Arabidopsis, of which completed gen-

ome sequences are available; however, the studies have

been extended to crops and medicinal plants, in which no

genome sequences are readily available [2]. This article

focuses on the current trends in phytochemical genomics

from the model plant Arabidopsis to crops and medicinal

plants.

Up-to-date technology advance inmetabolomicsMetabolomics is a key component in phytochemical

genomics [3]. One of the major bottlenecks of current

metabolomics is the annotation of metabolite peaks

detected by mass spectrometry (MS) or nuclear magnetic

resonance (NMR). In the last few years, progress has been

made in this annotation strategy, in particular, by com-

putational application as exemplified in [4]. Several data-

bases for plant metabolites and their mass spectra have

recently become available [1,5,6�,7]. Interpretation tools

of mass spectra [8] and NMR [9] for metabolite annota-

tion have also been developed. Annotation of elemental

formulae is achieved by the combination of stable isotope

labeling and an ultra-high resolution MS [10,11]. Along

with the improvement of peak annotation in non-targeted

metabolomics — a wide-target metabolomics technology

based on liquid chromatography (LC)–MS has been

developed for quantifying targeted metabolites in a

high-throughput manner [12]. Multiple reaction monitor-

ing using tandem quadrupole MS is adopted in this

technology to simultaneously monitor hundreds of pre-

defined metabolites. Capillary electrophoresis (CE)-MS

is particularly useful for wide-targeted analysis of ionic

metabolites [13], which are often enriched in central

metabolic pathways (Table 1). Consolidation of data

acquired from multi-MS analytical platforms is also one

of the challenging issues of metabolomics. Some

advances have been made for the bioinformatics of con-

solidation of the data [14] and applied to interpretation of

multi-platform metabolomic data [15].

In terms of spatially resolved metabolomics, single-cell or

single-cell-type analysis has been carried out [16]. The

metabolites of laser-micro-dissected vascular bundle cells

and epidermal cells of Arabidopsis have been analyzed by

gas chromatography (GC)–MS. The metabolome of bar-

ley vacuoles isolated by silicon-oil centrifugation has

been analyzed by comparison with the tonoplast pro-

teome, leading to identification of vacuole-specific

lant Biol (2013), http://dx.doi.org/10.1016/j.pbi.2013.04.001

Current Opinion in Plant Biology 2013, 16:1–8

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http://www.sciencedirect.com/science/journal/13695266

2 Physiology and metabolism

Figure 1

Metabolomics • Metabolite annotation • Change in development,

stress and genotype

Transcriptomics • Deep transcriptome • Co-expression network

Genomics • Phylogeny and evolution • GWAS • Gene cluster

Hypothesis generation bysystems analysis

Functional genomics (hypothesis validation) andbiotech application (synthetic biology)

Data processing, databasing, integration

Reverse genetics/biochemistry/chemistry,mathematical modeling

Current Opinion in Plant Biology

Data-driven systems biology and functional genomics for phytochemicals.


metabolites and correlation of vacuolar metabolite and

tonoplast proteins [17]. However, because these methods

required hundreds of respective cells for detection of

metabolites, they were not exactly ‘single-cell (or orga-

nelle)’ but ‘single-cell (or organelle) type’ analyses. Alter-

natively taking advantage of giant internodal cells of the

alga Chara australis, metabolomic analysis of an actual

single vacuole derived from a single cell has been con-

ducted [18�]. The metabolites in the vacuole and cyto-

plasm (extra vacuole) of a single Chara cell fluctuated

asynchronously under various stress conditions. In

another aspect of spatial resolution of metabolomics,

MS is applied to imaging of metabolite distribution in

plants. The chemical images of flavonoids by laser des-

orption/ionization MS on the surface of wild-type and

mutant (tt7) Arabidopsis flowers suggested the clear spatial

distribution of biosynthetic products along with the gene

expression profile [19].

Functional genomics for synthesis ofphytochemicals in ArabidopsisUsing Arabidopsis is a straightforward way to integrate the

multi-omics data for elucidation of the genomic basis of

biosynthesis of metabolites, since all necessary tools for

omics study were readily available soon after the com-

pletion of genome sequencing in 2000. Although the size

of the Arabidopsis metabolome is still a matter of debate,

there is an estimated 5000 metabolites [20], and at least

2000–3000 reported as detected in Arabidopsis by sum-

marizing recent publications [1�,21��,22�,23�].



Efforts for linking genes’ functions with metabolite

accumulation by non-targeted metabolomic analysis have

been recently attempted. A LC–MS-based approach has

been applied to metabolic analysis of a series of tag-

inserted mutants of gene families of glycosyltransferase

and methyltransferase [24], and generation of datasets of

AtMetExpress development [21��] and ecotype [25].

AtMetExpress datasets (http://prime.psc.riken.jp/lcms/

AtMetExpress/) can be efficiently used for hypothesis

generation regarding gene function by combination with

the developmental gene expression data of AtGenEx-

press [26] or single nucleotide polymorphism data of

ecotypes. Integration analysis can be performed on a

web browser developed for exploring large-scale bio-

logical datasets [27]. Another approach of non-targeted

analysis is by the ‘PlantMetabolomics.org’ project (http://

plantmetabolomics.vrac.iastate.edu/ver2/) [22�,23�]. The

metabolome of Arabidopsis mutants for 140 single knock-

out genes were analyzed by combination of different MS-

based platforms at multiple locations, resulting in detec-

tion of �1000 known and 2020 unknown metabolites.

Statistical tools together with morphological image data of

the mutants in the database allow biological researchers to

more easily generate hypotheses on gene function.

In contrast to non-targeted analysis, compound-group

targeted functional genomics has been developed. Repre-

sentative specialized (secondary) metabolites in Arabi-dopsis are flavonoids, glucosinolates, camalexin, the

compounds derived from phenylpropanoid pathway



http://prime.psc.riken.jp/lcms/AtMetExpress/

http://prime.psc.riken.jp/lcms/AtMetExpress/

http://plantmetabolomics.vrac.iastate.edu/ver2/

http://plantmetabolomics.vrac.iastate.edu/ver2/


Phytochemical genomics Saito 3

Table 1

Recent advances facilitating the progress of phytochemical genomics.

Technology Application Reference

High-throughput DNA sequencing Re-sequencing, SNP, draft genome sequence, deep

transcriptome (RNAseq), gene expression profile

[72��,73,77]

Ultra-high resolution MS Metabolomic annotation and structure elucidation [10,11]

LC-triple quadrupole (Q) MS, capillary

electrophoresis (CE)-MS

Wide-target metabolic profiling based on LC for neutral

metabolites and CE for ionic metabolites

[12,13]

Public data depositories and tool for meta-analysis Hypothesis generation for functional genomics

(AtMetExpress, PlantMetabolomics.org, among others)

[21��,22�]


(coumarins, and sinapoyl esters and amides) and caryo-

phyllene. This chemical diversity is indeed richer than

initially thought. Flavonoids are the best-known plant

specialized products and are classified into several sub-

categories in terms of structure. Arabidopsis accumulate

only flavonols, anthocyanins and proanthocyanidins, com-

prising at least 54 molecules (35 flavonols, 11 anthocya-

nins and eight proanthocyanidins) [28]. Scaffold

structures of these flavonoids are derived from kaemp-

ferol, quercetin and isorhamnetin for flavonols, cyanidin

for anthocyanins and epicatechin for proanthocyanidins.

From the final chemical structures of these flavonoids, the

groups of enzymes responsible for tailoring reactions

including glycosylation, methylation and acylation can

be predicted. Genes encoding these biosynthetic

enzymes have been extensively characterized mostly

by the integration of transcriptomics and metabolic pro-

filing followed by reverse genetic approaches [29]. Inde-

pendent component analysis on transcriptome data was

efficiently used for prediction of the involvement of

glycosyltransferase genes in the pathway [30]. In addition

to UDP-sugar-dependent glycosyltransferases localized

in cytosol, a novel acyl-sugar-dependent glycosyltransfer-

ase plays a role in the final modification of anthocyanins in

the vacuole [31]. For the production of proanthocyanidins

(condensed tannins) in seeds, several genes encoding

enzymes — responsible for branching from the main fla-

vonoid pathway to proanthocyaninds — were character-

ized [32]. Besides biosynthetic enzymes, the network of

transcription factors involved in the regulation of flavo-

noid biosynthesis has been extensively characterized

mostly by genetic analysis of visible mutants of antho-

cyanin or proanthocyanidin accumulation [33,34].

The second major class of specialized metabolites in

Arabidopsis is glucosinolate [35]. Genes for enzymes

[36], transporters [37] and transcription factor [38] have

been characterized by integrated omics approaches. Since

glucosinolates are sulfur-rich compounds, there is a tight

link between primary sulfate assimilation and glucosino-

late biosynthesis in terms of transcriptional network [39]

and metabolic flow by the enzymes [40]. Glutathion

(GSH) was demonstrated as the sulfur donor in the

biosynthesis of glucosinolates, and cytosolic plant g-glu-

tamyl peptidases were involved in the processing of GSH



conjugates in the glucosinolate and camalexin pathways

[41]. Combining genome-wide association analysis and

the transcriptome co-expression network led to the pre-

diction of genes controlling glucosinolates [42]. Two

members of the nitrate/peptide transporter family were

identified as glucosinolate-specific transporters essential

for long-distance translocation of glucosinolates to seeds

[43��].

Metabolomic analysis under stress conditions reveals the

correlation of metabolites and genes responsible for the

synthesis of the particular sets of metabolites. Often the

metabolites changing their levels under the given stress

conditions play roles as mitigating compounds to the

stresses. Under nitrogen deprivation, one of the major

nutrient deficient stresses impacting on metabolism [44],

the production of hemiterpenoid glycosides is induced in

leaves and correlates with the inducible production of

scopolin and coniferin in roots [45�]. Sulfur deficiency also

affects secondary metabolism such as enhanced degra-

dation and attenuated synthesis of glucosinolates [38,39]

and induction of anthocyanin biosynthesis. Under phos-

phorus-deprived stress, a new lipid class, glucuronosyl-

diacylglycerol, was identified as a stress-mitigating

metabolite [46��], indicating the power of lipidomics

for functional genomics in plants [47]. Besides nutrient

deficiency, other abiotic stresses such as drought, cold

[48] and UV B [49�] induce characteristic metabolomic

changes. These studies have been conducted with the

mutants of genes involved in the stress responses, and

thus led to more in-depth understanding of the genomic

basis of stress responses.

Combination of metabolic profiling and genetics in Ara-bidopsis is another way to identify the novel genes

involved in the synthesis of phytochemicals. A large

chemical diversity among Arabidopsis accessions has been

indicated, and 75% of metabolomic signatures have been

assigned to quantitative trait loci (QTLs) by using recom-

binant inbred lines. This study was followed by integ-

ration of transcriptomics and proteomic data to find six

QTL hot spots with major system-wide effects [50].

Focusing more on glucosinolates and flavonoids, gen-

ome-wide association analyses and QTL analysis indi-

cated possible association of genetic loci with metabolic






characters [25,42,51]. Metabolic signature and genetic

loci related to biomass have been investigated using

Arabidopsis heterotic hybrids and large panel accessions

[52,53].

Metabolomic investigation in major cropsSubstantial progress has been made in phytochemical

genomics in major crops in the last several years. In

particular, the advantages of assessments of phenotypic

and metabolic diversity in the genetic basis of complex

traits of crops were well illustrated recently [54].

Rice (Oryza sativa) is not only the first crop whose genome

has been sequenced but also one of the most important

crops feeding the majority of the world’s population. Both

gain-of-function [55] and loss-of-function [56] analyses of

rice genes with metabolomics reveal the novel function of

genes which modulate cellular metabolism. Metabolomic

QTL analysis has been conducted with 87 back-crossed

inbred lines (BILs) of a japonica variety having good flavor

and an indica variety exhibiting high yield, indicating the

presence of 802 QTLs in the rice genome [57��]. Pre-

dictive metabolome–trait regression models have been

successfully constructed based on the metabolomic com-

position of kernels from field-grown rice plants of the core

collections covering �90% of genetic diversity of rice

[58]. Similar study combining multi-platform metabolo-

mics with genome-wide genotyping by single nucleotide

polymorphism of three rice cultivars revealed unique

metabolic signatures relevant to nutrient value, taste

and flavor [59].

Drought-related metabolic changes have been studied in

wheat cultivars differing in tolerance to dehydration [60].

In maize, multi-omics (transcriptomic, proteomic and

metabolomic) studies demonstrated how systematic

responses take place during nitrogen deficiency [61]

and UV B exposure [62]. Tomato (Lycopersicon esculentum)

is not only a model plant for the study of fleshy fruit but

also commercially important as a widely grown vegetable.

Although metabolomic studies on tomato before 2008

were well documented in [63] (and references therein),

a novel pathway for monoterpene [64] and a gene for acyl

sugars [65�] in tomato trichome metabolites were recently

identified. Genetic mapping on soybean accessions led to

the identification of a glycosyltransferase for the biosyn-

thesis of soya-saponins [66�]. Insights on biosynthesis of

saponins and flavonoids in legumes have also been

obtained by the study of Medicago truncatula as a model

plant [67,68]. Another saponin glycosyltransferase was

identified by an activity-based assay against insect feed-

ing from a crucifer plant (Barbarea vulgaris) [69].

Phytochemical genomics in medicinal plantsThe concept for integrated functional genomics, that is,

correlation of co-responding elements in transcriptome

and metabolome, developed in Arabidopsis can be



successfully applied to medicinal plants, in which bioac-

tive specialized metabolites are produced. This is based

on the so-called ‘guilt-by-association’ theory that the co-

expressed gene set is presumably involved in the biosyn-

thesis of metabolites, which are accumulated in a similar

manner to the level of the genes’ expression [70]. Avail-

ability of so-called next generation sequencing technol-

ogy accelerates deep transcriptome studies in medicinal

plants, leading to the more feasible analysis of co-

responses in gene expression and metabolite accumu-

lation.

Co-expression analysis of transcriptome data of Cathar-anthus roseus identified candidate genes involved in mono-

terpenoid indole alkaloid biosynthesis. Subsequently the

functions of these genes’ products have been confirmed

to be cytochrome P450 hydroxylating at the 19-position of

the aspidosperma-type alkaloids [71] and a novel iridoid

synthase reductively converting 10-oxogeranial to iridoid

scaffold [72��]. A database and analytical platform (http://

metnetdb.org/mpmr_public/) for metabolomic and tran-

scriptomic data of 14 medicinal plants are available for

hypothesis development of genes’ function [73]. By a

similar strategy, two cytochrome P450 genes involved in

glycyrrhizin (a sweetener saponin) biosynthesis in Glycyr-rhiza uralensis (licorice) were identified [74��], leading to

the possible microbial production of glycyrrhetinic acid.

By the differential gene expression profiling of chemo-

varieties regarding quinolizidine alkaloids in Lupinusangustifolius, the gene encoding lysine decarboxylase

committed to the first step of the alkaloid biosynthesis

was characterized, and the molecular basis for catalytic

property of the enzyme was elucidated by site-directed

mutagenesis and protein modeling [75��]. Differential

transcriptome analysis together with metabolic profiling

for engineered cultured cells producing or not producing

camptothecin-type alkaloids, identified candidates genes

involved in the biosynthetic pathways of alkaloids and

anthraquinones [76,77]. Transcriptomics of glandular tri-

chomes from female cannabis (Cannabis sativa) flowers,

the primary site of cannabinoid biosynthesis, identified

the enzymes involved in cannabinoid biosynthesis, for

example, a polyketide synthase responsible for the syn-

thesis of olivetolic acid [78�] and an acyl-activating

enzyme [79]. In opium poppy (Papaver somniferum), most

genes involved in morphine biosynthesis have been

identified by omic approaches [80,81]. Furthermore, com-

bining transcriptome and genetic approaches suggested

the presence of a gene cluster for alkaloid synthesis in the

opium poppy genome [82��].

ConclusionsSo far, identification of genes involved in the biosynthesis

of specialized products in plants mostly relies on the co-

response theory of transcripts of biosynthetic enzymes

(and transcription factors) and metabolites synthesized by

the action of these enzymes being analyzed by correlation



http://metnetdb.org/mpmr_public/

http://metnetdb.org/mpmr_public/




networks [83]. This approach is based on a belief that the

biosynthetic genes in plants dispersedly locate in the

genome (unlike microbial genes, which cluster together

on the genome), and the expression of these genes are

coordinately regulated. However, increasing evidence

indicates that the genes of certain biosynthetic pathways

form gene clusters in plant genomes [82��,84��]. This

allows the easier identification of genes and also gives

more in-depth insights on evolution and function of

specialized metabolites. Furthermore, study on self-

resistance mechanisms of plants producing toxic metab-

olites provides clues for further understanding of evol-

ution and function of specialized metabolism [85]. The

recently-emerged cheap and massive DNA sequencing

technology definitely facilitates the completion of gen-

ome sequencing of non-model plants, including medic-

inal plants [86]. Phytochemical genomics study could be

advanced by taking advantage of publicly-available data-

bases for genomics and metabolomics, and natural and

artificial chemo-variants of plants. Unforeseen findings

and applications can be obtained in this new area in the

future.

AcknowledgementsThis work was partly supported by a Grant-in-Aid for Scientific Research onInnovative Areas from the Ministry of Education, Culture, Sports, Scienceand Technology of Japan, and by a Strategic International CollaborativeResearch Program (SICORP) of Japan Science and Technology Agency.

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28. Saito K, Yonekura-Sakakibara K, Nakabayashi R, Higashi Y,Yamazaki M, Tohge T, Fernie AR: The flavonoid biosyntheticpathway in Arabidopsis: structural and genetic diversity. PlantPhysiol Biochem 2013 http://dx.doi.org/10.1016/j.plaphy.2013.02.001.

29. Yonekura-Sakakibara K, Tohge T, Matsuda F, Nakabayashi R,Takayama H, Niida R, Watanabe-Takahashi A, Inoue E, Saito K:Comprehensive flavonol profiling and transcriptomecoexpression analysis leading to decoding gene–metabolitecorrelations in Arabidopsis. Plant Cell 2008, 20:2160-2176.

30. Yonekura-Sakakibara K, Fukushima A, Nakabayashi R, Hanada K,Matsuda F, Sugawara S, Inoue E, Kuromori T, Ito T et al.: Twoglycosyltransferases involved in anthocyanin modificationdelineated by transcriptome independent component analysisin Arabidopsis thaliana. Plant J 2012, 69:154-167.

31. Miyahara T, Sakiyama R, Ozeki Y, Sasaki N: Acyl-glucose-dependent glucosyltransferase catalyzes the final step ofanthocyanin formation in Arabidopsis. J Plant Physiol 2013http://dx.doi.org/10.1016/j.jplph.2012.12.001.

32. Kitamura S, Matsuda F, Tohge T, Yonekura-Sakakibara K,Yamazaki M, Saito K, Narumi I: Metabolic profiling andcytological analysis of proanthocyanidins in immature seedsof Arabidopsis thaliana flavonoid accumulation mutants. PlantJ 2010, 62:549-559.

33. Stracke R, Jahns O, Keck M, Tohge T, Niehaus K, Fernie AR,Weisshaar B: Analysis of production of flavonol glycosides-dependent flavonol glycoside accumulation in Arabidopsisthaliana plants reveals MYB11-MYB12- and MYB111-independent flavonol glycoside accumulation. New Phytol2010, 188:985-1000.

34. Dubos C, Stracke R, Grotewold E, Weisshaar B, Martin C,Lepiniec L: MYB transcription factors in Arabidopsis. TrendsPlant Sci 2010, 15:573-581.



35. Sønderby IE, Geu-Flores F, Halkier BA: Biosynthesis ofglucosinolates — gene discovery and beyond. Trends Plant Sci2010, 15:283-290.

36. de Kraker J-W, Gershenzon J: From amino acid to glucosinolatebiosynthesis: protein sequence changes in the evolution ofmethylthioalkylmalate synthase in Arabidopsis. Plant Cell2011, 23:38-53.

37. Sawada Y, Toyooka K, Kuwahara A, Sakata A, Nagano M, Saito K,Hirai MY: Arabidopsis bile acid:sodium symporter familyprotein 5 is involved in methionine-derived glucosinolatebiosynthesis. Plant Cell Physiol 2009, 50:1579-1586.

38. Hirai MY, Sugiyama K, Sawada Y, Tohge T, Obayashi T, Suzuki A,Araki R, Sakurai N, Suzuki H, Aoki K et al.: Omics-basedidentification of Arabidopsis Myb transcription factorsregulating aliphatic glucosinolate biosynthesis. Proc Natl AcadSci U S A 2007, 104:6478-6483.

39. Maruyama-Nakashita A, Nakamura Y, Tohge T, Saito K,Takahashi H: Arabidopsis SLIM1 is a central transcriptionalregulator of plant sulfur response and metabolism. Plant Cell2006, 18:3235-3251.

40. Mugford SG, Yoshimoto N, Reichelt M, Wirtz M, Hill L, Mugford ST,Nakazato Y, Noji M, Takahashi H et al.: Disruption of adenosine-5-phosphosulfate kinase in Arabidopsis reduces levels ofsulfated secondary metabolites. Plant Cell 2009, 21:910-927.

41. Geu-Flores F, Møldrup ME, Bottcher C, Olsen CE, Scheel D,Halkier BA: Cytosolic g-glutamyl peptidases processglutathione conjugates in the biosynthesis of glucosinolatesand camalexin in Arabidopsis. Plant Cell 2011, 23:2456-2469.

42. Chan EKF, Rowe HC, Corwin JA, Joseph B, Kliebenstein DJ:Combining genome-wide association mapping andtranscriptional networks to identify novel genes controllingglucosinolates in Arabidopsis thaliana. PLoS Biol 2011,9:e1001125.

43.��

Nour-Eldin HH, Andersen TG, Burow M, Madsen SR,Jorgensen ME, Olsen CE, Dreyer I, Hedrich R, Geiger D,Halkier BA: NRT/PTR transporters are essential fortranslocation of glucosinolate defence compounds to seeds.Nature 2012, 488:531-534.

Two members of the nitrate/peptide transporter family were identified ashigh-affinity, proton-dependent glucosinolate-specific transporters. Bothplasma membrane-localized transporters are essential for long-distancetransport of glucosinolates, presumably responsible for loading of glu-cosinolates from the apoplasm into the phloem.

44. Kusano M, Fukushima A, Redestig H, Saito K: Metabolomicapproaches toward understanding nitrogen metabolism inplants. J Exp Bot 2011, 62:1439-1453.

45.�

Ward JL, Baker JM, Llewellyn AM, Hawkins ND, Beale MH:Metabolomic analysis of Arabidopsis reveals hemiterpenoidglycosides as products of a nitrate ion-regulated, carbon fluxoverflow. Proc Natl Acad Sci U S A 2011, 108:10762-10767.

Metabolomic analysis of Arabidopsis using combined NMR-MS identifiedtwo hemiterpenoid glycosides that accumulated in leaf tissue underrepeated nitrate-deficient conditions. The formation of the hemiterpe-noids in leaves was strongly correlated with the induction of the phenyl-propanoids in roots of the same plants, suggesting shunts ofphotosynthetic carbon flow.

46.��

Okazaki Y, Otsuki H, Narisawa T, Kobayashi M, Sawai S,Kamide Y, Kusano M, Aoki T, Hirai MY, Saito K: A new class ofplant lipid is essential for protection against phosphorusdepletion. Nat Commun 2013, 4:1510.

Untargeted lipidomics of Arabidopsis and rice successfully led to thediscovery and elucidation of the biosynthetic pathway of a novel plantlipid class, glucuronosyldiacylglycerol. This new acidic lipid is importantfor protection of plants against phosphorus depletion.

47. Okazaki Y, Shimojima M, Sawada Y, Toyooka K, Narisawa T,Mochida K, Tanaka H, Matsuda F, Hirai A, Hirai MY et al.: Achloroplastic UDP-glucose pyrophosphorylase fromArabidopsis is the committed enzyme for the first step ofsulfolipid biosynthesis. Plant Cell 2009, 21:892-909.

48. Maruyama K, Takeda M, Kidokoro S, Yamada K, Sakuma Y,Urano K, Fujita M, Yoshiwara K, Matsukura S et al.: Metabolicpathways involved in cold acclimation identified by integrated







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analysis of metabolites and transcripts regulated by DREB1Aand DREB2A. Plant Physiol 2009, 150:1972-1980.

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Kusano M, Tohge T, Fukushima A, Kobayashi M, Hayashi N,Otsuki H, Kondou Y, Goto H, Kawashima M et al.: Metabolomicsreveals comprehensive reprogramming involving twoindependent metabolic responses of Arabidopsis to UV-Blight. Plant J 2011, 67:354-369.

Comprehensive metabolomic and transcriptomics analyses of Arabidop-sis mutants with impaired flavonoid or sinapoyl-malate biosynthesisunder UV B stress revealed that short-term responses occur only atthe level of primary metabolites, suggesting that these effectively primethe cell to facilitate the later production of UV-B-absorbing secondarymetabolites.

50. Fu J, Keurentjes JJ, Bouwmeester H, America T, Verstappen FW,Ward JL, Beale MH, de Vos RC, Dijkstra M, Scheltema RA et al.:System-wide molecular evidence for phenotypic buffering inArabidopsis. Nat Genet 2009, 41:166-167.

51. Routaboul JM, Dubos C, Beck G, Marquis C, Bidzinski P,Loudet O, Lepiniec L: Metabolite profiling and quantitativegenetics of natural variation for flavonoids in Arabidopsis. JExp Bot 2012, 63:3749-3764.

52. Meyer RC, Witucka-Wall H, Becher M, Blacha A,Boudichevskaia A, Dormann P, Fiehn O, Friedel S, von Korff M,Lisec J et al.: Heterosis manifestation during early Arabidopsisseedling development is characterized by intermediate geneexpression and enhanced metabolic activity in the hybrids.Plant J 2012, 71:669-683.

53. Sulpice R, Trenkamp S, Steinfath M, Usadel B, Gibon Y, Witucka-Wall H, Pyl E-T, Tschoep H, Steinhauser MC, Guenther M et al.:Network analysis of enzyme activities and metabolite levelsand their relationship to biomass in a large panel ofArabidopsis accessions. Plant Cell 2010, 22:2872-2893.

54. Carreno-Quintero N, Bouwmeester HJ, Keurentjes JJB: Geneticanalysis of metabolome–phenotype interactions: from modelto crop species. Trends Genet 2013, 29:41-50.

55. Albinsky D, Kusano M, Higuchi M, Hayashi N, Kobayashi M,Fukushima A, Mori M, Ichikawa T, Matsui K et al.: Metabolomicscreening applied to rice FOX Arabidopsis lines leads to theidentification of a gene-changing nitrogen metabolism. MolPlant 2010, 3:125-142.

56. Kusano M, Tabuchi M, Fukushima A, Funayama K, Diaz C,Kobayashi M, Hayashi N, Tsuchiya YN, Takahashi H et al.:Metabolomics data reveal a crucial role of cytosolic glutaminesynthetase 1;1 in coordinating metabolic balance in rice. PlantJ 2011, 66:456-466.

57.��

Matsuda F, Okazaki Y, Oikawa A, Kusano M, Nakabayashi R,Kikuchi J, Yonemaru J-I, Ebana K, Yano M et al.: Dissection ofgenotype–phenotype associations in rice grains usingmetabolome quantitative trait loci analysis. Plant J 2012,70:624-636.

This is the first report on metabolomic QTL analysis on rice grains. Themetabolome dataset generated using four MS pipelines consisted of 759metabolite signals from the grains of 85 back-crossed inbred lines of rice.An uneven distribution of 802 metabolomic QTLs on the rice genome withcoordinated control for groups of metabolites such as amino acids, linkedto the QTL hotspot. For flavonoids, the metabolic trait was tightlygoverned by genetic factors that alter the glycosylation of flavones.The genomic region and genes potentially responsible for the biosynth-esis of apigenin-6,8-di-C-a-l-arabinoside are indicated.

58. Redestig H, Kusano M, Ebana K, Kobayashi M, Oikawa A,Okazaki Y, Matsuda F, Arita M, Fujita N et al.: Exploring molecularbackgrounds of quality traits in rice by predictive modelsbased on high-coverage metabolomics. BMC Syst Biol 2011,5:176.

59. Calingacion MN, Boualaphanh C, Daygon VD, Anacleto R,Sackville Hamilton R, Biais B, Deborde C, Maucourt M, Moing A,Mumm R et al.: A genomics and multi-platform metabolomicsapproach to identify new traits of rice quality in traditional andimproved varieties. Metabolomics 2012, 8:771-783.

60. Bowne JB, Erwin TA, Juttner J, Schnurbusch T, Langridge P,Bacic A, Roessner U: Drought responses of leaf tissues fromwheat cultivars of differing drought tolerance at themetabolite level. Mol Plant 2012, 5:418-429.



61. Amiour N, Imbaud S, Clement G, Agier N, Zivy M, Valot B, Balliau T,Armengaud P, Quillere I, Canas R et al.: The use of metabolomicsintegrated with transcriptomic and proteomic studies foridentifying key steps involved in the control of nitrogenmetabolism in crops such as maize. J Exp Bot 2012, 63:5017-5033.

62. Casati P, Campi M, Morrow D, Fernandes J, Walbot V:Transcriptomic, proteomic and metabolomic analysis of UV-Bsignaling in maize. BMC Genomics 2011, 12:321.

63. de Vos RCH, Hall RD, Moing A: Metabolomics of a model fruit:tomato. In Annual Plant Reviews, Biology of Plant Metabolomics,vol. 43. Edited by Hall RD. Blackwell; 2011:109-155.

64. Schilmiller AL, Schauvinhold I, Larson M, Xu R, Charbonneau AL,Schmidt A, Wilkerson C, Last RL, Pichersky E: Monoterpenes inthe glandular trichomes of tomato are synthesized from aneryl diphosphate precursor rather than geranyl diphosphate.Proc Natl Acad Sci U S A 2009, 106:10865-10870.

65.�

Schilmiller AL, Charbonneau AL, Last RL: Identification of aBAHD acetyltransferase that produces protective acyl sugarsin tomato trichomes. Proc Natl Acad Sci U S A 2012, 109:16377-16382.

Glandular trichomes on the surface of tomato produce O-acyl sugars thatcontribute to insect resistance. The authors identified an acetyl-CoA-dependent acyltransferase enzyme capable of acyl sucrose acetylationby the genetic analysis of introgression lines differing in accumulation ofacetylated tetra-acyl sucroses.

66.�

Sayama T, Ono E, Takagi K, Takada Y, Horikawa M, Nakamoto Y,Hirose A, Sasama H, Ohashi M, Hasegawa H et al.: The Sg-1glycosyltransferase locus regulates structural diversity oftriterpenoid saponins of soybean. Plant Cell 2012, 24:2123-2138.

Genetic and mapping analysis indicated the glycosylation of triterpenesaponins of soybean was determined by a single locus, leading to theidentification of a UDP-sugar-dependent glycosyltransferase gene.

67. Naoumkina MA, Modolo LV, Huhman DV, Urbanczyk-Wochniak E,Tang Y, Sumner LW, Dixon RA: Genomic and coexpressionanalyses predict multiple genes involved in triterpene saponinbiosynthesis in Medicago truncatula. Plant Cell 2010, 22:850-866.

68. Zhao J, Huhman D, Shadle G, He X-Z, Sumner LW, Tang Y,Dixon RA: MATE2 mediates vacuolar sequestration offlavonoid glycosides and glycoside malonates in Medicagotruncatula. Plant Cell 2011, 23:1536-1555.

69. Augustin JM, Drok S, Shinoda T, Sanmiya K, Nielsen JK,Khakimov B, Olsen CE, Hansen EH, Kuzina V, Ekstrøm CT et al.:UDP-glycosyltransferases from the UGT73C subfamily inBarbarea vulgaris catalyze sapogenin 3-O-glucosylation insaponin-mediated insect resistance. Plant Physiol 2012,160:1881-1895.

70. Yonekura-Sakakibara K, Fukushima A, Saito K: Transcriptomedata modeling for targeted plant metabolic engineering. CurrOpin Biotechnol 2012 http://dx.doi.org/10.1016/j.copbio.2012.10.018.

71. Giddings LA, Liscombe DK, Hamilton JP, Childs KL, DellaPenna D,Buell CR, O’Connor SE: A stereoselective hydroxylation step ofalkaloid biosynthesis by a unique cytochrome P450 inCatharanthus roseus. J Biol Chem 2011, 286:16751-16757.

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Geu-Flores F, Sherden NH, Courdavault V, Burlat V, Glenn WS,Wu C, Nims E, Cui Y, O’Connor SE: An alternative route to cyclicterpenes by reductive cyclization in iridoid biosynthesis.Nature 2012, 492:138-142.

This paper reports the discovery of a novel iridoid synthase, whichgenerates the iridoid ring scaffold from 10-oxogeranial as substrate,coupling with an initial NAD(P)H-dependent reduction step with a sub-sequent cyclization step.

73. Wurtele E, Chappell J, Jones A, Celiz M, Ransom N, Hur M,Rizshsky L, Crispin M, Dixon P, Liu J et al.: Medicinal plants: apublic resource for metabolomics and hypothesisdevelopment. Metabolites 2012, 2:1031-1059.

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Seki H, Sawai S, Ohyama K, Mizutani M, Ohnishi T, Sudo H,Fukushima EO, Akashi T, Aoki T, Saito K et al.: Triterpenefunctional genomics in licorice for identification of CYP72A154






































































































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involved in the biosynthesis of glycyrrhizin. Plant Cell 2011,23:4112-4123.

This paper describes the identification of the second cytochromeP450 gene committed in the synthesis of glycyrrhizin, a naturalsweetener triterpene saponin from licorice, by an omics approach.Identification of two P450 genes, the first one reported by the sameauthors in 2008, led to the heterologous production of glycyrrhetinicacid in yeast.

75.��

Bunsupa S, Katayama K, Ikeura E, Oikawa A, Toyooka K, Saito K,Yamazaki M: Lysine decarboxylase catalyzes the first step ofquinolizidine alkaloid biosynthesis and coevolved withalkaloid production in Leguminosae. Plant Cell 2012, 24:1202-1216.

A gene encoding the first committed enzyme, lysine decarboxylase, in thebiosynthesis of quinolizidine alkaloids was identified by differential geneexpression profiling of alkaloid-accumulating ‘bitter’ and alkaloid-free‘sweet’ varieties of Lupinus. Structural and evolutionary basis for pro-miscuity of substrate specificity was elucidated.

76. Asano T, Kobayashi K, Kashihara E, Sudo H, Sasaki R, Iijima Y,Aoki K, Shibata D, Saito K, Yamazaki M: Suppression ofcamptothecin biosynthetic genes results in metabolicmodification of secondary products in hairy roots ofOphiorrhiza pumila. Phytochemistry 2012 http://dx.doi.org/10.1016/j.phytochem.2012.04.019.

77. Yamazaki M, Mochida K, Asano T, Nakabayashi R, Chiba M,Udomson N, Yamazaki Y, Goodenowe DB, Sankawa U, Yoshida Tet al.: Coupling deep transcriptome analysis with untargetedmetabolic profiling in Ophiorrhiza pumila to further theunderstanding of the biosynthesis of the anti-cancer alkaloidcamptothecin and anthraquinones. Plant Cell Physiol 2013http://dx.doi.org/10.1093/pcp/pct040.

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Gagne SJ, Stout JM, Liu E, Boubakir Z, Clark SM, Page JE:Identification of olivetolic acid cyclase from Cannabis sativareveals a unique catalytic route to plant polyketides. Proc NatlAcad Sci U S A 2012, 109:12811-12816.

By the transcriptome analysis of Cannabis sativa (marijuana), the authorsidentified a polyketide cyclase, which catalyzes an intramolecular aldolcondensation with carboxylate retention to form olivetolic acid. Theprotein is structurally similar to polyketide cyclases from Streptomycesspecies, demonstrating unexpected evolutionary parallels between poly-ketide biosynthesis in plants and bacteria.



79. Stout JM, Boubakir Z, Ambrose SJ, Purves RW, Page JE: Thehexanoyl-CoA precursor for cannabinoid biosynthesis isformed by an acyl-activating enzyme in Cannabis sativatrichomes. Plant J 2012, 71:353-365.

80. Gesell A, Rolf M, Ziegler J, Diaz Chavez ML, Huang FC,Kutchan TM: CYP719B1 is salutaridine synthase, the C-Cphenol-coupling enzyme of morphine biosynthesis in opiumpoppy. J Biol Chem 2009, 284:24432-24442.

81. Hagel JM, Facchini PJ: Dioxygenases catalyze the O-demethylation steps of morphine biosynthesis in opiumpoppy. Nat Chem Biol 2010, 6:273-275.

82.��

Winzer T, Gazda V, He Z, Kaminski F, Kern M, Larson TR, Li Y,Meade F, Teodor R, Vaistij FE et al.: A Papaver somniferum 10-gene cluster for synthesis of the anticancer alkaloidnoscapine. Science 2012, 336:1704-1708.

By transcriptomic and partial genomic analysis of a chemo-variety ofopium poppy indicated that a set of biosynthetic genes exists as acomplex gene cluster, allowing gene function to be linked to noscapinesynthesis.

83. Fukushima A, Kusano M, Redestig H, Arita M, Saito K: Integratedomics approaches in plant systems biology. Curr Opin ChemBiol 2009, 13:532-538.

84.��

Field B, Fiston-Lavier A-S, Kemen A, Geisler K, Quesneville H,Osbourn AE: Formation of plant metabolic gene clusters withindynamic chromosomal regions. Proc Natl Acad Sci U S A 2011,108:16116-16121.

In addition to a previously identified operon-like gene cluster required forthe synthesis and modification of the triterpene thalianol, the authorscharacterize a second operon-like triterpene cluster (the marneral cluster)from A. thaliana, concluding that common mechanisms are likely tounderlie the assembly and control of operon-like gene clusters in plants.

85. Sirikantaramas S, Yamazaki M, Saito K: Mutations intopoisomerase I as a self-resistance mechanism coevolvedwith the production of the anticancer alkaloid camptothecin inplants. Proc Natl Acad Sci U S A 2008, 105:6782-6786.

86. van Bakel H, Stout J, Cote A, Tallon C, Sharpe A, Hughes T,Page J: The draft genome and transcriptome of Cannabissativa. Genome Biol 2011, 12:R102.










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Phytochemical genomics—a new trend

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