Transcriptome data modeling for targeted plant metabolicengineeringKeiko Yonekura-Sakakibara1, Atsushi Fukushima1 and Kazuki Saito1,2

Available online at www.sciencedirect.com

The massive data generated by omics technologies require the

power of bioinformatics, especially network analysis, for data

mining and doing data-driven biology. Gene coexpression

analysis, a network approach based on comprehensive gene

expression data using microarrays, is becoming a standard tool

for predicting gene function and elucidating the relationship

between metabolic pathways. Differential and comparative

gene coexpression analyses suggest a change in coexpression

relationships and regulators controlling common and/or

specific biological processes. In conjunction with the newly

emerging genome editing technology, network analysis

integrated with other omics data should pave the way for robust

and practical plant metabolic engineering.

Addresses1 RIKEN Plant Science Center, 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 (ksaito@psc.riken.jp,

ksaito@faculty.chiba-u.jp)

Current Opinion in Biotechnology 2013, 24:285–290

This review comes from a themed issue on Plant biotechnology

Edited by Natalia Dudareva and Dean DellaPenna

For a complete overview see the Issue and the Editorial

Available online 4th December 2012

0958-1669/$ – see front matter, # 2012 Elsevier Ltd. All rights

reserved.

http://dx.doi.org/10.1016/j.copbio.2012.10.018

IntroductionIn most systems approaches used to understand cellular

biological processes, one key assertion is that the biological

systems can be modeled as a network [1–3]. In general, a

network can be described as a graph in which biological

entities such as genes, transcripts, proteins and metabolites

correspond to nodes, and the interactions between nodes

such as coexpression and protein-protein interaction, cor-

respond to edges. Network analysis, in turn, refers to the

use of graph-theoretic models and statistics to provide

topological information about a network.

In a broad sense, network analysis can be classified into

three major types, omics data modeling, stoichiometric

modeling and kinetic modeling, although they overlap

to a certain degree (Figure 1). Omics data modeling refers

to the use of statistical methods to identify and infer

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complex functional interactions among the components

in biological systems [4–6]. Stoichiometric modeling

entails network analysis based on knowledge of the stoichi-

ometry of a system, which predicts flux distributions of

biological pathways [7]. Kinetic modeling, which requires

reliable information about kinetic parameters, is utilized

for the evaluation of the dynamics of biological systems

such as time-course simulation, steady-state analysis and

metabolic control analysis [8]. Of these three types, omics

data modeling is becoming a standard analytical tool for

understanding whole biological systems and prediction of

gene function. Various networks including gene coexpres-

sion, differential coexpression, metabolite-to-metabolite,

gene-to-metabolite and protein-protein interaction have

been elucidated by means of network analysis using multi-

omics data [6,9–20]. Here, we focus on omics data model-

ing, especially on transcriptomic data, in network analysis

as a means to improve metabolic engineering strategies.

Transcriptome data modeling for identifyingtarget genesFunctional identification of target genes is one of the

major objectives of network analyses using omics data.

Gene coexpression analysis based on the so-called ‘guilt-

by-association’ principle [11] is frequently used for this

purpose [21–22]. This approach enables efficient exam-

ination of candidate genes that belong to a multigene

family (e.g. family 1 glycosyltransferases (UGTs), meth-

yltransferases, MYB), and accurate prediction of gene

function that is then corroborated by other experimental

evidences [23–26,27��,28�]. Maeda et al. identified a gene

encoding prephenate aminotransferase (PPA-AT) from

Arabidopsis thaliana and Petunia hybrida by first searching

for an aminotransferase gene coexpressed with those in

the shikimate and phenylpropanoid pathways of Arabi-

dopsis and then looking for homologs in petunia

expressed sequence tag (EST) databases [27��]. Enzy-

matic characterization of the recombinant proteins and invivo experiments using RNA interference (RNAi) tech-

nique verified the PPA-AT function. Before this discov-

ery, PPA-AT enzymatic characterization had been well

studied but the corresponding genes remained uniden-

tified in any organism. Analyses of PPA-AT RNAi lines

additionally revealed unknown post-transcriptional

regulation in the phenylalanine pathway. Moreover,

the use of co-expression analysis allowed Pfalz et al. to

identify genes that were later used to improve pro-

duction of the indole glucosinolate, indol-3-yl-methyl

(I3M), in Nicotiana benthamiana [28�]. The two-step

modification of I3M to 4-methoxy-indol-3-yl-methyl

Current Opinion in Biotechnology 2013, 24:285–290

286 Plant biotechnology

Figure 1

- Topologi calproperties

Omics datamodeling

Stoic hiom etricmodeling

- Geno me-sc alereconst ruction

- Quan titativepredictions

Time (h)

Leve

ls

Kineticmodeling

- Dyna micdescription

- Regula tor ymechani sm

Network analyses

Net work Infe rencesPearson ’s correla tionPartial correla tionMutual in for mation MICGene Coordina tion etc .

Data mining too lsPCA and ICAGraph clusteringNetwor k re const ructionPathway da taba ses

etc .

- Omics multine twork

- Functional map

Theoret ical app roac hesODEsOptimizationPrior kno wledgeEnzyme da taba ses etc .

“top-do wn” “bottom-up ”“middle-out ”

Network siz eLarge Small

.

.

.

.

.

.

.

.

.

.

.

. .

A BC

DV3

V3

V2V1

V1

V2

Current Opinion in Biotechnology

Schematic diagram of computational approaches of cellular networks.

A network analysis is a statistical method of identification and inference about functional interactions between elements in biological systems.

Mathematical modeling with detailed kinetic parameters (so-called ‘kinetic models’) evaluates the function and the dynamics in targeted biological

pathways. Stoichiometric analysis with genome-scale maps provides predictive flux distributions in cell metabolism. Omics data carry multilevel

network inferences. MIC, maximal information coefficient [64]; PCA, principal component analysis; ICA, independent component analysis; ODEs,

ordinary differential equations.

or 1-methoxy-indol-3-yl-methyl was achieved by

additional overexpression of both cytochrome P450

monooxygenases, CYP81Fs, and two O-methyltransfer-

ases that had been identified by gene coexpression

analysis using CYP81Fs [28�]. Similarly, based on coex-

pression with sucrose synthases (SUS5 and SUS6)

involved in the synthesis of the callose lining, Barratt

et al. identified a callose synthase, glucan synthase-like7,

in the sieve plate pores of stems and roots [29].

Thus, various genes encoding enzymes, members of

protein complexes and transcription factors have been

identified by gene coexpression network analysis using

public database (reviewed in [11,21], http://atted.jp/top_

publication.shtml).

As another approach based on transcriptome data, inde-

pendent component analysis (ICA), a form of unsupervised

algorithm, has been applied to microarray data analysis for

extraction and characterization of informative features,

Current Opinion in Biotechnology 2013, 24:285–290

clustering and classification of gene expression profiles

[30–33]. ICA of a total of 1877 genes including flavonoid

biosynthetic genes and genes annotated in AraCyc [34] was

performed on 1388 microarray data with ATTED-II [35�].A hierarchical cluster analysis of genes based on ICA

showed that the genes involved in the biosynthesis of

anthocyanins and flavonols form distinct clusters and the

cluster of anthocyanin biosynthetic genes can be divided

into two subclusters for skeleton biosynthesis and modi-

fication [35�]. Among putative anthocyanin UGTs,

UGT79B1 found in the anthocyanin modification subclus-

ter was identified as anthocyanin 3-O-glucoside: 200-O-

xylosyltransferase. In the anthocyanin skeleton biosyn-

thesis subcluster, UGT84A2 was discovered to be sinapic

acid glucosyltransferase that supplies 1-O-sinapoylglucose

as sinapoyl donor for anthocyanin sinapoyltransferase [35�].Interestingly, the cluster designation changes for some

bifunctional flavonoid glycosyltranferases depending on

the number of independent components (ICs) used for

analysis. Flavonoid 3-O-glucosyltransferase, for example,

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Transcriptome data modeling toward plant metabolic engineering Yonekura-Sakakibara, Fukushima and Saito 287

which can recognize flavonols and anthocyanins, falls into

the flavonoid cluster based on 8 ICs, but into the antho-

cyanin modification cluster based on 10 ICs [35�].

To further infer inter-pathway interactions, gene coex-

pression analysis can be expanded by conducting graph

clustering and differential coexpression analysis using

distinct data sets (e.g. organ specific data) [36�]. Gene

coexpression analysis was examined based on more than

300 tomato microarray data, and coexpression modules

were extracted by graph clustering. Graph clustering of

coexpression networks can be helpful for extracting den-

sely-clustered gene modules [37]. Significantly enriched

gene ontology terms in 88% of extracted coexpression

modules suggested the biological relevance of genes in

the modules. Coexpression analysis showed that gene

coexpression varied with the organ datasets used. In

the case of flavonoid biosynthetic genes, a strong positive

correlation between flavanone 3-hydroxylase and 4-cou-

marate:CoA ligase was observed in fruits (r = 0.89), but

not in roots (r = �0.23). In the case of chalcone synthase

(CHS) and chalcone isomerase, a strong negative corre-

lation in roots (r = �0.72) and a weak positive in fruits

(r = 0.50) were observed. Carotenoid biosynthetic genes

also showed similar results. Flavonoids are highly accu-

mulated in leaves and fruits, but not in roots of tomato,

suggesting that differential coexpression indicates a

change in gene coexpression relationships that may

reflect ‘reprogramming’ of transcriptome networks among

two biological tissues. This approach has been applied to

not only plants but also animals [36�,38–39].

A bioinformatics approach termed ‘gene coordination’

was developed in order to understand the coordinated

response of gene networks to environmental stimuli

[40]. An analysis using >1000 genes encoding enzymes

and transcription factors revealed possible stress-associ-

ated intra-pathway and inter-pathway interactions be-

tween genes from six energy-associated pathways

including the TCA cycle, glycolysis and photosynthesis

[41]. It may be useful for prediction of gene function

and deeper understanding of interaction and crosstalk

between pathways to elucidate whole biological sys-

tems in organisms.

Comparative network approachestransferring insights from model plants intocropsThe accumulation of gene expression data from various

plants enables us to conduct gene coexpression network

analysis in a range of plant species. Furthermore, integ-

ration of sequence similarity and gene (co)expression

profiles allows identification of conserved coexpression

clusters among multiple plant species (so-called ‘com-

parative coexpression’) [42]. As an example, compara-

tive coexpression analysis was examined using the CHS

gene as query by PlaNet [43�]. CHS belongs to the

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polyketide synthase family and is the first committed

enzyme in the biosynthesis of flavonoids [44]. Con-

served coexpression clusters containing CHSs and other

flavonoid biosynthetic genes from each plant (Medicago,

barley, soybean, wheat, rice, and poplar) were found as

most similar ones across seven species. In addition,

conserved coexpression clusters containing Arabidopsis

CHS-like genes were found. Arabidopsis has three

additional CHS-like polyketide synthase genes. At-PKS-B, one of polyketide synthases, is involved in fatty

acid and phenolics biosynthesis for pollen exine

developments. In accordance with this, the conserved

coexpression cluster for At-PKS-B contained several

genes which are required for biosynthesis of polyamine

which consist of sporopollenin surrounding the pollen

grains.

Furthermore, statistical methods based on the number of

orthologs between coexpression modules have been pro-

posed for proper cross-species comparison, although Pla-

Net applies a permutation test as statistical model. For

example, a method referred to as conserved modules

across organisms (COMODO) was developed to deter-

mine the most statistically optimal conserved coexpres-

sion module pairs between organisms [45]. By

COMODO, module ‘seed’ genes are selected from the

gene-to-gene threshold matrices, extended until optim-

ality assessed by a Pearson’s chi-square test is reached,

and finally the conserved module pair consisting of genes

in core and variable parts are shown [45]. In the case of

Escherichia coli and Bacillus subtilis, c.a. 80 conserved

module pairs linked through a statistically significant

set of homologous genes were identified. In those con-

served modules, genes in the variable parts accounting for

on average 40% are specific to one organism, suggesting

that these organisms have acquired new members and/or

have rewired the network during evolution. In addition,

the splitting of coexpression modules in one organism

into two modules in the other, and expression divergence

of modules contacting duplicated genes, were observed.

This result may suggest that we should also take into

account an evolutionary perspective including both evol-

utionary systems biology (i.e. how biological networks

evolved, see review [5]) and gene expression profiles,

because plants have multigene families encoding

enzymes and transcription factors involved in metab-

olism.

Genome editing tools for new plantbiotechnologyIn parallel with progress of network analysis, new plant

biotechnologies including zinc-finger nuclease technol-

ogy [46–47], oligonucleotide-directed mutagenesis [48]

and RNA-dependent DNA methylation [49] have been

developed and applied to model plants and crops [50].

These approaches are especially useful for plants with

long generation and fruition times.

Current Opinion in Biotechnology 2013, 24:285–290

288 Plant biotechnology

Figure 2

Network analysesomics data modelingstoichio metric modelingkine tic modeling

Geno me ed itingtechno log iesTALE N, ZFN , ODM ,cisgene sis andintra genetics, RdDM, grafting, re ver sebreeding, agro-infil tra tion

Custombreed ing

Multi-omicsdata

transc rip tome,epigenome,proteo me,intera ctome,metabolo me, etc

Systems biolog y

Biochem

ical dataAdvan ced analytic altechnolog iesRNA-Seq,ChIP-Seq,etc.

Current Opinion in Biotechnology

Schematic representation for custom breeding based on network

analysis.

TALEN, transcription activator-like effector nuclease; ZFN, zinc finger

nuclease; ODM, oligonucleotide directed mutagenesis; RdDM, RNA-

dependent DNA methylation.

Transcription activator-like effector nuclease (TALEN)

is a promising genome editing tool applicable to nearly

any organism [51–52]. Fusion of transcription activator-

like (TAL) effector proteins to the FokI nuclease creates

site-specific DNA nuclease for targeted DNA cleavage

[51]. Disease-resistant rice was developed by introducing

deletions or insertions into TAL effector-binding

element (EBE) in a promoter region of a disease-

susceptibility gene, Os11N3, a member of the SWEET

sucrose-efflux transporter family, using TALEN [53��].Xanthomonas Oryzae pv. oryzae TAL effectors bind EBE

and modify the Os11N3 expression to divert the sugar to

their advantage. The mutations were designed to inter-

fere the binding of X. Oryzae pv. oryzae TAL effectors,

AvrZa7 and PthXo3, but not to affect the function of

Os11N3.

Furthermore, gene activation and repression using engin-

eered TAL effector proteins were reported [54–55]. TAL

effector protein Hax3 fused with the EAR-repression

domain, SRDX, efficiently repressed the target RD29Agenes [54]. Designer TAL effectors with modified repeat-

variable diresidues of TALE repeat units, activated the

expression of target EGL3 and KNAT1 genes in Arabi-

dopsis [55]. A publicly available high-throughput system

using TALEN in human cells [56] may be applicable for

plants.

ConclusionsNetwork analyses, especially transcriptome data mod-

eling, have facilitated the functional identification of

unknown genes and the elucidation of metabolic net-

works. It also suggests the fine-tuning of regulatory

mechanisms under different conditions. Differential

and comparative network approaches [42] may give

us useful information about key regulators controlling

common biological processes among plant species and

diversified systems in specific plant(s). So far, the omics

data that serve as a basis for network analyses are still

not precise or distinct, and these factors contribute to

the limitations seen in network analyses. It will be

important to carefully filter out inaccurate data before

being utilized for network analysis and/or to develop

robust bioinformatics analysis that can tolerate a certain

degree of inaccuracy present in the large data set [57].

Further advances in analytical technologies like RNA-

Seq [58–59], while still containing a high degree of

inaccuracy, should generate more complete and

precise omics data and exploit the power of network

analysis.

Network-based integration of multiple omics data is

another promising strategy [60] because key regulatory

changes leading to phenotypes of interest (e.g. altered

metabolite accumulation) do not necessarily occur at

transcriptional levels. This limitation can be addressed

by integrating multiple omics data.

Current Opinion in Biotechnology 2013, 24:285–290

Discovery of gene functions and inter-pathway inter-

actions based on omics modeling is an important first

step. As more comprehensive and genome-wide data are

available at deep omics levels [59,61], integrated omics

modeling will shed light on plant metabolism at the

system-level and lead to robust and directed metabolic

engineering in plants (Figure 2). Furthermore, stoichio-

metric modeling and kinetic modeling will become

critical future steps toward identification of targets for

rational plant metabolic engineering [62–63].

AcknowledgementsThis work was partly supported by a Grant-in-Aid for Scientific Research onInnovative Areas (to K.S.), Scientific Research (C) (to K. Y.-S.) and YoungScientists (B) (to A.F.) from the Ministry of Education, Culture, Sports,Science and Technology of Japan.

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