ORTom: a multi-species approach based on conservedco-expression to identify putative functionalrelationships among genes in tomato

Laura Miozzi • Paolo Provero • Gian Paolo Accotto

Received: 25 August 2009 / Accepted: 11 April 2010 / Published online: 22 April 2010

� Springer Science+Business Media B.V. 2010

Abstract Co-expressed genes are often expected to be

functionally related and many bioinformatics approaches

based on co-expression have been developed to infer their

biological role. However, such annotations may be unre-

liable, whereas the evolutionary conservation of gene

co-expression among species may form a basis for more

confident predictions. The huge amount of expression data

(microarrays, SAGE, ESTs) has already allowed functional

studies based on conserved co-expression in animals. Up to

now, the implementation of analogous tools for plants

has been strongly limited probably by the paucity and

heterogeneity of data. Here we present ORTom, a tomato-

centred EST data-mining approach based on conserved

co-expression in the Solanaceae family. ORTom can be

used to predict functional relationships among genes and

to prioritize candidate genes for targeted studies. The

method consists in ranking ESTs co-expressed with a gene

of interest according to the level of expression pattern

conservation in phylogenetically-related plants (potato,

tobacco and pepper) to obtain lists of putative functionally-

related genes. The lists are then analyzed for Gene

Ontology keyword enrichment. The web server ORTom

has been implemented to make the results publicly-

available and searchable. Few biological examples on how

the tool can be used are presented.

Keywords Solanaceae � Conserved co-expression �Functional genomics � Expressed sequence tag (EST) �Systems biology

Introduction

Technological advances in the post-genomic era have

allowed measurement of the expression level of thousand

of genes simultaneously. As a consequence, a huge amount

of transcriptomic data is becoming publicly available. This

huge amount of information can be useful to get new

insight on how living systems work. A number of bioin-

formatics approaches have been developed to help in

deciphering the biological role of genes; several are based

on gene co-expression and the so-called ‘guilt-by-associa-

tion’ (GBA) principle (Eisen et al. 1998). According to

this, genes sharing a common expression pattern are likely

to be involved in related functions. Thus a gene of

unknown function, co-expressed with them, can be sup-

posed to be involved in the same process.

The importance of co-expression in inference of gene

function has been highlighted by several studies indicating

that proteins associated in stable complexes generally

show similar expression profiles (Jansen et al. 2002).

Co-expression can be also correlated to neighbouring genes

(Fukuoka et al. 2004) or to enzymes belonging to the same

metabolic pathway (Gachon et al. 2005). A systematic

survey of five co-expression networks using microarray

data from three mammalian organisms has demonstrated

the broad applicability of the GBA approach in predicting

gene functions (Wolfe et al. 2005). Co-expression has

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11103-010-9638-z) contains supplementarymaterial, which is available to authorized users.

L. Miozzi (&) � G. P. Accotto

Istituto di Virologia Vegetale, CNR, Strada delle Cacce 73,

10135 Turin, Italy

e-mail: l.miozzi@ivv.cnr.it

P. Provero

Molecular Biotechnology Center and Dipartimento di Genetica,

Biologia e Biochimica, Universita di Torino, Via Nizza 52,

10100 Turin, Italy

123

Plant Mol Biol (2010) 73:519–532

DOI 10.1007/s11103-010-9638-z

already been useful to annotate genes in yeast and barley

(Wu et al. 2002; Faccioli et al. 2005) and several bioin-

formatic tools exploiting microarray-based co-expression

have been developed (for a review on co-expression tools

for plant biology see Usadel et al. 2009).

However, prediction reliability can be compromised by

noise inherent in microarray and other technologies

employed. A way to improve prediction is to exploit evolu-

tionary conservation of co-expression, integrating data from

other species. Considering Saccharomyces cerevisiae and

Caenorhabditis elegans, Van Noort et al. (2003) have shown

that conserved co-expression among orthologs or para-

logs can more accurately predict function than simple

co-expression. Of the conserved co-expressed gene pairs, for

which functional annotation was available between those

two organisms, 89% were part of the same protein complex

(Teichmann and Babu 2002). Moreover, according to

Bhardwaj and Lu (2005), co-expression of interacting pro-

tein pairs tends to be conserved among human, mouse, yeast

and Escherichia coli. A comparative study of expression

profiles from S. cerevisiae, C. elegans, E. coli, A. thaliana,

D. melanogaster, and H. sapiens showed that functionally-

related sets of genes frequently belong to conserved

co-expressed modules, even when the evolutionary distance

among the organisms is considerable (Bergmann et al. 2004).

Functional annotation using conserved co-expression has

been exploited for model organisms, mainly animals (Daub

and Sonnhammer 2008; Ramani et al. 2008; Obayashi et al.

2008; Pellegrino et al. 2004) and particular attention has

been paid to the prediction of human candidate disease genes

(Ala et al. 2008; Oti et al. 2008). In plant biology, only the

GeneCAT tool (Mutwil et al. 2008) has considered the

co-expression of genes in more than one species, particularly

Arabidopsis and barley. However, it does not search for

statistically significant conserved co-expression but, given

two orthologs, it allow the users to compare the lists of

co-expressed genes.

Most of the work described above has involved the use

of microarray data. However, other kinds of experimental

data are available. The integration of array and SAGE

datasets significantly improved the functional annotations

of human genes (Miozzi et al. 2008), while Wu and

co-workers (Wu et al. 2005) pointed out the importance of

the already available EST resources. The huge amount of

EST data available in public databanks can be a remark-

ably rich alternative source of information but, as far as we

know, the use of EST-based conserved co-expression to

find functional relationships among genes in plants has not

yet been exploited.

Tomato (Solanum lycopersicum L.) is a model organism

among the Solanaceae, a family comprising several other

economically important crops (potato, tobacco, pepper,

eggplant) as well as ornamental and medicinal plants

(petunia, deadly nightshade). The Solanaceae is a medium-

size family of about 90 genera and 3,000–4,000 species,

almost half of which are in the genus Solanum. In spite of

its importance, the sequencing of the euchromatin portion

of the tomato genome, considered the gene rich region

(Wang et al. 2006) has been just completed and, at the

moment, only a provisional assembly is available (http://

www.sgn.cornell.edu/about/tomato_sequencing.pl). Major

array databases, such as ArrayExpress (http://www.ebi.ac.

uk/microarray-as/ae/) and GEO (http://www.ncbi.nlm.nih.

gov/geo/), as well as dedicated repositories like the Tomato

Functional Genomics Database (http://ted.bti.cornell.edu/)

contain data for only a few tens of microarray experi-

ments, moreover performed on several different platforms.

However, some EST repositories for tomato are available

(http://www.sgn.cornell.edu/; http://biosrv.cab.unina.it/tom

atestdb/) and, among them, the DFCI Gene Index Project

(http://compbio.dfci.harvard.edu/tgi/) contains thousands

of tomato ESTs assembled in virtual transcripts named

Tentative Consensus (TC) together with collections of TCs

from other Solanaceae. Putative orthologous relationships

among TCs are available through the eukaryotic gene

orthologues (EGO) database (http://compbio.dfci.harvard.

edu/tgi/ego/).

In this study we describe ORTom, a tomato-centred

data-mining method, that uses publicly-available EST data

for functional prediction and candidate gene prioritization

on the basis of the level of EST presence/absence profile

conservation. The core of our approach is the comparison

of transcriptional presence/absence patterns derived from

the best studied members of the Solanaceae family, i.e.,

tomato, potato, tobacco and pepper. Because of the limited

availability of information on EST library construction

(i.e., normalized, not normalized, subtracted), we reduced

the gene expression level at only two states: presence or

absence in a given library. Form now on, the EST presence/

absence profiles will be indicated by the term ‘‘expression

profiles’’ and the term ‘‘co-expression’’ will be used to

indicate similar EST presence/absence patterns. To show

how ORTom can be used to improve functional annotation

and to infer biological relationships among genes, few

biological examples are presented.

Materials and methods

Computational method

Expression data and measure of co-expression

We considered EST expression data for four Solanaceae

species: tomato, potato, tobacco and pepper. For each of

these, publicly available tentative consensus (TC) data

520 Plant Mol Biol (2010) 73:519–532

123

belonging to release 11, 11, 3 and 2, respectively, and

originating from different kinds of tissues and/or devel-

oping stages were downloaded through DFCI Gene Index

Project (http://compbio.dfci.harvard.edu/tgi/). According to

the DFCI definition, TCs are virtual transcripts created by

assembling ESTs. The number of ESTs, TCs and libraries

available for each species is reported in Table 1.

As a first step, a presence/absence matrix was con-

structed for all TC sequences belonging to each organism;

the presence/absence profile of each TC was a string con-

stituted of as many bits as libraries, with a bit equal to 1

indicating that the TC is represented in the corresponding

library and 0 otherwise. The best way to treat ESTs data

would be to differentiate among normalized, not normal-

ized and subtracted libraries. Unfortunately, the available

information on library construction is very limited, not

homogeneous and not standardized and at the moment it’s

not possible to set up an automatic way to extract this

information from the DFCI database. To correctly mine the

ESTs data, without such information, we decided to con-

sider only the presence/absence of genes, as already done

by Faccioli et al. (2005).

To measure the similarity between the presence/absence

profiles, for all pairs (TC1, TC2) of Tentative Consensus

we calculated the binary asymmetric distance, defined as

the ratio of the number of bits that are 1 for only one of the

two TCs over the total number of bits that are 1 in at least

one of the two TCs. The choice of the optimal similarity

measure is critical (Usadel et al. 2009). Shmulevich and

Zhang (2002) pointed out that several distances assume to

operate on continuous-level expression values and con-

sidered the Hamming distance as a natural choice for

investigating the differential expression using binary

expression data. However, this distance is given simply by

the number of different bits between the two strings, while

the binary asymmetric distance can capture the fact that

having both genes expressed in the same library is bio-

logically more significant than having both genes non-

expressed. Indeed, Glazko et al. (2005) showed that the

binary asymmetric distance (to which they refer as Jaccard

distance) performed best among several dissimilarity defi-

nitions in analyzing genome-wide binary expression data.

For each Tentative Consensus in a given species we

defined a group of TCs with similar presence/absence

profiles, considering no more than the first 400 TCs with

the highest similarity value. Other cut-off values (100 and

300) were tested and gave essentially similar results (data

not shown).

TCs with similar EST presence/absence profiles were

defined ‘‘co-expressed’’.

Definition of orthologous TCs

The second step was to define orthologous TCs. Orthology

relationships among TCs belonging to different species were

obtained from the latest release (release 13) of the Eukary-

otic Gene Orthologues database (http://compbio.dfci.har

vard.edu/tgi/ego/). This database, organized in orthologous

clusters generated by pair-wise comparison between TC

sequences, allowed us to directly link orthology and

expression data. Out of 20,680 tomato TCs, 12,612 (about

61%) had an ortholog in potato, 6,240 (about 30%) in

tobacco and 3,950 (about 19%) in pepper; 2,553 tomato TCs

(about 12%) had at least one ortholog in all three species.

Identification of groups of TCs with conserved

co-expression

Co-expressed tomato TCs were searched according to the

level of conservation of co-expression in at least one of the

other species considered (potato, tobacco and pepper),

using Fisher’s exact test. Given a tomato TC (TCtom) and

its ortholog in one of the other species (TCorth), the group

of tomato TCs co-expressed with TCtom was deemed to

show conserved co-expression if the number of tomato TCs

belonging to this group and having an ortholog among the

TCs co-expressed with TCorth was statistically significant

(P-value \ 10-3).

For a given tomato TCtom, the list of tomato TCs

showing conserved co-expression was ranked according to

the number of species for which the co-expression was

conserved.

Functional characterization of groups of TCs

with conserved co-expression

The groups of tomato TCs showing conserved co-expres-

sion were searched for GO term enrichment, using the

Gene Ontology annotations provided by the DFCI Gene

Index Project (http://compbio.dfci.harvard.edu/tgi/). For

each group we used Fisher’s exact test to evaluate the

probability that the TCs annotated to a given GO keyword

were significantly overrepresented. A TC was consid-

ered to be annotated to a GO term if it was directly

annotated to it or to any of its descendants in the GO graph.

A P-value \ 10-6 was considered as statistically signifi-

cant for overrepresentation.

Table 1 Dataset information

Species No. of libraries No. of TCs No. of ESTs

Tomato 104 20,680 196,114

Potato 60 30,152 194,910

Tobacco 48 9,912 51,044

Pepper 22 4,163 21,322

Plant Mol Biol (2010) 73:519–532 521

123

The percentage of false positives (PFP) among the

putative gene annotations was estimated using randomized

TC lists: we randomized the TC names 100 times inde-

pendently and recorded the number of putative annotations

obtained from each set of randomized lists. The PFP was

then calculated as the ratio between the number of pre-

dicted annotations obtained from the randomized TC lists

and the number obtained from the true lists. The overall

PFP was calculated as the average over all the obtained

PFPs.

Bench-based methods

Biological material

S. lycopersicum cv. Moneymaker seeds were surface ster-

ilized (Ethanol 70% (v/v) for 3 min; dip in Tween 20,

sodium hypochlorite 5% (v/v) for 13 min, rinse with dis-

tilled water), germinated in petri dishes with 0.6% (w/v)

agar for 5 days in the dark (25�C) and 4 days in the light.

Seedlings were then transferred to pots containing sterile

quartz sand. Plants were maintained in a growth chamber

with 14 h of light (24�C) and 10 h of dark (20�C) and

watered twice per week: once with 125 ml of the Modified

Long-Ashton solution (Trotta et al. 1996) and once with

water. 28 days after potting plants were inoculated with

TSWV (strain T1012). Approximately 1 g of infected

tomato leaf tissue was homogenized in 10 ml of inocula-

tion buffer (10 mM DIECA, 5 mM EDTA, 20 mM

Na2SO3). Inoculum was applied on the upper side of leaves

by rubbing with carborundum. Mock-inoculated plants,

used as control, were inoculated with non-infected tomato

leaf tissue and kept under the same conditions.

RNA extraction

Fourteen days after inoculation, systemically infected

leaves and the corresponding leaves of mock inoculated

plants were harvested and frozen in liquid N2. Total RNA

was purified using Trizol (Invitrogen, Carlsbad, CA, USA)

according to the manufacturer’s instructions. Quality and

quantity of total RNA were checked using the Experion

(Bio-Rad, Hercules, CA, USA). RNA extracted from three

to five plants were pooled to obtain three biological repli-

cates for each experimental condition.

Real-time quantitative RT–PCR analysis

Total RNA was treated with DNAse (Ambion, Foster City,

CA, USA) according to the manufacturer’s instructions and

RNA was subsequently quantified using a NanoDrop 1000

Spectrophotometer (Thermo Fisher Scientific, Waltham,

MA, USA).

For each sample, 4 lg of total RNA was used to

synthesize cDNA using Stratascript reverse transcriptase

(Stratagene, La Jolla, CA, USA) and SUPERase-In

RNase Inhibitor (Applied Biosystems/Ambion, Austin, TX,

USA) according to the manufacturer’s instructions. Primers

(Online Resource 1) were designed using Primer 3 software

(http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi),

on the basis of TC and the more recent corresponding SGN

sequences.

PCRs were carried out in 96-well plates with the

Applied Real-Time PCR Detection System (Applied Bio-

systems, Foster City, CA, USA), according to the following

cycling parameters: 95�C for 10 min (1 cycle), 15 s at

95�C 1 min at 60�C (40 cycles). Each reaction was con-

ducted in triplicate in a final volume of 10 ll containing

about 20 ng of template cDNA, 300 nM gene-specific

primers, 2X Power SYBR Green Master Mix.

A melting curve analysis (95–60�C with a cooling rate

of 0.5�C per 15 s and continuous fluorescence measure-

ment) was performed after each reaction to exclude the

generation of non-specific PCR products. PCR efficiency

was determined for each set of primers by using standard

curves on six standard tomato DNA serial dilutions.

The ubiquitin tomato gene Ubi3 (X58253), verified to be

not regulated in our experimental conditions (data not

shown), was used as a reference gene; the comparative

threshold cycle (Ct) method was used to calculate relative

expression levels (Rasmussen 2001).

Results and discussion

The aim of this work is to develop a data-mining approach

using publicly available EST expression data to identify

genes likely to be functionally related to a gene of interest

and provide a reliable method to prioritize candidate genes

for targeted experiments.

Our basic assumption is that genes belonging to the same

functional module are likely to be co-expressed and main-

tain their co-expression among closely related species. It

worth mentioning here the recently proposed neutral theory

of transcriptome evolution whereby divergence in transcript

abundance among taxa would be selectively neutral and

likely to be of little or no functional significance (Khaito-

vich et al. 2004; Broadley et al. 2008). In the neutral theory

of evolution, positive selection has little or no role in

determining divergence between species, but negative

(purifying) selection is assumed to be responsible of the

conservation of characters. Indeed the divergence of gene

expression profiles has been confirmed to be significantly

lower for orthologous genes than for randomly chosen ones

(Jordan et al. 2005). Therefore conserved co-expression

among orthologs can be used to achieve higher accuracy in

522 Plant Mol Biol (2010) 73:519–532

123

function prediction than simple co-expression. Such

co-expression, when phylogenetically conserved in more

than two species, may significantly increase the reliability

of predictions, allowing more accurate inferences of func-

tional relationships among genes. Being a condition-inde-

pendent approach (Usadel et al. 2009), ORTom consider

libraries from a variety of different tissues/conditions

(Fig. 1). It is therefore a good starting point for investi-

gating different aspect of tomato biology and design more

focused investigations.

A flow chart of the method is shown in Fig. 2. For each

tomato Tentative Consensus (TCtom) we identified a group

of tomato TCs showing conserved co-expression with it in

at least one species among potato, tobacco and pepper.

Given the list of tomato TCs co-expressed with the TCtom

and the list of TCs co-expressed with the orthologous TC

(TCorth) of the TCtom, we considered the co-expression as

conserved if the number of tomato co-expressed TCs having

an ortholog co-expressed with the TCorth was higher than

expected by chance. A Fischer Exact test P-value less than

10-3 was considered statistically significant. Tomato TCs

were then ranked by the number of species in which

co-expression was conserved. To functionally characterize

them, the lists of genes showing a conserved co-expression

were searched for Gene Ontology (GO) keyword enrich-

ment (http://www.geneontology.org/index.shtml).

In principle, the method can be applied to any group of

organisms for which ESTs are available. In particular it can

be a useful alternative for those species for which micro-

array expression data are not yet extensively available.

We decided to focus our attention on the Solanaceae

family, because of its economic as well as scientific

interest. Sequence conservation could be an important

factor in relation to the effectiveness and specificity of

predictions. A comparison of available transcript sequences

revealed that 76–78% of tomato sequences had a match in

potato; a high level of sequence conservation has also been

found between tomato and pepper or tobacco (Rensink

et al. 2005). The availability of a consistent amount of EST

data for these four species allowed us to reach a good

compromise between phylogenetic distances and conser-

vation of gene function. A percentage of transcripts ranging

between 21% for pepper and 49% for tobacco was found to

be unique in Solanaceae (Rensink et al. 2005), suggesting

leaf14%

fruit24%

root27%

stem2%

cellculture

2%

callus2%whole plant

3%

shoot3%

unspecified tissue

6%seed7%

flower11%

tuber27%

unspecifiedtissue23%

stolon7%

callus3%

meristem3%

root3%

shoot2%

flower2%

leaf30%

leaf31%

embryogenic microspores

2%

shoot2%

seedling4%seed

4%

unspecified tissue6%

root8%

flower13%

cell culture29%

fruit41%

flower5%leaf

9%

root18%

unspecified tissue27%

Tomato Potato

Tobacco Pepper

Fig. 1 Pie charts representing

the variety of tissues from

which the libraries were

originated

Plant Mol Biol (2010) 73:519–532 523

123

the existence of genes characteristic of this family. In this

perspective, our method will provide a useful tool to

investigate Solanaceae-specific functional modules.

Conserved co-expression and functional annotation

According to release 13 of the EGO database, 13,032 TCs

(about 63%) belonging to the DFCI tomato release 11 have

an ortholog in at least one species among potato, tobacco

and pepper. By our method, we found that for 1,609 of

those TCs, corresponding to approximately 12.3%, it is

possible to define groups of TCs showing conserved

co-expression in at least one of the other three species

considered (P-value \ 10-3); 2,553 TCs have an ortholog

in all the Solanaceae considered. Among them we selected

262 TCs for which it was possible to identify a list of TCs

with conserved co-expression in tomato, potato, tobacco

and pepper. This collection of transcripts could be con-

sidered as a core of best candidates to be experimentally

tested for functional relationships.

Lists of co-expressed TCs, ranked according to the

number of species in which we observe conserved

co-expression, can be already used to prioritize candidate

genes for targeted experiments (see example applications).

Moreover, to improve their functional annotation, we

searched each group of co-expressed TCs for Gene

Ontology term enrichment. Statistically significant GO

keywords (exact Fisher test P-value \ 10-6) were con-

sidered as new putative functional annotations, subject to

experimental validation. The percentage of false positives

(PFP) was estimated according to the procedure described

in the method section.

We calculated the sensitivity as the proportion of true

GO annotations recalled by the method over the number of

true annotations available on the DFCI website. The pre-

cision was evaluated as the percentage of recalled anno-

tations over all the putative GO annotations obtained

(Table 2). As expected, our approach slightly decreases in

sensitivity when the number of species in which the

co-expression is conserved increases, due to the lower

number of transcripts for which the conserved co-expres-

sion is detected. On the other hand, in the same conditions

the precision of the predictions tends to improve, sup-

porting the hypothesis that if the co-expression is con-

served in several species, the functional inference is more

reliable (Bergmann et al. 2004). Both sensitivity and pre-

cision of the method will be improved as new expression

data becomes available.

Structure and use of the ORTom web server

The results obtained with the proposed method have been

stored in a MySQL database, and an easy to use web

interface has been provided to query it for further investi-

gations (Fig. 3). ORTom can be inspected using five dif-

ferent query forms on: (1) tomato TC id, (2) gene

description keyword, (3) sequence, (4) GO annotation id,

and (5) GO annotation keyword. When a tomato TC id

is used as a query, the user will obtain as output a

co-expression report page with the list of tomato TCs

showing conserved co-expression with the query. A link

allows the user to download a tab-delimited file with this

list. Each tomato TC is associated with its orthologs in the

other species and is linked to the graphical presence/

absence profile page, displaying a graphical representation

of presence/absence profiles. Each profile is linked to a

page reporting the list of libraries used. To facilitate the

selection of candidate genes for targeted experiments, the

list of TCs is sorted according to the number of species in

which co-expression is conserved. A link allows the user to

obtain the putative GO annotations related to the TC query

(putative GO report page). Putative annotations are sorted

according to the Fisher’s exact test P-value.

ESTs expression

data

GOenrichment

ORTom web

interface

Lists of genes showing conserved

co-expression

MySQL database

Orthologs from EGO database

Calculate co-expression of genes for each species

Lists of co-expressed genes ranked according to the number of species where the co-expression

is conserved

tomato

potato

tobacco

pepper

ESTs expression

data

EST data from DFCI

database

Putative GO annotations

Fig. 2 ORTom data-mining approach flow chart

524 Plant Mol Biol (2010) 73:519–532

123

If the database is queried with a gene description key-

word, the output will be the TC description report page

with a list of tomato TCs having the keyword in their

description. Each TC is automatically linked to its

co-expression report page.

If the user has an anonymous sequence, either nucleo-

tide or amino acid, the first step is to find its corresponding

TC. In this case, the ORTom database can be queried

performing a blast search against the tomato TC sequences.

The user can select one algorithm among blastn, tblastn

and tblastx and define the E-value cut-off. The result will

be the blast report page, a typical blast output where each

blast hit is linked to the list of co-expressed TCs.

Finally, users could be interested in searching genes

involved in a particular biological function. For this, the

ORTom database can be queried by GO id and GO key-

word to find out the TCs putatively annotated to a given

GO term. If the query is a GO id, the output will be a list of

TCs (linked to their co-expression report page) putatively

annotated to it (putative TC annotation report page),

according to the conserved co-expression data. When a GO

description keyword is used as query, the result is the GO

description report page reporting the list of GO terms with

the keyword in their description. Through this page the user

can select the GO term of interest, obtaining the corre-

sponding putative TC annotation report page. The ORTom

web server is available at http://ortom.ivv.cnr.it.

Biological applications of the ORTom web server

ORTom can be used to address several biological aspects

and some examples are given in the following paragraphs.

Arabidopsis is a well known model organism for which

several tools based on co-expression have been developed

in the last years (Usadel et al. 2009). Therefore, our first

example was dedicated to verify how the ORTom approach

performs in two case studies already investigated in this

plant (Example application I). Subsequently, we selected a

well studied process, such as ‘‘tomato fruit ripening’’,

where many of the genes involved have already been

identified, and demonstrated that several TCs retrieved

using ORTom are actually involved in that process

(Example application II). Lastly, we showed how ORTom

can be used to address other less studied biological prob-

lems, i.e., ‘‘plant-virus interactions’’; experimentally vali-

dating by qRT–PCR the inferred functional annotations

(Example application III).

Example application I: ORTom results correlate

well with Arabidopsis co-expression case studies

Case study I: ribosomal protein genes Ribosomal pro-

teins are expected to maintain stoichiometric ratios for

efficient gene expression (Barakat et al. 2001). Therefore,

these genes are supposed to be under tightly controlled

transcriptional regulation and are good candidates to test

the effectiveness of co-expression analysis in highlighting

biological function relationships. Based on this assumption,

Jen et al. (2006) used ribosomal protein genes as an

example data set to demonstrate that their Arabidopsis co-

expression tool (ACT) could be used to highlight func-

tional relationships among genes. Using a gene coding for a

ribosomal protein L7Ae in Arabidopsis as a driver and

looking for genes co-expressed with it, they selected a list

of correlated genes, enriched in ribosomal proteins, clearly

functionally correlated with their query. Interestingly,

among the co-expressed genes, they observed a similar

proportion of 60S and 40S subunits, suggesting that no

separate coordinate regulation of genes comprising large

and small subunits can be observed (Jen et al. 2006).

Among co-expressed genes, they also found several ones,

functionally correlated with the driver since coding for

proteins potentially involved in the mRNA translation and

protein synthesis. Their results were consistent with the

coordinated mechanism of regulation of ribosomal protein

genes observed in rice by Lee et al. (2009).

In order to verify whether a similar coordinated regu-

lation of ribosomal protein genes is present in tomato and

whether ORTom can be used to address this question, we

queried the ORTom web server with two ribosomal protein

genes: (a) TC177036 and (b) TC187836, both coding for a

ribosomal protein L7. In both cases, the enrichment

of ribosomal protein genes among the conservatively

co-expressed TCs was evident (Online Resource 2). In the

case (a), we identified 194 TCs showing conserved

co-expression in at least one species beside tomato: 55 TCs

Table 2 Sensitivity and precision of the method

No. of species TC-GO annotations TCs GO terms Sensitivity (%) Precision (%) PFP

2 126,317 1,531 1,043 53 20 0.8e-3

3 49,088 718 787 48 27 0.2e-3

4 16,180 241 540 45 34 4.6e-5

Number of TC-GO annotations obtained, TCs and GO terms involved, sensitivity, precision and percentage of false positives (PFP) are reported

according to the number of species in which co-expression was conserved

Plant Mol Biol (2010) 73:519–532 525

123

of them showed conserved co-expression with the query in

two other species (potato and tobacco). As expected,

among these 55 TCs, more than half are related to trans-

lation. In particular, 27 TCs are annotated as ribosomal

proteins, and 5 TCs are translation initiation or elongation

factors.

In case (b), 107 TCs with conserved co-expression in 1

(93 TCs) or 2 (14 TCs) species were found. Also in this

case, the list of co-expressed TCs is enriched in ribosomal

protein genes (12 TCs out of the 14 co-expressed in 2

species). In agreement with what already observed by Jen

et al. (2006) in Arabidopsis and by Lee et al. (2009) in rice,

these ribosomal genes encode a balanced mixture of large

and small subunits, confirming that genes belonging to

such subunits are not subject to separate coordinate

regulation.

Case study II: genes responding to environmental stim-

uli In order to further investigate if ORTom predictions

do correlate with previously considered case studies in the

model plant Arabidopsis thaliana, we focused our attention

to the co-regulation of genes differentially expressed in

response to environmental stimuli, as previously done by

Jen et al. (2006).

Fig. 3 ORTom web server

screenshots. a Co-expression

report page; b graphical

presence/absence profile page

526 Plant Mol Biol (2010) 73:519–532

123

By using their co-expression based tool ACT, those

authors highlighted the functional correlation among genes

implicated in the heat shock response. Choosing as a

ORTom query the TC170016, coding for a heat shock

cognate 70 kDa protein 1, we selected 12 TCs with con-

served co-expression in 2 species (potato and pepper)

(Online Resource 2). Among them, 4 TCs were

clearly correlated to the query because of their involve-

ment in the response to environmental stimuli: TC175284

and TC179427 annotated as heat shock proteins (HSP),

TC169983 and TC170750 annotated as catalases, enzymes

involved in the response to oxidative stress. Since plant

HSPs are known to respond to a wide range of environ-

mental stresses, including heat, cold, drought, salinity and

oxidative stress (Wang et al. 2004b), the presence of

catalases among the co-expressed TCs is not surprising. A

fifth TC (TC186716) coding for an ATP-dependent Clp

protease ATP-binding subunit clpA, a chaperone involved

in the degradation of denatured proteins, was correlated to

the query by the fact that heat shock proteins are known to

function as chaperones, playing an important role in protein

folding, assembly, translocation and degradation (Wang

et al. 2004b). Such piece of evidence allows correlating our

query with three other co-expressed TCs (TC173852,

TC178671, and TC170633) involved in protein synthesis/

turnover. Eventually, more than 60% of the selected co-

expressed TCs were functionally related with the query.

These two case studies confirm that ORTom results

correlate well with those based on co-expression in Ara-

bidopsis, for which a wide amount of microarray data is

available.

Example application II: tomato fruit ripening

Tomato has been a model plant for the study of fleshy fruit

ripening and a lot of information is available in the liter-

ature on this process. Therefore, we decided to focus our

first example of ORTom web server application on this

biological process. As query we used the transcript

TC172177, annotated as a ripening regulated protein-like,

but lacking any other information from the literature. The

ORTom output consisted in 56 TCs showing conserved

co-expression with TC172177. Four of them showed con-

served co-expression in two other species (potato and

tobacco), and 52 only in one (potato or tobacco) (Online

Resource 3). According to the basic assumption of func-

tional correlation among conserved co-expressed genes,

several of the 56 selected TCs should be involved in the

ripening process and therefore functionally correlated with

the query. Alba et al. (2005), analyzing the transcriptional

changes during tomato fruit ripening, identified 869 genes

differentially expressed in developing tomato pericarp. Out

of 56 TCs putatively involved in ripening, according to

ORTom prediction, 13 have no homology (blastn

e-value B 10-10) among the sequences spotted in the

TOM1 array used in that study. Out of the remaining 43

TCs, 24 (more than 50%) have been found differentially

expressed during tomato ripening (Online Resource 3).

Interestingly, among them there are several genes indicated

by ORTom as involved in ripening but for which little or

no other evidence for such role is present in the literature.

In particular, among the TCs selected by ORTom, we

found two TCs (TC171077 and TC171403) clearly involved

in tomato ripening, as confirmed by their annotation and by

the data of Alba et al. (2005); they encode the fruit-ripening

protein E4 and the ripening-related mRNA ERT13,

respectively (Cordes et al. 1989; Picton et al. 1993). A third

TC (TC176732) shows similarity with the protein PM23

involved in seed maturation, fundamental in the ripening

process. Four are related to ethylene, a well known hormone

essential for fruit ripening (Alexander and Grierson 2002).

They are a 1-aminocyclopropane-1-carboxylate oxidase 4

(TC169966), a key enzyme in the ethylene biosynthesis; an

S-adenosylmethionine synthase 2 (TC170924), involved in

the Yang cycle, an early step in the biosynthesis of ethylene

(Wang et al. 2002); an S-adenosyl-L-homocysteine hydro-

lase (TC170042), involved in the S-adenosyl-L-methionine

cycle for the regeneration of methionine, the starting com-

pound in ethylene biosynthesis (Ravanel et al. 2004); and a

jasmonate and ethylene responsive factor 3 (TC170506), a

gene mainly induced by ethylene in tomato (Wang et al.

2004a). Three TCs (TC171281, TC177883, and TC174916)

are annotated as alcohol dehydrogenases (ADH); Longhurst

et al. (1990) showed that ADH activity decreases during the

early stages of ripening and then increases in the post-cli-

macteric period. The authors proposed that the increase

during ripening may contribute to flavour development.

TC171808 is annotated as a GDP-mannose phyrophos-

phorylase (GMP), an enzyme for the synthesis of the

ascorbic acid. Two studies on tomato (Zou et al. 2006) and

acerola (Badejo et al. 2007) indicate that GMP activity is

highest in fruits. TC170725 encodes an eukaryotic transla-

tion initiation factor 5A-4 (eIF-5A-4). Wang et al. (2005)

found that three members of tomato eIF-5A family were up-

regulated in parallel as the fruit begins to senesce and soften

and that plants where the activation of eIF-5A was inhibited

exhibited delayed fruit postharvest softening and senes-

cence. Interestingly, changes in transcriptional profiles of

ADH, GMP and eIF-5A were found associated with early

specialization of tomato fruit tissue (Lemaire-Chamley

et al. 2005). Finally, two TCs (TC 174083 and TC170496),

encoding a citrate synthase and an oxoglutarate/malate

translocator, respectively, can be related to the alteration of

citric and malic acid concentrations during tomato ripening

(Jeffery et al. 1984, 1986; Carrari et al. 2006).

Plant Mol Biol (2010) 73:519–532 527

123

Other TCs form a second class of transcripts, whose

relations with the ripening process are, according to the

limited literature available, still uncertain. The first one is

TC170007, encoding a 14-3-3 protein which was indicated

as an ethylene-dependent regulatory gene involved in rip-

ening (Alba et al. 2005) and found differentially expressed

particularly in exocarp during tissue specialization (Lem-

aire-Chamley et al. 2005); The 14-3-3 proteins, implicated

in the regulation of several physiological processes such as

regulation of primary metabolism, plant response to biotic

and abiotic stimuli (Finnie et al. 1999), have been already

isolated from ripening tomato fruits and their involvement

in fruit development was suggested (Laughner et al. 1994).

A second one is TC172410, annotated as a RAB7C protein.

It belongs to the RAB family, part of RAS superfamily of

small GTPases, regulators of membrane traffic pathways

(Stenmark and Olkkonen 2001). Other Rab mRNAs were

already observed to accumulate during tomato (Loraine

et al. 1996; Lu et al. 2001; Alba et al. 2005), mango (Zainal

et al. 1996), and apricot fruit ripening (Mbeguie-A-Mbeguie

et al. 1997). A third one is TC175454, which encodes a

disulfide isomerase protein, involved in the protein folding;

a precursor of this enzyme has been isolated in tomato fruits

in a survey of major protein variations during pericarp

development and ripening (Faurobert et al. 2007). The role

of these three TCs in the ripening process is therefore worth

investigating in depth by further experimental studies.

The 39 remaining TCs consist of ribosomal proteins (9

TCs), histones (2 TCs), unknown expressed proteins (4

TCs), TCs for which annotations are too generic to be

correlated with ripening in the literature (13 TCs), and TCs

with an annotation for which is difficult to suppose, at the

moment, an involvement in ripening on the basis of liter-

ature (11 TCs). Since these TCs were selected after a

search with ORTom, it is possible that some among them

are also involved in ripening, and they are therefore

potential candidates to be considered in studies on this

important biological process. The possible involvement of

17 of these genes is supported by the fact that they were

found differentially expressed during ripening in a tomato

microarray study (Alba et al. 2005). In our opinion, based

on these data, particular attention should be paid for

example to the nucleic acid binding protein (TC182771) or

to the Zinc finger transcription factor-like protein

(TC174072), as potential transcriptional regulators, or to

those TCs just annotated until now as expressed proteins

but for which no more information is available.

This example confirms that ORTom web server can

retrieve the functional annotation of those TCs whose the

function is already known from the literature and can be

useful to infer the biological role of TCs associated with

poor or no functional annotation.

Example application III: plant-virus interactions

To better elucidate how the ORTom web server can be used

to improve functional gene annotation, we used it to find new

candidate genes likely to be involved in plant-virus inter-

actions resulting in systemic infection. We chose as query a

tobacco gene, coding for an oxygen-evolving enhancer

protein 1, chloroplastic, OEE1 (Acc. No. X64349) known to

interact with Tobacco mosaic virus (TMV) replicase, and

tested our supposed functional annotation experimentally by

qRT-PCR.

Isolated in a yeast two-hybrid experiment by Abbink

et al. (2002), OEE1 was down-regulated in systemically

infected leaves, while its silencing resulted in a tenfold

increase of TMV accumulation. An analogous viral

increase was observed when silenced plants were infected

by two other RNA viruses, belonging to different genera,

Alfalfa mosaic virus (AMV) and Potato virus X (PVX),

suggesting this effect is not specific to a particular virus.

Focusing our attention on tomato as a model plant, we

speculated that: (1) a homologous tomato gene could be

involved in systemic virus infection; (2) genes showing

conserved co-expression with it are likely to be function-

ally correlated to this gene and therefore involved in the

systemic infection process.

To test the hypothesis (1), we first searched for

homologous genes in tomato and analysed their expression

in tomato leaves systemically infected by Tomato spotted

wilt virus (TSWV), an RNA virus, known causing huge

crop losses worldwide (Prins and Goldbach 1998).

Blasting the sequence X64349 reported in Abbink et al.

(2002) through the ORTom web server identified two TC

sequences (E-value = 0.0), TC171186 and TC170466,

both annotated as oxygen-evolving enhancer protein 1,

chloroplast precursor (OEE1), a 33 k subunit of the oxy-

gen-evolving system of photosystem II. Quantitative

RT-PCR experiments, with primers designed on TC171186

were performed on TSWV infected leaves, showing that

OEE1 is down-regulated. This indicates that OEE1 is

involved in the tomato-TSWV interaction, just as the

homologous gene is in systemic infection of N. benthami-

ana by TMV, AMV and PVX.

To test the hypothesis (2), we used as queries the two

TCs annotated as OEE1 and obtained two lists of TCs

showing conserved co-expression in potato, tobacco and

pepper. Considering that eight TCs were presents in both

lists and others showed high sequence similarity, the

dataset was reduced to a total of 11 transcripts (see Table 3

for gene description).

We hypothesized that these genes are involved in virus

infection, and investigated their regulation in TSWV-

infected leaves by qRT-PCR. For 7 out of 11 genes, down-

528 Plant Mol Biol (2010) 73:519–532

123

regulation was observed in all three biological replicates

(Table 3; Fig. 4).

Five of them are involved in photosynthesis: three

(plastidic aldolase; phosphoglycerate kinase; ribulose bis-

phosphate carboxylase small chain 3A/3C, chloroplast pre-

cursor) encode enzymes of the Calvin cycle, one

(chloroplast precursor of a chlorophyll a–b binding protein

1B) encodes a protein belonging to the light-harvesting

complex located in the thylakoid, and one (thioredoxin

peroxidase) encodes an enzyme that seems to play a role in

protecting the PSII from oxidative stress (Lamkemeyer et al.

2006). Therefore two pieces of functional evidence can be

used to correlate these genes: involvement in photosynthesis

and in the plant-virus interaction. The first confirms the

functional annotation of our queries as ‘‘33 k subunit of the

oxygen-evolving system of photosystem II’’. The abundance

Table 3 Experimental validation of ORTom results (Example application III)

TC id Description Expression (FC)

I II III

TC176756,

TC175143

Plastidic aldolase* 0.41 0.60 0.41

TC176604 Phosphoglycerate kinase* 0.01 0.27 0.29

TC171492 Chlorophyll a–b binding protein 1B, chloroplast precursor* 0.14 0.12 0.15

TC170931 Ribulose bisphosphate carboxylase small chain 3A/3C, chloroplast precursor* 0.08 0.24 0.20

TC171071 Thioredoxin peroxidise* 0.12 0.35 0.48

TC170915 S-adenosylmethionine synthase* 0.05 0.45 0.33

TC176778,

TC170178

Alpha-tubulin* 0.57 0.47 0.30

TC173712 Unknown protein 1.45 1.80 0.87

TC170305 Peptidyl-prolyl cis–trans isomerase (PPIase) (Rotamase) (Cyclophilin) (Cyclosporin A-binding

protein)

0.50 7.55 7.80

TC169983 Catalase isozyme 1 0.58 2.79 0.88

TC170750 Catalase isozyme 2 0.96 0.97 0.59

Expression level of the 11 genes resulting from ORTom search with TC171186 and TC170466 (OEE1 genes) as queries was measured by qRT-

PCR in tomato; columns I, II, III refer to the three biological replicates; asterisk indicates genes down-regulated in all replicates; FC fold change.

Raw data are available as Online Resource 4

0

1

2

3

4

5

6

7

8

TC

1767

56,T

C17

5143

TC

1766

04

TC

1714

92

TC

1709

31

TC

1710

71

TC

1709

15T

C17

6778

,TC

1701

78

TC

1737

12

TC

1703

05

TC

1699

83

TC

1707

50

Fo

ld c

han

ge

* * ** * * *

Fig. 4 Bar chart of qRT-PCR results; * indicates the genes confirmed to be down-regulated in all three biological replicates

Plant Mol Biol (2010) 73:519–532 529

123

of transcripts correlated with photosynthesis among those

co-expressed with the query in all the Solanaceae considered

makes this annotation more reliable.

Two more genes, not involved in photosynthesis, were

experimentally validated for coexpression with the query:

an alpha-tubulin and an S-adenosylmethionine synthase.

Previous proteomic studies highlighted the importance of

alpha-tubulin in viral infection, showing that TMV move-

ment protein (MP) (Heinlein et al. 1995) and viral RNA

(Mas and Beachy 1999) co-localized with microtubules

and endoplasmic reticulum. Microtubules directly interact

with the TMV MP during late stages of infection (Ashby

et al. 2006). Moreover, evidence of a role of TMV repli-

case in cell-to-cell movement (Hirashima and Watanabe

2003) suggests that virus replication and movement in

TMV are functionally linked.

S-adenosylmethionine synthetase (ADS) catalyzes the

formation of S-adenosylmethionine (AdoMet) from methio-

nine and ATP. AdoMet is the major methyl donor in plants

and is involved in the methylation of lipids, proteins and

nucleic acids (Fontecave et al. 2004). Moreover it is a

common precursor of polyamines and ethylene biosynthesis

(Walters 2000), two pathways known to be involved in

plant-virus interactions.

Several authors have observed an increase of conjugated

and free polyamines during the hypersensitive response

(HR) to TMV infection suggesting a role in virus resis-

tance, possibly inducing programmed cell death or affect-

ing virus multiplication (Walters 2003). Yamakawa et al.

(1998) showed that spermidine accumulates in tobacco

leaves reacting hypersensitively to TMV, and can induce

acidic pathogenesis-related proteins and resistance to TMV

via a salicylic acid-independent pathway. On the other

hand, ethylene is a phytohormone well known as principal

modulator in various mechanisms by which plants react to

pathogens (Broekaert et al. 2006). Genes encoding ADS

have been cloned from various plants, but until now no

specific regulation of them in response to pathogen attack

was reported, making it difficult to figure out the exact role

of ADS in the process; however, we obtained experimental

evidence that this enzyme is down-regulated under viral

infection.

Conclusions

We have shown that ORTom is a useful data-mining

method to extract information from publicly-available EST

data. Since this kind of data is still the most representative

for many organisms, including several plants, we believe

that ORTom is an effective approach to infer putative

functions for genes of interest and to prioritize candidate

genes for further experiments. The method, which was

applied to Solanaceae but could be extended to other

groups, has the potential to limit costly and time-consum-

ing non-targeted experiments and lead more rapidly to

improved gene annotations.

Acknowledgments This work was funded in part by the projects

‘‘GenoPom’’ (MIUR, Italy) and B74 (Ricerca Scientifica Applicata

2004, Regione Piemonte, Italy). P. P. gratefully acknowledges sup-

port from the Associazione Italiana per la Ricerca sul Cancro (AIRC).

The authors thank Christian Damasco, Stefano Ghignone and Matteo

Giaccone for their advice in developing the web site and Robert

G. Milne for revising the English.

References

Abbink TE, Peart JR, Mos TN, Baulcombe DC, Bol JF, Linthorst HJ

(2002) Silencing of a gene encoding a protein component of the

oxygen-evolving complex of photosystem II enhances virus

replication in plants. Virology 295:307–319. doi:10.1006/viro.

2002.1332

Ala U, Piro RM, Grassi E, Damasco C, Silengo L, Oti M, Provero P,

Di Cunto F (2008) Prediction of human disease genes by human-

mouse conserved coexpression analysis. PLoS Comput Biol

4:e1000043. doi:10.1371/journal.pcbi.1000043

Alba R, Payton P, Fei Z, McQuinn R, Debbie P, Martin GB, Tanksley

SD, Giovannoni JJ (2005) Transcriptome and selected metabo-

lite analyses reveal multiple points of ethylene control during

tomato fruit development. Plant Cell 17(11):2954–2965. doi:

10.1105/tpc.105.036053

Alexander L, Grierson D (2002) Ethylene biosynthesis and action in

tomato: a model for climacteric fruit ripening. J Exp Bot

53(377):2039–2055. doi:10.1093/jxb/erf072

Ashby J, Boutant E, Seemanpillai M, Groner A, Sambade A,

Ritzenthaler C, Heinlein M (2006) Tobacco mosaic virus

movement protein functions as a structural microtubule-associ-

ated protein. J Virol 80:8329–8344. doi:10.1128/JVI.00540-06

Badejo AA, Jeong ST, Goto-Yamamoto N, Esaka M (2007) Cloning

and expression of GDP-D-mannose pyrophosphorylase gene and

ascorbic acid content of acerola (Malpighia glabra L.) fruit at

ripening stages. Plant Physiol Biochem 45(9):665–672. doi:

10.1016/j.plaphy.2007.07.003

Barakat A, Szick-Miranda K, Chang IF, Guyot R, Blanc G, Cooke R,

Delseny M, Bailey-Serres J (2001) The organization of cyto-

plasmic ribosomal protein genes in the Arabidopsis genome.

Plant Physiol 127:398–415. doi:10.1104/pp.010265

Bergmann S, Ihmels J, Barkai N (2004) Similarities and differences in

genome-wide expression data of six organisms. PLoS Biol 2:E9.

doi:10.1371/journal.pbio.0020009

Bhardwaj N, Lu H (2005) Correlation between gene expression

profiles and protein–protein interactions within and across

genomes. Bioinformatics 21:2730–2738. doi:10.1093/bioinfor

matics/bti398

Broadley MR, White PJ, Hammond JP, Graham NS, Bowen HC,

Emmerson ZF, Fray RG, Iannetta PP, McNicol JW, May ST

(2008) Evidence of neutral transcriptome evolution in plants.

New Phytol 180(3):587–593. doi:10.1111/j.1469-8137.2008.

02640.x

Broekaert WF, Delaure SL, De Bolle MFC, Cammue BPA (2006)

The role of ethylene in host–pathogen interactions. Annu Rev

Phytopathol 44:393–416. doi:10.1146/annurev.phyto.44.070505.

143440

Carrari F, Baxter C, Usadel B, Urbanczyk-Wochniak E, Zanor MI,

Nunes-Nesi A, Nikiforova V, Centero D, Ratzka A, Pauly M,

530 Plant Mol Biol (2010) 73:519–532

123

Sweetlove LJ, Fernie AR (2006) Integrated analysis of metab-

olite and transcript levels reveals the metabolic shifts that

underlie tomato fruit development and highlight regulatory

aspects of metabolic network behavior. Plant Physiol 142(4):

1380–1396. doi:10.1104/pp.106.088534

Cordes S, Deikman J, Margossian LJ, Fischer RL (1989) lnteraction

of a developmentally regulated DNA-binding factor with sites

flanking two different fruit-ripening genes from tomato. Plant

Cell 1(10):1025–1034. doi:10.1105/tpc.1.10.1025

Daub CO, Sonnhammer EL (2008) Employing conservation of co-

expression to improve functional inference. BMC Syst Biol 2:81.

doi:10.1186/1752-0509-2-81

Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster

analysis and display of genome-wide expression patterns. Proc

Natl Acad Sci USA 95:14863–14868

Faccioli P, Provero P, Herrmann C, Stanca AM, Morcia C, Terzi V

(2005) From single genes to co-expression networks: extracting

knowledge from barley functional genomics. Plant Mol Biol

58:739–750. doi:10.1007/s11103-005-8159-7

Faurobert M, Mihr C, Bertin N, Pawlowski T, Negroni L, Sommerer

N, Causse M (2007) Major proteome variations associated with

cherry tomato pericarp development and ripening. Plant Physiol

143(3):1327–1346. doi:10.1104/pp.106.092817

Finnie C, Borch J, Collinge DB (1999) 14-3-3 proteins: eukaryotic

regulatory proteins with many functions. Plant Mol Biol 40(4):

545–554. doi:10.1023/A:1006211014713

Fontecave M, Atta M, Mulliez E (2004) S-adenosylmethionine:

nothing goes to waste. Trends Biochem Sci 29:243–249. doi:

10.1016/j.tibs.2004.03.007

Fukuoka Y, Inaoka H, Kohane IS (2004) Inter-species differences of

co-expression of neighboring genes in eukaryotic genomes.

BMC Genomics 5:4. doi:10.1186/1471-2164-5-4

Gachon CMM, Langlois-Meurinne M, Henry Y, Saindrenan P (2005)

Transcriptional co-regulation of secondary metabolism enzymes

in Arabidopsis: functional and evolutionary implications. Plant

Mol Biol 58:229–245. doi:10.1007/sl1103-005-5346-5

Glazko G, Gordon A, Mushegian A (2005) The choice of optimal

distance measure in genome-wide datasets. Bioinformatics

21(Suppl 3):iii3–iii11. doi:10.1093/bioinformatica/bti1201

Heinlein M, Epel BL, Padgett HS, Beachy RN (1995) Interaction of

tobamovirus movement proteins with the plant cytoskeleton.

Science 270:1983–1985. doi:10.1126/science.270.5244.1983

Hirashima K, Watanabe Y (2003) RNA helicase domain of tobamo-

virus replicase executes cell-to-cell movement possibly through

collaboration with its non conserved region. J Virol 77:12357–

12362. doi:10.1128/JVI.77.22.12357-12362.2003

Jansen R, Greenbaum D, Gerstein M (2002) Relating whole-genome

expression data with protein–protein interactions. Genome Res

12:37–46. doi:10.1101/gr.205602

Jeffery D, Smith C, Goodenough P, Prosser I, Grierson D (1984)

Ethylene-independent and ethylene-dependent biochemical

changes in ripening tomatoes. Plant Physiol 74(1):32–38

Jeffery D, Goodenough PW, Weitzman PDJ (1986) Enzyme activities

in mitochondria isolated from ripening tomato fruit. Planta

168(3):390–394. doi:10.1007/BF00392366

Jen CH, Manfield IW, Michalopoulos I, Pinney JW, Willats WG,

Gilmartin PM, Westhead DR (2006) The Arabidopsis co-

expression tool (ACT): a WWW-based tool and database for

microarray-based gene expression analysis. Plant J 46(2):336–

348. doi:10.1111/j.1365-313X.2006.02681.x

Jordan IK, Marino-Ramırez L, Koonin EV (2005) Evolutionary

significance of gene expression divergence. Gene 345(1):119–

126. doi:10.1016/j.gene.2004.11.034

Khaitovich P, Weiss G, Lachmann M, Hellmann I, Enard W, Muetzel

B, Wirkner U, Ansorge W, Paabo S (2004) A neutral model of

transcriptome evolution. PLoS Biol 2(5):E132. doi:10.1371/jour

nal.pbio.0020132

Lamkemeyer P, Laxa M, Collin V, Li W, Finkemeier I, Schottler MA,

Holtkamp V, Tognetti VB, Issakidis-Bourguet E, Kandlbinder A,

Weis E, Miginiac-Maslow M, Dietz KJ (2006) Peroxiredoxin Q

of Arabidopsis thaliana is attached to the thylakoids and

functions in context of photosynthesis. Plant J 45:968–981. doi:

10.1111/j.1365-313X.2006.02665.x

Laughner B, Lawrence SD, Ferl RJ (1994) Two tomato fruit

homologs of 14-3-3 mammalian brain proteins. Plant Physiol

105(4):1457–1458

Lee TH, Kim YK, Pham TT, Song SI, Kim JK, Kang KY, An G, Jung

KH, Galbraith DW, Kim M, Yoon UH, Nahm BH (2009)

RiceArrayNet: a database for correlating gene expression from

transcriptome profiling, and its application to the analysis of

coexpressed genes in rice. Plant Physiol 151(1):16–33. doi:

10.1104/pp.109.139030

Lemaire-Chamley M, Petit J, Garcia V, Just D, Baldet P, Germain V,

Fagard M, Mouassite M, Cheniclet C, Rothan C (2005) Changes

in transcriptional profiles are associated with early fruit tissue

specialization in tomato. Plant Physiol 139(2):750–769. doi:

10.1104/pp.105.063719

Longhurst TJ, Tung HF, Brady CJ (1990) Developmental regulation

of the expression of alcohol dehydrogenase in ripening tomato

fruits. J Food Biochem 14(6):421–433. doi:10.1111/j.1745-4514.

1990.tb00804.x

Loraine AE, Yalovsky S, Fabry S, Gruissem W (1996) Tomato

Rab1A homologs as molecular tools for studying Rab geranyl-

geranyl transferase in plant cells. Plant Physiol 110(4):1337–

1347

Lu C, Zainal Z, Tucker GA, Lycett GW (2001) Developmental

abnormalities and reduced fruit softening in tomato plants

expressing an antisense Rab11 GTPase gene. Plant Cell 13(8):

1819–1833. doi:10.1105/TPC.010069

Mas P, Beachy RN (1999) Replication of tobacco mosaic virus on

endoplasmic reticulum and role of the cytoskeleton and virus

movement protein in intracellular distribution of viral RNA.

J Cell Biol 147:945–958

Mbeguie-A-Mbeguie D, Gomez RM, Fils-Lycaon B (1997) Molecular

cloning and nucleotide sequence of a Rab7 small GTP-binding

protein from apricot fruit. Gene expression during fruit ripening

(PGR97-117). Plant Physiol 114:1569

Miozzi L, Piro RM, Rosa F, Ala U, Silengo L, Di Cunto F, Provero P

(2008) Functional annotation and identification of candidate

disease genes by computational analysis of normal tissue gene

expression data. PLoS ONE 3:e2439. doi:10.1371/journal.pone.

0002439

Mutwil M, Obro J, Willats WG, Persson S (2008) GeneCAT—novel

webtools that combine BLAST and co-expression analyses.

Nucleic Acids Res 36(Web Server issue):W320–W326. doi:

10.1093/nar/gkn292

Obayashi T, Hayashi S, Shibaoka M, Saeki M, Ohta H, Kinoshita K

(2008) COXPRESdb: a database of coexpressed gene networks

in mammals. Nucleic Acids Res 36:D77–D82. doi:10.1093/nar/

gkm840

Oti M, van Reeuwijk J, Huynen M, Brunner H (2008) Conserved co-

expression for candidate disease gene prioritization. BMC

Bioinformatics 9:208. doi:10.1186/1471-2105-9-208

Pellegrino M, Provero P, Silengo L, Di Cunto F (2004) CLOE:

identification of putative functional relationships among genes

by comparison of expression profiles between two species. BMC

Bioinformatics 5:179–189. doi:10.1186/1471-2105-5-179

Picton S, Gray J, Barton S, AbuBakar U, Lowe A, Grierson D (1993)

cDNA cloning and characterisation of novel ripening-related

mRNAs with altered patterns of accumulation in the ripening

Plant Mol Biol (2010) 73:519–532 531

123

inhibitor (rin) tomato ripening mutant. Plant Mol Biol 23(1):

193–207. doi:10.1007/BF00021431

Prins M, Goldbach R (1998) The emerging problem of tospovirus

infection and nonconventional methods of control. Trends

Microbiol 6:31–35. doi:10.1016/S0966-842X(97)01173-6

Ramani AK, Li Z, Hart TG, Carlson MW, Boutz DR, Marcotte EM

(2008) A map of human protein interactions derived from co-

expression of human mRNAs and their orthologs. Mol Syst Biol

4:180. doi:10.1038/msb.2008.19

Rasmussen R (2001) Quantification on the LightCycler. In: Meuer

SC, Wittwer C, Nakagawara K (eds) Rapid cycle real-time PCR

methods and applications. Springer, Heidelberg, pp 21–34

Ravanel S, Block MA, Rippert P, Jabrin S, Curien G, Rebeille F,

Douce R (2004) Methionine metabolism in plants: chloroplasts

are autonomous for de novo methionine synthesis and can import

S-adenosylmethionine from the cytosol. J Biol Chem 279(21):

22548–22557. doi:10.1074/jbc.M313250200

Rensink WA, Lee Y, Liu J, Iobst S, Ouyang S, Buell CR (2005)

Comparative analyses of six solanaceous transcriptomes reveal a

high degree of sequence conservation and species-specific

transcripts. BMC Genomics 6:124–138. doi:10.1186/1471-2164-

6-124

Shmulevich I, Zhang W (2002) Binary analysis and optimization-

based normalization of gene expression data. Bioinformatics

18(4):555–565. doi:10.1093/bioinformatics/18.4.555

Stenmark H, Olkkonen VM (2001) The Rab GTPase family. Genome

Biol 2(5):REVIEWS3007. doi: 10.1186/gb-2001-2-5-reviews

3007

Teichmann SA, Babu MM (2002) Conservation of gene co-regulation

in prokaryotes and eukaryotes. Trends Biotechnol 20:407–410.

doi:10.1016/S0167-7799(02)02032-2

Trotta A, Varese GC, Gnavi E, Fusconi A, Sampo S, Berta G (1996)

Interactions between the soil-borne root pathogen Phytophthoranicotianae var parasitica and arbuscular mycorrhizal fungus

Glomus mosseae in tomato plants. Plant Soil 185:199–209. doi:

10.1007/BF02257525

Usadel B, Obayashi T, Mutwil M, Giorgi FM, Bassel GW, Tanimoto

M, Chow A, Steinhauser D, Persson S, Provart NJ (2009) Co-

expression tools for plant biology: opportunities for hypothesis

generation and caveats. Plant Cell Environ 32:1633–1651. doi:

10.1111/j.1365-3040.2009.02040.x

Van Noort V, Snel B, Huynen MA (2003) Predicting gene function by

conserved co-expression. Trends Genet 19:238–242. doi:

10.1016/S0168-9525(03)00056-8

Walters DR (2000) Polyamines in plant–microbe interactions. Physiol

Mol Plant Pathol 57:137–146. doi:10.1006/pmpp.2000.0286

Walters D (2003) Resistance to plant pathogens: possible roles for

free polyamines and polyamine catabolism. New Phytol 159:

109–115. doi:10.1046/j.1469-8137.2003.00802.x

Wang KL, Li H, Ecker JR (2002) Ethylene biosynthesis and signaling

networks. Plant Cell 14(Suppl):S131–S151

Wang H, Huang Z, Chen Q, Zhang Z, Zhang H, Wu Y, Huang D,

Huang R (2004a) Ectopic overexpression of tomato JERF3 in

tobacco activates downstream gene expression and enhances salt

tolerance. Plant Mol Biol 55(2):183–192. doi:10.1007/s11103-

004-0113-6

Wang W, Vinocur B, Shoseyov O, Altman A (2004b) Role of plant

heat-shock proteins and molecular chaperones in the abiotic

stress response. Trends Plant Sci 9(5):244–252. doi:10.1016/j.

tplants.2004.03.006

Wang TW, Zhang CG, Wu W, Nowack LM, Madey E, Thompson JE

(2005) Antisense suppression of deoxyhypusine synthase in

tomato delays fruit softening and alters growth and development.

Plant Physiol 138(3):1372–1382. doi:10.1104/pp.105.060194

Wang Y, Tang X, Cheng Z, Mueller L, Giovannoni J, Tanksley SD

(2006) Euchromatin and pericentromeric heterochromatin: com-

parative composition in the tomato genome. Genetics 172:2529–

2540. doi:10.1534/genetics.106.055772

Wolfe CJ, Kohane IS, Butte AJ (2005) Systematic survey reveals

general applicability of ‘‘guilt-by-association’’ within gene

coexpression networks. BMC Bioinformatics 6:227–237. doi:

10.1186/1471-2105-6-227

Wu LF, Hughes TR, Davierwala AP, Robinson MD, Stoughton R,

Altschuler SJ (2002) Large-scale prediction of Saccharomycescerevisiae gene function using overlapping transcriptional clus-

ters. Nat Genet 31:255–265. doi:10.1038/ng906

Wu X, Walker MG, Luo J, Wei L (2005) GBA server: EST-based

digital gene expression profiling. Nucleic Acids Res 33:W673–

W676. doi:10.1093/nar/gki480

Yamakawa H, Kamada H, Satoh M, Ohashi Y (1998) Spermine is a

salicylate-independent endogenous inducer for both tobacco

acidic pathogenesis-related proteins and resistance against

tobacco mosaic virus infection. Plant Physiol 118:1213–1222

Zainal Z, Tucker GA, Lycett GW (1996) A rab11-like gene is

developmentally regulated in ripening mango (Mangifera indicaL.) fruit. Biochim Biophys Acta 1314(3):187–190

Zou LP, Li HX, Ouyang B, Zhang JH, Ye ZB (2006) Cloning,

expression, and mapping of GDP-D-mannose pyrophosphorylase

cDNA from tomato (Lycopersicon esculentum). Acta Genet Sin

33(8):757–764. doi:10.1016/S0379-4172(06)60108-X

532 Plant Mol Biol (2010) 73:519–532

123