only half the transcriptomic differences between resistant genetically

Only half the transcriptomic differences between resistantgenetically modified and conventional rice are associatedwith the transgeneMaria Montero1, Anna Coll1, Anna Nadal1, Joaquima Messeguer2 and Maria Pla1,*

1Institut de Tecnologia Agroalimentaria (INTEA), Universitat de Girona, Campus Montilivi, Girona, Spain2Departament Genetica Vegetal, Centre de Recerca en Agrigenomica, CSIC-IRTA-UAB, Barcelona, Spain

Received 12 July 2010;

revised 7 September 2010;

accepted 10 September 2010.

*Correspondence (Tel +34 972 41 98 52;

fax +34 972 41 83 99;

email [email protected])

Keywords: GMO (genetically modi-

fied organism), unintended effects,

transcriptomics, rice, transformation,

in vitro culture, fungal resistance,

stress.

SummaryBesides the intended effects that give a genetically modified (GM) plant the desired trait,

unintended differences between GM and non-GM comparable plants may also occur. Profil-

ing technologies allow their identification, and a number of examples demonstrating that

unintended effects are limited and diverse have recently been reported. Both from the food

safety aspect and for research purposes, it is important to discern unintended changes pro-

duced by the transgene and its expression from those that may be attributed to other factors.

Here, we show differential expression of around 0.40% transcriptome between conventional

rice var. Senia and Senia-afp constitutively expressing the AFP antifungal protein. Analysis of

one-fifth of the regulated sequences showed that around 35% of the unintended effects

could be attributed to the process used to produce GM plants, based on in vitro tissue culture

techniques. A further �15% were event specific, and their regulation was attributed to host

gene disruption and genome rearrangements at the insertion site, and effects on proximal

sequences. Thus, only around half the transcriptional unintended effects could be associated

to the transgene itself. A significant number of changes in Senia-afp and Senia are part of

the plant response to stress conditions, and around half the sequences for which up-regula-

tion was attributed to the transgene were induced in conventional (but not transgenic) plants

after wounding. Unintended effects might, as such, putatively result in widening the

self-resistance characteristics because of the transgene in GM plants.

Introduction

The use of genetically modified organisms (GMO) in basic

research and crop development is very extensive nowadays.

Genetically modified (GM) crops are subject to different legisla-

tion worldwide to cover aspects of consumer safety and protec-

tion; authorized GM events have been shown to be equivalent to

non-GM comparable varieties by targeted analysis of compounds

that are relevant to the species and trait that is introduced (OECD,

1993). Additionally, more unbiased profiling technologies, such

as metabolomics, proteomics and transcriptomics, have been

used to extend the scope of comparative analyses. Various studies

report the very limited difference between GM and comparable

non-GM plants, in contrast to the major impact of mutagenesis

techniques used to generate diversity in conventional breeding

approaches (Batista et al., 2008), and more generally to the

extensive variation between conventional varieties [see reviews in

(Bradford et al., 2005; Chassy et al., 2008; Kok et al., 2008)].

A number of recent publications report the differences, always

limited and of diverse identity, between specific GMOs and com-

parable non-GM plants [(El Ouakfaoui and Miki, 2005; Gregersen

et al., 2005; Dubouzet et al., 2007; Cheng et al., 2008; Coll

et al., 2008, 2010; Baudo et al., 2009; Abdeen et al., 2010; Bar-

ros et al., 2010) (Arabidopsis, maize, rice, soybean and wheat

transcriptomes); (Barros et al., 2010; A. Coll et al., 2010; Corpillo

et al., 2004; Khalf et al., 2010; Lehesranta et al., 2005; Ruebelt

et al., 2006) (Arabidopsis, maize, potato and tomato proteomes);

(Catchpole et al., 2005; Kristensen et al., 2005; Baker et al.,

2006; Beale et al., 2009; Zhou et al., 2009; Kogel et al., 2010)

(Arabidopsis, barley, potato, rice and wheat metabolome)].

Unintended differences in transgenic and non-GM plants can

be predictable or unpredictable as a function of whether they are

expected and explicable in terms of the present knowledge of

plant metabolism and physiology or whether they fall outside our

present level of understanding (Cellini et al., 2004). They may

occur as a consequence of pleiotropic effects of the integrated

DNA on the host plant genome, related to both the transgene

coding region and regulatory elements (Filipecki and Malepszy,

2006; Miki et al., 2009). As an example, they can arise as a result

of transgene products interacting with the regulation of other

genes or the activity of other proteins. These changes depend on

the presence and ⁄ or expression of the transgene.

They may also occur as a consequence of host gene disrup-

tion or DNA sequence rearrangements at the insertion site

(Forsbach et al., 2003) or because of interactions between

transgene elements and proximal host sequences (e.g. the cod-

ing sequences surrounding the insertion site may fall under the

influence of transgene promoters leading to sense or antisense

transcripts). The integration of a transgene in the host genome

is usually a random process, but with the preference for gene-

rich regions (Koncz et al., 1992), disruption or modification of

the activity of genes might occur. GM plants with undesired

transgene position effects are discarded and other events used.

These are event-specific unintended effects.

ª 2010 The Authors

Plant Biotechnology Journal ª 2010 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd 693

Plant Biotechnology Journal (2011) 9, pp. 693–702 doi: 10.1111/j.1467-7652.2010.00572.x

The host plant genome may also be affected by the process

to obtain GM plants. Most transformation techniques involve

tissue culture, plant cell dedifferentiation, plant regeneration

and acclimatization. In vitro culture technology gives rise to so-

maclonal variation (Larkin and Scowcroft, 1981), which mainly

relies on genetic changes (Ngezahayo et al., 2007; Noro et al.,

2007), but also on epigenetic and karyotypic changes. One

example of changes caused by in vitro culture is potato control

and tissue culture-derived tubers, which have been shown to

have compositional differences (Shepherd et al., 2006). Stress

factors are the main factors described as potentially responsible

for somaclonal variation. The host plant genome may also be

influenced by the infection with Agrobacterium, particle bom-

bardment or other transformation processes. AFLP and RAPD

analysis of transgenic rice plants produced by different transfor-

mation techniques have verified the presence of genomic

changes (Labra et al., 2001).

Especially in the context of public concern regarding health

and environmental safety of GMOs, it is essential to differenti-

ate the real extent of unintended changes produced by the

transgene and its expression from (i) event-specific changes

that can be minimized or avoided through proper selection

and (ii) those which are a consequence of technologies (such

as tissue culture) widely used with commercial applications

such as freeing plants from viruses or plant micropropagation.

In contrast to transgenic plants, conventionally bred crops

have not given rise to public safety concerns (Cellini et al.,

2004). For research purposes, it is often important to consider

the causes of unintended effects of the transgene insertion

and expression even if they fall within the natural variation of

the species, to avoid misinterpretation of the results, for exam-

ple in the evaluation of the expression patterns of transformed

genes under study.

Here, we used rice as a model cereal to assess unintended

differences between GM and conventional isogenic plants and

to evaluate their possible causes. Rice is commercialized as

inbred lines (not hybrids) often suitable for genetic transforma-

tion and regeneration. Both transgenic and isogenic lines can

be obtained, and they could have commercial interest. We anal-

ysed transcriptomic differences between the conventional line

Senia and the GM, fungal-resistant rice line Senia-afp (which

constitutively expresses the antifungal protein AFP) obtained by

Agrobacterium transformation. We further quantified the

impact of different factors on transcriptomic regulation, in par-

ticular the insertion site and associated rearrangements, the

processes to produce transgenic plants (e.g. in vitro culture) and

the transgene.

A detailed understanding of GMO unintended effects and

their causes is needed to develop strategies to minimize unin-

tended changes to transgenic plants. In the context of the pub-

lic concern regarding health and environmental safety of

GMOs, this study aims to give a more accurate perspective of

the unintended differences between GM and non-GM plants.

Results

Transcriptomics comparison of conventional andAFP-producing GM rice by microarray hybridization

We initially assessed transcriptional differences between the

conventional commercial rice line Senia and the GM line, Senia-

afp. Senia-afp was produced by Agrobacterium-based transfor-

mation of Senia and is self-resistant to fungal infections by

expression of the antifungal AFP protein, under the control of

the constitutive promoter ubi-P (Coca et al., 2004). In particular,

we used the homozygous stabilized line of event R25.14 that

has a single and full-length copy of the transgene cassette per

haploid genome.

To specifically focus on transcriptional changes related to the

transgenic character and avoid the effect of unrelated factors,

our approach was based on in vitro-cultured plantlets under

highly controlled experimental conditions. Furthermore, three

biological replicates, each from leaves of ten different plantlets,

were independently analysed in three microarrays per variety.

Microarray data are available at the European Bioinformatics

Institute (EMBL-EBI) ArrayExpress repository database under

accession E-MEXP-2730. The data obtained in the three repli-

cates were collectively analysed using the Robust Multichip

Average (RMA) software for gene expression summary values.

The estimated log2-fold changes and log odds values (T-test) for

differential expression of the data are shown in Figure 1.

The data were subsequently filtered by considering only

sequences with higher than twofold levels, Student test P-values

below 0.05 and fluorescence intensity values (in the rice line

with the highest expression) above 200 units. Microarray results

were subsequently validated by reverse transcription–real-time

polymerase chain reaction (RT-qPCR). A total of 44 sequences

were selected that corresponded to those with the highest fold-

values (above fivefold) plus a random selection of sequences

regulated down to twofold. Specific qPCR assays were designed

and optimized to target each selected sequence. The sequence

with probe set ID Os.22606.1.S1_at was not suitable for

designing a qPCR assay in the Beacon Designer software default

settings and was discarded. qPCRs produced unique amplicons,

as assessed by melting curve analysis, and had linearity and effi-

ciency values above 0.99 and 0.90, respectively (mean

R2 = 0.998 ± 0.002; mean E = 0.966 ± 0.026). Messenger RNA

levels of the 43 selected sequences were assessed in triplicate

samples of Senia-afp and Senia leaves by RT-qPCR. The three

reference genes, b-actin, elongation factor (EF-Ia) and 18S

Log2 AFP/Senia

–4 –2 0 2 4 6 8 10 12

–Log

10 P

-val

ue

0

1

2

3

4

5

6

7

Figure 1 Volcano plot representation of changes in gene expression in

Senia-afp and Senia rice lines. Each point represents one gene in the rice

Affymetrix microarray. The log odds for differential expression of all

genes (estimated from the Robust Multichip Average analysis of the

data) are plotted against the estimated log2-fold changes. Vertical bars

indicate the twofold increase or decrease in a given sequence.

Sequences further analysed by RT-qPCR are in bold.

ª 2010 The Authors

Plant Biotechnology Journal ª 2010 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd, Plant Biotechnology Journal, 9, 693–702

Maria Montero et al.694

ribosomal RNA, were used to normalize the data. They had sta-

bility values (M) below 0.5 (geNORM v3.4 statistical algorithm,

Ghent University, Ghent, Belgium) so were suitable for stan-

dardizing gene expression under these experimental conditions.

For each sequence, the RT-qPCR results in Senia-afp and Senia

were statistically analysed by the Student test coupled to the

Benjamini and Hochberg False Discovery Rate multiple testing

correction, with P < 0.05 (Table 1). Up to 82% of the RT-

qPCR results were in agreement with microarray data (mean

P = 0.017 ± 0.016), which is within the expected range (Dallas

et al., 2005).

The rice GeneChip� microarray used contains probes to query

51 279 transcripts, from which approximately 48 564 represent

japonica cultivars. Among the sequences analysed, 196 were dif-

ferentially expressed in Senia-afp and Senia with at least a two-

fold increase or decrease in the level of a given transcript

(Supplementary Table S2). A total of 12 sequences were repre-

sented by two or three probe sets, such that 183 genes were

regulated in GM and conventional rice. This corresponded to

0.40% of the analysed sequences. Most regulated sequences

were overexpressed in Senia-afp (162 sequences), whereas only

34 were down-regulated in the GM line (i.e. c. 83% and c. 17%

regulated sequences, respectively). Eighteen sequences (17 up-

regulated and 1 down-regulated) were differentially expressed

five- to 13-fold, and an additional sequence was highly up-regu-

lated in Senia-afp (above 1000-fold), with background levels in

Senia samples. This sequence corresponds to hygromycin B phos-

photransferase, used in Senia-afp as the selection gene under

the regulation of the cauliflower mosaic virus 35S promoter.

Because it was not an unexpected effect, the hgr sequence was

discarded from further expression analyses.

We identified a representative public ID and description of

each differentially expressed sequence and determined the bio-

logical process, molecular function and cellular component in

which these sequences were potentially involved, using the

Gene Ontology annotation files (Table 1 and Supplementary

Table S2). Among the regulated sequences, 175 were assigned

a biological process GO term. Gene annotation enrichment

analysis showed significant overrepresentation of sequences

related to the response to chemical stimulus and catabolic and

glucose metabolic processes (Table 2). Different tools for func-

tionally annotating gene sets and identifying significantly

enriched GO categories can give different results (van den Berg

et al., 2009), so they should be considered evaluative and not

conclusive.

Evaluation of the impact of the transformation processon transcriptomic differences

Transgenesis unintended effects may be a consequence of the

process applied to transform and regenerate transgenic plants,

and it is well known that tissue culture-based technologies give

rise to genomic variation. For Agrobacterium-mediated transfor-

mation, the process involves cell dedifferentiation to callus,

Agrobacterium infection, cell growth in different media (includ-

ing selection) and regeneration and acclimatization of GM

plants. To assess the weight of the transformation process on

unintended effects of transgenesis, we analysed null segregants,

i.e. non-GM plants that had been subjected to the same trans-

formation process as Senia-afp. After Agrobacterium tumefac-

iens infection of rice embryogenic callus and selection of

hygromicine-resistant cells, regenerated transgenic plants are

hemizygous for the transgene. Self-pollination of Senia-afp

R25-14 gave rise to homozygous (i.e. the previously analysed

Senia-afp event R25-14 stabilized line), hemizygous and non-

transgenic plants (25%, 50% and 25%, respectively, according

to Mendelian distribution of the transgene). The latter non-GM

plants, Senia-afp()), went through the whole transformation

process but did not contain the transgene.

A total of 130 Senia-afp R25-14 hemizygous grains were ger-

minated in vitro, and leaves were individually frozen in liquid

nitrogen. A small portion of each sample was analysed by qPCR

assays targeting afp, and 31 non-transgenic plants were identi-

fied. They were grouped into three biological replicates of ten

plants each for further RT-qPCR analysis of the 34 sequences

previously shown to be regulated in Senia-afp R25-14 and Senia

by microarray hybridization and RT-qPCR. The number of

sequences was above the minimal representative sample size

required (assuming a margin error of 15% and a confidence

level of 95%), and they represented around 20% of those reg-

ulated in Senia and Senia-afp, according to the microarray

results. The b-actin, EF-Ia and 18S rRNA reference genes (M-val-

ues <0.5 in these samples) were used for normalization. Pair-

wise comparison of their expression levels in Senia-afp()) and

Senia is shown in Table 3. As expected, most sequences were

similarly expressed in Senia and the non-GM Senia-afp()) line.

However, 12 of 34 sequences showed differential expression in

Senia and Senia-afp()), indicating that their regulation in Senia

and Senia-afp was not directly linked to the presence or expres-

sion of the transgene but to the transformation process.

Sequences regulated in Senia and Senia-afp()) were in most

cases similarly expressed in Senia-afp()) and Senia-afp, and

sequences similarly expressed in Senia and Senia-afp()) were

generally regulated in Senia-afp()) and Senia-afp. The differen-

tial regulation of OsA10, OsA11, OsA26 and OsA35 in Senia

compared to Senia-afp and Senia-afp()) was at different levels.

OsA32, OsA33 and OsA37 had intermediate expression levels in

Senia-afp()), which resulted in statistically similar values for

Senia and Senia-afp()) and also for Senia-afp()) and Senia-afp.

Note that their variation was below 2.85-fold in the microarray.

Evaluation of transcriptomic differences betweenSenia-afp and Senia in different GM events

Unintended gene expression differences in Senia-afp and Senia

may also originate from host gene disruption, DNA sequence

rearrangements at the insertion site or interactions between

transgene elements and proximal host sequences. As different

GM events arise from independently transformed cells and have

different insertion sites and associated rearrangements, we used

three different GM events from Agrobacterium-mediated trans-

formation of the same afp construct used above to evaluate to

what extent GMO unexpected effects could be associated to

these event-specific factors.

Senia-afp lines R25-14, R25-15 and R25-12 were obtained

in parallel in the laboratory. They all had a single copy of

the transgene (as confirmed by Southern hybridization, J.

Messeguer, pers. commun.) and stable lines, homozygous for

the transgene, were subsequently obtained. Seeds of each line

were in vitro cultured, and leaves were collected at the V2

stage (triplicate samples of ten plants each) for further gene

expression analysis. Messenger RNA levels of the afp transgene

were assessed by RT-qPCR in all nine samples, using the same

b-actin, EF-Ia and 18S rRNA reference genes, which had

stability values (M) below 0.5 (geNORM v3.4 statistical algo-

rithm) in these samples. The ANOVA statistical algorithm showed

ª 2010 The Authors


Transcriptomics comparison of GM and non-GM rice 695

that the three Senia-afp events expressed similar levels of afp

mRNA (P-value = 0.096). This excluded any further difference

among events being because of different transgene expression

levels.

The expression levels of the same 34 sequences with

differential expression in Senia and Senia-afp event R25-14

were further assessed in Senia-afp events R25-15 and R25-12

samples by RT-qPCR. Twenty-three of these sequences, i.e.

around 70% of the analysed sequences, were regulated in all

three events (Table 3), indicating their regulation was indepen-

dent of the insertion site. However, the expression of 11

sequences was not consistently regulated in the three analysed

events. Specifically in Senia and Senia-afp event R25-14, five

were differentially expressed so their sole dependence on the

Table 1 Validation of microarray data

Probe set ID Accession number Annotation (where available)

Sequence

internal

code

Microarray RT-qPCR

x-Fold*

Student test

P-value

Student test

P-value

RPTR-Os-K01193-1_at RPTR-Os-K01193-1 REPORTER hygromycin B phosphotransferase (hgh) gene OsA01 1261.45 0.000 0.000

OsAffx.19456.1.A1_at 9640.m00123 Zinc finger, C3HC4 type OsA02 13.39 0.031 0.006

Os.6776.1.S1_at AK067173.1 glucan endo-1,3-beta-glucosidase precursor, putative OsA03 9.62 0.000 0.000

Os.50256.1.S1_at AK120357.1 transcribed sequence OsA04 8.11 0.003 0.036

Os.7907.1.S1_a_at AK120896.1 putative ferredoxin-dependent glutamate synthase,

chloroplast precursor

OsA05 7.90 0.047 0.577

OsAffx.18617.1.S1_at 9639.m00033 clathrin heavy chain, putative OsA06 7.35 0.001 0.016

Os.15894.1.A1_a_at BU673129 putative peroxidase OsA07 6.94 0.048 0.137

OsAffx.22999.1.S1_at AK108619.1 retrotransposon protein, putative OsA08 6.82 0.000 0.000

Os.10183.1.S1_at AK067850.1 RSW1-like cellulose synthase catalytic subunit OsA09 5.24 0.000 0.005

Os.57456.1.S1_x_at D10956.1 GOS9 OsA10 5.15 0.006 0.008

Os.37717.1.A1_s_at BU673746 glycosyl hydrolase, putative OsA11 5.07 0.001 0.029

Os.6152.1.S1_at AK066089.1 expressed protein OsA12 5.03 0.033 0.018

Os.40352.1.S1_at CF336665 transcribed sequence OsA13 4.78 0.000 0.000

Os.52377.1.S1_s_at AK066722.1 expressed protein OsA14 4.60 0.003 0.017


Os.11821.1.S1_at AK071775.1 ATP synthase, putative OsA16 4.01 0.019 0.011

Os.52425.1.S1_x_at AK067023.1 trehalose-6-phosphate synthase, putative OsA17 3.97 0.011 0.002

Os.28397.1.S1_at BI807677 phenylalanine ammonia-lyase, putative OsA18 3.80 0.000 0.000

Os.6645.1.S1_s_at AU101638 thionin-like peptide, putative OsA19 3.69 0.002 0.008

Os.5504.1.S1_at AK059244.1 putative heat-shock protein OsA20 3.58 0.002 0.027

Os.15701.1.S1_x_at AK100652.1 putative potassium transporter OsA21 3.57 0.003 0.031

Os.51601.1.S1_at AK063147.1 ZOS7-10 – C2H2 zinc finger protein OsA22 3.45 0.015 0.016

Os.53343.1.S1_x_at AK072144.1 protein kinase domain containing protein OsA23 3.39 0.014 0.469

Os.5031.1.S1_at AB127580.1 root-specific pathogenesis-related protein 10 OsA24 3.30 0.022 0.239

Os.22995.1.S1_at CB096301 transcribed sequence OsA25 3.20 0.004 0.044

Os.46048.1.S1_x_at CB642477 zinc finger protein, putative OsA26 3.17 0.013 0.024


Os.26698.4.S1_s_at NM_189284.1 universal stress protein domain containing protein OsA28 3.15 0.002 0.142

Os.28011.1.S1_at AY256682.1 putative brown planthopper-inducible protein OsA29 3.09 0.008 0.027


Os.53062.1.S1_at AK070653.1 MYB family transcription factor, putative OsA31 2.93 0.001 0.112

Os.8032.1.S1_at AK107749.1 oryzain beta OsA32 2.87 0.009 0.049

Os.49627.1.S1_at AK105605.1 ascorbate oxidase OsA33 2.86 0.007 0.043

Os.48734.1.S1_s_at CR288034 proline-rich family protein, putative OsA34 2.81 0.003 0.618

Os.22577.2.S1_x_at BU673049 DUF260 domain containing protein, seed specific OsA35 2.80 0.000 0.007

Os.57475.1.S1_x_at D21280.1 glyceraldehyde-3-phosphate dehydrogenase OsA36 2.80 0.003 0.021

Os.609.3.S1_a_at AK066830.1 plastocyanin-like domain containing protein OsA37 2.80 0.003 0.013

Os.6542.1.S1_at CB678453 dihydroflavonol-4-reductase, putative OsA38 2.78 0.003 0.158

Os.8999.3.S1_x_at AK062270.1 aldolase C-1 OsA39 2.71 0.001 0.001

Os.6315.1.S1_at AK101374.1 transcribed sequence OsA40 0.50 0.003 0.004


Os.5725.1.S1_at BI810367 putative fructose-bisphosphate aldolase OsA42 0.33 0.010 0.026

Os.14921.1.S1_at AK069119.1 app1, putative OsA43 0.29 0.017 0.010

*Differential expression folds in Senia-afp vs. Senia. Note that values below 0.5 correspond to sequences that are down-regulated in Senia-afp.

ª 2010 The Authors



presence and expression of the transgene could be discarded.

Among sequences differentially expressed in an event-specific

manner, OsA30 and OsA32 were located on chromosome 4

(positions 29 280 748–29 281 890 and 33 988 248–33 991

626, respectively), whereas OsA12, OsA14 and OsA41 were on

chromosomes 5, 2 and 6, respectively. As shown in Table 1,

OsA32 was annotated (oryzain b) but OsA12, 14, 30 and 41

corresponded to expressed proteins with no associated putative

function or regulatory pathway.

Sequences induced by wounding and the transgene

Overall, the regulation of 17 of 34 of the analysed sequences

could not be associated to the insertion site or the transforma-

tion process, so it was attributed to the transgene itself. They

were all induced in the GM line. We further assessed the

expression patterns of these 17 sequences in non-GM and

Senia-afp plants in response to abiotic stress conditions using

wounding. Three biological replicates of in vitro-cultured V2

plants, with or without wounding, were analysed by RT-qPCR

with the same reference genes as before. The M-values were

<0.5 in these samples, which proved their suitability for normal-

ization purposes. As shown in Figure 2, nine sequences were

induced in non-GM Senia plants after wounding, while no sig-

nificant changes were observed for the eight other sequences.

In contrast, these sequences were not induced in Senia-afp

upon wounding, and two were down-regulated in stress

conditions. Control RT-qPCR assays targeting the afp transgene

confirmed its similar expression levels in wounded and control

Senia-afp plants.

Discussion

The recent research on assessment of unintended effects of

GMO has provided details of transcriptomic, proteomic and

metabolomic differences between conventional and a series of

transgenic plants, including different species and traits (Hoek-

enga, 2008; Kok et al., 2008). They consistently show a low

degree of unintended variation between GMO and non-GM

comparable plants, with no preferential functional categories

regulated and at expression levels usually falling into the gener-

ally acceptable range, naturally occurring in different cultivars

or ecotypes of the species. While the differences between GM

and non-GM plants are minor, a detailed understanding of their

causes would facilitate the development of strategies to mini-

mize them and would put the social debate on substantial

equivalence of GM and conventionally bred plants in a more

accurate context.

Transcriptomic comparison of the conventional rice line Senia

and the transgenic stabilized line Senia-afp (constitutively

expressing the antifungal protein AFP) showed differential

expression of around 0.40% in the analysed transcripts. The

effects of varying environmental conditions were minimized by

analysing leaves of V2 plantlets grown under in vitro homoge-

neous conditions. The magnitude of regulated transcripts is sim-

ilar to those reported for other species and transgenes. Minor

transcriptional regulation has been reported in the model Ara-

bidopsis species, expressing various marker (El Ouakfaoui and

Miki, 2005), herbicide resistance (Abdeen and Miki, 2009) or

drought tolerance-related transcription factor (Abdeen et al.,

2010) genes. Commercialized GMOs such as insect-resistant

MON810 maize have been shown to have very limited and vari-

ety-dependent transcriptome regulation (Coll et al., 2008). This

event, and also glyphosate-tolerant NK603 maize, had fewer

transcriptomic changes than those produced by environmental

and conventional breeding factors (Barros et al., 2010; Coll

et al., 2010). Similar results have been reported for rice plants

producing CsFv antibodies (Batista et al., 2008) and anthranilate

synthase a subunit (Dubouzet et al., 2007); glyphosphate-

tolerant soybean (Cheng et al., 2008) and wheat plants either

producing phytase (Gregersen et al., 2005) or a glutelin subunit

(Baudo et al., 2009). So they were considered adequate for use

as models to assess the possible causes of transcriptomic unin-

tended effects.

Further analysis of one-fifth of the sequences regulated in

Senia and Senia-afp indicated that the transgene is not the sole

cause of unintended differences between GM and non-GM iso-

genic rice plants.

By comparison of transgenic Senia-afp and non-GM Senia-

afp()) plants that had undergone the same Agrobacterium-

based transformation process, sequences were identified whose

differential expression was definitely not associated to the pres-

ence and ⁄ or expression of the transgene. Our results show that

it is possible to separate the effects of the transgene from those

of the transformation process when the appropriate comparator

is used, and that the transformation process caused around

35% of the unintended effects observed in Senia-afp and Se-

nia, with 21% and 52% being the lower and upper limits of

the 95% confidence interval (CI) calculated according to New-

combe (1998).

The introduction of a transgene into a plant genome usually

involves tissue culture technologies to induce dedifferentiation

of plant tissues and allow selection and regeneration of an

intact plant from a single GM cell. These procedures are associ-

ated with stresses such as wounding, osmotic stress, insufficient

Table 2 Singular enrichment analysis of sequences differentially expressed in Senia-afp and Senia plants using the AgriGO tool

GO ID GO term (biological process)

Annotated ⁄ total

no. in query list

Annotated ⁄ total

no. in background

Adjusted

P-value*

GO:0070887 Cellular response to chemical stimulus 6 ⁄ 175 7 ⁄ 30 241 8.1 e)11

GO:0009056 Catabolic process 13 ⁄ 175 553 ⁄ 30 241 0.0053

GO:0044248 Cellular catabolic process 10 ⁄ 175 432 ⁄ 30 241 0.029

GO:0006006 Glucose metabolic process 6 ⁄ 175 180 ⁄ 30 241 0.042

GO:0042221 Response to chemical stimulus 9 ⁄ 175 394 ⁄ 30 241 0.042

*Based on the Fisher’s statistical method and the Yekutieli FDR multiple test correction method (Benjamini and Yekutieli, 2001).

ª 2010 The Authors



nutrient, growth regulators and antibiotics (Carman, 1995) and

have been shown to induce genetic changes (Filipecki and

Malepszy, 2006; Latham et al., 2006). Plant in vitro culture is

used as a method to generate genetic variation for breeding

purposes (Larkin and Scowcroft, 1981; Veilleux and Johnson,

1998), and GM plants obtained through callus had higher vari-

ability than those obtained using the floral dip technique, which

does not include tissue culture (Labra et al., 2001). Agrobacte-

rium and other pathogen infections may also cause mutations

(Budziszewski et al., 2001; Lucht et al., 2002). This is in agree-

ment with our results associating as much as c. 35% of tran-

scriptional unintended differences between GM and

conventional rice to the transformation process. Some

sequences differentially expressed in Senia-afp and conventional

Senia-afp()) plants were also regulated in Senia and all three

Senia-afp events examined. We could not discount the tran-

scriptional regulation being linked to epigenetic changes (Kaep-

pler et al., 2000), preferential changes in certain sequences

associated to tissue culture technologies and ⁄ or genetic differ-

ences in the individual Senia plant, which gave rise to Senia-afp

and the Senia line as a whole.

We estimate that around 15% [95% CI: (6%, 30%)] of the

transcriptomic differences between Senia-afp R25-14 and the

conventional comparator Senia line were event specific. They

mostly affected unannotated sequences, with alignment to the

rice genome predicting their position on different chromo-

somes. This means that these position effects were not

restricted to sequences in the single transgene insertion site.

Agrobacterium T-DNA insertions usually result in small deletions

of plant DNA at the insertion site and insertion of superfluous

DNA, but they can also be associated with large-scale rear-

rangements or deletions (Latham et al., 2006). Interactions

between the transgene and proximal host sequences can also

result in unintended effects, which may also have more distant

downstream effects (Miki et al., 2009).

Differential expression of only half the sequences [95% CI:

(34%, 66%)] regulated in Senia-afp and Senia could, therefore,

be directly associated with the transgene or its expression; this

represented around 0.20% of the transcriptome in this GMO.

Senia transgenic plants harbouring the same construct as Senia-

afp, with the only exception being a weak promoter driving the

expression of afp, had around 1000-fold lower levels of afp

than Senia-afp and, compared to Senia, had similar mRNA lev-

els for 9 of the 17 sequences for which regulation in Senia and

Senia-afp was attributed to the transgene (data not shown).

This suggests this regulation was caused by the afp expression

level.

As in other GM plants [see e.g. (El Ouakfaoui and Miki,

2005; Cheng et al., 2008; Coll et al., 2008)], differentially

expressed sequences in Senia-afp and Senia rice lines are

involved in multiple biological processes. Regulated sequences

were mainly involved in response to chemical stimulus, catabolic

processes and glucose metabolic processes. The transformation

techniques, the insertion site and the transgene do not seem to

preferentially regulate any specific type of sequences among

those analysed by RT-qPCR, although the small number of

sequences in each group should be noted.

Among the nine sequences grouped under response to

chemical stimulus, five are involved in the stress response, i.e.

peroxidase reactions and response to oxidative stress

(BU673129, putative peroxidase; D21280.1, glyceraldehyde-3-

phosphate dehydrogenase; D10425.1, catalase), water depriva-

tion (AK107749.1, oryzain beta) and defence response to

bacteria and fungi (AY050642.1, seedling pathogenesis-related

protein PR4). The last four genes in this GO term (AK058556.1

and AK062728.1, auxin-responsive SAUR gene family members;

9636.m00152, similar to auxin-induced SAUR-like protein; and

Table 3 RT-qPCR expression patterns of a selection of sequences

differentially expressed in Senia-afp and conventional Senia plants.

T-test-based comparison of their expression levels in Senia-afp()) vs.

Senia; and in three different events of Senia-afp (R25-14, R25-15

and R25-12) vs. Senia. Pair-wise comparisons giving T-test values

with statistical significance (P < 0.05) are highlighted in grey. From

these data, the differential expression of each sequence in geneti-

cally modified and conventional rice was associated to the transfor-

mation process (t, dots), event-specific insertion site and associated

genomic rearrangements (e, white) or the transgene (T, dense dots)

*See Table 1.

REPORTER: hgr sequence; T, t and e: sequences which differential expression

was associated to the transgene (T), the transformation process (t) or the

specific event (e).

ª 2010 The Authors



9634.m02249, auxin-responsive Aux ⁄ IAA gene family member)

are related to the response to auxin. Auxin homoeostasis can

regulate the activation of defence responses in various plant

species, including Arabidopsis (Navarro et al., 2006; Wang

et al., 2007) and rice (Domingo et al., 2009).

Catabolic and glucose metabolic processes are overrepre-

sented GO terms as well. These include the five genes related

to the stress response and an enzyme of the phenylpropanoid

pathway (BI807677, phenylalanine ammonia-lyase activity, PAL,

involved in defence reactions). They also include four genes

involved in glycolysis (BU667041, fructose-1,6-bisphosphatase,

putative; AK062270.1, aldolase C-1; BI810367, putative fruc-

tose-bisphosphate aldolase; and D21280.1), two involved in cel-

lulose biosynthesis (AK120236.1, CESA3, cellulose synthase;

and AK067850.1, RSW1-like cellulose synthase catalytic sub-

unit), and an enzyme of the tricarboxylic acid cycle TCA

(AK106451.1, phosphoenolpyruvate carboxylase). Plant stress

responses are associated with a wide array of mechanisms

involving increased demands for energy and its redistribution.

Different publications profiling the plant response to pathogens

show regulation of primary metabolism genes that likely play a

role in providing energy for the resistance response [for review,

see (Bolton, 2009) and references therein]. Plant respiration is

stimulated during the stress response. This involves glycolysis (a

cytosolic pathway converting glucose to pyruvate and resulting

in a small net gain of ATP) and the mitochondrial TCA cycle

(generating reducing equivalents that are used by the electron

transport chain to fuel ATP synthesis). Reduction in photosyn-

thesis and changes in carbohydrate metabolism are also well

documented.

Through expression of afp, Senia-afp plants are protected

against infection by Magnaphorte oryzae (Coca et al., 2004).

Here, we show the unintended modulation of the expression of

a set of genes which have been related to the response to

stress, with the Senia-afp gene regulation partly mimicking an

abiotic stress response on wounding. In response to wounding,

conventional Senia plants overexpressed around half the

sequences that were up-regulated in Senia-afp vs. Senia in a

transgene-dependent manner. Remarkably, these sequences

were not further up-regulated in Senia-afp upon wounding.

From these results, we consider that unintended overexpression

of genes involved in the stress response could potentially

enhance the resistance of certain GM plants. In a defensome

study of GM Senia rice overexpressing the antimicrobial peptide

Cecropine A, Campo and collaborators (Campo et al., 2008)

showed altered levels of genes involved in protection against

oxidative stress, which correlated with the tolerance of Cecro-

pine A plants to diverse bacterial and fungal pathogens and oxi-

dative stress. The expression of genes encoding antimicrobial

peptides such as AFP and Cecropine A in transgenic plants

could involve unintended modifications in the transcription lev-

els of host stress-related genes and potentially extend resistance

to a broader range of stress conditions.

In conclusion, differences between transgenic Senia-afp and

conventional Senia lines were around 0.40% of the transcrip-

tome. Around 35% of these differences were attributed to the

procedure followed to obtain GM plants. In vitro culture tech-

niques, cell dedifferentiation and plant regeneration are used to

obtain GM plants but are also routinely used for micropropaga-

tion, to obtain virus-free material and numerous other commer-

cial applications. In addition, around 15% of the transcriptomic

changes in Senia-afp and Senia plants were event specific, asso-

ciated with the insertion site and genome rearrangements

occurring during transformation. The generation of many inde-

pendent transgenic lines would, therefore, create a range of

position effects and allow for the eventual identification of lines

in which unfavourable position effects are absent. Finally, only

around half the sequences regulated in GM Senia-afp and con-

ventional lines were associated to the transgene. A significant

number of the sequences unintentionally regulated in Senia-afp

were involved in the response to stress and could, as such,

enhance the resistance characteristics of the transgene in GM

plants. This should be taken into account in food safety

assessment studies.

Experimental procedures

Plant material

GM rice (Oryza sativa L. cv. Senia) lines resistant to fungal infec-

tions through constitutive expression of the Aspergillus gigan-

teus antifungal protein AFP were used (Senia-afp) (Coca et al.,

2004). Transgenic lines were produced by Agrobacterium-medi-

ated transformation using three independent transformation

events, Senia-afp R25.12, Senia-afp R25.14 and Senia-afp

R25.15, all harbouring a single copy of the transgene. The

expression cassette included the maize ubiquitin constitutive

promoter (ubi-P) and the A. tumefaciens nopaline synthetase

terminator (nos-T) driving afp expression, and the hygromycin

phosphotransferase (hptII) selection gene. Normally, stabilized

plants homozygous for the transgene were used, with the com-

mercial japonica line Senia used as the conventional isogenic

line. In addition, non-transgenic plants obtained by self-pollina-

tion of hemizygous Senia-afp R25.14 plants [Senia-afp())] were

included in the study.

Seeds were surface sterilized, germinated in vitro and sown in

glass tins, three in each, containing 100 mL sterile MS medium

(Murashige and Skoog, 1962) supplemented with 3% sucrose

Sequence internal code

OsA

09

OsA

13

OsA

15

OsA

16

OsA

17

OsA

18

OsA

19

OsA

20

OsA

21

OsA

22

OsA

25

OsA

27

OsA

29

OsA

33

OsA

36

OsA

37

OsA

39

Senia-afp/Senia

Senia: Wounding/control

Senia-afp: Wounding/control

Figure 2 Transcriptional response to wounding of 17 sequences with differential expression in Senia-afp and conventional Senia plants associated to

the transgene. Pair-wise comparisons giving T-test values with statistical significance (P < 0.05) are highlighted in black (sequences induced in the first

versus the second plant ⁄ conditions) or grey (sequences down-regulated in the first versus the second plant ⁄ conditions).

ª 2010 The Authors



and 0.7% agar. They were incubated in a culture chamber at

25 ± 1 �C with a photoperiod of 16 h light ⁄ 8 h dark under

fluorescent Sylvania Cool White lamps. All plants were grown

together in the same chamber with the glass tins placed ran-

domly. Rice plantlets were sampled at the vegetative two-leaf

stage (V2), immediately frozen in liquid nitrogen and stored at

)80 �C. Each sample consisted of two leaves of ten plantlets,

without lesions, taking three biological replicates. When

required, two 0.5-cm-diameter circular wounds were applied

per leaf, 4 h before sampling.

Genomic DNA extraction

Genomic DNA from 50 mg of T1 transgenic rice leaves was

extracted using the commercial NucleoSpin� Plant II kit (Mache-

rey-Nagel, Duren, Germany) according to the manufacturer’s

instructions. It was quantified by UV absorption at 260 nm in a

NanoDrop ND1000 spectrophotometer (Nanodrop technologies,

Wilmington, DE). The OD 260 ⁄ 280 nm absorption ratios [mean

and standard deviation (SD) = 1.93 ± 0.17] were used to con-

firm the purity of the DNA samples.

Total RNA extraction

Total RNA was extracted using a protocol based on the Trizol

reagent (Invitrogen Life Technologies, Carlsbad, CA) and puri-

fied with the Qiagen RNeasy MiniElute Cleanup kit (Qiagen, Hil-

den, Germany) according to the manufacturer’s instructions. It

was quantified by UV absorption at 260 nm in a NanoDrop

ND1000 spectrophotometer (Nanodrop technologies). Agarose

gel electrophoresis and OD 260 ⁄ 280 nm absorption ratios

(mean and SD = 2.09 ± 0.02) were used to confirm the integ-

rity and purity of the RNA samples.

Microarray hybridization and analyses

The GeneChip� Rice Genome Array (Affymetrix, Santa Clara,

CA) was used to search for transcriptome differences between

Senia and Senia-afp R25.14. The Rice GeneChip contains

sequences from the two most common rice cultivars, indica and

japonica. It contains 54 168 probe sets to analyse 48 564 tran-

scripts from the O. sativa japonica cultivar group. Sequence

information for this array includes public contents from Uni-

Gene Build #52 (7 May 2004), GenBank� mRNAs (13 July

2004) and 59 712 gene predictions from TIGR’s osa1 version

2.0 release (http://www.affymetrix.com/).

Three GeneChips were employed to analyse three indepen-

dent Senia and Senia-afp (R25.14) biological replicates. Hybrid-

ization and statistical analysis were performed at the Genomics

Unit, Parque Cientıfico in Madrid as previously described (Coll

et al., 2008). Briefly, the integrity of total RNA samples was

assessed by capillary electrophoresis using a Bioanalyser 2100

(Agilent Technologies, Palo Alto, CA). The GeneChip IVT Label-

ing kit (Affymetrix) was used to in vitro synthesize biotin-

labelled cRNA from complementary DNA (cDNA) obtained with

the One-cycle cDNA Synthesis kit (Affymetrix). Fifteen micro-

grams of biotinylated cRNA were fragmented into sequences of

around 100 nt and these hybridized to the GeneChip Rice Gen-

ome Array (Affymetrix) in the GeneChip Hybridization Oven

640 (Affymetrix) for 16 h at 45 �C. The chips were subse-

quently washed and fluorescently labelled with phycoerythrin

using the antibody amplification step in the GeneChip� Fluidics

Station 450, and the fluorescence quantified using the Gene-

Chip� 3000 scanner device. The data was extracted using the

RMA software (Irizarry et al., 2003), which includes background

adjustment, quantile normalization and summarization. Gene

ontology analysis was performed using the AgriGO computa-

tional tool, considering probes with expression changes greater

than twofold, P-values below 0.05 and minimal fluorescence

signal intensity of 200 units.

Reverse transcription and real-time PCR amplification

The expression of 43 selected sequences, the 18S ribosomal

RNA, b-actin and EF-Ia housekeeping genes and the afp trans-

gene were assayed by RT-qPCR. Reverse transcription was per-

formed on 2000 ng total RNA, previously treated with Turbo

DNase (Ambion, Austin, TX) using 50 U of MultiScribe Reverse

Transcriptase (Applied Biosystems, Foster City, CA) and random

hexamer primers (Applied Biosystems) according to the manu-

facturer’s protocol. For each sample, cDNA was prepared at

least in duplicate and the 43 sequences were analysed with all

cDNA preparations. The absence of remaining DNA targets was

confirmed by real-time PCR analyses of DNase-treated RNA

samples.

qPCR assays targeting the 43 selected sequences were devel-

oped based on SYBR-Green technology. PCR primers were

designed using the Beacon Designer 7.0 software (Premier Bio-

soft International, Palo Alto, CA), targeting the sequences used

for generation of the GeneChip� Rice Genome Array. qPCR

assays were performed in a 20 lL volume containing 1· SYBR

Green PCR Master Mix (Applied Biosystems), the optimized con-

centration of primers (see Supplementary Table S1) and 1 lL

cDNA. Reaction conditions were as follows: (i) initial denatur-

ation (10 min at 95 �C); (ii) amplification and quantification (50

repeats of 15 s at 95 �C and 1 min at 60 �C) and (iii) melting

curve program (60–95 �C with a heating rate of 0.5 �C ⁄ s).Melting curve analyses produced single peaks, with no primer–

dimer peaks or artefacts, indicating the reactions were specific.

All oligonucleotides (Supplementary Table S1) were purchased

from MWG Biotech AG (Ebersberg, Germany).

Reactions were run on a 7500 Fast Real-Time PCR System

(Applied Biosystems) in duplicate or triplicate. Linearity (R2) and

efficiency (E = 10[)1 ⁄ slope]) (Rasmussen, 2001) of each reaction

were within accepted values. The suitability of the housekeep-

ing genes as internal standards was confirmed in our samples

through the geNORM v3.4 statistical algorithm, with M-values

below 0.5 in all cases.

Senia-afp()) plants were identified by analysing genomic DNA

from T1 transgenic lines (R25.14) by means of qPCR targeting

the afp transgene and the b-actin reference gene.

Bioinformatics expression analysis

RT-qPCR data were normalized and statistically analysed using

the Genex software v.5.1.1.2 (MultiDAnalyses, Goteborg,

Sweden). The Benjamini and Hochberg False Discovery Rate

multiple testing correction was applied (Benjamini and

Hochberg, 1995).

The Affymetrix software was used to identify a representative

public ID and target description of each differentially expressed

sequence. Biological processes of these sequences were deter-

mined on the basis of the homology of genes with known func-

tions using the Gene Ontology annotation files.

The singular enrichment analysis of sequences differentially

expressed in Senia-afp and Senia plants was carried out using

the AgriGO computational tool (Zhou and Su, 2007) using Fish-

ª 2010 The Authors



er’s statistical method and the Yekutieli false discovery rate

(FDR) multiple test correction method (Benjamini and Yekutieli,

2001).

The singular enrichment analysis of sequences regulated by

the transformation techniques, the insertion site and the trans-

gene was performed with the AgriGO tool, using the whole set

of sequences analysed by RT-qPCR as the customized reference,

the hypergeometric statistical test with Yekuleti adjustment test

and P-value 0.05.

Acknowledgements

We thank R. Collado, N. Company (UdG) and M. Palaudelmas

(CRAG) for technical assistance; S. Burgess for revision of the

English; E. Mele (CRAG) for valuable suggestions; E. Garcia-Ber-

thou (UdG) for assistance with the statistics, and P. Puigdomen-

ech (CRAG) for critically reading the manuscript. This work was

financially supported by the Spanish MEC project, ref.

AGL2007-65903 ⁄ AGR. M.M. received a studentship from the

Fundacion Ramon Areces.

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Supporting information

Additional Supporting information may be found in the online

version of this article:

Table S1 Selected sequences and oligonucleotides used for RT-

qPCR.

Table S2 Representative public ID, target description and Gene

Ontology biological process, molecular function and cellular

component terms associated to the 196 sequences differentially

expressed in Senia-afp vs. Senia.

Please note: Wiley-Blackwell are not responsible for the content

or functionality of any supporting materials supplied by the

authors. Any queries (other than missing material) should be

directed to the corresponding author for the article.

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only half the transcriptomic differences between resistant genetically

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