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BMC Genomics

BioMed Central

Open Access

Research article

Comparative 454 pyrosequencing of transcripts from two olive
genotypes during fruit development
Fiammetta Alagna1, Nunzio D'Agostino2, Laura Torchia3, Maurizio Servili4,
Rosa Rao2, Marco Pietrella5, Giovanni Giuliano5, Maria Luisa Chiusano2,
Luciana Baldoni*1 and Gaetano Perrotta*3
Address: 1CNR – Institute of Plant Genetics, Via Madonna Alta 130, 06128 Perugia, Italy, 2Department of Soil, Plant, Environmental and Animal
Production Sciences, University of Naples 'Federico II', Via Università 100, 80055 Portici, Italy, 3ENEA, TRISAIA Research Center, S.S. 106 Ionica,
75026 Rotondella (Matera), Italy, 4Department of Economical and Food Science, University of Perugia, Via S. Costanzo, 06126 Perugia, Italy and
5ENEA, Research Center CASACCIA, S.M. Galeria 00163, Rome, Italy
Email: Fiammetta Alagna - fiammetta_a@hotmail.com; Nunzio D'Agostino - nunzio.dagostino@gmail.com;
Laura Torchia - laura.torchia@enea.it; Maurizio Servili - servimau@unipg.it; Rosa Rao - rosa.rao@unina.it;
Marco Pietrella - marco.pietrella@enea.it; Giovanni Giuliano - giovanni.giuliano@enea.it; Maria Luisa Chiusano - chiusano@unina.it;
Luciana Baldoni* - luciana.baldoni@igv.cnr.it; Gaetano Perrotta* - gaetano.perrotta@enea.it
* Corresponding authors

Published: 26 August 2009
BMC Genomics 2009, 10:399

doi:10.1186/1471-2164-10-399

Received: 15 April 2009
Accepted: 26 August 2009

This article is available from: http://www.biomedcentral.com/1471-2164/10/399
© 2009 Alagna et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract
Background: Despite its primary economic importance, genomic information on olive tree is still
lacking. 454 pyrosequencing was used to enrich the very few sequence data currently available for
the Olea europaea species and to identify genes involved in expression of fruit quality traits.
Results: Fruits of Coratina, a widely cultivated variety characterized by a very high phenolic
content, and Tendellone, an oleuropein-lacking natural variant, were used as starting material for
monitoring the transcriptome. Four different cDNA libraries were sequenced, respectively at the
beginning and at the end of drupe development. A total of 261,485 reads were obtained, for an
output of about 58 Mb. Raw sequence data were processed using a four step pipeline procedure
and data were stored in a relational database with a web interface.
Conclusion: Massively parallel sequencing of different fruit cDNA collections has provided large
scale information about the structure and putative function of gene transcripts accumulated during
fruit development. Comparative transcript profiling allowed the identification of differentially
expressed genes with potential relevance in regulating the fruit metabolism and phenolic content
during ripening.

Background
An improvement of our knowledge on gene composition
and expression is essential to investigate the molecular
basis of fruit ripening and to define the gene pool
involved in lipid and phenol metabolism in an oil crop

species as olive, characterized by a peculiar fatty acid and
antioxidant composition.
The availability of complete genome sequences and large
sets of expressed sequence tags (ESTs) from several plants
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has recently triggered the development of efficient and
informative methods for large-scale and genome-wide
analysis of genetic variation and gene expression patterns.
The ability to monitor simultaneously the expression of a
large set of genes is one of the most important objectives
of genome sequencing efforts. In this respect, the 454
pyrosequencing technology [1] is a rather novel method
for high-throughput DNA sequencing, allowing gene discovery and parallel efficient and quantitative analysis of
expression patterns in cells, tissues and organs.
In the past few years, several studies based on comparative
high throughput sequencing of plant transcriptomes
have, indeed, allowed the identification of new gene functions, contaminant sequences from other organisms,
alterations of gene expression in response to genotype, tissue or physiological changes, as well as large scale discovery of SNPs (Single Nucleotide Polymorphisms) in a
number of model and non model species, such us maize,
grapevine and eucalyptus [2-5].
Olive is the sixth most important oil crop in the world,
presently spreading from the Mediterranean region of origin to new production areas, due to the beneficial nutritional properties of olive oil and to its high economic
value.
It belongs to the family of Oleaceae, order of Lamiales,
which includes about 10 families for a total of about
11,000 species. Members of this order are important
sources of fragrances, essential oils and phenolics claiming for numerous health benefits, or providing valuable
commercial products, such as wood or ornamentals.
Information on the genome sequence and transcript profiles of the entire clade are completely lacking.
Olive is a diplod species (2n = 2x = 46), predominantly
allogamous, with a genome size of about 1,800 Mb [6,7].
In spite of its economical importance and metabolic peculiarities, very few data are available on gene sequences
controlling the main metabolic pathways.
Olive accumulates oil mainly in the drupe mesocarp and
its content can reach up to 28–30% of total mesocarp
fresh weight. Olive oil shows a peculiar acyl composition,
particularly enriched in the monounsaturated fatty acid
oleate (C18:1), deriving from the desaturation of stearate.
Oleate can reach percentages up to 75–80% of total fatty
acids, while linoleate (C18:2), palmitate (C16:0), stearate
(C18:0) and linolenate (C18:3) represent minor components. The final acyl composition of olive oil varies enormously among varieties. Environmental factors, such as
temperature and light during fruit ripening, can deeply
influence the balance between saturated and unsaturated
fatty acids [8].

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The chemistry of phenolic oleosides is attracting an
increasing interest of pharmacological research and agrifood biotechnology, and the biochemical pathway leading to their biosynthesis and regulation has been recently
deeply evaluated [9], even if the genetic control still
remains completely unknown.
Secoiridoids represent the most important class of phenolics and they arise from simple structures, like tyrosol and
hydroxytyrosol, to quantitatively more important conjugated forms like oleuropein, demethyloleuropein, 34DHPEA-EDA and ligstroside [10]. Oleuropein is the
main secoiridoid, representing up to the 82% of total
biophenols, known as the bitter principle of olives and
responsible for major effects on human health and for
releasing phytoalexins against plant pathogens [10].
Another secoiridoid with relevant health functions is oleocanthal (deacetoxy ligstroside aglycone) [11].
Developing olives contain active chloroplasts capable of
photosynthesis, thus representing significant sources of
photoassimilates. While chlorophyll is localized mostly
in the epicarp, the mesocarp contains significant amounts
of other photosynthetic pathway components, such as
phosphoenol pyruvate carboxylase [12].
Olive fruit development and ripening, takes place in
about 4–5 months and includes the following phases: i)
fruit set after fertilization, ii) seed development, iii) pit
hardening, iv) mesocarp development and v) ripening.
During the ripening process, fruit tissues undergo physiological and biochemical changes that include cell division
and expansion, oil accumulation, metabolite storage, softening, phenol degradation, colour change (due to
anthocyanin accumulation in outer mesocarp cells). Oil
synthesis starts after pit hardening, reaching a plateau
after 75–90 days, while the phenolic fraction is maximum
at fruit set and decreases rapidly along fruit development.
This work is aimed at defining the transcriptome of olive
drupes and at identifying ESTs involved in phenolic and
lipid metabolism during fruit development. Drupes from
two cultivars have been used: a widely cultivated variety
characterized by a very high phenolic content, and an
oleuropein-lacking natural variant; two developmental
stages, at completed fruit set and at mesocarp development, representing diverse sets of expressed genes, were
analyzed using 454 pyrosequencing.

Results
Sequencing output
The starting materials to explore the olive fruit transcriptome were fruit pools from two Olea europaea cultivars,
Coratina and Tendellone (C and T), showing striking differences in their biophenol accumulation pattern (Figure 1,

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Four enriched full-length ds-cDNA collections (see Methods) were obtained and their 454 pyrosequencing provided a total of 261,485 sequence reads, corresponding to
58.08 Mb, with an average read length of 217–224 nt,
depending on the cDNA sample (Table 1). The 4-step procedure adopted by the ParPEST pipeline to process the
454 EST reads is summarised in Figure 3.

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Figure 1
Plant material
Plant material. Fruit mesocarp and epicarp of cvs. Coratina
and Tendellone.

2). C is cultivated in the Apulia region and represents the
most widely cultivated variety of Italy, while T is a minor
cultivar locally spread in Central Italy. A previous SSR
analysis reported a very high genetic distance between
them [13]. These cultivars also differ markedly in their
oleuropein concentration (272.9 mg g-1 dw in C, decreasing strongly during fruit ripening, and 0.3 mg g-1 dw in T,

350
CORATINA
TENDELLONE

300

Polyphenols (mg g -1 DW)

decreasing only slightly during ripening). In contrast, the
content of the 3-4DHPEA-EDA intermediate compound
was similar between genotypes (data not shown).

250

200

150

100

50

ESTs were masked to eliminate sequence regions that
would cause incorrect clustering. Targets for masking
include simple sequence repeats (SSR, also referred to as
microsatellites), low complexity sequences (including
poly-A tails) and other DNA repeats. The number of ESTs
masked for each category, as well as the total nucleotides
that were masked, are shown in Table 2.
The most frequent DNA repeats, identified using RepBase
as the filtering database, were ribosomal RNA (both SSU
and LSU); LTR retrotransposons from the BEL type family,
Gypsy and Copia; non-LTR retrotransposons from the
CR1 superfamily and, finally, a batch of retro pseudogenes (CYCLO, L10, L31, L32) (data not shown).
In order to assess EST redundancy in the whole collection
and provide a survey of the Olea europaea drupe transcriptome, masked EST sequences were pair-wise compared
and grouped into clusters, based on shared sequence similarity. As a consequence, the obtained clusters are ESTs
which are most likely products of the same gene. Each
cluster was then assembled into one or more tentative
consensus sequences (TCs), which were derived from
multiple EST alignments. As described in Methods, TCs
within a cluster shared at least 90% identity within a window of 100 nucleotides. Therefore, the presence of multiple TCs in a cluster could be due to possible alternative
transcripts, to paralogy or to domain sharing. In addition,
all the ESTs that, during the clustering/assembling process, did not meet the match criteria to be clustered/assembled with any other EST in the collection, were defined as
singleton ESTs. The combination of TCs and singletons
are referred to as unique transcripts.

0
45

60

75

90

105

120

135

150

165

180

Days after flow ering

Figure 2 the course of fruit ripening
dellone in in polyphenol content between cv. Coratina and TenChanges
Changes in polyphenol content between cv. Coratina
and Tendellone in the course of fruit ripening. Arrows
indicate the dates of sample collection: at 45 and 135 DAF.

The total number of clusters generated was 22,904. They
were assembled into 26,563 TCs comprising 185,913 EST
reads. The TC length ranged between 102 (min) and
4,916 (max) nucleotides, while TC average length was 355
nucleotides. 2,406 were the clusters assembled into multiple TCs (ranging from 2 to 14). The total number of singleton ESTs (sESTs) was 75,570, with an average length of
179 nucleotides (Table 3, 4).

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Table 1: 454 sequencing raw data

SAMPLE

HQ READS

HQ BASES

AVERAGE LENGTH (bp)

Coratina 45 DAF
Coratina 135 DAF
Tendellone 45 DAF
Tendellone 135 DAF

51,659
61,488
71,112
77,226

11,215,346
13,769,788
15,963,353
17,127,266

217.10
223.94
224.48
221.78

261,485

58,075,754

222.82

HQ READS: Quality passed reads, HQ BASES: Quality passed bases.

The analysis of the full EST collection from this work
revealed an average GC-content of 42.5%, ranging from
less than 16% to more than 63%.
Database web interface
The OLEA EST database consists of a main relational database (MySQL) which collects raw as well as processed data
generated by ParPEST. This is supported by three local satellite databases: myENZYME, a local copy of the ENZYME
repository which was built by parsing the enzclass.txt and
the enzyme.dat files (release 04 Nov 2008) retrieved from
454 EST READS
RepBase

REPEAT MASKING
SIMPLE
REPEATS

LOW
COMPLEXITY

RepBase
TARGETS

MASKED ESTs

CLUSTERS

^^D>/E'
myKEGG
TCs

myENZYME

UniProtKB

&hEd/KE > EEKd d/KE

myGO

Figure 3
EST processing work-flow
EST processing work-flow.

BLAST REPORT

The web interface http://454reads.oleadb.it/ includes Java
tree-views for easy object navigation as well as the possibility to highlight on-the-fly the enzymes in the pathway
image files retrieved from the KEGG FTP site.
Functional annotation
In order to identify Olea unigenes coding for proteins with
a known function, we used a BlastX-based annotation that
provided 12,560 TCs with significant similarities to proteins in the UniProtKB database; the remaining 14,003
(52.7%) had no function assigned. A higher number of
sESTs with no function was obtained (58,835), representing 77.85% of the total.

When considering annotated TCs and sESTs with respect
to the origin of the protein data source, the bulk of the
identifications (73% – 75%), concerned proteins of plant
origin, as expected.

>h^dZ/E'

SINGLETONS

the ExPASy FTP site; myGO, a mirror of the Gene Ontology database, which was built by running the seqdblite
MySQL script (version 20081102) downloaded from the
GO database archives; myKEGG, which was built by parsing XML files of the KEGG pathways (release 21 October
2008), retrieved from the KEGG [14] FTP site. A PHPbased web application provides user-friendly data querying, browsing and visualization.

TCs and sESTs coding for enzymes with assigned EC
number were 5,040 and 5,864, respectively (Figure 4A)
following the ENZYME classification scheme http://
www.expasy.org/enzyme/. The majority of the enzymerelated unigenes encode for transferases (3,982), hydrolases (2,628) and oxidoreductases (1,895). Of particular
relevance for fruit metabolism are those TCs and sESTs
involved in the biosynthesis of secondary metabolites
(761) and lipids (1,005) (Figure 4B, C). Most frequently,
the same enzymatic function is redundantly encoded by
several unigenes, this may be the result of different proteins referenced with the same EC number or the effect of
different transcripts encoding specific enzyme subunits.
Given the limited sequence length typically provided by
454 pyrosequencing, it is also plausible that in some cases

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Table 2: Number of ESTs masked for each mask sequence category

Cultivar

Coratina

Tendellone

Developmental stage
Simple repeats

45 Days After Flowering
2,683 (97,992)

135 Days After Flowering
3,586 (129,807)

45 Days After Flowering
3,168 (113,243)

135 Days After Flowering
4,199 (146,670)

Low complexity
Match in RepBase

683 (22,961)
1,781 (29,8882)
5,147 (419,835)

779 (23,895)
2,959 (450,297)
7,324 (603,999)

814 (25,082)
2,663 (468,078)
6,645 (606,403)

894 (28,066)
3,176 (559,319)
8,269 (734,055)

*The total nucleotides masked is given in brackets

different TCs and sESTs cover non-matching fragments of
the enzyme transcript coding frame.
Changes in transcript abundance
In principle, the higher the number of ESTs assembled in
a specific TC, the higher the number of mRNA molecules
encoding that particular gene in a given tissue sample.
However, differences in transcript abundance may reflect
sampling errors rather than genuine differences in gene
expression. Hence, in order to identify differentially
expressed genes in the four sequenced fruit cDNA collections, the statistical R test [15] was applied, as a measure
of the extent to which the observed differences in the gene
transcription among samples reflect their actual heterogeneity. Applying this test and further filtering criteria to
select differentially expressed TCs among the four sets (see
Methods), we selected 2,942 differentially expressed TCs,
1,627 of them with a predicted annotation and 1,315 with
no similarity with other sequences in the public databases
[see Additional file 1].

Clustering of differentially expressed TCs distinguished
gene transcripts differentially expressed during fruit development from those differentially expressed between genotypes, evidencing that the former were more numerous
than the latter. C was the genotype showing the highest
expression differences between the two stages (Figure 5A).
This result was confirmed by PCA analysis, where transcripts from the second stage of C were significantly divergent from the remaining ones (Figure 5B).
Transcript differences affect several important physiological processes that promote fruit growth and development.
Transcripts identified as differentially expressed between
45 and 135 DAF in both genotypes and between genotypes, grouped in 13 categories on the basis of their predicted annotations, showed that different biological
processes are modulated at the molecular level by strin-

gent genetic and developmental signals (Figure 6). Transcripts involved in photosynthesis, in biosynthesis of
structural proteins (histones, aquaporins, ribosomal proteins, tubulins, pollen allergens), in terpenoid and flavonoid biosynthesis, in cell wall metabolism, in cellular
communication (hormone biosynthesis and regulation,
cascades of signal transduction) and responses to biotic
and abiotic stresses, were mainly expressed at 45 DAF,
whereas the majority of gene transcripts related to different primary metabolic pathways (carbohydrate, lipid,
amino acid and protein metabolism) as well as transcription factors and regulators and genes involved in vitamin
biosynthesis, were more expressed at 135 DAF (Figure 6).
A wide set of genotype-specific TCs, mainly related to hormone biosynthesis and signalling, responses to abiotic
and biotic stresses, biosynthesis of terpenes and phenylpropanoids, were also observed (Table 5).
Furthermore, TCs encoding structural enzymes synthesizing terpenoids and terpenoid precursors (such as dimethylallyl diphosphate (DMAPP) and isopentenyl
diphosphate (IPP)) fluctuated between developmental
stages (Figures 7 and 8). Transcripts involved in the
mevalonate (MVA) pathway for isoprenoid biosynthesis,
occurring in the cytoplasm, were predominantly not regulated, while six out of seven genes coding for the main
enzymes of the plastidial non-mevalonate (non-MVA)
pathway, presented TCs more abundant at 45 DAF.
Finally, transcripts involved in flavonoid biosynthesis
were also regulated between developmental stages and
genotypes (Figure 9).

Discussion
Sequencing output
This is the first report of a large-scale and comparative EST
analysis from olive fruit. Olive is one of the most impor-

Table 3: Summary of the EST assembly

Nr. of cluster

Nr. of clusters with multiple TCs

Nr. of sESTs

Nr. of TCs

Nr. of unique transcripts

22,904

2,406

75,570

26,563

102,133

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Table 4: Composition of the assembled dataset

TCs
Number of sequences

Average length (nts)

Min seq length

Max seq length

26,563

354.93

102

4,916

Number of sequences

Average length (nts)

Min seq length

Max seq length

185,913

239.47

101

412

Number of sequences

Average length (nts)

Min seq length

Max seq length

75,570

179.35

36

446

ESTs in TCs

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sESTs

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Figure 4
Depiction of enzyme-encoding TCs grouped by classes, each describing the main enzymatic activity
Depiction of enzyme-encoding TCs grouped by classes, each describing the main enzymatic activity. (A). B and
C represent TCs encoding enzymes involved in biosynthesis of plant metabolites and lipids, respectively. The number of TCs
encoding each enzyme class is reported on the x axis of each graph.

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0

1

5

,
ϰϱ  &
dϰϱ  &
dϭϯϱ  &
ϭϯϱ  &

W

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Figure 5
A. Hierarchical Clustering Analysis (HCA) of differentially expressed TCs
A. Hierarchical Clustering Analysis (HCA) of differentially expressed TCs. Color codes for expression values are
reported on the top. B – Principal component analysis (PCA) of differentially expressed TCs. The percentage of variance
explained by each component is shown within brackets. C45 = Coratina 45 DAF; C135 = Coratina 135 DAF; T45 = Tendellone
45 DAF; T135 = Tendellone 135 DAF.

tant oil crops in the world. It belongs to the Asterid clade
of angiosperms, that includes thousands of economically
important crops for which genomic information is still
scarce. The massive EST characterization described here
can be considered an initial platform for the functional
genomics of Olea europaea and will be a starting point for
the establishment of molecular tools for improving the
major quality traits in Olea species. Massively parallel EST
sequencing provided more than 102,000 unigenes consisting in 26,563 TCs and 75,570 singletons from four
fruit libraries. Considering 27 available data on expressed
genes of other plant species, such as Arabidopsis [16], it is
possible that the reported unigene set of Olea is an over

estimate of the actual number of transcripts expressed in
the fruit. This could in part be the result of unassembled
segments of TCs and sESTs pertaining to the same transcript unit. A certain amount of incomplete EST assembly
is expected as a result of the short reads provided by the
454 pyrosequencing technology.
Despite the fact that cDNA samples were prepared without any normalization process, we only found a moderate
degree of redundancy. Clustering of ESTs has indeed
reduced the number of sequences by 61% from 261,483
quality passed reads to 26,563 TCs plus 75,570 sESTs.
RepBase masking analysis has revealed a surprising

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Figure 6
gories (listed on the axis) on the basis of their predicted biological process
Transcripts identifiedxas differentially expressed between 45 DAF and 135 DAF in both genotypes grouped in functional cateTranscripts identified as differentially expressed between 45 DAF and 135 DAF in both genotypes grouped in
functional categories (listed on the x axis) on the basis of their predicted biological process.

amount of short repeats and transposable elements (TEs),
which could represent a valuable resource to develop TEderived molecular markers [17] and to investigate on Olea
genome size evolution. Also, the GC-content of 42.5%,
ranging from less than 16% to more than 63%, can provide a contribution to the evolution studies and gene
transfer dynamics within the Oleaceae taxon.
Functional annotation
The percentage of TCs and singletons with no putative
function assigned was considerably elevated, possibly as a
result of gene functions specifically evolved in Olea europaea and quite divergent from those of other plant species.
The Olea fruit, indeed, presents a number of exclusive
traits, like, above all, oil and biophenol accumulation.

These traits are encoded at genomic level. On the other
hand, the high incidence of unigenes with no assigned
function (about 70%), could be due to the poor annotation that still affects protein databases. Also, it is possible
that many TCs and sESTs could not be reliably annotated
because they did not cover the entire length of the transcript or because they represent untranslated regions
(UTRs). This could be particularly the case of our dataset
given that the 454 sequencing technology typically provides short sequence reads.
The identification in the Olea genome of transcribed
sequences similar to a wide range of phylogenetically distant organisms raises intriguing questions about the evolution of their physiological roles and about whether or

Table 5: TCs assembled from ESTs exclusively present in one of the two cultivars

Biological Process

N. of specific TCs for cv. Coratina

N. of specific TCs for cv. Tendellone

Hormone metabolism and regulation
Abiotic and biotic stress
Cell wall metabolism
Lipid metabolism
Steroid metabolism
Phenylpropanoid metabolism

13
10
9
7
9
7

5
4
3
7
0
3

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Dimethylallyl diphosphate

Isopentenyl diphosphate

Farnesyl pyrophosphate
synthetase

Geranyl diphosphate
(R)-limonene
synthase

Iridodial

C

(+)-R-limonene

Iridotrial
Deoxyloganic
acid
Loganin

7-epi-loganic
acid

Farnesyl diphosphate
Squalene

1,8-cineole
synthase C

Squalene
monooxygenase

7-ketologanin

7-ketologanic
acid

(+)-d-cadinene

C

Cycloartenol

Sterol
biosynthesis

Oleoside-11methyl ester

Brassinosteroid
biosynthesis

7-beta-1-D-glucopyranosil11-methyl oleoside

Secologanin

Ligstroside

Tryptamine
Oleuropein

Secoiridoid oleosides
biosynthesis

Polyneuridine aldehyde
C
esterase

Deacetoxyvindoline
4-hydroxylase

Vindoline

Cycloartenol
synthase

1,8-cineole

Secologanin synthase

3-alpha(S)Stryctosidine

Squalene
2,3-oxide

16-Epivellosimine

Vellosimine

spontaneous

Polyneuridine
aldehyde

Deacetylvindoline
Vinblastine
Vincrastine

Indole and ipecac
alkaloid
biosynthesis

Asparagine
Ajmaline

Figure 7
Partial representation of the metabolic pathway for terpenoid biosynthesis (Kegg map 00900)
Partial representation of the metabolic pathway for terpenoid biosynthesis (Kegg map 00900). TCs more
expressed at 45 DAF are boxed green, while those more expressed at 135 DAF are boxed purple. In black boxes are those
TCs expressed at all fruit ripening stages and in both genotypes. Genotype-specific TCs more expressed at 45 DAF, more
expressed at 135 DAF and with unchanged expression are included in green, purple and black circles, respectively, and
reported with C (Coratina) and T (Tendellone) when they are exclusively present in one of the two cultivars.

not these sequences and the related functions are the
result of recent gene transfer or the relic of an ancient past.
It is important to note that about 25% of the annotated
enzyme-coding transcripts are involved in biosynthesis of
lipids and fruit metabolites. The availability of the genetic
information related to these enzyme functions represents,
in our view, a fundamental tool for understanding the
molecular basis of the expression of traits related to fruit
phenotype and for establishing new strategies of metabolic engineering to improve the overall quality of olive
fruit.
Changes in transcript abundances
Large scale random sequencing of different fruit cDNA
collections has provided information on relative large

scale variation of gene expression. However, it should be
noted that no further experimental validation has been
performed on differentially expressed TCs passing the R
test [15].
Analysis of differentially expressed gene transcripts evidenced large differences in key genes involved in a
number of metabolic pathways that can potentially alter
most quality traits in olive fruits. In some cases, different
TCs with identical predicted annotation showed a contrasting accumulation pattern between developing stages
or between genotypes; this implies that similar, although
not identical, proteins and enzymes may undergo different expression patterns, determining a fine regulation of
metabolic pathways and the accumulation of alternative
metabolites.
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Ac-MVA pathway

Non-MVA pathway

(In cytoplasm)

(In plastids)

2 Acetyl-CoA
(AC)

Pyruvate + Glyceraldeide-3-P
DOXP synthase

ACC thiolase

1-deoxy-D-xylulose-5-P
(DOXP)

Acetoacetyl-CoA
(ACC)

(+ NADPH)

(+ NADPH)

HMG-CoA
synthase

2-C-methyl-D-erythritol-4-P
(MEP)

3-hydroxy-3-methylglutaryl-CoA
(HMG-CoA)
HMG-CoA
reductase
(HMGR)

DOXP
reductoisomerase

(+ CTP)

CDP-ME synthase

4-(CDP)-2-C-methyl-D-erythritol
(CDP-ME)

CoA

Mevalonate
(MVA)

(+ ATP)

CDP-ME kinase

MVA kinase

4-(CDP)-2-C-methyl-D-erythritol-2-P
(CDP-ME2P)

Mevalonate phosphate
(MVAP)

MECP synthase

CMP

MVAP kinase

2-C-methyl-D-erythritol 2,4-cyclo-PP
(MECP)
HMBPP synthase

Mevalonate diphosphate

MVAPP
decarboxylase

CO2
Isopentenyl diphosphate
delta isomerase

Isopentenyl diphosphate
(IPP)

1-hydroxy-2-methyl-2-(E)-butenyl-4-PP
(HMBPP)
HMBPP reductase

Dimethylallyl diphosphate
(DMAPP)

Partial representation of the metabolic pathway for biosynthesis of steroids (Kegg map 00100)
Figure 8
Partial representation of the metabolic pathway for biosynthesis of steroids (Kegg map 00100). Color codes for
boxes are the same as in Figure 7.

It is interesting to note that the C cultivar underwent a
larger degree of transcriptional modulation during fruit
development. It is possible that this is related to the very
high content in phenolic compounds at the beginning of
fruit development in this cultivar.
Comparison between fruit developmental stages
Expression differences were found for transcripts involved
in several physiological processes that promote fruit
growth and development. Plant cells require sugars to syn-

thesize lipids and acetyl-CoA is the precursor of carbon
chain elongation in all fatty acids. Photosynthesis is an
important source of sugars for mesocarp development
and olive oil biogenesis. Photoassimilates translocated
from leaves to fruit mesocarp by phloem are another
indispensable source of sugars in developing fruits [18].
At the end of the ripening process, concurrently to the
decrease of chlorophyll content in fruit mesocarp and to
the gradual color change, the intense mitochondrial respiration of photoassimilates translocated from leaves to

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Phenylalanine

Tyrosine

Caffeic acid 3-Omethyltransferase C

Caffeic acid
Cinnamic acid
C

Licodione
Kaempferol-3-Ogalactosyde

Isoliquiritigenin

Naringenin
chalcone

Naringenin

p-Coumaric
acid
4-coumarate-CoA
ligase T

p-Coumaroyl-CoA

Lignins

Kaempferol -3-Ogalactosyltransferase

Kaempferol
Flavonol
glycosydes

Liquiritigenin

Licodione
synthase

Ferulic acid

Myricetin

Flavonol
synthase/flavanone
3-hydroxylase

Flavonol-3-OC glycoside-7-O- Quercetin
glucosyltransferase

Dihydroflavonols
Dihydrokaempferol
T
4-reductase

Leucoanthocyanidins

Proanthocyanidins
(PAs)

Flavan-3-oli

Leucocyanidin
dioxygenase C

Anthocyanidins
Anthocyanidin-3-Oglucosyltransferase

Anthocyanins
Figure 9
00941, respectively)
Partial representation of the metabolic pathways for phenyl-propanoid and flavonoid biosynthesis (Kegg maps 009490 and
Partial representation of the metabolic pathways for phenyl-propanoid and flavonoid biosynthesis (Kegg maps
009490 and 00941, respectively). Color codes for boxes are the same as in Figure 7.

fruits through the phloem, becomes the main energy
source sustaining fruit ripening [19]. Consistent with this
fact, TCs with predicted functions related to photosynthesis (photoreception, Calvin cycle and oxidative phosphorilation) were more represented at 45 DAF, while
transcripts associated with carbohydrate metabolism (glycolysis/gluconeogenesis, citrate cycle, and fructose, mannose and galactose metabolism) were more represented at
135 DAF.
Generally, transcript fluctuations were consistent with the
physiological status of the fruit. The higher expression of
transcripts related to the biosynthesis of structural proteins at 45 DAF may be correlated with the intense and
rapid cell divisions during fruit growth, while the higher
expression of transcripts putatively associated with fatty
acid biosynthesis and with the assembly of storage triacylglycerols (TAGs) at 135 DAF, is in agreement with fatty
acid accumulation pattern in olive fruits, starting at about
90 DAF until the end of fruit maturation [18].

This work has allowed the identification of most TCs
related to secoiridoid conjugate (such as oleuropein) biosynthesis, deriving from the conjugation of an esterified
phenolic moiety (phenylpropanoid metabolism) and an
oleoside moiety (deoxyloganic acid deriving from the
mevalonic acid pathway) [9]. TCs related to the mevalonate pathway were not significantly regulated with the
exception of MVAPP decarboxylase, leading to the biosynthesis of IPP, a common precursor for all secoiridoid oleosides. Surprisingly, this TC was up-regulated in both
cultivars at fruit veraison, when the secoiridoid content is
decreasing. The non-MVA pathway, recently discovered in
plant chloroplasts, produces various classes of terpenoids,
mostly hemiterpenes, monoterpenes, diterpenes and carotenoids [20]. In higher plants, both pathways operate
simultaneously and their physical compartmentalization
does not preclude exchanges of metabolic intermediates,
although the nature of this crosstalk remains to be elucidated [20,21]. It is likely that the specialization of each

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BMC Genomics 2009, 10:399

pathway can play a key role in regulating the biosynthesis
of specific end products during olive fruit development.
Given the very high accumulation of secoiridoids (mainly
oleuropein) in developing fruits, the terpenoid metabolic
pathway must be strongly oriented toward the secoiridoid
biosynthesis branch. Unfortunately, the main enzymes
and related genes involved in oleuropein biosynthesis are
still unknown, hence the information provided by our
comparative genomics survey cannot provide direct
insight into the molecular basis of secoiridoids accumulation. Since C and T show extremely different oleuropein
accumulation patterns, it is likely that transcripts encoding key enzymes for oleuropein biosynthesis show specific differences in their accumulation patterns in these
two cultivars. In this respect, mining of TCs differentially
expressed in T vs. C, with functional annotations compatible with enzymes for oleuropein biosynthesis, or with no
functional annotation, is under way to address this point.
Other classes of phenolic compounds are known to be
less represented in olive fruits. Nonetheless, specific alteration of gene transcripts encoding a number of structural
enzymes suggests that even metabolites synthesized from
secondary branches can show differential expression following developmental and genetic cues.

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fruit phenolic content. On the other hand, the lack of
genetic information on the biosynthesis of secoiridoids in
plants makes it impossible to find orthologs in protein
databases, thus precluding the possibility to identify ESTs
directly correlated to their accumulation in the olive fruit.
Among genotype-specific transcripts, several TCs putatively involved in the biosynthesis of steroids with nutritional and health benefits, were reported exclusively in C.
Two TCs, specific to C, encode R-limonene synthase 1
(EC: 4.2.3.20) and 1,8-cineole synthase (EC: 4.2.3.11),
which are related to the biosynthesis of important flavour
compounds, such as (+)-R-limonene, one of the most
abundant monocyclic monoterpenes in nature [24] and
1,8-cineole, also known as eucalyptol, a monoterpenic
oxide present in many plant essential oils.
A number of other genotype-specific TCs could account
for biologically relevant differences between C and T and
provide a useful hint for focused biochemical analyses. In
general, genotype-specific TCs were prevalent in C, supporting the hypothesis that C fruits may synthesize a
wider array of secondary metabolites. The Olea EST database will be a useful tool for unravelling the biochemical
diversity of olive fruits.

Conclusion
The common precursor of both secoiridoids and indole
alkaloids, is deoxyloganic acid. Also loganin and secologanin, leading to indole alkaloid synthesis, could possibly
be involved in biosynthesis of secoiridoids [22]. The fact
that three TCs putatively encoding secologanine synthase
(EC: 1.3.3.9) are more represented at 45 DAF, while 2 TCs
are instead more represented at 145 DAF, deserves further
investigation to verify to which extent fine regulation of
different secologanine synthases can affect the actual accumulation of specific secoiridoid/indole alkaloid products.
Alternate regulation of distinct TCs sharing identical
annotations has proven to be a quite common condition
in the Olea transcriptome [see Additional file 1], suggesting that the well known plasticity of metabolite accumulation in olive fruits could be the result of the fine
modulation of genes encoding different enzyme isoforms.
Although the flavonoid content of olive fruits is relatively
low compared to other phenolic classes and the pattern of
their accumulation during fruit development is still
unknown, transcriptional differences observed between
45 DAF and 135 DAF are in general agreement with data
available from other fruit species [23].
Comparison between C and T genotypes
The EST database also contains comparative information
between fruits of the C and T genotypes. This can be particularly informative, considering that, as previously
reported, the two genotypes have extremely differentiated

In this work we describe the first large EST collection of
Olea europaea L. It represents a valuable resource to assist
a preliminary evaluation of features from the Olea europaea genome (i.e. GC content, SSR, genome annotation).
The EST database can be consulted through a user friendly
web interface that provides useful tools for data querying,
blast services, browsing and visualization.
Comparative sequencing of four fruit cDNA collections
has provided information on variation of gene expression
during fruit development and between two genotypes
with contrasting phenolic accumulation in fruits.
Analysis of differentially expressed gene transcripts evidenced large differences in key genes involved in a
number of metabolic pathways that can potentially control most quality traits in olive fruits.

Methods
Plant material
Olive drupes of two cultivars were used: Coratina (C), a
widely cultivated variety, characterized by a very high phenolic content, reaching 332,5 mg/g of total fruit dry
weight, and Tendellone (T), a low-phenolics natural variant (42,7 mg/g dw). Olive fruits were sampled from plants
of the Olive Cultivar Collection held by the CRA-OLI
(Collececco, Spoleto). The different olive trees were grown
using the same agronomic practices, including irrigation
conditions, that can affect phenolic concentration in the
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cDNA synthesis and 454 sequencing
Total RNAs were extracted from pooled fruits using the
RNeasy Plant Mini Kit (Qiagen). Contaminating genomic
DNA was removed by DNase I (Qiagen) treatment. The
RNA was quantified using a spectrophotometer at a wavelength of 260 nm and its quality was checked by running
5 μg of total RNA on 1.2% agarose gel under denaturing
conditions (Figure 10A).

The cDNA was prepared by using SMART PCR cDNA Synthesis protocol (Clontech) optimizing the conditions to
obtain high quantity of clean cDNA in a small volume.
For this reason numerous tests have been performed
applying different reaction conditions and purification
protocols.
First strand synthesis was performed using 6 ug of total
RNA for each sample, in three independent reactions,
with the use of Super Script II (Invitrogen) in a reaction
mixture containing 50 mM Tris-HCl pH 8.3, 75 mM KCl,
6 mM MgCl2, 2 mM DTT. The retro-transcription reaction
was primed with 3' SMART CDS Primer IIA (Clontech).
The SMART II™ A Oligonucleotide (Clontech), which has
an oligo(G) sequence at its 3' end, was used to create an
extended template useful for the full-length enrichment
provided by SMART™ technology. In fact, when reverse
transcriptase (RT) reaches the 5' end of the mRNA, the
enzyme's terminal transferase activity adds a few additional nucleotides, primarily deoxycytidine, to the 3' end
of the cDNA. The SMART™ II A Oligonucleotide base-pairs
with the oligo(G) sequence and RT then switches templates and continues replicating to the end of the oligonucleotide [25]. In cases where RT pauses before the end of
the template, the addition of deoxycytidine nucleotides is
less efficient than with full-length cDNA-RNA hybrids,
thus preventing base-pairing with the SMART™ II A Oligonucleotide. The SMART anchor sequences contained in
both 5' and 3' ends of cDNA serve as universal priming
sites for end-to-end cDNA amplification. In this manner,
SMART method is able to preferentially enrich for fulllength cDNAs http://www.clontech.com/images/pt/
PT3041-1.pdf.

M

T135

T45

Kb

C135

A

M

fruit. C and T cultivars were selected as the extreme variants in phenolic content among a set of twelve cultivars
(including Bianchella, Canino, Dolce d'Andria, Dritta,
Frantoio, Leccino, Moraiolo, Nocellara del Belice, Nocellara Etnea, Rosciola) surveyed for two years for oleuropein, demethyloleuropein, and 3–4 DHPEA-EDA content
(data not shown). Fruits were harvested at 45 and 135
days after flowering (DAFs). These stages correspond to
important physiological phases of fruit development:
completed fruit set and mesocarp development, respectively. Only fruit mesocarp and epicarp have been used for
RNA extraction.

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C45

BMC Genomics 2009, 10:399

6.0
4.0
3.0
2.0
1.5
1.0
0.5
0.2

B
10.0
2.0
1.0
0.5

0.1

Figure 10
cDNA sample preparation
cDNA sample preparation. A. Electrophoresis of total
RNA on denaturing agarose gel. B. Double stranded cDNA
separated on agarose gel electrophoresis. C45 = Coratina 45
DAF; C135 = Coratina 135 DAF; T45 = Tendellone 45 DAF;
T135 = Tendellone 135 DAF; M = Molecular Ladder.

For second strand synthesis, PCR was carried out on a
small aliquot (1/10th volume) of the primary template by
using Advantage 2 Polymerase Mix (Clontech). The following thermal cycling program was applied: initial denaturation at 95°C for 60 sec, followed by 15 cycles at: 95°C
denaturation for 30 sec; 55°C annealing for 30 sec; 68°C
extention for 6 min. All the PCR reactions, for each sample, were pooled together and purified by using QIAquick
PCR purification kit (Qiagen).
Double stranded cDNA was quantified with a spectrophotometer (NanoDrop 1000, Thermo Scientific) and microplate fluorimeter (Victor 2, Perkin Elmer, Wellesley, MA,
USA) and then concentrated by speed vacuum to a concentration of 500 ng/ul. The products were checked on a
2% agarose gel to verify cDNA quality and fragment
length. The main size distribution was included between
500 and 4,000 bp (Figure 10B).

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BMC Genomics 2009, 10:399

Approximately 5 μg of each cDNA sample were sheared
via nebulization into small fragments, and sequenced in a
single 454 run (the pico-titer plate was divided in four sectors) by using a GS-FLX sequencer (454 Life Sciences,
Branford, CT, USA).
Raw unprocessed EST sequences generated from this study
have been submitted to Short Read Archive (SRA) division
of the Genbank repository. 454 SFF file containing raw
sequences and sequence quality information can be access
through the SRA web site under accession number
SRA008270. Data files can also be directly accessed via
FTP ftp://ftp.ncbi.nlm.nih.gov/sra/Submissions/SRA008/
SRA008270/.
Bioinformatics EST processing protocol
ESTs were processed using the ParPEST (Parallel Processing of ESTs) pipeline [26] that has been tweaked in order
to properly manage shorter length error prone sequences,
such as 454 EST reads.

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Authors' contributions
FA carried out RNA extractions and cDNA synthesis. ND
performed sequence alignment, assembling, annotation
and database construction. LT participated in data mining
and design of some figures. MS provided samples and
contributed in biochemical inferences. RR supervised the
work of FA. MP participated in cDNA synthesis. GG participated in design, drafting and editing of the manuscript.
MLC supervised data analysis process and database construction. LB conceived of the study, and participated in
its design and coordination. GP conceived of the study,
and participated in its design and coordination.
All authors read and approved the final manuscript.

Additional material
Additional file 1
List of TCs. the data provided include the list of TCs. For each TC, ESTs
contribution per genotype and per developing stage is also provided.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712164-10-399-S1.xls]

Masking of simple sequence repeats (SSR) and low complexity sub-sequences was performed using the RepeatMasker tool http://www.repeatmasker.org. In addition,
EST reads were screened against RepBase (version 13.06)
[27], a library of repetitive elements, and returned as
masked sequences ready for the clustering/assembling
and annotation procedures. This process produced a set of
unique transcripts divided into singleton ESTs (sESTs)
and tentative consensus sequences (TCs), grouped by
clusters. Each cluster comprises TCs sharing at least 90%
of identity within a 100 nucleotide window.

Acknowledgements

BLAST similarity searches (e-value 1e10-3) against the UniProtKB database (version 13.3) [28] were performed to
assign a biological function to the unique transcripts.

References

In addition, Gene Ontology (GO) terms [29] and Enzyme
Commission (EC) numbers [30] were associated to each
transcript via the UniProtKB accession, and used for the
description of sequence function.
To assess the relative abundance of gene transcripts
among cDNA samples we applied the statistical R test
[15]. All TCs with R>8 (true positive rate of ~98%) and
with a minimum 3-fold EST number difference in at least
one sample out of the four sequence sets, were considered
as differentially expressed.
Hierarchical clustering analysis (HCA) and principal component analysis (PCA) of the data were performed using
GeneSpring version 7.3 (Agilent, Santa Clara, CA, USA).

The work has been supported by the Italian Ministry of Research, Project
FISR "Improving flavour and nutritional properties of plant food after first
and second transformation", Activity 2.1 'Identification of sequences
involved in the synthesis and degradation of secoiridoids in olive fruits' and
Project FIRB "Parallelomics". The Authors are very grateful to Dr. Giorgio
Pannelli for providing the olive fruit samples of the Olive Cultivar Collection held by the CRA-OLI (Collececco, Spoleto).

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