Minoia et al. BMC Research Notes 2010, 3:69
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RESEARCH ARTICLE

Open AccessA new mutant genetic resource for tomato crop
improvement by TILLING technology
Silvia Minoia1,3, Angelo Petrozza1, Olimpia D’Onofrio1, Florence Piron2, Giuseppina Mosca1, Giovanni Sozio1,
Francesco Cellini1, Abdelhafid Bendahmane2, Filomena Carriero1*

Abstract
Background: In the last decade, the availability of gene sequences of many plant species, including tomato, has
encouraged the development of strategies that do not rely on genetic transformation techniques (GMOs) for
imparting desired traits in crops. One of these new emerging technology is TILLING (Targeting Induced Local
Lesions In Genomes), a reverse genetics tool, which is proving to be very valuable in creating new traits indifferent crop species.
Results: To apply TILLING to tomato, a new mutant collection was generated in the genetic background of theprocessing tomato cultivar Red Setter by treating seeds with two different ethylemethane sulfonate doses (0.7%
and 1%). An associated phenotype database, LycoTILL, was developed and a TILLING platform was also established.
The interactive and evolving database is available online to the community for phenotypic alteration inquiries. To
validate the Red Setter TILLING platform, induced point mutations were searched in 7 tomato genes with themismatch-specific ENDO1 nuclease. In total 9.5 kb of tomato genome were screened and 66 nucleotide
substitutions were identified. The overall mutation density was estimated and it resulted to be 1/322 kb and1/574 kb for the 1% EMS and 0.7% EMS treatment respectively.
Conclusions: The mutation density estimated in our collection and its comparison with other TILLING populations
demonstrate that the Red Setter genetic resource is suitable for use in high-throughput mutation discovery. The
Red Setter TILLING platform is open to the research community and is publicly available via web for requesting
mutation screening services.

Background
Tomato (Solanum lycopersicum) is one of the mostimportant vegetable plants in the world. Its fruits are
end products both for the fresh market and food processing industry. Tomato presents a relatively small genome highly syntenic to others economically important
Solanaceae species and was selected as a reference species for sequencing a Solanaceae genome. In addition to
the availability of a number of genomic resources,
including transcriptome [1-3] and metabolome [4], large
collections of genetic resources are available to dissect
the biochemical and the metabolic pathways in tomato[5]. Large EMS and fast neutron mutant collections, inthe background of M82 tomato cultivar, have been generated and more then 3,000 phenotype alterations* Correspondence: fcarriero@agrobios.it
1
Metapontum Agrobios, SS Jonica 106 Km 448.2, 75010 Metaponto (MT),Italycatalogued [6]. An EMS-induced mutation library of theminiature dwarf tomato cultivar Micro-Tom has also
been produced and this constitutes another resource fortomato genetic studies [7].
In recent years, the genome sequencing program ofmany plant species [8-10], including tomato [11] has led
to the availability of a large number of gene sequencesin public databases which subsequently has encouraged
the development of reverse genetics approaches. T-DNA
and transposon insertional mutagenesis have been
exploited to inactivate genes in tomato [12,13]. However, unless a high-throughput tomato transformation
protocol is developed, systematic functional analysis of
tomato genes with these approaches is not realistic. In
recent years, TILLING (Targeting Induced Local Lesions
IN Genomes) [14,15] a new emerging technology that
doesn’t rely on genetic transformation techniques, allows
systematic functional genomic studies. The only

© 2010 Carriero 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 inany medium, provided the original work is properly cited.

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prerequisite for its application is the knowledge of thegene nucleotide sequences. TILLING is a reverse genetic
strategy that utilises chemical mutagenesis for inducing
variability and sensitive molecular screenings to identify
point mutations responsible for phenotype alteration.
The strength and potency of this reverse genetic strategy
has been validated by its successful application in bothplants (Arabidopsis thaliana [16-18], pea [19], wheat[20], rice [21,22], barley [23], maize [24], soybean [25],Lotus japonicus [26], sorghum [27], tomato [28,29]) andanimals (zebrafish [30], drosophila [31]).
In the present paper we report the construction of ahigh-quality tomato genetic mutant reference collection
which could be used for both forward and reverse
genetic studies. We have developed such a population
by mutagenizing the processing tomato variety Red Setter with EMS and establishing an associated phenotype
database, LycoTILL, and a TILLING platform. The database also serves as a portal for users to request materials
or TILLING experiments.

Results
Generation of the mutant collection

Red Setter is a processing tomato variety that completes
its reproductive cycle within 110 days, it is a high productive variety and its architecture permits mechanical harvesting. In order to optimize the EMS mutagenesis, wefirst conducted a ‘kill-curve’ analysis, using a range ofdoses from 0.3-1.5% EMS. Two EMS doses were then chosen to generate the mutant collection. The first mutagen
treatment was performed by incubating about 11,000
seeds with 0.7% EMS that caused 20% reduction in seedgermination (LD 20 ) with respect to untreated control
seeds. The second mutant tomato population was produced by treating 12,000 seeds with 1% EMS (LD49). Outof the 23,000 treated seeds, 13,000 seedlings were grown
to fruit maturity in controlled conditions and M2 seeds
were collected from individual M1 plants from different
plant internodes. In total we collected 6,667 distinct M2
seed stocks, among which 4,741 and 1,926 M2 seed stocks
were obtained with 0.7% and 1% EMS treatment respectively. For the production of M3 seed stocks, three seeds
per M2 family were sown in nursery, grown to fruit
maturity in open field and M3 seeds harvested from individual M2 plants. In total we collected 5,508 M3 seedstocks (Table 1) as 1,159 M2 families out of 6,667 M2
families didn’t produce M3 seeds. Specifically there were
585 M2 families (12.33%) generated from the 0.7% mutagenesis experiment and 574 M2 families (29.8%) generated
from the 1% EMS treatment.
M2 plant phenotyping

Three plants per M2 family of Red Setter mutant population were scored for visual phenotype alteration at key

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Table 1 Summary of the Red Setter tomato mutant
collection development
EMS
Mutagenized
Concentration Seeds (No)Transplanted
M1 plants

M2 seedFamilies

M3 seedFamilies
4,156

0.7% (LD 20)

11,000

8,500

4,741

1% (LD 49)

12,000

4,500

1,926

1,352

Total

23,00013,000

6,667

5,508

EMS concentrations, number of seeds treated with the mutagen, number ofM1 plants and collected M2 and M3 seed families are shown.

developmental stages, from germination until fruit
maturation. The data collected from individual plants
were organized in 17 classes and 51 subclasses of phenotypes. The vocabulary used to describe the phenotypes was derived from the plant phenotype ontology
and from previous investigation of systematic phenotyping of the mutant tomato collection [6]. We also introduced three new classes of phenotype alterations, thecotyledon, the fruit number and the seed germination
into fruit. The cotyledon class describes mutants showing alterations in the number, color and morphology of
the cotyledons. The fruit number class describes
mutants affected in the fruit yield and it contains threesubclasses, few, many or absence of fruits. By adding the
fruit number class and the subclass “absent” we distinguished the plant sterility due to the absence of fruits
from those caused by the seedless fruits. The class ofphenotype seed germination into fruit describes mutants
having pre-germinated seeds still in the fruit. This phenotype is presumed to result from an altered fruit flesh
pH or by a hormonal imbalance [32,33]. The complete
list of the vocabulary used and the number of lines
found in each major phenotype category are shown in
Table 2.
39% of tomato M2 plants showed at least one visualmutant trait and among these lines 37% displayed multiple phenotypes that fall into more than one major classof phenotypes. The most commonly observed phenotypes are related to the cotyledons (368), the leaf morphology (341), the habit (380) and the plant size (307)classes. In Figure 1 examples of tomato mutant traits
are shown.
LycoTILL database

In order to manage and integrate the recorded phenotypic data, we implemented the database LycoTILL. LycoTILL was developed according to a relational databasesystem, interconnecting three main modules: lines, classand subclass of phenotypes. The database interrogation
can be done according to the phenotypic catalog, previously reported, or by plant code number (plant name)or family name. The result displays all the collected phenotypic information as well as photos of the mutant
lines. LycoTILL, that is an evolving database, is publicly

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Table 2 List of phenotype classes and subclasses

1
2

Class

Subclass

No. ofplantsSeed

No germination

3,904

Cotyledons

Seedling lethality
Colour

1,674
264

Number82

Morphology

17

Size

2

Other cotyledon
development

3
303

3

Plant size

Small plant
Large plant

4

4

Plant habit

Aborted growth81

BranchingInternode length

109
16

Other plant habit

174

Leaf complexity

27

5

6

Leaf morphology

Leaf colour

7

Flowering

8
9

Inflorescence
Flower morphology

Leaf size98

Leaf texture

16

Leaf width12

Other leaf development

188

Dark green leaf
Dull green/grey leaf

19
19

Purple leaf

17

Variegation

20

White leaf

4

Yellow leaf

28

Yellow-green leaf

79

Late flowering

142

Inflorescence structure
Flower homeotic mutation

28
5

Flower organ size

10

Flower organ width12

Other flower morphology

4Pale yellow flower

13

10

Flower colour

White flower

5

11

Fruit size

Large fruit

212

12

Fruit morphology

Small fruit
Long fruit

192
43

Other fruit morphology

2

13

Fruit colour

Rounded fruit

4

Dark red fruit

0Green fruit

0

Orange fruit

0

Yellow fruit
14

Fruit number

15

Sterility

16

Seed germination into
fruit

Absent
Few

3
1,073
1,393

Many

64

Partial sterility

2,125

Total sterility

576

Seed germination into fruit

208

Table 2: List of phenotype classes and subclasses(Continued)
17

Disease and stressresponse

Necrosis

48

Wilting

80

Other disease response0

Number of plants bearing a specific mutant phenotype. Since a single plant
may also be recorded more than once, if it was scored for more than one
phenotype, the numbers are not additive.
The data reported in this table refer to the phase of M2 population
development.

accessible through the web interface: http://www.agrobios.it/tilling/index.html[34].
Mutation discovery in Red Setter mutant collection

To set up the tomato TILLING platform, DNA samples
were prepared from 5,221 M3 families corresponding to
3,924 and 1,297 families obtained by treatment with
0.7% and 1% EMS respectively. The selection of M3
families was based on the M3 seed abundance, 287
families were discarded due to their low seed set. DNA
samples were organized in pools of 8 M3 families. To
validate the 0.7% and the 1% EMS Red Setter TILLING
platforms and to estimate the mutation density of the
populations, we chose seven genes involved in fruit
quality traits. In particular we analysed ripening-inhibitor (RIN) and green ripe (Gr) genes involved in the
ripening of tomato fruit, rab11 GTPase (Rab11a) andexpansin 1 (Exp1) genes related to the tomato softening
control, polygalacturonase (PG) gene involved in the cellwall hydrolysis, and lycopene beta cyclase (Lcy-b) andlycopene epsilon cyclase (Lcy-e) involved in the carotenoid biosynthesis pathway.
The CODDLE program (Codons Optimized to Discover Deleterious Lesions [35,36]) combined with the
PRIMER3 tool [37] was used to define the best amplicon
for TILLING analysis. Mutations were detected in theamplified targets using the mismatch-specific endonuclease ENDO1 as previously described [19,38].
In total 9.5 kb of tomato genome were screened and
66 induced point mutations were identified (Table 3) ofwhich 41 and 25 mutations were derived from the 0.7%
and 1% EMS treated populations respectively. As
expected for EMS mutagenesis, single nucleotide substitutions were identified both in coding and non-coding
regions [17]. Among the exonic identified mutations,
37.6% were silent and 62.4% were missense mutations
while no stop codon type of mutations was found.
Using the SIFT programme (Sorting Intolerant From
Tolerant [39,40]), we analysed the putative impact of themissense mutations on the function of the tilled genes
and 57.14% of the missense mutations were predicted
deleterious for the protein’s activity.

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Figure 1 Examples of tomato mutant phenotypes. Mutations affecting: a) fruit morphology, b) fruit colour, c) plant habit, d) leaf morphology,e) cotyledon number, f) flower morphology, g) plant habit, h) inflorescence structure.

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Table 3 Mutation density in 0.7% EMS and 1% EMS Red Setter populations
No. of screened M3 families

No. of identified mutations

Overall mutation density

Name

Target gene
Amplicon size (kb)

0.7% EMS

1% EMS

0.7% EMS

1% EMS

0.7% EMS

Rab11a

0.407

1,373

713

1

3

1/559 kb

1/97 kb

PG

2.587

2,791

963

7

2

1/1031 kb

1/1246 kb

Exp1

1.025

3,885

1,284

14

6

1/284 kb

1/219 kb

RIN

1.331

3,885

1,284

4

8

1/1293 kb

1/214 kb

Gr

1.409

3,885

1,284

5

3

1/1095 kb

1/603 kb

Lcy-b

1.274

3,801

1,252

4

3

1/1211 kb

1/532 kb

Lcy-eTotal/mean

1.414
9.447

3,630

1,185

6
41

0
25

1/855 kb
1/574 kb

1/322 kb

1% EMS

The accession numbers of the analyzed seven target genes are the following: Rab11a [GenBank:AJ245570], PG [GenBank:M37304], Exp1 [GenBank:AF548376], RIN[GenBank:AF448522], Gr [GenBank:DQ372897], Lcy-b [GenBank:CQ788383], Lcy-e [GenBank:Y14387]. The number of screened M3 families, the number of identified
mutations and the overall mutation density, estimated as described in Methods, are reported both for 0.7% and 1% EMS Red Setter populations.

We calculated the mutation density in the seven targeted genes (Table 3) according to Dalmais et al. [19]and Greene et al. [17]. We estimated the mutation density at 1 mutation/322 kb in the 1% EMS and 1 mutation/574 kb in the 0.7% EMS Red Setter population.

Discussion
The optimization of mutagenesis is a critical parameterin establishing a good mutant collection for forward andreverse genetic studies. In order to balance maximum
mutation density with an acceptable plant survival rate
we decided to utilise two different doses of EMS, 0.7%
and 1%. A strict correlation was observed between the
EMS doses and the toxicity, the mutation density
obtained and the frequency of phenotype alterations. At
1% EMS the plant fertility rate was 41% less than theplant treated with 0.7%. In contrast, the 1% EMS yielded
1.78 fold more mutations per genome than 0.7% treatedplant. At the phenotype level, 60% of the mutant phenotypes scored in the M2 generation were derived fromthe 1% EMS treated seeds.
In the TILLING screens we analysed seven genes anddiscovered a total of 66 induced point mutations. The
spectrum of expected mutations in an EMS-treated population is essentially GC/AT transition because of the frequent alkylation of guanine residues by EMS [41]. In
Arabidopsis, maize, wheat and pea, more than 99% ofidentified mutations are GC/AT transition [17,19,20,24].
In our mutant Red Setter populations the percentage of
observed GC/AT transition was 60% in the 0.7% EMS
population and only 28.6% in the 1% EMS population. We
also identified GC/TA, AT/TA, AT/CG, GC/CG and AT/
GC transversions (Table 4). The spectrum of observed
nucleotide changes in the 0.7% EMS-treated tomato population is similar to the spectrum of mutations observed in
the reverse TILLING screens of rice and barley for whichtransitions in the range of 70% are reported [21,23]. In
contrast, the mutational spectrum of the 1% EMS population is different (AT/CG, GC/CG, see Table 4).

Table 4 Spectrum of mutations identified in Red Setter
populations and their comparison to other organisms
Mutation
Type

Tomato Red Setter

Barley [23] Rice [21]Change 0.7% EMS 1% EMS

Transition

GC/AT

60.0

28.6

70.0

Transversion

GC/TA

6.7

14.3

10.0

4.0

AT/TA

20.0

14.3

10.0

15.0

AT/GC

13.3

14.3

10.0

11.0

AT/CG
GC/CG

0
0

14.3
14.3

0
0

0
0

100

100

100

100

Total (%)

70.0

Distribution of the identified mutations in the different classes of nucleotidechanges. In addition to the Red Setter mutant population data, reported aspercentage values, the mutation spectrum of barley and rice are also shown.

In order to rule out the probability that natural polymorphisms, introduced through pollen or seed contamination, could be responsible for the non-GC/AT
changes observed in our mutant populations, we analysed the natural sequence variation of the tilled genes
using BLAST analysis [Additional file 1: Supplemental
Figure S1] and EcoTILLING [42] of 150 tomato varieties
among which 45 were Italian varieties (unpublisheddata). These analyses revealed that the nucleotidechanges identified by TILLING were present neither in
the available gene bank sequences nor in the screened
tomato varieties. Based on this, we concluded that the
non-GC/AT changes discovered in the TILLING screens
do not result from cross pollination, but are newallelic variants generated by the mutagen action. Thisconclusion is also consistent with the non recovery of
non-GC/AT changes in multiple genes in the same individual as reported for the Seattle Arabidopsis population, where rare contaminants were observed to
introduce polymorphisms in more than one gene in thesame plant [17].
Based on this we speculate that tomato might differ
from other plant species in its mutagenic response to
EMS doses. Moreover, we think that the choice ofMinoia et al. BMC Research Notes 2010, 3:69
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ENDO1 enzyme was fundamental in the detection of all
types of changes that we observed in our mutant populations. For its high specificity in recognizing mismatches at the same rate [38] we could identify
mutations never found in other plant species and with ahigher frequency.
The TILLING screening performed on seven tomato
genes permitted the calculation of the mutation density
in the two mutant Red Setter populations. We estimatedthe mutation density at 1 mutation/322 kb in the 1%
EMS and 1 mutation/574 kb in the 0.7% EMS Red Setter population. The mutation densities calculated in the
1% and 0.7% EMS Red Setter populations are 2.3 and1.2 times respectively higher than one mutation every
737 kb reported by Gady et al[29] in the 1% EMS
TPAADASU tomato population.
This comparison demonstrate that our populations havea higher number of mutations respect to those so far available and published for tomato. The high mutation densityof our populations, especially for the 1% EMS one,increases the size of allelic series that can be obtain andreduces the population size that needs to be screened.
Comparing the mutation densities estimated in the 1%
and 0.7% EMS Red Setter populations with thosedescribed in other plant species results that they are 1.9
and 3.4 times respectively lower than one mutation per
170 kb reported previously for Arabidopsis [17] but
their average (1/448 kb) is similar to those reported formaize (1/500 kb) [24] and rice (1/500 kb) [22] and 2.2
fold higher than one mutation per Mb found in barley
by Caldwell et al. [23].
So far higher mutation densities were observed only intetraploid wheat (1/40 kb) and hexaploid wheat (1/24kb) [20]. It’s likely that the polyploidy nature of theirgenomes helps in withstanding the mutagen action andconsequently higher mutation frequencies can beobtained.
A mutant population is considered saturated with atleast a single “hit” in every gene [6]. In the Red Setter
TILLING platform more than one mutation was identified per gene analysed. We can therefore conclude that
our mutant populations are sufficiently saturated.
Furthermore by comparing other plant species used inpublic TILLING projects we can also affirm that our
populations are suitable for use in high-throughput
mutation discovery.

Conclusions
We have developed a new genetic resource in thetomato Red Setter genetic background by means of
EMS mutagenesis. The mutant collection is organized assuch that it could be used for both forward (EMS saturated mutant collection and the associated phenotypic
database) and reverse (high-throughput TILLING

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platform) genetics in tomato, for both basic science orcrop improvement.
The Red Setter TILLING platform is open to thescientific community to request TILLING screenings in
genes of interest and to obtain material.
These services can be requested via database that also
serves as portal for user need. In addition to our platform, at present, other tomato TILLING platforms are
publicly accessible via web for requesting TILLING services (http://urgv.evry.inra.fr/UTILLdb and http://tilling.
ucdavis.edu/index.php/TomatoTilling). All the available
tomato TILLING platforms, including the Red Setter
one, utilise mutant collections generated in different
genetic backgrounds and with different EMS doses
which increase the chance of obtaining a larger spectrum of alleles. Thus, it is of interest for the scientificcommunity to have different tomato TILLING resources
for the possibility of identifying a greater number ofmutations of interest.

Methods
EMS mutagenesis

Tomato seeds (cv Red Setter) were treated with two different concentrations (0.7% and 1%) of the chemicalmutagen EMS (ethylmethane sulfonate) for 18 h at RTwith gentle shaking. The seeds were then thoroughly
washed, dried and sown in compost in 96 well seedtrays which allowed an accurate determination of germination frequency.
Control seeds, those not exposed to EMS treatment,
were treated in the same manner.
Plant material

M2 seeds: for the M2 seed production, M1 plants weregrown according to standard tomato agronomic practiceand at the end of the fruit-ripening phase, M2 seeds
were collected from individual M1 plants and kept
separate.
M3 seeds: 3 seeds belonging to each mutant M2 family
were sown in 96 well seed trays and the corresponding
seedlings transplanted in open field. M3 seeds were collected from single M2 plants.
M2 plant phenotyping and data collection tools

Phenotype scoring was performed at different developmental stages from seed germination through fruit ripening and seed harvest. Each mutant candidate wascharacterized according to 17 classes and 51 subclasses
which are reported in Table 2. The selection of classes andsubclasses was for the most part carried out on the basisof the phenotypic catalog reported by Menda et al. [6].
Data were collected using a hand-held Asus MyPal
730w while pictures were taken by using the Nikon
Coolpix 4500 digital camera.

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Database construction

The phenotype database was developed using MySQL[43] as a relational database system.
DNA extraction and sample pooling

For each M3 family, the genomic DNA was extracted
from four young leaves collected from four different
plants of the same family. The leaf samples were collected in 96-well plates and the DNA was isolated byusing DNeasy 96 Plant Kit (Quiagen, Hilden, Germany). The quantification of extracted DNA was carried out on a 0.8% agarose gel using l DNA(Invitrogen, Carlsbad, CA, USA) as a concentrationreference. Genomic DNA samples were then diluted
tenfold and pooled eightfold to obtain the working
material.
PCR amplification, mutation detection and validation

PCR amplification was based on nested-PCR and was
carried out using two couples of target-specific primers.
4 ng of pooled genomic DNA was used for the first
PCR and forward-strand primers and reverse-strand primers 5’-end labelled with IRDye 700 and IRDye 800 dye(LI-COR®, Lincoln, NE, USA) respectively were
employed for the second PCR [19].
Mutation detection was performed as previously
described [38]. Electrophoresis were performed on a LICOR 4300 (LI-COR®, Lincoln, NE, USA) and gel images
were analysed using Adobe Photoshop software (Adobe
Systems Inc., San José, CA, USA).
After discovery, mutations were validated by sequence
analysis.
The mutation frequency for each amplicon was calculated as previously described [19]. For the average mutation frequency we have summed the sizes of all
amplicons and we have divided by the total number ofidentified mutants. The data were multiplied for 0.75 inorder to eliminate the missing evaluation due to thepresence of one-fourth wild-type alleles in the 1:2:1
Mendelian segregation in M3 generation [17].
Additional file 1: Nucleotide alignment. Additional data file 1 is afigure showing a comparison analysis of a tilled 240 bp region of
Expansin1 gene. This analysis shows that the identified induced point
mutations are not part of natural variability.
Click here for file
[ http://www.biomedcentral.com/content/supplementary/1756-0500-3-69S1.DOC ]Abbreviations
BLAST: Basic Local Alignment Search Tool; CODDLE: Codons Optimized toDiscover Deleterious Lesions; ctrl: control; EMS: ethylmethane sulfonate; LD:
Lethal Dose; NCBI: National Center for Biotechnology Information; RT: Room
Temperature; SIFT: Sorting Intolerant From Tolerant; TILLING: Targeting
Induced Local Lesions IN Genomes.

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Acknowledgements
This work was mainly funded by the Italian Ministry of University andResearch (MIUR, ITALYCO Project, DD n° 603/RIC) and by the European
project EU-SOL (Contract number: FOOD-CT-2006-016214) through the 6thframework programme.
This work was also supported by a Marie Curie fellowship. The authors thankJ.J. Giovannoni for providing the Rin gene sequence and Stephan Summere
for reviewing the English. The authors wish to thank all colleagues ofMetapontum Agrobios for helping through all phases of the project.
Author details
1
Metapontum Agrobios, SS Jonica 106 Km 448.2, 75010 Metaponto (MT),
Italy. 2Unité de Recherche en Génomique Végétale, UMR INRA-CNRS, Rue
Gaston Crémieux, 91057 Evry Cedex, France. 3ENEA, Casaccia ResearchCenter, PO Box 2400 Roma 00100AD, Italy.
Authors’ contributions
FCa planned and headed the development of the mutant populations. AP
and GS took care and visually phenotyped the mutant populations; OD set
up the LycoTILL database; SM extracted the DNA; SM, FP and GM did
TILLING screens and analysis; AB supervised the TILLING platform set up in
Evry; FCe and FCa co-directed the TILLING project in M. Agrobios; FCa andSM were responsible for drafting and revising the manuscript with
contributions from co-authors.
All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 28 January 2010 Accepted: 12 March 2010
Published: 12 March 2010
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doi:10.1186/1756-0500-3-69
Cite this article as: Minoia et al.: A new mutant genetic resource fortomato crop improvement by TILLING technology. BMC Research Notes
2010 3:69.

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