study of candidate genes for their implication in early
TRANSCRIPT
Numéro d'ordre : 4742
THÈSE
PRÉSENTÉE A
L’UNIVERSITÉ BORDEAUX 1
ÉCOLE DOCTORALE SCIENCES DE LA VIE ET DE LA SANTÉ
Par Julien ASSALI
POUR OBTENIR LE GRADE DE
DOCTEUR
SPÉCIALITÉ : BIOLOGIE VÉGÉTALE
Identification et validation de nouveaux gènes candidats
impliqués dans la régulation du développement précoce du
fruit de tomate
Directeur de thèse : C. ROTHAN
Soutenue le 21 Décembre 2012
Devant la commission d’examen formée de :
Mme CAUSSE, Mathilde INRA Avignon Directeur de recherche
M. ZOUINE, Mohamed INRA/INP-ENSAT Toulouse Chargé de recherche
M. HERNOULD, Michel Université Bordeaux 2 Professeur
M. ROTHAN Christophe INRA Bordeaux Directeur de recherche
Table of contents
Abbreviations
Part I : Introduction 1
1) Generalities 1
a) History of tomato introduction 1
b) Tomato in food diet 2
Tomato production 2
Nutritional role of tomato 4
c) Tomato breeding 4
d) Tomato genetic resources 5
2) Tomato fruit development and regulations 7
a) Fruit set 8
Sugar signaling during fruit set 9
Hormone signaling during fruit set 10
Auxin signaling during fruit set 11
Gibberellins signaling during fruit set 12
Cross-talk between auxin and gibberellins signaling 12
Other hormonal signaling during fruit set 13
Target genes responsible for the initiation of fruit development 13
b) Cell division phase 14
Regulation of the cell division phase 14
c) Cell expansion phase 15
Hormone signaling during the cell expansion phase 16
Endoreduplication during the cell expansion phase 16
d) Fruit Ripening 18
Cell wall degradation during fruit ripening 19
Carotenoid accumulation during fruit ripening 20
Ethylene signaling during fruit ripening 22
Towards a regulatory network of tomato fruit ripening 26
3) Mining genomic data for identifying genes involved in the control of fruit quality traits 30
a) Technologies for "Omics" analysis 30
Transcriptomics 30
(1) Microarray 30
(2) RNA sequencing 32
Proteomics 33
Metabolomics 33
(1) Nuclear Magnetic Resonance 34
(2) Mass spectrometry 34
b) Data interpretation 35
Combining genetic and genomic data 35
Co-expression analysis 38
Correlative networks 39
c) Reverse genetics approaches for the validation of gene functions 42
RNAi and VIGS to silence expression 42
Gene Overexpression 44
CRES-T 44
4) Presentation of the PhD 45
Part II : Results and discussion 49
1) Functional analysis of F-Box encoding genes 49
a) State of the art at the beginning of my PhD thesis 50
b) Characterization of P35S:FB2RNAi
lines 52
c) Functional characterization of SlFB11 56
d) Functional characterization of SlFB24 60
e) Discussion and perspectives on F-Box functional characterization 61
SlFB2 62
SlFB11 64
SlFB24 65
2) Functional analysis of two transcription factors involved in the regulation of fruit
development 67
a) Functional analysis of SlTGA2.1 69
b) Functional analysis of SlTHAT22 78
c) Discussion and perspective about SlTGA2.1 and SlHAT22 85
PART III : Material and Methods 95
1) Biological material 95
a) Plant material and culture conditions 95
b) Bacterial strains and culture conditions 95
2) Nucleic acid analysis 96
a) Plant Genomic DNA extraction 96
b) Plasmid DNA extraction 96
c) Gel electrophoresis 96
d) Polymerase Chain Reaction 97
e) Total RNA extraction 97
f) Reverse transcription 98
g) Real Time PCR 98
3) Construction of transformation vectors and generation of stable transgenic plants 99
a) Gateway® cloning 99
b) Generation of RNAi, and over-expression Gateway compatible PCR products 100
c) Generation of CRES-T Gateway compatible products 101
d) Insertion into Donor vector 102
e) LR reaction 102
f) Bacteria transformation 103
g) Plant transformation 104
4) Characterization of transgenic plants 104
a) Ploidy control 104
b) Control of transgene presence 104
c) Segregation control 105
d) Phenotypic characterization 105
e) Biochemical characterization 105
f) Determination of T-DNA insertion site by inverse PCR 106
PART IV : Bibliography 113
ABBREVIATIONS
Databases
BAR Bio-Array Resource for Plant Biology
FAO Food and Agriculture Organization
SGN Solanaceae Genomic Network
TAIR The Arabidopsis Information Resource
TED Tomato Expression Database
TFGD Tomato functional genomic database
Metabolites/Chemicals
ABA Abcissic acid
ACC 1-aminocyclopropane-1-carboxylate
DAPI 4’,6-diamino-2-phenylindole
DEPC Diethylpyrocarbonate
EMS Ethyl methane sulfonate
GA Gibberellic acid
GM Glucomannan
GUS β-glucuronidase
GSL Glucosinolate
IAA Indole-3-acetic acid
MTA 5’-methylthioadenosine
PG Polygalacturonase
SA Salicylic acid
SAM S-adenosyl-L-methionine
TAE Tris/Acetic Acid/EDTA
TE Tris/EDTA
Tris Tris (hydroxymethyl)-aminomethane
XG Xyloglucan
Mutants
Cnr colorless non-ripening
DET1 DEETIOLATED 1
fas fasciated
Gr Green-ripe
hp high-pigment
ln locule-number
nor nonripening
Nr Never-ripe
Nucleic acids, nucleotides and related techniques
bZip Basic regions/leucine zipper
DNA Desoxyribonucleic acid
cDNA Complementary desoxyribonucleic acid
CRES-T Chimeric REpressor gene Silencing Technology
dNTP Desoxynucleotide 5’ triphosphate (dATP, dCTP, dGTP, dTTP)
EST Expressed sequence tags
EAR ERF-associated amphiphilic repression
HD Homeobox domain
LB, RB Left border, right border
miRNA Micro RNA
OE Over expression
ORF Open reading frame
PTGS Post transcriptional gene silencing
PCR Polymerase chain reaction
RNA Ribonucleic acid
RNAi RNA interference
RNAseq High-throughput cDNA sequencing
RNasin Ribonuclease inhibitor
RT Reverse transcription
SNP Single nucleotide polymorphism
UTR Untranslated region
VIGS Virus Induced Gene Silencing
WGS Whole Genome Sequencing
Zip Leucine zipper motif
Proteins/ genes
ACS ACC synthase
ACO ACC oxydase
AFB AUXIN F-BOX PROTEIN
APC/C Anaphase promoting complex/cyclosome or
AP2 APETALA2
ARF Auxin Response Factor
Ccs52 Cell cycle switch 52 kDa
CDK Cyclin-Dependent Kinase
Cyc Cyclin
CTR1 CONSTITUTIVE TRIPLE RESPONSE 1
DDB1 UV-DAMAGED DNA-BINDING PROTEIN 1
ERF Ethylene Response Factor
ETR Ethylene receptor
GFP Green fluorescent protein
INV Invertase
KRP Kip-related-protein
MC MACROCALYX
MIF Mitosis inducing factor
NptII Neomycin phosphotransferase II
PME Pectin methylesterase
PPC2 Phosphoenolpyruvate carboxylase 2
PSY1 Phytoene synthase 1
RBR Retinoblastoma Related protein
SBP SQUAMOSA PROMOTER BINDING PROTEIN
TAG1 tomato agamous-1
TAGL1 tomato agamous-like 1
TF transcription factor
TIR1 TRANSPORT INHIBITOR RESPONSE 1
Units
°C celsius degree
g acceleration
kDa kiloDalton
bp, kb, kbp, base pairs, kilobases, kilobase pairs,
Mb megabases
rpm round per minute
s, min, h seconde, minute, hour
w/w, w/v, v/v weight/weight, weight/volume, volume/volume
Miscellaneous
Bk Breaker
cv Cultivar
CaMV Cauliflower mosaic virus
CE Capillary electrophoresis
DPA Days post-anthesis
GC Gas chromatography
IL Introgression line
LC Liquid chromatography
Mg Mature Green
MS Mass spectrometry
MS/MS Tandem mass spectrometry
NIL Nearly isogenic lines
NMR Nuclear magnetic resonance
QTL Quantitative trait loci
RIL Recombinant inbred lines
SAM Shoot apical meristem
SAR Systemic Acquired Resistance
Remerciements
La thèse qui a abouti au manuscrit que vous tenez entre vos mains a représenté
une étape importante de ma vie professionnelle et personnelle. Je voudrais exprimer mes
plus sincères remerciements à toutes les personnes impliquées de près (ou de loin) dans ce
projet scientifique.
Tout d'abord, j'aimerais remercier MM. Dominique Rollin, Thierry Candresse et
Christian Chevalier pour m'avoir permis de travailler au sein de l'UMR 619, devenue par
la suite l'UMR 1332. J'ai pu y côtoyer de nombreux chercheurs et techniciens et cela a
toujours été un plaisir (malheureusement trop rare) d'échanger avec eux.
Je voudrais ensuite remercier Christophe Rothan, mon directeur de thèse, pour
m'avoir proposé un sujet incluant une approche originale et s'effectuant dans le cadre
d'une collaboration avec le Japon, un pays qui continue de me fasciner en dépit d'un
séjour long et difficile au cours de ma thèse. J'aimerais aussi remercier Hiroshi Ezura et
Erika Asamizu pour m'avoir accueilli et encadré pendant ce séjour au sein du laboratoire
Gene Research Center (Hidenshi Kenkyu Centa en version originale). Je m'estime
particulièrement chanceux d'avoir pu rencontrer des étudiants japonais et étrangers, et
d'avoir eu plusieurs discussions particulièrement passionnantes sur le plan scientifique,
mais aussi sur un plan historique et philosophique.
A mon retour en France, l'échec de mon travail au cours de ma première année
m'avait profondément affecté, et ce travail n'aurait sans doute pas été conduit aussi loin
sans la patience et la fermeté de Martine Lemaire-Chamley qui a assuré l'encadrement de
mes travaux lors de la seconde et troisième année de ma thèse, et dont les conseils et
corrections pour la rédaction et la présentation de ce travail de thèse ont été infiniment
précieux. J'aimerais donc tout particulièrement te remercier Martine, et te souhaite le
meilleur dans la continuation de ta carrière scientifique.
J'aimerais aussi remercier Joanna Jorly, qui a participé à de nombreuses manipes
effectuée pendant ma thèse. Ton aide m'a été précieuse, et je t'en remercie
chaleureusement.
Sur un plan professionnel mais aussi personnel, j'aimerais remercier mes collègues
de bureau et compagnons dans l'adversité, Lisa Bourreau et Antoine Monier. Pouvoir
réfléchir et travailler dans un bureau où règne une bonne ambiance est quelque chose
d'infiniment précieux que j'ai toujours apprécié à sa juste valeur. En tant que jeunes
docteurs, je vous souhaite la meilleure réussite dans vos futurs travaux, ainsi que dans
votre vie personnelle.
J'ai eu le bonheur d'interagir avec de nombreuses autres personnes, de mon équipe
ou non, que j'aimerais aussi remercier. Dans et hors de l'équipe, l'ambiance fut toujours
agréable, et je garderai un souvenir agréable des nombreux moments passés ensemble
autour du coin café.
Cette thèse a été un travail absorbant et je n'ai pas toujours pu être là pour mes
proches. Pour leur patience et leur compréhension, j'aimerais remercier mes soeurs et mes
parents, ainsi que mes amis, qui ont participé parfois à l'insu de leur plein gré à de
nombreux débats sur la biologie moléculaire et son impact sur la société moderne.
J'aimerais enfin remercier la personne qui compte le plus à mes yeux, et qui
comme moi a grandement souffert de la distance que nous a imposé ma thèse. Ursula,
malgré de nombreux moments difficiles, je ne regrette absolument pas ces deux dernières
années passées près ou loin de toi, et je te remercie profondément de m'avoir toujours
soutenu et encouragé quand j'en ai eu besoin.
1
Part I : Introduction
1) Generalities
a) History of tomato introduction
Tomato (Solanum lycopersicum) belongs to the Solanaceae family which includes several
plants with nutritional, pharmaceutical or decorative properties such as potato, pepper, eggplant,
tobacco, petunia, or belladonna. Tomato originates from the west area of South America, more
precisely from Bolivia, Colombia, Ecuador, and Peru. The main domestication area of tomato
seems to be Mexico, where tomato is represented by numerous cultivars showing high diversity in
fruit size, color and shape (Hobson and Grierson, 1993).
First description of tomato as “golden apple” (pomi d’oro) was written in 1544 by Pietro
Andrea Matthioli, an Italian doctor and botanist. Tomato was also described as the “love apple”
(pomme d’amour) in 1557 by Rembert Dodoens (Figure I.1). Since tomato was classified in the
same family as belladonna, it was first considered as toxic and only cultivated as a decorative
plant. Then it was consumed as a cooked ingredient in sauces in Italy, followed by its
consumption as fresh product in many Mediterranean countries. The tomato use for food reached
northern European countries at the end of the 18th century.
In 1753, Carl von Linné named tomato as Solanum lycopersicum, which means “wolf peach”
according to the greek ethymology. In 1754, Philip Miller separated tomato from the Solanum
gender and named it Lycopersicon esculentum. However, recent genetic, phylogenetic,
morphologic and geographic evidences confirmed the tomato as being part of the Solanum species,
and thus the tomato was renamed Solanum lycopersicum (Peralta et al., 2008).
2
Figure I.1 First visual description of tomato by Rembert Dodoens in 1557
b) Tomato in food diet
Tomato production
According to the FAO data, the global tomato production in 2010 was 151 million tons.
Between 1960 and 2010, global tomato production increased more than fivefold (Figure I.2) and
this increase was particularly important in Asia (Figure I.3). China alone produced 57.68% of
total tomato stock in 2010. In terms of production volume, tomato (151 Mt) is the first cultivated
fruit, before banana (102 Mt) and watermelon (99 Mt). In terms of value, the tomato production in
2010 was in 4th place behind rice, wheat and soybean (FAO, 2012). In France, tomato production
diminished during the last years, from more than 940k tons in 1985 to 587k tons in 2010 (Figure
I.4). France is now a minor producer of tomato, being less than the 25th contributor to tomato
production in terms of quantity and value (FAO, 2012).
3
Figure I.2. Evolution of global tomato production from 1960 to 2010 (FAO, 2012)
Figure I.3 : Evolution of tomato production in main producing countries (FAO, 2012)
Figure I.4 : Evolution of French tomato production (FAO, 2012)
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Nutritional role of tomato
Even if it is not the only factor, nutrition is a major criterion of public health. A large-scale
nutritional policy was recently adopted by the French state (the “Programme National Nutrition
Santé”; http://mangerbouger.fr) as a measure to fight against developing pathologies such as
cancer, cardiovascular diseases, obesity, osteoporosis, or type 2 diabete. The first objective of this
program is to increase fresh fruits and vegetables consumption for three main reasons. First, it
would contribute to bring micronutrients necessary for the smooth functioning of the human
organism. Second, it would protect against cardiovascular, neurodegenerative, and cancer diseases.
For example, consuming 400 grams of fresh fruit and vegetable each day would help to avoid
from 7% to 31% of cancers. Third, it would represent a good low caloric food replacement for the
high caloric food responsible for obesity.
In the context of this public health policy, tomato plays an important role since it represents
today more than 20% of fresh vegetable consumption and has important nutritional properties.
First of all, tomato is rich in water (90 to 95% of the fresh weight) and consequently poor in
calories (18 to 20 kcal for 100 grams of fruit). In addition, tomato is poor in lipids and is free of
cholesterol. Moreover, tomato is a source of minerals such as potassium (237 mg for 100 g of
tomato), which is a micronutrient necessary for reducing hypertension risks. Tomato is also the
source of substances beneficial to humans like vitamins (beta-carotene / pro-vitamin A and
ascorbic acid/ vitamin C) and lycopene, the most active antioxidant coming from human diet
responsible for the red color of tomato (Agarwal and Rao, 2000).
c) Tomato breeding
Tomato domestication has been applied since ancient times by South America natives and
took off after wide development of tomato culture in Europe (Pitrat and Foury, 2003). During the
19th century the first cultivars appeared as a result from selection of individuals originating from
mutations or cross-breeding. From seven tomato cultivars in 1856, intense agronomic research on
fruit characteristics led to several thousands of cultivars in the early 1950’s. From then,
genetically distant tomato homozygous lines were crossed, resulting in hybrid lines characterized
by an increased vigor for a given trait, also called “hybrid vigor” or “heterosis”. Research to
develop enhanced tomato cultivars was pursued according to four axis (Bai and Lindhout, 2007).
The production yield was the first target of breeders starting from the 1970’s, followed by the
improvement of fruit conservation starting from the 1980’s. Attention to fruit taste was then given
since the 1990’s, and led to the current focus on fruit nutritional qualities.
5
The domestication steps in Mexico and Europe combined with the autogamous reproductive
mode of tomato led to losses of genetic variability. To further enhance tomato cultivars and gain
in genetic variability, interspecific crosses with wild-type tomato cultivars became necessary. For
this, Pr. Charlie Rick organized several expeditions in the Andes Mountains to collect natural
tomato species holding genetic characteristics absent from domesticated tomatoes. These efforts
resulted in the creation of the C. M. Rick Tomato Genetics Resource Center (Davis, USA,
http://tgrc.ucdavis.edu) which makes the inventory, stock and distribute tomato seeds from more
than 1500 unique cultivars. Wild-type cultivars of tomato represent a very rich source of allelic
variability and have been used for breeding to recover natural phenotypes in domesticated
tomatoes.
d) Tomato genetic resources
More than 75,000 accessions are presently available in more than 120 countries. Most of
these resources were used during the last twenty years to improve disease and pathogens
resistance as well as tomato quality by selecting Quantitative Trait Loci (QTL) of interest
(Robertson and Labate, 2007). The high allelic variability present in wild-type species of tomato
was used to build Introgression Lines (ILs). The genome of ILs possesses chromosomal fragments
from natural species in a domesticated tomato background. Several collections of Solanum
lycopersicum ILs have been designed to integrate several species genome, such as Solanum
pennellii (Eshed and Zamir, 1995), Solanum pimpinellifolium (Doganlar et al., 2002), Solanum
hirsutum (Monforte and Tanksley, 2000) and Solanum lycopersicoides (Canady et al., 2005). ILs
populations have been used efficiently for QTL fine mapping and identification of genes in the
context of cultivar enhancement (Zamir, 2001). In the beginning of the 1990’s, the first molecular
markers allowed to build a reference map of tomato genome and highlighted numerous genes of
interest (Tanksley et al., 1992). Since then, numerous markers enriched this genetic map and most
of mapping data for tomato genome are available on the SGN website (http://solgenomics.net/).
An alternate approach for studying tomato is the study of mutants. Since the start of large-
scale studies, numerous mutants have been generated. Between 1956 and 2005, available data
evolved from 118 mutants whom 56 were mapped to 1000 mutations whom 400 are mapped (Ji
and Chetelat, 2007). The generation of mutants was mostly done through treatment of seed or
pollen with chemical agents like the ethyl methane sulfonate (EMS) or radiations (X or Gamma
rays). The development of TILLING (Target Induced Local Lesion In Genome) as a high-
throughput mutation detection technique allows the isolation of SNP in the sequence of genes of
interest from a DNA pool of mutated lines. Following these studies, seeds of 3417 mutants of
tomato cv. M82 have been listed from 13,000 famillies (http://zamir.sgn.cornell.edu/mutants/;
(Menda et al., 2004). In the same way, seeds of 3500 tomato cv. Micro-Tom mutants listed from
6
8500 families are also available (http://www.bordeaux.inra.fr/umr619/tilling.htm) and 1,048
tomato cv. Micro-Tom mutants were isolated from 9,183 M2 mutagenized families and are
available through the TOMATOMA database (Saito et al., 2011). The lycoTILL database was
created to search for phenotypes in a tomato cv. Red Setter mutants collection (Minoia et al.,
2010).
The last approach used to enrich available genetic resources on tomato is tomato genome
sequencing. For this, the genome of tomato cultivar ‘Heinz 1706’ was sequenced and assembled
by a multi-national team of scientists from 14 countries
(http://sgn.cornell.edu/about/tomato_sequencing.pl). The project was launched in 2004 and came
to its term in 2012 (Tomato Genome Consortium, 2012) . The predicted genome size is
approximately 900 megabases (Mb) among which 760 Mb were assembled in 91 scaffolds
aligned to the 12 tomato chromosomes. The remaining gaps in tomato genome are restricted to
pericentromeric regions. According to these data, 34,727 genes were predicted in tomato genome.
Up to now, 30,855 of these genes are supported by RNA seq data. All of these data are available
in the Sol Genomics Network website (http://solgenomics.net/), which associates for each
chromosome, the predicted genes with the corresponding unigenes, RNA-Seq coverage and
existing markers.
In the last decades, the diversity of tomato genetic resources associated with the existence of
precise genetic maps, and gene expression databases allowed deepening the knowledge of
mechanisms implicated in fruit development and quality (Causse et al., 2007). The availability of
these tools, together with the agronomic importance of tomato and its relative simplicity to
cultivate and to genetically transform conferred to tomato the status of model plant for the
development of fleshy fruits. In France, different laboratories of INRA (Institut National de
Recherche Agronomique) and CNRS (Centre National de Recherche Scientifique), in partnership
with professional actors, work on tomato fruit development and aim at improving tomato fruit
taste and nutritional quality.
7
2) Tomato fruit development and regulations
Tomato fruit is a complex organ which results from the development of ovary tissues after
ovule fertilization (Figure I.5). The pericarp comes from the differentiation of carpel walls and is
divided into outer pericarp, radial pericarp or "septum" (which divides the fruit into several
locules), and internal pericarp or "columella." The outer pericarp includes three tissues: the
exocarp or skin of the fruit, the mesocarp containing the conducting vessels and the endocarp
(Figure I.5B). The placenta is grafted onto the central vascular tissue and feeds the seeds that are
emerging in the locular cavities. A few days after fertilization, cell differentiation of placental
cells leads to the formation of a jelly tissue “the locular tissue” which will fill the locular cavities
and surround the seeds.
Figure I.5. Structure of tomato fruit during fruit development. A. Fresh section from 22-DPA Ferum fruit. B.
Tissue sections from ovary at anthesis (A) and from 6-, 12-, and 25-DPA fruit were cut from the equatorial region of the
fruit (cv Ferum). Numbers 1 to 5 indicate fruit regions where samples from 12- and 25-DPA fruits were taken. P,
Pericarp; Sep, septum; E, exocarp; M, mesocarp; En, endocarp; L, locular tissue; S, seed; C, columella; V, vascular
bundles (Lemaire-Chamley et al., 2005).
8
The functions of a fleshy fruit like tomato are to feed and protect the embryo along its
development from ovule fertilization to its latent state in fully developed seeds and to ensure seed
dispersal by its attractiveness for herbivorous animals. These two functions are respectively
achieved through early fruit development and fruit ripening, the two sequential phases of tomato
fruit development (Gillaspy et al., 1993). Early fruit development has been divided in three phases
according to their main characteristics: fruit set (Phase I), a short phase of cell division which
lasts for about 7 to 10 days (Phase II), and a longer phase of cell expansion which lasts for 20 to
30 days (Gillaspy et al., 1993). Further work showed that the separation between the division and
the expansion phases was not so clear because of a different spatio-temporal regulation of both
processes in the different fruit tissues (Joubes et al., 1999; Cheniclet et al., 2005). The different
phases of fruit development as well as their regulations will be described in the following parts.
Figure I.6. Tomato fruit development phases and associated hormonal changes (Gillaspy et al., 1993). Changes in
hormone levels throughout tomato fruit development are indicated by white diamonds.
a) Fruit set
Fruit set is the transition of the quiescent ovary to a young developing fruit (Ruan et al.,
2010). Apart particular genotypes (see below) and/or culture conditions, fruit set is strictly
dependent on ovule fertilization and fertilization default leads to flower abscission. In tomato,
fruit set is highly sensitive to biotic and abiotic stresses. The study of fruit set regulation focused
on the crucial role of source–sink ratio, which determine the carbon and nutrient sources feeding
9
the developing fruit, as well as on the role of different hormones in this process (Ruan et al.,
2012).
Sugar signaling during fruit set
Fruits are sink organs, which are dependent on source organs (leaves) providing carbon and
other nutrients necessary for their development. Sucrose constitutes the major nutrient transported
to tomato fruit (Ho, 1996). During fruit set, it is converted into glucose and fructose by
extracellular invertase active at the interface between placenta and seeds (Jin et al., 2009; Ruan et
al., 2010). In wheat, it has been demonstrated that the invertase activity regulates fruit and seed
set (Boyer and McLaughlin, 2007) and that in drought conditions, programmed cell death is
mostly dependent on sugar signaling (McLaughlin and Boyer, 2004; Boyer and McLaughlin,
2007). In the same way, tomato Lin5 gene, encoding a cell wall invertase, has been shown to be
implicated in fruit set. Indeed, silencing of this gene by RNA interference leads to an increase of
fruit abortion associated to a diminution in fruit size and seed number as well as changes in
hormone metabolism and signaling (Jin et al., 2009; Zanor et al., 2009).
Figure I.7. A model for regulation of seed and fruit set through sugar signaling. A. Under optimal conditions,
phloem unloaded sucrose is hydrolyzed by invertase (INV) in ovaries and ovules. The resultant glucose functions as a
signal to repress programmed cell death (PCD) genes and to promote cell division, which together lead to seed and fruit
set. Starch reserve may be remobilized to supplement glucose production, particularly under mild stress conditions. B.
Under severe stress, phloem import of sucrose is blocked, which in turn decreases INV activity. This, together with
depletion of starch reserve, reduces glucose levels that activate the PCD pathway and inhibit cell division.
Consequently, seed and fruit abort. (Ruan et al., 2012)
On the contrary, silencing of the invertase inhibitor encoding INVINH1 gene results in higher
sugar intake in the developing fruit as well as in a higher seed dry weight (Jin et al., 2009).
10
Interestingly, Lin5 and INVINH1 interact in the tomato fruit (Jin et al., 2009) and are both
required for the control of tomato fruit development through sugar signaling.
Hormone signaling during fruit set
The regulation of fruit set has been particularly studied in tomato because of agronomical
interest for seedless or parthenocarpic fruits (Gillaspy et al., 1993; Goetz et al., 2007). It was
demonstrated that exogenous application of auxins, cytokinins and gibberellins to tomato flowers
leads to the formation of parthenocarpic fruits in absence of pollination and fertilization (Gillaspy
et al., 1993); Figure I.8). It was further suggested that this particular fruit development is due to
the deregulation of the hormonal balance within the ovary, which substitutes for
pollination/fertilization (Schwabe and Mills, 1981; Gorguet et al., 2005).
Figure I.8. A model for multihormonal regulation of fruit set. Pollination and fertilization result in increased levels
of both auxin and gibberellins (GA), which triggers fruit growth through stimulation of cell division and expansion.
Auxin can stimulate fruit set either directly or via inducing GA biosynthesis. Each hormone seems to play a specific
role given that auxin application results in a high number of pericarp cells, whereas GA treatment results in fewer but
larger pericarp cells. Natural fruit set seems to require both hormones given that only parthenocarpic fruits induced by
concomitant auxin and GA treatment are similar to pollination-induced seeded fruits. Putative involvement of ethylene
and abscisic acid (ABA) in regulating fruit formation has been mainly suggested by transcriptomic studies. In particular,
during the transition from anthesis to post-anthesis, ethylene-related genes, along with those related to auxin, account
for most of the changes among all phytohormone-related genes, thus indicating that ethylene must play an active, but
yet not understood, role in fruit set. Exogenous application of cytokinin can induce parthenocarpic fruit yet the
underlying model of action remains unknown. Broken arrows represent effects that are still not sustained by solid and
multiple experimental data. (Ruan et al., 2012)
The role of auxins and gibberellins has been particularly investigated by direct application of
hormones on the unfertilized ovary or by generation and characterization of transgenic lines
affected in hormone synthesis or signaling. These studies suggest that auxins and gibberelins are
11
the two main hormonal regulators of fruit set, the implication of other hormones such as ethylene
and abcissic acid (ABA) remaining unclear for the moment (Ruan et al., 2012).
Auxin signaling during fruit set
Study of auxin signaling in plants, and particularly in Arabidopsis thaliana, generated a huge
mass of experimental data which allowed building a fairly complex scheme of molecular players
involved in this regulation as well as of their interactions (Chapman and Estelle, 2009; Kieffer et
al., 2010). Two elements of this model were particularly useful for the understanding of auxin
signaling during tomato fruit set: the importance of auxin fluxes for its regulation (Zazimalova et
al., 2007) and the implication of two families of transcription factors in auxin dependent
transcriptional regulation: the Auxin Response Factors (ARFs) which regulate specifically the
activity of target promoters and the Aux/IAAs proteins which are transcriptional repressors
interacting with the ARFs (Chapman and Estelle, 2009). Together with the experimental data
collected for seventy years on the role of auxin in tomato fruit set, it was possible to start drawing
a model for the regulation of fruit set by auxin in tomato (Figure I.9).
Figure I.9. A model for auxin regulation of fruit set. A. In WT plants, fertilization occurs after anthesis, induces a
local increase in auxin thus leading to the repression of ARF/IAA- ARF complex which prevents further carpel
development. Therefore, the regulation of auxin target genes involved in fruit development is effective and fertilization
leads to the development of seeded fruits. B. The ARF/IAA- ARF complex is formed by ARF8 and Aux/IAA9 proteins,
together with potentially other as yet unknown proteins (=?), and form a regulatory complex that can either directly
block transcription of target (fruit initiation) genes, or act indirectly by preventing ARF8 from functioning as a
transcriptional activator. After pollination and fertilization occur, auxin acts by binding to its receptor, TIR1, promoting
degradation of Aux/IAA9 proteins via the SCFTIR1 ubiquitin ligase complex. In the absence of Aux/IAA9, ARF8
together with additional signals and activators (A) stimulate expression of early auxin responsive genes, initiating fruit
growth and development. Destabilization of the regulatory complex or reduction of its functionality by aberrant ARF8
transcripts and possible products can lead to a reduction or loss of the inhibition of transcription of the fruit initiation
genes, resulting in parthenocarpic fruit growth (Goetz et al., 2007).
12
According to this model (Figure I.9), ovary development is blocked prior to fertilization by
an ARF/IAA- ARF complex which prevents further carpel development (Wang et al., 2005;
Goetz et al., 2007). Several Aux/IAA and ARF proteins have been proposed to fulfill this crucial
role in tomato ovary: SlARF7, SlARF8 and SlIAA9 (Wang et al., 2005; Goetz et al., 2007; Ren et
al., 2011). When pollination and fertilization occur, auxin level increases in the fertilized ovules /
ovary thanks to directed auxin efflux transport (Molesini et al., 2009; Nishio et al., 2010; Mounet
et al., 2012; Pattison and Catala, 2012). In the target cells, this signal leads to TIR1-mediated
proteolysis of Aux/IAA9 via the ubiquitin–proteasome pathway (Dharmasiri et al., 2005); Figure
I.9B), resulting in the derepression of the ARF partner and finally in the activation of fruit
development (Ren et al., 2011).
This pattern remains to be experimentally validated, but is consistent with existing data e.g.
the development of parthenocarpic fruit when the auxin signaling pathway is impaired by
alteration of auxin transport (Molesini et al., 2009; Carlos Serrani et al., 2010), mis-expression of
key ARFs or Aux/IAAs (Wang et al., 2005; Goetz et al., 2007; de Jong et al., 2009a; de Jong et al.,
2009b; Wang et al., 2009a) or increase of auxin level in ovules / ovary by over expression of
auxin biosynthesis genes (Ficcadenti et al., 1999; Pandolfini et al., 2002; Carmi et al., 2003).
Gibberellins signaling during fruit set
Studies of pat, pat-2 and pat3/4 parthenocarpic mutants (Mapelli et al., 1978; de Jong et al.,
2009a) as well as the induction of parthenocarpic fruits upon gibberellins application on tomato
unfertilized ovaries highlighted the importance of the regulation of gibberellins metabolism in
fruit set (Gillaspy et al., 1993). Further work showed that this regulation involves an increase of
gibberellin biosynthesis mainly through the up-regulation of GA 20-oxydase genes (Olimpieri et
al., 2007; Serrani et al., 2007). It was suggested that KNOTTED-type homeobox (KNOX)
transcription factors, already known to be responsible for the repression of GA 20-oxydase genes,
could be key elements of the regulation of gibberellins biosynthesis around fruit set (Olimpieri et
al., 2007). In addition, gibberellins signaling could take place thanks to the regulation of DELLA
protein, a negative regulator of GA responses (Wang and Deng, 2011). Indeed, the depletion of
SlDELLA in tomato transgenic lines leads to the development of parthenocarpic fruits (Marti et
al., 2007).
Cross-talk between auxin and gibberellins signaling
Numerous studies showed the interaction between auxins and gibberellins signaling for the
regulation of fruit set (see de Jong et al., 2009 and Ruan et al., 2012 for review ). These results
indicate that auxin is the primary signal that triggers fruit development, in particular by the
activation of gibberellin biosynthesis. However, if auxins or gibberellins treatments both give rise
13
to parthenocarpic fruits, these fruits do not present the same structure. Thus it was suggested that
the interaction between both signal transduction pathways induced by pollination and fertilization
do not form a single linear cascade via auxin to gibberellins (see de Jong et al., 2009). Studies on
SlARF7 silenced lines suggested that this transcription factor acts as a modulator of the auxin
response, but also as a modulator of the GA response, and could thus be a key element of the
cross-talk between auxin and gibberellins signaling during tomato fruit set (de Jong et al., 2011).
The recent characterization of the procera mutant affected in the SlDELLA protein reinforces this
hypothesis (Carrera et al., 2012a).
Other hormonal signaling during fruit set
In addition to auxins and gibberellins (Figure I.8), large scale expression analysis during
fruit set, or on parthenocarpic ovaries, highlighted the potential role of other hormones in the
regulation of fruit set. Genes involved in ethylene and ABA synthesis or signaling were
particularly pointed out (Vriezen et al., 2008). The study of the diageotropica (dgt) gene in
tomato showed that there is a possible interaction between auxin and ethylene for the control of
the early fruit development (Balbi and Lomax, 2003). This interaction between auxin and
ethylene may actually be necessary for communication between parental and newborn tissues
(Balbi and Lomax, 2003). The precursor of jasmonate, 12-oxo-phytodienoic acid (OPDA) could
also ensure this communication (Goetz et al., 2012). Since the seed coat is the major organ where
OPDA is produced, JA biosynthesis may also be temporarily and spatially involved in the control
of SlLIN5 to produce sufficient hexoses for fruit growth by down-regulating SlLIN5 negative
regulator, SlINVINH1 (Goetz et al., 2012).
Target genes responsible for the initiation of fruit development?
The final goal of the regulations operating around ovule fertilization is to trigger the
development of ovary tissue to form the fruit. MADS box proteins are prime candidates to play
this role. Indeed, this class of proteins has a wide implication in the regulation of flower and fruit
development (Dornelas et al., 2011; Klee and Giovannoni, 2011). A range of these genes were
found to be down-regulated in different tomato parthenocarpic lines (Mazzucato et al., 2008;
Vriezen et al., 2008; Wang et al., 2009a; Mounet et al., 2012). In addition, the down-regulation of
the tomato MADS box TM29 induces parthenocarpy (Ampomah-Dwamena et al., 2002) and
SlTAG1 silenced lines presented a “fruit inside a fruit” parthenocarpic fruits (Pan et al., 2010).
Other MADS box genes have not been functionally characterized but some of them are
upregulated in tomato carpel at anthesis in agreement with a possible implication in the regulation
of fruit set (Busi et al., 2003). It has been recently proposed that the MADS-box network controls
the identity of floral organs and the growth of floral pieces and fruit by targeting genes associated
14
with cell proliferation and growth (Dornelas et al., 2011). In Arabidopsis, one of the targets of the
MADS-box AGAMOUS is CRABS-CLAW, a YABBY TF implicated in the control of carpel
development (Alvarez and Smyth, 1999; Bowman and Smyth, 1999). The misregulation of
CRABS-CLAW in some parthenocarpic tomatoes suggests its implication in fruit set (Mounet et
al., 2012).
b) Cell division phase
After fruit set, fruit development is characterized by an intense activity of cell divisions that
lasts between seven to ten days after anthesis (Figure I.6, Gillaspy et al., 1993; Joubès et al.,
1999). This phase of fruit development will determine the final number of cells within the fruit.
Consequently, final fruit size is dependent on the number of cells established during the cell
division phase (Ho 1996).
These cell divisions do not occur uniformly within the fruit (Joubès et al., 1999; Cheniclet et
al., 2005). In the days that follow anthesis (2 to 4 DPA), the epidermis, pericarp and placental
tissues are the first to grow through cell division. Between 4 and 6 DPA, the outer pericarp and
placenta remain areas of active divisions. From 6 DPA, divisions occurring at the outer layer of
the placenta give rise to the locular tissue which begins to surround the seeds (Cheniclet et al.,
2005). From 10 DPA, only epidermal cells continue their divisions until the beginning of ripening
to support the considerable increase in volume of fruit during the phase of cell expansion (Joubès
et al., 1999). During this developmental phase, fruit cells remains small and have small vacuoles
(Gillaspy et al., 1993).
Regulation of the cell division phase
It was shown that the mitotic activity in the ovary is conditioned by the number of fertilized
ovules (Varga and Bruinsma, 1986). In addition, it has been observed that the development of
embryos and seeds, sources for hormonal factors, control the speed of cell divisions in the fruit.
Gibberellins and cytokinins present a peak of accumulation during the cell division phase,
(Figure I.6; Gillaspy et al., 1993; Matsuo et al., 2012). The high level of cytokinins in the
developing seeds suggests that the regulation by the fertilized ovules could be mainly due to
cytokinins (Srivastava and Handa, 2005).
However, different transgenic lines affected in auxin (e.g. SlARF7; de Jong et al., 2009) or
gibberellin signaling (e.g. SlDELLA; Carrera et al., 2012a) showed an alteration of cell division,
thus suggesting the implication of the corresponding hormones in the regulation of the cell
division phase. These different hormones may act together to regulate the expression/activity of
cell cycle genes. Indeed, it is well known that exogenous signals, such as auxin, cytokinin or
15
sugar levels, could promote cell cycle related gene expression and inhibitory protein degradation,
in order to modulate cell progression through G1, S and G2 phases of cell cycle (Hartig and Beck,
2005).
In complement to these molecular approaches, genetic approaches were developed to
characterize tomato fruit size variation, which is a quantitative trait controlled by different
Quantitative Trait Loci (QTL). Fw2.2 is the major locus among 30 QTLs controlling tomato fruit
weight (Frary et al., 2000). It accounts for 30% of the fruit weight variation between small fruit of
wild tomato species and large fruit of domesticated species. The allele present in domesticated
tomato cultivars carries a mutation in the regulatory region of fw2.2 which induces a difference in
the timing and level of the expression of fw2.2, resulting in augmented fruit size and weight
through an increased cell number. In wild type cultivars, the absence of the mutation results in
smaller fruit size due to a lower number of cells (Guo and Simmons, 2011). It has been
demonstrated that Fw2.2 encodes a small G Ras-like protein, addressed at the plasma membrane,
which is a negative regulator of fruit cell division, impacting on the cell number (Guo and
Simmons, 2011).
c) Cell expansion phase
The cell expansion phase takes place after the cell division phase (Figures I.5 and I.6).
During this phase, the vacuoles expand by accumulating water, sugars and organic acids as well
as other compounds, resulting in a cellular gain of volume (Lemaire-Chamley et al., 2005). This
process also involves modifications of the cell walls (Schopfer, 2006). Cell expansion can lead to
an increase of fruit weight, up by ninety percent, depending on the cultivars (Cheniclet et al.,
2005; Lemaire-Chamley et al., 2005). The cell size will almost not vary later during ripening. The
cell expansion phase is thus a major determinant of the final fruit size (Cheniclet et al., 2005;
Lemaire-Chamley et al., 2005).
Cell expansion is particularly impressive in the mesocarp and in the locular tissue (Mounet
et al., 2009), whereas both external and internal epidermis and subepidermal cells continue to
divide (Cheniclet et al., 2005). Transcriptional study of fruit development showed that these
different fruit tissues present specific expression patterns during the expansion phase (Lemaire-
Chamley et al., 2005; Mounet et al., 2009). In the locular tissue, genes related to cell expansion
where highlighted, like genes related to the control of water flow, organic acid synthesis, sugar
storage, and photosynthesis (Lemaire-Chamley et al., 2005; Mounet et al., 2009). The cell
expansion phase is also particularly important for the formation of tomato fruit cuticle, which will
protect the fruit until seeds dispersal (Matas et al., 2011).
16
Hormonal signaling during the cell expansion phase
During fruit development, auxin concentration peaks at the beginning of the cell expansion
phase (Gillaspy et al., 1993), suggesting a potential role in the regulation of cell expansion (Balbi
and Lomax, 2003). Seeds are the organs where auxin is the most concentrated during the cell
expansion phase (Pattison and Catala, 2012). Transcriptome studies have suggested that auxin
might be implicated in the development of fleshy tissues during the expansion phase (Lemaire-
Chamley et al., 2005; Mounet et al., 2009). Within the fruit tissues, DR5::GUS reporter gene
showed that auxin is more abundant in the placenta, where different auxin influx and auxin efflux
proteins are specifically expressed (Mounet et al., 2012; Pattison and Catala, 2012). The well
known effect of auxin in cell expansion process is the activation of cell wall release (Schopfer,
2006). Indeed, the assembly of polymers of cellulose and hemicellulose that compose the plant
cell wall constitutes the major barrier against the expansion of the cells under the effect of turgor.
Auxin acts at two levels: 1) by transcriptional activation of enzymes implicated in cell wall
loosening like expansins, b-glucanases and xyloglucanes-endotransglycosylases (Catala et al.,
2000), and 2) by the activation of a proton pump (H+-ATPase).which would reduce the pH in the
apoplaste, thus loosening the non covalent binding between cell wall constituents (Kutschera,
2006).
In the same way, gibberellins seem to be involved in the regulation of the cell expansion
phase. Indeed, gibberellins treatments have been shown to enhance fruit growth, mainly though
cell expansion (Serrani et al., 2007). It has been suggested that gibberellins influence cell
elongation through the increase in cell ploidy levels by endoreduplication in pericarp and locular
tissues (Joubes et al., 2000). (Joubes et al., 2000). As observed for auxin related genes,
transcriptome studies revealed that genes involved in gibberellins synthesis and/or signaling are
induced during the cell expansion phase (Lemaire-Chamley et al., 2005; Mounet et al., 2009).
Endoreduplication during the cell expansion phase
A characteristic feature of cell expansion is the increase of ploidy. Ploidy values in pericarp
cells are the highest observed in tomato and can reach 256 C, whereas ploidy in locular tissue
does not go over 128 C (Cheniclet et al., 2005). The mechanism responsible for the increase of
ploidy during the cell expansion phase is the endoreduplication process (Chevalier et al., 2011).
Endoreduplication is an altered cell cycle made of a succession of an undifferentiated G phase and
of a S phase for DNA synthesis. It results in the increase of ploidy levels by formation of
chromosomes with 2n chromatides (Bourdon et al., 2011; Chevalier et al., 2011).
Endoreduplicated cells present a larger nucleus and an increase of cytoplasm volume.
17
Endoreduplication is thus a major determinant for the size of the cells and consequently for the
determination of fruit size (Cheniclet et al., 2005).
To start and maintain endocycles, the exit from the classical cell cycle is largely linked to the
post-transcriptional regulation of the two main actors of a heterodimer complex formed by a
cyclin-dependent kinase (CDK) and by its regulatory subunit, the cyclin (CYC). The exit from the
classical cell cycle is due to the inhibition of the mitotic CDK/CYC complexes and to the
alternance between the S and the G phase of the endocycle, which is ensured by a G1/S type
CDK-cyclin. The three different post-translational mechanisms known to affect the components
of the CDK/CYC complexes have been found to be effective in the regulation of the endocyle in
tomato fruit: the phosphorylation/dephosphorylation status of the CDK, the availability and
binding of the CYC and the binding of specific CDK inhibitors (Figure I.10,Chevalier et al.,
2011).
The absence of mitosis in the endocyle suggested that the mitosis inducing factor (MIF),
which comprises a cyclin-dependent kinase (CDK) and a regulatory cyclin (CYC) subunit, was
impaired in endoreduplicating cells (Chevalier et al., 2011). CDKA and B form the two types of
CDK and play different roles that are interconnected during fruit development. CDKB 1 and 2
both negatively regulate the transition from mitosis to endoreduplication when associated with
CYCA2;3, as observed in transformed lines overexpressing CDKB1 and 2 (Boudolf et al., 2009;
Czerednik et al., 2012). The same plants also showed a reduced number of cell layers in the
pericarp. In addition, CDKA1 knock-out mutants showed the same phenotype as plants
overexpressing CDKB1 and CDKB2, implicating the existence of a molecular interaction between
CDKA1 and CDKB1 and CDKB2 for the control of cell size and regulation of cell division in the
pericarp (Czerednik et al., 2012).
The cyclic association between CDKA and CYCD during the G-phase, and between CDKA
and CYCA3;1 during the S-phase ensures the replication of DNA during the endocycle (Chevalier
et al., 2011). The G-S transition in endoreduplication is controlled by the retinoblastoma-related
protein (RBR) pathway. After being hyperphosphorylated by the CDKA-CYCD complex, the
RBR releases a E2F-DP dimeric transcription factor that activates genes necessary to have a
correct S phase (Figure I.10; Chevalier et al., 2011). In addition, the progression of the endocycle
requires the cyclic activation of the APC/C to target cyclins A and B (Boudolf et al., 2009). In
tomato fruit, CCS52A targeted the degradation of SlCYCA3;1, a typical S-phase cyclin (Mathieu-
Rivet et al., 2010b). (Mathieu-Rivet et al., 2010b). Finally, it has been shown that WEE1, a kinase
which inactivates CDKA by phosphorylation of the Tyr15 residue, is probably implicated is the
control of the G phase length during endoreduplication (Chevalier et al., 2011). Indeed, down
18
regulation of SlWEE1 results in higher CDKA activity, lower levels of ploidy and reduced fruit
size (Gonzalez et al., 2007)
Figure I.10: Schematic representation of endoreduplication cycle control in plants. The arrest of mitotic activity in
endoreduplicating tissues originates from the inactivation of the mitotic CDKB1/CYCA2;3 complex ( proposed as
being the mitosis inducing factor, MIF). This inactivation may be achieved by fixation of a CDK inhibitor (KRP) and
subsequent release of the cyclin moiety that is degraded by the APCCCS52A/26S proteasome pathway. During the
endocycle, an active CDKA/CYCD complex associates and phosphorylates the retinoblastoma protein (Rb). The GS
transition occurs due to the release of the E2F transcription factor which is necessary for the transcription of S-phase-
related genes, in particular the A-type cyclins (CYCA). At the GS transition, the CDKA/CYCD complex is likely to be
inhibited by KRP; CYCD is then degraded and CDKA can now associate with S-phase cyclins. In early S, the induced
CYCA3;1 associates with CDKA in a complex that is necessary for the progression throughout the S phase. At the end
of the S phase, CYCA3;1 is degraded by the APCCCS52A/26S proteasome pathway; CDKA is then released and
available for a new round of the endoreduplication cycle. In early G, CDKA is inactivated by the inhibitory
phosphorylation mediated by WEE1 as to allow a proper cell growth prior to commit to another round of DNA
synthesis (Chevalier et al., 2011).
A recent work showed that different MYB transcription factors are involved in the regulation
of cell expansion within the fruit pericarp (Machemer et al., 2011). This interaction allows the
fruit to remain intact while growing quickly by coordinating the growth of the different cell layers
comprised in the pericarp. At present we do not know if / how the regulation by theses MYB
transcription factors is connected to the regulation of the endocycle.
d) Fruit Ripening
Fruit ripening starts at the end of the expansion phase, when tomato fruit has almost reached
its final size and that seeds are mature ("mature green" stage). This developmental phase makes
the fruit attractive to herbivorous predators and appropriate for seed dispersal through
modifications of fruit color, texture, flavor (taste and aroma) and biochemical composition
(Hobson and Grierson, 1993; Seymour et al., 2012). Color changes are due to the transformation
of chloroplasts into chromoplasts and to the degradation of chlorophylls concomitant to the
19
synthesis of carorenoids like lycopene (Bramley, 2002; Egea et al., 2010). Texture changes are
mainly due to the degradation of cell wall components and to changes in cuticle composition and
architecture (Seymour et al., 2002; Saladie et al., 2007). Changes in fruit flavor are due to a large
shift of fruit metabolism between early development and fruit ripening (Carrari et al., 2006;
Carrari and Fernie, 2006). As in other climacteric fruits, tomato ripening is characterized by an
increase in respiration and a concomitant increase in ethylene synthesis, which is essential for
normal fruit ripening (Klee and Giovanonni, 2011). Large progresses have been made in
deciphering fruit ripening regulation thanks to the characterization of plants affected in ethylene
synthesis or perception (see below). In addition, transcriptome analyses have shown that fruit
transcriptome is largely reprogrammed during fruit ripening (Alba et al., 2005; Carrari et al.,
2006), and that these changes are mainly due to ethylene signaling.
Cell wall degradation during fruit ripening
Cell wall modifications during fruit ripening have been intensively studied because of their
relationship with fruit firmness and shelf-life (Brummel, 2006). Fleshy fruits such as tomato are
mostly constituted of parenchyma cells surrounded by a not lignified wall (Carrari and Fernie,
2006). This pectocellulosic wall consists of a rigid network of cellulose fibrils and hemicelluloses
sealed by pectins (Scheible and Pauly, 2004). Cellulose, a major component of the primary and
secondary wall, is a polymer of β(1-4)-D-glucose. Hemicellulose is composed of xyloglucans
(XG), glucomannans (GM), and glucuronoxylans. Finally, pectins are mainly composed of
galacturonic acid, galactose, arabinose and rhamnose. In addition, structural proteins as well as
enzymes involved in modeling and/or degradation of parietal compounds are present in the cell
wall.
A large number of enzymes are involved in the modification of cell wall (Rose et al., 2004).
This process is particularly important during fruit ripening. Indeed, the composition of pectins and
hemicellulose changes during fruit ripening (Davis and Hobson, 1981; Lahaye et al., 2012), and
the degradation of cell wall components is directly related to fruit softening or fruit shelf-life.
Transcriptomic analyses revealed that a large number of genes involved in cell wall metabolism
are up-regulated during fruit ripening, suggesting their implication in fruit softening (Alba et al.,
2005 and below). However, the complexity of the cell wall structure associated to the probable
multiple connections between cell wall-modifying processes and the multigenic families of cell
wall modifying enzymes hampered their functional validation by transgenic approaches (Bapat et
al., 2010).
A large part of research in this field focused on the degradation of pectins during fruit
ripening. Polygalacturonase (PG) is highly expressed (about 1% of the ripening fruit
20
transcriptome) and induces the depolymerization and solubilization of cell wall pectins. Its
activity requires the prior de-methyl-esterification of pectins by pectin methylesterase (PME;
(Brummell and Harpster, 2001). PME is induced before ripening and is down-regulated in an
ethylene-dependent fashion when ripening starts (Bapat et al., 2010). It was shown that a reduced
pectin depolymerization had a negative effect on tomato shelf-life (Carrari and Fernie 2006). Branched
pectins are depolymerized by rhamnogalacturonase and β-galactosidase (TBG), which are highly
active in tomato fruits. Functional studies of TBG4 and TBG7 by antisens transformation confirmed
their implication in fruit firmness (Bapat et al., 2010). Other enzymes have been identified like the
endo-β-1,4-glucanases (cellulase, EGase), which are a class of enzymes which degrade
carboxymethylcellulose. Their activity is associated with softening in tomato, suggesting a role in
ripening, but no functional analyses confirmed this hypothesis to date (Carrari and Fernie, 2006). A
specific expansin (LeExp1) is highly and specifically expressed during fruit ripening. It could be
implicated in fruit softening by an indirect effect on pectin depolymerization (Carrari and Fernie,
2006).
Carotenoid accumulation during fruit ripening
The characteristic change of tomato fruit color during the ripening phase is due to the
degradation of chlorophylls concomitant to the synthesis of a red pigment, the lycopene (Alba et
al., 2005; Egea et al., 2010). These changes are related to the conversion of chloroplasts into
chromoplasts, which occurs progressively from mature green to red ripe stages (Egea et al., 2010).
Ultrastructural studies revealed that chromoplast differentiation consists in large membrane
remodeling, with the disassembling of the thylakoids and the synthesis of a new membrane
system where the carotenoid crystals will be formed (Egea et al., 2010). In addition, an increase in
the number and size of plastoglobules, implicated in the sequestration of carotenoids and in the
biogenesis of chromoplasts, as well as an increase in the number and length of stromules, the
motile protrusions of the plastid membranes involved in the exchange with the cytoplasm, are
observed during the chloroplast-chromoplast conversion. These structural changes are
accompanied by a complete shift in the plastids metabolic activity from a photosynthetic
metabolism (Piechulla et al., 1985) to a metabolism devoted to the synthesis of carotenoids
(Bramley, 2002), where the stress-response proteins are highly represented (Barsan et al., 2010).
Proteins related to the biosynthesis of fatty acids (Kahlau and Bock, 2008; Barsan et al., 2010), as
well as proteins involved in energy and metabolite supply seems also to be particularly important
to sustain chromoplast metabolic activity (Barsan et al., 2010; Egea et al., 2010).
Regulation of carotenoids biosynthesis during tomato fruit ripening is dependent on the
regulation of genes involved in this metabolic pathway but also on the regulation of chloroplast
differentiation. Many of the genes involved in carotenoids biosynthesis during fruit ripening are
21
regulated by ethylene (Alba et al., 2005). The transcription factor SlERF6 regulates both ethylene
and fruit carotenoid synthesis pathways (Lee et al., 2012). Other transcription factors regulating
both tomato fruit ripening and carotenoids biosynthesis have been described such as CNR
SQUAMOSA promoter binding protein (Manning et al., 2006), TAGL1 MADS box (Itkin et al.,
2009; Vrebalov et al., 2009), LeHB-1 HBzip (Lin et al., 2008) and SlAP2a (Chung et al., 2010;
Karlova et al., 2011).
In addition to ethylene, light is a crucial element in the regulation of carotenoids biosynthesis.
Indeed, phytochromes regulate lycopene accumulation independently of ethylene biosynthesis
(Alba et al., 2000). This regulation by light is also related to an indirect effect due to the
regulation of chromoplasts formation (Ghassemian et al., 2006). Indeed, hp1 and hp2 mutants
showed an increase in fruit carotenoids at mature green stage, where they exhibit a dark-green
phenotype, and at red ripe stage together with an hypersensitivity to light due to phytochrome
action (Peters et al., 1989). The HP1 gene encodes a homolog of arabidopsis UV-DAMAGED
DNA-BINDING PROTEIN 1 (DDB1) protein, which interacts with the nuclear factor
DEETIOLATED 1 (DET1; Liu et al., 2004), while the HP2 gene encodes a tomato ortholog of
DET1 (Mustilli et al., 1999). It has been shown that DET1 plays a role in light signaling and
modulates stability of transcription factors (Schafer and Bowler, 2002; Lee et al., 2007). Although
chlorophyll and carotenoids biosynthesis are enhanced in DET1 RNAi lines, this increase of
pigment does not originate from alteration of the expression of the corresponding biosynthesis
genes, but more likely from a fruit ectopic increase of plastid number and size in the cells (Enfissi
et al., 2010).
Other genes associated with different hormones signaling pathways have been proposed to
affect chloroplast number/shape. HP3 is believed to change plastid structure and to increase
plastid number and lycopene content via ABA signaling (Galpaz et al., 2008). Down-regulation
of ARF4, an auxin response factor, resulted in a dark-green fruits characterized by a blotchy
ripening of tomato fruit, as well as in an increase of chloroplasts number and grana formation
(Jones et al., 2002). Finally, it has been shown that cytokinin treatment mimics the hp mutant
phenotype (Mustilli et al., 1999).
Some genes are implicated in the regulation of carotenoids biosynthesis at the transcriptional
level, by regulation of biosynthesis genes, and by regulation of chromoplast number/shape. It is
the case for the MADS-box transcription factor RIPENING INHIBITOR (RIN), which acts
upstream of both ethylene and non ethylene-mediated ripening control (Vrebalov et al., 2002).
Indeed, LeMADS-RIN interacts with the promoter of the phytoene synthase 1 (PSY1), the
enzyme responsible for the initiation of the carotenoid biosynthesis cascade (Martel et al., 2011).
22
In addition, the fruits of rin mutant have an increased number of chromoplasts, smaller than in the
wild type, which present a decreased number of stromules.
The complexity of the regulation of carotenoids biosynthesis during fruit ripening is not only
due to its dependence on fruit plastid status at the beginning of the ripening phase but also to its
dependence on events occurring during early fruit development (Egea et al., 2010; Enfissi et al.,
2010 ; Powell et al., 2012). Indeed, it has been shown that all the phenotypic changes observed in
det1 mutant during ripening are the consequences of changes induced at earlier stages and cannot
be observed if DET1 is silenced only during ripening (Davuluri et al., 2005). Up to now, the
initial signal which leads to chromoplast differentiation is not known. It is not fully dependent on
the status of the chloroplasts at the mature green stage, since in chlorophyll deficient lutescent
mutants chromoplast development and carotenoids biosynthesis are not impaired (Barry et al.,
2012). However, it seems to be somehow dependent on a chloroplastic metabolic status since it
has been shown that the overexpression of PSY-1 during fruit development induces metabolic
changes in mature green fruits resembling changes observed during ripening (Fraser et al., 2007b).
Accordingly with these observations, overexpression of PSY-1 also induces a premature transition
of chloroplasts into chromoplasts prematurely.
Ethylene signaling during fruit ripening
Tomato fruit ripening is strictly dependent on the ethylene phytohormone (Gillaspy et al.,
1993) and at least 37 % of the ripening related transcriptome changes are dependent on ethylene
signaling (Alba et al., 2005). Ethylene levels are low in the fruit before ripening, dramatically
increase at the onset of fruit ripening, and then drop to twenty-five to fifty percent of maximum
levels seven days after the breaker stage (Alba et al., 2005). Ethylene biosynthesis starts with the
conversion of S-adenosyl-L-methionine (SAM) to 1-aminocyclopropane-1-carboxylate (ACC) by
ACC synthase (ACS). Then ACC is converted into ethylene by ACC oxidase (ACO, Figure I.11).
The formation of ACC also leads to the production of 5’-methylthioadenosine (MTA), which is
recycled via the methionine cycle to yield a new molecule of methionine. Increased respiration in
the climacteric fruit provides the ATP required for the methionine cycle and can lead to high rates
of ethylene production without high levels of intracellular methionine (Barry and Giovannoni,
2007). ACS and ACO belong to multigenic families. Most research has focused on ACS
multigenic since ACS activity is the rate-limiting step of ethylene biosynthesis (Klee and
Giovannoni, 2011). The recent release of tomato genome revealed the existence of 11 tomato
ACS genes, among which eight were previously characterized (Tomato Genome Consortium,
2012).
23
Figure I.11 : Ethylene biosynthesis pathway. The synthesis of ethylene starts with the conversion of s-adenosyl
methionine (SAM) into 1-aminocyclopropane-1-carboxylic acid (ACC) by the ACC synthase (ACS). Then ACC is
oxidized by the ACC oxidase (ACO) into ethylene (Bapat et al., 2010).
Two systems of ethylene production exist in plant (Barry and Giovannoni, 2007; Klee and
Giovannoni, 2011). System 1 functions during normal growth and development, during stress
responses and during early fruit development. It is characterized by an autoinhibition of ethylene
synthesis; the ethylene treatment of immature fruits results in the induction ethylene-regulated
genes but does not lead to fruit ripening. On the contrary, system 2 functions specifically during
floral senescence and fruit ripening. It is characterized by an autocatalytic synthesis of ethylene
and ethylene treatment of mature fruits results in the induction of a large set of genes and leads to
fruit ripening.
Figure I.12. Differential expression of ACS and ACO genes associated with system 1 and system 2 ethylene synthesis
during fruit development and ripening in tomato. Autoinhibition of ethylene synthesis during system 1 ethylene
production is mediated by a reduction in LeACS1A and 6 expression. Autocatalytic ethylene synthesis at the onset of
fruit ripening is mediated through ethylene-stimulated expression of LeACS2 and 4 and LeACO1 and 4 (Barry and
Giovannoni, 2007).
Understanding of ethylene regulation during tomato fruit ripening greatly benefited from the
data collected on ethylene signaling in Arabidopsis (Yoo et al., 2009; Figure I.13) as well as from
the existence of numerous tomato ripening pleiotropic mutants, as already described above for
hp1 and hp2 mutants in relation with light signal (Klee and Giovannoni, 2011). Among the
24
tomato ripening mutants, rin (ripening-inhibitor), nor (nonripening) and cnr (colorless non-
ripening) mutants do not exhibit the ripening-related increase in ethylene synthesis (system 2).
The characterization of rin mutants using a combination of ethylene and ethylene-inhibitor
treatments allowed deciphering the specific role of the different ACS isoforms in tomato fruit
ripening (Barry and Giovannoni, 2007). The results suggested that SlACS6 is responsible for low-
level ethylene production in preclimacteric fruit (system 1), SlACS1A and SlACS4 are
responsible for initiating ethylene synthesis in system 2, and that system 2 is maintained by a
combination of SlACS2 and SlACS4 (Figure I.12). In addition to transcriptional regulations
within the ACS gene family in tomato, it was demonstrated that SlACS2 enzyme is submitted to a
post-translational regulation via phosphorylation/ dephosphorylation (Kamiyoshihara et al., 2010).
Phosphorylation of SlACS2 by a mitogen-activated protein kinase and a calcium dependent
protein kinase enhances the protein stability and guarantees a good enzymatic activity, whereas
dephosphorylation results in degradation of SlACS2 through the proteasome system
(Kamiyoshihara et al., 2010).
Figure I.13. Normal and mutant tomato fruit. Normal tomato cultivar Ailsa Craig ripe fruit ten days post breaker and
equivalent age fruit homozygous for the high-pigment 1 (hp1/hp1), high-pigment 2 (hp2/hp2), Never-ripe (Nr/Nr),
Green-ripe (Gr/Gr), Colorless non-ripening (Cnr/Cnr) and ripening-inhibitor (rin/rin) mutations. (Giovannoni, 2007)
After ethylene biosynthesis, ethylene signaling relies on the detection of ethylene signal by
receptors. These receptors act as negative regulators of ethylene signaling: in the absence of
ethylene, the receptors are functional and ethylene signaling pathway is blocked, whereas in the
presence of ethylene, the receptors are degraded through the 26S proteasome-dependent pathway
and ethylene signaling can proceed (Klee and Giovannoni, 2011; Figure I.14). In tomato, seven
genes coding for ethylene receptors have been identified, called LeETR1 to 7. They encode
protein kinases located in the endoplasmic reticulum membrane, distributed in subfamily 1 that
regroups histidine kinase proteins and in subfamily 2 that regroups serine kinase proteins (Klee
25
and Giovannoni, 2011). NR (subfamily 1) is upregulated at the onset of fruit ripening together
with LeETR4, and LeETR6 genes, which belong to subfamily 2. The nr (Never-ripe) tomato
mutant revealed the fundamental role of NR protein since a single mutation in this receptor was
sufficient to confer a dominant insensitivity to ethylene, which severely impairs fruit ripening
(Wilkinson et al., 1995). In addition, silencing of LeETR4 or LeETR6 results in an increased
sensitivity to ethylene leading to an early-ripening phenotype, because of a rapid degradation of
the ethylene receptors through the 26S proteasome-dependent pathway (Kevany et al., 2007). This
phenotype is reversed by over-expression of NR receptor.
Figure I.14. The ethylene signaling pathway. Ethylene receptors (upper left), shown as disulfide-linked dimers, actively
suppress ethylene responses in the absence of ethylene. Upon ethylene binding, receptors undergo a physical change
that permits them to be targeted for degradation by an as yet unidentified E3 ligase. Receptors physically interact with
the negative regulator CTR1. Ein3-like transcription factors (EILs) either activate transcription of ERF family members
or are targeted for degradation by the EIN3 binding factor E3 ligases Although EIN2 is required for ethylene signal
transduction, genetic analysis places it downstream of CTR1 and upstream of the EIL family. Its mechanisms of action
are undetermined. (Klee and Giovannoni, 2011).
In the absence of ethylene, the repression of ethylene signaling pathway is due to the
interaction of ethylene receptors with the MAPKK kinase CTR1 (CONSTITUTIVE TRIPLE
RESPONSE 1), thus leading to the repression of downstream components of the ethylene
signaling pathway (Zhong et al., 2008); Figure I.14). In the presence of ethylene, CTR1 is
inactivated by a yet unknown mechanism, and ethylene signaling pathway is activated. In tomato,
four genes encode proteins presenting a significant homology to Arabidopsis CTR1 protein
(Adams-Phillips et al., 2004). Among them, tCTR1 complements the Arabidopsis ctr1 mutant and
is the most highly expressed in the fruit, its expression being induced during ripening and by
ethylene (Klee and Giovannoni, 2011).
26
In Arabidopsis, the inactivation of CTR1 leads to the activation of EIN2, a positive regulator
of ethylene signaling crucial for the accumulation of EIN3 transcription factor (Yoo et al., 2009).
EIN3 and EIN3 like proteins (EIL proteins) are transcription factors which bind to PERE cis-
elements (Primary Ethylene Regulator Element) present in some ERF (Ethylene Response Factor)
promoters. In turn, ERFs transcription factors bind to the promoter of final target genes involved
in the different aspects of fruit ripening like texture changes or carotenoid biosynthesis (Figure
I.14). In tomato, four EIN3-like genes (LeEIL1 to 4) have been identified and the implication of
LeEIL1, LeEIL2 and LeEIL3 in fruit ripening has been shown. The level of LeEIL proteins is
regulated by targeted proteolysis through the ubiquitin/26S proteasome pathway. Two
functionally redundant EIN3 binding factors EBF F-box proteins, the LeEBF1 and LeEBF2 in
tomato, are believed to play this role (Klee and Giovannoni, 2011). Analysis of tomato genome
allowed recently the identification of 77 to 85 ERFs (Sharma et al., 2010; Pirrello et al., 2012).
Some of them present a specific up regulation at the onset of fruit ripening, thus suggesting their
implication in fruit ripening.
Towards a regulatory network of tomato fruit ripening
As described above, ethylene signaling is a major component of regulation during fruit
ripening. To fully decipher this process, the major objectives in the recent years were to identify
the master regulator(s) which is (are) responsible for the initiation of fruit ripening and to identify
the developmental genes regulated during this process (Klee and Giovannoni, 2011). The
discovery of fruit ripening mutants (Figure I.13) provides precious elements to achieve these
goals (Giovannoni, 2007; Klee and Giovannoni, 2011; Seymour et al., 2012). Three non allelic
mutants were particularly useful: the ripening-inhibitor (rin), non-ripening (nor), and Colorless
nonripening (Cnr). These mutants show a normal early development but fail to undergo the
climacteric rise in respiration, do not produce ripening-associated ethylene, do not ripen in
response to exogenous ethylene treatment, but respond to ethylene treatment by activation of
ethylene responsive genes. Taken together these data suggested that these mutants are implicated
in upstream processes regulating fruit ripening dependent and independent from ethylene
signaling (Giovannoni, 2007; Klee and Giovannoni, 2011; Seymour et al., 2012); Figure I.15 and
Figure I.16). The identification of the corresponding genes revealed that they encode
transcription factors, corresponding respectively to a MADS-Box (rin mutant / LeMADS-RIN
gene), a NAC (nor mutant / LeNAC-Nor gene) and a SQUAMOSA PROMOTER BINDING
PROTEIN (Cnr mutant/ LeSBP gene).
During fruit development, LeMADS-RIN gene is induced at the onset of ripening, but it is not
induced by ethylene (Vrebalov et al., 2002). The rin mutant corresponds to a deletion of 1kb of
the sequence between LeMADS-RIN and MACROCALYX (MC), a neighboring gene ortholog of
27
the Arabidopsis thaliana APETALA1. The product of this deletion is a fused MADS-RIN/MC
protein that does not show neither LeMADS-RIN function nor MC function (Klee and Giovannoni,
2011). In addition to the absence of ripening, the rin mutant presents large sepals, which are due
to the alteration of MACROCALYX protein. Both phenotypes of the rin allele were recreated
independently by antisense repression of LeMADS-RIN and MC, respectively (Vrebalov et al.,
2002). Due to its pleotropic phenotype, the rin mutation was exploited to produce tomato varieties
characterized by long shelf life and high fruit firmness (Klee and Giovannoni, 2011). The Cnr
mutation corresponds to a spontaneous epigenetic change in the promoter of the SQUAMOSA
PROMOTER BINDING PROTEIN which inactivates its upregulation during fruit ripening
(Manning et al., 2006).
Figure I.15 : Action model for several ethylene transcriptions factors (Klee and Giovannoni, 2011). Several
transcription factors regulate processes associated with ripening. RIN, NOR, CNR, TAGL1 and possibly TAG1 are
required for both ethylene dependent and ethylene independent gene regulation.
The genes regulated by these transcription factors have been studied by global expression
analyses and more recently through chomatin immunoprecipitation analyses (Eriksson et al.,
2004; Martel et al., 2011; Osorio et al., 2011; Fujisawa et al., 2012). Complementary, expression
analysis during fruit development and ripening in WT and Nr mutant also provided crucial
information on regulatory aspects. It was shown that LeMADS-RIN interacts with the promoters
of genes involved in the different aspects of fruit ripening, and notably NOR, LeSBP, TDR4 and
LeHB1 transcription factors (Martel et al., 2011; Fujisawa et al., 2012). In addition, this
interaction was dependent on the presence of a non-altered LeSBP protein, since it was not
detected in the Cnr mutant (Martel et al., 2011). LeMADS-RIN and LeSBP could thus act at the
same level in the regulation of tomato fruit ripening. Down-regulation of LeSBP in the rin mutant
rather suggests that LeSBP may act downstream of LeMADS-RIN. The hierarchical order
28
between LeMADS-RIN and LeSBP is not yet clear, because of numerous cross-regulations
between both partners (Eriksson et al., 2004; Osorio et al., 2011).
Direct comparison of rin and cnr mutants may
help to clarify the situation. Indeed, rin and
nor transcriptome were directly compared,
together with the transcriptome of WT and Nr
mutant (Osorio et al., 2011). This analysis
revealed that despite their close phenotype,
rin and nor were not equivalent, at the
transcriptome and metabolic levels. In
agreement with previous work, the
transcriptomic analysis suggested that nor
mutation has a more global effect on
ethylene/ripening-related gene expression
than rin mutation. As a conclusion, the
authors proposed that NOR is placed
upstream of LeMADS-RIN in tomato fruit
ripening signaling cascade (Osorio et al.,
2011; Figure I.16). Other studies however
showed that LeMADS-RIN can bind to the
NOR promoter, suggesting a slightly different
pattern of cross-regulations between both
regulators (Martel et al., 2011).
Figure I.16 : A potential genetic regulatory network
centered on ethylene governing tomato fruit development
and ripening. TF : transcription factor. NOR and MADS-RIN
regulate the biosynthesis of ethylene, which is signaled by
ethylene receptors and downward transcription factors to
induce the ethylene response (Osorio et al., 2011).
The transcriptional profiling of the ripening mutants also encouraged researchers to
investigate the implication of additional transcription factors in the regulation of tomato fruit
ripening (Klee and Giovannoni, 2011). Some of them are implicated in specific regulations during
ripening process such as LeHB1, a HD-zip homedomain protein, which is implicated in the
regulation of the LeACO1 gene. Indeed, repression of LeHB1 in transgenic plants resulted in
reduced LeACO1 expression, in a concomitant decrease in ethylene synthesis, and in a delayed
ripening (Lin et al., 2008); Figure I.15). In the same way, SlAP2a, a tomato APETALA2 (AP2)
29
gene family member induced at the onset of fruit ripening, was recently implicated in the control
of fruit ripening (Chung et al., 2010). RNAi repression of SlAP2a resulted in fruits that over-
produced ethylene, ripened early and presented a modification of carotenoid accumulation
profiles. These phenotypes are related to a negative regulation of SlAP2a on SlACS2 and SlACS4
during ripening, and a negative regulation on SlACS6 before ripening, suggesting the implication
of SlAP2a in the control of ethylene production and on the switch between system 1 and 2 for
ethylene biosynthesis (Chung et al., 2010). It is proposed that the role of SlAP2a is not only
limited to the regulation of ethylene biosynthesis, but also includes the regulation of carotenoid
levels through both ethylene-dependent and independent pathways (Chung et al., 2010). Indeed,
it was shown that SlAP2a upregulates SGR1 and the tomato homolog of Or, two genes
respectively implicated in the degradation of chlorophyll and the chloroplast to chromoplast
transition (Karlova et al., 2011). The mode of action of SlAP2a may necessitate the intervention
of auxin (Chung et al., 2010).
The implication of LeMADS-RIN in the regulation of tomato fruit ripening led rapidly to the
conclusion that other MADS-Box transcription factors may also be implicated in this regulation,
since these transcription factors act as dimers or heterogeneous multimers (Leseberg et al., 2008).
In addition, the expression of the MADS-Box TDR4 was reduced in the Cnr mutant (Eriksson et
al., 2004). Recent works allowed the identification of the MADS-Box TAGL1 (tomato agamous-
like 1) as a regulator of tomato fruit ripening (Vrebalov et al., 2009). In wild-type fruits TAGL1 is
expressed both during cell expansion and fruit ripening. Repression of TAGL1 by silencing or
expression of TAGL1 fused to the SRDX repressor domain, results in ripening inhibition and in
reduction in carpel thickness primarily due to a reduction of cell division (Vrebalov et al., 2009).
The reduction of ethylene and total carotenoids in these transgenic lines is related to a reduction
of LeACS2 expression. Expression of TAGL1 in the rin mutant and expression of LeMADS-RIN in
TAGL1 transgenic lines indicated that the pathways influencing TAGL1 versus LeMADS-RIN and
LeSBP are distinct, at least partially (Vrebalov et al., 2009). Interestingly, TAG1, the closest
homolog of TAGL1, is induced when TAGL1 is repressed, and may be able to complement
TAGL1 loss of function (Vrebalov et al., 2009).
As described above, a range of transcription factors are implicated in the initiation of tomato
fruit ripening, prior to ethylene autocatalytic biosynthesis. Recent results suggest the existence of
different pathways with LeMADS-RIN, NOR and LeSBP on one side and TAGL1/TAG1 on the
other side (Klee and Giovannoni, 2011); Figure I.15). Is one of these genes the master regulator
which is responsible for the initiation of fruit ripening? The question will be difficult to answer
since tomato fruit development is in reality a continuous process, and not a succession of different
well separated developmental phases. In addition, other processes which start only to be studied
could also be crucial regulatory elements of the transition between early fruit development and
30
ripening like the epigenetic regulations (Seymour et al., 2008; Moxon et al., 2008; Martel et al.,
2011).
3) Mining genomic data for identifying genes involved in the
control of fruit quality traits
With recent technological advances, large scale or global scale analyses of transcripts
(transcriptome), proteins (proteome) and metabolites (metabolome) have been made possible. Due
to the potential of the “omics” technologies for the global understanding of processes of interest,
such as fruit development, several technologies have been developed in the past years for
measuring each class of molecules. In parallel, new approaches have been designed for mining
and interpreting the large amount of data generated by the “omics” technologies e.g. through the
integration of genetic and metabolomics data (gene-QTL co-localization), or through correlation
analyses (gene co-expression analyses, trait/gene/protein/metabolites correlation networks…). In
the recent years, these approaches proved to be very powerful by allowing the identification of
key genes involved in the control of major biological processes, among which developmental and
metabolic processes linked to fruit quality traits.
a) Technologies for "Omics" analysis
Transcriptomics
The pattern of expression of a given gene gives a first insight on the involvement of that gene
in a process of interest, by knowing where (organ, tissue, cell) and when (time-course, response to
changing environmental conditions …) that gene is expressed. From another perspective, when
studying a process, getting a wide scale picture of the expression of hundreds or thousands of
genes allows the identification of the main genes involved in the process. Therefore, technologies
for monitoring large scale transcriptome changes have been widely used and a wealth of data have
been gathered in plants, including fruits and more particularly tomato fruit.
(1) Microarray
Microarray (or DNA chips) was the first technique allowing researchers to detect RNA and
measure gene expression on a large scale (Schena et al., 1996). Microarrays based on various
technologies using cDNAs or oligonucleotides can be designed to study gene expression at whole
genome scale or among a subset of genes for specific research applications (Lockhart et al., 1996;
Aharoni and Vorst, 2002; Alba et al., 2004). Following the first study carried out on a limited
number of genes in Arabidopsis (Schena et al., 1996), microarrays have soon been used to analyze
a large range of plant species, organs, tissues and cells and various processes such as response to
31
oxidative stress (Desikan et al., 2001; Alba et al., 2004), cold response (Fowler and Thomashow,
2002), light regulation of development (Ma et al., 2001; Devlin et al., 2003; Jiao et al., 2003),
response to pathogens (Maleck et al., 2000; Reymond et al., 2000; Puthoff et al., 2003; Gechev et
al., 2004; Blanco et al., 2009) and fruit development (Alba et al., 2004). As a plant and fleshy
fruit model, tomato was also largely studied using microarrays e.g. in response to fungi and virus
infections (Frick and Schaller, 2002; Mysore et al., 2002) and to other pathogens (Scheideler et al.,
2002), in response to salt stress (Ouyang et al., 2007), or for signaling and developmental
processes such as light regulation (Rutitzky et al., 2009), hormone synthesis (Soeno et al., 2010)
and fruit ripening (Moore et al., 2002; Srivastava et al., 2010).
Interestingly, one of the first large scale plant microarray study published to date was on a
fleshy fruit species, the strawberry, and on a fruit quality trait, the fruit flavor (Aharoni et al.,
2000). Combination of metabolic profiling and microarray transcriptome analysis of strawberry
fruit resulted in the identification of a strawberry alcohol acyltransferase (SAAT) gene involved
in aroma biogenesis. Based on this result, the involvement of enzymes with similar functions in
aroma synthesis was subsequently proved in several other fleshy fruit species (Yahyaoui et al.,
2002). This remarkable result paved the way for subsequent use of microarrays for gene discovery
and comprehensive understanding of fleshy fruit development and quality. In tomato, for which
large collections of Expressed Sequence Tags (ESTs), which are necessary for designing the
primers for cDNA amplifications or the oligoarray probes, were available, large scale
transcriptomic analyses using tomato microarrays explored the processes involved in fruit
ripening (Alba et al., 2004; Alba et al., 2005) and early fruit development (Lemaire-Chamley et
al., 2005). This led to the identification of candidate genes controlling major fruit quality traits e.g.
the GDSL lipase involved in cuticle synthesis (Mounet et al., 2012), or the GDP-mannose
epimerase involved in fruit ascorbate content and firmness modification (Gilbert et al., 2009).
Microarray analysis of tomato genetic diversity (introgression lines, recombinant inbred lines,
ripening mutants, transgenic lines) further investigated fruit quality traits such as fruit soluble
solid content (Baxter et al., 2005), fruit vitamin and other phytonutrient contents (Garcia et al.,
2009; Enfissi et al., 2010) and developmental processes e.g. fruit set (Wang et al., 2009a),
ripening (Osorio et al., 2011; Lee et al., 2012) and response to carbohydrate availability (Prudent
et al., 2010).Tomato transcriptomic analyses were also extended to the study of postharvest
treatments on tomato fruit shelf life (Liu et al., 2011) and to the study of fruit ripening in related
Solanacae species (Moore et al., 2005).
While microarray analysis has been widely used to gather transcriptome data, it shows
several limits (Chen et al., 2011). This technology relies on prior information and cannot detect
novel genes, transcripts or splicing variants. Therefore, RNA sequencing has been used in recent
32
studies in place of microarrays because it provides a more sensible and more suited technology
for RNA detection (Wang et al., 2009b; Chen et al., 2011).
(2) RNA sequencing
The striking advances in Next Generation Sequencing (NGS) technologies in the recent years
now allow deep coverage detection of expressed genes by sequencing (RNA seq) at low cost. In
contrast to the early quantification of ESTs by Sanger technology (electronic Northern blot),
which was at most semi-quantitative, and to the microarrays, which do not detect all transcripts
due to their lack of sensitivity, the RNA seq allows the precise quantification of all transcripts. It
therefore rapidly replaces transcriptome analysis by cDNA microarrays or by oligochips. The
principle and features of RNA sequencing have been reviewed (Wang et al., 2009b; Metzker,
2010). RNA sequencing makes use of deep sequencing technologies to sequence the pool of RNA
(mRNA, non-coding RNA and small RNA) present in a sample. First, RNA molecules are
converted into cDNA fragments with adaptors at one or both ends. All cDNA molecules are then
sequenced thanks to a high throughput sequencing technology, such as the Illumina (Bennett et
al., 2005) or the 454 sequencers (Emrich et al., 2007; Wang et al., 2009b).
RNA sequencing has been used to study RNA splicing (Guttman et al., 2010; Trapnell et al.,
2010), to distinguish between the different gene isoforms expressed (Chen et al., 2011), and to
analyze gene expression (Trapnell et al., 2010; Wang et al., 2010; Cumbie et al., 2011; Tarazona
et al., 2011) in sequenced (Guttman et al., 2010; Trapnell et al., 2010) or non sequenced species
(Robertson et al., 2010; Grabherr et al., 2011). Both 454 and Illumina sequencing have been
recently used for the analysis of expressed genes in tomato fruit and other tissues. This proved to
be very useful for the annotation of the tomato genome sequence (Tomato Genome Consortium ,
2012) In addition, by coupling it with Laser Capture Microdissection, it recently enabled the
comparative analysis of several tissues in developing fruit and therefore the detection of candidate
genes specifically expressed in a given tissue or cell type, e.g. genes with cuticle-related functions
specifically expressed in epidermal cells (Matas et al., 2011). Tomato cDNA sequences issued
from RNA seq analysis have also been used as a reference to study the proteome of tomato pollen
(Lopez-Casado et al., 2012) and the short RNAs in developing fruit (Mohorianu et al., 2011) .
Microarrays and RNA seq are two powerful techniques that allow detection and
quantification of most if not all of the transcriptome. However, protein activity can be regulated at
other levels than transcription; therefore analysis of proteome is also necessary to understand the
complex functional activity in a living organism.
33
Proteomics
Roughly, for proteome analysis, a set of proteins (proteome) isolated from plant tissues is
submitted or not to various separation techniques to reduce its complexity (sub-fractionation, 2D
gels), then fragmented to peptides that are further analyzed by mass spectrometer and compared
to reference sequences (genomic sequence, RNA seq data) to identify the proteins (Rubakhin et
al., 2011). Though less high throughput than the techniques used for gene expression analysis,
which presently allow the screening of all the expressed genes, proteome analysis enables the
detection of hundreds or thousands of expressed proteins and therefore emerge as a powerful tool
for studying fleshy fruit development and ripening (Palma et al., 2011). Following early studies
on tomato fruit (Schuch et al., 1989), comparison of proteome between two ecotypes allowed the
isolation of common and ecotype specific sets of proteins (Rocco et al., 2006). Detailed proteome
analysis of developing tomato fruit further showed that the tomato fruit proteome evolved in the
same way as the transcriptome, with a time-lapse (Faurobert et al., 2007). Proteome of specific
fruit tissues or organelles was also investigated e.g. during tomato cuticle biogenesis (Yeats et al.,
2010) and in tomato chromoplasts, in which more than two hundreds proteins not previously
characterized were identified (Barsan et al., 2010). In addition, the effects of storage conditions
on ripe tomato fruit were analyzed using this approach. Page et al. (Page et al., 2010). identified a
number of proteins differentially regulated between tomato lines with various sensitivity to
chilling stress and showed that cold stress resulted in the down-regulation of proteins involved in
ripening. Likewise, global analysis of proteome from control and cold stressed ripening fruit led
to the identification of 300 proteins differentially regulated between the two conditions (Vega-
Garcia et al., 2010).
Recently, proteome data (spectral mass) have been made available online as an in silico
resource comparable to the transcriptomics databases (Wienkoop et al., 2012). This resource
allows the retrieval of proteomics data in several species and also serves the purpose of being a
reference for identification of peptides.
Metabolomics
Metabolome analysis is also an important feature for understanding plant development and
metabolism. Metabolites are organic or inorganic molecules of low molecular weight (>1.5 kDa).
They participate as substrates or products of metabolism (primary metabolites) or act as
compounds with various properties, such as signaling or defense against pathogens (secondary
metabolites). Plants possess tens to hundred thousands of metabolites (Oksman-Caldentey and
Saito, 2005). The final aim of metabolomics is to provide a complete overview of the entire
metabolic set with a single experiment. Besides, the current trend in metabolome studies, like in
34
proteome or transcriptome studies, is to focus on specific tissues or even on single cells
(Rubakhin et al., 2011; Oikawa and Saito, 2012). However, the wide magnitude of chemical
diversity of plant metabolites results in the incapacity of analyzing the entire set of metabolites of
a sample by using a single technique (Fernie and Klee, 2011). Therefore, two main approaches are
often used to study metabolites: nuclear magnetic resonance (NMR) and mass spectrometry (MS).
(1) Nuclear Magnetic Resonance
NMR spectroscopy is a non-invasive and quantitative method for detection of metabolites
and has been successfully applied to single cell studies (Lee et al., 2006; Rubakhin et al., 2011;
Oikawa and Saito, 2012). It has the advantage of being a more uniform system than MS analyses
and can be used to identify and quantify metabolites (essentially major metabolites) in vivo (Hall,
2006). This technology allows for example the establishment of the metabolic profile of tomato
fruit tissues during fruit development (Mounet et al., 2007; Mounet et al., 2009) or the
observation of the responses to biotic or abiotic stresses (Fukushima et al., 2009). A technical
difficulty encountered by using NMR is the low sensitivity of metabolite detection but new probes
technologies using fluorescence have helped to partially solve the problem (Maguire et al., 2007;
Rubakhin et al., 2011). Recently, nuclear spin resonance has been used advantageously with very
small samples (Maguire et al., 2007).
(2) Mass spectrometry
Mass spectrometry analysis has been used in different contexts for a long period. Various
techniques exist to separate metabolites and to detect their spectral mass. GC-MS has been used
advantageously for detection of volatile compounds such as alcohols, monoterpenes and esters
and is also applicable to primary metabolites detection after converting them into volatile
compounds (Desbrosses et al., 2005; Hall, 2006). LC-MS is restricted to molecules able to be
ionized but can be used to detect large groups of secondary metabolites (Hall, 2006).
MALDI/TOF –MS was used to solve MS limitations for the study of carotenoids in tomato and to
study single cell metabolome (Fraser et al., 2007a; Amantonico et al., 2010). Tandem mass
spectrometry (MS/MS) spectral tag (MS2T) analysis was used to profile secondary plant
metabolites and identify many previously unknown tissue-specific metabolites (Matsuda et al.,
2009). To study the tomato fruit quality, the volatile compounds have also been profiled using
proton-transfer reaction MS, and flavor differences between cultivars have been established
(Farneti et al., 2012). Single cell metabolites were also analyzed using secondary ion MS
(Rubakhin et al., 2011).
Non-targeted metabolome analysis with known and unknown metabolites has been important
in the generation of comprehensive resources for the plant metabolome. For example,
35
metabolome profiling approaches have been applied to monitor changing metabolite accumulation
in response to stress conditions (Ishikawa et al., 2010; Kusano et al., 2011). Metabolome profiling
has also been applied to establish metabolomic profiles, not only in plant models but also in
various crop species (Mochida et al., 2009).
b) Data interpretation
One of the purposes of “Omics” based strategies is to identify new genes implicated in the
regulation of various processes of interest (Hirai et al., 2005). But this is not the sole objective. In
Arabidodpsis thaliana, for example, these strategies were used to study the diurnal cycle of the
rosette (Blasing et al., 2005; Gibon et al., 2006), as well as global responses to nutritional stresses
(Hirai et al., 2004; Nikiforova et al., 2005), transcriptional regulation of metabolic pathways
(Wei et al., 2006), response to nitrogen (Scheible et al., 2004; Gutierez et al., 2008), interaction
between light and carbon signaling pathways (Thum et al., 2008), regulation of flavonoid
synthesis (Yonekura-Sakakibara et al., 2008) and pathogen defense (Wang et al., 2006).
In tomato, transcript and metabolite profiling were used to study and identify regulatory
genes for diverse aspects of the tomato fruit development, such as fruit set (Wang et al., 2009a),
regulation of fruit metabolism (Carrari et al., 2006), early fruit development (Mounet et al., 2009),
fruit ripening (Alba et al., 2005; Osorio et al., 2011) and carotenoid accumulation (Lee et al.,
2012).
To cope with the enormous wealth of data generated by the “Omics” studies, large public
repositories have been created. The most important for Solanaceae studies is the Sol (Solanaceae)
Genomics Network database or SGN (http://solgenomics.net/). Bioinformatics and statistical tools
have then been developed to facilitate the interpretation of these data, to identify candidates gene
involved in the control of the biological processes under study and to unravel functional modules
of genes, proteins and metabolites. In addition to the mining of single transcriptome, proteome or
metabolome datasets, new strategies have been implemented more recently to pinpoint the genes,
mechanisms and regulations of interest e.g. by combining genetic and genomic data (mQTLs,
candidate gene approaches) or by integrating two or more datasets of different sources (gene co-
expression analyses, correlative networks between gene and metabolites etc …).
Combining genetic and genomic data
Tomato fruit development and quality traits such as size, color, or taste result from the
combination of a multitude of metabolic and physiological processes taking place in the fruit and
in the plant. These traits fluctuate in a continuous fashion and, in most cases, cannot be
distinguished into discreet classes of phenotypes. Indeed, they result from the complex association
36
of different quantitative trait loci or QTLs, which can be defined as genomic regions controlling
the quantitative variations of a trait. QTLs depend on the genetic background of the plant and are
partially regulated by environmental signals. Due to its importance as a model and crop species,
to the early availability of a saturated marker genetic linkage map and of populations issued from
crosses of cultivated tomato with other tomato wild species e.g. the Solanum pennellii species
used for generating the widely used S. pennellii introgression lines (Eshed and Zamir, 1995),
tomato has long been used for detecting and mapping QTLs for fruit quality traits. QTL analyses
have been carried out on two types of populations: the nearly isogenic lines (NIL) and the
recombinant inbred lines (RIL) (Fernie and Klee, 2011). Both populations are generated by
crossing two parents that show genetic diversity. For the NIL population, backcrosses with the
same parent are performed for several generations, which results in plants showing genetic
diversity only at a few loci. This kind of population allows the study of genetic diversity present
at a specific locus. The RIL populations are generated by several self-crossing, resulting in very
diverse individuals. RIL populations serve to establish high-density genetic maps, but cannot be
used to study a specific locus (Fernie and Klee, 2011).
In a first step, the identification of the gene underlying the QTLs was mainly carried out by
positional or map-based cloning. This approach was very successful since it allowed the
identification of the genes controlling major fruit traits in tomato, such as QTLs controlling fruit
weight (Frary et al., 2000) and locule number (Munos et al., 2011), fruit color (Isaacson et al.,
2002; Park et al., 2002; Liu et al., 2003), and fruit soluble soli content (Fridman et al., 2000). The
approach used is well illustrated by the work done for identifying the cell wall invertase LIN5
responsible for variations in tomato fruit soluble solid content between two tomato lines (Fridman
et al., 2004). The wild tomato Solanum pennellii shows a higher (11% to 25%) brix score than the
domesticated tomato Solanum lycopersicum M82 cultivar. The QTL Brix9-2-5, which is
responsible for the difference in brix score between the two cultivars, has been isolated and
delimited to a single nucleotide polymorphism (SNP) located in the coding sequence of the
tomato fruit cell wall invertase LIN5 (Fridman et al., 2000; Fridman et al., 2002). Further analysis
proved that the differences in total soluble content observed between Solanum pennellii and
Solanum lycopersicum were in fact caused by the SNP observed within the LIN5 coding sequence.
This SNP caused a change in the protein sequence from a glutamate to an aspartate, which
resulted in altered invertase activity of the enzyme (Fridman et al., 2004).
Though map-based cloning in tomato, which can be a lengthy and tedious process (Isaacson
et al., 2002; Park et al., 2002; Liu et al., 2003), is now considerably eased by the availability of
the tomato genome sequence and of thousands of markers covering the whole tomato genome
(Tomato Genome Consortium, 2012), this approach can still be difficult to carry out and can be
time-consuming. The recent advances of high throughput techniques for the analysis of expressed
37
genes and proteins and of a limited number of enzymes and metabolites (Stevens et al., 2006;
Steinhauser et al., 2011) as well as the current powerful metabolomic technologies allowing low
or medium-throughput identification of tens or hundreds of primary and secondary metabolites
(Fernie and Klee, 2011), considerably facilitated the mapping of new metabolic QTLs (mQTLs)
and the development of QTL/candidate gene approaches for fruit quality traits (fruit phenome) in
the last years.
As an example, metabolome analysis of a set of the Solanum pennellii introgression lines by
Schauer et al. (2006) identified as much as 889 quantitative metabolic loci among which 332 were
associated with fruit yield-associated quantitative traits. Studies by the same authors later found
than 174 of these QTLs were dominantly inherited (Schauer et al., 2008). Likewise, analysis of
the metabolite composition of fruits harvested from plants issued from a cross between S.
lycopersicum and the wild species Solanum chmielewskii and cultivated under two fruit loads
conditions indicated that a greater number of metabolic quantitative trait loci were observed under
high fruit load (240) than under low fruit load (128) cultivations and that development and
cultivation had more influence than genotype on fruit composition (Do et al., 2010). At the
opposite, in another study using a different population, QTLs responsible for tomato seed
metabolite contents were found to be more dependent on genetic background than on environment
(Toubiana et al., 2012).
Information on the location of the genomic regions responsible for variations in quantitative
traits can be subsequently linked with information on the genes present in that region and on their
possible role in the control of the trait studied (candidate gene approach). Causse et al (2004) led a
candidate gene-QTL analysis in tomato in order to identify putative candidate genes involved in
tomato fruit size and composition. A number of genes were found co-localized with fruit quality
QTLs, among which the gene coding for a PEP carboxykinase that was co-localized with two
QTLs controlling fruit acidity and reducing sugars (Causse et al., 2004). QTL analysis was also
carried out on three tomato populations (NILs and RILs) to identify conserved and different QTLs
responsible for variations in fruit ascorbic acid (vitamin C) content between the populations
(Stevens et al., 2007). Thirteen gene candidates were found co-localized with 23 ascorbic acid
QTLs, of which two were later shown to be effectively involved in the control of ascorbate
biosynthesis in the fruit (Garcia et al., 2009; Gilbert et al., 2009) and in the control of ascorbate
concentration during postharvest storage (Stevens et al., 2008). Likewise, a number of other
studies based on QTL-candidate gene approaches have been carried out in tomato, e.g. the
analysis of ethylene production in tomato fruit that led to the localization of seventeen QTLs
controlling this trait and to the co-localization of putative candidate genes (the ethylene
biosynthetic genes ACS and ACO genes) with these QTLs (Dal Cin et al., 2009), and the
38
identification of enzymes involved in flavor volatiles in tomato (Tieman et al., 2010) and in
sesquiterpene biosynthesis in S. habrochaites (Sallaud et al., 2009).
However, QTL analysis combined with candidate gene approach alone does not necessarily
give insights into the molecular role of the candidate genes. In addition, as shown by studying
QTLs for the activity of enzymes from the primary metabolism that may control fruit quality traits
such as sugar and organic acid contents, trans-regulatory mechanisms implicating e.g.
transcription factors or protein degradation rather than cis-effects linked to structural genes and
proteins can be mainly responsible for quantitative variations of the traits studied (Steinhauser et
al., 2011). Searching for correlations between the transcriptome, proteome and/or metabolome
data is an alternative and complementary strategy to establish functional modules and to get a
global and multi-leveled understanding of processes of interests and of their regulatory
mechanisms.
Co-expression analysis
Development of transcriptomics led to a global approach known as gene co-expression
analysis. This approach is based on the theory of guilt by association: genes involved in the same
biological process are co-regulated and show the same expression pattern as they belong to the
same regulatory system (Aoki et al., 2007; Saito et al., 2008). Thus, co-expression analysis of
transcriptome data is a useful tool to study the regulation of a metabolic process, to identify genes
that belong to a same functional module (set of co-expressed genes potentially sharing similar
function and regulation) and, in some cases, to infer the function of the genes discovered.
As an example, gene co-expression analysis integrated with metabolite quantification was
used for the identification of two MYB transcription factors in glucosinolate (GSL) biosynthesis in
Arabidopsis thaliana (Hirai et al., 2007). Co-expression analysis between genes involved in
metabolic pathways and transcription factor encoding genes revealed that Myb28 and Myb29 were
co-expressed with genes implicated in aliphatic GSL biosynthesis in a discreet module apart from
other genes, resulting in the hypothesis that these two transcription factors are important
regulators of GSL biosynthesis (Hirai et al., 2007). Additionally, transcriptome analysis of myb28
mutant revealed that genes involved in GSL biosynthesis are positively regulated by Myb28. In
tomato, gene co-expression analyses were used early to identify regulators of the ethylene-
dependent fruit ripening (Alba et al., 2005). Likewise, transcriptomic analysis of tomato fruit set
and early fruit development highlighted hormone-related gene modules possibly involved in the
regulation of fruit set (Vriezen et al., 2008; Wang et al., 2009a) and early fruit development
(Lemaire-Chamley et al., 2005).
39
In Arabidopsis thaliana, hundreds or thousands of transcriptome experiments have been
stored into databases such as the Bio-Array Resource for Plant Biology (BAR) (Toufighi et al.,
2005), GENEVESTIGATOR (Zimmermann et al., 2004), or ATTED-II (Obayashi et al., 2007),
which allow in silico mining of gene co-expression modules. In tomato, transcriptomic data from
the TOM1 microarrays (Hoeven et al., 2002; Alba et al., 2004) have been made available online
in the Tomato Expression Database (TED; Fei et al., 2006) which later evolved into the tomato
functional genomic database (TFGD; Fei et al., 2011), and more recently was enriched with more
data from other sources, such as RNA sequencing. Such databases offer online tools to perform in
silico analyses and allow a researcher to compare gene co-expression analysis results from public
data with his own data. A great advantage of public databases is that in silico analyses take very
short time to be performed in comparison with approaches that necessitate the generation of local
datasets.
Using gene co-expression to study gene function in plant models is not the only use of co-
expression analysis. The conservation of gene modules between species has been studied to
propose gene functions even in non-model plants where genomic data are scarce and gene
function are mostly unknown (Movahedi et al., 2012).
However, co-expression analysis shows limitations (Saito et al., 2008). This strategy does not
take in account regulatory mechanisms such as post-transcriptional or post-translational
regulations. Thus, only co-expressed genes are detected, which can result in incomplete or false
comprehension of a process of interest. Moreover, the information obtained from this strategy
does not reveal if the regulation between genes is positive or negative. It also does not reveal
which genes are the main regulatory elements in a co-expression network, and which genes are
regulated.
Correlative networks
In the same way as gene-to-gene correlations are established to define gene co-expression
modules, gene-to-protein, gene-to-metabolite or metabolite-to-metabolite correlations can be
calculated to search for common co-regulated modules along fruit development, in different fruit
tissues or in genotypes with contrasted fruit quality traits and to infer gene function. One of the
main limitations is technical. While for a model crop species like tomato, almost the whole
expressed genome, which represents about 35 000 genes (Tomato genome consortium 2012), is
known and can be measured, proteomic analyses can currently identify only a subset of the
expressed proteome. Though primary and secondary metabolites can now be analyzed
exhaustively, most analyses still result in the identification of tens to few hundreds metabolites at
most. Metabolite-to-metabolite correlation analyses proved however to be very robust and
40
sufficient to identify functional relationships between clusters of interconnected metabolites
(Toubiana et al., 2012).
To further investigate the degree of correlation between transcripts, proteins and/or
metabolites and visualize and interpret the results, new statistical tools have been developed or
adapted to help researchers. Among these are the Kohonen’s self-organizing maps (SOMs) that
can be used to cluster the biological data (Garcia et al., 2009), and the network analysis, which
enables the visualization of gene-to-gene/protein/metabolite associations via a graph of nodes and
edges. In tomato, such analyzes helped deciphering the relationships between fruit organoleptic
and nutritional traits and whole-plant phenotype (Schauer et al., 2006), and showed that the
metabolic processes in the seed are more interdependent than in the fruit (Toubiana et al., 2012).
Correlative network analysis further indicated that in the seeds, the amino acids were hubs and
had tight links with primary metabolites such as sugars and organic acids. A recent and
remarkable study, based on the extensive transcriptome profiling of tomato fruit, defined gene-to-
gene co-expression modules in the fruit, among which a module enriched in genes implicated in
the biosynthesis of flavonoids, which are major fruit phytonutrients (Ozaki et al., 2010;
Fukushima et al., 2012). To verify the relationships between module genes, the functional role of
a gene of unknown function encoding a non-enzyme non-transcription factor gene encoding a
zinc finger protein was assessed in planta. Flavonoid pathway genes included in the module were
up-regulated in the plant overexpressing the zinc finger gene, thereby indicating that this approach
can be used to identify and infer the function of tomato genes with previously unknown roles. A
similar approach was used by our group (Mounet et al., 2009), who analyzed two fruit tissues at
three stages of early tomato fruit development in order to identify key regulatory genes implicated
in the coordination of development/metabolic traits linked to the cell expansion phase. Among the
genes identified as key regulatory hubs in this study, three were already or later shown to be key
regulators of the cell expansion to ripening transition stage in tomato: the RIN and NOR genes
(Vrebalov et al., 2002; Osorio et al., 2011) and a close homolog of the AP2 gene (Chung et al.,
2010; Karlova et al., 2011). The functional study in planta of two of the remaining genes of
unknown function is one of the main objectives of the PhD work.
In summary, as shown in (Figure I.17), “Omics” based approaches make typically use of
“Omic” in silico resources to establish a hypothesis and design experiments to verify it. The result
obtained is then compared with the theory underlying the experiment (Fukushima et al., 2009).
An additional objective is to establish models for predicting the consequences of changes in
transcriptome, proteome or metabolome on phenotype (Richards et al., 2010). Ideally, large
“Omics” datasets and genetic data can be further combined and used to define the ideal genotype
that will achieve the desired traits e.g. high fruit yield associated with high fruit sugar content in
processing tomato.
41
Recently, such a model for predicting the phenotype of ripe tomato fruit has been developed
by using transcriptomic, metabolomic and phenomic (fruit quality traits) data obtained from a
collection of tomato recombinant inbreed lines (Carrera et al., 2012b). By focusing on a subset of
genes with potential effect on transcription regulation, the authors considered that their model
could predict changes in agronomic properties, such as fruit organoleptic properties, which would
be produced by specific changes in genetic expression. Though this probably remains a long term
goal, such a study highlights the potential of integrating “Omics” datasets for deciphering the
mechanisms involved in fruit development and metabolism and for designing tomato cultivars
with the desired traits.
Figure I.17 : Schematic representation of a synergetic integration strategy using multiple omics data. Within individual
experiments, the hypothesis–experiment cycle with in silico simulation of a biological phenomenon is repeated. In
addition to this, the data deposited contributes greatly to the construction of a general prediction database (e.g. co-
expression database). This also generates a further testable hypothesis and adjusts the hypothesis for individual
experiments.(Fukushima et al., 2009)
However, the functional validation of the target genes identified through these approaches
remains a prerequisite. This can be done either through association studies (Xu et al., 2012) and/or
through reverse genetic approaches (below).
42
c) Reverse genetics approaches for the validation of gene function.
RNAi and VIGS to silence expression
RNA interference (RNAi) commonly designates a strategy that uses the plant Post
transcriptional gene silencing (PTGS) mechanisms to silence a plant gene of interest. The PTGS
is dependent on the use of small RNA (sRNA) duplexes. sRNA duplexes are categorized into
miRNAs and siRNAs and result from the cleavage of double stranded RNA (dsRNA) molecules
by dicer-like enzymes (DCL) (Baulcombe, 2004).
To silence a gene, a small sequence of the gene DNA is assembled in a plasmid construct so
that the RNA transcript forms a hairpin recognized by the PTGS mechanism (Baulcombe, 2004).
Thanks to recombination techniques, this hairpin encoding sequence can be put under the
dependence of a chosen promoter so that it is expressed in a particular developmental stage or
organ. The constructed plasmid is usually delivered to the plant genome by using the
Agrobacterium tumefaciens mediated plant transformation. This approach necessitates several
months at least for the transformation of tomato, but results in a stable transformation.
miRNAs are formed from short (Voinnet, 2009) or long (Rajagopalan et al.,2006) fold-back
stem-loops that are cleaved by DLC1. siRNAs are formed thanks to the action of DCL2, DCL3 or
DCL4 that process dsRNAs in molecules of 21,22 and 24-nt molecules, respectively (Parent et al,
2012). The dsRNA precursor of siRNA can come from the fold-back of inverted-repeat transcripts
(Kasschau et al., 2007), a convergent transcription (Borsani et al., 2005), or assembling of single
stranded RNA by RNA-dependent RNA polymerases (RDR). After RNA cleaveage, miRNAs and
21-nt siRNAs are assembled with argonaut and other proteins in a RNA induced silencing
complex (RISC). They induce PTGS by translation inhibition of target genes (Brodersen et al.,
2008) or slicing (Baugerger and Baulcombe, 2005). The 24-nt siRNAs associate with different
argonaut proteins and form a RISC that induces transcriptional gene silencing (TGS) (Brosnan
and Voinnet, 2011). The role of 22-nt siRNAs has been described only as a backup for 21-nt or
24-nt siRNAs (Gasciolli et al., 2005).
sRNAs take part in resistance to viruses, bacterias. A miRNA (miR393) encoded by
Arabidopsis thaliana have been shown to be upregulated by bacteria aggressions (Navarro et al.,
2006). This miRNA downregulates genes involved in the auxin response, as a way to switch to
plant activity from development to pathogen defense (Dharmasiri et al., 2005). miRNAs and
siRNAs have also been shown to be implicated in the regulation of developmental genes (Voinnet,
2009; Dunoyer et al., 2010) and also take part in feedback loops to regulate DCL1 and AGO1,
implicated in the miRNA pathway (Xie et al., 2003, Vaucheret et al., 2004, 2006; Mallory and
Vaucheret, 2009). siRNAs also helps preventing genome damage originating from transposon
43
activity by silencing transposable elements (Kasschau et al., 2007; Slotkin et al., 2009).
Incidentally, this also affects transgene expression (Parent et al., 2012).
Numerous studies have shown the efficiency of reverse genetics to get insights into a gene
function in plants. RNAi have been widely applied even in model species such as Arabidopsis
thaliana or Solanum lycopersicum , but also in non model species such as various kinds of algae
(Cerutti et al, 2011), or even fungi (Nakayashiki and Nguyen, 2008). Among genes studied
through RNAi, transcription factors have been given special attention such as AP2 (Chung, 2010;
Karlova et al, 2011), TAGL1 and TAG1 (Vrebanov et al, 2009; Pan et al, 2010) for the
understanding of ripening in tomato. RNAi have also been used to bio-engineer different traits in
crop species (Mansoor et al, 2006; Hebert et al, 2008; Jagtap et al, 2011), for example in maize
(Segal et al, 2003; Houmard et al, 2007), wheat (Regina et al, 2006), rice (Qiao et al., 2007) or
even peanut (Dodo et al, 2008). Last but not least, a link between the RNAi machinery and
epigenetic regulatory mechanisms have been showed in several studies (Huettel et al, 2007;
Hawkins and Morris, 2008; Djupedal and Ekwall, 2009; Schmitz and Zhang, 2011).
Virus Induced Gene Silencing (VIGS) is another application of PTGS in research. The use of
VIGS as a research tools allows the transient silencing of any gene of interest by modifying viral
DNA or RNA. Modifying viral nucleic acids by adding plant derived gene sequence allows the
silencing of the targeted gene. The studied gene is cloned in a vector carrying infectious
component of a viral DNA or cDNA (derived from viral RNA). Vectors can then be delivered to
the host genomes using several methods, such as agroinfiltration or biolistic. After delivery of the
VIGS vector, the plant defense mechanisms will initiate PTGS and thus silence the targeted gene
(Baulcombe, 2004).
The use of VIGS in reverse genetics has several advantages but also shows limitations.
Advantages are numerous. First, the time-consuming steps of plant transformation are avoided,
resulting in a great gain of time but also in being able to modify a gene expression in species
where plant transformation is not (or hardly) possible. Second, this technique can be applied
during all stages of plant development thanks to novel positive controls available (Quadrana et al.,
2011). Moreover, this technique can also be used in tomato fruits detached from the plant.
Silencing occurs for three weeks to three months after delivery of VIGS vector to the plant, but
can be extended to up to two years (Senthil-Kumar and Mysore, 2011). One of the drawbacks of
this specific technique, compared to RNAi based plant transformation, is the inability of VIGS to
silence completely the target gene. Only the infected cells show silencing and the efficiency of the
silencing can vary from plant to plant. In addition, the silenced plant does not always display
phenotypic alterations, which are not always uniform when their appear (Sahu et al., 2012). Thus
the presence of a reporter gene is often necessary to visualize the zone of infection.
44
For both RNAi and VIGS, a good knowledge of the plant genome is also necessary. Indeed,
when targeting a gene which possesses closely homologous genes, the choice of the target
sequence is crucial to avoid off-target silencing. Additional validation of the efficiency of gene
silencing by RNAi and VIGS approaches is usually done by analyzing the accumulation of the
target transcripts in the plant tissues (Northern blot, RT-PCR).
Gene Overexpression
In addition to the inactivation of genes by the RNAi strategy, another reverse genetic tool is
commonly used to study the function of target genes in planta. The overexpression approach
consists in enhancing the expression of a gene of interest by transforming a plant with a plasmid
carrying the full coding sequence of the gene under the control of a strong promoter. A classic
choice for the promoter is the cauliflower mosaic virus 35S promoter, which induces ectopic
expression of the sequence under his dependence. Of course, as for the RNAi and CRES-T
strategies, the use of a promoter active at specific developmental stages or in specific plant organs,
tissue or cell types can be necessary to affect only the developmental stages and tissues targeted.
In tomato, sets of plant transformation vectors harboring fruit-specific promoters targeting
specific developmental stages and tissues have been designed (Fernandez et al., 2009), among
which the SlPPC2 (PEP carboxylase) promoter driving an expression specifically during the cell
expansion phase till the onset of ripening (Guillet et al., 2012).
CRES-T
Another approach is the Chimeric REpressor gene Silencing Technology (CRES-T).
Transcription factors in plants usually belong to multigene families in which close members can
complement each other’s inability to function. The repression domains of the class II
ETHYLENE RESPONSIVE ELEMENT BINDING FACTOR (ERF) and TFIIIA type finger
repressors of transcription that include SUPERMAN (SUP) contain the EAR (ERF-associated
amphiphilic repression) motif (Hiratsu et al., 2003). The EAR motif encodes a 12 amino acids
peptide called SRDX. Using the CRES-T approach consists in fusing the coding sequence of the
transcription factor of interest to the SRDX domain, which produces a dominant transcription
inhibitor, and therefore does not directly modify the expression of the transcription factor studied.
As a consequence, all the targets of the transcription factor are expected to be silenced, even
though the native transcription factor is still active (Hiratsu et al., 2003). The CRES-T has been
shown to be efficient in stable and transient transformation studies (Matsui and Ohme-Takagi,
2010).
CRES-T has been used to study several transcription factor implicated in reproductive
processes such as EIN3, CUC1, AGAMOUS, AP3, LEAFY (Hiratsu et al., 2003; Fujita et al.,
45
2004; Matsui et al., 2004; Tohge et al., 2005; Mitsuda et al., 2006). According to the authors, the
SRDX domain by itself does not alter the conformation and structure of a protein. It also acts
properly to silence the targets of the studied transcription factors even in multimeric complexes
(Matsui and Ohme-Takagi, 2010). Thus, CRES-T represents an alternative tool of great interest to
PTGS mediated strategies.
4) Presentation of the PhD
The research program in UMR 1332 “Biologie du Fruit et Pathologie” focuses on two major
projects: (1), the study of early fruit development and its relationship with fruit quality traits,
tomato being the main fleshy fruit studied, and (2), the study of non-cultivable pathogens and
their interaction with host plants and vectors. Main objectives of fruit-related projects are to get
insights upon the biological factors underlying fruit development and quality and, whenever
possible, to transfer knowledge and tools developed to other academic research teams and to
professionals.
In this context, the « Génomique Fonctionnelle du Développement du Fruit » team, in which
the present PhD study was done, is particularly interested in the study of molecular processes
involved in the regulation of fruit cell expansion and their impact on size, composition and quality
of tomato fruit. Fruit size and flavor usually behave as opposite traits, which may limit breeding
strategies for enhancing fruit taste in medium sized tomatoes. To get insights into these processes,
the team focuses its researches on the cell expansion phase, which can contribute by up to 90% of
the increase in fruit weight and during which fruit cells accumulate nutrients (starch, soluble
sugars, organic acids, and secondary metabolites) and undergo cell wall and cuticle modifications
that will affect fruit texture and other quality traits in the ripe fruit. Identification of key
regulatory genes involved in the control of the mechanisms underlying changes in fruit size and
composition is thus a major objective of the team.
To identify genes linked with differentiation of specialized tissues during early fruit
development, a strategy without a priori was initiated by the team in 2000. This strategy was
based on the use of the microarray technology to study the changes in transcriptome during fruit
early development in exocarp and locular tissue (Lemaire-Chamley et al., 2005). This study
demonstrated that early tomato fruit development is not directed by a set of fruit-specific genes,
but rather by the coordinated expression of genes also expressed in other organs. This work also
allowed pinpointing several candidate genes possibly implicated in fruit tissues differentiation and
in fruit growth. It also gave insight into the fleshy fruit trait acquisition during the tomato fruit
development.
46
Next step was the acquisition and integration of both transcriptomic and metabolomic data in
two different expanding fruit tissues (mesocarp, locular content) at different (three) stages of
tomato fruit development during the cell expansion phase. Analyzing the data generated by this
study showed correlations between cell size, metabolite concentration, and transcript levels in
mesocarp and locular tissue (Mounet et al., 2009). The correlation data was summarized into a
correlative network between genes and metabolites. Several genes appeared to be hubs in the
network, sharing correlations with numerous genes or metabolites and therefore are potential
regulators of early fruit development (Figure I.18) (Mounet et al., 2009).
Figure I.18. : Regulatory gene and metabolite network implicated in the control of cell expansion in tomato fruit
tissues(Mounet et al., 2009). The network was visualized with the Pajek software package. The distance between two
vertices is based on 1 – the absolute value of the Pearson correlation coefficient. Metabolite vertices are in red.
Hormones and polyamine-related transcripts are in green. Redox-related transcripts are in turquoise. Transcription-
related transcripts are in yellow. Transcripts related to the regulation of protein activity are in violet. Epigenetic-related
transcripts are in pink. Signaling-related transcripts are in brown. The metabolites are indexed with the following
abbreviations. Car, Carotene; Chloa, chlorophyll a; Chlob, chlorophyll b; Chol, choline; Cit, citrate; Mal, malate; Tol,
tomatidenol; Tom, tomatidine; TG2, tomatidine glycoside2; Trig, trigonelline; UDPG, UDP-Glc; UnkS5.4, unknown
sugar S5.4; Xant, xanthophylls (Mounet et al., 2009).
My work consisted into the functional validation of several of these candidate genes and was
divided into 2 main projects. First, I continued the functional validation of several F-box genes
that was initiated by Nicolas Viron during his PhD. Second, I initiated the functional validation of
two transcription factors that were isolated as hubs by the work of Mounet et al. in 2009.
47
The first project of the PhD consisted in characterizing F-box with putative roles in early
fruit development. Several studies showed that hormonal regulation during fruit development is a
central element of the regulation of fruit development. Among these regulations, the targeted
degradation of proteins through the 26S proteasome system has a key role in hormonal
signalization. Specificity of this degradation mechanism is carried by the SCF complex (SKP1-
Cullin-F-box) that possesses an E3 ubiquitin ligase activity. More specifically, the F-box protein
is responsible for the recognition of the target protein to degrade, as suggested by the high
diversity of the F-box encoding gene family. Four genes encoding F-box were selected for their
tissue-specific expression profile during fruit early development. Transgenic plants were
generated to study the role of the selected F-box encoding genes. The transformation affecting
these plants was designed to modify the expression of the targeted F-box genes by using RNAi
and overexpression technologies. Molecular and phenotypic characterization was started during
the PhD of Nicolas Viron and pursued during my PhD.
The second research project of my PhD was the functional characterization of two
transcription factors that are hubs in the correlative network resulting from the work of Mounet et
al. in 2009. The HD-Zip family regroups several transcription factors with the ability to form
homo- or hetero-dimers implicated in the regulation of various developmental processes. The B-
zip transcription factors are also regrouped in a gene family with properties close to these of the
HD-Zip transcription factors, such as the ability to form homo- or hetero-dimers. Some of them
have been shown to be implicated in reproductive organ development in tobacco. I initiated the
functional characterization of a HD-Zip transcription factor and of a b-Zip transcription factor and
generated transgenic plants affecting the expression and function of the two candidates.
Transgenic plants were generated to inactivate the target genes by RNAi and CRES-T strategies
biotechnologies or to overproduce the target protein by gene overexpression specifically during
the cell expansion phase in the fruit (use of the SlPPC2 fruit- and cell expansion phase-specific
promoter). To date, only the CRES-T lines present morphological and biochemical alterations of
the fruit. But interestingly, major fruit developmental and/or quality traits are affected for both
target genes, thus validating a posteriori the “Omics” integrative approach used to identify
regulatory genes involved in major developmental changes during fruit development. Due to the
high interest of both genes for deciphering the regulation of gruit development and for applied
research and use, transgenic lines are currently still submitted to in-depth analysis.
49
Part II : Results and discussion
1) Functional analysis of F-Box encoding genes
Protein degradation processes are particularly important for the regulation of tomato fruit
development, e.g. for the regulation of cell-cycle during fruit development (Mathieu-Rivet et al.,
2010a). In addition, hormone signaling have a central position in the regulation of tomato fruit
development (Mathieu-Rivet et al., 2010a) and is dependent on specific protein degradation
(Santner and Estelle, 2010). The hormone-related protein degradation is dependent on the
recognition of the targeted protein by a specific SKP1-Cullin-F-Box (SCF) complex (Hershko,
2005; Lechner et al., 2006). The specificity of target recognition by the SCF complex is due to the
F-Box proteins, a large family of over 700 members in plants (Gagne et al., 2002). There is a great
diversity of sequences within this protein family. F-Box proteins are characterized by the presence
at the N-terminus of a conserved F-Box domain of fifty amino acids involved in protein - protein
interactions with SKP1 (Bai et al., 1996). Various types of domains have been identified at the C-
terminus of the F-Box which are supposed to be involved in substrate recognition (Gagne et al.,
2002).
In tomato, five F-Box have been shown to be involved in tomato fruit development.
Among them, silencing of SlFbf (Xing et al., 2012) and COI1 (Li et al., 2004) leads to defects in
pollen viability and seed development, and therefore can interfere with fruit development. In
contrast, the F-Box SlTIR1 on one side and SlEBF1 and SlEBF2 on the other side are respectively
implicated in tomato fruit set and in fruit ripening. Indeed, the overexpression of SlTIR1, a
homologue of Arabidopsis auxin receptor TIR1, results in a pleiotropic phenotype including
parthenocarpic fruit formation (Ren et al., 2011); see § I.2). Likewise, transient silencing of the
two F-Box Sl-EBF1 and Sl-EBF2 results in early fruit ripening (Yang et al., 2010). These two F-
Box are believed to be implicated in the degradation of EIN3/EIL proteins which are involved in
ethylene signaling during fruit ripening (Yang et al., 2010; Figure I.14)
According to these elements, a functional analysis of four different F-Box proteins was
undertaken during a previous PhD (Nicolas Viron, 2010). During my PhD, I continued the
characterization of the transgenic plants generated for three of these F-Box proteins (SlFB2,
SlFB11 and SlFB24). This chapter describes the progress made concerning the functional
characterization of these F-Box proteins potentially involved in the development of tomato fruit.
50
a) State of the art at the beginning of my PhD thesis
Given the large number of F-Box proteins existing in Arabidopsis and in tomato, different
databases were screened to identify a list of relevant candidate genes potentially involved in the
development of tomato fruit. Among the 95 sequences annotated as F-Box proteins in the Tomato
Gene Indices (The Gene Index Project, http://compbio.dfci.harvard.edu/tgi/), 15 unigenes were
selected, corresponding to the F-Box genes represented in early fruit development EST libraries.
Then the expression of these genes was studied by qRT-PCR in fruit tissues presenting mainly
cell division (exocarp at 20 DAA) or mainly cell expansion (pericarp and locular tissue of fruit at
20 DAA). According to this analysis, four genes were selected for fucntionnal analysis, since they
presented a preferential expression in the pericarp (SlFB2, SlFB11 and SlFB12) or in the locular
tissue (SlFB24) (Table II.1).
For these four genes, RNAi transgenic lignes were generated using an hair-pin construct
targeted against a specific sequence of 100-150 b located in the 3 'UTR, under the control of
constitutive 35S promoter (pK7GWIWG2 vector). T0 generated transgenic seedlings (cultivar
Micro-Tom) were selected according to their ploidy level (2C) and the presence of the transgene
was controled by PCR on genomic DNA. T0 and T1 lines were already generated at the beginning
of my PhD thesis as well as T2 homozygous lines for P35S:FB12RNAi
plants. I started my PhD work
with the selection of homozygous lines for P35S:FB2RNAi
, P35S:FB11RNAi
and P35S:FB24RNAi
lines.
The various lines available now are presented in Table II.2. The generation of these homozygous
lines allowed progressing in the functional analysis of the targeted F-Box proteins, and will be
described in the following sections.
51
Table II.1. List of F-Box genes selected for functional analysis. The Unigene Number SGN-Uxxxxxx refers to the identification of genes on Sol Genomics Network
website (SGN: http://solgenomics.net/). TC reference number refers to the identification of genes at the DFCI site (http://compbio.dfci.harvard.edu/tgi/cgi-
bin/tgi/gimain.pl?gudb=tomato).
N° F-Box
Tomato Arabidopsis
Unigene (SGN-)
Predicted protein
Gene Accession Number
Annotation
Gene Accession Number
Annotation E-value
SlFB2
TC179590 U574471 344 aa Solyc10g080610 kelch repeat-containing F-Box family protein
AT2G44130 kelch repeat-containing F-Box family protein 4.00E-54
SlFB11
TC170139 U567358 443 aa * Solyc06g053340 F-Box protein SKIP16
AT1G06110 SKP1/ASK-interacting protein 16 e-136
SlFB12
TC188199 U565968 641 aa Solyc01g005300 Flavin-binding kelch repeat F-Box
AT1G68050 F-Box family protein (FKF1) / adagio 3 (ADO3) 0.0
SlFB24
TC178013 U569207 623 aa * Solyc04g074980 Auxin F-Box protein 5
AT5G49980 Auxin F-Box protein 5 0.0
Table II.2. Summary of the different RNAi transgenic lines generated for each F-Box
Transformants Annotation of the closest
Arabidopsis homolog
Independant T0
transformants
T0
transformants
2C, 1 insertion
T2
Homozygous
Lines
P35S:FB2RNAi
Kelch repeat containing F-Box protein 8 6 4
P35S:FB11RNAi
F-Box SKIP 16 8 5 5
P35S:FB12RNAi
Flavin-binding kelch repeat protein 10 4 4
P35S:FB24RNAi
Auxin F-Box protein 5 14 9 2
52
b) Characterization of the P35S:FB2RNAi
lines
SlFB2 protein is homologous to a F-Box protein of Arabidopsis containing a kelch repeat
domain, with no putative function to date (Table II.1). P35S :FB2RNAi
T0 plants showed no
noticeable phenotype. In contrast, a phenotype of locular tissue disappearance associated to an
alteration of seed shape was observed in the T1 and T2 generations of P35S :FB2RNAi
line 2 (Figure
II.1A). This interesting phenotype being present only in a unique T0 line, we hypothesized that it
was due to the locus of T-DNA insertion. In agreement with this hypothesis, SlFB2 expression
was not repressed in P35S :FB2RNAi
-2 plants (Figure II.1B).
Figure II.1 Phenotype and expression analysis of SlFB2 in P35S:FB2RNAi lines. (A) Fruit and seed shape at Red Ripe
stage in WT and P35S:FB2RNAi -2. (B) The relative expression (arbitrary units) corresponds to the expression of SlFB2 in
Bk fruits normalized with the expression of actin, β-tubulin and eIF4a. The expression was measured on a pool of three
fruit collected at the breaker stage. The vertical bars represent the standard deviation (3 technical replicates) and the star
indicates a significant difference between WT and transgenic lines according to Student’s t test (p < 0.05).
Further analysis was performed in order to characterize more precisely the phenotype of
P35S :FB2RNAi
-2 fruits. It was shown that the locular tissue does not develop in the P35S :FB2RNAi
-2
fruits (Figure II.2A, C and E), while it occurs within 4 to 6 days after fruit set in WT (Figure
53
I.5). As a result, tomato seeds are not embedded in this protective gelatinous tissue and remain
stick together and to the columella (Figure II.2C and E). Consequently, the seeds have abnormal
forms (Figure II.2B). Instead of being round as WT seeds, they are curved and present sharp
edges, and we can feel that the seeds are square. In addition, fruit pericarp seemed to be enlarged
(Figure II.2D), as cell size, and fruits were extremely firm. It should be noticed that the
vegetative development of the plants was comparable to the one of the WT.
Figure II.2 Development of fruit tissues and seeds in P35S:FB2RNAi-2 and WT plants (A) Internal vue of the carpel at
three stages of fruit development. The size of the fruit is indicated at the top of the picture. (B) Mature seeds isolated
from Red-Ripe fruits. Transversal section of fruits (Bk stage) stained with toluidine blue showing the carpel structure
(C), the pericarp (D) and the seed (E).
Towards the identification of the T-DNA insertion site in P35S:FB2
RNAi -2 transgenic line
Since the phenotype observed in P35S :FB2RNAi
-2 plants was very interesting, we initiated the
identification of the insertion site by reverse PCR. For this, genomic DNA was extracted from
leaves of P35S :FB2RNAi
-2 T2 lines, and digested with different restriction enzymes. After a ligation
of the digestion product, a PCR followed by a nested PCR allowed the amplification of the
flanking regions of the insertion sites which were sequenced (see Material and Methods § IV.4.f).
54
Five fragments were isolated and sequenced (Table III.3). Homology search in tomato genome
(SGN website) allowed mapping of four of these sequences on tomato genome.
Table II.3. Flanking regions of the T-DNA insertion in P35S :FB2RNAi
-2
Fragment Name Restriction
Enzyme Insertion site
FB2-2_B4 BglII No Hit
FB2-2_H5 HindIII Chr 8, no gene predicted
FB2-2_S1.6 SpeI Chr 1, no gene predicted
FB2-2_S1.9 SpeI Chr 9, promoter of Solyc09g074510
FB2-2_S2.10 SpeI Chr 11, in 3’ of Solyc11g065550, Unigene SGN-U571631
According to these results, it was difficult to assume a potential phenotypic effect for FB2-
2_H5 and FB2-2_S1.6 insertions, since they were not localized in a gene neiboorhood. In the
same way, it was not possible to further study FB2-2_B4 insertion, since it does not hit with
tomato genomic DNA, neither with other known DNA sequence (data not shown). On the
contrary, the two remaining detected insertion were more promising since they were localized
closely to two identified genes: in the promoter of Solyc09g074510 for FB2-2_S1.9 insertion, and
in the 3’ region of Solyc11g065550 for FB2-2_S2.10 insertion. The insertion of the T-DNA in the
promoter of Solyc09g074510 could activate or repress the expression of Solyc09g074510,
depending on the location of the insertion in the Solyc09g074510 promoter. The effect of the
insertion of the T-DNA in the Chromosome 11 is less clear. Indeed, the data available for tomato
genome in this area suggests the presence of a very short gene (Solyc11g065550) which does not
fit with the mRNA encoded by this region as suggested by the presence of an unigene (SGN-
U571631) and RNAseq results.
For these two insertions, primer pairs were designed in order to discriminate between the
wild-type DNA and the DNA within the T-DNA insertion (Figure II.3A, Annexe IV). These
primers were first tested on DNA extracted from the leaves of T0 SlFB2 transgenic plants. As
shown in Figure II.3B for FB2-2_S1.9, they allowed discrimination of an heterozygous
transgenic plant with FB2-2_S1.9 insertion (plant T0-FB2-2) by the presence of an amplification
band with both primer pairs (Figure II.3B, lines 4 and 5), from an independent transgenic plant
that presents another insertion (plant T0-FB2-3) by the presence of an amplification band only
with the primers present on tomato genomic sequence (Figure II.3B, lines 4 and 5).
55
Figure II.3. Genotyping by PCR of the T-DNA insertions in P35S:FB2RNAi
lines. a) Primers were designed
in the T-DNA and in tomato genomic DNA in order to reveal the T-DNA insertion (NV2X_S /
NV2X_vect) or designed only in wild-type DNA (NV2X_S / NV2 X_AS) to reveal the DNA without the
insertion. B) Example of PCR results on a T0 plant with the insertion FB2-2_S1.9 (T0 FB2-2) or not (T0
FB2-3).
In order to definitely associate the phenotype of P35S :FB2RNAi
-2 fruit and seeds to one or the
other integration site of the T-DNA, we checked the segregation of the phenotype in the
descendance of an heterozygous T2 line (P35S :FB2RNAi
-2.10) and genotyped each plant for the
identified insertions. For this 163 T3 seeds were sown directly in soil (without kanamycin
selection). Phenotyping and genotyping by PCR was performed on the 110 resulting plants. For
the genotyping of each insertion site, a Mendelian segregation involving a single locus was
expected i.e. 1:2:1 ratio corresponding respectively to the homozygous insertion, heterozygous
insertion, homozygous wild-type (no insertion). At the phenotypic level the results depend on the
dominance/recessivity of the effect of the insertion as well as on the possibility that two insertions
are necessary to obtain the phenotype.
At the phenotypic level, 27/110 plants presented the “square-seed” phenotype, thus
corresponding to 24.5 % of mutant phenotype appearance in agreement with the occurence of a
single recessive mutation responsible for the trait. Only two insertions were phenotyped by PCR:
FB2-2_S1.9 and FB2-2_S2.10. As expected, the segregation of FB2-2_S1.9 insertion fits to the
Mendelian segregation involving a single locus with 26.8 % of the plants with the homozygous
insertion, 49.1 % of heterozygous plants and 24.1 % of homozygous wild-type plants. Concerning
FB2-2_S2.10 insertion, genotyping by PCR was done on a subset of the whole progeny and
revealed that the “square-seed” phenotype was not associated to this insertion. From these results,
it is clear that the “square-seed” phenotype observed in P35S :FB2RNAi
-2 line is not due to the FB2-
2_S1.9 or FB2-2_S2.10 insertions. Further analyses are required to indentify the insertion site.
56
c) Functional characterization of SlFB11
SlFB11 protein is homologous to SKIP16 from Arabidopsis, a F-Box protein with unknown
function. Five independent P35S :FB11RNAi
T0 plants were generated by N. Viron during his PhD.
One T0 line presented a strong alteration of vegetative growth and stopped growing in the
greenhouse after in vitro culture. Another T0 plant (P35S :FB11RNAi
-3) showed a similar
phenotype in vitro but could develop after transfer in the greenhouse. In the same way, a strong
delay of T1 plantlets development was observed in vitro, when the seeds of P35S :FB11RNAi
-3
and -4 were sown in the presence of the kanamycin selectable-marker. As a consequence, it was
difficult to clearly differentiate between kanamycin resistant and susceptible seedlings. Growth
delay was observed in three independent P35S :FB11RNAi
T1 lines (2, 3 and 4), with the arrest of
the apical meristem and the development of axillary buds which developed secondary stems to
replace the main axis of the plant, resulting in small bushy plants (Figure II.4).
Homozygous lines were selected for the five T0 transformants (P35S :FB11RNAi
-1, -2 , -3, -4 et
-8). Again, T2 plantlets of P35S :FB11RNAi
-3 and -4 lines showed large growth defects during in
vitro culture, which led to the death of some plantlets after their introduction in the greenhouse.
According to the strong growth defects of the plantlets on selective medium in different
independent lines, we wondered if these plants were still resistant to kanamycin. PCR
amplification on genomic DNA of the five transgenic T0 lines was performed to ascertain the
presence of NptII gene, which confers kanamycin resistance. In addition, the expression of this
gene was tested on Bk fruits from T0 plants, in parallel to the expression of SlFB11 (Figure II.5).
This expression analysis confirmed the down-regulation of SlFB11 in the five T0 plants and
showed that the expression of NptII gene was very low in P35S:FB11RNAi
T0-3 and -4, the lines
presenting the strongest developmental defects, whereas lines which presented a less affected
phenotype presented a higher expression of NptII gene (Figure II.4). Thus, the phenotype
observed in P35S:FB11RNAi - 3 and -4 lines could be due either to a partial loss of kanamycin
resistance in these lines, or to an effect of SlFB11 down-regulation.
57
Figure II.4 Alteration of vegetative development in P35S :FB11RNAi lines. Whole plant morphology (A) and plantlets
presenting a stop of the apical meristems (B), a start of the axillary buds (C) and the resulting bushy phenotype in fully
developed plants (D)
In order to test these hypotheses, T3 seeds from the five P35S :FB11RNAi
lines were sown in
the presence or in the absence of kanamycin and their development was observed after one month
in the growth chamber (Figure II.6). This experiment revealed that in the absence of kanamycin,
P35S:FB11RNAi T3- 3 and -4 plants still present a strong phenotypic alteration characterized by poor
root and leaf development. These plantlets look indeed like kanamycin sensitive plants. In other
transgenic lines, plants grown in the absence of kanamycin present a WT phenotype, with the
development of leaves and secondary roots.
58
Figure II.5. Expression analysis of SlFB11 and NptII genes by qRT-PCR on Bk fruits from P35S:FB11RNAi T0 plants.
The relative expression (Arbitrary unit) corresponds to the expression of the SlFB11 or NptII normalized with the
expression of actin, β-tubulin and EiF4a. The expression was measured on a poll of three fruits collected at Bk stage.
The error bars represent the standard deviation of two technological replicates.
Figure II.6 Effect of kanamycin on plantlets growth in P35S :FB11RNAi lines. T3 seeds were sown on MS1/2 medium in
the presence (+K) of in the absence (-K) of kanamacin. Plant growth was observed after one monthe culture in the
growth chamber, before introduction of the plants in the greenhouse.
Further plant development was observed regularly after their tranfer on a soil support in the
greenhouse (Figure II.7). As shown here for a less affected line (P35S:FB11RNAi
- 2), most of the
plantlets presented a progressive growth alteration, with the disappearance of the apical meristem
within 10 to 20 days after repotting the plantlets (Figure II.7B). Eleven weeks after repotting, the
59
plants reached their final size and most of them flowered. At this time, the phenotypic alteration
previously identified in the other T0 lines was also visible on the P35S:FB11RNAi
-2 T3 plants: small
bushy plants resulting from the development of axillary buds, and plants exhibiting a complete
growth arrest due to the disappearance of all meristems (Figure II.7C).
The plants which were able to flower set fruits, which developed normally and were
observed at the Red Ripe stage. However, transgenic lines presented an increase of locule number
per fruit (Figure II.8). Fruits with only two locules where only present in WT and in the
P35S:FB11RNAi
- 8 line. In addition the frequency of fruits with five locules or more were increased
in the P35S:FB11RNAi
- 1, -3 and -4 lines. The characterization of the P35S:FB11RNAi
transgenic lines
was stopped at this stage.
Figure II.7 Growth and development of P35S :FB11RNAi lines after transfer in soil substrate. Plantlets were observed 10
days (A), 20 days (B) and 7 weeks (C) after transfer in the greenhouse.
60
Figure II.8 Quantification of locule number in red ripe fruit in P35S :FB11RNAi lines and WT. (A) Equatorial section of
fruits with 3, 4 and 5 locules. (B) The frequency of locule number per fruit was performed on 40 to 110 fruits harvested
on 10 to 20 plants for each genotype. Fruits were observed at Red Ripe stage, approximately 11 weeks after repotting
d) Functional characterization of SlFB24
SlFB24 F-Box is the closest homologue to Arabidopsis thaliana AFB5 (AUXIN F-BOX
PROTEIN 5). AFB5 is one of the five homologues of TIR1 (TRANSPORT INHIBITOR
RESPONSE 1), the F-Box implicated in auxin signaling. AFB5 is distant from TIR1-AFB1-
AFB2-AFB3, these four proteins being involved in the same processes (Dharmasiri et al., 2005).
In Arabidopsis, it has been shown that AFB5 can bind to different classes of auxinic compounds
like the picolinates, but its role is not established (Walsh et al., 2006). In tomato, SlTIR1 is
implicated in auxin signaling during fruit set (Ren et al., 2011).
During his PhD, N. Viron generated nine independant P35S:FB24RNAi
T0 plants with a unique
transgene insertion (Table II.2). The T0 and T1 plants do not present a noticeable vegetative and
reproductive phenotype. Six lines presenting a strong (P35S:FB24RNAi
-21) or moderate reduction
of SlFB24 expression (P35S:FB24RNAi
-1, -2, -7, -14 and -21) were selected for selection of
homozygous lines (Figure II.9). It was possible to select an homozygous line for P35S:FB24RNAi
-2
and -7, but only heterozygous lines were identified for P35S:FB24RNAi
-14 and -21. In addition,
P35S:FB24RNAi
-1 and -22 presented aberrant segregation of kanamycin resistance (Table II.4).
Due to these incoherent data and to the lack of phenotype, the characterization of P35S:FB24RNAi
transgenic lines was stopped at this stage.
61
Figure II.9 Expression analysis of SlFB24 by qRT-PCR on Bk fruits from P35S:F241RNAi T0 plants. The relative
expression (Arbitrary unit) corresponds to the expression of the SlFB24 normalized with the expression of actin, β-
tubulin and EiF4a. The expression was measured on a poll of three fruits collected at Bk stage. The error bars represent
the standard deviation of three technological replicates. Lines with grey expression bar were selected for further
analysis.
Table II.4 Segregation of the kanamycine resistance in P35S:FB24RNAi -2 and -7 lines. R, resistant; S, sensible
Generation Line Segregation
T1 1 40R/20S
T2 1-1 54R/36S
T2 1-2 62R/31S
T2 1-3 60R/28S
T2 1-4 58R/38S
T2 1-5 48R/24S
T2 1-6 47R/41S
T2 1-7 54R/33S
T1 22 67R/12S
T2 22-6 7R/87S
T2 22-8 57R/33S
T2 22-9 15R/80S
T2 22-10 57R/32S
e) Discussion and perspectives on F-Box functional characterization
The purpose of this part of my PhD was to pursue the functional characterization of three F-
Box proteins, selected by N. Viron for their expression during tomato fruit development and
characterized by a preferential expression in the fleshy tissues of the fruit. Toward this end, RNAi
lines have been generated for the three candidate genes. When I started my PhD, the objective
was to select homozygous lines for the three F-Box and to establish whether the modification of
their expression leads to a phenotypic alteration of tomato, in particular at the fruit level.
62
With the data collected during my PhD, we cannot conclude yet about the role of one of
these F-Box in tomato fruit development even if some preliminary results suggest that SlFB11
and SlFB24 could be implicated in tomato development. The major problem that we encountered
in this part of the work is the lack of phenotypic alterations in T0 plants, and the ill defined
phenotypes of T1 and T2 plants, especially at the fruit level. Several hypotheses can be put
forward to explain these results: 1) these proteins are not involved in tomato development, 2) the
chosen F-box proteins play a regulatory role in a very specific developmental process not revealed
in this study, 3) functional redundancy between the target protein and other proteins of the F-Box
family could mask the role of the targeted F-Box, as already described in the case of SlEBF1 and
SlEBF2 (Yang et al., 2010), 4) the reduction of the targeted F-Box expression was not sufficient
to have an effect on plant development. Other strategies, like the generation of negative dominant
lines with the CRES-T, might allow us to circumvent this problem (see § II.2). The conclusions
and perspectives specific to each candidate F-Box will be discussed in the following parts.
SlFB2
The generation of P35S :FB2RNAi
lines did not allow us to conclude on the role of SlFB2
during tomato development, because of the lack of phenotype in the silenced lines. However, it
should be noted that the three lines presenting a down-regulation of SlFB2 gene were only slightly
affected since they still presented 60-75 % of residual SlFB2 transcript level. To access to the
functional role of SlFB2 in tomato fruit development, further work would require the generation
of other transgenic lines with a higher repression of SlFB2 gene.
However, a very interesting phenotype was found by chance in P35S :FB2RNAi
-2 line due to
the insertion of the T-DNA in a particular site of the tomato genome. The resulting insertional
mutant presented a normal vegetative development and was only affected at the fruit level. It was
mainly characterized by the absence of locular tissue development and subsequent alteration of
seed shape, as well as by an increase in fruit firmness. This fruit specific mutant is particularly
interesting since it affects fruit quality parameters, by altering the early fruit development without
affecting fruit ripening. Now, the main objective it to successfully identify the insertion site. For
this, a segregating population of 110 plants was generated. It allowed the demonstratation that the
phenotype was due to a unique insertion which acts as a recessive trait, since only ¼ of the plants
presented the phenotype. This population is a crucial tool for the identification of the insertion site.
During this work, inverse PCR was used to clone T-DNA bording sequences and five
different sequences were identified. The genotyping of the segregationg population for two of the
five identified insertions (FB2-2_S1.9 or FB2-2_S2.10) demonstrated that they were not
responsible for the phenotype. The two other insertions matching with tomato genome have to be
63
genotyped in the population, even if they correspond apparently to noncoding regions (Table
II.3). In addition, it is highly probable that this first inverse PCR experiment was not exhaustive.
Southern blot hybridization of P35S :FB2RNAi
-2 with the T-DNA probe could help quantifying the
number of insertions in the transgenic lines. In addition, considering that five independent
insertion sites were already identified in P35S :FB2RNAi
-2 line and that kanamycin resistance
segregates as a single insertion according to the segregation test, it is likely that recombinations of
the T-DNA or partial T-DNA integrations occurred during tomato transformation (Thomas and
Jones, 2007).
In this case, given that the inverse PCR is based on primers defined on the sequence of the T-
DNA, different combinations of primer should be tested to properly identify the relevant T-DNA
insertion site. To avoid this kind of constraint Whole Genome Sequencing (WGS) of
P35S :FB2RNAi
-2 genomic DNA would allow a without a priori search for the different insertions
sites.
Further work on this mutant relies on the identification of the mutation. To our knowledge,
such a mutant with a complete disappearence of the locular tissue has not been described in
tomato. According to the available data, it is quite difficult to hypothetize the function of the
gene(s) responsible for this phenotype. Considering that locular tissue development is impaired, it
can be proposed that the gene affected could be a gene involved in a regulatory process specific of
this developmental phase. In addition, the fruits of P35S :FB2RNAi
-2 line presented an increased
firmness in green as well as in red ripe fruits. This phenotype suggests a modification of cuticule
thickness, pericarp thickness or changes in cell wall structure. Once again, the mutated gene could
be implicated in the regulation of one of this trait. At the moment, we have no clues to suggest
that one of these phenotypes (locular tissue development/fruit firmness) is the consequence of the
other, since both phenotypes do not show a trivial relationship. It is more probable that both
phenotypes are pleiotropic consequences of the alteration of one gene.
Once the mutated gene is identified, new transgenic lines will be generated to reproduce the
phenotype. Alternatively, if the mutation corresponds to the inactivation or down-regulation of a
protein, mutants will be searched by TILLING in the EMS mutant collection. The generated
transgenic lines and/or the selected mutants will be fully characterized for locular tissue
development as well as for fruit firmness. Other characteristics of fruit structure will also be
studied since preliminary characterization of P35S :FB2RNAi
-2 line leads to the conclusion that
pericarp thickness, as well as cell size seems to be increased in the mutant. Finally, the
experimentations will focus on the molecular mechanism underlying these phenotypic changes.
64
SlFB11
The candidate gene SlFB11 encodes an ortholog in tomato of the SKIP16 F-box
(SKP1/ASK-Interacting Protein16; At1g06110) identified in Arabidopsis thaliana. The only data
available in the literature concerns the interaction of AtSKIP16 with the protein ASK2 in a SCF
complex via its F-box motif (Risseeuw et al., 2003). The target proteins ubiquitinated via their
interaction with SKIP16 are not known to date.
In T1 generation, P35S:FB11RNAi
transformants showed altered vegetative growth of seedlings.
Indeed, T1 seedlings present an arrest of the apical meristems after their transfer to the
greenhouse. After a few days, one or more secondary axes generally emerge from the hypocotyls,
replace the main axis to sustain further plant development, thus resulting in a bushy phenotype.
The secondary stems give rise to fertile inflorescences which developed fruits presenting an
increase in locule number. During this work, we were able to show that these phenotypes were not
related to the loss of kanamycin resistance observed in two lines, since it was still observed when
these sensible lines were grown in the absence of kanamycin.
With the data currently available, we can hypothesize that SlFB11 F-Box protein could be
involved in maintaining the shoot apical meristem (SAM) in tomato. The SAM enables the
generation of leaves, stems and flower structures throughout plant development. It allows the
plant to maintain a population of stem cells while initiating the development of leaves (Barton,
2010). In tomato (Shani et al., 2009) as well as in Arabidopsis (Barton, 2010), mutants affected in
the SAM are often affected in leaves development or in plant phyllotaxis. Here, P35S:FB11RNAi
transformants were only affected in the maintain of meristem stem cells in the main axis, the
lateral meristems develop normally and present normal leaves.
The integrity of the stem niche in the SAM relies on the expression of WUSCHEL
transcription factor which regulates the expression of CLAVATA3 gene. It has been shown that
feedback signaling mediated by CLAVATA3 and WUSCHEL specify and maintain stem-cells in
the SAM (Besnard et al., 2011). This regulatory loop is connected to a more complex gene
regulatory network where hormones, nutrients availability and chromatin remodeling are also
involved. Although hormones signaling pathways are known to go through proteolytic
degradation involving F-box proteins (Tan and Zheng, 2009; Vanneste and Friml, 2009; Gfeller et
al., 2010), the involvement of F-Box proteins in the regulation of SAM was not previously
described. However, it has been shown that the 26S proteasome is involved in maintaining
inflorescence and floral meristem functions (Zhang et al., 2011). Likewise, in Arabidopsis the F-
Box protein MAX2 (At2g42620), which has no homology with SlFB11 or SKIP16, is involved in
the suppression of axillary bud development (Stirnberg et al., 2002).
A number of genes are specifically implicated in the acquisition and maintenance of floral
meristems identity and in the determination of floral organs (Liu et al., 2009). However, the floral
65
meristem derives from an undifferentiated meristem and shared common regulators, like
CLAVATA and WUSCHEL. As a consequence, alteration of the SAM can also lead to alteration of
the floral meristem (Barton, 2010). In tomato, locule number is controlled by several QTLs. Two
of them have been identified: ln (locule-number) on chromosome 2 and fas (fasciated) on
chromosome 11. FASCIATED gene codes for a YABBY transcription factor. It has been shown
that the increase in locule number in tomato modern cultivars is mainly due to the repression of
this gene during floral development (Cong et al., 2008). Recently, it has been reported that the lc
QTL corresponds to SNPs in a non-coding region located downstream from the 3' end of
WUSCHEL, which is implicated in the regulation of stem cell fate (Munos et al., 2011). Up to
now, the relationship between this mutation in a non–coding region near to WUSCHEL and the
regulation of locule number is not clear and how SlFB11 could be implicated in this process needs
further investigations.
The work on P35S:FB11RNAi
transgenic lines will be continued in order to better characterize
their phenotypes and to decipher the underlying molecular mechanisms. Toward this end,
cytological analysis of the meristems (SAM and Floral meristem), expression of the genes
involved in their maintainance, as well as in-situ hybridization of these genes will be performed.
The identification of SlFB11 target will also be necessary to link proteasome dependent
degradation and well known regulations within the vegetative and floral meristems. In addition,
the phenotype of P35S:FB11RNAi
transgenic lines needs to be clarified. Indeed, a crucial question
needs answers: how is it possible that the apical meristem aborted whereas the secondary
meristems seemed to function normally? One can hypothesize that the stress to which plants are
subjected when they are transferred in the greenhouse, corresponding to changes of soil,
hygrometry, light and temperature conditions, could be responsible for the SAM abortion. If this
is the case, the plantlets directly grown on soil in the greenhouse should not present this
phenotype. Otherwise, a developmental or a nutritional signal should be considered.
SlFB24
Concerning the functional characterization of SlFB24, the closest homologue to Arabidopsis
thaliana AFB5, six independent lines presenting a strong or moderate reduction of SlFB24
expression were studied. A major problem was encountered during the selection of homozygous
lines, since we were able to select homozygous line only for two T0 plants. Indeed, for two T0
lines only heterozygous plants were detected among 10 T1 descendants and for two other lines,
kanamycin resistance presented an aberrant segregation in T2 plants. Whether these results are a
true phenotypic alteration or are due to experimental defaults
remains to be determined. One can think that homozygous lines could be lethal and distort
the frequency of the gametes and/or the frequency of the resistant/sensible plantlets during the
66
segregation test. However, this hypothesis could not explain the different segregation results
observed in one transgenic line. The first step to further work on these transgenic lines will thus
be to grow new T1 plants, collect their seeds (T2), and repeat the segregation test. Then, further
work on this F-Box will depend on the identification of a particular phenotype, which has not
been found yet on the T0 plants, as well as on the T1 plants and on the two homozygous lines
generated.
67
2) Functional analysis of two transcription factors involved in
the regulation of fruit development.
Tomato fruit development has been described as a sequence of distinct developmental phases,
which have been well characterized (Gillaspy et al., 1993). After fruit set, fruit early development
starts with an intense phase of cell division for 7 to 10 days (Joubes et al., 1999), then fruit grows
mainly through a long phase of cell expansion for 20 to 30 days depending on tomato cultivars
(Gillaspy et al., 1993; Mounet et al., 2009). During this phase, cells within the pericarp and
locular tissue expand by accumulation of water, sugars and organic acids in their vacuoles thanks
to the concomitant release of cell walls (Schopfer, 2006). After cell expansion, the ripening starts
with an increase in respiration and a concomitant increase in ethylene synthesis, which is essential
for normal fruit ripening (Moore et al., 2002; Klee and Giovannoni, 2011). Fruit ripening
corresponds to large metabolic changes within the fruits leading to changes in fruit quality traits
such as fruit color, texture, aroma, and taste (Hobson and Grierson, 1993; Carrari and Fernie,
2006; Seymour et al., 2012).
The different phases of tomato fruit development are tightly regulated through hormonal
signaling (Frary et al., 2000; Cheniclet et al., 2005; Cong and Tanksley, 2006; Guo and Simmons,
2011). The understanding of ethylene regulation during tomato fruit ripening greatly benefited
from the data collected on ethylene signaling in Arabidopsis (Yoo et al., 2009) as well as from the
existence of numerous tomato ripening pleiotropic mutants (Giovannoni, 2007). Among these
mutants, the ripening-inhibitor (rin), non-ripening (nor), and Colorless non-ripening (Cnr) were
particularly useful since they are affected in ethylene dependent and independent upstream
processes that regulate fruit ripening (Giovannoni, 2007; Klee and Giovannoni, 2011; Seymour et
al., 2012). The identification of the corresponding genes emphasized the role of transcription
factors in the regulation of tomato fruit ripening, since they respectively encode a MADS-Box TF
(rin mutant / LeMADS-RIN gene), a NAC TF (nor mutant / LeNAC-Nor gene) and a SQUAMOSA
PROMOTER BINDING PROTEIN TF (Cnr mutant/ LeSBP gene). Cross-regulations and
interactions have been showed among these three transcription factors (Eriksson et al., 2004;
Martel et al., 2011; Osorio et al., 2011) and they seem to constitute the basis of a regulatory
network for the control of fruit ripening. Transcriptomic and metabolomic analysis of nor and rin
mutants revealed that LeNAC-Nor regulates more gene and metabolites than LeMADS-RIN
(Osorio et al., 2011) and could thus play a role upstream of LeMADS-RIN or LeSBP.
The technological development and improvement of proteomics, metabolomics and
transcriptomics resulted in the generation of large datasets (Fukushima et al., 2009). To interpret
this data, correlative approaches between, first between transcripts, and then between transcripts,
68
proteins and metabolites have been developed. This type of approach was used to further our
understanding in processes of interest such as ripening by defining functional modules of genes
(Aoki et al., 2007; Ozaki et al., 2010), to identify candidate genes for the regulation of these
processes (Seymour et al., 2012) and to propose new functions for known genes (Fukushima et al.,
2009; Osorio et al., 2011). Such an approach was used to point out crucial regulatory genes during
tomato fruit cell expansion phase (Mounet et al., 2009). Transcriptome and metabolome data were
summarized in a correlative network which revealed six transcriptions factors as potential hubs in
the regulation of this process. Among them, two were already known to be involved in the
regulation of tomato fruit ripening: LeMADS-RIN (Vrebalov et al., 2002) and LeNAC-Nor (Osorio
et al., 2011). In addition, two others were homologues of known ripening regulators and
corresponded respectively to a NAC transcription factor and an AP2 domain-containing TF
homologous to AP2a (Chung et al., 2010). The two other TFs were members of the bZip
(SlTGA2.1) and HD-Zip (SlHAT22) TF families and were pointed out for the first time as
potential regulators of tomato fruit development. A subsequent work also highlighted the potential
role of SlHAT22 in tomato fruit ripening network (Osorio et al., 2011).
Basic regions/leucine zippers transcription factors (bZIP TFs) form a multigenic superfamily
of about 75 members that can be separated into 10 groups in Arabidopsis thaliana according to
their sequence homology (Jakoby et al., 2002). The bZip TFs possess a basic region that binds
specifically to target DNA sequences, which is coupled with a leucine zipper motif that allows
dimerization of the TFs (Jakoby et al., 2002). It has been shown that bZip TFs are regulated by
post-translational modifications like protein-protein interactions (Pontier et al., 2002; Schuetze et
al., 2008). Up to now, functional analyses of bZIP TFs mostly report their involvement in abiotic
and biotic signaling. However, some data also demonstrate their implication in developmental
processes (Kegler at al., 2004; Wu et al., 2012). The HD-Zip superfamily is subdivided into four
subgroups of transcription factors according to their roles and structures (Ariel et al., 2007). These
TFs are characterized by a homeobox domain (HD), which allows the recognition and binding of
the TF to a specific DNA sequence, coupled with a leucine zipper motif (Zip) that allows the
dimerization of the transcription TF. Both domains are critical for the role of HD-Zip TFs (Ariel
et al., 2007). AtHAT22 belongs to the HD-Zip II subgroup, which includes nine members. This
subgroup is characterized by a conserved N-terminal sequence of unknown function and by a
conserved “CPSCE” motif located in the C-terminal part of the protein. This motif is involved in
the formation of high molecular weight protein complexes in oxidative medium (Tron et al., 2002;
Ariel et al., 2007). In Arabidopsis thaliana, a network analysis of light and carbon signaling
pathways proposed AtHAT22 as a regulatory hub playing a major role in the integration of plant
metabolism with light and carbon signaling (Thum et al., 2008).
69
The aim of this work was to unravel the function of SlTGA2.1 and SlHAT22 TFs in tomato
fruit development. For this, transgenic lines were generated with an alteration of TFs expression
level (OE and RNAi lines). In addition, we altered SlTGA2.1 and SlHAT22 function by
expressing a dominant repressor using the CRES-T technology. Phenotypical characterization of
the transgenic lines, together with preliminary metabolic analyses revealed that SlTGA2.1 and
SlHAT22 are implicated in regulatory processes during tomato early fruit development, including
for the SlTGA2.1 gene, the transition from the cell expansion phase to fruit ripening.
a) Functional analysis of SlTGA2.1
SlTGA2.1 belongs to the bZip family of transcription factors
The tomato gene SlTGA2.1 (Solyc10g080770) that belongs to the subgroup D of bZip
transcription factors regrouping the TGA transcription factors in Arabidopsis thaliana (Schütze et
al., 2008). SlTGA2.1 is also a close homolog of NtTGA2.1 and NtTGA2.2 from Nicotiana tabacum.
Sequence homology indicates that the closest homolog of our candidate gene is NtTGA2.1, since
both genes form a subgroup separated from the other genes (Figure II.10). Two tomato genes
(Solyc11g064950 and Solyc01g008730) are closely homologous to Nicotiana tabacum NtTGA2.2
and these three genes form a distinctive
subgroup (Figure II.10). Arabidopsis
thaliana AtTGA2, AtTGA5 and
AtTGA6 genes also form a distinctive
subgroup in which no homolog gene
from Solanum lycopersicum is
included (Figure II.10).
Figure II.10 Phylogenetic tree of bZip genes
from group D from Arabidopsis thaliana,
Nicotiana tabacum and Solanum lycopersicum.
The phylogenetic tree was constructed using the
neighbor joining algorithm with bootstrapping
between the predicted protein sequences from
the three species. AtHY5 is a b-Zip
transcription factor from the subgroup H and
was used as a root for the phylogenetic tree.
The predicted protein sequence of AtTGA2, SlTGA2.1 (Solyc10g080770), SlTGA2.2a
(Solyc11g064950) and SlTGA2.2b (Solyc01g008730) were aligned with protein sequences of
NtTGA2.1 and NtTGA2.2 (Figure II.11). Both SlTGA2.1 and NtTGA2.1 possess an extra N-
terminal domain (of 113 amino acids and 129 amino acids, respectively) when compared to the
NtTGA2.2
SlTGA2.2a(Solyc11g064950)
SlTGA2.2b(Solyc01g008730)
AtTGA5
AtTGA6
AtTGA2
NtTGA2.1
SlTGA2.1(Solyc10g080770)
AtPAN
Solyc05g009660
AtTGA1
AtTGA4
Solyc04g011670
AT1G77920
AtTGA3
Solyc12g056860
Solyc04g072460
AtTGA9
Solyc11g068370
Solyc10g080410
Solyc10g078670
AtTGA10
AtHY5
99
99
94
59
64
50
19
19
98
44
99
99
100
30
99
81
80
64
89
92
0.2
Solyc11g064950
Solyc01g008730
70
protein sequence of AtTGA2, NtTGA2.2, SlTGA2.2a and SlTGA2.2b. All homologs possess a
highly conserved protein sequence besides the N-terminal domains of SlTGA2.1 and NtTGA2.1.
This alignment confirms the observation made through phylogenetic analysis of TGA2 homologs
(Figure II.10).
Figure II.11. Protein sequence alignement of TGA2 homologs from Nicotiana tabacum, Solanum lycopersicum and
Arabidopsis thaliana. The alignment was realized using the multalin software. The high consensus sequences are
represented in red whereas the low consensus sequences are represented in blue. Sequences differing from the
consensus are represented in black.
SlTGA2.1 is widely expressed in tomato organs and is up-regulated during fruit ripening.
Expression analysis revealed that SlTGA2.1 was widely expressed in tomato plant, in flower
organs as well as in vegetative organs (Figure II.12). In the flower, SlTGA2.1 is equally
expressed in all tissues (Figure II.12A). In the vegetative organs, the expression of SlTGA2.1 is
stronger in the mature leaves and root than in the shoot and young leaves (Figure II.12B).
71
Figure II.12 Expression analysis of SlTGA2.1 in flower (A) and vegetative (B) organs in tomato plants (Solanum
lycopersicum cv Micro-Tom). The relative expression (arbitrary units) corresponds to the expression of SlTGA2.1
normalized with the expression of actin, β-tubulin and EiF4a. 0 DPA corresponds to the unfertilized ovary at anthesis.
The vertical bars represent the standard deviation (4 biological replicates).
The expression of SlTGA2.1 was measured in tomato fruit along its development from the
ovary stage (0 DPA) to the red ripe stage (Bk+7) and in the different fruit tissues (Figure II.3).
The results show that SlTGA2.1 is highly expressed at anthesis (Figure II.3A). Then SlTGA2.1
expression drops dramatically and remains very low during the cell division phase and during the
first part of the cell expansion phase. From 27 DPA to the ripe stage, the expression of SlTGA2.1
increases regularly and significantly to reach a maximum at the ripe stage (Bk+7). At 20 DPA,
SlTGA2.1 does not present a differential expression within fruit tissues, but it was doubled in
seeds when compared to columella, exocarp, locular tissue and pericarp (Figure II.3B).
Figure II.13 Relative expression of SlTGA2.1 during fruit development (A) and in fruit tissues and seeds (B) in
Solanum lycopersicum cv Micro-Tom. The relative expression (arbitrary units) corresponds to the expression of
SlTGA2.1 normalized with the expression of actin, β-tubulin and EiF4a. 0 DPA corresponds to the unfertilized ovary at
anthesis. Mg, Mature Green; Bk, Breaker.
Generation of stable transgenic lines
To study the role of SlTGA2.1 during the fruit development, we generated three types of
transgenic lines in an attempt to silence the expression of SlTGA2.1 by RNA interference (RNAi;
PPPC2:TGA2.1RNAi
lines), to increase the expression of SlTGA2.1 by over expression (OE;
PPPC2:TGA2.1OE
lines) and to produce of a chimeric TF by Chimeric Repressor Gene Silencing
Technology (CRES-T; PPPC2:TGA2.1CRES-T
lines). In order to avoid potential pleotropic effects of
the misregulation of a transcription factor and to take into accoutn the up-regulation of SlTGA2.1
during the cell expansion phase well before the onset of ripening, these constructs were placed
under the control of the tomato phosphoenolpyruvate carboxylase 2 promoter (SlPPC2) which is a
cell expansion phase fruit-specific promoter (Fernandez et al., 2009; Guillet et al., 2012).
For all constructions, T0 transgenic plantlets were selected according to their ploidy level
(2C) through cytometry analysis and the presence of the transgene was checked by PCR. A
72
segregation test on T1 seeds resulting from the auto fertilization of T0 plants allowed to evaluate
the number of independent T-DNA insertion in tomato genome. Finally, expression analysis of
SlTGA2.1 gene allowed the identification of the transgenic lines with the expected decrease (for
RNAi lines) or increase (for OE lines) in SlTGA2.1expression. Among the 16 PPPC2:TGA2RNAi
generated T0 lines, five lines (lines 1, 4, 11, 12 and 16) were chosen for further characterization.
Among the 13 PPPC2:TGA2OE
generated T0 lines, two lines were chosen for further
characterization (lines 5 and 8). Among the seven PPPC2:TGA2CRES-T
generated T0 lines, two lines
were chosen for further characterization (lines 5 and 7). A summary of the different lines
available is presented in Table II.5.
Table II.5 Summary of the RNAi, OE and CRES-T transgenic lines generated for the functional validation of SlTGA2.1
gene
Tomato plants affected in SlTGA2.1 expression levels do not show phenotypical
modifications
Expression analysis was performed on PPPC2:TGA2RNAi
lines and PPPC2:TGA2
OE lines to
determine the effect of each construct on SlTGA2.1 gene expression in the different transformed
line (Figure II.14). Among the PPPC2:SlTGA2OE
T0 plants, PPPC2:SlTGA2OE
-5 presented a slight
increase (x 1.7) of SlTGA2.1 expression, whereas the expression increase in PPPC2:SlTGA2OE
-8
was not significant compared to SlTGA2.1 expression in WT fruits. Among the PPPC2:SlTGA2RNAi
T0 plants, PPPC2:SlTGA2RNAi
-1, -4, and -12 showed a decrease of SlTGA2.1 expression of seventy
percent, PPPC2:SlTGA2 RNAi
-11 showed a decrease of SlTGA2.1 expression of fifty percent whereas
the expression decrease in PPPC2:SlTGA2 RNAi
-16 was not significant compared to SlTGA2.1
expression in WT fruits.
Construct Number of T0
independent lines
Number of 2C T0 lines
carrying 1 insert
Stage of plant
material
Number of
homozygous lines
PPPC2:TGA2.1RNAi 16 5 T1, T2 5
PPPC2:TGA2.1OE 13 5 T1, T2 2
PPPC2:TGA2.1CRES-T 7 2 T2 2
73
Figure II.14 Expression analysis of SlTGA2.1 in Bk+7 fruits from T0 PPPC2:SlTGA2OE lines (A) and PPPC2:SlTGA2RNAi
lines (B). The relative expression (arbitrary units) corresponds to the expression of SlTGA2.1 normalized with the
expression of actin, β-tubulin and EiF4a. The vertical bars represent the standard deviation (3 technological replicates).
Phenotyping of T0 and T1 plants from the two PPPC2:SlTGA2OE
lines and the five
PPPC2:SlTGA2RNAi
lines do not reveal morphological alterations at the vegetative nor at the flower
and fruit level. In the same way, plant growth and fruit development kinetics were not impaired in
these plants. Culture of T2 homozygous lines is currently underway and will allow us to definitely
confirm these observations.
Expression of a chimeric SlTGA2.1-SRDX TF leads to alteration of fruit development
T0, T1 and T2 plants from PPPC2:TGA2.1CRES-T
-5 and -7 lines were grown in the greenhouse
between autumn 2011 and summer 2012. Phenotypical observations do not reveal morphological
alterations at the vegetative nor at the flower level (Figure II.15). However, a major alteration of
fruit color and ripening was observed in these two CRES-T lines. During early development,
fruits from PPPC2:TGA2.1CRES-T
lines had a pale color, when compared to WT fruits (Figure II.16).
In addition, fruit ripening evolved very slowly in PPPC2:TGA2.1CRES-T
fruits, when compared to the
WT fruits (Figure II.16 and Figure II.17). Indeed, during ripening the color of WT fruits turned
from green to yellow, orange and red in seven days (Figure II.16). In PPPC2:TGA2.1CRES-T
lines, 12
to 15 days were necessary for a fruit to turn between the appearance of the yellow color on the
fruit (Breaker stage) and a fully red fruit. In addition, on the contrary to WT plants, fruit color did
not change uniformly in PPPC2:TGA2.1CRES-T
fruits. In these fruits, the onset of ripening was
characterized by the apparition of the yellow color near to fruit peduncle. Then color changes
progressed along fruit pericarp to reach the opposite extremity of the fruit within five days
(Figure II.16, Bk+5 stage). This shift in tomato fruit ripening between WT and PPPC2:TGA2.1CRES-
T lines was also clearly visible in transverse fruit sections (Figure II.17).
74
Figure II.15 Vegetative development of PPPC2:TGA2.1CRES-T T1 plant (left) and WT plant (right).
Figure II.16 Fruit color and ripening in PPPC2:TGA2.1CRES-T and WT fruits. IMG, immature green fruit ; Bk, breaker
stage.
Figure II.17 Observation of four stages of PPPC2:TGA2.1CRES-T fruits. IMG : Immature green stage. Bk : Breaker stage
75
These phenotypes were not related to a modification of expression of SlTGA2.1 in these
transgenic lines. Indeed, qRT-PCR analysis revealed a down-regulation of SlTGA2.1 of about
twenty percent for PPPC2:TGA2.1CRES-T
- 5 and about forty percent for PPPC2:TGA2.1CRES-T
-7
(Figure II.18), but it was shown that a down-regulation of SlTGA2.1 of seventy percent does not
lead to these phenotypes (Figure II.14).
According to the alteration of fruit color during ripening in PPPC2:TGA2.1CRES-T
lines, we
wondered if these lines were specifically affected at the pigment level, or if fruit ripening was
globally affected. In a first attempt, total soluble contents and firmness were measured in WT and
PPPC2:TGA2.1CRES-T
fruits harvested at Bk+7 on T2 plants (Figure II. 19). No significant
differences were observed for the total soluble contents, due to a great variability in WT fruits
(Figure II. 19A). On the contrary, fruit firmness was increased in PPPC2:TGA2.1CRES-T
fruits when
compared to WT fruits (Figure II. 19B). Thus, another characteristic of fruit ripening (fruit
firmness), was affected in PPPC2:TGA2.1CRES-T
fruits. Whether this increase in fruit firmness is
only due to an increased length of fruit ripening phase in these lines, or corresponds also to
irreversible effects of the expression of a chimeric SlTGA2.1 transcription factor remains to be
determined.
Figure II.18 Expression analysis of SlTGA2.1 in PPPC2:TGA2.1CRES-T T0 -5 and -7. The analysis was carried on fruits
from T0 plants harvested at Bk+7 stage. The relative expression (arbitrary units) corresponds to the expression of
SlTGA2.1 normalized with the expression of actin, β-tubulin and EiF4a. The vertical bars represent the standard
deviation (3 technological replicates).
76
Expression of a chimeric SlTGA2.1-SRDX TF leads to alteration in fruit metabolite content
and firmness
Figure II.19 Analysis of total soluble contents (A) and firmness (B) in PPPC2:TGA2.1CRES-T fruits harvested at Bk+7
stage. The vertical bars represent the standard deviation (10 biological replicates).
To further characterize the metabolic changes in PPPC2:TGA2.1CRES-T
fruits, preliminary
quantifications of organic acids (malate and citrate, Figure II.20) and sugars (starch, glucose,
fructose and sucrose; Figure II.21) were performed in T0 fruits harvested at Bk+7 stage. The
content in citrate in PPPC2:TGA2.1CRES-T
- 5 and -7 fruits was not significantly different from the
WT. However, the malate concentration showed a significant increase in PPPC2:TGA2.1CRES-T
-5
and -7 fruits in comparison with the WT.
Figure II.20 Analysis of organic acid contents in T0 PPPC2:TGA2.1CRES-T fruits harvested at Bk+7. (A) Citrate
concentration (µmol/gFW). (B) Malate concentration (mM/gFW). The vertical bars represent the standard deviation (2
technical replicates).
77
Both PPPC2:TGA2.1CRES-T
lines showed a clear decrease in glucose and fructose contents in
Bk+7 fruits, when compared to WT fruit (Figure II.21A and B). In the same way, starch content
was decreased but only in the PPPC2:TGA2.1CRES-T -
7 line (Figure II.21C). Concerning sucrose
content, a significant decrease was observed in PPPC2:TGA2.1CRES-T
-5, but no significant variation
was measured in PPPC2:TGA2.1CRES-T
-7 compared to the WT (Figure II.21D). These
quantification data, together with the fact that soluble sugars in ripe fruit comes in part from the
degradation of starch synthesized during early fruit development (Schaffer and Petreikov, 1997;
Mounet et al., 2007), prompted us to quantify starch content in early developing fruits. As a first
approach, fruit was stained thanks to lugol reagent for WT and PPPC2:TGA2.1CRES-T
-5 and -7 lines
at 10 DPA (Figure II.22). Lugol reagent revealed the presence of starch in the outer pericarp,
radial pericarp (septum) as well as in the columella in WT as well as in PPPC2:TGA2.1CRES-T
lines.
As expected, starch accumulation was decreased in PPPC2:TGA2.1CRES-T
lines. Indeed, lugol
staining was thinner in these lines, and was restricted to the internal part of the pericarp: the area
between the vascular bundles and the endocarp. On the contrary, in WT plants lugol staining
expanded beyond the vascular bundles, which were not stained and appeared as white dots.
Figure II.21 Analysis of sugar content in PPPC2:TGA2.1CRES-T T0 fruits harvested at Bk+7. (A) Glucose concentration.
(B) Fructose concentration. (C) Starch concentration. (D) Sucrose concentration. All concentrations were measured in
µg/gFW. The vertical bars represent the standard deviation (2 technical replicates).
78
Figure II.22 Lugol staining of WT and PPPC2:TGA2.1CRES-T T2 fruits harvested at 10 DPA. (A) Equatorial sections (0.5
mm thick) of four representative fruits are presented for each genotype. (B) Equatorial section of WT fruit pericarp. E,
exocarp; M, mesocarp; En, endocarp; VB, vascular bundle
b) Functional analysis of SlTHAT22
SlHAT22 belongs to the HD-Zip transcription factors superfamily.
The tomato gene SlHAT22 (Solyc02g091930) is the closest homolog in tomato of AtHAT22,
that belongs to the subgroup II of the HD-Zip superfamily transcription factors. Ten tomato genes
coding for a HD-Zip II protein were identified in tomato genome, whereas this family includes
nine members in Arabidopsis thaliana (Ariel et al., 2007). As observed in Arabidopsis, where
AtHAT22 and AtHAT9 are closely related, SlHAT22 (Solyc02g091930) predicted protein was
closely related to another HD-Zip II predicted protein (Solyc02g063520, Figure II.23).
Figure II.23 Phylogenetic tree of HD-Zip
II members from Arabidopsis thaliana and
Solanum lycopersicum The phylogenetic
tree was constructed using the neighbor
joining algorithm with bootstrapping
between the predicted protein sequences
from the two species.
AtHAT22
AtHAT9
SlHAT22 (Solyc02g091930)
Solyc02g063520
Solyc04g077220
Solyc01g073910
Solyc10g080540
AtHAT14
Solyc01g090460
AtHAT3
AtHB4
AtHAT4
Solyc08g078300
Solyc06g060830
Solyc08g007270
AtHAT2
AtHAT1
AtHAT17
Solyc05g008050
ATHB14
83
97
94
94
77
82
77
51
24
34
51
42
4
1
7
45
26
0.2
79
SlHAT22 is expressed in reproductive and vegetative organs
Expression analysis revealed that SlHAT22 was expressed in all tomato organs (Figure
II.24). In the flower (Figure II.24A), SlHAT22 is mainly expressed in the style and anther, much
less in sepals, petals and ovary. In the vegetative organs, the expression of SlHAT22 is stronger in
the mature leaves and root than in the shoot and young leaves (Figure II.24B).
Figure II.24 Expression analysis of SlHAT22 in flower (A) and vegetative (B) organs in tomato plants (Solanum
lycopersicum cv Micro-Tom). The relative expression (arbitrary units) corresponds to the expression of SlHAT22
normalized with the expression of actin, β-tubulin and EiF4a. 0 DPA corresponds to the unfertilized ovary at anthesis.
The vertical bars represent the standard deviation (4 biological replicates).
The expression of SlHAT22 was measured in tomato fruit along its development from the
ovary stage (0 DPA) to the red ripe stage (Bk+7) and in the different fruit tissues (Figure II.25).
The results show that the maximal expression of SlHAT22 is in ovary at anthesis (Figure II.25A).
After fertilization, the expression of SlHAT22 decreases strongly to 1/7th of its expression at
anthesis. Later on, the expression of SlHAT22 increases to peak at 12 DPA, at the beginning of the
cell expansion phase. Afterwards, the expression of SlHAT22 decreases slowly until the mature
green stage and remains at its lowest level during fruit ripening. At 20 DPA, SlHAT22 does not
present a differential expression within fruit tissues, but is at least quadrupled in seeds when
compared to columella, exocarp, locular tissue and pericarp (Figure II.25B).
80
Figure II.25 Relative expression of SlHAT22during fruit development (A) and in fruit tissues and seeds (B) in Solanum
lycopersicum cv Micro-Tom. The relative expression (arbitrary units) corresponds to the expression of
SlHAT22normalized with the expression of actin, β-tubulin and EiF4a. 0 DPA corresponds to the unfertilized ovary at
anthesis. Mg, Mature Green; Bk, Breaker.
Generation of stable transgenic lines
To study the role of SlHAT22 during the fruit development, we generated three types of
transgenic lines in an attempt to silence the expression of SlHAT22 by RNA interference (RNAi;
PPPC2:HAT22RNAi
lines), to increase the expression of SlHAT22 by over expression (OE;
PPPC2:HAT22OE
lines) and to produce of a chimeric TF by Chimeric Repressor Gene Silencing
Technology (CRES-T; PPPC2:HAT22CRES-T
lines). As already described for the functional
validation of SlTGA2.1 (cf §II.2a), these constructs were placed under the control of the tomato
SlPPC2 cell expansion phase fruit-specific promoter (Fernandez et al., 2009; Guillet et al., 2012)
For all constructions, T0 transgenic plantlets were selected according to their ploidy level
(2C) through cytometry analysis and the presence of the transgene was checked by PCR. A
segregation test on T1 seeds resulting from the auto fertilization of T0 plants allowed the
evaluation of the number of independent T-DNA insertion in tomato genome. Finally, expression
analysis of SlHAT22 gene allowed identifying the transgenic lines with the expected decrease (for
RNAi lines) or increase (for OE lines) in SlHAT22 expression. Among the 8 PPPC2:HAT22RNAi
T0
lines generated, two lines (lines 5 and 8) were chosen for further analysis. Among the 13
PPPC2:HAT22OE
T0 lines generated, four lines (lines 4, 10, 12 and 13) were chosen for further
analysis. Among the seven PPPC2:HAT22CRES-T
T0 lines generated, three lines (lines 2, 3 and 4)
were chosen for further analysis. A summary of the different lines available is presented in Table
II.6.
81
Table II.6 Summary of the RNAi, OE and CRES-T transgenic lines generated for the functional validation of SlHAT22
gene
Construction
Numbers of T0
independent
lines
Numbers of 2C
T0 carrying 1
insert
Stage of plant
material
Number of
homozygous
lines
PPPC2:HAT22RNAi
8 2 T1,T2 2
PPPC2:HAT22OE
13 4 T1,T2 4
PPPC2:HAT22CRES-T
6 3 T2 3
Tomato plants affected in SlHAT22 expression levels do not show phenotypical
modifications
Expression analysis was performed on PPPC2:HAT22RNAi
and PPPC2:HAT22OE
lines
to
determine the effect of each construct on SlHAT22 gene expression in the different transformed
line (Figure II.26).
Among the PPPC2:HAT22OE
T0 plants, three of them (-4, -10 and -13) showed an over-
expression of SlHAT22 gene ranging from 1.6 fold (PPPC2:HAT22OE
-10) to 4 fold
(PPPC2:HAT22OE
-4) (Figure II.26A). In PPPC2:HAT22OE
-12, SlHAT22 expression was close to the
WT. The two PPPC2:HAT22RNAi
T0 plants (-5 and -8) showed a diminution of the expression of
SlHAT22 by fifty percent.
Figure II.26 Expression analysis of SlHAT22 in Bk+7 fruits from T0 PPPC2:HAT22OE lines (A) and PPPC2:HAT22RNAi
lines (B). The relative expression (arbitrary units) corresponds to the expression of SlHAT22 normalized with the
expression of actin, β-tubulin and EiF4a. The vertical bars represent the standard deviation (3 technological replicates).
82
Phenotyping of T0 and T1 plants from the PPPC2:SlHAT22OE
lines and PPPC2:SlHAT22RNAi
lines did not reveal morphological alterations at the vegetative nor at the flower and fruit level. In
the same way, plant growth and fruit development kinetics were not impaired in these plants.
Culture of T2 homozygous lines is currently underway and will allow to definitely confirm these
observations.
Expression of a chimeric SlHAT22-SRDX TF leads to alteration in fruit development
Phenotypical characterization of PPPC2:HAT22CRES-T
plants from the three independent lines -2,
-3 and -4 (T0, T1 and T2 generations) showed that they presented fruits with a darker green than
the WT fruits at immature and mature green stages of fruit development (Figure II.27). Then,
during fruit ripening, fruit color was comparable to that of the WT fruit. The color difference
between PPPC2:HAT22CRES-T
fruits and WT fruits implies a difference in pigment accumulation
between both genotypes, which could be due to an increased quantity of chloroplasts per cell
and/or an increased chlorophyll content per chloroplasts in the PPPC2:HAT22CRES-T
fruits when
compared to the WT.
Figure II.27 Mature green fruits of (A) PPPC2:HAT22CRES-T line and (B) WT plant.
These phenotypes were not related to a modification of the expression of SlHAT22 in these
transgenic lines. Indeed, even if PPPC2:HAT22CRES-T
-2 T0 lines showed a 10-fold overexpression
of the SlHAT22 gene (Figure II.28), this phenotype was also observed in the two other
PPPC2:HAT22CRES-T
T0 lines which do not present such an increase in SlHAT22 expression. In
addition, it was shown that the 4-fold overexpression of SlHAT22 does not lead to such a
phenotype (Figure II.26A).
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Figure II.28 Expression analysis of SlHAT22 in PPPC2:HAT22CRES-T T0 lines. The analysis was carried on fruits from
T0 plants harvested at Bk+7 stage. The relative expression (arbitrary units) corresponds to the expression of SlHAT22
normalized with the expression of actin, β-tubulin and EiF4a. The vertical bars represent the standard deviation (3
technical replicates).
Expression of a chimeric SlHAT22-SRDX TF leads to alteration in fruit metabolite content
and in firmness
According to the alteration of fruit color during early fruit development in PPPC2:HAT22CRES-T
lines, we wondered whether this modification could lead to alterations of the ripe fruit
characteristics. In a first attempt, total soluble contents and firmness were measured in WT and
PPPC2:HAT22CRES-T
fruits harvested at Bk+7 on T2 plants (Figure II.29). Both total soluble content
and fruit firmness were increased in PPPC2:HAT22CRES-T
fruits compared to WT fruits, but a large
variability was observed in the measurement of soluble solids.
Figure II.29 Analysis of total soluble contents (A) and firmness (B) in PPPC2:HAT22CRES-T fruits harvested at Bk+7
stage. The vertical bars represent the standard deviation (10 biological replicates).
84
To further characterize the metabolic changes in PPPC2:HAT22CRES-T
fruits, preliminary
quantifications of organic acids (malate and citrate, Figure II.30) and sugars (starch, glucose,
fructose and sucrose; Figure II.31) were performed in T0 fruits harvested at Bk+7 stage.
The content in citrate was not significantly different in PPPC2:HAT22 CRES-T
- 2 and -4 fruits
from the WT (Figure II.30). However, citrate content was clearly decreased in PPPC2:HAT22 CRES-
T- 3 when compared to WT. Concerning malate content, no significant difference was observed
among the different PPPC2:HAT22CRES-T
lines when compared to WT.
Figure II.30 Analysis of organic acid contents in T0 PPPC2:HAT22CRES-T fruits harvested at Bk+7. (A) Citrate
concentration (µmol/gFW). (B) Malate concentration (mM/gFW). The vertical bars represent the standard deviation (2
technological replicates).
Lines PPPC2:HAT22CRES-T
-2 and -3 showed a clear increase in glucose and fructose contents in
Bk+7 fruits, when compared to WT fruit whereas the concentration of these sugars was not
significantly different from the WT in PPPC2:HAT22CRES-T
-4 (Figure II.31A and B). Line
PPPC2:HAT22CRES-T
-3 also accumulated more sucrose and starch than WT, whereas the situation
was less clear for the other lines due to the large variability in the measure (Figure II.31C and D).
These sugars measurements in Bk+7 fruits were reinforced by a visualization of starch
accumulation in immature green fruits (Figure II.32). Indeed, the increase in lugol starch staining
in PPPC2:HAT22CRES-T
-2 and -4 clearly showed an increase in starch content in these lines. Due to a
delay in introduction of PPPC2:HAT22CRES-T
-3 line in the greenhouse, lugol staining was not
performed on this line.
85
Figure II.31 Analysis of sugar content in PPPC2:HAT22CRES-T T0 fruits harvested at Bk+7. (A) Glucose concentration.
(B) Fructose concentration. (C) Starch concentration. (D) Sucrose concentration. All concentrations were measured in
µg/gFW. The vertical bars represent the standard deviation (2 technological replicates).
Figure II.32 Lugol staining of WT and PPPC2:HAT22 CRES-T T2 fruits harvested at 10 DPA. (A) Equatorial sections (0.5
mm thick) of four representative fruits are presented for each genotype. (B) Equatorial section of WT fruit pericarp. E,
exocarp; M, mesocarp; En, endocarp; VB, vascular bundle
c) Discussion and perspective about SlTGA2.1 and SlHAT22
Correlative network analysis of transcriptome and metabolome data has proved to be an
efficient strategy to get insights upon the relationships between genes, metabolites and biological
processes in tomato (Schauer et al., 2006; Toubiana et al., 2012) and to discover new gene
functions. The recent study of Ozaki et al. (2010) clearly demonstrated how a well conducted
86
gene expression profiling of tomato fruit followed by correlation network analysis of the
transcriptomic data could be used to highlight a gene-to-gene co-expression module enriched in
genes implicated in the biosynthesis of flavonoids, which are major fruit phytonutrients.
Furthermore, the authors could identify in this module a gene of unknown function that was not
an enzyme nor a transcription factor, which, when overexpressed in planta, resulted in the up-
regulation of the flavonoid pathway genes included in the module, thereby showing that this
approach can be used to identify and infer the function of tomato genes with previously unknown
roles. In a previous work (Mounet et al., 2009), we used a similar approach to identify key
regulatory genes differentially regulated in tomato fruit tissues during the cell expansion phase.
Among the genes identified as key regulatory hubs putatively implicated in the coordination of
development/metabolic traits in this study, three were already or later shown to be key regulators
of the cell expansion to ripening transition stage in tomato: the RIN and NOR genes (Vrebalov et
al., 2002; Osorio et al., 2011) and the AP2 gene (Chung et al., 2010; Karlova et al., 2011).
Among the other genes belonging to the same group, the HD-Zip SlHAT22 and the bZip
SlTGA2.1 presented interesting features unraveled by mining the scarce literature data existing on
the subject (Thurow et al., 2005; Thum et al., 2008). In addition, both genes had not been
previously characterized in tomato, their possible role and implication in fruit development and
metabolism were unknown and their expression along fruit development displayed opposite
profiles, suggesting that they could have different or even opposite roles in fruit development.
Here, I therefore used a reverse genetics approach to gain first insights upon the function of these
two candidate genes, possibly implicated in the regulation of the early fruit development (Mounet
et al., 2009)
SlTGA2.1 and SlHAT22 have opposite effects on tomato fruit early development
Besides their high expression at anthesis stage, SlHAT22 and SlTGA2.1 showed
complementary expression profiles. Indeed, SlHAT22 presented a peak of expression during early
fruit development at 12 DPA and a low expression during fruit ripening, whereas the expression
of SlTGA2.1 was low during early fruit development and increased regularly from 20 DPA to red
ripe stage. Accordingly, CRES-T transgenic lines for SlHAT22 and SlTGA2.1 presented opposite
phenotypes during early fruit development. Indeed, PPPC2:HAT22 CRES-T
plants presented immature
green fruits darker than WT fruit with an increase in starch content whereas fruits from
PPPC2:TGA2.1 CRES-T
plants were pale with a decrease in starch content. Variation of the green
intensity of immature tomato fruit have been described in different mutants exhibiting an
increased intensity of the green color like in the high-pigment (Peters et al., 1989) and in uniform
ripening U/U plants (Powell et al., 2012) or a decrease of the green intensity like in the lutescent
(Barry et al., 2012) and ghost (Shahbazi et al., 2007) mutants. In these plants, such color changes
87
were respectively associated with an increase or a decrease of chlorophyll content which could be
associated with a modification of chloroplast structure and/or chloroplast number (Enfissi et al.,
2010; Barry et al., 2012; Powell et al., 2012). At the moment, such data are not available for
PPPC2:HAT22 CRES-T
and PPPC2:TGA2.1 CRES-T
fruits, but plant material has been collected for
pigment quantification and plant culture is underway to collect fruits for chloroplast quantification
and analysis of chloroplast structure. The changes in starch content, which is increased in
PPPC2:HAT22 CRES-T
fruits and decreased in PPPC2:HAT22 CRES-T
fruits, is fully consistent with the
modification of photosynthesis capacity of these fruits. It should be noted that, PPPC2:HAT22 CRES-T
and PPPC2:TGA2.1 CRES-T
fruits do not present visual changes in ripe fruit color. This observation,
has to be confirmed by pigment quantifications, but it already suggests that chloroplast number
may not be increased enough to affect chromoplast number which is related to carotenoid
accumulation.
Does SlHAT22 regulation interplays with light and cytokinin signaling?
Data from the literature collected on AtHAT22 suggest a cross-talk between AtHAT22
transcriptional function and light and cytokinin signaling. Indeed, in Arabidopsis thaliana the
regulatory role of AtHAT22 has first been highlighted in multinetwork analyses focused on
carbon/nitrogen metabolism (Gutierrez et al., 2007) and on the interaction between light and
carbon signaling pathways (Thum et al., 2008). It has been proposed that AtHAT22 is a central
element for integrating the light and carbon regulation of genes involved in carbon metabolism,
amino acid metabolism and energy. In addition, genome-wide expression profiling of cytokinin
response revealed that AtHAT22 is induced by cytokinin treatment (Brenner et al., 2005).
The phenotype of plants with an overexpression of AtHAT22 as well as our phenotype in
PPPC2:HAT22 CRES-T
plants are fully consistent with such an interplay. Indeed, in Arabidopsis the
overexpression of AtHAT22 lowered the seedlings chlorophyll content and caused an earlier onset
of leaf senescence, as in tomato. The overexpression of an inactive HAT22 TF led to the opposite
result i.e. an increase in fruit chlorophyll content. Cytokinin could be implicated in this regulatory
process since it is well known that these hormones are involved in the mediation of a number of
light-regulated processes, such as de-etiolation and chloroplasts differentiation (Mok and Mok,
2001). In addition, it has been shown in tomato that cytokinin treatment mimics the dark-green
phenotype of hp mutant (Mustilli et al., 1999). Both hp1 and hp2 mutants are hypersensitive to
light due to phytochrome action (Peters et al., 1989). The HP1 gene codes for an homolog of the
Arabidopsis UV-DAMAGED DNA-BINDING PROTEIN 1 (DDB1) protein, which interacts with
the nuclear factor DEETIOLATED 1 (DET1) (Liu et al. 2004 ), while the HP2 codes for a tomato
ortholog of DET1 (Mustilli et al., 1999). It has been shown that DET1 plays a role in light
88
signaling and modulates stability of transcription factors (Schafer and Bowler, 2002; Lee et al.,
2007).
Cross-talks between light and cytokinin signaling via SlHAT22 regulation will be tested in
the next future to refine the implication of SlHAT22 in the regulation of tomato fruit development.
Cytokinins and light treatments on WT and PPPC2:HAT22 CRES-T
fruits may reveal potential
alterations of sensitivity or response in PPPC2:HAT22 CRES-T
fruits. Moreover, transcriptome
analysis of the WT and PPPC2:HAT22 CRES-T
fruits in standard conditions and exposed to light or
cytokinin treatments should highlight differentially expressed genes and reveal possible
misregulation of known regulators of chloroplast development (see above). Taken together, these
data will allow defining the regulatory network in which SlHAT22 takes part and further our
insight of the regulation of early fruit development.
Does SlHAT22 play a regulatory role during fruit ripening?
Ripening was poorly affected in PPPC2:HAT22 CRES-T
fruits. Indeed, no remarkable changes
were noted between these plants and the WT, considering ripening kinetic and associated color
changes. However, preliminary results suggested an increase in fruit firmness and total soluble
content in ripe fruit, which was confirmed by the increase in sugar levels in these fruits. Whether
these faint modifications are the result of specific regulation during tomato fruit ripening phase
remain to be proved. Alternatively, one can hypothesize that these changes are the consequences
of the modification of sugar metabolism during early tomato fruit development. Indeed, the
accumulation of starch during the early fruit development is impaired in these lines. Since this
starch is transiently accumulated in the growing fruit and is used for the production of sugars
during ripening (Schaffer and Petreikov, 1997), its reduction could lead to defects in sugar
metabolism in ripe fruit. A recent publication suggested that SlHAT22 may participate in ethylene
signaling or ethylene-dependent processes during fruit ripening (Osorio et al., 2011). The
weakness of the phenotypes observed in PPPC2:HAT22 CRES-T
ripe fruits could also result from the
weak activity of the SlPPC2 promoter, which drives the expression of the SlHAT22 CRES-T
construct, at the ripening stage (Fernandez et al., 2009; Guillet et al., 2012). Further experiments
are is necessary to provide answers to this question. The use of a ripening specific promoter like
the PG promoter or of SlHAT22 promoter to control SlHAT22CRES-T
expression could help solving
this question.
SlTGA2.1 is implicated in the regulation of the onset of tomato fruit ripening
On the contrary to PPPC2:HAT22 CRES-T
plants, major modifications of fruit ripening were
observed in PPPC2:TGA2.1CRES-T
fruits. Indeed, heterogeneous color changes were observed at the
surface of these fruits starting from the peduncle area. In addition, full fruit ripening was achieved
89
in between 12 to 15 days instead of seven days in WT plants. Taken together, these observations
suggest that fruit ripening hardly progress as within fruit tissues in PPPC2:TGA2.1CRES-T
plants.
This altered and slow pigment accumulation was associated with an increase in fruit firmness, a
decrease in sugar content and an increase in malate content at Bk+7 stage. Interestingly, the lower
starch content as well as the variation in malate could both affect sugar content in
PPPC2:TGA2.1CRES-T
fruits (Centeno et al., 2011). These global alterations of fruit characteristics
during ripening thus suggested a global alteration of the ripening process in PPPC2:TGA2.1CRES-T
fruits. As described with PPPC2:HAT22CRES-T
plants, we do not know if one of the particular
aspects of this phenotype is due to the use of SlPPC2 promoter to drive the expression of
TGA2.1CRES-T
construct. Indeed, the slow ripening of these fruits could be related to the
progressive decrease of activity of the promoter during fruit ripening (Fernandez et al., 2009;
Guillet et al., 2012). In the same way, the heterogeneous patterns of fruit ripening could be due to
a differential apical-basal (style/peduncle area) regulation of SlPPC2 promoter activity. Up to
now, no data can sustain this hypothesis, but it will be tested by the observation of PPPC2:nls-
GFP-GUS and PPPC2:GUS transgenic lines, which are available in the laboratory (Fernandez et al.,
2009). In addition, further experiments are necessary to answer this question by using other
promoters (PG or TGA2.1) or transient expression systems for the expression of TAG2.1CRES-T
. To
our knowledge such an apical-basal gradient in fruit ripening progression has not been described
in other transgenic line or mutants. However, such color differences between the apical and basal
part of the tomato are often found in black tomatoes cultivars, where the peduncle area is darker
than the style area, as well as in green tomatoes cultivars, where the peduncle area can be greener
that the style area (Powell et al., 2012). Intuitively, this heterogeneity in fruit pericarp could be
related to the different proximity of fruit cells from the nutritive and signaling contributions of the
rest of the plant. In addition, it is well known that carpel development in dry fruit it related to an
auxin apical-basal gradient (Staldal and Sundberg, 2009).
Despite this open question concerning the role of SlPPC2 promoter in PPPC2:TGA2.1CRES-T
fruits phenotype, it is clear that the presence of an altered TGA2.1 TF before the onset of fruit
ripening impairs this developmental process. This ripening phenotype is not easy to link with the
modification of immature green fruits. Indeed, in the lutescent mutant, which presents similar pale
immature green fruits due to malformation of thylakoid membranes, alteration of chloroplast
formation and disruption of chlorophyll accumulation (Chen et al., 2005), ripening is delayed
from one week compared to WT, but ripening length is normal (Barry et al., 2012). The more
extreme ghost mutant, which has white immature green fruits, presents yellow/orange fruits at
maturity due to defects in chloroplasts and chromoplasts biogenesis (Barr et al., 2004). According
to these phenotypical discrepancies, TGA2.1 is probably not implicated in the processes affected
in lutescent and ghost mutants.
90
To further our understanding of the SlTAG2.1 implication in tomato fruit ripening, it will be
necessary to compare the characteristics of PPPC2:TGA2.1CRES-T
ripe fruit at Bk+15 with WT fruit
at Bk+7 stage in terms of firmness, composition and color. In addition transcriptome analyses will
be necessary to access to the general transcriptional changes occurring before the onset and during
fruit ripening. This would possibly reveal the misregulation of genes implicated in the regulation
of specific ripening processes such as carotenoid biosynthesis or cell wall modification as well as
of genes involved in the regulation of the ripening process such as LeMADS-RIN, LeNAC-NOR
and LeSBP transcription factors (Giovannoni, 2007). In addition, it will be necessary to study a
possible relationship between SlTGA2.1 and ethylene signaling, which plays a crucial role in the
tomato fruit ripening process (Giovannoni, 2004). For this, the sensitivity and response to
ethylene of PPPC2:TGA2.1CRES-T
fruits will be investigated.
Molecular mechanisms underlying regulation of fruit development by SlHAT22 and
SlTGA2.1
In this work, phenotypes were obtained with the CRES-T SlTGA2.1 and SlHAT22 dominant
repressor construct, but not with the RNAi constructs. This result suggests that functional
redundancy could take place within these two transcription factors family. Such a functional
redundancy has been shown in Arabidopsis thaliana, where AtTGA5 and AtTGA6 can
compensate for the absence of AtTGA2 (Zhang et al., 2003). In Solanaceae (tobacco and tomato),
two proteins are close homologs of AtTGA2: TGA2.1 and TGA2.2. Both proteins share a highly
similar sequence which differs at their N-terminal part, TGA2.1 being characterized by the
presence of an N-terminal extension of approximately 130 amino acids (this work; Thurow et al.,
2005; Niggeweg et al., 2000b). Whether these two proteins can compensate for the absence of
each other is not established. In the same way, SlHAT22 is highly homologous to SlHAT9, and
functional redundancy has been shown in Arabidopsis HD-Zip II TF family (Ciarbelli et al., 2008).
Protein interaction is a crucial element in the regulation of gene expression by SlTGA2.1 and
SlHAT22 transcription factors. Indeed, they belong respectively to the bZip and HD-Zip
transcription factors that are multigenic superfamilies and share in common the leucine zipper
(Zip) motif, which is involved in homo- and hetero-dimerization of the transcription factors
(Schütze et al., 2008). Accordingly, data available for TGA2 indicate that interactions with other
transcription factors are an absolute prerequisite for its function. TGA2 transcription factor has
been well studied in Arabidopsis thaliana where it is implicated in signaling mechanisms
inducing the systemic acquired resistance (SAR) to resist to pathogen agents. In this context,
AtTGA2 interacts with its close homologues, AtTGA5 and AtTGA6 (Zhang et al., 2003) and the
AtTGA2/5/6 protein complex interacts with AtNPR1 (Johnson et al., 2008; Boyle et al., 2009). In
the absence of SA signaling, NPR1 accumulates as a complex of high molecular weight outside of
91
the nucleus and the AtTGA2/5/6 complex remains inactive. The signalization of SA induces a
redox change that results in the monomerization of NPR1 that is then imported in the nucleus to
bind and activate the AtTGA2/5/6 complex, thus inducing the response to SA (Pieterse and Van
Loon, 2004; Johnson et al., 2008). Still in the context of plant defense, TGA transcription factors
are also required to mediate the cross-talk between SA and ET/JA signaling pathways to favor the
most suitable defense mechanism. However, different protein interactions may also take place in
different developmental contexts since AtTGA2 is involved in the regulation of petal development
through interaction with AtROXY1 (Li et al., 2009). Data available on tobacco suggest that
NtTGA2.2 plays an equivalent role in tobacco as AtTGA2 in Arabidopsis and is implicated in
defense against pathogens (Niggeweg et al., 2000a; Niggeweg et al., 2000b; Kegler et al., 2004)
whereas NtGA2.1 has been shown to be implicated in determination of stamen identity (Thurow et
al., 2005) and not in defense against pathogens (Niggeweg et al., 2000b). AtHAT22 can also be
part of a protein complex and it has been shown that redox conditions may be involved in
intermolecular dimerization of HD-ZipII transcription factors (Tron et al., 2002). The results
suggest that redox conditions may operate to regulate the activity of these groups of plant TFs
within plant cells. Double-hybrid screening could help deciphering the regulatory mechanisms
controlled by SlTGA2.1 and SlHAT22, by the+ identification of the interacting proteins. In the
same way, single-hybrid or new technologies such as ChIP-on-chip and ChIP-seq may also allow
the identification of the target genes regulated by these transcription factors.
In addition to protein interactions, TGA transcription factors are submitted to different post-
translational regulations like phosphorylation/dephosphorylation (Schuetze et al., 2008) and to
specific degradation through proteolysis via the plant ubiquitin/26S proteasome pathway (Pontier
et al., 2002). These elements should also be taken into account for further characterization of the
molecular mechanisms related to tomato fruit regulation by SlTGA2.1. Up to now, such processes
have not been described concerning HAT22, but few data are available in the literature
concerning this transcription factor.
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Conclusion
During the past three years, I worked as PhD student in a fundamental research program
focused on the regulation of fleshy fruit early development with tomato as the model species. I
participated to two different tasks. On one hand, I pursued the functional characterization of
several F-Box proteins, and on the other hand I began the characterization of two genes encoding
transcription factors, SlHAT22 and SlTGA2.1.
I spent the first year of my PhD in the Gene Research Center in Japan. I was responsible for
the generation of Micro-Tom transgenic lines affected in the expression and function of SlHAT22
and SlTGA2.1. However, because of my inexperience as a researcher and inability to cope with
personal issues, I was unable to successfully carry out this task. Thus, the generation of the
necessary plant material was delayed by one year. This important drawback prevented me from
carrying the analysis of SlHAT22 and SlTGA2.1 as far as I would have wished. However, my
preliminary results revealed very interesting research tracks and will be pursued and published.
The study of the F-box encoding genes did not allow setting any conclusions about the
function of these genes in tomato fruit development. However, it allowed the isolation of an
insertional mutant showing alterations in seed shape as well as a severe alterations of locular
tissue development. The identification of the insertion site has not been completed yet, but this
insertional mutant will be particularly interesting to understand the interaction between seed and
locular tissue during the fruit early development. Considering the functional characterization of F-
Box proteins, the fusion with high-weight molecules to alter their function may be a valuable
strategy and could give further insights into the mechanisms regulating tomato fruit development.
Considering the functional characterization of SlHAT22 and SlTGA2.1 transcription factors,
the preliminary study of the CRES-T lines allowed confirmation of the results obtained by
Mounet et al in 2009 and validate the use of a correlative approach to isolate new regulatory
genes involved in a process of interest. Indeed, the phenotype observed on CRES-T fruits for
SlHAT22 and SlTGA2.1 indicates that these two genes are involved in the regulation of tomato
fruit development. The fruits carrying the CRES-T construct for SlTGA2.1 also revealed a
possible role of SlTGA2.1 in the onset of ripening.
The functional characterization of SlHAT22 and SlTGA2.1 is a promising project which will
hopefully deepen the understanding of tomato fruit development. In particular, the study of these
two genes may reveal new and exciting interactions with known processes regulating fruit
ripening, such as hormonal perception, signaling and transcriptional regulation of key genes
51
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PART III : Material and Methods
1) Biological material
a) Plant material and culture conditions
Tomato (Solanum lycopersicum) cultivar Micro-Tom was used as a control (wild-type, WT)
and to generate transgenic tomato plants. For in vitro culture, seeds were sterilized 10 min in
sodium hypochlorite (3.2% of active chloride), rinsed six times with sterile water and sown on
MS ½ medium (Annexe I) with kanamycin (150 mg/L) for selection of transgenic plants. Plants
were cultivated in vitro in a culture room for one month under controlled conditions: 14 hours of
light at 25°C and 10 hours of dark at 20°C.
Plants were then transferred in the greenhouse and repotted in 9 x 9 x 7 cm pots containing
Agrofino® substrate (Peltracom). After 2-3 months, T0 plants were transferred in 1 L pots in order
to promote fruit development. Watering was done two times per week by sub-irrigation of the
plants in a first nutritive solution until fruit set (pH=5.8; KNO 3.5 mM, K2SO4 1 mM, KH2PO4
2 mM, Ca(NO3)2 6 mM, MgSO4 2 mM), and with a second nutritive solution after fruit set (KNO3
4 mM, K2SO4 1.5 mM, KH2PO4 1.5 mM, Ca(NO3)2 4 mM, MgSO4 1.5 mM.
Fruits were tagged at breaker stage.On T0 plants, fruits were harvested at breaker + 7 stage
(Bk+7) and pericarp was frozen in liquid nitrogen. On further generations (T1, T2), immature
green (1-1.5 cm diameter), Bk, Bk+3 and Bk+7 fruits were harvested and pericarp was frozen in
liquid nitrogen. A minimum of three fruits was pooled to constitute a biological sample
representative of one plant (T0) or one line (T1, T2). Seeds were collected on Bk+7 fruits and
sterilized as described above. For several applications, fresh fruits were harvested at the same
stages and directly processed (Material and Methods, 4d).
b) Bacterial strains and culture conditions
DH5α™ F- E. coli strain (U169recA1, hsdR17, lacZα complementation, Invitrogen) was
used to amplify cloning vectors and recombining plasmids used during this work. GV3101
Agrobacterium tumefaciens strain was used for stable plant transformation. This strain contains a
non harmful Ti (“tumor inducing”) plasmid containing the Vir bacterial genes and has the
capacity to transfer DNA comprised between the LB and RB borders of the T-DNA into the host
genome DNA. We used this ability to transfer the sequences generated thanks to the gateway
system into tomato plants.
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All bacterial strains were cultivated in sterile liquid LB medium (Bactotryptone 1% (w/v);
yeast extract 0.5% (w/v); NaCl 1% (w/v)) under shaking at 150 rpm with or without antibiotic.
Solid medium was obtained by addition 15 g/L of bacterial agar to liquid LB medium before
autoclaving. All medium were autoclaved at 120°C during 20 min. Bacterial cultures were
performed at 37°C (E. coli) or 28°C (A. tumefaciens GV3101).
2) Nucleic acid analysis
a) Plant Genomic DNA extraction
Young leaves were collected, frozen in liquid nitrogen and grinded with a pillar and a mortar
in the presence of liquid nitrogen. DNA was extracted from grinded leaves (100 mg) with Plant
DNAzol® Reagent (Invitrogen) according to the instructions of the manufacturer. After extraction,
DNA was solubilized in 50 µL of TE buffer (Tris-HCl 10 mM pH=8, EDTA 1mM pH=8) and
stored at -20°C. Extracted DNA was quantified using a NanovueTM
spectrophotometer (GE
Healthcare Life Sciences). DNA quality was checked by gel electrophoresis.
b) Plasmid DNA extraction
Plasmid DNA was extracted using the QIAprep®
Spin Miniprep (Qiagen) according to the
protocol supplied by the manufacturer. Extracted DNA was quantified using a a NanovueTM
spectrophotometer (GE Healthcare Life Sciences). DNA quality was checked by gel
electrophoresis.
c) Gel electrophoresis
Gel electrophoresis was used to control the presence, the size and/or the quality of nucleic
acids. Nucleic acids were separated in non denaturing conditions in TAE buffer 0.5 X (TAE 50
X : Tris-Acetate 2 M, EDTA 50 mM, pH=8.0). Before electrophoresis, 10X loading buffer
(glycerol 30% (v/v), bromophenol blue 0.25% (w/v), xylene cyanol 0.25% (p/v)) was added to the
samples. The migration takes place on agarose gel (1 to 2 % (w/v)) in 0.5 X TAE buffer. Nucleic
acids were revealed with a Dark Reader®
(Clare Chemical Research) thanks to the incorporation
of Gel Green TM
(FluoProbes) present in the gel (1/50000 dilution). Promega gel wizard kit was
used to extract DNA fragments from agarose gel according to the protocol of the manufacturer.
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d) Polymerase Chain Reaction
The PCR technique, described by Saiki et al in 1985 allows the amplification of gene
sequences through several amplification cycles divided in 3 steps : template DNA thermal
denaturation (95°C for 20 seconds), primers hybridation on template DNA (Primers melting
temperature minus 2 degrees for 30 seconds) and complementary DNA synthesis from primers
(72°C during an elongation time depending on the length of the amplified DNA fragment).
Amplification of DNA was done in a Primus HT multiblocks (MWG)
thermocycler.Template DNA was first denaturated at 95°C for 2 min, then went through 30 to 35
amplification cycles described above, and the PCR product was finally extended at 72°C for
5 min.
Different quantitites of DNA were used as template in the PCR reaction according to the type
of the DNA and its extraction method: 0.1 µg of genomic DNA, 0.1 ng of plasmid DNA, 2 µL of
1/10 diluted retrotranscription product, or directly from a bacterial colony containing a plasmid of
interest. PCR amplification was performed in 25 µL or 50 µL composed of 1X Green GoTaq®
reaction buffer, 0.2 mM of each dNTP, 0.1 to 1 µM of each primer and 1.25 units of GoTaq®.
The high fidelity Phusion DNA Polymerase (New England Biolabs) was used to generate
accurate PCR fragments used in overexpression and CRES-T constructs. The PCR reaction was
performed in 50 µL total volume containing 1X Phusion HF Buffer, 0.2 mM of each dNTP,
0.5 µM of each primer and 1 unit of Phusion DNA polymerase.
e) Total RNA extraction
Plant material was grinded in the presence of liquid nitrogen. The plasticware was sterilized
by autoclaving (120°C during 20 min) or treated with chloroform and rinsed with ethanol. Diethyl
pyrocarbonate treated sterile water (DEPC, Sigma, 0.1% v/v) was used for solubilization and
dilution of RNA.
The RNA extraction was performed with TRIzol® reagent (Invitrogen) as described below.
One ml of TRIzol was added to frozen leaf powder (100 mg) or frozen fruit powder (120 mg).
Tubes were vortexed and centrifuged (12000g, 10 min). The supernatant was recovered and
incubated at 20°C for 5 min. Chloroform (200 µL) was added and the solution was vortexed for
1 min and incubated at 20°C for 5 min. After centrifugation (12000g, 15 min), 400 µL of the
upper phase were carefully taken and 400µL of isopropanol were added. After hand shaking, the
tube was incubated 10 min at 20°C and centrifuged 10 min at 12000g. The supernatant was
eliminated and the pellet was rinced with 75% ethanol/DEPC-treated water solution. After
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vortexing and another centrifugation (12000g for 10 min), ethanol was eliminated, the pellet was
dried and RNA was resuspended in 50 µL of DEPC-treated water. All centrifugation steps were
performed at 4°C. After extraction, RNA was quantified with a NanovueTM
spectrophotometer
(GE Healthcare Life Sciences) and RNA quality was checked by gel electrophoresis.
Total RNA (10 µg) was treated with Turbo DNA free DNAse (Ambion, Life Technologies)
following the protocol of the manufacturer. RNA quantity and quality was checked after this
treatement as described above. Residual contamination with genomic DNA was tested by PCR
using actin, β-tubulin or EiF4a constitutive gene primers (Annexe II).
f) Reverse transcription
DNA-free RNA (500 ng) was used as template for reverse transcription using iScript reverse
transcriptase (Invitrogen) in a 20 µL final volume according to the intructions of the manufacturer.
RT reaction was diluted (1/10) and used in a PCR using actin, β-tubulin or EiF4a constitutive
gene primers (Annexe II) as a control for DNA amplification before real time PCR was
performed.
g) Real Time PCR
Real time PCR was used to quantify the expression of candidate genes in transformed plants
and WT relatively to the expression of control genes that are expressed constituvely (such as
EiF4a, actin, or β-tubulin). Primers used for real time PCR are presented in Annexe II.
Real time PCR was performed with Promega Go Taq®
qPCR Master Mix according to the
instructions of the manufacturer. This real-time PCR mix contains a proprietary dye presenting
spectral properties similar to those of SYBR® Green I, but provides brighter dsDNA-dependent
fluorescence and less PCR inhibition than SYBR® Green I.
The amplification of the cDNA was performed in a BioRad CFX 96 thermocycler on 2 µL of
diluted RT (1/10 dilution) in a final volume of 20 µL containing 0.2M of each primers and 1X
Promega Go Taq®
qPCR Master Mix. After an initial activation of the GoTaq®
Hot Start
polymerase (3 min at 95°C), 40 cycles of a two step PCR program were performed: denaturation
at 94°C for 15 s and annealing/amplification at 60°C for 25 s. The dsDNA-dependent
fluorescence was recorded at the end of each cycle. Finally a fusion curve was systematically
performed after the 40 cycles. For this, the temperature was increased from 65°C to 95°C by
0.5°C increments every 5 s. The fluorescence was recorded at each step.
The efficiency of the PCR was measured for each primer pair in a standard curve performed
with dilution series of a PCR fragment purified on a S-300HR column (MicroSpinTM GE-
Healthcare). A PCR efficiency of 100% is ideal. It corresponds to a doubling of copy number at
each PCR cycle. Furthermore, the realization of the melting curves allowed to check the synthesis
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of a single PCR product. It allows the determination the dissociation temperature of the double-
stranded products and could reveal the presence of several PCR products as well as of primer
dimers.
Data acquisition and analysis were performed using the CFX manager software (version
2.0.885.0923, Biorad). EiF4a, actin and β-tubulin were used as reference genes to calculate the
relative expression of the genes of interest. A Student test was realised by using mean values and
standard deviation values from replicates.
3) Construction of transformation vectors and generation of
stable transgenic plants
a) Gateway® cloning
The Gateway®
system was used in this work to generate all vectors used for plant stable
transformation. This system takes advantage of the lambda bacteriophage site specific
recombination ability. It allows the integration of DNA from lambda bacteriophage to E.coli
chromosomic DNA and induces the switch between the bacteriophage lytic and lysogenic
pathways (Guarante et al., 1992). This integration of DNA occurs through recombination between
phagic and bacterial DNA at specific attachment (att) sites.
The Gateway®
system consists in two steps of recombination reactions (Figure IV.1). First,
the BP reaction allows the integration of linear DNA flanked by attB sites in an attP substrate
(donor vector) to form an attL product (entry clone). The second step is the LR reaction. During
the LR reaction the entry clone recombines with the attR destination vector to form an attB
containing expression clone.
Figure IV.1 BP and LR reactions in Gateway® system
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b) Generation of RNAi, and over-expression Gateway compatible PCR
products
For the RNAi contructs, a small sequence (100-150 pb) located in the 3’ UTR of the gene of
interest was amplified on a mix of RT from different tomato samples (leaves, fruits, flowers) with
Phusion DNA polymerase thanks to a specific 3’ primer containing an attB1 extension and a
specific 5’ primer containing an attB2 extension (ANNEXE III, Figure IV.2).
Figure IV.2 Location of primers used for Gateway constructs
For the over-expression (OE) constructs, the coding sequence from the ATG codon to the
stop codon was amplified on a mix of RT from different tomato samples (leaves, fruits, flowers)
with Phusion DNA polymerase thanks to specific primers containing an attB1 (5’ primer
containing the ATG codon) or an attB2 (3’ primer containing the stop codon) extension
(ANNEXE III, Figure IV.2).
For SlTGA2.1, the ATG codon considered as the initiation codon was based on the initiation
codon of Arabidopsis AtTGA2 gene. But, according to the data available in tobacco (Thurow et al.,
2005), two homologues of AtTGA2 exist in Solanaceae species: TGA2.1 a larger transcription
factor (456 amino acids), and TGA2.2 a shorter transcription factor (325 amino acids) of the same
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size than AtTGA2 (Niggeweg et al., 2000b; Thurow et al., 2005). Thus, in the present work, the
OE construct of SlTGA2.1 probably corresponds to a truncated or alternative protein.
c) Generation of CRES-T Gateway compatible products
To generate CRES-T constructs, the full coding sequences of SlHAT22 and SlTGA2.1 were
fused with the SRDX repression domain (Figure IV.3). Two successive steps of amplification
were required.
In the first PCR the full length coding sequences of SlHAT22 and SlTGA2.1 were amplified
from LEFL clones LEFL1017AH03 and LEFL2025G23 respectively (Figure IV.3A). The 5’
primer included the attB1 site and a gene specific sequence starting at the ATG
(HAT22CREST_attB1 and NPR1iCREST_attB1, ANNEXE II). The 3’ primer corresponded to
the last codons of the gene, excluding the stop codon, followed by the 5’ end of the SRDX
sequence (HAT22 MixR, NPR1 MixR, ANNEXE II).
The first PCR product was purified and used as a matrix for a second amplification step
(Figure IV.3B). This second PCR was performed in the presence of the full SRDX fragment
(GGGCTCGATCTGGATCTAGAACTCCGTTTGGGTTTCGCTTAA), the attB1-gene specific
5’ primer (HAT22CREST_attB1 and NPR1iCREST_attB1, ANNEXE II) and a 3’ primer
containing the last codons of the SRDX domain followed by the attB2 site (SRDX_attb2r,
ANNEXE II).
Figure IV.3. CRES-T two-step construction.
A. PCR amplification of the target gene coding sequence with attB1 site and with a partial SRDX sequence. B.
Generation by PCR of the full length target gene sequence fused to attB1 in 5’ and to SRDX-attB2 sequences at the 3’
end for use with Gateway system.
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d) Insertion into Donor vector
The BP reaction was carried out
between the donor vector pDONR201
(Figure IV.4) and attB1-attB2
containing gene sequences generated
in the previous step. According to the
manufacturer protocol the reaction
was done at 25°C for 3 hours in a
final volume of 10 µL containing 150
ng of PCR product, 2 µL of BP
reaction buffer, 150 ng of
pDONR201 and 2 µL of BP clonase
enzyme. Four µL of reaction volume
were then used to transform DH5αTM
F- E-coli bacteria by heat shock (see
3f).
e) LR reaction
The LR reaction allowed the transfer of the attB1-gene of interest-attB2 from the
pDONR201 to the destination vector convenient for tomato stable transformation. In order to
avoid potential pleiotropic effects, we decided to use plant expression vectors specific of the early
fruit development stage. The PEP carboxylase promoter (PPC2) is specifically active from the
beginning of the cell expansion phase of the fruit development to the end of the mature green
stage (Guillet et al., 2012; Fernandez et al., 2009). Different Gateway vectors with PPC2
promoter were available for plant expression at the beginning of this work (Fernandez et al.,
2009; Nafati et al., 2011). The pK8GWUWG-PPC2-B4 destination vector was used for RNAi
constructs, whereas the pK2GW7-PPC2 destination vector constructed by Nafati et a was used for
OE and CRES-T constructs (Figure IV.5).
LR reaction was performed overnight at room temperature (25°C) in a final volume of 5 µL
containing 75 ng of entry clone (resulting from previous BP reaction), 75 ng of destination and
1 µL of LR clonase II.
Figure IV.4 pDONR201 vector used in BP reaction. The attB sites
present in DNA of interest recombine with the attP sites located in
the pDONR 201.
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Figure IV.5. Destination vectors used in LR reaction. A, Destination vector used for RNAi constructs. B, Destination
vector used for OE and CRES-T constructs in which P35S was replaced by SlPPC2 promoter
f) Bacteria transformation
DH5αTM
F- E coli (U169recA1, hsdR17, lacZα complementation, Invitrogen) was used to
amplify plasmid DNA of interest. Transformation was done after BP or LR reactions by mixing
4 µL of the recombination product with (400 µL) of thermocompetent bacteria. After 30 min on
ice, the heat shock was performed at 42°C for 45 seconds followed by 2 min on ice. After addition
to 1 mL liquid LB medium, bacteria were shaked for 1 hour at 37 °C and plated on LB-agar plates
containing the appropriate antibiotic.
The plates were incubated ON at 37°C, and the presence of the recombined plasmid DNA
was checked by PCR on the resulting bacterial colonies. Colonies showing positive result were
cultivated in five mL of liquid LB medium containing the appropriate antibiotic. Plasmid DNA
was extracted with QIAprep Spin Miniprep kit (Qiagen) according to the instructions of the
manufacturer. After a control by PCR, the insert was controlled by sequencing.
After the LR reaction, the expression clone was used to transform Agrobacterium
tumefaciens. Forty µL of electrocompetent GV3101 strain were placed on ice and transformed
with 200 ng of the expression plasmid by electroporation at 1800 volts using a MicroPulser™
(Bio-Rad). Bacteria were shaked for one hour at 28°C in 1 mL of LB medium and plated on LB-
agar plates containing the appropriate antibiotic. After 48 h at 28°C, transformed colonies were
checked by PCR. One positive colony was selected and cultivated overnight at 28°C in liquid LB
(10 mL) containing the appropriate antibiotics.
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g) Plant transformation
Plant transformation was performed with a modified version of the protocol described by
Hamza and Chupeau (1993). Briefly, Micro-Tom cotyledons were cut into explants and incubated
in the dark for 2 days in pre-culture medium (ANNEXE I). Then, cotyledons were infected with
transformed Agrobacterium tumefaciens harvested during the exponential growth phase and
resuspended in pre-culture medium. Excess of bacterial solution was eliminated by absorption
with sterile paper.
Then the plant material was co-cultivated with Agrobacterium tumefaciens for two days at
25°C in the dark. Explants were transferred on shoot inducing medium (ANNEXE I). When
plantlets started to regenerate, they were cut from cals and transferred on root inducing medium
(ANNEXE I). Plantlets showing antibiotic resistance and root development were then transferred
to the greenhouse and called T0 plants.
4) Characterization of transgenic plants
a) Ploidy control
The transformation protocol which includes regeneration of plantlets from callus in vitro may
induce the regeneration of polyploid plants. Such plants may present phenotypes related to their
ploidy status rather that to the expression of the inserted transgene. In order to eliminate such
polyploid plants, the ploidy level of all T0 plants was controlled by flow cytometry on cell
nucleus isolated from young leaves.
Young leaves are placed in a petri dish and lacerated in one mL of “Cystain UV ploidy”
buffer (Partec) containing DAPI. Nucleus ploidy was measured with a Partec Pas-II flow
cytometer (Münster, Germany). The computer system (DPAC, Partec) coming with the cytometer
transforms the light signal into histograms representing the number of nuclei for each ploidy
category (2C, 4C, 8C, 16C, ...). The numbers of nuclei analyzed varied from 10,000 to 20,000
according to the availability of nuclei. Only diploid (2C) plants were conserved for further
analyses.
b) Control of transgene presence
The transformation of the T0 plant was controlled by PCR. For this, leaves were collected on
each T0 plant, frozen in liquid nitrogen and grinded in a mortar cooled down with liquid nitrogen
at -80°C. DNA was extracted (see Material and Methods section 2a) and the presence of the
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construct was checked by PCR with attB1and attB2 primers (ANNEXE III). Only plants showing
this amplification of the T-DNA were conserved for expression analysis and phenotyping.
c) Segregation control
The kanamycin resistance marker was used to study the transgene segregation and to
determine the number of copies present in the transformed plants. For this, 100 T1 seeds collected
in the fruits of the primo-transformed plants (T0) were sterilized and cultivated on MS1/2 medium
containing kanamycin (150 µg/ml). After one month, the sensitivity/resistance of the seedlings to
kanamycin was observed. A ratio of ¼ sensitive plants and ¾ resistant plants indicates a single
loci of insertion, whereas a ratio of 1/16 sensitive plants and 15/16 resistant plants indicates two
independent loci of insertion. Plants chosen for further analyses carried one insertion, except for
one RNAi line and one SRDX line that carried two insertions and were kept for analyses due to
the small number of successfully transformed lines.
The same test was performed on the third-generation (T2) seeds to determine which T1
plants are homozygous for the loci carrying the T-DNA. In that case the percentage of resistant
seedlings to kanamycin is one hundred percent.
d) Phenotypic characterization
The kinetic of fruit ripening in the different transgenic lines and in the WT was analyzed on
fruits detached from the plants. Fruits (six per genotype) were harvested at Bk stages and left on
the bench in the greenhouse. Pictures were taken daily.
Differences in fruit firmness between the WT and the transgenic lines Bk+7 fruits (10 fruits
per genotype) were assessed by penetrometry using a Fruit Texture Analyser (GüSS, South
Africa) equipped with 5 mm diameter probe. The parameters were as follows: trigger threshold:
0.05 kg, forward speed: 10 mm/s, reverse speed: 20 mm/s, measure speed: 5 mm/s, measure
distance: 3.5 mm, reverse increment: 20 mm.
e) Biochemical characterization
An estimation of soluble solid content in the fruits was assessed by the measurement of the
refraction indice using a refractometer. For this, the pericarp of Bk+7 tomato fruits freshy
harvested were pressed in a garlic press. The juice was recovered and centrifuged for 5 min at
16000g. The refractometry measurement was performed on this clear juice (10 fruits per
genotype).
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Localisation of starch in WT and the transgenic lines fruits was performed using Lugol
solution (I2 0.5 %, KI 1.5 %). Fruit equatorial sections (0.5 cm) were immersed in Lugol for 30s
and rinsed in water before observation using an Olympus SZX16 binocular equipped with a
Moticam 2300 Camera.
Rapid quantification of tomato fruit main pigments was performed as follows according to
(Kitagawa et al., 2005). Fruit powder stored at -80°C (300 mg) was mixed by vortex in 3 mL
Acetone-Hexane solution (4:6, v/v) in a glass vial. After sedimentation of the sample, the
supernatant was withdrawn and the absorbances at 663 nm (A663), 645 nm (A645), 505 nm
(A505) and 453 nm (A453) were measured using spectrophotometer. The contents of the main
pigments were calculated from the following equations.
Chlorophyll A=0.999*A663 -0.0989*A645
Chlorophyll B =-0.328*A663 +1,77*A645
Lycopen =-0.0458*A663 +0.204*A645 +0.372*A505 -0.0806*A453
β-caroten =0.216*A663 -1.22*A645 -0.304*A505 +0.452*A453
A first analysis of metabolite compostion was performed on Bk+7 fruits in the WT and in the
different transgenic T0 plants on the high-throughput system for robotized metabolic phenotyping
from the metabolome plateforme of the CGFB (Gibon et al., 2009). Twenty mg of frozen powder
was used to quantify sugars (glucose, fructosse, saccharose, starch), organic acids (citrate, malate)
as well as chlorophylls A and B, proteins and total free amino acids.
f) Determination of T-DNA insertion site by inverse PCR
Inverse PCR was used to determine the insertion site of the T-DNA in P35S :FB2RNAi
-2 line
(Figure IV.6). This technique allows the amplification and cloning of unknown DNA that flanks
one end of a known DNA sequence and for which no primers are available. According to the
sequence of the T-DNA, several compatible end restriction enzymes (BglII, BamHI, HindIII, and
SpeI) were used to analyse the genomic sequence near to the Left Border (LB) of the T-DNA and
different primers were selected for the amplification (Figure IV.6, Table IV.1).
The digestion of genomic DNA (3 µg) extracted from leaves of P35S :FB2RNAi
-2.2 T3 plants
was carried overnight at 37°C with 10 to 20 units of enzyme in a total reaction volume of 50 µL.
Enzyme was inactivated by a thermal treatment at 65°C for 20 min. Digested DNA (500 ng) was
then precipitated and self-ligated by 3 µl of T4 ligase (3 u/µL) at 15°C overnight in a final volume
of 100 µL. A first PCR was performed on the circular DNA (7 µl of the ligated DNA) with
Phusion Taq polymerase in 50 µL of reaction volume (see Table IV.1 for the choice of primers).
A nested PCR was performed in order to limit the occurrence of false positive amplifications on
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the first PCR product (3µL of the PCR) with Phusion Taq polymerase in 50 µL of reaction
volume (see Table IV.1 for the choice of primers).
Figure IV.6 Identification of the T-DNA insertion site by inverse PCR. A. Principle and steps of inverse PCR. After
restriction of the genomic DNA containing the T-DNA (1), restriction fragments are circularized by ligation and
primers located on the T-DNA are used to amplify genomic DNA located nearby the T-DNA. B. Map of the T-DNA
including the restriction sites detected by NEB cutter V2.0 (http://tools.neb.com/NEBcutter2/) and primers located on
the T-DNA used in the PCR reaction. Sequences of the primers is available in ANNEXE IV.
The PCR product was purified with Wizard SV gel and PCR Clean-Up system (Promega)
and 500 ng was poly-adenylated 20 min at 70°C with Taq DNA polymerase in the presence of
200 µM dATP. The poly-adenylated product was purified on S400-HR column (GE Healthcare
Life Sciences) and the fragment was cloned in pGEM-T with T4 DNA ligase (3 u, Promega). The
resulting DNA was used to transform E. coli DH5αF-. Bacteria were grown on LB-agar plates
including ampicillin, IPTG and X-Gal for white/blue selection of the recombinant colonies. White
colonies were screened by PCR, using M13 forward and reverse primers. Plasmid DNA was
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extracted from positive DH5α- colonies with the Bio Basic EZ-10 Spin column plasmid DNA
minipreps kit (Bio Basic Inc). The extracted DNA was sequenced with M13 primers.
Table IV.1 Primer pairs used to amplify the T-DNA insertion site after restriction and ligation
The sequence of the primers is indicated in ANNEXE IV
Restriction Enzyme
Primers Pair For PCR1
Primers Pair For PCR2
BamHI Kan272S + Kan272S_RC2 LB + Kan272S
BglII 13935R + Kan272S_RC2 LB + RB
HindIII 13935FBis + Kan272S_RC2 LB + 13935Fbis
SpeI Kan272S + Kan272S_RC2 LB + Kan272S
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ANNEXE I
Medium composition for plant culture
Medium Composition
MS MS (Including vitamins, Duchefa , MO222) 4.4 g/L
Sucrose 30 g/L
Phytoagar 8 g/L
pH=5.8
MS 1/2 MS (Including vitamins, Duchefa , MO222) 2.2 g/L
Sucrose 15 g/L
Phytoagar 5 g/L
pH=5.8
Pre-culture medium MS medium plus :
KH2PO4 200 mg/L
Thiamine 0.9 mg/L
Acetophenone 200 μM
2,4 D 200 μg/L
Kinetin 100 μg/L
Shoot inducing medium MS medium plus :
Zeatine Riboside (Duchefa) 2 mg/L
Nitsch vitamins 1X
Timentin (Duchefa) 250 mg/L then 150 mg/L
Kanamycin (Duchefa) 100 mg/L
Root inducing medium MS (Basal salt, Duchefa MO221) 2.2 g/L
Sucrose 10 g/L
Phytoagar 5 g/L
Nitsch vitamins 1X
Timentin (Duchefa) 75 mg/L then 150 mg/L
Kanamycin (Duchefa) 150 mg/L
Nitsch vitamins 1X Biotin 0.05 mg/L
Folic acid 0.5 mg/L
Glycine 2 mg/L
Myo-inositol 100 mg/L
Nicotinic acid 5 mg/L
Pyrixodin-HCl 0.5 mg/L
Thiamin-HCl 0.5 mg/L
110
ANNEXE II
Primer used in expression analysis
Amplicon Name Sequence
SlHAT22 COR40_QS1 TTTTTGGTTAAAAGGCTTGTATGA
COR40_QAS1 TGGACCATAATAAAGAAACAAATTAAA
HAT22 QS2 GGCTTCACAAGGAATTGCAG
HAT22 QAS2 TCCTTTCGCATGAAGGACAC
SlTGA2.1 U316694_QS1 TGACATGCATTGCGATATTC
U316694_QAS1 TTCTGCACTTCCACAAGCAC
TGA2 QS2 ATCAGTTGGAGCCTCTGACC
TGA2 QAS2 ATGTCTCTGCCAAGGATTGC
TGA2 QS3 TCACTGCACATTACGATGAGG
TGA2 QAS3 GGGTTTTCCACATCCCTGAC
SlFB2 U315070_QS2 AGCATGTGGTGTGGATCTTG
U315070_QAS2 TCTACGAAAAAGAACCCATGC
SlFB11 U323558_QS1 AAGGTTAGGTGGGCTGTCG
U323558_QAS1 TGGACCATAGCAAATCATCG
SlFB24 U323712_QS1 TTCTTCAACTCTTCCTGGATCTG
U323712_QAS1 GGGAAGTGTTTCTCTGAAATCTG
NptII NPT2-q250 AGGAAGCGGTCAGCCCAT
NPT2-q309 GCGTTGGCTACCCGTGATAT
β-tubulin bTubQPCR_S1 AACCTCTCGTGGATCACAGC
bTubQPCR_AS1 GGCAGAAGCTGTCAGGTAACG
Eif4A Eif4AQPCR_S1 AGTGGACGATTTGGAAGGAAG
Eif4A Eif4AQPCR_AS1 GCTCCTCGATTACGACGTTG
Actin ActinQPCR_S2 GGACTCTGGTGATGGTGTTAG
Actin ActinQPCR_AS2 CCGTTCAGCAGTAGTGGTG
111
ANNEXE III
Primers used for DNA constructs with GATEWAY System
Amplicon Name Use Sequence
SlHAT22 HAT22RNAi_attB1 RNAi GGGGACAAGTTTGTACAAAAAAGCAGGCTTGATAAATG
CCATATATAAAATCATG
HAT22RNAi_attB2 RNAi GGGGACCACTTTGTACAAGAAAGCTGGGTGATTAGTGAC
CTTAATTCTAATTACTC
HAT22OE_attB1 OE GGGGACAAGTTTGTACAAAAAAGCAGGCTAGATGGGTT
TTGATGATATTTGC
HAT22OE_attB2 OE GGGGACCACTTTGTACAAGAAAGCTGGGTTCATTAACAA
GCTGCCGAC
HAT22CREST_attB1 CRES-T GGGGACAAGTTTGTACAAAAAAGCAGGCTAGATGGGTTTTGATGATA
TTTGC
HAT22 MixR CRES-T CAACAATCCGTCGGCAGCTTGTGGGCTCGATCTGGATC
SlTGA2.1 NPR1iRNAi_attB1 RNAi GGGGACAAGTTTGTACAAAAAAGCAGGCTTGACATGCA
TTGCGATATTC
NPR1iRNAi_attB2 RNAi GGGGACCACTTTGTACAAGAAAGCTGGGTTTCTGCACTT
CCACAAGCAC
NPR1iOE_attB1 OE GGGGACAAGTTTGTACAAAAAAGCAGGCTACATGGCAG
ATTCTGGTTCTC
NPR1iOE_attB2 OE GGGGACCACTTTGTACAAGAAAGCTGGGTGCATGTCATA
CTTATTGCTCTCG
NPR1iCREST_attB1 CRES-T GGGGACAAGTTTGTACAAAAAAGCAGGCTACATGGCAG
ATTCTGGTTCTC
NPR1 MixR CRES-T GATCCAGATCGAGCCCTTGCTCTCGTGGTCTGGCAAG
SRDX SRDX_attb2r CRES-T GGGGACCACTTTGTACAAGAAAGCTGGGTTTAAGCGAA
ACCCAAACGGAG
attL attL1 PCR TCGCGTTAACGCTAGCATGGATCTC
attL attL2 PCR GTAACATCAGAGATTTTGAGACAC
attB attB1 PCR GGGGACAAGTTTGTACAAAAAAGCAGGCT
attB attB2 PCR GGGGACCACTTTGTACAAGAAAGCTGGGT
112
ANNEXE IV
Primers used for the determination of the T-DNA insertion site in P35S :FB2RNAi
-2 plants
Name Sequence
LB GGCAGCAACGCTCTGTCATCGTTA
RB TTTGACAGGATATATTGGCGGG
Kan 272S TGCGCGCTATATTTTGTTTTC
Kan 272S_RC2 CTAGGATAAATTATCGCGCGC
13935FBis GCTCAACACATGAGCGAAAC
13935 R CGCACAATCCCACTATCCTT
108
113
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ABREGE
La tomate (Solanum lycopersicum) est l’espèce modèle pour l’étude des fruits charnus. Le
fruit de tomate est un organe complexe, issu du développement de l’ovaire après autofécondation
de la fleur. Il est composé d’au moins deux carpelles résultant de la fusion de deux parois
carpellaires adjacentes. Le péricarpe, issu du développement de la paroi carpellaire, entoure la
cavité loculaire qui contient les graines attachées à l’axe parenchymateux central ou columelle.
Après la fécondation des ovules, le tissu placentaire, situé à la périphérie de la columelle, se
développe autour des graines pour former le tissu loculaire ou gel.
Le développement du fruit est divisé en deux grandes périodes: le développement précoce,
pendant lequel le fruit va atteindre un nombre de cellules et une taille quasi définitives et le
mûrissement, pendant lequel le fruit va acquérir ses caractéristiques de qualité finale (couleur,
texture, arôme, acidité, ….). Le développement précoce du fruit est divisé en deux phases
successives. La première phase débute avec la fécondation. Elle est caractérisée par une très forte
activité de division cellulaire dans tous les tissus du fruit et se déroule jusqu'à 8 à 10 jours après
anthèse (JAA). La croissance de la tomate au cours de la seconde phase est essentiellement due à
l’expansion cellulaire des cellules du péricarpe et du gel. Cette phase d’expansion cellulaire se
déroule jusqu'au stade vert mature. Le mûrissement va alors débuter, correspondant à des
modifications métaboliques de la composition des cellules de la tomate et à des modifications
profondes de la structure des parois cellulaires.
La qualité sensorielle du fruit dépend de nombreux facteurs, comprenant la couleur, la
texture, les arômes et la composition en métabolites primaires (sucres, acides organiques et acides
aminés). Les caractéristiques nutritionnelles et sensorielles des fruits sont élaborées lors des
phases successives du développement du fruit. Il a été montré que les régulations hormonales
jouent un rôle crucial dans la régulation des différentes phases du développement du fruit. Elles
impliquent notamment l’auxine et les gibbérellines, lors de l’initiation du développement, et
l’éthylène, lors du mûrissement du fruit. Afin d’identifier des gènes liés à la différenciation des
tissus au cours du développement précoce du fruit, une stratégie sans a priori a été développée
dans l’équipe « Génomique Fonctionnelle du Développement du Fruit » de l’UMR Biologie du
Fruit et Pathologie. Une étude de transcriptomique a permis de montrer que le développement
précoce du fruit de tomate n'est pas dirigé par un ensemble de gènes spécifiques des fruits, mais
plutôt par l'expression coordonnée de gènes s'exprimant également dans d'autres organes. Ce
travail a également permis de mettre en évidence plusieurs gènes candidats éventuellement
impliqués dans la différenciation des tissus et la croissance du fruit et a également donné un
aperçu des processus moléculaires mis en jeu lors de l'acquisition du caractère charnu du fruit de
tomate. L'étape suivante a été l'acquisition et l'intégration de données de transcriptomique et de
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métabolomique de deux tissus du fruit (le mésocarpe et le tissu loculaire) au cours de la phase
d'expansion cellulaire. L'analyse des données générées a montré des corrélations entre des
concentrations en métabolites et des niveaux de transcrits. Les données de corrélation ont été
synthétisées dans un réseau de corrélation entre les gènes et les métabolites. Plusieurs gènes sont
corrélés à un grand nombre d’autres gènes, et semblent donc être des carrefours de ce réseau de
régulation. Parmi ces gènes deux facteurs de transcription, SlHAT22 et SlTGA2.1, sont des
régulateurs potentiels du développement précoce du fruit et de la transition entre la fin de
l'expansion cellulaire et le début de la maturation.
Afin de mieux comprendre la régulation du développement du fruit de tomate, l'objectif de
cette thèse est de contribuer à la validation fonctionnelle de gènes candidats mis en évidence dans
les travaux menés précédemment au laboratoire: trois protéines à F-Box (SlFB2, SlFB11 et
SlFB24) et deux facteurs de transcription (SlHAT22 et SlTGA2.1).
Les protéines à F-Box jouent un rôle particulièrement important dans les processus de
régulation chez les plantes grâce à leur fonction de reconnaissance de protéines régulatrices cibles
par les complexes SCF (SKP1-Cullin-F-Box), qui sont alors marquées par ubiquitinylation, pour
leur dégradation par le protéasome 26S. Pour cette analyse fonctionnelle, trois protéines à F-Box
avaient été retenues en raison de leur expression au cours du développement précoce du fruit,
caractérisée par une expression préférentielle dans le mésocarpe (SlFB2 et SlFB11) ou dans le
tissu loculaire (SlFB24). Au cours de cette thèse, la caractérisation de lignées RNAi
précédemment générés pour les gènes SlFB2, SlFB11 et SlFB24 a été poursuivie.
Ce travail n’a pas permis pas de conclure quant au rôle de ces protéines à F-Box dans le
développement du fruit de tomate. Cependant, une lignée P35S :FB2RNAi
présentait un phénotype
très intéressant, spécifique du fruit : une absence totale de développement du tissu loculaire,
entrainant une modification de la forme des graines, associé à une augmentation de la fermeté du
fruit. Au cours de ce travail de thèse, il a été montré que ce phénotype était lié au site d’insertion
de l’ADN de transfert (ADN-T) dans le génome de la tomate. Afin de faciliter l’identification du
site d’insertion de l’ADN-T, une population en ségrégation de 110 plantes a été générée. Elle a
permis de démontrer que le phénotype est dû à une insertion unique qui agit comme un caractère
récessif, puisque seulement ¼ des plantes a présenté le phénotype. Au cours de ce travail, la PCR
inverse a été utilisée pour cloner des séquences bordantes de l’ADN-T et cinq séquences
différentes ont été identifiées. Le génotypage de deux de ces insertions dans la population e
ségrégation a montré qu’elles n'étaient pas responsables du phénotype. Deux autres insertions
présentant une homologie avec le génome de la tomate doivent encore être génotypées dans la
population. Si ces insertions ne sont pas responsables du phénotype, d’autres insertions devront
être recherchées par la même approche, ou par des approches plus exhaustives comme le Whole
Genome Sequencing (WGS). La poursuite de la caractérisation de ce mutant repose sur
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l'identification de la mutation, mais il s’agit-là d’un mutant très original, une telle disparition
complète du tissu loculaire n'ayant pas encore été décrite chez la tomate. D’après les données
disponibles, il est assez difficile d’émettre une hypothèse sur la fonction du (des) gène (s)
responsable (s) de ce phénotype. Une fois que le gène muté sera identifié, de nouvelles lignées
transgéniques devront être générées pour reproduire le phénotype. Par ailleurs, si la mutation
correspond à l'inactivation ou la répression d'une protéine, les mutants seront recherchés par
TILLING dans la collection de mutants EMS disponible au laboratoire. Les lignées transgéniques
générées et / ou les mutants sélectionnés seront caractérisés pour le développement du tissu
loculaire ainsi que la fermeté des fruits. D'autres caractéristiques de structure du fruit seront
également étudiées car la caractérisation préliminaire des plantes de la lignée P35S :FB2RNAi
-2
suggère également une augmentation de l'épaisseur du péricarpe, ainsi que de la taille des cellules.
Enfin, les travaux se concentreront sur l’étude des mécanismes moléculaires sous-jacent à ces
changements phénotypiques.
Le gène candidat SlFB11 code pour l’orthologue chez la tomate de la protéine à F-Box
SKIP16 (SKP1/ASK-Interacting Protein16; At1g06110) identifiée chez Arabidopsis thaliana. Les
seules données disponibles dans la littérature concernent l'interaction de AtSKIP16 avec la
protéine ASK2 dans un complexe SCF. Les protéines ubiquitinylées via l’action de SKIP16 ne
sont pas connues à ce jour. La caractérisation des lignées transgéniques RNAi générées pour
SlFB11(P35S :FB11RNAi
) a permis de mettre en évidence une altération de la croissance végétative
chez ces transformants. En effet, les plantules de différentes lignées indépendantes ont montré un
arrêt du méristème apical après leur transfert en serre. Après quelques jours, un ou plusieurs axes
secondaires émergent généralement des hypocotyles et remplacent l'axe principal entraînant le
développement de plantes à port buissonnant. Les tiges secondaires des plantules donnent lieu à
des inflorescences fertiles qui présentent des fruits avec une augmentation du nombre de loges.
L’ensemble de ces données suggère que la protéine à F-Box SlFB11 est impliquée dans le
maintien du méristème apical (SAM) chez la tomate. L’intégrité du SAM dépend de régulations
très complexes impliquant différents gènes comme CLAVATA3 et WUSCHEL, des régulations
hormonales, métaboliques, ainsi que des modifications épigénétiques. Le rôle de SlFB11 dans ce
processus reste à étudier. Pour cela, des analyses cytologiques des méristèmes (SAM et floral),
l'expression des gènes impliqués dans leur maintien, ainsi que l'hybridation in situ de ces gènes
sera effectuée. L'identification des cibles de SlFB11 sera également nécessaire, afin de relier la
dégradation par le protéasome aux processus de régulations connus des méristèmes végétatifs et
floraux.
Le troisième gène codant pour une protéine à F-Box étudié dans ce travail de thèse, SlFB24,
code pour l'homologue le plus proche de l’auxine F-Box AFB5 d’Arabidopsis thaliana. Un
problème majeur a été rencontré lors de la sélection de lignées homozygotes chez ces
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transformants, puisqu’il n’a pas été possible de sélectionner de telles lignées pour quatre des six
lignées T0 étudiées. En raison de ce problème et de l’absence de phénotype sur les fruits de ces
plantes, ce travail n’a pas été poursuivi. La poursuite de ce travail nécessiterait de déterminer si ce
phénotype est lié à la transgénèse ou à un problème expérimental.
La caractérisation fonctionnelle de SlHAT22 et SlTGA2.1 a été initiée par la génération de
lignées transgéniques présentant une altération du niveau d'expression de ces facteurs de
transcription (sur-expression et RNAi) ou une altération de la fonction du facteur de transcription
en utilisant la technologie CRES-T. Aucun phénotype n’a été observé sur les lignées sur-
exprimant ou sous-exprimant (RNAi) ces facteurs de transcription. Par contre, les plantes
présentant une altération fonctionnelle des facteurs de transcription SlTGA2.1 et SlHAT22 sont
affectées au niveau du développement précoce du fruit, ce qui suggère leur implication dans la
régulation de ce processus. En effet, les fruits immatures des plantes PPPC2:HAT22 CRES-T
sont verts
foncés et ont une teneur en amidon augmentée, tandis que les fruits immatures des plantes
PPPC2:TGA2.1 CRES-T
sont pâles et ont une teneur en amidon réduite. Des analyses supplémentaires
sont nécessaires afin de relier ces phénotypes à une augmentation de la quantité de chlorophylle
par plastes, une augmentation du nombre de plastes par cellule et/ou une modification de la
structure des plastes. Par ailleurs, les données de la littérature indiquent une interaction potentielle
entre la régulation par HAT22 et la signalisation par la lumière et les cytokinines. Ces signaux
seront donc à prendre en compte pour affiner l'implication de SlHAT22 dans la régulation du
développement précoce du fruit de tomate.
Les fruits des plantes PPPC2:TGA2.1 CRES-T
ont montré un mûrissement hétérogène et plus
lent que chez la plante WT, suggérant l’implication de SlTGA2.1 dans l’initiation du mûrissement
du fruit. Pour approfondir cette étude, une analyse transcriptomique sera nécessaire afin d’accéder
aux changements généraux de transcription qui se produisent avant le début et au cours du
mûrissement du fruit dans ces plantes. Par ailleurs, il sera indispensable de comprendre comment
ce nouveau régulateur s’intègre avec les régulateurs connus du mûrissement du fruit tels que
LeMADS-RIN, LeNAC-NOR et LeSBP. En outre, il sera nécessaire d'étudier un lien possible
entre SlTGA2.1 et la signalisation par l'éthylène, qui joue un rôle crucial dans le processus de
mûrissement des fruits de tomate.
Enfin, pour progresser dans la compréhension des mécanismes moléculaires mis en jeu avec
ces deux facteurs de transcription, l’identification de leurs gènes cibles sera indispensable, de
même que l’identification de protéines avec lesquels ils pourraient interagir dans des complexes
de régulation.
L’ensemble du travail réalisé au cours de cette thèse à permis de mettre en évidence de
nouveaux gènes impliqués dans la régulation du développement du fruit de tomate. Il a permis
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d’isoler un mutant insertionel présentant une modification de la forme des graines associé à une
modification sévère du développement du tissu loculaire. L'identification du site d'insertion n'a
pas encore été réalisée, mais ce mutant d'insertion sera particulièrement intéressant afin de mieux
comprendre l'interaction entre la graine et le tissu loculaire pendant le développement précoce du
fruit.
La caractérisation fonctionnelle de SlHAT22 et SlTGA2.1 est un projet prometteur qui
permettra d’approfondir la connaissance du développement du fruit de tomate. En particulier,
l'étude de ces deux gènes peut révéler des interactions nouvelles et passionnantes avec les
éléments connus de la régulation du mûrissement du fruit de tomate, tels que la perception
hormonale, la signalisation et la régulation de la transcription de gènes clés. Cette étude, en
confirmant l’implication de ces deux facteurs de transcription dans la régulation du
développement du fruit a permis de valider à posteriori l’approche intégrative «omic » utilisée
pour identifier des gènes régulateurs impliqués dans d'importants changements développementaux
au cours du développement du fruit.
RESUME
La tomate (Solanum lycopersicum) est l’espèce modèle pour l’étude des fruits charnus. Le
développement du fruit de tomate a été décrit comme une succession de phases de développement distinctes,
dans lesquelles les régulations hormonales jouent un rôle crucial. Afin de mieux comprendre la régulation
du développement du fruit de tomate, des approches ciblées sur des catégories particulières de protéines
régulatrices (F-Box) ainsi que des approches non ciblées de génomique fonctionnelle ont été menées dans le
laboratoire. L'objectif de cette thèse est de contribuer à la validation fonctionnelle de gènes candidats mis en
évidence dans ces travaux: trois protéines à F-Box (SlFB2, SlFB11 et SlFB24) et deux facteurs de
transcription (SlHAT22 et SlTGA2.1).
Au cours de cette thèse, la caractérisation de lignées RNAi précédemment générés pour les protéines
SlFB2, SlFB11 et SlFB24 a été poursuivie. Ce travail n’a pas permis pas de conclure sur le rôle de ces
protéines à F-Box dans le développement du fruit de tomate. Mais elle a permis d'isoler un mutant
d'insertion dans une lignée RNAi SlFB2, spécifiquement affecté au niveau des fruits. Ce mutant est
caractérisé par l'absence de développement de tissu loculaire, ce qui entraine une altération de la forme des
graines, ainsi qu'une augmentation de la fermeté du fruit. Le site d'insertion de l'ADN-T dans ce mutant
n'est pas encore identifié. En outre, la caractérisation des lignées RNAi SlFB11, a permis de proposer
l'implication de cette protéine dans la régulation des méristèmes apicaux et floraux.
La caractérisation fonctionnelle de SlHAT22 et SlTGA2.1 a été initiée par la génération de lignées
transgéniques présentant une altération du niveau d'expression de ces facteurs de transcription (sur-
expression et RNAi) ou une altération de la fonction du facteur de transcription par utilisation de la
technologie CRES-T. La caractérisation phénotypique des lignées transgéniques, ainsi que des analyses
métaboliques préliminaires ont révélé que SlTGA2.1 et SlHAT22 sont impliqués dans des processus de
régulation au cours du développement précoce du fruit de tomate. En outre, ils ont également suggéré que
SlTGA2.1 est impliqué dans la régulation du murissement du fruit de tomate.
Mots clés : Solanum lycopersicum, tomate, fruit, développement, murissement, F-Box, facteur de
transcription
SUMMARY
Tomato (Solanum lycopersicum) is a model species for the study of fleshy fruits. Tomato fruit
development has been described as a sequence of distinct developmental phases where different hormones
play crucial regulatory roles. To further our insights into the regulation of tomato fruit development,
targeted approaches focused on particular classes of regulatory proteins (F-Box) as well as global functional
genomics approaches were undertaken in the laboratory. The aim of this thesis was to contribute to the
functional validation of candidate genes isolated from such approaches: three F-Box proteins (SlFB2,
SlFB11 and SlFB24) and two transcription factors (SlHAT22 and SlTGA2.1).
During this PhD thesis, the characterization of SlFB2, SlFB11 and SlFB24 RNAi lines previously
generated was pursued. This work does not allow us to reach any conclusion about the role of these F-Box
in tomato fruit development. But it allowed the isolation of an insertion mutant in a SlFB2 RNAi line,
specifically affected at the fruit level. This mutant is characterized by the absence of locular tissue
development and subsequent alteration of seed shape, as well as by an increase in fruit firmness. The
insertion site of the T-DNA in this mutant is not yet identified. In addition, characterization of SlFB11
RNAi lines, allowed us to propose the implication of this protein in the regulation of the shoot apical and
floral meristems.
Functional characterization of SlHAT22 and SlTGA2.1 was initiated by the generation of transgenic
lines carrying an alteration of the transcription factor (TF) expression level (OE and RNAi lines) or with an
alteration of TFs function using the CRES-T technology. Phenotypical characterization of the transgenic
lines, together with preliminary metabolic analyses revealed that SlTGA2.1 and SlHAT22 are implicated in
regulatory processes during tomato early fruit development. In addition, they also suggested that SlTGA2.1
is involved in the regulation of tomato fruit ripening.
Keywords: Solanum lycopersicum, tomato, fruit, development, ripening, F-Box, transcription factor.