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UNIVERSITÀ POLITECNICA DELLE MARCHE
FACOLTÀ DI AGRARIA
Dipartimento di Scienze Agrarie, Alimentari ed Ambientali
Scuola di Dottorato di Ricerca
Curriculum Produzioni Vegetali e Ambiente
X Ciclo (2009-2011)
Selection and analysis of differentially expressed genes in the interaction
between Fusarium and Verticillium wilt pathogens and eggplant carrying the
Rfo-sa1 resistance locus introgressed from S. aethiopicum.
Tutore
Prof. Bruno Mezzetti
Co-tutore
Dott. Giuseppe Leonardo Rotino
Dottoranda
Dott.ssa Valeria Barbierato
Coordinatore del Curriculum
Prof. Bruno Mezzetti
Direttore della Scuola
Prof. Natale Giuseppe Frega
Index
Chapter 1…………………………………………………………………………………………………...p004
1.1Epplant………………………………………………………………………………………………….p004
1-1.2 Production……………………………………………………………………………………………p005
1-1.3 Features………………………………………………………………………………………………p006
1-1.4 Production method and grafting ………………………………………………………….................p008
1-1.5Genepool
…………………………………………………………………………………………..p009
1-2 Eggplant genome ……………………………………………………………………………………...p010
1-3.1 Fusarium oxysporum ………………………………………………………………………………..p011
1-3.2 Epidemilogy and management ……………………………………………………………………...p013
1-4 Verticillium Dahliae …………………………………………………………………………………..p014
1-5 Plant responses to pathogens ……………………………………………………………….................p015
1-5.1 PTI or PAMP-Triggered Immunity …………………………………………………………………p018
1-5.2 ETI or Effectors-Triggered Immunity…………………………………………………….................p018
1-5.3 Resistance (R) proteins………………………………………………………………………………p019
1-5.4 Active Oxygen Species, Lipoxygenases, and Disruption of Cell Membranes ……………………p021
1-5.5 Transduction of Pathogen Signals in Plants……………………………………………… ………..p022
1-5.6 Nitric oxide in signal transduction………………………………………………………..................p023
1-5.7 Salicylic acid in signalling defence response in plants……………………………………………..p023
1-5.8 Jasmonate signalling (JAs) in induction of defence responce………………………………………p024
1-5.9 Ethylene-dependent signalling pathway…………………………………………………………….p024
1-5.10 Abscisic acid signalling……………………………………………………………………………p025
1-5.11 Pathogenesis-related proteins (PRs) ………………………………………………………………p026
Chapter 2 Phenotypical analysis of Rfo-sa1 resistant eggplants interaction with
Fusarium oxysporum f. sp. melongenae and/or Verticillium dahliae …………………………..................p029
2-1 Introduction…………………………………………………………………………………………….p029
2-3 Materials and methods…………………………………………………………………………………p030
2-4 Results and Discussion………………………………………………………………………………...p032
Chapter 3 Molecular analyses of Rfo-sa1 resistant eggplant interaction with
Fusarium oxysporum f. sp. melongenae and/or Verticillium
dahliae……………………………………………………...........................................................................p039
3-1 Introduction…………………………………………………………………………………………….p039
3-2.1-Plant material and growing conditions; Fusarium, Verticillium and mixed
inoculations………………………………………………………………………………………………...p043
3-2.2-Functional characterization……………………………………………………………….................p044
3-2.3-SSH validation: qRT-PCR…………………………………………………………………………..p045
2
3-2.4-Microarray ………………………………………………………………………………..................p046
3-2.5-Functional characterization and validation by qRT-PCR……………………………………………p047
3-3 Results………………………………………………………………………………………………….p048
3-3.1-SSH and functional characterization………………………………………………………………...p052
3-3.2-SSH validation: qRT-PCR…………………………………………………………………………...p056
3-3.3-Induction after Fom inoculation: ……………………………………………………………………p057
3-3.4-Induction after Vd inoculation. ……………………………………………………………………...p057
3-3.5-Induction after Fom +Vd inoculation: ………………………………………………………………p060
3-3.6-Genes with an interesting expression profiles……………………………………………………….p060
3-3.7-Microarray and functional classification of the differentially expressed genes…………..................p066
3-3.8-Array validation by qRT-PCR……………………………………………………………………….p070
3-3.9- F3C3 : Fusarium oxysporum f. sp. lycopersici six1 gene, fot5 gene,
six2 gene, shh1 gene and ORF2……………………………………………………………………………p071
3-4 Discussion……………………………………………………………………………………………...p073
Chapter 4
Housekeeping gene selection using an external control for qRT-PCR
analysis of differentially expressed genes in eggplant roots after three
different fungal inoculations…………………………………………………………………….................p079
4-1 Introduction…………………………………………………………………………………………….p079
4-2.1 Plant materials and growth conditions……………………………………………………………….p082
4-2.2 RNA isolation and reverse transcription…………………………………………………..................p082
4-2.3 Primer design ……………………………………………………………………………..................p083
4-2.4 Two step real-time quantitative PCR……………………………………..………………………….p084
4-2.4 Data acquisition……………………………………………………………………………………...p085
4-3 Results and discussion…………………………………………………………………………………p086
4-3.1 Pre-analytical assessment of the panel of candidate genes………………………………..................p087
4-3.2 Evaluation of the expression stability
of the External Reference Gene……………………………………………………………………………p088
4-3.3 Evaluation of the relative expression levels of the candidate reference gene with respect
to the external control. …………………………………………………………………………………......p088
4-4 Discussion……………………………………………………………………………………………...p092
Refrences…………………………………………………………………………………………………...p097
3
Chapter 1
1-1.1 Eggplant
Eggplant (Solanum melongena L. 2n = 2x = 24) is an economically important
non tuberous crop belonging to Solanaceae family. The Solanaceae family is one of
the plant families most employed in our daily lives that included tomato (Solanum
lycopersicum), potato (Solanum tuberosum) and pepper (Capsicum annuum).
Eggplant and the closely related Solanum species belonging to the subgenus
Leptostemonum are some of the most important vegetable crops in Asia, the Middle
and Near East, Southern Europe and Africa (Daunay and Lester, 1988). Solanum
melongena L. (known as eggplant in the United States and aubergine in France and
4
England) is one of the few cultivated solanaceous species originating from the Old
World. It is assumed to have been first domesticated in South and East Asia
(Polignano et al., 2010) and brought to Europe by Arab traders and immigrants
around 600 CE (Daunay and Lester, 1988). The scientific name Solanum melongena
is derived from an Arabic term of 16th century and used for one variety.
1-1.2 Production
Eggplant world production has been grown year by year during the last two
decades, and reached 35 million tons in 2009 (FAOSTAT, http://faostat.org). In
production terms, eggplant is the third most important solanaceous crop species (after
potato and tomato; http://faostat.fao.org), and is cultivated all over the world, but
most intensively in China and India(Table 1). About 2.4% of world production in
2009 is sited in Europe, with Italy being the single largest producer. More than
2,043,788 hectares are devoted to the cultivation of eggplant in the world.
Country
Production (Tonnes)
Area harvested (Ha)
China
25912524
738797
India
10377600
600300
Egypt
1250000
50000
Turkey
816134
27461
Indonesia
449997
46000
Iraq
396 155
21200
Japan
349 200
10400
Italy
245300
9400
Philippines
200 942
21200
Spain
205000
3500
Table 1. Production (tonnes) and area harvested (Ha) regarding the countries
that in 2009 reached the 150000 tonnes of eggplant production (FAOSTAT).
5
1-1.3 Features
Germination takes 8–12 days at the optimalm range of temperatures (22–
28°C). The expansion of the cotyledons takes a few days and the first true leaf
appears after one week. Depending on the cultivar, the first flowers appear when the
plant has developed 5–12 leaves (20–30 cm tall). Vegetative growth and flowering
are then continuous: every 2 leavesm developed, a new flower appears on each
branch.
In temperate climates eggplant is grown as an annual, in tropical climates it is a shortlived perennial (up to 2 years in commercial fields).
The eggplant is a shrub that grows up from 20 cm to over 2 m in height, often
much-branched, with long taproot; stems and leaves could be with or without
prickles. Leaves are alternate, simple, the petiole is 6–10 cm long; the leaf blade may
be ovate or ovate-oblong, 3–25 cm × 2–15 cm, the leaf shape could be straight or
dentate. Flowers usually are bisexual, regular, the pedicel is 1–3 cm long, but up to 8
cm in fruit; the calyx is campanulate, sometimes whit prickles. The corolla shows a
broad range of colours, from white to pink and violet; stamens are inserted near the
base of the corolla tube and alternate with corolla lobes, filaments are short and thick,
anthers connivent, yellow, opening by terminal pores; the ovary is superior, 2–manycelled, the style should be long, longer or less than stamens, the stigma is green,
capitate and lobed ( Fig 1a). Eggplant is autogamous but with a fairly high rate of
cross pollination. Pollination occurs mostly by insects (mostly bumble bees or bees
such as Exomalopsis), as shown in Fig 1b)
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Fig 1 a:. Bumble bee during flower pollination; b: Eggplant flowers
Fig. 2 a and b : Eggplant fruits
The fruit is a globose or snake-shaped, furrowed or smooth berry, 2–35 cm long
(sometimes longer) and 2–20 cm broad, whit smoothness and shininess variable. The
colours at commercial stage are white, green, violet-purple or black, sometimes
striped, many-seeded (some examples of different fruit shapes and colours are
showed in Fig 2 a and b). Seeds are lenticular to reniform, flattened, 3 mm × 4 mm.
Fruit sets one week after anthesis, and 3–6 weeks are needed to reach commercial
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ripeness, depending on climatic conditions. Fruits reach physiological maturity 6–13
weeks after flowering, also depending on the climate.
1-1.4 Production method and grafting
Eggplant is traditionally grown in open field, also large scale culture in heated
or unheated glass and plastic houses was developed in Europe from the 1970s.
Grafting is mostly used in conditions of intensive production. In Europe, eggplant is
grafted mostly onto tomato or tomato interspecific hybrids (L. esculentum x L.
hirsutum) which, in addition to their resistance to several soil born pathogens, have a
good tolerance to low soil temperatures (Ginoux and Laterrot, 1991). Solanum
torvum, a wild species which has also a wide range of soil borne disease resistances,
is another valuable rootstock which brings also a higher yield, but its use is limited so
far by the difficulty to get a rapid and homogeneous germination (Ginoux and
Laterrot, 1991) (Fig 3). An other type of rootstocks are based on the use of S.
integrifolium - i.e. S. aethiopicum Aculeatum Group (Fig 4) - which carries resistance
traits to Fusarium and bacterial wilts, and presents a good graft affinity with S.
melongena. It is used directly as a rootstock (Yoshida et al., 2004b) or as parent
crossed with S. melongena varieties for producing interspecific hybrid rootstocks [S.
integrifolium x S. melongena] cumulating resistances from both parents (Mian et al.,
1995).
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Fig 3 Plantlets of eggplant grafted onto Solanum torvum
Fig 4 Fruits of S. integrifolium
1-1.5 Genepool
Eggplant genetic resources consist of three genepools. The primary genepool
consists of traditional and modern cultivars of Solanum melongena; the diversity is
important in terms of fruit size (from some tens of g to over one kg), fruit shape and
fruit colour (white, green, pink to violet or purple or even black, uniform, striped,
mottled or netted). The secondary genepool is formed by some 20 related Solanum
species that are relatively easily crossable with eggplant and give relatively fertile
hybrids; Solanum aethiopicum belongs to this genepool, but the hybrids, though quite
easily obtained, have very low fertility. The tertiary genepool consists of about 20
other Solanum species that are crossable with eggplant using particular procedures
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such as embryo rescue or colchicine treatment, and produce interspecific hybrids of
low fertility; Solanum macrocarpon belongs to this genepool.
1-2 Eggplant genome
The eggplant genome is still rather unexplored, the estimated size is about 1.1
Gbp (Arumuganathan et al, 1991). Knowledge of its genome organization is limited
compared
to
that
of
either
tomato
or
potato
(http://solgenomics.
net/,
http://www.potatogenome.net), but different genetic maps are publicly available. The
first one, developed in 2002 by Doganlar et al, is based on a inter-specific cross (S.
linnaeanum Jaegaer & Hepper MM195 and S. melongena L. MM738 were used).
This map, based on restriction fragment length polymorphism (RFLP), consists of 12
linkage groups, spans 1480 cM, and contains 233 markers. Comparison between the
eggplant and tomato maps revealed conservation of large traits of colinear markers, a
common feature of genome evolution in the Solanaceae and other plant families.
However, eggplant and tomato were differentiated by 28 rearrangements, which
could be explained by 23 paracentric inversions and five translocations. A second
inter-specific map was developed in 2009 by Wu et al. This map was based on
Conserved Ortholog Set II (COSII) markers (Wu et al. 2006). This last map was
constituted of 347 COS and RFLP markers spanning 1,535 cM.
Two different intra-specific maps, based on SSR (simple sequence repeat)
markers, were constructed by Nunome et al (2003) and Barchi et al (2010) and
comprise 236 markers, spanning 951.4 cM and 238 markers, spanning 718.7,
respectively. Barchi et al (2011) also combined the recently developed Restrictionsite Associated DNA (RAD) approach with Illumina DNA sequencing for rapid and
mass discovery of both SNP and SSR markers for eggplant. Whit this method, a total
of 384 SNPs were developed from an original dataset of 2435 candidate for
genotyping assay. The screening of the non redundant genomic dataset originated
from Illumina genotyping resulted also in the identification of 1885 microsatellites.
10
This high number of molecular marker represent an important building materials for
the construction of a new genetic map, based on intra-specific cross and improved
whit SNP, SSR and other molecular markers. The releasing of the new framework
map is in progress.
1-3.1 Fusarium oxysporum
Fusarium oxysporum f. melongenae is a major soil-borne pathogen of eggplant
(Solanum melongena). The distribution of Fusarium oxysporum is known to be
cosmopolitan, however, the different special forms (formae speciales) of F.
oxysporum often have varying degrees of distribution. The Fusarium wilt occurs in
Europe both in greenhouse and open-field cultivation (Urrutia Herrada et al. 2004;
Altinok, 2005). In plant inoculation studies, the recovered isolate could not infect
other Solanaceae species (including Lycopersicon esculentum, Nicotiana tabacum,
Solanum tuberosum, and Capsicum annuum). Fusarium oxysporum and its various
formae speciales have been characterized as causing the following symptoms:
vascular wilt, yellows, corm rot, root rot, and damping-off. The most important of
these symptoms is vascular wilt. In general, wilts caused by Fusarium first appear as
slight vein clearing on the outer portion of the younger leaves, followed by epinasty
(downward drooping) of the older leaves. At the seedling stage, plants infected by F.
oxysporum may wilt and die soon after symptoms appear. In older plants, vein
clearing and leaf epinasty are often followed by stunting, yellowing of the lower
leaves, formation of adventitious roots, wilting of leaves and young stems,
defoliation, marginal necrosis of remaining leaves, and finally death of the entire
plant. Browning of the vascular tissue is strong evidence of wilt.
In solid media culture, such as potato dextrose agar (PDA), the different special
forms of F. oxysporum can have varying shapes (Fig.5). In general, the aerial
11
mycelium first appears white, and then may change to a variety of colors - ranging
from violet to dark purple-according to the strain (or special form) of F. oxysporum.
Fig. 5 F. oxysporum in solid media culture (potato dextrose agar)
F. oxysporum produces three types of asexual spores: microconidia,
macroconidia, and chlamydospores. Microconidia are composed by one- or twocelled, and are the type of spore most abundantly and frequently produced by the
fungus under all the conditions. It is also the type of spore most frequently produced
within the vessels of infected plants. Macroconidia are three to five celled, gradually
pointed and curved toward the ends. These spores are commonly found on the surface
of plants killed by this pathogen. Chlamydospores are round, thick-walled spores,
produced on older mycelium or in macroconidia. These spores are either by one or
two cells. F. oxysporum is an abundant and active saprophyte in soil and organic
compounds. Its saprophytic ability enables it to survive in the soil between crop
cycles in infected plant debris. The fungus can survive either as mycelium, or as any
of its three different spore types. Healthy plants can become infected by F.
oxysporum if the soil in which they are growing is contaminated with the fungus. The
fungus can invade a plant either with its sporangial germ tube or its mycelium by
invading the plant's roots. The roots can be infected directly through the root tips
(Mendgen et al., 1996), through wounds in the roots or at the formation point of
12
lateral roots. Once inside the plant, the mycelium grows through the root cortex
between the cells. When the mycelium reaches the xylem, it invades the vessels
through the xylem's pits. At this point, the mycelium remains in the vessels, where it
usually advances upwards toward the stem and crown of the plant. As it grows, the
mycelium branches and produces microconidia, which are carried upward within the
vessel by way of the plant's sap stream. When the microconidia germinate, the
mycelium can penetrate the upper wall of the xylem vessel, enabling more
microconidia to be produced in the next vessel. The fungus can also advance laterally
as the mycelium penetrates the adjacent xylem vessels through the xylem pits.
Adhesion or close contact of the fungal spores with plant surface appears to be
important in sensing the plant signals. The recognition process is initiated almost at
the first contact of the plant surface by pathogen. Initiation of the signaling process
has been demonstrated even within 20 s of the first .
1-3.2 Epidemilogy and management
F. oxysporum is primarily spread over short distances by irrigation water and
contaminated farm equipment. The fungus can also be spread over long distances
either in infected transplants or in soil. It is also possible that the spores are spread by
wind.
Strategies to control this soil-borne disease have been based, especially in
greenhouse cultivation, on soil treatments with methyl bromide, but this compound
has officially been phased out in the European Union. As F. oxysporum and its many
special forms affect a wide variety of hosts, the management of this pathogen
includes: disinfestation of the soil and planting material with fungicidal chemicals,
crop rotation with non-hosts plants, or by using resistant cultivars (Jones et al., 1982;
Smith et al., 1988).
13
1-4 Verticillium Dahliae
Over 300 woody and herbaceous plant species are known to be susceptible to
Verticillium dahliae including tomato, eggplant, pepper, potato, peppermint,
chrysanthemum, cotton, asters, fruit trees, strawberries, raspberries, roses. V. dahliae
occurs worldwide but is more important in temperate zones, and naturally occurs at
low levels in soils and grows better at slightly higher temperatures 25 -28° C. The
fungus belongs to the fungal class Deuteromycetes , a group of fungi which do not
have a known sexual stage. The vegetative mycelium is septate and multinucleate.
The nuclei are haploid in culture. Conidia are ovoid or ellipsoid and usually singlecelled.
Symptoms vary among hosts, and none is absolutely diagnostic. Premature
foliar chlorosis and necrosis and a tan to brown colored discoloration or streaking of
the vascular system, however, are characteristic of all hosts. Symptoms of wilting are
most evident on warm, sunny days. The fungus can overwinter as mycelium in
perennial hosts, plant debris, and vegetative propagative parts. The fungus can
survive for many years (10 years or more) in soil in the form of tiny, black, seed-like
structures called microsclerotia that are stimulated to germinate by root exudates of
both host and non-host plants.
The fungus penetrates a root of a susceptible plant in the region of elongation
and the cortex is colonized. From the cortex, the hyphae penetrate the endodermis
and invade the xylem vessels where conidia are formed. Vascular colonization occurs
as conidia are drawn up into the plant along with water. As the diseased plant
senesces, the fungus reached the cortical tissue and produces microsclerotia, which
are released into the soil with the decomposition of plant material.
The management of this fungal disease is similar to F. oxysporum:
disinfestation of the soil and planting material with fungicidal chemicals, crop
rotation with non-hosts of the fungus, or by using resistant cultivars (Jones et al.,
1982; Smith et al., 1988) are the most common.
14
1-5 Plant responses to pathogens
When a plant and a pathogen come into contact, close communications occur
between the two organisms (Hammond-Kosack and Jones 1996). Pathogen activities
focus on colonization of the host and utilization of its resources, while plants are
adapted to detect the presence of pathogens and to respond with antimicrobial
defences and other defence responses. Plant and pathogen species are often highly coevolved, meaning for example that standard plant barriers to microbial infection can
be circumvented by particular pathogen species. As an infection plays out, the plant's
metabolism often represents a variable mixture of disease resistance and disease
susceptibility responses. Interactions between plants and pathogens induce a series of
plant defence responses (Hammond-Kosack and Jones 1996). Plants rely on
mechanisms of innate immunity, that can be present in two forms: basal (or
horizontal) resistance and R gene-based (or vertical) resistance. The first one
(horizontal resistance) is based on the recognition of a pattern recognition receptor
(PRR) and a pathogen-associated molecular pattern (PAMP), that trigger basal or
non-cultivar-specific defense responses in plants. The second one (R gene-based
resistance) is based on the highly specific interaction of pathogen effectors and the
products of plant R genes according to the gene-for-gene theory. This recognition
event leads to hypersensitive response, characterized by rapid apoptotic cell death
and local necrosis (Boller and Felix, 2009). The perception of danger signals occur in
the immediate surroundings of pathogen invasion sites. Plant species and plant
cultivar-specific resistance represent evolutionarily linked types of immunity that are
collectively referred to as the plant innate immune system. Signal transduction
cascades that mediate activation of innate immune responses comprise elements that
are common to both forms of plant immunity, such as the alterations in cytoplasmic
calcium levels, the mitogen activated protein kinase activities or the production of
reactive oxygen species. Not surprisingly, host transcriptional activity is substantially
modulated and redirected over the course of such defense responses (Scheideler et al.,
2002). When a pathogen-derived avirulence (avr) protein of a virus, bacterium,
15
fungus, nematode or insect is recognized directly or indirectly by the corresponding
resistance (R) protein in the plant, the R protein typically activates defence response
to make the infection unsuccessful (Dangl and Jones 2001). Hence R genes form an
important “front end” of the plant immune system, and are exploited widely for
disease control in crop plants. The R gene-mediated pathogen surveillance system
allows particularly rapid activation of the defence responses. The hypersensitive
response (HR), a programmed plant cell death response at the site of pathogen
infection, is often associated with gene-for-gene disease resistance. Systemic
acquired resistance (SAR) and induced systemic resistance (ISR) are related but
distinct versions of systemic host response, and they share two components: elevated
production of antimicrobial compounds are activated more strongly and rapidly in
response to subsequent infections (Glazebrook et al., 2005).
The term “pathogenesis-related protein” (PR protein) was introduced in the
1970s in reference to the proteins that are newly synthesized or present at
substantially increased levels after a plant has been infected (van Loon et al., 2006).
A number of the classically defined PR genes do encode proteins such as chitinases,
glucanases or defensins that have been shown to carry antimicrobial activity.
However, individual PR proteins apparently make only small quantitative
contributions to defence, and the contribution will vary depending on the pathogen
target.
16
Fig 6 Microbe-associated molecular patterns (MAMPs), damage-associated
molecular patterns (DAMPs), and effectors are perceived as signals of danger.
Extracellular MAMPs of prototypical microbes and DAMPs released by their
enzymes are recognized through pattern recognition receptors (PRRs). In the course
of coevolution, pathogens gain effectors as virulence factors, and plants evolve new
PRRs and resistance (R) proteins to perceive the effectors. When MAMPs, DAMPs,
and effectors are recognized by PRRs and R proteins, a stereotypical defense
syndrome is induced. RLK, receptor-like kinase; RLP, receptor-like protein; NBLRR, nucleotide binding-site–leucine-rich repeat. (Boller and Felix, 2009)
17
1-5.1 PTI or PAMP-Triggered Immunity
PAMPs (pathogen-associated molecular pattern) constitute highly conserved
determinants typical of whole classes of pathogens that are not found in potential host
organisms and that are indispensable for the microbial lifestyle, such as chitin for
fungi or peptidoglycan for bacteria. PAMPs can be also divided in microbeassociated molecular patterns (MAMPs) derived only from pathogen, and damageassociated molecular patterns (DAMPs), derived from plant itself because of the
damage caused by microbe. Anyhow, plants posses pattern recognition receptors
(PRRs) able to perceive both MAMPs and DAMPs. The perception of PAMPs by
PRRs initiates an active defence response, called basal immunity. Well-adapted
microbial pathogens, however, have found ways to breach this first line of active
defence. Plants have evolved a second line of defence, called R-gene-based resistance
which is higher specific than the basal one.
1-5.2 ETI or Effectors-Triggered Immunity
ETI , or R-gene-based resistance, is based on direct or indirect interaction of
pathogen effectors and the products of plant R genes according to the gene-for-gene
theory. This recognition events leads to a vigorous type of defence reaction called
hypersensitive response, characterized by rapid apoptotic cell death and local necrosis
(Martin et al., 2003).
The genetic basis for plant cultivar-specific disease resistance is determined by
gene pairs called pathogen-derived avirulence (Avr) genes and plant derived
resistance (R) genes. Avr gene-encoded proteins are likely (sometimes dispensable)
effectors that contribute to host infection.
An important consideration regards the defence program induced by PTI or
ETI: plants seem not to discriminate from PAMPs and elicitors. The perception of all
these signals appears to trigger the same defence responses, albeit with kinetic and
18
quantitative differences in induction. The response induced by ETI seems to be
stronger and longer than the response induced by PTI (Tao et al., 2003).
1-5.3 Resistance (R) proteins
Innate immunity relies on specialized receptors that can be divided into two
groups: the PRRs and the R proteins. PPRs recognize PAMPs, that are highly
conserved molecules, and allow plants to recognize distinct invaders using a limited
set of receptors (Gerber van Ooijen et al., 2007). In contrast to PPRs, R proteins
respond to molecules called avirulence proteins (avr) or elicitors, that are generally
not conserved between species or isolates of given pathogen. R protein are encoded
by large gene families, numbering several hundred of genes per genome (Meyers et
al., 2003). Resistance mediated by R protein is often associated with hypersensitive
response. R genes confer resistance to very different pathogens, but the encoded
proteins share a limited number of conserved domain. Based on these, R proteins can
be divided in four classes. Most of these contain a central nucleotide-binding (NB)
domain as part of a larger entity called NB-ARC domain. C-terminal to the NB-ARC
domain lies a leucine-rich repeat (LRR) domain, witch is sometimes followed by an
extension of variable length. Hence, this group of R proteins is collectively referred
to as NB-LRR proteins. On the basis of their N-terminal region, we can classified
TNL and CNL proteins. If the N-terminal region shows homology to a protein
domain found in the Drosophila Toll and human Interleukin-1 Receptor (IL-1R), it is
called the TIR domain and these protein referred to as TIR-NB-LRR or TNL protein
(Whitam et al., 1994). Because some non-TIR proteins contain predicted coiled-coil
structures (CC) in their N-terminal domain, non-TIR-NB-LRR proteins are referred
to as CC- NB-LRR or CNL proteins. A limited number of R proteins are
extracellularly and they contain a predicted extracellular LRR (eLRR) domain at their
N-terminus. This eLRR is connected via a transmembrane domain to a variable
cytoplasmatic C-terminal region. When the cytoplasmic domain contains a protein
kinase domain the protein belong to the RLK class (Receptor Like Kinase). if no such
19
domain are present, the protein is placed in the RLP class (Receptor Like Protein). A
schematic representation of the typical members of the four R protein classes is show
in Fig.2.
Fig 7 Schematic representation of typical members of the four R protein
classes. Protein domains and putative cellular localization are indicated. The
Receptor-Like Protein (RLP) and the Receptor-Like Kinase (RLK) classes of R
proteins span the plasma membrane (PM) and contain an extracellular Leucine Rich
Repeat (LRR) domain. The CNL and TNL classes of R proteins are located
intracellularly (cytoplasmic, nuclear, or membrane-bound) and contain a central NBARC domain (consisting of NB, ARC1 and ARC2 subdomains) coupled to an LRR
domain. TNLs carry an N-terminal TIR domain, while CNLs contain either a CC or
an extended CC domain (van Ooijen et al., 2007).
In the Solanaceae, the larger class of R protein is the CNL class ( van Ooijen et
al., 2007). We can find indirect and direct Avr/R interaction; for most R protein, this
mechanism is still unknown. Activation of NB-LRR proteins likely requires a series
of conformational changes, mediated via nucleotide hydrolysis by the central
20
nucleotide binding site. Determine the 3-D structure of NB-LRR and RLP proteins
will improve the understanding of molecular mechanisms underlying their function.
1-5.4 Active Oxygen Species, Lipoxygenases, and Disruption of Cell Membranes
The plant cell membrane consists of a phospholipid bilayer in which many
different kinds of protein and glycoprotein molecules are embedded. The cell
membrane is also an active site for the induction of defense mechanisms; as, it serves
as the anchor of R gene-coded proteins that recognize the elicitors released by the
pathogen and subsequently trigger the hypersensitive response. The most important
membrane- associated defence responses include the release of molecules important
in signal transduction within and around the cell and, possibly, systemically through
the plant and the release and accumulation of reactive oxygen “species” and of
lipoxygenase enzymes.
The first events of the defence response are perturbations in ion fluxes and the
pattern of protein phosphorylation, which precede the accumulation of ROS (mainly
O−2 and H2O2) and NO as well as the transcriptional activation of defence-related
genes (McDowell and Dangl 2000; Cohn et al., 2001). The attack of cells by
pathogens, or exposure to pathogen toxins and enzymes, often results in structural
and permeability changes of the cell membrane. In many plant-pathogen interactions,
one of the first events detected in attacked host cells is the rapid and transient
generation of activated oxygen species, including superoxide (O2 -), hydrogen
peroxide (H2O2), and hydroxyl radical (OH). The generation of superoxide and of
other reactive oxygen species as defence response happens most dramatically in
localized infections, but also in general and systemic infections, as well as in plants
treated with chemicals that induce systemic acquired resistance. These highly reactive
oxygen species are thought to be released by the multisubunit NADPH oxidase
enzyme complex of the host cell plasma membrane, they appear to be released in
affected cells within seconds or minutes from contact of the cell with the pathogen.
The activated oxygen species trigger the hydroperoxidation of membrane
21
phospholipids, producing mixtures of lipid hydroperoxides. The latter are toxic, as
their production disrupts the plant cell membranes, and they seem to be involved in
normal or HR-induced cell collapse and death. The presence of active oxygen
species, however, also affects the membranes and the cells of the advancing pathogen
either directly or indirectly through the hypersensitive response of the host cell. The
production of reactive oxygen species in affected but surviving nearby cells is kept
under control by the radical scavenger enzymes superoxide dismutase, catalase,
ascorbate peroxidase, etc. Several isoenzymes of each of these molecules are
produced, with different ones of them appearing at different stages after inoculation.
The oxygenation of membrane lipids seems to involve various lipoxygenases as well.
These are enzymes that catalyze the hydroperoxidation of unsaturated fatty acids,
such as linoleic acid and linolenic acid, which have been released previously from
membranes by phospholipases. The lipoxygenase-generated hydroperoxides formed
from such fatty acids, in addition to disrupting the cell membranes and leading to
HR-induced cell collapse of host and pathogen, are also converted by the cell into
several biologically active molecules, such as jasmonic acid, that play a role in the
response of plants to wounding and other stresses.
1-5.5 Transduction of Pathogen Signals in Plants
Plants are able to recognize pathogen-derived elicitor molecules that trigger a
number of induced defenses in plants. The recognition of a potential pathogen results
in activation of intracellular signaling events including ion fluxes, phosphorylationdephosphorylation cascades, kinase cascades, and generation of reactive oxygen
species (ROS) (Radman et al., 2003). Intercellular signaling system involves ROS,
nitric oxide (NO), salicylic acid (SA), jasmonic acid (JA), and ethylene (ET). Two
major pathways in defence signalling, one SA-dependent and the other SAindependent but involving JA and ET, are recognized (Kunkel and Brooks, 2002).
These signalling events lead to reinforcement of plant cell walls and the production of
defence proteins and phytoalexins. These events proceed in both susceptible and
22
resistant interactions, probably with different speed and intensity. The pathogens also
produce suppressor molecules to counteract the action of elicitors, resulting in
susceptibility.
1-5.6 Nitric oxide in signal transduction
NO is a gaseous free radical that diffuses readily through biomembranes
(Bethke et al., 2004). It is now well established that NO is involved in the plant
defence signalling (Delledonne et al., 2001;). NO production was observed in tobacco
cells within 5 min after treatment with the cryptogein elicitor, and reaches the
maximum within 30 min (Lamotte et al., 2004). Plants synthesize NO from nitrite.
Nitrate reductase has been found to catalyze the NAD(P)H-dependent reduction of
nitrite to NO (Morot-Gaudry-Talarmain et al., 2002). Nitrate reductase reduces nitrate
to nitrite and can further reduce nitrite to NO. Nitrite-dependent NO production has
been observed in soybean (Delledonne et al., 1998) and sunflower (Rockel et al.,
2002). NO induces defense gene expression via signaling pathways that likely
involve cyclic GMP and cADPR .
1-5.7 Salicylic acid in signalling defence response in plants
SA is a phenolic compound commonly present in the plant kingdom. Plants
synthesize SA (O-hydroxybenzoic acid) by the action of PAL (phenylalanine
ammonia lyase), which is a key regulator of the phenylpropanoid pathway and yields
a variety of phenolics with structural and defense-related functions. SA has been
reported as one of the most important signal molecules, which acts locally in
intracellular signal transduction and also systemically in intercellular signal
transduction (Raskin, 1992). SA accumulates in plants inoculated with pathogens, its
level increases both in proximal and distal tissue with respect to the infection. The
increased levels of SA resulted in induction of various defence-related genes (Dorey
et al., 1997). The importance of SA-signalling system in induction of host defences
was studied by developing transgenic plants expressing the bacterial gene NahG. This
23
gene encodes for the enzyme salicylate hydroxylase, which inactivates SA by
converting it to catechol. Some of the NahG transgenic plants were unable to
accumulate SA and consequently incapable of developing HR, indicating that SA
accumulation is required for HR to occur (Delaney et al., 1994). Disease resistance is
also induced in plants by spray treatments with SA (Navarre and Mayo, 2004)
1-5.8 Jasmonate signalling (JAs) in induction of defence responce
JAs, which were first detected in essential oils of Jasminum grandiflorum
(Demole et al., 1962), occur ubiquitously in all plant tissues, and they are a major
group of signalling compounds in inducing host defence. JA and its cyclic precursors
and derivatives are collectively referred to as JAs (Li et al., 2005). The JAs, derived
from peroxidized linolenic acid, are members of a large class of oxygenated lipids
called oxylipins (Hamberg and Gardner, 1992). Oxylipins are acyclic or cyclic
oxidation products derived from the catabolism of fatty acids (Creelman and Mulpuri,
2002). JA, MeJA, 12-oxo-phytodienoic acid (OPDA), and other oxylipins act as
signals for defence against pathogens (Krumm et al., 1995). The accumulation of JAs
is followed by the activation of JA-mediated defense responses (Wasternack and
Hause, 2002). The importance of JA in signaling induction of defense genes has been
demonstrated by using plant mutants deficient in JA synthesis and perception.
Constitutive production of JA in an Arabidopsis mutant was accompanied by
constitutive expression of defensin PDF1.2, thionin Thi2.1, and chitinase CHI genes
(Ellis et al., 1999), and this mutant showed enhanced resistance against E.
cichoracearum and a bacterial pathogen Pseudomonas syringae .
1-5.9 Ethylene-dependent signalling pathway
The increased production of ET is one of the earliest chemically detectable
events in pathogen-infected plants or in plants treated with elicitors (Toppan and
Esquerre-Tugaye, 1982). The role of ET in plant–pathogen interaction is complex
(Geraats et al., 2003). ET stimulates defence mechanisms against several pathogens,
24
and it also induces susceptibility to several other pathogens (Boller, 1991). ET
applied as pre-treatment induces resistance against Botrytis cinerea in tomato (Dı´az
et al., 2002), whereas exogenous application of ET enhances B. cinerea (gray mold)
incidence in tomato, pepper, cucumber, bean, rose, and carnation (Boller, 1991). The
ET-insensitive mutant of tomato showed enhanced resistance to Fusarium oxysporum
(Lund et al., 1998), and soybean mutants with reduced sensitivity to ET were less
susceptible to Phytophthora sojae (Hoffman et al., 1999). By contrast, ET
insensitivity enhanced susceptibility to various pathogens in different plants, for
example Arabidopsis mutant ein2-1 (for ethyleneinsensitive 2-1) showed enhanced
susceptibility to B. cinerea (Thomma et al., 2001a) After its synthesis, ET is
perceived and its signal is transduced through transduction machinery to trigger
specific biological responses. The signaling system consists of two proteins, a
histidine kinase and a response regulator. The histidine kinase acts as the sensor that
autophosphorylates an internal histidine residue in response to signals, and the
response regulator activates the downstream components upon receiving a phosphate
from the histidine residue of the sensor on its aspartate residue (Pirrung, 1999).
1-5.10 Abscisic acid signalling
Several recent papers have proposed that ABA signalling, in addition to
regulating plant development and response to abiotic stress, also plays a role in the
regulation of innate immunity (Adie et al., 2007, Berrocal-Lobo, M. et al., 2002).
Meta-analysis of pathogen-inducible genes in Arabidopsis reveals that a significant
subset of ABA-regulated genes are activated upon pathogen infection (Adie et al.,
2007). In some plant-pathogen interactions, such as that between Arabidopsis and the
vascular bacterium Ralstonia solanacearum, ABA signalling plays a direct function
in the activation of the defensive response (Hernandez-Blanco et al., 2007) . Instead,
in other plant-pathogen interactions, ABA seems to play a negative regulatory
function by inactivating other defence signalling pathways, such as those mediated by
SA or JA/ET (Anderson et al., 2004). These negative function of ABA has been
25
proposed to be a mechanism used by some pathogens to suppress plant basal
resistance (Melotto et al., 2006)
1-5.11 Pathogenesis-related proteins (PRs)
Pathogenesis-related
proteins
were
discovered
in
tobacco
reacting
hypersensitively to Tobacco mosaic virus (TMV) and later in other plant species. The
recognized PRs currently comprise 17 families described in Table 1, numbered whit
respect to the order of discovery (van Loon et al., 2006). A type member, usually the
first one, was chosen and families were defined on the basis of their common
biochemical and biological properties. A role of several families in limiting pathogen
activity, growth and spread fits the identification of PR-2 family as So, the member
of PR-2 family as β -1,3-endonucleases, PR -3, -4, -8, and -11 as endochitinases and
PR-6 as proteinase inhibitors. Members of PR-8 family also play an important role
against bacteria whit their lysozyme activity, while PR-12 (defencins) and PR-13
(thionins) have both antibacterial and antifungal activities (Lay and Anderson, 2005;
Epple et al., 1997). PR-14 family included also lipid transfer proteins whit
antibacterial and antifungal activities (Garcia-Olmedo et al., 1997), while member of
PR-1 and PR-5 (thaumatin-like) families have been associated with activity against
oomycetes. PR-7 is an endoproteinase that might aid in microbial cell wall
dissolution (Jorda et al., 2000) . PR-9 is a specific type of peroxidase that could act in
cell wall reinforcement by catalyzing lignification (Passardi et al., 2004). The
families PR-15, -16, and -17 have been added recently. PR-15 and -16 are typical of
monocots and comprise families of germinlike oxalate oxidases and oxalate oxidaselike proteins with superoxide dismutase activity (Bernier and Berna, 2001),
respectively. These proteins generate hydrogen peroxide that can be toxic to different
types of attackers or could directly or indirectly stimulate plant-defence responses.
PR-17 proteins have been found as an additional family of PRs in infected tobacco,
wheat, and barley and contain sequences resembling the active site of
zincmetalloproteinases (Christensen et al., 2002), but have remained uncharacterized
26
so far. A putative novel family (PR-18) comprises fungus- and SA-inducible
carbohydrate oxidases, as exemplified by proteins with hydrogen peroxide-generating
and antimicrobial properties from sunflower (Custer set al., 2004).
Tab 2 recognised families of PRs proteins (van Loon et al., 2006)
PR proteins, through their specific hydrolytic activities, may also be expressed
during plant development in specific stages or organs and contribute to the generation
of signal molecules that can act as morphogenetic factors. However, their widespread
induction upon pathogen attack and their regulation by the defence regulatory
hormones SA, JA, and ET suggest that they play an important role in alleviating the
effects of attack by pathogens and insects, as well as some forms of abiotic stress. In
several instances, quantitative resistance against pathogens has been shown to be
associated with constitutively expressed PRs (Liu et al., 2004). In SAR, the presence
27
of induced PR-type proteins is likely to contribute to some extent to the enhanced
defensive capacity. In contrast, in ISR, no defence-related proteins are present in
induced leaves before challenge, but upon infection activation of JA-responsive genes
in particular is accelerated and enhanced, a phenomenon known as priming (Conrath
et al., 2002).
28
Chapter 2
Phenotypical analysis of Rfo-sa1 resistant eggplants interaction with Fusarium
oxysporum f. sp. melongenae and/or Verticillium dahliae
2-1 Introduction
The two fungal wilts caused by Verticillium dahliae (Vd) (Bhat et al., 1999)
and Fusarium oxysporum f.sp melongenae (Fom) (Urrutia Herrada et al., 2004,
Cappelli et al. 1995) are among the most serious diseases of eggplant (Kennet et al.,
1970; Stravato et al., 1993; Urrutia Herrada et al., 2004). The resistance to Fom was
introgressed from the allied specie S. aethiopicum and molecular characterization of
the ILs enabled to demonstrate that the introgressed resistance trait is controlled by a
single locus (named Rfo-sa1, Resistance to Fusarium oxysporum f. sp. melongenae
from Solanum aethiopicum 1). In order to characterize the plant-pathogen interactions
of eggplant lines subjected to inoculation with Fom and/or Vd, a phenotypical
investigation was set up. The aim of this approach was to confirm the capacity of the
locus Rfo-sa1 to protect eggplant under Fom inoculation and the severity of
symptoms after Vd inoculation. Another aspect investigated was the improved
tolerance of ILs to Vd after simultaneous inoculation with Vd+Fom compared with
inoculation with Vd alone, previously observed in inoculation test due to breeding
intent. The hypothesis was that the activation of Rfo-sa1 caused by Fom inoculation
improve the response also to Vd. In order to better understand this method of Fom/Vd
interaction, a set of mixed inoculations were planned. Obviously, a single Fom and
Vd inoculations were done in comparison to mixed inoculations, while mock
inoculation whit water was used as control. Five different eggplant lines were
subjected to the same conditions, three of those carrying the locus Rfo-sa1.
The three resistant lines were: All96/6, All96/6x1F59 and 305E40, while the
susceptible ones were 1F59 and Tal 1/1. All96/6x1F59 is an advanced breeding line
29
derived from the IL All96/6, while 1F59 was used as recurrent susceptible parent in
backcrosses. The correlation between All96/6x1F59 and 1F59 was the same between
305E40 and Tal 1/1. Tal 1/1 was used as recurrent susceptible parental in backcrosses
to the achievement of the line 305E40.
An experiment scheme was designed on the basis of literature (Beye et al.,
1985 and 1988).
The criteria were based on the extent and intensity of foliar symptoms and on
the disease progression along the stem.
2-2 Materials and Methods
Five different eggplant lines (All96/6, All96/6x1F59 and 305E40 resistant to
Fom, 1F59 and Tal 1/1 susceptible) were separately inoculated whit a conidial
suspension of Fom and/or Vd. Both the pathogenic strains of Fom and Vd were
conserved in Potato Dextrose Agar (PDA, Merk) at 25° C without light. Before use
fungi were transferred on CZAPEC liquid medium. The liquid cultures were shaken
in the orbital incubator for 4 days at 25°C. The inoculation was carried out using a
Fom conidial suspension
whit a concentration of 1.5x106/ml, intead for
Vd
inoculation was used a concentration of 1.0x106/ml. In the mixed inoculation the
fungal suspension was prepared using the same concentrations reported before.
Artificial inoculation was performed according to the root-dip method
described in Cappelli et al. (1995), using plantlets at the 3-4th true leaf stage.
The experiments were conducted in order to understand the Fom/Vd
interaction, so five different types of mixed inoculations were prepared. In addiction
to a Fom and Vd inoculation alone, the two fungi were used to inoculate the plantlets
together or a two-steps. So, the plantlets were inoculated before with Fom and then
after 24 and 48 hours with Vd. The same experiment was conducted using before Vd
30
and then after 24 and 48 hours Fom. In Table 1 was reported the experimental design
used for the five eggplant lines described before.
Fungal inoculation
Fom
Vd
Fom + Vd
Control
Fom + 24h Vd
Vd + 24h Fom
Control
Fom + 48h Vd
Vd + 48h Fom
Control
inoculation day
Fom
Vd
Fom+ Vd
Water
Fom
Vd
Water
Fom
Vd
Water
24 h after
48 h after
Vd
Fom
Water
Vd
Fom
Water
Tab.3: Schematic representation of the different types of fungal inoculations.
The same experimental design was followed for each eggplant lines.
As shown in Table 1, three control (mock-inoculation with water) were
performed. This multiple-control set ensured the same experimental conditions at
each inoculation. The phenotypical observations were conducted after 15, 22 and 30
days of inoculation. Two distinct parameters were used to evaluate the severity of
symptoms: the extent and intensity of foliar symptoms and on the disease progression
along the stem. This parameters were used by Beye et al., (1985) to compare each
others different criteria of evaluation of fungal disease, so the same original
abbreviation in French were used also in this work. Thereby, the percentage value
attributed to disease progression along the stem was called I.E.S.F. (from the original
“indice d’étendue des symptoms foliares”), and the percentage value of intensity of
foliar symptoms was called I.A.F. (from “indice d’altération foliares”). I.E.S.F. was
calculated observing the progression of the disease along the stem from the ground to
the first leaf that present symptoms. A note was attributed to each plant in function to
the sick portion, following the criteria:
0: no symptoms
31
1: 20% of the plants presents symptoms of the wilt
5: plant totally compromised
To calculate I.E.S.F. was applied the formula:
note attributed /max note x100
Maximum note is 5. if a group of plants were considered, the formula was:
sum of note /max note x100
in this case, the maximum note was 5 x number of plants.
I.A.F. was calculated on the base of intensity of foliar symptoms. At each leaf
was attributed a note from 0 to 4. Zero was a leaf without symptoms, 4 was a died
leaf. the intermediated 1-2-3 represented various disease manifestations, from
yellowing to necrosis.
To calculate I.A.F. was applied the formula:
sum of note of a plant /max note x100
Maximum note is 4.
A consistent amount of phenotypical data was collected, considering this two
different parameters, the five eggplant lines and the 3 time points. All this data were
submitted to statistical analysis, using ANOVA (analysis of variance) and performing
the means comparison with Tukey’s test.
2-3 Results and discussion
The aim of this work is a detailed investigation about the improved tolerance of
resistant-to-Fom eggplants to Vd after simultaneous inoculation with Vd+Fom
32
compared with inoculation with Vd alone. The experimental design was planned to
make clear the mechanism of protection caused by the activation of Rfo-sa1 after
Fom, also in concomitance with Vd treatment. Five different mixed inoculations
were conducted : Fom+Vd at the same time, Fom and after 24h Vd, Fom and after
48h Vd, Vd and after 24h Fom and Vd and after 48h Fom. were performed also Fom
and Vd inoculation using individually the two fungi.
I.E.S.F.
The disease progression along the stem to the top of the plant (I.E.S.F.) during
30 days after inoculations showed 2 distinct trends. Very similar I.E.S.F. values were
observed considering the 3 resistant and the 2 susceptible eggplant lines.
All96/6, All96/6x1F59 and 305E40 presented a good resistance after Fom
inoculation as reported in Tab 4 (a, b and c).
All 96/6
Type of inoculation
Fusarium
Fom+ Vd
Verticillium
Fom+ 24h Vd
Fom+ 48h Vd
Vd + 24h Fom
Vd + 48h Fom
15 dai %
3,33
6,67
51,67
40,00
38,33
35,83
33,33
± se
22 dai %
4,32
6,18
5,18
5,31
5,30
3,36
7,19
6,67
53,33
71,67
40,00
43,33
60,00
65,00
± se
6,18
1,57
6,76
1,60
2,50
2,75
1,94
30 dai %
43,33
56,67
100,00
41,67
45,00
65,00
86,67
± se
1,92
3,36
0,00
0,96
2,45
0,99
3,81
Tab 4a: I.E.S.F percentage variation and standard error of All96/6 line under
all the inoculation type at three time points: 15, 22 and 30 days after inoculation.
All 96/6 X 1F5(9)
Type of inoculation
Fusarium
Fom + Vd
Verticillium
Fom + 24h Vd
Fom + 48h Vd
Vd + 24h Fom
Vd + 48h Fom
15 dai %
20,00
41,67
83,33
61,67
71,67
83,33
81,67
± se
22 dai %
4,45
5,05
3,47
4,09
3,82
3,47
3,46
33
25,00
60,00
81,67
60,00
65,00
83,33
81,67
± se
3,78
3,60
1,29
3,23
2,51
3,47
2,33
30 dai %
25,00
66,67
95,00
65,00
55,00
83,33
98,33
± se
3,42
0,00
3,74
3,01
3,31
1,49
3,74
Tab 4b: I.E.S.F percentage variation and standard error of All96/6x1F59 line
under all the inoculation type at three time points: 15, 22 and 30 days after
inoculation
305E40
Type of inoculation
15 dai %
Fusarium
Fom + Vd
Verticillium
Fom + 24h Vd
Fom + 48h Vd
Vd + 24h Fom
Vd + 48h Fom
± se
0,00
6,67
25,00
18,33
13,33
38,33
21,67
22 dai %
0,00
6,18
3,78
10,78
3,81
3,37
8,49
0,00
23,33
83,33
20,00
35,00
56,67
66,67
± se
0,00
1,31
2,42
3,27
2,51
3,44
7,33
30 dai %
13,33
48,33
90,00
20,00
30,00
61,67
76,67
± se
2,38
0,96
1,86
3,27
2,72
3,37
2,17
Tab 4c: I.E.S.F percentage variation and standard error of All96/6x1F59 line
inoculation.
305E40 was the line with the less disease progression after Fom inoculation, as
demonstrated by the I.E.S.F. percentage (from 0% at 15 dai to 13,33% at 30 dai),
especially in comparison to All96/6 (from 3,33% to 43,33% at 15 and 30 dai,
respectively). Also the tolerance after Vd was better with respect to All96 and
All96/6x1F59 (90%, 95% and 100% at 30dai, respectively). About the different
mixed inoculations, the 3 resistant lines followed the same trend: after Fom+Vd at the
same time, Fom+24h Vd and Fom+48h Vd inoculations the I.E.S.F. percentages were
lower than after Vd treatment. This evidence was particularly pronounced at 30 days
after inoculation. Instead, Vd +24h Fom and Vd +48h Fom inoculations showed
values more similar to Vd inoculation but lower. The addiction of Fom after 24 and
48 h seems to improve the defence response, also weakly with respect to mixed and
the Fom+24h and 48h Vd .
The two susceptible eggplant lines (1F59 and Tal 1/1) presented the same
trend, as showed in Table 5 (a and b).
34
1F59
Type of inoculation
Fusarium
Fom+ Vd
Verticillium
Fom+ 24h Vd
Fom+ 48h Vd
Vd + 24h Fom
Vd + 48h Fom
15 dai %
100,00
100,00
56,67
100,00
95,00
100,00
76,67
± se
22 dai %
0,00
0,00
2,50
0,00
5,41
0,00
1,31
100,00
100,00
93,33
100,00
100,00
100,00
100,00
± se
0,00
0,00
6,45
0,00
0,00
0,00
0,00
30 dai %
100,00
100,00
100,00
100,00
100,00
100,00
100,00
± se
0,00
0,00
0,00
0,00
0,00
0,00
0,00
Tab 5a: I.E.S.F percentage variation and standard error of 1F59 line under all
the inoculation type at three time points: 15, 22 and 30 days after inoculation.
TAL 1-1
Type of inoculation
Fusarium
Fom + Vd
Verticillium
Fom + 24h Vd
Fom + 48h Vd
Vd + 24h Fom
Vd + 48h Fom
15 dai %
100,00
88,33
76,67
100,00
95,00
86,67
85,00
± se
22 dai %
0,00
6,53
2,98
0,00
3,74
2,38
2,75
100,00
100,00
88,33
100,00
100,00
100,00
100,00
± se
0,00
0,00
5,91
0,00
0,00
0,00
0,00
30 dai %
100,00
100,00
100,00
100,00
100,00
100,00
100,00
± se
0,00
0,00
0,00
0,00
0,00
0,00
0,00
Tab 5b: I.E.S.F percentage variation and standard error of Tal 1-1 line under all
1F59 and Tal 1/1 had a very clear disease progression: after Fom treatment
(alone or in concomitance with Vd) the plants showed a 100% of sick portion, in 1522 days.
I.A.F.
The intensity of foliar symptoms (I.A.F.) during 30 days after inoculations
showed 2 distinct trends, as observed by I.E.S.F. analysis. Obviously, an important
difference of the values was observed among the 3 resistant and the 2 susceptible
eggplant lines. All96/6, All96/6x1F59 and 305E40 presented a good resistance after
Fom inoculation as reported in Tab 6 (a, b and c).
35
All 96/6
Type of inoculation
Fusarium
Fom+ Vd
Verticillium
Fom+ 24h Vd
Fom+ 48h Vd
Vd + 24h Fom
Vd + 48h Fom
15 dai %
5,17
1,30
20,83
13,37
9,29
13,58
6,22
± se
22 dai %
2,43
2,09
1,92
1,47
0,64
2,26
2,09
4,95
24,48
47,81
19,20
14,79
30,42
45,52
± se
3,31
3,52
2,28
1,78
1,68
1,62
2,85
30 dai %
12,25
24,72
58,37
19,24
14,48
31,71
50,90
± se
0,94
3,22
1,82
1,50
2,38
1,26
4,35
Tab 6a: I.A.F percentage variation and standard error of All96/6 line under all
All 96/6 X 1F5(9)
Type of inoculation
Fusarium
Fom + Vd
Verticillium
Fom + 24h Vd
Fom + 48h Vd
Vd + 24h Fom
Vd + 48h Fom
15 dai %
20,31
30,80
34,38
22,40
29,34
38,19
34,20
± se
22 dai %
1,72
1,96
1,31
2,63
0,95
3,55
1,27
16,67
38,13
53,75
28,96
30,63
49,41
49,58
± se
7,30
1,46
0,85
1,42
1,44
1,32
3,28
30 dai %
15,21
31,91
60,02
22,60
20,76
42,99
57,57
± se
2,10
1,51
0,64
3,25
3,52
2,77
0,88
Tab 6b: I.A.F percentage variation and standard error of All96/6x1F59 line
inoculation
305E40
Type of inoculation
Fusarium
Fom + Vd
Verticillium
Fom + 24h Vd
Fom + 48h Vd
Vd + 24h Fom
Vd + 48h Fom
15 dai %
1,56
1,56
6,77
4,69
4,58
6,30
2,71
± se
22 dai %
0,70
2,98
1,46
3,90
1,67
2,74
4,80
0,42
16,67
48,02
3,54
12,08
24,65
39,69
± se
1,51
1,19
1,27
5,53
2,60
0,23
4,65
30 dai %
4,90
16,25
60,45
6,04
10,38
30,76
46,15
± se
1,47
1,54
1,14
2,26
1,84
1,61
2,14
Tab 6c: I.A.F percentage variation and standard error of 305E40 line under all
the inoculation type at three time points: 15, 22 and 30 days after inoculation
36
All these 3 lines demonstrated its resistance to Fom, and not very significant
differences among All96/6, All96/6x1F59 and 305E40 subjected to Fom inoculation
were reported, though 305E40 was the less affected in terms of I.A.F..
About the mixed inoculations, the observations were very similar to those
conducted on I.E.S.F.: after Fom+Vd at the same time, Fom+24h Vd and Fom+48h
Vd inoculations the I.A.F. percentages were lower than after Vd treatment alone. This
evidence was particularly pronounced at 30 days after inoculation. Instead, Vd +24h
Fom and Vd +48h Fom inoculations showed values more similar to Vd inoculation
but lower.
About 1F59 and Tal 1/1, the intensity of foliar symptoms was very high after
all the Fom inoculation types (Table 7 a and b).
1F59
Type of inoculation
Fusarium
Fom+ Vd
Verticillium
Fom+ 24h Vd
Fom+ 48h Vd
Vd + 24h Fom
Vd + 48h Fom
15 dai %
76,91
72,12
16,67
74,65
74,24
61,35
35,78
± se
22 dai %
1,44
0,91
2,58
2,49
2,99
1,42
1,36
100,00
100,00
58,13
99,17
98,96
98,96
93,85
± se
0,00
0,00
1,16
2,63
2,94
2,94
4,97
30 dai %
± se
100,00
100,00
69,76
100,00
100,00
100,00
100,00
0,00
0,00
0,56
0,00
0,00
0,00
0,00
Tab 7a: I.A.F percentage variation and standard error of 1F59 line under all the
inoculation type at three time points: 15, 22 and 30 days after inoculation.
TAL 1-1
Type of inoculation
Fusarium
Fom + Vd
Verticillium
Fom + 24h Vd
Fom + 48h Vd
Vd + 24h Fom
Vd + 48h Fom
15 dai %
62,05
58,40
27,08
64,93
68,75
79,03
33,51
± se
22 dai %
1,50
2,47
0,94
1,68
1,18
11,35
1,35
100,00
99,48
58,75
100,00
100,00
100,00
89,76
± se
0,00
2,07
0,74
0,00
0,00
0,00
5,97
30 dai %
100,00
100,00
65,69
100,00
100,00
100,00
100,00
± se
0,00
0,00
1,39
0,00
0,00
0,00
0,00
Tab 7b: I.A.F percentage variation and standard error of Tal 1-1 line under all
37
Despite Fom inoculations, the Vd inoculation was particularly interesting
following I.A.F.. The five eggplant lines showed a foliar symptoms very similar if
compared each others. The range of I.A.F. percentage was about 60% in all the 5
lines.
The aim of this work was the confirmation of a preliminary observation,
related to the improved tolerance to Vd in eggplants resintant inoculated with Fom.
38
Chapter 3
Molecular analyses of Rfo-sa1 resistant eggplant interaction with Fusarium
oxysporum f. sp. melongenae and/or Verticillium dahliae
3-1 Introduction
Eggplant (S. melongena L.) is widely grown in both open fields and
greenhouses in Asia, Africa, and the subtropical areas, including the southern USA
and the Mediterranean region. The two fungal wilts caused by Verticillium dahliae
(Vd) (Bhat et al., 1999) and Fusarium oxysporum f.sp melongenae (Fom) (Urrutia
Herrada et al., 2004, Cappelli et al. 1995) are among the most serious diseases of
eggplant (Kennet et al., 1970; Stravato et al., 1993; Urrutia Herrada et al., 2004).
Fusarium is most important for the subtropical area, while Verticillium is mostly
present in the Mediterranean climates. The resistance levels found in the gene pool of
S. melongena are often insufficient for effective utilization in breeding programs
(Rotino et al. 2005), while a source of valuable traits of resistance to diseases may be
represented by the allied species of S. melongena . The resistance to Fom was
introgressed from S. aethiopicum through somatic hybridization followed by anther
culture of the tetraploid somatic hybrid to obtain dihaploid plants (Rizza et al., 2002)
which were successfully backcrossed with different typology of recurrent eggplants.
Advanced introgression lines (IL) were obtained through 6-8 cycles of backcross and
selection, followed by selfing and/or anther culture to obtain pure lines. Molecular
characterization of the ILs enabled to demonstrate that the introgressed resistance
trait is controlled by a single dominant gene (named Rfo-sa1, Resistance to Fusarium
oxysporum f. sp. melongenae from Solanum aethiopicum 1) and to develop molecular
markers associated to the resistance locus (Toppino et al., 2008).
39
Perception of the plant-pathogen interaction in model plants (e.g. Arabidopsis
thaliana-Fusarium, Berrocal-Lobo & Molina, 2008) follows the concept of the
elicitor-induced immune response, which in turn activates several defence signalling
pathways. In susceptible model plants, this fungal disease is able to multiply and
spread throughout the plant, leading to the appearance of the typical disease
symptoms: the fungus penetrates through the roots and proliferates in the vascular
tissue, and wilting progresses from lower to upper leaves, followed by collapse of the
plant. In resistant plants, the activation of a rapid and localized cell death at the site of
infection, known as the hypersensitive response (HR), limits the pathogen growth and
minimizes disease symptoms. In many plant-pathogen interactions, the development
of a HR is associated with several cellular responses that contribute to resistance
(Hammond-Kosack and Jones 1996). These responses include the production of
reactive oxygen species (ROS), transient opening of ion channels, cell wall
fortifications, production of phytoalexins, and synthesis of pathogenesis-related (PR)
proteins.
Resistance or susceptibility in the plant is dictated by the genetic backgrounds
of both the host and the invading pathogen. The recent development of genomics
techniques for the study of gene expression profiles (cDNA-AFLP, PCR-select,
RNAseq, microarray analysis) , together with the availability of sequenced genomes
and expressed sequence tag (EST) databases for many plant species, has allowed a
significant progress in the characterization of the plant responses to pathogen attack
(Wan et al. 2002). However, much remains to be learned about defence responses and
signalling pathways activated during the interaction of eggplant either with Fusarium
oxysporum f.sp. melongena, for which reports are limited (Mutlu et al 2008, Toppino
et al 2008) or Verticillium dahliae.
Therefore, in order to characterize genes involved during the early phase of
these plant-pathogen interactions , we analysed the radical extracts of the advanced
introgression lines resistant to Fusarium oxysporum sp melongenae (Fom) inoculated
with Fom and Verticillium dahliae (Vd). Another aspect that we investigated was the
40
improved tolerance of ILs to Vd after simultaneous inoculation using Vd+Fom
compared with inoculation with Vd alone.
Our choice to characterize the very early timings after inoculation was based
on preliminary biochemical studies conducted by Mennella et al. (2010). In this work
the radical extract of the advanced introgression line All96/6x1F59 was analysed
after inoculation with Fom at different timings. The spectrophotometer and RP-HPLC
analyses of the total protein contents suggested that the first 8 hours after inoculation
were the more suitable (among T0+24h to 72h) to study the eggplant interaction with
Fom.
As a first step toward the identification of plant genes involved in the eggplant
defence response to Fom and/or Vd, we plan to construct three cDNA libraries
enriched for Fom-, Vd- and Fom+Vd-
modulated genes, using suppression
subtractive hybridization (SSH) (Diatchenko et al 1996). SSH is a capable technique
for the isolation of genes expressed in plants subjected to both biotic and abiotic
stress, because it increases the relative abundance of some cDNA species
(Diatchenko et al 1996). The advantages of this technique include the detection of
low-abundant and differentially expressed transcripts through suppression of the
abundant ones, and the capacity of isolating genes with no previous knowledge of
their sequence or identity (Diatchenko et al., 1999). To help the unscrambling of the
complex molecular mechanisms of eggplant defence response to fungal inoculation,
we decided to combine the SSH technique with cDNA microarray technologies. In a
previous study, Yang et al (1999) combined SSH and cDNA microarray analysis for
the identification of differentially expressed genes in a human breast cancer cell line,
but also in works regarding tomato (Gibly et al., 2004; Oliveira, Magalhães, & Lima,
2008; Ouyang et al., 2007) and bamboo (Lin et al., 2006) the same approach was
used.
The array technology opens up significant opportunities to identified
pathogenesis-related genes and the associated regulatory systems, and to reveal
interaction between different signalling pathways (Wan, Dunning, & Bent, 2002) as it
41
allows to study contemporaneously the expression of many genes in one single
experiment. As no ready-to-use microarray chips are available for eggplant, our idea
is to construct a new CombiMatrix platform, with a 4x2K customized chip and
containing 2000x4 eggplant probe sets. The sequences for the chip design will be
selected from the genes collection retrieved from the three eggplant SSH cDNA
libraries of which after categorization will result to be putatively involved in the
plant-pathogen interaction. The probe set will be will be enriched with a panel of
resistance eggplant genes, selected from NCBI (The National Centre for
Biotechnology
Information,
http://www.ncbi.nlm.nih.gov/)
and
PRGdb
(http://www.prgdb.org), 5 sequences of putative housekeeping genes (β tubulin,
elongation factor 1- α, catalitc subunit of phosphatase 2A, 18s rRNA and
glyceraldeyde-3-phosphate dehydrogenase) and in addition 200 genes selected from a
collection of RAD-derived sequences (Barchi et al., 2011) which after GO
categorization resulted to be related to biotic stresses. We plan to compare changes in
gene expression between three different timings (0, 4 and 8 hours after dipping in
fungal suspension), considering the different fungal inoculations and the control
(mock inoculation with water). All the modulated genes identified will be then
functionally assigned according to the principal GO categories and their expression
profiles following Fusarium, Verticillium and mixed inoculations. Both SSH and
microarray techniques will be validated using qRT-PCR.
At present, qRT-PCR is the most suitable tool in quantitative gene expression
studies due to its precision and sensitivity (Gutierrez et al., 2008). The most diffused
approach with this technique is relative quantification, whereby the expression level
of a target gene is normalized depending on an internal reference gene, also called
housekeeping (Brunner et al, 2004). In our work, the selection of an adequate internal
control is particularly challenging, considering that if our purpose is to compare the
expression levels of the selected genes among the three different plant-pathogen
interactions, the putative reference gene should be not affected by any of the fungal
42
inoculations and any timing considered after root dipping. However, this topic will be
argued with more details in chapter 4.
The combination of suppression subtractive hybridization (SSH) and
microarray analysis will allow to identify a set of genes that are putatively involved
in the plant-pathogen interaction in order to select some candidate for a functional
study.
3-2 Materials and methods
3-2.1-Plant material and growing conditions; Fusarium, Verticillium and mixed
inoculations
Seed-derived plantlets of an advanced introgression line (All 96-6 x 1F5(9))
resistant to Fusarium oxysporum have been grown under greenhouse conditions.
Artificial inoculation was performed according to the root-dip method
described in Cappelli et al. (1995), using plantlets at the 3-4th true leaf stage.
Samples of inoculated and mock-inoculated (dipping in water) roots were harvested
at 0, 4 (T0+4h) and 8 hours (T0+8h) after artificial inoculation using a conidia
suspension of Fom (1.5x106/ml), or Vd (1.0x106/ml) or both the pathogens. Root
samples were subsequently frozen in liquid N2 and stored at -80°C. For each
treatment and timings, root samples from 8 inoculated plantlets were harvested,
pooled and used for the extraction of mRNA or total RNA.
T0+4h and T0+8h stages where chosen because preliminary Northern analysis
of tobacco chitinase IV gene expression and RP-HPLC analyses of the total protein
contents suggested that T0+4h and T0+8h stages were the more suitable (among
T0+4h to 72h) to study early interaction with Fom.
For subtractive hybridization, we employed samples of inoculated and mockinoculated roots of the resistant breeding line ALL96-6 x 1F5(9), harvested 8 hours
after root dipping in the fungal suspension or in water. mRNA was isolated through a
phenol-chloroform extraction, enriched for poly(A)+ RNA by chromatography on
43
oligo(dT)-cellulose (Sigma). The poly(A) RNA was then used for cDNA and used for
cDNA synthesis, followed by the digestion with RSA I. A two-step subtraction
followed by PCR amplification was performed using the Clontech PCR-select cDNA
subtraction Kit (BD Bioscience): mRNA from mock-inoculated roots (Driver) was
subtracted from mRNA from inoculated roots (Tester) (Diatchenko et al., 1996),
leading to enrichment of the resulting sample in differentially expressed sequences.
The product of the subtraction was amplified using two-step PCR in
accordance with the procedure recommended by the manufacturer. The amplified
products were cloned into the PGEM T-easy vector (Promega) to obtain three
libraries (one for each inoculation) of genes involved in the interaction between
eggplant and fungi. For molecular characterization, the most promising 1000 cDNAs
were selected from the three libraries, by comparing the intensity of spots in Dot blot
analysis. The clones were selected through comparison of the different hybridization
intensity of the correspondent spots in the two filter series obtained using both
mRNAs of inoculated and mock-inoculated samples as labeled probes (confirmed in
the inverted hybridization, as well). The selected clones were grown in LB containing
100 mg L-1 ampicillin overnight. Plasmids were extracted using the Pure Yield TM
Plasmid Miniprep System (Promega) and sequenced. FASTA sequences were
trimmed and cleaned using the Vector NTI software. (www. Invitrogen.com)
3-2.2-Functional characterization
Cleaned sequences were subjected to Blast analyses, using the BlastN
homology search tool, employing the NCBI (The National Centre for Biotechnology
Information, http://www.ncbi.nlm.nih.gov/), SGN (SOL Genomics Network,
http://www.sgn.cornell.edu/)
and
MiBASE
(MicroTom
Database,
http://www.kazusa.or.jp/jsol/microtom/) databases. All the analysed sequences were
grouped into three major categories: clones with no alignment in the database, clones
aligned with sequences of unknown or hypothetical function and clones aligned with
sequences of known function. The sequences belonging to the last category were
44
subjected to:
Uniprot (http://www.uniprot.org/), Brenda (http://www.brenda-
enzymes.info/) and Kegg (http://www.genome.jp/kegg/pathway.html) databases for
the allocation in metabolic groups of interest.
3-2.3-SSH validation: qRT-PCR
For molecular analysis, pooled samples of inoculated and mock-inoculated
roots of the advanced introgression line (All 96-6 x 1F5(9)), harvested at 0, 4 and 8
hours after inoculation, were employed. Total RNA was purified from root samples
using the RNeasy® plant RNA extraction kit (Qiagen). Root tissue was ground into a
fine powder in liquid nitrogen and dispersed into extraction buffer. RNA integrity and
quantification was determined with Nanodrop (Thermo Scientific Wilmington, USA).
Contaminating DNA was then removed from pooled RNA using RQ1 RNase-Free
DNase Treatment (Promega). Then reverse transcription was performed with
ImProm-II™ Reverse Transcription System (Promega). The reactions were incubated
at 25°C for 5 min (primer annealing) at 42°C for 1 h (cDNA synthesis) 15 min at
70°C (stop), and diluted 20-fold with sterile water. The resulting cDNA was
amplified using primers specific to the 18s gene. A total of 50 putative differentially
expressed sequences were chosen from the three subtractive libraries. The selection
was based on the previous functional characterization and covers all the functional
categories, unknown sequences included. Primer pairs were designed from these
sequences using PRIMER3 software and checked for secondary structure using
MFold program (http://www.bioinfo.rpi.edu/applications/mfold/cgi-bin/dna). Realtime analysis was performed in 72-Well Rotor with Rotor-Gene RG-6000 (Corbett
Research) using SYBR Green (IQTM Supermix Master, Bio-Rad) detection
chemistry. For each gene, the performance of the designed primers was tested by
PCR. Efficiency of primers was calculated using Rotor-Gene software on a standard
curve generated using a serial dilution of cDNA in triplicate and ranged from 88.0 to
101.0 %. The cycling conditions were set as follows: initial denaturation step of 95°C
for 3 min , followed by 50 cycles of denaturation at 95°C for 15 s, annealing and
45
extension at 59°C for 40 s. The amplification process was followed by a melting
curve analysis, ranging from 55°C to 95°C, with temperature increasing steps of
0.5°C every 5 s. All primer pairs were optimized for equivalent annealing
temperatures. The threshold was set at 0.004 fluorescent units, and the cycle
threshold (Ct) values were plotted against the starting template concentration. We
tested the 49 genes in two independent runs, each one composed at least of two
technical replicates. We performed a Tukey’s test to calculate the significance of Ct
between the means of expression levels in inoculated roots compared with the
corresponding control tissues, and only the statistically significant results were taken
under consideration.
3-2.4-Microarray
For array analysis, we employed samples of inoculated and mock-inoculated
roots of the advanced introgression line (All 96-6 x 1F5(9)) harvested at 0, 4 and 8
hours after the
fungal inoculation from an independent experiment. For array
analysis, we prepared a biological replicate of the three fungal inoculations using the
same conditions described before: plantlets of All 96-6 x 1F5(9) at the 3-4th true leaf
stage have been grown under greenhouse conditions, artificial inoculation was
performed according to the root-dip method described in Cappelli et al. (1995). Total
RNA was purified from tissue samples using the RNeasy® plant RNA extraction kit
(Qiagen). Root tissue (100 mg) was ground into a fine powder in liquid nitrogen and
dispersed into extraction buffer. RNA integrity and quantification were determined,
respectively, with Bioanalyzer (Agilent Bioanalyzer 2100) and Nanodrop. Expression
analysis was performed on a custom 4x2K CombiMatrix array (CustomArray,
Mulkiteo, USA) containing
2000 probes of 35-40 bp in length designed using
OligoArray 2.1 software (Rouillard JM et al, 2003). Three biological replicates were
used for each sample. Reverse transcription, amplification and labelling was
performed with
manufacturer’s
RNA AmpULSe amplification and labelling kit
instructions
(Kreatech
Diagnostics,
46
The
according to
Netherlands).
Pre-
hybridization, hybridization, washing and imaging were performed according to the
manufacturer's protocols (CustomArray, Mulkiteo, USA). The array was scanned
with a GenePix 4400A scanner and data extraction was done using GenePix Pro 7
software. The normalization between arrays was performed using the “quantile”
method. Analysis of differentially expressed genes was performed using linear
modelling and empirical Bayes methods, as implemented in the Limma R package.
P-values were adjusted for multiple testing with the Benjamini and Hochberg
method. Genes were called significant when log2 fold change was >= 1 (up-regulated
gene) or <= -1 (down-regulated gene) and the adjusted P-value was <=0.05.
3-2.5-Functional characterization and validation by qRT-PCR
The modulated genes were assigned to the principal GO categories using their
A. thaliana orthologs (http://www.arabidopsis.org/tools/bulk/go/index.jsp) as input
in GoSlim database (http://www.agbase.msstate.edu/cgi-bin/tools/goslimviewer.pl).
The array validation was conducted on 8 modulated genes (about 5% of the
modulated ones). Primer pairs were designed from these sequences using PRIMER3
software as described before. Real-time analysis was performed respecting the run
conditions described above: all the analysis were performed in 72-Well Rotor with
Rotor-Gene RG-6000 (Corbett Research) using SYBR Green (IQTM Supermix
Master, Bio-Rad) detection chemistry. For each gene, the performance of the
designed primers was tested by PCR. Efficiency of primers was calculated using
Rotor-Gene software on a standard curve generated using a serial dilution of cDNA
in triplicate and ranged from 88.0 to 101.0 %. The cycling conditions were set as
follows: initial denaturation step of 95°C for 3 min , followed by 50 cycles of
denaturation at 95°C for 15 s, annealing and extension at 59°C for 40 s. The
amplification process was followed by a melting curve analysis, ranging from 55°C
to 95°C, with temperature increasing steps of 0.5°C every 5 s. All primer pairs were
optimized for equivalent annealing temperatures. The threshold was set at 0.004
fluorescent units, and the threshold cycle (Ct) values were plotted against the starting
47
template concentration. We tested the genes in three independent runs, each one
composed at least of two biological replicates. We performed a Tukey’s test to
calculate the significance of
Ct between the means of expression levels in
inoculated roots compared with the corresponding control tissues, and only the
statistically significant results were taken under consideration.
3-3 Results
3-3.1-SSH and functional characterization
As a first step toward the identification of eggplant genes involved in the
defence response mechanism to Fom and Vd, we constructed three subtractive cDNA
libraries from Fom, Vd and Fom+Vd inoculated roots, using a mock inoculation
(water) as control. Each library was composed by 960 clones containing putative
differentially accumulated transcripts. Root tissues used for preparation of the three
libraries derived from plants of the advanced IL All96/x1F(5)9, carrying resistance to
Fom. For each treatment, root samples were harvested 8 hours after dipping in fungal
suspension or in water, were pooled and used for the extraction of mRNA,cDNA
synthesis and subtractive hybridization. Dot Blot analysis was carried out to select the
putative differentially expressed genes by comparing the different intensity of spots
in the two filter series obtained using both mRNAs of inoculated and mockinoculated samples as labeled probes.
After this previous screening, a total of 822 cDNAs were chosen for
sequencing analysis. As first consideration, we reported that the selected clones of the
three libraries bearing an insert were about 54% of the total (Tab.1). Considering the
three libraries, a higher number of up-regulated clones was always found with respect
to the down-regulated ones (Tab.8).
48
Library
Clones
Clones with insert
Total
Sequenced
Total
Upregulated
Downregulated
Fusarium
960
233
155
76.7 %
23.3 %
Verticillium
960
236
119
95.8 %
4.2 %
Fusarium / Verticillium
960
353
168
88.1 %
11.9 %
Tab.8 Total number, number of sequenced clones and percentage of up- and
down-regulated clones of the three cDNA libraries.
The three libraries of selected clones were normalized by eliminating
redundant sequences and a total of 100, 88 and 132 sequences from libraries of roots
inoculated with Fom, Vd and Fom+Vd, were respectively obtained. Putative function
was assigned to each sequence on the basis of its significant alignment in the
databases Kegg, Uniprot and Brenda.
All the sequences were subsequently grouped in fourteen functional categories:
primary metabolism and photosynthesis, DNA replication/ regulation and expression,
translation, protein synthesis/ degradation and modification, cell wall/ division and
cytoskeleton, secondary metabolism, development, signal transduction, transport and
translocation/membrane associated, stress induced, disease resistance, fungal,
unknown function, no matches (Tab.9). Considering the assigned categories, the
different expression profiles distinctive for each inoculation experiment (Fom, Vd and
Fom+Vd) were evaluated and subjected to comparison .
49
Functional category
Unknown function
No match
Primary metabolism and photosynthesis
Secondary metabolism
Protein Synthesis, Degradation and Modification
DNA Replication, Regulation and Expression
Translation
Cell Wall, Division and Cytoskeleton
Signal Transduction
Transport and translocation/ Membrane associated
Development
Stress induced
Defence response
Fungal
Fom
updownregulated regulated
20%
12%
6%
4%
7%
8%
4%
4%
9%
16%
4%
0%
9%
12%
11%
4%
2%
4%
8%
4%
1%
4%
0%
4%
18%
24%
1%
0%
Vd
22%
26%
13%
50%
1%
14%
8%
3%
2%
50%
1%
4%
0%
2%
4%
0%
Fom + Vd
14%
22%
21%
5%
11%
9%
2%
0%
8%
9%
0%
5%
2%
9%
6%
5%
9%
13%
13%
9%
0%
5%
5%
0%
9%
9%
0%
0%
Table 9. Percentage distribution and frequency (in percentage) of the upregulated and down-regulated sequences belonging to the three libraries, grouped
according to their functional categorisation
Particular interest was dedicated to the investigation of genes specifically
associated to the different inoculations utilized. In the library from Vd inoculated
roots, only two down-regulated genes (belonging to “basal metabolism” and “cell
wall division and cytoskeleton”) were found, while all the other clones were upregulated (98%) among them, 22% of the sequences having “unknown function”
and 26% with no matches were observed. Most of the sequences with known function
were associated to primary and secondary metabolism, while few sequences
belonging to the “defence response” group were identified (4%); 2% of the known
sequences were stress induced. Conversely, in the library from Fom inoculated roots,
“genes involved in defence responses” was the most represented category of upregulated genes (18%). With regard the up-regulated genes involved in “defence
response” and “cell wall division and cytoskeleton” categories, marked differences
between libraries from Fom (18% and 11%) and Vd (4% and 2%) inoculated roots
were identified, suggesting that a specific resistance reaction is triggered in the Fom
resistant line when the Rfo-sa1 gene is activated by Fom attack. Cell wall
modifications represent a well characterized defence response (Hammond-Kosack
50
and Jones, 1996) and in our experimental system could also represent a Rfo-sa1 genespecific response to Fom. Some of the ESTs were expected as originating from fungi,
because of the inoculation system, but only one gene was found to align with
Fusarium sequences and was obtained from the library of Fom inoculated roots. In
the library Fom+Vd, besides the well represented category of sequences with no
matches (21%) and unknown function (14%), a significant number of up-regulated
genes were classified as “related to defence” (9%), “cell wall” (6%), “transport”
(13%), “signal transduction” (9%) and “stress induced” (5%). Therefore, genes
derived from roots of Fom and Fom+Vd inoculations have a more similar expression
profiles with each other than when compared to the Vd library. Moreover, the plants
infected with Fom+Vd showed significanty lower symptoms with respect to plants
inoculated
with
Vd
(see
chapter
2).
Both
phenotypical
and
molecular
characterizations lead to the conclusion that a defence strategy mediated by the Rfosa1 locus in the IL seems to be able to improve the responses against a different
fungal wilt infection (i.e. Vd), towards which the plant wouldn’t be otherwise able to
organize a response.
1 a: upregulated
1 b: downregulated
100%
100%
90%
80%
80%
70%
60%
60%
50%
40%
40%
30%
20%
20%
10%
0%
0%
1
Fom
Fom2+ Vd
Vd3
1
Fom
Fungal
Translation
Stress induced
Development
Defence responce
DNA Replication, Regulation and
Expression
Cell Wall, Division and Cytoskeleton
Transport and translocation/ Membrane
associated
Primary metabolism and photosynthesis
Protein Synthesis, Degradation and
Modification
Unknown function
Signal Transduction
No alignment
51
Fom 2+ Vd
Fig. 8 Distribution of the up-regulated (1a) and down-regulated (1b) sequences
belonging to the three libraries, grouped in each functional group and graphical
representation of the percentage number assigned to each functional category.
When the sequences of the three libraries were compared, we observed that
very few sequences (15) are in common between at least two of them. The higher
similarity degree (9 common sequences) was observed between Fom and Fom+Vd
libraries, (common genes are for example xyloglucan endonuclease inhibitors, PR
proteins, osmotin precursors and TMV induced proteins). Three common genes were
identified between Vd and mixed inoculation libraries, and only two between Fom
and Vd libraries (2-nitropropane dioxigenase releated, caffeoyl CoA methyl
transferase, but in opposing expression). Finally, only one sequence was shared by
the three libraries (a TMV-induced protein). To further investigate the biological
processes implicated in these plant-pathogen interactions, a panel of putative genes of
interest was validated by qRT-PCR.
3-3.2-SSH validation: qRT-PCR
A subset of 49 putative defence-induced genes were selected for further
analysis by qRT-PCR to validate the SSH results by analysis of their expression
patterns in a eggplant line resistant to Fom. Primer pairs were designed for each gene
and tested for specificity by Blast comparisons against the NCBI database.
The list of the primer pairs referred to relative sequence code and the
corresponding annotation is reported in Table 10.
Considering the complexity of the experiment (three fungal inoculations and
three time points), the first critical aspect relied on the choice of the correct
housekeeping gene. In order to find the most suitable housekeeping gene for our
study, we performed a preliminary characterization of
the most common
housekeeping genes known in literature for plant-pathogen interaction (β tubulin,
elongation factor 1- α, ubiquitin, 18S rRNA and glyceraldeyde-3-phosphate
52
dehydrogenase). The selection of the best candidate was particularly challenging and
time expensive, this topic is presented in detail in a following dedicated chapter., The
final result of this work showed that GAPDH (glyceraldeyde-3-phosphate
dehydrogenase) was the best housekeeping gene under our conditions
and was used to normalize gene expression by means of the comparative Ct
method
53
FUNCTIONAL CATEGORY
SEQUENCE
CODE
Defence response
M4B10
Stress induced
Unknow function
no match
Primary metabolism
Transport and traslocation
for 5'AGGAACTCCGTGAAGAAGGA 3' rev
3'CGATTAGGGAGATAACCAGCA 5'
172
119
PR5-like protein osmotin-like protein (OSM1)
SGN-U314100
osmprec osmotin-like protein (OSM1)
SGN-U314100
for 5'CACGTATATGGGGCCGTACT 3' rev
3'AGTTGCCGAATTGATCCAAG 5'
150
179
F5A12
Nitroxyrel 2-Nitropropane dioxygenase- related
SGN-U580200
for 5'TGTTGATGCAGGTGTAGATGC 3'
3'CCAGAGCAGCAACATAACCA 5'
F8E4
Xylogluinib Putative xyloglucanase inhibitor
SGN-U314071
for 5'GAATCAAAACAAGCCCCAAA 3' rev
3'AGGTGTCGGTGGAACAAAAC 5'
150
for 5'TACCTCAGACCCCACCCTT3'
rev
3'GCAACTGTCTGGTGAAACGA 5'
rev
158
V6A5
Disresprot Disease resistance protein
SGN-U585507
M4H2
PR10/TSI pathogenesis-related protein 10
SGN-U312370
for 5'TTCCAATTTGTCTCCAAGAGC 3'
3'AGGGAGATGGTGTTGGAAGT 5'
150
rev
152
F9G6
TMVind TMV induced protein 1-2 (Tin1-2)
SGN-U571888
for 5'ACGATGCACCCAACAACTCT 3' rev
3'GGGCATCAAATTACATGAACG 5'
F9B9
BPR1 PR-1 protein precursor
SGN-U312367
for 5'TACCACCCATTGTTGCATCT 3' rev
3'GTCAAGATGTGGGTCGATGA 5'
149
M3H1
chitin Chitinase
SGN-U313266
for 5'GTACCCGATCCGATCTTCC 3' rev
3'ATGCCATGACGTTATCATCG 5'
153
V1F11
Bet v
SGN-U314971
for 5'CCAACAGTACCCCATTCACC 3' rev
3'TGAAGGAAGGTTGGTTTCACA 5'
150
136
M7G2
STH-21 S.tub pathogenesis-related protein
SGN-U315737
for 5'TGGAGGATGTGTTTGCAAGT 3' rev
3'AGGATTGGCGAGGAGATATG 5'
V1B10
radicalind dehydration-responsive protein-related
SGN-U317230
for 5'GAGTACCTCCACCAGGGAAAG 3' rev
3'AAGAAGTCAATTGGATGGCCTA 5'
142
V5F4
REF rubber elongation factor
SGN-U321960
for 5'GGCCGTAGAAGCCACTGTTA 3' rev
3'TTAACAGAGCTTTGGATGATGC 5'
170
lipocalin temperature stress-induced lipocalin
SGN-U313836
for 5'ACAGTGCCATCTGGATTCAA 3' rev
3'GGCTACAAAAGTAATGGAAGTGG 5'
M7H1
stressrelat stress-related protein
SGN-U313525
for 5'TTACACCAAATACGAGCCAATG 3'
3'CTGCTGTTTGCTGGACCAT 5'
F4H6
extensinlike extensin-like protein
SGN-U313108
for 5'GAACATGCACGTCTTCACCTT 3' rev
3'GCTGAGGATTTGGACAATCAA 5'
F5H7
Caffemetran caffeoyl-CoA O-methyltransferase
SGN-U581378
V3C10
ACT101 Actin-101
SGN-U318495
for 5'CCAGTCTACAAGCACAAATCTCC 3'
3'AACTGGAGGAGGTGGTGGA 5'
155
150
rev
132
for 5'CAAGGCCAACAGAGAGAAAA 3' rev
3'TGACTGACACCATCACCAGA 5'
for 5'TCCCTCTCGATATCCTTCTCAA 3'
3'AAGGCATCCCTGAGGAAGTC 5'
150
rev
147
rev
F2C6
spindle putative spindle disassembly related protein
SGN-U580237
F8F3
GPIanc GPI-anchored protein
SGN-U323291
for 5'CTGCTATTGTCGTCCCAACA 3' rev
3'GGTGATGGGTTTCCAACAAA 5'
M1D1
glucan endo-1,4-beta glucanase
SGN-U328622
for 5'ACGGCAGGTCAATTTTGC 3'
rev
3'ATGGCAGCCAATGTATTTCC 5'
161
M4F4
mucin mucin-like glycoprotein
SGN-U315189
for 5'GTTGGTGAGGGATGATACGG 3'
3'ACCACCAGCGACACCATAC 5'
111
M10C5
cinnamoyl-CoA reductase
SGN-U320036
for 5'ACACTGTTCACGCCACTGTT 3' rev
3'AATGCAAGGAGAAGCAAGGT 5'
179
M3D8
caffeoyl caffeoyl-CoA O-methyltransferase S. tuberosum
SGN-U313985
for 5'AAGGTTCCAAGGGTGTTTTG 3' rev
3'GTCGAGCAATGGAGAAAATG 5'
163
F3H2
hypotetical protein
SGN-U318407
for 5'ACAAGATAGTGCCCAAGGATG 3'
3'ATTCCCAAGTCCACGCTTC 5'
V4B11
Solanum melongena BAC 77N19
SGN-U322775
for 5'CCCCAAGTGTAATGAACTAGCA 3'
3'CACCTCGGTATTGTTTTTAACG 5'
M5C11
hypotetical protein
SGN-U314996
F5F4
rev
rev
155
rev
150
for 5'GCTGGCAGAACAAGGAAAAA 3' rev
3'CCACATGTTACTTGCGAGCTT 5'
for 5'TCATTTGTAGAATAATGCCCACA 3'
3'GGCGGAAATAAGAGTCTTGC 5'
no mach
158
153
152
rev
127
V4A5
Cytb5 Cytochrome b5
SGN-U314583
for 5'CTGAATTCCAGCTTCCATCG 3' rev
3'GCACATAGCAATTGTGAAGCA 5'
V1D10
Trepho Trehalose-phosphate phosphatase
SGN-U319739
for 5'ATTCAACCCCAACGATTCAA 3' rev
3'TGGACGAAGAGAGTTGGTCA 5'
167
170
152
154
M6F5
Chlorlyc Chloroplast genome
SGN-U575915
for 5'GGATCCGCATATGTTTGGTA 3' rev
3'GAATTCATTGGATCCTTGTCC 5'
M5D11
glucosid glucan 1,3-beta-glucosidase
SGN-U312944
for 5'CAGGCTTTCTTGGACTACCC 3' rev
3'CTTGGATCAAACAGGACAGG 5'
156
149
M3H2
desat microsomal oleic acid desaturase
SGN-U346467
for 5'TGTCCGAAATGTGATGGAAG 3' rev
3'TGAACGGCTTCATAGTGTTGA 5'
V5H6
PEMT Phosphoethanolamine N-methyltransferase
SGN-U314975
for 5'GGAAGTCACCTCCTCCAATG 3' rev
3'TGGCATACTACGCTATGAACGC 5'
V2A8
chlortub Chloroplast
M9B3
LTP protease inhibitor/seed storage/lipid transfer protein family protein
SGN-U575965
F3F2
PutImp7 putative Importin 7
SGN-U564933
for 5'TACTCCGAAAGGAAGGATGG 3'
3'ACAGGCATCCTTTGTGCAT 5'
tryinib trypsin and protease inhibitor family prot
SGN-U573941
for 5'ATGTTTTTGCTAATTTCGATGG 3'
3'TGCTTCTCTCAAAGGATTGC 5'
rev
131
rev
153
for 5'GACGATTTTAGCGATGACGA 3' rev
3'TGCTTGATAACGGAAGTCCA 5'
for 5'GTTGTGAGGATTTTACAGTGTGG 3'
3'CCTGAACCAGTTCCTTATTCG 5'
153
rev
101
M9C11
Miraculin-like protein
SGN-U315288
for 5'CCACCGGCAAGATGTAGTAA 3' rev
3'TGAAGACCAACCAACTTTTCC 5'
V8G8
methsynt S-adenosyl-L-methionine synthetase
SGN-U312579
for 5'TTGGTCCGCAACAAGAATTA 3' rev
3'TGAGAGGAGGCAACTACAGGT 5'
180
M3D12
protinibII protease inhibitor type II, CEVI57
SGN-U312589
for 5'CTCTCCTGCAGTGCAACAAT 3' rev
3'AAAGACGGCAAGTTTGTGTG 5'
150
V8F11
methsytub methionine synthase (mennella)
SGN-U334026
for 5'CGGTGCTTCTTGGATTCAGT 3' rev
3'CTCAGCAGGAACATCAGCAA 5'
157
139
150
M2G5
AADC aromatic amino acid decarboxylase
SGN-U578403
for 5'CCTGGCAGTCGTAATGGTTT 3' rev
3'TTCCTGCTTGTTGAAGACGA 5'
V8B10
Lemept Metallothionein-like protein
SGN-U313038
for 5'TCATAGGTGCAACACCCTCA 3' rev
3'ACATGTCTTGCTGTGGAGGA 5'
135
THT7 N-hydroxycinnamoyl transferase
SGN-U312993
for 5'AGCAAGTTGGCTAAGGAGGA 3' rev
3'GCATCATCGGAAAACAACAA 5'
170
sesqui sesquiterpene synthase
SGN-U572430
F8G3
Signal Transduction
SGN-U314559
F2C9
M6D10
DNA Regulation , Expression
PRIMER SEQUENCE
F3G1
M5D1
Protein synthesis
Lipoxygenase
AMPLICON
(bp)
SGN CODE
for 5'CCACGTTTGGAGGACAACA 3' rev
3'CGTTGGATCATCTTGAGGGTA 5'
M1H3
Cell wall
ANNOTATION
for 5'GGTTGAACCTGTCAGGATATGA 3'
3'CGAGGAATCAACCATTCAAA 5'
rev
135
V8H4
TGA bZIP family transcription factor
SGN-U566338
for 5'CTTCCCATTCCAGTGGTCAT 3' rev
3'TGAGCCATTGTCAGATCAGC 5'
V1F10
zinc finger(C3HC4-type RING finger)family protein
SGN-U583727
for 5'CCCTTTGTCGTTTTTCTAGTCC 3' rev
3'GGCTATCATGAGAAAATCGTTG 5'
159
F7E11
leurich leucine-rich repeat protein
SGN-U583216
for 5'CTGTGGCTGTAAAGGGGAAT 3' rev
3'CTCCATTGCAAGTGACATGA 5'
141
54
147
Tab 10: List of the sequences selected for qRT-PCR analysis, with the
corresponding SGN code, primer pairs sequences and amplicon length (bp)
The selection of the 49 candidate genes was conducted after the functional
classification, also considering their appearance in more than one library (common
sequences) and was based on the annotations reported in Table 3. Investigation was
especially performed on the “defence response” category, 12 primer pairs were
designed on the sequences from this group. These putative genes were: PR5-like
protein, Nitropropane dioxygenase- related protein, osmotin-like protein, putative
Xyloglucanase inhibitor, disease resistance protein, Pathogenesis-related protein 10,
TMV induced protein 1-2 (Tin1-2), PR-1 protein precursor, Chitinase, Bet v, STH-21
pathogenesis-related protein and Lipoxygenase. Only lipoxygenase was selected as
down-regulated from Dot-Blot, while the other 11 putative genes were up-regulated.
From the “stress induced” category four up-regulated putative stress-related genes
were chosen: dehydration-responsive protein-related, rubber elongation factor,
temperature stress-induced and stress-related protein. . The “cell wall” category
provided extensin-like protein, Caffeoyl-CoA O-methyltransferase,
Actin-101,
putative spindle disassembly related protein, GPI-anchored protein, Endo-1,4-beta
glucanase, mucin-like glycoprotein, cinnamoyl-CoA reductase and caffeoyl-CoA Omethyltransferase; in this category two down-regulated putative genes GPI-anchored
protein and caffeoyl-CoA O-methyltransferase are included. Cytochrome b5,
trehalose-phosphate phosphatase, two sequences belonging to chloroplast genome,
plus
1,3-beta-glucosidase,
microsomal
oleic
acid
desaturase
and
phosphoethanolamine N-methyltransferase were picked out from the “primary
metabolism” category, all up-regulated on the basis of the Dot-Blot analysis. From
“transport and translocation” group five primer pairs were designed on the upregulated sequences corresponding to LTP l(lipid transfer protein), putative importin,
55
trypsin, protease inhibitor and miraculin-like protein. Other selected putative upregulated
genes
belonged
to
“protein
synthesis”
(S-adenosyl-L-methionine
synthetase, protease inhibitor type II, methionine synthase), “secondary metabolism”
(AADC aromatic amino acid decarboxylase, metallothionein-like protein, Nhydroxycinnamoyl transferase and sesquiterpene synthase), “DNA regulation and
expression” (TGA bZIP family transcription factor, C3HC4-type zinc finger family
protein) and a down-regulated leucine-rich repeat protein sequences belonging to
“signal transduction” category. From the “unknown function” and “no alignment”
categories, with4 primer pairs were designed to analyze the correspondent sequences.
The higher number of up-regulated genes with respect to the down-regulated ones
reflects the different percentage of clones classified as up- and down-regulated in the
subtractive libraries.
The list of the genes analyzed by qRT-PCR and the corresponding conditions
of induction were reported in Tab 4.
For each of the 49 genes qRT-PCR analyses was carried out considering all the
inoculations and timings (despite the library from which it had been selected), in
order to validate the different expression pattern between the inoculated samples and
the control, and also to find significant differences in gene expression after Fom, Vd
and Fom+Vd inoculations.
Concerning candidate genes identified through Dot Blot analysis, a modulation
in at least one conditions or timings was demonstrated for 40 of 49 candidate genes
by qRT-PCR. A total of 9 genes were not significantly induced, in contrast to the
prediction from Dot-Blot results. About the others 40, a significant proportion was
up-regulated in response to fungi inoculation.
3-3.3-Induction after Fom inoculation:
After Fom inoculation, 32 out of the 49 analyzed genes were found to be upregulated in the eggplant roots. The relative abundance of the transcripts of 13 of
these 32 genes was in agreement with the outcomes of the Dot-blot confirming their
56
up-regulation following inoculation with Fusarium. On the contrary, the other 19
were up-regulated according to the
qRT-PCR analysis although they had been
considered unaffected in the Dot-Blot analysis of the clones after Fom inoculation. .
This fact can be easily explained if we taken under consideration the much lower
sensibility of Dot Blot when compared to qRT-PCR analysis, the last technique is
much more reliable and therefore the results obtained through qrt-PCR were
considered valid, About the 17 genes not affected by Fom inoculation, 9 of these had
never been inducted. Considering the two different timings analyzed (T0+4h and
T0+8h), most of the genes (15) were found to be induced both at 4 and 8 hours after
Fom inoculation. Interestingly, 13 genes were induced only at T0+4h after
inoculation, instead only 4 were inducted at T0+8h.
3-3.4-Induction after Vd inoculation.
After Vd inoculation, 30 genes showed a significant induction. The 66,6% of
the induced genes were up-regulated and the 33,4% was down-regulated after qRTPCR analysis. Five genes were exclusively induced after Vd inoculation, the
remaining were equally up-regulated after both Fom and Vd inoculation. With respect
to Dot-Blot results, 7 genes were confirmed in qRT-PCR analysis, 8 showed an
opposite induction (selected as up-regulated were down-regulated in qRT-PCR or
vice-versa) and half of them (15/30) resulted as Vd induced without a specific
selection. On the contrary of the Fom inoculation, most of the Vd-induction (14 of the
30 genes) happened 8 hours after inoculation , 10 genes were induced at both time
points and 6 were over-expressed only 4h after inoculation.
3-3.5-Induction after Fom +Vd inoculation:
After Fom+Vd inoculation, a total of 31 differentially expressed genes were
identified. The percentage of up-regulated genes was higher than down-regulated
ones(80,64% and 19,36%, respectively). Almost all the genes (28/31) were coinduced with respect to at least one other inoculation: 22 genes resulted co-induced
57
under the three types of inoculations, 5 were common to Fom and mixed inoculation
and only one (Phosphoethanolamine N-methyltransferase) was common to Vd and
mixed inoculation. The 3 genes specifically induced following the Fom+Vd
inoculation wereEndo-1,4-beta glucanase , dehydration-responsive protein related
and one with no match.
A low level of agreement was revealed between the Dot-Blot and qRT-PCR
analyses:
only 11 out of the 31 genes showed the same type of induction. About the
others genes, 17 were differentially expressed only in qRT-PCR analysis, the
remaining 4 genes showed an opposite induction. Similarly to the Fom inoculation,
the most of the genes (16) were induced at both T0+4h and T0+8h after inoculation,
followed by 10 genes induced exclusively at T0+4h.
Tab 11: Total genes analyzed by qRT-PCR. The ↑ represent a positive
induction, the ↓ represent a negative induction.
58
coordinate
Annotation
Fom
M4B10
Lipoxygenase
F2C9
PR5-like protein
F3G1
F5A12
osmotin-like protein (OSM1)
2-Nitropropane dioxygenase- related
F8E4
Putative xyloglucanase inhibitor
V6A5
M4H2
F9G6
Disease resistance protein
TMV induced protein 1-2 (Tin1-2)
F9B9
PR-1 protein precursor
M3H1
Chitinase
V1F11
Bet v
M7G2
STH-21 pathogenesis-related protein
V1B10
dehydration-responsive protein-related
V5F4
rubber elongation factor
M1H3
temperature stress-induced lipocalin
M7H1
stress-related protein
F4H6
extensin-like protein
F5H7
caffeoyl-CoA O-methyltransferase
V3C10
Actin-101
F2C6
putative spindle disassembly related protein
F8F3
GPI-anchored protein
M1D1
M4F4
endo-1,4-beta glucanase
mucin-like glycoprotein
M10C5
cinnamoyl-CoA reductase
M3D8
F3H2
Dem2
V4B11
smBAC Solanum melongena BAC 77N19
M5C11
no match
F5F4
no match
V4A5
Cytochrome b5
V1D10
Trehalose-phosphate phosphatase
M6F5
Chloroplast genome
M5D11
glucan 1,3-beta-glucosidase
M3H2
microsomal oleic acid desaturase
V5H6
Phosphoethanolamine N-methyltransferase
V2A8
Chloroplast
M9B3
LTP lipid transfer protein
F3F2
putative Importin 7
M5D1
trypsin and protease inhibitor family prot
M9C11
V8G8
S-adenosyl-L-methionine synthetase
M3D12
protease inhibitor type II
V8F11
methionine synthase (mennella)
M2G5
aromatic amino acid decarboxylase
V8B10
Metallothionein-like protein
M6D10
THT7 N-hydroxycinnamoyl transferase
F8G3
sesquiterpene synthase
V8H4
TGA bZIP family transcription factor
V1F10
zinc finger(C3HC4-type RING finger)family protein
F7E11
leurich leucine-rich repeat protein
T0+4h
T0+8h
↑
↑
↑
↑
↑
qRT-PCR
Vd
T0+4h
T0+8h
↑
↑
↑
↓
↑
↑
↓
↑
↑
↓
↑
↑
Fom+Vd
T0+4h
T0+8h
↑
↑
↑
↑
↑
↓
↑
↑
↓
↑
↓
↑
↓
↓
↑
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59
↑
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↑
↑
↑
↑
↓
↓
3-3.6-Genes with an interesting expression profiles
After these time-expensive qRT-PCR experiments, we conducted another
selection step among the 40 induced genes. Our intention was to identify a set of
genes strictly correlated with the introgression of Rfo-sa1, locus that triggered the
resistance to Fom. Obviously, the genes most interesting for our purposes were
belong to the collection of genes inducted by Fom and mixed inoculations. Also the
genes induced by the three fungal inoculations were taken under consideration, in
particular if it was a significant difference in the gene expression profiles among the
three different inoculations. So, we focused on genes that showed significant
induction under Fom inoculation (only Fom or with Vd in the mixed inoculation), or
induced also by Vd but together with the other inoculations. That way, our interest
was fixed on: PR5-like protein, osmotin-like protein, lipoxygenase, PR-1 protein
precursor, PR-10 protein, STH-21 pathogenesis-related protein, caffeoyl-CoA Omethyltransferase, trehalose-phosphate phosphatase, LTP or lipid transfer protein,
miraculin-like protein, protease inhibitor, and sesquiterpene synthase. Some of those
showed very important differences in gene expression, as reported in Fig2.
3-3.7-Microarray and functional classification of the differentially expressed
genes.
In order to identify transcripts expressed at different levels during fungal
inoculation (after inoculation with Fusarium, Verticillium and both fungi together), a
customized chip containing probes from a set of cDNA clones putatively involved in
defence response was constructed. The cDNA collection derived from the SSH
analysis was further enriched with a new set of clones chosen randomly from the
three subtractive libraries. In total, we added 17, 28 and 34 sequences from Fom, Vd
and mixed library, respectively. Globally, probes were designed on more than 400
clones, selected from the three SSH libraries (Toppino et al 2010), and synthesised in
60
triplicate on microarray slides. The array also included 50 more sequences derived
from the databases NCBI and PRGdb (Sanseverino et al. 2010) and encoding for
known eggplant defence-related genes and 200 new eggplant sequences involved in
root’s stress response and retrieved from a RAD-tag sequencing of two eggplant
genotypes (Barchi et al., 2011). In addition, we selected 5 sequences of putative
housekeeping genes (β tubulin, elongation factor 1- α, catalitc subunit of phosphatase
2A, 18s rRNA and glyceraldeyde-3-phosphate dehydrogenase).
For array hybridization, a technical replicate was prepared of the three fungal
inoculations as described before: plantlets of All 96-6 x 1F5(9) at the 3-4th true leaf
stage have been grown under greenhouse conditions and inoculated with Fom, Vd and
Fom+Vd, while water inoculation was performed as control. In total, 8 different
conditions were analyzed: the 4 different treatments (inoculations with Fom, Vd and
Fom+Vd and un-inoculated control) at 2 time points (T4/T0 and T8/T0), using three
biological replicates for each treatment. Total RNA was extracted from roots and was
used for the preparation of fluorescent probes, which were then hybridized to the
microarray slide.
With this approach, almost 25% of the probes (more than 150 genes) resulted
differentially expressed with statistical significance (P<=0.05) in at least one
treatment. The genes were identified and then functionally assigned according to the
principal GO categories (molecular function, biological process and cellular
localization), and their expression profiles were examined following Fusarium,
Verticillium and mixed inoculations.
This high percentage of modulation with respect to a whole transcriptome
microarray approach can be easily explained by considering the probes were
previously selected by means of SSH procedures. The number of differentially
expressed genes at every conditions and timings is reported in Fig.3. It includes 105
up-regulated and 94 down-regulated genes.
61
70
upregulated
60
downregulated
59
54
51
49
48
50
45
37
37
36
Control
Vert.
39
36
Mixed
37
Fus.
38
40
34
37
31
30
20
T4/T0
T8/T0
T4/T0
Mixed
Fus.
Vert.
Control
Mixed
Fus.
Vert.
Control
Mixed
Fus.
Vert.
0
Control
10
T4/T0
Fig 9 Number of up- and down-regulated genes after the 3 different fungal
inoculations and the control at T4h versus T0 and T8h versus T0.
The number of up-regulated genes is higher than down-regulated ones and
slightly higher in T8 versus T0 than in T4 versus T0. Whilst, the down-regulated
genes remains comparable between T8/T0 and T4/T0. The predominance of the
genes with a positive induction was a confirmation of the results obtained first by
SSH-Dot-Blot and, then, by qRT-PCR analysis. The functional classification of the
genes that showed an induction after the fungal inoculations was performed
consulting TAIR database (http://www.arabidopsis.org/tools/bulk/go/index.jsp) . This
alternative database was used in order to improve the number of annotations, because
the first functional classification, based on Kegg, Uniprot and Brenda and referred to
the three subtractive libraries, assigned a metabolic function only to the 50% of the
available sequences. In this new characterization, we didn’t obtain the expected
results, and the percentage of sequences with a putative annotation was 53,8%, only
slightly higher than the first one.
62
The modulated genes were distributed according to the principal GO
categories, Molecular Function (MF) Cellular Component (CC) and Biological
Process (BP) (Fig. 10 a and b).
BP
CC
MF
%
Fig 10a: Functional characterization of the up-regulated genes according to the
GO categories (Biological Process, Cellular Component, Molecular Function)
63
BP
CC
MF
%
Fig 10 b: Functional characterization of the down-regulated genes according to
the GO categories (Biological Process, Cellular Component, Molecular Function)
64
About the BP terms, the first evidence is the high percentage of unknown
sequences. The eggplant genome is still rather unexplored, this may explain why a lot
of sequences find no correlation match in any databases, even using a new
classification method. Regarding the GO categorization of the assigned sequences,
we can resume that among the up-regulated sequences, the most representative
categories are “metabolic, cellular and biological processes”. The principal interest
was focused on the sub-categories: “response to jasmonic acid”, “incompatible
interaction”, “lateral root primordium development”, “response to abscisic acid”,
“response to oxidative stress”, “systemic acquired resistance”. All of these subcategories are strictly correlated with response to pathogen attack. Highly represented
after Fusarium inoculation is also the “response to biotic stimulus” sub-category, that
included the “response to fungus” GOs. Looking at BP classification of downregulated sequences (Fig.3b), should be noted that the most representative categories
are also “metabolic, cellular and biological processes”, but in this case the subcategories regarded the primary metabolism and the oxidation-reduction process. The
MF terms “catalytic activity” and “hydrolase activity” occurred most frequently both
in the up- and down-regulated genes, but in the classification of down-regulated ones
the MF term “binding” was also well represented. Finally, the CC terms indicated
that the differentially expressed genes were active in every category, with a particular
induction in the “cell wall”, “extracellular region”, “plastid” and “plasma
membrane”. The same trend was observed between up- and down-regulated genes.
A critical observation about the total number of the genes positively coinduced was that the majority of the inducted genes showed a modulation after all the
four treatments, i.e. the three kinds of fungal inoculations and the mock inoculation
(control), as well. Not surprisingly, these co-induced genes were PR proteins,
osmotin precursor, xiloglucanase inhibitor, proteinase inhibitor and others genes
strictly correlated with the defence response mechanism. About the down-regulated
genes, we observed a similar co-induction, but the selected genes were less -involved
in the defence mechanism: stress related protein, helicase, tubulin beta, 265
oxoglutarate-dependent dioxygenase and xyloglucan endotransglucosylase-hydrolase
were some examples of this panel of genes.
Subsequently, we performed the array validation using qRT-PCR and a
comparison between the results of the two approach (SSH + qRT-PCR and array +
qRT-PCR).
The array was validated by qRT-PCR analysing the expression profiles of 8
genes, the results obtained will be discussed in the next section.
3-3.8-Array validation by qRT-PCR
The microarray results required verification and validation by an alternative
and complementary gene expression profiling method. We decided to use quantitative
Real-time PCR, because represented the most rigorous, reliable and commonly used
technology for our purpose, and SYBER Green was the easiest and least expansive
qRT-PCR detention method. It was no doubt about the major bottleneck to array
confirmation: the process of design and optimization of gene-specific primer pairs.
Taking in consideration all the specific conditions in the primer design (e.g. GC
content, melting temperature, possible development of secondary structures), the
most important rule regarded the position of the amplicon over the sequence to
amplify. The primer pairs had to amplify the probe sequence (30-40 bp) for every
gene under validation. We respected these condition for all
the
primer pairs
designed in order to validate the chip results. Fortunately, 7/8 primer pairs previously
designed satisfied the conditions reported above, so we designed only 1 new primer
pair (Tab.12)
66
SEQUENCE
CODE
ANNOTATION
SGN CODE
F2C9
PR5-like protein osmotin-like protein (OSM1)
SGN-U314100
F3G1
osmprec osmotin-like protein (OSM1)
SGN-U314100
F8E4
SGN-U314071
M2G5
AADC aromatic amino acid decarboxylase
SGN-U578403
M3H1
Chitinase
SGN-U313266
M9B3
LTP protease inhibitor/seed storage/lipid transfer protein family protein
SGN-U575965
M9C11
SGN-U315288
*F3C3
Fusarium oxysporum f. sp. lycopersici six1 gene
PRIMER SEQUENCE
for 5'CCACGTTTGGAGGACAACA 3'
rev
3'CGTTGGATCATCTTGAGGGTA 5'
for 5'CACGTATATGGGGCCGTACT 3' rev
3'AGTTGCCGAATTGATCCAAG 5'
for 5'CCTGGCAGTCGTAATGGTTT 3' rev
3'TTCCTGCTTGTTGAAGACGA 5'
for 5'GTACCCGATCCGATCTTCC 3'
rev
3'ATGCCATGACGTTATCATCG 5'
for 5'ATGTTTTTGCTAATTTCGATGG 3' rev
3'TGCTTCTCTCAAAGGATTGC 5'
for 5'CCACCGGCAAGATGTAGTAA 3' rev
3'TGAAGACCAACCAACTTTTCC 5'
for 5'GACGGGATGGACCTCTTGAAA 3' rev
3'CAGTAGCTGTCCGTGAAGCA 5'
AMPLICON
(bp)
119
150
150
139
153
153
150
171
Tab 12: List of the primer pairs used for array validation by qRT-PCR. The * is
associated to the primer pair newly designed.
So, Eight genes were selected from the total of modulated ones (5% of the
total, as suggested in literature by Morey et al., 2006). We decided to select both upand down-regulated genes, and also genes that showed marked or slight differences in
gene modulation. The selection was based also on functional classification, hence
defence-related genes were considered more interesting than other ones. So, we
obtained a panel of 8 genes in representation of the whole array. The results from
qRT-PCR whth the specific primers confirmed the reliability of the microarray data.
The expression value determined in inoculated roots versus control by qRTPCR analysis were higher than the fold-change determined by our array
hybridization; in some cases, with a dramatic difference. We found a similar
tendency for the inoculated versus control expression value, albeit not to the same
extent. These results reflect the fact that qRT-PCR is a more sensitive technique than
transcript profiling using arrays and, likely, is not affected by related transcripts that
cause problems of cross-hybridization, at least with gene-specific primers. On the
basis of this consideration, we planned to extent our qRT-PCR experiments to others
nine modulated genes, in order to and to better discriminate the specific fungalresponse. In this sense, the microarray gave us a general information about the
modulated genes, and allowed us to screen a panel of genes for a specific and more
accurate second step of qRT-PCR experiments. The nine genes selected with the
corresponding primer pair sequences are reported in Tab 13.
67
SEQUENCE
CODE
ANNOTATION
SGN CODE
M3D8
SGN-U313985
V8G8
methsynt S-adenosyl-L-methionine synthetase
SGN-U312579
M4H2
SGN-U312370
V1D10
SGN-U319739
M7G2
STH-21 S.tub pathogenesis-related protein
SGN-U315737
F8E4
SGN-U314071
M3D12
protinibII protease inhibitor type II, CEVI57
SGN-U312589
*F10F7
xyloglucan endotransglucosylase-hydrolase xth3
SGN-U579445
*M9E2
xyloglucan-specific fungal endoglucanase inhibitor
SGN-U274748
*M9F6
fe-superoxide dismutase
SGN-U271296
*M4C2
RNA helicase SDE3
AMPLICON
(bp)
PRIMER SEQUENCE
for 5'AAGGTTCCAAGGGTGTTTTG 3' rev
3'GTCGAGCAATGGAGAAAATG 5'
for 5'TTGGTCCGCAACAAGAATTA 3' rev
3'TGAGAGGAGGCAACTACAGGT 5'
for 5'TTCCAATTTGTCTCCAAGAGC 3' rev
3'AGGGAGATGGTGTTGGAAGT 5'
for 5'ATTCAACCCCAACGATTCAA 3' rev
3'TGGACGAAGAGAGTTGGTCA 5'
for 5'TGGAGGATGTGTTTGCAAGT 3' rev
3'AGGATTGGCGAGGAGATATG 5'
for 5'CTCTCCTGCAGTGCAACAAT 3' rev
3'AAAGACGGCAAGTTTGTGTG 5'
for 5'CATTGTAATTGGGGGCAAGT 3' rev
3'GAGCTGCATGTGAATTTTACCA 5'
for 5'GCTGCATCAAGATTGGGATT 3' rev
3'ATGCGCATTATTCACACCTG 5'
for 5'AATCGGCGACCTGACTACAT 3' rev
3'CTTAATGCGCATCTCCCTTC 5'
for 5'CAGCACTCAAACCCGAAACT 3' rev
3'GGCAAGTGAATACCTTTCCACA 5'
163
180
152
167
136
150
150
169
224
160
158
Tab 13: list of the 9 genes analysed after the array validation. The * symbol is
for the new primer pairs designed.
In total, we analysed 18 genes, 5 of those were newly selected after the array
results. The mRNA used for both microarray and qRT-PCR experiment was extracted
from a technical replicate of the starting material. We chose 9 up-regulated and 9
down-regulated genes, and the correlation between qRT-PCR and the induction trend
(from microarray output) was almost 100%: only AADC (aromatic amino acid
decarboxylase), S-adenosyl-L-methionine synthetase and fe-superoxide dismutase
showed an opposite induction if we compare array and qRT-PCR results.
If we considered the single inoculation type and the two distinct time points,
the agreement between array and qRT-PCR results was lower: the details of these
comparison are in Tab 14.
coordinate
Annotation
qRT-PCR
Vd
T0+8h
Fom
F2C9
F3G1
F8E4
M4H2
T0+4h
T0+8h
↑
↑
↑
T0+4h
↑
↑
PR5-like protein
Fom+Vd
T0+4h
T0+8h
↑
↑
↑
M3H1
Chitinase
M7G2
V1D10
M9B3
M9C11
V8G8
M3D12
M2G5
M3D8
F3C3
Fusarium oxysporum f. sp. lycopersici six1 gene
F10F7
xyloglucan endotransglucosylase-hydrolase xth3
M4C2
RNA helicase SDE3
M9E2
xyloglucan-specific fungal endoglucanase inhibitor
M9F6
fe-superoxide dismutase
↓
↑
↑
↑
↑
↓
↑
↓
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↓
↑
↑
↑
↑
↓
↑
↓
↓
↓
↓
↑
↑
↑
↓
↓
↑
68
array
Vd
Fom
T0+4h
T0+8h
T0+4h
T0+8h
↑
↑
↑
↑
↓
↑
↑
↑
↑
↑
↑
↑
↑
↑
↓
↑
↓
↑
↑
↓
↑
↑
↑
↑
↑
↓
↑
↓
↑
↑
↓
↑
↓
↓
↓
↓
Fom+Vd
T0+4h
T0+8h
↑
↑
↑
↑
↑
↑
↑
↑
↑
↓
↑
↓
↑
↓
↑
↑
↑
↑
↓
↓
↑
↑
↑
↓
↓
↑
↑
↓
↑
↓
↓
↑
↓
↑
↓
↓
↑
↑
↓
Tab 14: Expression trends of the 18 genes selected to validate the array in
qRT-PCR compared with the array output in the different conditions (Vd, Fom and
Vd+Fom) and time points (T0+4h, T0+8h) . The ↑ represent a positive induction, the
↓ represent a negative induction
For our purposes, the most important and significant comparison was between
the same genes analysed in the two technical replicates by qRT-PCR: only the genes
with a confirmed expression profiles represented a good candidate for a subsequent
functional study.
Then, attention was focused on a set of twelve genes analysed in both
replicates. As showed in Tab 8, osmotin like precursor, putative xyloglucanase
inhibitor, STH21, caffeoyl Co-A methyltransferase, LTP miraculin and proteinase
inhibitor were the genes having a most consistent correlation between the two
replicates. To facilitate this comparison, the induction was analysed considering only
the fungal inoculation and not the two distinct time points. Not surprisingly, all of
this genes were up-regulated (Tab.15).
coordinate
F2C9
Annotation
qRT-PCR (1 replicate)
Fom
Vd
Fom+Vd
↑
↑
↑
PR5-like protein
F3G1
F8E4
M4H2
↑
↑
↑
↑
qRT-PCR (2 replicate)
Fom
Vd
Fom+Vd
↑
↑
↑
↑
M3H1
Chitinase
M7G2
V1B10
dehydration-responsive protein-related
M3D8
M9B3
M9C11
V8G8
M3D12
M2G5
↑
↑
↑
↑
↑
↑
↑
↑
↓
↑
↑
↓
↑
↑↓
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↑
↓
↑
↓
↑
↑
↑
↑
↓
↑
↑
Tab 15: comparison between the genes analysed in the two technical replicates
by qRT-PCR. The emphasised genes showed the most correspondence in the two
replicates.
In conclusion, our verification studies by SSH, array and qRT-PCR yielded a
total of 7 genes corresponding to a range of functional classes, the most prominent
69
one being defence response. These seven genes represent the panel of candidates for
a subsequent functional study, based on an accurate and complete analysis of their
expression profiles under three fungal inoculations, two time points and two technical
replicates, each one composed by three biological replicates.
3-3.9-F3C3 : Fusarium oxysporum f. sp. lycopersici six1 gene, fot5 gene, six2
gene, shh1 gene and ORF2
Another important result regarded the sequence F3C3, the only one sequence
identified from Fom and selected in the Fom inoculation library. This sequence was
annotated at the first functional classification using Blast homology search tool, and
its expression profile was analyzed after array hybridizations. To confirm the array
induction after Fom and mixed inoculation, we designed a specific primer pair for
qRT-PCR analysis. The expression profile of this gene is reported in Fig 5. In this
case, , after Vd inoculation and in the control there was no detection of the Fom
gene, and after Fom and mixed inoculation we found a positive induction. These data
are a confirmation of the array results.
18
16
14
12
c
10
8
f
v
6
4
m
2
0
T0
T4
T8
Fig 11. Expression profile of the six 1 gene after Fom inoculation (red line) and
mixed inoculation (blue line).
70
For the unraveling of molecular mechanisms of disease resistance in plants, it
is important to identify the avirulence factors from the pathogen. Identification of
such factors may also lead to a better understanding of the molecular basis of
pathogenicity, as their secretion in planta could play a positive role in colonization of
a susceptible host plants. From fungal plant pathogens, few avirulence factors have
yet been identified. The majority of these are small cysteine-rich proteins (Rep et al.,
2004). However, six1 gene is required for I-3 mediated resistance of tomato towards
Fusarium oxysporum f.sp. lycopersici (Rep et al., 2004). The biological function of
the six 1 protein remains to be established.
3-4 Discussion
The combination of suppression subtractive hybridization (SSH) and
microarray techniques allowed to identify a large number of eggplant genes
differentially regulated during the response of an eggplant introgression line
(All96/6x1F59) to three fungal inoculations: Fom, Vd and Fom +Vd. As a first step
for the elucidation of cellular events taking place during the interaction between fungi
and the eggplant line resistant to Fom, SSH libraries were prepared from root tissues
of the resistant plants inoculated with the three kinds of fungal suspensions.
SSH combines suppression PCR with subtraction and normalization steps in a
single reaction, thus increasing, therefore the possibility rescuing low expressed
genes (Diatchenko et al., 1999). Genes with unknown roles were identified in the
three subtractive libraries, which indicates the possibility of identifying new genes
which have not yet been reported in previous studies of stress/defense response.
Similar results were observed in SSH libraries from Arabidopsis, potato and tomato,
in biotic stress conditions (Oliveira et al., 2008). Table 1 shows that the higher
number of genes involved in defense mechanisms were found in the Fom and mixed
inoculation libraries.
71
Plant genes with known roles involved in biotic stress response were identified
both in Fom and mixed inoculation libraries, while in the Vd inoculation library the
most representative categories are “primary metabolism” and “protein synthesis”.
Therefore, the kind of genes belonging to the libraries derived from roots inoculated
with Fom and Fom+Vd and also their expression profiles are more similar with each
other than when compared to the Vd library. This evidence is strictly correlated to the
fact that the eggplant line used in this work triggers the resistance locus to Fom,
therefore it seems that the genes activated as a consequence to the Fom inoculation
(also when present in the mixed inoculation) allow the plant to set a more organized
and specific response to the infection;
otherwise, while the response after Vd
inoculation , for which the line considered doesn’t possess a tolerance locus, seems to
be less specific and limited to the modulation of genes involved in primary
metabolism.. Some of the genes identified in the “defense response” category in both
Fom and mixed inoculation libraries are xyloglucan, endonuclease inhibitors, PR
proteins, osmotin precursors and TMV induced proteins. The group of unknown
genes represents the 20%, the 22% and the 14% of the Fom, Vd and mixed
inoculation libraries of up-regulated genes, respectively. By monitoring kinetics of
the defense-related gene expression at two time points during a 8h time course after
fungal inoculations, initial changes in the eggplant transcriptome were detected at 4h
and 8h after inoculations. Assuming that, during this limited period of time, the
defense responses were already induced by the plants. This assumption is based on
previous biochemical analyses (Mennella et al, 2010). qRT-PCR analysis was
performed on 49 genes from the three SSH libraries selected on the basis of their
functional classification . The results of these time-expensive experiments
represented an important starting points for the next analysis conducted by array. Of
the 49 genes analyzed, 40 showed a significant modulation after at least one type of
inoculation or timing. The agreement between Dot-Blot and qRT-PCR data was
slightly higher than 50%: from the screened 49 genes from Dot-Blot filters, 9 didn’t
show any induction and 11 were modulated but in an opposite trend. Twenty-eight of
72
the genes analyzed by qRT-PCR were consistent with the Dot-Blot results, but also
among these genes we found some discrepancies: some genes showed a significant
induction after a type of fungal inoculation, but ever were selected as inducted from
another inoculation type. These genes were considered as differentially expressed
because the different reliability of the two techniques used, qRT-PCR represent the
most reliable technique in studies for monitoring gene expression (Gutierrez et al.,
2008), while SSHis most suitable as screening method.
Some of the genes selected showed a very high differences in gene expression
if we compare the three inoculation profiles with the control (Fig 2). For example,
lipoxygenase, proteinase inhibitor, miraculin and sesquiterpene shyntase were genes
that showed differences in the expression level after Fom and mixed inoculation and
not after Vd when compared to the control. All these genes were correlated with
defense mechanism. Sesquiterpene shyntase showed a similar induction profile, but
over-expression of this gene was particularly evident after Fom inoculation.
In this work, SSH and microarray approaches were used to identify a panel of
genes differentially expressed during fungal inoculation from a eggplant line resistant
to Fom. This study focused on the early stages of fungal inoculation in root tissues
due to important role of early response genes in mediating the effect of fungal attack.
A customized Combimatrix chip was used in order to analyze all the 400 sequences
previously selected using SSH. In addition, 200 sequences from RAD tag sequencing
of two eggplant genotypes
(Barchi et al., 2011)and from NCBI and PRGdb
(Sanseverino et al., 2010) databases were included. Others 5 sequences were selected
as putative housekeeping genes used in plant/pathogen interaction works. Probes on
all these selected sequences were designed for the chip hybridization..
A technical replicate of the three fungal inoculations described in this work
was prepared. In total, 8 different treatments were analyzed (4 inoculation types at 2
time points (T4/T0 and T8/T0).
73
The hybridisations of the chip lead to the identification of 150 induced genes
(25%). This high percentage was easily explained by the previous selection of the
probes among a set of defence-related genes.
The number of up-regulated genes was higher than down-regulated ones and
slightly higher in T8 versus T0 than in T4 versus T0. Instead, the down-regulated
genes remains comparable between T8/T0 and T4/T0. The predominance of the
genes with a positive induction was a confirmation of the results obtained first by
Dot-Blot and then by
qRT-PCR analysis. The functional classification of the
modulated
was
genes
performed
consulting
Tair
database
(http://www.arabidopsis.org/tools/bulk/go/index.jsp) instead of Uniprot, Kegg and
Brenda databases used for the functional classification of the SSH libraries. The use
of an alternative database for functional classification was done because of the low
number of annotations obtained by using Kegg, Uniprot and Brenda. However, using
TAIR the percentage of sequences with a putative annotation was only slightly higher
(53,8%) than the previous ones (50%).
The modulated genes were distributed according to the principal GO
categories, Molecular Function (MF) Cellular Component (CC) and Biological
Process (BP) (Fig. 3a and b).
About the BP terms, the first evidence is the high percentage of unknown
sequences. Regarding the GO categorization of the assigned sequences, we can
resume that among the up- and down-regulated sequences, the most representative
categories are metabolic, cellular and biological processes. The MF terms “catalytic
activity” and “hydrolase activity” occurred most frequently both in the up- and downregulated genes, the MF term “binding” was also well represented in the
classification of down-regulated genes. Finally, the CC terms indicated that the
differentially expressed genes were active in every categories, especially in the “cell
wall”, “extracellular region”, “plastid” and “plasma membrane”
The array validation by qRT-PCR was conducted on a percentage of 5% of the
modulated genes. The array(?) was less sensitive than qRT-PCR, like reported in
74
literature (Morey et al., 2006). As example, the modulation of the gene “protein like
precursor” ranked from 1,5- to 3,7-fold by array results, instead by qRT-PCR the
same gene showed an induction between 4- to 18-fold.
The qRT-PCR analysis was extended to 10 additional genes, of which four
never analysed by real time. This panel of genes was investigated , in a technical
replicate. Considering the first 8 genes selected for array validation, a total of 18
genes were tested, and its expression profiles were compared first with the array
results, than with the expression profiles of the same genes previously analyzed by
qRT-PCR (Tab. 7 and 8). Seven genes showed a tight agreement across the
techniques utilized, especially qRT-PCR and microarray, because these data were
obtained from independent biological and technical replicates, the results were
consistent, reliable and repeteable.
These seven genes were: osmotin like precursor, putative xyloglucanase
inhibitor, STH21, caffeoyl Co-A methyltransferase, LTP (Lipid transfer protein),
miraculin and proteinase inhibitor.
The sequence F3G1 was selected from the Fom inoculation library, and its
annotation was complex. We had a SGN code (SGN-U314100) corresponding to an
osmotin like precursor, but we found no At code and no EC number corresponding to
this SGN. The osmotin like precursor was up-regulated, and its expression profiles
was confirmed after Fom inoculation in both the qRT-PCR experiments. By array,
F3G1 results always modulated. Typically, osmotin is correlated whit defence
mechanism, there are some evidence in literature that is one “stress protein” isolated
from tobacco cell cultures (Singh et al., 1989). The accumulation of osmotin mRNA
is developmentally regulated and controlled by a variety of hormonal or
environmental signals, including abscisic acid (ABA), ethylene, viral infection,
salinity, desiccation, and wounding (Zhu et al., 1995). Based on its structure and
expression patterns, osmotin has also been classified as a member of PR 5 proteins
and, thus, implicated to have dual function in osmotic stress and plant pathogen
defence (Zhu et al., 1995).
75
The sequence F8E4 was selected from the Fom inoculation library, and its
annotation was composed of the annotation (SGN-U314071) corresponded to
xyloglucanase inhibitor. The role of xyloglucanase inhibitor in plant-pathogen
interaction is related to cell wall protection. The cell wall of plants is composed of
various polysaccharides, such as cellulose and hemicellulose. Cellulose is a major
component of the plant cell wall, and cellulose microfibrils are linked via
hemicellulose. The network of cellulose–hemicellulose provides tensile strength. In
most dicotyledonous plants, hemicellulose comprises xyloglucan, which consists of a
cellulosic backbone substituted with side chains. These b-linked glucans, namely
cellulose and xyloglucan, are constantly exposed to degradation by various endoβglucanases, such as cellulase and xyloglucanase from pathogenic bacteria and fungi.
To protect the cell wall from degradation by such enzymes, plants secrete
proteinaceous inhibitors against endo-b-glucanases. The first endo-b-glucanase
inhibitor protein discovered was the so-called xyloglucan-specific endo-b-1,4glucanase inhibitor protein (XEGIP) (Yoshizawa et al., 2011) a tomato protein that
inhibits fungal xyloglucan-specific endo-b-1,4-glucanase (XEG), an enzyme
classified as a member of the PR protein.
The sequence M7G2 was selected from the mixed inoculation library, and its
annotation was referred only to a SGN code (SGN-U315737) corresponding to a
STH21 pathogenesis-related protein. This gene showed a positive induction after
Fom and mixed inoculation, but also after Vd treatment (only in the qRT-PCR
experiment regarding the second replicates).
Caffeoyl Co-A methyltransferase (M3D8) was selected from the mixed
inoculation library. Its SGN code (SGN- U313985) corresponds to the functional
classification of
a transferases that specifically transfers one-carbon group
methyltransferases. The systematic name of this enzyme class is S-adenosyl-Lmethionine:caffeoyl-CoA 3-O-methyltransferase. Other names in common use
include
caffeoyl
coenzyme
A
methyltransferase,
caffeoyl-CoA
3-O-
methyltransferase, and trans-caffeoyl-CoA 3-O-methyltransferase. This enzyme
76
participates in phenylpropanoid biosynthesis, a diverse family of organic compounds
that are synthesized by plants from the amino acid phenylalanine. The name
”phenylpropanoid” is derived from the six-carbon, aromatic phenyl group and the
three-carbon propene tail of cinnamic acid, which is synthesized from phenylalanine
in the first step of phenylpropanoid biosynthesis. Phenylpropanoids are found
throughout the plant kingdom, where they serve as essential components of a number
of structural polymers, provide protection from ultraviolet light, defend against
herbivores and pathogens, and mediate plant-pollinator interactions as floral pigments
and scent compounds.
LTP (M9B3- SGN-U575965) was selected from the mixed inoculation library
and was up-regulated after the three fungal inoculations. The induction after Fom and
mixed inoculation was higher than after Vd treatment and control. The involvement
of LTP in plant-pathogen interaction was previously reported in literature (Blilou et
al., 2000). One of the major inducible plant defence responses is the accumulation of
plant defence proteins, including PR proteins and other with toxic or inhibitory
activity towards pathogens. In this sense, plant lipid transfer protein or LTP,
previously thought to be involved in the transfer of a broad range of lipids between
membranes in vitro (Kader 1996) have also been implicated in plant defence (Blilou
et al., 2000). The defensive role of plant LTPs was found because of their ability to
inhibit bacterial and fungal pathogens growth, their distribution at high concentration
over exposed surfaces, and the response of Ltp gene expression to infection with
pathogens (Garcia-Olmedo et al., 1995).
Miraculin (M9C11) was selected from the mixed library. Annotation to this
sequence was given only using its SGN code (SGN- U315288). Miraculin showed a
very strong positive induction after Fom and mixed inoculation, in both the
replicates. Miraculin is a plant protein, which can modify a sour taste into a sweet
taste, purified from extracts of red berries of Richadella dulcifera (Masuda et
al.,1995). Purified native miraculin protein has an amino acid sequence of 191
residues with a molecular mass of 24,600, and a cDNA encoding miraculin also has
77
been cloned and sequenced (Masuda et al., 1995). LeMir is rapidly induced and
localized in tomato root tips by nematode infection, and is predicted to be secreted
from the roots into the surrounding environment (Brenner et al., 1998). Some other
cDNAs appearing in an internet database from EST programs of model plants, such
as Arabidopsis, also showed sequence similarity to miraculin and have been
designated as miraculin-like protein genes (Tsukuda et al., 2006).
PR-6 or proteinase inhibitors (M3D12- SGN-UU312589) was also selected
from the mixed inoculation library, and its induction was the strongest among all the
qRT-PCR analysis carried out. After Fom and mixed inoculation its expression level
reached, respectively, 200- and 300-fold with respect to the control.
In conclusion, by using SSH combined with microarray approach, we
identified a group of genes differentially expressed during the response of eggplant to
Fom and/or Vd inoculations. The qRT-PCR experiments performed on a panel of
these genes allowed us to better understand the expression trends of the selected
genes. These genes represent candidates for further functional genomic studies.
Moreover, their potential use as molecular markers could be explored by looking for
the allelic variation in the eggplant gene pool to discover superior alleles having an
improved response to pathogen attacks.
78
Chapter 4
Housekeeping gene selection using an external control for qRT-PCR analysis of
differentially expressed genes in eggplant roots after three different fungal
inoculations
4-1 Introduction
The two fungal wilts caused by Verticillium dahliae (Bath R.G. et al 1999) and
Fusarium oxisporum f. sp. melongenae (Urrutia Herrada M.T. et al 2004) are among
the most serious diseases of eggplant. They occur in Asian countries and in Europe,
both in greenhouse and open-field cultivation (Baysal et al, 2010). At the moment,
little is known about the mechanisms involved in the plant-pathogen interaction
occurring in eggplant during these two fungal infection, therefore our purpose is to
highlight the role of many differentially expressed genes which we isolated, and
putatively acting in response to these fungal infections and at different timings after
root inoculation.
The locus Rfo-Sa1 carrying resistance to Fusarium oxysporum (Toppino et al,
2008), was introgressed from the allied species S. aethiopicum into Solanum
melongena background by somatic hybridization, followed by several cycles of
backcrosses of the androgenetic dihaploids from the somatic hybrid with different
cultivated lines of eggplant. One of the so-obtained Fusarium-resistant Advanced
Backcrossed eggplant Lines (ABLs), ALL96-6 x 1F5(9), was utilized for
identification and characterization of the differentially expressed genes involved in
the plant-pathogen interaction. Plantlets of this ABL were separately inoculated with
Fusarium, Verticillium and both fungi together, while roots dipping in water was
used as mock inoculation. Through selective-suppression hybridization we created
three cDNA libraries of differentially expressed genes putatively involved in the
plant-pathogen interaction; from these libraries we chosen the most interesting genes
which underwent a more accurate characterization. Our final purpose is to investigate
79
the expression of these selected genes among the three inoculations and at four
different times: 0, 4, 8 and 24 hours after roots dipping.
At present, qPCR is the most suitable tool in quantitative gene expression
studies due to its precision and sensitivity (Gutierrez et al., 2008). The most diffused
approach with this technique is relative quantification, whereby the expression level
of a target gene is normalized depending on an internal reference gene, also called
housekeeping (Brunner et al, 2004). However, the reliability of the results is strictly
correlated whit the selection of the internal reference gene, which is usually chosen
among the list of genes expressed at a constant level under different experimental
conditions.
Evidently, the first critical aspect in a Real Time analysis lays in the selection
of an adequate housekeeping, considering that a failure in this preliminary step may
result in biased gene expression profiles and therefore leading to false conclusions
(Gutierrez et al, 2008). The stable expression of a reference gene turn out to be
mandatory in any qPCR analysis (Turabelidze et al 2010).
Many recent studies (Vandesompele et al 2002) showed that also internal
standard genes could vary depending on different experimental conditions, and not
often a reliable control has been reported. In addiction, Vandesompele et al (2002)
showed that the use of only one reference gene may lead to errors in expression data
rising up to 20-fold, and therefore recommended, for an accurate normalisation, the
use at least two or three reference genes. In our work, the selection of an adequate
internal control is particularly challenging, considering that if our purpose is to
compare the expression levels of the selected genes among the three different plantpathogen interactions, the putative reference gene should be not affected by any of
the fungal inoculations and any timing considered after root dipping. Therefore, an
accurate validation of the stability of candidate housekeeping genes is essential as
first step.
For our purposes, a list of putative housekeeping genes was selected from
literature, searching for genes acknowledged to be used as housekeeping in
80
experiments of pathogen-mediated stress induction in plant. We found 7 potential
candidates from those most frequently used as references: β tubulin, elongation factor
1- α, ubiquitin, catalitc subunit of phosphatase 2A, 18s rRNA, glyceraldeyde-3phosphate dehydrogenase and actin (Lovdal and Lillo, 2009). Actin was discharged
from this analysis, considering its presence in our libraries of putative differentially
expressed genes. The expression stability of the remaining candidate genes was tested
in our experimental conditions.
Nevertheless, also to validate the supposed stable expression of each putative
housekeeping gene, we would need a prior knowledge of a stable gene-expression
measure, to be used itself as control for the tested candidate genes.
To solve this circular problem, and therefore to make possible an accurate gene
expression normalization, several statistical algorithms have been recently developed,
like geNorm (Vandesompele et al 2002), BestKeeper (Pfaffl et al, 2004) and
Normfinder (Andersen et al, 2004).
The statistical method geNorm is a freely available and well-recognized Excel
based tool for normalization of experimental data from gene expression analysis
(http://allserv.ugent.be/;jvdesomp/genorm/index.html). This method based on the
principle that in all samples the expression ratio of two housekeeping genes remains
constant and invariable. This algorithm uses pair wise comparison and geometric
averaging across a matrix of candidate genes. The output is the gene-stability
measure M: at the lowest M values corresponds the most unvarying couple of genes;
the gene corresponding to the highest M value is eliminated until the two most stable
expressed genes remains. This simple approach is largely used in gene-expression
studies in mammals, yeast and bacteria, but remains undervalued in studies regarding
plants (Gutierrez et al., 2008).
Smith et al (2003) proposed an alternative method to verify the stability of the
candidate housekeeping genes in human cells among different experimental
conditions. Its method uses an exogenous sequence (RuBisCo transcript) as an
external reference gene which allows comparison between the variation of the target
81
genes of interest. We developed the same approach but in plant, as suggested by
McMaugh et al (2003), as external standard we used the bacterial gene for the
resistance to Kanamycin, but we used the external reference gene like a fixed point to
normalize the candidate internal reference genes. We also applied the geNorm
algorithm to all the genes tested, external transcript included, and we find a strong
correlation by the two different approaches. This work can serve as resource to help
select and screen eggplant reference genes for gene expression studies in root tissue
under biotic stress.
4-2 Materials and methods
4-2.1 Plant materials and growth conditions
Seed-derived plantlets of an advanced introgressed line of eggplant resistant to
Fusarium oxysporum, (ALL 96-6 x 1F5(9)), grown in greenhouse, were individually
inoculated at the 3-4th true leaf stage, according to the root-dip method described in
Cappelli et al. (1995) with a conidia suspension of Fusarium (1,5 x 106 conidia/ml),
Verticillium (1 x 106 conidia/ml), or both fungi together (mixed inoculation), while
root dipping in water was used as mock-inoculation. Inoculated and mock-inoculated
eggplant roots were harvested at 0, 4, 8 and 24 hours after artificial inoculation,
frozen in liquid N2 and stored at -80 °C.
4-2.2 RNA isolation and reverse transcription
100 mg of root tissue were ground into a fine powder in liquid nitrogen, and
total RNA was purified using the RNeasy® plant RNA extraction kit (Qiagen, Clifton
Hill, Victoria, Australia) according to the manufacturer’s instructions. RNA purity
and quantification was determined with Nanodrop (Thermo Scientific Wilmington,
82
USA). A fixed amount of 30 ng of the heterologous Kanamycin 1.2 kb Control RNA
(Kan 1.2; Promega, Madison, WI, USA) was then added to 3000 ng of total RNA (the
concentration ratio kanamicin RNA /total RNA was 1/100 in all the samples), in
order to introduce an External Reference Transcript (ERT) in each sample which
would undergo the processing of reverse transcription together with the endogenous
sequences.
Contaminating DNA was then removed from each sample of “pooled” RNA
(endogenous plus ERT) using RQ1 RNase-Free DNase Treatment 1U/µl (Promega)
according to the manufacturer’s instructions. Reverse transcription was then
performed with the ImProm-II™ Reverse Transcription System (Promega, Madison,
WI, USA), in a total volume of 20 L. The reactions were incubated at 25°C for 5
min (primer annealing), then at 42°C for 1 h (cDNA synthesis). The cDNA solutions
were then incubated for 15 min at 70°C to stop the reaction, and diluted 20-fold with
sterile water
4-2.3 Primer design
We selected from databanks 6 potential reference genes (Table 16) which are
commonly used as internal control for expression studies in tomato, such as GAPDH
(glyceraldehyde-3-phosphate dehydrogenase), EFα1 (elongation factor α1), TUB (
alpha-tubulin), PP2Asc (catalytic subunit of protein phosphatase 2A), 18S (18s
rRNA) and UBI (ubiquitin).
Primers to amplify each candidate housekeeping in eggplant were designed on
the basis of the homologous sequences of the corresponding genes in tomato,
retrieved
from
the
DFCI-TGI
(Tomato
Gene
Index)
EST
database
(http://compbio.dfci.harvard.edu/tgi/cgi-bin/tgi/gimain.pl?gudb=tomato).
Six primer pairs were designed (Table1) on these sequences (170 bp maximum
length, optimal Tm at 59°C, GC% between 40% and 60%) using PRIMER3 software.
A check for secondary structure within the amplicon was performed using the MFold
program
(http://www.bioinfo.rpi.edu/applications/mfold/cgi-bin/dna).
83
Primer’s
specificity was confirmed by checking of the correct PCR product sizes on an 1%
agarose gel and then by sequencing of the amplicons. The S. melongena amplified
sequences were compared to Tomato sequences with BLAST 2 sequences software
(http://www.ncbi.nlm.nih.gov/blast/bl2seq/bl2.html). All the amplified sequences
shared more than 96% identities with their tomato homologues. Specific primer pairs
were then designed on the sequence of the external Kan 1.2 reference gene as
described before.
Table 16. primers used in this work. (1) Optimal annealing and elongation
temperature in PCR program. (2) Percentage sequence identity between the amplicon
and the corresponding homolog tomato sequence from Genbank. (3) Measure of the
real-time PCR reaction efficiency (calculated by standard curve method). (4)
Reproducibility of the real-time PCR reaction. n.d. = no data because the gene was
excluded from the study.
4-2.4 Two step real-time quantitative PCR
Real-time amplifications were performed in a Rotor-Gene RG-6000 thermal
cycler (Corbett Research) using SYBR Green (IQTM Supermix Master Bio-Rad)
detection chemistry. For each gene, the performance of the designed primers was
tested by real-time. Two negative controls and a 4-fold dilution series of pooled
84
cDNA were included in each run. This pooled samples consisted of cDNA from roots
from both groups (inoculated and mock-inoculated).
The cycling conditions were set as follows: initial denaturation step of 95°C for
3 min, followed by 50 cycles of denaturation at 95°C for 15 s, annealing and
extension at 59°C for 40 s. The amplification process was followed by the
measurement of fluorescence during a melting curve in which the temperature raised
from 55 to 95°C in sequential steps of 0.5°C for 5 seconds. This insured the detection
of one gene-specific peak and the absence of primer-dimer peaks.
The 4-fold dilution series with 4 measuring points were used to construct a
relative standard curve to determine the PCR efficiency. Each reaction was run in
duplicate, whereby two negative controls were included. The real-time PCR
efficiency (E) was determined for each gene with the slope of a linear regression
model. The efficiency of primers was calculated using Rotor gene software according
to the equation:
E= 10[-1/slope]
Primer conditions were optimized by determining the correspondent best
annealing temperature and primer concentration. We analyzed the expression of the
candidate reference transcript in all the experimental conditions (i.e. types of fungal
inoculation and timings). All samples were amplified in duplicate and the mean was
obtained for further calculations. CT values over 45 cycles were excluded from
further mathematical calculations.
4-2.4 Data acquisition
Expression levels were determined as the number of cycles needed for the
amplification to reach a constant fluorescence level (threshold) fixed in the
exponential phase of PCR reaction (Ct) . The threshold was set at 0.004 fluorescent
85
units, and the threshold cycle (Ct) values were plotted against the starting template
concentration.
Next, in order to compare the transcription level of the selected genes across
different type of fungal inoculation and timings, the average Ct-value of each
duplicate reaction was converted to raw data (relative quantities) for subsequent
analysis
with
the
geNorm
software
(http://allserv.ugent.be/;jvdesomp/genorm/index.html).
4-3 Results and discussion
4-3.1 Pre-analytical assessment of the panel of candidate genes
Total RNA was isolated from eggplant roots at different timings from the three
inoculations plus the mock inoculations. All the collected RNA samples were
characterized with respect to their concentration and purity, in order to determine the
quality of the RNA that is used for the subsequent expression analyses. RNA purity
and quantification was determined with Nanodrop (Thermo Scientific Wilmington,
USA).
Primer pairs were designed on the basis of the consensus sequence retrieved
from tomato and used in a PCR-based screening of the six potential reference genes
on cDNA samples of eggplant and confirmed that all these genes were expressed in
eggplant roots. Amplification of all the candidate genes gave an univocal amplicon of
the expected size in a 1% agarose gel electrophoresis, except for Ubiquitin, which
despite the use of several alternative primer pairs designed on the consensus
sequence, still revealed the presence of un-specific amplification, therefore was
excluded from this study.
86
All the other amplicons were sequenced for verification and all shared more
than 96% of identity with the tomato consensus sequences on which primer design
was based (Table 1).
For each candidate reference gene, a qPCR standard curve was then generated,
using 4-fold serial dilutions of cDNA obtained from both inoculated and mockinoculated eggplant roots. All the five candidate genes (TUB, EF1, PP2Asc, GADPH
and 18S) displayed good PCR efficiency varying from 0.88 to 1.01 (the PCR
efficiency value E characterizing each standard curve is given in Table 16). The
identity of each qPCR product was confirmed by observation of a single melt peak at
the end of each real time course.
4-3.2 Evaluation of the expression stability of the External Reference Gene
A fixed amount of 1.2 Kanamycin mRNA transcript was added in a constant
ratio (1:100) to the RNA of each sample extracted from inoculated and mockinoculated eggplant roots before DNAse treatment and retrotranscription (see
methods, the experimental design is shown in Fig.1). A qPCR standard curve was
generated also for the 1.2 Kanamycin transcript which showed a very high PCR
efficiency: E = 1.05.
qPCR analysis was then performed to establish the stability of expression of
the ERT among the samples; duplicate reactions at each experimental condition were
amplified along with no-template controls, and the identity of each qPCR product
was confirmed by observation of a single melt peak at the end of each real time
course. In total, the exogenous Kanamycin transcript was amplified in 228 replicates,
giving a mean Ct value of 5,96 cycles with a standard error of 0,30, thus revealing a
very stable expression which remains nearly unaltered among all our experimental
conditions. After proving the reliability of expression of the kanamycin transcript, we
used it as a reference gene to normalize the experimental data of the other candidate
genes, in order to correct for any difference in the amount of starting material.
87
4-3.3 Evaluation of the relative expression levels of the candidate reference gene
with respect to the external control.
qPCR analysis was then performed to establish the expression of the five
candidate reference genes (TUB, EF1, PP2Asc, GADPH and 18S) in all the root
cDNA samples at different fungal inoculations (plus mock inoculation) and timings
0, 4, 8, 24 h after root dipping, in order to identify among the panel the gene whose
expression is less affected by the different experimental conditions and therefore is
eligible as housekeeping.
The expression levels of these five candidates were normalized against the
ERT. For each one, Ct between candidate gene and kanamycin were calculated and
the results are shown as boxplot in Figure 12. Each data point represents the average
of two experiments (performed in duplicate) and the error bars indicate the standard
error of the mean of four replicates. The five genes displayed a wide range of relative
expression levels with respect to the kanamycin transcript, the mean values of CT
ranging from +16,78 (PP2Asc) and -3,5 (18S).
Figure 12
Expression profiles of the candidate housekeeping genes after
different types of fungal inoculation (C: control, V: Verticillium, F: Fusarium, M:
Mixed) and timings (0, 4, 8, 24 hours) using the external control as reference gene
88
For each candidate reference gene, differences in the expression levels across
the considered samples representing different fungal inoculations and timings were
evaluated.
The highest
CT variability can be detected in the expression levels of
elongation factor 1- α (EF, green line), which proves severely affected by all the
inoculations ( Ct between a minimum value of 7,02 and a maximum of 10,4); a
slighter variability also happens both for 18S rRNA and β tubulin (18S and TUB,
pink and red line, respectively) whose fluctuating expression is evident under all the
considered samples. Protein Phosphatase 2A (PP2Asc, violet line in figure 1) showed
variation ( Ct value between 14,7 and 16,7) in gene expression after Fusarium and
mixed inoculation at every times considered, while no differences are detected after
Verticillium and mock inoculations. GAPDH (blue line) reveals to be the most stable
gene and shows the slightest CT value variability (between 9,7 and 10,6) in mock
and fungal inoculations among different timings.
Expression level stability of our panel of external and native candidates was
also investigated with geNorm, the freely available and well-known Excel-based tool
for gene expression normalisation (Vandesompele et al., 2002). The geNorm
algorithm calculates the gene expression stability measure “M” for each reference
gene as the average pairwise variation “V” for each one with respect to all other
reference genes. Stepwise exclusion of the gene with the highest M value allows
ranking of the tested genes according to their expression stability, giving way to the
selection of the couple of candidates which show the most stable expression with
respect to each other. This approach assumes that stably expressed genes stay in a
constant ratio with respect to each other; co-regulated genes are an exception to this
assumption and they must not to be included in the test. The panel of chosen
candidates included genes that are involved in basal metabolism, but distantly related
in metabolic function, therefore being all suitable to be employed in a test for stability
of expression through the geNorm algorithm.
89
Therefore, the Ct values obtained from the qPCR analysis of all these candidate
genes were converted in raw data (relative quantities) in order to apply geNorm
algorithm.
The graph in figure 3A represents the output of the geNorm software, which
leads to the identification of Gapdh/KANAr as best couple of reference genes for our
given conditions. In Table 17 is shown the expanded ranking of the native candidate
genes and the ERT, according to their M value (average expression stability): from
the most stable (lowest M value) to the least stable (highest M value) Gapdh/KANAr
< 18S < PP2Asc < Tub < EF1.
Table 17. Candidate reference genes for normalization ranked according to
their expression stability (calculated as the average M Value after stepwise exclusion
of the worst scoring gene) by geNorm.
GeNorm algorithm confirm our hypothesis: the best gene-pair for qPCR
analysis in eggplant roots affected by fungal inoculation is Gapdh and KANAr. The
geNorm programme can also determine the optimal number of genes required for
accurate normalisation, based on the pairwise variation between two sequential
normalization factors containing an increasing number of genes (Vn/Vn+1). The cutoff
Vn/Vn+1
value
was
set
at
0,15
by
geNorm
manual
(http://allserv.ugent.be/;jvdesomp/genorm/index.html), but as suggested in the
manual itself must not to be considered as a very strict threshold. As shown in figure
3B, the V4/5 value of 0,165 obtained in this study was close to the cut-off threshold
of 0,15, in addition it is lower than V5/6 value (0,298). An increasing variation in the
ratio V5/V6 corresponds to a reduction in expression stability due to the addition of a
relative unstable 5th gene, so for accurate normalisation external kanamycin and three
internal gene (Gapdh, 18S and PP2A) are required. GeNorm algorithm allows to
90
confirm the selection of the native candidate Gapdh as the best reference gene for
qPCR analysis in eggplant roots affected by fungal inoculation.
Fig 13A: Average expression stability values of control genes: elongation
factor 1- α (EF), β tubulin (TUB) , catalitc subunit of phosphatase 2A (PP2A), 18s
rRNA (18S), glyceraldeyde-3-phosphate dehydrogenase (GAPDH) and Kanamycin
(KANAr).
Fig 13 B. Pair wise variation analysis between the normalisation factors NFn
and NFn+1, to determine the minimum number of reference genes for normalisation
91
4-4 Discussion
Gene expression studies by qPCR technique often use reference gene levels as
a means for assessing sample processing and normalizing for the mRNA content of a
sample (Andersen et al., 2004). For an accurate evaluation of gene expression, it is
essential to normalize experimental data to one or, better, to more reference genes
that are stably expressed at the same level among all the samples and whose
expression is not affected by the experimental conditions (Huggett et al., 2005).
However, while these reference genes constitute the most appropriate normalization
strategy, a major problem is that their expression is often influenced by the
experimental conditions (Schmittgen et al.,2000). The expression stability of several
genes commonly used as references is often untrustworthy (Dheda et al., 2004),
indicating that their use as references is inappropriate (Vandesompele et al., 2002).
To date, the validation of reference genes in plants has received very little attention
and only few candidate genes have been investigated with some detail in rice (Jain et
al., 2006; Ding et al.2004, Kim et al.,2003 ) poplar (Brunner et al., 2004) potato
(Nicot et al., 2005), coffee (Barsalobres- Cavallari et al., 2009), tobacco (Schmidt et
al., 2010), soybean (Jian et al., 2008; Libault et al., 2008) , tomato (ExpósitoRodríguez et al., 2008).
Suitable reference genes have not been yet defined for a great number of crop
species, including eggplant, and still putative housekeeping genes tend to be used as
references without any appropriate validation. The implications of using an
inappropriate reference gene for normalization of experimental data, could lead to
severe effect on data analysis (Bustin et al., 2002; Drehda et al., 2005; Gutierrez et
al., 2008): if unrecognized, unexpected changes in reference gene expression can
result in erroneous conclusion about real biological effects. In addition, this type of
changes often remains unnoticed because most experiments only include single
reference gene which cannot in turn be subdued to check for stability.
92
For all these reasons, the experimenter needs to carefully assess whether a
certain reference gene is stably expressed in the experimental design under study
(Hong et al., 2010, Schmidt et al., 2010).
The large number of publications that focus on the validation of an internal
reference gene
reflects the ongoing difficulty in selecting the most suitable
candidates (Logan et al., 2009): Also several companies have identified the problem
and now provide validated reference gene panels for various organisms
(Vandesompele et al., 2009).
The accurate choice of the best reference genes is mandatory, considering that
more and more recent works highlight the lack of a systematic validation of the
reference genes; but how can expression stability of a candidate be evaluated if no
reliable measure is available to evaluate the stability of expression of a candidate?
The debates on the unraveling of this circular problem and on the criteria for
selecting the best reference are still a hot spot among the scientific community,
(Vandesompele et al., 2009) as demonstrated by the continue raising of workgroups
and focused on the argument (like the External RNA controls consortium), or the
development of platforms and forums devoted to discussion about gene expression
(like the qPCRforum or the portal www.Gene-Quantification.info ) and also by the
development of more and more Algorithms and software for evaluation of candidate
reference genes like BestKeeper (Pfaffl et al., 2004) Genorm (Vandesompele et
al.,2002), or Normfinder (Andersen et al., 2004).
GeNorm is one of the best known and freely available systems for selecting
the best candidate reference gene for a given experimental scenario. Its algorithm
calculates and compares the so called M-value of all candidate genes, eliminate the
gene with highest M-value, and repeats the process until there is only two genes left
and determines the optimum pair of reference gene from a set of tested genes in a
given cDNA sample panel. According to Vandesompele et al., (2002), to best
perform geNorm analysis in an ideal condition the user usually should measures the
expression of 6 to 12 reference genes in a representative panel of samples; this kind
93
of analysis it’s time and sample consuming and requests pre-analytical knowhow
about candidate gene sequences. Acceptable results can be however obtained also
from test of a reduced number of candidate genes, although a slighter accuracy is
endangered.
The aim of this work was to develop an efficient strategy to assist the selection
of the most stable candidate housekeeping gene in gene expression studies in which
different experimental conditions (different biotic stresses) have to be compared, at
least one of them that could affect the expression levels of the reference genes
themselves.
Most common reference genes are involved in basic cellular functions (e.g. βactin and ubiquitin genes) and are often assumed to have a uniform expression
pattern, but there are experimental conditions like biotic stress whose could have
severe effects on the plant metabolism and also interfere with expression of the so
called housekeeping genes.
On the other hand, in vitro produced artificial RNA molecules (also called
RNA spike-ins) can be introduced into the sample RNA extract prior to reverse
transcription (Smith et al., 2003) and can act as valuable tool for internal
standardization of real-time PCR experiments as they are completely independent of
the biological process (Gilsbach et al., 2006). Spiking of the native RNA with an
artificially synthesized sequence is a strategy known in human (Smith et al., 2003)
while is an extremely less applied in plants. The only information about spiking in
plants regards an expression investigation in Bermuda grass following infection with
the fungal root pathogen Ophiosphaerella narmari (McMaugh et al., 2003) and
confirmed
the suitability of the external reference gene for experimental data
normalization.
In our work, we decided to combine the two different approaches of spiking an
artificial kanamycin transcript in the RNA samples and of evaluating all the panel of
native and artificial transcripts with the geNorm algorithm, basing on the idea that an
94
heterologous transcript which is stably expressed as unaffected by experimental
conditions can be used not only as reference gene itself but also as a normaliser to
evaluate the expression of the candidate native genes.
We investigated the expression data of the 6 native candidate genes and of the
spike: after confirming the reliability of the expression of the spike sequence, we
decided to use it as external control for the expression of the housekeeping
candidates.
Ct comparison of gene expression of the candidate genes with respect to the
extreme stability of expression of the kanamycin transcript, allows to indicate Gapdh
as the gene that display the slighter variation among all the samples.
However, to compare this technical method whit a statistical one, the geNorm
algorithm was applied. With the algorithm, the best pair of housekeeping genes was:
Gapdh/KANAr. In our case, the pair wise variation suggest that three or four genes
are the minimum number request for a robust validation. There are strong similarities
between the two different approaches, as they both assigned the best expression
stability to Gapdh and KANAr. Although all these candidate genes are reported in
literature to be suitable as reference genes in plant, our approach enables us to reveal
their slight variability of expression among our different experimental conditions, and
also to select Gapdh as the best reference for the evaluation of the expression of our
collection of genes involved in different plant/pathogen interactions.
We have explored the possibility of using kanamycin as external control to
check the internal reference genes in eggplant root, but this approach should find a
broad range of other applications. External reference should turn into the ideal
reference gene, because its expression is independent from tissue or experimental
condition. Considering the reliability of this result, the proposed method should be
exploited in any qPCR based study.
We suggest that use of an external control may led to a fast and easy solution
when validation of the best reference genes is particularly difficult due to the
experimental conditions, or when in literature there are few example of candidate
95
housekeeping, as it may speed up the process of identification of the best reference
gene also if the panel is constituted by a reduced number of candidates.
96
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