The Metabolic Profile of Sour Cherry
Leaves (Prunus cerasus) and the Relation to
Cherry Leaf Spot Resistance
Master thesis, Maja Eline Petersen, 20105561
English title: The Metabolic Profile of Sour Cherry Leaves
(Prunus cerasus) and the Relation to Cherry Leaf Spot
Resistance
Danish title: Den Metaboliske Profil for Surkirsebærblade
(Prunus cerasus) og Forholdet til Kirsebærbladpletresistens
Maja Eline Petersen
20105561
Master Thesis in Agrobiology (45 ECTS)
March 2016
Main supervisor
Morten Rahr Clausen
Postdoc
Department of Food Science
Aarhus University
Co-supervisor
Katrine Heinsvig Kjær
Assistant professor
Department of Food Science
Aarhus University
1
Front page photos by Maja Eline Petersen
Acknowledgements
ACKNOWLEDGEMENTS
First and foremost, I owe a huge thanks to my supervisors Morten Rahr Clausen and Katrine Heinsvig
Kjær for giving me the opportunity to work with this project, for continuous guidance, supervision
and support throughout the process.
Furthermore, I wish to sincerely thank Martin Jensen (Department of Food Science, Aarhus
University) for sharing his great knowledge about the project and for the conversations giving
inspiration for new ideas.
I would furthermore like to thank the laboratory technicians Annette Steen Brandsholm, Nina Eggers
and Karin Henriksen at Department of Food Science for valuable guidance and assistance through
the laboratory work.
Thanks to “Special-Klassen” (Martina Skjellerudsveen, Betina Zacher Jensen, Malene Theilgaard and
Sissel Geyti) for good company and great laughs, and thanks to everyone at Department for Food
Science, for a pleasant working environment.
Last, but not least, I wish to thank friends and family for continuous encouragement and support.
Especially I would like to thank my older brother Rasmus Jes Petersen for introducing and helping
me through the wondrous universe of Matlab and for proofreading. Finally, a special thanks to Simon
for his everlasting support, for proofreading and for always being there when I needed it.
Aarhus, March 2016
Maja Eline Petersen
i
Abstract
ABSTRACT
A central problem in the production of sour cherry is the fungal disease, cherry leaf spot (CLS). The
prevalence of this pathogen, which can severely impair infected orchards, is in turn creating a high
demand for resistant sour cherry genotypes. The aim of this study was to investigate physical and
biochemical resistance in sour cherry to the CLS fungus. This was carried out by a field experiment,
where six different genotypes of sour cherry with varying degrees of resistance towards CLS were
selected and divided into three resistance categories; resistant, intermediate and susceptible, based on
the percentage of leaf area infected by CLS. On the leaves from these genotypes, different stomatal
characteristics were measured, in order to examine whether there was a difference in the physical
barriers at the leaf surface to the fungal infection between the resistant and susceptible genotypes.
These measurements did not, however, explain the differences in resistance degree among the
selected genotypes.
Furthermore, 1D- and 2D-NMR analyses were performed on leaf samples from the different
genotypes. The resulting metabolic profiles of the CLS-resistant and susceptible plants were analyzed
with univariate and multivariate data analysis, in order to identify the metabolites involved in CLS
resistance. A total of 15 metabolites were identified, including carbohydrates, phenolics,
organonitrogen compounds, cyclic polyalcohol and organic acids. The main factor in discriminating
the sampled leaves based on resistance category was the level of carbohydrates. Susceptible
genotypes had an overall lower content of sucrose and sorbitol and a higher hexose-to-sucrose ratio
than the resistant genotypes. This indicates that the carbon metabolism in these leaves was impaired
due to CLS infection. Additionally the susceptible genotypes contained a significantly higher
concentration of phenolic compounds. This finding is presumably an expression of the stress situation
in these leaves caused by CLS. Of the identified phenolic compounds, chlorogenic acid and
epicatechin were important biomarkers for infection. Furthermore, an unidentified compound in the
phenolic region was detected in the multivariate analysis as an important biomarker in the separation
between the susceptible and resistant genotypes.
Experiments showed that it was possible to artificially infect leaves in the laboratory with conidia
spores from dry naturally infected leaves. Artificial inoculation tests could be a useful method in the
future for analyzing constitutive and induced resistance by controlled infections of the leaves, moving
towards a full understanding of the metabolic profile related to resistance in sour cherries.
Keywords: Prunus cerasus, sour cherry, Blumeriella jaapii, Coccomyces hiemalis, Phloeosporella
padi, cherry leaf spot, phenolics, breeding, host plant resistance, stomata, NMR metabolomics
RESUMÉ (IN DANISH)
Svampesygdommen kirsebærbladplet (CLS) er et centralt problem i produktionen af surkirsebær.
Udbredelsen af dette patogen, som kan gøre betydelig skade på inficerede planter, har medført en stor
efterspørgsel efter resistente surkirsebærgenotyper. Formålet med dette studie var at undersøge fysisk
ii
Resumé (in Danish)
og biokemisk resistens i surkirsebær overfor CLS. Seks forskellige surkirsebærgenotyper med
varierende grad af resistens mod CLS blev udvalgt og opdelt i 3 resistenskategorier; resistent,
intermediær og modtagelig, baseret på hvor stor en del af bladet der var inficeret. På blade fra disse
genotyper, blev der foretaget målinger af forskellige stomatakarakteristika, med henblik på at
undersøge, om der var forskel i de fysiske barrierer på bladoverfladen mellem de resistente og
modtagelige genotyper. Disse målinger kunne dog ikke forklare forskellene i graden af resistens
blandt de udvalgte genotyper.
1D- og 2D-NMR-analyser blev ligeledes udført på blade fra de forskellige genotyper. De resulterende
metaboliske profiler for de CLS-resistente og modtagelige blade blev analyseret med univariat og
multivariat dataanalyse, med henblik på at identificere de metabolitter, der er involveret i resistens
mod CLS. I alt blev 15 metabolitter identificeret, herunder kulhydrater, fenoler, organonitrogen
forbindelser, cykliske polyalkoholer og organiske syrer. Den vigtigste faktor i opdelingen af blade på
baggrund af resistenskategorien var indholdet af kulhydrater. Modtagelige genotyper havde et lavere
indhold af sukrose og sorbitol og et højere hexose-til-sukrose-forhold end de resistente genotyper.
Dette indikerer, at carbon-metabolismen i disse blade var forringet på grund af CLS-infektion.
Derudover indeholdt de modtagelige genotyper en signifikant højere koncentration af fenoler. Dette
var formentlig et udtryk for en stress-situation i disse blade forårsaget af CLS. Af de identificerede
fenoler, var klorogensyre og epicatechin vigtige biomarkører for CLS infektion. Endvidere blev en
uidentificeret forbindelse i det fenoliske område påvist i den multivariate analyse som en vigtig
biomarkør i adskillelsen mellem de modtagelige og resistente genotyper.
Studiet viste, at det var muligt kunstigt at inficere blade i laboratoriet med konidie-sporer fra tørre,
naturligt inficerede blade. En kunstig inokuleringstest kunne være en nyttig metode i fremtiden for at
analysere konstitutiv og induceret resistens ved kontrollerede infektioner i bladene, på vejen mod en
fuld forståelse af hvordan den metaboliske profil er relateret til resistens i surkirsebær.
iii
Abbreviations
ABBREVIATIONS
1D: One-dimensional
2D: Two-dimensional
ANOVA: Analysis of variance
CLS: Cherry leaf spot
COSY: Correlated spectroscopy
CPMG: Carr-Purcell-Meiboom-Gill
DM: Dry matter
DPI: Days postinoculation
DS: Disease score
GC: Gas chromatography
GUDP: Danish: Grønt Udviklings- og Demonstrationsprogram (Green development and
demonstration projects)
HPLC: High-performance liquid chromatography
HR: Hypersensitive response
HSQC: Heteronuclear single quantum coherence
LA: Leaf age
LC: Liquid chromatography
LOX: Lipoxygenase
MS: Mass spectrometry
NMR: Nuclear magnetic resonance
OPLS-DA: Orthogonal projections to latent structures – discriminant analysis
PAL: Phenylalanine-ammonia-lyase
PC: Principal component
PCA: Principal component analysis
PDA: Potato dextrose agar
Ppm: Parts per million
PPO: Polyphenol oxidase
Q2: Predicted variation
R2: Goodness of fit
RMSECV: Cross validated prediction error
iv
Abbreviations
RMSEE: Root mean square error of estimation
SA/LA: Stomatal area per leaf area
SAR: Systemic acquired resistance
TMSP: 3-(trimethylsilyl)propionic acid-[2,2,3,3-d4]
UPLC-MS/MS: Ultra-performance liquid chromatography tandem mass-spectrometry
VIP: Variable Importance on Projection
v
TABLE OF CONTENTS
Acknowledgements ............................................................................................................................................ i
Abstract ............................................................................................................................................................. ii
Resumé (in Danish) ........................................................................................................................................... ii
Abbreviations ................................................................................................................................................... iv
1
Introduction ............................................................................................................................................... 1
2
Background................................................................................................................................................ 3
2.1
Sour Cherry (Prunus cerasus) ........................................................................................................... 3
2.2
Cherry Leaf Spot (Blumeriella jaapiis (Rehm) var. Arx.) ................................................................ 3
2.2.1
2.3
Genetic Resistance............................................................................................................................. 6
2.4
Structural Defenses - Stomata ........................................................................................................... 6
2.5
Biochemical Defenses – Phenolic Compounds ................................................................................. 7
2.6
Selection of Resistant Sour Cherry Genotypes ................................................................................ 11
2.7
Metabolic Profiling .......................................................................................................................... 11
2.7.1
2.8
3
4
Nuclear Magnetic Resonance Spectroscopy ............................................................................ 12
Chemometrics .................................................................................................................................. 15
2.8.1
Principal Component Analysis ................................................................................................ 15
2.8.2
Orthogonal Projections to Latent Structures - Discriminant Analysis..................................... 16
Materials and Methods ............................................................................................................................ 19
3.1
Plant Materials and Harvesting of Samples for NMR- and Image Analysis ................................... 19
3.2
Artificial Inoculation Test ............................................................................................................... 22
3.3
Percentage of Infected Leaf Area measured by Image Analysis ..................................................... 24
3.4
Stomatal Measurements ................................................................................................................... 25
3.5
Metabolic Profiling using NMR Analysis ....................................................................................... 27
3.6
Data Processing and Statistical Analysis ......................................................................................... 28
Results ..................................................................................................................................................... 31
4.1
Digital Image Analysis .................................................................................................................... 31
4.2
Artificial Inoculation Test ............................................................................................................... 31
4.3
NMR ................................................................................................................................................ 33
4.3.1
Identification of Metabolites by 1D 1H-NMR and 2D COSY and HSQC Spectra.................. 33
4.3.2
Univariate and Multivariate Data Analysis of NMR Data ...................................................... 35
4.4
5
Effects of the CLS Pathogen on the Physiological Functions of Sour Cherry .......................... 5
Stomatal Traits................................................................................................................................. 43
Discussion................................................................................................................................................ 45
5.1
Carbon Metabolism ......................................................................................................................... 45
5.2
Phenolics.......................................................................................................................................... 47
5.2.1
Quinic Acid ............................................................................................................................. 49
5.2.2
Malate ...................................................................................................................................... 50
5.3
Structural Defenses – Stomata......................................................................................................... 51
6
Conclusion ............................................................................................................................................... 52
7
Implications ............................................................................................................................................. 53
8
Bibliography ............................................................................................................................................ 55
9
Appendix ................................................................................................................................................. 61
9.1
Table of Phenolics Identified in Sour Cherry Leaves ...................................................................... 61
9.2
Sample Names ................................................................................................................................. 63
9.3
Stomatal Measurements from ImageJ ............................................................................................. 64
9.4
Dry Matter Content.......................................................................................................................... 65
9.5
Table of Metabolites Identified in the 600 MHz Spectrometer of Sour Cherry Leaves .................. 66
9.6
1
9.7
1
9.8
PCA Score Scatter Plot .................................................................................................................... 75
9.9
OPLS-DA Model Statistics ............................................................................................................. 75
H NMR Spectra with Assignment of Identified Metabolites ......................................................... 69
H-13C HSQC NMR Spectra with Assignment of Identified Metabolites ....................................... 74
9.9.1
OPLS-DA Model 1 (0.5-9 ppm) resistant vs susceptible: ....................................................... 76
9.9.2
OPLS-DA Model 2 (5.5-9 ppm) resistant vs susceptible ........................................................ 78
9.9.3
OPLS-DA Model 3 (5.5-9 ppm) resistant vs susceptible, young leaves ................................. 80
9.9.4
OPLS-DA Model 4 (0.5-9 ppm) young vs old leaves ............................................................. 83
9.10
Hexose-to-Sucrose Ratio ................................................................................................................. 85
9.11
Concentration of Identified Metabolites .......................................................................................... 86
Introduction
1 INTRODUCTION
Sour cherry (Prunus cerasus) is an important high value crop belonging to the genus Prunus in the
Rosaceae family. Over the course of the last decade, the total area dedicated to sour cherry production
has decreased rapidly in Denmark (Danmarks Statistik, 2014). A central problem in the production
of sour cherry is fungal diseases, with cherry leaf spot (CLS), and blossom blight (Monilinia laxa),
being the most prevalent ones (Pedersen et al., 2012). Currently none of the commercial sour cherry
genotypes show complete resistance to CLS and studies have only found some moderate resistant
individuals (Pedersen et al., 2012; Stegmeir et al., 2014). Therefore, the fungal pest control mainly
depends on the use of fungicides. However, most pesticides have a short term effectiveness and
frequent spraying is thus required (Leiss et al., 2011). Since varieties differ in their susceptibility to
CLS, research in identifying sources of resistance against this disease is of high importance. Selection
by phenotypic registration is often uncertain, since both a certain amount of inoculum and the right
climate have to be present to be able to register a variation between genotypes. A different method of
identifying genotypes with a high resistance could be to study the chemical resistance of the host
plant, by analyzing the plant’s secondary metabolite profile. For technical reasons the main focus in
plant resistance studies has, up till now, been limited to the identification of single compounds.
However, biological processes are complex and usually many compounds are involved (Leiss et al.,
2011). Metabolomics represents a useful tool for investigating the role of chemical resistance towards
CLS in sour cherries. Metabolomics is an explorative approach to study the molecules (metabolites),
involved in metabolism in plants and living organisms (Martínez-Gómez et al., 2012). Different
metabolomics approaches exist, making it possible to detect a wide range of compounds within the
plant. One of the most universally used metabolomics approaches comprises nuclear magnetic
resonance (NMR) (Leiss et al., 2011). The application of NMR has been used in a handful of studies
to identify candidate compounds for host plant resistance to western flower thrips (Frankliniella
occidentalis), which has been used as a model for different plant species (Mirnezhad et al. 2010; Leiss
et al., 2009a; Leiss et al., 2009b). Metabolic profiling in plants associated with resistance to pathogens
using NMR has also been conducted. Choi et al. 2004, found that infection in Catharanthus roseus
leaves with phytoplasma caused an increase of metabolites related to the biosynthetic pathways of
phenylpropanoids and terpenoid indole alkaloids. Arabidopsis thaliana cultures infected by the rootpathogenic oomycete Pythium sylvaticum caused an increase in indolics upon infection (Bednarek et
al., 2005) while metabolites such as phenylpropanoids, flavonoids and glucosinolates were highly
associated with fungal infection in Brassica rapa leaves (Abdel.Farid, 2009).
In line with this, studies using other metabolic approaches than NMR have shown that the resistance
of sour cherries towards CLS is related to the content of phenolics in the leaves (Niederleitner et al.,
1993; Oszmiański & Wojdylo, 2014). However, other metabolites may also be related. We chose
NMR as the metabolic tool, since untargeted NMR spectra are unique and specific for each single
compound and can thus be used to identify metabolites of which no a-priori knowledge is needed
(Leiss et al., 2011). Furthermore, 1H-NMR is a nonbiased method compared with other metabolomics
1
Introduction
approaches, since the vast number of biological metabolites contain hydrogen and 1H-NMR provide
a unique signal for each chemically distinct hydrogen nucleus. NMR is emerging as the standard
metabolic profiling approach despite its relative low sensitivity, compared to other metabolomics
approaches (Goodacre et al., 2005). In this study a broad analysis of all metabolites was made, mainly
focusing on the phenolic compounds.
Plant physical structures may also play a role in plant-pathogen relationships. Stomata are pores
positioned on the surface of plant leaves, which act as the interface between the environment and
internal plant tissues where water, O2 and CO2 are exchanged. However due to their pore structure,
they are also convenient gates for endophytic colonization by pathogens (Gudesblat et al., 2009;
Melotto et al., 2008). In order to understand the role of stomata in sour cherry disease resistance we
analyzed the genotypic differences in stomata size and density.
This master thesis will investigate:





How is the metabolic profile of sour cherry leaves affected by CLS infection?
Which phenolic compounds are correlated to CLS resistance?
How is the composition and concentration of phenolic compounds and other metabolites
affected over time in the different genotypes?
Is there a coherence in how the different genotypes react to infection by CLS?
Is there a difference in the stomatal traits between resistant and susceptible genotypes?
These questions leads to the following hypotheses:






Infection will change the carbon metabolism and upregulate sink metabolism in the infected
leaves.
Leaves from genotypes with a high level of resistance throughout the season contain a high
concentration of phenolic compounds in young leaves.
A combination of several phenolic compounds is necessary to obtain resistance in leaves.
If the phenolic content is higher in the old leaves than in the young leaves, the leaves become
less susceptible over time. This increase in phenolic content can either be constitutive (happen
naturally with increasing age) or induced by the fungus attack.
Leaves from some susceptible genotypes never obtain a sufficiently high level of phenolic
compounds to achieve resistance, due to a low naturally occurring content of phenolic
compounds and the disability to induce a production.
Resistant genotypes have fewer and smaller stomata, which makes it more difficult for the
fungus to penetrate the epidermis.
The present study is linked to the science project “Disease-resistant varieties for organic sour cherry
growing by NMR metabolomics analyses” which aims to use new and advanced methods of
identifying robust varieties of sour cherries with high resistance towards the fungal diseases cherry
leaf spot and Monilinia laxa. This is funded by the Danish research program for green development
and demonstration projects (GUDP), and runs from January 2014 to December 2016.
2
Background
2 BACKGROUND
The following sections contain a presentation of sour cherry and cherry leaf spot, with focus on how
the plant defends itself by structural and biochemical mechanisms. In addition, the applied
techniques, including NMR spectroscopy and multivariate data analysis will be presented.
2.1 SOUR CHERRY (PRUNUS CERASUS)
Sour cherry is a tetraploid species (2n=4x=32 chromosomes), which has originated through natural
hybridization between the tetraploid ground cherry, Prunus fruticosa (2n=4x=32), and unreduced
pollen of the diploid sweet cherry, Prunus avium (2n=2x=16). Prunus cerasus is now a species in its
own right having developed beyond a hybrid and stabilized (Oldén & Nybom, 1968; Schuster et al.,
2011). It blossoms in the beginning of May and the fruit can be harvested from July to August. In
production, sour cherries are mainly used in processed products, typically undergoing freezing,
canning or juicing processes (Kim et al., 2005). Due to severe problems with fungal diseases, sour
cherries are difficult to produce (Korsgaard & Lindhard Pedersen, 2007). CLS is one of the main
fungal diseases in both sweet and sour cherries, with sour cherries being the more susceptible of the
two. To manage CLS infection growers perform up to eight fungicide applications in the course of
each growing season. This results in substantial costs to the growers and a significant amount of
pesticides released into the environment (Stegmeir et al., 2014). The prevalence of this disease makes
organic production of sour cherry especially challenging, and is certainly part of the reason why only
three hectares of organic sour cherries are grown in Denmark (Jørgensen, 2015). However, there is
an increasing demand for organic sour cherry, which in turn causes an increased need for robust
organic sour cherry genotypes (Jensen, n.d.).
2.2 CHERRY LEAF SPOT (BLUMERIELLA JAAPIIS (REHM) VAR. ARX.)
Cherry leaf spot (CLS) is indigenous to North America but was first reported in cherries in Europe
around 1940 and in Denmark it was detected in 1948. Today CLS is common in all cherry growing
areas in North America and Europe (Wharton, 2003).
CLS mainly affects the leaves causing chlorosis and premature leaf defoliation, with strongly affected
trees being defoliated by mid-summer. Early defoliation results in reduced tree vigor with trees
producing dwarfed, soft and unevenly ripened fruit with poor coloration and an inferior taste.
Furthermore, premature defoliation reduces winter hardiness of the tree causing weak fruit buds,
reduced shoot growth, and the death of fruit spurs or even entire trees. The disease may also cause
lesions on fruit, petioles, and fruit stems (Schuster, 2004; Wharton et al., 2003; Pedersen et al., 2012).
The disease is caused by the ascomycete fungus Blumeriella jaapii, called Phloeosporella padi at its
conidial anamorph stage. Formerly the fungus was known as Coccomyces hiemalis (Ellis, 2008).
The CLS fungus is spread by two different kinds of spores; ascospores and conidiaspores. The fungus
overwinters as a saprophyte in fallen diseased leaves on the orchard floor, which were colonized
during the previous growing season. Mild winters help the fungus overwinter successfully on the
orchard floor. During wet periods in the spring, the sexual fruiting bodies (apothecia) develop and
release ascospores (primary inoculum). The discharge of ascospores occurs over a period of
3
Background
approximately six to eight weeks, starting at petal fall. The optimal temperature for ascospore
discharge is 16°C and higher, with very few ascospores discharged at temperatures below 8°C
(Pedersen et al. 2012). The ascospores are spread by wind or splashing raindrops to new enlarging
leaves where they stick to the leaf surface, germinate in a film of water, and penetrate the leaf through
the stomata on the lower side of the leaves. From the stomata, hyphae grow through the intercellular
spaces of the mesophyll and haustoria penetrate the cell walls (Gruber et al., 2012). For the
penetration to succeed, leaves have to remain wet for several hours. Lesions appear on the upper leaf
surfaces approximately 10 to 14 days after the infection as small, reddish spots. The incubation period
varies with temperature but can occur within five days. Temperatures of 16-20°C are most favorable
for disease development (Pedersen et al., 2012). The lesions rapidly enlarge into brown or purple
irregular or round spots, which die from the center out (figure 1). The lesions occur over the entire
leaf surface. Individual spots merge together to kill larger areas of the leaf. After six to eight weeks,
the necrotic parts separate from healthy tissue and drop out, leaving a "shot-holed" appearance. This
first infection is limited, as new leaves are small and the temperatures are often low for efficient
infection (Pedersen et al., 2012). Once lesions have developed, during moist and rainy periods,
asexual fruiting bodies (acervuli) are formed on the abaxial (lower) side of the leaf (figure 2). From
the stroma of the acervuli, conidiophores (specialized hyphal branches) are developed giving rise to
conidia spores (secondary inoculum). When the conidia have accumulated in sufficient number, the
epidermis is broken and they appear as cream-colored sticky spore masses in the center of the spots
on the lower side of the leaves. These secondary spores are responsible for the extensive spread of
the disease (Díaz et al., 2007; Pedersen et al., 2012; Holb, 2009; Higgins, 1914).
When the mycelium develops inside the leaf, the host cells are not killed at first, except those cells in
contact with the stroma. This indicates that the fungus does not produce any poisonous toxins or
enzymes, since the dying of cells in direct contact with the stroma is brought by drying (Higgins,
1914).
Figure 1. Lesions on sour cherry leaf infected by B. jaapii
(photo; Maja Eline Petersen)
Figure 2. Acervuli on the lower leaf surface (photo;
Maja Eline Petersen)
The conidia spores are rain splashed to neighbor foliage, causing severe secondary infections. If
weather conditions for disease development are conductive, with frequent rainfall, secondary
infection cycles can repeat throughout the summer and fall leading to a rapid and progressive increase
in the disease incidence, where eventually all leaves are infected, turn yellow, and drop. An overview
of the CLS disease cycle can be seen in figure 3. Symptoms similar to those on the leaves may also
4
Background
appear on leaf petioles and fruit pedicels, causing fruit to ripen unevenly. However, spots usually do
not form on the fruit. In addition to conidia produced during the summer, the CLS fungus also
produces winter conidia in the overwintering leaves. However, the role of these winter conidia in the
epidemiology of cherry leaf spot is still unknown (Díaz et al., 2007; Pedersen et al., 2012; Holb, 2009;
Higgins, 1914).
Figure 3. Disease cycle of cherry leaf spot (Travis et al., n.d.)
2.2.1
Effects of the CLS Pathogen on the Physiological Functions of Sour
Cherry
The CLS fungus, like other pathogens, infects the plant host in order to obtain nutrition for its own
metabolism. By doing so, the different physiological functions of the infected plant, in this case sour
cherry is affected (Agrios, 2005). Since CLS cause chlorosis and necrosis on the sour cherry leaves,
photosynthesis and growth are affected (Gruber et al., 2012).
Photosynthesis enables green plants to transform light energy into chemical energy, which can be
used in all cell activities (Gruber et al., 2012). Photoassimilates produced by photosynthesis are
converted either to sucrose in the cytosol or to transitory starch, in order to remobilize the inorganic
phosphate incorporated in the primary products of the photosynthesis. All photoassimilates that are
not required for the support of the leaf function are converted into sucrose and transported to other
parts of the plant (Marselis et al., 2014). The sucrose is actively loaded into the phloem, which results
in diffusion of water from the xylem into the phloem where the sucrose concentration is high. This
creates an area of high pressure in the region surrounding the sucrose source. As a result sucrose is
transported by a pressure flow to sink tissues (net importer of assimilate). Here sucrose is unloaded
and the turgor pressure drops, which drives the mass flow of sucrose from source organs (net
exporters of assimilate) to sink organs. The sucrose is unloaded from the sieve elements in the phloem
into the apoplast. Here it is cleaved by extracellular invertase to the hexose monomers glucose and
fructose, which is taken up by the sink cells by monosaccharide transporters (Roitsch, 1999; Hay &
Porter, 2006). The infection of CLS in sour cherries, will affect these processes. The photosynthetic
rate will decline as a result of premature canopy senescence, which reduces the interception of
5
Background
radiation. The activation of the defense reaction requires energy, which causes the infected leaves to
upregulate the sink metabolism, to meet the energy requirements (Roitsch, 1999). Hereby the
partitioning of assimilates from the infected leaves to the reproductive structures and other sinks will
decrease and this may alter the source-sink relationship (Hay & Porter, 2006; Agrios, 2005). In sour
cherry trees, this will mainly affect the production of the cherry fruits, since these depend on the
import of photoassimilates from the photosynthetic leaves.
A reduction in stomatal conductance and transpiration rate has also been measured in cherry leaves
infected by B. jaapii (Oszmiański & Wojdylo, 2014). This is contrary to other results, showing that
transpiration rates usually increase in infected leaves, as a result of the destruction of a considerable
portion of the cuticle and epidermis (Agrios, 2005).
2.3 GENETIC RESISTANCE
For a disease to be successful, a compatible genetic interaction between a host plant and a pathogen
is required, meaning that the host resistance has to be low and the pathogen virulence high (Lucas,
2009). The response of the plant, when plant host and pathogens interact, can lead to different degrees
of resistance to the pathogen. Plants may be highly resistant, intermediate resistant or susceptible. If
a plant avoids infection simply because factors such as weather or vector movement are not
appropriate, its lack of disease is considered an escape. If the disease has developed but has little or
no impact on the final yield, the host plant can be described as tolerant (Schumann & D’Arcy, 2006).
Perennial plants, such as sour cherry, grow in the same location for many years and are thus constantly
exposed to many different microorganisms. Plants respond to the threat of invasion by fungal
pathogens with a variety of defenses, some of which are always present (constitutive) and others that
are triggered in response to invasion (induced). Constitutive defenses act as continuous barriers. The
induced defenses make the plants able to detect the presence of an invading pathogen so that it can
fight back. Constitutive and induced defenses can be divided into structural and biochemical defenses
(Schumann & D’Arcy, 2006).
2.4 STRUCTURAL DEFENSES - STOMATA
Structural constitutive defenses can consist of many different physical components, such as a waxy
cuticle, the size and shape of stomata on the leaf surfaces, the thickness of the cortex, a dense layer
of hairs etc. Many structural defenses can also be induced by pathogen invasion, for example
thickening and lignifying of the cell wall, both of which increase the physical barrier and prevent the
invasion by fungi (Schumann & D’Arcy, 2006).
For a pathogen to establish a parasitic relationship and complete infection, it must penetrate the host.
B. jaapii invades sour cherry leaves through the stomata. Stomata are small pores located on the leaf
surface. They consist of a pair of specialized epidermal cells referred to as guard cells that allow
plants to exchange gases with the environment by opening and closing the stomatal pore (figure 4).
They thus play an essential role in the uptake of CO2 for photosynthesis, but at the same time, they
allow water loss by transpiration. Their position at the interface between the environment and internal
6
Background
plant tissues make them convenient gates for endophytic colonization by pathogens (Gudesblat et al.,
2009; Melotto et al., 2008).
Figure 4. The opening and closing of stomata regulated by the guard cells (Hempel, n.d.)
The mode of infection by B. jaapii suggests that differences in stomata traits between genotypes could
affect the resistance of different sour cherry genotypes to CLS.
2.5 BIOCHEMICAL DEFENSES – PHENOLIC COMPOUNDS
Several plant defense strategies against pathogens exist, including the production of biochemical
defense compounds (Oszmiański & Wojdylo, 2014). These compounds are known as secondary
metabolites, which in contrary to primary metabolites are not necessary for the plant’s primary
metabolism, such as photosynthesis, respiration, formation of carbohydrates, proteins and lipids.
Instead, secondary metabolites are chemicals required for plant interactions with the environment.
Since plants are immobile, they must have mechanisms that make them resistant to adverse
environments, herbivores, pests and pathogens (Taiz & Zeiger, 2010). In principle, plants can grow
and develop in a protected environment without secondary metabolites, but these substances play a
key role for plants ability to compete and survive within the natural environment (O’Doherty Jensen,
2001; Kliebenstein, 2013). In plant-pathogen interactions, secondary metabolites play an important
role for the plant’s ability to resist infection. The secondary metabolites either can be present in the
plant before infection or induced by attack (Agrios, 2005).
Secondary metabolites are often classified into three larger sub-groups based on their biosynthetic
origins (Crozier et al., 2006);
1. Phenolics
2. Terpenoids
3. Nitrogen-containing alkaloids and sulphur-containing compounds
Phenolics exist in different parts of the plant, such as root, shoot, leaf and flower (Leiss et al., 2009).
Soluble phenolics are stored in plant cell vacuoles while the insoluble phenolics are found in cell
7
Background
walls (Naczk & Shahidi, 2006). They play several roles in plant defenses against pathogens (Naczk
& Shahidi, 2006; Prasad et al., 2010) including the defense in sour cherries against CLS (Oszmiański
& Wojdylo, 2014; Niederleitner et al., 1993). A number of phenolics are regarded as pre-infection
inhibitors that provides the plants with a certain degree of basic resistance against pathogenic
microorganisms (Oszmiański & Wojdylo, 2014). It is suggested, that the first stage of the defense
mechanism of plants involves a rapid accumulation of phenolics at the infection site, which slows
down the growth of the pathogens (Oszmiański & Wojdylo, 2014).
Phenolics are aromatic acid compounds containing at least one aromatic 6-carbon ring bonded to one
or more hydroxyl groups (figure 5). They vary from low
molecular-weight, single aromatic-ringed compounds to large and
complex structures. More than 8000 phenolic structures have
been identified within a wide range of plants (Crozier et al., 2006).
Phenolics can protect the plant by different mechanisms. They can
be deposited inside the cell wall as an important defense barrier
Figure 5. The simplest phenolic
compound (C6H5OH).
for infecting pathogens or they can be oxidized and react with
proteins which can cause a loss of enzyme function, limiting the
viability of the pathogen (Schwalb & Feucht, 1999).
The phenolics consist of a very diverse group of chemical compounds such as;




Flavonoids
Polyphenolics
Phenolic acids
Hydroxycinnamates (Crozier et al., 2006).
Flavonoids consist of a 15-carbon skeleton with two aromatic rings connected by a three-carbon
bridge. They are present in high concentrations in the epidermis of
leaves and are involved in disease resistance, among other (Crozier
et al., 2006; Taiz & Zeiger, 2010). Epicatechin (figure 6) is a
flavonoid, occurring especially in woody plants (Wishart et al.,
2013). It is a precursor for pro-anthocyanidins, which are
compounds with putative antifungal properties (Oliva et al., 2015).
Figure 6. Molecular structure of epicatechin.
Non-flavonoids comprise the phenolic acids, polyphenolics, and the hydroxycinnamates.
Hydroxycinnamates are found ubiquitously in plants. They can either occur in free form such as
cinnamic acid, as derivatives of cinnamic acid, such as the hydroxyl derivative m-coumaric acid or
they may be conjugated to other molecules, usually to quinic acid (figure 7) (Nollet & Toldrá, 2012).
Chlorogenic acid is an ester formed between quinic acid and the hydroxycinnamate caffeic
acid (figure 7).
8
Background
Figure 7. Molecular structure of cinnamic acid, quinic acid, caffeic acid, m-coumaric acid and chlorogenic acid.
The biosynthesis of phenolics involves a complex network of paths, mainly the shikimic and malonic
acid pathways (figure 8) (Taiz & Zeiger, 2010).
Only a few studies have examined the phenolic content in sour cherry leaves. In a study examining
the influence of naturally-occurring phenolic acid mixtures from selected plants against the grain
aphid, the phenolic profile for leaves of sour cherry was investigated (Chrzanowski et al., 2012). Here
they found 10 different phenolic compounds. A study conducted by Niederleitner et al. in 1993 and
one by Oszmiański & Wojdylo in 2014 examined the influence of CLS on changes in the pattern of
phenolic compounds in sour cherry leaves. In appendix 9.1 a table with a list of phenolic compounds
found in sour cherry leaves in previous studies can be seen.
The HPLC analysis of sour cherry leaf extract by Niederleitner et al. (1993) showed that leaves
infected with CLS had a significant accumulation of catechin, epicatechin and the procyanidins B2,
B5 and C1, while noninfected leaves did not contain any of these metabolites. All these compounds
belong to the group of flavan-3-ols, which, according to Niederleitner et al. (1993) are accumulated
by infection of several pathogens in tissues around the infection zone. Within this group of flavanols,
the synthesis of catechin seemed to be present in most genotypes (Niederleitner et al., 1993).
In 2014, Oszmiański & Wojdylo characterized and quantified phenolic compounds from green and
yellow sour cherry spotted leaves by UPLC-MS/MS, hereby comparing infected leaves at the early
period and at the terminal period of infection. A total of 35 phenolic compounds found in spotted sour
cherry leaf extracts were identified; 14 hydroxycinnamates, 15 flavonols, five flavan-3-ols and one
flavon derivative. The content of phenolics in the yellow and green leaves of sour cherry varied
significantly, with a much higher total content of phenolics in the yellow leaves (10058.00 mg/100 g
dry matter (dm)) than in the green leaves (8737.13 mg/100 g dm). The yellow leaves were
characterized by having a larger content of hydroxycinnamate and flavonol compounds than the green
leaves. However, green sour cherry leaves contained much more monomeric catechins and polymeric
procyanidins than the yellow leaves.
9
Background
Glycolysis
Glyceraldehyde-3-phosphate
Photosynthesis
(Calvin cycle)
Oxidative pentose
phosphate pathway
Erytrose-4-phosphate
↓
Phosphoenolpyruvate
Glyceraldehyde-3-phosphate
↓
Pyruvat
Shikimic acid pathway
3-Dehydroquinic acid
Malonic acid pathway
Acetyl-CoA
Phenylpropanoid precursors
Phenylalanine
↓
Malonyl-Coa
Flavonoids,
condensed
tannins,
stilbenes
↓
Cinnamic acid
↓
p-coumaric acid
↓
3-Dehydroshikimic acid
↓
Shikimic acid
↓
p-coumaroyl-CoA
Simple
phenolic acids
Coumaric and
caffeic acid
esters, lignans
Figure 8. Plant phenolics are biosynthesized in several different ways, mainly the shikimic acid and malonic acid pathways. Figure
adapted from Natural Chemistry Research Group, n.d.
10
Background
2.6 SELECTION OF RESISTANT SOUR CHERRY GENOTYPES
Finding resistant genotypes is usually done by
selection based on phenotypic registration.
However, this method can be very uncertain since
both a certain amount of inoculum and a favorable
environment have to be present, to be able to
register disease and a variation between genotypes.
This can be represented by the disease triangle,
which represents the three fundamental elements
required for disease in plants; a susceptible plant, a
pathogen capable of causing disease and a favorable
environment (figure 9) (Schumann & D’Arcy,
2006).
Figure 9. The disease triangle
The phenotypical selection of resistant sour cherry genotypes poses several additional challenges.
Prunus species show a long juvenile period of three to seven years, which prolongs the selection
process and produces differences in trait expression between juvenile and mature trees. When sour
cherries are vegetatively propagated onto rootstocks, the rootstock can interact with the graft, making
the selection processes more difficult. For these reasons, the development of new resistant sour cherry
genotypes is expensive and time-consuming and involves the generation of a large population of
seedlings from which the best genotypes are selected (Martínez-Gómez et al., 2012). Therefore, the
development of an efficient selection strategy for detecting resistance could be particularly useful.
2.7 METABOLIC PROFILING
The study of the chemical defense in host plant resistance has mainly been restricted to chemical
analyses of single compounds. However, usually more than one compound is involved in biological
processes. An approach to simultaneous detection of a wide range of compounds is metabolic
profiling, which provides an immediate image of the metabolome of the plant (Leiss et al., 2011).
The metabolome is defined as the complete set of all the molecules (metabolites) involved in
metabolism in plants and other living organisms (Martínez-Gómez et al., 2012; Kim et al., 2011) and
is thus essential in order to maintain physiological homeostasis (Rivas-Ubach et al., 2013).
Metabolites are defined as small molecules (30–3000 Da) which vary widely with respect to size,
polarity, quantity and stability. Polymeric biomolecules, such as proteins, polysaccharides, lignin,
peptides, DNA and RNA, are excluded from this category (Kim et al., 2011). Changes in the
metabolome typically reflect gene and protein expression. External perturbations can potentially
cause changes in a biological system, which can be explored by genomics and proteomics. However
genomics and proteomics do not always predict gene function accurately, since mRNA and protein
levels are often poorly correlated. Furthermore, a synthesized protein may not be enzymatically
active, making the proteome and genome different from the metabolome (Goodacre et al., 2005). This
11
Background
makes metabolic profiling a useful tool for understanding the biochemical reaction to stress, since it
can give valuable information about the functional state of an organism due to the closeness of
metabolites to the functional endpoints and to the phenotypes of the organisms (Choi et al., 2004).
Because of this, metabolites and metabolic profiles are ideal as biomarkers (Smolinska et al., 2012).
In sour cherries, the capacity of metabolic tools to identify metabolites and profiles that may serve as
biomarkers for the CLS disease is of particular interest, since these methods may enable rapid and
cost-effective assessment of resistant sour cherry genotypes.
2.7.1 Nuclear Magnetic Resonance Spectroscopy
The main analytical techniques used for the analysis of the metabolome are nuclear magnetic
resonance (NMR)- and mass spectrometry (MS) -based analyses, the latter requiring a pre-separation
of the metabolic components using either gas chromatography (GC) or liquid chromatography (LC)
(Koek et al., 2011; Beckonert et al., 2007). Both MS and NMR are suitable techniques for studying
metabolites in a wide range of biological systems, but they have different analytical strengths and
weaknesses (Beckonert et al., 2007). MS is a more sensitive method than NMR and therefore detects
metabolites that are present in concentrations below the detection limit of NMR (Kim et al., 2011).
NMR, on the other hand, has advantages unsurpassed by other methods since it is fast, quantitative,
nondestructive and requires no derivatization or separation (Wishart, 2013). Due to these advantages
NMR has been the predominant profiling method for many applications in plant metabolomics,
especially 1H NMR since most biological molecules are proton-rich (Smolinska et al., 2012; Lane,
2012).
NMR is a robust, reliable and reproducible technique for investigating the structure and dynamics of
molecules by using the magnetic property of certain atomic nuclei. It gives a metabolic 'snapshot' at
a particular time point by simultaneously detecting a wide range of structurally diverse metabolites
(Beckonert et al., 2007).
A sample can be investigated by NMR by using the properties of active nuclei to absorb
electromagnetic radiation at a frequency characteristic of the isotope when placed in a magnetic field.
When a magnetic nucleus is placed in a magnetic field, it adopts one of a small number of allowed
orientations of different energy depending on the spin number quantum of the nucleus. For a nucleus
with a spin of ½ (e.g. 1H, 13 C, 15 N), there are two allowed orientations of the nucleus; against the
field (alpha spin state) and parallel to the field (beta spin state). The two spin states are separated by
an energy, ΔE, depending on the strength of the interaction between the nucleus and the magnetic
field. Irradiation of the sample with energy corresponding to the exact spin state separation of a
specific set of nuclei, will cause the nuclei in the lower energy level to absorb energy and flip to the
high energy state. The nucleus is said to be in resonance with the applied magnetic field, when the
nucleus in the lower energy state excite to the higher energy state. When the nucleus falls back down
from the high energy state, the energy is detected as a signal whose intensity varies with time. To
ease the following analysis, the signals are transformed to a frequency domain by applying a Fourier
transformation. On the resulting spectrum, the frequency is plotted on the horizontal axis and the
intensity of the signal on the vertical axis (figure 10) (Butler, 2002).
12
Background
Figure 10. Fourier transformed NMR spectrum with intensity plotted on the vertical axis and frequency on the horizontal axis
(Butler, 2002).
The value of the resonance frequency is mainly determined by the nuclear structure. Every isotope
has a particular combination of protons and neutrons in its nucleus and thus has a specific basic
resonance frequency (table 1). The resonant frequency of a nucleus relative to a standard in a
magnetic field, is termed the chemical shift, δ, which is usually expressed in parts per million (ppm).
Table 1. Basic resonance frequency for some NMR active isotopes (Lane, 2012)
Nucleus
1
H
H
13
C
14
N
15
N
2
Basic resonance frequency quoted for
a 14.1T magnet (MHz)
600
92
151
43
61
The local atomic environment in which an isotope is situated, also affects the resonance frequency.
However, this effect is relatively small compared to the basic resonance frequency. The electrons
surrounding the nucleus act as a source of magnetic shielding. Thus, atoms bonded to and surrounding
the isotope of interest, are able to shield the nucleus from the applied magnetic field. Irradiation can
therefore be increased in order to achieve resonance, resulting in frequency variations between
different functional groups, making them distinguishable, and even identical functional groups with
differing neighboring substituents still give distinguishable signals (figure 11).
13
Background
Figure 11. Approximate proton chemical shifts (Richardson, 2011).
The out coming signals can help us understand the structure of molecules, since they can be assigned
by comparison with libraries of reference compounds, or by 2D-NMR (Leiss et al., 2011; Hore, 1995).
The intensity is a measure of the signal strength. The integral of the signal peak is proportional to the
number of nuclei contributing to a signal at a particular frequency. The intensity consequently
provides quantitative information about the chemical structure.
Signals can be split into more peaks due to an effect known as spin-spin coupling. This is caused by
the magnetic interaction between neighboring nuclei in different local environments. The derived
coupling between n equivalent nuclei splits the signal into an n+1 multiplet with intensity ratios
following Pascal’s triangle (table 2).
Table 2. Multiplets and Pascal’s triangle
Multiplicity
Singlet
Doublet
Triplet
Quartet
Quintet
Sextet
No. of neighboring nuclei
0
1
2
3
4
5
Intensity Ratio
1
1:1
1:2:1
1:3:3:1
1:4:6:4:1
1:5:10:10:5:1
Coupling between nuclei that have the same chemical shift and are magnetically equivalent has no
effect on the NMR spectra and couplings between nuclei that are distant are usually too small to cause
observable splittings. The magnitude of the coupling constant is affected by the number of intervening
bonds between the coupled nuclei, bond order and length (Lane, 2012).
The NMR metabolic profile of a sample is obtained when each peak of the NMR spectrum is assigned
to its corresponding metabolite. The NMR spectra are unique and specific for each single compound
and can be used to identify metabolites of which no a-priori knowledge is needed. The NMR method
14
Background
provides simultaneous access to both qualitative and quantitative information, since the signal
intensity is directly proportional to the molar concentration. The fact that concentrations of
components in a mixture can be determined is one of the key benefit of NMR over other analytical
methods (Smolinska et al., 2012; Kim et al., 2011).
Often signals overlap in 1D-NMR spectrum that may hinder signal identification and accurate peak
integration. In most cases, this overlapping can be solved by using 2D-NMR, which has a much better
resolution and gives easier access to information that is difficult to obtain from the crowded 1D-NMR
spectrum (Kim et al., 2005; Lane, 2012). 2D experiments essentially use the same principles as 1D
experiments. However, the data are plotted as a grid, with one chemical shift range on one axis and
another chemical shift range on the other axis. This results in a contour plot, where contour lines
correspond to signal intensity (Pavia et al., 2008). Different types of 2D experiments are available. In
this project heteronuclear single quantum coherence (HSQC) spectroscopy and correlation
spectroscopy (COSY) is applied. In HSQC, any 13C atom directly attached to a proton is detected,
giving a spectrum with 1H chemical shifts along the X-axis and 13C chemical shifts along the Y-axis
(Kim et al., 2011). COSY provides information on which hydrogens in a molecule are close in
chemical bond term (Smolinska et al., 2012).
2.8 CHEMOMETRICS
The NMR spectrum of each sample contains huge amounts of variables. The application and
development of mathematical and statistical multivariate methods to extract information from
chemical data, also known as chemometrics, is therefore useful for interpreting the NMR data and for
identification of metabolites relevant to a specific phenotypic characteristic. In chemometrics
different multivariate methods for data exploration, visualization, classification and prediction are
available (Smolinska et al., 2012). The first step of data analysis, after preprocessing, is to explore
the overall structure of the data by unbiased unsupervised methods. Here you explore trends and
groupings in the data without assuming any prior knowledge. If groupings are found, this known
structure can be used in supervised techniques, to learn patterns and rules to predict new data
(Smolinska et al., 2012). Below, an overview of the methods used in this experiment is given.
2.8.1 Principal Component Analysis
Principal component analysis (PCA) is the main unsupervised multivariate statistical analysis used in
metabolomics. The objective of PCA is to provide the most compact representation of all the variation
in a data table, X. PCA is an ordination method that converts the multidimensional data space into a
low dimensional model plane, by rotating the coordinate system for the raw data points into a new
coordinate system. The axes in the new system, called principal components (PCs), are ordered
according to the amount of variance they explain from the original data. This means that the highdimension spectral data can be described by just a few PCs, allowing us to extract and display the
systematic variation in the data (Smolinska et al., 2012). This gives an overview that uncovers the
relationships between observations and variables and may reveal groups of observations, trends, and
outliers (Eriksson et al., 2006).
15
Background
Statistically, PCA finds lines, planes
and
hyperplanes
in
the
multidimensional space, which
approximate the data as well as
possible in the least squares sense.
The first PC is the direction through
the data that accounts for as much
variance in plot dispersion from the
centroid as possible (figure 12). The
second PC must be orthogonal to the
previously calculated PC and explain
the maximum of the remaining
variance. The following PCs are
chosen based on the same criteria.
Figure 12. PCA derives a model that fits the data as well as possible in the
Each PC is thus a linear combination least squares sense. Alternatively, PCA may be understood as maximizing the
variance of the projection co-ordinates (Eriksson et al., 2006).
of the original variables, where each
successive
PC
explains
the
maximum amount of variance, which was not accounted for by the previous PCs.
By using PCA your data table X is modelled as
X = TP’ + E (1)
Where T represents the scores, P’ the transposed loadings while E forms the residuals.
The scores, T, are the new coordinates for the samples in the space defined by the PCs (Davies &
Fearn, 2004; Smolinska et al., 2012). By plotting the scores, it is possible to see how the samples are
related to each other. Points close to each other in the score plot illustrate samples with similar NMR
profiles while points far from each other represent samples characterized by different metabolic
properties (Smolinska et al., 2012).
The loadings, P, define the orientation of the PC plane in regard to the original X-variables.
Algebraically, the loadings inform how the variables are linearly combined to form the scores. The
magnitude of the loading reveals if the correlation is large or small while the direction reveals if the
correlation is positive or negative (Eriksson et al., 2006). Thus, the loading plots can be used to detect
the metabolites responsible for the separation in the NMR data (Bro & Smilde, 2014).
2.8.2 Orthogonal Projections to Latent Structures - Discriminant Analysis
While PCA is an unsupervised pattern recognition technique where no class identity is assigned to
the samples a priori, the class specific segregation can be refined using a supervised technique such
as orthogonal projections to latent structures – discriminant analysis (OPLS-DA). In this method the
relation between a matrix of predictors, X (i.e. NMR spectra) and a matrix or vector of responses, Y
(i.e. class membership) is found. The technique is similar to PCA since it assumes that the data can
be well approximated by a low dimensional subspace, by latent variables, which are assumed to be
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Background
linear combinations of the original variables. OPLS-DA uses information in the Y matrix (equation
2)
Y = UQ’ + F (2)
to decompose the X matrix into blocks of structures with variation correlated to and orthogonal to Y,
respectively (equation 3):
X = ToP’o + TpP’p + E (3)
In this way, OPLS-DA can separate predictive from non-predictive (orthogonal) variation and when
interpreting the model the irrelevant variation can be filtered out (Smolinska et al., 2012).
Supervised methods are very powerful statistical tools, but since it often is possible to construct a
model, which fits the data perfectly, giving 100% correct classification even if there is no real relation
in the data, it is very important to validate the model (Smolinska et al., 2012). The OPLS-DA model,
as well as other statistical models, is primarily judged using the two parameters R2X and Q2 (cum).
R2X represents the total explained variation in the data whereas the Q2 (cum) explains the extent of
separation between the classes as well as the predictability of the model (Choi et al., 2004).
The data analysis makes it possible to interpret the biological question of interest, which can open to
further scientific inquiry. The typical workflow of metabolomics is shown in figure 13.
Figure 13. Schematic of a typical workflow in metabolomics.
17
Background
18
Materials and Methods
3 MATERIALS AND METHODS
3.1 PLANT MATERIALS AND HARVESTING OF SAMPLES FOR NMR- AND
IMAGE ANALYSIS
From field 26, six different sour cherry
genotypes
with
different
parent
combinations were chosen according to
differences in their degree of resistance
(figure 15). The percentage of infected
leaf area of leaves from 50 of the
genotypes in field 26 had been measured
in October 2014 (figure 14). The six
genotypes for this experiment were
chosen as representatives of three degrees
of resistance; resistant, intermediate and
susceptible (table 3).
Frequency
From 2004 to 2009, the research center at Aarslev, Denmark has carried out a breeding project in
sour cherry, by crossing genotypes with a high fruit-bearing capacity and genotypes with the highest
fruit quality from Denmark and Europe. Since 2007-2008 a collection of approximately 3500 seed
plant progeny from these crossings have been planted in a single-tree plot at Aarslev research center.
The genotypes for this experiment were taken from field 26, which consists of 969 genotypes planted
as controlled crosses with known parents in October/November 2007. The trees were planted in five
rows with a distance of approximately 40-45 cm between each tree. No irrigation, fertilization, or
pesticides have been applied and no pruning has been done for at least the last two years. The short
distance between the trees and the lack of pruning have resulted in a dense tree canopy with a high
humidity, giving optimal conditions for plant diseases to develop. This has given beneficial conditions
for the evaluation of the susceptibility of each genotype towards plant diseases.
16
14
12
10
8
6
4
2
0
5
10
15
20
25
30
Percentage of infected leaf area
35
Figure 14. Histogram of the percentage of infected leaf area measured in
October 2014 on leaves from 50 different sour cherry genotypes located
at field 26 in Aarslev.
Table 3. Three resistance categories of the six genotypes based on the level of attack measured by WinRHIZO in October 2014
Category (level of attack)
Resistant
Resistant
Intermediate
Intermediate
Susceptible
Susceptible
Genotype
Res1
Res2
Int1
Int2
Sus1
Sus2
Mean degree of attack (%)
4,9
4,3
11,3
8,6
31,5
29,4
19
Parent combination
06-36 (Birgitte x Årslev 2510)
06-40 (Birgitte x Zarga Vysne)
06-39 (Birgitte x K27/2)
06-48 (Viki x K27/2)
06-38 (Birgitte x Safir)
06-49 (Viki x Zarga-Vysne)
Materials and Methods
Figure 15. The six sour cherry genotypes examined by NMR, image analysis, stomata imprint and artificial inoculation test (photos;
Maja Eline Petersen)
Field Trial and Experimental Design
The following field experiment on the six selected trees was designed to investigate the effect of
resistance towards CLS attack depending on leaf age in sour cherry. Cherry leaves for the field trial
were collected from the selected trees on August 13, 2015. In August, the CLS infection pressure is
at its highest, which presumably makes it possible to find a great variation in degree of resistance
among the different genotypes. Leaf age was investigated through sampling of leaves with different
ages from the same branch initiated in spring 2015. Three leaves were taken from a branch initiated
in spring 2015; the first unfolded leaf closest to the terminal bud (young), a leaf from the middle of
the branch (middle) and a leaf from the bottom of the branch a couple of leaves above the old wood
from last year’s growth (old). This was done for three branches from the same larger branch, and the
three leaves from the same position from the three branches were pooled as one sample. Three
replicate samples were made from three different larger branches, giving a total of nine samples (each
20
Materials and Methods
consisting of three leaves) for
each genotype. This was done
twice providing nine samples for
NMR and nine samples for image
analysis, in total 108 samples
(figure 16). An overview of the
samples is given in appendix 9.2.
The physiological processes in
plants may vary throughout the
course of a day. To minimize the
influence of this variance, the
samples were gathered within a
short period of time (9:40-11:40
AM).
Figure 16. Leaf sampling. The colored rings symbolize the three different leaf ages
taken from branches from this year’s growth (black = youngest, red = middle, blue
= oldest). Leaves surrounded by the same color of ring were pooled together.
Three replicates were made.
The samples were placed in a cooling box where they remained for the duration of the collection
period. Thereafter the samples for NMR were immediately frozen for approximately 30 seconds in
liquid nitrogen and stored at -24ºC until NMR analysis (figure 17).
Figure 17. Samples were collected in paper bags and stored in a cooling box until being freeze dried in liquid nitrogen (photo; Maja
Eline Petersen)
Samples for image analysis were dried in a paper press at 25ºC for three days and then stored at 4 ºC
until image analysis.
21
Materials and Methods
3.2 ARTIFICIAL INOCULATION TEST
To confirm the resistance categories made from the phenotypical field evaluation, the degree of
resistance of the six genotypes was further investigated by an artificial inoculation test. A disease
severity index was used to characterize the susceptibility of detached leaves from the chosen
genotypes when artificially infected with B. jaapii.
Plant Materials
Cherry leaves for the artificial inoculation test were collected at the experimental plot on August 25,
2015. Four young unfolded and uninfected leaves were taken from each of the six genotypes. The
leaves were divided into three replicates and one control leaf. Young leaves were chosen since
previous studies have documented the importance of inoculating young leaves to achieve successful
infection and fungal growth, since infection is drastically reduced if leaves are more than five days
old (Wharton et al, 2003).
Media Preparation
Potato dextrose agar (PDA) was prepared according to the instructions on the container (½ L
demineralized water and 19.5 g of PDA), added to a ½ L flask and autoclaved at 121°C for 20 min.
Fungal Culture
Heavily infected leaves were collected from naturally infected sour cherry trees and stored in open
trays at room temperature.
In September 2015, the leaves were
placed on filter paper and transferred to a
germination box. By supplying the filter
with demineralized water, wick papers
were used for constant moistening (figure
18). The leaves were incubated in a
heating cabinet at 20ºC for 7 days.
For preparation of CLS spores, spores
from the lower side of the leaves were Figure 18. Leaves stored in a germination box and incubated in heating
cabinet (photo; Maja Eline Petersen).
transferred with an inoculation needle to
petri dishes containing a thin layer of
PDA (figure 19). The plates were incubated at 20ºC for 11 days until white hyphae and spores were
developed.
One petri dish with a monoculture of B. jaapii was flooded with sterile distilled-deionized water
containing a droplet of Tween® 20. The petri dish was then swirled gently and a glass spreader was
used to gently scrape the surface of the media to favor detachment of conidia (figure 19). The conidia
suspension was transferred to a sterile spray bottle and supplied with sterile distilled-deionized water
to reach a higher volume.
22
Materials and Methods
Figure 19. Conidia spores transferred to PDA petri dish, incubated for 11 days and used for inoculation of leaves (photos; Maja Eline
Petersen).
Detached-Leaf Inoculations
Leaves were sterilized in a 1% sodium hypochlorite solution with one drop of Tween® 20 added for
2 minutes and rinsed with sterile distilled-deionized water before being dried between two pieces of
sterile filter paper. The leaves were placed abaxial (lower) surface up in sterile petri dishes on filter
paper soaked with 1% solution of sucrose. The spore suspension was added to the leaves with a spray
bottle (figure 20). The petri dishes were then wrapped with parafilm and incubated in a heating cabinet
at 20°C in a 12-h photoperiod for 13 days. The temperature of 20 °C was chosen, since the optimum
temperature range for germination and infection of sour cherry leaves by B. jaapii conidia has
previously been identified as 17.2 to 22°C (Wharton et al., 2003). The control leaves were treated
with sterile distilled-deionized water only.
Figure 20. Leaves were sterilized in 1% sodium hypochlorite solution and inoculated with a spore suspension (photos; Maja Eline
Petersen).
Evaluation of the Detached-Leaf Assay
At 13 days post inoculation (DPI) the leaves were removed from the petri dishes and observed under
a dissecting microscope and scored visually for symptom development using a severity scale with 4
reaction types (table 4).
23
Materials and Methods
Table 4. Disease scoring scheme used for inoculation test, based on the symptom development on sour cherry leaves inoculated with
B. jaapii after 13 DPI. Modified after Wharton et al. (2003)
Disease Scores
1
2
3
4
chlorotic or necrotic points or small pigmented lesions , no spores
1 to few spores, no acervuli
2 to 10 sporulating lesions or lesions covering up to 25% of the leaf area, acervuli
11 or more sporulating lesions with lesions covering up to 100% of the leaf area, acervuli
Plants with an average disease
score of 1-2 were considered
to be resistant, 2-3 were
considered moderately
susceptible (intermediate) and
plants with a disease score
above 3 were considered
highly susceptible to CLS
(figure 21).
Acervuli developed on leaves
from sample Sus2 after 13
DPI were transferred to an
Eppendorf tube filled with
ethanol, stored at -24ºC and
saved for NMR analysis.
Figure 21. Characteristic disease symptoms and the corresponding disease scores. 1.
Scattered lesions, 2. Few spores, 3. 2-10 sporulating lesions, acervuli, 4. 11 or more
sporulating lesions and acervuli (photos; Maja Eline Petersen).
3.3 PERCENTAGE OF INFECTED LEAF AREA MEASURED BY IMAGE ANALYSIS
To verify the level of attack of the chosen genotypes from the field trial, the severity of leaf damage
caused by CLS was quantified as the percentage of the infected leaf area using digital image analysis.
The severity of leaf damage was defined as the proportion of leaf area exhibiting symptoms or signs
of infection. The harvested leaves were dried in a paper press at 25ºC for 3 days and then stored at 4
ºC until image analysis. The image of the adaxial (upper) leaf surface was digitized using a flatbed
RÉGENT scanner and WinRHIZO software (figure 22). The proportion of leaf surface affected by
necrosis was detected in Photoshop using the “Magic Wand” tool (with a tolerance factor of 10),
which allows you to select an area of an image based on its color. Furthermore, areas that were not
detected by this tool were marked manually by checking the “Contiguous” box to only select areas,
which were joined together. When all necrotic areas were marked on all leaf samples, a color analysis
in WinRHIZO was used to quantify the percentage of necrosis, and thus the level of attack, for each
leaf.
24
Materials and Methods
Figure 22. Image analysis of sour cherry leaves using a flatbed RÉGENT scanner and WinRHIZO software (photos; Maja Eline
Petersen).
3.4 STOMATAL MEASUREMENTS
To study the difference between genotypes on stomatal anatomy, the length, width, area and surface
density of stomata, as well as the pore length, width, area and surface density were assessed in the six
genotypes. Anatomical measurements were determined using the silicon rubber impression technique
(Weyers & Meidner, 1990). Measurements were carried out on the abaxial leaf surface, since a test
imprint of both the abaxial and adaxial surface of the sour cherry leaves showed that sour cherry is a
hypostomatous species.
The stomata imprints were made at midday when stomata were open, on leaves still attached to the
tree (figure 23). Three replicates of the third leaf from the terminal bud was chosen.
Vinylpolysiloxane dental resin was attached to the abaxial leaf side with a dispensing gun and
removed after drying.
The resin epidermal imprints were covered with clear nail polish, which was removed when it had
dried. This gave a mirror image of the resin imprint, which could then be analyzed under a light
microscope with a Nikon AZ100 camera mounted. For determining stomatal anatomy, a
magnification of 19.2× was used. Stomatal images were taken from three fractions of the leaf avoiding
major veins, since veins lack stomata. The images were later analyzed to determine aperture size and
stomata density using the Java-based image processing program ImageJ. Sour cherry leaf images
showing stomatal measurements made in ImageJ can be seen in appendix 9.3.
25
Materials and Methods
Figure 23. Stomata imprints of sour cherry leaves (photos; Maja Eline Petersen).
To calculate stomatal area, it was assumed that the stomata are elliptical (the major axis as the stomata
length and the minor axis as the stomatal width) (Savvides et al., 2012). The area was thus calculated
as the semi major axis (a) multiplied by the semi minor axis (b) multiplied by pi (equation 4):
𝐀 𝐞𝐥𝐥𝐢𝐩𝐬𝐞 = 𝛑𝐚𝐛 (4)
The same procedure was used for estimating the pore area. For stomatal and pore area, five randomly
selected stomata were chosen in each image and width and length was measured (figure 24). Stomatal
area per leaf area was measured by counting the numbers of stomata in a rectangular field with distinct
stomata and calculating the proportion of stomatal area to the total area of the rectangular field. The
stomatal density was calculated as the number of stomata per mm2.
Figure 24. Stomatal measurements used for image analysis. The grey area represents the guard cells, the black area the pore walls,
and the white area depicts the pore area (Savvides et al., 2012).
26
Materials and Methods
3.5 METABOLIC PROFILING USING NMR ANALYSIS
The leaves were freeze-dried (Christ Gamma
1-16 LSC) for 2 days before ground in a ball
mill (Retsch MM 200) at 22.0 s-1 for 2 minutes
(figure 25). Fifty mg of the lyophilized cherry
leaves was weighed into a 1½ ml Eppendorf
tube.
Acervuli from B. jaapii propagated from the
artificial inoculation test, was also examined
using NMR, to investigate its metabolic
profile. The Eppendorf tube containing
acervuli was evaporated using the Eppendorf
Concentrator plus to remove ethanol.
The metabolite extraction was based on the
Figure 25. Leaves were grinded using a ball mill (picture 1-3),
protocol for NMR-based metabolomics extracted with methanol in an ultrasound bath (picture 4) and a
centrifuge (picture 5) (photos; Maja Eline Petersen).
analysis of plants by Kim et al. (2010). 500 µl
90mM phosphate buffer (pH 6.0) and 500 µl methanol were added to the 54 Eppendorf tubes with
leaf samples and the Eppendorf tube with acervuli. The phosphate buffer was prepared by adding
1.232 g of KH2PO4 and 400 µL 1.0 M NaOH to a beaker subsequently filled up to 100 ml with
distilled-demineralized water. According to Kim et al. (2010), a mixture of methanol and aqueous
phosphate buffer with pH 6.0 provides a good overview of both secondary and primary metabolites.
After stirring until total dissolution, the pH was adjusted using some droplets of 1.0 M NaOH until it
reached pH 6.0 measured on a Metrohm 691 pH Meter.
Samples were vortexed for 1 minute in an IKA MS2 minishaker, ultrasonicated for 15 minutes at
room temperature in a Branson 5210 ultrasonic bath and centrifuged for 10 minutes at 15.000g and
4ºC in an Eppendorf centrifuge 5417 R. 700 µL of the supernatant was transferred to a 1½ ml
Eppendorf tube and the solvent was evaporated using the Eppendorf Concentrator plus for 3 ½ hours
at ambient heating level and application mode for aqueous sample solvent. 600 µL distilleddemineralized water was added to the Eppendorf tube and vortexed for 1 minute, sonicated for 15
minutes and centrifuged for 10 minutes as previous step. 500 µl supernatant from the prepared sample
and 100 µL D2O (containing 0.01% 3-(trimethylsilyl)propionic-2,2,3,3-d4 acid (TMSP) and sodium
salt) were transferred to a NMR tube.
NMR spectra of extracts were obtained on a 600 MHz Bruker 600 UltraShieldTM NMR spectrometer
equipped with a TXI probe (Bruker Biospin, Rheinstetten, Germany) operating at a 1H frequency of
600.13 MHz and 298 K. One-dimensional (1D) proton NMR spectra were acquired with a CarrPurcell-Meiboom-Gill (CPMG) sequence. CPMG uses the fast relaxation of protons in
macromolecules (short T2) to filter particularly those signals out, highlighting the signals from the
small molecule metabolites. In total 64 scans were made giving 65535 data points for each sample.
All spectra were referenced to TMSP.
27
Materials and Methods
After running 1D-NMR, samples were stored in a -21ºC freezer and transferred after 1 day to a -80ºC
freezer until 2D-NMR.
Cherry leaf metabolites were identified using 2D-NMR experiments (1H−1H homonuclear shift
correlation (COSY) and 1H−13C heteronuclear single-quantum correlation spectroscopy (HSQC)) on
sample Sus2-1-1, Chenomx software (Chenomx Inc., Edmonton, Canada), comparison with
published data (Human Metabolome Database (Wishart et al., 2007) and BioMagResBank (Ulrich et
al., 2008)) and correlation values calculated for the ppm values of interest.
3.6 DATA PROCESSING AND STATISTICAL ANALYSIS
1
The H-NMR spectra were aligned using the Icoshift procedure in Matlab version 8.1 (The
Mathworks Inc., Natick, MA). The intervals were manually chosen based on intervals between peaks
and aligned to the corresponding segments of the average spectrum.
The regions from 9.0 to 0.5 ppm were used for multivariate data analysis and the spectra were
subdivided into 0.01 ppm integral regions, reducing the number of variables for each spectrum.
Signals from residual water and nondeuterated solvents, which corresponded to the 4.75−4.95 ppm
region were left out. Preprocessing of data reduced the dataset into 826 bins per spectrum. Prior to
multivariate data analysis, NMR spectra were Pareto scaled (equation 5) to reduce the importance of
large features, so resultant multivariate models did not become dominated by bins representing
metabolites high in concentration.
𝐏𝐚𝐫 =
𝐱−𝐱̅
√𝐒𝐃
(5)
Multivariate statistical analysis and graphics were obtained using Simca-P+ software (v. 14.0,
Umetrics, Umeå, Sweden). PCA was applied to observe clustering behavior of the samples for the
entire profile (0.5-9 ppm) and in the phenolic region (5.5-9 ppm).
OPLS-DA models were built to visualize the class specific segregation and to obtain the significant
bins contributing to the variation across the resistance categories and different leaf ages. The OPLSDA approach uses class information to make the utmost discrimination among classes of
observations. This approach thus assists in determining and discovering latent and vital biomarkers
related to specific groupings.
Outliers were detected by investigating score plots, Hotelling’s T2 and DModX functions. Hotelling’s
T2 plots a T2 value for each observation, showing the distance in the model plane (score space) from
the origin. T2 is calculated as the sum over the selected range of components of the scores in square
divided by their standard deviation in square (equation 6), where values larger than the 95%
confidence limit are suspect (Simca®-P+ 14.0, 2014).
𝑡𝑓2
𝑇 2 = ∑𝑁𝑢𝑚𝑏𝑒𝑟𝐶𝑜𝑚𝑝
(6)
𝑓=1
𝑠2
𝑓
The DModX examines the residual standard deviation of every X observation. This shows the
distance of an observation in the training set to the X model plane. Observations with a DModX twice
28
Materials and Methods
as large as the critical value (Dcrit) are moderate outliers indicating that these observations are
different from the other observations with respect to the correlation structure of the variables.
For OPLS-DA model validation, a systematic cross validation was applied taking out the three
replicates for each genotype at a time to avoid overfitting.
Number of components for the OPLS-DA models were chosen based on the cross validated prediction
error (RMSECV), the goodness of fit (R2) and the predicted variation (Q2), ensuring that the models
describe the systematic variation and leave out noise and irrelevant variation.
Model strength was assessed using both R2 and Q2 metrics. R2 values report the total amount of
variance explained by the model in both the 1H-NMR data (R2X) and independent variables (R2Y;
e.g. resistance category). Q2 reports model accuracy.
Permutation tests were further used to validate the models, making sure that the OPLS-DA models
have a higher R2 and Q2 than randomly permuted models. Furthermore, observed vs. predicted plots
were made to verify the validity of the models (appendix 9.9).
Potential biomarkers were assigned based on S-plot and variable-importance plots (VIPs).
The concentrations of the identified metabolites where calculated by fitting the reference compounds
in Chenomx Profiler to the 1H NMR spectra for each sample and comparison of the peak intensity
with the internal standard TSMP. The effects of genotype and leaf age were analyzed using ANOVA
based on concentrations of the individual secondary metabolites. A multiple comparison test made in
Matlab was used to test if the concentration means were significantly different among leaf ages and
genotypes.
29
Materials and Methods
30
Results
4 RESULTS
The following section includes results from the digital image analysis and artificial inoculation test,
identification of metabolites found in NMR, univariate and multivariate data analysis made on the
NMR data, quantification of the identified metabolites, and finally results on stomatal traits.
4.1 DIGITAL IMAGE ANALYSIS
The digital image analysis verified the grouping of the six genotypes (table 5). The percentage of
mean degree attack was overall lower in the analysis of leaves from 2015 compared to 2014, but the
grouping of low, intermediate and high level of attack was consistent between the two years. The
mean level of attack for leaves from the susceptible genotype Sus2 was just a bit higher than the mean
level of attack for leaves from the intermediate genotype Int2, while the mean level of attack of leaves
from the intermediate genotype Int2 only was a bit higher than that of leaves from the resistant
genotype Res2 in 2015.
Table 5. Three resistance categories of the six genotypes based on the level of attack measured by WinRHIZO in October 2014
Category (level of attack)
Genotype
Resistant
Resistant
Intermediate
Intermediate
Susceptible
Susceptible
Res1
Res2
Int1
Int2
Sus1
Sus2
Mean degree of attack (%)
2015
0,50
1,39
2,32
5,15
24,80
6,07
Mean degree of attack (%)
2014
4,9
4,3
11,3
8,6
31,5
29,4
4.2 ARTIFICIAL INOCULATION TEST
The susceptible genotypes (Sus1 and Sus2) were heavily infected with a high degree of brown spots
on two out of three leaves, whereas the intermediate and resistant genotypes only had a few brown
spots (figure 26). This demonstrated that it was possible to make controlled infections of the leaves
and confirmed the categories of the chosen genotypes based on the digital image analysis (table 6).
One exception was sample Int2, which was scored as being resistant to fungal attack even though it
was categorized as an intermediate genotype. However, two out of three leaves from Int2 had dried
out in the petri dish, making it uncertain to evaluate the level of attack for this genotype.
Table 6. Disease scores of detached leaves from 6 different genotypes of sour cherry inoculated with B. jaapii conidia from naturally
infected sour cherry leaves. n =dried out leaves not suitable for interpretation. a resistant, bintermediate, csusceptible category.
Genotype
Disease score
Res1
Res2a
Int1b
Int2b
1. sample
1
1
N
1
2. sample
N
2
3
N
3. sample
2
2
3
N
Mean
1.5 (resistant)
1.67 (resistant)
3 (moderate susceptible)
1 (resistant)
Sus1c
Sus2c
4
3
4
4
4
4
4 (highly susceptible)
3.67 4 (highly susceptible)
a
31
Results
DS 1
DS 2
DS n
Res1
DS n
Res1
Res1
DS 3
DS 3
Int1
Int1
Int1
DS 4
DS 4
Sus1
DS 1
DS 4
Sus1
Sus1
DS 2
DS 2
Res2
Res2
Res2
DS n
DS 1
DS n
Int2
Int2
Int2
DS 4
DS 3
Sus2
DS 4
Sus2
Sus2
Figure 26. Disease scores (DS) of detached leaves from 6 different genotypes of sour cherry inoculated with B. jaapii conidia from
naturally infected sour cherry leaves. n =dried out leaves not suitable for interpretation (photos; Maja Eline Petersen).
32
Results
4.3 NMR
The NMR analysis was successfully applied for the analysis of the metabolic profile of the sour cherry
leaves, whereas the NMR analysis of the CLS fungi extraction, using the same extraction method as
for the leaf samples, did not reveal any metabolites. This suggests that none of the compounds found
with 1H NMR in the leaf extracts were produced by the fungi.
To make sure that the differences between the groups was not caused by differences between dry and
fresh weight, the percentage of dry matter for the different genotypes and resistance groups was
calculated (appendix 9.4). However, this did not seem to cause any effect.
Identification of Metabolites by 1D 1H-NMR and 2D COSY and HSQC
Spectra
Figure 27 shows the 600.13 MHz 1H NMR spectrum of young sour cherry leaves extract in
methanol/water from a resistant genotype (Res2) and a susceptible genotype (Sus2), indicating that
peaks in the area of the phenolics were more expressed in the susceptible genotypes.
4.3.1
Res2-3-1
Sus2-3-1
Figure 27. 600.13 MHz 1H NMR spectrum of methanol/water extraction of young sour cherry leaves from genotype Res2 (resistant
category) and genotype Sus2 (susceptible category). Internal standard (TMSP) at 0.0 ppm. The y-scale, expressing the intensity,
differs between the two spectra.
33
Results
The table in appendix 9.5 summarizes the chemical shift assignments obtained for the sour cherry
leaves. In total, 15 metabolites were identified belonging to different groups, including carbohydrates,
phenolics, organonitrogen compounds, cyclic polyalcohol and organic acids. The identification of
these metabolites was based on the analysis of 1D- and 2D-NMR experiments (COSY and HSQC),
together with previously reported data. Two of the metabolites were identified as hydroxycinnamate
derivatives (cinnamate-derivative and m-coumaric acid like compound) and one compound was only
identified by the identification of five peaks in the 1H NMR spectra belonging to the same compound
(unknown a). Assigned 1H NMR spectra and 1H-13C HSQC NMR spectra can be seen in appendix
9.6 and 9.7.
The 1H signals of phenolics were assigned to chlorogenic acid, epicatechin, a cinnamate-derivative,
and an m-coumaric acid like compound. All possible 1H and 13C signals for chlorogenic acid and
epicatechin were found in the investigated spectra.
For the cinnamate-derivative, a proton at δ6.5 equivalent to the proton neighboring a carboxyl group
in cinnamate was detected in the 1H NMR spectra (figure 28). Furthermore, signals were detected at
δ7.6 and δ7.4. However, the shape of the 1H NMR peaks
at these frequencies did not match the shape of the
reference cinnamate compound in Chenomx.
Furthermore, the 1H-13C resonance for cinnamate at
δ7.4; δ143.6 was not detected. This indicates that the
detected compound is a derivative of cinnamate with
some equivalent and some nonequivalent protons to
cinnamate.
For the m-coumaric acid like compound, 1H NMR peaks Figure 28. Proton at δ6.5 in cinnamate (Chenomx).
were found at δ6.48, δ6.91 and δ7.32 with
corresponding 13C NMR peaks at δ127.09, δ119.37 and
δ132.94. However, the 1H NMR peaks at δ6.91 and δ7.32 did not match the shape of the reference
m-coumaric acid compound in Chenomx. Furthermore the 1H-13C resonances for m-coumaric acid at
δ7.16; δ122.8 were not detected.
Other signals were detected close to those of chlorogenic acid (δ7.59 and δ6.44) with the same
coupling constant as chlorogenic acid. This indicates that these signals originate from another
chlorogenic acid isomer (assigned unknown a). However, the COSY spectrum revealed a neighboring
signal at δ8.13. At this frequency, protons are deshielded by groups, which withdraw electron density,
indicating that electronegative elements such as oxygen- and nitrogen atoms are substituted on the
same carbon as the detected proton (Pavia et al., 2008). A correlation line plot in Matlab for the 1H
NMR resonance at δ8.13 revealed that the doublet signal at δ5.83 also originate from that molecule
(r=0.9962), which was not possible to detect by 1H-1H COSY spectrum.
Peaks in the phenolic region of δ6.74-6.79 were also detected. However, it was not possible to identify
from which compound they originate.
34
Results
4.3.2
Univariate and Multivariate Data Analysis of NMR Data
4.3.2.1 Multivariate Data Analysis
NMR data were analysed using multivariate data analysis.
The PCA of the data for the entire metabolite profile (0.5-9.0 ppm) showed a separation and a
clustered pattern among the various resistance categories in the first two components, explaining 35%
and 21% of the variance respectively.
Figure 29. PCA score scatter plot of the entire metabolic profile (0.5-9ppm) of PC1 vs PC2 (a) and PC1 vs PC3 (b) colored according
to resistance category and numbered according to the genotype. PCA loading line plot for PC1 (c). High loadings are seen at δ3.503.90 ppm (red ring).
Figure 29a and b show the PCA score scatter plots of 1H-NMR spectra of sour cherry leaves colored
according to the phenotypic grouping of resistance degree, based on the leaf image analysis. Samples
close to each other have similar properties, whereas those far from each other differ in their metabolic
profile. The samples were clustered separately along particularly PC1 and PC2. PC1 separated the
one susceptible genotype (Sus1) from the other categories and the resistant genotypes from the
intermediate and susceptible genotypes. On PC2, the other susceptible genotype (Sus2) was separated
from the other categories and again the resistant genotypes were separated from the intermediate and
35
Results
susceptible genotypes. This indicates that some patterns of the metabolic profiles of the susceptible
genotypes differ greatly form the rest. However, in the score scatter plot for PC1 vs PC3 (figure 29b)
and PC1 vs PC4 (appendix 9.8) the susceptible genotypes (Sus1 and Sus2) were clustered, indicating
similar metabolic properties for these genotypes.
Examination of the loading line plot can give explanations for the groupings in the score plot, since
directions in the scores correspond to directions in the loadings. The loading line plot for PC1 shows
that the separation is mainly caused by signals from protons in the carbohydrates (figure 29c). This
indicates that the concentrations of carbohydrates are higher in resistant genotypes than in
intermediate and susceptible genotypes.
In order to investigate the specific metabolites driving the separation between the resistance
categories, an OPLS-DA model of the genotypes from the resistant category versus genotypes from
the susceptible category was made. This showed a good separation between the metabolic profile of
sour cherry leaves from the resistant and susceptible category with 32% variation in X related to the
separation of the two groups (appendix 9.9.1).
In order to identify the spectral bins varying significantly between the classes, the S-line was
investigated. Here variables with higher p and p(corr) values have the greatest influence on cluster
formation among the groups (appendix 9.9.1, figure 52). This demonstrated that the absolute most
important metabolite in discriminating these groups is sorbitol at δ3.85.
Second, the biomarkers from the s-line plot underwent a screening procedure for their Variable
importance on Projection (VIP) values. The VIP plot summarizes the importance of the variables both
to explain X and to correlate to Y. A list of variables driving separation in metabolite fingerprints
among resistant and susceptible sour cherry genotypes can be seen in table 7.
Table 7. Variables driving separation in metabolite fingerprints among resistant and susceptible sour cherry genotypes from OPLSDA model 1 (0.5-9 ppm)
Increased/decreased
Metabolite
Increased in sour
cherry leaf metabolic
profile of susceptible
genotypes
Unknown a
Unknown a
Chlorogenic acid
Unknown a
Glucose
Sorbitol
Sorbitol
Sorbitol
Sucrose
Sucrose
Decreased in sour
cherry leaf metabolic
profile of susceptible
genotypes
Bin (chemical
shift)
8.13
7.58
6.90
5.82
3.94
3.85
3.67
3.74
5.41
4.21
Loading
values
0.08
0.08
0.07
0.06
0.09
-0.37
-0.21
-0.16
-0.10
-0.10
Variable importance on
projection (VIP) values
2.20
2.43
2.12
1.60
2.45
10.52
6.08
4.45
2.84
2.88
P(corr)
0.81
0.76
0.77
0.80
0.85
0.79
0.72
0.62
0.76
0.74
To avoid the effect of carbohydrates to cover up the effect from the phenolics, the phenolic region
was analyzed separately by multivariate data analysis. A PCA model showed a clustering between
the resistance groups, with a higher level of compounds from the phenolic region in the susceptible
36
Results
genotypes (figure 30). The OPLS-DA model showed segregation of the two groups with 25.9%
variation in X related to the separation of the two groups (appendix 9.9.2).
Figure 30. PCA score scatter plot of PC1 vs PC3 of the phenolic region colored according to resistance category and numbered
according to the genotype (a). PCA loading line plot for PC1 (b).
The S-line plot, loadings and VIP values showed that chlorogenic acid, epicatechin and the unknown
compound were found in higher levels in the leaves of susceptible genotypes, whereas there were no
compounds found in higher concentrations in the resistant genotypes (table 8).
Table 8. Variables driving separation in metabolite fingerprints among resistant and susceptible sour cherry genotypes from OPLSDA model 2 (5.5-9 ppm)
Increased
Metabolite
Increased in sour
cherry leaf
metabolic profile
of susceptible
genotypes
Unknown a
?
Unknown a
Unknown a
Chlorogenic acid
Chlorogenic acid
Epicatechin
Chlorogenic acid
Unknown a
Bin (chemical
shift)
8.13
8.05
7.74
7.58
7.15
7.08
6.94
6.90
5.83
Loading values
0.17
0.06
0.14
0.20
0.12
0.13
0.16
0.18
0.13
Variable importance on
projection (VIP) values
2.24
1.16
2.63
3.68
2.33
2.36
2.96
3.29
2.34
P(corr)
0.70
0.81
0.71
0.68
0.52
0.61
0.77
0.70
0.68
When designing the experiment, young leaves were harvested to get the “clean” plant profile without
any infection. To avoid analyzing leaves affected by infection, multivariate analysis was made on
these young leaves, to see if the metabolic profile was different. The OPLS-DA tested the variation
between young resistant leaves and young intermediate/susceptible leaves (appendix 9.9.3). The
investigation of important biomarkers revealed the same metabolites discriminating between resistant
and non-resistant groups as when analyzing all leaf ages. However, the unidentified peaks at δ6.746.79 proved to have a high importance for the discrimination in young leaves, with a higher level in
37
Results
intermediate/susceptible than resistant leaves, which was not observed when looking at all leaf ages
(appendix 9.9.3, table 18).
Multivariate analysis was further made to investigate how the metabolic profile changed with age. A
PCA for the entire metabolic profile found a separation of young leaves from older leaves on PC3,
explaining 11% of the variation. Statistics for the OPLS-DA model analyzing the variation between
the young and old leaves from the entire metabolic profile can be found in appendix 9.9.4.
Investigation of biomarkers that had the greatest influence on cluster formation among the groups,
showed that old leaves had a higher concentration of sucrose and malate, while young leaves had a
higher concentration of glucose, fructose, sorbitol, myo-inositol and chlorogenic acid (table 9).
Table 9. Variables driving separation in metabolite fingerprints among young and old sour cherry leaves from OPLS-DA model 4
(0.5-9 ppm).
Increased/decreased
Increased in sour
cherry leaf metabolic
profile of old leaves
Decreased in sour
cherry leaf metabolic
profile of old leaves
Metabolite
Sucrose
Malate
Sucrose
Sucrose
Sucrose
Malate
Malate
Glucose
Glucose
Fructose
Sorbitol
Sorbitol
Myo-inositol
Chlorogenic acid
Bin (chemical
shift)
5.42
4.36
4.21
3.82
3.69
2.71
2.48
5.24
4.65
4.15
3.85
3.65
3.55
2.05
Loading
values
0.10
0.11
0.10
0.14
0.14
0.13
0.12
-0.08
-0.10
-0.16
-0.08
-0.08
-0.15
-0.14
Variable importance on
projection (VIP) values
2.82
3.18
2.84
4.07
4.07
3.79
3.33
2.23
2.84
4.61
2.16
2.35
4.34
4.04
P(corr)
0.44
0.44
0.44
0.46
0.41
0.52
0.56
0.55
0.55
0.73
0.10
0.18
0.72
0.76
When doing multivariate analysis on the phenolic region according to leaf age, no grouping was
found.
4.3.2.2 Univariate Data Analysis
The two-way ANOVA, assessing the main effect of leaf age and genotype, found that 9 out of 14 metabolites
were significantly affected by leaf age while 13 out of 14 were significantly affected by genotype (table 10).
The concentrations of the identified metabolites for the different genotypes and leaf ages can be seen in
appendix 9.11.
38
Results
Table 10. p-values from two-way ANOVA for single metabolites in sour cherry leaves of nine genotypes and three leaf ages (young,
intermediate and old).
Metabolite
Alanine
Chlorogenic acid
Choline
Cinnamate-derivative
Epicatechin
Formate
Fructose
Sorbitol
Glucose
Malate
Quinic acid
Sucrose
m-Coumaric acid like compound
Myo-Inositol
Leaf age
0.0001***
0.3037 NS
0.1797 NS
0.0116 *
0.0298*
0.8576 NS
0.0011**
0.4289 NS
0.0011**
0.0012**
0.0000***
0.0000***
0.1412 NS
0.0000***
Genotype
0.0000***
0.0512NS
0.0000***
0.0000***
0.0002***
0.0000***
0.0005***
0.0000***
0.0000***
0.0000***
0.0011**
0.0000***
0.0004***
0.0000***
Interaction
0.2962 NS
0.1183 NS
0.1156 NS
0.8263 NS
0.0721 NS
0.1762 NS
0.8127 NS
0.6328 NS
0.1945 NS
0.0109*
0.7101 NS
0.0002***
0.3965 NS
0.0105*
NS, not significant.
*Statistically significant differences at P-value below 0.05.
**Statistically significant differences at P-value below 0.01.
***Statistically significant differences at P-value below 0.001.
4.3.2.2.1 Carbohydrates
Since most metabolites were identified in the region for carbohydrates, the quantification of these
metabolites can elaborate on the separation according to resistance category. All carbohydrates were
significantly affected by genotype, while all carbohydrates except sorbitol was significantly affected
by leaf age.
The OPLS-DA found sorbitol to be the most influential biomarker in separating genotypes from the
resistant category from genotypes from the intermediate/susceptible category. However, when
investigating the concentrations from the quantification, where the genotypes where investigated
individually, genotype Sus1 stood out from the other genotypes, by having a much lower
concentration of sorbitol (figure 31a). Thus, the separation of the two susceptible genotypes observed
in the PCA, might be explained by the difference in sorbitol concentration in these two genotypes.
Leaf age did not have any effect on sorbitol concentration.
Sucrose, which was another important biomarker, was quantified in significantly higher
concentrations in the resistant genotypes and in old leaves (figure 31b). The ANOVA test found an
interaction between genotype and leaf age (p=0.00), which indicates that the sucrose level in the
genotypes reacted differently with leaf age (table 10). The sucrose level did not seem to change with
leaf age for genotype Res1, Int1 and Int2, whereas the level increased with age for genotype Res2
and Sus2 and decreased with age for Sus1. The higher sucrose level in old leaves did not apply for
all genotypes.
39
180,00
70,00
160,00
60,00
140,00
50,00
Sucrose mM
Sorbitol mM
Results
120,00
100,00
80,00
40,00
30,00
20,00
10,00
60,00
0,00
40,00
-10,00
20,00
0,00
a)
Res1 Res2
Int1 Int2
b)
Sus1 Sus2
90,00
80,00
Fructose mM
Glucose mM
70,00
60,00
50,00
40,00
30,00
20,00
10,00
0,00
Young
c)
Middle
Res2
Int1
Int2
Sus1
Sus2
d)
80,00
Middle
Old
Res1
Res2
Int1
Int2
Sus1
Sus2
50,00
45,00
40,00
35,00
30,00
25,00
20,00
15,00
10,00
5,00
0,00
Old
Res1
Young
Young
Middle
Old
Res1
Res2
Int1
Int2
Sus1
Sus2
Young
Middle
Res1
Res2
Int1
Int2
Sus1
Sus2
25,00
60,00
Myo-imositol mM
Hexose-to-sucrose ratio
70,00
50,00
40,00
30,00
20,00
10,00
15,00
10,00
5,00
0,00
-10,00
Young
Middle
Old
0,00
-20,00
e)
20,00
Res1
Res2
Int1
Int2
Sus1
Sus2
f)
Old
Figure 31. The concentration of sorbitol (mM) (a) for the different genotypes, the concentration of sucrose (b), glucose (c), fructose
(d) and myo-inositol (f) (mM) in young, middle and old leaves in the different genotypes, and the hexose-to-sucrose ratio (e) for the
different genotypes.
40
Results
The concentration of glucose showed to be highest in young leaves (figure 31c). However, the two
genotypes from the same resistance category did not behave similarly. The one susceptible genotype,
Sus1, had the lowest concentration of glucose among the different genotypes while the other
susceptible genotype, Sus2, had the highest concentration of glucose among the different genotypes.
This can, along with the differences in sorbitol concentrations, explain the separation of these
genotypes in regards to the clustering of the PCA as mentioned earlier.
When examining the quantification of fructose, it displayed the same pattern as for glucose, of the
concentration decreasing with age (figure 31d). As was the case for glucose, the highest variation in
fructose concentration was between the two susceptible genotypes, with Sus1 having the lowest
content and Sus2 having the highest content.
To investigate how the carbon metabolism is affected, the hexose-to-sucrose ratio for each genotype
and leaf age was calculated by dividing the concentration of fructose and glucose with the
concentration of sucrose (figure 31e). This showed that the hexose-to-sucrose ratio decreased with
age for all genotypes. The ratio was highest for the susceptible genotypes, in particular Sus2, which
exhibited a very high ratio in the young leaves. When looking at the total hexose-to-sucrose ratio for
each genotype, the ratio changed according to the resistance categories. Genotypes with a low level
of CLS attack had a low hexose-to-sucrose ratio and the susceptible genotypes with the high CLS
attack had the highest ratios (appendix 9.10).
The concentration of myo-inositol was highest in the susceptible genotypes and lowest in the resistant
genotypes (figure 31f). The concentration decreased with age for the susceptible genotypes and Int2,
while it did not change in the resistant genotypes and Int1.
4.3.2.2.2 Phenolics
The identification of phenolics proved to be more challenging, as some of the important biomarkers
were not identified and therefore could not be quantified. This was the case for the unknown
compound a, which was one of the most important biomarkers in the OPLS-DA separation of the
resistance categories. The peaks in the phenolic region δ6.74-6.79, which were important biomarkers
for determining resistance category in the young leaves were neither identified nor quantified. Since
the exact identification of the two hydroxycinnamate derivatives was not possible, the concentrations
measured may vary from the actual concentrations. The method for estimation of the individual
hydroxycinnamate derivatives however was the same for each sample. For the quantification of the
cinnamate derivative, the doublet at δ6.53 was used as a reference, while the doublet at δ6.48 was
used for the quantification of the m-coumaric acid like compound.
Chlorogenic acid was found to be an important biomarker in determining the resistance category.
However, the effect of neither genotype (p=0.05) nor of leaf age (p=0.30) was significant, but the
quantification showed a trend of lower chlorogenic acid concentration in resistant genotypes (figure
32a).
The concentration of epicatechin was lowest in the resistant genotypes (figure 32b). In genotype Sus1,
the concentration decreased with age, whereas it was more constant with age for the rest of the
genotypes.
41
Results
The concentration of the cinnamate-derivative was affected by both leaf age and genotype (table 10).
From figure 32c it seems that the concentration of the cinnamate-derivative decrease with age.
25,00
8,00
20,00
6,00
Epicatechin mM
Chlorogenic acid mM
The concentration of the m-coumaric acid like compound however was not affected by leaf age and
genotypes from the same resistance category did not behave similarly (figure 32d).
15,00
10,00
5,00
4,00
2,00
0,00
0,00
c)
Middle
Old
Res1
Res2
Int1
Int2
Sus1
Sus2
b)
7,00
6,00
5,00
4,00
3,00
2,00
1,00
0,00
Young
Middle
Old
Res1
Res2
Int1
Int2
Sus1
Sus2
Middle
Old
-2,00
m-coumaric acid like
compound mM
Cinnamate-derivative mM
a)
Young
Young
d)
Res1
Res2
Int1
Int2
Sus1
Sus2
Young
Middle
3,00
2,50
2,00
1,50
1,00
0,50
0,00
Old
Res1
Res2
Int1
Int2
Sus1
Sus2
Figure 32. Concentration of chlorogenic acid (a), epicatechin (b), cinnamate-derivative (c) and m-coumaric acid like compound
(d)(mM) in the six genotypes and three leaf ages.
4.3.2.2.3 Other Compounds
The quinic acid concentration decreased with increasing age for all genotypes (figure 33a). The
concentration was highest in the resistant genotype Res2 and lowest in the susceptible genotype Sus1.
The young leaves from genotype Res1, Res2 and Int1 had the highest concentration of quinic acid
(166.4, 202.8 and 170.2 mM), while the concentration of genotype Int2, Sus1 and Sus2 was 135.0,
87.7 and 165.7 mM respectively.
The concentration of malate more than doubled from the young leaves to the old leaves for genotype
Res2, while the concentration remained more consistent for the other genotypes (figure 33b). The
concentration was highest for the two resistant genotypes.
42
250,00
120,00
200,00
100,00
Malate mM
Quinic acid mM
Results
150,00
100,00
50,00
60,00
40,00
20,00
0,00
0,00
Young
a)
80,00
Middle
Old
Res1
Res2
Int1
Int2
Sus1
Sus2
Young
b)
Middle
Old
Res1
Res2
Int1
Int2
Sus1
Sus2
Figure 33. Concentration of quinic acid (a) and malate (b) (mM) in the six genotypes and three leaf ages.
4.4 STOMATAL TRAITS
Stomatal size, represented by stomatal area, was not correlated with the level of susceptibility of the
genotypes examined (table 11). Likewise, the pore size did not show any correlation, where biggest
pore area was measured in the resistant genotype Res2 and the susceptible genotype Sus1. The pore
length was highest for the two resistant genotypes.
Table 11. Stomatal measurements on the abaxial surface of six sour cherry genotypes. Res1 and Res2 =resistant, Int1 and
Int2=intermediate, Sus1 and Sus2=susceptible.
Genotype
Res1
Res2
Int1
Int2
Sus1
Sus2
Pore Length (µm)
97.71
102.33
86.62
91.93
94.04
90.71
Pore Width (µm)
32.13
40.51
36.05
39.58
42.96
36.09
Pore Area (µm2)
2465.98
3255.99
2452.3
2857.67
3172.8
2571.11
Stomatal Length (µm)
134.07
132.27
124.96
127.4
128.76
120.27
Stomatal Width (µm)
69.6
77.02
70.76
75.11
73.78
64.7
Stomatal Area (µm2)
7328.58
8001.23
6943.91
7515.62
7460.71
6111.4
Stomatal Area/Leaf Area (µm2 mm-2) 6.97
5.78
8.69
7.88
8.97
6.62
Stomatal Density (no. pr. mm-2)
21.97
17.08
21.76
17.2
25.67
18.83
The stomatal area per leaf area (SA/LA) gives the best measurement of the entrance area for the
fungus. Here the susceptible genotype Sus1 had the highest SA/LA (8.97 µm2 mm-2), while the
resistant genotype Res2 had the lowest SA/LA (5.78 µm2 mm-2). The ranking was as follows in table
12. The susceptible genotype Sus2 stands out in the ranking, since it was expected to have a high
SA/LA, but was measured to have the second lowest SA/LA.
43
Results
Table 12. The ranking of the genotypes from genotype with the highest stomatal area per leaf area (µm2 mm-2) to the lowest. Res1
and Res2 =resistant, Int1 and Int2=intermediate, Sus1 and Sus2=susceptible.
Sus1 > Int1 > Int2 > Res1 > Sus2 > Res2
44
Discussion
5 DISCUSSION
The image analysis of the leaves from the selected genotypes supported the resistance categories
established from the previous image analysis. Thus, the overall degree of attack of the selected
genotypes seemed to be consistent over the two seasons. However, all measurements were lower in
2015 than in 2014. This indicates that the results attained by this method varies somewhat depending
on who performs the analysis. Furthermore, we know that the severity of plant diseases differs from
season to season due to differences in environmental conditions, which implies that the general
conditions for CLS infection were more favourable in 2014.
The attack of Sus2 was much lower in 2015 than measured in 2014, and the level of attack for this
genotype was closer to that of the intermediate genotypes than to the other susceptible genotype.
When only considering the levels of attack measured in 2015, Sus1 seems to be the only genotype
fitting the susceptible category, while Sus2 would fit better into the intermediate category. In the PCA
for the entire metabolite profile, this separation between the two susceptible genotypes was shown on
PC1, with Sus1 deviating most from the other genotypes. The grouping according to leaf age was
made based on the leaf’s position on the branch. However, the age and ontogeny of the leaves was
not known and might in fact differ greatly between samples.
The artificial inoculation of leaves from the selected genotypes confirmed the division of genotypes
into the chosen resistance categories. Leaves from genotype Sus1 had the highest disease score, which
supports the assumption that this genotype may be particular susceptible to CLS. This experiment
showed that the artificial inoculation of leaves in the laboratory is an easy and fast method for
evaluating CLS resistance. It was possible to get a high amount of conidia spores from the dry
naturally infected leaves, which was also shown in a study by Schuster (2004). In the artificial
inoculation test of this experiment, identification of the isolated pathogen by a pathologist was not
performed. The spore morphology was thoroughly compared to illustrations of CLS spores, but for
future studies, this must be done with a higher degree of certainty. Since specific genotypes may be
susceptible only to specific strains of the pathogen, it is important to know whether the defense
mechanisms work equally well on all strains. Furthermore, a greater selection of samples is required
and climatic parameters, such as the humidity, must be controlled in order to avoid the desiccation of
the leaves.
1
H-NMR spectroscopy succeeded in detecting a division in the metabolic profile according to the
phenotypically chosen resistance categories in sour cherry leaves. The chemometrics approaches
furthermore elucidated the nature of the metabolites, which are the key to the separation between the
sample groups.
5.1 CARBON METABOLISM
The main factor in discriminating the sampled leaves based on resistance category and leaf age was
the level of carbohydrates. The overall depletion of carbohydrates in the susceptible leaves is most
likely caused by the reduction of healthy leaf area due to necrosis. Furthermore, some of the resources
45
Discussion
in the leaves may be diverted to fungal growth once the leaf is infected by the pathogen (Hay &
Porter, 2006).
The resistant genotypes as well as the old leaves had an overall higher content of sucrose. Most of
the carbon fixed by photosynthesis is converted to sucrose (Raven et al., 1999). Sucrose is thus the
most commonly translocated carbohydrate in plants, with regards to translocation from source to sink
tissue (Taiz & Zeiger, 2010). The high levels of chlorosis and necrosis, measured on the susceptible
leaves by image analysis, have most likely inhibited their ability to perform photosynthesis and hence,
decreased the levels of sucrose found in leaves from these genotypes. In addition, leaves need energy
to activate their defense mechanisms. Since the level of phenolics was higher in susceptible leaves,
this production could further explain the decline in sucrose levels in the CLS infected leaves.
The young leaves were harvested just after unfolding, and were therefore still small and acting as
sinks for assimilates. Since the photosynthetic capacity increases from the newly unfolded to the fully
expanded leaf, the older leaves had a greater accumulation of sucrose. The pattern for sucrose
accumulation with age, however, was not consistent within the different genotypes. For genotype
Res1, Res2, Int2 and Sus2 the concentration of sucrose increased with age as explained above. For
genotype Sus1 however, there was an increase from young to middle age and then a decrease in the
old leaves. Since Sus1 had a markedly higher degree of attack compared to the other genotypes, this
could have caused an earlier depletion of sucrose. Since the actual age was not known for the sampled
leaves, it may be quite different for the leaves categorized to be the same age. Thus, the oldest leaves
from genotype Sus1 may very well have begun to senescence. The old leaves from genotype Res2
had a very high level of sucrose and may be at a stage in the leaves’ lifecycle where the photosynthesis
is at its highest. Since the actual age of each individual leaf is not known, the comparison of the leaves
according to age may be somewhat uncertain.
Not all carbohydrates were depleted from the susceptible leaves. The highest concentrations of
fructose and glucose were found in leaves from the susceptible genotype Sus2. The haustorium of the
CLS fungus transfers nutrients from the sour cherry plant cell to fungal thallus and thus competes
with the leaves for photoassimilates and other nutrients (Szabo & Bushnell, 2001). Previous studies
have found that the penetration of leaves by the fungal hyphae and the development of haustoria cause
a rapid increase in invertase activity, which hydrolyses sucrose to fructose and glucose in the
mesophyll cells (Scholes et al., 1994). Studies indicate that glucose and fructose are the preferred
nutrients taken up by haustorial complexes in biotrophic fungi and the increased invertase activity
thus leads to an accumulation of glucose and fructose, which are available for the haustorium to
uptake (Scholes et al., 1994). The susceptible genotype Sus1 however was one of the genotypes with
the lowest concentration of the hexoses, glucose and fructose. Hence, the carbon metabolism seems
to be affected differently in response to infection in the different susceptible genotypes.
A better way to examine the carbon-metabolism and the source-sink relationship is to look at the ratio
of hexose-to-sucrose, since the level of hexoses and sucrose is interdependent. . This showed that the
ratio changed according to the resistance categories, where genotypes with a low level of CLS attack
had a low hexose-to-sucrose ratio and the susceptible genotypes with the high CLS attack had the
highest ratios. Hereby, genotype Sus1 fit into the theory of susceptible leaves acting as sink tissue.
46
Discussion
The ratio for the two susceptible genotypes was considerably higher than those of the resistant and
intermediate genotypes. Meanwhile the ratios measured in the intermediate and resistant samples
were quite close in relative comparison. This may indicate that the carbon metabolism of the
intermediate genotypes is not as impaired by infection with CLS. This is noteworthy in terms of the
selection of genotypes, where the intermediate leaves seem to be able to maintain the export of
assimilates to the high value sour cherries in spite of infection.
Since the hexose-to-sucrose ratio shows when the leaf is a source or sink organ, it could be used as
an indication of whether the leaf is infected by pathogens. An unnaturally high ratio in leaves could
indicate infection by CLS, even though signs of infection are not visible.
Sorbitol is another carbohydrate used in the translocation of nutrients in the phloem (Taiz, 2010).
Sorbitol was found to be the most important biomarker for resistance categories, with higher levels
detected in resistant genotypes compared to susceptible genotypes. However, when looking at the
quantification of sorbitol, it showed that the low concentration of sorbitol was due to the genotype
Sus1. Since the level of attack found in this genotype was by far the highest, it would seem that CLS
infection causes a reduction in the sorbitol concentration of the infected leaves. In some plants, such
as in apple, sorbitol, and not sucrose, is the major translocated form of carbon (Loescher et al., 1982).
The fact that sorbitol was the most influential biomarker in separating the genotype with the highest
degree of attack from the other genotypes, suggests that sorbitol might be a major translocated
carbohydrate in sour cherries, and hereby play an important role in sink-source interconversions of
this species.
The young leaves from the genotypes with the highest level of attack (Sus1, Sus2 and Int2) had the
highest concentration of the sugar alcohol myo-inositol, which decreased considerably with age. For
the remaining genotypes, no significant variation was observed between leaf ages. Myo-inositol is an
important cellular metabolite that forms the structural basis of many lipid signaling molecules. These
molecules function in diverse pathways, including stress responses, cell wall biosynthesis and the
regulation of cell death (Eckardt, 2010). It was not possible to find studies investigating the relation
between myo-inositol and plant resistance, and it is difficult to say how and if these are connected.
5.2PHENOLICS
As opposed to the stated hypotheses, the concentration of phenolics was higher in the susceptible than
in the resistant genotypes. Since the infection pressure was at its highest when harvesting the sour
cherry leaves, one could assume that the analysis showed the stress situation in the leaves rather than
expressing which metabolites are correlated to CLS resistance. Even in the young leaves, which were
meant to serve as “clean” plant profiles without any attack, the conclusion was the same; more
phenolics were found in the susceptible than in the resistant leaves. The young leaves were harvested
without any visible signs of infection, but since CLS has a biotrophic behavior and does not kill the
leaves immediately, symptoms on the leaves may not be visible right away. This could mean that the
young susceptible leaves were already infected with CLS and in response had induced the synthesis
of defense compounds. These early metabolic perturbations can be detected by metabolomics even
before visible disease symptoms appear (Smolinska et al., 2012).
47
Discussion
The multivariate analysis of phenolic metabolites showed chlorogenic acid as an important compound
in separating the resistance categories. Previous studies have found chlorogenic acid to serve as an
important defense compound in many plant species, with plants containing a high concentration of
chlorogenic acid exhibiting markedly reduced susceptibility to infection with fungal pathogens
(Villarino et al., 2011; Sung & Lee, 2010). This is contrary to our findings of resistant genotypes
containing a lower level of chlorogenic acid. The study by Villarino et al., 2011, investigated the
action of chlorogenic acid on spore germination and mycelial growth of Monilinia laxa in culture.
They found that chlorogenic acid did not inhibit spore germination or mycelial growth of M. laxa in
culture but markedly inhibited the production of melanin-like pigments in the mycelia of M. laxa in
culture (42% melanin reduction). Since melanin has been reported to enhance the survival and
competitive abilities of organisms in certain environments, Villarino et al. (2011) suggested that the
reduced susceptibility was due to a high concentration of chlorogenic acid causing interference with
fungal melanin production.
The study by Sung & Lee (2010) assessed the in vitro antifungal activity of chlorogenic acid against
the pathogenic yeast Candida albicans. The result showed that the hyphae of the fungus was not only
inhibited but also destroyed after treatment with chlorogenic acid, whereas the hyphae or
pseudohyphae extended normally in the absence of chlorogenic acid. The results suggested that
chlorogenic acid exerted antifungal activity by disrupting the structure of the cell membrane.
To see whether these modes of action are similar in the resistance of sour cherry to CLS, the effect
of chlorogenic acid on the melanisation, membrane potential and growth of B. jaapii needs to be
investigated. However, the results from the present experiment does not indicate that this is the case,
since chlorogenic acid did not seem to reduce the susceptibility of the sour cherry leaves.
It is often suggested in the literature, that the concentration of phenolics is higher in resistant
genotypes than in susceptible ones (Agrios, 2005; Schumann & D’Arcy, 2006; 2014; Mayr et al.,
1996; Dai et al., 1993). The multivariate analysis, however, found a higher level of phenolics in the
susceptible genotypes and the quantification found a higher content of epicatechin in the susceptible
genotypes. The two previous studies, examining the influence of CLS on the phenolic profile of sour
cherry, investigated the difference between healthy and infected leaves (Niederleitner, 1993;
Oszmiański & Wojdylo, 2014). Thus, their findings of greater concentrations of phenolics in the
infected leaves, most likely reflect how the plants respond to the stress induced by the CLS pathogen,
rather than how the phenolic compounds contribute to resistance. The assertion that phenolics are
pre-infection inhibitors, providing plants with a certain degree of basic resistance against pathogenic
microorganisms, might therefore not be correct in the case of sour cherry resistance to CLS. A variety
of studies on other plants has also exhibited how the synthesis of phenolic compounds was induced
by infection. Kang & Saltveit (2003) found that wounding during the preparation of fresh-cut lettuce
resulted in an accumulation of chlorogenic acid. According to Wharton et al. (2003) flavan-3-ols,
including epicatechin, are stress metabolites, that are produced in response to mechanical damage of
the tissue surrounding the infection site and not compounds that are important in resistance response.
They base this statement on the study conducted by Niederleitner et al. (1993), which showed that
flavan-3-ols enhanced conidia spore germination. This finding supports the argument that the high
48
Discussion
level of phenolics measured in the susceptible genotypes, is due to an induced response to infection,
as the present study indicates.
The multivariate analysis found that the unidentified compound from the phenolic region, designated
unknown a, was an important biomarker. Hence, it seems to be an important compound in the
infection response in sour cherry leaves. For this reason, it would be interesting to get this compound
identified in order to clarify the response mechanism in the sour cherry leaves.
In order to elucidate differences between resistant and susceptible genotypes the present experiment
could have been carried out just before and right after inoculation before symptoms became visible.
By testing the leaves before inoculation, it would be possible to examine whether the resistant
genotypes had a higher level of phenolics as a constitutive defense, compared to the susceptible
genotypes. By testing the phenolic content right after inoculation, perhaps as a time series, it would
furthermore be possible to measure how fast the different genotypes were able to induce their defense
system. How fast the defense mechanisms can be mobilized by invasion of a pathogen, is suggested
in the literature to be a very important factor for the degree of resistance in plants (Agrios, 2005;
Schumann & D’Arcy, 2006; Taiz & Zeiger, 2010). To detect how fast the defense mechanisms are
mobilized, a large number of samples that can give several snapshots of the development in the
metabolic profile after infection, would be necessary. Pinpointing exactly when the infection of the
leaf occurs would require controlled conditions. The artificial inoculation of sour cherry leaves made
in this experiment, showed that it is possible to perform controlled infections of the leaves. This
means that artificial inoculation tests could be a useful method for analyzing constitutive and induced
resistance and thereby finding the metabolic profile for “field resistance” against CLS.
Despite the fact that, no separation was found in the phenolic region according to leaf age, the
quantification of the metabolites showed that young leaves had a higher content of the phenolic
compounds; chlorogenic acid, epicatechin, and the cinnamate derivative. The variation of the
cinnamate derivative between leaf ages was highly significant, whereas the concentration for
chlorogenic acid and epicatechin did not show a statistically significant variation, but did display a
tendency towards higher levels in the youngest leaves. Chlorogenic acid was also found to be
important in the separation between young and old leaves in the multivariate analysis of the entire
metabolite profile. Since the infection pressure builds up over the course of summer, the higher
concentration in the young leaves might reflect the need of these tissues to increase their defensive
capability (Brown et al., 2003; Abdel-Farid et al., 2007).
5.2.1 Quinic Acid
When studying the resistance in sour cherry it may be inadequate, only to examine the phenolic
compounds. Constitutive defense compounds may be present as conjugated compounds to reduce
toxicity or as precursors of active compounds that can easily be activated upon damage (Taiz &
Zeiger, 2010). Quinic acid is a precursor of chlorogenic acid, which has been suggested to play an
active role in plant defense. In the quantification of metabolites, it was found that quinic acid was
present in higher levels in leaves from the resistant genotype Res2. It was also found that young leaves
from the genotypes with the lowest measured percentage of attack (Res1, Res2 and Int1) had higher
levels of quinic acid, compared to the young leaves from the remaining genotypes. However, it should
49
Discussion
be noted that the difference in concentrations between young leaves of Res1 and Sus2 was only very
slight. By analyzing the phenolic region, no clear separation was found between leaf ages. It might
suggest that this compound, rather than the phenolics, is important in determining the defense against
CLS infection, and might be a factor in how fast the different genotypes are in mobilizing their
defense mechanisms. For this reason, it would be interesting to investigate how quinic acid affects
CLS infection. Possible courses of investigation could be testing whether the high level of quinic acid
could induce a faster production of phenolic compounds or to test if this compound could have an
impact on the CLS fungus (e.g. germination of the spores, inhibition of the hydrolytic enzymes of the
pathogen etc.).
5.2.2 Malate
Since the quantification of malate showed a higher concentration of malate in the two resistant
genotypes, this might also be a compound of interest when investigating the metabolic profile
determining resistance. The concentration was particularly high in the old leaves from genotype Res2,
whereas the concentration in Res1 did not differ particularly from the rest of the genotypes. Malate
plays many roles within the plant, including acting as an essential carbon storage molecule, an
intermediate of the citric acid cycle and as an osmoticum involved in stomatal movement (Martinoia
& Rentsch, 1994). Furthermore, it is thought to function in the generation of apoplastic NADH, which
stimulates the production of hydrogen peroxide. Hydrogen peroxide is needed to sustain lignification,
which is a well-documented plant defense mechanism (Libik-Konieczny et al., 2012). This indicates
that malate metabolism may play an important function in plant defense. Further investigation of the
role of malate in sour cherry defense against CLS attack would therefore be interesting.
Many parameters affect the metabolic profile and the concentrations of metabolites vary greatly
during a 24-hour period (Taiz & Zeiger, 2010). This can make it difficult to compare the profiles for
different samples. The division in the three resistance groups was, as stated earlier, an artificial
grouping based on the image analysis performed the previous season. When measuring the percentage
of attack, Sus1 stood out with a particularly high level of attack (24.80%). In the multivariate and
univariate analysis, this genotype stood out by having a particularly low level of sorbitol and formate.
This genotype, as well as the other susceptible genotype and the two intermediate genotypes, had
high levels of the phenolic compounds chlorogenic acid and epicatechin, which was low in the two
resistant genotypes. In regard to the phenolic content, it thus seems that the intermediate and
susceptible genotypes resemble each other. However, as concerns the carbohydrate sucrose and the
hexose-to-sucrose ratio, the intermediate genotypes resembled the resistant genotypes more closely
than the susceptible ones. This indicates that the carbon metabolism will not be critically affected
until the level of attack is severe, whereas the production of the defense compounds increase by low
level of attack. During a plant’s life cycle, it undergoes considerable changes in the dynamics of
carbon metabolism and transport in both source and sink tissues as well as in the degree of
competition among various sinks for the common pool of carbohydrates available (Marcelis et al.,
2014). This can make the comparison of different samples difficult, but it is clear from the results that
carbon metabolism is affected by a high CLS infection.
50
Discussion
The genotype Res1, with the lowest measured level of attack (0.50%), stood out in the quantification
by having the highest concentration of sorbitol, sucrose, quinic acid, malate, alanine and formate.
These metabolites thus might represent a healthy metabolic profile, resistant to CLS attack. However,
exactly which metabolites are decisive in defeating the pathogen needs further investigation.
If future studies succeed in developing a model for a metabolic profile of sour cherries to predict
resistance, this will have a huge impact on the effectiveness of breeding, since it could replace the
current very time consuming method of phenotypic selection of sour cherry genotypes. By building
a calibration model, the resistance of new sour cherry genotypes can be predicted. However, the new
data must be in the range of the prediction model, meaning that the data have to be similar to the data
used for the model, in regards to factors such as harvest time of the leaves, extraction method etc.
Other pathogens than CLS are problematic in sour cherry. If the metabolites involved in host plant
resistance for these pathogens are known, a single NMR analysis may be sufficient in the future to
predict resistance to these different pathogens simultaneously and thus facilitate multi-resistance
breeding.
Looking at different compounds at the same time decreases the ability of pests to break host plant
resistance. Due to the long lifespan of 25 to 30 years of a cherry orchard, it is important that the
resistance to CLS is durable (Wharton et al., 2003). Metabolomics is a method that allows the
simultaneous detection of a wide range of compounds and thus ought to be a useful tool for
investigating durable resistance.
5.3 STRUCTURAL DEFENSES – STOMATA
Stomatal characteristics did not explain the differences in resistance between the genotypes. There
was a trend for increasing stomatal area per leaf area with higher susceptibility, but since the study
only consisted of a few samples and since one out of two susceptible genotypes did not match the
expected ranking, it cannot be concluded that the measured stomatal traits are determining factors in
the resistance of the individual genotypes. Stomatal regulation has been observed in some plantfungal interactions, where different elicitors from the fungi and from the degradation of the cell wall,
influenced the stomata movement in tomato, Arabidopsis and the tobacco plant (Melotto et al., 2008).
Moreover, some wheat varieties possess resistance to stem rust, because their stomata open late in
the day. By this method, the germ tubes of spores germinating in the night dew desiccate due to
evaporation of the dew before the stomata begin to open (Agrios, 2005). Thus, other stomata
characteristics than those measured in this experiment can influence resistance. Consequently,
whether the stomata play a role in sour cherry resistance to CLS cannot be ruled out.
When selecting new sour cherry cultivars it is important to have in mind that tolerance to biotic stress
is not the only desirable trait. To obtain genotypes with acceptable commercial value it is necessary
to combine resistance to disease with other traits such as high yielding, high fruit quality, high and
stable fruit set, high tree vigor, tolerance to abiotic stress etc. As an example, it was observed that the
resistant genotype Res1 did not bear any fruit at harvest time in August. Despite its high resistance,
this genotype would therefore not be suitable for sour cherry production.
51
Conclusion
6 CONCLUSION
This study concluded that CLS infection primarily has an impact on carbon metabolism. Susceptible
genotypes had an overall lower content of sucrose and sorbitol and a higher hexose-to-sucrose ratio
than the resistant genotypes. Thus, carbon partitioning between source and sink tissues is affected in
these leaves, which is likely to influence plant growth and development.
Furthermore, the study showed that susceptible genotypes contained higher concentrations of
phenolic compounds, especially chlorogenic acid and epicatechin. This finding is presumably an
expression of the stress situation in these leaves caused by CLS. Furthermore an unidentified
compound in the phenolic region was detected in the multivariate analysis as an important biomarker
in the separation between the susceptible and resistant genotypes. The phenolic precursor quinic acid
was found in high concentration in young leaves from resistant genotypes and could perhaps be
correlated with resistance. This study suggest that quinic acid could assist in a faster initiation of a
biochemical defense response in the young leaves from the resistant genotypes, but further
investigation is needed to qualify this. Another compound found in higher concentration in the two
resistant genotypes was malate. Thus, this compound might also be of interest to future investigations
of the metabolic profile determining resistance.
The stomatal measurements conducted in this study, showed that there was a trend for increased
stomatal area per leaf area with higher susceptibility. However, it did not provide enough evidence to
conclude that the measured stomatal traits are determining factors in the resistance of the individual
genotypes.
Experiments in this study showed that it was possible to artificially infect leaves in the laboratory
with conidia spores from dry naturally infected leaves. It is suggested that experiments investigating
the metabolic profile before and right after inoculation is necessary in order to get a more
comprehensive profile of the constitutive and induced biochemical defense in resistant genotypes. In
this context, artificial inoculation test could be a useful method for controlled infections of the leaves.
52
Implications
7 IMPLICATIONS
Unfortunately, this study did not find a general metabolic profile for host plant resistance for future
use in the prediction of plant resistance. Plant interactions are very complex and many parameters
can affect their metabolic profile and their capability to resist infection. Thus, further analyses are
necessary to investigate all parameters involved in plant resistance. At the present time, no single
extraction technique or analytical instrument is able to quantify all the metabolites within a biological
sample (Goodacre et al., 2005). Phenolics vary from simple to very highly polymerized substances
and their different chemical properties thus affect their solubility. Furthermore, some phenolic are
capable of interacting with other plant components such as carbohydrates and proteins, which can
create complexes that may be quite insoluble.
The polarity of the solvent used affects the solubility of phenolics, and it can be difficult to develop
an extraction procedure that can extract all plant phenolics (Naczk & Shahidi, 2006). An example of
a phenolic compound that is difficult to extract, because of its covalent bonds to cellulose and other
polysaccharides of the cell wall, is lignin. Lignification in leaves can block the growth of pathogens
and is a common response to infection (Taiz & Zeiger, 2010). It would thus be interesting to
investigate how the resistant and susceptible leaves differ in the level of lignin.
Currently NMR is the method of choice for metabolomics studies, since it is the only method where
the physical characteristics of compounds will always be the same (Goodacre et al., 2005; Leiss et
al., 2011). However, because of its comparatively poor sensitivity, complementing this approach with
MS-based technologies could provide more detail. The high throughput NMR method, that require
minimal sample preparation, can thus be considered for a first macro-level analysis of the
metabolome, whereas MS-based metabolomics methods can be used to allow quantification and
identification of additional metabolites.
For the identification of metabolites in this experiment, the inadequacy of the metabolite libraries and
databases was a limitation, in particularly for the phenolic compounds. Most of the listed compounds
were detected in human fluids and NMR spectra for many of the phenolic compounds were lacking.
However, metabolomics is not the only approach for studying the interactions involved in plant
defense reactions. Among the ‘omics’ studies is also genomics, which provides an overview of the
complete set of genetic instructions provided by the DNA and proteomics, which studies dynamic
protein products and their interactions (ISAAA, 2005).
Taiz & Zeiger (2010) state that the ability of resistant plants to rapidly detect the invading pathogen
is determined by the presence of specific R genes. These R genes might be identified and targeted by
the application of genomics as a step towards understanding the resistance in sour cherry leaves.
These R genes encode protein receptors that recognize and bind specific molecules that originate
from the pathogen. The product from these R-genes are nearly all proteins with a leucine-rich domain
(Taiz & Zeiger, 2010). Proteomics approaches could further elucidate this aspect of sour cherry
resistance.
53
Implications
Furthermore, by use of proteomics methods the enzymes involved in the synthesis of phenolics could
be analyzed. The most phenolic compounds in plants are derived from phenylalanine via the
elimination of an ammonia molecule to form cinnamic acid; a process catalyzed by the enzyme
phenylalanine-ammonia-lyase (PAL). A study by Mayr et al. (1997) tested whether phenolics play
a role in the resistance of apple to scab, by inhibiting this PAL enzyme in young shoots of a resistant
apple genotype. By doing this, they found that the inhibitor-treated leaves showed severe symptoms of
the disease after inoculation. This indicates that pathways downstream of PAL are important in plant
defense against this pathogen. The reaction that PAL catalyzes is at a branch point between primary
and secondary metabolism, and it is thus an important regulatory step in the formation of many
phenolic compounds (Taiz & Zeiger, 2010).
Polyphenol oxidase (PPO) and lipoxygenase (LOX) are other important enzymes in plant defense
responses. PPO catalyzes the oxidation of polyphenolic compounds to quinones (Lin et al., 2011).
According to Oszmiański & Wojdylo (2014), the toxicity of phenolics in plant disease resistance is
largely based on these oxidation products and therefore PPO has frequently been suggested to
participate in plant defense against pests and pathogens (Ngadze et al., 2012; Vanitha et al., 2009).
LOX catalyzes the addition of molecular oxygen to certain polyunsaturated fatty acids to produce an
unsaturated hydroperoxy fatty acid (Porta & Rocha-Sosa, 2002). In plants, products of the LOX
pathway, such as jasmonic acid, traumatin and ketodienes, have several diverse functions, here among
signaling in wounding and pathogen attack (Porta & Rocha-Sosa, 2002).
By measuring the enzymatic activity of these defense-related enzymes in relation to the degree of
sour cherry resistance to CLS infection, the mechanisms of how sour cherry leaves defend themselves
against CLS attack can be further elucidated.
There is an ongoing discussion concerning which ‘omics’ approach is the preferred in plant resistance
studies. While comprehensive data of the genotype has been presented by genomics and proteomics,
they provide little information on the phenotype. Since the metabolomics approach studies the
interactions between gene and protein downstream products and environmental stimuli, this is the
closest link to the plant phenotype (Wanichthanarak et al., 2015). However, metabolomics by itself
may not be sufficient to fully characterize the complexity of sour cherry resistance and plant-pathogen
interactions. A better method would therefore be a combination of the aforementioned omic
approaches.
54
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Leaf Spot." Plant Disease 87 (5): 471-477.
Wishart, DS. 2013. "Exploring the Human Metabolome by Nuclear Magnetic Resonance Spectroscopy and
Mass Spectroscopy; Lutz NW, Editor".
Wishart, D. S., D. Tzur, C. Knox, R. Eisner, A. C. Guo, N. Young, D. Cheng, et al. 2007. "HMDB: The
Human Metabolome Database." Nucleic Acids Research 35 (Database issue): D521-6.
60
Appendix
9 APPENDIX
9.1 TABLE OF PHENOLICS IDENTIFIED IN SOUR CHERRY LEAVES
Table 13. Phenolics identified in sour cherry leaves from previous studies. 1 = Chrzanowski et al., 2012, 2 = Oszmiański & Wojdylo,
2014, 3=Niederleitner et al., 1993.
Phenolic subgroup
Phenolic subclass
Phenolic compound
Flavonoids
Flavan-3-ols
(_)-Epicatechin
2,3
(þ)-Catechin
2,3
Procyanidin B2
2,3
Procyanidin tetramer
Procyanidin trimer
Non-flavonoids
Reference
2
2,3
Flavon
Luteoin-3-O-rutinoside
2
Flavonol
Isorhamnetin-acylated-glucoside
2
Isorhamnetin-hexoside-rhamnoside
2
Isorhamnetin-pentoside
2
Kaempferol-3-O-6-acetylglucoside
2
Kaempferol-3-O-glucoside
2
Kaempferol-dihexoside-deoxyhesoside
2
Kaempferol-hexoside-dideoxyhesoside
2
Quercetin-3-O-glucoside
2
Quercetin-3-O-hexoside-rhamnoside
2
Quercetin-3-O-rutinoside
2
Quercetin-acylated-glucoside
2
Quercetin-diglucoside
2
Quercetin-glucoside glucoronide
2
3,4-di-O-caffeoylquinic acid
1
3,5-di-O-caffeoylquinic acid
2
Caffeic acid
1
Caffeoylhexose
1
Caffeoyl-p-coumaroyl-quinic acid
2
Caffeoylquinic acid
2
Hydroxycinnamate
Chlorogenic acid
61
1, 2
Appendix
Cryptochlorogenic acid
Phenolic acid
1, 2
Ferulic acid
2
Feruoylhexose
2
Neochlorogenic acid
2
p- coumaric acid
1
p-Coumaroylhexose
2
p-Coumaroylquinic acid
1
Trans-cinnamic acid
1
Benzoic acid
1
Gallic acid
1
p-hydroxybenzoic acid
1
Syringic acid
1
Vanillic acid
1
62
Appendix
9.2 SAMPLE NAMES
Table 14. Sample names for the six selected genotypes with varying resistance.
Genotype
Leaf age
Branch 1 (3 replicates) Branch 2 (3 replicates) Branch 3 (3 replicates)
Young leaf
Res2-1-1
Res2-2-1
Res2-3-1
Middle leaf
Res2-1-2
Res2-2-2
Res2-3-2
Old leaf
Res2-1-3
Res2-2-3
Res2-3-3
Young leaf
Res1-1-1
Res1-2-1
Res1-3-1
Middle leaf
Res1-1-2
Res1-2-2
Res1-3-2
Old leaf
Res1-1-3
Res1-2-3
Res1-3-3
Young leaf
Int1-1-1
Int1-2-1
Int1-3-1
Int1 (intermediate) Middle leaf
Int1-1-2
Int1-2-2
Int1-3-2
Old leaf
Int1-1-3
Int1-2-3
Int1-3-3
Young leaf
Int2-1-1
Int2-2-1
Int2-3-1
Int2 (intermediate) Middle leaf
Int2-1-2
Int2-2-2
Int2-3-2
Old leaf
Int2-1-3
Int2-2-3
Int2-3-3
Young leaf
Sus1-1-1
Sus1-2-1
Sus1-3-1
Middle leaf
Sus1-1-2
Sus1-2-2
Sus1-3-2
Old leaf
Sus1-1-3
Sus1-2-3
Sus1-3-3
Young leaf
Sus2-1-1
Sus2-2-1
Sus2-3-1
Middle leaf
Sus2-1-2
Sus2-2-2
Sus2-3-2
Old leaf
Sus2-1-3
Sus2-2-3
Sus2-3-2
Res2 (resistant)
Res1 (resistant)
Sus1 (susceptible)
Sus2 (susceptible)
63
Appendix
9.3 STOMATAL MEASUREMENTS FROM IMAGEJ
Example of images analyzed using the ImageJ software.
Figure 34. Pore length
Figure 35. Pore width
64
Figure 36. Stomata length
Appendix
9.4
DRY MATTER CONTENT
Table 15. Percentage of dry matter in the different genotypes.
Genotype
Percentage of dry matter measured in the leaves (%)
Res1
54.48
Int1
61.31
Sus1
60.72
Res2
56.79
Int2
63.38
Sus2
58.08
Resistant category
55.64
Intermediate category
62.35
Susceptible category
59.40
65
Appendix
9.5
TABLE OF METABOLITES IDENTIFIED IN THE 600.13 MHZ SPECTROMETER OF
SOUR CHERRY LEAVES
Table 16. Summary of the metabolites identified in the 600.13 MHz spectrometer of sour cherry leaves. 1H chemical shifts are
reported with respect to TMSP signal (δ0.00 ppm), s=singlet, d=doublet, m=multiplet, *=peaks not able to detect in HSQC because
of signal overlap.
Class
Key
Compound
Carbohydrates
1
Glucose
2
Sorbitol
3
Fructose
4
Sucrose
1
H
(ppm)
5.23
4.64
3.89
3.84
3.82
3.76
3.72
3.70
3.53
3.48
3.46
3.41
3.40
3.24
3.85
3.83
3.83
3.77
3.74
3.65
3.62
4.11
4.10
4.02
3.99
3.89
3.82
3.80
3.79
3.71
3.70
3.67
3.59
3.56
3.55
5.23
4.64
4.04
3.88
3.83
3.83
66
Multiplicity
[j (Hz)]
d
d
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
m
d
m
m
m
m
m
m
m
d
m
m
m
m
m
d
d
m
m
m
m
13
C (ppm)
94.95
98.75
63.71
63.66
63.66
63.52
63.62
63.62
74.26
74.26
72.42
78.78
78.78
77.09
75.94
75.94
75.94
73.95
73.95
74.06
75.46
77.84
77.84
66.31
72.13
72.67
70.52
70.52
70.52
66.49
66.49
66.49
65.74
65.74
65.74
94.92
*
76.34
*
*
*
Appendix
Phenolics
5
Chlorogenic acid
6
Cinnamate-derivative
7
Epicatechin
11
m-coumarate like
compound
10
Unknown a
Organonitrogen
compound
11
Choline
Cyclic
polyalcohol
12
Myo-inositol
Organic acid
13
Quinic acid
67
3.82
3.82
3.79
3.76
3.68
3.66
3.55
3.47
7.59
7.14
7.06
6.89
6.44
5.31
4.24
3.89
2.18
2.13
2.02
2.01
7.82
7.62
7.45
7.43
7.39
6.53
7.01
6.91
6.91
6.09
6.06
4.89
4.34
2.94
2.78
6.48
6.91
7.32
8.13
7.74
7.57
6.44
5.83
4.06
3.51
3.18
4.06
3.62
5.53
3.27
4.14
4.00
m
m
m
m
s
s
m
m
d [13.49]
d [8.38]
m
d [8.38]
d [16.35]
m
m
m
m
m
m
m
d[15.73]
d [16.35]
d [2.04]
m
m
d [2.25]
d [1.94]
s
m
m
m
d
m [8.17]
d [1.33]
d [15.93]
d [16.35]
d
m
m
s
m
m
m
m
m
m
*
*
*
*
*
*
*
*
148.95
117.93
125.38
118.96
124.56
73.96
73.72
*
41.33
39.95
*
41.07
130.41
131.64
133.41
133.41
127.33
117.33
*
*
101.82
101.82
*
*
*
*
127.09
119.37
132.94
*
*
56.75
75.16
75.59
72.46
77.15
73.39
70.03
Appendix
14
Malate
15
Formate
3.53
2.06
1.96
1.87
4.33
2.72
2.45
8.44
68
m
m
m
m
m
m
m
s
78.35
40.46
40.34
43.65
72.93
45.07
44.92
Appendix
9.6
1
H NMR SPECTRA WITH ASSIGNMENT OF IDENTIFIED METABOLITES
Figure 37. 1H NMR spectra of the young leaf of genotype Sus2 with assignment of identified metabolites, ppm 2.1-1.5.
Figure 38. 1H NMR spectra of the young leaf of genotype Sus2 with assignment of identified metabolites, ppm 2.7-2.1.
69
Appendix
Figure 39. 1H NMR spectra of the young leaf of genotype Sus2 with assignment of identified metabolites, ppm 3.7-2.7.
Figure 40. 1H NMR spectra of the young leaf of genotype Sus2 with assignment of identified metabolites, ppm 3.85-3.4.
70
Appendix
Figure 41. 1H NMR spectra of the young leaf of genotype Sus2 with assignment of identified metabolites, ppm 4.9-3.9.
Figure 42. 1H NMR spectra of the young leaf of genotype Sus2 with assignment of identified metabolites, ppm 6.2-5.
71
Appendix
Figure 43. 1H NMR spectra of the young leaf of genotype Sus2 with assignment of identified metabolites, ppm 7-6.
Figure 44. 1H NMR spectra of the young leaf of genotype Sus2 with assignment of identified metabolites, ppm.7.5-7.0.
72
Appendix
Figure 45. 1H NMR spectra of the young leaf of genotype Sus2 with assignment of identified metabolites, ppm 8.1-7.5.
73
Appendix
9.7
1
H-13C HSQC NMR SPECTRA WITH ASSIGNMENT OF IDENTIFIED
METABOLITES
Figure 46. 1H-13C HSQC NMR spectra of sample Sus2-1-1 (young, susceptible sour cherry leave). 1: Quinic acid, 2: Chlorogenic
acid.
Figure 47. 1H-13C HSQC NMR spectra of sample Sus2-1-1 (young, susceptible sour cherry leave). 1: Epicatechin, 2: Chlorogenic
acid, 3: m-coumarate derivative, 4: Cinnamate-derivative.
74
Appendix
9.8
PCA SCORE SCATTER PLOT
Figure 48. PCA score scatter plot of the entire metabolic profile (0.5-9ppm) of PC1 vs PC4 colored according to resistance category
and numbered according to the genotype.
9.9
OPLS-DA MODEL STATISTICS
Table 17. Model statistics. R2X (cum) and R2Y (cum) represent the goodness of fit between the X (metabolite data) and Y (predictor
values) matrices. Q2 (cum) assesses the accuracy and predictability of the model. A Q2 value close to 1.0 represents a more predictive
model. RMSEcv is the Root Mean Square Error of Cross Validation and RMSEE is the Root Mean Square Error of Estimation.
OPLS-DA Model
Model 1 (0.5-9 ppm) resistant vs susceptible
Model 2 (5.5-9 ppm) resistant vs susceptible
Model 3 (5.5-9 ppm) young leaves, resistant vs
intermediate/susceptible
Model 4 (0.5-9 ppm) young vs old leaves
R2X(cum)
0.61
0.73
0.77
R2Y(cum)
0.87
0.99
0.99
Q2(cum)
0.77
0.94
0.89
RMSEcv
0.24
0.13
0.16
RMSEE
0.19
0.06
0.03
0.77
0.96
0.86
0,19
0.11
75
Appendix
9.9.1
OPLS-DA Model 1 (0.5-9 ppm) resistant vs susceptible:
Figure 49. OPLS-DA CV score plot showing the variation between metabolic profiles of the entire metabolic profile (0.5-9 ppm) in
sour cherry leave (model 1). The model was constructed from 1H NMR data of 36 samples of sour cherry leaves from 4 sour cherry
genotypes divided in two resistance groups (green circle = samples from resistant genotypes and blue circle = samples from
susceptible genotypes). Each point in the plot represents one sample 1H NMR spectra. The PLS 1 axis represents the predictive
variation among the classes and the OPLS 1 axis represents the variation orthogonal to the class specific variation. R2Y(cum) = 0.87
and Q2(cum) = 0.77.
Figure 50. OPLS-DA model 1 permutation plot showing R2 and Q2 for randomly permuted Y-observations (left) and R2 and Q2 for
the original Y-matrix (right). The test randomly permute the Y-observations 20 times, while the X-matrix is kept intact.
76
Appendix
Figure 51. OPLS-DA model 1 Y observed vs Y predicted plot displaying the observed versus predicted values of the selected yvariable. RMSEE = root mean square error of estimation, RMSEcv = root mean squared error of the cross-validated procedure.
Figure 52. Representative OPLS-DA S-line plot for the entire metabolic profile of resistant vs susceptible genotypes, showing
relative contribution of bins/spectral variables to clustering of resistant and susceptible sour cherry genotypes (OPLS-DA model 1).
Each point in the figure represents a bin. The color of the lines represents the correlation of the bin towards the predictive variation.
The height of the lines shows the intensity of the spectral bins. A positive p-value on the y-axis indicates a high level of the respective
signal in genotypes from the susceptible category (low level in genotypes from the resistant category), while a negative p-value
indicates a low level of the respective signal in genotypes from the susceptible category (high level in genotypes from the resistant
category).
77
Appendix
9.9.2
OPLS-DA Model 2 (5.5-9 ppm) resistant vs susceptible
Figure 53. OPLS-DA CV score plot showing the variation between metabolic profiles in the phenolic region (5.5-9 ppm) in sour
cherry leave (model 2). The model was constructed from 1H NMR data of 36 samples of sour cherry leaves from 4 sour cherry
genotypes divided in two resistance groups (green circle = samples from resistant genotypes and blue circle = samples from
susceptible genotypes). Each point in the plot represents one sample 1H NMR spectra. The PLS 1 axis represents the predictive
variation among the classes and the OPLS 1 axis represents the variation orthogonal to the class specific variation. (R2Y(cum) =
0.99 and Q2(cum) = 0.94.
Figure 54. OPLS-DA model 2 permutation plot showing R2 and Q2 for randomly permuted Y-observations (left) and R2 and Q2 for
the original Y-matrix (right). The test randomly permute the Y-observations 20 times, while the X-matrix is kept intact.
78
Appendix
Figure 55. OPLS-DA model 2 Y observed vs Y predicted plot displaying the observed versus predicted values of the selected y-variable.
RMSEE = root mean square error of estimation, RMSEcv = root mean squared error of the cross-validated procedure.
Figure 56. Representative OPLS-DA S-line plot showing relative contribution of bins/spectral variables to clustering of resistant and
susceptible sour cherry genotypes (5.5-9 ppm). Each point in the figure represents a bin. The color of the lines represents the
correlation of the bin towards the predictive variation. The height of the lines shows the intensity of the spectral bins.
79
Appendix
9.9.3
OPLS-DA Model 3 (5.5-9 ppm) resistant vs susceptible, young leaves
Figure 57. OPLS-DA CV score plot showing the variation between metabolic profiles in young leaves in the phenolic region (5.5-9
ppm) in sour cherry leave (model 3). The model was constructed from 1H NMR data of 18 samples of sour cherry leaves from 6 sour
cherry genotypes divided in two resistance groups, with intermediate and susceptible genotypes gathered in one group (blue circle =
samples from resistant genotypes, green circle = samples from intermediate resistant genotypes and blue circle = samples from
susceptible genotypes). Each point in the plot represents one sample 1H NMR spectra. The PLS 1 axis represents the predictive
variation among the classes and the OPLS 1 axis represents the variation orthogonal to the class specific variation. R2X(cum) =
0.77, R2Y(cum) = 0.99 and Q2(cum) = 0.89.
Figure 58. OPLS-DA model 3 permutation plot showing R2 and Q2 for randomly permuted Y-observations (left) and R2 and Q2 for
the original Y-matrix (right). The test randomly permute the Y-observations 20 times, while the X-matrix is kept intact.
80
Appendix
Figure 59. OPLS-DA model 3 Y observed vs Y predicted plot displaying the observed versus predicted values of the selected yvariable. RMSEE = root mean square error of estimation, RMSEcv = root mean squared error of the cross-validated procedure.
Figure 60. Representative OPLS-DA S-line plot showing relative contribution of bins/spectral variables to clustering of young leaves
from resistant and susceptible sour cherry genotypes (5.5-9 ppm). Each point in the figure represents a bin. The color of the lines
represents the correlation of the bin towards the predictive variation. The height of the lines shows the intensity of the spectral bins.
81
Appendix
Table 18. Variables driving separation in metabolite fingerprints among young leaves of resistant and intermediate/susceptible sour
cherry genotypes from OPLS-DA model 3 (5.5-9 ppm).
Increased/decreased
Metabolite
Increased in young sour
cherry leaf metabolic
profile of
intermediate/susceptible
genotypes
Unknown a
Unknown a
Unknown a
Epicatechin
Chlorogenic acid
Chlorogenic acid
?
?
?
Unknown a
Chlorogenic acid
Epicatechin
Unknown a
Bin (chemical
shift)
8.13
7.74
7.59
6.94
6.92
6.90
6.79
6.77
6.74
6.44
6.37
6.08
5.83
82
Loading
values
0.17
0.16
0.20
0.15
0.14
0.16
0.15
0.16
0.13
0.12
0.10
0.12
0.12
Variable importance on
projection (VIP) values
3.11
3.01
3.71
2.72
2.70
3.04
2.82
3.05
2.50
2.20
1.84
2.19
2.31
P(corr)
0,60
0.70
0.77
0.60
0.47
0.56
0.59
0.57
0.62
0.59
0.38
0.56
0.59
Appendix
9.9.4
OPLS-DA Model 4 (0.5-9 ppm) young vs old leaves
Figure 61. OPLS-DA CV score plot showing the variation between young and old leaves from the entire metabolic profile (0.5-9
ppm) in sour cherry leave (model 4). The model was constructed from 1H NMR data of 36 samples of sour cherry leaves from 4 sour
cherry genotypes divided in two age groups (green circle = samples from old leaves and blue circle = samples from young leaves).
Each point in the plot represents one sample 1H NMR spectra. The PLS 1 axis represents the predictive variation among the classes
and the OPLS 1 axis represents the variation orthogonal to the class specific variation. R2Y(cum) = 0.96 and Q2(cum) = 0.86.
Figure 62. OPLS-DA model 4 permutation plot showing R2 and Q2 for randomly permuted Y-observations (left) and R2 and Q2 for
the original Y-matrix (right). The test randomly permute the Y-observations 20 times, while the X-matrix is kept intact.
83
Appendix
Figure 63. OPLS-DA model 4 Y observed vs Y predicted plot displaying the observed versus predicted values of the selected yvariable. RMSEE = root mean square error of estimation, RMSEcv = root mean squared error of the cross-validated procedure.
84
Appendix
9.10 HEXOSE-TO-SUCROSE RATIO
Table 19. Hexose-to-sucrose ratio for the different genotypes.
Category (level of attack)
Genotype
Hexose-to-sucrose ratio
Resistant
Resistant
Intermediate
Intermediate
Susceptible
Susceptible
Res1
Res2
Int1
Int2
Sus1
Sus2
1.83
4.00
3.61
4.11
6.70
23.94
85
Appendix
9.11 CONCENTRATION OF IDENTIFIED METABOLITES
Table 20. The content of single metabolites (mean ± standard deviation in mM) in sour cherry leaves calculated for the different genotypes and leaf ages.
Metabolite
Res1
Int1
Sus1
Res2
Int2
Sus2
Young
Intermediate Old
Chlorogenic acid
7.15±4.06
12.12±4.59
10.21±3.81
9.30±4.46
12.32±6.09
13.18±2.52
11.40±4.84
11.33±5.22
9.41±4.15
Epicatechin
0.97±0.70
3.32±1.39
3.30±2.14
1.05±0.60
1.74±1.48
2.74±1.19
2.71±2.08
2.28±1.49
1.56±1.09
m-Coumaric acid like
compound
Quinic acid
1.61±0.74
2.24±0.47
1.22±0.28
1.93±0.49
1.53±0.55
2.26±0.31
2.00±0.59
1.69±0.55
1.70±0.68
Cinnamate derivative
1.49±0.69
1.80±0.46
1.57±0.59
2.09±1.14
4.70±1.94
3.26±2.06
3.30±2.13
2.13±1.16
2.02±1.50
Fructose
16.01±6.34
21.39±11.99
12.24±4.86
61.11±10.34
22.63±4.56
30.02±9.28
26.87±10.94
21.44±9.33
15.94±7.09
Sorbitol
135.40±36.75 126.00±33.76 47.95±21.12 161.96±19.18 149.13±17.44 145.93±26.93 135.08±48.00 124.80±34.77 123.31±52.59
Sucrose
23.72±4.21
16.79±5.78
13.10±5.63
34.98± 6.22
18.33±7.38
11.35±9.29
13.12±6.81
21.63±9.16
24.38±15.19
myo-Inositol
7.61±1.40
8.49±1.60
12.20±3.50
8.46±1.62
12.44±3.77
16.80±5.26
12.90±5.43
11.48±4.32
8.62±1.91
Glucose
25.18±9.14
29.04±14.67
23.06±7.69
40.26±18.36
41.31±9.93
58.91±17.56
43.95±20.93
37.33±16.64
27.59±12.35
Choline
0.76±0.22
0.75±0.25
0.72±0.08
1.41±0.30
1.02±0.26
1.15±0.29
0.93 ± 0.28
0.91 ± 0.30
1.05 ± 0.44
Formate
0.26±0.09
0.13±0.04
0.15±0.03
0.32±0.06
0.22±0.08
0.26±0.12
0.22±0.12
0.22±0.07
0.23±0.10
Malate
39.45 ± 18.10 22.78 ±6.84
36.12 ±7.56 56.73 ±.25.38 36.59 ±13.22 27.26 ±8.95
30.52 ±6.24
33.12 ±14.22 45.83 ±25.21
Alanine
0.20 ± 0.07
0.33 ± 0.06
0.48 ± 0.26
0.28 ± 0.08
111.19±55.75 116.36±51.09 79.98±20.81 158.37±48.91 103.55±25.76 122.10±39.76 154.63±53.30 108.61±30.70 82.53±24.44
0.24 ± 0.12
0.55 ± 0.23
86
0.49 ± 0.18
0.39 ± 0.14
0.33 0.13
Appendix
87
Appendix
88