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B’sweet or B’sour
An ecological, metabolomic and molecular analysis of slug resistance in Solanum dulcamara
Onno Wouter Calf
Calf OW (2019) B’sweet or B’sour—An ecological, metabolomic and molecular analysis of slug resistance in Solanum dulcamara. PhD thesis, Radboud University, Nijmegen, the Netherlands.
Design and layout: Onno W. Calf
Cover: The very moment Rosy faces ZD11… It is both fascinating and frustrating that, no matter the effort, I will never be able to draw a complete sharp image of the ‘ordinary’ world around me.
ISBN: 978-94-6323-793-2
Printed by: Gildeprint, Enschede, the Netherlands
The research presented in this thesis was performed at the department of Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Faculty of Science, Radboud University, Nijmegen, the Netherlands under the auspices of the Graduate School of Experimental Plant Sciences (EPS) and supported by grant number 823.01.007 of the open programme of the Netherlands Organisation for Scientific Research (NWO-ALW).
© Onno W. Calf, all rights reserved
B’sweet or B’sour
An ecological, metabolomic and molecular analysis of
slug resistance in Solanum dulcamara
Proefschrift
ter verkrijging van de graad van doctor
aan de Radboud Universiteit Nijmegen
op gezag van de rector magnificus prof. dr. J.H.J.M. van Krieken,
volgens besluit van het college van decanen
in het openbaar te verdedigen op vrijdag 25 oktober 2019
om 12.30 uur precies
door
Onno Wouter Calf
geboren op 23 maart 1986
te Vlaardingen
Promotoren:
Prof. dr. ir. Nicole M. van Dam
Prof. dr. Celestina Mariani
Copromotoren: dr. Heidrun Huber dr. Janny L. Peters
Manuscriptcommissie:
Prof. dr. Leon P. M. Lamers
Prof. dr. Caroline Müller (Universität Bielefeld, Duitsland) dr. ir. Rob C. Schuurink (Universiteit van Amsterdam)
B’sweet or B’sour
An ecological, metabolomic and molecular analysis of
slug resistance in Solanum dulcamara
Doctoral thesis
to obtain the degree of doctor
from Radboud University Nijmegen on the authority of the Rector Magnificus prof. dr. J.H.J.M. van Krieken,
according to the decision of the Council of Deans
to be defended in public on Friday, October 25, 2019
at 12.30 hours
by
Onno Wouter Calf
born on March 23, 1986
in Vlaardingen (the Netherlands)
Supervisors:
Prof. dr. ir. Nicole M. van Dam
Prof. dr. Celestina Mariani
Co-supervisors: dr. Heidrun Huber dr. Janny L. Peters
Doctoral thesis committee:
Prof. dr. Leon P. M. Lamers
Prof. dr. Caroline Müller (Bielefeld University, Germany) dr. ir. Rob C. Schuurink (University of Amsterdam)
Aan: Norah, Thijn, Finn, Evi, Lars en ieder ander die vol verwondering kan genieten van de ‘alledaagse’ wereld om ons heen.
En aan hen die het willen leren…
To: Norah, Thijn, Finn, Evi, Lars and everyone who can enjoy, full of wonder, the ‘ordinary’ world around us.
And to those who want to learn it…
Table of contents
Chapter 1. General introduction 11
Chapter 2. Glycoalkaloid composition explains variation in slug resistance in 23 Solanum dulcamara
Chapter 3. Gastropods and insects prefer different Solanum dulcamara 47 chemotypes
Chapter 4. Induced resistance to slug herbivory in Solanum dulcamara is related to 83 the temporal and spatial dynamics of metabolomic and transcriptomic responses
Chapter 5. The genetic basis of ecologically relevant chemical diversity in Solanum 115 dulcamara
Chapter 6. Summary and conclusions 159
Samenvatting en conclusies 165
References 173
Dankwoord / Acknowledgements 189
Curriculum vitae and education statement 195
Chapter 1:
General introduction Chapter 1: General introduction
Plant chemical diversity in relation to diverse biotic interactions Plants are not just passive collections of green leaves, waiting to be eaten by the first trespassing herbivore. In fact, it is safe to say that all plants are at least partly resistant to most of the biotic threats they may encounter (Fig. 1). For instance, one will neither find the cabbage white’s caterpillars feeding on potato plants, nor potato beetles on Brussels sprouts. In order to survive, plants possess successful defence mechanisms against a vast diversity of enemies. They may be attacked by grazing mammals, leaf chewing caterpillars, sap sucking aphids, soil dwelling nematodes, but also fungal and bacterial pathogens (Coley and Barone 1996; van Dam 2009). Without defences, plants would not survive. Structural defence traits, such as thorns, spines, prickles, trichomes, cuticles and waxes may form a direct barrier to herbivores and pathogens (Hanley et al. 2007; Müller and Riederer 2005; Serrano et al. 2014). A more spectacular way to defend is by means of help from the third trophic level of organisms. Plants may offer shelter, carbohydrates or nutrients to build mutualistic relationships, following the rule: ‘The enemy of my enemy is my friend’ (Heil 2008; Richardson et al. 2000). Many species of ants, for example, live in mostly facultative association with plants, from which they scavenge herbivores or their eggs (Heil and McKey 2003). Another example of such indirect plant defences comes from the emission of volatiles to attract parasitoids, which oviposit in other insect eggs or their larvae (Dicke 2009). Upon hatching, the parasitoids larvae ultimately kill their host and liberate the plant from further damage. Plants may thus deploy diverse strategies to manage the interaction with various damaging and beneficial organisms. Yet, by far the most diverse set of relation management tools is formed by the secondary metabolites. More than 200,000 compounds in the plant kingdom are estimated to be secondary metabolites (Hartmann 2007). Such compounds are not vital for basic plant growth, development or reproduction, but can enhance plant performance (Bennett and Wallsgrove 1994; Howe and Jander 2008). For example, they can be toxic or repellent to herbivores, or provide colours or smells that are attractive to pollinating insects. Secondary metabolites are highly diverse and can be sorted into classes based on their chemical structure, such as terpenes, phenolics, glucosinolates or alkaloids (Bennett and Wallsgrove 1994). Each plant has a chemical profile which is mostly characteristic for the species. However, not only among plant species, but also within a single species a multitude of chemical varieties, so- called chemotypes, may exist (Bálint et al. 2016; Kliebenstein et al. 2001; Macel et al. 2004). Such chemotypes are a form of intraspecific variation, which, in turn, is an important aspect in the evolutionary process of natural selection. In this process, a trait of a species is: 1) variable among individuals, 2) adaptive to specific stress conditions and 3) heritable. Whereas chemical diversity may originate from random events, it is believed to be maintained by the interactions of plants with the herbivore community (Hartmann 1996; Jones and Firn 1991; Speed et al. 2015). Indeed, many chemotypes were found to be heritable and to provide resistance against specific herbivores (see for example: Macel et al. 2004 and 2005 or van Leur et al. 2006 and 2008). This illustrates that each herbivore species may pose a different selection pressure on chemical diversity.
12 Chapter 1: General introduction
Fig. 1 Plant-herbivore interactions are common practise in most vegetable gardens. Whereas caterpillars of the cabbage white butterfly (Pieris brassicae) will damage Brassica plants (e.g. Brussels sprouts or broccoli), Colorado potato beetles (Leptinotarsa decemlineata) are only found on Solanum species (mostly potato). This illustrates that each plant, at least in part, has successful defences against some of the herbivores it may encounter
Whether a particular chemical compound indeed acts as a defence largely depends on the level of specialization of the herbivore species (Ali and Agrawal 2012). Herbivores can be roughly classified as generalists or specialists. Whereas generalists are capable of feeding on a wide range of plant species, their growth and development is generally impaired by specific defence compounds. In contrast, specialist herbivores are well adapted to specific defences of some or single plant species, but unable to survive on other plants. Generalists and specialists may exert opposing selection on the levels of defence compounds. Generalist herbivores are usually deterred by high levels of secondary metabolites, whereas specialist herbivores may even be attracted to the same compounds (Ali and Agrawal 2012; Bennett and Wallsgrove 1994; Miles et al. 2005; van Loon et al. 2002). Most plants are attacked by multiple herbivore species (van Dam 2009), which all share a specific co-evolutionary history with their host. Therefore, each plant-herbivore interaction may involve specific metabolites or defence strategies. The dominant herbivore species that is posing the strongest selection pressure on defence traits may differ among plant populations (Agrawal 2007; Johnson
13 Chapter 1: General introduction
2011). As a consequence, some chemotypes may be under strong selection by a specific herbivore species in some populations, whereas these same varieties may thrive in other populations where the particular herbivore is absent. Local differences in the relative abundance of herbivore species may therefore explain the maintenance of chemical diversity within plant species (Hartmann 1996).
Herbivore specific defence induction as a strategy to save resources The production of defences requires energy and nutrients and is therefore considered to be costly (Strauss et al. 2002; Wittstock and Gershenzon 2002). After all, resources that are invested in defence cannot be invested in growth or reproduction. Moreover, mutualists may be deterred by high defence levels. Therefore, plants need to balance the costs and benefits of producing defences (Herms and Mattson 1992). The existence of this balance can already be derived from observed intraspecific variation in basal defence production (Wittstock and Gershenzon 2002). Some individuals of a species constitutively produce high levels of defences, whereas others may save costs by having low levels of defence production. These less defended plants may grow faster and produce more offspring in the absence of herbivores, but they may lose competition with better defended conspecifics when a herbivore arrives. In addition to genetically fixed differences in constitutive defence expression, plants are able to increase defence production upon herbivory (Howe and Jander 2008; Karban and Baldwin 1997; Walling 2000). Such induced responses are generally observed in both locally, damaged and systemic, undamaged organs (Chung et al. 2013; Erb et al. 2009; van Dam and Raaijmakers 2006), where they may confer induced resistance (Agrawal 1998; van Zandt 2007). Deployment of inducible defence mechanisms, in concert with reorganisation of the primary metabolism, allows the plant to balance the investment of resources in growth and defence under variable herbivore pressure (Orians et al. 2011; Schwachtje and Baldwin 2008; Steinbrenner et al. 2011; van Zandt 2007; Vos et al. 2013). However, the ability to induce defences may trade-off with constitutive defence levels (Kempel et al. 2011; Rasmann et al. 2015). Plants with low constitutive defence levels may rely more on inducible defences. Although these plants probably receive some damage when a herbivore arrives, they may be able to upregulate defence levels and induce resistance before the level of damage becomes critical. This strategy is particularly beneficial when the severity of herbivore feeding is low (Backmann et al. 2019). In contrast, when local herbivore densities are high, a plant with high constitutive defence levels is unlikely to receive much damage and consequently has a high chance to survive without defence induction. High herbivore feeding pressures may therefore particularly select for plant individuals with high constitutive levels and low inducibility of defence expression.
14 Chapter 1: General introduction
An additional advantage of defence induction is that the response can be tailored to specific herbivores (Agrawal 2000; Chung and Felton 2011; van Zandt and Agrawal 2004). The ability to induce specific defences illustrates that plants actively differentiate between wound and herbivore specific signals. Endogenous cell compounds, for instance, act as signalling molecules for physical damage (Heil et al. 2012). However, herbivore specific compounds, such as saliva, are as well detected by the plant (Basu et al. 2018; Bonaventure et al. 2011; Mithöfer and Boland 2008; Voelckel and Baldwin 2004). This combination of damage and herbivore specific signals elicits molecular and chemical reactions, which occur within minutes upon damage (Leon et al. 2001; Maffei et al. 2007). These reactions are regulated by phytohormones, of which jasmonic acid (JA) and salicylic acid (SA) specifically drive defence responses (Erb et al. 2012; Lorenzo and Solano 2005; Lortzing and Steppuhn 2016). Every herbivore species is characterised by a different fingerprint or pattern of elicitors, which allows the plant to differentiate herbivore species (Basu et al. 2018). The JA- and SA-signalling pathways, and other signalling hormones, act in cross-talk and ultimately result in specific responses to the attacking herbivore (Beckers and Spoel 2006; Schweiger et al. 2014; Thaler et al. 2012). Defence induction is a dynamic process. Because the production and allocation of metabolites take time, the observed molecular or metabolic response depends on the time past herbivore feeding (de Vos et al. 2005; Mathur et al. 2011). Moreover, plants do not always (directly) upregulate their defence status upon herbivory (Backmann et al. 2019; Karban 2011; Underwood 2012). Tolerance mechanisms, in which plants under attack sustain high performance and low investment in defence, can act in synchrony with resistance mechanisms (Núñez-Farfán et al. 2007; Stout 2013; Tucker and Avila-Sakar 2010). These different strategies can all occur within the same plant at the same time. For instance, the strength or direction of responses at the local site of damage can be different from those observed in systemic, undamaged tissue (Florencio-Ortiz et al. 2018; Schmidt et al. 2009; van Dam and Raaijmakers 2006). This heterogeneity of responses within a plant partly depends on the balancing of primary resources between source and sink leaves, and is therefore associated with the ontogenetic stage of the attacked tissue (Barton and Koricheva 2010; Boege and Marquis 2005; Meldau et al. 2012). Upregulated defence levels, in turn, are not necessarily degraded once the herbivore is no longer feeding. Therefore, depending on the turnover rate, induced responses can have long-lasting effects on the defence status of the plant. Since induced responses may be herbivore specific, host suitability for later arriving herbivores might depend on the herbivore species that has previously been feeding. As such, early season herbivory may have far-reaching consequences for the herbivore community (Erb et al. 2011; Poelman et al. 2008; Underwood 2012; Utsumi 2011). Taken together, the plant defence status depends on the balancing of available primary resources between damaged and undamaged tissues, the time past herbivore feeding and the herbivore species that have previously been feeding. Highly standardised species-specific plant-herbivore interaction studies are therefore required to understand how plants respond to the many members of the natural herbivore community.
15 Chapter 1: General introduction
Plants may know that gastropods are not insects Gastropods, commonly known as slugs and snails, are part of many herbivore communities as well as important pests to many crops (Gall and Tooker 2017). Surprisingly, the mechanisms and regulation of plant defence against terrestrial gastropods have received little attention. Instead, plant defence mechanisms are mostly studied in relation to insect herbivores. Gastropods may be considered chewing generalist herbivores, with similar host preference as generalist insect species (Kempel et al. 2015). However, their feeding mode is highly distinct from that of chewing insects. Gastropods possess a radula, a tongue-like chitinous structure covered with minute teeth, which is used for scraping in food material. Moreover, locomotion mucus is a unique feature of this distinct class of herbivores and may contain many specific elicitors of induced responses. It is therefore expected that plants are able to discriminate between feeding damage inflicted by gastropod and insect feeding. As a result, plants may have evolved specific defences. Most studies on gastropod-plant interactions have had an ecological focus and illustrate how gastropod feeding can influence plant species richness and survival (see for instance Allan and Crawley 2011; Fabian et al. 2012; Hulme 1996 or Scheidel and Bruelheide 1999). This effect is mostly obtained from selection of plant species or chemotypes in the seedling stage (Buschmann et al. 2006; Hanley et al. 1995; Korell et al. 2016; Strauss et al. 2009). It is long known that gastropod feeding preference and host selection is often based on the presence of chemical defences (Stahl 1888). Also more recent studies illustrated that gastropod feeding is affected by specific secondary metabolites (Asplund et al. 2010; Linhart and Thompson 1995; Moshgani et al. 2014; van Dam et al. 1995; Wink 1984). The selective feeding behaviour of gastropods on plants may impose specific selection on plant chemical diversity (O'Reilly-Wapstra et al. 2007; Orians et al. 2013). This may eventually lead to locally adapted plant populations, particularly where herbivorous gastropods are abundant (Kalske et al. 2012; Laine 2009; Scriber 2002). In contrast to constitutive gastropod resistance, little is known about how plants respond to gastropods. Whereas gastropod-induced responses in marine macro-algae have gained attention for about two decades (see for instance: Pavia and Toth 2000; Rempt et al. 2012; Ritter et al. 2017 or Rohde et al. 2004), similar studies on terrestrial plants are surprisingly scarce. The few available studies on the molecular regulation of gastropod- induced responses in terrestrial plants have all used artificial approaches as resemblance of gastropod feeding, such as application of locomotion mucus to mechanical wounds or intact leaves (Falk et al. 2014; Meldau et al. 2014; Orrock 2013). These studies revealed the induction of local and systemic responses, which involve similar pathways as induced responses upon feeding by insects or pathogen infection. Indeed, the mucus of gastropods may contain most gastropod specific elicitors, but also their mode of feeding might be essential to induce specific responses. A few studies have shown that gastropod feeding can induce the production of secondary metabolites for direct and indirect defence (Danner et al. 2017; Desurmont et al. 2016; Khan and Harborne 1990). Insect feeding-induced defences,
16 Chapter 1: General introduction in turn, can affect gastropod feeding preference and performance (Kozlowski et al. 2016; Neylan et al. 2018; Viswanathan et al. 2008). Remarkably, the induced effect of actual gastropod feeding on plant resistance to later arriving gastropods has never been tested. Moreover, the studies on the regulation of gastropod-plant interactions have focussed on metabolites and processes that are known to be related to insect feeding resistance. By such targeted approaches, the specific plant mechanisms that are regulated upon gastropod feeding may have been overlooked. These specific mechanisms may only be revealed by untargeted eco-metabolomic (Peters et al. 2018) and transcriptomic (Anderson and Mitchell- Olds 2011) analyses of plant responses upon gastropod feeding.
Objective and experimental model system The objective of this thesis is to get more insight in the specific role of gastropods in plant- herbivore interactions. To this end, I performed a comprehensive analyses of the molecular and metabolomic regulation, as well as the ecological consequences of constitutive resistance and induced responses to gastropod feeding. I used Solanum dulcamara (bittersweet nightshade) as a model system (Fig. 2). This wild perennial woody vine is native to north-western Europe, northern Africa and Asia. High levels of phenotypic plasticity in response to light and water availability allow this species to thrive in contrasting habitats, ranging from exposed coastal dunes to wet forest understories (Dawood et al. 2014; Visser et al. 2016). Recent studies have shown that S. dulcamara is characterized by within and among population genetic and phenotypic trait variation (D'Agostino et al. 2013; Zhang et al. 2016). Resistance to herbivory may as well be such a variable trait. The herbivore community on S. dulcamara includes many specialist insects (Calf and van Dam 2012; Castillo Carrillo et al. 2016), but also gastropods may cause significant damage. For instance, up to 50 % of the plants in a German natural S. dulcamara population showed evidence of substantial gastropod feeding (Lortzing et al. 2016). Moreover, in a semi-natural Canadian population, taildropper slugs (Prophysaon sp.) inflicted up to 15 % leaf area loss early in the growing season (Viswanathan et al. 2005). It is therefore expected that gastropods pose selection on the expression of herbivore defences in S. dulcamara.
17 Chapter 1: General introduction
Fig. 2 Bittersweet nightshade (Solanum dulcamara) naturally faces diverse biotic interactions, including herbivores and mutualists (Calf and van Dam 2012). I used S. dulcamara as a wild model species to assess the mechanisms and consequences of gastropod feeding (-induced) resistance in plants
18 Chapter 1: General introduction
Plants from the Solanum genus are well known to contain toxic steroidal glycoalkaloids (GAs), which serve as resistance compounds to herbivores and pathogens (Eich 2008; Friedman 2015; Milner et al. 2011). These secondary metabolites may also be related to slug resistance in S. dulcamara. Interestingly, diversity in the relative proportion of different GAs among S. dulcamara chemotypes has long been recognised (Mathé 1970; Willuhn 1966). Natural selection by gastropods may be an underlying cause for this source of chemical diversity, but may also have selected for yet unidentified resistance compounds. Moreover, induced responses in S. dulcamara upon mechanical wounding, insect feeding and egg deposition affect later arriving herbivores (Geuss et al. 2018; Lortzing et al. 2017; Nguyen et al. 2016; Viswanathan et al. 2008). It is therefore expected that gastropod feeding similarly triggers immune responses, which might be specific to gastropod feeding and have significant ecological consequences for the natural herbivore community. As a member of the Solanaceae plant family, S. dulcamara is closely related to tomato (S. lycopersicum), potato (S. tuberosum) and eggplant (S. melongena). Knowledge obtained from studying this wild species may therefore have an applied value for breeding strategies in economically important crop species. For instance, S. dulcamara has been a natural source for eriophyid mite and late blight resistance factors (Bronner et al. 1991; Golas et al. 2010; Golas et al. 2013). Interestingly, S. dulcamara successfully deploys an indirect defence against gastropods (Lortzing et al. 2016). Herbivore damaged leaves attract ants by producing extrafloral nectar on the edges of inflicted wounds. This inducible mechanism of establishing an indirect defence was never described before. These examples illustrate the novel aspects in plant defence that may be found by studying gastropod resistance in this wild species.
Thesis outline With this thesis, I provide a comprehensive ecological, metabolomic and molecular analysis of constitutive and induced gastropod feeding resistance in Solanum dulcamara plants. In chapter 2, I investigate to what extend S. dulcamara plants from eight native Dutch populations vary in their constitutive resistance against gastropod feeding and whether this variation can be explained by chemical diversity. To this end, I combined slug preference assays with an eco-metabolomic analyses approach to identify the chemical mechanisms underlying natural variation in gastropod feeding resistance. In chapter 3, I used the chemical diversity that was identified in chapter 2 to assess whether different gastropod species and insect herbivores show similar feeding preferences for six S. dulcamara chemotypes. Moreover, I combined greenhouse and common garden observations with metabolomic profiling to assess the role of constitutive and gastropod-induced defence levels in resistance against generalist and specialist herbivores. In chapter 4, I follow up on the elucidation of gastropod-induced defence regulation. I specifically tested induced responses and resistance in both local and systemic leaves at different time points after 24 h or 72 h exposure to gastropod feeding. Moreover, I combined untargeted transcriptomic and
19 Chapter 1: General introduction metabolomic analyses to assess the key compounds and primary and secondary processes that are regulated after gastropod feeding. In chapter 5, I investigate the molecular mechanisms underlying the previously identified S. dulcamara chemical diversity. To do so, I used a genetic crossing design to assess the inheritability of chemotypes. I combined the observed segregation patterns in F1 and F2 populations with RNA sequencing of the parental chemotypes and qPCR analyses to assess the role of candidate genes. In addition, I used preference assays with segregating F1 chemotypes to explicitly relate the S. dulcamara chemotypes to gastropod feeding resistance. Finally, in chapter 6, I summarise the main results and conclusions and highlight the potential role for gastropods in the scientific field of plant-herbivore interactions.
20 Chapter 1: General introduction
21
Chapter 2:
Glycoalkaloid composition explains variation in slug resistance in Solanum dulcamara
Onno W. Calf Heidrun Huber Janny L. Peters Alexander Weinhold Nicole M. van Dam
Published in: Oecologia (2018) DOI: 10.1007/s00442-018-4064-z Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Abstract In natural environments, plants have to deal with a wide range of different herbivores whose communities vary in time and space. It is believed that the chemical diversity within plant species has mainly arisen from selection pressures exerted by herbivores. So far, the effects of chemical diversity on plant resistance have mostly been assessed for arthropod herbivores. However, also gastropods, such as slugs, can cause extensive damage to plants. Here we investigate to what extent individual Solanum dulcamara plants differ in their resistance to slug herbivory and whether this variation can be explained by differences in secondary metabolites. We performed a series of preference assays using the grey field slug (Deroceras reticulatum) and S. dulcamara accessions from eight geographically distinct populations from the Netherlands. Significant and consistent variation in slug preference was found for individual accessions within and among populations. Metabolomic analyses showed that variation in steroidal glycoalkaloids (GAs) correlated with slug preference; accessions with high GA levels were consistently less damaged by slugs. One, strongly preferred, accession with particularly low GA levels contained high levels of structurally related steroidal compounds. These were conjugated with uronic acid instead of the glycoside moieties common for Solanum GAs. Our results illustrate how intraspecific variation in steroidal glycoside profiles affects resistance to slug feeding. This suggests that also slugs should be considered as important drivers in the co-evolution between plants and herbivores.
24 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Introduction Plants interact with a large diversity of organisms such as herbivores and pathogens (van Dam 2009). Toxic or deterrent secondary metabolites, such as phenolics, terpenoids and alkaloids are known to govern plant-herbivore interactions (Bennett and Wallsgrove 1994; Howe and Jander 2008). It has been postulated that the large chemical diversity observed in plants today has resulted from the multitude of interactions with herbivores. Each herbivore species may require a specific defence strategy. Generalist herbivores are usually deterred by high levels of secondary metabolites, whereas specialist herbivores have evolved mechanisms to overcome plant defences and may even be attracted by specific secondary metabolites (Ali and Agrawal 2012). However, herbivore communities are not constant in time and space. Therefore, the dominant herbivore species which is exerting the strongest selection pressure on local defence traits may differ among plant populations (Agrawal 2007; Johnson 2011). Since plants evolve specific defence mechanisms against the most damaging herbivore species, differences in dominant herbivore species among plant populations may lead to intraspecific chemotypic variation in secondary metabolite composition (Jones and Firn 1991; Speed et al. 2015). Slugs and snails are an important component of many herbivore communities in temperate climate zones. These gastropods are widespread generalist herbivores which require moist conditions and intermediate temperatures (Astor et al. 2017; Sternberg 2000). Therefore, slugs are generally more abundant in shaded and moist habitats than in dry habitats with high sunlight exposure. Though often being unnoticed due to their cryptic nocturnal mode of life, gastropods exert a strong selection pressure on natural plant communities and populations (Strauss et al. 2009). Gastropods can affect plant species diversity by selective feeding on seedlings of particular plant species (Barlow et al. 2013; Korell et al. 2016; Strauss et al. 2009). Selective slug feeding may also result in reduced palatability of the surviving plants, as was shown for hybrid willows (Orians et al. 2013). Surprisingly, in the latter example the reduced palatability could not be related to particular differences in defence chemistry, such as phenolic glycosides or tannins (Orians et al. 2013). On the other hand, pine needles with high terpene concentrations as well as artificial diets laced with either one of the terpenes Δ3-carene or α-pinene were eaten significantly less by slugs (O'Reilly-Wapstra et al. 2007). Similarly, high-alkaloid accessions of the legume Lupinus angustifolius suffered less feeding damage from three different slug species than those with low concentrations of lupin alkaloids (Kozlowski et al. 2017). Together these studies indicate that gastropods commonly respond to chemical variation within a plant species. Thus they may be an important driver for natural variation in chemical plant defence traits equally to, or even more than, insect herbivores (Gall and Tooker 2017).
25 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
The present study focuses on intraspecific variation in gastropod resistance in bittersweet nightshade, Solanum dulcamara. This wild solanaceous perennial woody vine is native to north-western Europe, northern Africa and Asia. It is characterized by within and among population genetic and phenotypic variation (D'Agostino et al. 2013; Zhang et al. 2016). High levels of phenotypic plasticity in response to abiotic stress factors allow this species to thrive in habitats with contrasting abiotic conditions, ranging from exposed coastal dunes to wet forest understories (Dawood et al. 2014; Visser et al. 2016). The herbivore community on S. dulcamara includes both gastropods and specialist insects (Calf and van Dam 2012; Lortzing et al. 2016; Viswanathan et al. 2005). In a semi-natural Canadian population taildropper slugs (Prophysaon sp.) inflicted up to 15 % damage early in the season (Viswanathan et al. 2005). In a German natural population, we found that up to 50 % of the plants showed evidence of substantial gastropod feeding (Lortzing et al. 2016). These observations illustrate the importance of slugs as natural herbivores and potential drivers of defence evolution in S. dulcamara. S. dulcamara produces steroidal glycoalkaloids (GAs), which are highly toxic and deterrent to many organisms (Eich 2008; Kumar et al. 2009; Milner et al. 2011). Previous studies found that there is (genetically fixed) chemotypic variation in GA profiles among individuals of S. dulcamara (Mathé 1970; Willuhn 1966; Willuhn and Kunanake 1970). It is known that alkaloidal secondary metabolites generally deter gastropod feeding (Bog et al. 2017; Speiser et al. 1992; Wink 1984). Thus it is very likely that differences in GA concentrations and profiles also affect resistance to slugs. However, to our knowledge the ecological consequences of S. dulcamara GA concentrations or profiles for plant-herbivore interactions have not been investigated so far. We utilized the naturally available genetic variation within and among populations of S. dulcamara to address the following specific questions: 1) Is there intraspecific variation in gastropod resistance in S. dulcamara? 2) What are the underlying chemical mechanisms explaining variation in gastropod resistance in S. dulcamara? We addressed these questions in a series of bioassays using the grey field slug (GFS, Deroceras reticulatum) as a gastropod model species. Although there are no actual data available on its natural hosts, GFS is an abundant generalist gastropod species which occurs sympatrically with S. dulcamara (South 1992). Adults are relatively small (3–4 cm), and well suited to be used in high-throughput preference assays on leaf discs in Petri dishes (Hendriks et al. 1999). We hypothesised that intraspecific variation in GFS resistance is related to plant chemical diversity. The preference assays were combined with a metabolomics approach to identify the chemical mechanisms underlying differences in slug preference. This allowed us to link the slug’s choice behaviour directly to variation in the chemical profiles of the different accessions.
26 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Materials and Methods
Plant material Eight native S. dulcamara populations from the Netherlands were selected based on the criterion of covering a wide geographic area and range of abiotic conditions, ranging from dry open coastal dune areas to river floodplains and lake borders (Fig. 1 and Table S1). This selection was made to capture intraspecific variation in local conditions, such as differences in herbivore community composition, which may be a causal agent for selection on plant defence traits. Seed batches of the source populations, which were field collected as in Zhang et al. (2016), were provided by the Radboud University Genebank. Seeds were cold- stratified at 4 °C in the dark on a 2 cm layer of glass beads (1 mm Ø) and tap water in a plastic container (8 × 8 × 6 cm, L × W × H, www.der-verpackungs-profi.de GmbH, Göttingen, Germany) covered with a transparent lid for at least 2 weeks. Germination was initiated by transferring the container to greenhouse conditions. After approximately eight days, when cotyledons had unfolded, seedlings were transplanted into individual pots (11 × 11 × 12 cm, L × W × H) containing potting soil (Lentse Potgrond nr. 4, Horticoop, Katwijk, the Netherlands) supplemented with 4 g L-1 slow-release fertilizer (Osmocote® Exact Standard, Everris International B.V., Geldermalsen, the Netherlands). Each seedling, hereafter referred to as ‘accession’, received a unique accession-number. This number is composed of a two- letter population abbreviation followed by a number (1–12). To generate sufficient plant material for experimentation, the accessions were propagated by cloning. Stem cuttings, consisting of a single node with at least 2 cm of woody stem internodes on the distal and proximal side, were potted directly in the same soil mixture as above. The soil was kept moist to stimulate adventitious root formation. All plants were grown in net cages within a greenhouse to prevent insect infection (0.3 mm gauze, 7.50 × 3 × 2.75 m, L × W × H). Greenhouse conditions were set to maintain a 16 h photoperiod with minimum temperatures of 20 °C / 17 °C (day / night). Light levels were supplemented with 1000 W sodium lamps (Philips GreenPower, Amsterdam, the Netherlands) fixed above the net cages providing ~280 µmol m-2 s-1 light intensity at the plant level. Details on the size and age of plants used for experiments are given below.
Fig. 1 (next page) Experimental design of the consecutive assays. Column A: short title indicating the aim of the three consecutive slug preference assays. Column B: graphical representation of the accession selection and testing procedures. Column C: geographic locations of eight Solanum dulcamara populations in the Netherlands. Source population abbreviations: TD Texel Dry, TW Texel Wet, FW Friesland Wet, ZD Zandvoort Dry, OW Ooijpolder Wet, VW Voorne Wet, GD Goeree Dry, LD Limburg Dry
27 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Fig. 1 (caption on previous page)
Slug preference assays A series of three sequential slug preference assays was performed to investigate natural variation in slug resistance in S. dulcamara (assay 1–3 in Fig. 1). In assay 1, accessions (n = 12 per population) grown from seeds collected in eight native source populations were screened to assess within-population variation in GFS preference. In assay 2, leaves of stem cuttings, hereafter referred to as clones, of the most- and least-preferred accession in each population were offered to GFS in a dual-choice assay to test whether the feeding preference was consistent. In assay 3, leaves of clones of 15 out of the 16 accessions used in assay 2 were offered to GFS in a full-choice preference assay to assess overall preference for accessions among the eight original populations.
General approach The same general experimental set-up was used for all three assays. Specific details are given per assay (see below). Plants used for the bioassays were of equal age, between 60 cm and 100 cm tall and any inflorescences that were emerging were regularly removed. Because the accessions showed variation in growth rates, only fully expanded leaves at a position of about 2/3rd of the stem length measured from the apex were used to ensure that leaves of similar developmental stage and metabolic composition were used in all assays. Leaves were numbered from the apex downwards, starting with the first leaf below the first internode that was greater than 2 cm. Within each assay, leaves from the same position were used for all accessions/clones. Leaf discs were made of interveinal tissue using a cork-borer (1.5 cm Ø). One leaf disc of each treatment/accession was placed in a Petri dish (9 cm Ø) with the
28 Chapter 2: Glycoalkaloid composition explains variation in slug resistance adaxial (i.e. upper) side down. The leaf discs adhered sufficiently strong to the bottom of the Petri dish to prevent them from changing their position due to slug activities. For each Petri dish, leaf positions of the different accessions within a given Petri dish were based on a unique pre-determined and completely randomised order which was printed on paper and placed under the transparent dish when setting up the assay. The Petri dishes were gently sprayed with de-ionised water before, during and after placing the leaf discs to create a moist environment for maintaining leaf disc quality. Depending on the size of the slugs, either two or three individuals were placed on the lid of each Petri dish after which the dishes were closed and placed in the slug-culture cabinet. After 24 h, slugs were removed and the leaf material remaining in the Petri dish was photographed with a 14 cm ruler as scale reference for analyses of consumed area using ImageJ v. 1.48 (Schneider et al. 2012). Each slug was only used once for experimentation.
Assay 1: eight population screenings Twelve randomly selected 4- to 5-week-old seedlings per source population (n = 8) were used for preference assays to assess within-population variation in slug feeding resistance. Each individual was given an accession identifier consisting of the population code (Fig. 1) and a sequential number. Three leaves were selected from each accession (leaves 6–8 from the apex). From each leaf, 8 leaf discs were punched, providing a total of 24 discs (3 leaves × 8 discs) per accession. Individual discs were randomly allocated to Petri dishes (n = 16) for preference assays. Each Petri dish thus contained 12 leaf discs, each representing 1 of the 12 accessions of a single population. One accession of Limburg Dry (LD12) was discarded right before the onset of the preference assay due on the suspicion of being infected with a disease, leaving 11 accessions for this source population.
Assay 2: within-population dual-choice For each of the eight source populations, the most- and least-preferred accessions in assay 1 were selected. As the initially chosen accessions TD11 and FW03 appeared to be infected by a disease, these were replaced by the second most-preferred (FW09) or least-preferred (TD01) accession. Per accession, three 4- to 5-week-old clones with similar stem lengths were selected for a within-population dual-choice assay to test the consistency of the slugs’ preference between the most and least preferred accession for each source population. From each plant, 1 leaf was selected (leaf 7 from the apex) from which 8 leaf discs were made, thus providing a total of 24 discs (1 leaf × 3 clones × 8 discs). Discs were randomly allocated over Petri dishes (n = 8 per population). Each Petri dish received 3 discs of the most-preferred (one disc of each clone) and three of the least preferred (idem) accession, resulting in 6 leaf discs presented to the slugs in each Petri dish.
29 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Assay 3: among population full-choice New clones were made from the 16 accessions selected for assay 2. Because accession TD01 appeared diseased and was excluded from further assays, only 15 accessions were used in assay 3. Three 7 week-old clones with similar stem length were selected for each accession. From each plant, two leaves were chosen (leaves 10 and 11 from the apex) and 4 leaf discs were made from each leaf, resulting in 24 discs for each accession (2 leaves × 3 clones × 4 discs). The leaf discs were pooled and single discs were randomly allocated to Petri dishes (n = 19) for the 15-choice assay. Three replicates of the preference assay were excluded from statistical analyses due to excessively low or high consumption rates or being unable to reconstruct the original leaf disc position, leaving a total of 16 suitable replicates. Four leaf discs of each accession were oven-dried to constant weight and used to determine the specific leaf area (cm2 g-1 dry weight).
Statistical analyses of preference assays Absolute leaf disc consumption data (cm2) of all preference assays were analysed using nonparametric statistical methods from the R “stats” package R Core Team (2016). Friedman’s rank sum test was applied to evaluate overall preferences for accessions in the multiple choice assays (assays 1 and 3) using the Petri dish number as grouping factor. Paired Wilcoxon signed-rank tests with continuity correction, excluding ties (“no choice”), were applied to assess differences in preferences for two accessions (assay 2). For presentation in figures, the absolute consumed leaf area was converted to the relative consumption per
Petri dish ( ) to correct for individual differences 2( ) 퐼퐼퐼퐼퐼퐼퐼퐼퐼퐼 �푙�푙 𝑑푑푑 �푎𝑎 �푐𝑐�푐𝑐 ��푐 � 2 among slugs,푇� 푇across𝑇 �푙�푙 𝑑Petri푑푑 �푎 dishes,𝑎 �푐𝑐� 푐and𝑐 �experimental� 푃𝑃𝑃 𝑑푑ℎ �푐 series. A Pearson’s correlation test was performed to test the relation between the relative leaf disc consumption and the specific leaf area in assay 3.
Metabolic profiling using HPLC-qToF-MS The leaf tissue immediately surrounding the area of the leaf discs used for assay 3 was dissected at ~1 cm circumference around the original hole, collected in screw cap tubes (57.0 × 15.3 mm, Sarstedt AG&Co. Nümbrecht, Germany), flash frozen in liquid nitrogen and stored at -80 °C until further processing. These small leaf tissue samples taken from three clones per accession were pooled per accession, resulting in 15 leaf samples for metabolomic analyses. A semi-untargeted analysis with particular emphasis on abundant compounds was performed to determine which chemical compounds relate to slug preference. Leaf samples were extracted following a procedure derived from de Vos et al. (2012). In short, fresh leaf material was ground in liquid nitrogen. About 100 mg of ground sample was double extracted with, respectively 1.0 and 0.9 ml MeOH:acetate buffer (pH 4.8) in 2 ml reaction
30 Chapter 2: Glycoalkaloid composition explains variation in slug resistance tubes holding two glass beads (5 mm Ø) by shaking in a TissueLyser (Qiagen, Venlo, the Netherlands) at 50 Hz for 5 minutes followed by centrifugation at 15.000 rpm at 4 °C. Clear supernatants were combined and stored at -20 °C until further processing.
Two sets of diluted crude extracts (1:5 and 1:50) were analysed with an UltiMate™ 3000 Standard Ultra-High-Pressure Liquid Chromatography system (UHPLC, Thermo Fisher Scientific, Waltham, MA, USA) equipped with an Acclaim® Rapid Separation Liquid Chromatography (RSLC) 120 column (150 × 2.1 mm, particle size 2.2 μm, Thermo Fisher Scientific) using the following gradient at a flow rate of 0.4 ml min-1: 0–2 min, isocratic 95 % A (water / formic acid 99.95 / 0.05 (v / v %)), 5 % B (acetonitrile / formic acid 99.95 / 0.05 (v / v %)); 2–15 min, linear from 5 % to 40 % B; 15–20 min, linear from 40 % to 95 % B; 20–22 min, isocratic 95 % B; 22–25 min, linear from 95 % to 5 % B; 25–30 min, isocratic 5 % B. Compounds were detected with a maXis impact™ quadrupole time-of-flight mass spectrometer (qToF-MS, Bruker Daltonik GmbH, Bremen, Germany) applying the following conditions in positive ionization mode: scan range 50–1400 m/z; acquisition rate 3 Hz; end plate offset 500 V; capillary voltage 3500 V; nebulizer pressure 2 bar, dry gas 10 L min-1, dry temperature 220 °C. Mass calibration was performed using sodium formate clusters (10 mM solution of NaOH in 50 / 50 (v / v %) isopropanol water containing 0.2 % formic acid). The 50 most prominent peaks (signal : noise > 10) in the chromatograms of 15 accessions—hereafter referred to as compounds—were selected for further analyses. Their intensities were determined based on the most characteristic fragment and normalised by extracted fresh weight. The mean relative consumption of GFS on leaf discs of the 15 -1 accessions was correlated with the log10-transformed peak intensities g FW of all 50 compounds using Pearson’s correlation analyses applying correction for the false discovery rate (FDR) using the online R-based tool MetaboAnalyst 3.0 (Xia et al. 2015). Tandem mass spectrometry (MS2) spectra were acquired by injection of samples that contained the highest amount of compounds of interest using the same chromatographic conditions as described before. MS2 spectra were collected using the automated MSMS function of the Bruker oToF Control software. Spectra were evaluated for compounds of interest with particular emphasis on fragmentation of the parental compound, to understand the structural composition of backbones and possible conjugations. Putative identifications were made based on comparison of mass spectra reported in the literature (Lu et al. 2008; Munafo and Gianfagna 2011; Shakya and Navarre 2008). Solasonine and solamargine were identified by injection of authentic standards (Carbosynth Limited, Berkshire, United Kingdom) and comparison of retention time and mass spectra. The raw data files, pre-processed peak matrix and protocol descriptions are available under the accession number MTBLS738 of the Metabolights repository (https://www.ebi.ac.uk/metabolights/).
31 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Results Intraspecific variation in slug feeding resistance Slugs showed significant variation in feeding preferences in all eight independent population screenings (assay 1, Fig. 2, Friedman test Table 1). Differences in the relative leaf disc consumption between the most and least preferred accessions within populations ranged from 8 % in Texel Dry (TD07: 13 %; TD11: 5 %) to 62 % in Zandvoort Dry (ZD11: 63 %; ZD04: 1 %). Pair-wise assays with the most- and least-preferred accessions from each population (assay 2) showed that the preference ranking remained consistent when using vegetative clones of the original plant (insets Fig. 2, Wilcoxon test Table 1). In this second assay the difference in relative consumption between the two accessions was lowest for Limburg Dry (28 %) and highest in Zandvoort Dry (89 %), which illustrates a particularly strong difference in slug preference for accessions from the latter population. Significant differences in slug preference were also observed when all accessions were offered simultaneously (assay 3, Fig. 3). The relative ranking between the most- and least-preferred accessions of each population remained largely the same. Note, however, that due to variation in overall palatability among populations, some of the accessions that were highly preferred in the within-population screenings (such as LD07 and OW05) appeared to be among the least preferred accessions in this overall assay, and vice versa (FW01). Relative leaf disc consumption did not correlate with specific leaf area (Pearson’s r = 0.07, P = 0.81).
Table 1 Test statistics on relative leaf disc consumption by the grey field slug (D. reticulatum) in two independent preference assays. Friedman’s rank sum test statistics for eight population screenings testing within-population preference (assay 1 in Fig. 1) and Wilcoxon signed-rank test results for the within-population paired dual-choice assays using the most- and least-preferred accessions from assay 1 (assay 2 in Fig. 1)
Friedman test (assay 1) Wilcoxon test (assay 2) Population n df χ2 n Ties V TD 16 11 28.93** 8 1 28.0*
TW 16 11 89.06*** 8 0 33.0*
FW 16 11 28.65** 8 1 28.0*
ZD 16 11 57.29*** 8 0 36.0*
OW 16 11 82.75*** 8 0 36.0*
VW 16 11 103.54*** 8 0 36.0*
GD 16 11 26.94** 8 0 36.0*
LD 16 10 22.37* 8 1 28.0*
*** P ≤ 0.001, ** P ≤ 0.01, * P ≤ 0.05.
32 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Fig. 2 Mean relative consumption (± SE) of grey field slugs (Deroceras reticulatum) in independent preference assays on Solanum dulcamara leaf discs. Large panels show results of eight population screenings testing within-population preference (assay 1 in Fig. 1, n = 16). Insets show results of eight within-population dual- choice assays using clones of most-preferred and least preferred accessions from each population (assay 2 in Fig. 1, n = 8). Test statistics are provided in Table 1. The boxes surrounding accession names indicate the accessions used in assay 2. Dashed lines indicate the damage distribution when slugs would have equally preferred all accessions tested
33 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Chemical leaf profiles and their relation with slug preference The abundance of the 50 most prominent compounds found in the S. dulcamara leaf samples illustrate the chemical diversity among the 15 accessions (Fig. 4). Based on mutual Pearson correlations we were able to distinguish 10 clusters. Correlation analyses of the mean relative consumption of GFS (assay 3) with the 50 most prominent compounds in the metabolic profiles of the accessions used in assay 3 revealed 20 compounds which were significantly correlated with slug preference (FDR-adjusted P-value < 0.05 as summarised in Table S2). The compounds that correlated with slug preference were found in five clusters; those in clusters 6, 7, 9 and 10 were negatively correlated with slug preference and the compounds in cluster 2 were positively correlated with slug preference. The chemical structure of prominent compounds representing the clusters was further evaluated (GA1–4 from cluster 6, 7, 9 and 10, GA5–7 from cluster 8 and X1–6 from cluster 2, Table S3). All four prominent compounds from clusters that negatively correlated with slug preference were (putatively) identified as structurally related glycosylated steroidal alkaloids (Fig. 5). Based on their mass spectra and co-elution with reference standards, GA3 (cluster 9) and GA4 (cluster 10) were identified as the solasodine-type glycoalkaloids (GAs) solasonine and solamargine, respectively. GA1 and GA2 were tentatively identified as tomatidenol-type glycoalkaloids which are conjugated with different glycoside moieties (Fig. 5).
Fig. 3 Mean relative consumption (± SE) of grey field slugs (Deroceras reticulatum) on leaf discs of 15 Solanum dulcamara accessions characterized by contrasting feeding preference in within-population comparisons (assays 1 and 2 in Fig. 1) when offered simultaneously (assay 3 in Fig. 1). Friedman’s rank sum test for overall preference: n = 16, df = 14, χ2 = 109.09, P = < 0.001. Codes of S. dulcamara accessions as in Fig. 2. Dashed line indicates the damage distribution when slugs would have equally preferred all accessions tested
34 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Fig. 4 (caption on next page)
35 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Fig. 4 (previous page) Abundance of 50 most prominent compounds in single samples of 15 Solanum dulcamara accessions which were used in a full-choice preference assay with the grey field slug (Deroceras reticulatum, assay 3 in Fig. 1). Accessions are arranged from least preferred (left) to most preferred (right). Unidentified compounds are indicated by a compound number, the quantified ion mass (m/z) and its retention time (sec). A selection of 13 compounds (GA1–7, X1–6) was putatively identified to have a steroidal aglycon backbone (m/z [M+H]) + glycoside or uronic acid (UrAc) conjugate and given a putative identity (Table S3). The ID of compounds with significant correlations (FDR-corrected P < 0.05) with slug preference are preceded by an asterisk. Compounds were grouped in clusters based on mutual Pearson correlation (indicated and separated by red dashed lines and numbers in red on the left). The numbers of the individual S. dulcamara accessions are preceded by their population code (Fig. 1) and ordered by their rank in assay 3 (Fig. 3)
GA1–4 were the dominant compounds in all accessions but TW12 and ZD11 (Fig. 4). The four GAs occurred in relatively equal ratios in GD04, ZD04, TW01, and GD10. In accessions LD02, LD07, OW05, OW09, and VW11, the tomatidenol-type GAs (GA1–2) were the most prominent, and in two, VW08 and FW01, the solasodine-type GAs (GA3 and 4) dominated the chemical profile (Fig. 4). FW09 mainly contained a single GA, namely GA3 (solasonine) and TD07 mainly contained GA1 and GA3. Accessions TW12 and ZD11 were found to have particularly deviant chemical profiles compared to the other accessions. TW12 was the only accession with a high level of soladulcidine/tomatidine-type GAs (cluster 8, GA5–7 in Fig. 4). Additionally, this accession contained intermediate levels of the two tomatidenol- and two solasodine-type GAs 1–4. Interestingly, the highly preferred accession ZD11 only contained minor levels of the common GAs (GA1–4) found in the other accessions. Instead, it contained mainly saponins (X1, 4–6 within cluster 2 in Fig. 4) as well as GAs (X2 and 3), which were all conjugated with glucuronic acid instead of the more common combinations of monosaccharides (Fig. 5, Table S3).
Fig. 5 Chemical structure of glycoalkaloid aglycons and their glycoside moieties found in Solanum dulcamara using LC-qToF-MS. The configuration of
the F-ring on position R1 and the saturation level of the C-5,6 bond determine the type of aglycon backbone, which is conjugated to a glycoside
moiety on position R2
36 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Discussion
Our study revealed significant constitutive variation in plant resistance to the slug D. reticulatum within and among eight wild S. dulcamara populations from the Netherlands. By utilizing a metabolomics approach to analyse the underlying chemical mechanisms, we identified four prominent steroidal glycoalkaloids (GAs) showing particularly strong negative correlations with slug feeding preference. This is in line with previous studies reporting toxic or repellent effects of different types of alkaloidal secondary metabolites to gastropods (Aguiar and Wink 2005; Bog et al. 2017; Speiser et al. 1992; Wink 1984) and insect herbivores (Altesor et al. 2014; Hare 1983).
In addition we found considerable chemotypic diversity in GA composition among accessions. The consistency of GFS preference for accessions, as tested using clones of the original seedling in three sequential assays, suggests that the chemical composition of GAs is genetically determined (Willuhn 1966). Moreover, this also suggests that overall slug preference or relative GA levels were not affected by environmental factors, such as seasonal differences (Hare 1983), when plants are grown under regulated greenhouse conditions. It did not seem to matter which of the common S. dulcamara GAs dominated the profile; plants with either solasodine-based (GA3, GA4) or tomatidenol-based (GA1, GA2) alkaloids as the main GAs were equally resistant to slug feeding. Moreover, TW12, which contained at least three additional GAs of the soladulcidine/tomatidine type (GA5–7), was not significantly more or less preferred than, for example, accession GD10, which only contained GA1–4. This suggests that the different classes of GAs do not show synergistic effects on slug preference as previously reported for snails feeding on potato (Smith et al. 2001). It thus seems likely that differences in total GA-concentration in the leaves were underlying the observed variation in feeding preferences, rather than GA structural diversity per se. Additional experiments that specifically manipulate GA composition, for example gene editing technologies such as CRISPR/Cas9, are needed to establish a firm correlation between variation in GA composition and slug resistance.
Previous studies found β-solamarine, solasonine and solamargine from various wild Solanum species to be lethal to aquatic snails when administered to the water (Alzerreca and Hart 1982; Njeh et al. 2016; Wanyonyi et al. 2002). When ingested, GAs affect neurotransmitters and additionally disrupt cell function by complexation with sterols in the cell membrane (Milner et al. 2011; Moses et al. 2014; Roddick et al. 2001). However, gastropods may also be able to endure toxic substances. Some gastropod species have been shown to possess effective microsomal detoxification mechanisms to cope with alkaloids to a certain extent (Aguiar and Wink 2005). The same authors suggested that cytochrome P450 oxidizing enzymes play a central role (Aguiar and Wink 2005). However, further experimental testing of GA-metabolism, for example by feeding labelled GAs to slugs, is necessary to support this hypothesis. We did not explicitly test for potential toxic effects in our study; this would require longer term performance assays including measurements of slug survival. In
37 Chapter 2: Glycoalkaloid composition explains variation in slug resistance our assays, the GAs were likely serving as deterrents due to the bitter taste that GAs may cause, as evidenced by the common name of S. dulcamara; bittersweet nightshade.
In addition to the different GA chemotypes which have been described in S. dulcamara previously (Eich 2008; Mathé 1970; Willuhn 1966), we also found a hitherto undiscovered chemotype which basically lacked the typical S. dulcamara GAs. The most preferred accession, ZD11, appeared to possess a novel type of GA, consisting of a common GA aglycon conjugated with uronic acid (Table S2). Whereas glucuronic acid conjugates of triterpenoid saponins have been reported before in the congeneric Solanum lyratum (Sun et al. 2006; Yahara et al. 1985), we found no records in the literature that similar conjugates, as found in accession ZD11, have been reported for GAs (Eich 2008). Seen the close structural similarity and biosynthetic relationships between saponins and GAs, it is not improbable that these glucuronic conjugates might co-occur in a single plant species. In eggplant (Solanum melongena) it was found that two similar, though separate, glucosyltransferases with a low substrate specificity were responsible for the 3-O-glucosylation of steroidal saponins as well as GAs (Paczkowski et al. 1998). S. dulcamara likely has similarly unspecific glycosyltransferases, which makes it plausible that we would find both saponins and GAs conjugated to glucuronic acid in S. dulcamara. Further studies comparing the genomes or transcriptomes of ZD11 with those of the other accessions may reveal the underlying differences in biosynthetic genes (see for example Itkin et al. 2013).
Triterpenoid saponins, such as diosgenin, are not only structurally closely related to GAs, but may also serve similar functions in protecting the plant against herbivores and pathogens (Eich 2008). In Barbarea vulgaris, for instance, saponins confer resistance to specialist flea beetles, which are not affected by glucosinolates, the typical defences in B. vulgaris and other Brassicaceae (Kuzina et al. 2009). Given the fact that the insect herbivore community of S. dulcamara is dominated by several specialist (flea) beetle species (Calf and van Dam 2012; Lortzing et al. 2016; Viswanathan et al. 2005), it is very well possible that the loss of resistance to slug feeding in ZD11 is traded-off by an increased resistance to beetle feeding. Moreover, the source population of ZD11 is located in the dry coastal sand dunes of the Dutch western coast (Fig. 1). In this environment slug feeding may be less frequent, thus providing a window of opportunity for these chemotypes to survive and propagate in this particular population. Our recent finding of another individual with the same chemotype in the same seed batch as ZD11 seems to point in this direction (data not presented). However, an assessment of the local gastropod and insect abundance in combination with transplantation experiments would be needed to unequivocally assess whether herbivore community composition may play a role in the selection for specific chemotypes.
Our results also stress the role and importance of the glycosylation of bioactive molecules, such as GAs. In potato (Solanum tuberosum), for instance, the feeding inhibitory effect of chacotriose conjugates on snails was found to be stronger than that of solatriose conjugates (Smith et al. 2001). Another example of the importance of glycosylation comes
38 Chapter 2: Glycoalkaloid composition explains variation in slug resistance from the Colorado potato beetle (Leptinotarsa decemlineata). This specialist beetle which feeds on a wide range of solanaceaous plants also uses S. dulcamara as a wild host in Europe and the USA (Calf and van Dam 2012; Hare 1983). However, it did not feed on species that contain high levels of tetraose conjugates as was found in a comparison of six resistant wild Solanum species (Tai et al. 2014). This would lead to the hypotheses that accession TW12, the only accession possessing a tetraose side chain (Table S3), may be more resistant to these beetles than the others.
Our assays revealed high levels of intraspecific variation in slug-resistance within populations of S. dulcamara, with 2–60 fold variation in preference for accessions in a population. This suggests that gastropods may impose strong selection on defence traits in natural populations by choosing among the different chemotypes present in a population. This may eventually lead to locally adapted populations, particularly when gastropods are abundant (Kalske et al. 2012; Laine 2009; Scriber 2002). The results of the full-choice comparison of all selected accessions (Fig. 3) also suggest that there may be a degree of population differentiation, as some populations overall were more preferred by slugs than others. However, this difference did not appear to be linked to the local abiotic conditions at the sites where seeds for this study were collected. For example, local hydrological conditions both in the FW and OW populations are likely favouring gastropod abundance and should be favouring selection of resistant genotypes. However, on average FW accessions were considerably more preferred by GFS when given the choice than accessions from other populations, indicating that other factors may contribute to chemical population characteristics than habitat type.
In conclusion, plants may employ different strategies and different combinations of secondary plant compounds to reduce herbivore damage. Intraspecific variation in resistance is the basis for the evolution of herbivore resistance traits. We found that S. dulcamara shows significant variation in slug resistance, which was closely linked to differences in their chemical profiles, especially that of GAs. This does not preclude that other defences known to be present and to vary in S. dulcamara, such as polyphenoloxidases, peroxidases, protease inhibitors and extrafloral nectar (Lortzing et al. 2016; Nguyen et al. 2016a; Viswanathan et al. 2008), play an additional role in slug resistance. We argue that slugs, in addition to insects and pathogens, thus may exert a strong selection pressure on the chemical profiles of plants. This may be especially so during seedling establishment, a stage which had been shown to be exceptionally vulnerable to slug herbivory (Elger et al. 2009; Fritz et al. 2001; Hanley et al. 1995). Therefore, slugs and the damage they do to plants should be more often considered when studying the ecological roles and evolutionary origins of chemical variation in plants.
39 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Acknowledgements
The authors thank Gerard van der Weerden of the Radboud University Genebank in Nijmegen for providing seeds and Yvonne Poeschl from iDiv for assistance in metabolomic data processing and analyses. OWC also thanks Anke Steppuhn and the Molecular Ecology group at the Free University of Berlin for valuable discussions during the project. OWC is supported by grant number 823.01.007 of the open programme of the Netherlands Organisations for Scientific Research (NWO-ALW). NMvD and AW gratefully acknowledge the support of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig funded by the German Research Foundation (FZT 118).
40 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Supplemental material
Table S1 Details of eight Solanum dulcamara source populations
Table S2 Pearson correlation test statistics of 50 compounds with the mean relative consumption by the grey field slug of 15 Solanum dulcamara accession leaf discs
Table S3 Putative identification based on MS-MS spectra of 13 selected compounds which were selected from metabolic profiling of 15 Solanum dulcamara accessions
41 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Table S1 Details of eight Solanum dulcamara source populations which were used for population screenings (assay 1 in Fig. 1). Population abbreviations, accession numbers, latitudinal and longitudinal coordinates and description of local conditions are given for each population
Population (Abbr.) Accession nr.* Coordinates Habitat description 53°07'40.9"N Dry open primary coastal dunes, Texel Dry (TD) B34750130 4°47'21.4"E isolated on island 53°07'17.5"N Coastal dune lake border, Texel Wet (TW) B24750045 4°47'13.6"E isolated on island 52°58'36.2"N Continuously inundated inland Friesland Wet (FW) B24750030 5°30'59.4"E fresh water lake border 52°21'07.3"N Zandvoort Dry (ZD) B34750110 Dry open primary coastal dunes 4°30'44.3"E 51°51'36.3"N Inland river floodplain dominated Ooijpolder Wet (OW) B24750035 5°54'03.5"E by Phragmites australis
51°50'58.6"N Muddy understory of deciduous Voorne Wet (VW) B24750025 4°04'34.3"E coastal dune forest 51°49'23.8"N Goeree Dry (GD) B24750010 Dry open primary coastal dunes 3°53'19.2"E 50°48'59.0"N Limburg Dry (LD) A94750152–177** Bushy well drained inland chalk hill 5°40'50.7"E
* Accessions obtained from Radboud University Genebank ** Seeds stored under individual accession numbers for each mother plant. One seed of each acession (n = 26) was used for germination and potting. Only 11 were grown successfully and used for the population screening
42 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
Table S2 Two-sided Pearson correlation test statistics (df = 13) with correction for the false discovery rate (FDR) of 50 compounds with the mean relative consumption by the grey field slug of 15 Solanum dulcamara accession leaf discs (assay 3 in Fig. 1). Compounds are listed according the order of appearance in hierarchical clustering (Fig. 4). Colour coding of Pearson r indicates the strength of significant (FDR-adj. P-value < 0.05) positive (red) or negative (blue) correlations
Clustered order Compound Pearson r r2 P-value FDR 1 50_508.32_857 0.09 0.01 0.74 0.91 2 19_362.29_619 0.05 0.00 0.85 0.99 3 48_524.32_807 0.31 0.10 0.26 0.40 4 02_163.03_330 0.00 0.00 0.99 0.99 5 03_163.03_348 0.01 0.00 0.96 0.99 6 06_611.16_507 -0.01 0.00 0.97 0.99 7 11_595.17_555 0.16 0.03 0.57 0.77 8 37_751.39_720 0.40 0.16 0.14 0.24 9 23_590.37_646 0.58 0.33 0.02 0.06 10 46_753.40_778 0.74 0.55 0.00 0.01 11 49_589.33_830 0.87 0.75 0.00 0.00 12 45_590.37_769 0.87 0.75 0.00 0.00 13 47_589.33_790 0.87 0.75 0.00 0.00 14 34_739.42_710 0.09 0.01 0.75 0.91 15 17_196.68_609 0.23 0.05 0.40 0.58 16 04_183.67_503 0.12 0.01 0.67 0.86 17 15_184.67_581 0.15 0.02 0.58 0.77 18 01_166.08_123 -0.22 0.05 0.44 0.61 19 32_739.42_702 -0.67 0.44 0.01 0.02 20 14_884.50_577 -0.63 0.40 0.01 0.03 21 44_1031.53_748 -0.87 0.75 0.00 0.00 22 22_868.50_636 -0.82 0.67 0.00 0.00 23 26_868.50_676 -0.75 0.56 0.00 0.01 24 09_884.50_532 -0.62 0.39 0.01 0.03 25 43_867.47_744 -0.55 0.31 0.03 0.07 26 12_900.48_563 -0.50 0.25 0.06 0.11 27 28_722.44_686 -0.49 0.24 0.06 0.11 28 24_722.44_655 -0.79 0.62 0.00 0.00 29 25_884.50_665 -0.81 0.66 0.00 0.00 30 41_883.47_732 -0.66 0.44 0.01 0.02 31 21_884.50_626 -0.91 0.82 0.00 0.00 32 36_1063.53_715 -0.84 0.70 0.00 0.00 33 42_1047.53_738 -0.83 0.69 0.00 0.00 34 30_870.52_691 -0.28 0.08 0.30 0.46 35 10_594.40_535 -0.05 0.00 0.85 0.99 36 33_578.40_707 -0.02 0.00 0.95 0.99 37 35_870.52_713 0.02 0.00 0.95 0.99 38 40_741.44_728 -0.04 0.00 0.88 0.99 39 38_739.42_721 -0.57 0.32 0.03 0.06 40 18_576.38_613 -0.24 0.06 0.40 0.58 41 07_592.38_519 -0.33 0.11 0.23 0.36 42 29_576.39_688 -0.43 0.19 0.11 0.19 43 39_738.44_728 -0.02 0.00 0.93 0.99 44 27_884.50_676 -0.63 0.40 0.01 0.03 45 05_900.50_504 -0.50 0.25 0.05 0.11 46 13_882.48_574 -0.53 0.28 0.04 0.09 47 08_884.50_524 -0.39 0.16 0.15 0.24 48 20_868.50_619 -0.64 0.41 0.01 0.03 49 16_866.49_587 -0.67 0.45 0.01 0.02 50 31_868.50_698 -0.78 0.61 0.00 0.00
43 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
- β α - α - A? A? dulcine accessions.
ola Putative ID Solasonine Solamarine Solamarine Soladulcine Soladulcine S Solamargine
name Glycoside Solatriose Solatriose Chacotriose Chacotriose Chacotriose Chacotriose Chacotriose Lycotetraose
------
Solanum dulcamara Solanum Hex
Hex Hex Hex Pent - DeOxHex DeOxHex DeOxHex DeOxHex DeOxHex DeOxHex ------Putative glycoside DeOxHex DeOxHex DeOxHex DeOxHex Hex Hex Hex Hex Hex composition** Hex Hex
Putative ladulcidine aglycone Solasodine Solasodine Tomatidenol Tomatidenol / Tomatidine / Tomatidine / Tomatidine Soladulcidine Soladulcidine So
Hex Hex Hex Hex - - - -
Hex Hex Hex
- - -
Hex
-
Hex DeOxHex DeOxHex DeOxHex DeOxHex - Pent - - - -
- DeOxHex DeOxHex DeOxHex Hex Hex DeOxHex Hex Pent DeOxHex DeOxHex M M M M M M ------Hex Pent DeOxHex DeOxHex DeOxHex DeOxHex DeOxHex DeOxHex M M M ------mass)** Hex Hex Hex M M M M M - - - Soladulcine ? Hex Hex Hex DeOxHex DeOxHex DeOxHex M M DeOxHex - - M - - - - - DeOxHex DeOxHex DeOxHex M M DeOxHex M M M M - - - M - M M M M fragmentation (M=parental Putative annotation of chemical
Δ 1.34 2.64 1.60 1.56 2.99 1.54 1.60 1.80 2.02 1.81 0.90 0.13 2.99 1.81 1.42 0.13 4.19 1.88 2.71 0.01 4.08 1.33 2.71 0.01 1.60 2.28 3.84 3.06 0.23 ppm
]+ 5
O ]+ ]+ ]+ ]+ ]+ ]+ 10 ]+ 5 5 5 5 5 5 5 H
O O O O O O 6
O
C 10 10 10 10 10 10 ]+ ]+ ]+ ]+ ]+ ]+ 10 ]+ - 4 4 4 4 4 4 4 5 H H H H H H H 6 6 6 6 6 6
O O O O O O 6 O O C C C C C C 8 C 10 10 10 10 10 10 ]+ ]+ ]+ - ]+ - - - ]+ - ]+ - ]+ 10 - 5 4 5 4 4 4 4 4 4 5 4 5 5
H 4 H H H H H H 5 H 6 6 6 6 6 6 O O O O O O O O O O O O O 6 ]+ O C C C C C C C 8 - C 10 10 10 10 10 10 10 10 10 10 10 10 10 ------5 - 5 4 5 4 4 4 H 4 H H H H H H H H H H H H H 5 O 6 6 6 6 6 6 6 6 6 6 6 6 6 O O O O O O O C C C C C C C C C C C C C C 10 8 - 10 10 10 10 10 10 ------[M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H [M+H]+ 5 4 4 4 4 4 4 H H H H H H H H 5 6 O 6 6 6 6 6 6 O O O O O O [M [M [M [M [M [M [M C C C C C C C C 10 - - 10 10 10 10 10 10 ------5 H H H H H H H (M=parental mass) 6 O 6 6 6 6 6 6 [M [M [M [M [M [M [M C C C C C C C 10 ------H 6 [M [M [M [M [M [M [M C - Putative chemical fragmentation
[M MS spectra of 13 selected compounds which were selected from metabolic profiling of 15 -
+ + + + + + + + + + + + + + +
+ + + + + + + + + + + + + + 7 2 7 2 7 2 7 2 7 2 7 2 7 2 hexose, UrAc uronic = acid 16 11 15 11 16 11 15 11 15 11 15 11 21 16 12 NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO NO 54 44 54 44 56 46 56 46 56 46 54 44 54 44 74 64 74 64 76 66 76 66 84 74 66 74 64 74 64 H H H H H H H H H H H H H H H H H H H H H H H H H H H H H Putative 33 27 33 27 33 27 33 27 33 27 33 27 33 27 chemical structure 45 39 45 39 45 39 45 39 50 44 39 45 39 45 39 C C C C C C C C C C C C C C C C C C C C C C C C C C C C C
parental parental the from moiety glycoside the of fragmentation chemical putative the and (RT) time retention by its described and ID ashort 3 ed 00 mass 884.502 724.464 884.5014 722.449 576.3904 414.3373 868.5079 722.4485 576.3904 414.3374 870.5246 724.4644 578.4067 416.3523 870.5245 578.4067 416.3523 872.5022 740.4608 578.4069 416.3524 722.4487 576.39 414.3366 868.5079 722.4487 576.3903 414.3366 Measur 1034.5547 monoisotopic
RT 665 676 696 676 713 691 707 (sec)
Putative identification based on MS on based identification Putative
GA1 GA2 GA5 GA6 GA7 GA3 GA4 Compound* Table S3 given was compound Each GA* glycoalkaloid, = X unknown = yet uronic acid conjugate Hex** hexose, = Pent pentose, = DeOxHex = deoxy - compound to the aglycon. Compounds in bold were identified by comparison to purchased standards purchased to by comparison identified were bold in Compounds aglycon. the to compound
44 Chapter 2: Glycoalkaloid composition explains variation in slug resistance
? ? ? ? ? ? ID Putative
name Glycoside
UrAc UrAc - - UrAc UrAc UrAc UrAc Hex Hex composition** Putative glycoside
diosgenin diosgenin diosgenin - - asodine / asodine amogenin amogenin amogenin Putative dro y y aglycone - - -y y Yamogenin Diosgenin / Solasodine / Sol Tomatidenol Tomatidenol / / / Dehydro Deh Dehydro -
UrAc UrAc
- - Hex Hex UrAc UrAc UrAc UrAc M M M M M M ------Hex Hex M M M M M M - - mass)** M M fragmentation (M=parental Putative annotation of chemical
Δ 4.63 5.35 2.10 3.48 0.13 2.30 1.10 2.02 4.30 1.34 0.29 5.14 5.41 ppm 12.39
]+ ]+ 6 6
O O 8 8 ]+ ]+ ]+ ]+ ]+ ]+ 5 5
H H
6 6 6 6 6 6 O O O O O O C C 8 8 8 8 - - 10 10 5 5 H H H H H H 6 6 6 6 O O 6 6 C C C C C C 10 10 ------[M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ [M+H]+ H H 6 6 [M [M [M [M [M [M C C - - (M=parental mass) [M [M
Putative chemical fragmentation
+ + + + + + 8 2 8 2 + + + + + + + + hexose, UrAc uronic = acid 9 3 9 3 9 3 9 3 14 14 O O O O O O O O O O NO NO NO NO 50 43 49 41 49 41 49 41 60 59 52 44 52 44 H H H H H H H H H H H H H H 33 27 33 27 33 27 33 27 39 39 Putative 33 27 33 27 chemical structure C C C C C C C C C C C C C C
mass 753.4021 591.3496 415.3198 590.3702 414.3366 590.3701 414.3362 589.3383 413.3068 589.3379 413.3049 751.3938 589.3403 413.2999 Measured monoisotopic (2/2)
RT 781 646 769 790 830 720 (sec)
Continued d*
X1 X2 X3 X4 X5 X6 Compoun Table S3 GA* glycoalkaloid, = X unknown = yet uronic acid conjugate Hex** hexose, = Pent pentose, = DeOxHex = deoxy -
45
Chapter 3:
Gastropods and insects prefer different Solanum dulcamara chemotypes
Onno W. Calf Heidrun Huber Janny L. Peters Alexander Weinhold Yvonne Poeschl Nicole M. van Dam
Published in: Journal of Chemical Ecology (2019) DOI: 10.1007/s10886-018-0979-4 Chapter 3: Gastropods and insects prefer different chemotypes
Abstract
Solanum dulcamara (Bittersweet nightshade) shows significant intraspecific variation in glycoalkaloid (GA) composition and concentration. We previously showed that constitutive differences in overall GA levels are correlated with feeding preference of the grey field slug (GFS; Deroceras reticulatum). One particularly preferred accession, ZD11, contained low GA levels, but high levels of previously unknown structurally related uronic acid conjugated compounds (UACs). Here we test whether different slug species as well as insect herbivores show similar feeding preferences among six S. dulcamara accessions with different GA chemotypes. In addition, we investigate whether slug feeding can lead to induced changes in the chemical composition and affect later arriving herbivores. A leaf disc assay using greenhouse-grown plants showed that three slug species similarly preferred accessions with low GA levels. Untargeted metabolomic analyses showed that previous slug feeding consistently increased the levels of N-caffeoylputrescine and a structurally related metabolite, but not the levels of GAs and UACs. Slug-induced responses only affected slug preference in one accession. A common garden experiment using the same six accessions revealed that ZD11 received the highest natural gastropod feeding damage, but suffered the lowest damage by specialist flea beetles. The latter preferred to feed on accessions with high GA levels. Our study indicates that different selection pressures imposed by generalist gastropods and specialist insects may explain part of the observed chemical diversity in S. dulcamara.
48 Chapter 3: Gastropods and insects prefer different chemotypes
Introduction
Plants display a significant degree of chemical diversity. Not only among plant species, but also within a single species a multitude of natural chemotypes may exist. A well-studied example for within species chemical diversity are the glucosinolates in Arabidopsis thaliana. A study on 39 ecotypes of A. thaliana showed that each ecotype is characterized by a specific glucosinolate profile which is a subset of 34 different glucosinolates (Kliebenstein et al. 2001). Another example comes from Jacobaea vulgaris (syn. Senecio jacobaea). Plants collected in 11 European populations show a high diversity in their pyrrolizidine alkaloid composition (Macel et al. 2004). It was postulated that the vast intraspecific chemical variation observed in plants today, to a great extent has resulted from co-evolutionary processes with the many members of the herbivore community (Hartmann 1996). Whether a particular chemical compound indeed acts as a defence depends on the level of specialization and feeding strategy of the herbivore species present (Ali and Agrawal 2012). Herbivore communities are not homogenous in time and space. Intraspecific chemical variation may thus result from local or temporal variation in herbivore community composition (Jones and Firn 1991; Speed et al. 2015). Many chemotypes were found to be heritable (see for example: Macel et al. 2004; van Dam and Baldwin 2003; van Leur et al. 2006). In addition to these genetically fixed differences in constitutive defences, plants are able to induce defences upon herbivory (Howe and Jander 2008; Walling 2000). This enables plants to tailor their response to specific herbivores. Moreover, it allows them to optimize resource allocation to growth or defence under conditions characterized by variable herbivore pressure (Vos et al. 2013; Wittstock and Gershenzon 2002). Herbivore-induced responses can have far-reaching consequences for other members of the herbivore community (Kessler and Halitschke 2007; Poelman et al. 2010). So far, induced responses have mainly been studied in relation to insect herbivory, whereas the effects of gastropod feeding damage are much less well understood. The little information there is suggests that slug feeding, and even their mucus, induces similar responses as chewing insect herbivores and pathogens do (Falk et al. 2014; Meldau et al. 2014; Orrock 2013). On the other hand, slug-induced volatile emissions by Brassica rapa were sufficiently different from those induced by insects to be discriminated by parasitoids hunting for caterpillars (Danner et al. 2017; Desurmont et al. 2016). Taken together, this calls for an experimental assessment whether and how slug feeding changes chemical defence levels. Bittersweet nightshade (Solanum dulcamara) shows large intraspecific variation in constitutive profiles of steroidal glycoalkaloids (GAs, Calf et al. 2018; Eich 2008; Mathé 1970). The fact that clones of the same plant consistently express the same chemotype suggests that there is a strong genetic background for GA composition (Willuhn 1966). We recently showed that resistance to the grey field slug (GFS, Deroceras reticulatum) in S. dulcamara is associated with high levels of GAs. One strongly preferred accession, ZD11, was found to have particularly low GA levels. Instead, it contained high levels of previously
49 Chapter 3: Gastropods and insects prefer different chemotypes unknown structurally related steroidal compounds which were conjugated with uronic acid (Calf et al. 2018), rather than the mono- or polysaccharides commonly found as glucoside moieties for GAs (Eich 2008; Milner et al. 2011). The observed association between the uronic acid conjugated compounds (UACs) and slug preference illustrates that not only the GA concentration, but also small changes in the glycoside moiety may have large ecological consequences. This is supported by research on potato (Solanum tuberosum), which showed that the biological activity of GAs towards gastropods and insects was particularly associated to differences in the glycosylation of the alkaloid backbone (Smith et al. 2001; Tai et al. 2014). The question arises how these very susceptible UACs-containing accessions survive in natural populations. Slug feeding can exert a strong selection pressure, especially by affecting seedling survival and seedling establishment (Hanley et al. 1995; Rathcke 1985; Strauss et al. 2009). Given the overall high resistance to GFS in the other S. dulcamara accessions investigated, it is surprising that UAC-rich chemotypes are not ‘weeded out’ before setting seed (Calf et al. 2018). Possibly UACs provide resistance to other herbivores than GFS. Both slugs and specialist flea beetles are common in natural S. dulcamara populations and can affect plant performance (Calf and van Dam 2012; Lortzing et al. 2016; Viswanathan et al. 2005). Generally it has been found that generalist and specialist herbivores differ in their response to chemical defences (Ali and Agrawal 2012). Whereas generalists avoid plants with high levels of chemical defences, specialists may use the same compounds to recognize their preferred hosts (Miles et al. 2005, van Loon et al. 2002). Thus co-occurring generalists and specialists may both play an important role in the maintenance of chemical diversity by differentially selecting high and low GA S. dulcamara accessions and affecting their performance. Like many other plant species, S. dulcamara is known to increase its levels of defences upon insect herbivore feeding (Nguyen et al. 2016b; Viswanathan et al. 2005). Whether slugs can also induce defence responses in S. dulcamara is as yet unknown. Our previous studies were carried out using leaf discs taken from undamaged plants, which ignores the role of inducible defence responses. Possibly, the constitutively susceptible accessions invest more in induced responses, thereby saving resources for growth in absence of herbivores (Strauss et al. 2002; Vos et al. 2013). Whether or not this applies, should be tested by assessing the preference for different chemotypes after exposure to slug feeding. Here we report on a series of experiments designed to test whether S. dulcamara accessions, representing different GA chemotypes, are uniformly resistant to different herbivores under greenhouse and field conditions. First, we tested whether constitutive variation among chemotypes similarly affects feeding preferences of three different slug species. Moreover, we tested whether slug herbivory affects subsequent feeding preference of gastropod and insect herbivores. To do so, we first performed a greenhouse-based preference assay using six S. dulcamara accessions. The accessions were chosen to represent the full range of GFS resistant to GFS susceptible chemotypes (Calf et al. 2018). Leaf discs of
50 Chapter 3: Gastropods and insects prefer different chemotypes undamaged plants and plants previously exposed to GFS feeding were offered to three slug species to analyse their preference. The leaf metabolomes of GFS damaged leaves were compared to undamaged leaves to analyse differences in constitutive and induced chemical profiles. Additionally, we set up a common garden experiment in which plants of the same accessions were exposed to the natural herbivore community. Half of the plants were repeatedly treated by Arion sp. feeding early in the season. This allowed us to test the preference of different members of the natural herbivore community for slug-sensitive and resistant chemotypes, as well as the effect of early season slug feeding.
Materials and Methods
Plant material Solanum dulcamara is a perennial woody vine with its native range stretching from north- western Europe, northern Africa to the Asian region north of the Himalayas. It is considered an invasive weed in northern America, where it serves as a host for economically important pest insects, such as the Colorado potato beetle (Leptinotarsa decemlineata) and potato psyllid (Bactericera cockerelli) (Castillo Carrillo et al. 2016; Hare 1983). Slug damage commonly occurs on S. dulcamara and thus may constitute a significant selection pressure on chemical defences (Lortzing et al. 2016). Six S. dulcamara accessions differing in susceptibility to slug herbivory were selected from a previously performed screening of 95 accessions from the Netherlands (Calf et al. 2018). The accessions used in the present study ranged in resistance to the grey field slug from low to highly preferred in the following sequence: GD04-VW08-OW09-TW01-FW09-ZD11 (Calf et al. 2018). Clones of these six accessions were obtained by means of stem cuttings which were potted into individual pots (11 × 11 × 12 cm, L × W × H) containing potting soil (Lentse Potgrond nr. 4, Horticoop, Katwijk, the Netherlands) supplemented with 4 g L-1 slow-release fertilizer (Osmocote® Exact Standard, Everris International B.V., Geldermalsen, the Netherlands). Plants were grown in a greenhouse in net cages to prevent insect infection (0.3 mm gauze, 7.50 × 3 × 2.75 m, L × W × H) at a 16 h photoperiod and minimum temperatures set to 20 °C / 17 °C (day / night). Light levels were supplemented with 1000 W Sodium lamps (Philips GreenPower, Amsterdam, the Netherlands) fixed above the cages providing ~280 µmol m-2 s-1 light intensity at the plant level.
Slugs Three different slug species were used for preference assays, namely the grey field slug (GFS; Deroceras reticulatum) and two species complexes belonging to the family Arionidae. Because slugs in the Arionidae family can only be accurately identified by their genitalia, we broadly classified them as Arion rufus/ater (ARA) and Arion fuscus/subfuscus (AFS). Being relatively small, adults measure about 4 cm, GFS is well suited to be used in small scale
51 Chapter 3: Gastropods and insects prefer different chemotypes preference assays. ARA and AFS grow much larger, adults measure up to 15 cm, which is why we used juveniles of these species. All three slug species are believed to be generalist herbivores, which most commonly feed on fresh leaves, fruits and seedlings. However, this does not imply that they show the exact same preferences. Slugs were frequently collected in fields and gardens in the vicinity of Nijmegen (the Netherlands) and individually kept in clear 50 ml plastic containers (6 cm Ø, www.der- verpackungs-profi.de GmbH, Göttingen, Germany) lined with sieved (2 mm mesh) humid potting-soil. Containers were placed in a climate cabinet (Snijders Scientific, Tilburg, the Netherlands) under 16 h photoperiod of ~50 µmol m-2 s-1 light intensity at temperatures set to 17 °C / 14 °C (day / night). Diet consisted of self-grown organic lettuce (Bio Pluksla ‘Mesclun’, Dille & Kamille, Zoetermeer, the Netherlands), which was refreshed twice a week. Containers were cleaned every week by removing faeces, diet residues and excess water. Slugs were transferred to clean containers with fresh soil monthly.
Experiment 1: greenhouse experiment Plant growth and feeding-induction procedure Plants were grown from stem cuttings (Calf et al. 2018), replicates of one accession are hereafter called clones. All plants were 38 days old and at least 60 cm tall when used for experiments. Plants were fitted with clip cages, either containing one adult GFS (feeding- induction treatment, 4 clones / accession) or empty clip cages (control treatment, 4 clones / accession) on the tip of two leaves (number 10 and 11 from the apex) to prevent total leaf consumption. The cages and the slugs were removed after 72 h. All plants in the slug treatment showed substantial feeding damage (estimated by eye, > 1 cm2). The plants were left for an additional 24 h before sampling. In preliminary experiments this procedure was found suitable for detecting induced plant responses (OW Calf, unpublished results). The part of the leaf that was outside of the clip cage was used to produce six leaf discs using a cork-borer (1.5 cm Ø), thus creating a total of 48 leaf discs (2 leaves × 4 clones × 6 discs) per treatment group. Leaf veins were avoided while punching out the discs. Leaf discs were pooled per accession and treatment group. The remaining leaf tissue was sampled in liquid nitrogen and stored at -80 °C until further processing for chemical analyses.
Preference assays One leaf disc of each treatment and accession was placed in a Petri dish (9 cm Ø) with the adaxial (i.e. upper) side down. The Petri dishes were gently sprayed with de-ionised water before, during and after placing the leaf discs to maintain leaf disc turgor. This procedure also ensured that the discs adhered to the Petri dish and stayed at the same position while the slugs were feeding. Each dish contained one disc of each of the six selected accessions in both the constitutive and GFS feeding-induced state, adding up to twelve discs per dish.
52 Chapter 3: Gastropods and insects prefer different chemotypes
Remaining leaf discs were discarded. Within each Petri dish, the disc positions were randomised. The positions were printed on a piece of paper which was placed under the transparent dish. Depending on the size of the slugs, either two or three individuals of either GFS, ARA or AFS (n = 12 Petri dishes per species) were placed on the lid of each Petri dish. Thereafter the dishes were closed and placed in a climate cabinet under the same conditions as described before. After 24 h, the slugs were removed and the leaf material remaining in the Petri dish was photographed with a 14 cm ruler as scale reference. The consumed area (in mm2) was assessed using ImageJ v. 1.48 (Schneider et al. 2012).
Metabolic profiling An untargeted metabolomics approach was used to assess the effect of GFS feeding- induction on the metabolic profiles of the six S. dulcamara accessions. Leaf samples (n = 4 per accession and treatment group) were extracted following a procedure derived from de Vos et al. (2012). In short, fresh leaf material was ground in liquid nitrogen. About 100 mg of each sample was double extracted with respectively 1.0 and 0.9 ml MeOH:Acetate (50 / 50, v / v %) buffer (pH 4.8) in 2 ml reaction tubes holding two glass beads (5 mm Ø) by shaking in a TissueLyser (Qiagen, Venlo, the Netherlands) at 50 Hz for 5 minutes followed by centrifugation at 14.000 rpm at 4 °C. Clear supernatants were combined and stored at -20 °C until further processing. Two sets of diluted crude extracts (1:5 and 1:50) were analysed with an UltiMate™ 3000 Standard Ultra-High-Pressure Liquid Chromatography system (UHPLC, Thermo Fisher Scientific, Waltham, MA, USA) equipped with an Acclaim® Rapid Separation Liquid Chromatography (RSLC) 120 column (150 × 2.1 mm, particle size 2.2 μm, Thermo Fisher Scientific) using the following gradient at a flow rate of 0.4 ml / min: 0–2 min isocratic 95 % A (water / formic acid 99.95 / 0.05 (v / v %)), 5 % B (acetonitrile / formic acid 99.95 / 0.05 (v / v %)); 2–15 min, linear from 5 % to 40 % B; 15–20 min, linear from 40 % to 95 % B; 20–22 min, isocratic 95 % B; 22–25 min, linear from 95 % to 5 % B; 25–30 min, isocratic 5 % B. Compounds were detected with a maXis impact™ quadrupole time-of-flight mass spectrometer (qToF-MS, Bruker Daltonik GmbH, Bremen, Germany) applying the following conditions in positive mode: scan range 50–1400 m/z; acquisition rate 3 Hz; end plate offset 500 V; capillary voltage 3500 V; nebulizer pressure 2 bar, dry gas 10 L min-1, dry temperature 220 °C. Mass calibration was performed using sodium formate clusters (10 mM solution of NaOH in 50 / 50 (v / v %) isopropanol water containing 0.2 % formic acid. Mass spectra of the dataset obtained from analysing the 1:5 diluted samples were processed using XCMS and CAMERA packages in R (Kuhl et al. 2012; Smith et al. 2006). Peak picking and alignment was done within a retention time between 45 and 700 seconds, signal:noise ratio ≥ 50, maximum deviation of 5 ppm, 3 seconds retention time window, mass/charge window of 0.005 and minimum occurrence in three out of four samples in at least one treatment group. Resulting features were grouped to belong to the same
53 Chapter 3: Gastropods and insects prefer different chemotypes compound after symmetric retention time correction, within a retention time window of maximum 5 seconds and minimum mutual correlation of 0.8. Feature intensities were multiplied by their dilution factor (5) and samples normalised by the fresh weight of the leaf material used for extraction. Missing values were replaced by the treatment group mean value only when one of four replicates was missing or else by a random value between 1 and 7722 (half of the minimum value in the complete dataset). Feature groups, potentially representing single compounds, were reduced to one feature to represent the respective compound in later analysis. This was done by applying the in-house maximum heuristic approach, which selects the feature with the highest intensity in the majority of the samples. Intensities of the 50 most prominent peaks (signal : noise > 10) were manually identified from the datasets obtained from the 1:5 and 1:50 diluted samples, following the same procedure as described in Calf et al. (2018). Feature intensity values were multiplied by their dilution factor (5 or 50) and values in the 1:5 dilution dataset exceeding the saturation intensity threshold value of 6 × 106 g-1 FW were replaced with the corresponding value from the 1:50 dilution dataset. The resulting dataset included all GAs and UACs of interest, which were thus reliably quantified and no subject of random errors caused by automatic data processing. The dataset of abundant features and that processed by XCMS and CAMERA were then merged to a single dataset. Features which were quantified by both procedures, were replaced by the manually retrieved feature intensities. This approach resulted in a dataset with 286 features, hereafter referred to as compounds. Tandem mass spectrometry (MS2) spectra were acquired by injection of samples that contained the highest amount of the compounds of interest (see section below: Statistical analyses experiment 1). The separation was achieved by using the same chromatographic conditions as described before. MS2 spectra were collected by using the automated MSMS function of the Bruker oToF Control software. Spectra were evaluated for compounds of interest with particular emphasis on fragmentation of the parental compound. Putative identifications were made based on comparison of mass spectra reported in the literature.
Statistical analyses experiment 1 Absolute leaf disc consumption (mm2) in the preference assays of experiment 1 were transformed to preference ranks ranging from 1 (least preferred) to 12 (most preferred) within each Petri dish. Leaf discs with equal damage were assigned the mean of the two consecutive ranks that would have been assigned. Note, when for instance five plants received no damage each would receive rank number 3 using this approach. Data were analysed using nonparametric statistical methods from the R “stats” package (R Core Team, 2016). Friedman’s rank sum test was applied to evaluate overall preference for each slug species using the Petri dish number as grouping factor. Chi-square (χ2) tests were performed on the mean ranks for each treatment group to test similarity in preference among different slug species. The effect of the feeding induction treatment was evaluated individually for
54 Chapter 3: Gastropods and insects prefer different chemotypes each S. dulcamara accession using Paired Wilcoxon signed-rank tests with continuity correction, excluding ties (‘no difference’). The complete metabolic dataset of 286 compounds was further analysed using the online R-based tool MetaboAnalyst 3.0 (Xia et al. 2015). Principal component analyses (PCA) was performed using log10-transformed and auto-scaled intensity values to evaluate the overall metabolic variation among accessions and treatments. The effect of GFS feeding- induction on metabolic profiles was assessed with discriminant analyses using orthogonal projection models to latent structures (OPLS-DA, Wiklund et al. 2008; Worley and Powers 2013). These models separate metabolic variation correlated with the treatment from random variation among samples, which allowed for reliable selection of compounds of interest which responded to the GFS feeding-induction treatment. First, the intensity value of each compound was normalised to the mean value of the same compound in the control samples of the respective accession. OPLS-DA models were built to compare the log10- transformed relative intensity values of all compounds in control samples and GFS feeding- induced samples. Two approaches were used: 1) an overall model was built including all six accessions to assess the shared response (n = 24 for each treatment); 2) Individual models for each accession were built to assess accession specific responses (n = 4 for each treatment). Predictive significance of the model was assessed with 1000 permutations using cross-validated predictive ability (Q2) as performance measure (Westerhuis et al. 2008; Worley and Powers 2013). Compounds of interest were selected from the S-plot with the OPLS-DA models. The S-plot depicts the response (covariance) and reliability of the response (correlation) of each compound in relation to the first predictive component (p) of the model. Thresholds for selection as compound of interest were set to absolute covariance(p) ≥
1.5 and correlation(p) ≥ 0.5. Venn diagrams were built using the online tool Venny 2.1.0 (Oliveros 2007–2015) and used to illustrate unique and shared metabolic responses among accessions. Significance of the treatment effect within each accession on the abundance of compounds was further evaluated using Student’s t-tests with application of P-value correction for the false discovery rate (FDR) using the Bonferroni method. The list of compounds included the set of pre-selected compounds (four GAs and six UACs), which was supplemented with compounds of interest selected from the S-plot of the overall OPLS-DA model. The abundance of different compounds can only be compared on a relative scale. Therefore the relative level of each compound of interest to the maximum mean value across all treatment groups was used for presentation of compound abundance in a heat map (%).
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Experiment 2: common garden experiment Natural herbivory in a common garden Plants were grown in the greenhouse for 29 days. Plants of similar size and appearance within accessions were planted (n = 10 per accession) following a randomised block design in a common garden field plot in the experimental garden of Radboud University in Nijmegen on 14 May 2016. S. dulcamara grows as a climbing vine. Therefore, each plant was supported by a cylindrical wire structure (1 mm stainless steel wire, mesh = 20 cm, height = 100 cm, Ø = 50 cm) eight weeks after planting. Because GFS slugs were not available in sufficient numbers at the time, juvenile ARA slugs were used for the feeding-induction treatment on five clones per accession. Slugs were placed in a clip cage for 24 h on the 10th leaf counted from the apex. This treatment was repeated twice a week for three consecutive weeks, whereby the treated leaf was chosen following the same criteria. When the designated leaf was not/no longer available due to excessive herbivore feeding, the leaf on the internode over the designated leaf was used. All species that were observed on the plant throughout the field season of 2016 were recorded. The plants were monitored from late-May to October 2016 (calendar weeks 21– 41), with the exception of weeks 25, 36 and 37. The numbers of all invertebrate organisms on each plant in the plot were recorded twice every week by surveying the entire plant for one minute. The small size and cryptic life strategy of most observed herbivore species makes it unlikely to record them all at each census date. Moreover, daily variation in weather conditions affects their activity level and thus the likelihood of observing them. To compensate for this day-to-day variation, only the maximum number of each species on each plant recorded each week were entered into the data analyses (Table 1). The percentage gastropod and flea beetle damage on each plant was scored once every week. The damage was classified in six equally sized damage classes, ranging from no damage at all (class 0) to severe damage (class 5). In the latter category > 75 % of the leaf material had been removed which often had led to leaf and stem deformations (Table S1). Gastropod feeding causes large holes often starting from the edge of the leaf. These could be identified as slug damage by the presence of typical bite marks of the radula and slime residues. Slug damage was estimated over the entire plant, thereby excluding the leaves that had been subject to the ARA treatment. Flea beetle feeding also causes a specific damage pattern, seen as numerous small holes, which are also described as a shot-gun pattern. Fresh flea beetle damage was only assessed on the top 20 cm of all shoots, where feeding typically occurs. Plant size and herbivore abundance have been shown to be positively correlated (Castells et al. 2017; Wei et al. 2015; Windig 1993). Therefore we recorded the total plant size, calculated as the summed length of all living shoots, nine times throughout the growing season. This allowed us take variation in plant size into account when comparing herbivore abundance. Plants that were completely consumed in the course of the experiment, were excluded from further analyses.
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Statistical analyses experiment 2 Similar to the greenhouse assays described before, the observed damage classes of gastropod and flea beetle damage in the common garden observations were transformed to preference ranks ranging from 1 (least preferred) to 60 (most preferred) within the garden plot. Data were analysed using nonparametric statistical methods from the R “stats” package (R Core Team 2016). Unpaired Mann Whitney and Kruskal-Wallis rank sum tests were performed to test for the effect of slug feeding-induction and the differences between accessions for each week, respectively. Two-way ANOVA with Tukey’s post hoc test was performed to test for differences in final total stem length between accessions. Total stem lengths were square root transformed to ensure normal distribution and homogeneity of the variances.
Results
Experiment 1: greenhouse experiment Slug preference assays The three different slug species showed the same preference for the six S. dulcamara accessions. Their preference was independent of whether the discs were from plants previously subjected to GFS feeding-induction (Fig. 1a, b, c, GFS vs ARA: χ2 = 2.325, df = 11, P = 0.997, GFS vs AFS: χ2 = 5.689, df = 11, P = 0.893, ARA vs AFS: χ2 = 2.535, df = 11, P = 0.996). By pooling the preference ranks of the three different species, we found that slugs overall showed a significant preference among accessions (Fig. 1d, Friedman χ2 = 240.3, df = 11, P < 0.001). Accessions were preferred in the exact same order as reported in the original study from which they were selected (Calf et al. 2018), with ZD11 being the most preferred accession. Previous GFS feeding significantly reduced subsequent slug feeding preference only for accession TW01 (Fig. 1d).
Metabolomic profiling of GFS feeding-induced responses Accessions differed with respect to their metabolic profiles. A PCA on the metabolomes of the six S. dulcamara accessions revealed that accession ZD11 clustered separately from the other five accessions. ZD11 mainly separated on the first principal component (PC1), which explained 15.1 % of the overall variation (Fig. 2a). The other five accessions separated on the second principal component (PC2, 11.6 %), with exception of VW08 and FW09. Separation in the PCA was mainly based on the abundance of UACs (PC1) and tomatidenol/solasodine- type GAs (PC2, Fig. 2b). This is in accordance with chemotypic data from the original study based on which the accessions were selected (Calf et al. 2018). Samples of plants subjected to GFS feeding always clustered with their respective undamaged controls.
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Fig. 1 Mean relative consumption (± SE) by a) Deroceras reticulatum (n = 12); b) Arion rufus/ater (n = 12) and c) Arion fuscus/subfuscus (n = 12) as well as d) the combined mean preference rank (± SE) of all three slug species (n = 36) on leaf discs of Solanum dulcamara accessions that were either left undamaged (control; dark grey bars) or induced by feeding damage of D. reticulatum (72 h of feeding followed by 24 h relaxation, light grey bars). Asterisks indicate a significant treatment effect according to Wilcoxon signed-rank test with Bonferroni correction for multiple comparisons (** P < 0.01)
Fig. 2 (next page) Metabolic profiling of leaves taken from six Solanum dulcamara accessions that were either left undamaged (C) or induced by feeding damage of Deroceras reticulatum slugs (I; 72 h of feeding followed by 24 h relaxation). Profiles are based on 286 compounds retrieved by untargeted HPLC-qToF-MS analyses. a) PCA scores plot of accessions subjected to both treatments (n = 4 clones / accession / treatment); b) compound loadings in the PCA scores plot. Filled dots indicate pre-selected compounds of interest (GA1–4 and UAC1–6); c) T-score plot of the first predictive component (p) and orthogonal component (o) in the overall OPLS-DA model including all six accessions (n = 24 / treatment); d) S-plot for compound importance in the same OPLS-DA model. Filled dots indicate selected compounds of interest (C1–C3); panel e and f) Venn diagrams of respectively up- and downregulated compounds selected from three significantly cross-validated individual accession OPLS-DA models
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Fig. 2 (Caption on previous page)
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Because slug-induced responses may be too subtle to be visible in the overall PCA model, OPLS-DA models were built to compare the metabolomes of control and GFS feeding-induced plants from the whole data set, as well as for each accession separately (Table 1). The overall model, including all six accessions, revealed that the metabolomes of damaged and undamaged plants were significantly different (Fig. 2c and Table 1). The model had significant (P < 0.001) cross-validated predictive ability of 71.6 %. However, the predictive component (p) explained merely 3.9 % of the total variance, whereas 8.9 % of total variance was explained by the orthogonal component (o). This illustrates that only a small portion of the metabolome showed a shared response to the treatment and more variation is explained by differences among accessions. Three compounds of interest (C1– C3) were selected that showed the most consistent and reliable response among accessions to the GFS feeding-induction treatment, based on the S-plot with the OPLS-DA model (Fig. 2d and Table S2). C1 and C2 were putatively identified as two isomers of the phenolamide N- caffeoylputrescine (Table S3). C3 is a related compound which was proposed to be an N- caffeoylputrescine metabolite in a study by Li et al. (2015).
Table 1 Results of OPLS-DA models comparing the overall or individual metabolic profiles of six Solanum dulcamara accessions that were either left undamaged or induced by feeding damage of the grey field slug (Deroceras reticulatum). The number of plants per treatment (n), cross-validated predictive ability (Q2 in %), 2 explained variance by the first predictive component (R X(p) in %), explained variance by the first orthogonal 2 component (R X(o) in %) and number (n) of up- and down-regulated compounds of interest are provided for each model
2 2 2 Model n Q R X(p) R X(o) n Up n Down Overall 24 71.6*** 3.9 8.9 3 0 GD04 4 43.0 17.1 17.8 19 29 VW08 4 67.8+ 20.4 14.3 42 32 OW09 4 65.4+ 22.1 18.6 41 28 TW01 4 75.4* 27.3 19.0 33 36 FW09 4 70.3* 22.1 15.0 42 18 ZD11 4 74.0* 23.2 19.5 31 40 Symbols indicate significance of the cross-validated model based on 1000 permutations (***P < 0.001, * P < 0.05, + P < 0.1)
Induced responses in each accession were tested using individual models comparing undamaged control and slug-induced plants per accession. Interestingly, only the models of the three most sensitive accessions, ZD11, FW09 and TW01 showed significant predictive ability after cross-validation (P < 0.05; Q2 = 70.3–75.4 %; Table 1). The three significant models explained between 22.1–27.3 % of the total variance, which is much higher than the overall model. This illustrates that responses to GFS feeding induction are relatively specific for each accession. Compounds of interest of the individual models were selected based on the same criteria as the overall model. The majority of regulated compounds in accession FW09 were upregulated, whereas a similar or higher number of compounds was downregulated in ZD11 and TW01. However, there was very little overlap in the compounds that were up- (Fig. 2e) or downregulated (Fig. 2f) among these three accessions, which again points to accession specific responses.
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Fig. 3 Mean log10-transformed peak intensities relative to the maximum observed intensity of selected compounds of interest in leaves of six Solanum dulcamara accessions that were either left undamaged (C, n = 4 clones / accession) or induced by feeding damage of Deroceras reticulatum slugs (I; 72 h of feeding followed by 24 h relaxation, n = 4 clones / accession). The peak intensities of four steroidal glycoalkaloids, five uronic acid conjugated compounds and three selected regulated compounds are shown. Asterisks indicate significant treatment effects according to FDR-adjusted P-values of independent t-tests for each accession (***P < 0.001, ** P < 0.01, * P < 0.05)
GFS feeding-induction did not affect the abundance of GAs and UACs in any accession (Fig. 3). However, each accession, except for VW08 and OW09, showed upregulation of at least one of the previously selected phenolamides.
Experiment 2: common garden experiment None of the analyses on herbivore numbers or damage revealed significant effects of previous slug feeding (unpaired Wilcoxon signed-rank test, P > 0.05, data not shown). For this reason, the data for control and slug-induced plants were pooled for further analyses.
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s /
individual
205) 1) n
Mean plant / week (41) 0.206 ( 0.522 (69) 0.375 (2) 0.004 (26) 0.133 (125) 1.019 (10) 0.044 (3) 0.006 (8) 0.037 (23) 0.125 NA NA (4) 0.005 (6) 0.023 (10) 0.014 (3) 0.006 (5) 0.010 (20) 0.118 ( 0.004 (51) 0.166
mining larvae in flowers - Leaf mining Gall Leaf chewing Leaf chewing pollen,Adults on in larvae anthers Leaf chewing Leaf chewing Leaf chewing Cell sucking Cell sucking Sap sucking Sap sucking Leaf chewing Leaf chewing Predator Predator Predator Predator Predator Predator Feeding type season
Solanum dulcamara plants dulcamara 60 Solanum during of each on eachnumber of 19 weeks
idae
idulidae
) Lepidoptera, Glyphipterigidae Diptera, Cecidomyiidae Coleoptera, Chrysomelidae Coleoptera, Chrysomelidae Coleoptera, Nit Coleoptera, Chrysomelidae Coleoptera, Chrysomelidae Coleoptera, Chrysomelidae Hemiptera Hemiptera Hemiptera, Aphididae Hemiptera, Psyllidae/Triozidae Lepidoptera Pulmonata, Helicidae Coleoptera, Coccinell Syrphidae Diptera, Hymenoptera, Formicidae Hymenoptera, Ichneumonidae Neuroptera, Chrysopidae Trombidiformes, Trombidiidae Order, Family
Philaenus spumarius ) is among these, only noticed in last weeks observationsof
Acrolepia autumnitella Contarinia solani Epitrix pubescens Leptinotarsa decemlineata Pria dulcamarae Psylliodes affinis Psylliodes dulcamarae Lilioceris lilii Species
herbivores
umping lice*** plant Bittersweet leaf miner moth Nightshade gall midge beetle Nightshade flea Colorado potato beetle Bittersweet sap beetle Potato flea beetle Bittersweet flea beetle Scarlet lily beetle Cicadas* Heteropteran bugs Aphids** J Lepidopteran larvae**** Typical snails Ladybirds Hoverflies Ants Common parasitoids Green lacewings Red velvet mite
Common name A. Specialist herbivores B. Other C. Third trophic level of common garden observations in 2016. The maximum number observed on all plants at one census date is given in brackets in given is date census one at plants all on observed number maximum The 2016. in observations garden common of recorded Not **: absolutein numbers, as being instead but absent, present or abundant only startin of observed mean and of feeding mode Table their of organisms, 2 type The *: Earlysignificant season number of meadow ( spittlebugs ***: Possibly the potato specialist ( Bactericera cockerelli ****: Fair number supposedly belonging family to of leafroller moths (Tortricidae)
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Overall herbivore abundance In total we observed 20 types of organisms on S. dulcamara in the common garden experiment (Table 2). Eight of the observed insect species, mainly beetles, belonged to specialist herbivores on Solanaceae. Seven were classified as ‘other herbivores’. This group mainly included generalists, as well as Lilioceris lilii, which is a specialist on plants in the Liliaceae family, but also a facultative visitor of S. dulcamara (Cox 2001). Six other herbivores were classified as third trophic level insects, mainly predators of arthropod herbivores. To analyse differences among accessions, we focussed on the most abundant leaf feeding specialist insects, namely Psylliodes affinis, Epitrix pubescens and Acrolepia autumnitella (Table 2). For each of these species we counted > 30 individuals on at least one of the census dates. The remaining herbivore species either were found too infrequently to be reliably analysed, or they fed on flowers, such as the larvae of Contarinia solani gall midges and Pria dulcamarae sap beetles. The flea beetle P. affinis was the most abundant herbivore, with on average > 1 individual per plant each week (Table 2). The second most abundant herbivore was another flea beetle E. pubescens, which abundance was about a third of that of P. affinis. Over the growing season, the abundance of both flea beetle species was synchronised. There were two generations of adults; a spring generation with peak abundance at the end of May (week 22) and a mid-summer generation peaking in the end of August (week 33–35, Fig. 4a). The larvae of the specialist moth A. autumnitella are leaf miners. They appeared to have three generations, with peak abundances of new mines in mid-June, early August and September (week 24, week 31 and weeks 35–38, Fig. 4a). Their peak abundance increased with time, which indicates a typical population increase over the season. Slugs and snails were not frequently observed because of their nocturnal feeding habit. Differences in gastropod abundance among accessions could therefore not be tested.
Accession specific herbivore abundance The distribution of insects was evaluated in absolute numbers per plant as well as relative to stem length. The latter was done because total stem length was found to differ significantly among accessions at the end of the growing season (one-way ANOVAWk41: df = 5, F = 9.25, P < 0.001, Fig. S1). Absolute and relative flea beetle abundance significantly varied among accessions (Table S4). Only in week 26 both the absolute and relative numbers of beetles were differently distributed among accessions (Kruskal-Wallis Test P < 0.05, Fig. 4b–c, Table S4). In that week, most individuals were observed on accession GD04. However, the accession on which most flea beetles were observed differed throughout the season. The lowest numbers of P. affinis and E. pubescens were always observed on accession ZD11 (Kruskal-Wallis Test, P < 0.05, Table S4). In contrast to flea beetle distribution, individuals of the moth A. autumnitella were not differently distributed among accessions on any census date (Kruskal-Wallis χ2 < 0.99, df = 4, P > 0.05, data not shown).
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Fig. 4 Abundance of specialist leaf herbivores in a common garden experimental area. a) Seasonal pattern of occurrence of three species of specialist leaf herbivores from late-May to October 2016 (calendar week 21–41). Lines are broken at the time points that the plants were not monitored; panel b and c) Means (± SE) of respectively the absolute numbers and relative numbers to stem length (± SE) of two species of flea beetles in week 26 on six Solanum dulcamara accessions
Gastropod and flea beetle damage scores We recorded gastropod and flea beetle damage in damage classes, as a measure for herbivore preference (Fig. 5, Table S1). Overall gastropod damage was greatest at the start of the experiment and gradually decreased over the season (Fig. 5a). Flea beetle damage (Fig. 5a) was strongly synchronised with flea beetle abundance (Fig. 4a). Gastropod damage significantly differed among accessions over the entire season, except for the last census week, and was always the greatest on accession ZD11 (Fig. 5b). Three plants of ZD11 died because of excessive gastropod feeding (one in week 32, two in week 38). These plants were excluded from further analyses. Accession GD04 showed the lowest gastropod damage in the first half of the season. The gastropod damage ranks of GD04, OW09, TW01 and FW09 significantly changed with time (Kruskal-Wallis χ2 > 41.80, df
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= 17, P < 0.001), with average rank numbers becoming more equal over time. This effect is explained by the fact that overall gastropod damage decreased over the season (Fig. 5a). Therefore fewer plants incurred any damage and were assigned the same average rank number. Also after exclusion of ZD11 from the statistical analyses, gastropod damage remained different among accessions for the first half of the season (week 22, 23, 26–29, Kruskal-Wallis χ2 > 9.70, df = 4, P < 0.05, data not shown). Flea beetle damage differed among accessions only at the start (week 21–24 and 27) and the end (week 35 and 39–41) of the season (Fig. 5c). Generally, accession ZD11 received the lowest amount of flea beetle damage, which is in line with the low flea beetle abundance observed on this accession (Fig. 4b and 4c, Table S4), but opposite to gastropod feeding preference (Fig. 5b and Fig. S2). The flea beetle damage rank changed throughout the season for FW09 (Kruskal-Wallis χ2 > 49.64, df = 17, P < 0.001). The mean damage rank of this accession was among the lowest in spring (week 21–24), but gradually increased towards the end of the season.
Discussion
Our study revealed that generalist gastropods and specialist flea beetles display opposite feeding preferences among six S. dulcamara accessions with distinctive GA chemotypes. Previous feeding by slugs had little effect on this preference. The UAC-rich accession ZD11, which was uniformly preferred by slugs in both experiments, suffered the least damage by specialist flea beetles in the field. Gastropods and flea beetles thus seem to exert opposing selection pressures on chemical diversity in GA and UAC profiles. All three slug species tested in the greenhouse experiment showed highly similar preferences for accessions with low GA levels. These results are similar to those of the original experiment with GFS only, based on which the accessions were selected (Calf et al. 2018). The gastropod feeding damage incurred by the different accessions in the common garden experiment confirmed that gastropods also prefer low GA accessions under field conditions. Our findings are in line with previous studies, reporting effects of several types of alkaloids on gastropod feeding damage (Bog et al. 2017; Smith et al. 2001; Speiser et al. 1992). This suggests that the observed preference of generalist gastropods is driven by differences in GA levels among S. dulcamara accessions. Interestingly, the preference and damage patterns of flea beetles were diametrically different. The accession that was the most preferred by the slugs was the least preferred by these specialist insect herbivores, which are commonly associated with S. dulcamara (Calf and van Dam 2012; Castillo Carrillo et al. 2016; Viswanathan et al. 2005).
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Fig. 5 Seasonal pattern of herbivory as observed on six Solanum dulcamara accessions (n = 10) in a common garden experiment from late-May to October 2016 (calendar week 21–41). a) overall mean gastropod and flea beetle damage classes on all accessions, b) mean gastropod and c) mean flea beetle damage ranks derived from scored damage classes per accession. Lines are broken at the time points that the plants were not monitored. Symbols indicate significant differences according to Kruskal-Wallis signed-rank test (*** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.10)
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Specialists often use the presence of specific chemical defences, such as GAs, to recognise their host (van Loon et al. 2002). Seen the particular taste of GAs–which gave S. dulcamara its common name “bittersweet nightshade”–it is likely that herbivores use them as cues. Possibly, the flea beetle species observed in this study did not feed on UAC-rich accession ZD11, because of the lack of GAs as a feeding stimulant. Moreover, specialists often possess specific mechanisms to deal with their host’s toxins. The Colorado potato beetle, which also commonly occurs on S. dulcamara (Calf and van Dam 2012, Hare 1983), was shown to excrete GAs (Armer 2004). Also the larvae of tortoise beetles feeding on S. dulcamara in northern America excrete GAs. They even use them for their own benefit by incorporating the compounds in faecal shields as defence against predators (Vencl et al. 1999). Sequestration of plant chemical defences in the insect’s haemolymph frequently occurs in specialist insects, including flea beetles (Beran et al. 2014; Nishida 2002). However, this has not been observed for Solanum GAs yet (Opitz and Müller 2009). We did not explicitly test for toxic effects of GAs and UACs on different herbivores, which would require measurements of herbivore performance. This question could be assessed by bio-assays in which herbivores are offered different concentrations of GAs and UACs in artificial diets, or genetically modified S. dulcamara plants with altered GA concentrations (Voelckel et al. 2001). Previous slug damage significantly reduced the relative feeding preference of slugs in only one accession (TW01) in the greenhouse experiment and not at all in the experimental garden. Moreover, none of the accessions showed a significant increase in GA levels within 96 h after the onset of slug feeding. Induction of the biosynthesis pathway of GAs is regulated by jasmonate-responsive transcription factors (Thagun et al. 2016). Two earlier studies showed that GFS, in contrast to other gastropods, secretes salicylic acid in its locomotion mucus (Kästner et al. 2014, Meldau et al. 2014), which attenuates jasmonic acid dependent defence signalling (Schweiger et al. 2014; Thaler et al. 2012). Besides, the induction of secondary metabolites is dynamic in time and induced responses are known to differ between damaged and undamaged tissues (Chung et al. 2013; Erb et al. 2009; Mathur et al. 2011; van Dam and Raaijmakers 2006). It may thus well be that changes in GA abundance are only observed in other tissues or at a later time point. In the garden experiment, we had to use ARA instead of GFS to induce the plants, which may have elicited different responses. Additionally, the natural gastropod feeding occurring immediately after transplantation to the garden may have overruled the effect of our induction treatment with single slugs. Both factors may have prevented us from finding effects of our controlled slug induction treatment in the common garden experiment. Further research on the dynamics of local and systemic slug-induced resistance, in combination with metabolic profiling, is needed to better understand defence regulation upon slug feeding under greenhouse and field conditions. Our untargeted metabolomic analyses revealed that only the metabolomes of the three most susceptible accessions (TW01, FW09 and ZD11) were significantly changed in
67 Chapter 3: Gastropods and insects prefer different chemotypes response to slug feeding. This suggests that these accessions may rely more on induced responses, potentially saving resources for growth in absence of herbivores (Strauss et al. 2002; Vos et al. 2013). Alternatively, the slugs may have caused more feeding damage on these susceptible accessions, thereby triggering a stronger response. This may also explain the accession specific responses, as illustrated by the little overlap in the metabolites that were up- or downregulated. Only the levels of two isomers of the phenolamide caffeoylputrescine and a related metabolite were consistently increased among S. dulcamara accessions. Phenolamides, also known as hydroxycinnamic acid amides, are commonly found in plants. These compounds have a diverse role in plant development, such as flowering and adaptive responses to stress conditions (reviewed by Bassard et al. 2010; Edreva et al. 2007). Phenolamide metabolism is particularly well-studied in wild tobacco, Nicotiana attenuata. Various phenolamides in this species, including caffeoylputrescines, are induced in local and systemic tissue after insect feeding, as well as in response to UV-B radiation (Demkura et al. 2010; Gaquerel et al. 2014). Because the polyamides were induced in slug-sensitive and -resistant S. dulcamara accessions alike, these compounds can serve as a biochemical indicator for defence responses. Based on our experiments we cannot correlate them to (induced) resistance to slugs, as we observed only in one accession that slug damage reduced slug preference. However, their induction may have consequences for other organisms, as several studies correlated high levels of phenolamides with increased resistance to herbivores or pathogens (Fixon-Owoo et al. 2003; Kaur et al. 2010; Tai et al. 2014; Tebayashi et al. 2007; Yogendra et al. 2014). It should be noted that herbivore preference is not exclusively determined by polar secondary metabolites, as detected by LC-qToF-MS. For example, S. dulcamara is known to produce and induce other defences upon herbivore feeding, such as polyphenoloxidases, peroxidases and protease inhibitors, which have not been measured here. These compounds mainly affect the plant’s nutritional value, but may also affect the spatial distribution of herbivores through the season (Nguyen et al. 2016b; Viswanathan et al. 2008). Additionally, S. dulcamara successfully deploys indirect defences against slugs. Herbivore damaged plants produce extrafloral nectar from the wounds which attracts ants (Lortzing et al. 2016). In our common garden experiment, only a few ants or other predators were observed, probably because ant nests were removed when preparing the area. This makes it unlikely that differences in herbivore abundance or damage in this experiment are correlated with differences in nectar secretion and ant attendance. However, this does not rule out that variation in other defence traits, such as leaf toughness or trichome density (Agrawal and Fishbein 2006), may have contributed to the observed patterns. The intraspecific variation in GAs that was reported for S. dulcamara before (Eich 2008; Mathé 1970) now also includes UACs as related compounds (Calf et al. 2018). The fact that three plants of the UAC-rich accession ZD11 did not survive the season due to excessive gastropod damage illustrates the potential consequences of relatively small chemical differences. It is expected that natural chemical variation among plant populations reflects
68 Chapter 3: Gastropods and insects prefer different chemotypes adaptation to the locally dominant herbivores, in terms of inflicted damage (Kalske et al. 2012; Laine 2009; Scriber 2002). For gastropods and insect herbivores, differences in the herbivore community composition may concur with differences in local abiotic conditions among S. dulcamara populations (Zhang et al. 2016). Slugs and snails generally require moist conditions and intermediate temperatures (Astor et al. 2017). In dry natural populations, such as the sand dunes where ZD11 originates from, gastropods may be less abundant than specialist insects (Calf and van Dam 2012). The low abundance of gastropods in these populations may provide a ‘window of opportunity’ for chemotypes producing UACs to survive and propagate. This hypothesis is supported by the fact that only two accessions containing UACs were found, both from the same coastal dune population (OW Calf, pers. obs.). Closer monitoring of natural feeding damage incurred by UAC-rich accessions in different natural populations would be needed to test the role of abiotic conditions in survival of this chemotype. The genetic mechanisms underlying the GA polymorphism in S. dulcamara can be elucidated using our knowledge of their chemistry and biosynthesis in tomato and potato. S. dulcamara is closely related to these crop species and the genetic mechanisms for GA biosynthesis seem to be highly conserved (Cárdenas et al. 2015; Eich 2008; Itkin et al. 2013). Further studies comparing genomes or transcriptomes of GA and UAC chemotypes will allow us to dissect the GA-biosynthetic genes that contribute to this chemical polymorphism. One of the first students of chemical ecology, Ernst Stahl, observed already 130 years ago that plants produce chemical defences to protect themselves against slug and snail feeding (Stahl 1888, see also the historic review by Hartmann 2008). Our study is in line with this and other studies, which show that gastropods may constitute a severe selection pressure in the field, especially by ‘weeding out’ specific seedlings (Rathcke 1985; Strauss et al. 2009). We also show that generalist gastropods and specialist insects may impose different selection pressures on chemical diversity. Heterogeneity in herbivore communities often concurs with abiotic differences among the habitats that plant species with broad ecological amplitudes can occupy. Together with random molecular events, such as gene mutation or duplication, this may be the reason for the intraspecific chemical diversity we observe in S. dulcamara and other plant species today (Hartmann 1996).
Acknowledgements
The authors thank Gerard van der Weerden of the Radboud University Genebank in Nijmegen for providing seeds. We also thank Anke Steppuhn and the Molecular Ecology group at the Free University of Berlin for valuable discussions during the project. OWC is supported by grant number 823.01.007 of the open programme of the Netherlands Organisations for Scientific Research (NWO-ALW) to NMvD and HH. NMvD, AW and YP gratefully acknowledge the support of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig funded by the German Research Foundation (FZT 118).
69 Chapter 3: Gastropods and insects prefer different chemotypes
Supplemental material
Table S1 Classification scheme for gastropod and beetle damage in a common garden field plot
Table S2 Compound covariance and response reliability with respect to the first predictive component in an OPLS-DA model comparing the metabolomes of undamaged and damaged leaves
Table S3 Putative identification and details of compounds of interest as deduced from MS2 experiments
Table S4 Abundance of two species of flea beetles observed in a common garden experimental area on six Solanum dulcamara accessions
Fig. S1 Mean stem of six Solanum dulcamara accessions in a common garden experiment during the growing season of 2016
Fig. S2 Difference in gastropod and flea beetle preference for six Solanum dulcamara accessions in a common garden experiment during the growing season of 2016
70 Chapter 3: Gastropods and insects prefer different chemotypes
Table S1 Classification scheme for gastropod and beetle damage on Solanum dulcamara plants in a common garden field plot
Damage class Gastropod damage Beetle damage 0 No visual damage No visual damage 1 No more than little bites (0.5 cm2) 1–4 per leaf 2 < 25 % leaf area eaten 5–8 per leaf 3 25–50 % leaf area eaten 9–12 per leaf 4 50–75 % leaf area eaten > 12 per leaf 5 Severe damage, > 75 % leaf area eaten Severe damage, including stem deformations
71 Chapter 3: Gastropods and insects prefer different chemotypes
Table S2 Compound covariance and reliability of the response (correlation) with respect to the first predictive component (p) in an OPLS-DA model comparing the metabolomes of undamaged leaves of six Solanum dulcamara accessions with those damaged by feeding damage of the grey field slug (n = 24). Compounds with absolute covariance > 1.5 and correlation > 0.5 were selected as compounds of interest
Compound Covariance(p) Correlation(p) ABS(Covariance(p)) ABS(Correlation(p)) Selected compound of interest (ID_mz_rt) 39_251.14_185 3.274 0.621 3.274 0.621 C1 47_251.14_116 2.408 0.584 2.408 0.584 C2 120_347.20_435 1.626 0.535 1.626 0.535 C3 38_183.68_509 -2.651 -0.447 2.651 0.447 334_868.51_619 -1.551 -0.425 1.551 0.425 139_145.93_693 1.736 0.295 1.736 0.295 74_177.05_383 -1.298 -0.334 1.298 0.334 317_152.06_72 1.277 0.425 1.277 0.425 146_204.16_160 1.243 0.271 1.243 0.271 385_434.2_468 1.141 0.252 1.141 0.252 218_383.15_90 1.139 0.344 1.139 0.344 29_193.05_345 -1.070 -0.250 1.070 0.250 316_137.05_73 1.066 0.290 1.066 0.290 163_417.17_483 1.022 0.269 1.022 0.269 24_130.05_45 -1.017 -0.302 1.017 0.302 142_414.34_582 0.998 0.347 0.998 0.347 67_265.15_251 0.978 0.426 0.978 0.426 399_900.5_691 -0.931 -0.309 0.931 0.309 58_265.15_307 0.928 0.517 0.928 0.517 286_314.14_690 -0.918 -0.250 0.918 0.250 213_375.22_83 -0.905 -0.286 0.905 0.286 223_443.3_442 -0.879 -0.375 0.879 0.375 152_391.16_341 0.878 0.271 0.878 0.271 196_538.32_603 0.876 0.311 0.876 0.311 275_401.15_330 -0.842 -0.275 0.842 0.275 419_221.09_89 -0.831 -0.305 0.831 0.305 222_342.14_105 0.828 0.201 0.828 0.201 283_246.06_48 0.827 0.245 0.827 0.245 165_362.29_638 0.826 0.266 0.826 0.266 208_163.1_241 0.785 0.209 0.785 0.209 143_301.07_674 0.775 0.282 0.775 0.282 258_345.68_312 -0.766 -0.337 0.766 0.337 220_524.34_597 0.752 0.300 0.752 0.300 330_361.05_51 0.751 0.213 0.751 0.213 170_267.17_234 0.745 0.342 0.745 0.342 294_337.09_49 -0.739 -0.314 0.739 0.314 296_266.16_268 -0.737 -0.169 0.737 0.169 151_279.14_372 0.732 0.305 0.732 0.305 292_263.05_49 0.713 0.241 0.713 0.241 125_461.71_676 -0.689 -0.269 0.689 0.269 199_325.07_323 -0.681 -0.263 0.681 0.263 309_142.95_69 -0.681 -0.257 0.681 0.257 232_267.1_74 -0.666 -0.250 0.666 0.250 382_868.51_593 0.665 0.279 0.665 0.279 319_295.1_341 -0.655 -0.210 0.655 0.210 225_106.06_141 -0.649 -0.174 0.649 0.174 176_186.15_262 -0.644 -0.223 0.644 0.223 185_291.17_363 0.642 0.234 0.642 0.234 32_611.16_507 -0.620 -0.280 0.620 0.280 42_209.15_575 0.604 0.282 0.604 0.282
72 Chapter 3: Gastropods and insects prefer different chemotypes
Table S2 Continued (2/5)
Compound Covariance(p) Correlation(p) ABS(Covariance(p)) ABS(Correlation(p)) Selected compound of interest (ID_mz_rt) 226_175.09_89 -0.603 -0.283 0.603 0.283 GA5_870.52_713 -0.599 -0.128 0.599 0.128 80_357.11_332 0.595 0.190 0.595 0.190 390_416.35_693 -0.594 -0.210 0.594 0.210 216_276.22_361 0.593 0.259 0.593 0.259 52_98.06_73 -0.589 -0.222 0.589 0.222 210_295.1_226 -0.579 -0.188 0.579 0.188 315_396.01_382 0.576 0.288 0.576 0.288 329_408.18_383 0.564 0.200 0.564 0.200 314_365.08_382 0.557 0.205 0.557 0.205 243_353.08_173 -0.555 -0.235 0.555 0.235 45_147.04_401 -0.549 -0.152 0.549 0.152 167_171.03_87 0.548 0.203 0.548 0.203 424_566.43_552 0.547 0.238 0.547 0.238 323_348.07_70 0.545 0.292 0.545 0.292 57_177.05_485 0.541 0.194 0.541 0.194 416_342.18_241 -0.529 -0.208 0.529 0.208 392_597.22_582 0.527 0.207 0.527 0.207 65_149.96_70 0.519 0.184 0.519 0.184 186_319.15_306 0.517 0.190 0.517 0.190 73_295.1_519 0.505 0.317 0.505 0.317 262_185.13_498 -0.503 -0.328 0.503 0.328 90_151.07_91 0.491 0.130 0.491 0.130 256_899.49_606 0.479 0.265 0.479 0.265 304_303.05_525 -0.479 -0.136 0.479 0.136 328_503.19_520 -0.465 -0.150 0.465 0.150 173_295.17_284 -0.459 -0.164 0.459 0.164 19_188.07_231 0.458 0.424 0.458 0.424 141_145.93_660 -0.457 -0.095 0.457 0.095 137_412.32_513 0.456 0.199 0.456 0.199 193_138.09_139 -0.452 -0.135 0.452 0.135 238_446.33_416 0.441 0.297 0.441 0.297 242_1068.57_697 -0.438 -0.212 0.438 0.212 270_263.15_466 -0.431 -0.253 0.431 0.253 415_432.35_569 0.426 0.221 0.426 0.221 417_528.27_614 0.416 0.246 0.416 0.246 211_448.19_292 0.414 0.186 0.414 0.186 183_355.1_289 0.414 0.116 0.414 0.116 191_114.09_324 -0.412 -0.083 0.412 0.083 312_449.06_440 0.409 0.214 0.409 0.214 253_412.22_600 -0.406 -0.151 0.406 0.151 212_434.36_613 0.404 0.160 0.404 0.160 298_747.15_348 -0.402 -0.279 0.402 0.279 202_386.2_327 -0.398 -0.131 0.398 0.131 237_469.13_436 -0.397 -0.151 0.397 0.151 192_553.26_696 0.395 0.197 0.395 0.197 307_310.09_588 0.394 0.153 0.394 0.153 235_253.13_84 -0.389 -0.140 0.389 0.140 179_225.15_488 0.387 0.082 0.387 0.082 273_419.7_420 0.386 0.207 0.386 0.207 240_463.22_591 0.382 0.154 0.382 0.154 75_307_51 0.373 0.190 0.373 0.190 269_331.3_554 0.366 0.200 0.366 0.200 278_157.01_51 0.361 0.070 0.361 0.070 279_198.15_51 -0.360 -0.194 0.360 0.194 245_315.13_547 0.359 0.136 0.359 0.136 78_342.13_500 0.359 0.183 0.359 0.183 164_295.16_332 0.353 0.123 0.353 0.123 UAC4_589.33_790 -0.348 -0.217 0.348 0.217 138_145.93_671 0.346 0.061 0.346 0.061 172_433.17_518 -0.345 -0.084 0.345 0.084 404_339.7_285 -0.344 -0.164 0.344 0.164 272_199.07_533 0.343 0.178 0.343 0.178 246_163.09_86 -0.343 -0.132 0.343 0.132 310_212.05_68 0.332 0.115 0.332 0.115
73 Chapter 3: Gastropods and insects prefer different chemotypes
Table S2 Continued (3/5)
Compound Covariance(p) Correlation(p) ABS(Covariance(p)) ABS(Correlation(p)) Selected compound of interest (ID_mz_rt) UAC3_590.37_769 0.329 0.238 0.329 0.238 418_203.08_89 -0.326 -0.121 0.326 0.121 207_317.07_569 -0.321 -0.163 0.321 0.163 282_154.09_48 0.318 0.091 0.318 0.091 131_202.14_193 -0.313 -0.083 0.313 0.083 149_437.25_479 0.309 0.151 0.309 0.151 87_418.7_277 0.307 0.125 0.307 0.125 327_440.15_393 0.304 0.183 0.304 0.183 C_508.32_857 0.301 0.173 0.301 0.173 229_385.15_530 -0.300 -0.122 0.300 0.122 C_524.32_807 0.298 0.178 0.298 0.178 GA_739.42_702 0.297 0.285 0.297 0.285 247_295.1_450 -0.296 -0.117 0.296 0.117 372_516.3_430 -0.292 -0.155 0.292 0.155 174_323.07_301 0.286 0.105 0.286 0.105 194_409.22_275 -0.283 -0.168 0.283 0.168 188_433.33_686 -0.274 -0.131 0.274 0.131 234_606.37_559 0.274 0.152 0.274 0.152 132_225.15_384 0.273 0.232 0.273 0.232 259_347.12_527 -0.273 -0.123 0.273 0.123 230_504.3_411 -0.272 -0.098 0.272 0.098 391_435.17_582 -0.266 -0.100 0.266 0.100 422_366.12_331 0.265 0.113 0.265 0.113 110_225.15_430 0.264 0.218 0.264 0.218 GA_741.44_728 -0.262 -0.216 0.262 0.216 249_325.68_331 0.260 0.083 0.260 0.083 241_520.33_502 0.259 0.093 0.259 0.093 305_578.4_678 0.257 0.103 0.257 0.103 147_358.27_543 0.251 0.068 0.251 0.068 227_453.74_576 -0.248 -0.147 0.248 0.147 209_517.16_235 -0.247 -0.071 0.247 0.071 GA_722.44_655 0.246 0.287 0.246 0.287 C_362.29_619 0.245 0.172 0.245 0.172 221_395.29_626 -0.244 -0.104 0.244 0.104 224_295.1_615 -0.244 -0.097 0.244 0.097 81_265.05_620 -0.242 -0.140 0.242 0.140 59_595.17_556 -0.239 -0.111 0.239 0.111 116_464.22_273 0.237 0.133 0.237 0.133 GA_882.48_574 0.235 0.317 0.235 0.317 31_276.23_679 0.234 0.078 0.234 0.078 228_455.19_487 -0.233 -0.116 0.233 0.116 402_508.29_332 0.219 0.091 0.219 0.091 255_539.33_603 0.213 0.137 0.213 0.137 53_344.13_340 0.212 0.313 0.212 0.313 265_450.14_510 0.211 0.139 0.211 0.139 22_257.65_269 0.207 0.061 0.207 0.061 219_287.06_536 -0.206 -0.086 0.206 0.086 264_526.22_658 0.206 0.079 0.206 0.079 257_509.26_454 -0.201 -0.069 0.201 0.069 267_474.19_553 -0.200 -0.116 0.200 0.116 UAC5_589.33_830 0.195 0.195 0.195 0.195 48_258.15_269 0.191 0.072 0.191 0.072 GA_1063.53_715 -0.183 -0.100 0.183 0.100 248_342.13_566 0.177 0.079 0.177 0.079 GA_739.42_721 0.177 0.182 0.177 0.182 16_414.34_627 0.174 0.052 0.174 0.052 295_172.98_55 0.169 0.072 0.169 0.072 285_367.11_49 -0.167 -0.049 0.167 0.049 83_219.1_563 0.166 0.050 0.166 0.050 261_446.73_309 -0.163 -0.075 0.163 0.075 44_430.33_577 -0.156 -0.039 0.156 0.039 233_144.08_279 -0.156 -0.087 0.156 0.087 C_592.38_519 -0.153 -0.111 0.153 0.111 184_446.33_645 0.149 0.051 0.149 0.051 345_396.01_335 0.147 0.050 0.147 0.050
74 Chapter 3: Gastropods and insects prefer different chemotypes
Table S2 Continued (4/5)
Compound Covariance(p) Correlation(p) ABS(Covariance(p)) ABS(Correlation(p)) Selected compound of interest (ID_mz_rt) 71_207.06_394 0.147 0.068 0.147 0.068 GA_868.50_636 0.145 0.103 0.145 0.103 175_312.12_544 -0.141 -0.044 0.141 0.044 159_183.06_325 -0.140 -0.057 0.140 0.057 280_332.13_50 0.139 0.073 0.139 0.073 GA_868.50_619 0.139 0.155 0.139 0.155 GA_867.47_744 0.137 0.172 0.137 0.172 GA_594.40_535 -0.135 -0.100 0.135 0.100 C_595.17_555 0.132 0.231 0.132 0.231 414_391.16_327 -0.131 -0.051 0.131 0.051 260_323.13_520 -0.130 -0.060 0.130 0.060 GA_884.50_577 0.129 0.260 0.129 0.260 55_147.04_457 0.128 0.253 0.128 0.253 91_360.13_334 0.124 0.282 0.124 0.282 254_866.49_532 -0.122 -0.061 0.122 0.061 423_545.2_331 -0.120 -0.051 0.120 0.051 411_451.34_686 0.119 0.063 0.119 0.063 C_163.03_348 0.117 0.316 0.117 0.316 318_175.09_72 -0.116 -0.046 0.116 0.046 8_138.05_46 0.115 0.508 0.115 0.508 GA_1047.53_738 0.114 0.202 0.114 0.202 231_303.05_446 -0.113 -0.041 0.113 0.041 276_277.09_47 -0.113 -0.028 0.113 0.028 389_372.72_693 -0.112 -0.045 0.112 0.045 GA_576.39_688 0.111 0.273 0.111 0.273 51_420.19_381 -0.110 -0.265 0.110 0.265 396_204.06_341 0.109 0.044 0.109 0.044 GA6_870.52_691 -0.106 -0.093 0.106 0.093 421_750.41_558 -0.105 -0.067 0.105 0.067 277_130.09_50 0.103 0.172 0.103 0.172 13_198.67_647 -0.103 -0.091 0.103 0.091 79_420.19_395 0.103 0.035 0.103 0.035 64_588.35_607 -0.099 -0.090 0.099 0.090 136_145.93_683 -0.097 -0.018 0.097 0.018 15_116.07_47 0.096 0.240 0.096 0.240 20_150.05_49 0.092 0.282 0.092 0.282 40_177.05_441 0.082 0.109 0.082 0.109 21_215.02_54 0.081 0.246 0.081 0.246 297_726.16_392 0.081 0.032 0.081 0.032 35_274.22_657 -0.080 -0.033 0.080 0.033 GA_866.49_587 0.079 0.222 0.079 0.222 27_215.02_65 0.077 0.316 0.077 0.316 10_136.06_48 0.077 0.388 0.077 0.388 311_372.14_509 0.076 0.041 0.076 0.041 130_242.14_66 -0.075 -0.024 0.075 0.024 GA_900.50_504 -0.074 -0.057 0.074 0.057 GA7_578.40_707 0.073 0.092 0.073 0.092 37_121.06_69 0.073 0.027 0.073 0.027 155_606.37_691 -0.072 -0.054 0.072 0.054 GA1_884.50_665 0.067 0.173 0.067 0.173 GA_739.42_710 0.066 0.045 0.066 0.045 394_589.34_674 -0.066 -0.030 0.066 0.030 GA_1031.53_748 0.066 0.084 0.066 0.084 C_184.67_581 0.063 0.187 0.063 0.187 GA2_868.50_676 0.061 0.151 0.061 0.151 C_183.67_503 0.061 0.183 0.061 0.183 GA_884.50_524 0.061 0.101 0.061 0.101 25_163.04_392 0.060 0.168 0.060 0.168 GA_883.47_732 -0.056 -0.052 0.056 0.052 11_414.34_689 0.055 0.123 0.055 0.123 GA_738.44_728 -0.053 -0.050 0.053 0.050 121_448.34_593 -0.053 -0.030 0.053 0.030 288_366.11_47 -0.051 -0.373 0.051 0.373 268_127.07_103 -0.051 -0.028 0.051 0.028 381_459.28_435 0.050 0.022 0.050 0.022
75 Chapter 3: Gastropods and insects prefer different chemotypes
Table S2 Continued (5/5)
Compound Covariance(p) Correlation(p) ABS(Covariance(p)) ABS(Correlation(p)) Selected compound of interest (ID_mz_rt) 236_317.12_326 0.050 0.027 0.050 0.027 397_259.08_341 -0.048 -0.019 0.048 0.019 306_869.51_678 -0.047 -0.039 0.047 0.039 UAC6_751.39_720 -0.047 -0.046 0.047 0.046 291_244.15_49 0.043 0.127 0.043 0.127 C_196.68_609 0.042 0.111 0.042 0.111 290_217.07_49 -0.041 -0.013 0.041 0.013 34_268.1_71 -0.041 -0.081 0.041 0.081 C_163.03_330 0.039 0.146 0.039 0.146 GA4_868.50_698 0.038 0.081 0.038 0.081 313_222.08_412 -0.038 -0.013 0.038 0.013 GA3_884.50_676 0.038 0.078 0.038 0.078 GA_576.38_613 -0.038 -0.024 0.038 0.024 263_138.97_67 -0.037 -0.029 0.037 0.029 GA_722.44_686 0.037 0.055 0.037 0.055 9_142.12_50 0.036 0.008 0.036 0.008 UAC2_590.37_646 0.031 0.033 0.031 0.033 289_189.04_49 0.029 0.104 0.029 0.104 393_606.37_582 -0.029 -0.015 0.029 0.015 332_325.14_396 0.026 0.015 0.026 0.015 UAC1_753.40_778 0.021 0.032 0.021 0.032 C_611.16_507 0.017 0.032 0.017 0.032 250_425.21_354 -0.017 -0.007 0.017 0.007 293_321.12_49 0.016 0.063 0.016 0.063 72_738.45_633 0.016 0.015 0.016 0.015 299_182.08_66 0.013 0.020 0.013 0.020 41_207.14_412 0.013 0.067 0.013 0.067 420_495.18_487 -0.012 -0.004 0.012 0.004 281_124.04_49 0.012 0.030 0.012 0.030 251_773.22_362 0.011 0.004 0.011 0.004 GA_884.50_532 0.010 0.014 0.010 0.014 284_174.11_47 -0.010 -0.004 0.010 0.004 GA_884.50_626 0.009 0.009 0.009 0.009 127_295.1_468 -0.008 -0.003 0.008 0.003 C_166.08_123 0.007 0.017 0.007 0.017 252_172.98_63 -0.007 -0.004 0.007 0.004 GA_900.48_563 0.006 0.006 0.006 0.006 18_430.33_505 0.005 0.007 0.005 0.007 326_224.07_394 -0.003 -0.001 0.003 0.001 17_120.08_123 -0.003 -0.006 0.003 0.006 395_408.18_372 -0.002 -0.001 0.002 0.001
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Table S3 Putative identification of compounds of interest and their proposed elemental formulas, fragments and proposed neutral losses and moieties as deduced from MS2 experiments
Measured RT Elemental Δ Compound monoisotopic Annotation Losses and moieties Putative ID (sec) Formula ppm mass + 251.1378 C13H19N2O3+ [M+H] 7.03 putrescine loss C1 185 + N-Caffeoylputrescine isomer 163.0385 C9H7O3+ [M-C4H11N2] 4.41 caffeoyl moiety + 251.1389 C13H19N2O3+ [M+H] 2.65 putrescine loss C2 116 + N-Caffeoylputrescine isomer 163.0388 C9H7O3+ [M-C4H11N2] 1.05 caffeoyl moiety + 347.1946 C19H27N2O4+ [M+H] 7.15 putrescine loss unknown + C3 307 259.0941 C15H15O4+ [M-C4H11N2] 11.3 unknown loss N-Caffeoylputrescine + 163.0385 C9H7O3+ [M-C6H8O] 6.25 caffeoyl moiety metabolite
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Table S4 Abundance of two species of flea beetles observed in a common garden experimental area on six Solanum dulcamara accessions (n = 10 clones / accession) during the growing season of 2016. Mean numbers of Psylliodes affinis and Epitrix pubescens are presented in absolute numbers (respectively a and c) and relative to stem length (respectively b and d). Weeks in grey represent significant different distributions of beetles among accessions according to Kruskal-Wallis signed-rank test (P < 0.05). No data are available for weeks with empty cells a) b)
Week GD04 VW08 OW09 TW01 FW09 ZD11 Week GD04 VW08 OW09 TW01 FW09 ZD11 21 0.8 1.0 1.0 0.7 0.9 0.1 21 2.6 1.7 2.1 1.8 1.8 0.3 22 1.4 3.6 2.6 1.8 1.4 0.2 22 23 1.3 1.5 3.5 0.9 1.3 0.4 23 2.5 1.7 4.7 1.5 1.6 1.2 24 1.6 1.8 1.5 1.2 1.1 0.5 24 25 25 26 2.0 1.7 1.4 0.4 0.8 0.1 26 2.4 1.4 0.9 0.4 0.7 0.2 27 0.9 1.5 2.0 0.2 0.5 0.0 27 28 1.1 1.6 1.4 0.7 1.0 0.0 28 29 0.2 1.1 1.0 0.3 0.5 0.1 29 0.2 0.4 0.4 0.2 0.3 0.2 30 0.4 0.8 1.0 0.2 1.0 0.4 30 31 1.3 1.4 1.4 0.9 0.9 0.3 31 32 1.5 1.1 0.7 0.7 1.1 0.0 32 0.7 0.2 0.1 0.4 0.3 0.0 33 1.1 2.3 1.2 1.0 1.7 0.4 33 34 2.1 3.5 1.7 1.9 2.3 0.6 34 35 2.1 3.7 1.8 1.7 2.9 0.3 35 0.9 0.7 0.4 0.8 0.8 0.4 36 36 37 37 38 0.4 0.7 0.5 0.5 0.7 0.0 38 0.2 0.1 0.1 0.2 0.7 0.0 39 0.3 0.6 0.0 0.6 0.1 0.0 39 40 0.5 0.6 0.5 0.8 0.6 0.1 40 41 0.8 0.5 0.5 0.4 0.2 0.3 41 0.3 0.1 0.1 0.2 0.1 0.2
c) d)
Week GD04 VW08 OW09 TW01 FW09 ZD11 Week GD04 VW08 OW09 TW01 FW09 ZD11 21 0.0 0.1 0.2 0.3 0.0 0.0 21 0.0 0.2 0.5 0.8 0.0 0.0 22 0.8 0.7 1.1 1.3 0.5 0.2 22 23 0.3 0.4 0.6 0.9 0.0 0.0 23 0.6 0.4 0.7 1.4 0.0 0.0 24 0.6 0.3 0.4 0.3 0.2 0.0 24 25 25 26 0.9 0.1 0.4 0.2 0.1 0.0 26 0.9 0.1 0.3 0.2 0.1 0.0 27 0.4 0.7 0.3 0.1 0.3 0.1 27 28 0.0 0.4 0.7 0.1 0.0 0.0 28 29 0.0 0.7 0.2 0.1 0.0 0.0 29 0.0 0.2 0.1 0.1 0.0 0.0 30 0.7 0.5 0.5 0.1 0.2 0.0 30 31 0.2 0.2 0.3 0.0 0.2 0.0 31 32 0.7 0.2 0.6 0.1 0.3 0.0 32 0.3 0.0 0.1 0.1 0.1 0.0 33 1.0 1.7 1.2 0.3 0.9 0.2 33 34 1.5 1.3 1.1 1.0 1.5 0.6 34 35 0.8 1.4 0.6 0.6 0.6 0.2 35 0.3 0.3 0.1 0.3 0.2 0.1 36 36 37 37 38 0.3 0.6 0.3 0.1 0.2 0.0 38 0.1 0.1 0.1 0.0 0.0 0.0 39 0.1 0.2 0.6 0.0 0.1 0.0 39 40 0.1 0.4 0.4 0.0 0.1 0.0 40 41 0.1 0.4 0.1 0.0 0.2 0.0 41 0.0 0.1 0.0 0.0 0.0 0.0
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Fig. S1 Mean stem length (± SE) of six Solanum dulcamara accessions in a common garden experiment during the growing season of 2016. One-way ANOVAWk41: n = 10, df = 5, F = 9.25, P < 0.001. Different letters indicate significant differences (P < 0.05) according to Tukey's post hoc test
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Fig. S2 Difference in gastropod and flea beetle preference for six Solanum dulcamara accessions (n = 10) in a common garden experiment during the growing season of 2016. Lines are broken at the time points that the plants were not monitored
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81
Chapter 4:
Induced resistance to slug herbivory in Solanum dulcamara is related to the temporal and spatial dynamics of metabolomic and transcriptomic responses
Onno W. Calf Tobias Lortzing Alexander Weinhold Yvonne Poeschl Janny L. Peters Heidrun Huber Anke Steppuhn Nicole M. van Dam
Manuscript in preparation Chapter 4: Dynamics of induced responses and resistance to slug herbivory
Abstract The dynamics and regulation of induced plant responses to insect herbivores are well described. In contrast, we know very little about how plants respond to gastropods. Here we aim to identify key regulated defence processes after slug feeding in Solanum dulcamara. Plants were exposed to feeding by the grey field slug (GFS; Deroceras reticulatum) for 24 h or 72 h. Leaf discs were used in preference assays to assess local and systemic induced resistance. Moreover, we measured changes in phytohormone, chlorophyll, anthocyanin, trypsin protease inhibitor (TPI) levels, and polyphenol oxidase (PPO) activity. This was combined with untargeted transcriptomic and metabolomic profiling. A 24 h feeding period sufficed to induce local and systemic resistance to GFS. Continuous exposure to slugs for 72 h most strongly affected the metabolome and induced the strongest increase of the glycoalkaloid solasonine, phenolamides, PPO activity, TPI activity and anthocyanin levels. Chlorophyll levels and transcriptomic analyses indicated that photosynthesis remained unaffected, which is unlike the common response to insects. Whereas GFS activated genes involved in jasmonic acid (JA), abscisic acid (ABA) and salicylic acid signalling, only JA, JA- isoleucine and ABA levels were elevated in damaged leaves. Twenty-four hour exposure to GFS feeding mostly upregulated gene expression, but strong response relaxation was observed after an additional 24 h without feeding. We conclude that employment of dynamically induced responses may allow S. dulcamara to balance resource investment in defence production under variable slug feeding pressure. Induced responses and resistance to GFS feeding involve common insect-induced signalling pathways and anti-herbivore defences. GFS-induced responses may therefore have far-reaching consequences for the other herbivores in the community and vice versa.
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Introduction
In addition to constitutively expressed defences, plants are able to induce defences upon herbivory to attain induced resistance (Agrawal 1998; Howe and Jander 2008; Walling 2000; Wittstock and Gershenzon 2002). Such induced responses are generally observed in local, damaged as well as in systemic, undamaged organs (Chung et al. 2013; van Dam and Raaijmakers 2006). Deployment of dynamically inducible defence mechanisms, in concert with a reorganisation of the primary metabolism, allows the plant to optimize resource investment under variable herbivore pressure (Orians et al. 2011; Schwachtje and Baldwin 2008; Steinbrenner et al. 2011; Zhou et al. 2015). The temporal dynamics of induced responses include a build-up phase upon the first signal of damage and potential decline after the herbivore has left (de Vos et al. 2005; Mason et al. 2017; Mathur et al. 2011). Tolerance and relaxation often act in synchrony with resistance mechanisms to attain an optimal response (Núñez-Farfán et al. 2007; Stout 2013; Stowe et al. 2000; Strauss and Agrawal 1999). For example, attenuation or time-lags of induced responses are cost-saving strategies to limit resource investment in defence when the chances of recurring feeding damage and/or the benefit of defence production is low (Backmann et al. 2019; Karban 2011; Underwood 2012). Moreover, plants may tolerate transient herbivory, e.g. by reallocating resources to unattacked organs for later regrowth (Orians et al. 2011; van der Meijden et al. 1988). An additional advantage of induced defences is that the response can be tailored to specific herbivores (Agrawal 2000; Chung and Felton 2011; van Zandt and Agrawal 2004). Plants perceive herbivory by chemical signals associated with feeding damage and the herbivore itself, such as compounds in the herbivore's saliva (Bonaventure et al. 2011; Heil et al. 2012; Mithöfer and Boland 2008). Differences in these signals among various herbivores allow plants to mount responses specific to the attacker (Basu et al. 2018; Voelckel and Baldwin 2004). Herbivore-induced responses lead to extensive molecular reprogramming (Leon et al. 2001; Maffei et al. 2007). This process is regulated by phytohormones, most importantly jasmonic acid (JA) and salicylic acid (SA). Cross-talk between the JA and SA signalling pathways results in specific responses to herbivores and pathogens (Beckers and Spoel 2006; Erb et al. 2012; Lorenzo and Solano 2005; Schweiger et al. 2014; Thaler et al. 2012). While the mechanisms and consequences of insect-induced plant responses are intensively studied, similar studies addressing the effect of gastropod (slug or snail) feeding damage are scarce. However, it is likely that plants use specific inducible defences to gastropod feeding, because of the selective pressure they pose on plant populations (Korell et al. 2016; Strauss et al. 2009). The few studies there are indeed show that gastropod feeding can induce the production of secondary metabolites, with diverse consequences for plant volatile emissions and defence levels in leaves (Danner et al. 2017; Desurmont et al. 2016; Khan and Harborne 1990; Viswanathan et al. 2005). Surprisingly, studies on the molecular regulation of gastropod-induced responses focussed on the effect of their
85 Chapter 4: Dynamics of induced responses and resistance to slug herbivory locomotion mucus, instead of actual feeding. These studies provided evidence that gastropod mucus applied to—artificially wounded—leaves can induce local and systemic responses (Falk et al. 2014; Kästner et al. 2014; Meldau et al. 2014; Orrock 2013). These involve both JA- and SA-related defence signalling (Falk et al. 2014; Meldau et al. 2014). Interestingly, the mucus of the grey field slug (GFS; Deroceras reticulatum) was found to contain SA (Kästner et al. 2014), which may suppress JA-dependent defence responses via negative cross-talk (Beckers and Spoel 2006; Schweiger et al. 2014; Thaler et al. 2012). In addition to gastropod-specific elicitors in locomotion mucus, also the feeding mode of gastropods is highly distinct from that of chewing or sucking insects. Gastropods possess a radula, a tongue-like chitinous structure covered with minute teeth, which is used to scrape in leaf material. Mimicked herbivory would thus unlikely fully cover the array of responses elicited by actual gastropod feeding (see also Lortzing et al. 2017). Therefore, it is essential to apply real feeding damage to be able to fully understand the mechanisms and regulation of induced responses to gastropod feeding. We recently showed that feeding by GFS can induce resistance to later slug feeding in local, damaged leaves of bittersweet nightshade (Solanum dulcamara, Calf et al. 2019). The underlying mechanisms and temporal dynamics of slug-induced responses in both local and systemic leaves are yet unknown. As pointed out before, slug-induced responses are likely different from responses to insects, because slugs have a distinct feeding mode and potential specific elicitors in their locomotion mucus. Moreover, slugs are opportunistic generalist herbivores with erratic occurrence. We therefore expect that tolerance and relaxation are part of slug-induced responses. These strategies would be reflected in the temporal expression of slug-induced responses. For instance, continuous exposure to feeding could elicit stronger responses than short-term feeding and the induced response may relax after feeding has stopped. Untargeted analyses are required to unveil whether and how plants respond to different temporal patterns of slug feeding. Here we report on two independent experiments designed to assess the temporal and spatial dynamics of changes in primary and secondary metabolism after exposure to slug feeding in relation to local and systemic induced resistance. We specifically addressed the following questions: 1) Does GFS feeding on S. dulcamara induce GFS resistance in both local, damaged and systemic, undamaged leaves? 2) What are the chemical, physiological and molecular mechanisms underlying induced responses and resistance to GFS feeding? 3) Do induced responses and resistance depend on the duration of, and the time elapsed since GFS feeding? In experiment 1, we exposed S. dulcamara plants to GFS feeding for 24 h or 72 h and sampled the local as well as systemic leaves at different time points thereafter. We used an eco-metabolomic analyses approach (Peters et al. 2018) in which we combined untargeted metabolomic profiling of leaf samples with slug preference assays. This approach allowed us to assess the local and systemic induced metabolomic responses and their consequences for later arriving slugs. We further quantified the levels of polyphenol oxidase (PPO) activity and trypsin protease inhibitors (TPI), which are well-known insect inducible defences (Lortzing et al. 2017; Nguyen et al. 2016a; Viswanathan et al. 2005). In addition to
86 Chapter 4: Dynamics of induced responses and resistance to slug herbivory increased production of defence compounds, herbivore feeding often reduces photosynthesis and induces leaf senescence (Mellway et al. 2009; Wingler and Roitsch 2008; Zhu et al. 2017). We therefore also included chlorophyll and anthocyanin measurements to assess effects on photosynthesis-related primary metabolism. In experiment 2, we exposed plants to GFS feeding for 24 h and performed microarray analyses on the transcriptome of local leaves directly at the end of the treatment period, as well as after an additional 24 h without feeding. In this experiment we also quantified the levels of the phytohormones jasmonic acid (JA), its bioactive isoleucine conjugate (JA-Ile), salicylic acid (SA) and abscisic acid (ABA). This allowed us to assess the signalling events and regulation of processes after feeding by GFS.
Materials and Methods
General experimental design Two independent experiments were performed to assess 1) induced resistance and metabolomic responses in local and systemic leaves, and 2) transcriptomic responses and phytohormone regulation after feeding by GFS in S. dulcamara. Details of each experiment are given below. In general, we used plants that were grown from seeds collected in native S. dulcamara populations. The seed batches were field collected following the procedure described by Zhang et al. (2016). All plants and slugs were cultured under the same conditions as described in Calf et al. (2018 and 2019). Plants were fitted with clip cages on the tip of one or two leaves to prevent the consumption of the whole leaf in the feeding treatments. Clip cages were left empty or received one adult GFS to initiate feeding. Depending on the experiment, plants were exposed to GFS for 24 h or 72 h (Fig. 1). This exposure to slugs, that are mostly active at night, includes both locomotion mucus application and feeding. We did not assess when the slugs were actually feeding, but plants in the slug treatments showed substantial feeding damage (estimated by eye, > 1 cm2).
Experiment 1 Seeds of a single S. dulcamara population in the Netherlands (Goeree: 51°49'23.8"N, 3°53'19.2"E, provided by the Radboud University Genebank) were used to grow plants for 26 days. At that time, plants were 30–51 cm tall. Each plant was randomly assigned to one of five treatments (n = 6 plants / treatment; Fig. 1, top half). Leaf number 10 and 11 from the apex were exposed to GFS feeding for 24 h at three different days before harvest or continuously for 72 h (Fig. 1). Another group (control) was fitted with empty clip cages (Fig. 1). Slugs were removed after the assigned period, but the empty clip cages were left on the plant until the last treatment had ended. At that point, the plants were left for an additional 24 h before sampling the leaves. Harvest of the different treatments took place at the exact same moment (Fig. 1).
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Fig. 1 Treatment overview of two independent experiments in which Solanum dulcamara plants were exposed to an undamaged control treatment (C; white box) or to feeding by the grey field slug (GFS; Deroceras reticulatum) for 24 h (grey box) or 72 h (black box). Depending on the experiment and treatment, leaf harvest (dashed line) took place 0 h, 24 h, 48 h or 72 h after the end of the treatment period
The part of the leaf that was outside of the clip cage was used to produce leaf discs using a cork-borer (1.5 cm Ø). One leaf disc was punched from each leaf (n = 12 discs / treatment / position). Leaf veins were avoided while punching out the discs and leaf discs of different plants in a single treatment were pooled. The remaining leaf material was sampled in liquid nitrogen and stored at -80 °C until further processing. Leaf discs and samples of leaf number 5 and 6 from the apex, which vascular tissue is fully connected to the treated leaves (Viswanathan and Thaler 2004), were used to assess the induced response in systemic leaves.
Slug preference assays Leaf discs were used in multiple choice slug preference assays to test the effect of GFS feeding on slug preference. One leaf disc of each treatment was offered to GFS in Petri dishes (n = 12; 5 discs per Petri dish) following the procedure described in Calf et al. (2018 and 2019). Local and systemic leaves were tested separately.
Untargeted metabolomic profiling An untargeted metabolomics approach was used to assess the effect of GFS feeding on the metabolomic profiles of S. dulcamara leaves. Leaf samples obtained from experiment 1 were extracted and analysed by LC-qToF-MS following the method described in Calf et al. (2019). For details on the LC gradient and flow rate see Document S1. Local and systemic leaf samples were analysed separately. This approach resulted in a dataset with 142 and 170 compounds for the local and systemic sample sets, respectively.
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Targeted analyses of compounds The levels of total proteins, polyphenol oxidase (PPO) activity, trypsin protease inhibitor (TPI) activity, chlorophyll and anthocyanin in local and systemic leaves were quantified spectrophotometrically to assess the effect of GFS feeding. Three technical replicates were analysed in microplate assays on a SpectraMax® 190 absorbance plate reader (Molecular Devices, San Jose, CA, USA) using SoftMax® Pro software. The levels of total proteins, PPO activity and TPI were quantified from the same protein extract obtained from 90–110 mg ground fresh leaf material using 0.8 ml 0.1M KPO4 buffer (pH 7.3) containing 5 % (w / v) PVPP (Sigma-Aldrich, Zwijndrecht, the Netherlands, P6755) and 0.83 % (v / v) Triton™ X-100 (Sigma-Aldrich, T8787). Total protein content (mg g-1 FW) was determined relative to the absorbance of albumin (735078, Boehringer- Mannheim, Mannheim, Germany) at 595 nm using the Bradford method (Bradford 1976). -1 -1 Activity of PPO was quantified by the change in absorbance at 435 nm (ΔOD435 min mg protein) using caffeic acid (Sigma-Aldrich, C0625) as substrate following a method derived from Thaler et al. (1996). TPI activity (μg mg-1 protein) was quantified relative to the inhibition activity of bovine trypsin protease (Sigma-Aldrich, T8003) by soybean TPI (Sigma- Aldrich, T9003). We used N-benzoyl-DL-arginine-ß-naphtylamide hydrochloride (BANA, Sigma-Aldrich, B4750) as substrate for the TPI assay and measured inhibition by difference in absorbance at 550 nm following the method derived from Bode et al. (2013). Total chlorophyll levels (mg g-1 FW) were quantified by measuring absorbance at 654 nm of a leaf extract obtained from 30–40 mg ground fresh leaf material using 1.8 ml absolute EtOH (Emsure®, Sigma-Aldrich) according to the method described by Wintermans and Demots (1965). -1 Relative levels of total anthocyanins (OD530 g FW) were quantified by measuring absorbance at 530 and 657 nm of a leaf extract obtained from 75–90 mg ground fresh leaf material using 0.6 ml MeOH:AcOH buffer (45 % / 5 %, v / v, pH 2.68) following the protocol by Nakata and Ohme-Takagi (2014).
Experiment 2 Four seed batches were selected from S. dulcamara populations in the Netherlands (Goeree: 51°49'23.8"N, 3°53'19.2", registration nr. B24750010; Friesland: 52°58'36.2"N, 5°30'59.4"E, registration nr. B24750030, both provided by the Radboud University Genebank) and Germany (Erkner: 52°25'07.3''N, 13°46'26.2''E; Siethen: 52°16'53.7"N, 13°11'18.7"E). Plants were 25 days old and 27–45 cm tall at the onset of the experiment. Plants were randomly assigned to one of four treatments (n = 4 plants / population / treatment, Fig. 1, bottom half). Leaf number 10 from the apex was exposed to feeding by GFS for 24 h or fitted with an empty clip cage for the same time period (undamaged control). Harvest took place directly (0 h) after removing the slugs or after an additional 24 h without exposure to GFS (Fig. 1).
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The total leaf area that was covered by the clip cage was collected for analyses of gene expression. The leaf material outside the clip cage was sampled separately for quantification of phytohormones. This ensured that the analyses were not affected by contamination with locomotion mucus, which possibly contains SA (Kästner et al. 2014). All samples were flash frozen in liquid nitrogen and stored at -80 °C until further processing.
Transcriptomic analyses The transcriptomic response upon GFS feeding was assessed using microarray analyses. Total RNA extraction and purification were performed on ground fresh leaf material using the RNeasy® Plant Mini kit (Qiagen, Venlo, the Netherlands) and DNAse I (Fermentas, RNAse-free) following the manufacturer’s instructions. Quality and quantity of the RNA were estimated using a NanoDrop 1000 device (Thermo Fisher Scientific, Waltham, MA, USA) and 1.5 % agarose gel electrophoresis. Equal quantities of total RNA from the four plants of a single plant population at each time point were pooled, resulting in four total RNA samples per treatment (control or induced). The RNA samples were further inspected for concentration, integrity and purity by electrophoretic analysis with the RNA 6000 Pico Kit using a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). All samples had an RNA integrity number (RIN) between 6.6 and 7.2 and were hybridized on 8x60K Agilent microarrays. cDNA labelling, microarray hybridization, design and technical validation were performed as described in Lortzing et al. (2017). Upon publication of this chapter, the design and the experimental data of the microarray will be made available at NCBI Gene Expression Omnibus (GEO Accession: GSE131208).
Phytohormone analyses Phytohormones were quantified as described in Geuss et al. (2018). In brief, 100–110 mg of fresh leaf material were ground and extracted 2 times with 1.0 ml ethyl acetate. Deuterated internal standards were included for salicylic acid (SA; OlChemIm Ltd., Olomouc, Czech Republic), abscisic acid (ABA; OlChemIm Ltd.), jasmonic acid (JA; Purity Compounds Standards GmbH, Cunnersdorf, Germany) and jasmonic acid-isoleucine (JA-Ile; Purity Compounds Standards GmbH). Extracts were vacuum-dried and re-eluted in 0.4 ml 70 % MeOH containing 0.1 % formic acid (v / v). Phytohormones were separated on a UPLC C18 column in a water and MeOH gradient (ACQUITY UPLC BEH-C18, 50 × 2.1 mm, particle size 1.7 μm), fragmented and detected in an ESI-MS/MS-qTOF detector (Synapt G2-S HDMS; Waters®, Milford, MA, USA) and quantified according to the respective internal standard.
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Statistical analyses experiment 1 and 2 Absolute leaf disc consumption (mm2) in the preference assays (experiment 1) was analysed using nonparametric statistical methods from the R “stats” and “PMCMR” packages (Pohlert, 2014; R Core Team, 2016). Friedman’s rank sum test was applied followed by Conover’s post hoc test with Bonferroni correction for multiple comparisons to evaluate overall preference differences among treatments. The Petri dish number was used as grouping factor (n = 12). The minimum consumption to accurately assess preference was set at 100 mm2 (11.7 % of the offered leaf material). Therefore, one replicate with local leaves was excluded, leaving 11 suitable replicates. The metabolomic datasets from experiment 1 were analysed using the online R- based tool MetaboAnalyst 4.0 (Chong et al. 2018). The effect of GFS feeding on metabolomic profiles was assessed with discriminant analyses using orthogonal projection models to latent structures (OPLS-DA, Wiklund et al. 2008; Worley and Powers 2013). These models separate metabolomic variation that is correlated with the treatment from random variation among samples. Individual OPLS-DA models were built to compare the generalized log- transformed (glog) intensity values of all compounds in the control treatment and each of the GFS feeding treatments. Predictive significance of the models was assessed based on 1000 permutations using cross-validated predictive ability (Q2) as performance measure (Westerhuis et al. 2008; Worley and Powers 2013). The abundance of different compounds among samples can only be compared on a relative scale. Therefore, the absolute variance among treatments was not evaluated. Instead, the reliability of the response of each compound (correlation) in relation to the first predictive component (p) from the model was used as a proxy for compound induction by GFS feeding. The explained variance by the 2 predictive component (R X(p)) was used as a proxy for the modelled effect size of the treatment. The local and systemic response were tested separately. Compounds that showed high correlation with the model (pcorr > 0.7) and significant regulation according to
Student’s t-test after Bonferroni correction for multiple comparisons (Padj < 0.05), were selected as compounds of interest. The relative level of each compound of interest to the maximum mean value across all treatments was used for presentation of the treatment effect on compounds in a heat map (%). The raw data files, pre-processed peak matrix and protocol descriptions are available under the accession number MTBLS1132 of the Metabolights repository (https://www.ebi.ac.uk/metabolights/). Microarray data were analysed using the “limma” software packages from Bioconductor in “R” (R Core Team, 2015; Ritchie et al., 2015). Quantile normalised reads of 21261 out of 62970 contigs were included for data analyses after setting a critical level of quantification based on dark corner intensity (90 % percentile of non-labelled hairpin DNA probes) as well as removal of structural spots, repeatedly spotted probes and averaging repeatedly spotted contigs. Average fluorescence values of contigs were log2 transformed and visualised by principal component analyses (PCA) using the “prcomp” function. Data were fitted to a linear model using the “lmFit” function using dual contrasts (induced
91 Chapter 4: Dynamics of induced responses and resistance to slug herbivory response at 0 h or 24 h after exposure to GFS feeding versus the respective control). Contigs that showed an absolute log2-fold difference in expression > 1 and significance (Padj) < 0.05 after correction for false discovery rate according to the Benjamini-Hochberg method were considered significantly regulated. Contig expression levels in the microarray were validated based on Pearson’s correlation between fold-change in expression as estimated by microarray and RT-qPCR analyses for 10 genes related to primary and secondary metabolism (PR1a, OPR3, PI1, KPI4, LOXD, PPOA, ERF4, PPO, NIM1 and CS, R2 = 0.97, Fig. S1 and Document S1). Gene primers for qPCR (Table S1) were designed using the primer BLAST-tool of the National Centre for Biotechnology Information (NCBI). Functional descriptions of regulated contigs were obtained from the Sol Genomics Network (SGN, solgenomics.net/) and based on homology with tomato (D'Agostino et al. 2013). Further functional analyses of regulated contigs was performed using gene ontology enrichment analyses for overrepresentation of molecular functions and biological processes using the “TOPGO” package (Alexa and Rahnenfuhrer, 2010). GO annotation was based on the annotation by Nguyen et al. (2016a). GO enrichment was assessed by comparing the distribution of the list of differentially expressed contigs to the distribution of all targets included in the data analysis. We used the “elim” algorithm, which corrects for the annotation of contigs in multiple parental GO terms (Alexa et al. 2006). Only GO terms that included a minimum of 50 annotated contigs were included for analyses. The P-values for the enrichment of each GO term are based on Fisher’s exact tests. The effect of GFS feeding on levels of total proteins, PPO activity, TPI activity, anthocyanin and chlorophyll in leaf samples of experiment 1, as well as phytohormone levels from samples of experiment 2, were statistically tested using parametric statistical methods from the “stats”, “car” and “agricolae” software packages in “R” (de Mendiburu 2017, Fox and Weisberg 2011, R Core Team 2016). Data were log10 or square root transformed when assumptions for homogeneity of variances among treatments and/or normal distribution of residuals were not met according to Levene’s and Shapiro-Wilk normality test, respectively. Treatment effects on compounds and enzymes in experiment 1 were tested using one-way ANOVA, followed by Tukey’s post hoc test for differences between treatments. Phytohormone levels in experiment 2 were statistically tested using paired t-tests, including plant population of origin as paired factor.
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Results
Induced effect on GFS feeding preference (experiment 1) Slugs preferred to feed on leaf discs of plants that were left undamaged (control treatment) compared with those of plants exposed to GFS feeding (Fig. 2). This preference was significant for both local and systemic leaves. A 24 h feeding period was sufficient to reduce slug preference using local leaves, but 72 h continuous exposure to GFS resulted in the highest resistance. In contrast to the local leaves, systemic induced leaves were equally resistant, without a significant effect of the temporal feeding pattern.
Fig. 2 Mean consumption relative to the total consumed leaf area (± SE, n = 12) on Solanum dulcamara leaf discs in preference assays with the grey field slug (GFS; Deroceras reticulatum). Slugs were offered leaf discs from plants that were exposed to an undamaged control treatment (C; white bar) or to feeding by GFS for 24 h (grey bars) or 72 h (black bar). Leaf discs were collected 24 h, 48 h or 72 h after the end of the treatment period (see also Fig. 1). Discs of either local or systemic leaves were offered in independent preference assays. Different letters over the bars indicate significant preference differences (P < 0.05) according to Conover’s 2 post hoc test of Friedman ANOVA (χ local =17.065, Plocal = 2 0.002; χ systemic = 11.661, Psystemic=0.022) after Bonferroni correction of P-values for multiple comparisons
Metabolomic profiling (experiment 1) OPLS-DA models were built to assess the effect of different temporal patterns of GFS feeding on leaf metabolomes (Table 1). All models, except one (systemic 24 h / 24 h; Q2: 48.8, P < 0.1, Table 1) had a significant cross-validated predictive ability (Q2: 60.6–83.9 %). The effect 2 sizes (R X(p)) of models testing the local response were always greater than the respective models testing the systemic response (Table 1). In both leaf positions, the effect size of the model increased with time after 24 h of exposure to GFS, but 72 h continuous exposure to slugs elicited the strongest response. We selected compounds showing a strong correlation with the predictive component of each model (pcorr > 0.7) and significant regulation according to Student’s t-test after
Bonferroni correction for multiple comparisons (Padj < 0.05, Table S2). Moreover, we focused on the induction pattern of those of which the levels were consistently regulated in at least two treatments, or in both local and systemic leaves within a treatment (Fig. 3a).
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Table 1 Results of OPLS-DA models comparing the metabolomic profiles (n = 6) of undamaged Solanum dulcamara leaves with those exposed to feeding by the grey field slug (GFS; Deroceras reticulatum) for 24 h or 72 h. Leaves were harvested at 24 h, 48 h or 72 h past the end of the treatment period (see also Fig. 1). Model codes indicate the duration of GFS feeding exposure and the time between the end of GFS exposure and harvest (feeding / harvest). Separate models were built for local and systemic leaves. The cross-validated 2 2 predictive ability (Q ) as well as explained percentage of variance by the predictive component (R X(p)) are provided for each model. Symbols indicate significance of the cross-validated model based on 1000 permutations (*** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.10)
2 2 OPLS-DA model Q R X(p) Local response 24 h / 24 h 64.6* 14.9 24 h / 48 h 66.3** 16.2 24 h / 72 h 75.2** 19.9 72 h / 24 h 83.9** 24.1 Systemic response 24 h / 24 h 48.8+ 11.5 24 h / 48 h 60.8* 13.1 24 h / 72 h 60.6* 14.8 72 h / 24 h 75.3** 16.0
Fifteen compounds were consistently regulated in at least two treatments (Fig. 3a; 10 local, 5 systemic). In local leaves, most selected compounds (7 out of 10) showed a gradually progressing increase in their response (either up or down) with time after 24 h of GFS feeding, and the strongest response upon 72 h continuous feeding exposure (Fig. 3a, Local). Two of these gradually regulated compounds were downregulated upon GFS feeding (L119 and L088), whereas five were upregulated (L026, L005, L053, L050 and L007). Among the gradually increased compounds there are two putatively identified phenolamides; N- caffeoylputrescine and a related metabolite (L025 and L026 in Fig. 3a, see also Fig. S2). In addition to the gradually regulated compounds, one compound was fully downregulated in local leaves in all slug feeding treatments (L097, < 20 % of the control level). Two other compounds showed the strongest response upon 24 h of exposure to GFS (L099 and L024). The changes in systemic leaves were more erratic. The gradual response that was observed for most locally regulated compounds was only seen for two compounds in systemic leaves (Fig. 3a, Systemic). One of these compounds was upregulated (S129) and the other one downregulated (S116). Two compounds were found to be significantly regulated both in local and systemic leaves at one single time point only (Fig. 3a, Both). One of these compounds was the glycoalkaloid solasonine (LGA3 / SGA3 in Fig. 3a), the levels of which were induced after 72 h continuous exposure to slug feeding (see also Fig. S2). In the 24 h feeding treatments, solasonine levels gradually increase after slugs were removed. The levels of an unidentified compound were also upregulated at both leaf positions (L124 / S147, m/z 377.15 in Fig. 3a), but only 72 h after 24 h of exposure to GFS feeding. The relative increase of this compound was small, as constitutive levels (C) were high to start with (~60 %).
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Targeted analyses of proteins and compounds (experiment 1) We assessed the effect of GFS feeding on well-known insect inducible defensive proteins and compounds (Table 2 and Fig. 3b). The levels of PPO activity, TPI activity and anthocyanin significantly increased in response to 72 h continuous exposure to slug feeding (Fig. 3b), but only in local leaves (Table 2). A 24 h feeding period resulted in intermediate levels of these compounds. Total protein and chlorophyll levels were not affected by slug feeding (Table 2).
Fig. 3 Induced responses in Solanum dulcamara leaves after feeding by the grey field slug (GFS, Deroceras reticulatum). Plants (n = 6) were left undamaged (control treatment: C, white) or exposed to feeding by GFS for 24 h (grey) or 72 h (black). Samples were collected 24 h, 48 h or 72 h after the end of the treatment period (see also Fig. 1). a) Mean relative abundance of compounds of interest, the levels of which were affected by GFS feeding in at least two treatments or in both local and systemic leaves in within one treatment. The compound ID for local (L) and systemic (S) leaves is composed of a unique number and the quantified mass (m/z [M+H]). b) Mean relative levels (± SE) of polyphenol oxidase (PPO) activity, trypsin protease inhibitor (TPI) activity and total anthocyanins in local leaves. Treatment effects were significant according to one-way ANOVA (Table 2). Different letters over the bars indicate significant differences among treatments according to Tukey’s post hoc test (P < 0.05)
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Table 2 One-way ANOVA table showing test results for the effect of feeding by the grey field slug (Deroceras reticulatum) on the levels of total proteins, polyphenol oxidase (PPO) activity, trypsin protease inhibitor (TPI) activity, anthocyanin and chlorophyll in local and systemic Solanum dulcamara leaves (n = 6, df = 4). Symbols indicate significance at the following levels: *** P < 0.001, * P < 0.05. Significant treatment effects are shown in Fig. 3b
Parameter FLocal FSystemic Proteins mg g-1 FW 0.508 0.348 PPO rel. act. mg-1 protein 2.976* 0.164 TPI rel. µg mg-1 protein 3.297* 0.491 Anthocyanin rel. amount g-1 FW 11.850*** 0.182 Chlorophyll rel. amount g-1 FW 0.366 1.754
Transcriptomic analyses (experiment 2) To assess the molecular mechanisms underlying the observed metabolomic changes, we analysed transcriptomes of plants that were sampled directly (0 h) after 24 h of exposure to GFS feeding as well as after an additional 24 h without feeding (Fig. 1, lower half). Principal component analyses were performed on the expression values of all quantified contigs (21261) to assess differences in the transcriptomic profiles (Fig. 4a). The first principal component (PC1) explained the effect of GFS feeding, which accounted for 32.1 % of the total variance among samples. Samples taken directly (0 h) after exposure to GFS feeding separated further from the control samples than samples taken after an additional 24 h without feeding. The second principal component (PC2, 14.9 %) revealed that transcriptomic profiles differed among plant populations. The German populations (Siethen and Erkner) separated from the Dutch populations (Goeree and Friesland). A total of 1526 contigs (7.2 % of the total set) was significantly up- or downregulated in response to feeding by GFS at either one of both time points (Fig. 4b). The total number of regulated contigs directly (0 h) upon 24 h of exposure to GFS feeding (1424) was > 5 times greater than after an additional 24 h without feeding (262, Fig. 4b). Overall, more contigs were upregulated than downregulated upon slug feeding. In the direct response treatment (0 h), the number of upregulated contigs (838) was twice as high as the number of downregulated contigs (426), whereas about an 8-fold difference was observed after an additional 24 h without feeding (90 up, 12 down, Fig. 4b). Two PPO enzymes (c2630 and c16387), five protease inhibitors (c460, c673, c1119, c4199 and c22651) and an anthocyanidin synthase (c10758) were among the 25 strongest regulated contigs at either one of both time points (Table S3, 11–208 fold-change in expression).
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Fig. 4 Transcriptomic profiles in Solanum dulcamara leaves upon 24 h of exposure to feeding by the grey field slug (Deroceras reticulatum). Samples were collected directly (0 h; blue) or 24 h (red) after the end of the treatment period (see also Fig. 1). a) Principal component analysis of overall transcriptomic differences among samples. Ellipses show 95 % confidence intervals. Letters indicate the plant population of origin; E: Erkner, S: Siethen, G: Goeree, F: Friesland. b) Numbers of significantly up- and downregulated contigs that are unique for, or shared among harvest time points (fold change > 2, Padj < 0.05, n = 4). The total number of regulated contigs at each time point is given in brackets. c) Percentage of contigs that was significantly up- or downregulated in 83 gene ontology (GO) terms. GO terms were grouped according to overall involvement in biological processes and sorted based on the number of contigs in each term (group numbers correspond to the numbers in Table 3)
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Enrichment analyses of gene ontology terms were performed to assess the biological processes that are overrepresented at either one or both time points. The 77 (out of 339)
GO terms that were most significantly enriched upon slug feeding (Padj < 0.0001) were selected and grouped according to the overall biological process they are involved in (group 1–6 in Table 3). In addition to the most strongly enriched terms, we selected six GO terms related to photosynthesis (group 7 in Table 3), which were previously found to be regulated upon insect feeding (Lortzing et al. 2017; Nguyen et al. 2018). The total number of up- and downregulated contigs in each selected term as well as the specific numbers at each time point were further evaluated (Fig. 4c and Table 3). Most contigs in the selected GO terms were upregulated in response to slug feeding (Fig. 4c). The numbers of regulated contigs were always higher directly (0 h) after 24 h of exposure to slug feeding than after an additional 24 h without feeding (Fig. 4c; red bar lower than blue bar). Approximately 10–16 % of all the contigs in terms related to defence responses (group 1) were significantly regulated. This group included various terms that involve responses to wounding, fungi, bacteria or nematodes (Table 3). The many responses related to abiotic stimuli (group 2) indicate that there is large overlap in regulation of responses to biotic and abiotic stress. Various terms related to defence-signalling phytohormones (group 3) were regulated upon slug feeding; JA (4 terms), SA (3 terms), ABA (2 terms) as well as single terms related to auxin, karrikin, gibberellin, ethylene and brassinosteroids (Table 3). The highest percentage of phytohormone-related contigs was regulated for JA metabolic (GO:31) and biosynthetic processes (GO:33, both about 23 %, Fig. 4c and Table 3). The overall largest percentage of regulated contigs was observed for terms involved in organic compound metabolism (up to 35 %, group 4). This group includes the biosynthetic processes for many defence-related compounds, such as flavonoids, phenylpropanoids, coumarins and alkaloids. Also terms related to primary metabolism, such as phenylalanine and tyrosine metabolism, were enriched (group 4). Overall, terms involved in cell wall biogenesis (group 5) revealed more unique responses at both time points than other GO groups (Fig. 4c; black bar higher than red or blue bar). The upregulation of many basic cellular metabolic and transport processes (group 6) indicates extensive reprogramming of the plants’ primary metabolism upon slug feeding. In contrast, only relatively few contigs involved in photosynthesis were regulated (group 7). Within this group, only term 81 (chlorophyll biosynthetic process) was significantly enriched (Padj = 0.007), of which most genes were upregulated (Fig. 4c).
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Table 3 Gene Ontology (GO) terms that were enriched strongest upon 24 h of exposure to feeding by the grey field slug (Deroceras reticulatum) as well as 6 terms that involve photosynthesis (Group 7). GO term numbers (#) correspond to the number in Fig. 4c. GO terms were grouped according to overall involvement in biological processes and sorted based on the number of contigs in each term. The number (n) of regulated contigs is given for the total response as well as separate for two time points (0 h and 24 h) after the end of the treatment. All terms in group 1–6 were significantly regulated (Padj < 0.0001) according to Fisher’s exact test after correction for false discovery rate using to the Bonferroni method. Symbols in group 7 indicate significance on the following level: ** Padj < 0.01
Contigs in n regulated contigs # GO.ID GO term description term Total 0 h 24 h Group 1: Defence response 1 GO:0006952 defence response 4146 440 407 76 2 GO:0009620 response to fungus 1567 192 178 42 3 GO:0042742 defence response to bacterium 1361 152 136 35 4 GO:0009611 response to wounding 1314 190 186 31 5 GO:0050832 defence response to fungus 1106 129 119 30 6 GO:0009627 systemic acquired resistance 1056 126 118 19 7 GO:0010200 response to chitin 968 107 105 16 8 GO:0010363 regulation of hypersensitive response 889 97 96 15 9 GO:0009624 response to nematode 361 58 54 12 Group 2: Response to abiotic stimulus 10 GO:0009651 response to salt stress 2286 252 231 47 11 GO:0009409 response to cold 1714 182 161 40 12 GO:0006979 response to oxidative stress 1462 168 160 28 13 GO:0009414 response to water deprivation 1421 204 186 40 14 GO:0042538 hyperosmotic salinity response 528 87 81 14 15 GO:0010167 response to nitrate 417 75 68 12 16 GO:0010224 response to UV-B 322 44 43 8 17 GO:0010583 response to cyclopentenone 252 37 33 11 18 GO:0010106 cellular response to iron ion starvation 190 40 35 10 19 GO:0009269 response to desiccation 126 34 30 10 20 GO:0071456 cellular response to hypoxia 113 30 29 9 Group 3: Phytohormonal response 21 GO:0009737 response to abscisic acid 1932 221 207 36 22 GO:0009753 response to jasmonic acid 1243 171 166 26 23 GO:0009751 response to salicylic acid 1202 147 139 22 24 GO:0009733 response to auxin 1045 133 125 24 25 GO:0009738 abscisic acid-activated signalling pathway 798 90 88 13 26 GO:0009867 jasmonic acid mediated signalling pathway 721 94 89 18 27 GO:0080167 response to karrikin 580 94 91 15 28 GO:0009739 response to gibberellin 575 75 71 11 29 GO:0009862 SA-mediated systemic acquired resistance 524 64 60 8 30 GO:0009696 salicylic acid metabolic process 474 60 55 13 31 GO:0009694 jasmonic acid metabolic process 440 99 94 15 32 GO:0009873 ethylene-activated signalling pathway 368 53 48 12 33 GO:0009695 jasmonic acid biosynthetic process 338 78 73 10 34 GO:0016132 brassinosteroid biosynthetic process 264 43 41 14
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Table 3 Continued (2/2)
Contigs in n regulated contigs # GO.ID GO term description term Total 0 h 24 h Group 4: Organic compound metabolism 35 GO:0009813 flavonoid biosynthetic process 623 93 91 17 36 GO:0009698 phenylpropanoid metabolic process 601 116 102 32 37 GO:0009699 phenylpropanoid biosynthetic process 489 92 81 24 38 GO:0006633 fatty acid biosynthetic process 424 56 48 12 39 GO:0016126 sterol biosynthetic process 320 56 50 18 40 GO:0009805 coumarin biosynthetic process 282 47 45 10 41 GO:0009963 positive regulation of flavonoid biosynthesis 257 38 37 9 42 GO:0006084 acetyl-CoA metabolic process 216 34 31 10 43 GO:0042398 cellular modified amino acid biosynthesis 210 36 34 5 44 GO:0006570 tyrosine metabolic process 174 31 30 6 45 GO:0006558 L-phenylalanine metabolic process 140 25 25 3 46 GO:0006598 polyamine catabolic process 123 35 33 6 47 GO:0006639 acylglycerol metabolic process 95 19 17 4 48 GO:0018874 benzoate metabolic process 89 18 18 4 49 GO:0042343 indole glucosinolate metabolic process 76 17 17 3 50 GO:0019852 L-ascorbic acid metabolic process 75 17 11 8 51 GO:0071616 acyl-CoA biosynthetic process 68 15 14 2 52 GO:0009821 alkaloid biosynthetic process 65 18 17 3 53 GO:0016104 triterpenoid biosynthetic process 62 20 20 3 54 GO:0019745 pentacyclic triterpenoid biosynthetic process 55 15 15 3 Group 5: Cell wall biogenesis 55 GO:0042546 cell wall biogenesis 899 130 106 45 56 GO:0010383 cell wall polysaccharide metabolic process 729 102 77 41 57 GO:0045492 xylan biosynthetic process 357 54 36 26 58 GO:0010413 glucuronoxylan metabolic process 355 53 35 26 59 GO:0009808 lignin metabolic process 227 47 39 17 60 GO:0009809 lignin biosynthetic process 212 39 32 13 61 GO:0009834 plant-type secondary cell wall biogenesis 143 38 29 16 Group 6: Cellular metabolic process and transport 62 GO:0055114 oxidation-reduction process 2068 216 201 41 63 GO:0005982 starch metabolic process 817 98 86 24 64 GO:0009744 response to sucrose 575 73 68 7 65 GO:0000041 transition metal ion transport 532 76 66 21 66 GO:0009750 response to fructose 435 64 56 17 67 GO:0005985 sucrose metabolic process 421 69 63 14 68 GO:0015706 nitrate transport 386 76 70 14 69 GO:0010310 regulation of hydrogen peroxide metabolism 358 47 44 7 70 GO:0006826 iron ion transport 273 39 34 10 71 GO:0042744 hydrogen peroxide catabolic process 248 36 31 10 72 GO:0000272 polysaccharide catabolic process 211 33 27 9 73 GO:0006857 oligopeptide transport 185 36 35 4 74 GO:0015824 proline transport 153 28 27 4 75 GO:0009225 nucleotide-sugar metabolic process 95 21 17 4 76 GO:0015837 amine transport 87 22 20 5 77 GO:0048359 mucilage metabolism (seed coat development) 62 16 15 2 Group 7: Photosynthesis 78 GO:0015979 photosynthesis 793 51 35 22 79 GO:0009658 chloroplast organization 601 37 31 10 80 GO:0019684 photosynthesis, light reaction 585 35 22 18 81 GO:0015995 chlorophyll biosynthetic process 341 38** 30 13 82 GO:0010207 photosystem II assembly 264 9 6 3 83 GO:0009902 chloroplast relocation 232 10 7 3
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Phytohormone analyses (experiment 2) Jasmonic acid as well as the levels of its bioactive isoleucine conjugate (JA-Ile) were elevated at both time points, with the strongest increase measured directly (0 h) after 24 h of exposure to GFS feeding (Fig. 5). Salicylic acid levels were not affected, whereas ABA levels were elevated directly (0 h) after 24 h of exposure to slug feeding, but not after an additional 24 h without feeding.
Fig. 5 Levels of phytohormones in Solanum dulcamara leaves upon 24 h of exposure to feeding by the grey field slug (Deroceras reticulatum). a) jasmonic acid, b) jasmonic acid-isoleucine, c) salicylic acid, and d) abscisic acid. Samples were collected directly (0 h) or 24 h after the end of the treatment period (see also Fig. 1). Bars represent mean levels (± SE, n = 4) in undamaged control (white bars) and damaged leaves (grey bars). The symbols indicate significant differences between treatments on a single time point according to Welch’s t-test on the following levels: ** P < 0.01, * P < 0.05, + P < 0.1
Discussion
Our study revealed that S. dulcamara has a dynamically inducible defence system against GFS. The induced responses elicited by GFS feeding reduced feeding preference of conspecifics. Hence we may speak of induced resistance (Karban and Baldwin 1997). Twenty- four h of exposure to feeding was sufficient to induce metabolomic changes and induce resistance in both local and systemic leaves. The metabolomic response was increasing with time after 24 h feeding. However, 72 h of continuous exposure to slugs elicited the strongest metabolomic response and accordingly induced the highest resistance. Plants responded to GFS feeding by increasing commonly known defences, such as solasonin, TPIs, PPOs, phenolamides and anthocyanins. GFS feeding triggered transcription of genes related to diverse pathways, including JA and SA phytohormone signalling. While physiological analyses confirmed an induction of JA, JA-Ile and ABA, the level of SA was not affected. Induced responses to GFS are therefore regulated by similar compounds and pathways that cause insect resistance. However, other than insect feeding, slug herbivory did not alter chlorophyll levels or downregulate transcription of photosynthesis-related genes. Moreover, most induced responses showed relaxation after feeding had stopped. Taken together, this suggests that S. dulcamara limits resource investment in defence and maintains primary performance in the absence of further slug feeding damage.
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Glycoalkaloids likely play a key role in induced resistance to GFS. Solasonine levels were significantly higher in both local and systemic leaves upon 72 h of exposure to slug feeding. Moreover, the GO term for alkaloid biosynthetic processes (GO:0009821) was among the most strongly enriched terms overall. We previously showed that constitutive resistance to GFS in S. dulcamara is associated with high levels of glycoalkaloids (Calf et al. 2018). Taken together, this suggests that glycoalkaloids cause both constitutive and induced resistance to slugs in S. dulcamara. We also reported that gastropods and the specialist insect community show opposite feeding preference for specific glycoalkaloid chemotypes (Calf et al. 2019). Therefore, slug feeding, and glycoalkaloid induction, may have different effects on the many members of the natural herbivore community. In the same study, however, glycoalkaloid levels and slug resistance in clones of glycoalkaloid chemotypes were unaffected by the same 72 h GFS feeding treatment as used in this study (Calf et al. 2019). This difference with our current finding may be due to the fact that we used younger and therefore smaller plants that were grown from seeds this time. The inducibility of plants often varies with the developmental and ontogenetic stage of a plant (Barton and Koricheva 2010; Boege and Marquis 2005). Whether induced glycoalkaloid production upon slug feeding and the costs thereof differ between seed-grown plants and clones should be tested in controlled experiments.
In addition to GAs, we found several more induced defences that possibly affect slug preference or performance. Genes related to TPI and PPO activity were among the most strongly regulated genes after 24 h exposure to GFS feeding. This is in line with their increased activity in local leaves, in particular after 72 h continuous exposure to slugs. Also the induction of phenolamides and anthocyanins was found in both the transcriptomic and metabolomic analyses. Each of these compounds was previously found to be induced upon insect feeding and pathogen infection or even primed by insect egg deposition in S. dulcamara and other plant species (Bandoly et al. 2015; Gaquerel et al. 2014; Geuss et al. 2017; Gould 2004; Karageorgou and Manetas 2006; Kaur et al. 2010; Muroi et al. 2009; Nguyen et al. 2016a; Raj et al. 2006; Turra and Lorito 2011; Viswanathan et al. 2005). This indicates that feeding by GFS induces defences that constitute part of the general inducible defence arsenal against biotic stresses and may therefore enhance plant resistance to later arriving herbivores or pathogens. However, several GFS-induced compounds were not yet identified and possibly serve a specific role in slug resistance in S. dulcamara.
GFS feeding triggered several phytohormones involved in defence signalling (Erb et al. 2012). In line with the transcriptomic response, JA, JA-Ile and ABA levels were significantly elevated upon slug feeding. However, SA levels were unaffected, whereas, on the transcriptomic level, genes involved in SA metabolism and signalling were upregulated. Similar results were reported for the response of S. dulcamara to beetle and caterpillar feeding (Geuss et al. 2018; Lortzing et al. 2017; Nguyen et al. 2018). It may be that SA-levels only increased very locally at the feeding site, which was not included for phytohormone analyses. Interestingly, the locomotion mucus of GFS was reported to contain SA and induce
102 Chapter 4: Dynamics of induced responses and resistance to slug herbivory the local expression of the PR1 marker gene for SA defence signalling in Arabidopsis thaliana (Kästner et al. 2014). However, also the locomotion mucus of other gastropods species, without SA, can induce local PR1 expression (Meldau et al. 2014). We did not test whether our field-collected slugs also have SA in their slime, but expression of a S. dulcamara PR1 homologue was unaffected in our study (c374 in Fig. S1). SA and JA are generally involved in response to herbivorous insects; both phytohormones often act in negative cross-talk (Beckers and Spoel 2006; Schweiger et al. 2014; Thaler et al. 2012). It was speculated that GFS may apply SA to suppress herbivore defences in unwounded leaves (Kästner et al. 2014). In our study, this interaction, if at all, was not sufficient to antagonise the wound-induced responses and prevent induced resistance in local and systemic leaves. Nevertheless, it would be interesting to test whether all GFS individuals have SA in their locomotion mucus, and whether feeding by GFS without SA induces similar, weaker or stronger responses.
The many transcriptomic responses related to abiotic stimuli indicate that there is large overlap in responses to biotic and abiotic stress factors. Similar results were reported in an experimental analyses on interactive responses to drought, flooding and insect feeding in S. dulcamara (Nguyen et al. 2018). Such cross-talks between signalling pathways allow the plant to orchestrate both resource acquisition and defence allocation. For instance, overall downregulation of primary metabolic processes is frequently observed upon insect feeding (Zhou et al. 2015). In contrast to this common response, we observed overall upregulation of transcriptional responses, including those for primary metabolism, upon slug feeding. More specifically, negative effects of beetle and caterpillar feeding were reported on photosynthesis related processes in S. dulcamara (Lortzing et al. 2017; Nguyen et al. 2018) and other plant species (Bilgin et al. 2010; Lawrence et al. 2008; Tang et al. 2006; Zangerl et al. 2002). In our current study, only few genes involved in photosynthesis were significantly regulated and chlorophyll levels did not change. These results provide evidence that the photosynthetic capacity of the leaves was maintained, although we did not measure actual photosynthesis levels. The overall absence of transcriptomic downregulation and maintenance of photosynthetic capacity suggest that GFS-induced defence in S. dulcamara does not come at a cost in terms of reduced primary metabolism and performance.
The observed temporal and spatial patterns suggest that S. dulcamara invests in an optimal response to GFS feeding. The strength of the induced response depended both on the period the slug was on the plant and the time past after the slug was removed. The transcriptomic response, for instance, showed a strong relaxation in the absence of further damage. This may well explain why the metabolomic response upon 72 h continuous exposure to feeding is stronger than after a single bout of feeding damage. In contrast to the transcriptomic response, the induced metabolomic response to 24 h of feeding got stronger with time after feeding. The observed metabolomic induction pattern is in line with the observed effect on slug preference in local leaves, but not in systemic leaves, which showed highest resistance already directly (0 h) after 24 h feeding. The induced production and reallocation of metabolites takes time and not all metabolites responding may serve as
103 Chapter 4: Dynamics of induced responses and resistance to slug herbivory deterrent to slugs. Our results therefore suggest that young systemic leaves more rapidly attain effective levels of defence metabolites, such as glycoalkaloids (see before), than older local leaves. This is in agreement with the optimal defence hypothesis, in which younger leaves are more valuable to the plant, have higher constitutive defence levels, and are better inducible than older leaves (Meldau et al. 2012).
In conclusion, our results illustrate that induced responses and resistance to GFS feeding in S. dulcamara are regulated by similar pathways and compounds as those involved in insect resistance (Geuss et al. 2018; Lortzing et al. 2017; Nguyen et al. 2018). Yet, plants maintain photosynthetic capacity upon slug feeding, which is in contrast to what commonly happens after insect feeding. This illustrates that gastropods and insects may induce specific responses, despite of similarity in regulating pathways. Highly standardized experiments comparing the response of S. dulcamara to various gastropod and insect herbivores are required to assess in how far induction patterns are specific to different classes of herbivores. Besides, we show that relaxation plays a role in the dynamics of GFS-induced defences, and that sustained feeding can boost the locally induced response. The temporal and spatial dynamics of GFS-induced responses, in particular with respect to those related to primary and secondary metabolism, illustrate that S. dulcamara plants possibly balance resource investment after induction. Together with rapidly induced local and systemic resistance, we may conclude that S. dulcamara shows a functional response to GFS, which is well-balanced with the incidence of feeding damage. Depending on the turnover rate of defence compounds in the absence of feeding, this induced response may have far-reaching consequences for the other herbivores in the community and vice versa (Viswanathan et al. 2005). Further elucidation of the specific interactions between gastropods and plants may therefore provide novel insight to the general regulation and diversity of plant-herbivore interactions.
Acknowledgements
The authors thank Gerard van der Weerden of the Radboud University Genebank in Nijmegen for providing seeds. OWC is supported by grant number 823.01.007 of the open programme of the Netherlands Organisation for Scientific Research (NWO-ALW). NMvD, AW and YP gratefully acknowledge the support of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig funded by the German Research Foundation (FZT 118). AS, NMvD, TL and OWC thank the German Research Foundation (Deutsche Forschungsgemeinschaft DFG) for funding the collaboration (STE2014/1-1) and a temporal stipend for OWC in the CRC 973 (project B2).
104 Chapter 4: Dynamics of induced responses and resistance to slug herbivory
Supplemental material
Table S1 Primer sequences used for qPCR validation of microarray
Table S2 Results of OPLS-DA model and t-test statistics for comparison of metabolite profiles of Solanum dulcamara plants that were undamaged with those exposed to feeding by the grey field slug
Table S3 Fold-differences in expression of the 25 most strongly regulated contigs according to microarray analyses of Solanum dulcamara leaves after exposure to feeding by the grey field slug
Fig. S1 Validation of microarray analyses based on expression as measured by qPCR
Fig. S2 Induced response on four compounds in Solanum dulcamara leaves after feeding by the grey field slug
Document S1 Protocols for untargeted metabolic profiling and RT-qPCR validation of microarray
105 Chapter 4: Dynamics of induced responses and resistance to slug herbivory
Table S1 Primer sequences used for qPCR validation of microarray
Gene name S. dulcamara contig Primer Sequence (5'-3') Type CAC comp3969_c0_seq3 Forward AGTTTGTTGTTGAGGCTGTTACAC Reference gene CAC comp3969_c0_seq3 Reverse ACCGGACACCTTCCTGAGTAATG Reference gene EF1α comp28_c0_seq4 Forward TGGTACCTCCCAGGCTGACTGTG Reference gene EF1α comp28_c0_seq4 Reverse GCAACGCATGTTCACGGGTCT Reference gene Exp comp12933_c0_seq1 Forward CTAAGAACGCTGGACCTAATGACAAG Reference gene Exp comp12933_c0_seq1 Reverse AAAGTCGATTTAGCTTTCTCTGCATATTTC Reference gene SAND comp6973_c0_seq1 Forward TGCTTACACATGTCTTCCACTTGC Reference gene SAND comp6973_c0_seq1 Reverse AAACAGGACCCCTGAGTCAGTTAC Reference gene PR1a comp374_c0_seq1 Forward CATAAGGGCCACCAGAGTGT Microarray validation PR1a comp374_c0_seq1 Reverse CCCTCAATATCCCAAGCTCA Microarray validation OPR3 comp2015_c0_seq1 Forward CTGTGACGACTGCTTGAACCAC Microarray validation OPR3 comp2015_c0_seq1 Reverse AGCTCACGGGTACTTGATCGAC Microarray validation PI I comp460_c0_seq1 Forward GCAAGCGAATTGAGTGATGA Microarray validation PI I comp460_c0_seq1 Reverse GTGGCCTAACCATACCAGGA Microarray validation KPI 4 comp673_c0_seq1 Forward CAGGTGCAAAGTCGGGAAATCC Microarray validation KPI 4 comp673_c0_seq1 Reverse CCACTTTGTAATTGGACGATGCCA Microarray validation LOX D comp3729_c0_seq1 Forward TGTAGGCAGCAGCACTGATCTC Microarray validation LOX D comp3729_c0_seq1 Reverse CTCGCCAGAGCTTACTCAATGC Microarray validation PPO A comp2630_c0_seq2 Forward ACAAAAGGCCTTACCGTGTG Microarray validation PPO A comp2630_c0_seq2 Reverse CCATCTTGTGGAGTGGGAAT Microarray validation ERF 4 comp3023_c0_seq1 Forward AGAGCTTATGATTGTGCGGCGTT Microarray validation ERF 4 comp3023_c0_seq1 Reverse TTCTTTTCCGGCCAATATTCGC Microarray validation PPO comp16387_c0_seq1 Forward CGATGACGGGGAATATCATC Microarray validation PPO comp16387_c0_seq1 Reverse CCATCGCAATAAGCACAATG Microarray validation NIM1 comp9738_c0_seq1 Forward CAGCTTCATACGCAAATCATCG Microarray validation NIM1 comp9738_c0_seq1 Reverse GCATTGAGATTCTGGAGCAAGC Microarray validation CS comp14877_c0_seq1 Forward CTGGCGCGTACCTCTTCTTCTG Microarray validation CS comp14877_c0_seq1 Reverse TTGGGCATGAAAGTTCCAACGT Microarray validation
106 Chapter 4: Dynamics of induced responses and resistance to slug herbivory
Table S2 Results of OPLS-DA model and t-test statistics for comparison of metabolite profiles of Solanum dulcamara plants that were undamaged with those exposed to feeding by the grey field slug (GFS; Deroceras reticulatum) for 24 h or 72 h. Models represent the plant response 24 h, 48 h or 72 h after the end of the treatment period (see also Fig. 1) in: a–d) local, damaged leaves; e–h) systemic, undamaged leaves. Model names are derived from the duration of feeding exposure and time point of harvest (duration / harvest).
Compound ID with modelled correlation with the predictive component (pcorr) and Student's t-test statistics after Bonferroni correction for multiple comparisons are provided for each model. Compounds with adjusted P- values (Padj) < 0.05 are shown for each model, sorted in ascending order based on absolute pcorr
a) 24 h / 24 h b) 24 h / 48 h Compound OPLS-DA model t-test Compound OPLS-DA model t-test (ID_mz_rt) pcorr ABS(pcorr) t Padj (ID_mz_rt) pcorr ABS(pcorr) t Padj 97_389.16_423 -0.91 0.91 6.7 0.000 99_139.91_56 -0.97 0.97 12.7 0.000 99_139.91_56 -0.85 0.85 4.6 0.001 97_389.16_423 -0.92 0.92 7.9 0.000 136_125.09_463 -0.84 0.84 5.5 0.000 118_245.09_430 0.87 0.87 -5.6 0.000 42_225.15_341 -0.76 0.76 3.6 0.005 22_219.1_442 -0.74 0.74 3.6 0.005 7_188.07_262 0.74 0.74 -3.8 0.003 50_267.17_264 0.73 0.73 -3.5 0.006 18_152.06_90 0.70 0.70 -3.2 0.009 119_385.15_429 -0.73 0.73 3.4 0.007 61_363.1_259 -0.71 0.71 3.1 0.011 24_253.15_140 0.70 0.70 -3.1 0.012
c) 24 h / 72 h d) 72 h / 24 h Compound OPLS-DA model t-test Compound OPLS-DA model t-test (ID_mz_rt) pcorr ABS(pcorr) t Padj (ID_mz_rt) pcorr ABS(pcorr) t Padj 88_395.2_471 -0.94 0.94 8.7 0.000 101_227.13_350 0.99 0.99 -19.1 0.000 26_347.2_377 0.93 0.93 -7.5 0.000 24_253.15_140 0.97 0.97 -12.9 0.000 24_253.15_140 0.90 0.90 -6.9 0.000 88_395.2_471 -0.95 0.95 8.7 0.000 7_188.07_262 0.86 0.86 -5.6 0.000 26_347.2_377 0.94 0.94 -8.0 0.000 97_389.16_423 -0.86 0.86 5.1 0.000 7_188.07_262 0.90 0.90 -6.2 0.000 119_385.15_429 -0.85 0.85 4.9 0.001 97_389.16_423 -0.90 0.90 6.0 0.000 53_219.15_383 0.81 0.81 -4.7 0.001 5_251.14_236 0.84 0.84 -4.7 0.001 81_275.15_440 -0.81 0.81 4.1 0.002 127_322.97_63 0.84 0.84 -5.0 0.001 5_251.14_236 0.79 0.79 -4.0 0.002 53_219.15_383 0.83 0.83 -4.4 0.001 50_267.17_264 0.76 0.76 -3.7 0.004 50_267.17_264 0.80 0.80 -3.9 0.003 124_377.15_358 0.73 0.73 -3.2 0.010 15_209.15_454 -0.79 0.79 3.8 0.004 18_152.06_90 0.72 0.72 -3.4 0.006 84_265.14_301 -0.79 0.79 3.7 0.004 131_361.13_316 -0.72 0.72 3.1 0.012 31_348.07_85 0.77 0.77 -3.9 0.003 117_356.13_89 -0.71 0.71 3.1 0.011 59_201.11_429 -0.76 0.76 3.8 0.004 25_295.16_291 0.71 0.71 -3.1 0.011 49_207.05_83 -0.74 0.74 3.8 0.003 130_525.15_346 0.70 0.70 -3.0 0.013 GA3_884.49_702 0.74 0.74 -3.3 0.008 47_433.17_418 -0.72 0.72 3.1 0.011 28_265.15_304 0.72 0.72 -3.1 0.012 40_434.2_394 0.72 0.72 -3.2 0.010 71_221.09_88 0.72 0.72 -3.2 0.010 110_182.08_83 -0.71 0.71 3.2 0.009 119_385.15_429 -0.71 0.71 3.1 0.011 67_189.13_374 -0.71 0.71 3.3 0.009 115_268.1_89 -0.70 0.70 3.1 0.011
107 Chapter 4: Dynamics of induced responses and resistance to slug herbivory
Table S2 Continued (2/2)
e) 24 h / 24 h f) 24 h / 48 h Compound OPLS-DA model t-test Compound OPLS-DA model t-test (ID_mz_rt) pcorr ABS(pcorr) t Padj (ID_mz_rt) pcorr ABS(pcorr) t Padj 145_406.16_343 -0.86 0.86 4.8 0.001 77_217.07_270 0.97 0.97 -11.8 0.000 116_224.13_60 -0.72 0.72 3.2 0.010 120_263.15_403 0.96 0.96 -11.1 0.000 94_358.27_447 -0.72 0.72 3.2 0.010 94_358.27_447 -0.86 0.86 5.2 0.000 97_122.1_450 0.71 0.71 -2.9 0.015 116_224.13_60 -0.81 0.81 4.6 0.001 135_432.25_82 0.80 0.80 -4.4 0.001 101_332.13_242 -0.72 0.72 3.2 0.010 35_293.1_343 0.72 0.72 -3.4 0.007 96_529.24_260 -0.70 0.70 3.1 0.010 58_276.22_329 0.70 0.70 -3.2 0.010
g) 24 h / 72 h h) 72 h / 24 h Compound OPLS-DA model t-test Compound OPLS-DA model t-test (ID_mz_rt) pcorr ABS(pcorr) t Padj (ID_mz_rt) pcorr ABS(pcorr) t Padj 129_723.45_476 0.99 0.99 -35.7 0.000 129_723.45_476 1.00 1.00 -43.0 0.000 162_570.22_379 0.83 0.83 -4.7 0.001 116_224.13_60 -0.99 0.99 16.2 0.000 GA4_868.49_726 0.74 0.74 -3.4 0.006 101_332.13_242 -0.87 0.87 5.7 0.000 78_206.08_384 -0.73 0.73 3.2 0.010 69_248.19_320 -0.86 0.86 5.4 0.000 147_377.15_358 0.72 0.72 -3.3 0.008 121_244.09_58 0.82 0.82 -4.6 0.001 58_276.22_329 0.71 0.71 -3.6 0.005 39_147.04_390 -0.74 0.74 3.4 0.007 149_495.06_59 -0.71 0.71 3.0 0.013 154_232.19_347 -0.73 0.73 3.2 0.009 42_161.15_427 0.70 0.70 -3.0 0.014 GA3_884.49_702 0.72 0.72 -3.2 0.010 71_204.16_210 -0.71 0.71 3.2 0.009 58_276.22_329 0.71 0.71 -3.1 0.012
108 Chapter 4: Dynamics of induced responses and resistance to slug herbivory
Table S3 Log2-transformed fold-differences (FD) in expression of the 25 most strongly regulated contigs according to microarray analyses of Solanum dulcamara leaves a) directly (0 h) after 24 h exposure to feeding by the grey field slug (Deroceras reticulatum) or b) after an additional 24 h without exposure to slug feeding. P- values were adjusted (Padj) for the false discovery rate. Contigs are sorted based on FD in descending order
a) 0 h
Contig ID ITAG 2.3 ID logFD Padj Description comp521_c0_seq1 Solyc07g006570.2.1 7.8 0.000 S8-Rnase comp460_c0_seq1 Solyc09g089510.2.1 7.7 0.000 Proteinase_inhibitor_I comp2630_c0_seq3 Solyc08g074680.2.1 7.3 0.005 Polyphenol_oxidase comp6001_c0_seq1 Solyc07g064600.2.1 7.1 0.000 Endoribonuclease_L-PSP_family_protein comp4507_c0_seq1 Solyc08g006850.2.1 7.1 0.002 Patatin-like_phospholipase_family_protein Hydroxycinnamoyl_CoA_shikimate/ comp15183_c0_seq1 Solyc11g071470.1.1 6.2 0.000 quinate_hydroxycinnamoyltransferase-like_protein comp673_c0_seq1 Solyc11g022590.1.1 6.2 0.001 Kunitz_trypsin_inhibitor_4 Acetylornithine_deacetylase_or_ comp7825_c0_seq1 Solyc08g076970.2.1 6.2 0.000 succinyl-diaminopimelate_desuccinylase comp1691_c0_seq2 Solyc07g041900.2.1 6.1 0.003 Cathepsin_L-like_cysteine_proteinase comp4767_c0_seq1 Solyc05g053960.2.1 6.0 0.000 Cysteine-rich_extensin-like_protein-2 comp1119_c0_seq1 Solyc11g022590.1.1 6.0 0.046 Kunitz_trypsin_inhibitor_4 comp23535_c0_seq1 Solyc10g078780.1.1 5.9 0.000 Seed_maturation_protein_LEA_4 comp10758_c0_seq1 Solyc10g076660.1.1 5.8 0.000 Anthocyanidin_synthase Hydroxycinnamoyl_CoA_shikimate/ comp21613_c0_seq1 Solyc11g066640.1.1 5.8 0.000 quinate_hydroxycinnamoyltransferase-like_protein comp3101_c0_seq2 Solyc08g076990.2.1 5.8 0.000 Acetylornithine_deacetylase comp4199_c0_seq1 Solyc03g098700.1.1 5.8 0.000 Cysteine_protease_inhibitor_8 comp21413_c0_seq1 Solyc01g095960.2.1 5.8 0.000 Diacylglycerol_O-acyltransferase comp769_c0_seq1 Solyc03g006700.2.1 5.8 0.002 Peroxidase comp6006_c0_seq1 Solyc02g077970.2.1 5.7 0.001 Late_embryo_abundance_protein comp27958_c0_seq1 Solyc04g078340.2.1 5.5 0.000 Cytochrome_P450 comp21537_c0_seq1 Solyc02g070130.1.1 5.4 0.000 FAD-binding_domain-containing_protein comp730_c0_seq1 Solyc06g062370.2.1 5.4 0.008 Acid_phosphatase comp9579_c0_seq1 Solyc05g054890.2.1 5.4 0.001 ABC_transporter_G_family_member_1 comp9050_c0_seq1 Solyc00g138060.2.1 5.3 0.001 2-oxoglutarate-dependent_dioxygenase comp4088_c0_seq1 Solyc09g008670.2.1 5.3 0.000 Threonine_ammonia-lyase_biosynthetic
b) 24 h
Contig ID ITAG 2.3 ID logFD Padj Description comp11770_c0_seq1 Solyc01g080010.2.1 5.5 0.022 Xylanase_inhibitor comp1691_c0_seq2 Solyc07g041900.2.1 5.2 0.039 Cathepsin_L-like_cysteine_proteinase comp6001_c0_seq1 Solyc07g064600.2.1 5.1 0.023 Endoribonuclease_L-PSP_family_protein comp29736_c0_seq1 Solyc01g108360.2.1 4.7 0.001 Copper_ion_binding_protein comp460_c0_seq1 Solyc09g089510.2.1 4.5 0.015 Proteinase_inhibitor_I comp521_c0_seq1 Solyc07g006570.2.1 4.5 0.033 S8-Rnase comp1206_c0_seq1 Solyc07g008560.2.1 4.4 0.044 Purple_acid_phosphatase comp18975_c0_seq1 NA 4.3 0.011 NA Hydroxycinnamoyl_CoA_shikimate/ comp21613_c0_seq1 Solyc11g066640.1.1 4.2 0.002 quinate_hydroxycinnamoyltransferase-like_protein comp22651_c0_seq1 Solyc03g098680.2.1 4.1 0.036 Kunitz_trypsin_inhibitor comp21537_c0_seq1 Solyc02g070130.1.1 3.9 0.013 FAD-binding_domain-containing_protein comp1237_c0_seq1 Solyc06g065970.1.1 3.9 0.035 Cortical_cell-delineating_protein comp20137_c0_seq1 Solyc05g046040.1.1 3.8 0.000 CONSTANS-like_zinc_finger_protein comp25366_c0_seq1 Solyc02g070090.1.1 3.8 0.012 FAD-binding_domain-containing_protein comp13315_c0_seq1 NA 3.7 0.044 NA comp6358_c0_seq1 Solyc02g084850.2.1 3.7 0.044 Unknown_Protein comp16999_c0_seq1 Solyc07g063560.2.1 3.7 0.003 Cotton_fiber_expressed_protein_1 comp13503_c0_seq1 Solyc05g050880.2.1 3.6 0.002 Peroxidase comp16387_c0_seq1 Solyc02g078650.2.1 3.5 0.024 Polyphenol_oxidase comp25796_c0_seq1 Solyc09g075330.2.1 3.5 0.042 Pectinesterase comp18980_c0_seq1 Solyc02g092820.2.1 3.5 0.022 Indole-3-acetic_acid-amido_synthetase_GH3.8 comp15647_c0_seq1 Solyc02g014730.2.1 3.5 0.022 Cytochrome_P450 comp4199_c0_seq1 Solyc03g098700.1.1 3.5 0.022 Cysteine_protease_inhibitor_8 comp7708_c0_seq1 Solyc07g008120.2.1 3.3 0.027 Blue_copper_protein comp14378_c0_seq2 Solyc07g052790.1.1 -3.8 0.037 Nbs-lrr,_resistance_protein
109 Chapter 4: Dynamics of induced responses and resistance to slug herbivory
Fig. S1 Validation of microarray analyses based on expression of 10 contigs as measured by qPCR (see Document S1) in Solanum dulcamara leaves upon feeding by the grey field slug (GFS, Deroceras reticulatum). a) mean (± SE) difference in expression relative to undamaged control samples directly (0 h) after 24 h of exposure to GFS or after an additional 24 h without exposure to slug feeding. b) Correlation of contig expression as quantified by both methods
110 Chapter 4: Dynamics of induced responses and resistance to slug herbivory
Fig. S2 Induced responses in Solanum dulcamara leaves after feeding by the grey field slug (GFS, Deroceras reticulatum). Plants (n = 6) were left undamaged (control treatment: C, white bar) or exposed to feeding by GFS for 24 h (grey bars) or 72 h (black bar). Samples were collected at 24 h, 48 h or 72 h past the end of the treatment period (see also Fig. 1). Mean relative abundance (± SE) is shown of four defence compounds that were selected from untargeted metabolomic analyses (Fig. 3). Treatment effects were further tested using one- way ANOVA. a) Solasonine in local leaves (LGA3, F = 3.864, P = 0.014); b) solasonine in systemic leaves (SGA3, F = 3.283, P = 0.027); c) N-caffeoylputrescine in local leaves (L005, F = 8.225, P = < 0.001); d) unknown N- caffeoylputrescine metabolite in local leaves (L026, F = 12.360, P = < 0.001). Different letters over the bars indicate significant differences among treatments according to Tukey’s post hoc test (P < 0.05)
111 Chapter 4: Dynamics of induced responses and resistance to slug herbivory
Document S1 Protocols for untargeted metabolic profiling and RT-qPCR validation of microarray
Metabolic profiling Extraction
Fresh leaves were ground in liquid nitrogen. About 100 mg of each sample was double extracted with respectively 1.0 and 0.9 ml MeOH:Acetate (50 / 50, v / v %) buffer (pH 4.8) in 2 ml reaction tubes holding two glass beads (5 mm Ø) by shaking in a TissueLyser (Qiagen, Venlo, the Netherlands) at 50 Hz for 5 minutes followed by centrifugation at 14.000 rpm at 4 °C. Clear supernatants were combined and stored at -20 °C until further processing.
LC-qToF-MS analyses
Two sets of diluted crude extracts (1:5 and 1:50) were analysed with an UltiMate™ 3000 Standard Ultra-High- Pressure Liquid Chromatography system (UHPLC, Thermo Fisher Scientific) equipped with an Acclaim® Rapid Separation Liquid Chromatography (RSLC) 120 column (150 × 2.1 mm, particle size 2.2 μm, Thermo Fisher Scientific):
1:5 dilutions were analysed using the following gradient at a flow rate of 0.4 mL / min-1: 0–2 min isocratic 95 % A (water / formic acid 99.95 / 0.05 (v / v %)), 5 % B (acetonitrile / formic acid 99.95 / 0.05 (v / v %)); 2–7 min, linear from 5 % to 30 % B; 7–12 min, linear from 30 % to 35 % B; 12–15 min, linear from 35 % to 95 % B; 15–17 min, isocratic 95 % B; 17–19 min, linear from 95 % to 5 % B; 19–23 min, isocratic 5 % B. Compounds were detected with a maXis impact™ quadrupole time-of-flight mass spectrometer (qToF-MS, Bruker Daltonik GmbH, Bremen, Germany) applying the following conditions in positive mode: scan range 50– 1000 m/z; acquisition rate 3 Hz; end plate offset 500 V; capillary voltage 3500 V; nebulizer pressure 2 bar, dry gas 10 L min-1, dry temperature 220 °C. Mass calibration was performed using sodium formate clusters (10 mM solution of NaOH in 50 / 50 (v / v %) isopropanol water containing 0.2 % formic acid.
1:50 diluted extracts were analysed using the following gradient at a flow rate of 0.4 mL / min-1: 0–2 min isocratic 95 % A (water / formic acid 99.95 / 0.05 (v / v %)), 5 % B (acetonitrile / formic acid 99.95 / 0.05 (v / v %)); 2–15 min, linear from 5 % to 40 % B; 15–20 min, linear from 40 % to 95 % B; 20–22 min, isocratic 95 % B; 22–25 min, linear from 95 % to 5 % B; 25–30 min, isocratic 5 % B. Compounds were detected with a maXis impact™ quadrupole time-of-flight mass spectrometer (qToF-MS, Bruker Daltonik) applying the following conditions in positive mode: scan range 50–1400 m/z; acquisition rate 3 Hz; end plate offset 500 V; capillary voltage 3500 V; nebulizer pressure 2 bar, dry gas 10 L min-1, dry temperature 220 °C. Mass calibration was performed using sodium formate clusters (10 mM solution of NaOH in 50 / 50 (v / v %) isopropanol water containing 0.2 % formic acid.
Data processing
The LC-qToF-MS raw data were recalibrated and then converted to the mzXML format by using the CompassXport utility of the DataAnalysis software (Bruker Daltonik). Mass spectra of the dataset obtained from analysing the 1:5 diluted samples were processed using XCMS and CAMERA packages in R (Smith et al., 2006; Kuhl et al., 2012). The datasets from local and systemic leaves were processed separately, because of the large fraction of position specific metabolites. Peak picking and alignment were done within a retention time between 50–480 seconds, signal:noise ratio ≥ 50, maximum deviation of 5 ppm, 3 seconds retention time window, mass/charge window of 0.005 and minimum occurrence in four out of six samples in at least one treatment. Resulting features were grouped to belong to the same compound after symmetric retention time correction, within a retention time window of maximum 5 seconds and minimum mutual correlation of 0.8. Feature intensities were multiplied by their dilution factor (5) and samples normalised by the fresh weight of
112 Chapter 4: Dynamics of induced responses and resistance to slug herbivory the leaf material used for extraction. Missing values were replaced by the treatment mean value only when one of six replicates was missing or else by a random value between 1 and half of the minimum value in the complete dataset. Feature groups, potentially representing single compounds, were reduced to one feature to represent the respective compound in later analysis. This was done by applying the in-house maximum heuristic approach, which selects the feature with the highest intensity in the majority of the samples.
Quantification and identification of highly abundant compounds
Peak intensities of four putatively identified glycoalkaloids (α-solamarine, β-solamarine, solasonine and solamargine) exceeded the saturation intensity threshold and were therefore manually quantified from the 1:50 diluted samples. Values corresponding to these compounds in the automatically processed dataset were replaced by the manually quantified intensities after dilution correction.
Putative identification was based on Calf et al. (2019). Tandem mass spectrometry (MS2) spectra were acquired by injection of samples that contained the highest amount of the compounds of interest. The separation was achieved by using the same chromatographic conditions as described before. MS2 spectra were collected by using the automated MSMS function of the Bruker oToF Control software. Spectra were evaluated for compounds of interest with particular emphasis on fragmentation of the parental compound. Putative identifications were made based on comparison of mass spectra reported in the literature.
RT-qPCR for microarray validation
New total RNA isolates were made from pools of ground leaf tissue, each including the four plants of a single plant population in each treatment, as hybridized on the microarray. Extraction and purification were performed using the RNeasy® Plant Mini kit (Qiagen) and DNAse I, RNAse-free (Fermentas) following the manufacturer’s instructions. Then, cDNA was synthesised from ~1000 ng total RNA using the iScript™ cDNA Synthesis kit (BIO-RAD Laboratories B.V., Veenendaal, the Netherlands) and diluted to an equivalent of 4 ng total RNA per µl. RT-qPCR was performed using the amount of cDNA corresponding to 20 ng total RNA in a 20 µl mix containing 12.5 µl iQ™ SYBR® green Supermix (BIO-RAD) and 250 nM of both the forward and reverse primer. Reactions were performed on a CFX96™ Real-Time System (BIO-RAD) using a protocol of 40 cycles of 10 s at 95 °C and 30 s at 60 °C followed by a melt-curve analysis.
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Chapter 5:
The genetic basis of ecologically relevant chemical diversity in Solanum dulcamara
Onno W. Calf Jules Beekwilder Alexander Weinhold Heidrun Huber Nicole M. van Dam Janny L. Peters Chapter 5: Genetic basis of ecologically relevant chemical diversity
Abstract
The wild bittersweet nightshade (Solanum dulcamara) has several naturally occurring chemotypes differing in their steroidal glycoalkaloid (GA) profile. These chemotypes are based on the relative presence of three structural polymorphisms in GA chemistry: 1) configuration as 25S- or 25R-stereoisomers, 2) saturation of the C-5,6 position, and 3) conjugation of the aglycon with uronic acids or simple sugars. These chemical differences have ecological relevance. Chemotypes with unsaturated GAs are generally more resistant to insect herbivores and pathogens than chemotypes with saturated GAs. Moreover, a S. dulcamara individual containing newly discovered uronic acid conjugates (UACs) is highly preferred by slugs, but not by specialist beetles. In this study, we investigated the genetic background of S. dulcamara chemotypes and how differences therein relate to the different GA profiles and slug resistance. To this end, five well-defined S. dulcamara chemotypes were used to perform RNA sequencing. In parallel, we generated F1 and F2 individuals to assess the segregation patterns of GA and UAC chemotypes. We identified two sequence variants of the SdGAME8 gene with a codominant role in the configuration of 25S- or 25R- stereoisomers. Moreover, our results support a role for SdGAME25 in the saturation of the steroidal backbone at the C-5,6 position. Chemotype segregation analyses in F1 and F2 populations revealed that the conjugation of uronic acids to steroidal backbones is related to a recessive allele of a yet unidentified gene. These genetic analyses were combined with slug preference assays. Slugs showed less preference for F1 chemotypes with predominantly unsaturated GAs compared to those with saturated GAs. UAC chemotypes were overall the most susceptible to slugs. Our results illustrate how plant chemotypes may arise from single gene differences. This implies that slug feeding may pose a strong selection pressure on chemical diversity in natural S. dulcamara populations.
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Introduction
Steroidal glycoalkaloids (GAs) are prominent secondary metabolites of plant species in the genus Solanum (Eich 2008). This genus includes many important crop species, such as tomato (Solanum lycopersicum), potato (S. tuberosum) and eggplant (S. melongena), in which they serve as resistance compounds to herbivores and pathogens (Friedman 2015; Milner et al. 2011). GAs have a bitter taste, are neurotoxic and affect cell functioning by disrupting cell membranes (Roddick et al. 2001). Moreover, the pharmacological aspects of GAs are studied in medicinal science, because these compounds are used in cancer treatment (Eich 2008; Friedman 2015; Milner et al. 2011; and references therein). Because of the aforementioned anti-nutritive effects, crop breeders have mostly selected for variants with low GA contents. This illustrates that their role as resistance compound is in conflict with commercial interests. Solanum GAs are glycosides of nitrogen-containing steroidal aglycon backbones (see Fig. 1). For more than fifty years it is known that the wild bittersweet nightshade (Solanum dulcamara) displays chemotypic variation in GA profiles (Mathé 1970; Willuhn 1966; Willuhn and Kunanake 1970). We recently reported that the diversity in GA leaf profiles among individuals from eight native Dutch populations is related to variation in slug resistance (Calf et al. 2018). Both the type of the most abundant aglycon, as well as the conjugated moieties differ among the S. dulcamara accessions studied (Calf et al. 2018; or see Fig. S1). The GA profiles in most plants are dominated by unsaturated tomatidenol- and/or solasodine-type GAs. One accession, TW12, also contains saturated soladulcidine- and/or tomatidine-type GAs. This particular accession belonged to a class of intermediate-preferred accessions by slugs (Calf et al. 2018). In other studies, saturated GAs were also shown to be less potent resistance factors towards insect herbivores and pathogens than unsaturated GAs (Friedman 2002; Sonawane et al. 2018). The common glycoside moieties attached to the alkaloid backbones are tri- and tetrasaccharides, such as solatriose, chacotriose and lycotetraose (Calf et al. 2018; Eich 2008). Interestingly, one S. dulcamara accession (ZD11) has low levels of these common GAs. Instead, its profile contains high proportions of various, previously unknown, uronic acid conjugated compounds (UACs). This particular chemotype was highly susceptible to feeding by generalist gastropods, but not to specialist flea beetles (Calf et al. 2019). The ecological relevance of the natural chemical variation observed in S. dulcamara motivated the current study on the genetic background of GA and UAC biosynthesis.
Fig. 1 Chemical structure of solasonine illustrating the overall structure of steroidal glycoalkaloids (GAs) in Solanum dulcamara. Both the configuration of the F- ring (25R, red oval) and saturation level of the C-5,6 bond (unsaturated, red circle) determine the type of aglycon backbone (here solasodine), which is conjugated to a glycoside moiety (here solatriose, Gal- Glu-Rha)
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The genes involved in the biosynthesis of steroidal GAs are well studied for tomato and potato and seem to be highly conserved among Solanum species (Cárdenas et al. 2015; Itkin et al. 2013; Mariot et al. 2016; Nakayasu et al. 2017; Sonawane et al. 2018). It is therefore expected that the same or homologous genes are involved in the biosynthesis of GAs in S. dulcamara (summarised in Fig. 2). GAs, like structurally related steroidal compounds, are derived from the common precursor cholesterol (1 in Fig. 2). The differences between chemotypes are defined by variation in the configuration, saturation or conjugation of the steroidal backbone. 1) Configuration: steroidal backbones occur as 25S or 25R stereoisomers. Upon hydroxylation of cholesterol at C-22 (2), a second hydroxylation occurs at either C-26 (3) or C-27 (4). A direct consequence of this second hydroxylation step is the downstream configuration of the F-ring (Fig. 1). The resulting backbone becomes either a 25S or 25R stereoisomer, depending upon whether the hydroxylation took place at C-26 (7, 9) or C-27 (8, 10), respectively. In tomato and potato, C-26 hydroxylation is performed by GAME8 enzymes (Itkin et al. 2013; Mariot et al. 2016). Whether GAME8 also regulates C-27 hydroxylation is unclear, because 25R-type GAs do not occur in the model systems tomato and potato (Eich 2008). In contrast, eggplant exclusively contains 25R-type GAs (Eich 2008), but GAME8 expression was not explicitly analysed in this species. Interestingly, 25R and 25S stereoisomers co-occur in all S. dulcamara chemotypes, but in different relative proportions (Calf et al. 2018; or see Fig. S1). By comparing the expression of GAME8 homologues in different S. dulcamara chemotypes, we may gain insight in how this GA polymorphism arises. This knowledge could be an important stepping stone to better understand GA biosynthesis in different solanaceous crop species. 2) Saturation: steroidal backbones are either unsaturated or saturated at the C-5,6 position. Tomatidenol (7, 25S) and solasodine (8, 25R) are both unsaturated alkaloids with a double bond at the C-5,6 position. In tomato and eggplant, these compounds serve as substrate for the short-chain dehydrogenase/reductase GAME25, which catalyses the first step in the reduction of the double bond (Sonawane et al. 2018). The authors proposed that two additional, unidentified, reductase enzymes are required to yield the saturated GAs tomatidine (9) and soladulcidine (10). Untargeted analyses of differentially expressed genes between S. dulcamara chemotypes dominated by either saturated or unsaturated GAs enable verification of the involvement of GAME25 enzymes and may provide insight into the genes that are additionally involved. 3) Conjugation: steroidal backbones are either conjugated with sugars or uronic acid moieties, resulting in GAs or UACs, respectively. Usually, simple sugars are conjugated to the aglycon by various glycosyltransferases. In tomato for instance, α-tomatine is produced by glycosylation of tomatidine (9) by enzymes coded subsequently by GAME1, GAME17, GAME18 and GAME2 (Itkin et al. 2013). Steroidal alkaloids conjugated with uronic acids,
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Fig. 2 Schematic of the putative role of glycoalkaloid metabolism (GAME) enzymes, including glycosyltransferases (GTs), in the biosynthesis of steroidal saponins, glycoalkaloids (GAs) and related uronic acid conjugated compounds (UACs) in Solanum dulcamara. The structure of the pathway is based on tomato (S. lycopersicum). Symbols and enzymes in red highlight the molecular steps that are likely responsible for the configuration, saturation and conjugation chemotypes that are investigated in this study. Dashed arrows indicate multiple enzymatic conversions. Bold numbers are used for in-text reference in the introduction. Asterisks indicate two alternative hypotheses for UAC biosynthesis. * Uronic acids may be directly conjugated to steroidal backbones, ** Sugar moieties of saponins and GAs may be oxidised to uronic acids after conjugation
119 Chapter 5: Genetic basis of ecologically relevant chemical diversity such as glucuronic or galacturonic acid, had not been reported, until we reported it in S. dulcamara accession ZD11 (Calf et al. 2018). Structurally related sapogenins (diosgenin) conjugated with glucuronic acid were found in S. lyratum, but we did not find published records on their bioactivity (Sun et al. 2006; Yahara et al. 1985). Alkaloids and sapogenins differ in the amine-group (-NH), which is incorporated by GAME12 enzymes in the F-ring of alkaloids (7, 8, 9, 10), and which is absent in sapogenins (5, 6). Both alkaloid and sapogenin UACs are observed in accession ZD11 (Calf et al. 2018; or see Fig. S1). The conjugate contains either a single uronic acid, or a combination of a hexose with a uronic acid. This suggests that UAC biosynthesis is regulated by enzymes that are able to take a broad range of structurally similar backbones as a substrate. We hypothesise that there are two possible scenarios: a) uronic acids are directly conjugated to steroidal backbones, or b) the sugar moieties of GAs and saponins are oxidised to uronic acids after conjugation. In this study, we used the naturally available chemical diversity in S. dulcamara to examine the genetic background of GA chemotypes and explicitly relate this to slug resistance. We thereby combined selfing and crossing designs of chemotypes with next generation RNA sequencing (RNAseq), qPCR analyses and slug bioassays. In order to assess how chemotypes are inherited, we produced F1 and F2 individuals using five chemotypes from our earlier study (Calf et al. 2018). The observed segregation patterns provide information about the number and dominance of genetic loci involved in inheritance of the chemotypes. In parallel, we carried out RNAseq analyses of the five parental chemotypes.
We combined these two approaches by qPCR of candidate GAME genes, in segregating F1 and F2 chemotypes. We specifically related the expression of SdGAME8 and SdGAME25 to the configuration and saturation of steroidal backbones, respectively. Moreover, the RNAseq data were used to identify candidate genes and biological processes that may be related to additional enzymes potentially required for the saturation of steroidal backbones, or the conjugation of uronic acids. As a final step, slug preference assays on selected F1 individuals allowed us to explicitly relate the chemotypes with their resistance status.
Materials and Methods
Plant material Based on our earlier study (Calf et al. 2018), we selected five S. dulcamara accessions which represent different chemotypes (Fig. S1): OW09, in which unsaturated 25S-type GAs dominate (S-); VW08, in which unsaturated 25R-type GAs dominate (R-); ZD04, in which both 25S- and 25R-type unsaturated GAs codominate (RS-); TW12, in which both 25S- and 25R- type GAs codominate, but the saturated types are most abundant (RS+) and ZD11, which is rich in UACs and low in predominantly 25S-type GAs (UAC/S-). Throughout the years, these accessions were kept as clones and therefore represent the exact same genetic background as when used in the original study. These five selected chemotypes were used for RNAseq analyses, as well as to produce F1 and F2 individuals by selfing and crossing.
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Five selected chemotypes for RNAseq analyses Stem cuttings of each of the selected chemotypes, consisting of a single node with at least two cm of woody stem internodes on the distal and proximal side, were potted in individual pots (11 × 11 × 12 cm, L × W × H). Each pot contained potting soil (Lentse Potgrond nr. 4, Horticoop, Katwijk, the Netherlands), which was supplemented with 4 g L-1 slow-release fertilizer (Osmocote® Exact Standard, Everris International B.V., Geldermalsen, the Netherlands). Plants were grown in a greenhouse in net cages to prevent insect infection (0.3 mm gauze, 7.50 × 3 × 2.75 m, L × W × H) at a 16 h photoperiod and minimum temperatures set to 20 °C / 17 °C (day / night). Light levels were supplemented with 1000 W Sodium lamps (Philips GreenPower, Amsterdam, the Netherlands) fixed above the cages, providing ~280 µM m-2 s-1 light intensity at the plant level. Plants were grown for two months before the onset of the experiment. Because the accessions showed variation in growth rates, only fully expanded leaves at a fixed position were sampled. This ensured that all leaves had a comparable developmental stage and metabolic composition. Leaves were numbered from the apex downwards, starting with the first leaf below the first internode that was greater than 2 cm. Leaves 6 and 11 were treated with methyl jasmonate (MeJA; Sigma-Aldrich, Zwijndrecht, the Netherlands, product 392707) to boost the expression of defence-related genes, expectedly including those of GA- synthesis (Lortzing et al. 2017; Thaler et al. 1996). Both leaves received 150 µg MeJA dissolved in 25 µl lanolin which was applied to the base of the main leaf blade, thereby excluding the side lobes of the leaves. Leaves 6–11 (n = 6) were cut from the plant after 24 h. Two leaf discs were produced from each leaf using a cork borer (1.8 cm Ø). The leaf discs included the main vein, but excluded the hormone treated area (Fig. S2). All leaf discs of a single plant (n = 12) were pooled into a single sampling tube, flash frozen in liquid nitrogen, manually ground and stored at -80 °C until further processing for chemotyping and gene expression analyses.
Production of F1 and F2 individuals Each of the five selected chemotypes was selfed by repeatedly pollinating the stigma for three days with freshly collected pollen from the same flower to produce F1 seeds. In addition, VW08 and ZD11 were crossed, using VW08 as pollen donor and ZD11 as receptor. To this end we emasculated flower buds of ZD11 before petal expansion. Moreover, one of the F1 individuals that resulted from this crossing was selfed to produce F2 seeds. After pollination, organza bags (14 × 23 cm, L × W, www.pandahall.com) were used to protect the flowers from further pollination. The berries were left to ripen in these bags. Seeds were collected by crushing the ripe berries and rinsing them with ample tap water. Dried seeds were stored at 4 °C in the dark.
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Seed germination was supported by cold-stratification for at least 2 weeks at 4 °C in the dark. Seeds were sown on a 2 cm layer of glass beads (1 mm Ø) and tap water in a plastic container (8 × 8 × 6 cm, L × W × H, www.der-verpackungs-profi.de GmbH, Göttingen, Germany), which was covered with a transparent lid. Germination was initiated by transferring the container to greenhouse conditions. After approximately eight days, when the cotyledons had unfolded, the F1 seedlings were potted in individual pots and grown under the same conditions as the parental clones (see before). F2 seedlings were potted in small individual pots (5.5 × 5.5 cm, Ø × H) and grown in a climate chamber at a 14 h photoperiod and minimum temperatures set to 20 °C / 18 °C (day / night). Six young fully developed leaves of each plant were collected when plants were about two months (F1) or three weeks old (F2). Leaves were pooled, flash frozen in liquid nitrogen, manually ground and stored at -80 °C until further processing for chemotyping and gene expression analyses.
Chemical profiling and analyses of chemotype inheritance Leaf samples were extracted and analysed by LC-qToF-MS following the same procedure as described in Calf et al. (2018). Details can be found in Document S1. To determine the chemotype of each sample, we specifically quantified the relative levels of two putatively identified unsaturated S-type GAs (α- and β-solamarine, S-), two positively identified unsaturated R-type GAs (solasonine and solamargine, R-), three unspecified saturated GAs (+) and five unspecified UACs with either sapogenin or unsaturated alkaloid backbones (Calf et al. 2018). The abundance of different compounds can only be compared on a relative scale, because of the lack of internal standards for most compounds. Therefore, the relative level of each compound to the maximum value across all samples was used for presentation of compound abundance in a heat map (%). The observed chemotype frequencies were statistically analysed using nonparametric Chi-square (χ2) tests from the R “stats” package (R Core Team 2016). If the inheritance of the chemotypes would follow Mendelian inheritance rules, we expect that: 1) non-segregating populations (1:0) indicate the same homozygous genetic background of both parents; 2) segregating populations with a 3:1 ratio of chemotypes indicate a heterozygous genetic background, where both parents have a dominant and recessive allele; 3) segregating populations with a 1:2:1 ratio of chemotypes indicate the involvement of two codominant alleles for which both parents are heterozygous.
Transcriptional analyses RNA sequencing Total RNA for RNA sequencing was extracted from the five selected chemotypes (n = 3 for each chemotype). Of each plant, 50.0 to 100.0 mg fresh leaf material was extracted using the RNeasy Plant Mini kit (Qiagen, Venlo, the Netherlands), following the manufacturers
122 Chapter 5: Genetic basis of ecologically relevant chemical diversity manual. A TruSeq® stranded mRNA Library Prep kit (Illumina, San Diego, CA, USA) was used for preparing cDNA. Transcripts were analysed on a Hiseq2500 (Illumina). The CLC genomics workbench module for RNAseq analysis (Qiagen, v11.0) was used to map reads from different accessions to all contigs in the S. dulcamara cDNA library registered by the Sol Genomics Network (SGN, solgenomics.net/). These contigs were putatively annotated based on homology with tomato (D'Agostino et al. 2013). The total number of reads was used as a proxy for the expression level of each contig. We additionally used the RPKM (reads per kilobase million) value as a normalised read count to compare expression among samples.
Differential expression analyses The complete set of 32,157 contigs from RNAseq mapping was filtered by selecting only those contigs with ≥ 100 total reads in all three replicates of any accession. This yielded a list of 18,241 contigs that were consistently expressed among biological replicates. Differential expression analyses were performed to identify contigs of interest which may be involved in: a) the saturation of steroidal backbones at the C-5,6 position (TW12: RS+ vs OW09: S-, VW08: R- and ZD04: RS-) or b) the conjugation of uronic acids in UAC biosynthesis (ZD11: UAC vs OW09: S-, VW08: R- and ZD04: RS-, and TW12: RS+). Our experimental design for RNAseq did not include replications of genetically independent R- and S-dominated chemotypes, which is why we could not perform differential expression analyses for identification of related contigs to backbone configuration. Analyses were performed using the DESeq2 R-package for modelling of gene expression (Love et al. 2014). The default ‘betaPrior’ function, which corrects for outliers, was not implemented, because these outliers are possibly of most interest to our study. The total read numbers of contigs in target accessions were compared pairwise with those in each of the reference accessions. All contigs with absolute fold-difference in expression ≥ 2 and Benjamini-Hochberg adjusted P-values ≤ 0.05 in relation to each of the reference accessions were considered differentially expressed contigs (DECs). DECs with absolute fold- difference in expression ≥ 10-fold and absolute RPKM difference ≥ 10 were considered highly differentially expressed.
Nucleotide sequence and expression analyses of GAME8 and GAME25 homologues We used tBLASTn to identify homologues of tomato GAME8 (SGN-E747188) and GAME25 (Solyc01g073640.2) in the S. dulcamara cDNA library (D'Agostino et al. 2013). Four transcripts (contig ID: comp57_c0_seq1–4) were identified by tBLASTn to have high homology with tomato GAME8 of which comp57_c0_seq1 was best aligned (83.85 % match). Therefore, we selected it for further analysis and refer to it as SdGAME8 from here onwards. Similarly, S. dulcamara contig comp59_c0_seq1 was identified as a homologue of tomato GAME25 (85.23 % match) and is therefore referred to as SdGAME25.
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The “Basic variant detection” module in CLC Genomics Workbench was used to detect single nucleotide polymorphisms (SNPs) relative to the registered SdGAME8 and SdGAME25 sequences. SNPs which were present in all three biological replicates of each accession with coverage > 2000 reads and a frequency of > 10 % were considered consistent. For each consistent SNP we determined whether it was homozygous or heterozygous. Targeted qPCR analyses were performed to assess the role of SdGAME8 and
SdGAME25 enzymes in chemotype biosynthesis using segregating F1 and F2 populations. The obtained nucleotide sequences from SNP analyses were used to design chemotype-specific primers. Gene primers were designed using the primer BLAST-tool of the National Centre for Biotechnology Information (NCBI). Total RNA was extracted from ground leaf tissue using Invitrogen™ TRIzol reagent (Ambion, Carlsbad, CA, USA) following the manufacturer’s protocol. Then, cDNA was synthesised from ~1 µg total RNA using the iScript™ cDNA Synthesis kit (BIO-RAD Laboratories B.V., Veenendaal, the Netherlands) and diluted to an equivalent of 4 ng total RNA per µl. We performed qPCR using the amount of cDNA corresponding to 20 ng total RNA in a 20 µl mix containing 12.5 µl iQ™ SYBR® green Supermix (BIO-RAD) and 250 nM of the forward and reverse primer. Reactions were performed on a CFX96™ Real-Time System (BIO-RAD) using cycles of 10 s at 95 °C and 30 s at 60 °C followed by a melt-curve analysis. The qPCR cycle threshold (Ct) for accurate quantification was set to 35 cycles. Relative quantification of gene expression was based on expression of CAC, EF1α, SAND and EXP as reference genes (Table S1) using the geNorm calculation algorithm (Vandesompele et al. 2002). One-way ANOVA with Tukey’s post hoc test from the R “stats” package (R Core Team 2016) was used to test differences in expression level of SdGAME8 sequence variants among chemotypes. Data were log10 transformed to meet data assumptions for parametric statistics.
Gene Ontology enrichment Functional analyses of DECs, which were selected for accession ZD11 (UAC/S-) compared to the four reference accessions (see before), was performed using gene ontology enrichment analyses. We used the “TOPGO” package (Alexa and Rahnenfuhrer 2010) based on the GO annotation of (Nguyen et al. 2016a). GO enrichment was assessed by comparing the distribution of DECs in ZD11 to the distribution of all contigs previously annotated in the S. dulcamara transcriptome (D'Agostino et al. 2013). We used the “elim” algorithm, which corrects for the annotation of contigs in multiple parental GO terms (Alexa et al. 2006). Only GO terms which included a minimum of 250 annotated contigs were included for analyses. The P-values for the enrichment of each GO term were based on Fisher’s exact tests.
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Slug preference assay A bioassay was performed to test the preference of roundback slugs (Arion rufus/ater) for different chemotypes among F1 individuals. Juvenile slugs were field collected 2–3 days before the bioassays and kept without food in clean humid 50 ml plastic containers (6 cm Ø, www.der-verpackungs-profi.de GmbH, Göttingen, Germany). The containers were placed in a climate cabinet (Snijders Scientific, Tilburg, the Netherlands) under 16 h low light photoperiod of ~50 μM m−2 s−1 light intensity at temperatures set to 17 °C / 14 °C (day / night).
Clones of seven F1 individuals (n = 3 clones per individual), representing chemotypes with different parental backgrounds, were used for this assay. Each clone was pruned to grow three main stems. Leaf number 7 of each stem, counting from the apex, was used to make leaf discs using a cork borer (1.5 cm Ø, n = 2 per leaf). A single leaf disc of each chemotype was placed in a Petri dish (9 cm Ø) with the adaxial (i.e. upper) side down. Remaining leaf discs were discarded. The Petri dishes were gently sprayed with de-ionised water before, during and after placing the leaf discs to maintain leaf disc turgor. This procedure also ensured that the discs adhered to the Petri dish and stayed at the same position while the slugs were feeding. Within each Petri dish (n = 15), the disc positions were randomised. The positions were printed on a piece of paper which was placed under the transparent dish. One slug was placed on the lid of each Petri dish. Thereafter the dishes were closed and placed back in the climate cabinet. The slugs were removed after 24 h and the leaf material remaining in the Petri dish was photographed with a 14 cm ruler as scale reference. The consumed area (in mm2) was assessed using ImageJ v. 1.48 (Schneider et al. 2012). The slugs showed large variation in the total consumed area among replicates (0.8– 7.5 cm2, equalling 0–60 %). Therefore, only replicates in which we observed sufficient feeding activity (> 4–7 cm2, equalling 32–57 %) were used for statistical analyses (n = 8). Differences in absolute leaf disc consumption in the preference assays were analysed using nonparametric statistical methods from the R “stats” and “PMCMR” packages (Pohlert T 2014, R Core Team, 2016). Friedman’s rank sum test followed by Conover’s post hoc test with Bonferroni correction for multiple comparisons was applied to evaluate overall slug preference differences for chemotypes. The Petri dish number was used as grouping factor.
Results
Inheritance of chemotypes Clones of the parental accessions showed the same characteristic chemotypes as found in the previous study (Fig. 3a, Calf et al. 2018). Accessions OW09 and VW08 had unsaturated 25S- (S-, GA1 and 2 in Fig. 3) and 25R-dominated (R-, GA3 and 4 in Fig. 3) chemotypes, respectively, whereas ZD04 had a chemotype codominated by both 25S- and 25R-type
125 Chapter 5: Genetic basis of ecologically relevant chemical diversity unsaturated GAs (RS-). TW12 also had an RS-codominated chemotype, but in addition contained saturated GAs (RS+, GA5, 6 and 7 in Fig. 3). ZD11 was rich in UACs (UAC1–5 in Fig. 3) and contained low levels of predominantly unsaturated 25S-type GAs (UAC/S-). The inheritance of the configuration (S and R), saturation (- and +) and conjugation (GA and UAC) chemotypes was studied with F1 and F2 individuals that were produced by selfing or crossing the parental accessions. The segregation of each of the three independent structural polymorphisms will be described in separate sections.
Inheritance of configuration chemotypes
With a focus on the representative 25R- and 25S-type GAs (GA1–4), all F1 individuals (Fig. 3b) that were produced by selfing OW09 (S) or VW08 (R) showed a chemotype corresponding to the parental chemotype (Fig. 3a). Moreover, β-solamarine (GA2) is the only GA, which levels were above detection limit in ZD11 F1 individuals (Fig. 3b). This suggests that these plants also had the same S-dominated chemotype as the parent (Fig. 3a). In contrast, the leaf profiles of F1 individuals that were produced by selfing ZD04 or TW12 (both RS) segregated into different chemotypes (Fig. 3b). Six ZD04 (RS) F1 individuals had the same chemotype as the parent, one individual was dominated by 25S-type GAs and two individuals by 25R-type
GAs. In the F1 population of TW12 (RS), five individuals had an RS-codominated chemotype, three individuals were dominated by 25S-type GAs and two individuals by 25R-type GAs. Assuming that the configuration chemotype is coded by the same gene(s) with the same heritability in both ZD04 and TW12, we combined the observed frequencies for statistical analyses (S:RS:R = 4:11:4). This segregation pattern does not significantly differ from a 1:2:1 ratio of Mendelian inheritance (χ2 = 0.4737, df = 2, P = 0.79).
Fig. 3 (next page) Relative abundance of steroidal glycoalkaloids (GAs) and uronic acid conjugated compounds
(UACs) in Solanum dulcamara leaves of: a) clones of five parental accessions (n = 3); b) F1 individuals produced by selfing or crossing parental accessions (n = 5–17) and c) F2 individuals (n = 72) produced by selfing one F1 individual (indicated by black triangle). The chemotypes are classified by the relative abundance of 25R- and 25S-type unsaturated (-) or saturated (+) GAs as indicated above each profile. Red triangles indicate individuals that were selected for slug preference assays. * Relative abundance could not be calculated because the compound was not detected in any sample
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Fig. 3 (caption on previous page)
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Crossing VW08 (R) with ZD11 (S) resulted in eight non-segregating F1 individuals in which 25R- and 25S-type GAs occurred in equal proportions (RS, Fig. 3b). One of the individuals (black triangle in Fig. 3b) from this population was selfed to analyse chemotype heritability in the F2 population (Fig. 3c). Of the 55 GA-rich F2 plants (Fig. 3c: segregation of conjugation chemotypes is described in a later section), 9 and 14 individuals had profiles which were dominated by 25S- or 25R-type GAs, respectively. In 32 individuals, 25S- and 25R-type GAs co-occurred in equal proportions (RS-). The segregation ratio (S:RS:R = 9:32:14) is not significantly different from a 1:2:1 ratio of Mendelian inheritance (χ2 = 2.3818, df = 2, P = 0.30). Therefore, this population shows the same segregation pattern of stereoisomers as was observed for ZD04 (RS) and TW12 (RS) F1 individuals (see before). Taken together, the observed segregation patterns for offspring of selfed RS-codominated parents and the non- segregating offspring of selfed 25S- or 25R-dominated parents suggests that two codominant alleles determine the configuration of the GAs.
Inheritance of saturation chemotypes
For the presence of saturated GAs, segregation was observed among the TW12 F1 individuals (Fig. 3b). Whereas eight out of ten individuals produced saturated GAs (+), the chemical profiles of two individuals were dominated by unsaturated GAs (-). This ratio (8:2) does not significantly differ from a 3:1 ratio of Mendelian inheritance (χ2 = 0.1333, df = 1, P = 0.72).
Neither the F1 individuals produced by selfing of the other accessions nor the F2 individuals contained saturated GAs. Therefore, the ability to produce saturated GAs appears to result from a dominant allele at a single locus for which accession TW12 is heterozygous, whereas the other accessions are homozygous for a recessive allele.
Inheritance of conjugation chemotypes
F1 individuals produced by selfing accession ZD11 (UAC) all displayed the UAC chemotype
(Fig. 3b). Strikingly, two out of eleven individuals in the ZD04 (GA) F1 population also produced the UAC chemotype, whereas the parent did not produce UACs (Fig. 3a). This proportion (9:2) is not significantly different from a 3:1 ratio of Mendelian inheritance (χ2 = 0.2727, df = 1, P = 0.60).
None of the F1 individuals resulting from the cross between VW08 (GA) and ZD11
(UAC) produced UACs (Fig. 3b). However, in addition to the 55 GA-rich F2 individuals
(discussed before), 17 out of 72 F2 individuals produced the UAC chemotypes (Fig. 3c). This segregation ratio of chemotypes (55:17) is not significantly different from a 3:1 ratio of Mendelian inheritance (χ2 = 0.0741, df = 1, P = 0.79). Taken together, our results of the selfing and crossing experiments suggest that the formation of UACs is regulated by a recessive allele at a single locus.
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Gene expression analyses Genetic regulation of configuration chemotypes Our experimental design for RNAseq included an insufficient number of R- and S-dominated chemotypes to perform untargeted differential expression analyses. Instead, we focussed on the role of GAME8 as candidate gene for determining the 25S- or 25R-configuration of GAs (Itkin et al. 2013; Mariot et al. 2016). RNAseq analyses of the five selected chemotypes revealed that each of the chemotypes showed expression of SdGAME8 (280–490 RPKM, Table S2). A SNP analysis was performed to assess whether different variants of SdGAME8 exist
(Fig. 4), which may explain segregation of 25R and 25S chemotypes among F1 and F2 individuals (Fig. 3, see before). We identified 62 SNPs in 7 independent sequence variants by comparing the nucleotide sequences of SdGAME8 in the five selected accessions to the 1585-bp reference sequence (comp57_c0_seq1 in D'Agostino et al. 2013). The reference sequence was based on a mixed cDNA library from other accessions than the ones used here. Overall, five SNPs were consistently present among all SdGAME8 sequence variants (Fig. 4, bp: 928, 1007, 1130, 1266 and 1424 in yellow). These are consistent differences of the screened accessions with the registered reference sequence and therefore not related with the chemotype. SNPs in the region between base-pair 1209 and 1499 showed a consistent sequence pattern among different accessions (Fig. 4, highlighted region in black rectangle). This means that the entire sequence in the region between base-pairs 1206 and 1585, including non-SNPs, showed this consistent pattern. We therefore focussed on this consistent 380-bp region. Accession VW08 (R) expressed a single variant of which the sequence was the same as the reference sequence, except for two consistent SNPs among sequence variants (Fig. 4, bp: 1266 and 1424 in yellow). In contrast, OW09 and ZD11 (both S) expressed another sequence variant with 27 unique SNPs in the 380-bp region. Interestingly, accession ZD04 and TW12 (both RS) expressed both sequence variants. This suggests that the two sequence variants have a codominant role in the configuration of steroidal backbones.
To analyse the expression of each SdGAME8 sequence variant in the segregating F2 population, variant-specific qPCR primers (Table S1) were designed based on the consistently coded base-pair region 1206–1585 (Document S2). Each sequence variant was specifically expressed in R- or S-dominated chemotypes, whereas intermediate expression of both variants was observed in RS-codominated plants (Fig. 5). Thus, the configuration chemotypes seem to be related to the relative co-expression of both SdGAME8 sequence variants.
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Fig. 4 Single nucleotide polymorphisms (SNPs) detected in five Solanum dulcamara accessions when compared to the reference sequence of SdGAME8 (comp57_c0_seq1 in D'Agostino et al. 2013). Accessions were selected based on their glycoalkaloid chemotype (VW08: R-, OW09: S-, ZD11: UAC/S-, ZD04: RS-, TW12: RS+). Colours indicate nucleotide similarity with the reference (blue: reference type, red: non- reference type, green: insertion, yellow: common SNP). The black rectangle indicates a region that shows a completely consistent pattern of SNP occurrence among different accessions
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Low but detectable expression (Ct = 25–35) of the R-sequence variant was observed in S-dominated chemotypes (~14 % in Fig. 5a) as well as the S-sequence variant in the R- dominated chemotypes (~0.2 % in Fig. 5b). This consistent co-expression indicates that the two sequence variants might not concern alleles at a single locus, but instead represent two genetic loci.
Fig. 5 Mean normalised expression (± SE) of: a) R-type and b) S- type sequence variants of SdGAME8 in Solanum dulcamara
glycoalkaloid (GA) chemotypes from a segregating F2 population (R: n = 14, RS: n = 13, S: n = 9). Chemotypes are defined by the relative abundance of 25R- and 25S-type GAs. Normalised expression of the sequence variant was measured using qPCR and scaled to the mean expression in the target chemotype for which specific primers were designed (R-type in a, S-type in b; Table S1). Different letters over the bars indicate significant differences in expression (P < 0.05) according to Tukey’s post hoc test of one- way ANOVA (a: F = 94.5, df = 2, P = < 0.001; b: F = 263.2, df = 2, P = < 0.001)
Genetic regulation of saturation chemotypes A number of 147 DECs was selected for accession TW12 (containing saturated GAs, +) after comparison to three reference accessions characterised by unsaturated GAs (ZD04: RS-, OW09: S- and VW08: R-; Table S3). From this list we selected twelve contigs that were highly differentially expressed in this accession (> 10-fold expression difference, P < 0.05, absolute RPKM difference > 10, Table 1). Contig c59 (SdGAME25) was identified as the most strongly overexpressed contig. This supports the hypothesized key role for SdGAME25 enzymes in GA saturation, based on literature (Sonawane et al. 2018). Primers (Table S1) were designed based on the transcript sequence that was obtained from RNAseq (Document S3). A qPCR analyses on all F1 individuals in Fig. 3b confirmed that only plants that express saturated GAs in their chemotype showed detectable expression levels of SdGAME25 (Ct = 25.5–31.7, Table S4). Sonawane et al. (2018) proposed that in addition to GAME25, two reductase enzymes are required to synthesize saturated GAs. None of the DECs that were selected from RNAseq analyses were annotated as reductases. For instance, the overexpressed polyphenol oxidase (c2630) oxidises phenolic compounds to quinones (Mayer 2006). The underexpressed ferredoxin-thioredoxin reductase (c1515) is involved in the catalysis of light- dependent activation of photosynthesis enzymes (Arner and Holmgren 2000). Two contigs (c8050 and c169) were not annotated by D'Agostino et al. (2013).
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Table 1 Contigs of interest that are selected by differential expression analyses comparing Solanum dulcamara accession TW12, containing saturated glycoalkaloids (GAs), with three reference accessions (ZD04, OW09 and VW08), all containing unsaturated GAs. Mean RPKM (reads per kilobase million, n = 3) in each accession are shown for differentially expressed contigs (Benjamini-Hochberg adjusted P-values ≤ 0.05, Table S3) with a > 10- fold modelled difference in expression and absolute mean RPKM difference > 10. Additional data are shown for accession ZD11, which produces predominantly uronic acid conjugates (UACs) and was not used as a reference in statistical analyses
Mean RPKM per accession S. dulcamara contig Tomato homolog Description ZD04 OW09 VW08 TW12 ZD11 Overexpressed comp59_c0_seq1 Solyc03g095970.2.1 0.5 0.0 0.7 444.4 0.2 3-beta hydroxysteroid dehydrogenase comp2630_c0_seq1 Solyc08g074680.2.1 2.0 2.9 1.4 39.8 1.1 Polyphenol oxidase comp3987_c0_seq3 Solyc07g021540.2.1 0.0 0.0 0.0 25.9 0.0 FIP1 / ABA-responsive-like prot. comp7405_c0_seq1 Solyc07g052380.2.1 0.0 0.0 0.0 24.0 14.8 Multidrug resistance protein mdtK comp3457_c0_seq2 Solyc04g008340.2.1 0.0 0.0 0.0 15.1 0.0 Zinc transporter / IAA-alanine resist. prot. 1 comp9226_c0_seq4 Solyc08g076020.2.1 0.6 0.0 0.1 13.2 0.0 Cobalamin synthesis prot. / P-family prot. comp8050_c0_seq1 NA 0.0 0.0 0.0 13.2 0.0 NA Underexpressed comp733_c0_seq4 Solyc02g086740.1.1 25.2 20.7 10.7 0.0 29.0 50S ribosomal protein L12-C comp2808_c0_seq1 Solyc08g067090.2.1 17.9 11.9 29.3 0.1 27.0 PpiC-type isomerase / cyclophilin-like prot. comp169_c0_seq1 NA 82.9 63.4 106.7 2.1 98.0 NA comp1515_c0_seq1 Solyc02g087230.2.1 237.2 102.0 56.0 0.8 157.4 Ferredoxin-thioredoxin reductase cat. chain comp4507_c0_seq1 Solyc08g006850.2.1 216.3 309.5 1335.3 0.0 239.1 Patatin-like phospholipase family prot.
Genetic regulation of conjugation chemotypes We identified 170 DECs in the UAC-rich accession ZD11 (UAC) when compared to the four GA-rich accessions devoid of UACs (ZD04: RS-, OW09: S-, VW08: R-, and TW12: RS+, Table S5). Fifteen DECs were highly differentially expressed (> 10-fold expression difference, P < 0.05, absolute RPKM difference > 10, Table 2). None of these selected contigs had a putative annotated molecular function that could be directly related to UAC biosynthesis, e.g. UDP- glucuronosyltransferases or glycoside oxidases. Several of the selected contigs were involved in defence responses. For instance, a thionin-like antimicrobial protein (c4602 in Table 2) was highly expressed in ZD11. The exact function of the overexpressed N-acetyltransferase (c9686) is unknown, but it is possibly involved in xenobiotic metabolism. A closer look at the complete list of DECs (Table S5) revealed that two glutathione-S transferases were overexpressed (c1223 and c1273). Glutathione-S transferases also aid in the elimination of xenobiotics (Dixon et al. 2010). In contrast to these overexpressed genes, the expression of an antinutritive protease inhibitor (c1119), a defence regulating WRKY transcription factor (c12641) and an immune regulating lectin superfamily protein (c5292) were significantly lower in ZD11 (Table 2). In addition to these genes involved in defence responses, we observed that genes involved in primary metabolic processes were less expressed in the UAC chemotype ZD11. For example, the expression of an essential light receptor (CAB4, c15 in Table 2) was very low (3.9 RPKM) compared to the four reference accessions (> 2500 RPKM). Also contigs related to ion signalling (c3223 and c6424) and cell wall biosynthesis (c3582) were underexpressed in the UAC-rich accession ZD11 as well as three sequences related to a single oxygenase (c1766), with a proposed role in flavonoid biosynthesis. The underexpression of a sterol regulatory element-binding (SREB) transcription factor (c11905) may have an overall effect on GA- and UAC-metabolism, because it is possibly involved in cholesterol biosynthesis.
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Table 2 Contigs of interest that are selected by differential expression analyses comparing Solanum dulcamara accession ZD11, predominantly containing steroidal uronic acid conjugated compounds (UACs), with four reference accessions (ZD04, OW09, VW08 and TW12) producing glycoalkaloids (GAs). Mean RPKM (reads per kilobase million, n = 3) in each accession are shown for differentially expressed contigs (Benjamini-Hochberg adjusted P-values ≤ 0.05, Table S5) with a > 10-fold modelled difference in expression and mean RPKM difference > 10 Mean RPKM per accession S. dulcamara contig Tomato homolog Description ZD04 OW09 VW08 TW12 ZD11 Overexpressed comp4602_c0_seq1 Solyc10g007870.2 1.3 66.2 136.1 6.7 1617.1 Tumor-related / thionin-like prot. comp9686_c0_seq2 Solyc05g041830.2 0.7 0.5 0.6 0.9 13.1 N-acetyltransferase Underexpressed comp11905_c0_seq8 Solyc01g106820.2 12.3 14.0 12.6 10.5 0.0 Peptidase M50 fam. / SREBP 2 protease comp10659_c0_seq1 Solyc09g083010.2 17.5 16.7 24.1 14.6 0.0 Endonuclease III-like prot. 1 comp5292_c0_seq1 Solyc09g083030.1 11.9 20.3 21.3 25.6 0.7 Mannose-binding lectin superfam. prot. comp3223_c0_seq1 Solyc12g056910.1 16.3 15.1 30.0 28.4 0.0 Zinc finger CCCH domain prot. comp1766_c0_seq2 Solyc07g054870.2 26.8 24.2 26.0 38.0 0.2 2-oxoglutarate-FE(II)-dep. oxygenase comp12641_c0_seq1 Solyc01g079360.2 17.0 18.8 69.0 22.9 0.0 WRKY transcription factor 3 comp3582_c0_seq2 Solyc07g009380.2 47.4 46.3 37.2 67.6 0.0 Xyloglucan endotransgluco- / hydrolase 2 comp6424_c0_seq2 Solyc12g005400.1 58.9 38.4 69.7 42.5 0.0 Cyclic nucleotide gated channel comp1986_c0_seq1 Solyc08g082120.2 44.3 26.7 44.7 96.4 2.0 Methanol inducible prot. comp1766_c0_seq1 Solyc07g054870.2 73.5 64.5 75.2 95.2 0.3 2-oxoglutarate-FE(II)-dep. oxygenase comp1766_c0_seq3 Solyc07g054870.2 95.5 79.6 116.4 126.9 0.5 2-oxoglutarate-FE(II)-dep. oxygenase comp1119_c0_seq1 Solyc11g022590.1 228.7 158.5 1227.7 1542.3 0.2 Kunitz trypsin inhibitor 4 comp15_c0_seq10 Solyc07g047850.2 2562.5 6312.3 2695.4 3623.9 3.9 Chlorophyll a-b binding prot. 4
We performed a GO term enrichment to better assess the molecular function of the complete set of DECs and the biological processes they are involved in. Terms related to three molecular functions were significantly enriched (Table 3). Oxidoreductase activity- related contigs (GO:0016705) were both over- and underexpressed, whereas eight glycosylhydrolase enzymes (GO:0016798 and GO:0004553) were all underexpressed in accession ZD11. Additionally, contigs involved in signal transduction pathways were differentially expressed, including ion transporters (GO:0046873 and GO:0015077) and calmodulin binding (GO:0005516). The enrichment of biological processes included various terms related to (a)biotic stimuli, ion transport, sugar metabolism and immune responses (Table 3). Genes involved in stress responses (GO:0006810) were mostly overexpressed, whereas immune responses (GO:0006955 and GO:0045087) and sucrose metabolism (GO:0005985) were mostly underexpressed. This functional analyses of the DECs suggests that overall secondary and primary metabolism are differentially regulated in the UAC chemotype.
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Table 3 Gene ontology (GO) terms, related to molecular functions and biological processes, that were significantly overrepresented in the set of differentially expressed contigs (DECs) in Solanum dulcamara accession ZD11. This accession produces predominantly steroidal uronic acid conjugated compounds (UACs), whereas chemical profiles of four reference accessions are dominated by glycoalkaloids (GAs) n contigs Expected n n DECs (over-/ GO term Description annotated DECs underexpressed) Molecular function oxidoreductase activity, acting on paired donors, with GO:0016705 298 2.45 8 (4/4)** incorporation or reduction of molecular oxygen GO:0016798 hydrolase activity, acting on glycosyl bonds 348 2.86 8 (0/8)** GO:0004553 hydrolase activity, hydrolyzing O-glycosyl compounds 300 2.46 7 (0/7)* GO:0005516 calmodulin binding 311 2.55 7 (4/3)* GO:0046873 metal ion transmembrane transporter activity 251 2.06 6 (5/1)* oxidoreductase activity, acting on the CH-OH group of GO:0016616 270 2.22 6 (2/4)* donors, NAD or NADP as acceptor monovalent inorganic cation transmembrane transporter GO:0015077 290 2.38 6 (5/1)* activity GO:0016614 oxidoreductase activity/ acting on CH-OH group of donors 295 2.42 6 (2/4)* Biological process GO:0009642 response to light intensity 666 5.16 12 (3/9)** GO:0006950 response to stress 7672 59.49 73 (47/26)** GO:0006810 transport 5864 45.47 58 (24/34)* GO:0009991 response to extracellular stimulus 922 7.15 14 (6/8)* GO:0009595 detection of biotic stimulus 251 1.95 6 (2/4)* GO:0005985 sucrose metabolic process 407 3.16 8 (0/8)* GO:0015672 monovalent inorganic cation transport 672 5.21 11 (6/5)* GO:0051179 localization 6500 50.40 62 (26/36)* GO:0051234 establishment of localization 6023 46.70 58 (24/34)* GO:0030001 metal ion transport 1122 8.70 15 (8/7)* GO:0000041 transition metal ion transport 460 3.57 8 (4/4)* GO:0031668 cellular response to extracellular stimulus 828 6.42 12 (6/6)* GO:0006955 immune response 2517 19.52 28 (9/19)* GO:0045087 innate immune response 2409 18.68 27 (9/18)* GO:0006811 ion transport 2422 18.78 27 (12/15)* GO:0010260 organ senescence 470 3.64 8 (3/5)* GO:0071496 cellular response to external stimulus 849 6.58 12 (6/6)* GO:0033554 cellular response to stress 3480 26.98 36 (13/23)* GO:0015706 nitrate transport 309 2.40 6 (2/4)* GO:0002376 immune system process 2910 22.56 31 (11/20)* GO:0006812 cation transport 1686 13.07 20 (8/12)* GO:0071229 cellular response to acid chemical 1695 13.14 20 (8/12)* GO:0009744 response to sucrose 581 4.51 9 (3/6)* GO:0034285 response to disaccharide 584 4.53 9 (3/6)* GO:0042221 response to chemical 7300 56.60 66 (30/36)* GO:0010114 response to red light 334 2.59 6 (2/4)*
Asterisks indicate significant enrichment according to Fisher’s exact test (** Padj < 0.01/ * Padj < 0.05)
Slug preference assays
Seven F1 individuals with well-defined chemotypes were selected from different populations (Fig. 3b, marked with red triangles). Leaf discs from these plants were used to assess slug resistance in a preference assay. Leaf discs from UAC chemotypes were most preferred by slugs, independent whether ZD11 (UAC/S-) or ZD04 (RS-) was the parent (Fig. 6). Similarly, plants with unsaturated GAs (RS- and S-) were least preferred, independent whether ZD04 (RS-) or TW12 (RS+) was the parental accession. Slugs showed an intermediate preference for plants with saturated tomatidine-/soladulcidine-type GAs (RS+). Overall, slug preference for chemotypes appears independent of the genetic background, since equal chemotypes of different genetic origin were equally preferred by slugs.
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Fig. 6 Mean relative consumption (± SE) by roundback slugs (Arion rufus/ater, n = 8) on leaf discs of seven Solanum dulcamara chemotypes of different parental accessions (in brackets; see red triangles in Fig. 3b for selected individuals). Different letters over the bars indicate significant preference differences (P < 0.05) according to Conover’s post hoc test with Bonferroni correction of P-values for multiple comparisons after Friedman ANOVA (χ2 = 37.085, df = 6, P < 0.001). The dashed line indicates the theoretical damage distribution when slugs would not have shown a preference
Discussion
Our results support a key role for SdGAME8 enzymes in the configuration of 25S- and 25R- type GAs, as well as for SdGAME25 enzymes in GA saturation. We identified two SdGAME8 sequence variants, one more highly expressed in 25S- and the other in 25R-dominated GA chemotypes. These sequence variants were codominantly inherited and expressed at intermediate levels in RS-codominated chemotypes. SdGAME25 acts as a dominant allele in chemotypes containing saturated GAs. The identity of the recessive gene responsible for UAC biosynthesis remains elusive. Slugs prefer plants with saturated GAs over conspecifics with unsaturated GAs. Plants with saturated GAs, in turn, are less preferred than UAC-rich accessions. This provides strong evidence that single molecular differences among S. dulcamara individuals result in ecologically relevant chemical diversity.
Genetic background of configuration chemotypes Our combined analyses of chemotype segregation and transcript expression using RNAseq and qPCR show that the configuration of 25R- and 25S-stereoisomers corresponds with the co-expression of two sequence variants of SdGAME8. Plants in which 25S- or 25R-type GAs dominate, specifically expressed one of both sequence variants, whereas RS-codominated chemotypes showed intermediate expression levels. The fact that all chemotypes express at least low levels of both SdGAME8 sequence variants indicates that the two variants cannot be different alleles of a single gene, but instead represent two genetic loci. In tomato and potato, two copies of GAME8 (GAME8a and GAME8b) are located on chromosome 6 (Cárdenas et al. 2016; Mariot et al. 2016). Possibly the two sequence variants of SdGAME8 are homologues of GAME8a and GAME8b. In contrast to tomato and potato, the two sequence variants of SdGAME8 show differences in the coding sequence. This may explain why S. dulcamara produces both configurations, whereas tomato only produces 25S-type GAs. Potato is characterised by solanidane-type GAs, in which the F-ring is fused to the E- ring, which is why no R- and S-configuration chemotypes occur (Eich 2008).
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It is not yet clear how the expression of the two SdGAME8 sequence variants is regulated. We observed that selfing of 25S- or 25R-dominant chemotypes produced non- segregating populations, whereas RS-codominated chemotypes produced offspring with a segregation pattern that is not significantly different from a Mendelian 1:2:1 ratio. Crossing of a 25S- and a 25R-dominant chemotype, in turn, produced a non-segregating F1 in which both stereoisomers co-occur. These segregation patterns are characteristic for the inheritance of a single independent gene, possibly coding for a single transcription factor regulating the transcription and co-expression of both SdGAME8 sequence variants. In potato, StGAME8a and StGAME8b contain different cis-regulatory elements in the promotor region, which suggests that both variants can be independently regulated in response to abiotic and biotic stress (Mariot et al. 2016). Whether these differences in promotor sequence also apply to the sequence variants of SdGAME8 remains unclear, because the S. dulcamara genome is not fully sequenced yet. However, the consistent relative proportions of 25R- and 25S-type GAs in our studies (Calf et al. 2018 and 2019) suggests that the chemotype is not altered in response to environmental stress, such as slug feeding (Calf et al. 2019). Further research is needed to unravel how both sequence variants of SdGAME8 are differentially expressed and whether this can be regulated by (a)biotic stimuli.
Genetic background of saturation chemotypes Our analyses support a key role for GAME25 enzymes in the saturation of the GA backbone in S. dulcamara. This is in line with what has been reported from in vivo and in vitro studies on cultivated Solanum species (Sonawane et al. 2018). In our untargeted differential expression analyses of RNAseq data, SdGAME25 was the most strongly overexpressed gene in accession TW12, which is rich in saturated GAs. Moreover, both RNAseq and qPCR analyses revealed that only plants expressing SdGAME25 produced saturated GAs in their leaves. The observed segregation pattern in the F1 offspring of TW12 indicates that the expression of SdGAME25 is regulated by a dominant allele of a single gene, and that the original TW12 parent is heterozygous for that gene. This does not mean that the gene coding for the SdGAME25 enzyme itself is heterozygous in TW12. The absence of SdGAME25 mRNA in unsaturated chemotypes suggests a regulatory region mutation. Moreover, in a previous study, the expression of GAME25 and the levels of saturated GAs were shown to be highly tissue specific (Sonawane et al. 2018). Taken together, this suggests that the responsible heterozygous region in TW12 may be found in the gene coding for a tissue-specific transcription factor, or the SdGAME25 promotor. GAME25 is proposed to catalyse only the first of a three-step reduction of the GA backbone in tomato (Sonawane et al. 2018). We could not identify candidate genes for the subsequent reduction steps in S. dulcamara. It may well be that the yet unidentified reductase enzymes serve a multifunctional role and are therefore also expressed in plants that do not produce saturated GAs. If so, these genes would not be detected in differential expression analyses. Alternatively, our analyses of the S. dulcamara transcriptome is based
136 Chapter 5: Genetic basis of ecologically relevant chemical diversity on homology with tomato, in which the responsible genes may not have been annotated (D'Agostino et al. 2013). Moreover, homologous genes can serve different functions in different plant species, which is why selected DECs with non-matching annotated functions may still be related with the chemotype. Gene expression analyses in segregating individuals by means of qPCR might be an effective approach to investigate the relationship between our selection of DECs and the saturated chemotypes. In case of strong correlations, the function should be confirmed using knock-out or overexpression lines.
Genetic background of conjugation chemotypes
The observed segregation pattern among F2 individuals suggests that UAC biosynthesis is a recessive trait coded by a single gene. This hypothesis is supported by two other observations: 1) the lack of UAC chemotypes among the F1 progeny of the cross between
VW08 and ZD11 and 2) the emergence of two F1 individuals with UAC chemotypes after selfing the non-UAC accession ZD04. The responsible recessive allele therefore appears to be commonly present in the natural population from which both ZD11 (homozygous for the recessive allele) and ZD04 (heterozygous at this locus) were collected: Zandvoort Dry (Calf et al. 2018). Despite our efforts to identify the genetic mechanism underlying UAC biosynthesis, the identity of the responsible gene remains unknown. None of the differentially expressed contigs in the UAC-rich accession, ZD11, was annotated with a molecular function that is in line with the hypothesised pathway of UAC biosynthesis, such as UDP- glucuronosyltransferase or glycoside oxidase. It may be that the transcript that is responsible for UAC biosynthesis is not mapped in the S. dulcamara reference transcriptome (D'Agostino et al. 2013) and therefore not detected in our analyses. Alternatively, the responsible transcript could be a gene that is expressed at reference levels but results in a dysfunctional enzyme. After all, a loss-of-function mutation can easily arise from a single point-mutation, which is not detected by differential expression analyses. Gene mapping and subsequent sequencing may reveal whether this is indeed the case. Besides, it should be noted that UACs are also observed in very low proportions in regular GA-producing plants. It is possible that UACs are a common, but hitherto unnoticed, intermediate or by-product of GA metabolism. UACs therefore also could accumulate if the function of an essential enzyme in GA metabolism is impaired.
Interestingly, the UAC chemotype, ZD11, showed differential expression of many genes involved in secondary metabolism/defence processes, compared to general GA- producing accessions. For instance, eight glycoside hydrolase enzymes were underexpressed in ZD11. Such enzymes break the glycosidic bond between two or more carbohydrates or between a carbohydrate and a non-carbohydrate moiety. These enzymes could well be involved in GA catabolism and recycling. Moreover, the UAC-chemotype shows signs of an affected immune response, such as overexpression of a thionin-like protein and glutathione- S transferase enzymes, which aid to mobilize and eliminate xenobiotic compounds (Dixon et al. 2010). Uronic acid conjugation, or glucuronidation, is a common physiological process to
137 Chapter 5: Genetic basis of ecologically relevant chemical diversity increase the water solubility of compounds. As such, it is often deployed as a phase II detoxification reaction in the mobilisation and elimination of xenobiotics in plants and animals, similar to the function of glutathione-S transferases (Bender and Murray 2015). An alternative explanation of the UAC-chemotype may therefore be that GAs in these chemotypes act as effector molecules and elicit an auto-immune response which converts GAs into UACs. However, UACs themselves may also be autotoxic similar to non-glycosylated GA backbones (Eich 2008). The observed auto-immune response could therefore also result from the accumulation of UACs due to impaired GA metabolism.
We hypothesised that UACs are either produced by direct conjugation of uronic acids to the aglycon backbone, or that sugar moieties of saponins and GAs are oxidised to uronic acids after conjugation. Based upon our results we cannot reject or support either of these two scenarios. Further analyses of the UAC chemotypes in the F2 offspring likely provide a better avenue to understand the genetic mechanisms underlying this chemotype.
Chemical diversity under selection by slugs
The use of segregating F1 individuals allowed us to investigate the resistance status of S. dulcamara chemotypes with varying genetic backgrounds. S-dominated and RS-codominated unsaturated chemotypes were equally resistant. This suggests that 25R- and 25S-type GAs are equally bioactive towards slugs, which is in agreement with our previous study (Calf et al.
2018). Moreover, F1 individuals that produce unsaturated GAs are more resistant to slug feeding than conspecifics with chemotypes dominated by saturated GAs. This, in turn, is in line with studies on tomato, in which saturated GAs were less bioactive towards insect herbivores and pathogens than unsaturated GAs (Friedman 2002; Sonawane et al. 2018). Our results also provide further support that UAC-biosynthesis is at the expense of resistance to slugs and other generalist herbivores (Calf et al. 2018 and 2019). In conclusion, slug resistance in S. dulcamara is particularly related to the overall levels of unsaturated GAs.
Conclusion We illustrate that ecologically relevant chemical diversity can arise from single heritable molecular differences. The variation in S. dulcamara chemotypes mirrors the combined diversity of GAs that is observed among various solanaceous crop species (Eich 2008). Further research on the regulation of the genes involved in the configuration, saturation and conjugation of GAs may therefore shed a new light on GA biosynthesis and metabolism in domesticated crops. Natural selection by slugs and other generalist herbivores may explain the rare occurrence of UACs (Calf et al. 2018), as well as the heterogeneous geographic distribution of saturated GA-chemotypes (Mathé 1970) in natural populations.
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Acknowledgements
The authors thank Peter de Groot, Jan Zethof, Redouan Anaia, Ruud Grootens, Leonne van der Enden, Sieme Pelzer and Adèle van Houwelingen for practical assistance. We also thank and Bas te Lintel Hekkert for RNA sequencing. OWC is supported by grant number 823.01.007 of the open programme of the Netherlands Organisation for Scientific Research (NWO-ALW). NMvD and AW gratefully acknowledge the support of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig funded by the German Research Foundation (FZT 118).
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Supplemental material
Table S1 Primer sequences used for qPCR Table S2 Mean number of RPKM of GAME genes according to RNAseq analyses of five selected Solanum dulcamara accessions Table S3 Differentially expressed contigs in Solanum dulcamara accession TW12 in comparison to three reference accessions Table S4 Non-normalised cycle threshold for expression estimation of SdGAME25 in parental Solanum dulcamara accessions and plants from F1 populations Table S5 Differentially expressed contigs of Solanum dulcamara in accession ZD11 in comparison to four reference accessions
Fig. S1 Relative abundance of steroidal glycoalkaloids (GAs) and uronic acid conjugated compounds (UACs) in leaf material of 15 Solanum dulcamara accessions as derived from Calf et al. (2018) Fig. S2 Treatment and leaf sampling in the experiment for RNAseq analyses
Document S1 Chemical profiling using HPLC-qToF-MS Document S2 Nucleotide sequence variants of SdGAME8 Document S3 Nucleotide sequence variants of SdGAME25
140 Chapter 5: Genetic basis of ecologically relevant chemical diversity
Table S1 Primer sequences used for qPCR
Gene name S. dulcamara contig Primer Sequence (5'-3') CAC comp3969_c0_seq3 Forward AGTTTGTTGTTGAGGCTGTTACAC CAC comp3969_c0_seq3 Reverse ACCGGACACCTTCCTGAGTAATG EF1α comp28_c0_seq4 Forward TGGTACCTCCCAGGCTGACTGTG EF1α comp28_c0_seq4 Reverse GCAACGCATGTTCACGGGTCT Exp comp12933_c0_seq1 Forward CTAAGAACGCTGGACCTAATGACAAG Exp comp12933_c0_seq1 Reverse AAAGTCGATTTAGCTTTCTCTGCATATTTC SAND comp6973_c0_seq1 Forward TGCTTACACATGTCTTCCACTTGC SAND comp6973_c0_seq1 Reverse AAACAGGACCCCTGAGTCAGTTAC SdGAME25 comp59_c0_seq1 Forward TCCATCGGAACAGACAAGGC SdGAME25 comp59_c0_seq1 Reverse TCACGTTCATGTCGAGGACG SdGAME8_R-type comp57_c0_seq1 Forward CCTGAAGCCAATGCTTTCATCCACT SdGAME8_R-type comp57_c0_seq1 Reverse TTTCATCGCAACATACAGAAATTGC SdGAME8_S-type comp57_c0_seq1 Forward TGTGAATCCAATGCTTTCATCGACA SdGAME8_S-type comp57_c0_seq1 Reverse GTTCATCACAACACTCACAAAATGC
141 Chapter 5: Genetic basis of ecologically relevant chemical diversity
Table S2 Mean number of reads per kilobase million (RPKM, n = 3 ± SE) of glycoalkaloid metabolism (GAME) genes according to RNAseq analyses of five selected Solanum dulcamara accessions. Genes are sorted in ascending order of putative participation in GA biosynthesis (Fig. 2)
Mean RPKM Gene S. dulcamara contig ZD04 (RS-) OW09 (S-) VW08 (R-) TW12 (RS+) ZD11 (UAC) GAME7 comp1232_c0_seq1 4.7 ± 0.5 10.7 ± 1 8.1 ± 1.4 7.9 ± 0.5 11 ± 0.6 GAME8 comp57_c0_seq1 400.8 ± 17.9 374.1 ± 16.5 280.2 ± 19.3 490 ± 49.1 451.3 ± 31.7 GAME11 comp13_c0_seq1 10367 ± 200 4879.3 ± 380.7 7104.8 ± 212.8 7545.1 ± 637.5 7590.3 ± 477.6 GAME6 comp341_c0_seq1 461 ± 16.2 212.1 ± 10.7 295.3 ± 6.1 327.6 ± 30.6 447.1 ± 15.1 GAME4 comp185_c0_seq1 780.1 ± 36.3 569.6 ± 32.5 539.6 ± 15.2 594.9 ± 34.6 585.2 ± 47.2 GAME12 comp47_c0_seq1 3417.1 ± 22.1 2069.7 ± 120.4 3671.1 ± 185.1 2864.6 ± 306.7 2875.5 ± 316.9 GAME25 comp59_c0_seq1 0.5 ± 0 0 ± 0 0.7 ± 0.2 444.4 ± 12.7 0.2 ± 0.1 GAME1 comp839_c0_seq1 299.6 ± 21 120.8 ± 5.4 154.2 ± 16.3 162.2 ± 5.6 329.5 ± 22 GAME17 comp2023_c0_seq1 373.2 ± 11.8 56.8 ± 1.6 130.3 ± 15.6 294 ± 1.9 548.3 ± 44.9 GAME18 comp1608_c0_seq1 523.9 ± 39.7 169.5 ± 13.7 314.1 ± 2 283.5 ± 6.8 536.9 ± 52 GAME2 comp611_c0_seq1 289.3 ± 11.9 116.6 ± 10.9 258.4 ± 6.5 177.4 ± 18.1 225.6 ± 19.3 GAME9 comp2527_c0_seq1 18.5 ± 1.1 11.3 ± 1.4 11.5 ± 1.2 12 ± 2.2 15.3 ± 1.6
142 Chapter 5: Genetic basis of ecologically relevant chemical diversity
ference dif -
fold - 2
ino et al. 2013) gs with absolute log gs absolute with
binding protein/permease C9B6.09cbinding protein/permease
-
se gatY subunit
phosphate dehydrogenase ITAG2.3 description
- C -
Pol polyprotein
- itol 6 transporter ATP in like family
A Gag
- bisphosphate aldola bisphosphate
- protein ligase MARCH3 protein ligase MARCH3 - - 1,6 1 homolog 2 - - like phospholipase like family protein glucosidase - - H2 finger prote D loop GTPase 1 - 1c protein - -
-
tagatose beta hydroxysteroid dehydrogenase - opper transport protein 86 - FIP1 Zinc transporter NA Protein sel Patatin NA D GPN NA Choline transporter like family 50S ribosomal protein L12 Glutaredoxin family protein Uncharacterized ABC E3 ubiquitin inhibitorNuclear protein of phosphatase 1 inhibitorNuclear protein of phosphatase 1 bindingMyrosinase protein C Beta 3 import Mitochondrial membraneinner translocase subunit Tim8 Multidrug resistance protein mdtK RING NA NADP sorb dependent synthase Glycogen kinase Transposon Ty1 PAR Choline transporter E3 ubiquitin
06g072600.2.1
Tomato homolog Solyc07g021540.2.1 Solyc04g008340.2.1 NA Solyc03g118670.2.1 Solyc08g006850.2.1 NA Solyc03g118640.2.1 Solyc08g081330.2.1 NA Solyc01g111370.2.1 Solyc02g086740.1.1 Solyc02g087850.1.1 Solyc02g087870.2.1 Solyc03g116030.2.1 Solyc02g089360.2.1 Solyc02g089360.2.1 Solyc09g083030.1.1 Solyc02g080650.2.1 Solyc11g071640.1.1 Solyc03g095970.2.1 Solyc Solyc07g052380.2.1 Solyc01g107290.2.1 NA Solyc01g110450.2.1 Solyc02g086670.2.1 Solyc00g075040.1.1 Solyc03g025680.2.1 Solyc01g111370.2.1 Solyc03g116030.2.1
9.7 9.3 14.5 14.4 14.2 14.1 13.9 13.8 13.7 13.5 13.5 13.4 13.2 13.0 13.0 12.7 12.6 12.5 12.0 11.9 11.7 11.5 11.4 11.3 11.2 10.9 10.9 10.6 10.5 10.3 Mean AbsLFD
values ≤ 0.05 in relation to each of the reference accessions were considered differentially expressed. accession TW12 in comparison to three reference accessions (ZD04, OW09 and VW08). Mean number of - 0.0 0.0 0.0 0.0 0.0 0.0 8.6 4.2 9.1 0.0 0.0 1.0 0.0 0.2 0.2 7.7 6.2 0.0 9.4 5.5 0.0 0.0 24.5 11.2 29.0 15.0 14.8 13.1 27.6 ZD11 ZD11 239.1 (UAC)
= 3)=
n 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.6 8.4 0.0 0.0 0.0 9.4 25.9 15.1 13.2 24.0 11.6 (RS+) 444.4 TW12
) - Hochberg adjusted P adjusted Hochberg 0.0 0.0 0.0 9.6 9.6 9.4 3.4 3.5 3.5 5.8 0.7 0.0 5.2 0.0 0.0 4.7 2.3 0.9 0.0 0.0 - (R 18.3 15.7 21.0 10.7 10.7 11.9 11.7 17.2 10.3 VW08 1335.3 = 3)= are shown for each accession, including ZD11 which was not used as reference. All conti
) - 0.0 0.0 0.0 3.6 8.4 5.4 5.2 3.6 9.1 3.8 4.1 5.2 4.2 7.4 0.0 8.8 0.0 1.9 0.0 0.1 4.8 4.0 3.7 5.2 0.0 (S 15.5 11.2 15.4 20.7 309.5 OW09 OW09 Mean RPKM per accession (
)
- 0.0 0.0 0.0 6.3 7.5 3.3 9.1 8.0 6.0 2.1 4.3 1.6 1.6 4.6 1.2 3.8 0.5 2.8 0.0 3.2 0.0 0.0 5.1 3.1 2.1 0.0 3.5 16.7 25.2 (RS ZD04 ZD04 216.3
_seq4 Differentially expressed contigs in dulcamara Solanum
4507_c0_seq1 mp5749_c0_seq3 mp12476_c0_seq1 S. dulcamara contig comp3987_c0_seq3 comp3457_c0_seq2 comp8050_c0_seq1 comp7929_c0_seq2 comp comp8050_c0_seq2 comp5384_c0_seq2 comp3138_c0_seq1 comp3308_c0_seq1 comp6174_c0_seq1 comp733_c0 comp14106_c0_seq2 comp6961_c0_seq2 comp3970_c0_seq2 comp5749_c0_seq1 co comp4062_c0_seq1 comp12956_c0_seq1 comp4531_c0_seq6 comp59_c0_seq1 comp7843_c0_seq1 comp7405_c0_seq1 comp12241_c0_seq1 comp20111_c0_seq1 comp3699_c0_seq2 comp8410_c0_seq2 co comp8887_c0_seq2 comp6174_c0_seq2 comp3970_c0_seq4 Table S3 (AbsLFD) (AbsLFD) in expression ≥ 2 and Benjamini tions are based on homology with tomato (D'Agost with tomato on homology based tions are descrip Functional order. descending in AbsLFD mean the on based sorted are Contigs reads per kilobase million (RPKM, n
143 Chapter 5: Genetic basis of ecologically relevant chemical diversity
ke proteinke li
-
protein kinase, RLP -
like -
carboxylate oxidase
25
isomerase - -
Pol polyprotein 1 -
-
ITAG2.3 description
trans
- A Gag - cis like serine/threonine - thioredoxin reductase catalytic chain - prolyl - lrr, resistance protein - loop GTPase 1 ear inhibitorear protein of phosphatase 1 - repeat protein repeat protein related protein Rab - - - nbs
rredoxin - aminocyclopropane lass I heat shock proteinlass I shock heat 3 - Wd Wd NA Copper transport protein 86 Cobalamin protein/Pfamily synthesis protein Fe Peptidyl NA GPN inhibitorNuclear protein of phosphatase 1 Nuclear receptor corepressor 2 NA Cc dismutaseSuperoxide Nucl Ectonucleoside triphosphate diphosphohydrolase 6 1 NHL repeat containing 2 NA Cytochrome P450 NA Polyphenol oxidase Octanoyltransferase NA Polyphenol oxidase NA NA Transposon Ty1 synthaseAnthocyanidin Peptide transporter Cytochrome P450 NA synthetaseGlutathione Ras LRR receptor Receptor like kinase, RLK Serine/threonine protein kinase C NA
.2.1
Tomato homolog Solyc09g065290.2.1 Solyc09g065290.2.1 NA Solyc02g080650.2.1 Solyc08g076020.2.1 Solyc02g087230.2.1 Solyc08g067090.2.1 NA Solyc08g081330 Solyc02g089360.2.1 Solyc04g049120.2.1 NA Solyc06g062440.2.1 Solyc06g049080.2.1 Solyc02g089360.2.1 Solyc12g098540.1.1 Solyc09g089730.2.1 Solyc01g100650.2.1 NA Solyc04g079660.2.1 NA Solyc08g074680.2.1 Solyc02g077460.1.1 NA Solyc08g074680.2.1 NA NA Solyc10g017730.1.1 Solyc08g006770.2.1 Solyc05g005970.2.1 Solyc04g079660.2.1 NA Solyc01g098610.2.1 Solyc01g096220.2.1 Solyc05g055190.1.1 Solyc03g005960.2.1 Solyc07g018140.2.1 Solyc04g014480.2.1 NA
9 9.0 9.0 8.8 8.6 8.4 7.2 7.0 6.1 5.6 5.4 5.3 5.3 5.2 5.2 5.2 4.9 4.9 4.8 4.6 4.6 4.5 4.4 4.4 3.9 3.8 3.8 3.5 3.4 3.3 3.3 3.1 3.1 3.0 2.9 2. 2.8 2.8 2.7 2.6 Mean AbsLFD
2.3 5.2 5.4 0.0 8.0 6.0 1.5 6.5 5.0 4.5 3.5 1.0 3.1 0.2 5.9 0.2 1.1 0.1 5.4 2.0 5.8 2.3 0.8 5.6 7.9 7.1 7.8 3.2 0.6 0.8 46.5 27.0 21.8 98.0 42.7 41.4 33.7 20.5 ZD11 ZD11 157.4 (UAC)
= 3)=
n 3.6 3.2 3.2 0.8 0.1 0.0 4.9 0.0 2.1 0.1 0.1 3.9 0.1 0.5 0.1 0.3 5.8 0.3 0.5 5.4 7.1 2.7 0.8 0.1 1.0 0.8 0.3 7.4 5.9 62.9 13.2 19.8 39.2 15.5 39.8 57.4 94.6 26.0 98.6 (RS+) TW12
) 08 08 - .2 0.7 0.5 0.0 0.0 0.1 1.2 2.2 1.7 6.9 3.9 2.1 0.8 9.8 0.2 0.7 1.4 0.0 6.6 5 0.2 0.9 5.0 1.6 1.5 2.2 3.8 2.1 1.5 (R 56.0 29.3 10.2 10.9 13.9 13.0 27.1 40.1 10.9 13.5 106.7 VW
) - 0.0 0.0 0.0 0.0 2.4 0.0 0.0 0.9 6.6 5.7 0.0 0.4 5.8 5.3 2.9 8.2 2.9 0.8 4.5 2.7 4.8 2.0 0.8 3.0 1.6 3.9 4.4 9.7 2.4 0.7 0.7 (S 30.1 11.9 63.4 25.2 17.0 14.3 27.4 102.0 OW09 OW09 Mean RPKM per accession (
)
-
0.0 0.0 0.0 1.2 0.6 1.3 7.7 1.7 0.5 3.1 3.1 1.4 1.4 0.5 9.0 0.2 4.7 2.9 2.0 0.9 2.3 5.5 4.7 0.3 0.5 5.3 0.8 6.9 9.3 1.3 1.0 1.0 17.9 82.9 10.9 31.2 11.1 10.7 (RS ZD04 ZD04 237.2 2/4) (
c0_seq2 _c0_seq1 Continued Continued
2411_c0_seq1 S. dulcamara contig comp7491_c0_seq3 comp7491_c0_seq1 comp169_c0_seq3 comp12956_ comp9226_c0_seq4 comp1515_c0_seq1 comp2808_c0_seq1 comp15767_c0_seq1 comp3138_c0_seq2 comp5749_c0_seq2 comp4355_c0_seq4 comp169_c0_seq1 comp13631_c0_seq1 comp14269_c0_seq1 comp5749_c0_seq4 comp8429_c0_seq1 comp5303_c0_seq2 comp9220_c0_seq2 comp12867_c0_seq1 comp15191_c0_seq2 comp4847_c0_seq1 comp2630_c0_seq1 comp1910_c0_seq2 comp15051_c0_seq1 comp2630_c0_seq4 comp4227_c0_seq2 comp23703_c0_seq1 comp23955_c0_seq1 comp comp8529_c0_seq1 comp24380_c0_seq1 comp4227 comp3794_c0_seq2 comp4476_c0_seq1 comp6866_c0_seq1 comp3694_c0_seq5 comp10314_c0_seq1 comp3629_c0_seq2 comp24537_c0_seq1 Table S3
144 Chapter 5: Genetic basis of ecologically relevant chemical diversity
protein expressed
1 subunit -
it B1 2.3 description
2 2 actor family protein -
ning protein 23 ning protein ITAG
containing protein
contai - -
like protein -
like protein
binding protein alpha
- containing subun -
associated domain associated containing like 5 - polymerase 3 - like protein like thioredoxin reductase catalytic chain -
- - rich extensin like protein - pol - - like protein - dicarboxylate transporter/malic acid transport family protein
- acetyltransferase - Octanoyltransferase NA N Ferredoxin Macrophage migration inhibitory f NA C4 Adiponectin receptor 2 nucleotide Guanine Thioredoxin Hydroxycinnamoyl CoA quinate transferase Decarboxylase Nodulin Deoxyribonuclease TatD family Thaumatin Katanin p80 WD40 NA Lysine decarboxylase Cysteine Genomic DNA chromosome 5 P1 clone MUL8 LTRGag Pectinacetylesterase NA Carbohydrate kinase FGGY Receptor protein kinase CXE carboxylesterase Glycerol kinase Heavy metal RLK, like Receptor protein, putative resistance protein with an antifungal domain Zinc finger CCCH domain Cytochrome P450 NA NA Major facilitator superfamily MFS_1 Peroxidase Chromatin 1 subunit assembly factor B Unknown Protein Lipase Pentatricopeptide repeat
.1
Tomato homolog Solyc02g077460.1.1 NA Solyc00g272810.1.1 Solyc02g087230.2.1 Solyc10g085350.1.1 NA Solyc03g007770.2.1 Solyc02g092230.2 Solyc01g109110.2.1 Solyc10g078920.1.1 Solyc12g096790.1.1 Solyc07g016020.1.1 Solyc05g005850.2.1 Solyc02g078980.2.1 Solyc11g066130.1.1 Solyc02g087730.2.1 NA Solyc07g016050.2.1 Solyc05g053960.2.1 Solyc02g088270.2.1 Solyc01g010200.2.1 Solyc03g025600.2.1 NA Solyc11g042940.1.1 Solyc00g289230.1.1 Solyc02g069800.1.1 Solyc03g117490.2.1 Solyc09g008200.2.1 Solyc02g079990.2.1 Solyc11g065320.1.1 Solyc08g079420.2.1 NA NA Solyc02g063020.1.1 Solyc07g052510.2.1 Solyc11g005800.1.1 Solyc08g083050.1.1 Solyc00g056680.1.1 Solyc01g088750.2.1
.7 2.6 2.5 2.5 2.5 2.5 2.4 2.4 2.4 2.3 2.3 2.3 2.2 2.2 2.2 2.2 2.2 2.1 2.1 2.1 2.1 2.1 2.1 2.0 2.0 2.0 2.0 1.9 1.9 1.8 1.8 1.8 1.8 1.8 1.8 1.8 1.7 1 1.7 1.7 Mean AbsLFD
1.8 3.1 1.1 3.4 5.9 2.1 3.8 0.6 8.6 4.1 6.1 1.3 5.1 2.6 1.6 4.7 1.8 8.1 8.0 5.3 2.2 2.2 5.7 17.6 14.5 19.4 44.5 16.8 17.5 46.5 16.8 55.6 14.1 19.9 12.0 20.2 42.1 12.5 ZD11 ZD11 179.8 (UAC)
= 3)=
n 1.3 2.1 0.6 0.5 0.5 0.3 7.7 1.3 3.1 0.9 0.8 1.5 4.5 5.3 3.2 2.2 2.4 4.1 1.5 2.0 2.1 4.8 3.0 6.1 3.0 17.5 19.1 23.2 5 18.0 18.2 72.6 14.5 26.9 16.6 16.7 20.8 (RS+) 233.5 128.4 217.6 TW12
) - 3.1 5.2 2.6 2.8 2.2 2.5 7.7 3.6 7.6 4.4 5.8 3.6 6.6 6.1 1.4 4.5 1.3 3.7 9.2 8.6 1.4 2.8 1.5 (R 15.5 26.9 60.3 14.4 14.9 65.4 18.5 11.5 10.5 17.2 10.2 51.7 16.2 130.0 232.8 137.3 VW08
) - 4.8 4.3 9.6 2.8 4.5 1.3 2.7 3.7 6.7 5.0 2.7 4.8 2.1 8.7 1.2 9.5 0.9 4.1 6.7 8.1 4.2 7.3 8.5 1.3 2.8 0.8 (S 11.5 99.7 22.4 26.1 12.5 19.0 71.9 80.5 10.0 23.2 70.7 10.0 270.2 OW09 OW09 Mean RPKM per accession (
)
-
1.9 1.6 6.0 1.4 1.9 2.2 1.1 5.7 4.6 4.7 3.0 3.1 4.6 2.7 4.6 0.8 7.4 2.2 5.4 5.0 6.8 7.9 4.3 4.5 5.3 1.7 5.7 0.8 0.7 11.9 23.2 31.2 17.2 17.4 72.4 13.6 44.1 78.0 (RS ZD04 ZD04 285.7 3/4) (
q1 seq1 Continued Continued
S. dulcamara contig comp1910_c0_seq3 comp29339_c0_seq1 comp10512_c0_seq1 comp1515_c0_seq2 comp15732_c0_seq1 comp4269_c0_seq1 comp10685_c0_seq2 comp16103_c0_seq2 comp5754_c1_seq1 comp9177_c0_seq1 comp1110_c0_seq1 comp13518_c0_seq1 comp16246_c0_seq1 comp11270_c0_seq2 comp901_c0_seq1 comp10178_c0_seq1 comp18276_c0_seq1 comp13518_c0_seq3 comp4767_c0_seq1 comp5718_c0_ comp8865_c0_seq1 comp18177_c0_seq2 comp23811_c0_seq1 comp4353_c0_seq4 comp21255_c0_seq1 comp6058_c0_se comp5332_c0_seq2 comp1220_c0_seq2 comp5360_c0_seq3 comp3409_c0_seq5 comp7316_c0_seq2 comp24816_c0_seq1 comp8587_c0_seq1 comp9074_c0_seq4 comp669_c0_seq1 comp10727_c0_seq1 comp1577_c0_seq1 comp15520_c0_seq1 comp19071_c0_seq2 Table S3
145 Chapter 5: Genetic basis of ecologically relevant chemical diversity
ITAG2.3 description
like protein - containing protein containing protein - -
like
phospholipid synthase phospholipid synthase
- - -
acyl transferase MBOAT like -
- acyl acyl - - ce protein
dependent dioxygenasedependent
-
fatty fatty containing protein - - - like like cysteine proteinase - like protein kinase 2
- nctional protein lrr, resistan
- chain dehydrogenase/reductase SDR -
binding protein dependent RNA helicase dependent
- - nbs RNase
- - oxoglutarate box family protein kinasereceptor - - - DTW domain Cyclopropane Saccharopine dehydrogenase family protein expressed Epsin Leaf senescence protein 2 Receptor like kinase, RLK Short Unknown Protein Unknown Protein NA Palmitoyl protein thioesterase family protein Transcriptional activator Nup98 synthetaseGlutathione F8A5.1 protein Cathepsin B (Dimethylallyl)adenosine tRNA methylthiotransferase miaB Pectinesterase F GTP S8 Palmitoyl protein thioesterase family protein NA Sulfate transporter like protein Membrane O bound FolD bifu Pentatricopeptide repeat Cyclopropane ATP Tyrosine aminotransferase Tir Pentatricopeptide repeat Receptor S Unknown Protein FIP1 NA NA
lyc06g065620.1.1 Tomato homolog Solyc02g087600.2.1 Solyc09g090510.2.1 Solyc11g065240.1.1 Solyc08g065890.2.1 Solyc02g089690.2.1 Solyc02g062460.2.1 Solyc03g005960.2.1 Solyc00g009140.2.1 Solyc12g096170.1.1 Solyc04g072940.2.1 NA Solyc01g090410.2.1 Solyc11g007570.1.1 Solyc02g093330.2.1 Solyc01g098610.2.1 So Solyc02g076750.1.1 Solyc02g084750.2.1 Solyc06g009190.2.1 Solyc08g048430.2.1 Solyc01g006180.2.1 Solyc07g006570.2.1 Solyc01g090410.2.1 NA Solyc03g119930.1.1 Solyc06g066630.1.1 Solyc08g048240.2.1 Solyc11g073270.1.1 Solyc09g090510.2.1 Solyc01g079800.2.1 Solyc07g053720.2.1 Solyc11g011090.1.1 Solyc03g120510.1.1 Solyc04g079690.2.1 Solyc04g015460.2.1 Solyc00g049520.2.1 Solyc07g021540.2.1 NA NA
6 1.7 1.6 1.6 1. 1.6 1.6 1.6 1.6 1.6 1.5 1.5 1.5 1.5 1.5 1.4 1.4 1.4 1.4 1.4 1.4 1.4 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.2 1.2 1.2 1.2 1.2 1.2 1.1 1.1 Mean AbsLFD
6.4 4.1 2.2 7.9 3.3 9.1 9.4 1.4 4.4 3.8 1.4 2.4 2.0 1.6 4.0 5.6 7.0 5.2 1.5 8.1 43.1 12.2 11.5 50.3 11.1 17.8 54.1 43.0 11.9 22.0 21.0 40.6 11.0 40.7 33.1 64.2 13.4 ZD11 ZD11 248.7 (UAC) 9323.9
= 3)=
4 n 1.7 9.4 1.4 3.5 2.8 2. 7.5 4.8 5.9 3.3 5.1 1.2 1.1 3.4 5.8 0.5 4.8 6.3 4.8 2.4 1.3 2.2 5.3 2.2 2.6 2.1 5.5 3.9 27.0 32.1 11.7 18.3 40.9 13.1 17.9 33.6 19.1 (RS+) 105.3 TW12 16571.5
) - 6.7 5.2 1.3 2.1 9.4 1.8 4.1 3.7 8.9 3.3 1.6 2.0 7.3 3.9 5.9 6.6 7.0 1.0 (R 27.3 10.0 92.9 11.5 11.5 14.7 27.4 44.9 47.8 42.6 13.6 17.8 23.9 31.8 14.5 48.6 88.4 45.2 16.7 10.5 VW08 5122.5
) - 4.4 6.7 1.4 7.5 8.0 3.5 9.9 2.6 3.1 3.7 8.0 2.8 1.5 1.7 6.3 3.9 5.0 6.1 7.2 1.0 8.4 (S 30.4 13.5 12.6 14.9 32.1 58.0 42.7 16.1 16.9 11.4 34.8 12.7 39.6 83.4 41.6 11.9 107.6 OW09 OW09 Mean RPKM per accession ( 8324.7
)
-
.5 5.8 2.5 0.9 8.0 4.9 2.3 6.6 1.3 2.9 2.0 1.3 1.2 9.2 2.0 5.4 2.6 5 4.0 5.1 0.9 9.9 7.2 31.8 56.1 12.1 14.9 14.7 23.3 11.6 45.9 37.7 14.2 17.8 32.9 12.3 40.1 67.7 43.5 (RS ZD04 ZD04 6970.7 4/4) (
eq1 _seq1 _c0_seq2 Continued Continued
21_c0_seq1 . dulcamara contig S comp12341_c0_seq1 comp5138 comp11354_c0_seq1 comp3542_c0_seq1 comp13190_c0_seq1 comp125_c0_seq1 comp3694_c0_seq3 comp8348_c0_seq3 comp7523_c0_seq1 comp5898_c0_seq1 comp2317_c0_seq2 comp8134_c0_seq2 comp11822_c0_seq1 comp2317_c0_seq3 comp3794_c0_seq3 comp11723_c0_seq1 comp12656_c0_seq1 comp4599_c0_seq1 comp892_c0_seq1 comp8531_c0_seq1 comp15928_c0_seq1 comp5 comp8134_c0_seq1 comp18441_c0_seq1 comp6556_c0_s comp7941_c0_seq1 comp8506_c0_seq4 comp16469_c0_seq1 comp5138_c0_seq11 comp17484_c0_seq1 comp12354_c0_seq1 comp9197_c1_seq5 comp7607_c0_seq1 comp14884_c0_seq1 comp19274_c0_seq1 comp1246_c0_seq1 comp3987_c0_seq4 comp10833_c0_seq1 comp20233_c0 Table S3
146 Chapter 5: Genetic basis of ecologically relevant chemical diversity
Table S4 Non-normalised cycle threshold (Ct) for expression estimation of SdGAME25 in parental Solanum dulcamara accessions and plants from F1 populations. Each individual is characterised by saturated (+) and unsaturated (-) chemotypes
Individual Chemotype Ct Individual Chemotype Ct Parental accessions F1 individuals (continued) 04-TW12-1 + 27.87 VW08 - 1 - >35 05-TW12-2 + 26.49 VW08 - 2 - >35 06-TW12-3 + 26.37 VW08 - 3 - >35 01-ZD04-1 - >35 VW08 - 4 - >35 02-ZD04-2 - 33.9 VW08 - 5 - >35 03-ZD04-3 - >35 VW08 - 6 - >35 07-OW09-1 - >35 VW08 - 7 - >35 08-OW09-2 - >35 VW08 - 8 - >35 09-OW09-3 - >35 VW08 - 9 - >35 10-ZD11-1 - >35 VW08 - 10 - >35 11-ZD11-2 - >35 VW08 - 11 - >35 12-ZD11-3 - >35 VW08 - 12 - >35 13-VW08-1 - >35 VW08 - 13 - >35 14-VW08-2 - >35 VW08 - 14 - >35 15-VW08-3 - >35 VW08 - 15 - >35 F1 individuals VW08 - 16 - >35 TW12 - 1 + 27.89 VW08 - 17 - >35 TW12 - 2 + 25.58 ZD11 X VW08 - 1 - >35 TW12 - 3 + 27.74 ZD11 X VW08 - 2 - >35 TW12 - 4 + 29.79 ZD11 X VW08 - 3 - >35 TW12 - 5 + 28.59 ZD11 X VW08 - 4 - >35 TW12 - 6 + 30.07 ZD11 X VW08 - 5 - >35 TW12 - 7 + 30.33 ZD11 X VW08 - 6 - >35 TW12 - 8 + 31.70 ZD11 X VW08 - 7 - >35 OW09 - 1 - >35 ZD11 X VW08 - 8 - >35 OW09 - 2 - >35
OW09 - 3 - >35
OW09 - 4 - >35
OW09 - 5 - >35
OW09 - 6 - >35
OW09 - 7 - >35
OW09 - 8 - >35 OW09 - 9 - >35 OW09 - 10 - >35 ZD11 - 1 - >35 ZD11 - 2 - >35 ZD11 - 4 - >35 ZD11 - 5 - >35 ZD11 - 6 - >35 TW12 - 9 - >35 TW12 - 10 - >35 ZD04 - 1 - >35 ZD04 - 2 - >35 ZD04 - 3 - >35 ZD04 - 4 - >35 ZD04 - 5 - >35 ZD04 - 6 - >35 ZD04 - 7 - >35 ZD04 - 8 - >35 ZD04 - 9 - >35 ZD04 - 10 - >35 ZD04 - 11 - >35
147 Chapter 5: Genetic basis of ecologically relevant chemical diversity
-
ssed MPR - expressed
diphosphate pyrophosphatase diphosphate -
containing protein
- like - like -
phosphate receptor CI
-
6
- are sorted based on the mean AbsLFD AbsLFD mean the on based sorted are
coil protein 2 ITAG2.3 description -
methyltransferase
- anchored protein At1g61900 ) - - triphosphate 3&apos triphosphate - like proteinlike 1
- /Phox/Bem1p domain rich coiled N(1) -
-
b binding 3C protein methionine salicylic acid carboxyl methyltransferase - - type like zinc fingerlike protein L - 5&apos - - - semialdehyde dehydrogenasesemialdehyde difference (AbsLFD) in expression ≥ 2 and Benjamini - - independent mannose independent - damage inducible protein DDI1damage inducible 1c protein - fold - -
2 adenosyl - Os11g0198100 protein Os11g0198100 Cyclic nucleotide gated channel Xyloglucan endotransglucosylase/hydrolase 2 Aspartate Octicosapeptide Protein translocase subunit secA Arginine/serine Peptidase M50 family Endonuclease III (Guanine tRNA Alpha/beta hydrolase superfamily protein Os03g0385301 DNA CONSTANS Cupin RmlC NA Cation HAT dimerisation family domain containing protein Guanosine HAT dimerisation family domain containing protein expre Chlorophyll a Uncharacterized GPI Protein translocase subunit secA kinaseUridine PAR Protein translocase subunit secA trypsin inhibitorKunitz 4 NA WRKY transcription factor 3 S
1g079360.2.1 10g005890.2.1
Tomato homolog olyc11g005020.1.1 Solyc11g017420.1.1 Solyc12g005400.1.1 Solyc07g009380.2.1 Solyc01g005250.2.1 Solyc04g007460.2.1 Solyc11g005020.1.1 Solyc12g020000.1.1 Solyc01g106820.2.1 Solyc09g083010.2.1 Solyc05g006200.2.1 Solyc08g075100.2.1 Solyc12g056910.1.1 Solyc Solyc05g046040.1.1 Solyc03g114510.2.1 NA Solyc05g052460.2.1 Solyc10g080090.1.1 Solyc01g100260.2.1 Solyc10g080090.1.1 Solyc03g005780.1.1 Solyc02g080500.2.1 Solyc11g005020.1.1 Solyc09g065860.2.1 Solyc03g025680.2.1 S Solyc11g022590.1.1 NA Solyc0 Solyc01g005230.2.1
.8 16.7 16.6 15.8 15.4 14.6 14.5 14.5 14.3 14.2 14.1 13.7 13.5 13.2 13.0 13.0 12.9 12.5 12.4 12.2 12.2 12.1 11.9 11 11.6 11.6 11.5 11.5 11.1 10.9 10.6 Mean AbsLFD
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 3.9 0.0 0.2 0.0 0.0 11.3 64.8 ZD11 (UAC) ZD11
in accession ZD11 in comparison to four reference accessions (ZD04, OW09, VW08 and TW12). Mean
6.2 7.1 9.4 1.4 4.2 8.7 5.0 2.2 3.2 1.9 3.9 2.0 1.6 0.0 7.7 0.0 1.4 0.0 = 3)= 12.3 42.5 67.6 10.4 16.8 10.5 14.6 28.4 17.3 12.8 22.9
1542.3 n TW12 (RS+)
) -
4.7 7.2 9.1 5.0 4.9 3.6 3.9 1.8 1.9 1.0 0.0 1.6 0.0 0.1 13.2 69.7 37.2 23.6 10.3 22.3 12.6 24.1 14.0 62.9 30.0 17.5 10.8 19.5 69.0 = 3) are 3) shown= for each accession. All contigs with absolute log 1227.7
VW08 (R
) -
1 2.7 8.3 8.1 9.2 7.4 4.8 6.6 6. 4.3 4.0 3.5 3.1 3.3 1.9 0.0 4.2 0.0 0.0 Mean RPKM per accession ( 11.2 38.4 46.3 14.0 16.7 12.0 15.0 15.1 15.1 10.2 18.8 158.5 OW09 (S
) -
0.7 4.4 9.0 2.9 9.5 3.5 8.3 5.7 4.2 1.7 1.0 1.7 2.9 1.4 0.3 0.0 2.4 3.8 1.1 58.9 47.4 14.1 12.3 17.5 16.3 14.9 13.4 17.0 21.1 228.7 ZD04 (RS values ≤ 0.05 in relation to each of the reference accessions were considered differentially expressed. Contigs -
_seq8 Differentially expressed contigs ofdulcamara Solanum
S. dulcamara contig comp5921_c0_seq2 comp6424_c0_seq2 comp3582_c0_seq2 comp3789_c0_seq2 comp4316_c0_seq1 comp12919_c0_seq1 comp4743_c0_seq1 comp11905_c0 comp10659_c0_seq1 comp5635_c0_seq2 comp6482_c0_seq2 comp3223_c0_seq1 comp3677_c0_seq3 comp20137_c0_seq2 comp8139_c0_seq1 comp11086_c0_seq1 comp12555_c0_seq1 comp13510_c0_seq3 comp10537_c0_seq1 comp13510_c0_seq4 comp15_c0_seq3 comp4215_c0_seq3 comp12919_c0_seq3 comp10616_c0_seq3 comp8887_c0_seq1 comp12919_c0_seq2 comp1119_c0_seq1 comp15714_c0_seq1 comp12641_c0_seq1 comp4110_c0_seq1 Table S5 number of reads per kilobase million (RPKM, n Hochberg adjusted P adjusted Hochberg tomato (D'Agostino with homology on et al. based 2013) are descriptions Functional order. descending in
148 Chapter 5: Genetic basis of ecologically relevant chemical diversity
containing protein -
like -
carboxylate oxidase 1 - containing protein 28 dependent oxygenasedependent oxygenasedependent oxygenasedependent like - 1
- - - - -
ITAG2.3 description
pane monophosphate dehydrogenasemonophosphate dehydrogenasemonophosphate associated gene 2 protein homolog - - iron(II) iron(II) iron(II)
- - - - b binding 4, chloroplastic protein -
thioredoxin reductase catalytic chain - 5&apos 5&apos glucosidase glucosidase glucosidase related protein ------induced protein - D D D - - -
ta methyltransferase aminocyclopro oxoglutarate oxoglutarate oxoglutarate - - - - - erredoxin Ankyrin repeat domain Chlorophyll a Inosine 1 NA NA NA 2 2 2 Auxin Inosine Be Ubr7 protein NA Ovarian cancer O Ubr7 protein Beta NA Beta NA Tumor NA NA Aldo/keto reductase family protein F Cyclic nucleotide gated channel NA NA Pol polyprotein HGWP repeat containing protein Cyclic nucleotide gated channel bindingMyrosinase protein NA Octicosapeptide/Phox/Bem1p domain Methanol inducible protein Unknown Protein RING finger protein Peptidase M50 family
Tomato homolog Solyc09g010690.2.1 Solyc07g047850.2.1 Solyc10g079500.1.1 Solyc02g071490.2.1 NA NA NA Solyc07g054870.2.1 Solyc07g054870.2.1 Solyc07g054870.2.1 Solyc05g041870.1.1 Solyc10g079500.1.1 Solyc06g073750.2.1 Solyc08g082320.2.1 NA Solyc06g060570.2.1 Solyc03g097700.2.1 Solyc08g082320.2.1 Solyc11g071640.1.1 NA Solyc11g071640.1.1 NA Solyc10g007870.2.1 NA NA Solyc09g097980.2.1 Solyc02g087230.2.1 Solyc12g005400.1.1 NA NA Solyc00g131710.1.1 Solyc09g014660.1.1 Solyc03g098210.2.1 Solyc09g083030.1.1 NA Solyc04g007460.2.1 Solyc08g082120.2.1 Solyc11g010670.1.1 Solyc01g109200.2.1 Solyc01g106820.2.1
D
.0 9.8 9.5 9.5 9.0 8.8 8.0 7.9 7.8 7.5 7.5 7.4 7.4 7.3 7.3 7.2 7.1 7.1 6.8 6.7 6.6 6.5 6.4 6.0 5.9 5.9 5.8 5.7 5.6 5.4 5.3 5.0 4.9 4.7 4.7 4.7 4.5 4.4 4.2 10.4 10 Mean AbsLF
2.9 3.9 0.0 0.0 6.9 0.3 0.5 0.2 0.0 0.1 0.1 0.0 0.0 0.1 0.1 5.1 0.1 0.1 0.1 0.1 0.0 1.1 0.1 0.1 0.2 0.2 6.5 0.7 2.0 0.1 18.1 13.1 13.7 20.0 70.3 10.0 21.4 16.0 27.9 1617.1 ZD11 (UAC) ZD11
0.0 0.0 4.6 3.7 0.0 5.1 7.3 3.0 9.4 6.6 0.0 6.4 1.2 6.7 0.1 4.0 0.7 0.8 2.7 7.3 5.7 4.4 1.1 0.7 9.5 6.8 1.4 0.3 = 3)= 17.4 95.2 38.0 12.4 68.6 40.2 14.2 25.6 96.4
126.9 233.5 3623.9 n TW12 (RS+)
) -
0.0 0.0 0.0 8.9 0.0 4.6 8.6 6.5 0.0 5.2 4.8 0.0 0.1 4.4 0.8 8.3 8.5 1.8 0.0 0.0 0.0 6.6 0.3 16.4 75.2 26.0 12.9 10.8 29.9 11.4 10.2 10.2 16.9 12.9 21.3 44.7 116.4 136.1 130.0 2695.4 VW08 (R
) -
0.0 0.0 6.9 0.0 7.8 0.0 5.2 1.9 3.6 7.3 4.5 1.8 8.8 0.0 0.1 7.0 0.7 8.9 5.5 9.6 9.8 0.0 2.4 8.5 5.7 4.7 5.6 Mean RPKM per accession ( 64.5 79.6 24.2 15.0 32.5 66.2 99.7 13.3 20.3 26.7 213.3 122.2 6312.3 OW09 (S
) -
8 0.0 6.9 0.0 6.8 2.5 4.5 5.4 1.0 3.1 2.8 5.6 3.0 1.9 0.8 6. 1.0 3.6 1.3 5.9 2.8 1.0 6.0 0.3 3.2 0.4 5.7 6.3 1.2 0.6 7.6 3.7 1.3 7.3
10.2 73.5 95.5 26.8 11.9 44.3 2562.5 ZD04 (RS
1
Continued (2/5) Continued
502_c0_seq1 686_c0_seq1 p9240_c0_seq2 S. dulcamara contig omp11905_c0_seq7 comp1893_c0_seq2 comp15_c0_seq10 comp2175_c0_seq2 comp18776_c0_seq1 comp20111_c0_seq1 comp17049_c0_seq1 comp11086_c0_seq2 comp1766_c0_seq1 comp1766_c0_seq3 comp1766_c0_seq2 comp9 comp2175_c0_seq1 comp4531_c0_seq8 comp6021_c0_seq4 comp10234_c0_seq1 comp17597_c0_seq1 com comp6021_c0_seq1 comp4531_c0_seq3 comp15692_c0_seq1 comp4531_c0_seq2 comp6082_c0_seq4 comp4602_c0_seq1 comp19 comp16063_c0_seq1 comp3370_c0_seq2 comp1515_c0_seq2 comp6424_c0_seq3 comp12880_c0_seq1 comp3348_c0_seq2 comp19188_c0_seq comp12880_c0_seq2 comp10035_c0_seq1 comp5292_c0_seq1 comp13003_c0_seq1 comp4316_c0_seq2 comp1986_c0_seq1 comp13501_c0_seq1 comp13018_c0_seq2 c Table S5
149 Chapter 5: Genetic basis of ecologically relevant chemical diversity
protein kinase, RLP -
like -
ment bundling protein
like - like protein like protein
- -
ITAG2.3 description
protein
rich splicing factor -
transferase transferase like serine/threonine - - - like like cysteine proteinase
- binding - like potential actin fila 6 fatty acid desaturase sheath defective protein 2 family - related protein - - - glucuronosyltransferase - like, Serine/threonine kinase protein, resistance protein -
acetyltransferase - N NA NA UDP Unknown Protein NA Omega Bundle Potassium 17 transporter NA Potassium 17 transporter Decarboxylase Cathepsin B NA Tumor Serine/threonine protein kinase Transcriptional corepressor SEUSS NA Potassium 17 transporter NA NA LRR receptor Arginine/serine Pto NA Oxysterol NA S Glutathione Fimbrin protein Defensin synthetaseGlutathione NA HGWP repeat containing protein Glutamine cyclotransferase NA Unknown Protein S Glutathione Heparanase embryogenesis Late abundant protein 5 1 Sesquiterpene synthase
Tomato homolog Solyc05g041830.2.1 NA NA Solyc04g074380.2.1 Solyc01g057540.1.1 NA Solyc01g006430.2.1 Solyc12g009440.1.1 Solyc04g008450.2.1 NA Solyc04g008450.2.1 Solyc07g016020.1.1 Solyc03g006210.1.1 NA Solyc10g007870.2.1 Solyc08g013940.2.1 Solyc11g005990.1.1 NA Solyc04g008450.2.1 NA NA Solyc05g055190.1.1 Solyc09g075090.1.1 Solyc02g014030.1.1 NA Solyc07g008920.2.1 NA Solyc09g011630.2.1 Solyc07g049600.2.1 Solyc07g007760.2.1 Solyc01g098610.2.1 NA Solyc10g050070.1.1 Solyc03g007920.2.1 NA Solyc07g045530.1.1 Solyc09g011550.2.1 Solyc07g007550.2.1 Solyc09g075210.2.1 Solyc06g059930.2.1
4.2 4.1 4.1 4.1 3.8 3.7 3.7 3.6 3.4 3.4 3.3 3.3 3.3 3.3 3.2 3.2 3.2 3.2 3.2 3.1 3.1 3.1 2.8 2.8 2.8 2.7 2.7 2.7 2.7 2.7 2.6 2.6 2.6 2.6 2.5 2.5 2.5 2.5 2.5 2.4 Mean AbsLFD
C)
0 7.1 4.0 0.2 8.0 3.8 0.5 0.6 0.4 2.5 0.7 7.1 0.6 8.4 8.9 7.4 3.4 0.4 4.4 0.4 5.0 13.1 47.0 48.1 25.3 12.9 49.4 68.2 17. 16.8 33.7 27.7 14.3 20.4 18.3 12.4 13.9 14.1 14.5 90.7 36.9 ZD11 (UA ZD11
0.9 0.8 1.1 1.8 4.4 0.0 5.2 9.1 1.5 5.3 0.8 3.2 4.2 0.1 2.0 6.3 0.9 0.1 4.1 0.8 3.0 0.9 2.1 2.5 3.2 5.1 1.3 3.8 2.2 = 3)= 18.0 64.0 25.1 11.7 82.0 86.2 17.6 10.8 12.9
180.3 1473.7 n TW12 (RS+)
) -
7 0.6 0.4 0.2 0.0 3.2 1.2 1.3 1.0 5.2 0. 3.6 3.0 1.6 0.2 1.8 1.3 1.6 6.2 2.2 0.4 2.6 3.2 2.9 1.8 0.8 3.5 1.7 1.9 1.2 11.2 39.3 14.8 44.9 85.5 45.3 16.1 84.7 188.2 159.5 121.8 VW08 (R
) -
0.5 0.1 1.2 3.1 2.4 5.1 1.9 9.2 0.8 3.7 0.2 0.8 2.0 1.4 1.6 3.2 9.7 0.5 2.3 3.4 2.8 0.7 1.3 5.3 3.5 Mean RPKM per accession ( 19.0 11.7 10.3 10.7 71.3 20.7 32.1 77.8 23.7 15.7 11.5 213.5 127.0 179.1 2235.3 OW09 (S
) -
0.7 6.6 1.0 1.3 1.6 1.3 2.9 9.6 8.9 1.8 5.1 4.6 3.9 2.5 0.7 2.9 5.4 5.6 0.8 7.0 2.4 0.4 2.4 0.6 1.9 4.4 4.5 0.8 6.5 0.9
11.1 19.6 23.2 90.7 23.3 28.4 10.4 243.2 107.2 112.4 ZD04 (RS 3/5) (
q1 q2 c0_seq3 Continued Continued
242_c0_seq2 mp14619_c0_seq2 S. dulcamara contig comp9686_c0_seq2 comp4227_c1_seq1 comp24822_c0_seq1 comp2790_c0_seq2 comp13260_c0_seq1 comp15767_c0_seq1 comp523_c1_seq1 comp524_c0_se comp15458_c0_seq6 comp8972_c0_seq1 comp15458_c0_seq4 comp13518_c0_seq1 comp14878_c0_seq1 comp21987_c0_seq1 comp288_c0_seq1 comp17577_c0_seq1 comp20861_c0_seq1 comp18281_c0_seq1 comp15458_c0_seq8 comp4227_c0_seq1 comp10822_ comp6866_c0_seq1 comp727_c0_seq2 co comp12986_c0_seq1 comp17208_c0_seq1 comp2041_c0_seq2 comp1273_c0_seq6 comp20408_c0_seq1 comp251_c0_seq1 comp3794_c0_seq3 comp2434_c0_seq1 comp4 comp4370_c0_seq1 comp21912_c0_seq1 comp4430_c0_se comp1273_c0_seq3 comp13004_c0_seq1 comp434_c0_seq1 comp13011_c0_seq2 Table S5
150 Chapter 5: Genetic basis of ecologically relevant chemical diversity
like protein like protein - - se
2 -
1
-
ITAG2.3 description
carboxylate oxidase carboxylate oxida - - like protein 1 1 - - -
transferase - n dehydrogenase/reductase family protein rich extensin - nucleotidase surE nucleotidase -
activating protein 1 homolog chai chain dehydrogenase/reductase family protein - - - 1c protein - minocyclopropane
R a aminocyclopropane - - NA Receptor like kinase, RLK Lysine decarboxylase Cysteine NA Pectinesterase NA S Glutathione PA Receptor like kinase, RLK Amino acid transporter family protein NA Nup98 Nup98 NA Short Transcription factor CYCLOIDEA GRAS family transcription factor 60S ribosomal protein L23A RAG1 NA Major facilitator family transporter NA NA Zinc knuckle containing protein Pathogenesis related protein PR GDSL esterase/lipase At5g42170 1 Oligopeptide transporter 9 Glutamate dehydrogenase Cell division protein kinase 10 Cytochrome P450 Ulp1 protease family protein NA U11/U12 small nuclear ribonucleoprotein 48 kDa protein 1 5&apos Short Methyltransferase type 11 Hydroxycinnamoyl CoA shikimate/quinate hydroxycinnamoyltransferase
Tomato homolog olyc07g042900.2.1 NA Solyc02g071800.2.1 Solyc07g016050.2.1 Solyc05g053960.2.1 NA Solyc09g075350.2.1 NA Solyc01g081270.2.1 Solyc10g085010.1.1 Solyc03g096190.1.1 Solyc03g013440.2.1 NA Solyc02g093330.2.1 Solyc02g093330.2.1 NA Solyc03g025370.2.1 Solyc12g014140.1.1 Solyc01g100200.2.1 Solyc10g084610.1.1 Solyc05g024260.2.1 NA S NA NA Solyc05g042130.1.1 Solyc09g007010.1.1 Solyc12g089350.1.1 Solyc12g006380.1.1 Solyc08g082990.2.1 Solyc05g052100.2.1 Solyc05g014760.2.1 Solyc09g092640.2.1 Solyc01g094420.2.1 NA Solyc04g050760.2.1 Solyc12g006380.1.1 Solyc09g075990.2.1 Solyc06g071060.1.1 Solyc07g025520.2.1 Solyc07g005760.2.1
2.4 2.4 2.4 2.3 2.3 2.3 2.3 2.2 2.1 2.1 2.1 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 1.9 1.9 1.9 1.9 1.8 1.8 1.8 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.7 1.6 1.6 1.6 1.6 1.6 Mean AbsLFD
6.8 1.7 1.3 5.1 0.7 6.6 7.9 8.9 1.5 9.2 5.0 1.7 5.1 0.6 8.8 3.9 8.3 6.0 2.2 2.0 5.5 9.2 4.4 31.7 27.4 13.2 10.2 13.2 11.1 13.4 20.2 11.1 10.7 49.8 29.3 21.8 14.5 13.9 19.0 156.1 ZD11 (UAC) ZD11
.7 6.3 4.4 2.1 4.6 3.2 1.5 3.3 2.9 1.2 1.7 3.9 6.6 2.7 4.9 1.2 2.0 1.4 2.0 2.1 5.2 7.6 5.5 7.2 5.9 3.1 1.2 = 3)= 23.5 18.2 72.6 51.0 22.8 26.5 25.9 11.0 27.9 13.5 16.5 71.8 24 11.0
n TW12 (RS+)
) -
1 .7 6.4 3.6 3.5 8. 5.4 2.5 2.6 0.8 2.1 4.1 4.0 2.8 5.2 2.3 5.8 6.4 3.9 4.2 4.0 8.5 4 5.0 2.9 5.1 1.7 48.6 17.1 14.9 23.0 36.4 1 18.2 10.2 19.9 31.2 34.2 11.8 13.4 49.2 25.7 19.6 VW08 (R
) -
6.0 4.8 7.8 3.3 2.6 4.6 0.9 3.0 3.5 3.1 2.8 3.7 5.9 2.2 8.2 1.9 7.3 1.4 1.6 3.8 6.9 9.9 6.1 3.7 5.0 2.0 Mean RPKM per accession ( 44.8 19.0 29.0 29.5 19.1 12.8 10.0 61.7 20.6 11.2 10.0 40.9 31.3 14.8 OW09 (S
) -
6.7 4.6 9.3 1.5 1.5 1.0 3.2 3.8 2.9 1.0 2.2 4.2 1.5 6.3 1.2 4.5 3.3 1.8 2.1 6.4 3.7 4.4 5.1 3.4 1.8 7.3
22.6 17.4 10.4 27.7 36.9 12.1 14.5 11.0 23.9 11.2 18.2 10.9 58.2 21.1 ZD04 (RS 4/5) (
eq5 _c0_seq3 Continued Continued
p11652_c0_seq1 S. dulcamara contig omp2317_c0_seq7 comp1700_c0_seq1 comp15047_c0_seq1 comp13518 comp4767_c0_seq1 comp23709_c0_seq1 comp13243_c0_seq1 comp9748_c0_seq2 comp1223_c0_seq1 comp14379_c0_seq2 comp8088_c0_seq1 comp17962_c0_seq1 comp15245_c0_seq1 c comp2317_c0_seq3 comp27728_c0_seq1 comp17507_c0_seq1 comp6566_c0_seq3 comp5799_c0_seq1 comp4217_c0_seq1 comp10194_c0_seq2 comp17958_c0_seq1 comp10508_c0_seq4 comp8587_c0_seq1 comp19257_c0_seq1 comp14782_c0_seq1 com comp20386_c0_seq1 comp15070_c0_seq1 comp7939_c0_seq2 comp8291_c0_seq1 comp2072_c0_seq3 comp3086_c0_seq1 comp10465_c0_seq1 comp2771_c3_s comp12419_c0_seq4 comp12588_c0_seq1 comp17404_c0_seq2 comp3796_c0_seq1 comp11602_c0_seq1 comp21601_c0_seq1 Table S5
151 Chapter 5: Genetic basis of ecologically relevant chemical diversity
MPR -
phosphate receptor CI - CoA synthase 6 - -
ketoacyl ITAG2.3 description ribosylphosphate transferase ribosylphosphate
- - O -
2&apos
-
independent mannose independent -
Unknown Protein Nitrilase 2 Kinase family protein Mitochondrial carrier protein expressed NA Pollen e 1 Ole allergen and extensin Fatty acid elongase 3 Nitrilase 2 Cell number regulator 2 Auxin response factor 14 FRIGIDA synthase Sucrose Multidrug resistance protein ABC transporter family Oxidoreductase aldo/keto reductase family protein expressed Helicase sen1 A64 tRNA Unknown Protein Unknown Protein Cation Vacuolar sorting receptor 7
41280.2.1 g053600.2.1
Tomato homolog Solyc01g066440.2.1 Solyc07g041280.2.1 Solyc08g014450.2.1 Solyc10g085220.1.1 NA Solyc02g089250.2.1 Solyc06g065560.1.1 Solyc07g0 Solyc04g007900.2.1 Solyc05g056040.2.1 Solyc03g117930.2.1 Solyc03g098290.2.1 Solyc03g117540.2.1 Solyc06 Solyc03g124010.2.1 Solyc12g008450.1.1 Solyc06g068670.2.1 Solyc06g068670.2.1 Solyc05g052460.2.1 Solyc02g081970.2.1
1.6 1.5 1.5 1.5 1.5 1.5 1.5 1.4 1.4 1.3 1.3 1.3 1.3 1.3 1.3 1.2 1.2 1.2 1.1 1.1 Mean AbsLFD
1.9 7.6 6.8 3.6 6.0 2.8 8.7 3.7 1.5 6.4 3.1 1.9 7.0 66.8 17.2 15.2 27.8 16.5 11.5 20.7 ZD11 (UAC) ZD11
4.7 3.3 2.5 9.1 0.9 3.5 5.9 6.6 8.5 3.5 2.1 6.1 3.6 2.6 8.8 = 3)= 13.6 52.3 40.5 27.6
160.8 n TW12 (RS+) ion (
) -
8.3 2.5 1.9 1.0 4.1 7.9 6.1 4.0 2.9 8.8 4.3 3.4 9.9 12.6 16.0 10.4 74.0 38.6 25.7 266.3 VW08 (R
) -
4.6 2.0 1.9 8.5 1.3 2.5 7.0 4.2 8.5 3.1 2.8 7.1 4.1 3.2 9.3 Mean RPKM per access 22.3 59.3 35.6 24.1 134.9 OW09 (S
) -
3.3 1.9 2.3 8.2 0.7 2.5 3.9 5.4 7.2 3.7 1.9 5.6 3.9 2.7 7.7
12.8 67.5 27.9 19.6 171.5 ZD04 (RS 5/5) (
Continued Continued
S. dulcamara contig comp16539_c0_seq1 comp16946_c0_seq4 comp7977_c0_seq1 comp2073_c0_seq1 comp14199_c0_seq1 comp205_c0_seq1 comp4677_c1_seq2 comp9107_c0_seq2 comp6750_c0_seq1 comp6932_c0_seq2 comp7900_c0_seq1 comp15093_c0_seq1 comp16299_c0_seq1 comp3984_c0_seq1 comp8863_c0_seq1 comp14921_c0_seq2 comp6350_c0_seq1 comp2492_c0_seq1 comp12555_c0_seq2 comp13569_c0_seq1 Table S5
152 Chapter 5: Genetic basis of ecologically relevant chemical diversity
Fig. S1 Relative abundance of steroidal glycoalkaloids (GAs) and uronic acid conjugated compounds (UACs) in leaf material of 15 Solanum dulcamara accessions as derived from Calf et al. (2018). The chemotypes are defined by differences in the abundance of 25R- and / or 25S-type unsaturated (-) or saturated (+) GAs as indicated above each profile. Red triangles indicate chemotypes that were selected for the current study
153 Chapter 5: Genetic basis of ecologically relevant chemical diversity
Fig. S2 Treatment and leaf sampling in the experiment for L1 RNAseq analyses. Two leaves (L6 and L11) were treated with L2 150 µg methyl jasmonate (MeJA) dissolved in 25 µl lanolin wax (grey area). After 24 h L6–11 (n = 6) were picked and each L3 contributed two leaf discs (1.8 cm Ø, red dashed circles) to a L4 pooled leaf sample per clone L5 L6 L7
L8
L9
L10
L11
L12 L13
154 Chapter 5: Genetic basis of ecologically relevant chemical diversity
Document S1 Chemical profiling using HPLC-qToF-MS
Extraction
An amount of 90.0–115.0 mg frozen leaf tissue was twice extracted with respectively 1.0 and 0.9 ml MeOH:Acetate (50 / 50, v / v %) buffer (pH 4.8). The material was extracted in 2 ml round bottom reaction tubes holding two glass beads (5 mm Ø) by shaking in a TissueLyser (Qiagen, Venlo, the Netherlands) at 50 Hz for 5 minutes followed by centrifugation at 14.000 rpm at 4 °C. The supernatants were combined and stored at -20 °C until further processing.
LC-qToF-MS analyses
We analysed 1:50 dilutions of the crude extracts with an UltiMate™ 3000 Standard Ultra-High-Pressure Liquid Chromatography system (UHPLC, Thermo Fisher Scientific, Waltham, MA, USA) equipped with an Acclaim® Rapid Separation Liquid Chromatography (RSLC) 120 column (150 × 2.1 mm, particle size 2.2 μm, Thermo Fisher Scientific). The flow rate was set to 0.4 mL min-1 with the following eluent gradient: 0–2 min isocratic 95 % A (water / formic acid 99.95, 0.05 (v / v %)), 5 % B (acetonitrile / formic acid 99.95, 0.05 (v / v %)); 2–15 min, linear from 5 % to 40 % B; 15–20 min, linear from 40 % to 95 % B; 20–22 min, isocratic 95 % B; 22–25 min, linear from 95 % to 5 % B; 25–30 min, isocratic 5 % B. Compounds were detected with a maXis impact™ quadrupole time-of-flight mass spectrometer (qToF-MS, Bruker Daltonik GmbH, Bremen, Germany) in positive mode applying the following conditions: scan range 50–1400 m/z; acquisition rate 3 Hz; end plate offset 500 V; capillary voltage 3500 V; nebulizer pressure 2 bar, dry gas 10 L min-1, dry temperature 220 °C. Mass calibration was performed using sodium formate clusters (10 mM solution of NaOH in 50 / 50 (v / v %) isopropanol water containing 0.2 % formic acid.
Data processing and chemical profiling
The LC-qToF-MS raw data were recalibrated and then converted to the mzXML format by using the CompassXport utility of the DataAnalysis software (Bruker Daltonik). We used the peak intensity of a single fragment ion for relative quantification of levels of GAs and UACs. All intensities were normalised by the extracted fresh weight.
155 Chapter 5: Genetic basis of ecologically relevant chemical diversity
Document S2 Nucleotide sequence variants of SdGAME8. The registered Solanum dulcamara reference sequence of comp57_c0_seq1 (D'Agostino et al. 2013) was identified as closest homolog to tomato GAME8. In addition, the variant specific sequences of S- and R-dominated glycoalkaloid chemotypes are provided, which includes single nucleotide polymorphisms in the region starting at base-pair 1206 (underscored) as determined by RNA sequencing
>comp57_c0_seq1 len=1585 path=[0:0-1005 1006:1006-1050 1051:1051-1341 1342:1342-1428 1429:1429- 1474 3341:1475-1498 1499:1499-1584]
TTTAAATTTAAATTATTATATTTGGACGGAACGATAGGTAACCCTCTTACACTCGTTATTGTGAAGTTTGAGGAAACGGTTAGTGGTGGATTAC TTAAGCTAACTATAATAAGTTGTTTGGAATAATAATAAAAGGAATCATACGAAATTAAATAATGATAAAATTAATTGCTGGATATAGTTTTATT GACAAATATTTGATTAGTTGGACTAAAAATGAAATATACCTATAATATAAAATATATGTGACTTGTTAAGAAACAGGAAAAATCTCTTCTCTCT CTGCGCAAGTCACTGTGCACTAACTTGCACCTCGCCAGAAAAAGGGAAGAGGACGACCACGAAAGGATACTCAGAAATGGATATAGATTTACTA TTTAGGTAAGAAAACTACTCAGATGAGAAAATTTAGCTTACTTGGGCGTCAAAAAGCTATGGAGTGATAGTATAGCCCAATTTTTGAACCATTT CTAATATGTTAATGACATTTTTGACTTAATAAGTAAAAAAAAGTACAAATTTATTCCAATAGATAAAAAAATGACATTTTAAAACTATTTCTAT ACGTTAAAAATACAGCCCAATTTATTTTTTTAATTTATAAACTGTTTTTAATTAAAATAATTATTTTAGTATTGATCTAACAAGTGAGTGCGTG GTTGGTGAGGGAGGGGCGTAGAACTCTAGAAACGTGAAAAAAGAGTTGGGAGGTGCGCTTAGAAAGATCACAAAAGATGACAACATTTTAAAGT ATTTTATTAGTTTCAACTGTGTCCAAATTACAATAAACGGTGTGTTTATAAGTATTACCCTTTTCTTTGCTCCGCTCTCAAGAAAATAGTCTCA CTACTCAGTTTGTAGAAAATAGAGTTCTAACTATGGCAGCTGCAACAATAATTGCTGCAATAGTTGGGATTTTGGTGGCCTGTTTTTGTGGGAA AGTGTTGTACACAATATGGTGGTGGCCAAAGATGATAGAAAAGAAGCTGAAGAAGGAAGGTATCCACGGAGAACCCTACAAATTGCTGTTTGGA AATCTTAAAGAGATGATGAAAATGTCTAGAGAAGCAAAGAAAAAACCCTTGTTGGATCATGATATCATTCCTTGGGTTAATCCTTTTCTTCTCC ACCTTTCTAACACTTACAAGAAAATATTTGTGCTGTGGGCTGGACCAACACCTCGCGTGACAGTAACAGATCCAAAGCTAATAAGAGAAGTGCT GAACAGATATAATGAATTCCACAAGCCTGAAGCCAATGCTTTCGTCCACTTGTTTGTTACTGGACTTGCCAGTTACGATGGTGAAAAATGGGAT ACCCACAGAAAGATACTTAACCCTGCCTTTCATGTAGAGAAGCTAAAGAGGATGTTCCCAGCAATTTCTGTATGTTGCGATGAAATGATAAATA GATGGGAGGAATTAGTTAGCAAAAAAGGAAGTTGTGAGTTGGATGTGGCAGACGAGTTTCTTAACTTAGGTGGAGATGTTATATCAAGAGCTGC ATTTGGTAGCAATATTGAAGAAGGAAGGTCCATTTTCCTACTTCAAAAGGAGCAGTGTGAGCTTATTTTGGCTTCTCCATT
>SdGAME8_seqR len=380 path=[1206:0-379]
CTAATAAGAGAAGTGCTGAACAGATATAATGAATTCCACAAGCCTGAAGCCAATGCTTTCATCCACTTGTTTGTTACTGGACTTGCCAGTTACG ATGGTGAAAAATGGGATACCCACAGAAAGATACTTAACCCTGCCTTTCATGTAGAGAAGCTAAAGAGGATGTTCCCAGCAATTTCTGTATGTTG CGATGAAATGATAAATAGATGGGAGGAATTGGTTAGCAAAAAAGGAAGTTGTGAGTTGGATGTGGCAGACGAGTTTCTTAACTTAGGTGGAGAT GTTATATCAAGAGCTGCATTTGGTAGCAATATTGAAGAAGGAAGGTCCATTTTCCTACTTCAAAAGGAGCAGTGTGAGCTTATTTTGGCTTCTC CATT
>SdGAME8_seqS len=380 path=[1206:0-379]
CTAGTTAAAGAAGTGGTGAACAGATATAATGAATTCCAGAAGTGTGAATCCAATGCTTTCATCGACATGTTTGTTACTGGACTTGCTAGTTACA ACGGTAAAAAATGGGACACCCACAGAAAGATACTTAACCCTGCTTTTCATGTAGAGAAGATAAAGAGGCTGTTCCCAGCATTTTGTGAGTGTTG TGATGAACTGATAAATAGATGGGAGGAATTGGTTAACAAAAAAGGAAGTTGTGAGTTGGATGTGGCAGACGAGTTTCTTAACGTAGGTGGAGAT GTTATATCAAGAGCTGCATTTGGTAGCAATATTGAAGAAGGAAGGTCCATTTTCCTACTTCAAAAGGAGCAGTGTGAGCTTATTTTGGCTTCTC CATT
156 Chapter 5: Genetic basis of ecologically relevant chemical diversity
Document S3 Nucleotide sequence variants of SdGAME25. The registered Solanum dulcamara reference sequence of comp59_c0_seq1 (D'Agostino et al. 2013) was identified as closest homolog to tomato GAME25. In addition the sequence of accession TW12 is provided, which includes the single nucleotide polymorphisms in the region starting at base-pair 155 (underscored) as determined by RNA sequencing
>comp59_c0_seq1 len=1133 path=[0:0-1132]
TTATTGCAAATTACAGAAAGTTCCTGATATTGTATGTACGTACTCTTATAATGGCAAATAAGCTCAGGTTGGAGGGTAAAGTGGCTATAATTAC GGGTGCTGCAAGTGGCATTGGTGAAGCAAGTGCAAGATTATTCGTCCAACATGGTGCTTGTGTGATCGTCACTGATATTCAAGATGAACTCGGT CTACAAGTAGTGGCGTCCATCGGAACAGACAAGGCCACCTACCGTCACTGCGACGTCACAGACGAGAAACAAGTAGAGGATACCGTAGCCTACG CGGTCGAGAAATATGGTACTTTCGACGTCATGTTTAGTAATGTCGGGACGTTGCACTTTTGCAGCGTCCTCGACATGAACGTGACGGTCTTCGA TGAGACCATGGTCATCAATGCTCGAGGATCCGCTTTAGCCGTTAAGCATGCGGCTAGAGTCATGGTTGCTAAGAAAATCCGCGGATCCATTATA TGCACCGCGAGTTTAGAGGGTATCCTAGCGGGGGCTGCTTCCTTAGCCTACGTAGCGTCAAAGCATGCAGTCGTAGGCATTGTGAAAGCGGCCG CACGTGAGCTAGGTCCGCACGGGATCAGGGTGAATGGGGTGTCGCCTTATGGCATAGCGACGCCCCTGGTGTGCAAAGCCTACGGGTTGGACAT GGCTCCACTGGAAGCAGCAATATGTGGAAATGCTCACTTGAAAGGTGTTACGTTGAGCACGATGCATGTAGCACAAGCAGCACTTTTCTTGGCG TCCGATGAATCCGCTTACATAAGTGGTCAAAATTTGGCTGTTGATGGTGGTCTTAGTTCTATTTTGAAGTTAGAATAAATCATCCCTCTCTCGT TGGTGTGCTGTGATGTTGGCCTTACTTCTATTTTGAAGCTACAATAAATCTTTTCCTCTCTTGTGAGTGTACTGTTTCCTCTTCTTAAGGTTCC ACACAATATTTGACAGGTCAACTTTGAGTTTACTTATTTGCTTGTGAATATCTACTCAATTTTAAGCGACTTGCGTAGATTATATATGTTTGGG CTTATGTTAAACACTGCTAATATCGAGAGAAATATAAAGTAAAATGATACATTTAGAAATAATTATCAGATTATAAGCATTAGGAGAAAGACAT CCAGA
>SdGAME25_seqTW12 len=979 path=[155:0-978]
TGTGGTTGTCGCCGATATTCAAGATGAACTTGGTCTTCAAGTAGTGGAGTCCATCGGAACAGACAAGGTCAGCTATCGTCACTGCGACGTCACA GACGAGAAACAAGTAGAGGATACCGTAGCCTACACGGTCGAGAAATACGGTACTCTCGACGTCATGTTTAGTAATGTCGGGACGTTGAACTTCT GCAGCGTCCTCGACATGGACGTGACGACCTTCGATGAGACCATGGTCATCAACGCTCGAGGATCCGCGTTAGCCATCAAGCACGCGGCTAGAGT CATGGTTTCTAAGAAAATCCGCGGATCCATTATATGCACCGCGAGTTTAGAGGGTATCCTAGCTGGGGCTGCTTCCTTAGCCTACATAGCGTCA AAGCATGCCGTCGTAGGCATTGTGAAAGCCGCCGCACGTGAGCTAGGTCAGCACGGGATCAGGGTGAATGGGGTGTCGCCTTATGGCATAGCGA CACCCCTGGTGTGCAAAGCCTACGGGTTGGACCCGGCTCCACTGGAAGCAGCAATATATGGAAATGCTCACTTAAAAGGTGTTACGTTGAGCAC CATGCATGTAGCACAAGCAGCACTTTTCTTGGCGTCCGATGAATCCGCTTACATAAGTGGTCAAAATTTGGCTGTCGATGGTGGCCTTAGTTCT ATTTTGAAGTTAGAATAAATCATCCCTCTCTTGTTGCTGTGCTGTGATGTTGGCCTTGCTTCTATTTTGAAGCTACAATAAATCTTTTCCTCTC TTGTGAGTGTACTGTTTCCTCTTCTTAAGGTTCCACACAATATTTGACAGGTCAACTTTGAGTTTACTTATTTGCTTGTGAATATCTACTCAAT TTTAAGCGACTTGCGTAGATTATATATGTTTGGGCTTATGTTAAACACTGCTAATATCGAGAGAAATATAAAGTAAAATGATACATTTAGAAAT AATTATCAGATTATAAGCATTAGGAGAAAGACATCCAGA
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Chapter 6:
Summary and conclusions
Chapter 6: Summary and conclusions
The interaction of plants and herbivores reflects an evolutionary arms race between toxic or repellent plant defence mechanisms and co-adaptations of the herbivore (Coley and Barone 1996; van Dam 2009). In addition to mechanical defences, such as thorns and trichomes, chemical defence plays an important role in this interaction. Many compounds do not fulfil an essential role in the growth or development of the plant, but are important in the interaction with other organisms. These compounds are therefore referred to as secondary metabolites. Intraspecific variation in the basal defence levels and profiles of plants partly results from selection that is imposed by specific herbivores (Hartmann 1996; Speed et al. 2015; Wittstock and Gershenzon 2002). In addition to these constitutive defence levels, plants may upregulate defences upon damage as cost-saving strategy under variable herbivore pressure (Karban and Baldwin 1997; Strauss et al. 2002; Vos et al. 2013). Such induced responses may be tailored to the specific herbivore that is feeding (Agrawal 2000; Chung and Felton 2011) and have specific consequences for later arriving members of the herbivore community (Poelman et al. 2008; Viswanathan et al. 2005). While plant-herbivore interactions are being intensively studied in relation to insects, little is known about the responses of plants after feeding by gastropods (slugs and snails). This is surprising, because the mode of feeding of these voracious herbivores is distinct from that of chewing insects, and the typical locomotion mucus of gastropods itself may already serve as specific elicitor for induced responses (Kästner et al. 2014; Orrock 2013). The mechanisms and regulation of plant defence against gastropods may therefore be specific to this class herbivores. To gain more insight in the role of gastropods in plant-herbivore interactions, I specifically studied plant resistance mechanisms against gastropods as well as their ecological consequences. I thereby used bittersweet nightshade (Solanum dulcamara) as a wild model system. To my knowledge, this thesis comprises the first comprehensive ecological, metabolomic and molecular analyses of both constitutive resistance and induced responses to gastropod feeding in plants.
I assessed the relation between intraspecific variation in gastropod resistance and plant chemical diversity in chapter 2. Slug preference assays revealed that there is substantial variation in constitutive resistance to the grey field slug (Deroceras reticulatum, GFS) among S. dulcamara accessions. This variation exists for individual accessions both within and among natural populations. Eco-metabolomic analyses revealed that resistance to slug feeding is positively related to overall high levels of glycoalkaloids (GAs). This analysis underscored the long known chemotypic diversity in GA composition among S. dulcamara accessions (Mathé 1970; Willuhn 1966). One particularly preferred accession was found to contain very low GA levels. Instead of GAs, this accession contained high levels of previously unknown uronic acid conjugated compounds (UACs). Although UACs are structurally closely related to GAs, these compounds appear to provide no resistance against GFS. I therefore concluded that GFS and other gastropod species, in addition to insects and pathogens, may exert strong selection on overall GA levels and chemical diversity in S. dulcamara.
160 Chapter 6: Summary and conclusions
In chapter 3, I assessed whether three different slug species as well as insect herbivores show similar feeding preference for S. dulcamara accessions with distinct GA chemotypes, which were identified in chapter 2. In addition, I investigated whether gastropod feeding induces changes in the metabolomic profile and affect the feeding preference of later arriving herbivores. Different gastropod species indeed shared a similar feeding preference for accessions with low GA levels. Continuous exposure to GFS feeding for 72 h consistently increased the levels of phenolamides in different accessions, but not the levels of GAs or UACs. Moreover, induced responses upon slug feeding only significantly affected gastropod feeding preference in one accession in a greenhouse assay, but not at all in a common garden experiment. This suggests that gastropod feeding induced responses have no or little effect on later arriving herbivores. Besides gastropods, various specialist insects are commonly associated with S. dulcamara (Calf and van Dam 2012). Interestingly, generalist gastropods and specialist flea beetles showed opposite feeding preferences for S. dulcamara chemotypes in the common garden. These distinct classes of herbivores may therefore pose opposite selection pressures on constitutively expressed chemical diversity in S. dulcamara.
Following chapter 3, I further examined the temporal and spatial dynamics of induced responses and resistance upon GFS feeding in chapter 4. In contrast to the results in chapter 3, in this study, only 24 h exposure to slug feeding already sufficed to induce both local and systemic resistance to GFS. However, the plant metabolome was most strongly affected by continuous exposure to GFS for 72 h. This treatment also induced the highest level of induced resistance and levels of plant defences, such as GAs, phenolamides, polyphenol oxidase activity, trypsin protease inhibitor activity and anthocyanins. Sustained slug feeding therefore provided a boost to the induced response when compared with a single day of feeding. Untargeted transcriptomic analyses revealed that this metabolomic response was regulated by well-known signalling phytohormones, such as jasmonic acid (JA), abscisic acid (ABA) and salicylic acid (SA). The same hormones are also involved in regulating insect-induced responses (Erb et al. 2012; Leon et al. 2001). Physiological analyses revealed that, indeed, the levels of JA, its isoleucine conjugate (JA-Ile) and ABA were increased upon slug feeding. However, SA-levels were unaffected. This may be explained by the fact that SA and JA often act in negative cross-talk (Beckers and Spoel 2006; Schweiger et al. 2014; Thaler et al. 2012). The plants seemed to maintain photosynthetic capacity upon slug feeding. This is unlike the common plant response to insects, which generally results in downregulation of photosynthesis (Lortzing et al. 2017; Nguyen et al. 2018; Zhou et al. 2015). Moreover, overall relaxation of the transcriptomic response was observed after 24 h without further damage. Given the observed dynamics of responses, I concluded that S. dulcamara shows a functional response to GFS, which is well-balanced with the incidence of feeding damage. The employment of such dynamically inducible responses may allow the plant to optimise the investment of resources in defence when the chance for recurring gastropod feeding damage is low.
161 Chapter 6: Summary and conclusions
In chapter 5, I combined gene expression studies by RNA sequencing and qPCR with an inheritance study of S. dulcamara GA chemotypes, to assess their genetic background. I therefore assessed chemotype segregation patterns in F1 and F2 offspring from populations which I obtained from selfing or crossing of chemotypes from chapter 2. Additionally, I explicitly related these chemotypes to gastropod resistance in preference assays. The GA chemotypes are based on the relative presence of three structural polymorphisms regarding the configuration, saturation and conjugation of the steroidal aglycon backbone. Based on homology with the glycoalkaloid metabolism (GAME) genes in tomato, I was able to identified two sequence variants of SdGAME8 with a codominant role in the configuration of 25S- or 25R-stereoisomers. These variants are possibly homologues of the two copies of GAME8 (GAME8a and GAME8b) in tomato and potato (Cárdenas et al. 2016; Mariot et al. 2016). In contrast to these and other solanaceous crop species, the two variants of SdGAME8 do show differences in the coding sequence, which may explain why both stereoisomers co-occur in S. dulcamara. I could also support a dominant role for SdGAME25 in the saturation of the steroidal backbone at the C-5,6 position. This gene was most strongly differentially expressed in untargeted transcriptomic analyses and only recently emerged as a candidate gene from literature (Sonawane et al. 2018). The identity of a third, recessive, gene that is responsible for conjugation of uronic acids to form UACs remained elusive. Interestingly, the UAC chemotype showed differential expression of many genes involved in secondary metabolism/defence processes, which suggests that UAC-biosynthesis is related to an auto-immune response. Roundback slugs (Arion rufus/ater) showed less preference for
F1 chemotypes with predominantly unsaturated GAs compared to those with saturated GAs. The UAC chemotypes were overall most susceptible to slugs. In conclusion, these results illustrate that single gene differences result in ecologically relevant chemical diversity among S. dulcamara individuals.
Taken together, it is evident that S. dulcamara has an effective constitutive and inducible defence system against gastropods. Most individuals are at least partly resistant to gastropods on a constitutive level (chapter 2). Moreover, plants can rapidly induce resistance in both local and systemic leaves (chapter 3 and 4). However, the induced effect of 72 h continuous exposure to slug feeding differed between the experiments presented in chapter 3 and chapter 4. This may be explained by technical differences between these studies, in which plants had a different age and were grown from seeds or cuttings. As a consequence, these plants were probably in a different ontogenetic and developmental stage, which often relates with differences in inducibility of the plant (Barton and Koricheva 2010; Boege and Marquis 2005). These partly contradicting results in the different chapters illustrate that defence induction is not straight forward, but instead differs from plant to plant under both experimental and natural conditions. Further controlled experiments are therefore required to fully understand the process of defence induction upon gastropod feeding. The here presented untargeted transcriptomic and metabolomic analyses provide a good foundation on which to continue.
162 Chapter 6: Summary and conclusions
GAs appear to play a key role in gastropod resistance in S. dulcamara. This is supported by the observation that plants with high overall levels of GAs were most resistant to various species of gastropods in greenhouse and common garden assays (chapter 2 and 3). Moreover, induced resistance concurred with GA induction (chapter 4) and variation in resistance was directly linked to structural chemical diversity of GAs (chapter 2, 3 and 5). However, this does not exclude the additional involvement of other resistance traits, such as phenolamides (chapter 3 and 4), defensive proteins (chapter 4) or indirect defence by mutualistic ants (Lortzing et al. 2016). It is important to realise that each of these defences does not exclusively affect gastropods. Besides, gastropod resistance involves common insect-inducible signalling pathways and anti-herbivore defences (chapter 4). Gastropod- induced selection on defence traits as well as induced responses upon gastropod feeding may therefore also affect the other herbivores in the community and vice versa.
Heterogeneity in herbivore communities often concurs with different abiotic conditions among habitats. Interestingly, bittersweet nightshade thrives under contrasting abiotic conditions (Zhang et al. 2016). Gastropods particularly favour moist conditions and intermediate temperatures (Astor et al. 2017). It might therefore be expected that the impact of gastropod feeding on local chemical diversity and induced plant resistance is lower in, for instance, dry sun-exposed dunes, compared to wet forest understories. This may explain how UAC chemotypes, collected from a dune area where gastropod populations are expected to be low, can survive in nature. The discovery of this novel UAC chemotype illustrates that simple chemical modifications may have significant implications. The transition of a sugar to a sugar acid conjugate determines whether the outcome of the interaction between S. dulcamara and gastropods will be sweet or sour. This highlights the specific impact that gastropod feeding might have on plant chemical diversity and overall resistance to herbivory. Gastropods should therefore be more often considered in plant- herbivore interaction studies investigating the ecological significance or applied value of plant defence traits.
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Samenvatting en conclusies Samenvatting en conclusies
De interactie tussen plant en herbivoor kan omschreven worden als een evolutionaire wapenwedloop tussen afweermechanismen van de plant en aanpassingen van de planteneter om met deze afweer om te gaan (Coley and Barone 1996; van Dam 2009). Naast voor de hand liggende mechanische afweermethoden zoals stekels en haren, speelt chemische afweer een belangrijke rol in deze interactie. Veel stoffen vervullen geen essentiële rol in de groei of ontwikkeling van de plant, maar wel in de interactie met andere organismen. Deze stoffen worden daarom ook wel secundaire metabolieten genoemd. Daarmee beïnvloeden deze stoffen dan ook de overlevingskansen van de plant en de kans op nageslacht. Niet alleen tussen, maar ook binnen plantensoorten komt veel variatie voor in het basale afweerniveau en de samenstelling van verschillende secundaire metabolieten. Dergelijke intraspecifieke variatie is gedeeltelijk het resultaat van de selectiedruk die door specifieke herbivoren wordt opgelegd (Hartmann 1996; Speed et al. 2015; Wittstock and Gershenzon 2002). Ter aanvulling van het basale (constitutieve) afweerniveau kunnen planten de productie van afweer ook verhogen nadat schade is opgetreden. Dit is een kostenbesparende strategie waar de plant voornamelijk baat bij heeft wanneer de selectiedruk door herbivoren variabel is door de tijd heen (Karban and Baldwin 1997; Strauss et al. 2002; Vos et al. 2013). Dergelijke geïnduceerde responsen kunnen specifiek worden gericht op de identiteit van de herbivoor die aan het eten is (Agrawal 2000; Chung and Felton 2011) en daarmee ook specifieke gevolgen hebben voor de leden van de herbivorengemeenschap die later arriveren (Poelman et al. 2008; Viswanathan et al. 2005). Hoewel plant-herbivoor interacties veelvuldig bestudeerd worden in relatie tot insecten is er nog maar weinig bekend over de responsen van planten na slakkenvraat. Dit is opvallend aangezien deze vraatzuchtige herbivoren een geheel eigen manier van voedselinname hebben en daarnaast kan slakkenslijm alleen al als specifieke herkenning dienen voor geïnduceerde responsen (Kästner et al. 2014; Orrock 2013). De mechanismen en regulatie van plantenafweer tegen slakken zou daarom best eens specifiek kunnen zijn voor deze klasse van herbivoren. Om meer inzicht te krijgen in de rol van slakken in plant-herbivoor interacties heb ik dan ook specifiek de afweermechanismen van planten tegen slakken bestudeerd, alsmede de ecologische gevolgen daarvan. Daarbij heb ik gebruik gemaakt van bitterzoet (Solanum dulcamara) als wilde modelplant. Voor zover mij bekend behelst dit proefschrift de eerste uitgebreide analyse van de ecologische, metabolomische én moleculaire achtergrond van zowel constitutieve resistentie alsook geïnduceerde responsen tegen slakkenvraat in planten.
In hoofdstuk 2 heb ik de relatie tussen intraspecifieke variatie in slakkenafweer en chemische variatie bestudeerd. Keuzeproeven lieten zien dat verschillende bitterzoet planten (accessies) substantiële variatie vertonen in de constitutief aanwezige afweer tegen de gevlekte akkerslak (Deroceras reticulatum). Deze variatie bestaat niet alleen tussen, maar ook binnen natuurlijke populaties. Metaboloom analyses (studie naar de totale samenstelling van secundaire metabolieten) lieten zien dat resistentie tegen slakken positief gerelateerd is met hoge waarden van verschillende glycoalkaloïden (GAs). Deze analyse benadrukte daarnaast ook nog eens de reeds langbekende chemotypische variatie in GA-
166 Samenvatting en conclusies samenstelling tussen bitterzoet accessies (Mathé 1970; Willuhn 1966). Eén accessie, die door slakken zeer veel schade ondervond, had erg lage GA-waarden. In plaats daarvan bevatte deze accessie hoge hoeveelheden van voorheen onbekende stoffen die gekenmerkt worden door een verbinding met uronzuur (uronzuur-conjugaten; UZCs). Hoewel deze UZCs chemisch nauw verwant zijn aan GAs lijken zij geen afweerfunctie tegen grijze akkerslakken te hebben. Ik concludeerde daarom dat grijze akkerslakken en andere slakken, maar waarschijnlijk ook insecten en pathogene ziekteverwekkers, een sterke selectiedruk kunnen uitoefenen op de GA-waarden alsook de chemische diversiteit binnen de bitterzoet nachtschade.
In hoofdstuk 3 heb ik bestudeerd of drie verschillende slakkensoorten en tevens de natuurlijke insectengemeenschap dezelfde voorkeur hebben voor bitterzoet accessies met onderscheidende GA chemotypen, die ik heb geïdentificeerd in hoofdstuk 2. Daarnaast heb ik onderzocht of slakkenvraat veranderingen teweegbrengt in het metaboloom van planten en of deze veranderingen de voorkeur van later arriverende herbivoren beïnvloeden. Verschillende slakkensoorten bleken inderdaad dezelfde voorkeur te hebben voor accessies met lage GA-waarden. Voortdurende blootstelling aan de grijze akkerslak voor 72 uur zorgde voor een consistente verhoging van fenolamide-waarden in verschillende accessies, maar bracht geen verandering in de GA- of UZC-waarden dan wel samenstelling teweeg. Bovendien hadden door slakkenvraat geïnduceerde responsen slechts in één enkele accessie een significant, negatief, effect op slakkenvraat in een kasproef, en geen enkel effect tijdens een experiment in de proeftuin. Dit suggereert dat door slakkenvraat geïnduceerde responsen weinig tot geen gevolgen hebben voor later arriverende herbivoren. Naast slakken komen er verschillende specialistische insecten voor op bitterzoet (Calf and van Dam 2012). Wat interessant was is dat generalistische slakken en specialistische aardvlooien (kevers) een tegengestelde voorkeur hadden voor de verschillende bitterzoet chemotypen in de proeftuin. Het is dan ook goed mogelijk dat deze verschillende klassen van herbivoren een tegengestelde selectiedruk uitoefenen op de constitutief aanwezige chemische diversiteit in bitterzoet nachtschade.
In navolging van hoofdstuk 3, heb ik de temporele dynamiek en ruimtelijke aspecten van geïnduceerde responsen en resistentie tegen slakken in bitterzoet verder bestudeerd in hoofdstuk 4. In tegenstrijd met de resultaten in hoofdstuk 3 was 24 uur blootstelling aan slakkenvraat ditmaal al voldoende om resistentie tegen de grijze akkerslak te induceren in zowel aangevreten alsook onbeschadigde bladeren. Het metaboloom van de planten werd echter nog het sterkst beïnvloed door een continue blootstelling aan vraat door de grijze akkerslak voor 72 uur. Dezelfde behandeling induceerde tevens het hoogste niveau van resistentie alsook van afweerstoffen, zoals GAs, fenolamides, polyfenoloxidase activiteit, trypsine protease-remmer activiteit en anthocyaan. Voortdurende slakkenvraat van drie dagen zorgt dus voor een sterkere geïnduceerde respons dan een enkele dag aan vraat. Verdere analyses van het transcriptoom (expressie van alle genen) onthulden dat deze metabolomische respons wordt gereguleerd door welbekende signaalhormonen, zoals
167 Samenvatting en conclusies jasmonzuur, abscisinezuur en salicylzuur. Dezelfde hormonen zijn ook betrokken bij de respons van planten tegen insecten (Erb et al. 2012; Leon et al. 2001). Fysiologische analyses onthulden dat, inderdaad, de waarden van jasmonzuur, alsook het isoleucine-conjugaat daarvan en abscisinezuur hoger werden na slakkenvraat. Salicylzuurwaarden bleven echter onveranderd. Dit zou kunnen worden verklaard door het feit dat salicylzuur- en jasmonzuur- gestuurde signalen elkaar vaak negatief beïnvloeden (Beckers and Spoel 2006; Schweiger et al. 2014; Thaler et al. 2012). Ik zag ook dat de planten hun fotosynthesecapaciteit leken te behouden na slakkenvraat. Dit is dan weer tegenstrijdig met de gebruikelijke respons na insectenvraat, waarbij vaak wordt gezien dat de fotosynthese wordt verlaagd (Lortzing et al. 2017; Nguyen et al. 2018; Zhou et al. 2015). Bovendien zag ik een sterke relaxatie van de transcriptomische respons na 24 uur zonder vervolgvraat. Gegeven de dynamische aard van de responsen concludeerde ik dat bitterzoet een functionele respons vertoont tegen de grijze akkerslak, die in balans is met de duur van vraat. De toepassing van dergelijke dynamische responsen zou de plant in staat kunnen stellen om de investering van primaire bronnen (zoals koolhydraten, eiwitten en nutriënten) te optimaliseren, wanneer de kans op vervolgvraat door slakken klein is.
In hoofdstuk 5 heb ik gen-expressiestudies middels RNA sequentieanalyse en qPCR gecombineerd met een erfelijkheidsstudie van bitterzoet GA-chemotypen om de genetische achtergrond daarvan te achterhalen. Ik heb daarvoor de segregatiepatronen van chemotypen in F1 en F2 generaties bestudeerd in populaties die ik heb verkregen uit zelf- of kruisbestuiving van chemotypen uit hoofdstuk 2. Daarnaast heb ik deze chemotypen expliciet gerelateerd aan resistentie tegen slakken middels voorkeursproeven. De GA- chemotypen zijn gebaseerd op de relatieve aanwezigheid van drie structurele verschillen (polymorfismen) betreffende de configuratie, saturatie en conjugatie van het steroïde suikerloze basismolecuul. Ik was in staat om op basis van de homologie van glycoalkaloïdemetabolisme (GAME) genen in bitterzoet en tomaat (Solanum lycopersicum) twee sequentievarianten aan te wijzen van het gen SdGAME8 met een codominante rol in de configuratie van 25S en 25R GA-stereoisomeren. Deze varianten zijn mogelijk homologen van de twee kopieën van GAME8 (GAME8a en GAME8b), zoals die bekend zijn in zowel tomaat als aardappel (Cárdenas et al. 2016; Mariot et al. 2016). In tegenstelling tot deze en andere cultuurgewassen uit de Solanaceae plantenfamilie, vertonen de twee varianten van SdGAME8 verschillen in hun coderende sequentie, wat mogelijk verklaart hoe beide GA- stereoisomeren gelijktijdig voor kunnen komen in bitterzoet. Ik was ook in staat een dominante rol voor SdGAME25 te bevestigen voor de saturatie van het steroïde basismolecuul op de C-5,6 positie. Dit gen vertoonde de sterkste overexpressie in transcriptoomanalyses en kwam pas recentelijk aan het licht als kandidaatgen in de literatuur (Sonawane et al. 2018). De identiteit van een derde, recessief, gen dat verantwoordelijk is voor de conjugatie van suikerzuren (met UZCs als resultaat) is nog onduidelijk. Het was opvallend dat de UZC chemotypen een sterke overexpressie vertoonde van genen die te maken hebben met secundair metabolisme en afweerprocessen, wat suggereert dat UZC-biosynthese is gerelateerd aan een auto-immuunrespons. Wegslakken
168 Samenvatting en conclusies
(Arion rufus/ater) vertoonden een verminderde voorkeur voor F1 chemotypen met hoofdzakelijk onverzadigde GAs in vergelijking met die met verzadigde GAs. De UZC- chemotypen waren over het geheel het meest gevoelig voor slakkenvraat. Concluderend kan gezegd worden dat verschillen binnen een enkel gen resulteren in ecologisch relevante chemische diversiteit tussen verschillende bitterzoet planten.
Bij elkaar genomen is het duidelijk dat bitterzoet een doeltreffend constitutief en induceerbaar afweersysteem heeft tegen slakken. De meeste plantindividuen zijn enigszins resistent tegen slakken op constitutief niveau (hoofdstuk 2). Bovendien kunnen planten snel hun afweerstatus verhogen in zowel aangevreten als onbeschadigde bladeren (hoofdstuk 3 en 4). Echter, het geïnduceerde effect van 72 uur blootstelling aan slakkenvraat verschilde tussen de experiment in hoofdstuk 3 en hoofdstuk 4. Dit zou kunnen worden verklaard door technische verschillen tussen deze studies, waarbij de planten een verschillende leeftijd hadden en waren opgegroeid uit zaad of door middel van stekken. Als gevolg daarvan verkeerden deze planten mogelijk in een ander ontogenetisch- of ontwikkelingsstadium, wat vaak gerelateerd is met verschillen in de induceerbaarheid van de plant (Barton and Koricheva 2010; Boege and Marquis 2005). Deze deels tegenstrijdige resultaten in verschillende hoofdstukken illustreren echter ook dat afweerinductie niet rechttoe rechtaan werkt, maar kan verschillen van plant tot plant onder zowel experimentele, alsook natuurlijke condities. Verdere, sterk gecontroleerde, experimenten zijn daarom nodig om het gehele proces van afweerinductie tegen slakken volledig te begrijpen. De hier gepresenteerde transcriptoom- en metaboloomanalyses bieden een goede fundering om het onderzoek voort te zetten.
Glycoalkaloïden lijken een sleutelrol te spelen in slakkenafweer van bitterzoet. Dit wordt ondersteund door de observatie dat planten met algeheel hoge GA-waarden het meest resistent waren tegen verschillende slakkensoorten onder zowel kas- als veldomstandigheden (hoofdstuk 2 en 3). Bovendien ging geïnduceerde resistentie samen met GA-inductie (hoofdstuk 4) en was variatie in resistentie direct gekoppeld aan chemische diversiteit in GAs (hoofdstuk 2, 3 en 5). Dit neemt echter niet weg dat ook andere afweermechanismen een rol kunnen spelen, zoals fenolamiden (hoofdstuk 3 en 4), afweergerelateerde eiwitten (hoofdstuk 4) en indirecte afweer middels mutualistische mieren (Lortzing et al. 2016). Het is daarnaast belangrijk te beseffen dat elk van deze afweermethoden niet alleen effect op slakken heeft en dat slakkenresistentie overeenkomsten vertoond met door insecten induceerbare signaleringsnetwerken en afweer (hoofdstuk 4). Door slakken opgelegde selectie op afweermethoden, alsook geïnduceerd responsen na slakkenvraat, kunnen daarom ook effect hebben op andere herbivoren in de gemeenschap en vice versa.
Verschillen in de samenstelling van herbivorengemeenschappen gaan vaak samen met verschillen in de abiotische omstandigheden tussen habitats waar de planten groeien. Het is dan ook een interessant gegeven dat bitterzoet goed kan groeien onder
169 Samenvatting en conclusies contrasterende abiotische omstandigheden (Zhang et al. 2016). Slakken worden gekenmerkt door hun voorkeur voor vochtige condities en gematigde temperaturen (Astor et al. 2017). Het ligt daarom binnen de verwachting dat de impact van slakkenvraat op chemische diversiteit en geïnduceerde resistentie van planten lager is in bijvoorbeeld droge zonnige zandduinen in vergelijking met de bodem van natte broekbossen. Dit zou kunnen verklaren hoe het UZC chemotype (verzameld in een duingebied waar weinig slakken te verwachten zijn) kan overleven in de natuur. De ontdekking van dit nieuwe UZC chemotype illustreert hoe een eenvoudige chemische verandering grote gevolgen kan hebben. De verandering van een suiker- naar een suikerzuurverbinding bepaalt of de uitkomst van de interactie tussen bitterzoet en slakken zoet zal zijn of zuur. Dit benadrukt tevens de specifieke impact die slakkenvraat kan hebben op chemische diversiteit in het plantenrijk en algehele resistentie tegen herbivoren. Slakken zouden daarom vaker in beschouwing moeten worden genomen in plant-herbivoor interactiestudies naar het ecologisch belang of de toegepaste waarde van plantenafweermechanismen.
170 Samenvatting en conclusies
171
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Dankwoord / Acknowledgements Dankwoord / acknowledgements
Het dankwoord, het deel van het proefschrift waar haast iedere ontvanger van dit boekje begint met lezen… Of niet soms? Eindelijk mag ik zonder op mijn woorden te letten zeggen wat ik denk. Niks geen wetenschappelijk verantwoorde formuleringen en dichtgespijkerde zinnen, die dan ook weer vooral niet te lang mogen zijn. Nee, ik mag heerlijk al mijn slakkenslijm uitstorten over alles en iedereen. Maar geen zorgen, ik zal het netjes houden. Mocht het toch teveel zijn, hier een tip van de ervaringsdeskundige: slakkenslijm moet je eerst even aan een droge doek afvegen, kort wassen, nog eens afvegen en dan kun je het vervolgens vrij goed met water en zeep van je handen krijgen. Het deel dat toch achterblijft moet je maar zien als een goede herinnering… Graag wil ik beginnen met hen die in dit onderzoek de hoofdrol hebben gehad, maar helaas doorgaans slechts als lijdend voorwerp worden versleten. Allereerst, Solanum dulcamara, met je schitterende naam, al ruim negen jaar zijn we haast onafscheidelijk. We hebben vele wegen bewandeld en je blijft me verbazen met je diversiteit aan eigenschappen. Eén leven is niet genoeg om je te doorgronden, maar welke weg ik ook in zal slaan, ik zal altijd een oogje op je houden. Als tweede, Rosy (Deroceras reticulatum) en je slakkenvriend(inn)en (altijd een beetje van beiden). Vooraf was het maar de vraag of ik je taal zou kunnen verstaan, maar vanaf het allereerste experiment heb je me overduidelijke antwoorden gegeven. We lijken op elkaar, want we zijn beiden nogal kieskeurig als het ons dieet aangaat. Lust je iets niet dan eet je het niet, en gelijk heb je, want juist dat maakte het onderzoek met jou zo interessant! Hoewel je vaak als ongedierte wordt beschouwd heb je mij laten zien hoe mooi je ook kunt zijn en wat zijn je kinderen betoverend wanneer ze als engelen in hun eitje dansen. Nicole, zonder jou had ik deze en vele andere ervaringen nooit gehad. Je begeleide me al bij de eerste stage van mijn master en was direct een inspiratiebron door de eenvoud waarmee je moeilijke materie weet uit te leggen en te presenteren. Tijdens je inauguratie als EGG-head (hoogleraar ecogenomics) liet je me zien dat er kleur en leven in wetenschap mag zitten in plaats van dat het uitgekauwd en zwart/wit moet zijn. Ik heb je evolutie naar MIE- head (hoogleraar molecular interaction ecology) in Nijmegen en vervolgens in Leipzig gadegeslagen en bewonder je kracht en uithoudingsvermogen waarmee je staat waarvoor je staat. Enorm bedankt voor de kansen en support die je me geboden hebt door al die jaren heen. Mijn copromotoren Heidi en Janny wil ik bedanken voor hun vertrouwen in mij. Jullie zijn altijd een fijne steun, vraagbaak en kletspartner voor me geweest en ik ben dankbaar voor de vele malen dat jullie geholpen hebben richting aan mijn denken te geven. Heidi, ik wil je vooral ook bedanken dat je me gewoon in het diepe gooide door me een practicum en college voor de cursus populatie- en evolutiebiologie te laten ontwikkelen welke ik meerdere jaren heb mogen uitvoeren met en voor de derdejaars studenten. Het heeft mij kanten van mijzelf laten zien die ik anders nooit had (h)erkend. Janny, we delen het overmatige enthousiasme voor kleine dingen. Je haast eeuwige positivisme is aanstekelijk en ik hoop dat je daarmee nog vele generaties na mij mag inspireren. Wel blijven fietsen hè!
190 Dankwoord / acknowledgements
Titti, dank dat je me met Nicole’s aanstelling in Leipzig hebt ‘geadopteerd’ binnen je afdeling. Je warmte wordt alom geprezen en zo ook door mij. Je persoonlijke interesse waardeer ik zeer en wat heb ik goede herinneringen aan je rondleiding door je geboortestad Taranto. Geniet van je welverdiende pensioen, en alle tijd die je nu vrijelijk kunt besteden aan je ruime sociale leven. We houden contact! Natuurlijk wil ik ook alle andere collega’s van MIE/MPP bedanken voor een geweldige tijd. Die goede sfeer was vaak te danken aan mijn kamergenoten van de laatste jaren: Mieke (als je niet weer een paar maanden weg was), Peter (wat kun je toch geweldig uit de hoek rechtsachter komen), Mirka (bedankt voor je vele bemoedigende woorden) en Isa (jij komt zo aan de beurt ;). Else, jij bent sowieso mijn favoriete secretaresse van de afdeling. Ook jij bedankt voor je oprechte interesse en voor hoe je altijd voor mij en de afdeling klaarstaat. Verder natuurlijk dank aan alle andere directe collega’s: Ivo, Duy, Stuart, Gai Gai, Martijn, maar ook de oudgedienden Nicky, Emma, Florian, Jiemeng, Holger, Hanjing en de generaties daarvoor… Wat voor mij geldt, geldt ook voor planten. Alleen onder de juiste combinatie van omgevingsfactoren kun je groeien en bloeien. De kassen zijn door de jaren heen als een tweede thuis voor mij geweest en de perfecte plek voor mijn planten om te groeien. Gerard, Koos, Walter, Harry, Yvette, Dorine, Ronald, maar ook iedereen die ik er heb mogen ontmoeten in de koffiekamer… Altijd kon ik terecht voor praktische hulp, advies, diepgaande en flauwekulgesprekken. Een bezoek aan de kas is als therapie, waar zowel plant als mens worden voorzien van licht (energie), water (thee of koffie) en voeding (voor de geest). Dit onderzoek barst van de Duitse invloeden. Vielen Dank an alle meine deutschen Kollegen! I treasure very good memories of my time at FU Berlin. Anke, thanks for hosting me for my second master internship and the year as stipend holder, during which I could prepare for my PhD. Thanks for all the lively discussions on science, politics, society… not only in the institute’s kitchen, but also at home with your family, e.g. during the famous annual BBQ. Tobi, thanks a lot for your help on molecular analyses, riddles in R and all so many other questions! You are a great young scientist and honest partner in crime. Similar thanks to Daniël, Sylvia, Michelle and all other FU-colleagues with whom I have shared this unforgettable period! I also like to thank the many colleagues at iDiv in Leipzig, where I have spent various weeks as a guest researcher. Special thanks to Alex, your contributions to the chemical analyses are evident. Sorry for all the tiring questions I asked and the time you’ve spent to explain the basics of chemistry! Yvonne, thanks a lot for your help on data analyses and statistics. You are an R miracle. And Su, sorry for all the kind insults. Jules, dank voor je altijd snelle en accurate bijdragen aan de genetische achtergrondstudie. Dat je als expert van begin af aan enthousiast was en ten volle bereid mee te denken voelde als een fijne aanmoediging en bevestiging dat het niet alleen een leuk hersenspinsel in mijn eigen hoofd was.
191 Dankwoord / acknowledgements
Mijn paranimfen kennen de bitterzoete aspecten van mijn onderzoek als geen ander. Duy, my first master internship was on the topic on which you later started your PhD research. Ever since, we have always shared our bittersweet findings. Thanks a lot for the many discussions we had. I have always enjoyed our friendship a lot and was honoured to be your paranymph two years ago. This time I am proud to have you on my side during my own defence. Isa, jij hebt als geen ander ieder moment van euforie meegemaakt als ik weer eens wat leuks dacht te zien in mijn data. Maar ook was je er bij iedere zucht, steun, wiebel, rek, draai, hurk, kreun en weet ik welke uiting wanneer ik vastliep. Je bent naast studiegenoot, collega en klimmaatje ook een goede vriendin geworden. Bedankt voor alle momenten dat je me hebt opgevangen, niemand neemt het zekeren (in de hal, maar ook privé) zo serieus als jij! Stages kunnen leidend zijn voor de keuzes die je later gaat maken. Daarmee zijn ze dan ook zo’n beetje het belangrijkste onderdeel van een opleiding/studie. Dat ik als dagelijks begeleider een rol mocht spelen in de stage en dus ook de toekomst van anderen was me dan ook zowel een eer als genoegen. Kai, Sven, Bob, Adam, Ruud, Yari, Leonne, Sieme en Ids, dank dat ik direct of indirect betrokken mocht zijn en dat ik jullie met mijn enthousiasme mocht inspireren. Hoewel jullie het misschien zelf niet altijd inzagen, hebben jullie een wezenlijke bijdrage geleverd aan dit project en ook mij geïnspireerd met jullie verhalen, levensdoelen en inzichten. Het is erg leuk om te zien dat iedereen naderhand een eigen weg gaat, al staat die soms ver af van slakken, bitterzoet of biologie in het algeheel. Duizend maal dank aan alle slakkenleveranciers die hebben gereageerd op mijn vele oproepen naast de koffieautomaat. Dankzij jullie heb ik dit onderzoek kunnen doen, want hoewel ik mijn best heb gedaan, is het kweken van slakken in bakjes een grotere uitdaging dan een blik in menig groentetuin doet vermoeden. Naast de collega’s en studenten die direct of indirect aan dit project hebben bijgedragen heb ik ook daarbuiten onnoemelijk veel mensen leren kennen. Ik doe sowieso mensen tekort als ik daar een lijst van zou samenstellen, want bij wie begin je, en waar hou je op? De wandelgangen zijn lang, aan de lunchtafel is het een komen en gaan van mede PhD’s, studenten en ander volk, mijn kantoorplek met uitzicht op de koffieautomaat resulteerde in de meest uiteenlopende interacties en wat te doen met collega’s die inmiddels in de vriendenhoek vallen?. Ik ben een gevoelsmens, en zo kan het kortste moment voor mij al veel betekend hebben. Ik zou iedereen persoonlijk willen bedanken, maar weet je, dat doe ik dan wel de eerst volgende keer dat ik je spreek. Laat ik het hier zo zeggen: “Ik ben dankbaar voor elke hoi in het voorbijgaan, elk gesprek, elk advies, elke lach en elk moment van stilte dat ik met je gedeeld heb. Het kan zomaar doorslaggevend zijn geweest voor net dat ene moment…”
192 Dankwoord / acknowledgements
Ik loop doorgaans niet zo te koop met wat er speelt in mijn dagelijkse leven, waardoor vrienden en familie waarschijnlijk vaak geen idee hadden/hebben van waar ik nou eigenlijk mee bezig was/ben. Dat vind ik prima, want ik geniet veel liever van het moment dat we samen zijn dan het te verspillen met lastige verhalen over wetenschap. Een ieder doet immers zijn eigen ding. Toch hoop ik dat ik met mijn verhalen zo nu en dan toch mensen bewust kan maken van de mooie kleine dingen die altijd om ons heen te zien zijn. Ria en Johan, bedankt voor de vele avonden die ik bij/met jullie op de bank, in de tuin, op de hei of waar ook heb mogen doorbrengen. Jullie warmte is ongeëvenaard en het brietje van Rietje een remedie tegen elke dip! Marieke, Sander, Renske en Gerrit, al in Leeuwarden hebben jullie bijgedragen aan de onderzoeker (en mens) die ik nu ben. We zien elkaar helaas veel te weinig, maar elk moment is even vertrouwd, in voor en tegenspoed! Janneke, Myrna, aan het begin van mijn promotieonderzoek had ik nooit kunnen bedenken dat nu Norah rond zou lopen! Bedankt voor de onverwachte wending waarmee jullie haar in mijn (ons) leven hebben gebracht. Het blijft bijzonder en ik ben heel gelukkig dat we een deel van haar leven mogen zijn! Zussies en zwagers, dank voor jullie geduld en begrip voor de onbegrijpelijke inhoud van mijn dagelijks bestaan. De kids zijn de mooiste afleiding die jullie me hebben kunnen geven! Hun onbevangenheid is zo heerlijk om te zien en elke keer dat ze mij als speeltoestel gebruiken ben ik weer helemaal in mijn element. Dit proefschrift is niet voor niets mede aan hen opgedragen. Hoewel ze het waarschijnlijk nooit zullen lezen, hoop ik dat het een inspiratie kan zijn voor wat ze kunnen bereiken als ze maar getrouw aan zichzelf hun eigen pad bewandelen. Ik ben zo benieuwd wie en wat ze gaan worden; bakker, zanger, juf, meester, of misschien wel hetzelfde als “ome Onno”. Papa, muttie, duizend maal dank dat jullie me mijn eigen weg hebben laten lopen en dat jullie jezelf nooit als barricade op deze weg hebben geplaatst, maar puur als richtingaanwijzer hebben gefungeerd. Al sinds ik op mijn hurken in de tuin naar de miertjes zat te kijken was voor jullie duidelijk dat dat de wereld was waarin ik later terecht moest komen. Wat hebben we genoten tijdens onze onvergetelijke reis door Zuid-Afrika en Namibië vorig jaar. Het is haast eng hoe ik tijdens deze intensieve tijd samen mijzelf in jullie herkende. Door jullie ben ik wie ik ben, en ik zou niemand anders willen zijn… Maik, lieverd, bedankt voor al je geduld, dat je me beschermt (vooral tegen mijzelf), dat je het altijd voor me opneemt en dat je zorgt dat ik thuis ook echt thuis kan zijn. Na al die jaren met zoveel lusjes zijn we zo aan elkaar verbonden dat ik een beetje jou ben en jij een beetje mij. Dit boekje is slechts een hoofdstuk van ons leven en ik kijk uit naar de vele hoofdstukken die we beiden nog gaan schrijven, de zoete en de zure, elk op onze eigen eigenwijze wijze. Bedankt dat je er bent, want jij bent het die me echt laat genieten van de ‘alledaagse’ wereld om me heen.
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Curriculum vitae and education statement Curriculum vitae and education statement
I, Onno Wouter Calf–van Schravendijk, was born in Vlaardingen (the Netherlands) on the 23rd of March 1986. Already as a child, I was always fascinated by the ‘wild, nature’ in our back garden. In particular the weeds and bugs, that no one really liked always, drew my special attention. I was lucky that my parents allowed me to have my own little ‘experimental’ garden plot in which nature could thrive under my thorough supervision. After finishing lower general secondary education (MAVO) in 2002, I studied environmental supervision on secondary vocational level (MBO) at Holland College in Delft. After my graduation in 2006 I was by far not satisfied. I therefore followed up with a BSc in environmental science (HBO) at van Hall Larenstein in Leeuwarden. Part of this study was a BSc minor for five months in applied freshwater and marine biology at the Galway–Mayo Institute of Technology in Galway (Ireland). During my studies, I particularly enjoyed courses with an ecological focus. I therefore went to Bargerveen Society in Nijmegen for a graduation project on the effect of nitrogen deposition on soil macrofauna communities in Dutch inland drift sand landscapes. I graduated in 2009 and concluded that I had to continue with a MSc in biology, which I did at Radboud University in Nijmegen. Soon I got to know Solanum dulcamara, the bittersweet nightshade that has received my full attention ever since. I wrote my first MSc thesis on the cross-talk between insect herbivory and drought or flooding. Then, I left to the Free University in Berlin (Germany) for my second MSc thesis on the attraction of mutualistic ants to wound-induced sugar secretions for indirect defence. I graduated in 2012 and went back to FU Berlin to work for one year as stipend holder, during which I continued on the topic of my second MSc thesis and additionally studied the role of priming in herbivore defence, including gastropod feeding. This was the preparation of my PhD project which I officially started at Radboud University in 2014 and comes to an end with the defence of this thesis. No matter the direction in which my career will evolve, I will always keep my fascination for the world around us and remain an ambassador for the non- huggable and stubborn little wonders of nature.
E-mail [email protected] LinkedIn https://www.linkedin.com/in/owcalf ORCID ID https://orcid.org/0000-0001-5280-2265 ResearchGate https://www.researchgate.net/profile/Onno_W_Calf
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Scientific publications Calf OW, Huber H, Peters JL, Weinhold A, Poeschl Y, van Dam NM (2019) Gastropods and insects prefer different Solanum dulcamara chemotypes. Journal of Chemical Ecology 45:146-161. Peters K, Worrich A, Weinhold A, Alka O, Balcke G, Birkemeyer C, Bruelheide H, Calf OW, Dietz S, Dührkop K, et al. (2018) Current challenges in plant eco-metabolomics. International Journal of Molecular Sciences 19:e1385. Calf OW, Huber H, Peters JL, Weinhold A, van Dam NM (2018) Glycoalkaloid composition explains variation in slug resistance in Solanum dulcamara. Oecologia 2:495-506. Lortzing T, Calf OW, Boehlke M, Schwachtje J, Kopka J, Geuss D, Kosanke S, van Dam NM, Steppuhn A (2016) Extrafloral nectar secretion from wounds of Solanum dulcamara. Nature Plants 2:e16056. Calf OW, van Dam NM (2012) Bittersweet bugs: the Dutch insect community on the nightshade Solanum dulcamara. Entomologische Berichten 72:193-198.
Awards 1st Price Poster award: A slug’s view on Bittersweet defence. NWO-ALW Symposium of the Experimental Plant Science graduate school (EPS), Apr 10-11, 2017, Lunteren. 1st Price Poster award: Mechanisms and ecological consequences of plant resistance to gastropod herbivores. 15th International Symposium on Insect-Plant Relationships (SIP), Aug 17-22, 2014, Neuchatel, Switserland. 1st Price Poster award: Bittersweet sugars as indirect defence strategy. 43rd annual meeting of the Ecological Society of Germany, Austria and Switzerland (GFÖ), Sep 9-13, 2013, Potsdam, Germany.
In the media Television interview about research for plant resistance to herbivores. Rob’s grote tuinverbouwing, SBS6, Broadcasted on Okt 7, 2017.
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199 Notes
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