Genetic and chemical diversity of the toxic herb Jacobaea vulgaris Gaertn. (syn. Senecio jacobaea L.) in Northern Germany

Phytochemistry 172 (2020) 112235

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Genetic and chemical diversity of the toxic herb Jacobaea vulgaris Gaertn. (syn. Senecio jacobaea L.) in Northern Germany

T

Stefanie Junga,∗, Jan Lauterb,c, Nicole M. Hartungb,c, Anja Theseb, Gerd Hamscherc, Volker Wissemanna a

Systematic Botany, Justus Liebig University Giessen, Germany German Federal Institute for Risk Assessment, Department Safety in the Food Chain, Berlin, Germany c Institute of Food Chemistry and Food Biotechnology, Justus Liebig University Giessen, Germany b

ARTICLE INFO

ABSTRACT

Keywords: Jacobaea vulgaris Gaertn. Asteraceae Tansy ragwort Liquid chromatography High resolution mass spectrometry AFLP marker Alkaloids Pyrrolizidine alkaloids

Tansy ragwort, Jacobaea vulgaris Gaertn. (syn. Senecio jacobaea L.), is a common Asteraceae in Europe and Asia and known to be an invasive pest in several regions in the world. Recently it is also spreading immensely in native regions like Northern Germany. Pyrrolizidine alkaloids (PAs), which are found in high amounts in Jacobaea vulgaris, are toxic for humans and potentially lethal for grazing animals. In this study we investigated 27 populations of tansy ragwort in Northern Germany for their PA concentration and composition using liquid chromatography coupled to high resolution mass spectrometry. Furthermore, we investigated the genetic structure of selected populations using amplified length polymorphism markers. We detected 98 different PAs in the samples and considerable differences of PA composition between populations. In contrast, PA content of populations did not differ significantly. Genetic (4%) differentiation among populations was low while average genetic diversity was high (0.35). There was no correlation between genetic and geographic distance. Neither genetic markers nor chemical composition revealed any connection to the geographic pattern. As we could not detect any pattern in genetic or chemical diversity, we suggest that the existence of this diversity is a result of a broad interaction with the environment rather than that of evolutionary constraints in the current selection process driving PA composition in J. vulgaris in certain chemotypes.

1. Introduction Selective pressure is the main driver of evolution in plant communities. The interaction of plants and herbivores resulted in the development of defence mechanisms on both sides. On one hand, plants have developed the ability to produce a multitude of different specialised metabolites to defend themselves against herbivores or pathogens (Coley et al., 1985; Pichersky and Gershenzon, 2002; Sonnewald, 2014), whereas on the other hand specialised insects detoxify these metabolites to coexist with their host plants. Concentration and composition of metabolite diversity are often associated with environmental factors as nutrient availability (Close et al., 2005; Kirk et al., 2010). Pyrrolizidine alkaloids (PAs) belong to the group of specialised plant metabolites which are produced by different plant families especially in Asteraceae (tribes Eupatorieae and Senecioneae), Boraginaceae (most genera), Fabaceae (basically the genus Crotalaria) and Orchidacea (ten genera) (Hartmann and Witte, 1995). They are hepatotoxic for humans, grazing mammals like horses or cattle and toxic for many insects (Edgar

∗

et al., 2011; Neumann et al., 2015). Once ingested, PAs are absorbed in the digestive tract and transported to the liver. Here, they are metabolized to reactive pyrroles (Mattocks, 1968; Ruan et al., 2014) which can interact with proteins or other biopolymers and show concentration-related acute or chronic toxicity. A proliferation of toxic plants in managed farmland might be problematic to agriculture. Furthermore the risk for humans and livestock to get poisoned increases, especially if a specific metabolite lineage becomes invasive and dominant in the landscape. Traces of PAs may enter the human food chain via herbal tea, milk, eggs, food supplements or honey (Mulder et al. 2015, 2018). The impact of J. vulgaris on interacting herbivores (e.g. insects) is a subject of current research. On one hand, generalist herbivores or nonspecialised interacting insects can be negatively affected by PAs, Drosophila melanogaster or Frankliniella occidentalis for example show negative impacts caused by PAs. Furthermore different PAs show different effects on various insects (Lindigkeit et al., 1997; Naumann et al., 2002; Liu et al., 2017). In other cases, PAs are even used by specialised herbivores to find a host plant (Macel, 2011; Macel and Vrieling, 2003).

Corresponding author. Heinrich-Buff-Ring 38, 35392, Giessen, Germany. E-mail address: [email protected] (S. Jung).

https://doi.org/10.1016/j.phytochem.2019.112235 Received 4 July 2019; Received in revised form 14 October 2019; Accepted 19 December 2019 0031-9422/ © 2020 Elsevier Ltd. All rights reserved.

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Assuming that there are numerous different insect herbivores living in J. vulgaris populations they might be able to constitute some selective pressure on PA content and diversity of J. vulgaris individuals and vice versa. Jacobaea vulgaris Gaertn, syn. Senecio jacobaea L., belongs to the family of Asteraceae and is an annual or biennial herb. It occurs naturally in Europe and Western Asia but it has also managed to reach North America, Australia and New Zealand (Harper and Wood, 1957). Numerous flowerheads in combination with a long flowering time increase the possibility of pollination and thus to produce countless seeds (Harper and Wood, 1957). J. vulgaris is one of the species that produce PAs and this becomes relevant for protection against generalist herbivores. Previous studies detected 27 different PAs in J. vulgaris (Joosten et al., 2011): the major ones are jacoline, jacozine, jacobine, seneciphylline, senecionine and erucifoline (Crews et al., 2009; Witte et al., 1992). Depending on the major PAs they contain, two different chemotypes were defined. The jacobine type basically containing jacobine and jacozine and the erucifoline type basically containing erucifoline and its o-acetyl ester (Witte et al., 1992). The basis for secondary metabolite diversity is genetic diversity. In this study we use a neutral molecular fingerprint marker technique to characterize genetic diversity called amplified length polymorphism (AFLP), which is suitable to detect genetic differences between individuals. For this, genomic DNA is cut into defined pieces using two restriction enzymes. Thereafter, several PCR runs with different pairs of primers produce molecular patterns, unique for every individual. Finally, this pattern is used to calculate genetic diversity and differentiation. In contrast to other plant species that are suffering from fragmentation, populations of J. vulgaris increase and therefore are not expected to suffer genetic inbreeding effects. Furthermore, population size and genetic variation are positively correlated and genetic variation affects ecological fitness in a positive way (Frankham, 1996; Leimu et al., 2006). Currently J. vulgaris populations in Northern Germany are proliferating faster than in other regions in Germany. We assume that one PA chemotype might be dominant in this region enabling the recent spread of J. vulgaris in Northern Germany due to selective advantages against herbivores. We characterize the chemical PA profile of J. vulgaris and compare the chemical and genetic diversity in Northern Germany. Due to the fact that J. vulgaris is currently proliferating, the number of populations and their sizes are increasing and thus we expect the genetic variation and fitness to increase as well. Therefore we hypothesize that there is evidence for directed evolution towards specific PA chemotypes and that PA diversity and content of PAs might be a driver for the invasiveness of J. vulgaris in our study area.

Fig. 1. Geographic distribution of the collection sites of J. vulgaris populations.

of PAs when compared to the locations in Schleswig-Holstein. Population 17 (Breklum) has a higher PA content, than the remaining populations (Kruskal-Wallis, p < 0.0001). However, given our hypothesis, this content might not be relevant for selection by big herbivores, as all found concentrations do not reach the level of harmfulness for big grazing mammals like cattle. For insect herbivores it is known that the effect of the concentration of PAs depends on both PA and insect species (Macel et al., 2005). Nonetheless compared to other investigations in Germany, we found lower overall PA contents in dry weight (These et al., 2013). Explanations for different contents of PAs are rare. A possible influence might be soil composition and nutrient availability. This was shown by Kirk et al. (2010) who found higher PA concentrations in plants growing in soils with limited nutrients. Kirk et al. (2010) also suggested that there is no selective pressure on PA content by different herbivores. However, other studies did not point towards relevant effects of locational factors including soil composition or herbivory insects on PA content (Joosten et al., 2009; Van der Meijden et al., 1989; Vrieling and Wijk, 1994), indicating the need for further research in this field. PA diversity was investigated by counting the different PAs that are produced by individuals and subsequently averaged within populations (Fig. 3B). Individuals from population 5 (70 ± 2), 6 (71 ± 3), 15 (73 ± 3), 16 (74 ± 3), and 19 (71 ± 3) produced a greater range of different PAs than the average of all individuals hence are chemically more diverse. Population 2 (49 ± 2), 3 (51 ± 2), 12 (57 ± 3), 23 (49 ± 3), 24 (50 ± 2), 25 (51 ± 5) and 27 (57 ± 2) in contrast produced a lower range of different PAs (ANOVA, F (27, 706) = 18.86, P < 0.0001). Population 15 was genetically investigated, too and

2. Results and discussion 2.1. PA profile of J. vulgaris We coupled chromatography with high resolution mass spectrometry (LC-HR-MS) in order to detect low PAs contents and different structures of PAs. A total of 98 different PA structures were determined in 367 J. vulgaris plant samples of which 347 were located in SchleswigHolstein and 20, respectively 2 populations, in Hesse (Fig. 1, Table .1). At the time of the study, 13 of these 98 PAs were available as reference standards and could therefore be unambiguously determined. The most abundant PA in J. vulgaris individuals was erucifoline-N-oxide (16.33%) followed by senecionine-N-oxide_senecivernine-N-oxide (13.75%) and jacobine-N-oxide (10.84%) (Table 2). Fig. 2 shows the structural formula of the most abundant PAs and a comprehensive list of all identified PAs is shown in Table 5. The average total PA content of an individual was 1032 ± 365 mg/ kg corresponding to 0.1% of dry weight. The mean amount of PA between populations differed from the lowest 777 ± 237 mg/kg of dry weight (Heiligenhafen) to the highest 1666 ± 476 mg/kg of dry weight (Breklum) (Fig. 3A). Both locations in Hesse show similar sums 2

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Table 1 Location of collection sites of J. vulgaris populations with number of individuals used for chemical analysis (sample size PA analysis), number of individuals used for genetic analysis (sample size AFLP analysis), and coordinates. Chemotype of locations including significant differences between relative abundance of erucifoline and jacobine. - = not used, ns = not significant, *p < 0.05, **p < 0.01, ***p < 0.001. Nr.

Location

Sample size PA analysis

Sample size AFLP analysis

Lat.

Long.

Chemotype

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Rauischholzhausen Cleeberg Stodthagen Goosefeld Dazendorf Neustadt Eutin Mühlenbarbek Drögen Eider Bordesholm Preetz Langwedel Rastorf Dithmarscher Geest Heiligenhafen Neumünster Breklum Flensburg Füsing Hamburg Rehburg Schwarzenbek Niebüll Lottorf Barkauer See Forstkrug Bültsee

11 9 9 15 15 15 15 15 15 15 15 11 8 15 15 15 15 15 15 15 16 15 15 6 14 15 13

9 – 9 – 8 7 –

50.762199 50.454095 54.281263 54.428561 54.362362 54.088625 54.133031 53.954671 54.164423 54.193261 54.235568 54.214269 54.275717 54.130057 54.377679 54.039343 54.607393 54.728386 54.524689 53.643896 54.284511 53.553038 54.767187 54.453106 54.086987 53.486318 54.497838

8.879012 8.556469 10.115665 9.808093 10.944441 10.798694 10.6095 9.654996 10.123386 10.047096 10.296812 9.921167 10.300127 9.254603 10.931665 9.980943 8.987367 9.367222 9.637699 9.928421 10.288206 10.414365 8.866052 9.571611 10.641261 10.733531 9.753609

Mixed, ns Erucifoline, ** Mixed, ns Jacobine, * Erucifoline, *** Mixed, ns Erucifoline, *** Jacobine, * Jacobine, * Jacobine, * Jacobine, * Jacobine, * Mixed, ns Jacobine, * Erucifoline, ** Jacobine, * Erucifoline, *** Mixed, ns Jacobine, * Jacobine, * Mixed, ns Mixed, ns Mixed, ns Mixed, ns Jacobine, *** Mixed, ns Mixed, ns

– – – – – 8 9 – – 7 10 8 – – – – – – –

Table 2 Description of the ten most abundant PA structures detected in J. vulgaris plants in Northern Germany. Senecionine and senecivernine as well as their N-oxides were respectively quantified as sum due to coelution. SEM: standard error of the mean.

Erucifoline-N-Oxide Senecionine-NOxide_Senecivernine-NOxide Jacobine-N-Oxide Jacobine Erucifoline Seneciphylline-N-Oxide Seneciphylline Retrorsine-N-Oxide Senecionine_Senecivernine Retrorsine

average [mg/kg]

SEM

Minimum [mg/kg]

Maximum [mg/kg]

168.43 141.87

6.93 5.77

1.10 8.23

487.79 671.95

111.82 90.50 83.03 82.33 64.05 54.34 48.91 19.43

5.10 4.10 3.90 4.44 3.36 1.84 2.45 0,71

1.45 0.00 0.94 2.48 1.72 6.58 2.53 1.80

473.07 393.19 431.43 475.52 457.44 204.07 344.73 97.72

Fig. 2. Structural formula of the 6 most abundant PAs in this study.

population produce considerably different amounts of different PAs. As we cannot figure out any consistency or dominant PA profile we suggest that there might not be a direct evolutionary constraint as a selective pressure on PA composition in J. vulgaris at all. At least for specialist herbivores other studies confirm that they do not put any selective pressure on PA composition (Macel et al., 2002; Macel and Vrieling, 2003; Vrieling and Boer, 1999). In contrast, the same authors showed that different PAs had different effects on generalist and specialist herbivores and thus hypothesized that herbivores could play a role in the evolution of PA diversity (Macel et al., 2005; Macel and Klinkhamer, 2010). Furthermore, soil constitution and its content of microorganisms can affect PA composition (Joosten et al., 2009). Although diversity within populations does exist, averaged values of our data do not support any directed evolution, therefore we interpret the overall diversity of PAs in J. vulgaris, lacking specific chemotypes to be the result of a panmictic metapopulation with no directing selection on the geographic range of our study.

shows one of the highest genetic diversity values (Table 4). Previous studies have specified three (Macel et al., 2004) respectively four chemotypes of J. vulgaris (Witte et al., 1992). According to the dominant PAs, a jacobine type, an erucifoline type, a senecionine type and a mixed type were assigned. We found mainly individuals with both PA jacobine as well as erucifoline. However, ANOVA revealed that averaged trough all populations, 11 populations contained a significantly higher relative share of jacobine. In contrast, 5 populations produced more erucifoline (Fig. 3C), confirming Marcel at al. (2004) who postulated that erucifoline chemotypes are not only restricted to South-East Europe. Moreover, one population (10) contained no erucifoline and 5 populations contained no jacobine at all (2, 5, 7, 15, and 17) or less than 2% (Fig. 4). Remarkably individuals from Population 3 (Stodhagen) produced neither much of jacobine nor erucifoline but plenty seneciphylline and its N-oxide. However, our data regarding PA composition widely spread so that individuals from the same 3

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are somehow cross-linked to each other, leading to the conclusion that J. vulgaris forms a panmictic metapopulation in Northern Germany. This could be explained by long distance achene dispersion (e.g. via wind or hay transport) and a connection between populations via pollinators. At least 36 species are known to pollinate J. vulgaris, mainly Diptera like Syrphidae and Hymenoptera (Vanparys et al., 2008). Therefore, the distances pollinating insects are able to overcome are very various. In human modified landscape for example insects can spread pollen up to 400 m (Rader et al., 2011). In addition, J. vulgaris seeds are dispersed by wind, water and human activity (Harper and Wood, 1957). Especially rail- and motorways seem to be good propagation vectors for seeds from J. vulgaris (Harper and Wood, 1957). For the related species Senecio inaequidens DC. studies have already shown the importance of rail- and motorways as dispersion way (Blanchet et al., 2015; Griese, 1996). In addition, the comparison between the genetic distance and the geographical distance (Mantel test) showed a small positive correlation which was not significant (r = 0.083, p = 0.08), hence we could not detect any isolation by distance, confirming the conclusion that J. vulgaris forms a metapopulation in Northern Germany. Genetic diversity values range between 0.31 ± 0.01 (Neustadt) and 0.4 ± 0.07 (Rauischholzhausen). The average assumption is 0.35 (Table 4). This is a high to moderate genetic diversity on average and fits to the results of other common Asteraceae with similar life traits. Mandák et al. (2009) investigated Carduus acanthoides L. in its native range using allozymes. C. acanthoides also shows high levels of genetic diversity and small genetic differentiation. Both species, J. vulgaris and C. acanthoides, are invasive in several parts of the world (Desrochers et al., 1988; Doorduin et al., 2010). This also applies to Centaurea scabiosa L. in Denmark (Ehlers, 1999). In this case a connection between genetic variation and population reproductive success could be detected. According to the same probability to disperse seeds via wind and the general positive correlation between genetic variation and fitness (Leimu et al., 2006) we believe that this is also true for J. vulgaris. This would mean, that J. vulgaris has a great potential of spreading further und thus would aggravate the problem of toxic herbs on grazing fields of livestock. We interpret the overall genetic diversity in J. vulgaris, lacking specific differentiated genotypes to be the result of a panmictic metapopulation with no directing selection on the geographic range of our study.

Fig. 3. PA content and composition of J. vulgaris. A) Averaged PA content in mg/kg dry weight of the individuals per population ( ± SEM). At first total PA content of every individual was calculated and afterwards the mean content of all individuals per population. Significant differences to the total averaged PA content (dotted line) are indicated by *. B) PA diversity of the populations shown by mean numbers of different PAs detected in populations ( ± SEM). Significant differences to the total averaged number of PA (dotted line) are indicated by *. C) Categorisation of J. vulgaris chemotypes: Difference in relative abundance of erucifoline and jacobine per population. Bars below 0 indicate jacobine type and bars over 0 indicate erucifoline type populations. *p < 0.05, **p < 0.01, ***p < 0.001.

3. Conclusion In this study we showed that J. vulgaris in Northern Germany is highly diverse and with only low differentiation between populations. This is based on our data from genetic neutral markers indicating high genetic diversity within populations and low differentiation among populations and from a screening of specialised metabolites with a significantly diverse PA profile. Our study revealed that J. vulgaris contains a multitude of different PAs. As previously 27 different PAs were found in roots and shoots of the species J. vulgaris (Joosten et al., 2011), our study suggests that the diversity found in blossoms is even much higher. Although more that 80% of the total PA content is represented by 10 PAs, the remaining PA provide the individual variation for evolutionary selection. However, currently our data do not support any directed evolution related to a specific PA, thus we cannot support our hypothesis that either PA content or PA diversity is a driver for a specific invasive lineage among J. vulgaris. The PA composition and content vary strongly between different individuals and populations. We could not detect any geographic pattern, neither in relationship with genetic data, nor in relationship with specialised metabolism composition, thus genotypes and chemical composition are the expression of natural diversity in this species. This leads us to the conclusion that based on our data we cannot confirm any directed evolutionary constraints from selection on J. vulgaris in Northern Germany driving populations in specific evolutionary directions. As diversity is

2.2. Genetic profile of J. vulgaris Our results generated with AFLP markers draw the same picture as our former results using ISSR markers (Jung et al., 2017). In total, three primer combinations produced 243 loci for 75 individuals of which 241 (99.2%) were polymorphic, hence show differences between individuals. AMOVA shows that the main part of the total genetic differentiation (96%) is located within populations while 4% of the genetic differentiation is among populations (Table 3). The principal co-ordinate analysis (PCoA) was performed to visualize genetic similarities. It shows scattered individuals along with any clustering (Fig. 5). Coordinate 1 explains 29% of the variability and coordinate 2 explains 10%. As we do not see any grouping of individuals belonging to one population, PCoA results confirm AMOVA results and show that there is not too much differentiation among populations. Genetic structure of J. vulgaris populations in its native and invasive region has been investigated before (Doorduin et al., 2010). This study shows higher differentiation between populations (13.26%) in its native region which might be explained by the much greater distances between the different populations used in that study. The low differentiation we found in our study indicates that the J. vulgaris populations 4

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Fig. 4. Mean PA composition per population. Percentage proportion of single PAs from all individuals were averaged and summarized in populations. Only single PAs that count for more than 2% of total PA concentrations were included.

often associated with ecological fitness (Leimu et al., 2006), our data of genetic and PA diversity indicate that J. vulgaris populations in Northern Germany might have a sufficient level of fitness. Further studies should concentrate on possible reasons for different PA concentrations and the inheritance of PA composition as well as the biosynthesis of the different PAs. In combination with ecological research on environmental factors like soil composition and herbivore community at the sampling locations, this will shed light on the role of PAs for the evolutionary fate of J. vulgaris in Northern Germany.

Table 3 Analysis of molecular variance (AMOVA) for genetic diversity of all populations of J. vulgaris. PhiP = Differentiation, *p 0.01, d.f. = degree of freedom. Source of variation

PhiP

d.f.

Sums of square

Variation %

Among Populations Within Populations Total

0.039*

8 66 74

447.588 2756.892 3204.480

4 96 100

Table 4 Genetic diversity of J. vulgaris populations based on AFLP data. n = sample size, loc = number of loci scored, LocP = number of polymophic loci, PLP (%) = percent polymorphic loci, Hj = expected heterozygosity, SE (Hj) = standard error of Hj. Population

n

Loc

Loc P

PLP (%)

Hj

SE (Hj)

1 3 5 6 14 15 18 19 20

9 9 8 7 8 9 7 10 8

243 243 243 243 243 243 243 243 243

238 222 213 202 206 243 207 219 213

97.9 91.4 87.7 83.1 84.8 100.0 85.2 90.1 87.7

0.39276 0.34413 0.33061 0.31759 0.35668 0.36319 0.34047 0.35207 0.36099

0.00738 0.00930 0.00989 0.00992 0.01022 0.00796 0.01002 0.00972 0.00990

4. Experimental Altogether 367 Jacobaea vulgaris (Asteraceae) plants were collected from 27 different populations during flowering time in 2015. 25 of these populations were based in Northern Germany (23 in SchleswigHolstein and 2 in Hamburg). Plants from two populations in Hesse, central Germany, served as control groups. 4.1. PA analysis Liquid chromatography coupled to high resolution mass spectrometry (LC-HR-MS) was used to analyse the individual PA. For the extraction of PAs 1.0 g ± 0.1 g of comminuted dried plant material (flowers) were weighed into a centrifuge tube. For single extraction a volume of 20 mL aqueous extraction solution containing 25%

5

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Fig. 5. Principal co-ordinate analysis (PCoA) ordination of the genetic similarity of 75 individuals of J. vulgaris from Northern Germany. Coordinate 1 explains 29.47% of the shown variability and coordinate 2 explains 10.00% of the shown variability. Colours indicate membership to the different populations.

of methanol and 2% formic acid was added. Subsequent extraction was performed in an ultrasonic bath for 10 min. After filtering with a paper filter, the extracts were adjusted to pH 6–7 using a 6.4% ammonia solution. Aliquots of the solution were centrifuged and then stored at 4 °C. Afterwards, samples were analysed with an Orbitrap mass spectrometer (Q Exactive Focus, Thermo Scientific). PAs were analysed in the positive ionization mode using variable data-independent acquisition (vDIA). Two scan events were recorded in parallel by permanent switching. First, a full scan mass range of m/z 100–650, applying a resolving power of 70,000 was recorded; additionally, fragments were generated with collision energy of 30 eV and acquired in a mass range of m/z 100–1500 using a resolving power of 17,500. For each analyte two confirming ions were extracted. Reversed phase separation was achieved on a 150 × 2.1 mm; 1.9 μm C18 Hypersil Gold column with guard protection (Thermo Fisher, Runcorn, UK) at a flow rate of 0.3 mL/min on an UltiMate 3000 (Thermo Scientific, Germany) UHPLC system and 2 μl were injected. Eluent A was prepared with 100% water containing 0.1% formic acid and 5 mM ammonium formate and eluent B with 95% MeOH and 5% water containing 0.1% formic acid, 5 mM ammonium formate. A gradient elution was adopted as follows: 0–0.5 min A:95%/B:5%, 7.0 min A:50%/B:50%, 7.5 min A:20%/B:80%, 7.6–9.0 min A:0%/B:100%, 9.1–15.0 min A:95%/B:5%. Methanol and water were obtained in LC-MS grade from Merck (Darmstadt, Germany). Ammonium formate (VWR, Germany) and formic acid (Honeywell, Germany) were purchased in analytical quality. The PA standard substances erucifoline, erucifoline-N-oxide, jacobine, jacobine-N-oxide, retrorsine, retrorsine-N-oxide, senecionine, senecionine-N-oxide, seneciphylline, seneciphylline-N-oxide, senecivernine, senecivernine-N-oxide and senkirkine were obtained from PhytoLab (Verstenbergsgreuth, Germany). Depending on solubility PAs were either dissolved in methanol or acetonitrile. The resulting stock solutions were stored at 4 °C. For calibration, a matrix matched multianalyte PA-standard with individual concentrations of 50, 300 and 450 ng/mL was used. To simulate matrix effects Sisymbrium loeselii leaves were extracted in the same way as the J. vulgaris samples and used for matrix matched calibration. For the quantification the software TraceFinder 4.1 (Thermo Fisher Scientific, Carlsbad, USA) was applied. Identification of PAs was performed via retention time, the exact mass of the protonated molecular ion (Table. 5) and at least two characteristic fragments within a mass accuracy of at least 5 ppm. Peak area of the protonated molecular ion was used for quantification. The content of each individual PA was determined in each sample and the average

of each PA as well as the sum of all PAs per population was subsequently calculated among the samples. 4.2. Genetic screening Genetic differentiation and diversity of J. vulgaris was assessed by amplified fragment-length polymorphism (AFLP). The analysis was performed with genetic material from 90 individual plants, originating from nine representative populations equally distributed across the sampling area (marked in Fig. 1). Choice of population was based on a previous study investigating genetic diversity of J. vulgaris in Northern Germany by ISSR-PCR (Jung et al., 2017). Plant leaf material was dried with silica gel and DNA was subsequently extracted employing the DNeasy Mini Plant kit (Qiagen, Hilden, Germany), following the manufacturer instructions. The protocol for AFLP was adapted from Vos et al. (1995). A volume of 10 μl extracted DNA (30 ng/μl) was double-digested using 9.8 μl purified H2O and 5 μl 10x buffer tango (ThermoFisher Scientific, Carlsbad, USA), with the enzymes EcoR1 and Tru1l (both 10 U/μl; 0.1 μl, ThermoFisher Scientific, Carlsbad, USA) for 1 h at 37 °C and afterwards for 1 h at 65 °C. Afterwards ligation of adapters Eco+/(5 pmol, 0.5 μl, MWG Biotech, Ebersberg, Germany) and Mse+/(50 pmol, 0.5 μl, MWG Biotech, Ebersberg, Germany) was carried out applying 2U T4-ligase (0.5 μl, ThermoFisher Scientific, Carlsbad, USA), 10 x T 4-ligase buffer (0.5 μl, ThermoFisher Scientific, Carlsbad, USA) and ATP (10 mM, 3 μl, ThermoFisher Scientific, Carlsbad, USA) for 1 h at 22 °C. The reaction was inactivated at 75 °C for 10 min. Subsequently two PCRs were performed. The first one using primers with one selective nucleotide and the second one with primers with three selective nucleotides. 1.5 μl ligation product was mixed with 17.36 μl purified H2O, 2.5 μl 10x dream Taq buffer (ThermoFisher Scientific, Carlsbad, USA), 2.5 μl dNTP (10 mM each, ThermoFisher Scientific, Carlsbad, USA), 0.5 μl primer EcoR1 – A, 0.5 μl primer Mse1- C (both 5 μM, Metabion international AG, Planegg/Steinkirchen) and dream Taq polymerase (0.14 μl, ThermoFisher Scientific, Carlsbad, USA). PCR conditions were as follows: initial step at 94 °C for 3 min, followed by 30 cycles at 94 °C for 30 s, 56 °C for 60 s, 72 °C for 60 s, ending with a final extension step at 72 °C for 7 min. Products of this PCR were dilute 1:30 bevor starting the second PCR which contained 13.36 μl purified H2O, 2.0 μl 10x dream Taq buffer, 2.5 μl dNTP, 0.65 μl fluorescent labelled primer EcoR1 - NNN (1 μM, Metabion international AG, Planegg/Steinkirchen), 0.65 μl primer Mse1 - NNN (5 μM, Metabion 6

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international AG, Planegg/Steinkirchen) and 0.11 μl dream Taq polymerase. Except primer annealing temperature which was adapted to each primer, PCR conditions were the same as for the first PCR. From a total of 21 primer combinations three combinations, EcoR1-ACA/Mse1CAC, EcoR1-ACA/Mse1-CCT, EcoR1-AAC/Mse1-CAA, were selected for the final analyses based on the quality of AFLP profiles. Reproducibility was controlled by including replications of six individual reactions in each PCR set. Fragment analysis of PCR products was outsourced to IfB GmbH (IfB Institut für Blutgruppenforschung GmbH, Cologne, Germany) and achieved by capillary gel electrophoresis.

Kruskal-Wallis and Dunn's test as post-hoc test. PA diversity was investigated by counting the different PAs per individual and subsequently the mean number of PAs for every population was calculated. Differences of averaged PA number in every population was tested against PA number of all populations using a one-way ANOVA and a Dunnet's post-hoc test. In order to determine a classification of the populations into different chemotypes differences between the relative abundance of the PAs jacobine and erucifoline for each population were calculated. A paired t-test was used, if data passed normality test. If this was not the case a paired Wilcoxon test was employed. If relative abundance of erucifoline was significant higher that the relative abundance of jacobine, the population was determined as erucifoline type and vice versa. If the difference between the relative abundance of jacobine and erucifoline was not significant, the population was decided to be a mixed type population. For all tests for significances the program GraphPad Prism 7 (GraphPad Software Inc., La Jolla, USA) was applied.

4.3. Data evaluation Before data evaluation of AFLP-PCR results 15 individuals were excluded from the analyses due to incomplete genetic data. The genetic profiles of the remaining 75 individuals were scored using the program Genographer 2.1.4 (Benham et al., 1999) by analyzing fragments between 50 and 400 base pairs. Genetic differentiation between populations was assessed with AMOVA using GeneAlex in Microsoft Excel (2013 (Peakall and Smouse, 2006, 2012). Genetic similarity between samples was determined by including a principal coordinate Analyses (PCoA) and a Mantel test was performed to examine the relationship between genetic and geographic distance. The genetic diversity (Hj) of each population was calculated employing the Lynch and Milligan method in AFLPsurv (Laboratoire de Genetique et Ecologie Vegetale, Universite Bruxelles, Belgium). The number of bootstraps for genetic distances was 1000. For every population, mean PA content in mg/kg dry weight was calculated. PA content of every population was tested for significant differences against the averaged PA content of all populations applying

Acknowledgments We kindly thank the laboratory staff of the institutes of Systematic Botany and of Food Chemistry and Food Biotechnology of the Justus Liebig University Giessen and the laboratory staff of the German Federal Institute for Risk Assessment for their technical assistance. We are also grateful to Prof. Dr. Birgit Gemeinholzer for her scientific support. And finally we are very thankful to the collectors of the plants: Dr. Katrin Romahn, Dr. Hans-Ulrich Piontkowski, Regina HaaseZiesemer, Dr. Irene Timmermann-Trosiener, Cordelia Triebstein, HeinzUlrich Schimkat, Inke Rabe, Hauke Drews, Fritz Heydemann, PD Dr. Tobias Donath and Dr. Ulrich Mierwald.

Appendix Table 5

PA content of all PAs detected in all J. vulgaris. Exact mass of [M+H]+-adduct, Ret = retention time, average [mg/kg] = averaged content of PA of all individuals, SEM = standard error of mean. CycNO = cyclic diester N-oxide, cyc = cyclic diester, oto = otonecine, cyc_glc = PA glycoside, pla = platynecine. Nr.

PA

estertype

molecular formula

[M+H]+ [m/z]

Ret [min]

average [mg/kg]

SEM

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

Erucifoline-N-Oxide Senecionine-N-Oxide_Senecivernine-N-Oxide Jacobine-N-Oxide Jacobine Erucifoline Seneciphylline-N-Oxide Seneciphylline Retrorsine-N-Oxide Senecionine_Senecivernine Retrorsine cycNO_C18H25NO7_5.55 cycNO_C18H25NO7_5.89 oto_C19H27NO6_7.17 Riddelliine-N-Oxide oto_C19H27NO7_7.28 oto_C19H27NO7_6.33 oto_C19H27NO7_6.52 cyc_glc_C24H33NO11_4.25 cycNO_C18H25NO8_4.03 cyc_C18H25NO7_4.14 cyc_glc_C24H35NO10_6.89 oto_C19H27NO7_6.97 Riddelliine cycNO_C18H25NO8_4.62 cycNO_C18H27NO9_2.7 cyc_C18H27NO8_2.34 cyc_C18H27NO8_2.36 pla_C18H27NO6_7.55 cycNO_C20H25NO8_7.23 Senkirkine Platyphylline oto_C19H25NO8_6.6

cycNO cycNO cycNO cyc cyc cycNO cyc cycNO cyc cyc cycNO cycNO oto cycNO oto oto oto cyc_glc cycNO cyc cyc_glc oto cyc cycNO cycNO cyc cyc pla cycNO oto pla oto

C18H23NO7 C18H25NO6 C18H25NO7 C18H25NO6 C18H23NO6 C18H23NO6 C18H23NO5 C18H25NO7 C18H25NO5 C18H25NO6 C18H25NO7 C18H25NO7 C19H27NO6 C18H23NO7 C19H27NO7 C19H27NO7 C19H27NO7 C24H33NO11 C18H25NO8 C18H25NO7 C24H35NO10 C19H27NO7 C18H23NO6 C18H25NO8 C18H27NO9 C18H27NO8 C18H27NO8 C18H27NO6 C20H25NO8 C19H27NO6 C18H27NO5 C19H25NO8

366.15473 352.17546 368.17038 352.17546 350.15981 350.15981 334.1649 368.17038 336.18055 352.17546 368.17038 368.17038 366.19111 366.15473 382.18603 382.18603 382.18603 512.21264 384.16529 368.17038 498.23337 382.18603 350.15981 384.16529 402.17586 386.18094 386.18094 354.19111 408.16529 366.19111 338.1962 396.16529

4.88 7.47 5.17 5 4.42 6.79 6.55 6.44 7.26 6.29 5.55 5.89 7.17 5.74 7.28 6.33 6.52 4.25 4.03 4.14 6.89 6.97 5.58 4.62 2.7 2.34 2.36 7.55 7.23 7.96 7.16 6.6

168.43 141.87 111.82 90.50 83.03 82.33 64.05 54.34 48.91 19.43 19.39 19.31 6.26 5.99 5.98 5.91 5.83 5.64 5.49 5.15 5.10 4.73 4.48 3.52 2.49 2.30 2.30 1.67 1.62 1.60 1.36 1.32

6.93 5.77 5.10 4.10 3.90 4.44 3.36 1.84 2.45 0.71 0.90 0.90 0.33 0.53 0.34 0.76 0.76 0.39 0.27 0.18 0.37 0.30 0.22 0.16 0.10 0.07 0.07 0.04 0.41 0.29 0.02 0.01

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Table 5 (continued) Nr.

PA

estertype

molecular formula

[M+H]+ [m/z]

Ret [min]

average [mg/kg]

SEM

33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98

oto_C19H27NO7_5.51 oto_C19H29NO7_5.76 oto_C19H27NO9_6.09 oto_C19H25NO7_5.72 oto_C19H29NO9_3.95 oto_C19H29NO8_5.53 oto_C17H25NO6_3.9 cyc_glc_C24H33NO11_5.64 oto_C21H29NO7_9.05 pla_C18H27NO6_5.18 oto_C19H25NO6_7.42 pla_C18H27NO6_8.53 cycNO_C20H25NO7_8.59 cyc_C18H23NO7_3.69 cyc_C20H25NO6_8.43 cyc_C20H25NO7_6.98 oto_C21H27NO7_8.73 cyc_C18H25NO6_5.55 pla_C18H27NO5_9.09 oto_C19H25NO7_5.01 oto_C23H33NO11_6.59 pla_C18H29NO5_8.48 pla_C18H27NO5_7.56 pla_C18H29NO5_8.05 oto_C19H27NO8_5.19 cyc_C17H25NO6_4.19 cyc_C18H27NO5_7.75 cycNO_C23H33NO11_6.77 cyc_C20H27NO7_7.36 oto_C19H27NO7_5.23 cyc_glc_C24H35NO12_4.48 cyc_C18H27NO7_4.28 cycNO_C18H27NO6_7.95 cyc_C20H27NO6_8.82 pla_C18H29NO7_7.39 pla_C18H29NO7_5.82 oto_C19H25NO6_6.7 CycNO_C20H27NO8_5.51 cyc_C23H33NO10_6.67 pla_C19H31NO8_5.51 pla_C18H29NO6_6.09 oto_C22H27NO12_3.5 cyc_C18H24NO6Cl_4.67 oto_C21H29NO8_7.78 cyc_C20H27NO7_5.23 cyc_glc_C24H35NO13_4.75 cyc_C18H26NO7Cl_4.82 oto_C21H31NO9_6.37 oto_C21H29NO8_8.16 cyc_C18H25NO5_7.54 oto_C21H31NO9_7.66 cyc_C18H26NO6Cl_5.72 oto_C21H29NO8_7.28 cycNO_C21H31NO9_7.24 oto_C23H33NO11_6.94 oto_C21H29NO8_7.4 oto_C19H28NO8Cl_5.66 cycNO_C20H27NO7_8.9 oto_C19H28NO7Cl_6.76 cycNO_C18H26NO7Cl_5.15 cycNO_C20H27NO8_7.49 cyc_C18H26NO6Cl_4.91 cyc_C18H24NO7Cl_4.34 oto_C21H30NO8Cl_8.33 oto_C19H28NO8Cl_4.57 cycNO_C18H26NO7Cl_5.95

oto oto oto oto oto oto oto cyc_glc oto pla oto pla cycNO cyc cyc cyc oto cyc pla oto oto pla pla pla oto cyc cyc cycNO cyc oto cyc_glc cyc cycNO cyc pla pla oto cycNO cyc pla pla oto cyc oto cyc cyc_glc cyc oto oto cyc oto cyc oto cycNO oto oto oto cycNO oto cycNO cycNO cyc cyc oto oto cycNO

C19H27NO7 C19H29NO7 C19H27NO9 C19H25NO7 C19H29NO9 C19H29NO8 C17H25NO6 C24H33NO11 C21H29NO7 C18H27NO6 C19H25NO6 C18H27NO6 C20H25NO7 C18H23NO7 C20H25NO6 C18H27NO8 C21H27NO7 C18H25NO6 C18H27NO5 C19H25NO7 C23H33NO11 C18H29NO5 C18H27NO5 C18H29NO5 C19H27NO8 C17H25NO6 C18H27NO5 C23H33NO11 C18H27NO8 C19H27NO7 C24H35NO12 C18H27NO7 C18H27NO6 C20H27NO6 C18H29NO7 C18H29NO7 C19H25NO6 C20H27NO8 C23H33NO10 C19H31NO8 C18H29NO6 C22H27NO12 C18H24NO6Cl C21H29NO8 C20H27NO7 C24H35NO13 C18H24NO7Cl C21H31NO9 C21H29NO8 C18H25NO5 C21H31NO9 C18H27NO8 C21H29NO8 C21H31NO9 C23H33NO11 C21H29NO8 C19H28NO8Cl C20H27NO7 C19H28NO7Cl C18H26NO7Cl C20H27NO8 C18H26NO6Cl C18H24NO7Cl C21H30NO8Cl C19H28NO8Cl C18H26NO7Cl

382.18603 384.20168 414.17586 380.17038 416.19151 400.19659 340.17546 512.21264 408.20168 354.19111 364.17546 354.19111 392.17038 366.15473 376.17546 386.18094 406.18603 352.17546 338.1962 380.17038 500.21264 340.21185 338.1962 340.21185 398.18094 340.17546 338.1962 500.21264 386.18094 382.18603 530.2232 370.18603 354.19111 378.19111 372.20168 372.20168 364.17546 410.18094 484.21772 402.21224 356.20676 498.1606 386.13649 424.19659 394.18603 546.21812 402.13141 442.20716 424.19659 336.18055 442.20716 386.18094 424.19659 442.20716 500.21264 424.19659 434.15762 394.18603 418.16271 404.14706 410.18094 388.15214 402.13141 460.17327 434.15762 404.14706

5.51 5.76 6.09 5.72 3.95 5.53 3.9 5.64 9.05 5.18 7.42 8.53 8.59 3.69 8.43 2.36 8.73 5.55 9.09 5.01 6.59 8.48 7.56 8.05 5.19 4.19 7.75 6.77 2.36 5.23 4.48 4.28 7.95 8.82 7.39 5.82 6.7 5.51 6.67 5.51 6.09 3.5 4.67 7.78 5.23 4.75 4.34 6.37 8.16 7.54 7.66 2.36 7.28 7.24 6.94 7.4 5.66 8.9 6.76 5.15 7.49 4.91 4.34 8.33 4.57 5.95

1.32 1.31 1.31 1.31 1.29 1.28 1.26 1.26 1.24 1.21 1.20 1.16 1.15 1.14 1.14 1.13 1.13 1.12 1.11 1.11 1.10 1.09 1.09 1.09 1.09 1.06 1.05 1.02 1.01 1.01 0.98 0.91 0.85 0.85 0.84 0.83 0.82 0.82 0.78 0.69 0.68 0.63 0.51 0.50 0.47 0.46 0.45 0.44 0.42 0.35 0.30 0.20 0.19 0.17 0.17 0.10 0.06 0.06 0.05 0.05 0.05 0.04 0.04 0.02 0.01 0.01

0.02 0.01 0.04 0.02 0.01 0.08 0.02 0.01 0.02 0.01 0.15 0.01 0.03 0.01 0.02 0.13 0.02 0.03 0.01 0.03 0.03 0.02 0.02 0.03 0.02 0.02 0.02 0.03 0.02 0.03 0.02 0.03 0.04 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.04 0.21 0.03 0.03 0.03 0.06 0.03 0.03 0.03 0.03 0.02 0.02 0.03 0.02 0.02 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Justy, F., Machon, N., Noël, F., 2015. Multivariate analysis of polyploid data reveals the role of railways in the spread of the invasive South African Ragwort (Senecio inaequidens). Conserv. Genet. 16, 523–533. https://doi.org/10.1007/s10592-0140676-y. Close, D.C., McArthur, C., Hagerman, A.E., Fitzgerald, H., 2005. Differential distribution of leaf chemistry in eucalypt seedlings due to variation in whole-plant nutrient availability. Phytochemistry 66, 215–221. https://doi.org/10.1016/j.phytochem.

References Benham, J., Jeung, J., Jasieniuk, M., Kanazin, V., Blake, T., 1999. Genographer: a graphical tool for automated fluorescent AFLP and microsatellite analysis. J. Agric. Gen. 4, 399. Blanchet, É., Penone, C., Maurel, N., Billot, C., Rivallan, R., Risterucci, A.-M., Maurice, S.,

8

Phytochemistry 172 (2020) 112235

S. Jung, et al.

org/10.1007/s10682-009-9303-7. Macel, M., Vrieling, K., 2003. Pyrrolizidine alkaloids as oviposition stimulants for the cinnabar moth, Tyria jacobaeae. J. Chem. Ecol. 6, 1435–1446. https://doi.org/10. 1023/A:1024269621284. Macel, M., Vrieling, K., Klinkhamer, P.G.L., 2004. Variation in pyrrolizidine alkaloid patterns of Senecio jacobaea. Phytochemistry 65, 865–873. https://doi.org/10.1016/ j.phytochem.2004.02.009. Mandák, B., Zákravský, P., Kořínková, D., Dostál, P., Plačková, I., 2009. Low population differentiation and high genetic diversity in the invasive species Carduus acanthoides L. (Asteraceae) within its native range in the Czech Republic. Biol. J. Linn. Soc. Lond. 98, 596–607. https://doi.org/10.1111/j.1095-8312.2009.01304.x. Mattocks, A., 1968. Toxicity of pyrrolizidine alkaloids. Nature 217, 723–728. https://doi. org/10.1038/217723a0. Mulder, P., Sánchez, P., These, A., Preiss‐Weigert, A., Castellari, M., 2015. Occurrence of pyrrolizidine alkaloids in food. pp. 116 EFSA Supporting Publications 2015:EN-859. Mulder, P., López, P., Castellari, M., Bodi, D., Ronczka, S., 2018. Occurrence of pyrrolizidine alkaloids in animal- and plant-derived food: results of a survey across Europe. Food Addit. Contam. Part A Chem. Anal. Control Expo. Risk Assess. 1, 118–133. https://doi.org/10.2903/sp.efsa.2015.EN-859. Naumann, C., Hartmann, T., Ober, D., 2002. Evolutionary recruitment of a flavin-dependent monooxygenase for the detoxification of host plant-acquired pyrrolizidine alkaloids in the alkaloid-defended arctiid moth Tyria jacobaeae. Proc. Natl. Acad. Sci. U. S. A. 99, 6085–6090. https://doi.org/10.1073/pnas.082674499. Neumann, M.G., Cohen, L., Opris, M., Nanau, R.M., Hyunjin, J., 2015. Hepatotoxicity of pyrrolizidine alkaloids. J. Pharm. Pharm. Sci. 18, 825–843. https://doi.org/10. 18433/J3BG7J. Peakall, R., Smouse, P., 2012. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research- an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1093/bioinformatics/bts460. Peakall, R., Smouse, P., 2006. Genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295. https://doi.org/10. 1111/j.1471-8286.2005.01155.x. Pichersky, E., Gershenzon, J., 2002. The formation and function of plant volatiles: perfumes for pollinator attraction and defense. Curr. Opin. Plant Biol. 237–243. https:// doi.org/10.1016/S1369-5266(02)00251-0. Rader, R., Edwards, W., Westcott, D.A., Cunningham, S.A., Howlett, B.G., 2011. Pollen transport differs among bees and flies in a human-modified landscape. Divers. Distrib. 17, 519–529. https://doi.org/10.1111/j.1472-4642.2011.00757.x. Ruan, J., Yang, M., Fu, P., Ye, Y., Lin, G., 2014. Metabolic activation of pyrrolizidine alkaloids: insights into the structural and enzymatic basis. Chem. Res. Toxicol. 27, 1030–1039. https://doi.org/10.1021/tx500071q. Sonnewald, U., 2014. Stoffwechselphysiologie. In: Kadereit, J.W., Körner, C., Kost, B., Sonnewald, U. (Eds.), Strasburger − Lehrbuch der Pflanzenwissenschaften. Springer Berlin Heidelberg, pp. 337–446. These, A., Bodi, D., Ronczka, S., Lahrssen-Wiederholt, M., Preiss-Weigert, A., 2013. Structural screening by multiple reaction monitoring as a new approach for tandem mass spectrometry: presented for the determination of pyrrolizidine alkaloids in plants. Anal. Bioanal. Chem. 405, 9375–9383. https://doi.org/10.1007/s00216-0137365-4. Van der Meijden, E., van Zoelen, A.M., Soldaat, L.L., 1989. Oviposition by the cinnabar moth, Tyria jacobaeae, in relation to nitrogen, sugars and alkaloids of ragwort, Senecio jacobaea. Oikos 54, 337–344. https://doi.org/10.2307/3565295. Vanparys, V., Meerts, P., Jacquemart, A.-L., 2008. Plant–pollinator interactions: comparison between an invasive and a native congeneric species. Acta Oecol. 34, 361–369. https://doi.org/10.1016/j.actao.2008.06.008. Vos, P., Hogers, R., Bleeker, M., Reijans, M., van de Lee, T., Hornes, M., Friters, A., Pot, J., Paleman, J., Kuiper, M., Zabeau, M., 1995. AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res. 23, 4407–4414. https://doi.org/10.1093/nar/23.21. 4407. Vrieling, K., Boer, N. de, 1999. Host-plant choice and larval growth in the cinnabar math: do pyrrolizidine alkaloids play a role? Entomol. Exp. Appl. 251–257. https://doi.org/ 10.1046/j.1570-7458.1999.00491.x. Vrieling, K., Wijk, C., 1994. Estimating costs and benefits of the pyrrolizidine alkaloids of Senecio jacobaea under natural conditions. Oikos 449–454. https://doi.org/10.1007/ 978-94-011-1654-1_23. Witte, L., Ernst, L., Adam, H., Hartmann, T., 1992. Chemotypes of the two pyrrolizidine alkaloid-containing Senecio species. Phytochemistry 559–565. https://doi.org/10. 1016/0031-9422(92)90038-R.

2004.11.004. Coley, P.D., Bryant, J.P., Chapin, F.S., 1985. Resource availability and plant antiherbivore defense. Science (New York, N.Y.) 230, 895–899. https://doi.org/10.1126/science. 230.4728.895. Crews, C., Driffield, M., Berthiller, F., Krska, R., 2009. Loss of pyrrolizidine alkaloids on decomposition of ragwort (Senecio jacobaea) as measured by LC-TOF-MS. J. Agric. Food Chem. 57, 3669–3673. https://doi.org/10.1021/jf900226c. Desrochers, A.M., Bain, J.F., Warwick, S.I., 1988. The biology OF Canadian weeds: 89. Carduus nutans L. and Carduus acanthoides L. Can. J. Plant Sci. 68, 1053–1068. https://doi.org/10.4141/cjps88-126. Doorduin, L.J., van den Hof, K., Vrieling, K., Joshi, J., 2010. The lack of genetic bottleneck in invasive Tansy ragwort populations suggests multiple source populations. Basic Appl. Ecol. 11, 244–250. https://doi.org/10.1016/j.baae.2009.12.007. Edgar, J.A., Colegate, S.M., Boppré, M., Molyneux, R.J., 2011. Pyrrolizidine alkaloids in food: a spectrum of potential health consequences. Food Addit. Contam. Part A Chem. Anal. Control Expo Risk 28, 308–324. https://doi.org/10.1080/19440049.2010. 547520. Ehlers, B.K., 1999. Variation in fruit set within and among natural populations of the selfincompatible herb Centaurea scabiosa (Asteraceae). Nord. J. Bot., Le 19, 653–663. https://doi.org/10.1111/j.1756-1051.1999.tb00675.x. Frankham, R., 1996. Relationship of genetic variation to population size in wildlife. Conserv. Biol. 10, 1500–1508. https://doi.org/10.1046/j.1523-1739.1996. 10061500.x. Griese, D., 1996. Zur Ausbreitung von Senecio inaequidens DC. an Autobahnen in Norddeutschland. Braunschw. Natkd. Schr. 5, 193–204. Harper, J.L., Wood, W.A., 1957. Biological flora of the British isles.: Senecio jacobaea L. J. Ecol. 617–637. https://doi.org/10.2307/2256946. Hartmann, T., Witte, L., 1995. Chemistry, biology and chemoecology of the pyrrolizidine alkaloids. In: ninth ed. In: Pelletier, S.W. (Ed.), Alkaloids: Chemical and Biological Perspesctives, vol. 9. Elsevier, pp. 155–233. Joosten, L., Cheng, D., Mulder, P.P.J., Vrieling, K., van Veen, J.A., Klinkhamer, P.G.L., 2011. The genotype dependent presence of pyrrolizidine alkaloids as tertiary amine in Jacobaea vulgaris. Phytochemistry 72, 214–222. https://doi.org/10.1016/j. phytochem.2010.11.013. Joosten, L., Mulder, P.P.J., Klinkhamer, P.G.L., van Veen, J.A., 2009. Soil-borne microorganisms and soil-type affect pyrrolizidine alkaloids in Jacobaea vulgaris. Plant Soil 325, 133–143. https://doi.org/10.1007/s11104-009-9963-7. Jung, S., Henniges, M., Mutz, S., Kibar, G., Gemeinholzer, B., Wissemann, V., 2017. Senecoi jacobaea L. (Jacobaea vulgaris Gaertn.) Artinformation und vorläufige Ergebnisse zur genetischen Variabilität in Schleswig-Holstein. Kiel. Not. Pflanzenkd. 37–58. Kirk, H., Vrieling, K., van der Meijden, E., Klinkhamer, P.G.L., 2010. Species by environment interactions affect pyrrolizidine alkaloid expression in Senecio jacobaea, Senecio aquaticus, and their hybrids. J. Chem. Ecol. 36, 378–387. https://doi.org/10. 1007/s10886-010-9772-8. Leimu, R., Mutikainen, P.I., Koricheva, J., Fischer, M., 2006. How general are positive relationships between plant population size, fitness and genetic variation? J. Ecol. 94, 942–952. https://doi.org/10.1111/j.1365-2745.2006.01150.x. Lindigkeit, R., Biller, A., Buch, M., Schiebel, H.-M., Boppre, M., Hartmann, T., 1997. The two faces of pyrrolizidine alkaloids: the role of the tertiary amine and its N-oxide in chemical defense of insects with acquired plant alkaloids. Eur. J. Biochem. 245, 626–636. https://doi.org/10.1111/j.1432-1033.1997.00626.x. Liu, X., Klinkhamer, P.G.L., Vrieling, K., 2017. The effect of structurally related metabolites on insect herbivores: a case study on pyrrolizidine alkaloids and western flower thrips. Phytochemistry 138, 93–103. https://doi.org/10.1016/j.phytochem. 2017.02.027. Macel, M., 2011. Attract and deter: a dual role for pyrrolizidine alkaloids in plant-insect interactions. Phytochem. Rev. 10, 75–82. https://doi.org/10.1007/s11101-0109181-1. Macel, M., Bruinsma, M., Dijkstra, S.M., Ooijendijk, T., Niemeyer, H.M., Klinkhamer, P.G.L., 2005. Differences in effects of pyrrolizidine alkaloids on five generalist insect herbivore species. J. Chem. Ecol. 31, 1493–1508. https://doi.org/10.1007/s10886005-5793-0. Macel, M., Klinkhamer, P.G., Vrieling, K., van der Meijden, E., 2002. Diversity of pyrrolizidine alkaloids in Senecio species does not affect the specialist herbivore Tyria jacobaeae. Oecologia 133, 541–550. https://doi.org/10.1007/s00442-002-1074-6. Macel, M., Klinkhamer, P.G.L., 2010. Chemotype of Senecio jacobaea affects damage by pathogens and insect herbivores in the field. Evol. Ecol. 24, 237–250. https://doi.

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