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Phytochemistry
Published as: Phytochemistry. 2011 August ; 72(11-12): 1379–1389.
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Metabolic fingerprinting of Leontopodium species (Asteraceae)
by means of 1H NMR and HPLC–ESI-MS
Stefan Safera, Serhat S. Ciceka, Valerio Pieria, Stefan Schwaigera, Peter Schneidera, Volker
Wissemannb, and Hermann Stuppnera,⁎
aInstitute of Pharmacy/Pharmacognosy, Faculty of Chemistry and Pharmacy, Center for
Molecular Biosciences Innsbruck (CMBI), University of Innsbruck, Innrain 52c, A-6020 Innsbruck,
Austria
bInstitute
of Botany, Systematic Botany Group, Justus-Liebig-University Gießen, Heinrich-BuffRing 38, D-35392 Gießen, Germany
Abstract
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Graphical abstract—1H NMR and LC–MS metabolic fingerprinting of 11 different
Leontopodium species gained insights on metabolic patterns and revealed information on
taxonomic relationships between some closely related species.
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Highlights—► We investigated different Leontopodium species by comparing their metabolic
profiles. ► PCA of 1H NMR data revealed two species groups, PCA of LC-MS data three groups.
► Discriminators could be identified as ent-kaurenoic acids and bisabolane derivatives. ►
Intraspecific metabolic differences could be observed using a PLS-DA. ► The used techniques
helped to assign species to mutual groups.
Abstract
The genus Leontopodium, mainly distributed in Central and Eastern Asia, consists of ca. 34–58
different species. The European Leontopodium alpinum, commonly known as Edelweiss, has a
long tradition in folk medicine. Recent research has resulted in the identification of prior unknown
secondary metabolites, some of them with interesting biological activities. Despite this, nearly
© 2011 Elsevier Ltd.
⁎
Corresponding author. Address: Leopold-Franzens-Universität Innsbruck, Innrain 52c, Josef-Moeller-Haus, A-6020 Innsbruck,
Austria. Tel.: +43 512 507 5300; fax: +43 512 507 2939. Hermann.Stuppner@uibk.ac.at.
This document was posted here by permission of the publisher. At the time of deposit, it included all changes made during peer
review, copyediting, and publishing. The U.S. National Library of Medicine is responsible for all links within the document and for
incorporating any publisher-supplied amendments or retractions issued subsequently. The published journal article, guaranteed to be
such by Elsevier, is available for free, on ScienceDirect.
Safer et al.
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nothing is known about the Asian species of the genus. In this study, we applied proton nuclear
magnetic resonance (1H NMR) spectroscopy and liquid chromatography–mass spectrometry (LC–
MS) metabolic fingerprinting to reveal insights into the metabolic patterns of 11 different
Leontopodium species, and to conclude on their taxonomic relationship. Principal component
analysis (PCA) of 1H NMR fingerprints revealed two species groups. Discriminators for these
groups were identified as fatty acids and sucrose for group A, and ent-kaurenoic acid and
derivatives thereof for group B. Five diterpenes together with one sesquiterpene were isolated
from Leontopodium franchetii roots; the compounds were described for the first time for L.
franchetii: ent-kaur-16-en-19-oic acid, methyl-15α-angeloyloxy-ent-kaur-16-en-19-oate, methylent-kaur-16-en-19-oate, 8-acetoxymodhephene, 19-acetoxy-ent-kaur-16-ene, methyl-15β–
angeloyloxy-16,17-epoxy-ent-kauran-19-oate. In addition, differences in the metabolic profile
between collected and cultivated species could be observed using a partial least squaresdiscriminant analysis (PLS-DA). PCA of the LC–MS fingerprints revealed three groups.
Discriminating signals were compared to literature data and identified as two bisabolane
derivatives responsible for discrimination of group A and C, and one ent-kaurenoic acid
derivative, discriminating group B. A taxonomic relationship between a previously unidentified
species and L. franchetii and Leontopodium sinense could be determined by comparing NMR
fingerprints. This finding supports recent molecular data. Furthermore, Leontopodium dedekensii
and L. sinense, two closely related species in terms of morphology and DNA-fingerprints, could
be distinguished clearly using 1H NMR and LC–MS metabolic fingerprinting.
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Keywords
Leontopodium; Asteraceae; Metabolic fingerprinting; 1H NMR; LC–MS; Chemotaxonomy
1 Introduction
The genus Leontopodium R.Br. ex Cassini comprises between 34 (Dickoré, unpublished), 41
(Handel-Mazzetti, 1927), and 58 (Wu et al., 1994) different species. The main distribution
of the genus is in Central and Eastern Asia. The centre of diversity is south-western China,
where 15 to 18 different species can be found. Two species also occur in Europe: The
widespread Leontopodium alpinum Cass., and its endemic sister species, Leontopodium
nivale (Ten.) Huet. ex Hand.-Mazz., which has a disjunct distribution in the Central
Apennines in Italy and the Pirin Mountains in Bulgaria. For people living in the European
Alps, especially L. alpinum, which is known as the Alpine Edelweiss, is a very important
part of their cultural heritage.
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The Alpine Edelweiss (L. alpinum Cass. or L. nivale subsp. alpinum (Cass.) Greuter) has a
long tradition in folk medicine. References from the year 1582 already mentioned the use of
Edelweiss for the treatment of diarrhoea and dysentery (Tabernaemontanus, 1582). Several
other applications in traditional medicine for extracts and plant parts of Edelweiss were
described throughout the years. Recent phytochemical research on L. alpinum has resulted in
the detection of almost 50 different, partly uncommon secondary metabolites, including
sesquiterpenes (Dobner et al., 2003a; Gray et al., 2000; Schwaiger et al., 2004; Stuppner et
al., 2002), diterpenes (e.g., Schwaiger et al., 2004), lignanes (Dobner et al., 2003a;
Schwaiger et al., 2004), benzofurans (e.g., Dobner et al., 2003a), and phenolic compounds,
such as the novel described leontopodic acids (Schwaiger et al., 2005). Some of these
compounds are highly bioactive, which was demonstrated in several different
pharmacological models. Hence, antibacterial (Dobner et al., 2003b), antioxidative and
DNA-protecting (Schwaiger et al., 2005), and anti-inflammatory (Schwaiger et al., 2004)
properties were observed, as well as an enhancement of cholinergic transmission in the brain
(Hornick et al., 2008) and an inhibition of intimal hyperplasia of venous bypass grafts
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(Reisinger et al., 2009). Despite these results, nearly nothing is known about bioactive
compounds in other Leontopodium species. Until now, only a few phytochemical and
pharmacological investigations have been conducted on these species (e.g., Leontopodium
longifolium Ling: Li et al., 2006; Leontopodium leontopodioides Beauverd: Li et al., 1994;
Leontopodium andersonii C.B. Clarke: Schwaiger et al., 2010; Leontopodium nanum Hand.Mazz.: Wang et al., 2002), although many species were used in Traditional Asian Medicine,
e.g., in Tibet (Kletter and Kriechbaum, 2001).
Whereas the metabolome is clearly defined as the ‘complete complement of small molecules
present in an organism’ (Hall, 2006), there are different approaches to detect and investigate
the metabolome. Throughout the years, various terms were defined, such as metabonomics,
metabolomics, metabolic profiling and metabolic fingerprinting. A metabolic fingerprinting
approach is defined as a ‘high-throughput qualitative screening of an organism or tissue with
the primary aim of sample comparison and discrimination analysis’ (Hall, 2006).
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Commonly used techniques for metabolic fingerprinting are LC–MS (liquid
chromatography–mass spectrometry) and 1H NMR (proton nuclear magnetic resonance)
spectroscopy. LC–MS as a fingerprinting technique was applied successfully in various
fields of plant research, such as chemotaxonomy (Urbain et al., 2009), plant biochemistry
(Kim and Park, 2009), food chemistry (Pongsuwan et al., 2008), and for the quality control
of medicinal plants (e.g., Tianniam et al., 2008). The main advantage of mass spectrometry
is its high sensitivity, which allows the detection of low molecular weight compounds at
concentrations below the nanogram per millilitre range if optimal MS conditions can be
provided (Seger and Sturm, 2007).
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On the other hand, 1H NMR spectroscopy in combination with multivariate statistics has
become a frequently used technique for metabolic fingerprinting. NMR spectroscopy has a
long history in the qualitative and quantitative assessment of secondary plant metabolites
(Holmes et al., 2006). 1H NMR spectroscopy is also commonly applied for quality control in
food science and technology (e.g., Ali et al., 2009; Belton et al., 1998; Lachenmeier et al.,
2005). NMR techniques are reproducible with rich structure information. The only essential
requirement for compound detection in 1H NMR experiments is the availability of
observable protons in a molecule, thus resulting in the applicability of 1H NMR to a wide
range of plant metabolites. In this regard, 1H NMR enables the detection of constituents that
could otherwise not be detected in LC–MS experiments, e.g., as in case of insufficient
ionisation. Another major advantage compared to other analytical techniques is the
matchless reproducibility. In contrast to NMR-based analysis, day to day variations are often
a problem for LC–MS-based systems. Nevertheless, one of the great disadvantages of NMR
spectroscopy is its relatively low sensitivity compared to modern mass spectrometry
instrumentations (Holmes et al., 2006). Low concentration compounds may not be
detectable with NMR. In addition, signal overlapping is often a problem if more than one
compound is present in an NMR sample, e.g., when analysing plant extracts.
In our study, we used both 1H NMR spectroscopy and LC–MS in combination with
multivariate statistics as an approach to detect metabolic fingerprints of species within the
genus Leontopodium. We investigated roots of 11 different species, which were collected in
the field, and the roots of 12 different cultivated species (Table 1). The main aim of this
study was to reveal information about similarities and differences between the species of the
genus Leontopodium by comparing their metabolic fingerprints, and to conclude on their
relationship to each other.
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2 Results and discussion
2.1
1H
NMR spectroscopy
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2.1.1 Extraction of plant material and acquisition of NMR spectra—Powdered
plant material was extracted directly with DMSO-d6. This is adequate for qualitative
purposes and simplifies the extraction process. DMSO was the most appropriate solvent for
our samples; both apolar and polar compounds were extracted, resulting in a broad range of
metabolites. The extraction method used was simple and convenient, requiring just a small
amount of plant material suspended in the solvent and extracted on a flat-bed shaker for
24 h. After centrifugation, an aliquot of the supernatant was analysed directly by 1H NMR
spectroscopy. Due the use of an auto sampler, NMR experiments were also accomplished
overnight, which was additionally time-saving. Three sample replicates (and accordingly
only two sample replicates for Leontopodium himalayanum due to a lack of plant material)
were used to test the precision of the method.
2.1.2 Multivariate statistical analysis and pattern recognition—The spectra were
imported into AMIX and pre-processed using the bucketing function. By generating a
number of integrated regions for each dataset, complexity of the NMR spectra was reduced.
Here, we found a bucket width of δ 0.04 suitable for our data. A table of bucket-integrated
spectra was exported as a spreadsheet.
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Principal component analysis (PCA) was performed in SIMCA-P. PCA is an unsupervised
method and was used to reduce the dataset in order to obtain the maximum variation
between the samples. Mean-centering was chosen for scaling. The method focuses on the
fluctuating part of the data, and leaves only the relevant variation (i.e., the variation between
the samples) for analysis (van den Berg et al., 2006). A ten-component model was calculated
and explained 98.1% of the variation, with the first two components explaining 84.2%.
Principal component (PC) 1 was the dominant factor for classification of the groups,
whereas PCs 3–10 did not influence the results. Intragroup clustering for each group
highlights the good method precision (Fig. 1A). Discriminating NMR signals are presented
in the loadings plot (Fig. 1B), typical 1H NMR spectra of the different species are displayed
in Fig. 2A.
Two groups can be identified. The main group A consists of eight species with a similar
metabolic pattern. Signals at δ 1.26 and δ 1.22 were compared to literature data and assigned
to the lipid region, corresponding to the major signal of fatty acids (Rasmussen et al., 2006).
These signals are responsible for the discrimination of Leontopodium cf. stracheyi.
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Due to comparison with 1D (1H NMR) and 2D (HSQC) NMR spectra of sucrose, signals at
δ 3.82, δ 3.78, δ 3.66, and δ 3.62 could be assigned to sucrose, the signal at δ 5.18
(J = 3.7 Hz) to its anomer. Species like Leontopodium artemisiifolium and Leontopodium
calocephalum are discriminated by these signals. Furthermore, intraspecific variations in
terms of primary metabolites (i.e., sucrose) can be found for species with more than one
population included (e.g., L. andersonii, Leontopodium dedekensii, Leontopodium souliei).
Metabolic discrepancies between populations are responsible for these variations; as a
consequence, different populations belonging to the same species are misaligned in PCA.
This variation may be a result of environmental influences, and highlights that the
metabolite pattern (i.e., mostly primary metabolites) is strongly affected by ecological
factors.
The metabolic fingerprint of L. dedekensii is most similar to NMR spectra of species
belonging to group B (see below and Fig. 2B), even though the signals in the aliphatic
region are missing. Furthermore, the characteristic signals at δ 8.30, δ 7.90, and δ 7.00 could
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be assigned to an already described benzofuran (1-{(2R∗,3S∗)-3-(β-Dglucopyranosyloxy)-2,3-dihydro-2-[1-(hydroxymethyl)vinyl]-1-benzofuran-5-yl}ethanone;
Dobner et al., 2003a; see online supplementary data, Fig. S1). These signals were also
identified for other Leontopodium species (L. artemisiifolium and L. cf. stracheyi).
Nevertheless, the corresponding benzofuran is irrelevant for discrimination of those species
(Fig. 1B).
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Group B comprises L. franchetii, L. sinense, and an unidentified species, L. sp. The
discriminating signals for this group can be found mostly in the aliphatic region (δ 2.50–
0.50), other important signals have a higher chemical shift (i.e., δ 4.78 and δ 4.70).
Comparison of the metabolic fingerprints of these species (Fig. 2B) shows that the spectrum
of L. sp. is a mixture of the spectra of two other species, L. sinense and L. franchetii.
Whereas the signals at δ 4.78 and δ 4.70 can be found in L. franchetii and L. sp., these
resonances are missing in L. sinense. On the other hand, the characteristic benzofuran
signals at δ 8.30, δ 7.90 and δ 7.00 (Dobner et al., 2003a), are present in the spectra of L.
sinense and L. sp., and nonexistent in the spectrum of L. franchetii. The unidentified species
was first considered to be L. dedekensii or L. sinense, because several morphological
characters are congruent. Recent phylogenetic research based on DNA-fingerprinting
(AFLP; Amplified Fragment Length Polymorphism) has shown that the unidentified species
is closely related and a sister species to L. dedekensii and L. sinense. Due to similar
morphology, also hybridisation between L. dedekensii and L. sp. has been probably partly
responsible for the classification obtained with AFLP (Safer et al., 2011). Our results show,
that L. sp. might be closely related to L. franchetii as well. Shared morphological features
can be found in both species, but results from AFLP were in this case not clear enough to
tell if there is a close phylogenetic relationship (Safer et al., 2011). Nevertheless, due to
similarities in morphology, genetic and metabolic profile, L. dedekensii, L. franchetii, L.
sinense and L. sp. can be definitely assigned to a mutual group. In addition, hybridisation
between L. sinense and L. franchetii could also be a possible explanation for the similarities
in the metabolic fingerprints of the three species within group B.
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2.1.3 Isolation and identification of discriminating compounds from L.
franchetii—Discriminating compounds for group B were isolated from the roots of L.
franchetii using standard procedures (i.e., silica gel column chromatography, Sephadex
column chromatography, preparative HPLC, etc.) as described in section 4.5. Structures of
the pure compounds were elucidated using 1D (1H, 13C) and 2D (HSQC, HMBC, COSY)
NMR spectra, and identified by comparison with NMR literature data (all substances have
already been described: Bohlmann et al., 1980a,b; Bohlmann and Zdero, 1977; Bohlmann et
al., 1981; Gray et al., 2000; Hasan et al., 1982; Ohno et al., 1979). Five diterpenes and one
sesquiterpene (Fig. 3) were isolated and described the first time for L. franchetii: entkaur-16-en-19-oic acid (1), methyl-15α-angeloyloxy-ent-kaur-16-en-19-oate, (2), methylent-kaur-16-en-19-oate (3), 8-acetoxymodhephene (4), 19-acetoxy-ent-kaur-16-ene (5),
methyl-15β-angeloyloxy-16,17-epoxy-ent-kauran-19-oate (6).
The main compound in L. franchetii roots is compound 1 (ent-kaurenoic acid). To determine
the influence of this compound on the discrimination of the samples, 1D and 2D NMR
spectra of L. franchetii were compared with 1D and 2D NMR spectra of ent-kaurenoic acid
(see online supplementary data, Fig. S2). The signals responsible for discrimination of L.
franchetii (Fig. 1B) can be assigned to 1H resonances of ent-kaurenoic acid. Signals in the
spectral region δ 1.90–0.90 are part of the basic structure of ent-kaurenoic acid, and the
signals at δ 4.78 and δ 4.70 can be assigned to the exocyclic double bond at position C-16.
Similar signals can also be found in the 1D NMR spectra of other diterpenes isolated in this
study, indicating that ent-kaurenoic acid and its derivatives are the discriminators for L.
franchetii and the other two species of group B.
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2.1.4 Cultivated samples vs. collected samples—As observed in the PCA of the
NMR fingerprints, intraspecific variations depending on ecological factors can be found
within the genus Leontopodium. Therefore, 1H NMR spectra of collected species were
compared to spectra of cultivated species using a partial least squares-discriminant analysis
(PLS-DA). Contrary to PCA, which is an unsupervised method and can be applied without
prior knowledge about samples, PLS-DA is a supervised method and uses information about
the samples to maximise the differences between two or more a priori defined classes
(Holmes et al., 2006).
Here, the samples were divided into two classes: class 1 represented the plants collected in
China, whereas class 2 comprised all cultivated samples. A ten component model was
calculated and explained 95.5% of the variation, with the first three PLS components
explaining 79.5%. The result of the PLS-DA is displayed in a 3D-scores plot (Fig. 4). The
scores plot showed a clear differentiation between the two classes, although some species
(e.g., L. calocephalum, L. dedekensii, L. sinense, L. souliei; see Table 1) were present as
both collected and cultivated samples.
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These findings suggest a correlation between metabolic patterns and ecological factors.
Cultivated species were grown in the Botanical Garden of Giessen (Germany) and therefore
not exposed to climatic conditions which can be found in the natural habitat (QinghaiTibetan plateau, QTP, south-western China), such as high UV radiation, high precipitation
and similar. Environmental and climatic stress may cause changes in the production of
primary and secondary metabolites. Furthermore, higher average temperatures in Giessen,
Germany (compared to the QTP in China) could lead to increased plant growth and
biochemical activity of the plants. Moreover, the plot clearly shows a tighter clustering
within the cultivated sample group. These plants were grown under the exact same
conditions and their metabolic profile is similar. Plants belonging to the class of collected
species occupied different habitats and were exposed to unequal environmental conditions.
As a consequence, the scattering within this group is more distinctive.
2.2
LC–MS
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2.2.1 Acquisition of mass spectra—Extracts prepared for NMR fingerprinting (see
Section 2.1.1) were also used for LC–MS analysis. Due to the broad and complex metabolite
spectrum obtained by DMSO extraction, the LC method had to be rather long to enable
separation of a large number of compounds (65 min). Both positive and negative ionisation
modes were tested for mass detection. The positive mode was chosen for further analyses
since ionisation in negative mode was not satisfying. LC–MS analyses were only performed
for collected species. Triplicates of 22 different samples belonging to 11 species resulted in
a total analyses time of nearly 5 days (for L. himalayanum only duplicates were analysed
due to a lack of plant material).
2.2.2 Multivariate statistical analysis and pattern recognition—For analysis of
the acquired dataset with multivariate methods, LC–MS chromatograms were pre-processed
using MZmine to compensate for variations in retention time and m/z value between the
chromatographic runs. The pre-processed chromatograms were exported as a peak list table,
with rows representing the individual samples, and columns representing the integrated and
normalised peak areas.
A ten-component model was calculated and explained 93.9% of the variance, with the first
two components explaining 46.7%. The remaining components contributed as follows: PC 3
(11.9%), PC 4 (10.3%), PC 5 (7.3%), PC 6 (5.3%), PC 7 (4.3%), PC 8 (3.6%), PC 9 (2.4%),
and PC 10 (2.0%). Using PC 1 and PC 2, the species were found to be clustered into three
groups (Fig. 5A). Intragroup clustering for each group indicates the good method precision.
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Discriminating m/z values are displayed in a loadings plot (Fig. 5B); typical total ion
chromatograms (TICs) of the investigated species are presented in Fig. 6.
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Group A consists of four species. Species of this group are morphologically (Dickoré,
unpublished) and genetically diverse (Safer et al., 2011), ranging from tall woody herbs like
L. artemisiifolium to small shrubs like L. himalayanum. Discriminating compounds for
group A are defined by m/z values of 501.1, 477.1, and 459.0. By comparison of retention
times and mass spectra with literature data, the m/z value of 501.1 was assigned as [M+Na]+
to an already described bisabolane derivative (Fig. 3, compound 7: 3-methyl-1-{2-[(1R∗,
2R∗,5R∗,6S∗)-2,5,6-tris(acetyloxy)-4-methylcyclohex-3-en-1-yl]propyl}but-2-enyl (2Z)-2methylbut-2-enoate; i.e., an isomeric mixture;) with a calculated mass of 478 (Stuppner et
al., 2002). The compound was detected as a dominant double peak at a retention time of
42 min (see TICs of L. himalayanum and L. artemisiifolium, Fig. 6; an extracted ion
chromatogram (EIC) of the corresponding m/z value is provided as online supplementary
data, Fig. S3). Unfortunately, m/z values of 477.1 and 459.0 could not be identified.
Comparison with literature data suggested that the m/z value of 459 corresponds to a
bisabolane derivative isolated from L. longifolium (= L. souliei; Li et al., 2006).
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Leontopodium sinense and L. sp. are forming a clearly differentiated group B (mainly
discriminated with PC 1). These species were already described as closely related (Safer et
al., 2011), and this finding can be confirmed with both NMR and LC–MS fingerprinting.
Unlike NMR spectra, TICs do not exhibit many differences between the two species. The
group is discriminated by m/z values of 469.2, 329.3 and 311.4, which correspond to an entkaurenoic acid derivative described for L. alpinum (Fig. 3, compound 8: methyl-ent-7α,9αdihydroxy-15β-[(2Z)-2-methyl-but-2-enoyloxy]kaur-16-en-19-oate, calculated mass 446;
Schwaiger et al., 2004). Again, identification was done by comparing retention times and
mass spectra: 469.2 [M+Na]+; 329.3 [M−C5H7O2−H2O]+; 311.4 [M−C5H7O2−2H2O]+.
Furthermore, the m/z value of 915.1 could be assigned to [2 M+Na]+ (EICs of corresponding
m/z values are provided as online supplementary data, Fig. S4).
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Regarding the detection of ent-kaurenoic acid derviatives, the two approaches revealed
different patterns. NMR metabolic fingerprinting resulted in a grouping of L. franchetii, L.
sinense and L. sp (group B), and ent-kaurenoic acid (compound 1) and its derivatives
(compounds 2, 3 and 6) could be determined as discriminating compounds. In LC–MS
analysis, L. franchetii was not included within group B, and a previously isolated entkaurenoic acid derivative (compound 8; methyl-ent-7α,9α-dihydroxy-15β-[(2Z)-2-methylbut-2-enoyloxy]kaur-16-en-19-oate, calculated mass 446; Schwaiger et al., 2004) was
identified as discriminator. LC–MS peaks for compound 1 could not be identified for any of
the three species, suggesting that ionisation of compound 1 is limited. On the other hand,
LC–MS peaks for compound 8 with m/z 469, 329, and 311 (Schwaiger et al., 2004) could
only be recognised for L. sinense and L. sp. This determines compound 8 with molecular
weight 446 as main discriminator for the two species but not for L. franchetii.
Group C consists of L. andersonii, L. caespitosum, L. dedekensii, L. franchetii and L. cf.
stracheyi. L. franchetii, which occupied a conspicuous position within group B in NMR
fingerprinting, is classified within group C in LC–MS analysis. In terms of LC–MS, the
main compound of L. franchetii, ent-kaurenoic acid, does not have any influence on the
discrimination of this species (see above). L. andersonii is morphologically distinct and
occupies a genetically unique position within the genus (Safer et al., 2011). NMR analysis
did not reveal characteristic metabolic fingerprints for the species, placing L. andersonii
within the large group A. In contrast, results of LC–MS fingerprinting showed a different
pattern. Taking PC 3 (not shown) into account, the discrimination of L. andersonii was
explicit. The species is discriminated by an m/z value of 457. Recent phytochemical
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investigations of L. andersonii discovered a novel bisabolone derivative (Fig. 3, compound
9; (1R∗,5S∗,6S∗)-5-(acetyloxy)-6-[3-(acetyloxy)-1,5-dimethylhex-4-enyl]-3methylcyclohex-2-en-4-on-1-yl (2Z)-2-methyl-but-2-enoate, calculated mass 434;
Schwaiger et al., 2010), which was not described for other Leontopodium species yet.
Hence, the m/z value of 457 could be identified as [M+Na]+, and determines the bisabolone
as the discriminating compound for L. andersonii (an EIC of the corresponding m/z value is
provided as online supplementary data, Fig. S5).
3 Conclusions
We found both 1H NMR spectroscopy and LC–MS useful for metabolic fingerprinting of
species of the genus Leontopodium. The combination of the two methods offered valuable
insights about metabolic patterns of the different species. Whereas with NMR the total
metabolic status could be recorded including primary and secondary metabolites, LC–MS
fingerprinting exhibited details on specific secondary metabolites.
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In NMR fingerprinting, the major compounds responsible for discrimination were identified
as fatty acids, sucrose, and ent-kaurenoic acid and derivatives thereof. Ent-kaurenoic acid
was identified as the main compound of L. franchetii. Altogether, five diterpenes and one
sesquiterpene were isolated and described for the first time for L. franchetii. Furthermore,
PLS-DA analysis between collected and cultivated species highlighted the influence of
environmental and ecological factors on the production of metabolites as a result of
modified biochemical activity.
With LC–MS fingerprinting, several discriminating compounds could be identified for the
different groups, including two bisabolane derivatives and one ent-kaurenoic acid derivative.
Since LC–MS did not offer much information on chemical structures of the compounds,
comparison of the recorded mass spectra and retention times with literature data revealed
attribution of the signals to the corresponding compounds. Furthermore, information about
secondary metabolites of species not investigated yet could be obtained by checking their
group assignment within PCA.
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In addition, new insights concerning species relationships within the genus could be
acquired with both fingerprinting approaches. The unidentified species, L. sp., which was
considered to be closely related to L. sinense in molecular analysis, showed similarities in
NMR fingerprints with L. franchetii as well. This indicates possible hybridisation events
between L. sinense and L. franchetii. L. sinense and L. dedekensii are closely related to each
other, and therefore often wrongly identified because they share several morphological
characters. This close relationship was confirmed in a recent study (Safer et al., 2011)
dealing with DNA-fingerprinting of Leontopodium species. Our results exhibited clear
differences in the metabolic pattern of those two species, classifying L. sinense and L.
dedekensii unambiguous into two groups. Where identification with morphological and
molecular methods may be difficult, NMR and LC–MS fingerprinting approaches could
offer additional information on species relationship and facilitate classification of the
species.
4 Experimental
4.1
General experimental procedures
1D NMR spectra for metabolic fingerprinting were acquired on a Bruker Avance II
spectrometer (Bruker BioSpin, Rheinstetten, Germany) equipped with an automated sample
exchanger at a temperature of 300 K, operating at 600 MHz. 1D and 2D NMR spectra for
structure elucidation were acquired on a Bruker DRX 300 spectrometer (Bruker BioSpin,
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Rheinstetten, Germany), operating at 300 MHz. LC–MS analyses were performed on an
Agilent 1100 HPLC system (Agilent, Waldbronn, Germany) coupled with a Bruker
Daltonics esquire 3000plus mass spectrometer (Bruker Daltonics, Bremen, Germany)
equipped with an electrospray (ESI) interface. Semi-preparative HPLC was carried out on a
Dionex preparative HPLC system (P580 pump, ASI 100 automated sampler, Ultimate 3000
column department, UVD 170 U detector; Dionex Softron, Germerling, Germany) equipped
with a Gilson Abimed 206 fraction collector (Gilson International, Middleton, WI, USA).
Column chromatographies were performed with Sephadex LH-20 (Pharmacia Biotech AB,
Stockholm, Sweden) and silica gel 60 (0.040–0.063 mm; Merck, VWR, Darmstadt,
Germany). CDCl3 and DMSO-d6 were obtained from Euriso-Top (Paris, France). All
solvents used for HPLC analysis were gradient grade, all solvents for extraction technical
grade.
4.2
Plant material
Whole plants were collected in south-western China in 2008 (Safer et al.). Vouchers are
deposited in the herbaria of the University of Vienna, Austria (WU), and the Chinese
Academy of Sciences in Beijing, China, (PE). Roots of cultivated plants were obtained from
the Botanical Garden in Giessen (Germany). Only dried plant material was used for all
analyses. Population numbers, species names and voucher information (WU) are listed in
Table 1.
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4.3
Extraction and sample preparation
Roots were frozen with liquid nitrogen and powdered using mortar and pestle. 100 mg of
finely powdered plant material was weighed into a 1.5 ml Eppendorf tube. 1.2 ml of DMSOd6 (containing 0.03% TMS) was added to each sample. The tubes were mixed thoroughly on
a flat-bed shaker for 24 h. The samples were spun down in a micro-centrifuge at 14,000 rpm
for 5 min. 700 μl of the supernatant was filtered through cotton wool into a 5 mm NMR
tube. Triplicates were prepared for each sample (for Leontopodium himalayanum, only two
samples were prepared due to a lack of plant material). The same samples were used for
LC–MS analyses; for each sample, the extract was diluted with DMSO (1:5).
4.4
1H
NMR spectroscopy
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Five hundred and twelve scans were accumulated, resulting in an acquisition time of 30 min
per sample. A water suppression pulse sequence was used. The relaxation delay was 2.40 s,
the acquisition time 1.36 s. Spectral width was δ 20.00, size of FID 32 k, and size of real
spectrum 64 k. Fourier transformation and polynomial baseline correction were carried out
automatically, phase correction was done manually using TOPSPIN 2.0 (Bruker
Biospin). 1H NMR chemical shifts in the spectra were referenced to TMS at δ 0.00. To
reduce the size of the spectra to a number of variables suitable for statistical analysis, 1H
NMR spectra were imported into AMIX (Analysis of MIXtures software v.3.7.5, Bruker
Biospin). Spectral intensities were bucket-integrated to equal width (δ 0.04). The regions
between δ 3.60 and 3.00 (residual water) and δ 2.56–2.46 (residual DMSO) were removed
prior to statistical analysis. Spectra were normalised to the total signal area. The preprocessed spectra were exported as a bucket table with rows representing the individual
NMR spectra, and columns (comprising 220 variables) representing the integrated regions.
Principal component analysis (PCA) and partial least squares-discriminant analysis (PLSDA) were performed with the programme SIMCA-P ver. 10.0 (Umetrics, Umeå, Sweden).
The mean-centering scaling method (ctr) was applied to both PCA and PLS-DA.
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4.5
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Isolation of discriminating compounds from L. franchetii roots
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Dried roots (115 g) were ground using a laboratory mill (IKA MF10 basic). The finely
powdered roots were extracted with CH2Cl2 using an ultrasonic bath and repeated
maceration. The solvent was evaporated under reduced pressure to obtain 10.7 g crude
extract. The extract was subjected to silica gel column chromatography (50 × 5 cm) and
eluted with a pet-ether-Me2CO gradient (9:1 to 4:6) yielding seven fractions (Lf1A–Lf1G).
Lf1H and Lf1I were obtained by flushing the column with Me2CO and MeOH, respectively.
Fraction Lf1B (3.35 g) was applied to a Sephadex LH-20 column (100 × 4 cm) and eluted
with CH2Cl2–Me2CO (85:15), yielding five subfractions (Lf2A–Lf2E). Lf2E gave 1.50 g of
compound 1. Fraction Lf2B was subjected to a silica gel column chromatography
(90 × 3.5 cm) using a solvent system of pet-ether–CH2Cl2 by gradient elution (8:2 to 2:8).
This resulted in ten subfractions (Lf3A–Lf3J), whereas Lf3H gave 40 mg of compound 2.
Lf3K was obtained by flushing the column with pure CH2Cl2. Lf3E was subjected to a semipreparative HPLC (column: Waters XTerra C18 5 μm, 100 × 7.80 mm; solvent system: H2O
(A)–MeOH (B); gradient: 0 min 70% B, 15 min 98% B, 30 min 98% B), yielding 13 mg of
compound 3. Lf3F was also separated with the semi-preparative HPLC system (isocratic
H2O–MeOH 20:80), resulting in compound 4 (5 mg) and compound 5 (9 mg). Lf3K was
purified with a silica gel column chromatography (38 × 3 cm) using CH2Cl2 with 2%
Me2CO as a solvent system. The column was flushed with CH2Cl2–Me2CO (8:2) and pure
Me2CO at the end, resulting in a total of 10 subfractions (Lf4A–Lf4J). Lf4C gave 67 mg of
compound 6. Structures of the compounds were elucidated via 1D and 2D NMR
spectroscopy using CDCl3 as an NMR-solvent; NMR experiments (1H, 13C, HSQC, HMBC
and COSY) were carried out using Bruker standard acquisition parameters. Spectroscopic
data for compounds 1, 2, 3, 5, and 6 are provided as online supplementary material (Tables
S1 and S2).
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4.6
LC–MS
The separation was carried out using a Phenomenex LUNA C18 column (3 μm,
150 × 2.00 mm) at 40 °C, with a mobile phase including H2O (A), and a mixture of MeOH
and MeCN (1:10, v/v) containing 0.9% HCO2H and 0.1% HOAc (B). Analyses were
performed at a flow rate of 0.2 ml/min using the following gradient: 0 min 15% B, 15 min
25% B, 25 min 45% B, 30 min 85% B, 55 min 95% B, 65 min 95% B. The injection volume
was 10 μl. Detection was performed in both positive and negative ionisation mode in the m/z
range of 100–1000. The following ESI conditions were used: Nebulizer 40.0 psi, dry gas
5.0 l/min, dry temperature 300 °C, and capillary voltage 1500 V. Acquired spectra were
saved as total ion chromatograms (TICs) in NetCDF format.
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TICs were pre-processed with the programme MZmine ver 1.97 (Katajamaa and Oresic,
2005). Mass peaks were detected, chromatograms were retention time normalised,
deconvoluted, isotopic peaks were grouped, and the chromatograms were aligned. To avoid
missing data, gaps were filled via the peak finder function. Duplicate peaks were filtered and
a linear normalizer was applied. Pre-processed spectra were exported as a peak list table,
with rows representing the individual mass spectra, and columns (comprising 199 variables)
representing the integrated and normalised peak areas. The peak list table was imported into
SIMCA-P 10.0 (Umetrics, Umeå, Sweden); a PCA was carried out using mean-centering
(ctr) for scaling.
References
Ali K. Maltese F. Zyprian E. Rex M. Choi Y.H. Verpoorte R. NMR metabolic fingerprinting based
identification of grapevine metabolites associated with downy mildew resistance. Journal of
Agricultural and Food Chemistry. 2009; 57:9599–9606. [PubMed: 19785416]
Published as: Phytochemistry. 2011 August ; 72(11-12): 1379–1389.
Safer et al.
Page 11
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Belton P.S. Colquhoun I.J. Kemsley E.K. Delgadillo I. Roma P. Dennis M.J. Sharman M. Holmes E.
Nicholson J.K. Spraul M. Application of chemometrics to the 1H NMR spectra of apple juices:
discrimination between apple varieties. Food Chemistry. 1998; 61:207–213.
Bohlmann F. Zdero C. Eine neue Diterpensaeure aus Perymenium ecuadoricum. Phytochemistry.
1977; 16:786–787.
Bohlmann F. Fritz U. King R.M. Robinson H. Sesquiterpene and diterpene derivatives from Solidago
species. Phytochemistry. 1980; 19:2655–2661.
Bohlmann F. Suding H. Cuatrecasas J. Robinson H. King R.M. Tricyclic sesquiterpenes and further
diterpenes from Espeletiopsis species. Phytochemistry. 1980; 19:2399–2403.
Bohlmann F. Ziesche J. King R.M. Robinson H. Eudesmanolides and diterpenes from Wedelia
trilobata and an ent-kaurenic acid derivative from Aspilia parvifolia. Phytochemistry. 1981;
20:751–756.
Dobner M.J. Ellmerer E.P. Schwaiger S. Batsugkh O. Narantuya S. Stutz M. Stuppner H. New lignan,
benzofuran, and sesquiterpene derivatives from the roots of Leontopodium alpinum and L.
leontopodioides. Helvetica Chimica Acta. 2003; 86:733–738.
Dobner M.J. Schwaiger S. Jenewein I.H. Stuppner H. Antibacterial activity of Leontopodium alpinum
(Edelweiss). Journal of Ethnopharmacology. 2003; 89:301–303. [PubMed: 14611896]
Gray A.I. Hook I.L. James P. Sheridan H. Sesquiterpenes from Leontopodium alpinum.
Phytochemistry. 2000; 54:551.
Hall R.D. Plant metabolomics: from holistic hope, to hype, to hot topic. New Phytol.. 2006; 169:453–
468. [PubMed: 16411949]
Handel-Mazzetti H. Systematische Monographie der Gattung Leontopodium. Beihefte Bot.
Centralblatt. 1927; 44:1–178.
Hasan C.M. Healey T.M. Waterman P.G. Kolavane and kaurane diterpenes from the stem bark of
Xylopia aethiopica. Phytochemistry. 1982; 21:1365–1368.
Holmes E. Tang H.R. Wang Y.L. Seger C. The assessment of plant metabolite profiles by NMR-based
methodologies. Planta Medica. 2006; 72:771–785. [PubMed: 16881014]
Hornick A. Schwaiger S. Rollinger J.M. Vo N.P. Prast H. Stuppner H. Extracts and constituents of
Leontopodium alpinum enhance cholinergic transmission: brain ACh increasing and memory
improving properties. Biochemical Pharmacology. 2008; 76:236–248. [PubMed: 18541221]
Katajamaa, M., Oresic, M., 2005. Processing methods for differential analysis of LC/MS profile data.
BMC Bioinformatics 6.
Kim H. Park S.H. Metabolic profiling and discrimination of two cacti cultivated in Korea using
HPLC–ESI-MS and multivariate statistical analysis. Journal of the Korean Society for Applied
Biological Chemistry. 2009; 52:346–352.
Kletter, C.; Kriechbaum, M., editors. Tibetan Medicinal Plants. Scientific Publishers; Stuttgart: 2001.
Lachenmeier D.W. Frank W. Humpfer E. Schafer H. Keller S. Mortter M. Spraul M. Quality control of
beer using high-resolution nuclear magnetic resonance spectroscopy and multivariate analysis.
European Food Research and Technology. 2005; 220:215–221.
Li, L.Y., Ye, J.M., Yin, H., Zhu, Y.M., Tian, J.M., Gao, F., 1994. Effect of Leontopodium
leontopodioides (Willd.) Beauv. on inflammation induced by animal reversed passive arthus
(RPA). Zhongguo Zhongyao Zazhi 19, 174–176, 192.
Li J.X. Lin C.J. Yang X.P. Jia Z.J. New bisabolane sesquiterpenes and coumarin from Leontopodium
longifolium. Chemistry and Biodiversity. 2006; 3:783–790. [PubMed: 17193310]
Ohno N. Mabry T.J. Zabel V. Watson W.H. Tetrachyrin, a new rearranged kaurenoid lactone, and
diterpene acids from Tetrachyron orizabaensis and Helianthus debilis. Phytochemistry. 1979;
18:1687–1689.
Pongsuwan W. Bamba T. Harada K. Yonetani T. Kobayashi A. Fukusaki E. High-throughput
technique for comprehensive analysis of Japanese green tea quality assessment using ultraperformance liquid chromatography with time-of-flight mass spectrometry (UPLC/TOF MS).
Journal of Agricultural and Food Chemistry. 2008; 56:10705–10708. [PubMed: 18973299]
Rasmussen B. Cloarec O. Tang H.R. Staerk D. Jaroszewski J.W. Multivariate analysis of integrated
and full-resolution 1H NMR spectral data from complex pharmaceutical preparations: St. John’s
wort. Planta Medica. 2006; 72:556–563. [PubMed: 16773541]
Published as: Phytochemistry. 2011 August ; 72(11-12): 1379–1389.
Safer et al.
Page 12
Sponsored Document
Sponsored Document
Reisinger U. Schwaiger S. Zeller I. Messner B. Stigler R. Wiedemann D. Mayr T. Seger C. Schachner
T. Dirsch V.M. Vollmar A.M. Bonatti J.O. Stuppner H. Laufer G. Bernhard D. Leoligin, the major
lignan from Edelweiss, inhibits intimal hyperplasia of venous bypass grafts. Cardiovascular
Research. 2009; 82:542–549. [PubMed: 19228707]
Safer S. Tremetsberger K. Guo Y.P. Kohl G. Samuel R. Stuessy T. Stuppner H. Phylogenetic
relationships in the genus Leontopodium (Asteraceae: Gnaphalieae) based on AFLP. Botanical
Journal of the Linnean Society. 2011; 165:364–377.
Schwaiger S. Adams M. Seger C. Ellmerer E.P. Bauer R. Stuppner H. New constituents of
Leontopodium alpinum and their in vitro leukotriene biosynthesis inhibitory activity. Planta
Medica. 2004; 70:978–985. [PubMed: 15490327]
Schwaiger S. Cervellati R. Seger C. Ellmerer E.P. About N. Renimel I. Godenir C. Andre P. Gafner F.
Stuppner H. Leontopodic acid – a novel highly substituted glucaric acid derivative from Edelweiss
(Leontopodium alpinum Cass.) and its antioxidative and DNA protecting properties. Tetrahedron.
2005; 61:4621–4630.
Schwaiger S. Hehenberger S. Ellmerer E.P. Stuppner H. A new bisabolane derivative of Leontopodium
andersonii. Natural Product Communications. 2010; 5:667–668. [PubMed: 20521527]
Seger C. Sturm S. Analytical aspects of plant metabolite profiling platforms: current standings and
future aims. Journal of Proteome Research. 2007; 6:480–497. [PubMed: 17269705]
Stuppner H. Ellmerer E.P. Ongania K.H. Dobner M. Bisabolane derivatives from Leontopodium
alpinum. Helvetica Chimica Acta. 2002; 85:2982–2989.
Tabernaemontanus, J.T., 1582. Das Ander Buch von Kreutern. In: Bauhin, H. (Ed.), D. Jacobi
Theodori Tabernaemontani neu vollkommen Kraeuter-Buch. Reprint Basel: König, 1731. Verlag
Kölbl, Grünwald bei München, 1993, pp. 779–782.
Tianniam S. Tarachiwin L. Bamba T. Kobayashi A. Fukusaki E. Metabolic profiling of Angelica
acutiloba roots utilizing gas chromatography-time-of-flight-mass spectrometry for quality
assessment based on cultivation area and cultivar via multivariate pattern recognition. Journal of
Bioscience and Bioengineering. 2008; 105:655–659. [PubMed: 18640606]
Urbain A. Marston A. Marsden-Edwards E. Hostettmann K. Ultra-performance liquid
chromatography/time-of-flight mass spectrometry as a chemotaxonomic tool for the analysis of
Gentianaceae species. Phytochemical Analysis. 2009; 20:134–138. [PubMed: 19140109]
van den Berg, R.A., Hoefsloot, H.C.J., Westerhuis, J.A., Smilde, A.K., van der Werf, M.J., 2006.
Centering, scaling, and transformations: improving the biological information content of
metabolomics data. BMC Genomics 7.
Wang Y. Zhu Q.X. Yang M. Jia Z.J. Chemical components from Leontopodium nanum. Journal of the
Chinese Chemical Society. 2002; 49:259–261.
Wu, Z.Y.; Raven, P.H.; Hong, D.Y. Science Press, Missouri Botanical Garden Press; Beijing, St.
Louis: 1994. Flora of China.
Appendix A Supplementary data
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Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
The authors thank Michael Jäger from the Botanical Garden of Giessen (Germany) for maintaining cultivated plants
and providing dried plant material for our study. We also thank Yan-Ping Guo (Beijing Normal University, China)
for help with plant material collection. We thank the Institutes of Botany (Beijing and Kunming) of the Chinese
Academy of Sciences (China) for supporting the collection trip to China. We thank Ernst P. Ellmerer (University of
Innsbruck, Austria) for performing 1D and 2D NMR spectroscopy for structure elucidation. We also thank W.B.
Dickoré (Botanische Staatssammlung Munich, Germany) for voucher identifications and morphological
investigations. This study was financed by the Austrian Science Fund (FWF), Grant No. P19480.
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Fig. 1.
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Scores plot (A) and loadings plot (B) of principal component analysis (PCA) results
obtained from 1H NMR spectra of 11 collected Leontopodium species using PC 1 (68.8%)
vs. PC 2 (15.4%). Discriminating NMR signals are highlighted in the loadings plot.
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Fig. 2.
Typical 1H NMR spectra of DMSO-d6 root extracts of the 11 investigated Leontopodium
species in the range of δ 9.00–0.00 (A). Comparison of 1H NMR spectra of the closely
related L. sinense, L. franchetii, L. dedekensii, and an unidentified species, L. sp. in the
range of δ 9.00–4.00 (B).
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Fig. 3.
Chemical structures of compounds 1–9 [ent-kaur-16-en-19-oic acid (1), methyl-15αangeloyloxy-ent-kaur-16-en-19-oate (2), methyl-ent-kaur-16-en-19-oate (3), 8acetoxymodhephene (4), 19-acetoxy-ent-kaur-16-ene (5), methyl-15β–angeloyloxy-16,17epoxy-ent-kauran-19-oate (6), 3-methyl-1-{2-[(1R∗,2R∗,5R∗,6S∗)-2,5,6-tris(acetyloxy)-4methylcyclohex-3-en-1-yl]propyl}but-2-enyl (2Z)-2-methylbut-2-enoate (7), methyl-ent-7α,
9α-dihydroxy-15β-[(2Z)-2-methyl-but-2-enoyloxy]kaur-16-en-19-oate (8), (1R∗,5S∗,6S∗)-5(acetyloxy)-6-[3-(acetyloxy)-1,5-dimethylhex-4-enyl]-3-methylcyclohex-2-en-4-on-1-yl
(2Z)-2-methyl-but-2-enoate (9)].
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Fig. 4.
3D-scores plot of partial least squares-discriminant analysis (PLS-DA) results obtained
from 1H NMR spectra of 11 collected and 12 cultivated Leontopodium species using PLS
components 1–3. Class 1: collected plants, class 2: cultivated plants.
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Fig. 5.
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Scores plot (A) and loadings plot (B) of principal component analysis (PCA) results
obtained from LC–MS fingerprints of 11 collected Leontopodium species using PC 1
(29.6%) vs. PC 2 (17.1%). Discriminating m/z values are highlighted in the loadings plot.
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Fig. 6.
Typical total ion chromatograms (TICs) of DMSO-d6 root extracts of the 11 investigated
Leontopodium species, acquired in positive ionisation mode by electrospray ionisation
(ESI).
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Table 1
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Population number, species names, sample origin, and voucher informationa for the investigated species.
Populations
Species
Sample origin
Voucher informationa
Collected plants (Province of Yunnan, south-western China)
SSG-06
L. andersonii C.B. Clarke
China, Yunnan, Lijiang, Yuhu Village
WU 0044003
SSG-14
L. andersonii C.B. Clarke
China, Yunnan, Zhongdian, Haba Village
WU 0043998
SSG-27
L. andersonii C.B. Clarke
China, Yunnan, Luquan, JiaoZiShan
WU 0043958
SSG-13A
L. artemisiifolium Beauverd
China, Yunnan, Zhongdian, Haba Village
WU 0043997
SSG-13B
L. cf. artemisiifolium Beauverd
China, Yunnan, Zhongdian, Haba Village
WU 0043997
SSG-26
L. caespitosum Diels
China, Yunnan, Luquan, JiaoZiShan
WU 0043960
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SSG-11
L. calocephalum Beauverd
China, Yunnan, Zhongdian, Haba Village
WU 0043999
SSG-17
L. dedekensii Beauverd
China, Yunnan, Benzilan, Dongzhulin Monastery
WU 0043994
SSG-19
L. dedekensii Beauverd
China, Yunnan, Deqen, Atuntze
WU 0044004
SSG-22
L. dedekensii Beauverd
China, Yunnan, Weixi, Langping-Laching
WU 0043954
SSG-24
L. dedekensii Beauverd
China, Yunnan, Bingzhongluo
WU 0043956
SSG-09
L. franchetii Beauverd
China, Yunnan, Lijiang-Zhongdian
WU 0044016
SSG-15
L. franchetii Beauverd
China, Yunnan, Zhongdian, Cuo Bu La Ka Mt.
WU 0044008
SSG-18
L. himalayanum DC.
China, Yunnan, Zhongdian-Deqen
WU 0043993
SSG-04
L. sinense Hemsl. ex Forb. & Hemsl.
China, Yunnan, Dali, CangShan
WU 0043975
SSG-05
L. sinense Hemsl. ex Forb. & Hemsl.
China, Yunnan, Heqing, Da Shi Village
WU 0044001
SSG-08
L. souliei Beauverd
China, Yunnan, Lijiang-Zhongdian
WU 0044013
SSG-12
L. souliei Beauverd
China, Yunnan, Zhongdian, Haba Village
WU 0044000
SSG-20
L. cf. stracheyi C.B. Clarke ex Hemsl.
China, Yunnan, Weixi, La Ba Di Village
WU 0043995
SSG-21
L. cf. stracheyi C.B. Clarke ex Hemsl.
China, Yunnan, Weixi, Langping-Laching
WU 0043996
SSG-25
L. cf. stracheyi C.B. Clarke ex Hemsl.
China, Yunnan, GongShan
WU 0043957
SSG-16
L. sp.
China, Yunnan, Zhongdian
WU 0044007
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Cultivated plants (Botanical Garden Giessen, Germany)
J22
L. alpinum Cass.
Austria, Styria, Rax Alpe
07/J/700
J01
L. calocephalum Beauverd
China, Yunnan, Da Xue Shan
03-678
J10
L. cf. calocephalum Beauverd
China, Sichuan, Min Shan
07-524
J03
L. dedekensii Beauverd
China, Yunnan, Hengduan Shan
03-302
J03A
L. dedekensii Beauverd
China, Yunnan, Hengduan Shan
03-302
J07
L. dedekensii Beauverd
China, Xizang, E-Tibet
03-664
J16
L. discolor Beauverd
Russia, Shakalin, Tymorsky distr.
07-365
J11
L. cf. haplophylloides Hand.-Mazz.
China, Sichuan, Shaluli Shan
07-526
J13
L. cf. haplophylloides Hand.-Mazz.
China, Sichuan, Litang
07-531
J06
L. cf. himalayanum DC.
China, Sichuan, Litang
05-520
J15
L. cf. leontopodinum Hand.-Mazz.
Tadjikistan, Pamir
07-547
J20
L. pusillum Hand.-Mazz.
China, Tibet, Bamda
07-556
J03B
L. aff. sinense Hemsl. ex Forb. & Hemsl.
China, Yunnan, Hengduan Shan
03-302
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Populations
Species
Sample origin
Voucher informationa
J09
L. cf. souliei Beauverd
China, Sichuan, Shaluli Shan
07-523
J14
L. cf. souliei Beauverd
China, Sichuan, Litang
07-532
J08
L. stracheyi C.B. Clarke ex Hemsl.
China, Xizang, E-Tibet
03-665
J12A
L. sp.
China, Quinghai, Huashixia
07-530A
J12B
L. sp.
China, Quinghai, Huashixia
07-530B
a
For the collected species, voucher numbers are indicated, for the cultivated samples cultivar numbers.
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