http://orcid.org/0000-0002-8004-4526
Figures
Abstract
Sarraceniaceae is a New World carnivorous plant family comprising three genera: Darlingtonia, Heliamphora, and Sarracenia. The plants occur in nutrient-poor environments and have developed insectivorous capability in order to supplement their nutrient uptake. Sarracenia flava contains the alkaloid coniine, otherwise only found in Conium maculatum, in which its biosynthesis has been studied, and several Aloe species. Its ecological role and biosynthetic origin in S. flava is speculative. The aim of the current research was to investigate the occurrence of coniine in Sarracenia and Darlingtonia and to identify common constituents of both genera, unique compounds for individual variants and floral scent chemicals. In this comprehensive metabolic profiling study, we looked for compound patterns that are associated with the taxonomy of Sarracenia species. In total, 57 different Sarracenia and D. californica accessions were used for metabolite content screening by gas chromatography-mass spectrometry. The resulting high-dimensional data were studied using a data mining approach. The two genera are characterized by a large number of metabolites and huge chemical diversity between different species. By applying feature selection for clustering and by integrating new biochemical data with existing phylogenetic data, we were able to demonstrate that the chemical composition of the species can be explained by their known classification. Although transcriptome analysis did not reveal a candidate gene for coniine biosynthesis, the use of a sensitive selected ion monitoring method enabled the detection of coniine in eight Sarracenia species, showing that it is more widespread in this genus than previously believed.
Citation: Hotti H, Gopalacharyulu P, Seppänen-Laakso T, Rischer H (2017) Metabolite profiling of the carnivorous pitcher plants Darlingtonia and Sarracenia. PLoS ONE 12(2): e0171078. https://doi.org/10.1371/journal.pone.0171078
Editor: Vijai Gupta, Tallinn University of Technology, ESTONIA
Received: May 23, 2016; Accepted: January 17, 2017; Published: February 21, 2017
Copyright: © 2017 Hotti et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: This work was supported by the Finnish Doctoral Program in Plant Science, Societas pro Fauna et Flora Fennica, Eteläsuomalaisten ylioppilaiden säätiö, Oskar Öflunds Stiftelse, the Otto A. Malm Foundation (all to HH), VTT and the Academy of Finland (decision number 138808 to HR, and 265966 to PG). VTT provided support in the form of salaries for authors [HH, TS-L, HR], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the "author contributions" section. The other funders did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: Authors Hannu Hotti, Tuulikki Seppänen-Laakso, and Heiko Rischer are employed by VTT Technical Research Centre of Finland Ltd, Espoo, Finland. This does not alter our adherence to PLOS ONE policies on sharing data and materials.
Introduction
Sarraceniaceae is a New World carnivorous plant family comprising three genera: Darlingtonia Torr. (monotypic), Heliamphora Benth. (ca. 23 species [1]) and Sarracenia L. (ca. 11 species [2]). The distribution of Darlingtonia is limited to a few locations along the western coast of North America, Heliamphora occurs mainly on tepuis of the Guiana Highlands in South America and Sarracenia is the most widespread genus in the family, found in the eastern coastal plains of North America. Darlingtonia californica, Sarracenia, and Heliamphora are able to compete in nutrient-poor habitats due to their insectivorous nature, i.e. the ability to attract, capture, and digest insects to supplement their nutrient uptake. A common feature for all three genera is that they lure insects to their elongated tubular leaves. In order to attract insects, they produce extrafloral nectar [3], emit insect attractants [4], and most species are brightly colored. They utilize various methods to capture their prey. Darlingtonia californica and S. psittacina, for example, hide their entry/exit hole from the inside, displaying multiple translucent false exits so that insects finally get exhausted and fall into the pitcher. Other Sarracenia and Heliamphora species utilize downward pointing hairs and waxy surfaces in their pitchers in order to trap insects.
The family is relatively poorly described in terms of chemical constituents [5], which is surprising given the fact that Sarracenia species have long been used as traditional medicine by many aboriginal communities in North America, and have attracted renewed pharmaceutical interest due to recent investigations revealing their cytoprotective activities in cell models [6]. Darlingtonia californica has not been chemically investigated at all to date, but several insect-attracting constituents have been described from the spoon-shaped lid structures of pitchers of two Heliamphora species [4]. Various compounds found in Sarracenia have also been reported, including volatiles [7,8], flavonoids [9–11], phytochemicals [12–14] and pitcher fluid composition [3,11,15,16]. Sarracenin, an enol diacetal monoterpene, was first identified in S. flava [17] and later found in a number of Sarracenia [18] and Heliamphora [4] species. Sarracenia flava is the most studied species with respect to its chemical composition [7,8,19,20]. Interestingly, S. flava contains coniine [21], a toxic alkaloid, which is otherwise only known from the unrelated Conium maculatum (Apiaceae) and several Aloe species (Xanthorrhoeaceae) [22,23]. In C. maculatum, a polyketide synthase (PKS) initiates the biosynthesis of coniine [24]. The original study [21] referred to earlier research on S. purpurea, indicating that it could also contain coniine or related alkaloids. Mody et al. [21] speculated that coniine in S. flava paralyzes insects, whereas Harborne [25] postulated insect attraction. Systematic investigations of the compound’s wider occurrence in the genus have hitherto not been performed. In order to follow up on the earlier findings in S. flava and to expand our knowledge on coniine distribution in Sarracenia, we aimed at investigating a number of accessions using a sensitive gas chromatography-mass spectrometry (GC-MS) method applying selected ion monitoring (SIM) to detect coniine reliably in plant material even at low concentrations. Additionally, the transcriptomes of S. psittacina and S. purpurea were analysed for encoded candidate PKSs putatively involved in coniine biosynthesis.
Three previous studies derived the phylogeny of Sarraceniaceae using gene sequence data, with incongruent results [26–28]. Stephens et al. [2] recently addressed this inconsistency by applying a target enrichment approach to assess the phylogenetic relationships among 75 Sarracenia accessions. Unlike the mutations from highly conserved genomic loci, the chemical composition usually differs even between closely related species and hence is not suitable for deriving reliable taxonomies [29]. In a biochemical profiling study of volatiles, Jürgens et al. [8] applied an approach based on multidimensional scaling to study the similarities among different species. They then used similarity percentage analysis (SIMPER) to obtain compounds that explained the highest amount of dissimilarity among the samples. Thus, Jürgens et al. [8] focused on the variability in the data without focusing directly on the phylogenetic structure. The phylogeny, on the other hand, may explain the chemical diversity of the species. The aim of our current study was to provide a comprehensive catalogue of chemical constituents of Sarraceniaceae and to examine the extent to which the known phylogenetic information explains the chemical composition of the plants. Therefore, we employed a comprehensive metabolic profiling approach using GC-MS to detect all ions in SCAN mode in a large sample collection. The genus Sarracenia comprises 44 recognised intraspecific taxa [30] within 11 Sarracenia species [2]. By contrast, only one Darlingtonia species is known, occurring in a geographically restricted area. Our collection was selected to cover adequately the diversity of pitcher plants that had been examined in phylogenetic studies [26]. We found common chemical constituents among the plants, unique compounds for individual variants and possible floral scent chemicals as classified according to Knudsen et al. [29], and studied whether the biochemical profiles can be explained by the taxonomy presented in Stephens et al. [2].
Materials and methods
Plant material
Pitchers of cultivated plants were investigated in order to exclude environmental effects. Sarracenia L. (56 accessions) and Darlingtonia californica Torr. were provided by C. Klein, Germany (http://www.carnivorsandmore.de). Metabolite and coniine content screening was performed using global metabolomics in a set of 48 accessions (Table 1) that contained one D. californica and 47 Sarracenia accessions. The Sarracenia accessions included S. alata Alph.Wood (5 accessions), S. flava L. (11 accessions), S. leucophylla Raf. (5 accessions), S. minor Walt. (3 accessions), S. oreophila (Kearney) Wherry (2 accessions), S. psittacina Michx. (4 accessions), S. purpurea L. (13 accessions) and S. rubra Walt. (4 accessions).
Targeted metabolomics for sensitive detection of coniine was performed in 17 accessions, including eight accessions that were also analyzed using global metabolomics (Table 2). These accessions included S. alata (2 accessions), S. flava (4 accessions), S. leucophylla (1 accession), S. minor (1 accession), S. oreophila (1 accession), S. psittacina (2 accessions), S. purpurea (4 accessions) and S. rubra (2 accessions).
Cultivated poison hemlock (Conium maculatum L.) and barley (Hordeum vulgare L. ‘Golden Promise’), were used as alkaloid-containing or alkaloid-free reference material, respectively.
Metabolite extraction
Lids and pitchers were separated, washed with tap water and ground up. Fresh (2 g; metabolite profiling) or freeze dried (200 mg; coniine analysis) plant material was used for extraction as described in [31]. Lipids were removed from the plant material with 3.0 ml petroleum ether (puriss p.a., Sigma-Aldrich Munich, Germany). The plant material was diluted with 2.0 ml ultrapure water and a pH above 9 was obtained by addition of 10% ammonium hydroxide solution (25% stock solution, pro analysi, Merck KGaA, Darmstadt, Germany). Metabolites were extracted twice with 2.0 ml dichloromethane (HPLC grade, Rathburn Chemicals Ltd, Walkerburn, Scotland, UK). The combined dichloromethane extracts were evaporated to dryness and dissolved in 100 μl dichloromethane for further analysis.
Gas chromatography-mass spectrometry
Samples (1 μl) were analysed by GC-MS consisting of a 6890A Series GC (Agilent Technologies, Inc., Santa Clara, CA, USA) combined with an Agilent 5973 Network MSD and a Combipal automatic sampler (Varian Inc., Palo Alto, CA, USA). Analytes were separated by an Agilent HP-5MS capillary column (25 m × 0.2 mm i.d, 0.33 μm). The temperature program started at 50°C with 1 min holding time and then increased at 10°C/min up to 300°C. MSD was operated in electron impact mode at 70 eV.
Pure coniine (Sigma-Aldrich, Munich, Germany) was used as the reference compound in developing the GC-MS method. To determine the detection limit of coniine in the SIM-method, 1, 5, 10 and 20 μg was spiked into alkaline water and extracted as described in [31]. Cotinine (20 μg/sample) (Sigma-Aldrich, Munich, Germany) was used as an internal standard.
PKS-encoding genes in transcriptomes of S. psittacina and S. purpurea
Available transcriptomes of S. psittacina (accession number SRX060168 in the NCBI database) and S. purpurea (accession number SRX060177 in the NCBI database) [32] were analyzed for PKSs using Geneious (version 9.0.4) [33]. The tblastn algorithm in Geneious was used to search the sequence database with the Medicago sativa CHS2 amino acid sequence [34] as the template and a stringency setting of 1e-10. The obtained nucleotide sequence hits were translated to amino acid sequences, and the correct reading frames were chosen and aligned using the Geneious alignment option.
Data handling
Peaks in GC-MS chromatograms were integrated automatically using MSD ChemStation software (version E.02.01.1177, Agilent Technologies, Inc., Santa Clara, CA, USA). Peaks were identified with the Palisade Complete 600K Mass Spectral Library (Palisade Mass Spectrometry, Ithaca, NY, USA) and the NIST Mass Spectral Search Program (The Standard Reference Data Program of the National Institute of Standards and Technology, Gaithersburg, MD, USA). The computer-generated identifications were sorted manually, with a cut-off at 70% identification [35], into an Excel spreadsheet (Microsoft, Redmond, WA, USA) according to their chemical structure, elution time and origin. When peaks with same retention time were identified as different hydrocarbons in multiple samples, they were treated as n-alkanes at the specific retention time. The relative peak abundances were used in the data input.
Data mining
The metabolite data were treated in two formats: (1) a qualitative format representing presence (i.e. concentration level above the detection limit) or absence (concentration level below the detection limit) of a compound in a sample, by coding the presence and absence as 1 and 0, respectively, and (2) a quantitative or continuous format in which the concentration level is given as the percentage of the total peak area. The main aim of our data mining was to visualize any patterns present in the data. Towards this goal, it was first noted that the current data are very high dimensional (i.e. contain a large number of compounds), very sparse (91.35% zeros in the lids dataset and 91.86% in the pitchers dataset), and that the distinct species show huge chemical diversity (i.e. the metabolite composition of different plants is largely distinct). Therefore, it is reasonable to expect that only a small proportion of compounds are likely to be useful for clustering the samples. A feature selection approach for clustering [36] was applied in order to identify the most important features required for deriving hierarchical clusters. This approach computes and reweights the overall dissimilarity matrix while applying a lasso-type penalty, which results in a dissimilarity matrix sparse in features [36]. This sparse clustering was applied using the R package sparcl. In order to compute the hierarchical clustering with the qualitative format of the data, the hamming distance was used as the dissimilarity measure. For the quantitative format of the data, the Euclidean distance was used. The complete linkage method was used for the clustering.
In order to compare the phylogenetic structure with the chemical profiles, the MP-EST accession tree from [2] was downloaded. Then the accessions in the two studies were mapped based on the location of sample collection, which resulted in a many-to-many mapping (Table 1) with one or more of 42 nodes in the phylogenetic tree matching one or more of 48 species in our study. From this, 36 possible bijective maps were enumerated, and compound-based distances corresponding to each bijective map were calculated as follows. The distance between every pair of accessions was calculated using hamming distance for the binary and Euclidean distance for the continuous data of the selected metabolite features. These distances are referred to below as species-level distances (SLD). Using the clades resolved in the MP-EST accession tree (i.e. D. californica, S. flava, S. psittacina, S. minor, S. purpurea complex, S. rubra complex, S. alata, S. leucophylla, and S. oreophila), distances within and between the clades were calculated. A within-clade distance (WCD) was calculated as the average of all pairwise SLDs of accessions within the clade. A between-clade distance (BCD) was calculated as the average of all SLDs of accession-pairs across the pair of clades. Average species-level and clade-level distance matrices were calculated over all 36 bijective maps to derive the average within-clade (aWCD) and between-clade distances (aBCD), as well as the average species-level distances (aSLD). These averaged distances were used to assess how well the metabolite data supports the phylogenetic structure. If the phylogenetic structure explains the compound data, the aWCDs are expected to be lower than the aBCDs. This was assessed by comparing aWCDs against not only aBCDs but also aSLDs as an additional test. More precisely, we (I) visualized aWCDs against the background distance distribution formed by aSLDs (Fig 1B and Fig 2B, S1B and S2B Figs), (II) visualized the difference between the distribution of aWCDs and aBCDs (Fig 1C and Fig 2C, S1C and S2C Figs) and (III) performed one-sided Wilcoxon’s rank sum tests to assess whether aWCDs are less prevalent than aBCDs.
(A) Heat map visualization of selected metabolite features from the qualitative data of lids. The phylogenetic tree from [2] is displayed as the column dendrogram. Six samples of our dataset (11, 31, 35, 38, 42, and 46) are omitted from this heat map based on the sample selection procedure described in the Methods section. (B) Comparison of average within-clade distances (aWCDs) against the background distribution of average species-level distances (aSLDs) and average between-clade distances (aBCDs). Distribution of aSLDs was calculated using qualitative data of the selected metabolite features and displayed in a density plot. The black vertical lines mark the individual aWCDs. The orange dashed and dotted lines show the mean and median of aSLDs. The purple dashed and dotted lines show the mean and median of aBCDs. (C) Comparison of aWCDs (green continuous density line) with aBCDs (orange dashed density line).
(A) Heat map visualization of selected metabolite features from the qualitative data of pitchers. The phylogenetic tree from [2] is displayed as the column dendrogram. Six samples of our dataset (1, 11, 14, 31, 38, and 46) are omitted from this heat map, based on the sample selection procedure described in the Methods section. (B) Comparison of average within-clade distances (aWCDs) against the background distribution of average species-level distances (aSLDs) and average between-clade distances (aBCDs). Distribution of aSLDs was calculated using qualitative data of the selected metabolite features and displayed in a density plot. The black vertical lines mark the individual aWCDs. The orange dashed and dotted lines show the mean and median of aSLDs. The purple dashed and dotted lines show the mean and median of aBCDs. (C) Comparison of aWCDs (green continuous density line) with aBCDs (orange dashed density line).
In order to visualize the metabolite features selected for clustering alongside the phylogenetic structure presented in [2], the best mapping of samples between the MP-EST accession tree and our compound data was obtained. The best bijective map is expected to result in the maximum BCD and minimum WCD among all possible bijective maps. To achieve this objective, we chose the map that yields the maximum difference between the mean values of BCD and WCD i.e. mean(BCD)–mean(WCD) for these visualizations (Fig 1A and Fig 2A, S1A and S2A Figs). Thus, the heat maps shown in Fig 1A and Fig 2A, S1A and S2A Figs contain only one sample from our compound dataset for each node in the MP-EST accession tree chosen to maximize the mean(BCD)–mean(WCD). Since only 42 nodes in the accession tree map to our dataset, each heat map omits 6 samples from our study. In particular, the samples numbered 31 and 46 (Table 1) were omitted in all four heat maps (Fig 1A and Fig 2A, S1A and S2A Figs). Apart from these two samples, 11, 35, 38, and 42 were omitted from Fig 2A; 14, 35, 37, and 44 were omitted from S1A Fig; 1, 11, 14, and 38 were omitted from Fig 2A; and 1, 11, 38, and 42 were omitted from S2A Fig.
All the statistical analyses and visualizations were performed using the R statistical software [37] and its packages such as gplots, sparcl, metadar (http://code.google.com/p/metadar), ihm (http://code.google.com/p/ihm), and RColorBrewer.
Results
Coniine identification and occurrence in Sarracenia
With the GC-MS method used, coniine elutes at a constant retention time (6.33±0.01min) even in spiked barley material and C. maculatum leaf extract. The samples were analysed on the basis of their SCAN mass spectra and were compared to a database. Pure coniine matched the database with 86%, or in plant matrix with 78%-86% identity. The retention time of coniine was very stable, and the ions 80, 84, and 126 exhibited the same relative abundances in the sample matrix and in the coniine reference substance (Fig 3). Therefore, a match lower than 90% can be considered acceptable. Using the SCAN mode, coniine was detected in S. alata, S. flava, S. leucophylla, S. oreophila, S. psittacina and S. purpurea (incl. S. rosea) (Table 1). In D. californica, only the fragment m/z 84 was detected, whereas in S. jonesii (3) none of the ions were detected at 6.33 min.
Mass spectrum of pure coniine in SCAN mode (A) and selected fragments in SIM mode (B). Coniine detection in sample matrix (S. flava) in SCAN (C) and SIM modes (D).
In order to detect coniine at low concentrations, we operated the GC-MS in SIM mode. Based on the fragmentation pattern of coniine (m/z 43, 56, 70, 80, 84, 97, 110, and 126), the characteristic ions m/z 56, 70, 80, 84 (base peak) and 126 (mass peak) were selected. The fragments m/z 80, 84, and 126 are specific for coniine, in contrast to the ions m/z 56 and 70, which are shared with many other molecules.
The limit of detection for coniine in SIM was 1 μg/ml, which corresponds to 1 μg/g dry weight. Using SIM detection, coniine was identified from S. alata, S. flava, S. leucophylla, S. minor, S. oreophila, S. psittacina, S. purpurea (incl. S. rosea) and S. rubra (incl. S. alabamensis) (Table 2). Of these, S. flava and S. alata samples only contained coniine traces close to the detection limit, whereas other samples accumulated clearly higher levels of coniine. No coniine was detected in the pitchers of S. minor var. okefenokeensis or the lids of S. oreophila.
PKSs in Sarracenia transcriptomes
Sarracenia psittacina and S. purpurea transcriptomes were analysed using the tblastn algorithm with the stringency set to 1e-10 and M. sativa CHS2 as a template, resulting in 8 and 12 sequences, respectively. Correct reading frames were selected and aligned with each other after the nucleotide sequences were translated to amino acid sequences. This resulted in three unique contigs per species. Of these, one represents the N-terminus and two the C-terminus when compared to full-length PKS-enzyme. None of the contigs cover the middle part of the PKS-enzyme sequence, but they do contain all the conserved amino acids in the active site in the observed area [38] when compared to other full-length PKSs (S3 Fig).
Metabolite profiles
The metabolite profiles of lids and pitchers were analysed separately. In addition to analysing the metabolite profiles using the quantitative (concentration) data, we also investigated the qualitative (presence or absence) data in which compounds with non-zero concentration levels (i.e. with levels above the detection limits) were treated as present and compounds with levels below the detection limits as absent.
The manually aligned lid dataset consisted of a total of 560 compounds detected in at least one sample. Among these, there were library matches (≥70%) for 69 alcohols, 70 aldehydes and ketones, 53 esters, 58 ethers, 30 carboxylic acids and sterols, 45 hydrocarbons (including some identified as alkanes), 148 n-alkanes, 75 nitrogen compounds, and 12 sulphur compounds. However, each individual plant's lid contained an average of only 48 compounds. The lid sample of S. purpurea subsp. purpurea (16) contained the lowest number of compounds (n = 20) and S. rubra subsp. wherryi (23) had the highest number of compounds (n = 85). The barplot in S4 Fig shows the distribution of compounds across all the lid samples. Furthermore, every lid sample had on average approximately six compounds uniquely found in that sample but in no other sample, one of which could be classified as a floral scent component which had previously been detected from intact flowers [29]. Sarracenia leucophylla (17) displayed the highest number (n = 4) of floral scent compounds (Table 3). The sample S. purpurea subsp. venosa var. burkii (39) is an exception in that it did not accumulate unique compounds, whereas S. flava var. atropurpurea (35) had the largest number (n = 18) of unique compounds. S1A Table shows the compounds unique to each sample along with their concentration levels. Finally, when we compared the lid samples in pairs, we observed that, on average, every lid sample contained 32 unique compounds (S2A Table).
The pitcher dataset contained 589 compounds detected in at least one sample. Among these, there were library matches (≥ 70%) for 67 alcohols, 60 aldehydes and ketones, 72 esters, 60 ethers, 52 carboxylic acids and sterols, 50 hydrocarbons (including those identified as alkanes), 139 n-alkanes, 74 nitrogen-containing compounds and 15 sulphur-containing compounds. Each individual plant's pitcher sample had an average of 48 compounds. The pitcher sample S. purpurea subsp. venosa var. montana (41) did not contain a single compound at a detectable concentration level and S. leucophylla var. alba (26) had the highest number of compounds (n = 78). The barplot in S5 Fig shows the distribution of compounds across all the pitcher samples. Furthermore, every pitcher sample had approximately seven unique compounds, one of which, on average, can be considered as a floral scent component [29]. Sarracenia flava var. atropurpurea (31) and S. minor var. okefenokeensis (5) had the highest number (n = 6) of floral scent compounds (Table 3). Four samples, S. leucophylla (12), S. psittacina f. heterophylla (6), Sarracenia purpurea subsp. purpurea (16) and S. purpurea subsp. venosa var. montana (41) did not contain unique compounds, whereas S. flava var. atropurpurea (31) had the highest number of unique compounds (n = 25). S1B Table shows the compounds unique to each sample along with their concentration levels. Similar to the lids, pitcher pairs had an average of 32 unique compounds (S2B Table).
A sarracenin-like compound was found at an elution time of 18.2 min. Its mass peak was m/z 225, major fragments m/z 180 and 138, and further fragments were m/z 162, 120, 93, 67 and 43.
Selection of metabolites
Overall, both the lid and pitcher datasets are very sparse, with 91.35% zeros in the lid dataset and 91.86% in the pitcher dataset. These datasets are also high dimensional, as described above, with 560 and 589 compounds, respectively, in the lid and pitcher datasets. We performed sparse hierarchical clustering of the data in order to reduce the dimensionality of the datasets and identify the compounds important for clustering. The metabolite features selected using the qualitative and quantitative formats of the data are visualized as heat maps (S6–S9 Figs).
Integration of phylogenetic clustering
The MP-EST accession tree presented in [2] was integrated with metabolite profiling data. Firstly, the selected metabolite features were visualized as heat maps with the MP-EST accession tree (Fig 1A and Fig 2A, S1A and S2A Figs). Since the best bijective map between the samples of the two studies was selected for these visualizations, six samples from our compound dataset are omitted from each of the heat maps (Fig 1A and Fig 2A, S1A and S2A Figs). Secondly, the MP-EST accession tree was used to assess whether the metabolite profiles support the clade-level classification of the plant family. This was done by comparing the aWCDs against aBCDs as well as the background distance distribution formed by the aSLDs. The aWCDs were lower than aBCDs (Fig 1 and Fig 2, S1 and S2 Figs), indicating that the compound data was consistent with the clade-level classification. From the qualitative data of lids, all aWCDs were less than the mean and median values of the aBCDs. In comparison to the background distribution, eight out of nine aWCDs were less than the mean of the aSLDs and all the aWCDs were less than the median of the aSLDs (Fig 2B). Finally, the aWCDs were significantly lower than the aBCDs (Wilcoxon test P-value = 1.42e-05; Fig 2C). From the qualitative data of pitchers, all aWCDs were less than the mean and median values of the aBCDs as well as the aSLDs (Fig 2B), and the aWCDs were significantly lower than aBCDs (P-value = 5.109e-06; Fig 2C). The quantitative data weakly supported the clade-level classification (S1 and S2 Figs). From the quantitative data of lids, seven out of nine aWCDs were lower than the mean and median values of the aBCDs and aSLDs (S1B Fig), and the difference between aWCDs and aBCDs was marginally significant (P-value = 0.02; S1C Fig. From the quantitative data of pitchers, all aWCDs were lower than the mean of aBCDs, eight out of nine aWCDs were lower than the mean of aSLDs, seven aWCDs were less than the median of aBCDs, and six aWCDs were less than the median of aSLDs (S2B Fig). The difference between aWCDs and aBCDs was marginally significant (P-value = 0.004; S2C Fig).
Discussion
Coniine in Sarracenia sp.
The presence of coniine has been reported from poison hemlock and twelve Aloe species [22,23]. The only report of coniine in Sarraceniaceae is by Mody et al. [21], who isolated 5 mg of coniine from 45 kg fresh pitchers of S. flava via steam distillation. This is in contrast to the results of Romeo et al. [11], who did not detect any alkaloids or volatile amines in Sarracenia. We have now confirmed the findings of Mody et al. [21] and also found that coniine occurs, often in low amounts, in at least seven other species, e.g. S. purpurea (Table 2). It remains unknown where exactly coniine is biosynthesized in Sarracenia spp., since the compound was detected both in lids and in the actual pitchers. Biosynthesis of coniine has been studied in poison hemlock. In this case the carbon backbone is derived from the iterative coupling of butyryl-CoA and two malonyl-CoAs by a PKS, CPKS5 [24]. According to our analysis, genes encoding such enzymes are present in the transcriptomes [32] of S. psittacina and S. purpurea. Both species harbour three contigs which represent two to three PKSs. The exact number could not be determined because the N-terminal contig cannot be assigned to either of the C-terminal contigs. The contigs do not represent full-length sequences and therefore it is impossible to clearly assign them as PKSs for coniine biosynthesis in Sarracenia spp. Important mutations might be located outside the observed area, preventing distinction from chalcone synthases involved in anthocyanin synthesis [9,10].
An important question is the function of coniine in Sarracenia. Why should plants living in nutrient-poor environments produce a nitrogenous compound if there are no benefits? Butler and Ellison [39] studied nitrogen acquisition of S. purpurea and reported that the pitchers are in fact very efficient in prey capture and could thus greatly enhance the available nitrogen for the following growth season. Mody et al. [21] postulated that coniine could be an insect-stunning agent. Coniine did indeed paralyze fire ants, but probably the tested concentrations were not physiological [21]. Another function for coniine could be insect attraction, as suggested by Harborne [25] and Roberts [40], who identified coniine as a floral scent compound in poison hemlock. In conclusion, it appears that an investment in coniine biosynthesis could have a double benefit by enhancing both insect attraction and retention.
Metabolite profiles of Sarracenia and Darlingtonia
There are several previous reports on Sarracenia volatiles [7,8]. For example, Miles et al. [7] reported benzothiazole, benzyl alcohol, heptadecane and tridecane from S. flava, which we also found from Sarracenia spp. Nonanal, a floral scent compound widespread in the plant kingdom [28], was found from Sarracenia spp. lids in our study. The compound is known to attract mosquitos [41], and Miles et al. [7] described it as one of S. flava’s volatile organic compounds. The Venus flytrap (Dionaea muscipula), another carnivorous plant, emits this volatile organic compound when it is feeding on fruit flies (Drosophila melanogaster) [35].
Sarracenin (Fig 4A) has previously been reported from S. flava [17], S. alata, S. leucophylla, S. minor and S. rubra [18]. Our study confirmed the presence of this compound in all the aforementioned species, except S. minor, and revealed several new species containing sarracenin, namely, S. psittacina, S. purpurea and D. californica. The compound is volatile and attracts insects to Heliamphora sp. [4]. A possible explanation of why S. minor did not accumulate sarracenin in our study could be that our samples were not feeding on insects at the time of collection, and as a result, they did not synthesize the compound [4].
(A) Common and (B) specific constituents of Sarracenia and D. californica.
We also found (Z)-13-docosenamide (erucamide) to be a common compound in Sarracenia spp. and D. californica. It has previously been reported from H. tatei and H. heterodoxa [4], where it is a possible lubricating component of the nectar.
Other common compounds from Sarracenia sp. and D. californica are carboxylic acids (fatty acids) such as tetradecanoic, hexadecanoic and (Z)-9-hexadecenoic acids. All three are floral scent compounds and the latter is known from Hydnora africana [42]. Hexadecanoic acid is emitted by the Venus fly trap as a volatile organic compound after feeding [35].
Sarracenia spp. display a huge variety of unique compounds which are found only in their lid and/or pitcher. Actinidine is a floral scent compound known from Sauromatum guttatum [43] and an insect pheromone in Hymenoptera [44]. Trans-Jasmone acts either as an insect attractant or repellent depending on the insect species. Pulegone (Fig 4B) is a floral scent compound of Tilia sp. [45] and Agastache sp. [46], and functions as an insecticide [47]. 14-β-Pregna is a sex pheromone of the insect Eurygaster maura [48]. Lagumicine was found from S. oreophila lid. Previously it had been found from Alstonia angustifolia var. latifolia [49]. Miles et al. [17] suggested, on the basis of the possible cleavage of sarracenin, that terpene indole alkaloids could be synthesized in Sarracenia spp.
The studied accessions of Sarraceniaceae are characterized by a large number of diverse metabolites, with nearly 600 metabolites identified in lids as well as in pitchers. They are also characterized by a huge chemical diversity, as the metabolite compositions of different plants were largely distinct. Unlike mutation data from highly conserved genomic loci, the data that mainly displays wide heterogeneity of samples is not suitable for constructing taxonomies. Knudsen et al. [29] concluded that the usability of floral scent compounds in chemotaxonomy is limited because chemical composition usually differs even between closely related species. The composition may also vary among genera of a specific family, as it may vary among species of a given genus. Thus, the chemical composition alone is of little use for phylogenetic estimates above the genus level. As expected, clustering derived from our data alone does not agree with the phylogenetic structure of the accessions (see the column dendrograms in S6–S9 Figs).
The available phylogenetic information, on the other hand, may help us to understand the current data. We sought to explain the metabolite composition of plants with the known phylogenetic information from [2]. We successfully demonstrated that the metabolite data conform with the clade-level classification of the plant family and hence that the phylogeny can explain the metabolite composition of the plants to some extent. Notably, whereas the qualitative data could be largely explained by phylogeny (Fig 1 and Fig 2), the concordance of quantitative data with the clade-level classification was relatively weaker (S1 and S2 Figs). Thus, we speculate that evolution may more directly affect the presence or absence of specific chemicals than the exact amount in which the chemicals are present.
We have limited the focus of the current data mining to cataloging and visualizing the data. Given the dominance of zeroes, the current datasets may benefit from computational methods specially designed for zero-inflated or left-censored data. But such a detailed computational analysis is out of the scope of this biochemical profiling study.
Conclusion
Studied accessions of Sarraceniaceae possessed a diverse variety of compounds. Lids and pitchers were studied separately and approximately 600 compounds were detected in both collections. The accessions also showed huge diversity, with every accession containing unique compounds. Coniine was newly detected in seven Sarracenia species in addition to the known source, S. flava. However, we could not identify a specific candidate gene involved in coniine biosynthesis in Sarracenia spp. Among the common constituents of Sarraceniaceae are sarracenin, erucamide, and nonanal. By integrating existing phylogenetic information of Sarraceniaceae, we successfully demonstrated that the phylogeny can explain the metabolite composition of the plants. Phylogeny explained the presence or absence of compounds more strongly than their concentrations.
Supporting information
S1 Fig. Visualization of selected metabolite features from the quantitative data of lids.
(A) Heat map visualization of selected metabolite features from the quantitative data of lids. The phylogenetic tree from [2] is displayed as the column dendrogram. Six samples of our dataset (14, 31, 35, 37, 44, and 46) are omitted from this heat map, based on the sample selection procedure described in the Methods section. (B) Comparison of average within-clade distances (aWCDs) against the background distribution of average species-level distances (aSLDs) and average between-clade distances (aBCDs). Distribution of aSLDs was calculated using qualitative data of the selected metabolite features and displayed in a density plot. The black vertical lines mark the individual aWCDs. The orange dashed and dotted lines show the mean and median of aSLDs. The purple dashed and dotted lines show the mean and median of aBCDs. (C) Comparison of aWCDs (green continuous density line) with aBCDs (orange dashed density line).
https://doi.org/10.1371/journal.pone.0171078.s001
(PDF)
S2 Fig. Visualization of selected metabolite features from the quantitative data of pitchers.
(A) Heat map visualization of selected metabolite features from the quantitative data of pitchers. The phylogenetic tree from [2] is displayed as the column dendrogram. Six samples of our dataset (1, 11, 31, 38, 42, and 46) are omitted from this heat map, based on the sample selection procedure described in the Methods section. (B) Comparison of average within-clade distances (aWCDs) against the background distribution of average species-level distances (aSLDs) and average between-clade distances (aBCDs). Distribution of aSLDs was calculated using qualitative data of the selected metabolite features and displayed in a density plot. The black vertical lines mark the individual aWCDs. The orange dashed and dotted lines show the mean and median of aSLDs. The purple dashed and dotted lines show the mean and median of aBCDs. (C) Comparison of aWCDs (green continuous density line) with aBCDs (orange dashed density line).
https://doi.org/10.1371/journal.pone.0171078.s002
(PDF)
S3 Fig. Alignment of Sarracenia PKSs with selected plant-PKSs translated into an amino acid sequence.
Conserved amino acids of the active site are bolded, and colored amino acids indicate mutated amino acids of the active site. GenBank accession numbers: Conium maculatum CPKS1 (KP726914), Conium maculatum CPKS2 (KP726915), Conium maculatum CPKS5 (KP726916), Gerbera hybrida 2PS (CAA86219.2), Gerbera hybrida CHS1 (Z38096.1), Medicago sativa CHS2 (L02902.1).
https://doi.org/10.1371/journal.pone.0171078.s003
(PDF)
S4 Fig. Barplot of distribution of compounds across lid samples.
https://doi.org/10.1371/journal.pone.0171078.s004
(PDF)
S5 Fig. Barplot of distribution of compounds across pitcher samples.
https://doi.org/10.1371/journal.pone.0171078.s005
(PDF)
S6 Fig. Heat map of selected features obtained from qualitative data of lids.
https://doi.org/10.1371/journal.pone.0171078.s006
(PDF)
S7 Fig. Heat map of selected features obtained from quantitative data of lids.
https://doi.org/10.1371/journal.pone.0171078.s007
(PDF)
S8 Fig. Heat map of selected features obtained from qualitative data of pitchers.
https://doi.org/10.1371/journal.pone.0171078.s008
(PDF)
S9 Fig. Heat map of selected features obtained from quantitative data of pitchers.
https://doi.org/10.1371/journal.pone.0171078.s009
(PDF)
S1 Table. Unique compounds with their concentration percentages in metabolite samples.
Unique compounds found in Sarracenia and D. californica (lids and pitchers separately).
https://doi.org/10.1371/journal.pone.0171078.s010
(XLSX)
S2 Table. Sample comparison in pairs.
Samples are compared to each other in lids and pitchers separately.
https://doi.org/10.1371/journal.pone.0171078.s011
(XLSX)
Acknowledgments
We thank Kaarina Viljanen, Anna-Liisa Ruskeepää and Airi Hyrkäs for GC-MS analyses, Jan Schlauer and Teemu Teeri for commenting on a draft of the manuscript, Michael Bailey for proofreading and language editing and Russell L. Malmberg for providing contigs of S. psittacina and S. purpurea transcriptomes.
Author Contributions
- Conceptualization: HH PG HR.
- Data curation: PG.
- Formal analysis: HH PG TSL.
- Funding acquisition: HH PG HR.
- Investigation: HH PG TSL HR.
- Methodology: HH PG TSL HR.
- Project administration: HR.
- Resources: HH PG TSL HR.
- Software: PG.
- Supervision: HR.
- Validation: PG TSL.
- Visualization: PG.
- Writing – original draft: HH PG HR.
- Writing – review & editing: HH HR.
References
- 1.
McPherson S, Wistuba A, Fleischmann A, Nerz J. Sarraceniaceae of South America. Poole, Dorset, England: Redfern Natural History Productions; 2011.
- 2. Stephens JD, Rogers WL, Heyduk K, Cruse-Sanders JM, Determann RO, Glenn TC, et al. Resolving phylogenetic relationships of the recently radiated carnivorous plant genus Sarracenia using target enrichment. Mol Phylogenet Evol. 2015; 85: 76–87. pmid:25689607
- 3. Dress WJ, Newell SJ, Nastase AJ, Ford JC. Analysis of amino acids in nectar from pitchers of Sarracenia purpurea (Sarraceniaceae). Am J Bot. 1997; 84: 1701–1706. pmid:21708574
- 4. Jaffé K, Blum MS, Fales HM, Mason RT, Cabrera A. On insect attractants from pitcher plants of the genus Heliamphora (Sarraceniaceae). J Chem Ecol. 1995;21: 379–384 pmid:24234068
- 5. Schlauer J, Nerz J, Rischer H. Carnivorous plant chemistry. Acta Bot Gall. 2005;15(2): 187–195.
- 6. Harris CS, Asim M, Saleem A, Haddad PS, Arnason JT, Bennett SAL. Characterizing the cytoprotective activity of Sarracenia purpurea L., a medical plant that inhibits glucotoxicity in PC12 cells. BMC Complement Altern Med. 2012;12: 245. pmid:23216659
- 7. Miles DH, Kokpol U, Mody NV. Volatiles of Sarracenia flava. Phytochemistry. 1975;14: 845–846.
- 8. Jürgens A, El-Sayed AM, Suckling DM. Do carnivorous plants use volatiles for attracting prey insects? Funct Ecol. 2009;23: 875–887.
- 9. Sheridan PM, Mills RR. 1998. Presence of proanthocyanidins in mutant green Sarracenia indicate blockage in late anthocyanin biosynthesis between leucocyanidin and pseudobase. Plant Sci. 1998;135: 11–16.
- 10. Sheridan PM, Griesbach RJ. Anthocyanidins of Sarracenia L. flowers and leaves. Hortscience. 2001;36: 384.
- 11. Romeo JT, Bacon JD, Marby TJ. 1977. Ecological considerations of amino acids and flavonoids in Sarracenia species. Biochem Syst Ecol. 1977;5: 117–120.
- 12. Hu J-F, Starks CM, Williams RB, Rice SM, Norman VL, Olson KM, et al. Secoiridoid glycosides from the pitcher plant Sarracenia alata. Helv Chim Acta. 2009; 92: 273–280.
- 13. Muhammad A, Haddad PS, Durst T, Arnason JT. 2013. Phytochemical constituents of Sarracenia purpurea L. (pitcher plant). Phytochemistry. 2013;94: 238–242 pmid:23810285
- 14. Cieniak C, Walshe-Roussel B, Liu R, Muhammad A, Saleem A, Haddad PS, et al. Phytochemical comparison of the water and ethanol leaf extracts of the Cree medicinal plant, Sarracenia purpurea L. (Sarraceniaceae). J Pharm Pharm Sci. 2015;18(4): 484–93. pmid:26626246
- 15. Cipollini DF Jr, Newell SA, Nastase AJ. Total carbohydrates in nectar of Sarracenia purpurea L. (Northern Pitcher Plant). Am Midl Nat. 1994;131: 374–377.
- 16. Deppe JL, Dress WJ, Nastase AJ, Newell SJ, Luciano CS. Diel variation of sugar amount in nectar from pitchers of Sarracenia purpurea L. with and without insect visitors. Am Midl Nat. 2000;144: 123–132.
- 17. Miles DH, Kokpol U, Bhattacharayya J, Atwood JL, Stone KE, Bryson TA, et al. Structure of sarracenin. An unusual enol diaceal monoterpene from the insectivorous plant Sarracenia flava. J Am Chem Soc. 1976;98: 1569–1573.
- 18. Newman T, Ibrahim S, Wheeler JW, McLaughlin WB, Petersen RL, Duffield RM. Identification of sarracenin in four species of Sarracenia (Sarraceniaceae). Biochem Syst Ecol. 2000;28: 193–195.
- 19. Miles DH, Kokpol U, Zalkow LH, Steindel SJ, Nabors JB. Tumor inhibitors I: Preliminary investigation of antitumor activity of Sarracenia flava. J Pharm Sci. 1974;63: 613–615. pmid:4828716
- 20. Miles DH, Kokpol U. Tumor inhibitors II: Constituents and antitumor activity of Sarracenia flava. J Pharm Sci. 1976; 65: 284–285. pmid:1255463
- 21. Mody NV, Henson R, Hedin PA, Kokpol U, Miles DH. Isolation of insect paralysing agent coniine from Sarracenia flava. Experientia. 1976;32: 829–830.
- 22. Dring JV, Nash RJ, Roberts MF, Reynolds T. Hemlock alkaloids in Aloes. Occurrence and distribution of γ-coniceine. Planta Med. 1984;50: 442–443. pmid:17340347
- 23. Nash RJ, Beaumont J, Veitch NC, Reynolds T, Benner J, Hughes CNG, et al. Phenylethylamine and piperidine alkaloids in Aloe species. Planta Med. 1992;58: 84–87. pmid:17226441
- 24. Hotti H, Seppänen-Laakso T, Arvas M, Teeri TH, Rischer H. Polyketide synthases from poison hemlock (Conium maculatum L.). FEBS J. 2015;282(21): 4141–56. pmid:26260860
- 25.
Harborne JB. Introduction to ecological biochemistry. 2nd ed. London, UK: Academic Press; 1982.
- 26. Ellison AM, Butler ED, Hicks EJ, Naczi RF, Calie PJ, Bell CD, et al. Phylogeny and biogeography of the carnivorous plant family Sarraceniaceae. PLoS One. 2012;7(6):e39291. pmid:22720090
- 27. Bayer RJ, Hufford L, Soltis DE. Phylogenetic relationships in Sarraceniaceae based on rbcL and ITS sequences. Syst Bot. 1996;21: 121–134.
- 28. Neyland R, Merchant M. Systematic relationships of Sarraceniaceae inferred from nuclear ribosomal DNA sequences. Madroño. 2006;53: 223–232.
- 29. Knudsen JT, Eriksson R, Gershenzon J, Ståhl B. Diversity and distribution of floral scent. Bot Rev. 2006;72: 1–120
- 30.
McPherson S, Schnell D. Sarraceniaceae of North America. Poole, UK: Redfern Natural History Productions Ltd; 2011.
- 31. Häkkinen ST, Moyano E, Cusidó RM, Palazón J, Piñol MT, Oksman-Caldentey K-M. Enhanced secretion of tropane alkaloids in Nicotiana tabacum hairy roots expressing heterologous hyoscyamine-6β-hydroxylase. J Exp Bot. 2005;56: 2611–2618. pmid:16105856
- 32. Srivastava A, Rogers WL, Breton CM, Cai L, Malmberg RL. Transcriptome analysis of Sarracenia, an insectivorous plant. DNA Res. 2011;18: 253–261. pmid:21676972
- 33. Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28(12): 1647–9. pmid:22543367
- 34. Junghans H, Dalkin K, Dixon RA. Stress responses in alfalfa (Medicago sativa L.). 15. Characterization and expression patterns of members of a subset of the chalcone synthase multigene family. Plant Mol Biol. 1993; 22: 239–253. pmid:8507827
- 35. Kreuzwieser J, Scheerer U, Kruse J, Burzlaff T, Honsel A, Alfarraj S, et al. The Venus flytrap attracts insects by the release of volatile organic compounds. J Exp Bot. 2014;65(2): 755–66. pmid:24420576
- 36. Witten DM, Tibshirani R. A framework for feature selection in clustering. JASA. 2010;105(490): 713–726. pmid:20811510
- 37.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2014. Available: http://www.R-project.org/.
- 38. Austin MB, Noel JP. The chalcone synthase superfamily of type III polyketide synthases. Nat Prod Rep. 2003;20(1): 79–110. pmid:12636085
- 39. Butler JL, Ellision AM. Nitrogen cycling dynamics in the carnivorous northern pitcher plant, Sarracenia purpurea. Funct Ecol. 2007;21: 835–843.
- 40.
Roberts MF. Enzymology of alkaloid biosynthesis. In: Roberts MF, Wink M, editors. Alkaloids: Biochemistry, ecology, and medicinal applications. New York, USA: Springer; 1998. pp. 109–146.
- 41. Syed Z, Leal WS. Acute olfactory response of Culex mosquitoes to a human- and bird-derived attractant. Proc Natl Acad Sci U S A. 2009;106(44): 18803–8. pmid:19858490
- 42. Burger BV, Munro ZM, Visser JH. Determination of plant volatiles 1: analysis of the insect-attracting allomone of the parasitic plant Hydnora africana using Grob-Habich activated charcoal traps. J High Resol Chromatogr. 1988;11: 496–499.
- 43. Borg-Karlson A-K, Valterová I, Nilsson LA. Volatile compounds from flowers of six species in the family Apiaceae: Bouquets for different pollinators? Phytochemistry. 1993;35(1):111–9.
- 44. Janssen E, Bestmann HJ, Hölldobler B, Kern F. N,N-dimethyluracil and actinidine, two pheromones of the ponerine ant Megaponera foetens (Fab.) (Hymenoptera: Formicidae). J Chem Ecol. 1995;21(12): 1947–55. pmid:24233898
- 45. Buchbauer G, Remberg B, Jirovetz L. Comparative headspace analysis of living and fresh cut lime tree flowers (Tiliae flores). Flav Fragr J. 1995;10: 221–224.
- 46. Wilson LA, Senechal NP, Widrlechner MP. Headspace analysis of the volatile oils of Agastache. J Agric Food Chem. 1992;40: 1362–1366.
- 47. Franzios G, Mirotsou M, Hatziapostolou E, Kral J, Scouras ZG, Mavragani-Tsipidou P. Insecticidal and genotoxic activities of mint essential oils. J Agric Food Chem. 1997;45: 2690–2694.
- 48. Durak D, Kalender Y. Fine structure and chemical analysis of the metathoracic scent gland of Eurygaster maura (Linnaeus, 1758) (Heteroptera: Scutelleridae). Folia Biol (Krakow). 2007;55(3–4): 133–41.
- 49. Kam TS, Choo YM. Alkaloids from Alstonia angustifolia. Phytochemistry. 2004;65(5): 603–8. pmid:15003424