Skip to main content

Can phylogeny predict chemical diversity and potential medicinal activity of plants? A case study of amaryllidaceae



During evolution, plants and other organisms have developed a diversity of chemical defences, leading to the evolution of various groups of specialized metabolites selected for their endogenous biological function. A correlation between phylogeny and biosynthetic pathways could offer a predictive approach enabling more efficient selection of plants for the development of traditional medicine and lead discovery. However, this relationship has rarely been rigorously tested and the potential predictive power is consequently unknown.


We produced a phylogenetic hypothesis for the medicinally important plant subfamily Amaryllidoideae (Amaryllidaceae) based on parsimony and Bayesian analysis of nuclear, plastid, and mitochondrial DNA sequences of over 100 species. We tested if alkaloid diversity and activity in bioassays related to the central nervous system are significantly correlated with phylogeny and found evidence for a significant phylogenetic signal in these traits, although the effect is not strong.


Several genera are non-monophyletic emphasizing the importance of using phylogeny for interpretation of character distribution. Alkaloid diversity and in vitro inhibition of acetylcholinesterase (AChE) and binding to the serotonin reuptake transporter (SERT) are significantly correlated with phylogeny. This has implications for the use of phylogenies to interpret chemical evolution and biosynthetic pathways, to select candidate taxa for lead discovery, and to make recommendations for policies regarding traditional use and conservation priorities.


During evolution, plants and other organisms have developed a diversity of chemical defence lines, leading to the evolution of various groups of specialized metabolites such as alkaloids, terpenoids, and phenolics, selected for their endogenous biological function [17]. Intuitively, a correlation between phylogeny and biosynthetic pathways is sometimes assumed [1, 810] and could offer a predictive approach enabling deduction of biosynthetic pathways [6, 1115], defence against herbivores [16, 17], more efficient selection of plants for the development of traditional medicine and lead discovery [1822] as well as inform conservation priorities [23].

Several studies have confirmed the usefulness of specialized metabolites such as glucosinolates, iridoids, sesquiterpene lactones, flavonoids, and phenolics to support molecular based phylogenies contradicting morphologic patterns [11, 12, 2429]. On the contrary, several studies have found inconsistency of specialized metabolite profiles at various taxonomic levels and indicated that specialized chemistry and anti-herbivore defence syndromes tend to be poorly correlated with plant phylogeny [6, 7, 13, 30].

Lack of congruence between specialized chemistry and phylogeny may be caused by several different phenomena. One contributing factor is convergent evolution by which the same or similar traits originate independently in taxa that are not necessarily closely related, often in response to similar environmental challenges [17, 31]. A striking example of convergent evolution is the common use of the sex pheromone (Z)-7-dodecen-l-yl acetate by over 120 species of primarily Lepidopteran insects and female Asian elephants, Elephas maximus[32]. In relation to plants, well known convergent morphological adaptations are the occurrence of prickles, thorns, and spines, which have evolved to avoid or limit herbivory [33], succulence as adaptation to dry environments in both North American Cactaceae and African Euphorbia[34, 35], and insectivorous plants, which have evolved several times in response to a nitrogen-deficient environment [36]. Likewise, chemical defence lines may also have arisen independently in unrelated taxa, and convergent evolution in plant specialized metabolism appears to be surprisingly common [6, 17, 31, 37]. For example, the ability to produce cyanogenic glycosides appears to have evolved independently in many different plant families [17, 31].

However, convergent evolution can be difficult to verify because absence of evidence is not evidence of absence and it is possible that some compounds presently considered to be limited to some lineages are indeed more universally found in plants [31]. Specialized compounds are not continuously expressed, but may be produced as a response to herbivory or other damage, the expression may also be dependent on the environment [38] and plants often use a combination of several defensive traits [7, 17]. In addition, chemosystematic data are scattered in the literature and negative results are often not reported. Absence or presence of a compound is also dependent on the amount of plant material investigated as well as the detection limit of the analytical methods [27, 39]. Finally, the existence of several different phytochemical methods can cause inconsistence in the results reported in the literature.

Nevertheless, reports of incongruence between phytochemistry and phylogeny have questioned the degree of correlation between phylogeny and specialized metabolites, indicating that such a correlation cannot simply be assumed [6]. However, this relationship has rarely been tested because of lack of accurate estimates of phylogeny and corresponding chemical data; lack of tradition for interdisciplinary studies bridging botany, chemistry, and molecular systematics; and appropriate statistical tools. Consequently, the potential predictive power is unknown [17]. In the present study, we use Amaryllidaceae subfamily Amaryllidoideae as a model system for testing the correlation between phylogenetic and chemical diversity and biological activity.

Amaryllidaceae subfamily Amaryllidoideae sensu APG III [40] (formerly recognized as a separate family, Amaryllidaceae J.St.-Hil.) is a widely distributed subfamily of 59 genera and about 850 species. Amaryllidoideae has centres of diversity in South Africa, South America, particularly in the Andean region, and in the Mediterranean, three of the recognized hotspots of biodiversity on Earth [41, 42] ( Plants of the Amaryllidoideae are used in traditional medicine to treat mental problems, primarily in Southern Africa [43, 44]. The traditional use of plants of Amaryllidoideae has been related to their unique and subfamily specific alkaloid chemistry (Figure 1). Over 500 alkaloids have been described from various species and have been subdivided into 18 major types based on hypothetical biosynthetic pathways [4547]. Extracts or isolated alkaloids of Amaryllidaceae species have shown activity in vitro in a range of assays related to disorders of the central nervous system, primarily Alzheimers disease (inhibition of acetylcholinesterase, AChE) [21, 4851], and anxiety and depression (affinity to the serotonin re-uptake transporter, SERT) [21, 52, 53]. Galanthamine is registered in a number of countries as an AChE inhibitor (Reminyl or Rezadyne; Janssen Pharmaceutica) [54]. Another Amaryllidaceous alkaloid, sanguinine (9-O-demethylgalanthamine), is shown to be a ten times more potent inhibitor of AChE than galanthamine in vitro[55].

Figure 1

Alkaloid types recovered in the present study. Alkaloids were classified to type according to the hypothetical biosynthetic pathways proposed by Jin [45, 46]. Marvin was used for drawing and displaying chemical structures, Marvin 5.9.0, 2012, ChemAxon (

Amaryllidaceae subfamily Amaryllidoideae is therefore an ideal model system for comparing phylogenetic and chemical diversity with bioactivity. Previous molecular phylogenetic studies based on plastid gene regions (rbcL, trnLF, and ndhF) have confirmed Amaryllidoideae as monophyletic and resolved many taxa into geographically confined monophyletic groups [42, 56]. The African tribe Amaryllideae has been well supported as sister group to the remaining taxa. However, the relationship among several other early diverging lineages, in particular the African tribes Haemantheae and Cyrtantheae, and the Australasian Calostemmateae are not well supported by previous studies and remain problematic [42]. In a study by Meerow and Snijman [42] based on parsimony analysis of plastid ndhF sequences, Amaryllideae also resolved as sister to the remainder of the subfamily. The next major split resolved a clade with American and Eurasian subclades, and an African/Australasian clade with Cyrtantheae as sister to a Haemantheae/Calostemmateae clade. However, this African/Australasian clade was not supported by bootstrap analysis.

The objectives of the present study were: (1) to produce a comprehensive and well supported phylogenetic hypothesis of Amaryllidaceae subfamily Amaryllidoideae based on total evidence from DNA regions from all three plant genomes; (2) to test for correlation between phylogenetic and chemical diversity and central nervous system (CNS) related activities.



The ITS region was the most informative region followed by the matK region (Table 1). The trnLF and the nad1 regions resolved 10% or less of the clades with strong support defined as ≥ 90% bootstrap support. There were no strongly supported conflicts among any regions (Bayesian consensus tree with posterior probabilities and parsimony bootstrap consensus tree for the total evidence analysis and bootstrap consensus trees of individual regions are provided as Additional file 1: Figures S1-S7). The number of resolved clades (87%; Table 1) and the number of resolved clades supported by  90% bootstrap (62%) was highest in the total evidence analysis, which was also the only analysis that resolved all the major lineages. The Bayesian analysis of the total evidence matrix provided the same overall topology as parsimony analysis and all major clades were strongly supported by Bayesian analysis (Figure 2; Additional file 1: Figures S1 and S2).

Table 1 Details of the matrices included in this study
Figure 2

Phylogenetic hypothesis for Amaryllidaceae subfamily Amaryllidoideae. Obtained after 1,000,000 replicates of Bayesian inference. Parsimony bootstrap percentages and Bayesian posterior probabilities (BS/PP) are indicated for major clades only. Examples of members are illustrated on the right hand side.

The topology (Figure 2) of the total evidence analysis largely supports previous studies [42, 56]. The African tribe Amaryllideae (100% BS; PP = 1.00) is sister to the remainder of the Amaryllidoideae (100% BS; PP = 1.00), and this is strongly supported by all analyses. The next major split resolves an American clade (66% BS; PP = 0.99) and a Eurasian clade (65% BS; PP = 1.00) as sisters (100% BS; PP = 1.00) and a clade (66% BS; PP = 1.00) with the African monogeneric tribe Cyrtantheae (100% BS; PP = 1.00), and tribe Haemantheae (100% BS; PP = 1.00) as sisters (100% BS; PP = 1.00), and the Australasian tribe Calostemmateae (100% BS; PP = 1.00) as sister to these.

In the ITS analysis ( Additional file 1: Figure S3), tribe Amaryllideae (100% BS) is sister to the remainder of Amaryllidoideae (61% BS). Within the remainder of Amaryllidoideae, tribe Calostemmateae (100% BS) is sister to a clade (54% BS) including tribes Cyrtantheae (100% BS), Haemantheae 95% BS) and the American and European Amaryllidoideae. Tribes Cyrtantheae and Haemantheae are sisters (96% BS). In the combined plastid analysis ( Additional file 1: Figure S6), Cyrtantheae is sister to the remainder of Amaryllidoideae except tribe Amaryllideae (75% BS). In both the matK ( Additional file 1: Figure S4 supporting online material) and the combined plastid analysis tribe Calostemmateae is sister to tribe Haemantheae (matK: 70% BS; plastid: 94% BS).

The low bootstrap support (65%) for the Eurasian clade in the total evidence analysis (Figure 2, Additional file 1: Figure S2) may be caused by uncertainty in the placement of the genus Lycoris. The remainder of the Eurasian clade is strongly supported in all analyses except trnLF and nad1, which are the two regions providing the least resolution and support in general ( Additional file 1: Figures S3-S7 in the supporting online material).

Relationship of phylogeny to chemistry and bioactivity

The relationship of individual types of compounds and biological activity could be assessed using the D metric developed to deal with discrete binary characters [57]. D is equal to 1 if the observed chemical component has a random distribution (i.e. no phylogenetic signal). D is equal to 0 if the component is distributed exactly as would be predicted under a Brownian motion model of gradual divergent evolution (i.e. strong phylogenetic signal). See Materials and Methods for details. Of the seven alkaloid types which are found in more than one species we found evidence for some phylogenetic signal of five types (Table 2a) [57]. With the exception of tazettine and galanthindole, D values were significantly different from that expected under a random distribution of the components across the phylogenetic tree (D = 1). Inhibition of acetylcholinesterase (AChE) and binding to the serotonin re-uptake transporter (SERT) were used as proxies for biological activity. Both measures of bioactivity, AChE activity and SERT activity also displayed significant phylogenetic signal (Table 2b). However, in none of the above cases was the phylogenetic signal sufficiently strong to be considered as indistinguishable from a Brownian motion model of evolution, where traits are strongly ‘clumped’ on the phylogeny (and D = 0) [57].

Table 2 Phylogenetic signal in chemistry and biological activity determined using Fritz and Purvis’s[57]D metric (see Materials and Methods for details)

There was a statistically highly significant correlation between differences in chemical profile and phylogenetic distance, indicating that closely-related species tend to have more similar chemical profiles than more distantly-related species, although the effect was not strong (Mantel test: r = 0.085, p = 0.002). There was also statistically significant correlation between chemical profile and phylogenetic distance in the genus level comparison (Mantel test: r = 0.090, p = 0.024), although the effect was also weak.


Phylogeny of Amaryllidoideae

For the purpose of the present study, we consider the total evidence approach to provide the best estimate of phylogeny and all major lineages are supported by both parsimony and Bayesian analyses. The present study has doubled previous sampling of Amaryllidoideae from 51 species [56] to 108 and from combined analysis of two DNA regions [42, 56] to four DNA regions representing all three genomes. The only previous study resolving relationships among basal lineages was based on plastid ndhF sequences [42] and resolved Calostemmateae and Haemantheae as sisters and tribe Cyrtantheae as sister to these. However, in the present study (Figure 2), the African tribes Cyrtantheae and Haemantheae are strongly supported by both bootstrap and Bayesian posterior probabilities as sister clades. Tribe Calostemmateae is sister to these, although this was only weakly supported by the bootstrap, but strongly supported by Bayesian posterior probabilities. A sister group relationship of the two African tribes Cyrtantheae and Haemantheae and the Australasian tribe Calostemmateae as sister to these appears more convincing than the alternative based on biogeography. However, in terms of morphology there may be some room to question this relationship. The indehiscent capsule of Calostemmateae has more in common with the indehiscent baccate fruit of Haemantheae (resembling the unripe fruit of Clivia, Scadoxus, Haemanthus, and Cryptostephanus) than with the dehiscent capsule of Cyrtanthus.

Phylogenetic signal of chemical diversity and bioactivity

Our approach to quantify overall correlation between chemical and phylogenetic diversity has previously been applied to show positive correlations between pheromone differences and nucleotide divergence in Bactrocera fruit flies [58] and phylogenetic correlation of cuticular hydrocarbon diversity in ants [59]. We have now shown the potential application of this approach to explore correlations between phylogenetic and chemical diversity of medicinal plants.

We found significant phylogenetic signal for five out of seven tested individual alkaloid types and for both AChE and SERT bioactivity proxies, although the signal was not strong enough to be indistinguishable from a Brownian model of evolution, where traits are strongly clumped on the phylogeny [57]. There was also a highly significant correlation between differences in chemical profiles and phylogenetic distance in both species and generic level analyses. Despite this, there was often still considerable difference in chemical make-up even between phylogenetically very close species (Figure 3) with the average number of alkaloid types differing between congeners being 2.26. Whereas members of some genera such as Crinum and Galanthus have generally similar chemical profiles, other genera such as Hippeastrum and Narcissus have striking diversity.

Figure 3

Relationship between phylogenetic distance and chemical diversity (number of alkaloid group differences) for Amaryllidaceae subfamily Amaryllidoidae. A) Scatter plot showing distribution of points and best-fit regression line. The size of each point is proportional to the log-transformed number of data points contributing to it. B) The same data showing mean (±s.e.) number of differences for categories of phylogenetic distance (number of substitutions per site) for ease of visualization. The overall relationship is significant. There is a statistically highly significant correlation between differences in chemical profile and phylogenetic distance, although the effect is not strong (Mantel test: r = 0.085, p = 0.002), indicating that closely related species tend to have more similar chemical profiles than more distantly related species.

An explanation for the moderate correlation found could be either methodological artefacts or underlying ecological or genetic differences [38]. We minimized methodological artefacts by using the same plant accessions for both phylogenetic, chemical, and bioactivity studies, and by analysing our data with consistent methods. Chemical profiles were based on types deducted from hypothetical pathways and could be an oversimplification of the chemical diversity contained by over 500 individual alkaloid structures known from the subfamily [45, 47].

The strength of correlation could be dependent on taxonomic scale. Whereas alkaloids derived from norbelladine and its derivatives are almost exclusively restricted to the subfamily Amaryllidoideae [45], and alkaloids with AChE activity appear to be phylogenetically constrained within Narcissus [18], the considerable variation at the species and genus level found in this study corresponds well with within species variation of alkaloid profiles in for example Galanthus [51, 60].

Evaluation of extensive historical drug data, marine natural products, medicinal plants and bioactive natural products suggests that drugs are derived mostly from pre-existing drug-productive families that tend to be clustered rather than randomly scattered in the phylogenetic tree of life [61]. Zhu et al. [61] further suggest that efforts to identify new potential drugs can therefore be concentrated on exploring a number of drug-productive clusters. However, based on our results, such a strong presumed correlation between phylogeny and bioactivity appears to be an oversimplification, at least at the taxonomic scale tested in our study. Based on our data for the medicinally important plant subfamily Amaryllidoideae, it appears that phylogeny can predict chemical diversity and bioactivity, but considerable caution must be emphasized. We also suggest that phylogenetic correlation of chemical traits of interest may need to be assessed for a particular phylogenetic framework before it is used for prediction of occurrence in un-investigated taxa.

Application of phylogenetic prediction and in silicodata mining

A predictive approach could enable deduction of biosynthetic pathways, defence against herbivores, more efficient selection of plants for the development of traditional medicine and lead discovery as well as inform conservation priorities as outlined in the introduction. A plethora of data on phylogenetic relationships, chemical constituents and bioactivity are available through public databases (e.g., GenBank) and in the literature. Systematic in silico data mining could enable more efficient use of predictive approaches to speed up all of the above applications [22, 6165].

However, a methodological framework still needs to be developed. In the present study, we have suggested an approach for testing correlations between phylogenetic and chemical diversity and biological activity using experimental data generated for this purpose. One method for subsequent identification of specific nodes in phylogenies with high bio-screening potential has been proposed by Saslis-Lagoudakis et al.[22] using tools from community ecology. In this approach, a matrix of ethnomedicinal use was composed and used to identify nodes in a phylogeny of Pterocarpus (Fabaceae), which have more medicinal taxa related to a specific category of use than expected by chance. This approach could be useful for identifying alternative resources or substitute taxa in cases where supply of a medicinal plant or natural product of interest is limited or where species in use are subject to conservation concerns [22]. However, for the purpose of increasing the chance of making truly new discoveries such as new compounds and/or new activity profiles, it may be more relevant to identify clades that possess activity of interest and at the same time do not correspond to well known compounds with well known activity profiles [66].

Other methods for predictive in silico data mining may be combined with a phylogenetic selection approach, e.g., exploration of natural product chemical space as developed by Backlund and co-workers [65, 67, 68]. Another computerized geospatial tracking tool linking bioactive and phylogenetic diversity has been developed for microorganisms [63]. The concept of virtual parallel screening developed for natural products by Rollinger [64], which simultaneously enables fast identification of potential targets, insight into a putative molecular mechanism and estimation of a bioactivity profile, could allow for optimal selection of relevant targets.


In conclusion, we have shown significant correlation between phylogenetic and chemical diversity and biological activity in the medicinally important plant subfamily Amaryllidoideae. However, a correlation cannot be assumed for other study systems without considerable caution or testing. This has implications for the use of phylogenies to interpret chemical evolution and biosynthetic pathways, to select candidate taxa for lead discovery, and to make recommendations for policies regarding traditional use and conservation priorities. Phylogenetic prediction of chemical diversity and biological activity may provide an evolutionary based tool alone or in combination with other recently developed tools for in silico data mining of natural products and their bioactivity.


Taxon sampling

Specimens were collected in their natural habitat or obtained from botanical gardens or specialist nurseries. Sampling included 108 (over 10%) of circa 850 species in Amaryllidaceae subfamily Amaryllidoideae [69] with Agapanthus campanulatus L. used as outgroup ( Additional file 2). Sampling represents 43 of circa 60 genera and all currently recognized tribes except Griffineae Ravenna [41, 42, 56]. Samples from tribes Galantheae and Haemantheae were partly retrieved from previous studies [21, 51]. The same accessions of plant material were used for both molecular, chemical, and bioactivity analysis to minimize effects of intraspecific and ecological variation.

Phylogenetic analyses

DNA was extracted using the Qiagen DNeasy kit (Qiagen, Copenhagen, Denmark) from 20 mg of dried leaf fragments. Amplification and sequencing of the nuclear encoded ITS and plastid encoded matK and trnLF regions followed Larsen et al.[51]. Amplification and sequencing of the mitochondrial nad1 region followed Cuenca et al.[70]. Primers used for amplification and sequencing are listed in Additional file 3. Both strands were sequenced for each region for all taxa whenever possible. Sequences were edited and assembled using Sequencer 4.8TM software (Gene Codes, Ann Arbor, MI, USA). All sequences are deposited in GenBank and accession numbers JX464256- JX464610 are listed in Additional file 2. Sequences were aligned using default options in MUSCLE [71] as implemented in the software SeaView [72].

Phylogenetic analyses were conducted using both parsimony and Bayesian inference. Most parsimonious trees (MP) were obtained with PAUP v. 4.0b10 [73] using 1,000 replicates of random taxon addition sequence and TBR branch swapping saving multiple trees. All characters were included in the analyses and gaps were treated as missing data. We analysed the four regions separately to identify strongly supported phylogenetic conflicts among the regions, prior to performing a combined total evidence analysis. By using total evidence the explanatory and descriptive power of the data is maximized [74]. Bootstrap analyses [75] of the four individual datasets and the combined dataset were carried out using 1,000 replicates. Bayesian analysis of the combined dataset was performed with MrBayes 3.1.2 [76]. We first selected the best fitting model (GTR + I + G; Parameters: lset NST = 6 RATES = gamma) of molecular evolution using the Akaike criterion (AIC) in Modeltest v. 3.8 [77]. The analysis was performed with 1,000,000 generations on four Monte Carlo Markov chains. The average standard deviation of the split frequencies was 0.01 after 232,000 generations and < 0.005 after 1 million generations corresponding to an effective sample size of 115 using the software Tracer v. 1.5.0 [78]. The first 2,500 (25%) trees of low posterior probability were deleted and all remaining trees were imported into PAUP. A majority rule consensus tree was produced showing the posterior probabilities (PP) of all observed bi-partitions. We also performed a partitioned analysis allowing different models for the three genomes. However, in consideration of the limited information present in our plastid and mitochondrial datasets (Table 1), partition rich strategies are not always the best ones and in some cases less complex strategies have performed better [79, 80]. Although the Bayesian MCMC approach is good at handling complex models, there is a risk of over-parameterization, which can result in problems with convergence and excessive variance in parameter estimates [81].

Chemical diversity

Alkaloids were extracted from 300 mg dried bulb scales using 0.1% H2SO4 and clean-up on ion-exchange solid phase columns as described by Larsen et al.[51]. All extracts were concentrated under vacuum until dryness and re-dissolved to a standard concentration of 5 mg ml-1 in MeOH. Alkaloid profiles were obtained by gas chromatography–mass spectrometry (GC-MS) as described by Larsen et al.[51] using a method developed by Berkov et al.[82]. Alkaloids were identified to type by comparison with the NIST 08 Mass Spectral Search Program, version 2.0 (NIST, Gaithersburg, Maryland) and with published spectral data. Alkaloid structures were scored to one of eighteen types (Figure 1) proposed by Jin [45, 46] based on hypothetical biosynthetic pathways [51]. Only nine of the eighteen alkaloid types were recorded in the present study (Figure 1). Each type show characteristic fragmentation patterns in the MS-spectra [83]. In most of the cases, the database proposals with highest similarity could therefore be used to score the candidate structure indirectly to one of the types. Candidate structures were excluded from the profile if they could not be scored unambiguously to types.

In vitro biological activity

AChE inhibition and SERT affinity of the standardized alkaloid extracts were tested using published methods [21]. AChE activity was conducted using isolated acetylcholinesterase (Electrophorus electricus, Sigma, Germany) and SERT activity using homogenates of whole rat brains except cerebellum. Galanthamine and fluoxetine hydrochloride were used as positive standards in the AChE and SERT assays, respectively. Data were analysed with the software package GraFit 5 (Erithacus Software Ltd.). Activity values are means of three individual determinations each performed in triplicate. In an initial screening, AChE inhibition was defined as minimum 50% inhibition at a concentration of 1.0 μg ml-1. Subsequently IC50 values were determined for all extracts deemed active according the initial screening. IC50 values < 50 μg ml-1 was considered active for the analysis. SERT activity was defined as more than 85% binding of extract at 5 mg ml-1. Subsequently IC50 values were determined for all extracts deemed active according the initial screening. IC50 values < 50 μg ml-1 was considered active for the analysis. These activity levels were designed to reflect the observed level of activity in the present study and do not necessarily reflect levels of pharmacological relevance, but within the range of proposed ecological relevance [84]. SERT activity data were not determined for eight species of Narcissus and these samples were pruned from the phylogenetic trees in the correlation tests.

Phylogenetic signal

We assessed the relationship of phylogeny to chemical diversity and biological activity by calculating the phylogenetic signal present for individual alkaloid types and types of biological activity. Each alkaloid type was coded as being either present (1) or absent (0) for each species. Likewise, for biological activity, species were scored for the presence or absence of AChE inhibition or SERT binding activity. Two of the alkaloid traits (belladine and cherylline) are found only in one species each, rendering calculation of phylogenetic signal for these traits meaningless, so they were not included in this part of the analysis.

To quantify phylogenetic signal we used the recently developed D metric [57], specifically developed to deal with discrete binary traits. D is calculated as follows:

D = s obs s ¯ b s ¯ r s ¯ b

where s obs is the observed number of changes in the binary trait (here, a chemical component) across the ultrametric phylogeny, s ¯ r is the mean number of changes generated from 1000 random permutations of the species values at the tips of the phylogeny, and s ¯ b is the mean number of changes generated from 1000 simulations of the evolution for the character by a Brownian motion model of evolution with likelihood of change being specified as that which produces the same number of tip species with each character state as the observed pattern. A D value of 1 ( s obs = s ¯ r ) indicates that the trait has evolved in a way that cannot be distinguished from a random manner (i.e., no phylogenetic signal), whilst a D value of 0 ( s obs = s ¯ b ) indicates that the trait has evolved in a phylogenetically highly correlated manner. Estimation of whether D differs significantly from 1 or 0 is achieved by evaluating where the observed number of changes (s obs ) fits within the distribution of the 1000 generated s r and s b values respectively. Thus if 95% or more values of s r are greater than s obs then P (D = 1) ≤ 0.05 and the trait is significantly more phylogenetically structured than random expectation. Calculation of D was carried out using the packages caper [85] and ape [86] in the R v2.14.0 framework [87].

We also quantified the relationship between overall chemical profile and phylogeny following an approach used to study the evolution of pheromone chemical diversity [57, 58]. We constructed pairwise matrices; one of ultrametric phylogenetic distances (summed branch lengths) between species, and the other of chemical difference calculated as the binary squared Euclidean distance (i.e., the total number of alkaloid types that are absent in one taxon but present in another and vice versa). In addition to using all included species as terminal taxa, we also pruned the phylogenetic tree to genera and compared the resulting distance matrix to summed chemical profiles for each genus. In the case of polyphyletic genera both clades were retained. The correlation between phylogenetic distance and chemical difference was calculated using Mantel tests, with rows and columns of the distance matrix being randomly perturbed and the correlation coefficient recalculated 999 times to generate a null frequency distribution. These tests were performed using the program GenAlEx [88].


  1. 1.

    Abbott HCDS: The chemical basis of plant forms. Studies in Plant and Organic Chemistry, and Literary Papers. Edited by: Michael HA. 1907, Cambridge, MA: The Riverside Press

    Google Scholar 

  2. 2.

    Stahl E: Pflanzen und Schnecken. Jena Z Med Naturwiss. 1888, 22: 557-684.

    Google Scholar 

  3. 3.

    Ehrlich PR, Raven PH: Butterflies and plants - a study in coevolution. Evolution. 1964, 18: 586-608. 10.2307/2406212.

    Article  Google Scholar 

  4. 4.

    Berenbaum MR, Zangeri AR, Nitao JK: Constraints on chemical coevolution: wild parsnips and the parsnip webworm. Evolution. 1985, 40: 1215-1228.

    Article  Google Scholar 

  5. 5.

    Bennett RN, Wallsgrove RM: Secondary metabolites in plant-defense mechanisms. New Phytol. 1994, 127: 617-633. 10.1111/j.1469-8137.1994.tb02968.x.

    CAS  Article  Google Scholar 

  6. 6.

    Wink M: Evolution of secondary metabolites from an ecological and molecular phylogenetic perspective. Phytochemistry. 2003, 64: 3-19. 10.1016/S0031-9422(03)00300-5.

    PubMed  CAS  Article  Google Scholar 

  7. 7.

    Agrawal AA, Fishbein M: Plant defense syndromes. Ecology. 2006, 87: S132-S149. 10.1890/0012-9658(2006)87[132:PDS]2.0.CO;2.

    PubMed  Article  Google Scholar 

  8. 8.

    Hegnauer R: Chemotaxonomie der Pflanzen, Vol. 1–6. 1962–1973, Basel: Birkhäuser Verlag

    Book  Google Scholar 

  9. 9.

    Dahlgren R: Angiospermernes taxonomi, Vol. 1–4. 1974–1976, Copenhagen: Akademisk Forlag

    Google Scholar 

  10. 10.

    Dahlgren R, Clifford HT, Yeo P: The families of the monocotyledons: Structure, evolution, and taxonomy. 1985, Berlin and New York: Springer-Verlag

    Book  Google Scholar 

  11. 11.

    Rodman JE, Soltis PS, Soltis DE, Sytsma KJ, Karol KG: Parallel evolution of glucosinolate biosynthesis inferred from congruent nuclear and plastid gene phylogenies. Am J Bot. 1998, 85: 997-1006. 10.2307/2446366.

    PubMed  CAS  Article  Google Scholar 

  12. 12.

    Rønsted N, Franzyk H, Mølgaard P, Jaroszewski JW, Jensen SR: Chemotaxonomy and evolution of Plantago. Pl Syst Evol. 2003, 242: 63-82. 10.1007/s00606-003-0057-3.

    Article  Google Scholar 

  13. 13.

    Wink M, Mohamed GIA: Evolution of chemical defense traits in the Leguminosae: mapping of distribution patterns of secondary metabolites on a molecular phylogeny inferred from nucleotide sequences of the rbcL gene. Biochem Syst Ecol. 2003, 31: 897-917. 10.1016/S0305-1978(03)00085-1.

    CAS  Article  Google Scholar 

  14. 14.

    Albach DC, Jenen SR, Ozgokce F, Garyer RH: Veronica: Chemical characters for the support of phylogenetic relationships based on nuclear ribosomal and plastid DNA sequence data. Biochem Syst Ecol. 2005, 33: 1087-1106. 10.1016/j.bse.2005.06.002.

    CAS  Article  Google Scholar 

  15. 15.

    Larsson S: The "new" chemosystematics: phylogeny and phytochemistry. Phytochemistry. 2007, 68: 2904-2908. 10.1016/j.phytochem.2007.09.015.

    PubMed  CAS  Article  Google Scholar 

  16. 16.

    Becerra JX, Noge K, Venable DL: Macroevolutionary chemical escalation in an ancient plant-herbivore arms race. PNAS. 2009, 106: 18062-18066. 10.1073/pnas.0904456106.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  17. 17.

    Agrawal AA: Current trends in the evolutionary ecology of plant defence. Funct Ecol. 2011, 25: 420-432. 10.1111/j.1365-2435.2010.01796.x.

    Article  Google Scholar 

  18. 18.

    Rønsted N, Savolainen V, Mølgaard P, Jäger AK: Phylogenetic selection of Narcissus species for drug discovery. Biochem Syst Ecol. 2008, 36: 417-422. 10.1016/j.bse.2007.12.010.

    Article  Google Scholar 

  19. 19.

    Schmitt I, Barker K: Phylogenetic methods in natural product research. Nat Prod Rep. 2009, 2009 (26): 1585-1602.

    Article  Google Scholar 

  20. 20.

    Bohlin L, Goransson U, Alsmark C, Weden C, Backlund A: Natural products in modern life science. Phytochem Rev. 2010, 9: 279-301. 10.1007/s11101-009-9160-6.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  21. 21.

    Bay-Smidt MGK, Jäger AK, Krydsfeldt K, Meerow AW, Stafford GI, van Staden J, Rønsted N: Phylogenetic selection of target species in Amaryllidaceae tribe Haemantheae for acetylcholinesterase inhibition and affinity to the serotonin reuptake transport protein. S Afr J Bot. 2011, 77: 175-183. 10.1016/j.sajb.2010.07.016.

    CAS  Article  Google Scholar 

  22. 22.

    Saslis-Lagoudakis CH, Klitgaard B, Forest F, Francis L, Savolainen V: The use of phylogeny to interpret cross-cultural patterns in plant use and guide medicinal plant discovery: an example from Pterocarpus (Leguminosae). PLoS One. 2011, 6: e22275-10.1371/journal.pone.0022275.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  23. 23.

    Forest F, Grenyer R, Rouget M, Davies TJ, Cowling RM, Faith DP, Balmford A, Manning JC, Proche S, van der Bank M, Reeves G, Hedderson TAJ, Savolainen V: Preserving the evolutionary potential of floras in biodiversity hotspots. Nature. 2007, 445: 757-760. 10.1038/nature05587.

    PubMed  CAS  Article  Google Scholar 

  24. 24.

    Grayer RJ, Chase MW, Simmonds MSJ: A comparison between chemical and molecular characters for the determination of phylogenetic relationships among plant families: An appreciation of Hegnauer's "Chemotaxonomie der Pflanzen". Biochem Syst Evol. 1999, 27: 369-393. 10.1016/S0305-1978(98)00096-9.

    CAS  Article  Google Scholar 

  25. 25.

    Jensen SR, Franzyk H, Wallander E: Chemotaxonomy of the Oleaceae: iridoids as taxonomic markers. Phytochemistry. 2002, 60: 213-231. 10.1016/S0031-9422(02)00102-4.

    PubMed  CAS  Article  Google Scholar 

  26. 26.

    Aguilar-Ortigoza CJ, Sosa V: The evolution of toxic phenolic compounds in a group of Anacardiaceae genera. Taxon. 2004, 53: 357-364. 10.2307/4135614.

    Article  Google Scholar 

  27. 27.

    Zidorn C: Sesquiterpene lactones and their precursors as chemosystematic markers in the tribe Chichorieae of the Asteraceae. Phytochemistry. 2008, 69: 2270-2296. 10.1016/j.phytochem.2008.06.013.

    PubMed  CAS  Article  Google Scholar 

  28. 28.

    Sareedenchai V, Zidorn C: Flavonoids as chemosystemtic markers in the tribe Cichorieae of the Asteraceae. Biochem Syst Ecol. 2010, 38: 935-957. 10.1016/j.bse.2009.09.006.

    CAS  Article  Google Scholar 

  29. 29.

    Stanstrup J, Rusch A, Agnolet S, Rasmussen HB, Mølgaard P, van Staden J, Stafford GI, Staerk D: Itoside A and 4-hydroxytremulacin from Dovyalis caffra and Dovylais zeyheri. Biochem Syst Ecol. 2010, 38: 346-348. 10.1016/j.bse.2010.02.006.

    CAS  Article  Google Scholar 

  30. 30.

    Becerra JX: Insects on plants: macroevolutionary chemical trends in host use. Science. 1997, 276: 253-256. 10.1126/science.276.5310.253.

    PubMed  CAS  Article  Google Scholar 

  31. 31.

    Pichersky E, Lewinsohn E: Convergent evolution in plant specialized metabolism. Ann Rev Plant Biol. 2011, 62: 549-566. 10.1146/annurev-arplant-042110-103814.

    CAS  Article  Google Scholar 

  32. 32.

    Rasmussen LEL, Lee TD, Roelofs WL, Zhang A, Doyle Davies G: Insect pheromone in elephants. Nature. 1996, 379: 684-10.1038/379684a0.

    PubMed  CAS  Article  Google Scholar 

  33. 33.

    Brodie ED: Convergent evolution. Pick your poison carefully. Curr Biol. 2010, 20: R152-R154. 10.1016/j.cub.2009.12.029.

    PubMed  CAS  Article  Google Scholar 

  34. 34.

    Landrum JV: Four succulent families and 40 million years of evolution and adaptation to xeric environments: What can stem and leaf anatomical characters tell us about their phylogeny?. Taxon. 2002, 2002 (51): 463-473.

    Article  Google Scholar 

  35. 35.

    Bennici A: The convergent evolution in plants. Riv Biol. 2003, 96: 485-489.

    PubMed  Google Scholar 

  36. 36.

    Albert VA, Williams SE, Chase MW: Carnivorous plants: phylogeny and structural evolution. Science. 1992, 257: 491-1495.

    Article  Google Scholar 

  37. 37.

    Jensen NB, Zagrobelny M, Hjerno K, Olsen CE, Houghton-Larsen J, Borch J, Moller BL, Bak S: Convergent evolution in biosynthesis of cyanogenic defence compounds in plants and insects. Nat Commun. 2011, 2: 273-

    PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Gatehouse J: Plant resistance towards insect herbivores: a dynamic interaction. New Phytol. 2002, 156: 145-168. 10.1046/j.1469-8137.2002.00519.x.

    CAS  Article  Google Scholar 

  39. 39.

    Nyman T, Julkunen-Tiitto R: Chemical variation within and among six northern willow species. Phytochemistry. 2005, 66: 2836-2843. 10.1016/j.phytochem.2005.09.040.

    PubMed  CAS  Article  Google Scholar 

  40. 40.

    Chase MW, Reveal JL, Fay MF: A subfamilial classification for the expanded asparagalean families Amaryllidaceae, Asparagaceae and Xanthorrhoeaceae. Bot J Linn Soc. 2009, 161: 132-136. 10.1111/j.1095-8339.2009.00999.x.

    Article  Google Scholar 

  41. 41.

    Meerow AW, Snijman DA: Amaryllidaceae. The families and genera of vascular plants. Edited by: Kubitzki K. 1998, Berlin: Springer-Verlag, pp. 83-pp. 110. vol. 3

    Google Scholar 

  42. 42.

    Meerow AW, Snijman DA: The never-ending story: multigene approaches to the phylogeny of Amaryllidaceae, and assessing its familial limits. Aliso. 2006, 22: 353-364.

    Google Scholar 

  43. 43.

    Neuwinger HD: African Traditional Medicine. A dictionary of plant use and applications. 2000, Stuttgart: Medpharm Scientific Publishers

    Google Scholar 

  44. 44.

    Stafford GI, Pedersen ME, van Staden J, Jäger AK: Review on plants with CNS-effects used in traditional South African medicine against mental diseases. J Ethnopharmacol. 2008, 119: 513-537. 10.1016/j.jep.2008.08.010.

    PubMed  Article  Google Scholar 

  45. 45.

    Jin Z: Amaryllidaceae and Sceletium alkaloids. Nat Prod Rep. 2007, 24: 886-905. 10.1039/b502163b.

    PubMed  CAS  Article  Google Scholar 

  46. 46.

    Jin Z: Amaryllidaceae and Sceletium alkaloids. Nat Prod Rep. 2009, 26: 363-381. 10.1039/b718044f.

    PubMed  CAS  Article  Google Scholar 

  47. 47.

    Jin Z: Amaryllidaceae and Sceletium alkaloids. Nat Prod Rep. 2011, 28: 1126-1142. 10.1039/c0np00073f.

    PubMed  CAS  Article  Google Scholar 

  48. 48.

    López S, Bastida J, Viladomat F, Codina C: Acetylcholinesterase inhibitory activity of some Amaryllidaceae alkaloidsand Narcissus extracts. Life Sci. 2002, 71: 2521-2529. 10.1016/S0024-3205(02)02034-9.

    PubMed  Article  Google Scholar 

  49. 49.

    Elgorashi EE, Stafford GI, van Staden J: Acetylcholinesterase enzyme inhibitory effects of Amaryllidaceae alkaloids. Planta Med. 2004, 70: 260-262.

    PubMed  CAS  Article  Google Scholar 

  50. 50.

    Jäger AK, Adsersen A, Fennell CW: Acetylcholinesterase inhibition of Crinum spp. S Afr J Bot. 2004, 70: 323-325.

    Article  Google Scholar 

  51. 51.

    Larsen MM, Adsersen AA, Davis AP, Lledó MD, Jäger AK, Rønsted N: Using a phylogenetic approach to selection of target plants in drug discovery of acetylcholinesterase inhibiting alkaloids in Amaryllidaceae tribe Galantheae. Biochem Syst Ecol. 2010, 38: 1026-1034. 10.1016/j.bse.2010.10.005.

    CAS  Article  Google Scholar 

  52. 52.

    Elgorashi EE, Stafford GI, Jäger AK, van Staden J: Inhibition of [3 H] citalopram binding to the rat brain serotonin transporter by Amaryllidaceae alkaloids. Planta Med. 2006, 72: 470-473. 10.1055/s-2005-916251.

    PubMed  CAS  Article  Google Scholar 

  53. 53.

    Neergaard JS, Andersen J, Pedersen ME, Stafford GI, van Staden J, Jäger AK: Alkaloids from Boophone disticha with affinity to the serotonin transporter. S Afr J Bot. 2009, 75: 371-374. 10.1016/j.sajb.2009.02.173.

    CAS  Article  Google Scholar 

  54. 54.

    Heinrich M, Teoh HL: Galanthamine from snowdrop – the development of a modern drug against Alzheimer’s disease from local Caucasian knowledge. J Ethnopharmacol. 2004, 92: 147-162. 10.1016/j.jep.2004.02.012.

    PubMed  CAS  Article  Google Scholar 

  55. 55.

    Bores GM, Huger FP, Petko W, Mutlib AE, Camacho F, Rush DK, Selk DE, Wolf V, Kosley RW, Davis L, Vargas HM: Pharmacological evaluation of novel Alzheimer’s disease therapeutics: Acetylcholinesterase inhibitors related to galanthamine. J Pharmacol Exp Ther. 1996, 227: 728-738.

    Google Scholar 

  56. 56.

    Meerow AW, Fay MF, Guy CL, Li Q-B, Zaman FQ, Chase MW: Systematics of Amaryllidaceae based on cladistic analysis of plastid rbcL and trnL-F sequence data. Am J Bot. 1999, 86: 1325-1345. 10.2307/2656780.

    PubMed  CAS  Article  Google Scholar 

  57. 57.

    Fritz SA, Purvis A: Selectivity in mammalian extinction risk and threat types: a new measure of phylogenetic signal strength in binary traits. Conserv Biol. 2010, 24: 1042-1051. 10.1111/j.1523-1739.2010.01455.x.

    PubMed  Article  Google Scholar 

  58. 58.

    Symonds MRE, Moussalli A, Elgar MA: The evolution of sex pheromones in an ecologically diverse genus of flies. Biol J Linn Soc. 2009, 97: 594-603. 10.1111/j.1095-8312.2009.01245.x.

    Article  Google Scholar 

  59. 59.

    van Wilgenburg E, Symonds MRE, Elgar MA: Evolution of cuticular hydrocarbon diversity in ants. J Evol Biol. 2011, 24: 1188-1198. 10.1111/j.1420-9101.2011.02248.x.

    PubMed  CAS  Article  Google Scholar 

  60. 60.

    Berkov S, Sidjimova B, Evstatieva L, Popov S: Intra-specific variability in the alkaloid metabolism of Galanthus elwesii. Phytochemistry. 2004, 65: 579-586. 10.1016/j.phytochem.2003.12.013.

    PubMed  CAS  Article  Google Scholar 

  61. 61.

    Zhu F, Qin C, Tao L, Liu X, Shi Z, Ma X, Jia J, Tan Y, Cui C, Lin J, Tan C, Jiang Y, Chen Y: Clustered patterns of species origins of nature-derived drugs and clues for future bioprospecting. PNAS. 2011, 108: 12943-12948. 10.1073/pnas.1107336108.

    PubMed  CAS  PubMed Central  Article  Google Scholar 

  62. 62.

    Larsson S, Backlund A, Bohlin L: Reappraising a decade old explanatory model for pharmacognosy. Phytochemistry Lett. 2008, 1: 31-134. 10.1016/j.phytol.2007.12.004.

    Article  Google Scholar 

  63. 63.

    Pacharawongsakda E, Yokwai S, Ingsriwang S: Potential natural product discovery from microbes through a diversity-guided computational framework. Appl Microbiol Biotech. 2009, 82: 579-586. 10.1007/s00253-008-1847-x.

    CAS  Article  Google Scholar 

  64. 64.

    Rollinger JM: Accessing target information by virtual parallel screening – the impact on natural product research. Phytochem Lett. 2009, 2: 53-58. 10.1016/j.phytol.2008.12.002.

    CAS  Article  Google Scholar 

  65. 65.

    Backlund A: Topical chemical space relation to biological space. Comprehensive Natural Products II Chemistry and Biology. Edited by: Mander L, Lui H-W. 2010, Oxford: Elsevier, pp.47-pp.79. volume 3

    Chapter  Google Scholar 

  66. 66.

    Bruhn JG, Bohlin L: Molecular pharmacognosy: an explanatory model. Drug Discov Today. 1997, 2: 243-246. 10.1016/S1359-6446(97)01048-9.

    CAS  Article  Google Scholar 

  67. 67.

    Larsson J, Gottfries J, Muresan S, Backlund A: ChemGPS-NP: Tuned for navigation in biologically relevant chemical space. J Nat Prod. 2007, 70: 789-794. 10.1021/np070002y.

    PubMed  CAS  Article  Google Scholar 

  68. 68.

    Rosén J, Lövgren A, Kogej T, Muresan S, Gottfries J, Backlund A: ChemGPS-NPweb – chemical space navigation online. J Comp Aided Mol Des. 2009, 23: 253-259. 10.1007/s10822-008-9255-y.

    Article  Google Scholar 

  69. 69.

    Govaerts R, Kington S, Snijman DA, Marcucci R, Silverstone-Sopkin PA: World Checklist of Amaryllidaceae. The Board of Trustees of the Royal Botanic Gardens, Kew. 2007, Published on the Internet; accessed 22 September 2011

    Google Scholar 

  70. 70.

    Cuenca A, Petersen G, Seberg O, Jahren AH: Genes and processed paralogs coexist in plant mitochondria. J Mol Evol. 2012, 74: 158-169. 10.1007/s00239-012-9496-1.

    PubMed  CAS  Article  Google Scholar 

  71. 71.

    Edgar RC: MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004, 35: 1792-1797.

    Article  Google Scholar 

  72. 72.

    Gouy M, Guindon S, Gacuel O: SeaView version 4: a multiplatform graphical user interface for sequence alignment and phylogenetic tree building. Conserv Biol. 2010, 27: 221-224.

    CAS  Google Scholar 

  73. 73.

    Swofford DL: PAUP*: Phylogenetic Methods Using Parsimony (*and other methods), version 4.0b.10. 2002, Sunderland, Massachusetts: Sinauer

    Google Scholar 

  74. 74.

    Kluge AG, Wolf AJ: Cladistics: What’s in a word?. Cladistics. 1993, 9: 183-199. 10.1111/j.1096-0031.1993.tb00217.x.

    Article  Google Scholar 

  75. 75.

    Felsenstein J: Confidence limits on phylogenies: an approach using the bootstrap. Evolution. 1985, 39: 783-791. 10.2307/2408678.

    Article  Google Scholar 

  76. 76.

    Ronquist F, Hulsenbeck JP: MRBAYES 3: Bayesian phylogenetic inference under mixed models. Bioinformatics. 2003, 19: 1572-1574. 10.1093/bioinformatics/btg180.

    PubMed  CAS  Article  Google Scholar 

  77. 77.

    Posada D, Crandall KA: Modeltest: testing the model of DNA substitution. Bioinformatics. 1998, 14: 817-818. 10.1093/bioinformatics/14.9.817.

    PubMed  CAS  Article  Google Scholar 

  78. 78.

    Rambaut A, Drummond AJ: Tracer v1.4. 2007, Available from

    Google Scholar 

  79. 79.

    Lemmon AR, Moriarty EC: The importance of proper model assumption in Bayesian phylogenetics. Syst Biol. 204, 53: 265-277.

    Article  Google Scholar 

  80. 80.

    Kergoat GJ, Silvain J-F, Delobel A, Tuda M, Anton K-W: Defining the limits of taxonomic conservatism in host–plant use for phytophagous insects: Molecular systematics and evolution of host–plant associations in the seed-beetle genus Bruchus Linnaeus (Coleoptera: Chrysomelidae: Bruchinae). Mol Phyl Evol. 2007, 43: 251-269. 10.1016/j.ympev.2006.11.026.

    CAS  Article  Google Scholar 

  81. 81.

    Ronquist F, Huelsenbeck JP, van der Mark P: MrBayes 3.1. 2011, Manual.

    Google Scholar 

  82. 82.

    Berkov S, Bastida J, Viladomat F, Codina C: Analysis of galanthamine-type alkaloids by capillary gas chromatography–mass spectrometry in plants. Phytochem Anal. 2008, 19: 285-293. 10.1002/pca.1028.

    PubMed  CAS  Article  Google Scholar 

  83. 83.

    Bastida J, Lavilla R, Viladomat F: Chemical and biological aspects ofNarcissusalkaloids. The alkaloids Chemistry and Biology. Edited by: Cordell GA. 2006, Amsterdam: Academic Press, pp. 87-pp. 179. vol. 63

    Google Scholar 

  84. 84.

    Wink M: Interference of alkaloids with neuroreceptors and ion channels. Studies in Natural Product Chemistry. Edited by: Atta-ur R. 2000, Amsterdam: Elsevier Science, pp. 3-pp. 122. vol. 21

    Google Scholar 

  85. 85.

    Orme D, Freckleton R, Thomas G, Petzoldt T, Fritz S, Isaac N: Caper. 2011, Comparative Analyses of Phylogenetics and Evolution in R. R package version 0.4. URL:

    Google Scholar 

  86. 86.

    Paradis E, Claude J, Strimmer K: APE: analyses of phylogenetics and evolution in R language. Bioinformatics. 2004, 20: 289-290. 10.1093/bioinformatics/btg412.

    PubMed  CAS  Article  Google Scholar 

  87. 87.

    R Development Core Team: R: A language and environment for statistical computing. 2011, Vienna, Austria: R Foundation for Statistical Computing, URL:

    Google Scholar 

  88. 88.

    Peakall R, Smouse PE: GenAlEx 6: genetic analysis in excel. Population genetic software for teaching and research. Mol Ecol Notes. 2006, 6: 288-295. 10.1111/j.1471-8286.2005.01155.x.

    Article  Google Scholar 

Download references


K. Krydsfeldt and C. Hansen are thanked for technical assistance. This research was supported by a Steno grant ´Phylodrugs´ (N° 09–063972) to NR and a grant (N° 272-06-0436) to Ole Seberg and GP, both grants from the Danish Council for Independent Research - Natural Sciences; a Marie Curie Incoming Fellowship (N°235167) from the European Commission to GS and NR; and grants from the Lundbeck Foundation, Aase and Ejnar Danielsens Foundation and Brødrene Hartmanns Foundation to NR.

Author information



Corresponding author

Correspondence to Nina Rønsted.

Additional information

Competing interests

The authors declare that they have no competing interests.

Authors' contributions

NRØN conceived and designed the study, assembled the matrices, except nad1, conducted the phylogenetic analyses. NRØN prepared the manuscript with MRES and GIS. ANKJ and PM participated in conceiving the study. ANKJ and NRØN designed the study of biological activity and chemical diversity and interpreted the results with GIS. SBC participated in interpretation of the chemical data. TB, MM, NRAS, and GIS produced sequences, chemical, and bioactivity data and participated in interpreting the results. GP provided and aligned nad1 sequences and participated in interpretation and discussion of the results of the phylogenetic analysis. MRES conducted the analyses for phylogenetic signal, and designed the analyses and interpreted the results with NRØN. AWM provided the classification scheme for Amaryllidoideae and participated in interpretation and discussion of the results of the phylogenetic analysis. JvS facilitated fieldwork in KwaZulu-Natal. All authors read and approved the final manuscript.

Electronic supplementary material


Additional file 1: Bayesian consensus tree with posterior probabilities and parsimony bootstrap consensus tree for the total evidence analysis and bootstrap consensus trees of individual regions. Figures S1-S7. (PDF 360 KB)

Additional file 2: Details of material included in the present study.(DOCX 34 KB)


Additional file 3: Primers used in this study for amplification and sequencing of four DNA regions in Amaryllidaceae subfamily Amaryllidoideae.(PDF 96 KB)

Authors’ original submitted files for images

Rights and permissions

This article is published under license to BioMed Central Ltd. 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 work is properly cited.

Reprints and Permissions

About this article

Cite this article

Rønsted, N., Symonds, M.R.E., Birkholm, T. et al. Can phylogeny predict chemical diversity and potential medicinal activity of plants? A case study of amaryllidaceae. BMC Evol Biol 12, 182 (2012).

Download citation


  • Amaryllidaceae
  • Phylogeny
  • Chemical diversity
  • Prediction
  • Lead discovery