MD Marine Drugs MD 1660-3397 Molecular Diversity Preservation International 10.3390/md9112438 marinedrugs-09-02438 Article Variability of Non-Polar Secondary Metabolites in the Red Alga Portieria PayoDioli Ann1* ColoJoannamel1 CalumpongHilconida2 de ClerckOlivier1* Phycology Research Group, Ghent University, Krijgslaan 281, S8, 9000 Ghent, Belgium; E-Mail: jm2marine@yahoo.com Institute of Environmental and Marine Sciences, Silliman University, Dumaguete City 6200, Philippines; E-Mail: hpcalumpong@yahoo.com Authors to whom correspondence should be addressed; E-Mails: dioli_20@yahoo.com (D.A.P.); olivier.declerck@ugent.be (O.d.C.); Tel.: +32-9-264-8500 (O.d.C.); Fax: +32-9-264-8599 (O.d.C.). 2011 21 11 2011 9 11 2438 2468 17 8 2011 01 11 2011 08 11 2011 © 2011 by the authors; licensee MDPI, Basel, Switzerland 2011

This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

Possible sources of variation in non-polar secondary metabolites of Portieria hornemannii, sampled from two distinct regions in the Philippines (Batanes and Visayas), resulting from different life-history stages, presence of cryptic species, and/or spatiotemporal factors, were investigated. PCA analyses demonstrated secondary metabolite variation between, as well as within, five cryptic Batanes species. Intraspecific variation was even more pronounced in the three cryptic Visayas species, which included samples from six sites. Neither species groupings, nor spatial or temporal based patterns, were observed in the PCA analysis, however, intraspecific variation in secondary metabolites was detected between life-history stages. Male gametophytes (102 metabolites detected) were strongly discriminated from the two other stages, whilst female gametophyte (202 metabolites detected) and tetrasporophyte (106 metabolites detected) samples were partially discriminated. These results suggest that life-history driven variations, and possibly other microscale factors, may influence the variation within Portieria species.

 Portieria secondary metabolite variation cryptic species life-history stages 1. Introduction

Natural products, and in particular secondary metabolites, have been the focus of study in many marine macroalgae. Seaweeds interact with their environment utilizing a rich variety of secondary metabolites [1–4]. These chemical compounds have no explicit role in the internal metabolism of the organisms [1,5,6] but serve as defense mechanisms against grazers, competitors, fouling organisms and pathogens [7–9]. The compounds are localized in specialized cells, have relatively low molecular weights (<3000 Daltons), are structurally diverse, often halogenated, and exist in low abundance (often <1% of total carbon) [1,2,6,10–12].

Secondary metabolites have drawn wide attention because of their pharmaceutical potentials, chemotaxonomic and ecological importance [13–16]. Macroalgae produce a wide range of compounds such as terpenes, phenols, fatty acids, lipopeptides, amides, alkaloids, terpenoids, lactones, pyrroles and steroids [9,15,17]. Red seaweeds belonging to the family Rhizophyllidaceae have been found to be especially rich in secondary metabolites, harboring a variety of halogenated monoterpenes. The tropical, Indo-Pacific genus Portieria (synonyms Chondrococcus and Desmia), is a prolific source of these halogenated compounds [11,12,18–24], about 80 of which have been isolated (Table S1). Among the most interesting of these compounds is halomon, a monoterpene with anti-tumor properties, isolated from samples collected in Batanes, Philippines [22,23]. However, the inconsistent availability of this compound from natural populations prevented further drug development.

Previous studies have mainly focused on discovery and pharmaceutical potentials of chemical compounds in P. hornemannii. Only a few studies have addressed inter- or intraspecific variation in secondary metabolite composition. Puglisi and Paul [25] tested the carbon/nutrient hypothesis, which postulates that the secondary metabolites produced by a certain alga are dependent on the nutrient availability. They found that variation of ochtodene concentrations in P. hornemannii cannot be attributed to nitrogen and phosphorus availability but suggested instead that light was a contributing factor. Matlock et al. [24] demonstrated strong site-to-site differences, variation within populations, and limited evidence for temporal variation in apakaochtodene levels. Some authors have emphasized the necessity to genotype the organisms in order to better understand the mechanisms that regulate the production of specific natural products [5,26–28].We found evidence for a large number of cryptic species within Portieria in the Philippines [29], but the effect of intraspecific genetic variation in relation to secondary metabolites has not been tested to date.

This study aims to understand if non-polar secondary metabolites of Portieria plants vary quantitatively or qualitatively between cryptic species and among life stage within species. We employed metabolite fingerprinting and multivariate analyses of chromatograms of extracts of samples collected in the Philippines. The large number of compounds produced by Portieria, the lack of commercial standards and the scarcity of compound identification resources (mass spectral databases) dedicated to marine secondary metabolites makes metabolite fingerprinting the most feasible analytical method to define variation between cryptic species and among life-history stages within species. In contrast to target analysis, metabolite fingerprinting does not separate individual metabolites, but instead compares spectra of whole extracts using multivariate statistics [30]. Metabolite fingerprinting is particularly useful for rapid classification of samples [31]. Our specific aims were to evaluate: (1) if there is variation of non-polar secondary metabolites between the gametophyte and tetrasporophyte life-history stages of Portieria, (2) if metabolite variation is due to the presence of phylogenetically distinct cryptic species, and (3) if patterns of variation are observed on a geographical and temporal level.

 2. Results and Discussion 2.1. Cryptic Diversity

Phylogenetic analyses of 152 cox2–3 sequences revealed 12 clades of closely related sequences, preceded by long well-supported branches. Seven lineages include specimens from Batanes and five clades are restricted to the Visayas (Figure 1). These clades will further be regarded as species. Details on cryptic diversity and species delimitation are reported in Payo [29].

 2.2. Identification of Compounds in Non-Polar Extracts

Owing to the lack of standard compounds and public repositories of mass spectral data specific to marine metabolites, the identity of the compounds was investigated only by comparing the mass spectra and the Kovats retention indices to those deposited in publicly available databases (MassBase, Pherobase, Lucero Library and the free limited NIST database in the MS Search Program and AMDIS). Identifications were based on a minimum of 80% similarity of mass fragments. Using a retention index calculator [32], plant volatiles with the nearest Kovats Index (KI) to that of the compounds in the samples were retrieved. The KI used for compound identification is based on a DB5 capillary column similar to the non-polar, low bleed HP-5MS column used for GC/MS in this study. The Lucero KI based identification was verified with the mass spectra and KIs listed in Pherobase. Comparison with mass spectra of compounds found in Portieria in the literature [11,12,33] did not reveal any positive identification. Examination of mass fragments did not show any similarity of components with that of halomon’s mass spectral fragments as reported by Egorin et al. [33].

 2.3. Metabolite Fingerprinting 2.3.1. Variation Between Life Stages

Plants used for this study belonged to a single species V1B (Figure 1) and were collected on the same day from a single site in Bantayan, Dumaguete. A total of 202 non-polar compounds were detected from gametophytes and tetrasporophyte samples by GC-MS (Table S2). Using an external standard and the databases, the first peak of each sample (RT = 6.18 min) was confirmed as β–myrcene (Figure 2a). Of the remaining compounds, only 11% (22) were identified using NIST and MassBase. Based on the NIST and MassBase identifications, five compounds were monoterpenes. Examination of the patterns of parent ions showed ten halogenated compounds containing one Cl or one Br. Two hundred compounds had a KI ranging 993–1969. Some peaks with succeeding retention times shared the same KI but had a different mass spectra composition. NIST and MassBase did not share the same identification of peaks except for β-myrcene.

KI for the last two peaks were not computed since it occurred later than the last alkane standard (C20 at 31.226 min) that was used for KI calculation. We also explored the potential of the Lucero library and Pherobase to identify compounds that Portieria might have in common with land plants. The Lucero library retrieved the nearest KIs of plant volatiles. Some of these identifications were verified at Pherobase, but mass spectra composition of the unidentified peaks was not similar to the spectra of the compound name retrieved from the database. Furthermore, we also tried to retrieve compounds with a certain KI and examined the corresponding spectra. β-Myrcene has a KI of 993.543 in this study. Pherobase listed 991 and 994 as KIs for β-myrcene in a DB5 column. Nine compounds listed in the database have a KI of 993.

The Lucero library lists KI 993 for β-myrcene. With the large number of metabolites retrieved, this method of verification proved cumbersome especially since Pherobase currently does not allow automated mass spectral and KI comparison. Of the 202 compounds detected, 155 compounds were present in the female samples, 102 in male plants and 106 in tetrasporophytes (Figure 3a). There were 55 compounds that were shared by all life stages. Sixty-three compounds were found exclusively in female gametophyte extracts, 15 in male gametophyte extracts, and 18 in tetrasporophyte extracts. There were 14 compounds common to male and tetrasporophyte extracts. The male and female gametophyte shared 18 compounds while female and tetrasporophyte shared 19 compounds. A precursor compound of the monoterpenes, myrcene, was detected in all of the life stages but not in all replicates.

We applied a principal component analysis (PCA) on the standardized relative abundances to determine if metabolites differ among life stages (Figure 4a). The matrix contained 202 compounds (active variables) and ten samples (active cases: six female gametophytes, two male gametophytes, and two tetrasporophytes). All samples were collected on the same day from a single site, and belong to a single species. Figure 4a shows the projection of the cases on the factor plane. Factor 1 and factor 2 accounts for 20.42% and 14.37% of the variation, respectively. Factor 1 clearly discriminated the male gametophytes from the others, whilst the female gametophytes and tetrasporophytes, although separated, were more closely aligned. Factor 2 does not discriminate between life stages but points towards additional variation within life stages.

 2.3.2. Variation Between Cryptic Species

A total of 67 compounds were detected from nine samples (three technical replicates) by GC-MS (Table S3). These samples belonged to the five species B8, B5, B21, B33 and B35, as determined by DNA sequence analysis. A sample consists of pooled extract of eight individuals belonging to one species as verified by DNA analysis. β-Myrcene was not detected in any of the samples (Figure 2b). Unfortunately the three databases used (NIST, MassBase, Lucero library) did not give identical identifications of compounds. NIST identified four compounds appearing at different times as 2,2-dimethyl-3-hexanone. Comparison of mass spectra with MassBase resulted in the retrieval of straight and branched alkanes and alkenes. Compounds with retention times from 8.11 to 30.69 min had retention indices from 1065 to 1965. No KIs were computed for the rest of the compounds. Comparison of computed KIs with those in the Lucero library indicated compounds from these samples contained monoterpenes, sesquiterpenes, esters, fatty acids and alcohols. NIST, MassBase and the Lucero databases did not share the same identification of compounds. Visual examination of parent ions showed only two compounds to be halogenated containing either one Cl or one Br.

The number of compounds detected varied between the five species (Table 1) and among biological replicates. Species B35 had the highest number of compounds detected (60/67), species B33 contained 51/67 compounds detected, species B8 and B5 both had 57/67 compounds detected and species B21 had the least number of compounds (49/67). There were 21 compounds that were detected in all nine samples, while 33 compounds were present in all five species.

To detect distinct groupings, we conducted a PCA based on the standardized relative abundances of the nine samples using the 67 compounds as variables (Figure 4b). The first principal axis accounted for 27.4% of the variance while the second 16.0%. Plotted scores from the PCA did not show distinct interspecific clusters. The largest variation was displayed by specimens belonging to species B5.

 2.3.3. Spatial and Temporal Variation

Six sites in the Visayas (Sawang, Pagubagubaan, Dapdap, Siaton, Liloan and Bantayan (Dumaguete); n = 44) were sampled from 2007 to 2009 and investigated using Spectconnect to determine if seasonality or geography explained the variation in secondary metabolites. A total of 33 compounds were detected from these samples (Table S4), however, β-myrcene was not detected (Figure 2c). As in previous datasets, the three databases (NIST, MassBase, Lucero library) used did not give identical identifications of compounds. Comparison of mass spectra with MassBase resulted in the retrieval of eleven compounds, most likely straight and branched alkanes and alkenes. Many of the peaks detected eluted later than the standard, icosane (C20), hence were not included. Comparison of computed RIs with those in Lucero library retrieved the following compound classes: monoterpenes, sesquiterpenes, esters and fatty acids. NIST library identified two compounds, 3-hexanone, 2,2-dimethyl- and disilane. No halogenated compounds were detected.

PCA was conducted using the standardized relative abundance file generated by Spectconnect (n = 44) using the 33 compounds as variables (Figure 5a). The first and second PCs accounted for 17.87% and 17.78% of the variance, respectively. No clear groupings could be deduced based on species, sampling period, or sampling site. Most of the samples clumped except for one Siaton and two Bantayan samples, which were clearly separated from the rest. A PCA excluding these three samples slightly spread the rest of the samples but likewise did not show any recognizable clusters (Figure 5a). A separate PCA analysis was conducted on samples belonging to the same species, V32, from Sawang, Pagubagubaan, and Liloan (Figure 5b). The first and second PCs accounted for 21.36% and 14.96% of the variance, respectively. Again, no clusters based on site or period of collection were detected.

 2.4. Patterns of Secondary Metabolite Distribution

Distribution of secondary metabolites in marine macroalgae exhibit macroscale and microscale patterns [34]. Macroscale patterns include global patterns within or across algal taxa, patterns within a specific habitat, and patterns correlated with biotic and abiotic factors. Microscale patterns are related to molecular and biochemical processes within an algal thallus, the spatial distribution of compounds within an algal thallus and temporal responses (short- and long-term responses). In Portieria neither macro- nor microscale patterns of secondary metabolite variation have been fully explored.

Although three decades of studies have been dedicated to the isolation of new compounds only two have investigated the variation of these, specifically ochtodene and apakaochtodene A and B [24,25]. However, a possible drawback related to these studies is the fact that individual compounds are not mutually independent but are linked via biological pathways as is the case for β-myrcene, which is the likely precursor of many monoterpenes [35], and whose presence influences the presence or absence of other halogenated monoterpene components. Hence in order to understand general or specific metabolite patterns, knowledge of the presence and absence of progenitor compounds is essential. The present study has successfully described the variation of the non-polar secondary metabolites of Portieria on a macroscale level using metabolite fingerprinting.

Reports on intraspecific variation of secondary metabolites in macroalgae have been based on variations within individuals, between individuals of a population and among geographically isolated populations [36,37]. Our results suggest substantial intraspecific variation among the life-history stages of Portieria. Male gametophytes form a clearly distinct group, while a partial discrimination was detected between female gametophytes and tetrasporophytes (Figure 4a). Furthermore, our results show that female gametophytes are chemically richer in terms of the number of secondary metabolites (Figure 3a), possibly providing protection to the carposporophyte from grazers. Many of the compounds detected appear to occur exclusively to a certain life-history stage while a relatively small group of compounds is shared among life-history stages. Similarly, male gametophytes of Asparagopsis armata were found to have lower concentration of the secondary metabolite bromoform compared to female gametophytes [37]. It has also been observed that the carposporophyte phase of A. armata was the least consumed life-history phase by the seahare, Aplysia parvula [37]. Likewise, in the brown alga Dictyota menstrualis, female gametophytes were found to produce more diterpenes compared to the other life-history stages [38].

Comparative analyses of the five cryptic species from Batanes (B5, B21, B33, B35, and B8) showed variation between, as well as within species (Figure 4b). Intraspecific variation is even more pronounced in the Visayas dataset (Figure 5). Neither species specific nor spatio-temporal patterns were evident.

In addition, a separate PCA including only specimens of species V32 (Figure 3b) did not elucidate patterns based on collection site or period. The results suggest that life history related variations combined with microscale factors have a large influence on the variation within Portieria species. Pelletreau and Targett [34] pointed out that the existence of inducible and activated defenses complicates the search for universal patterns of secondary metabolites and continues to highlight the importance of localized phenomena. We conducted a preliminary grazing experiment in outdoor flow-through aquaria to assess the influence of the herbivore (Aplysia) on the production of metabolites in Portieria. However, these efforts were beset by two factors: death of plants due to high water temperature and continuous development of microscopic Aplysia larvae on field collected plants undergoing acclimatization. Future grazing experiments will attempt to eliminate these factors. In addition more extensive sampling should confirm the patterns observed during the present study.

Finally, there was difficulty in achieving unequivocal identification of compounds in this study unveiling the need for mass spectral and KI databases dedicated solely to marine secondary metabolites. Such repositories will assist in the identification of previously reported compounds for investigations mainly focused on rapid detection and understanding of chemical patterns in marine organisms. In doing so, future studies will not just be focused in isolating and naming novel compounds as had been the trend in Portieria but will also seek to answer questions of biological or ecological relevance. Furthermore, the presence of cryptic species in Portieria highlights the need to genotype organisms ensuring that correct species identification is correlated with metabolite fingerprint, enabling more targeted approaches in future studies.

 3. Experimental Section 3.1. Collection and Storage

Field samplings were performed at different periods from several sites in the Philippines (Figure S1). Plants were collected by snorkeling and SCUBA, diving depths ranging from 1–20 m. For secondary metabolite characterization of the different life-history stages of Portieria, plants were collected randomly from Bantayan near Dumaguete (Figure S1c) on December 29, 2009. To allow both chemical characterization and life-history stage verification plants were blotted dry and stored over silica.

Plants for secondary metabolites analysis between species were collected from four sites in Batanes (Basco, White Beach, Coral, and in Chavayan) (Figure S1a,b) on April 23–26, 2009. Plants were preserved in 95% ethanol in the field and stored at −23 °C afterwards. Sampling for seasonal and spatial comparison of secondary metabolites were conducted from six sites in the Central Visayas (Dapdap, Siquijor, Siquijor; Pagubagubaan, San Juan, Siquijor; Sawang, San Juan Siquijor; Bantayan, Dumaguete, Negros Oriental; Siaton, Negros Oriental; Liloan, Santander, Cebu (Figure S1a,c) from 2007 to 2009. For the first four sites, plants were collected on a monthly basis when present and when abundant enough to afford a good amount for extraction. For sites close to the Silliman Marine Laboratory, plants were immediately packed and stored at −23 °C upon arrival. When immediate freezing was not possible, plants were preserved in 95% ethanol in the field and stored at −23 °C. Frozen plants were rinsed in freshwater or in ethanol and blotted dry.

As Portieria cannot be distinguished morphologically, species identification was based on DNA sequence data (see below). For DNA characterization, thallus clippings were collected from specimens and stored in silica while in the field.

 3.2. Extraction 3.2.1. Life-History Stages Comparison

Portieria has a tri-phasic isomorphic life history which includes free-living haploid male and female gametophytes, a diploid carposporophyte attached to and dependent on the female gametophyte, and a free-living diploid tetrasporophyte. Silica-dried specimens were examined under the microscope and segregated into different life-history stages (male gametophytes, female gametophytes, and tetrasporophytes). Biological replicates, each about 1 gram dry weight, were prepared for every life-history stage. Tissues were ground using mortar and pestle, dissolved in 6 mL dichloromethane (DCM) containing 50 μg·mL−1 naphthalene, and vortexed for a few seconds. The DCM extracts were allowed to stand at room temperature for 24 h in screw-cap tubes. The extracts were decanted and transferred to air tight screw-cap tubes and stored at −23 °C. The tissues were steeped in DCM for another 24 h. First and second extracts of a sample were pooled and filtered using Whatman GF/C. The combined extracts were loaded onto pre-conditioned SPE tubes containing 10 g of silica (Silicycle SiliaFlash® G60, 60–200 μm, 60 Å). Non-polar compounds were eluted using 12 mL hexane. The eluate was concentrated to 0.5 mL under N2 gas. Three 100 μL replicates were taken from this stock and used as technical replicates in the GC-MS analysis.

 3.2.2. Species Comparison

The samples were extracted individually. Tissue was ground using a mortar and pestle and extracted twice in 6 mL DCM overnight. Extracts were pooled, loaded onto 2 g silica and eluted using 6 mL of hexane. Eluates of eight samples belonging to the same species, approximately weighing 10–26 g, were pooled and evaporated down to 100 μL. To assess consistency in the GC-MS analyses, three technical replicates were prepared for each sample.

HPLC solvents for extraction and chromatography (DCM and n-hexane) were sourced from Mallinckrodt or HiperSolv Chromanorm). Naphthalene (Fisher) and β-myrcene (Sigma) were used as internal and external standards, respectively.

 3.2.3. Seasonal and Spatial Comparison

For seasonal analysis, plant material was ground using mortar and pestle. Twenty grams of ground material was extracted in 20 mL DCM for 24 h at room temperature. The material was extracted a second time in 20 mL DCM and the extracts were pooled. To measure the amount of green oil that was generated, one of the two pooled extracts was evaporated to dryness using nitrogen. The oil was weighed and redissolved in 20 mL DCM. The other extract was evaporated down to 20 mL. Both extracts were loaded in SPE columns containing 10 g of silica (Silicycle SiliaFlash® G60, 60–200 μm, 60 Å). Non-polar compounds were eluted from the column with 20 mL hexane and again reduced to 100 μL. A separate aliquot of ground plant tissue was used to determine dry mass.

 3.3. Phylogenetic Analysis

Assignment of the sampled specimens to cryptic species was based on phylogenetic analysis of the mitochondrial cox2–3 spacer region. The phylogeny includes specimens collected from five sites (Basco, Chanaryan, Coral, Mahatao, Chavayan) in Batanes and six sites (Dapdap, Siquijor; Sawang, San Juan, Siquijor; Pagubagubaan, San Juan, Siquijor; Malo, Siaton; Bantayan, Dumaguete; Liloan, Santander) in the Visayas, Philippines. Species delineation follows the rational outlined in Payo et al. [29]. DNA extraction, PCR amplification, sequencing and sequence alignment were performed as described in Payo et al. [29].The alignment of 152 Portieria sequences was 345 bp long. Bayesian inference (BI) of phylogeny was performed using MrBayes v3.1.2 [39] under a GTR + I + Γ model as determined by jModelTest [40]. BI analyses consisted of two parallel runs of three incrementally heated chains and one cold chain each, and 3 million generations with sampling every 1000 generations. A burnin value of 750 was determined using TRACER V1.4 [41].

 3.4. Metabolite Analysis, Data Processing and Multivariate Analysis 3.4.1. GC-MS Analysis

GC-MS analyses of the non-polar eluates were performed using an Agilent 6890 gas chromatograph and Agilent 6973 mass selective detector. Sample injection volume was 1 μL. Split injection with a split ratio of 20:1 was used. The carrier gas was helium with a total flow rate of 72.7 mL·min−1 and 26.20 psi column head pressure. Compounds were separated using a 30 m × 0.25 mm HP-5 MS non-polar capillary column (Hewlett-Packard, 5% phenyl methyl siloxane, 0.25 μm thickness) for a run time of 67 min. under the following oven temperature program: 50 °C initial held for 2 min, then increased at a rate of 5 °C·min−1 to 300 °C, held for 15 min. The spectrometers were run in electron-impact mode with ionization energy of 70 eV and an ion source temperature ranging 230–250 °C. The scan range was set to detect masses 50–500 amu.

 3.4.2. GC-MS Data Processing

The software AMDIS [42] was used for peak identification and deconvolution of the chromatogram. This method calculates and retrieves pure (background-free) mass spectra from raw GC-MS data files based from the parameters indicated by the user. The following parameters were used for all of the analyses: medium shape requirement, medium sensitivity, and medium resolution. The ELU files generated from AMDIS were submitted for analysis using Spectconnect [43] to generate matrices of component peaks (relative abundance, retention time, integrated signal and base peak).

 3.4.3. Compound Identification

In the absence of pure standards of compounds previously isolated from Portieria (except for β-myrcene), we used KI’s, freely available mass spectral databases to determine identity of component peaks, and literature. We computed the Kovat’s or retention index of each peak using the retention times of alkane standards C8–C20 and the Retention Index Calculator [32]. The retention index is derived from the interpolation (usually logarithmic), relating the adjusted retention volume (time) of the sample component to the adjusted retention volumes (times) of two standards eluted before and after the peak of a sample component (IUPAC definition). The calculated KI was automatically compared to the KI’s of compounds stored in the built-in library of the calculator. Mass spectra of component peaks were compared to compounds retrieved from MassBank [44], Pherobase [45] and the limited version of the NIST library in the MS Search Program and AMDIS. Mass spectra of components were inspected for similarity with those of the characteristic mass signals of components published in [11,12,33].

 3.4.4. Data Standardization, Metabolite Fingerprinting and Multivariate Analyses

Relative abundance matrices generated from the Spectconnect analyses were used for statistical analyses. A matrix contains relative abundance values of all detected components across all samples included in the analysis. The data were standardized by obtaining a ratio between the relative abundance of a component per replicate (or sample) and the total of all components per replicate (or sample). The ratios obtained among the technical replicates were subsequently averaged. Principal component analysis was performed using Statistica 7.0 (StatSoft Inc., Tulsa, OK, USA) on each dataset to detect any pattern or groupings based from the variables which are the component compounds.

 4. Conclusions

Portieria species in the Philippines are a rich source of secondary metabolites. This diversity in secondary metabolites amounts to at least 302 various compounds. The majority of which are exotic, remain undescribed and therefore are not available in natural product databases. Our study demonstrates that metabolic fingerprinting presents a practical approach to disclose intra- and interspecific patterns of secondary metabolites. Variation in secondary metabolites occurs between, as well as within, Portieria species. Preliminary results, based on a relatively small sample size, demonstrate that extensive intraspecific variation in secondary metabolites occurs between life-history stages. Female gametophytes (202 compounds) are chemically richer compared to that of the males (102) and tetrasporophytes (106). No spatio-temporal patterns were evident among the datasets. These results suggest that life-history driven variations and possibly other microscale factors may have an important influence on the variation of secondary metabolites within Portieria species. More exhaustive sampling is needed to confirm the life-stage specific metabolic fingerprints. In addition, to determine whether the intraspecific variation of the analyzed part of non-polar metabolome remains lower than the interspecific variation, it will be necessary to expand the taxon sampling across different cryptic species.

 Supplementary

List of compounds isolated from Portieria (syn. Desmia and Chondrococcus) hornemannii, based on previous studies.

Reference Cpd # Name Mol. Formula Description Specimen Source
[18] 1 Myrcene C10H16 acyclic halogenated monoterpene Amami Is., Japan
2 7-chloro-myrcene C10H15Cl " "
3 7-bromo-myrcene C10H15Br " "
4 (Z)-10-bromo-myrcene C10H15Br " "
5 (E)-10-bromo-myrcene C10H15Br " "
6 (Z)-10-bromo-7-chloro-myrcene C10H14BrCl " "
7 (E)-10-bromo-7-chloro-myrcene C10H14BrCl " "
8 3-chloro-7, (Z)-10-dibromo-myrcene C10H13Br2Cl " "
9 (Z)-10-chloro-3,7-dibromo-myrcene C10H13Br2Cl " "
10 3-bromo-7-chloro-myrcene C10H14BrCl " "
11 7-bromo-10-chloro-myrcene C10H14BrCl " "
12 - C10H15Br cyclic halogenated monoterpene "
[19] 1 chondrocole A C10H14BrClO " Hawaii
2 chondrocole B C10H14BrClO " Hawaii
[46] 3 - C10H15Cl3Br2 acyclic halogenated monoterpene Black Point, Oahu, Hawaii
4 - C10H16Cl3Br " "
5 - C10H15Cl2Br " "
6 - C10H16Cl2Br2 " Halona Blowhole Is, Oahu, Hawaii
10 - C10H16Cl2Br2 acyclic halogenated monoterpene "
11 6-bromo-2-chloromyrcene C10H15ClBr " Black Point, Oahu, Hawaii
12 chondrocole c C10H14Br2O cyclic halogenated monoterpene "
13 - C10H15Cl3Br2 acyclic halogenated monoterpene Hawaii
14 Z-3-bromomethylene-7-methyl-1,6-octadiene C10H16ClBr2 " Halona Blowhole Is., Oahu, Hawaii
15 - C10H14Br2Cl2 cyclic halogenated monoterpene "
[47] 1 myrcene C10H16 acyclic halogenated monoterpene Kada Coast, Wakayama Pref., Japan
2 7-chloro-myrcene C10H15Cl " "
3 7-bromo-myrcene C10H15Br " "
4 3,7-dichloro-myrcene C10H14Cl2 " "
5 (Z)-10-bromo-3-methoxy-α-myrcene C11H17OBr " "
6 (E)-10-bromo-3-methoxy-α-myrcene C11H17OBr " "
7 3-bromo-7-chloro-myrcene C10H14BrCl " "
8 (Z)-10-bromo-1-methoxy-myrcene C11H17OBr " "
9 (E)-10-bromo-1-methoxy-myrcene C11H17OBr " "
[48] (−)-3-bromomethyl-3-chloro-7-methyl-1,6-octadiene C10H16BrCl " Trincomalee (Foul Point) Sri Lanka
[49] 2-(1-chloro-2-hydroxyethyl)-4,4-dimethylcyclohexa-2, 5-dienone: a precursor of 4,5-dimethylbenzo [b] furan - - -
[20] 1 (2Z,6E)-1,8-dichloro-3-chloromethyl-7-methylocta-2,6-diene C10H15Cl3 acyclic halogenated monoterpene Rib Reef, Great Barrier Reef, Australia
2 (E)-1,2-dibromo-3-chloromethylene-7-methyloct-6-ene C10H15Br2Cl " "
3 (Z)-1-chloro-3-chloromethyl-7-methylocta-2,6-diene C10H16Cl2 " "
4 (Z)-1,6-dichloro-3-chloromethyl-7-methylocta-2,7-diene C10H15Cl3 " "
5 (2R*,3(8)E,4S*,6R*)-6-bromo-2-chloro-1,4-oxido-3,(8)-ochtodene C10H14BrClO epimeric bicyclic monoterpene "
6 (2S*,3(8)E,4S*,6R*)-6-bromo-2-chloro-1,4-oxido-3,(8)-ochtodene C10H14BrClO " "
[50] (Z)-3-bromo-8-chloro-6-chloromethyl-2-methylocta-1,6-diene (9,6-hydroxymethyl-2-methylocta-2,8-dien-6-ol - - -
[21] 1 (2Z)-6-bromo-3-chloromethyl-1,7-dichloro-7-methylocta-2-ene C10H16Cl3Br acyclic halogenated monoterpene Nelly Bay, Magnetic Island, Queensland, Australia
2 (2Z,6E)-3-chloromethyl-1-chloroocta-2,6-dien-8-al C10H14OCl2 " "
3 3-methoxymethyl-6-methoxyl-7-methylocta-1,7(10)-dien-3-ol C12H22 O3 " "
4 (2Z,6S)-3-chloromethyl-1-methylocta-2,7(10)-dien-6-ol C11H19 O2Cl " "
5 (2Z,6S)-3-chloromethyl-6-methylocta-2,7(10)-dien-1-ol C11H19 O2Cl " "
[22] 1 halomon/6(R)-bromo-3(S)-(bromomethyl)-7-methyl-2,3,7-trichloro-1-octene C10H15Br2Cl3 acyclic halogenated monoterpene Chanaryan, Batan Is., Batanes, Philippines
2 - C10H13Cl cyclic halogenated monoterpene Banilad, Bacong, Negros Oriental, Philippines
[23] 2 isohalomon C10H15Br2Cl3 acyclic halogenated monoterpene; isomeric with halomon Chanaryan, Batan Is., Batanes, Philippines
3 - C10H14BrCl3 acyclic halogenated monoterpene "
4 - C10H14Br2Cl2 " "
5 - C10H14Br3Cl cyclic halogenated monoterpene "
6 - C10H14BrCl3 " "
7 - C10H13Cl3 " "
8 - C10H15BrCl2 acyclic halogenated monoterpene "
9 - C10H16Br2Cl2 " "
10 - C10H16BrCl " Halona Blowhole Is, Oahu, Hawaii
11 - C10H15BrCl " "
12 - C10H14BrCl cyclic halogenated monoterpene "
[8] 1 apakaochtodene A/6(S*)-bromo-1,4(S*),8(R*)-trichloro-2(Z)-ochtodene C10H14Cl3Br cyclic halogenated monoterpene Apaka Point Beach, Guam
2 apakaochtodene B/6(S)-bromo-1,4(S), 8(R*)-trichloro-2(E)-ochtodene C10H14Cl3Br cyclic halogenated monoterpene Gun Beach; Double Reef; Pago Bay; Guam
[11] 1 myrcene/7-methyl-3-methylene-1,6-octadiene C10 H16O acyclic non-halogenated monoterpene microplantlet culture, Double Reef NW Guam
2 10E-bromomyrcene/E-3-bromomethylene-7-methyl-1,6-octadiene C10H15Br " "
3 10Z-bromomyrcene/Z-3-bromomethylene-7-methyl-1,6-octadiene C10H15Br " "
4 10E-bromo-3-chloro-α-myrcene/E-3-bromomethylene-6-chloro-1,7-octadiene C10H14BrCl " "
5 - C10H14Br2 " "
6 apakaochtodene B/6(S)-bromo-1,4(S), 8(R*)-trichloro-2(E)-ochtodene C10H14BrCl3 cyclic halogenated monoterpene "
7 - C10 H16O non-halogenated monoterpene "
8 bromomyrcene isomer C10H15Br halogenated monoterpene "
9 7-chloromyrcene/2-chloro-3-methylene-7-methyl-1,6-octadiene C10H15Cl " "
10 C10H14Cl " "
11 chloromyrcene derivative C10H16Cl " "
12 bromomyrcene isomer C10H15Br " "
13 chloromyrcene derivative C10H14Cl " "
14 - C10H18Cl2 " "
15 - C15H18 O4 sesquiterpene "
[13] 5 1,2-dibromoochtoda-3(8),5-dien-4-one C22H12Br2O cyclic halogenated monoterpene Cape Zampa, Okinawa, Japan
6 1-bromo-2-chloroochtoda-3(8),5-dien-4-one C10H12BrClO " "
7 1,2-dichloroochtoda-3(8),5-dien-4-one C10H12Cl2O " Gushichan coast, Okinawa, Japan
8 (1Z)-1-bromoochtoda-1,3(8),5-trien-4-one C10H11BrO " Cape Zampa, Okinawa, Japan
9 (1Z)-1-chloroochtoda-1,3(8),5-trien-4-one C10H11ClO " "
[12] 1 halomon/6(R)-bromo-3(S)-(bromomethyl)-7-methyl-2,3,7-trichloro-1-octene C10H15Br2Cl3 acyclic halogenated monoterpene Tolagniaro, Fort Dauphin, Madagascar
2 - C10H13ClBr " "
3 - C10H14Cl2Br " "
4 C10H15Br2Cl3 " "

Compound peaks detected from GC-MS analysis of samples used for characterization of the life history stages of P. hornemannii. Identifications based on comparison of either Kovat’s Indices (KI) of compounds retrieved from the Retention Index Calculator [37] or mass spectral comparison of compounds retrieved from NIST or MassBase. Asterisk (*) indicates compounds with parent ions showing halogenated mass spectral patterns.

# Sample Base Peak KI KI Based ID Mass Spectra Based ID
RT (min) Nearest KI Lucero et al. [37] NIST MassBase
1 F6_2 93 6.1863 994 993/994 beta-myrcene/6-methyl-5-hepten-2-ol b-myrcene b-myrcene
2 T1_1 104 6.5874 1008 1007 α-phellandrene - -
3 T1_2 104 6.5909 1008 1007 α-phellandrene - -
4 M1_1 91 6.7685 1016 1017 α-terpinene Benzene, tert-butyl- 4-methylacetophenone
5 F3_3 79 6.9501 1023 1024 r-cymene - -
6 M1_2 79 6.9532 1023 1024 r-cymene - -
7 M1_3 91 7.017 1026 1025 p-cymene carbonic acid 1-phenylpropan-2-one
8 M2_2 91 7.021 1026 1025 p-cymene - protopine
9 F3_2 91 7.0387 1027 1025 p-cymene - N-Methyl-Npropagylbenzylamine
10 T2_1 57 8.0381 1063 1062 y-terpinene - -
11 M2_2 132 8.8894 1089 1089 p-mentha-2,4(8)diene - -
12 T2_1 117 9.5127 1110 1099 linalool - -
13 F4_3 117 9.8874 1124 1123 chrysanthenone - -
14 M1_3 117 9.9855 1128 1127 α-campholenal - -
15 M2_2 91 10.2042 1136 1127 α-campholenal - -
16 F2_2 91 10.2132 1136 1127 α-campholenal - Dimethirimol
17 T2_1 119 10.4565 1145 1145 camphor - Benzimidazole
18 M1_1 68 10.8826 1160 1158 isobomeol - -
19 F7_3 68 10.8845 1160 1158 isobomeol - -
20 T2_1 68 10.8866 1160 1158 isobomeol - -
21 F3_3 68 10.8873 1160 1158 isobomeol - -
22 F4_1 117 11.3242 1174 1171 ethyl-benzoate - -
23 F4_1 50 11.5177 1180 1180 m-cymen-8-ol - -
24 T2_2 127 11.5317 1180 1180 m-cymen-8-ol - -
25 T2_3 134 11.7619 1188 1187 p-cymen-8-ol - -
26 M2_2 105 12.0654 1197 1196 methylchavicol - 3-cyanopyridine
27 F3_2 57 12.1827 1200 1203 n-decanal 3-Hexanone -
28 F7_3 113 12.3674 1207 1207 verbenone - -
29 M1_2 113 12.3831 1208 1207 verbenone - -
30 T1_3 91 12.3874 1208 1207 verbenone - -
31 M2_2 93 12.6294 1217 1217 trans-carveol - -
32 M1_2 69 12.7588 1222 1219 trans-carveol - -
33 M1_1 55 12.8397 1225 1229 nerol - -
34 F7_3 67 13.1203 1236 1236 thymol methyl ether - -
35 M1_1 79 13.1266 1236 1236 thymol methyl ether - -
36 F5_2 93 13.127 1236 1236 thymol methyl ether - Beta-pinene; y-eudesmol
37 M2_2 93 13.32 1243 1243 carvone - -
38 T1_1 93 13.327 1243 1243 carvone 2-chloropropionyl chloride -
39 T2_1 93 13.3305 1243 1243 carvone - -
40 *(Br) F6_2 69 13.6916 1256 1256 geraniol - -
41 M1_1 91 13.6953 1256 1256 geraniol -
42 F2_1 69 13.6967 1256 1256 geraniol -
43 F5_1 69 13.6975 1256 1256 geraniol - -
44 M2_1 91 13.9837 1266 1263 (E)-2-decenal - N-Methyl-Npropagylbenzylamine
45 F7_3 119 14.131 1271 1271 geranial - -
46 F2_3 119 14.1354 1271 1271 geranial - -
47 F3_2 69 14.4252 1281 1282 a-terpinen-7-al - -
48 M2_3 69 14.4368 1281 1282 a-terpinen-7-al - -
49 F5_1 91 14.6745 1289 1289 p-cymen-7-ol - -
50 T2_1 91 14.68 1289 1289 p-cymen-7-ol - -
51 F6_3 149 14.9157 1297 1297 perilla alcohol - Benzoylcholine
52 * (Cl) F6_3 131 15.0641 1302 1302 trans-ascaridole - 2-chloro-1-phenyl-2-butene
53 F6_1 166 15.3344 1313 1313 2E,4E-decadienal - 2-chloro-1-phenyl-2-butene
54 F6_3 81 15.5368 1320 1313 2E,4E-decadienal - -
55 M2_2 81 15.5381 1321 1313 2E,4E-decadienal - -
56 T1_2 81 15.5481 1321 1313 2E,4E-decadienal - -
57 M2_2 67 15.6269 1324 1313 2E,4E-decadienal - 1-Adamantanamine
58 M1_2 67 15.6354 1324 1313 2E,4E-decadienal - -
59 F3_1 57 15.7447 1329 1339 d-elemene - -
60 F2_1 57 15.7649 1329 1339 d-elemene - -
61 M2_2 91 15.8827 1334 1339 d-elemene - -
62 M1_2 91 15.8885 1334 1339 d-elemene - -
63 M2_2 139 16.5157 1357 1357 eugenol - -
64 F6_1 115 16.5278 1358 1357 eugenol - -
65 M2_3 166 16.5392 1358 1357 eugenol - -
66 M2_2 91 17.02 1375 1373 a-ylangene - -
67 M2_3 91 17.0254 1376 1373 a-ylangene - -
68 M1_2 91 17.0311 1376 1373 a-ylangene - -
69 T2_1 127 17.403 1389 1389 isolongifolene - Imidazole-4-acetate
70 T2_1 57 17.7476 1400 1399 1,7-di-epi-a-cedrene - -
71 M2_2 79 17.7625 1401 1402 methyleugenol - -
72 M2_3 127 17.7782 1402 1402 methyleugenol - -
73 T2_2 67 18.1041 1415 1415 cis-a-bergarnotene - -
74 T1_3 79 18.18 1418 1419 β-caryophyllene - -
75 F6_1 79 18.1866 1419 1419 β-caryophyllene - -
76 T1_1 79 18.1928 1419 1419 β-caryophyllene Pyridine -
77 F5_1 79 18.1934 1419 1419 β-caryophyllene - camphene
78 M1_2 79 18.1939 1419 1419 β-caryophyllene - -
79 * (Br) F7_2 212 18.3193 1424 1420 β-caroyophyllene - -
80 F6_2 210 18.4564 1429 1420 β-caroyophyllene - -
81 M2_3 131 18.4673 1430 1420 β-caroyophyllene - -
82 M1_1 67 18.5462 1433 1435 trans-a-bergamotene - -
83 M1_2 67 18.5503 1433 1435 trans-a-bergamotene - -
84 F2_2 133 18.6084 1435 1435 trans-a-bergamotene - -
85 T2_2 69 18.6925 1439 1438 trans-a-bergamotene - -
86 F3_3 69 18.6982 1439 1440 a-guaiene - -
87 M2_3 69 18.7018 1439 1440 a-guaiene - -
88 M1_1 91 18.7475 1441 1440 a-guaiene - -
89 F4_2 69 18.8243 1444 1440 a-guaiene - -
90 F7_2 69 18.8273 1444 1440 a-guaiene - -
91 T1_3 69 18.8306 1444 1440 a-guaiene - -
92 F2_2 210 19.1226 1456 1455 a-humulene - -
93 F3_3 133 19.3339 1464 1464 a-acoradiene -
94 T2_2 105 19.4843 1469 1469 drima-7,9(11)-diene - -
95 T2_1 105 19.4852 1469 1469 drima-7,9(11)-diene - -
96 F7_2 131 19.4915 1470 1469 drima-7,9(11)-diene - -
97 F5_1 91 19.6255 1475 1474 b-cadinene - -
98 F6_3 91 19.7468 1479 1479 y-curcurnene -
99 F2_1 91 19.7581 1480 1480 germacrene D - -
100 F4_1 153 20.0567 1491 1491 cis-β-guaiene - -
101 F4_1 153 20.0567 1491 1491 cis-β-guaiene - -
102 F3_2 71 20.2111 1496 1495 a-zingiberene -
103 F7_2 135 20.3621 1502 1499 a-muurolene - -
104 F5_1 91 20.3727 1503 1506 d-selinene - -
105 T2_1 91 20.3766 1503 1506 d-selinene - -
106 F2_2 91 20.3829 1503 1506 d-selinene -- -
107 F6_1 205 20.6221 1513 1513 g-cadinene - -
108 F4_3 205 20.6237 1513 1513 g-cadinene - -
109 F7_2 205 20.6252 1513 1513 g-cadinene - 5-bromo-4-methoxyoct-1-ene
110 T2_1 205 20.6278 1513 1513 g-cadinene p-Benzoquinone -
111 F3_2 133 20.6781 1516 1514 sesquicineole - -
112 F3_3 133 20.6803 1516 1514 sesquicineole - -
113 F6_3 133 20.685 1516 1514 sesquicineole - -
114 F5_1 69 20.8293 1522 1523 d-cadinene - --
115 F5_1 69 20.9154 1526 1525 eugenyl acetate - -
116 F6_3 69 20.9158 1526 1525 eugenyl acetate - -
117 F7_2 121 20.9204 1526 1525 eugenyl acetate - -
118 T1_3 69 20.9227 1526 1525 eugenyl acetate - -
119 F7_2 167 21.1103 1534 1533 cadina-1,4-diene1 - -
120 T2_2 91 21.1498 1536 1538 a-cadinene - -
121 F6_1 167 21.43 1547 1548 elemol - -
122 F7_2 167 21.4346 1547 1548 elemol - -
123 T1_3 69 21.467 1549 1549 elemol - --
124 F3_2 69 21.4709 1549 1549 elemol - -
125 F7_2 91 21.598 1554 1552 elemicin - -
126 F6_2 91 21.6852 1557 1557 germacrene B - -
127 F5_1 91 21.6859 1558 1557 germacrene B - -
128 F6_3 91 21.6869 1558 1557 germacrene B - -
129 M1_1 91 21.6895 1558 1557 germacrene B - -
130 F6_3 67 21.7268 1559 1557 germacrene B - -
131 F7_2 133 21.8253 1563 1564 β-calacorene - l-Asparagine
132 T1_1 91 21.8405 1564 1564 β-calacorene - -
133 F7_2 167 22.0012 1570 1574 prenopsan-8-ol - -
134 T1_3 213 22.018 1571 1574 prenopsan-8-ol - -
135 F6_3 132 22.0226 1571 1574 prenopsan-8-ol - -
136 F6_3 153 22.0226 1571 1574 prenopsan-8-ol - -
137 M1_1 91 22.3956 1586 1585 gleenol - -
138 F2_2 133 22.5362 1591 1590 viridflorol - -
139 F6_3 71 22.7179 1598 1590 viridflorol - -
140 F3_2 71 22.7193 1598 1590 viridflorol - -
141 F2_3 135 22.722 1598 1590 viridflorol - -
142 F3_3 57 22.7547 1600 1607 b-oplopenone - -
143 F3_2 57 22.7603 1600 1607 b-oplopenone - -
144 F3_3 57 22.761 1600 1607 b-oplopenone - -
145 F6_3 67 22.8782 1605 1607 b-oplopenone - -
146 T2_2 68 23.0541 1613 1612 tetradecanal - -
147 F4_3 148 23.2435 1622 1623 silphiperfol-6-en-5-one - -
148 F3_1 135 23.2485 1622 1623 silphiperfol-6-en-5-one - -
149 F5_3 91 23.52 1634 1633 y-eudesmol - -
150 F4_3 91 23.5226 1634 1633 y-eudesmol - -
151 F7_2 68 23.5261 1634 1633 y-eudesmol - -
152 F7_2 68 23.5261 1634 1633 y-eudesmol - --
153 T2_2 68 23.5278 1634 1633 y-eudesmol - -
154 F6_1 167 23.5309 1634 1633 y-eudesmol -
155 F6_2 67 23.7239 1643 1642 cubenol - allylcy-anide
156 T1_3 91 23.8325 1647 1646 a-muurolol - -
157 F7_2 132 23.8759 1649 1646 a-muurolol - -
158 F7_2 132 23.9988 1654 1653 a-cadinol - -
159 F6_2 149 24.3377 1669 1668 bulnesol - -
160 F7_2 149 24.3428 1669 1668 bulnesol - -
161 F6_3 167 24.6091 1680 1682 a-bisabolol - -
162 F6_1 57 24.6252 1681 1682 a-bisabolol - -
163 F7_2 69 24.8113 1689 1686 8-cedren-13-ol - -
164 F7_2 69 24.8207 1689 1686 8-cedren-13-ol - -
165 F2_3 67 24.9282 1693 1686 8-cedren-13-ol - -
166 F6_1 57 25.0854 1700 1686 8-cedren-13-ol - Heptadecane
167 F7_2 91 25.3006 1710 1686 8-cedren-13-ol - -
168 F3_2 71 25.3224 1711 1735 oplopanone - -
169 F3_3 68 25.4814 1718 1735 oplopanone - -
170 F6_3 67 25.7278 1730 1735 oplopanone - -
171 F7_2 103 25.7292 1730 1735 oplopanone - -
172 F2_1 67 25.7318 1730 1735 oplopanone - -
173 F2_3 133 25.8305 1735 1735 oplopanone - -
174 F3_1 117 25.8366 1735 1735 oplopanone - -
175 F7_2 167 26.3223 1757 1761 benzyl-benzoate - -
176 T2_1 167 26.5838 1769 1761 benzyl-benzoate - 6-Methylmer-captopurine
177 F6_2 117 26.7333 1775 1789 8-a-acetoxyelemol - -
178 T2_2 103 26.8111 1779 1789 8-a-acetoxyelemol - -
179 F3_1 67 26.8577 1781 1789 8-a-acetoxyelemol - -
180 F6_1 67 26.8599 1781 1789 8-a-acetoxyelemol - -
181 F3_2 67 26.8671 1781 1789 8-a-acetoxyelemol - -
182 T2_1 67 26.8697 1781 1789 8-a-acetoxyelemol - -
183 * (Cl2) F6_1 103 26.8838 1782 1789 8-a-acetoxyelemol - -
184 F3_1 103 26.8852 1782 1789 8-a-acetoxyelemol - -
185 * (Br) F6_1 67 27.4286 1806 1798 nootkatone - -
186 * (Br) F7_2 67 27.4342 1806 1798 nootkatone - -
187 F2_1 67 27.4383 1807 1798 nootkatone - -
188 F6_3 103 27.4961 1810 1798 nootkatone - -
189 F6_1 67 27.5012 1810 1798 nootkatone - -
190 F6_2 67 27.5676 1813 1827 isopropyl tetradecanoate - -
191 F6_2 69 28.0351 1836 1827 isopropyl tetradecanoate - -
192 F3_2 69 28.0387 1836 1827 isopropyl tetradecanoate - -
193 F6_1 91 28.3153 1849 1867 flourensiadiol - -
194 F3_2 149 28.735 1869 1867 flourensiadiol - -
195 * (ClBr) F6_2 69 29.2918 1895 1878 hexadecanol - -
196 F5_3 67 29.4766 1903 1927 methyl hexadecanoate - -
197 T2_1 67 29.4889 1904 1927 methyl hexadecanoate - -
198 F7_2 91 30.7705 1968 1999 eicosane - -
199 T2_2 91 30.7753 1969 1999 eicosane - -
200 F3_2 91 30.7855 1969 1999 eicosane - -
201 T2_2 64 31.6475 - - - - -
202 T2_2 129 38.661 - - - - -

Compound peaks detected from GC-MS analysis of samples for evaluation of non-polar secondary metabolite patterns of 5 cryptic species of P. hornemanni found in Batanes, Philippines. Identifications based on comparison of either Kovat’s Indices (KI) of compounds retrieved from the Retention Index Calculator [37] or mass spectral comparison of compounds retrieved from NIST or MassBase. Asterisk (*) indicates compounds with parent ions showing halogenated mass spectral patterns.

# Sample Base Peak KI KI Based ID Mass Spectra Based ID
RT (min) Nearest KI Lucero et al. [37] NIST Massbase
1 B4B 57 8.1122 1065 1062 y-terpinene - -
2 B4C 69 8.7386 1085 1085 artemisia-alcohol - -
3 B4C 57 9.1807 1098 1097 linalool - -
4 B4B 71 9.1841 1098 1097 linalool - -
5 B6C 57 9.4047 1106 1099 linalool 3-hexanone, 2,2-dimethyl- 2-methylbutane
6 B4C 69 11.2515 1172 1171 ethyl-benzoate - -
7- * (Cl) B3A 69 11.2560 1172 1171 ethyl-benzoate - -
8 B4A 57 14.3547 1279 1277 trans-carvone-oxide - 2,2-dimethylbutane
9 B4B 57 14.3593 1279 1277 trans-carvone-oxide - 3-ethylhexane
10 B3B 57 14.5560 1285 1286 borneol-acetate -
11 B3A 69 15.2678 1310 1306 undecanal - trans-4-octene
12 B3A 57 15.6497 1325 1314 2E,4E-decadienal -
13 B6B 57 15.6518 1325 1314 2E,4E-decadienal -
14 B4A 71 16.5884 1360 1357 eugenol - 2-methylpentane
15 B6C 57 17.8154 1403 1403 italicene - 2,2-dimethylbutane
16 B3A 57 17.8289 1404 1403 italicene -
17 B10 71 20.306 1500 1499 a-muurolene - Pentacosane
18 B3A 57 20.4025 1504 1506 d-selinene - tripropylamine
19 B4A 191 20.715 1517 1514 sesquicineole -
20 B6C 57 21.0008 1529 1532 cadina-1,4-diene -
21- * (Br) B3B 57 21.0024 1529 1532 cadina-1,4-diene - 1-octene
22 B4A 69 21.5566 1552 1552 elemicin -
23 B4A 57 25.1819 1704 1686 8-cedren-13-ol -
24 B4B 71 25.4142 1715 1735 oplopanone - Hexacosane
25 B4B 69 26.0498 1745 1747 6S-7R-bisabolone - 10-methyl-1-dodecanol
26 B4A 57 27.4031 1805 1798 nootkatone - 4-methyl-1-pentene
27 B3B 57 27.9515 1832 1827 isopropyl-tetradecanoate - 2,4-dimethylpentane
28 B3B 71 29.9932 1930 1927 methyl-hexadecanoate - docosane
29 B6B 57 30.4182 1951 1927 methyl-hexadecanoate - -
30 B10 69 30.5987 1960 1927 methyl-hexadecanoate - -
31 B4C 73 30.6949 1965 1999 eicosane - -
32 B6B 71 32.0267 - - 3-hexanone, 2,2-dimethyl- 2,2-dimethylbutane
33 B3B 71 32.0458 - - - 2,2-dimethylbutane
34 B6B 69 34.1452 - - - docosane
35 B3B 69 34.7128 - - - -
36 B4C 69 34.7233 - - - -
37 B6C 71 35.0713 - - - -
38 B3C 71 36.7162 - - - 3-ethylpentane
39 B9B 395 38.2697 - - - -
40 B6C 71 38.8875 - - - 3-ethylhexane
41 B6B 71 39.0147 - - - -
42 B4C 57 39.4792 - - - 3-ethylhexane
43 B6B 57 39.4822 - - - 3,3-dimethyl-1-butene
44 B4C 71 39.6266 - - - 3-ethylpentane
45 B3C 71 39.7884 - - - -
46 B6B 57 40.4997 - - 3-hexanone, 2,2-dimethyl- 2-methylbutane
47 B6C 57 40.7684 - - - 3-ethylpentane
48 B3C 71 40.7722 - - - -
49 B4C 57 43.09 - - - 3-ethylpentane
50 B6B 57 43.0982 - - - 2,2,4,6,6-pentamethylheptane
51 B6C 85 43.2865 - - - 2-methylnonane
52 B6B 71 43.3628 - - - 3-ethylpentane
53 B3C 71 43.3691 - - - -
54 B3B 85 43.7727 - - - 3,3-dimethyl-1-butene
55 B4B 85 44.3901 - - - Amitrole
56 B6B 71 44.3953 - - - 3-aminopropiononitrile
57 B4A 71 46.2638 - - - -
58 B3C 71 46.8143 - - - 2,3-dimethylbutane
59 B6B 71 46.822 - - 3-hexanone, 2,2-dimethyl- -
60 B6C 71 47.3544 - - - -
61 B4A 71 47.7467 - - - 3-ethylypentane
62 B3A 57 47.7529 - - - 3,3-dimethyl-1-butene
63 B4A 71 48.0072 - - - -
64 B6B 71 48.0159 - - - -
65 B6C 71 48.0169 - - - -
66 B4A 71 48.0321 - - - -
67 B4B 71 50.7731 - - - -

Compound peaks detected from GC-MS analysis of samples used for evaluation of temporal and spatial patterns of non-polar metabolites of three cryptic species of P. hornemanni found in the Visayas. Identifications based on comparison of either Kovat’s Indices (KI) of compounds retrieved from the Retention Index Calculator [37] or mass spectral comparison of compounds retrieved from NIST or MassBase. Asterisk (*) indicates compounds with parent ions showing halogenated mass spectral patterns.

# Sample Base Peak KI KI Based ID Mass Spectra Based ID
RT Nearest KI Lucero et al. [37] Massbase
1 3A 57 8.1118 1065 1062 y-terpinene undecane
2 15A 57 14.556 1285 1286 borneol acetate 1-nonene
3 9A 57 14.556 1285 1286 borneol acetate pentacosane
4 29A 57 20.3128 1500 1499 a-muurolene tricosane
5 15B 71 20.5458 1510 1509 β-bisabolene 3-ethylpentane
6 15A 57 25.186 1705 1686 8-cedren-13-ol -
7 34A 57 25.4206 1716 1735 oplopanone -
8 15B 71 26.6674 1772 1761 benzyl-benzoate 2-methylbutane
9 15A 57 27.8534 1827 1827 isopropyl tetradecanoate
10 44BB 71 30.0015 1930 1927 methyl hexadecanoate docosane
11 8B 57 30.4069 1950 1927 methyl hexadecanoate
12 15B 71 31.6111 - - - -
13 15A 57 31.8326 - - - -
14 15B 57 31.8342 - - - 1,4-dimethylhexane
15 44BA 71 34.1535 - - - docosane
16 15B 71 34.2967 - - - 3-ethylpentane
17 15A 57 34.76 - - - 1-octene
18 15A 71 35.4496 - - - 2-methylbutane
19 15B 57 35.5757 - - - -
20 15B 57 35.5757 - - - 2,2-dimethylbutane
21 44BA 71 37.9407 - - - docosane
22 15B 410 38.2743 - - - -
23 15A 71 39.3484 - - - 3-ethylhexane
24 15A 71 40.0877 - - - 2,2-dimethylbutane
25 15A 71 40.0981 - - - beta-Aminopropionitrile
26 15B 71 42.5358 - - - 2-methylbutane
27 15B 57 43.9626 - - - 4-methyloctane
28 44C 71 44.6454 - - - -
29 15A 57 45.2255 - - - 2,2-dimethylbutane
30 15A 71 47.3529 - - - -
31 15B 57 47.3534 - - - -
32 15B 57 47.3581 - - - 2,4-dimethylpentane
33 15A 71 47.6237 - - - 2,4-dimethylhexane

Map of sampling sites. (a) Map of the Philippines indicating location of Batanes and Visayas; (b) Sampling sites in Batan and Sabtang Islands in Batanes; (c) Sampling sites in Siquijor, Negros, and Cebu Islands in the Visayas).

 Acknowledgments

The authors are grateful to Abner Bucol, Zacharias Generoso and Jacinta Lucañas of IEMS-Silliman University for field assistance, Batanes MENRO Francis Domingo for field and dive assistance, Frederik Lynen (Department of Organic Chemistry, UGent) and co-workers for the helpful suggestions and access to GC-MS and HPLC instruments during the exploratory period of the chemical analysis. Special gratitude goes to Elie Verleyen and Wim Vyverman (Protistology and Aquatic Ecology, UGent) for allowing us to use the GC-MS equipment (FWO Grant 1516906N) and Renaat Dasseville for the valuable technical assistance. Funding for this research was provided by the Flemish Interuniversity Council (VLIR) as part of the PhD Grant to D.A. Payo.

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Phylogenetic tree reconstructed using Bayesian inference based on the mitochondrial cox2–3 spacer of Portieria specimens collected from Batanes (B) and Visayas (V) Islands in the Philippines. Branch support (posterior probabilities) ≥ 0.5 are indicated at the branches. The eleven clades represent cryptic lineages, likely equivalent to species. Species B34 and B38 were not included in the chemical analysis.

Portion of GC-MS total ion chromatograms of non-polar extracts of Portieria samples: (a) male gametophyte from Bantayan (Dumaguete), V1; (b) White Beach, Batanes, B21; (c) Liloan, V32. β-myrcene, a precursor of many halogenated monoterpenes, was detected only in the Bantayan specimens. Naphthalene is used as an internal standard.

Frequency distribution indicating number of shared and unique non-polar secondary metabolites: (a) within life-history stage of species V1B from Bantayan, female (F), male (M) and tetrasporophyte (T); (b) among sites of species V32 collected in Sawang (SAW), Liloan (LIL) and Pagubagubaan (PAG).

Principal component analysis of GC-MS standardized relative abundance datasets which includes the compounds detected in a 67-min run of Portieria extracts. (a) Male gametophyte (M1-2) samples are clearly discriminated occurring at the positive end of the plane while female gametophyte (F2-7) and tetrasporophyte samples (T1-2) are only partially discriminated by Factor 1; (b) Batanes dataset includes five cryptic species. The clustering of B35 replicates and the scattered pattern of B5 suggest variation in component compounds exists between species but at the same time suggested that variation within species can occur.

Principal component analysis of GC-MS standardized relative abundance datasets for detecting possible spatial and temporal patterns in: (a) the Visayas species; (b) the Visayas samples belonging to species V32. Letters indicate sampling site (DAP: Dapdap; PAG: Pagubagubaan; SAW: Sawang; SIA: Siaton) and numbers indicate month and year of sampling.

Frequency of compounds that are unique to or common to several species of Portieria found in Batanes. Values in parenthesis indicate total number of compounds found in a species, while 1 indicates presence and 0 absence of a compound.

B21 (49) B33 (51) B35 (60) B8 (57) B5 (57) Frequency (67)
0 0 0 1 0 1
0 0 1 1 0 3
0 0 1 1 1 3
0 1 0 0 0 1
0 1 0 1 0 2
0 1 1 1 0 1
0 1 1 1 1 7
1 0 1 0 1 3
1 0 1 1 0 2
1 0 1 1 1 4
1 1 0 0 1 2
1 1 0 1 1 1
1 1 1 0 1 4
1 1 1 1 1 33