Soft Corals Biodiversity in the Egyptian Red Sea: A Comparative MS

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Soft Corals Biodiversity in the Egyptian Red Sea: a Comparative MS and NMR Metabolomics Approach of Wild and Aquarium Grown Species Mohamed A. Farag, Andrea Porzel, Montasser A. Al-Hammady, Mohamed-Elamir F. Hegazy, Achim Meyer, Tarik A. Mohamed, Hildegard Westphal, and Ludger A. Wessjohann J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00002 • Publication Date (Web): 19 Feb 2016 Downloaded from http://pubs.acs.org on February 21, 2016

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Journal of Proteome Research

Soft

Corals

Biodiversity

in

the

Egyptian

Red

Sea:

a Comparative MS and NMR Metabolomics Approach of Wild and Aquarium Grown Species Mohamed A. Farag1*, Andrea Porzel2, Montasser A. Al-Hammady3, Mohamed-Elamir F. Hegazy4, Achim Meyer5, Tarik A. Mohamed4, Hildegard Westphal5, Ludger A. Wessjohann2* 1

Pharmacognosy department, College of Pharmacy, Cairo University, Cairo, Egypt, Kasr el

Aini st., P.B. 11562. 2

Leibniz Institute of Plant Biochemistry, Dept. Bioorganic Chemistry, Weinberg 3,

D-06120 Halle (Saale), Germany 3

National Institute of Oceanography and Fisheries, Red Sea Branch, Hurghada 84511, Egypt

4

Phytochemistry Dept., National Research Centre, 33 El Bohouth St Dokki, Giza, Egypt, P.

O. 12622. 5

Leibniz Institute of Tropical Marine Ecology, Fahrenheit Str.6, D-28359 Bremen, Germany

*Corresponding author: [email protected] Tel: +20-1004142567 *Co-corresponding author: [email protected]

1

ACS Paragon Plus Environment


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ABSTRACT Marine life has developed unique metabolic and physiologic capabilities and advanced symbiotic relationships to survive in the varied and complex marine ecosystems. Herein, metabolite composition of the soft coral genus Sarcophyton was profiled with respect to its species, and different habitats along the coastal Egyptian Red Sea via 1H-NMR and UPLCMS large-scale metabolomics analyses. Current study extends the application of comparative secondary metabolite profiling from plants to corals to reveal for metabolite compositional differences among its species viaa comparative MS and NMR approach . This was applied for the first time to investigate the metabolism of 16 Sarcophyton species in the context of their genetic diversity and or growth habitat. Under optimized conditions, we were able to simultaneously identify 120 metabolites including sixty five diterpenes (65), eight sesquiterpenes (8), eighteen sterols (18) and fifteen oxylipids (15). Principal component analysis (PCA) and orthogonal projection to latent structures-discriminant analysis (OPLSDA) were used to define both similarities and differences among samples. For corals classification based on species type, UPLC-MS was found to be more effective than NMR. The main differentiations emanate from cembranoids and oxylipids. The specific metabolites that contribute to discrimination between soft corals of S. ehrenbergi from 3 different growing habitats also belonged to cembrane type diterpenes, with aquarium S. ehrenbergi corals being less enriched in cembranoids compared to sea corals. PCA analysis using either NMR or UPLC-MS datasets was found equally effective in predicting the species origin of unknown Sarcophyton species. Cyclopropane containing sterols observed in abundance in the corals may act as cellular membrane protectant against the action of coral toxins, i.e. cembranoids. Keywords: Corals; cembranoids; Sarcophyton; metabolomic fingerprinting; nuclear magnetic resonance (NMR); ultra performance liquid chromatography mass spectrometry (UPLC-MS) ; cyclopropyl sterols

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INTRODUCTION Seas cover over 70% of the earth. The total global biodiversity is estimated to amount to some 500 x 106 species of prokaryote and eukaryote organisms. Of these, marine macrofauna comprise an estimated range of 0.5-30 x 106 species with a broader range of taxonomic diversity than that found in the traditional sources of natural products, the terrestrial macrofauna1. Only a few thousand compounds have been reported from marine origin and hence seas and oceans are believed to have an enormous potential of providers for new bioactive metabolites2. Marine natural products display an extraordinary chemical and pharmacological scope3. This could be attributed to the necessity of marine invertebrates to release secondary metabolites as their own chemical defense tools to survive in the specific temperature, salinity, and pressure conditions, and to resist their predators, or to provide chemical communication in symbiotic relationships4. Coral reef ecosystems support enormous biological diversity, including structurally and functionally complex benthic communities. The Red Sea is an epicenter for marine biodiversity with a high percentage of endemic biota including the northern most tropical reefs with stony corals and soft corals. Of the 180 soft coral species identified worldwide, approximately 40% are native to the Red Sea. Soft corals are marine invertebrates possessing a vast range of terpenoid metabolites5. These terpenes, mostly cembranoids, represent the main chemical defense of corals against natural predators. Soft corals of the genus Sarcophyton are particularly rich in cembrane terpenes6, 7. Cembranoids

contain

a

14-membered

macrocyclic

skeleton8,

biosynthesized

by

macrocyclization of geranylgeranyldiphosphate and exhibit a wide range of biological properties including anti-tumor, neuro-protective, antimicrobial, calcium-antagonistic, and anti-inflammatory activity 9, 10. In addition, marine organisms show intense symbiosis with other organisms like plants or bacteria. Chemical diversity is a critical factor in an organism’s adaptation and fitness and a primary reason for the large number of natural products found in marine organisms including their symbionts 11. Metabolomics strategies have recently emerged to help us gain a broader insight into the biochemical composition of living organisms12. With the recent developments in plant metabolomics techniques13,

14

, it is now possible to detect several hundreds of

metabolites simultaneously and to compare samples reliably for differences and similarities in a semi-automated and untargeted manner. Metabolomics makes use mostly of hyphenated techniques which rely on chromatographic separation of metabolites using either gas chromatography (GC) or liquid chromatography (LC) coupled to mass spectrometry (MS) or, 3



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upcoming, nuclear magnetic resonance spectroscopy (NMR), to analyze complex mixtures of extracted metabolites15. While an NMR metabolite profiling approach provides the complete and quantitative metabolite signature of a complex extract, LC/MS much better resolves individual chemical components into separate peaks, enhancing the opportunity to uncover novel metabolites of low abundance

16

. The profiling of secondary metabolites in terrestrial

plants recently has also been utilized, e.g., for the quality control of herbal drugs 17-19. Plants, fungi and (some) microorganisms are much richer in (secondary) metabolites then mammals or most other animals, and thus plant studies are more suitable as templates for studies on marine organisms. Although the use of metabolomics in plant analyses has been extensive over the past 20 years20, very little has been done in terms of applying it to marine organisms. One of the recent application of liquid chromatography–mass spectrometry (LC-MS) metabolomics was found to be quite effective for marine microbial strain prioritization to support drug discovery of unique natural products 21. With an increasing interest in utilizing NMR for metabolic fingerprinting, it is now possible to record NMR spectra from crude extracts, providing its valuable metabolite signature

22

.

Nevertheless, 1D-NMR on its own cannot always provide unambiguous metabolite identifications and suffers from signal overlap. Application of NMR in the field of marine drugs include analysis of spatial variation in soft corals from South China 23and profiling of stony reef corals from Hawaii 24, although in both studies limited number of metabolites were annotated,mostly targeting primary metabolites. In previous studies some of the present authors have successfully applied a comparative metabolomic approach combining both NMR and MS based technologies with multivariate data analyses for herbal drug analyses 18, 25, 26. The present work is focused on evaluating the capability of 1D and 2D-NMR for metabolomic fingerprinting and profiling of soft coral extracts, ideally without any preliminary chromatographic step, in parallel to more conventional chromatography-coupled MS techniques. Such a comparative metabolomics approach is the first time to be applied in a marine type metabolomics project, exploring the diversity of soft coral secondary metabolism in the context of its genotype and or growing habitat. The effect of the growing habitat on secondary metabolite accumulation in soft corals was assessed by collecting soft corals from different locations in the Egyptian part of the Red Sea and at different sea levels along with soft corals grown in an experimental aquarium facility under controlled conditions. Five species of the soft coral genus Sarcophyton, known to have many secondary metabolites including cembranoids, which are well represented along the Red Sea coast, have been 4


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comprehensively studied. To verify the effectiveness of the developed method, extracts from other soft corals were analyzed for comparison, including Lobophyton and Sinularia sp. from the Red Sea coast. Owing to the complexity of coral extracts, statistical multivariate analyses including principal component analysis (PCA) and partial least squares-discriminant analysis (OPLS-DA) were performed for samples classification. MATERIALS AND METHODS Soft coral material The soft coral Sarcophyton of the family Alcyoniidaeis the most common genus along the Red Sea coast. Soft corals of five species of this genus, namely S. glaucum, S. acutum, S. ehrenbergi, S. convolutum, S.regulare, as well as the two soft coral species Lobophyton pauciliforum and Sinularia polydactela are studied here (Fig. 1). Samples were collected from two widely geographically separated areas, of which offshore Al-Guna (20 km southern Hurghada) and offshore Makadi (55 Km southern Hurghada) at two locations along the Egyptian Red Sea coast, located 55 km apart (Al-Guna and Makadi bay Fig. 1). Using SCUBA diving, samples were collected from at water depths of 2 -5 m below sea-level (Table 1, Suppl. Table S1). Fragments of 5-7 cm2 were cut and collected from the disk of large colonies that were tagged to recur it more one time. On deck of the vessel, the samples were immediately transferred into boxes filled with dry ice for transportation to the laboratories. We are well aware, that these handling procedures are not ideal, but were the best adaptation available to field conditions. Taxonomy and determination of the corals was done by coauthor Dr. Montaser Al-Hammady el Zaiat (National Institute of Oceanography and fisheries, Red Sea Branch department, Suez Canal University) on the basis of Fabricious and Aldersdale 27. Identification of Sarcophyton includes the isolation of sclerites (Suppl. Fig. S1). Specimens of S. glaucum and S. ehrenbergi were additionally cultured in the aquarium facility of the Leibniz Center for Tropical Marine Ecology, Bremen, Germany. These corals were kept at 26°C in a 2500 L recirculation system with 50 cm distance between coral nubbins and a blue/white combination of two 39W fluorescence light bulbs. A total of 16 samples of various Sarcophyton and two other soft coral species belonging to the genera Sinularia and Lobophyton collected from different sites along the Red Sea coast were compared to the ones grown in aquarium tanks. For each specimen, 3 biological replicates representing 3 different coral bulbs were collected to assess biological variance. Among the collected samples, five were from Northern Hurgada (S. glaucum, n = 1; S. ehrenbergi, n = 1; 5



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S. acutum, n = 1; S. convolutum, n = 1; S. regulare, n = 1), nine from Northern Safaga (S. glaucum, n = 1; S. ehrenbergi, n = 2; S. acutum, n = 1; S. convolutum, n = 1; S. regulare, Sarcophyton spp., n = 1, Sinularia polydactela, n = 1; Lobophyton pauciliforum, n = 1) and two (S. glaucum, n = 1; S. ehrenbergi, n=1) were cultured in the aquarium facility (see Table 1).

Chemicals and reagents Methanol-D4 (99.80% D), acetone-D6 (99.80% D) and hexamethyldisiloxane (HMDS) were purchased from Deutero GmbH (Kastellaun, Germany). For calibration of chemical shifts, HMDS was added to a final concentration of 0.94 mM. Acetonitrile and acetic acid (LC–MS grade) were obtained from Baker (The Netherlands), milliQ water was used for LC analysis. Sarcophine standard was purchased from AG Scientific, San Diego, CA (St. Louis, MO, USA). Chromoband C18 (500 mg, 3 ml) cartridge from Macherey and Nagel (Düren, Germany). All other chemicals and standards were available from Sigma Aldrich (St. Louis, MO, USA).

(2R,3E,7R,8S,11E)-7,8-Dihydroxy-1(15),3,11-cembratrien-16,2-olide, 11(S)-

hydroperoxylsarcoph-12(20)ene,

12-hydroperoxylsarcoph-10-ene,

7-hydroxy-8-methoxy-

1(15),3,11-cembratrien-16,2-olide, crassocolide K, horiolide, hydroxyl dihydrobovolide, isosarcophinone, norcembrene, trochelioid A, 16-oxosarcophytonin E, 7b-acetoxy-8ahydroxydeepoxysarcophine and ent-sarcophine were isolated from S. ehrenbergi and S. glaucum.

Soft coral extraction procedure and sample preparation for NMR and MS analyses Approximately 100mg tissue from the umbrella were cut with a clean scalpel and transferred to liquid nitrogen. The powdered freeze-dried soft coral tissues were ground with a pestle in a mortar using liquid nitrogen. The powder was then homogenized with 5.0 ml 100% ethyl acetate containing 5 µg/ml umbelliferone (as internal standard for UPLC-MS) using an ultrasonic bath for 20 min. Extracts were then vortexed vigorously and centrifuged at 12000 g for 5 min to remove debris, 3 ml was aliquoted for NMR evaporated under nitrogen and the residue was resuspended in 800µl acetone-D6 containing HMDS at a concentration of 0.94 mM. After centrifugation (13000 g for 1 min), the supernatant was transferred to a 5 mm NMR tube. All 1H-NMR spectra for multivariate data analysis were acquired consecutively within a 48 h time-interval, with samples resuspended in deuterated acetone immediately before data acquisition. Repeated control experiments after 48 h showed no additional 6


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variation. For UPLC-MS analyses, 500 µl were aliquoted and placed on a (500 mg) C18 cartridge, preconditioned with methanol and water. Samples were then eluted using 6 ml methanol, the eluent was evaporated under a nitrogen stream and the obtained dry residue was resuspended in 1 ml methanol. Three µl were used for UPLC-MS analysis. For each specimen, 3 biological replicates were provided and extracted in parallel under the same conditions. High resolution UPLC-PDA-qTOF-MS analysis Chromatographic separations were performed on an Acquity UPLC system (Waters) equipped with a HSS T3 column (100 × 1.0 mm particle size 1.8 µm; Waters) applying the following elution binary gradient at a flow rate of 150 µL min–1: 0 to 1 min, isocratic 95% A (water/formic acid, 99.9/0.1 [v/v]), 5%B (acetonitrile/formic acid, 99.9/0.1 [v/v]); 1 to 16 min, linear from 5 to 95% B; 16 to 18 min, isocratic 95% B; 18 to 20 min, isocratic 5% B. The injection volume was 3.1 µL (full loop injection). The C18 bonded phase used for the HSS T3 sorbents is compatible with 100% aqueous mobile phase and provides ultra-low MS bleed, while promoting superior polar compound retention which has been successfully used for profiling of similar plant extracts. Eluted compounds were detected from m/z 100 to 1000 using a MicroTOF-Q hybrid quadrupole time-of-flight mass spectrometer (Bruker Daltonics) equipped with an Apollo-II electrospray ion source in positive ion modes using the following instrument settings: nebulizer gas, nitrogen,1.6 bar; dry gas, nitrogen, 6 l min–1, 190°C; capillary, –5500 V (+4000 V); end plate offset, –500V; funnel 1 RF, 200 Vpp; funnel 2 RF, 200 Vpp; in-source CID energy, 0 V; hexapole RF, 100 Vpp; quadrupole ion energy, 5eV; collision gas, argon; collision energy, 10 eV; collision RF 200/400 Vpp (timing 50/50); transfer time, 70 µs; prepulse storage, 5 µs; pulser frequency, 10 kHz; spectra rate, 3 Hz. UPLC-ESI-MSn analysis ESI-MSn mass spectra were obtained from a LCQ Deca XP MAX system (ThermoElectron, San José, USA) equipped with a ESI source (electrospray voltage 4.0 kV, sheath gas: nitrogen; capillary temperature: 275 ºC) in positive ionization mode. The Ion Trap MS system is coupled with the Exact Waters UPLC setup, using same elution gradient for the high resolution UPLC-ESI-TOF MS analysis. The MSn spectra were recorded during the UPLC runusing the following conditions: MS/MS analysis with a starting collisioninduced dissociation energy of 20 eV and an isolation width of +2 amu in a data dependent, positive ionization mode. MS data processing for multivariate analysis: PCA and OPLS 7



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Relative quantification and comparison of Sarcophyton metabolic profiles after UPLC-MS was performed using XCMS data analysis software under R 2.9.2 environment, which can be downloaded for free as an R package from the Metlin Metabolite Database (http://137.131.20.83/download/). This software approach employs peak alignment, matching and comparison to produce a peak list. The resulting peak list was processed using the Microsoft Excel software (Microsoft, Redmond, WA), where the ion features were normalized to the total integrated area (1,000) per sample and imported into the R 2.9.2 software package for principal component analysis (PCA). Absolute peak area values were autoscaled (the mean area value of each feature throughout all samples was subtracted from each individual feature area and the result divided by the standard deviation) prior to principal component analysis. This provides similar weights for all the variables. PCA was then performed on the MS-scaled data to visualize general clustering and trends, and outliers among the samples were identified based on the scores plot. Orthogonal projection to latent structures-discriminant analysis (OPLS-DA) was performed with the program SIMCA-P Version 13.0 (Umetrics, Umeå, Sweden). OPLS-DA is a supervised pattern recognition technique that aims to find the maximum separation between a priori groups

28

that was

applied to discriminate, e.g., between species. Biomarkers for species were subsequently identified by analysing the S-plot, showing the relation of covariance (p) and correlation (pcor). The PCA was run for obtaining a general overview of the variance of metabolites, and OPLS-DA was performed to obtain information on differences in the metabolite composition among samples. Distance to the model (DModX) test was used to verify the presence of outliers and to evaluate whether a submitted sample fell within the model applicability domain.

OPLS model was evaluated by the two parameters Q2 and R2X, where R2X is used

to quantify the goodness-of-fit; whereas Q2Y is employed to assess the predictability of the model.

To rule out the non-randomness of separation between groups, an iteration

permutation test was performed. In all cases, the values of Q2 and R2 resulting from the original model were found higher than the corresponding values from the permutation test confirming the model validity. R2 did not exceed Q2 by more than 1 units and with no negative Q2 values suggesting the validity of the model. The S loading plots of the OPLS-DA model was further used to identify the variables responsible for sample differentiation on the scores plot.

NMR analyses

8


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All spectra were recorded on an Agilent VNMRS 600 NMR spectrometer operating at a proton NMR frequency of 599.83 MHz using a 5 mm inverse detection cryoprobe. 1H NMR spectra were recorded with the following parameters: digital resolution 0.367 Hz/point (32K complex data points); pulse width (pw) = 3µs (45°); relaxation delay = 23.7 s; acquisition time = 2.7 s; number of transients = 160. Zero filling up to 128K and an exponential window function with lb = 0.4 was used prior to Fourier transformation. 2D NMR spectra were recorded using standard CHEMPACK 5.1 pulse sequences (gDQCOSY, gHSQCAD, gHMBCAD) implemented in Varian VNMRJ 4.0A spectrometer software. The HSQC experiment was optimized for 1JCH = 146 Hz with DEPT-like editing and

13

C-decoupling

during acquisition time. The HMBC experiment was optimized for a long-range coupling of 8 Hz; a 2-step 1JCH filter was used (130-165 Hz). NMR data processing and PCA analysis The 1H-NMR spectra were automatically Fourier transformed to ESP files using ACD NMR Manager Lab version 10.0 software (Toronto, Canada). The spectra were referenced to internal hexamethyldisiloxane (HMDS) at 0.062 ppm for 1H-NMR and at 1.96 ppm for 13CNMR. Spectral intensities were reduced to integrated regions, referred to as buckets, of equal width (0.04 ppm) within the region of δ 8.0 to -0.4 ppm. The regions between δ 2.00-2.15 ppm and δ 2.75-3.00 ppm corresponding to residual acetone and water signals, respectively, were removed prior to multivariate analyses. PCA was performed with R package (2.9.2) using custom-written procedures after scaling to HMDS signal and exclusion of solvent regions. Sclerite isolation from coral heads and microscopic analysis Sarcophyton has a mushroom-shaped polypary consisting of a smooth and marginally folded disc, which projects beyond clearly differentiated base or stalk (Suppl. Fig. S1). Most species of the genus Sarcophyton possess clubs some 0.20–0.30 mm in length, rarely exceeding 0.60 mm in length, with the clubs having low, rounded processes. The sclerite morphology was examined with an optical microscope at ×200 magnification. Sclerite identification followed protocol 29. RESULTS AND DISCUSSION The major goal of this study was to pave the way for metabolomics-based secondary metabolite investigation of corals exemplified with Sarcophyton spp. (Fig. 1) in an

9



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untargeted, holistic manner in the context of its different species, growing habitat and to some extent water-depth so as to enable metabolite pattern-based taxonomy, and to pave the way to identify species with constituents of medicinal potential in Sarcophyton (Table 1).

Development of a single pot extraction method for UPLC- MS and NMR analyses To allow for a comparative analysis of the metabolite data derived from these different technology platforms, a one pot extraction method compatible for both NMR and MS metabolomics was developed. Several solvents were initially tested including acetone, methanol, and ethyl acetate. Solvent selection for sample preparation was evaluated with respect to reproducibility, quality and recovery of Sarcophyton cembranoids secondary metabolites as revealed by 1H NMR. Compared with MS, 1H NMR can detect metabolites universally, thus providing an unbiased picture of differences among extraction methods. Compared with methanol, acetone and ethyl acetate show a better recovery rate of cembranoids based on the NMR signals as is evident from sarcophine resonances at δ 5.0–5.8 ppm (Suppl.Fig. S2). We are aware that methanol in principal covers more hydrophilic compounds which are sometimes relevant in terrestrial plants, while ethyl acetate shifts the focus slightly to more lipophilic metabolites abundant in corals. Previously, ethyl acetate was shown to possess high efficiency for the extraction of coral diterpenes from Sarcophyton, Sinularia and Dendronephthya species cembranoids

23

.

Considering our interest in profiling coral

30

, 100 % ethyl acetate was chosen for preparing the bulk extract, further

aliquoted for NMR and UPLC-MS analyses. The use of deuterated solvent, routinely used for NMR sample extraction was ruled out in our case due to possible deuterium exchange with sample components affecting further UPLC-MS acquisition and metabolites assignment

UPLC–ESI-MS peak identification LC–MS so far has not been used for large scale metabolomics based analyses of corals. Coral cembranoids are relatively non-polar with commonly a hydroxy or peroxy group in the molecule, and thus can be readily ionized in the electrospray ionization (ESI) source positive mode. Soft coral extracts were initially analyzed in both positive and negative ion electrospray ionization (ESI) MS modes as changes in ESI polarity can often alter competitive ionization and suppression effects revealing otherwise suppressed metabolite signals. Compared to the negative-ion ESI mode, positive-ion MS spectra revealed better sensitivity 10


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and more observable peaks, with a total of 3341 mass signals extracted in positive mode using XCMS software versus 2689 mass signals found in negative ionization mode. Representative UPLC–MS total ion chromatograms of S. glaucum (SG1) is presented in Fig. 2. The identities, retention times, and observed molecular and fragment ions for individual components are presented in Suppl. Table S2. Metabolite assignments were made by comparing high resolution TOF MS data (accurate mass, isotopic distribution and fragmentation pattern) of the compounds detected with Sarcophyton compounds reported in the literature. More than 91 terpenoid and sterol peaks, were resolved, of which 62 were tentatively identified; cembranoids accounted for the highest abundance (65 peaks) among species (Suppl. Table S2, Suppl. Fig. S3) of which the C20-diterpene sarcophine/entsarcophine and the C15-sesquiterpene guaiacophine (Suppl. Fig. S3) were the major constituents in most coral extracts examined. Several oxylipid classes were identified, i.e. cerebrosides, fatty acids, and their amides were detected at late elution times of the chromatogram 600-700 sec

31

. The enhanced resolution afforded by TOF MS measurement

assisted to distinguish between several cembranoid isobaric peaks with same nominal masses. The example compounds are tortuosene B (peak C15) and hydroxydeepoxy sarcophine acetate (peak C90), differing in mass by 0.038 amu, albeit such difference translates to different formulas of C21H29O6+ and C22H33O5+. In general, the mass spectra obtained by UPLC–TOF MS yielded quasi-molecular ions, e.g., [M+H]+ and with a predicted formula with high number of double bond equivalents accounting for 2 to 3 cycles, 3 double bonds and one carbonyl shared in most cembrane diterpene skeletons (Suppl. Fig. S3). The main observed product ions of cembranoids were the result of consecutive eliminations of 18 or 28 units, due to losses of H2O and CO, resp., on the basis of the chemical structure of a diterpene (i.e. excision of ether-like oxygen and hydroxy groups). Further fragmentation results in the opening of the γ-lactone ring and loss of the corresponding acyloxy group (C3H5O2, 71 amu) similar to sesquiterpene lactone fragmentations32. For cembranoid peaks, i.e. peak C7 that exhibit hydroperoxy group, major fragmentations appeared as consecutive losses of (-OH) and (-O) moieties attributed to the cleavage of the hydroperoxy group. Loss of 42 amu was evident in cembranoids containing an acetyl group and was used as a tool to screen for acylated diterpenes as in peaks C31, C51, C57 & C90. Notably, acylated derivatives were not identified in any sesquiterpene and only in one sterol (C68) suggestive for the presence of acylating enzymes with high substrate specificity towards diterpenes. Few terpenoids C3, C4 and C9 showed the loss of 44 amu corresponding to CO2 group and thus indicative of carboxylic acid groups. These polar compounds eluted much earlier at a high solvent polarity 11



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rt.300-350sec. Compared with diterpenes, sesquiterpene were much less abundant in all examined coral extracts. A total of 8 peaks were detected of a guaiane skeleton, with guaiacophine (C69) and calamanene (C95) the most common forms. It cannot be excluded that more volatile simple sesqui- and monoterpenes may be lost during solvent evaporation. Soft corals are recognized for their high diterpene content, of which cembrane diterpenes are the characteristic constituent of the genus33. It should be noted that unless confirmed by standard and the proper spectroscopic technique, neither the establishment of relative nor absolute configuration of terpene assigned peaks can be made. e.g., with several stereogenic centers being present in many of these compounds, six different stereoisomers A-F could be assigned to the sarcosolide peak C14

34

, Suppl. Fig. S3. Detailed MS fragmentation studies

for diterpene stereoisomers analyzed using chiral columns might help to clarify for that issue, but currently is more established for sesquiterpenes only35. Interestingly, biscembranoids reported in Sarcophyton spp.

36, 37

from Japanese or Kenyan coasts could not be identified in

any of our examined Sarcophyton species from the Red Sea, except for one diterpene dimer peak C73 (701.4603, C41H65O9+). Whether such discrepancy is due to ecological differences or rather due to analysis limitations, with failure of biscembranoids to elute or ionize under LCMS conditions has yet to be proved. In addition to the sesqui- and diterpenoids, several tetracyclic C-28 sterols possessing the ergostan (C45, C46, C52, C99 & C103) and cholestan skeleton (C103, C108, C112, C115, C118) were identified in coral extracts, all of which contained cyclopropane rings in their side chain backbone38, a sterol pattern common in zooxanthellae 39. Interestingly, gorgostene-diol 40, a sterol that bears an unusual C-23 methyl group and a cyclopropane ring in its side chain (C108)) was identified along with its mono hydroxy (C113, Suppl. Fig. S3) and desmethyl derivative (C118) in S. ehrenbergi samples but is likely to be derived from its harbored zooxanthellae. Gorgosterol is known to be produced by zooxanthellae, intracellular photosynthetic dinoflagellate symbionts living inside corals

41

. An acetyl derivative of a gorgostane sterol was also tentatively assigned in peak

C115 (535.3996 [M+H]+, C32H55O6) based on its MS data.

The last part analyzed in the chromatographic run (600–750 s), revealed the presence of several fatty acids (FAs) as major peaks, and expectedly with a low response in positive ionization mode. For the genus Sarcophyton the presence of several polyunsaturated fatty acids (PUFAs) is reported, with recent evidence for FA transfer to its symbiont42. The negative ion MS spectrum of the fatty acids arachidonic acid (20:4 (n−6), C94), tetracosapentaenoic acid (24:5 (n−6), C116), octadecatetraenoic acid (18:4 (n-3), C78), 12


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stearic acid (18:0, C120), and palmitic acids (16:0, C106) were straight forward to interpret based on their high resolution mass at 303.2307, 357.2747, 277.2168, 283.2597 and 255.232 with predicted molecular formulae of C20H32O2-, C24H37O2- , C18H29O2- , C18H35O2- and C16H31O2-, respectively. Several nitrogen containing lipids i.e., cerebroside (sarcoehrenoside B, C113) and stearamide (C117) were also detected in S. ehrenbergi extracts as evident from their even molecular ion peaks, albeit their origin (poly or symbiont) cannot be unequivocally determined.

Cerebroside derivatives have been previously isolated from various marine

invertebrates, such as sea stars, sponges, and soft corals including sarcoehrenoside B from S. ehrenbergi collected off the Taiwan coast

43

. Several different types of biological activities

have been ascribed to these compounds, including antifungal, antitumor, antiviral, cytotoxic, and immuno-modulatory properties

44

. This is the first report for the presence of fatty acid

amides in soft coral extracts.

Multivariate data analysis of soft corals chemical profiles via UPLC-MS Although different metabolite patterns could be observed by visual inspection of UPLC–MS traces from different specimens, principal component analysis (PCA) was employed as a more holistic approach to test for possible heterogeneities among different genotypes and for the chemotaxonomy of Sarcophyton in an untargeted approach (Suppl. Fig. S4). PCA is an unsupervised clustering method requiring no knowledge of the datasets and act to reduce the dimensionality of multivariate data while preserving most of the variance within 45. PCA was applied to the TIC data from the UPLC–Q-TOF-MS analysis retrieving a score plot in which Lobophyton and Sinularia species appeared as most distant from other Sarcophyton species, suggesting that our profiling method can distinguish Sarcophyton from other coral species. However, it should be noted that some Sarcophyton sp. are chemically more separated from each other than from Sinularia/Lobophyton. Distant clustering of Lobophyton specimen was due to its enrichment in mass signals m/z 397.349 & 401.3375 corresponding to ergosta-trienol (C99) and an unknown compound (data not shown). In contrast, Sarcophyton specimen was more enriched in diterpenes (data not shown). Considering that our interest was to establish Sarcophyton taxa relatedness based on its species, geographical origin and growing habitat, another multivariate analyses was performed excluding Sinularia and Lobophyton.

The metabolome clusters were located at different points in the two-dimensional space prescribed by two vectors, principal component 1 (PC1 = 47%) and principal component 2 (PC2 = 20%) (Fig. 3A). Generally, all replicates from each sample clustered together and 13



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were separated from other genotypes, confirming the repeatability of the methods used in this study. The PC1/PC2 scores plot (Fig. 3A) shows that two distinct clusters are formed, corresponding to the five different species studied. It should be noted that within a species, separation based on geographical region or growing habitat could not be observed from PCA as, e.g. values for S. ehrenbergi derived from different geographical sites or grown in tanks are all clustering together. Also the numbers collected do not give enough statistical safety. The same observation regarding geographical origin was observed in S. regulare specimens. The tight clustering in S. ehrenbergi and S. regulare individual specimens suggests that they share similar biosynthetic or enzymes involved in the production of cembranoids. Such a clustering pattern was not observed in S. glaucum specimens, with specimens from different origins (Table 1) failing to group together. S. glaucum chemical content was found to vary to a large degree and it was concluded there are at least nine chemotypes existing within S. glaucum, which might account for their dispersal

46

, i.e. the species is not suitable for

chemotaxonomic identification due to its chemical variability. Most of the species, were placed on the right side of the vertical line representing PC1 (positive score values), whereas S. ehrenbergi and S. acutum samples were placed on the left side (positive score values). Examination of the loadings plot suggests that the variables refer to the MS signals of nitrogen containing lipids and cembranoid diterpenes i.e., sarcophine contributes to the discrimination of species (Fig. 3B). In detail, cembrapentaen-20,10-olide, hydroxycembrapentaen-20,10-olide, cembratetraen-16,2-olide (sarcophytonin B), sarcophine (7,8epoxy-1(15),3,11-cembratrien-16,2-olide), ent-sarcophine and hydroperoxylsarcoph-12(20)ene were more enriched in S. regulare and S. convolutum, whereas oxylipid peaks C35 and C77 of (327.2301, C22H31O2+) and (355.2244, C23H31O3+) were more abundant in S. ehrenbergi and S. acutum specimens along with cembrene C. Our results are in agreement with findings on qualitative differences in cembranoids in different Sarcophyton chemotypes from Okinawa, Japan

46

. It should be noted that MS signals for sesquiterpenes and sterols

detected via MS (Suppl. Table S2) did not contribute for segregation in PCA loading plots along PC1 in the UPLCMS dataset, suggestive that sesquiterpenes and sterols are present at comparable levels in all Sarcophyton species or at least their extracts.

Our attempt is the first one to derive Sarcophyton species relatedness based on metabolite data, as molecular phylogenetic analyses alone so far has been insufficient to clearly identify Sarcophyton species. The lack of understanding in both intraspecific variations of diagnostic morphological characters within that genus in addition to a lack of solid taxonomic and 14


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ecological work on Sarcophyton poses problems to derive clear phylogenetic based analysis 47

. With regards to metabolites based classification as in current study, it should be noted that

results are also not yet conclusive considering that the biogenetic source of metabolites whether from corals or its symbionts is not definitive.

Differentiation of S. ehrenbergi grown in different regions and growing habitats With the effective differentiation of coral samples of different genetic origin, we examined whether multivariate statistical analysis can also differentiate the growing habitat within a single species.

Therefore, we used multivariate data analysis to analyze S. ehrenbergi

samples collected at different sea levels and growth habitats (reef flat) separately, and a PCA classification model was established (Fig. 4). The main principal components (PC) to differentiate between samples, i.e. PC1 & PC2 accounted for 71% of the variance. The score plots show that the samples could be differentiated without overlap. Most notably, corals grown in aquarium clustered separately from all sea samples with negative score values along PC1. No clear difference could be observed among sea corals from different habitats along PC1. Although along PC2, it should be noted that reef flat corals (shallower water) appeared to be separable from corals collected at 2 to 3 meter sea depth. In general and compared to aquarium specimens, sea corals contained higher levels of cembranoids i.e., cembrapentaene (C96), 10-oxocembrene (sarcophytonin A, C80) and cembratetraen-16,2-olide (sarcophytonin B, C85) as denoted from their high resolution masses of 271.243, 287.2379 and 301.2171 amu, respectively.

In contrast, samples collected from aquarium showed less diterpene

content, being more enriched in oxylipid peaks with high resolution masses of 355.2244 (C23H31O3)+ and 359.2190 (C23H35O3)+. We propose that these masses are for unknown oxylipids. Note the four extra hydrogen atoms in the predicted formula C23H35O3+, compared with C23H31O3+ common in lipids with a polyunsaturated acyl chain. Cembranoids play an ecological role in coral life and are likely to be produced at higher levels where corals are under more stressful marine life conditions compared to corals grown in an aquarium tank.

Supervised OPLS analyses of S. ehrenbergi, S. glaucum & Lobophyton pauciliforum analyzed via UPLC-MS

15



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In spite of the clear separation observed in PCA analysis for coral samples based on genotype, supervised OPLS-DA was applied to build a classification model that could allow to identify metabolite markers for coral species, and to confirm whether, i.e., S. ehrenbergi and L. pauciliforum UPLC-MS fingerprints were sufficiently unique to be identified as markers for each species. OPLS-DA also has greater potential in the identification of markers by providing the most relevant variables for the differentiation between two sample groups. Two new models were constructed with S. ehrenbergi (Suppl. Fig. S5A) and L. pauciliforum (Suppl. Fig. S5C) each modelled separately, one at a time, against all other corals data present in one class group. The S. ehrenbergi model showed one orthogonal component with R2 = 0.83 and Q2=0.79and for L. pauciliforum with R2=0.89 and Q2 = 0.85. R2 measures the goodness of fit while Q2 measures the predictive ability of the model. The S-plot results for the S. ehrenbergi model (Suppl. Fig. S5B) shows that this genus is particularly enriched in cembranoids viz. cembratetraene C110, sarcophytonin A C80 and unidentified lipid C76, though it should be noted that these constituents were found in all samples and therefore cannot serve as a chemical marker for S. ehrenbergi. In contrast, the S-plot of Lobophyton (Suppl. Fig. S5D) showed sterols enrichment viz. ergosta-tetraen-ol, present at trace levels in other Sarcophyton species. The attempt to build an OPLS-DA model for S. regulare and Sinularia polydactela classification revealed no distinct chemical markers (data not shown). To also distinguish between S. glaucum and S. ehrenbergi, appearing to cluster together in PCA analysis, and with no readily visible distinctive differences in their sclerite features (Suppl. Fig. S1), supervised OPLS-DA was used to build a classification model. S. glaucum and S. ehrenbergi were modelled against each other using OPLS-DA with the derived score plot showing a clear separation between both samples (data not shown). The OPLS score plot explained 78% of the total variance (R2 = 0.78) with the prediction goodness parameter Q2 = 0.69. Compared to S. ehrenbergi, for S. glaucum the S plot derived from OPLS contains more sarcophine/ent-sarcophine (C23), but less cembrene C (C107) and 10-oxcembrene (C80). Correlation analysis was performed and reveals that cembrene C and sarcophine were negatively correlated with R2 = 0.65. Such negative correlation is rational, considering that cembrene C is the building unit for the biosynthesis of sarcophine and decrease in its pool is likely due to its consumption acting as precursor for late step cembranoids formation i.e., sarcophine.

Interestingly, another mass of 327.2301, C22H31O2 (C35) enriched in S.

ehrenbergi was annotated as zooxanthellactone, a furanone found solely in Symbiodinium (Zooxanthellae) species

48

. Soft corals possess endosymbiotic dinoflagellates of the genus

Symbiodinium, with the microalgae providing photosynthates to the host coral tissue 16


49

.

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Although there is piling evidence that photosynthetic products are chemically incorporated into the tissue of the host, little is known about the quantity of metabolites produced by the symbiont, nor how the host (soft coral polyp) utilizes them to produce its secondary metabolites. Visual inspection of 1H NMR spectra and assignments of metabolites in coral crude extract The use of NMR has been previously reported in the study of marine drug extracts from Hawaii and Japan, although targeting mostly the more abundant primary metabolites23, 24. We chose S. glaucum (SG1) to positively demonstrate that NMR spectroscopic characterization of secondary metabolites (Fig. 5) investigated in this study is feasible. A comparative NMR spectrum of Sarcophyton species is depicted in Suppl. Fig. S6. In 1D and 2D NMR using 1

H–1H COSY (correlation spectroscopy), HMQC (heteronuclear multiple quantum

coherence), HMBC (heteronuclear multiple bond coherence), peaks were assigned whenever possible on the basis of spectra of reference standards. Chemical shifts of metabolites that were identified are listed in Table 2. The 1H NMR spectrum is characterized by two main regions: a low field region between 5.0 and 7.0 ppm with signals principally due to olefinic protons of cembranoids and a high-field region between 3.2 and 0.8 ppm with high density signals due to terpene methyl, methylene, and methine signals indicating that corals contain a large amount of terpenoids. Of the 73 terpenoids identified using UPLC-MS, only 6 terpenoids/sterol (Fig. 6) with skeletons commonly isolated from the genus Sarcophyton were detected using NMR. Despite such current limitations of NMR, the patterns observed in the coral metabolite compositions are intriguing as revealed from multivariate data analysis. The presence of guaiacophine (N1), a sesquiterpene isolated from Sarcophyton was readily assigned based on its characteristic signal H-6 appearing at δH 6.26 br. s (Kim et al. 2002) and the up-field chemical shift signals of H-9 2.39 dd (11.7, 7.5)/2.46 dd, (11.7, 7.5); H-10, (2.31); H3-12, 1.81 s; H3-13, 1.82 s; H3-14, 1.13 d (6.9),and H3-15, 0.89 d (6.0) using 1H-1H COSY and 1H-13C correlations observed in the HMBC spectra (Table 2)50. NMR spectra of all examined Sarcophyton accessions show the presence of diterpenes bearing a sarcophine skeleton

51, 52

as major constituents as revealed from two doublet signals at δH 5.71 d (10.1)

and 5.08 dq (10.1, 1.4) for H-2 and H-3, respectively, in the five-membered α,β-unsaturatedγ-lactone ring 53. The vicinal coupling constant of ca. 10.1 Hz between H-2 and H-3 suggests a cis configuration between the γ-lactone proton (H-2) and the olefinic proton (H-3). The clear downfield singlet signals integrate for three protons at δH 1.79 s, 1.94 s, and 1.64 s indicate 17



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the presence of olefinic methyl signals for H3-17, H3-18, and H3-20, respectively. Since enantiomers in achiral solvents show the same NMR spectra, sarcophine/ent-sarcophine (N2/3) cannot be distinguished. The 1H NMR spectrum of S. convolutum (SC1, Table 1) shows characteristic down-field signals of H-2, H-3 and H-11 at δH 5.71 dq (10.1, 1.4), 5.08 dq (10.1, 1.4) and 5.23, respectively, in 7α,8β-dihydroxydeepoxysarcophine (N4). The proton signal at δH 3.55 shows an HSQC correlation with δC 72.0 (C-7) as well as HMBC correlations with δC 26.1 (C-6) and 40.0 (C-9). Along with the HMBC correlations of H3-19 (1.25 ppm) with C-7 (72.0 ppm), C-8 (75.5 ppm), and C-9 (40.0 ppm) this shows the presence of two hydroxy groups at C-7/C-8 instead of an epoxy group which led to the identification of (N4) as 7α,8β-dihydroxydeepoxysarcophine. The presence of the (N4) epimer 7β,8βdihydroxydeepoxysarcophine (N5) was concluded from an oxygenated methine signal at δH 3.44, correlated via 1JCH with δC 72.3 (C-7) and in the HMBC spectrum with δC C-19 (26.8), C-6 (28.0) and C-8 (71.7). Due to too much signal overlap in N4/5, structural assignment are rather tentative compared to other structures (N1, N2/3 and N6) were assignments are unequivocal. Characteristic signals for a cyclopropane bearing gorgostane type side chain in a sterol were observed at δH -0.08 dd (5.9, 4.2) (H-30a), 0.48 dd (9.0, 4.2) (H-30b), 0.22 m (H22) in addition to an olefinic signal at δH 5.32 (H-6), commonly found in cholestan sterols. 1

H-13C correlations observed in the HMBC spectrum assigned these signals to gorgosterol

(N6), identified from UPLC-MS analysis. The HMBC spectrum of (N6) shows key correlations for both H3-26 at δH 0.90 and H3-27 at δH 0.80 cross-correlating to their respective carbon signals in addition to signals at δC 32.2 (C-25) and 51.8 (C-24). Furthermore, a methyl group signal at δH 0.88 (H3-29) showed HMBC correlations to δC 32.9 (C-22), 26.0 (C-23), and 21.8 (C-30). The high-field 13C chemical shifts of C-22, C-23 and C30, caused by the ring strain of the three-membered ring, confirmed the structure of N6. It should be noted that the absolute configuration for all terpenoids and sterols identified (Fig. 6) cannot be determined by the methods used in this study. It has been based on biogenetic or literature precedence or for relative stereochemistry, if no reference compound was available, is mostly based on comparison with previously reported NMR data9, 54, 55. Comparison of 1H NMR and UPLC–MS multivariate data analyses We further compared the performance of PCA from UPLC–MS and 1H NMR spectra of Sarcophyton extracts to better evaluate the classification potential of both technologies. PCA was performed for all Sarcophyton samples within the 1H NMR region of δ -0.4 till 8.0 ppm with a total of 167 spectral bins (variables) describing metabolite profiles. When compared to 18


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the PC plot obtained from UPLC–MS data (Fig. 4A), it is apparent that the NMR results (Suppl. Fig. S7A) were in general agreement. In both score plots, S. ehrenbergi and S. acutum were plotted on the left side (negative score values). In contrast, and contrary to UPLC–MS, S. convolutum specimens failed to cluster together and were spread along PC1 as for S. glaucum. It should be noted that several Sarcophyton samples showed considerable overlap in NMR scoring plots that even of biological replicas within the same sample failed to group together, as revealed from the PCA (Suppl. Fig. S7A). This finding suggests that for such soft coral samples, UPLC–MS datasets provide more efficient sample classifications than NMR. Similar results were observed by our group in case of some plant sample classifications 26. The separation observed in PCA can be explained in terms of the identified compounds, using the loading plots for PC1 (Suppl. Fig. S7B) exposing the most discriminatory NMR signals. Two major groups stand out in this plot. The first corresponds to the chemical shifts of sarcophine (H3-17, δ 1.79; H3-18, δ 1.94; H3-19, δ 1.25; H3-20, δ 1.64) contributing positively to PC1. The second group is characterized by resonances appearing at δ-0.1, 0.1 and 5.31 for gorgosterol. The remaining unidentified compounds show proton signals at δ 0.84 and 1.36 ppm matching spectral locations of lipids. Examination of the 1H NMR loading plots suggest that fatty acid signals and cembranoids contribute the most in coral samples discrimination. NMR can detect all 1H-containing species in a sample, i.e. including fatty acids posing NMR as a powerful technique in metabolomics if looking at more abundant metabolites. However, contents of such (primary) metabolites are usually dependent on growth conditions, and are often not indicative for the pharmaceutically relevant compounds. Considering that biological and technical replicates in several S. ehrenbergi specimens failed to cluster tightly together as observed in UPLC-MS dataset derived score PCA plots (Fig. 3A), no further attempt was made to use multivariate data analysis for assessing growing habitat effect on Sarcophyton chemical composition from NMR derived datasets. Aside from HMBC spectra providing structural assignment of terpenoids in extracts, careful examination of correlation pattern of coral extracts HMBC spectra (Suppl. Fig S8) clearly showed distinct fingerprint spectra in Sarcophyton acutum (SA) (A) & Sarcophyton ehrenbergi (SE1) (B) different from that of Sarcophyton glaucum (SG1) (C), Sarcophyton convolutum (SC1) (D) and Sarcophyton regulare (SR1) and concurring PCA results derived from UPLC-MS dataset (Fig. 4).

Such observation need to be further confirmed by

subjecting HMBC spectra to multivariate data analyses. Our previous work on hop resin revealed better classification results derived from 2D-NMR compared to 1D-NMR 56, which has yet to be investigated in case of corals. 19



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CONCLUSIONS To the best of our knowledge, this study provides the first comparative metabolomics approach to reveal for compositional differences in secondary metabolites among various soft coral species. NMR and UPLC-MS techniques coupled with multivariate data analyses were used and compared to obtain the experimental results, and interesting and meaningful differences between the various species and detection methods were identified. Our comparative metabolomics approach demonstrates that it can be a powerful tool for capturing the (secondary) metabolome status and peculiarities of soft corals and is likely to have value in monitoring corals and their genetically and environmentally caused metabolic differences. In this context, the presented study provides the first holistic approach to reveal for cembranoid compositional differences among Sarcophyton via metabolomics. Such strategy would prioritize coral species ought to be subjected for further future detailed chemical analysis and speed up natural product research in corals, thereby helping to reduce the gap that has opened to modern drug discovery demands.

In particular, corals from natural

seawater environments contained significantly higher levels of cembranoids than aquarium samples. It remains to be examined whether differential metabolite accumulation patterns are due to ecological differences like organismic interactions, to physical parameters (ion strength, temp., light etc.) or to precursor limitations such as the isoprene pool, or to genetic differences in expression, regulation and enzymatic activity as no clones were available. Coupling these differential metabolite profile data with gene transcript levels may assist in probing involved genes and biosynthetic pathway analyses. Probing enzymatic activity or gene expression levels involved in the biosynthesis of cembranoids could provide further understanding of our metabolomics results in Sarcophyton. Indeed, the correlative analysis of differential metabolic profiling and gene expression profiling in planta has proven a powerful approach for the identification of candidate genes and enzymes, particularly for those of secondary metabolism 57. Differences among examined Sarcophyton species mainly emanate from their content of cembranoid diterpenes and lipids. Compared to Sarcophyton species, the genus Lobophyton appeared more enriched (abundance) in sterols versus diterpenes and sesquiterpenes in Sarcophyton. The cyclopropane containing sterols in coral specimens examined might be linked with coral adaptation to the membranolytic activities of their own toxins i.e. cembranoids. Sarcophine-diol has shown a cytotoxic effect on a melanoma cancer cell line mediated via inhibition of cell membrane permeability 58. Such phenomenon, which involves the interdependent presence of two different types of secondary metabolites in an organism, i.e. a “biochemical coordination” of the type “membranolytic toxins—unusual 20


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sterols” are evidenced for several marine sponges

59

.

For example, preincubation of

carcinoma cells with a cyclopropane-containing sterol from the marine sponge Rhizochalina incrustata decreased the cytotoxic effects of rhizochalin, a toxic constituent from the same sponge

60

. Whether, such scenario is also true for the corals here, i.e. cytotoxic cembranoids

and gorgosterol to counteract their toxic effect, is still speculative and yet has to be examined. The same workflow of sample preparation, measurement and processing can be easily transferred to other coral metabolome studies, pertaining coral ecological monitoring and enabling researchers to monitor shifts in coral metabolic composition in response to environmental conditions like global warming or water pollution. We are aware that this first, method oriented metabolic fingerprinting study of selected coral species will require further in depth studies with many more accessions to provide reliable hierarchical clusters. But even in this first study, significant information could be obtained, i.e. that metabolic profiles of aquarium specimen clearly differ from those of their wild brothers, and that the profiles may be able to aid species determination..

Notes The authors declare no competing financial interest ACKNOWLEDGMENTS Dr. M. A. Farag thanks the Hanse-Wissenschaftskolleg (HWK), Germany, for financial support and the funding received by Science and Technology Development fund STDF, Egypt (grant number 12594). We also thank Dr. Christoph Böttcher for assistance with the UPLCMS. We are grateful to Dr. Steffen Neumann and Dr. Tilo Lübken for providing R scripts for UPLC-MS data analysis.

ASSOCIATED CONTENT Supporting Information Physico-chemical parameters at diving sites and metabolites annotated in Sarcophyton species via UPLC–qTOF–MS are presented in Suppl. Table S1 and S2, respectively. Sclerite photos from soft corals (Suppl. Fig. S1), 1H-NMR spectra of S. glaucum coral tissue extracted using different solvents (Suppl. Fig. S2), structure of the major terpenoids, sterols and fatty acids detected in Sarcophyton extract via UPLC-MS and discussed in the manuscript. (Suppl. Fig.

21



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S3), UPLC-qTOF-MS PCA analysis of Sarcophyton, Sinularia and L. pauciliforum (Suppl. Fig. S4), UPLC-qTOF-MS OPLS-DA score plot (Suppl. Fig. S5), 1H-NMR spectra of Sarcophyton species (Suppl. Fig. S6), 1H NMR (-0.8–8.0) peak based PCA of different Sarcophyton samples with the methylene region of fatty acids excluded from analysis (Suppl. Fig. S7), partial HMBC spectrum of Sarcophyton species (Suppl. Fig. S8).

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25



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Page 26 of 35

Figure legends Fig. 1 Photos of soft corals examined and location map of the collection area along the Red Sea, Egypt. “Photograph courtesy of Dr. Montasser A. Al-Hammady & Dr. Mohamed A. Farag. Copyright 2016.”

26


Page 27 of 35

Fig. 2 Representative UPLC-qTOF-MS base peak chromatograms of Sarcophyton glaucum (SG1) showing sarcophine C23 and gauiacophene C69 as the major terpenoid peaks. The identities, Rt-values, and MS data of all peaks are listed in Suppl. Table S2. Intens. x105

MS response

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


S. glaucum (SG1)

Guaiacophine, C69

Sarcophine/ent-Sarcophine, C23

6

4

2

0 300

300

350

350

400

400

450

450

500

500

550

550

Rt (sec)

27


600

600

650

650

700 Time [s]

700


A

S. glaucum S. ehrenbergi

S. regulare -5.0e+06

PC2 (20%)

5.0e+06 1.0e+07

Fig. 3. UPLC-qTOF-MS (m/z 100-600) Principal component analysis of Sarcophyton species based on group average cluster analysis of MS profiles (n = 3). The metabolome clusters are located at the distinct positions described by two vectors of principal component 1 (PC1 = 47%) and principal component 2 (PC2 = 20%). (A) Score plot of PC1 vs PC2 scores. (B) Loading plots for PC1 and PC2 with contributing mass peaks and their assignments, with each metabolite denoted by its mass/Rt (sec) pair. It should be noted that ellipses do not denote statistical significance, but are rather for better visibility of clusters as discussed. For sample codes, refer to Table 1. SG1 (○), SA (∆), SR1 (+), SC1(X), SE1 (◊), SR2 (∇), SE2 ( ), S (*), SG2 (⊕), SC2 ( ), SR3 ( ), SE3 ( ), SE4 ( ), SG3 (⊗).

-1.5e+07

S. acutum

-1.5e+07

-1.0e+07

-5.0e+06

0.0e+00

5.0e+06

PC1 (47%)

B

Sarcophine/ent-Sarcophine

Sarcophytonin B Hydroxy cembrapentaen-20,10-olide

Unidentified fatty acid

0.0

0.1

0.2

0.3

Cembrapentaen-20,10-olide Sarcophine/ ent-Sarcophine

PC1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 28 of 35

Cembrene C

Oxylipid peak C35 Oxylipid peak C77

28


1.0e+07

Page 29 of 35

0e+00 2e+06 4e+06

Red sea corals

Aquarium tank corals -4e+06

PC2 PC2 (32%)

6e+06

Fig. 4 UPLC–MS based score plots for differentiation of regional S. ehrenbergi samples modelled separately (n = 3). The model explains 71% of total variation. The aquarium coral samples cluster more with reef flat corals on the left side (negative side of PC1). For sample codes, refer to Table 1. SE1 (○), SE2 (∆), SE3 (+), SE4 (X).

0.2

0.3

-1e+07

-5e+06

PC1 (39%)

0e+00

5e+06

339.3/643

B

317.3/643 367.3/701

345.3/701 305.2/606 303.2/506 357.2/377 219.2/530 318.3/643 341.2/410317.2/377 201.2/530 253.2/564 340.3/643 355.2/385 329.2/598 332.3/626 284.3/661 301.2/410 459.3/530 277.2/551 235.2/564 339.2/444 275.2/564 368.3/701 355.2/548 361.2/528 241.1/530 303.2/575 319.2/410 383.2/528 280.2/580 357.3/565 354.3/626 351.2/598 306.2/606 393.3/554 239.2/590 205.2/527 327.2/606 346.3/701 343.2/403 205.2/538 367.2/500 301.2/455 355.2/398 333.2/548 229.2/583 255.2/596 335.2/377 386.3/681 302.2/580 257.2/640 383.2/455 319.2/368 473.3/518 397.3/566 460.3/530 389.2/428 357.2/393 301.1/522 325.2/575 304.2/507 203.2/530 358.2/377 299.2/378 303.2/403 339.2/475 220.2/530 315.2/548 400.4/531 249.2/501 325.2/471 299.2/551 433.3/554 399.2/456 318.2/377 306.3/661 437.3/530 287.2/557 287.2/606 415.3/619 408.3/681 357.3/632 381.3/659 342.2/410 373.2/360 283.2/410 333.3/626 343.2/497 357.2/520 377.2/600 315.2/385 356.2/548 473.8/518 395.3/676 281.2/613 285.3/661 361.2/455 330.2/598 217.2/564 389.2/410 310.3/671 239.2/640 356.2/385 315.2/505 328.2/606 297.2/536 254.2/564 273.2/530 278.2/551 221.1/357 337.2/505 202.2/530 449.3/489 378.3/619 356.3/619 373.2/606 302.2/410 354.2/378 378.2/600 387.2/422 357.2/444 251.2/456 275.2/536 344.2/506 394.3/554 251.2/529 362.2/528 355.2/433 340.2/444 282.3/615 482.3/495 433.3/619 381.3/645 473.3/561 352.2/598 359.3/659 338.2/410 319.3/643 281.3/580 237.2/596 320.2/410 365.3/499 355.3/626 310.3/657 281.2/510 417.2/571 400.4/504 282.3/605 358.3/565 434.3/553 279.2/579 398.3/566 211.2/583 236.2/564 276.2/564 304.2/566 341.2/580 384.2/528 493.3/552 461.3/530 259.2/530 347.2/440 261.2/590 240.2/590 257.1/530 353.3/672 321.2/564 454.3/516 383.3/701 387.3/681 304.2/575 373.2/519 308.3/633 399.2/382 233.1/460 315.2/486 459.4/640 333.2/559 474.3/561 377.2/456 219.1/243 263.2/613 326.3/602 356.3/498 330.3/592 369.3/701 341.3/642 441.3/640 335.2/428 435.2/569 346.2/377 273.1/462 356.2/398 273.1/440 257.1/372 438.3/530 283.2/455 337.2/486 242.2/530 275.2/444 341.2/392 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415.3/541 415.2/473 392.3/286 397.2/519 395.2/436 396.3/641 397.2/534 378.3/557 373.2/697 321.2/379 320.2/535 315.2/520 312.3/556 273.1/399 273.1/416 201.2/665 133.1/601 250.2/359 251.1/334 265.2/665 231.2/639 233.1/571 228.2/359 220.2/647 226.2/641 485.3/522 483.3/595 481.3/477 332.2/486 447.2/671 452.2/396 447.3/610 390.2/571 420.2/671 463.2/429 460.2/432 413.2/435 333.2/606 336.2/323 407.1/546 394.3/681 396.3/557 353.2/607 378.3/647 377.3/694 352.2/450 382.2/571 373.2/598 366.2/405 364.2/463 300.3/564 313.2/399 305.1/293 276.3/639 272.2/485 200.1/647 149.1/571 111.1/647 202.2/647 229.2/441 228.2/647 221.2/640 224.6/375 497.3/602 492.3/530 332.2/505 388.2/417 469.3/305 402.2/464 338.3/652 405.2/610 393.2/382 360.2/429 367.2/408 364.2/296 323.2/457 320.2/647 275.2/394 107.1/647 260.2/647 243.2/601 244.2/641 216.2/600 206.2/568 271.2/385 217.2/647 228.2/641 409/226 410/226 394/227 351/404 297/227 304/227 191/227 165/227 151/227 255/227 489.3/520 483.3/516 331.2/655 403.3/629 403.3/621 412.2/618 342.2/535 384.2/497 362.2/377 362.3/572 369.2/671 372.2/405 297.2/647 287.2/373 175.1/600 147.1/601 205.2/647 243.2/647 427/227 428/226 426/227 441/227 444/227 452/226 391/492 470/227 338/226 393/227 329/404 346/226 327/404 353/226 349/404 348/227 315/227 308/227 274/226 273/226 283/227 160/227 204/227 253/227 207/227 209/227 212/227 221/227 223/226 449.3/456 358.2/451 419.2/486 402.2/671 458.3/560 405.2/548 405.3/692 397.2/496 315.2/655 298.3/652 161.1/600 135.1/600 133.1/647 205.2/518 247.2/441 227.2/572 352/226 280/226 281/226 200/227 269/227 264/227 263/227 271/226 491.3/473 479.3/610 405.3/662 414.3/677 415.2/343 342.2/368 375.2/413 376.3/620 372.3/681 324.2/647 291.3/623 297.2/459 289.2/556 187.1/600 119.1/647 213.2/574 219.2/455 337/227 345/226 279/226 186/227 164/227 203/227 265/229 425.3/680 409.2/437 394.2/606 346.2/409 352.3/582 357.2/424 185.1/647 189.2/647 175.1/647 145.1/647 135.1/647 245.2/428 440.3/609 401.2/355 357.2/600 328.2/460 385.2/671 373.2/582 302.2/647 177.1/647 123.1/647 121.1/600 253.2/469 241.2/647 332.3/596 358.2/647 183.1/378 147.1/647 253.2/481 439.3/530 391.2/437 267.2/593 433.2/485 214.2/647 383.2/439 335.2/323 173.1/647 372.2/425 159.1/647 203.2/600 431.3/540 459.2/432 286.2/573 237.2/460 405.1/546 419.2/671 395.2/524 284.2/498 161.1/647 383.2/497 369.2/604 439.3/609 301.2/647 341.2/368 375.2/582 329.2/528 343.2/600 371.2/451 277.2/588 397.3/701 322.3/504 285.4/518 268.7/588 309.2/563 365.2/508 285.6/518 247.2/588 216.6/451 424.3/629 358.2/520 429.2/704 329.2/628 441.3/694 432.3/456 446.4/644 354.7/507 345.2/333 314.2/491 304.2/647 267.2/386 207.6/451 490.3/492 482.3/519 432.3/561 405.2/453 364.3/530 369.2/464 297.2/561 487.3/573 482.8/519 462.4/696 356.2/591 397.3/684 342.2/519 310.2/522 268.2/617 486.4/686 480.3/519 431.3/586 387.2/388 123.1/518 133.1/518 257.2/647 387.2/655 356.2/571 339.2/655 351.2/428 353.2/505 383.3/657 373.2/507 323.7/519 284.2/592 166.1/507 255.2/518 245.2/518 475.4/592 344.2/385 339.2/571 350.2/508 365.3/675 318.2/508 281.2/592 332.2/399 390.2/366 356.2/450 378.2/524 384.2/671 376.2/508 302.2/518 309.2/520 255.2/607 445.3/506 336.5/227 372.2/399 318.2/571 306.2/517 483.4/658 428.3/642 447.4/642 399.2/660 395.2/518 431.3/504 355.2/583 387.2/371 328.3/406 350.3/649 349.2/352 371.2/649 373.2/647 389.2/647 345.2/518 375.2/480 372.2/583 291.2/600 247.2/423 340.2/647 394.2/515 329.2/443 342.3/491 317.2/655 281.2/450 267.2/455 387.2/328 413.3/641 349.2/181 363.3/574 311.2/506 273.2/665 346.2/572 354.3/620 284.2/571 389.2/530 403.1/546 328.3/626 346.2/518 378.2/497 163.1/600 425/226 379.2/340 368.2/593 289.2/450 273.2/598 189.2/600 271.2/697 257.2/575 229.2/600 336/227 347/227 419.2/680 321.2/586 305.2/569 275.2/470 272/227 340.2/476 346.2/440 301.2/379 305.2/420 149.1/600 297.2/404 203.2/647 470.3/456 391.3/629 377.3/557 273.1/306 187.1/647 231.2/647 346.2/423 386.2/436 149.1/647 121.1/647 201.2/600 230.2/647 337.3/652 349.2/470 227.2/647 384.2/473 320.2/491 259.2/647 219.2/647 359.2/515 199.1/647 360.2/609 201.2/647 215.2/600 401.2/498 368.2/572 328.3/606 267.2/471 240.1/588 268.2/588 239.1/587 291.3/647 336.2/450 241.2/519 215.2/518 216.6/508 275.2/519 243.2/519 273.2/518 333.2/571 334.2/478 300.2/450 255.2/571 272.2/479 270.2/460 270.2/542 273.2/601 277.2/455 283.2/498 297.3/652 269.2/542 448.4/694 391.3/704 498.4/677 446.3/492 344.2/390 406.2/654 318.3/412 429.3/561 416.3/455 417.3/657 398.3/455 434.4/543 433.3/455 406.3/630 199.1/518 225.2/518 347.2/545 371.2/508 324.2/592 180.1/522 449.4/696 352.2/507 326.2/486 362.3/314 441.3/596 407.2/638 348.2/561 191.2/637 389.2/591 321.2/637 177.2/638 331.2/399 305.2/388 344.5/226 347.2/457 426.2/510 345.2/387 393.3/704 379.3/622 303.2/423 307.2/497 319.2/647 437.2/649 341.2/535 288.2/518 474.2/396 399.2/564 395.3/557 342.2/571 185/227 370.2/544 436.2/522 389.2/571 411.2/629 305.2/423 442.3/615 381.2/502 371.2/584 328.2/647 288.2/429 443.2/492 407.2/492 413.3/677 345.2/365 312.2/600 327.2/460 301.2/473 374.2/450 342.2/492 369.2/578 359.2/609 302.3/439 461.3/696 340.2/390 392.2/520 385.3/562 341.2/561 211.1/518 314.7/519 405.2/508 485.4/687 390.2/507 413.3/535 317.2/507 356.2/477 289.2/625 300.2/592 305.2/477 346.2/637 371.2/406 287.2/393 395.3/640 361.2/497 302.2/499 401.2/680 361.2/376 456.3/485 323.2/647 457.3/560 441.3/615 369.2/530 213.2/647 371.2/425 456.3/530 285.2/573 367.2/572 345.3/525 355.2/523 377.2/524 497.4/677 378.2/651 344.2/650 414.3/704 404.3/584 489.3/492 227.2/518 408.3/590 391.2/390 354.2/507 314.2/519 304.2/389 229.2/518 355.2/571 371.2/480 325.2/647 381.2/637 323.2/515 218.2/637 331.2/549 318.2/450 301.2/518 457.3/578 395.2/494 373.2/478 403.2/513 290.2/496 349.3/649 431.2/411 163/227 345.2/410 330.2/496 371.2/469 205.2/638 269.2/515 287.2/417 267.2/572 469.3/456 399.2/444 345.2/441 229.2/647 440.3/596 358.2/626 405.2/654 405.2/647 268.2/518 445.3/644 151.1/518 395.2/638 267.2/486 411.2/618 344/226 388.2/435 384.2/463 283.2/571 425.2/510 470.3/614 401.2/671 283.2/464 470.4/692 309.2/581 345.2/423 456.3/626 369.2/561 270.2/647 344.2/332 446.4/696 355.2/591 283.2/592 413.3/557 314.2/636 302.2/591 361.2/473 387.2/523 275.3/639 378.2/340 369.2/597 289.2/523 269.2/460 286.2/476 333.2/450 373.2/449 272.2/651 334.2/508 290.2/602 329.2/544 360.2/651 433.3/543 397.3/455 307.3/524 393.2/514 345.2/572 343.2/464 418.3/595 343.2/390 358.2/507 165.1/507 367.2/593 271.2/479 324.2/571 287.2/430 345.2/511 310.2/647 319.2/491 272.2/609 312.2/650 415.3/455 313.2/491 318.2/592 325.2/486 349.2/508 286.2/488 343.2/455 355.2/459 331.3/598 361.2/463 385.2/462 369.2/544 455.3/485 271.2/609 445.3/492 371.2/399 333.2/477 362.2/637 347.2/561 270.2/626 343.2/651 447.4/695 311.2/600 310.2/627 366.3/548 336.2/507 333.2/507 327.2/516 385.2/436 288.2/647 424.3/544 355.2/478 323.2/592 345.2/637 413.3/704 290.2/649 380.2/638 289.2/535 271.2/501 272.2/600 289.2/602 269.2/647 445.3/696 299.2/451 289.2/497 301.2/497 322.7/519 302.2/571 344.3/525 377.2/651 267.2/518 391.2/520 303.2/389 389.2/507 387.2/436 469.3/614 301.2/464 469.3/692 455.3/544 288.2/626 304.2/518 286.2/518 455.3/527 285.2/477 326.2/535 217.2/637 305.2/517 357.2/627 317.2/451 383.2/463 329.2/499 303.2/518 361.2/637 313.2/637 309.2/647 301.2/592 285.2/486 372.2/600 455.3/639 375.2/627 359.2/651 302.2/686 287.2/518 271.2/650 285.2/518322.2/519 455.3/540 357.2/507 323.2/571 269.2/626 317.2/592 269.2/578 311.2/650 344.3/548 330.3/439 335.2/507 339.2/453 289.2/649 301.2/571 379.2/638 287.2/626 325.2/535 326.2/518 274.3/637 325.2/518 371.2/600 301.2/686

PC2

-0.2

-0.1

0.0

0.1

Oxylipids

287.2/647 271.2/600

273.2/637 -0.1

29

cembrapentaene

Sarcophytonin A & B

-0.3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


0.0

PC1

0.1


0.2

0.3


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 35

Fig. 5 1H-NMR spectrum of S. glaucum (SG1) extract showing characteristic signals for secondary metabolites in the most relevant shift range (δ-0.3 - 6.5 ppm, A). Expanded spectral region from 3.256.25 (B), 0.7-2.9 ppm (C) and -0.1-0.8 ppm (D) with assigned peaks: N1 (guaiacophine), N2/3 (sarcophine/ent-sarcophine),

N4

(7α,

8β-dihydroxydeepoxysarcophine),

N5

(7β,

8β

dihydroxydeepoxysarcophine), N6 (gorgosterol). The assignments were established using NMR spectra of standards. Signal numbers correspond to those listed in Table 2 for metabolite identification using 1H NMR.

30


Page 31 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Fig. 6 Structures of the major C15, C20-terpenoids and sterols detected in Sarcophyton extract using NMR and discussed in the manuscript. Note the carbon numbering system for each compound is used throughout the manuscript for NMR assignment, and thus is based on analogy rather than IUPAC rules. 15 18

H 2 3 4

10

1

8

7

O

19 9

6 11

14

H O

O

3

O

7

5

H O

5

9

14

12

1

15

O 17

12 20

13

Guaiacophine

Sarcophine

N1

ent-Sarcophine

N2

H O

HO

O

HO

N3

H O

O 19 1

HO

31

7 , 8 -Dihydroxydeepoxysarcophine N5


29 15

5

Gorgosterol N6

26

24

17 11 10

28

20

18

3

7 , 8 -Dihydroxydeepoxysarcophine

30

21

HO

HO

N4

O

25 27


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 35

Table 1 The origin of soft corals Sarcophyton, Lobophyton and Sinularia samples used in analysis. Accession SG1

Species

Original Source

Sarcophyton glaucum

Northern

Hurghada

Depth (Al- Reef

Guna) SA

Sarcophyton acutum

Northern

flat Hurghada

(Al- 3 m

Hurghada

(Al- 3m

Hurghada

(Al- 2 m

Hurghada

(Al- Reef

Guna) SR1

Sarcophyton regulare

Northern Guna)

SC1

Sarcophyton convolutum

Northern Guna)

SE1

Sarcophyton ehrenbergi

Northern Guna)

SR2


flat

Northern Safaga (Makadi 3 m bay)

SE2


Northern Safaga (Makadi 2 m bay

S

SG2

Unidentified

Northern Safaga (Makadi 5 m

Sarcophyton sp.

bay

Sarcophyton glaucum

Northern Safaga (Makadi Reef bay

SC2

Sarcophyton convolutum

Northern Safaga (Makadi Reef bay

SR2


flat

flat


LP

Lobophyton pauciliforum Northern Safaga (Makadi 3 m bay

SP

Sinularia polydactela


SE3



SE4


Aquarium, ZMT, Germany

-

SG3

Sarcophyton glaucum

Aquarium, ZMT, Germany

-

32


Page 33 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2 Resonance assignments with chemical shifts of constituents identified in 1H (at 600 MHz) and 1H/13C 2D-NMR spectra of Sarcophyton species (acetone-D6). Metabolite

Assignment

N2/3 Sarcophine/entSarcophine

N4 7α,8β-Dihydroxydeepoxysarcophin e

N5 7β,8βDhydroxydeepoxy sarcophine

N6 Gorgosteol

33

H (multiplicity)

13

C

H-1 H-4 H-6

2.79 2.61 6.26 br s

45.9 40.8 117.7

H-9a/b

51.7

H-10 H3-12

2.39 dd (11.7, 7.5); 2.46 dd (11.7, 7.5) 2.31 1.81 s

36.7 21.1

H3-13

1.82 s

22.4

H3-14 H3-15 H-2 H-3 H-7 H-10a/b H-11 H3-17

1.13 d (6.9) 0.89 d (6.0) 5.71 d (10.1) 5.08 dq (10.1, 1.4) 2.63 2.25; 1.95 5.23 1.79 s

19.7 16.7 79.2 121.8 61.5 23.9 125.6 10.0

H3-18 H3-19 H3-20

1.94 s 1.25 1.64 s

16.1 17.4 16.0

H-2 H-3 H-7 H3-11 H3-17 H3-18 H3-19 H3-20

5.71 d (10.1) 5.08 dq (10.1, 1.4) 3.55 5.23 1.79 s 1.94 s 1.25 s 1.63 s

79.4 122.1 72.0 125.9 10.0 16.5 26.5 16.0

H-2 H-3 H-7 H-11 H3-17 H3-18 H3-19 H3-20

5.71 d (10.1) 5.08 dq (10.1, 1.4) 3.44* 5.23 1.79 s 1.94s 1.25 s 1.63 s

79.4 122.1 72.3 125.9 10.0 16.5 26.8 16.0

H-3 H-6 H-22 H3-26

3.48** 5.32 m 0.22 m 0.90**

72.0 121.5 32.9 21.5

N1 Guaiacophine

1

HMBC

C-1 (45.9), C-4 (40.8), C-8 (205.8), C-11 (136.9) C-1 (45.9), C-7 (138.5), C-8 (205.8), C-10 (36.7), C-15 (16.7) C-1 (45.9) C-7 (138.5), C-11 (136.9), C-13 (22.4) C-7 (138.5), C-11 (136.9), C-12 (21.1) C-3 (34.3), C-4 (40.8), C-5 (151.8) C-1 (45.9), C-9 (51.7), C-10 (36.7) C-3 (121.8) C-5 (37.9), C-18 (16.1),

C-1 (163.5), C-15 (122.9), C-16 (174.7) C-3 (121.8), C-4 (144.6), C-5 (37.9) C-7 (61.5), C-8 (59.8), C-9 (39.8) C-11 (125.6), C-12 (136.4), C-13 (36.9) C-3 (122.1) C-5 (38.2), C-18 (16.5) C-6 (26.1), C-9 (40.0) C-9 (40.0), C-20 (15.5) C-15 (122.5) C-3 (122.1), C-4 (144.9), C-5 (38.2) C-7 (72.0), C-8 (75.5), C-9 (40.0) C-11(125.9), C-12 (136.0), C-13 (37.0) C-3 (122.1) C-5 (38.2), C-18 (16.5), C-6 (28.0), C-8 (71.7), C-19 (26.8) C-9 (40.0), C-20 (16.0) C-15 (122.5) C-3 (122.1), C-4 (144.9), C-5 (38.2) C-7 (72.3), C-8 (71.7), C-9 (40.0) C-11 (125.9), C-12 (136.0), C-13 (37.0) C-2 (32.0)

C-24 (51.8), C-25 (32.2), C-27



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

H3-27

0.80

21.3

H3-29

0.88**

14.3

Page 34 of 35

(21.3) C-24 (51.8), C-25 (32.2), C-26 (21.5) C-22 (32.9), C-23 (26.0), C-30 (21.8)

H-30a/b

0.48 dd (9.0, 4.2); 21.8 -0.08 dd (5.9, 4.2) * and ** correspond to overlapping NMR signals in N5 and N6, respectively.

34


Page 35 of 35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


GRAPHICAL ABSTRACT

35


Soft Corals Biodiversity in the Egyptian Red Sea: A Comparative MS

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