Effect of Oxylipins, Terpenoid Precursors and Wounding on Soft Corals’ Secondary Metabolism As Analyzed Via UPLC/MS and Chemometrics

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Article Effect of Oxylipins, Terpenoid Precursors and Wounding on Soft Corals’ Secondary Metabolism as Analyzed via UPLC/MS and Chemometrics

Mohamed A. Farag 1,2,*, Hildegard Westphal 3,4, Tarek F. Eissa 5, Ludger A. Wessjohann 6 and Achim Meyer 3 ID 1 Department of Pharmacognosy, College of Pharmacy, Cairo University, Kasr El Aini St., Cairo 11562, Egypt 2 Department of Chemistry, School of Sciences & Engineering, The American University in Cairo, New Cairo 11835, Egypt 3 Leibniz Centre for Tropical Marine Research, Fahrenheit Str.6, D-28359 Bremen, Germany; [email protected] (H.W.); [email protected] (A.M.) 4 Department of Geosciences, Bremen University, Fahrenheit Str.6, D-28359 Bremen, Germany 5 Department of Pharmacognosy, College of Pharmacy, Modern Science and Arts University, Cairo 12566, Egypt; [email protected] 6 Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, D-06120 Halle, Germany; [email protected] * Correspondence: [email protected] or [email protected]

Received: 28 October 2017; Accepted: 2 December 2017; Published: 10 December 2017

Abstract: The effect of three oxylipin analogues, a terpenoid intermediate and wounding on the secondary metabolism of the soft corals Sarcophyton glaucum and Lobophyton pauciflorum was assessed. Examined oxylipins included prostaglandin (PG-E1), methyl jasmonate (MeJA), and arachidonic acid (AA) in addition to the diterpene precursor geranylgeranylpyrophosphate (GGP). Post-elicitation, metabolites were extracted from coral heads and analyzed via UPLC-MS followed by multivariate data analyses. Both supervised and unsupervised data analyses were used for sample classiﬁcation. Multivariate data analysis revealed clear segregation of PG-E1 and MeJA elicited S. glaucum at 24 and 48 h post elicitation from other elicitor samples and unelicited control group. PG-E1 was found more effective in upregulating S. glaucum terpene/sterol levels compared to MeJA. Metabolites showing upregulation in S. glaucum include campestene-triol and a cembranoid, detected at ca. 30- and 2-fold higher levels compared to unelicited corals. Such an elicitation effect was less notable in the other coral species L. pauciﬂorum, suggesting a differential oxylipin response in soft corals. Compared to MeJA and PG, no elicitation effect was observed for GGP, AA or wounding on the metabolism of either coral species.

Keywords: oxylipins; soft corals; cembranoids; chemometrics; sterols

1. Introduction Jasmonic acid and its derivatives comprise a family of important wound signaling transducers, that can efficiently stimulate secondary metabolism in planta [1]. Jasmonic acid (JA), and its methyl ester (methyl jasmonate, MeJA) are linolenic acid (LA)-derived cyclopentanone-based compounds of wide distribution in the plant kingdom [2]. Since the first report regarding the effect of MeJA on the accumulation of plant secondary metabolites [3], ca. 100 plant species have been demonstrated to respond to MeJA by accumulating secondary metabolites [4]. Previous targeted metabolite profiling reports demonstrated that MeJA can elicit a myriad of natural product classes, i.e., saponins [5], flavonoids [6], phenolic acids [7] and alkaloids [8]. A consensus is now perceived that jasmonates are ubiquitous chemicals that orchestrate natural product biosynthesis in planta.

Molecules 2017, 22, 2195; doi:10.3390/molecules22122195 www.mdpi.com/journal/molecules Molecules 2017, 22, 2195 2 of 15

Regarding the induction effect of jasmonates-stimulated secondary metabolite biosynthesis in plant cell cultures, to date, most efforts have been focused on the induction of plant defense responses by the addition of jasmonates. The eicosanoids i.e., “prostaglandins” have similar chemical structures to jasmonates. In mammals, arachidonic acid is the precursor that is metabolized by various enzymes to a wide range of biologically active eicosanoids [9]. These metabolites mediate localized stress and inflammatory responses in animal cells. Prostaglandin (PG) is known from octocorals where it seems to impede predation [10]. The protective power might relate to PGs capacity to modulate crucial areas of animal biology, as it has been described for insects who are close relatives of crustaceans [11]. Hence and compared to extensive reports on jasmonate effects in planta, there is nothing till now on their influence on marine soft corals metabolism. Soft corals are marine invertebrates possessing endosymbiotic dinoflagellates of the genus Symbiodinium, also known as zooxanthellae which provide photosynthates comprised of carbohydrates, fatty acids, glycerol, triglycerides, amino acids, and oxygen to the host coral tissue. The coral host, on the other hand, provides carbon dioxide and nutrients in the form of waste products (N, P, and S) and urea to the zooxanthellae [12,13]. A specific suite of associated microbes is also thought to contribute to these nutritional interactions, forming the coral ‘holobiont’ [14,15]. Within the coral holobiont MeJA targets the plant secondary metabolism, whereas AA and PG target the animal host to induce the metabolic stress response. Marine natural products display an extraordinary chemical and pharmacological scope, mostly attributed to the necessity of marine organisms to release secondary metabolites as their own chemical defense tools to survive in extreme temperature and salinity (or variations of those), and to resist their predators, or to provide chemical communication such as in symbiotic relationships. The growing interest in marine natural products, warrants for a better understanding of their regulation [16]. The same is true for an understanding and influence of ecological relationships and their change upon (man-made) environmental impact, like global warming or pollution [17]. Metabolomics is a comprehensive tool targeting the evaluation of the variation in the metabolites of organisms under different conditions [18]. It is a rapid and reliable method allowing the evaluation of metabolites variation of any biological system under various conditions. Metabolomics also provides the immediate detection of a wide range of metabolites, providing a simultaneous picture about organism metabolome [19]. Metabolomics is based on several techniques to measure metabolites variation, and ultrahigh performance liquid chromatography coupled to mass spectrometry (UPLC-MS) is one of the leading analytical techniques for monitoring and discriminating these differences in several scientific areas [20]. A multiplexed metabolomic approach for the profiling of marine soft corals secondary metabolites was previously applied for corals classification based on its genotype, growth habitat i.e., depth or aquarium cultured specimens [12]. Additionally, this tool was recently reported by our group to monitor variations in coral metabolism in response to biotic and abiotic water soluble elicitation [21]. Among eight different elicitors, salicylic acid (SA) was found effective in inducing acetylated diterpenes and sterol in the soft coral Sarcophyton ehrenbergi. We extend herein using the same UPLC-MS metabolomics platform to provide insight onto whether oxylipin signaling molecules can play a role in coral secondary metabolism. Specifically, we compared metabolic responses from two different coral species viz. Sarcophyton glaucum and Lobophytum pauciflorum known to accumulate cembranoid diterpenes [22–24]. Oxylipin elicitors examined in this study included methyl jasmonate (MeJA), prostaglandin E1 (PG), arachidonic acid (AA) as well as wounding. These signaling molecules are known to operate in several eukaryote wound signal transduction pathways, and are thus likely to function similarly in corals. Consequently, wounding was included as control in one of the coral treatment group. Further, the effect of the common biosynthetic precursor of diterpenes, geranylgeranylprophosphate (GGP) [25] was also assessed as part of this study. GGP is known to serve as substrate in diterpenes biosynthesis in Symbiodinium [26] which functions as protectant against predation in Sarcophyton [27]. Furthermore, GGP could have similar effects as green leaf volatiles, which coordinate metabolic repulse at different plant organs as well as between adjacent Molecules 2017, 22, 2195 3 of 14 organs as well as between adjacent plants [28]. A layout for the experimental design and elicitor chemical structures examined in this study is shown in Figures 1 and 2, respectively. Molecules 2017, 22, 2195 3 of 15

Molecules 2017, 22, 2195 3 of 14 plants [28]. A layout for the experimental design and elicitor chemical structures examined in this study is shownorgans in Figures as well 1as and between2, respectively. adjacent plants [28]. A layout for the experimental design and elicitor chemical structures examined in this study is shown in Figures 1 and 2, respectively.

Figure 1. Elicitors application to soft corals and temporal sampling theme used in this study. FigureFigure 1. 1. ElicitorsElicitors application application to to soft soft corals corals and and te temporalmporal sampling theme used in this study. O CH3 O - O O O OH H - O P O O OH O CH3 O O P O - CH3 O O - CH3 CH3 CH3 CH3 H3C O O OH CH3 O H CH3 - HO H OH O P O HO O CH3 O O P O O - O CH3 CH3 CH3 CH3 H3C CH3 Me Jasmonate Gernayl geranyl Arachidonic acid Prostaglandin (PGE1) CH3 pyrophosphate (GGP) (AA) (MeJA) HO H HO Figure 2. Chemical structure of GGP and the oxylipins used for elicitingO soft corals.

Me Jasmonate Gernayl 2.geranyl Results Arachidonic acid Prostaglandin (PGE1) pyrophosphate (GGP) (AA) (MeJA) 2.1. Experimental Design and Analytical Parameters FigureSoft 2. Chemical Chemicalcoral cultures structure structure generated of from GGP Sarcophyton and the glaucum oxy oxylipinslipins and Lobophytonused for pauciliforumeliciting soft[29] corals.were exposed independently to wounding, GGP and three oxylipin elicitors (Figures 1 and 2) and then harvested at 0, 24 and 48 h post elicitation. Biological samples were harvested in triplicate from 2. Results independent jars for both control and elicited corals. Coral tissue was extracted, and analyzed using reverse-phase ultrahigh performance (UPLC) coupled to MS electrospray ionization ion-trap mass spectrometry detection (UPLC-MS) to determine the extent of induction on corals metabolome. 2.1. Experimental The Design selected andchromatographic Analytical parameters Parameters described in the Materials and Methods section resulted in the separation of coral metabolites within 20 min. Details on the peak identification using MS have Soft coral been cultures previously generated described from[12]. No Sarcophyton obvious quantitative glaucum differences and Lobophyton were observed pauciliforum between [29][29] were exposed independently unelicited and to to elicited wounding, coral metabolite GGP GGP profilesand and three threeby visual ox oxylipin inspectionylipin elicitors of chromatgorams (Figures (data 11 not andand 2 )2) and and then then shown) which warranted the use of multivariate data analyses for samples classification. To better harvested at 0,visualize 24 and the 48impact hh postpostof elicitation elicitation.elicitation. on coral meta Biological bolism, UPLC-MS samples chromatograms were harvested were processed in to triplicate from independent jars jarsextract for mass both abundance control data and and furtherelicited subjected cora corals. ls.to multivariate CoralCoral tissuetissue data analyses waswas tool extracted,extracted, for better and analyzed using reverse-phase samples ultrahighclassification performance in an untargeted (UPLC)manner. The coupled detected metabolitesto MS electrospray were analyzed ionizationusing ion-trap principal component analysis (PCA) and orthogonal projection to latent discriminatory analysis mass spectrometry (OPLS) detection to define both (UPLC-MS) similarities and to differences determine among the thesoft coralextent extent specimens. of of induction induction on on corals corals metabolome. metabolome. The selected chromatographicchromatographic parametersparameters described described in in the the Materials Materials and and Methods Methods section section resulted resulted in inthe the separation separation of of coral coral metabolites metabolites within within 20 20 min. min. Details Details on on the the peak peak identificationidentification usingusing MS have been previouslypreviously described described [12 ].[12]. No obviousNo obvious quantitative quantitative differences differences were observed were observed between unelicitedbetween unelicitedand elicited and coral elicited metabolite coral profilesmetabolite by visualprofiles inspection by visual of inspection chromatgorams of chromatgorams (data not shown) (data which not warrantedshown) which the warranted use of multivariate the use of data multivariate analyses fordata samples analyses classification. for samples classification. To better visualize To better the visualizeimpact of the elicitation impact onof elicitation coral metabolism, on coral UPLC-MSmetabolism, chromatograms UPLC-MS chromatograms were processed were to processed extract mass to extractabundance mass data abundance and further data subjected and further to multivariate subjected data to analysesmultivariate tool fordata better analyses samples tool classification for better samplesin an untargeted classification manner. in Thean detecteduntargeted metabolites manner. wereThe analyzeddetected usingmetabolites principal were component analyzed analysis using (PCA)principal and component orthogonal analysis projection (PCA) to latent and discriminatoryorthogonal projection analysis to (OPLS) latent to discriminatory define both similarities analysis (OPLS)and differences to define among both similarities soft coral specimens. and differences among soft coral specimens.

2.2. Effect of Elicitors on S. glaucum Metabolism as Analyzed via PCA & OPLS Analysis In unsupervised analysis methods (i.e., PCA), the similarity patterns within the data are identiﬁed without taking into account the type or class of the study samples. They are often applied to Molecules 2017, 22, 2195 4 of 14

2.2. Effect of Elicitors on S. glaucum Metabolism as Analyzed Via PCA & OPLS Analysis In unsupervised analysis methods (i.e., PCA), the similarity patterns within the data are identifiedMolecules without2017, 22, 2195taking into account the type or class of the study samples. They are often4 applied of 15 to summarize the complex metabolomics data and provide an effective way to detect data patterns that are correlated with biological variables [30]. PCA was first applied to the UPLC-MS dataset, summarize the complex metabolomics data and provide an effective way to detect data patterns with the first two components (PC1 and PC2) accounting for 41% and 26% of the total variance, that are correlated with biological variables [30]. PCA was first applied to the UPLC-MS dataset, respectively. The score plot revealed that triplicate measurements from the same coral sample were with the first two components (PC1 and PC2) accounting for 41% and 26% of the total variance, foundrespectively. to be reproducible, The score plotclustering revealed altogether that triplicate in the measurements score plot. from The the PCA same score coral plot sample (Figure were 3A) showedfound the to segregation be reproducible, of PG clustering and MeJA altogether elicited in samples the score from plot. all The other PCA specimens score plot (Figurewith negative3A) scoreshowed values on the the segregation first dimension. of PG and In MeJA contrast, elicited other samples elicited from coral all otherspecimens specimens including with negative wounding, (AA) scoreand (GGP) values onshowed the first considerable dimension. Inoverlap contrast, and other clus elicitedtering coralaltogether specimens on the including positive wounding, side of PC1 suggesting(AA) and that (GGP) PC1 showedwas able considerable to only discrimina overlap andte between clustering MeJA altogether and PG on the from positive other side class of groups. PC1 PCA suggestinganalysis of that S. glaucum PC1 was elicitation able to only metabolites discriminate data between revealed MeJA that and PGno clear from othersamples class segregation groups. couldPCA be observed analysis of alongS. glaucum PC2.elicitation PCA loading metabolites plots, data which revealed define that no the clear most samples important segregation components could be observed along PC2. PCA loading plots, which define the most important components with respect with respect to the clustering behavior, revealed that the unknown mass weights of m/z 871.57184,+ to the clustering behavior, revealed that the unknown mass weights of m/z 871.57184, C54H79O9 C54H79O9+ made a larger contribution to the cluster segregation and being found more enriched in made a larger contribution to the cluster segregation and being found more enriched in samples samples clustering positively along PC1. Supervised methods (i.e., OPLS-DA) were further used to clustering positively along PC1. Supervised methods (i.e., OPLS-DA) were further used to build buildanother another classification classification model model to enhanceto enhance separation separation between between coral treatmentcoral treatment groups. groups.OPLS-DA OPLS-DA was was appliedapplied toto identifyidentify metabolic metabolic patterns patterns that that are correlatedare correlated with the with elicitation the elicitation variable variable of interest of while interest whiledown-weighting down-weighting the the other other sources sources of variance of variance [30]. [30]. OPLS-DA OPLS-DA score score plot (Figure plot (Figure3C) confirmed 3C) confirmed the the resultsresults derived derived from from PCA, asas clearerclearer discriminationdiscrimination was was then then observed observed of PGof PG and and MeJA MeJA elicited elicited samplessamples from from other other elicitors elicitors and and control control corals. corals.

GGP A Control C MeJA AA Wound MeJA PG MeJA PG PC2 (14%) PC2 (26%) PG

PC1 (41%) PC1 (17%) Unknown lipids Diepoxy-cembratriene B 871/16.24 D 871/16.61 PC2 PC2 PC1

Unknown lipids 871/16.24 871/16.61

PC1 PC1 FigureFigure 3. 3.PrincipalPrincipal component component analysis analysis (PCA) (PCA) an andd orthogonal projectionprojection to to latent latent structures-discriminantstructures-discriminant analysis analysis (OPLS-DA) (OPLS-DA) of of UPLC-MS UPLC-MS extracted extracted metabolites metabolites from S. glaucumfrom S.elicited glaucum elicitedwith 20with and 20020 µMand prostaglandin 200 μ (PG),M methylprostaglandin jasmonate (MeJA),(PG), geranylgeranlypyrophosphatemethyl jasmonate (MeJA), (GGP) and arachidonic acid (AA) in addition to wounding harvested at 0, 24 and 48 h post elicitation. geranylgeranlypyrophosphate (GGP) and arachidonic acid (AA) in addition to wounding harvested (A) PCA score plot of PC1 vs. PC2 scores and its corresponding loading plot (B) showing contributing at 0, 24 and 48 h post elicitation. (A) PCA score plot of PC1 vs. PC2 scores and its corresponding metabolites and their assignments. The clusters are located at the distinct positions in two-dimensional loadingspace plot described (B) showing by two contributing vectors of principal metabolites component and their 1 (PC1) assignments. = 41% and PC2 The = clusters 26%; (C) are OPLS-DA located at the distinctscore plot positions of PC1 in vs. two-dimensional PC2 scores and space its corresponding described by loading two vectors plot (D )of showing principal contributing component 1 (PC1)metabolites = 41% and and PC2 their = 26%; assignments. (C) OPLS-DA Selected score variable plot masses of PC1 are vs. highlighted PC2 scores inthe and loading-plot its corresponding with loadingm/z plot/retention (D) showing time (s)pair contributing and identiﬁcations metabolites are discussed and their in assignments. text. Selected variable masses are highlighted in the loading-plot with m/z/retention time (s) pair and identifications are discussed in text.

Molecules 2017, 22, 2195 5 of 14 Molecules 2017, 22, 2195 5 of 15 The loading plot obtained by the OPLS-DA model is represented in (Figure 3D). The mass weight of 871.57184, C54H79O9+ appeared again contributing the most for samples segregation in additionThe loadingto another plot mass obtained weight by of the m/z OPLS-DA 303.23, C model20H31O is2+ representedfound mostin enriched (Figure 3inD). MeJA The masssamples weight and + oftentatively 871.57184, assigned C54H79O 9 asappeared diepoxycembratriene again contributing as therevealed most for from samples its segregationtandem MS in additionspectrum to + another(Supplementary mass weight Figure of m/zS1A).303.23, It should C20H be31O noted2 found that mostno change enriched in sesquiterpene in MeJA samples pool and was tentatively revealed assignedfrom multivariate as diepoxycembratriene data analysis,as suggesting revealed from that itsat tandemleast in MScase spectrum of S. glaucum (Supplementary the examined Figure elicitors S1A). Ithad should no effect. be noted that no change in sesquiterpene pool was revealed from multivariate data analysis, suggestingConsidering that at leastthat inPG- case and of S.MeJA-elicited glaucum the examinedcorals were elicitors found had to nocluster effect. most distantly from controlConsidering unelicited that group PG- andin the MeJA-elicited OPLS plot corals (Figure were 3C), found another to cluster two most OPLS-DA distantly models from control were unelicitedconstructed group encompassing in the OPLS untreated plot (Figure control3 C),un elic anotherited coral two groups OPLS-DA modelled models one were at aconstructed time against encompassingPG (Figure 4A) untreated or MeJA controlelicited un group elicited (Figure coral 4C) groups one at modelled a time. R one2 and at aQ time2 values against were PG employed (Figure4A) as orindicative MeJA elicited for covered group variance (Figure4 C)and one prediction at a time. power R 2 and for Q 2assessingvalues were models employed validity. as indicativeBoth PG and for coveredMeJA models variance showed and predictionone orthogonal power component for assessing with models R2 = 0.86 validity. and Q2 Both= 0.84. PG A andparticularly MeJA models useful showedtool that one compares orthogonal the componentvariable magnitude with R2 =agains 0.86 andt its Qreliability2 = 0.84. Ais particularlythe S-plot obtained useful tool by thatthe comparesOPLS-DA themodel variable and magnituderepresented against in Figures its reliability 4B & 4D, is the where S-plot axes obtained plotted by thefrom OPLS-DA the predictive model andcomponent represented are the in Figurecovariance4B,D, p[1] where against axes plottedthe correlation from the p(cor)[1]. predictive For component the indication are the of covariance plots with p[1]retention against time the m/z correlation values, p(cor)[1].a cut-off Forvalue the of indication p < 0.01 was of plots used. with The retention S-plot results time m/z forvalues, the PG a cut-offmodel value(Figure of 4B)p < 0.01showed was that used. PG The elicited S-plot samples results for were the PGparticularly model (Figure enriched4B) showedin campestenetriol that PG elicited (m/z + samples433.36737, were C28H particularly49O3+) in addition enriched to inan campestenetriol unknown mass (weightm/z 433.36737, of (m/z C871.57184,28H49O3 )C in54H addition79O9+) likely to an a + unknownlipid based mass on weightits molecular of (m/z 871.57184,formula and C54 Hlate79O retention9 ) likely atime lipid RT based 15.58 on min its molecular eluting peak formula at high and lateorganic retention phase time solvent RT 15.58composition. min eluting Tandem peak atMS high spectrum organic of phase m/z 433.36737 solvent composition. showed the Tandemconsecutive MS spectrumloss of three of m/zwater433.36737 molecules showed (−18 amu) the consecutive with fragment loss masses of three appearing water molecules at m/z 415, (−18 497 amu) andwith 479, fragmentrespectively masses (Supplemen appearingtary at Figurem/z 415, S1B). 497 and 479, respectively (Supplementary Figure S1B).

A C MeJA PG Control Control u[1] u[1]

t[1] t[1] Campest-ene-triol & unknown lipids D B Diepoxy-cembratriene

Unknown lipids P (cor) (cor) [1] P P (cor) (cor) [1] P

p[1] p[1]

Figure 4.4. OPLS-DA score plotplot derivedderived fromfrom modellingmodelling S.S. glaucumglaucum coralscorals elicitedelicited withwith prostaglandinprostaglandin ((AA)) andand methyl methyl jasmonate jasmonate (C )(C at) aat dose a dose of 20 of and 20 200 andµ M200 each μM modelled each modelled one at a timeone at post a elicitationtime post versusversus controlcontrol unun elicitedelicited corals.corals. The S-plots from PG (B) andand MeJAMeJA ((D)) showshow thethe covariancecovariance p[1]p[1] againstagainst the correlationcorrelation p(cor)[1]p(cor)[1] of thethe variablesvariables ofof thethe discriminatingdiscriminating componentcomponent of thethe OPLS-DAOPLS-DA model. Cutoff values of p << 0.05 were used; selected variables are highlighted in the S-plotS-plot withwith m/zm/z andand retentionretention timetime inin minutesminutes andand identiﬁcationsidentifications areare discusseddiscussed inin thethe text.text.

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Molecules 2017, 22, 2195 6 of 14 Campestenetriol is a common constituent in several soft coral species including Sinularia dissecta and LobophytumCampestenetriol catala is[ 31a ]common and was constituent found to bein elevatedseveral soft in MeJAcoral species elicited includingS. glaucum Sinulariaspecimens. Thedissecta role andof this Lobophytum unknown catala mass weight[31] and m/z was 871.57184 found to cannot be elevated be clarified in MeJA as its masselicited isomers S. glaucum appearing atspecimens. different The RT role showed of this a differentunknown responsemass weight to elicitation, m/z 871.57184 with cannot that appearingbe clarified at as RT its 15.58mass min isomers appearing at different RT showed a different response to elicitation, with that appearing at found upregulated in PG samples versus that appearing at RT 16.24 min upregulated in the control RT 15.58 min found upregulated in PG samples versus that appearing at RT 16.24 min upregulated unelicited corals. in the control unelicited corals. 2.3. Effect of Biotic and Abiotic Elicitors on L. pauciliforum Soft Coral Metabolism via PCA & OPLS 2.3. Effect of Biotic and Abiotic Elicitors on L. pauciliforum Soft Coral Metabolism Via PCA & OPLS To further confirm whether the response observed in S. glaucum could be generalized for To further confirm whether the response observed in S. glaucum could be generalized for other other soft coral genus, same experimental design was repeated for a different soft coral species soft coral genus, same experimental design was repeated for a different soft coral species i.e., i.e., Lobophyton pauciliforum. Elicitation of L. pauciliforum with the aforementioned elicitors resulted in Lobophyton pauciliforum. Elicitation of L. pauciliforum with the aforementioned elicitors resulted in a aless less marked marked effect effect on on metabolite metabolite profiles profiles compared compared to that to that observed observed in S. in glaucumS. glaucum as revealedas revealed fromfrom thethe PCA analysis. analysis. PCA PCA was was applied applied to to L. L.pauciliforum pauciliforum UPLC-MSUPLC-MS dataset dataset with with a score a score plot plot(Figure (Figure 5A) 5A) showingshowing partialpartial segregationsegregation of of sample sample groups groups and and with with obvious obvious overlap overlap among among the differentthe different replicates alongreplicates PC1, along suggesting PC1, suggesting that PCA that was PCA not was able not to able discriminate to discriminate among among metabolite metabolite profiles profiles of of these differentthese different elicitor elicitor groups groups and control.and control.

A GGP C Control AA Wound MeJA PG PC2 (14%) PC2 (20%) PC2

PC1 (37%) PC1 (15%) D B PC2

PC2 Diepoxy-cembratriene

Diepoxy-cembratriene

PC1 PC1

Figure 5.5.Principal Principal component component analysis analysis (PCA) and(PCA) orthogonal and projectionorthogonal to latentprojection structures-discriminant to latent analysisstructures-discriminant (OPLS-DA) of analysis UPLC-MS (OPLS-DA extracted) of metabolites UPLC-MS extracte from Lobophytond metabolites pauciflorum from Lobophytonelicited with 20pauciflorum and 200 µelicitedM prostaglandin with 20 (PG),and methyl200 μM jasmonate prostaglandin (MeJA), (PG), geranylgeranyl-pyrophosphate methyl jasmonate (MeJA), (GGP) geranylgeranyl-pyrophosphate (GGP) and arachidonic acid (AA) in addition to wounding harvested and arachidonic acid (AA) in addition to wounding harvested at 0, 24 and 48 h post elicitation. at 0, 24 and 48 h post elicitation. (A) PCA score plot of PC1 vs. PC2 scores and its corresponding (A) PCA score plot of PC1 vs. PC2 scores and its corresponding loading plot (B) showing contributing loading plot (B) showing contributing metabolites and their assignments. The clusters are located at metabolites and their assignments. The clusters are located at the distinct positions in two-dimensional the distinct positions in two-dimensional space described by two vectors of principal component 1 space described by two vectors of principal component 1 (PC1) = 37% and PC2 = 20%; (C) OPLS-DA (PC1) = 37% and PC2 = 20%; (C) OPLS-DA score plot of PC1 vs. PC2 scores and its corresponding score plot of PC1 vs. PC2 scores and its corresponding loading plot (D) showing contributing loading plot (D) showing contributing metabolites and their assignments. Selected variable masses metabolites and their assignments. Selected variable masses are highlighted in the loading-plot are highlighted in the loading-plot with m/z/retention time (s) pair and identifications are discussed with m/z/retention time (s) pair and identiﬁcations are discussed in text. in text.

The ﬁrstfirst twotwo components components (PC1 (PC1 and and PC2) PC2) explained explained 37% 37% and 20%and of20% the of total the variance, total variance, respectively. Consequently,respectively. Consequently, a supervised a datasupervised analysis data method analysis was method adopted was to adopted drive better to drive samples better classiﬁcation.samples OPLS-DAclassification. was OPLS-DA applied to was identify applied metabolic to identify patterns metabolic thatare patterns correlated that withare correlated the elicitor with effect the while elicitor effect while down-weighting the other sources of variance. Compared to PCA score plot,

Molecules 2017, 22, 2195 7 of 15 Molecules 2017, 22, 2195 7 of 14

OPLS-DAdown-weighting score plot the (Figure other sources5C) showed of variance. better and Compared more clear to discrimination PCA score plot, among OPLS-DA sample score groups plot with(Figure PG5 C)elicited showed group better clustering and more only clear distantly discrimination from all other among treatment sample being groups located with at PG the elicited lower negativegroup clustering quadrant only of distantlythe PCA from score all plot. other The treatment loading being plot located derived at the from lower OPLS-DA negative model quadrant is representedof the PCA scorein Figure plot. 5D. The Metabolites loading plot contributing derived from to corals OPLS-DA segregation model isrevealed represented from inPC1 Figure loading5D. plotMetabolites included contributing m/z 303.23, C to20 coralsH31O2+ segregationcembranoid revealed diterpene from at higher PC1 loading levels in plot PG included elicited corals,m/z 303.23, and + alsoC20H detected31O2 cembranoid at higher levels diterpene in MeJA at higher elicited levels S. glaucum in PGelicited corals (Figure corals, and4C,D). also Albeit, detected no effect at higher for MeJAlevels incould MeJA be elicited observedS. glaucum on L. pauciliforumcorals (Figure and4C,D). withAlbeit, only PG no elicitor effect for appearing MeJA could to beimpart observed a slight on differentialL. pauciliforum metabolicand with response. only PG elicitor appearing to impart a slight differential metabolic response. TheThe OPLS-DA OPLS-DA model model and and its its derived derived loading loading S-plot S-plot were were thus thus further further employed employed to to identify identify metabolitemetabolite markers markers related related to to PG PG elicitation elicitation in in L.L. pauciliforum pauciliforum byby modeling modeling control control versus versus PG PG elicited elicited coralscorals (Figures (Figure6 A,B).6A,B). The The first first two two components components in inOPLS OPLS modelmodel presentedpresented inin FigureFigure6 A6A explained explained 0.800.80 of of the the total total variance variance (R2 (R), with2), with the prediction the prediction goodness goodness parameter parameter Q2 = 0.71. Q2 R=2 0.71.did not R exceed2 did notQ2 byexceed more Q 2thanby moretwo units than twoand unitswith andno negative with no negativeQ2 values Q suggesting2 values suggesting the validity the validityof the model. of the Diepoxy-cembratrienemodel. Diepoxy-cembratriene was found was foundmore moreelevated elevated in PG in elicited PG elicited corals corals as asrevealed revealed from from PCA, PCA, + + concurrentconcurrent withwith aa decrease decrease in inm/z m/z510.39130, 510.39130, C34 CH3443HN432NO2O2tentatively tentatively annotated annotated as chaetoglobosinas chaetoglobosin 510 . 510.Chaetoglobosin Chaetoglobosin 510 is510 an is alkaloid an alkaloid isolated isolated from from the marinethe marine fungi fungiPhomopsis Phomopsis asparagi asparagi[32] and[32] isand thus is thusunlikely unlikely to be to derived be derived from thefrom soft the coral soft itself.coral itself. The identity The identity of this of alkaloid this alkaloid still needs still toneeds be further to be furtherconfirmed confirmed using authentic using authentic standard standard as attempt as to attempt perform to tandem perform MS tandem to confirm MS its to structure confirm wasits structurenot successful was not (data successful not shown). (data Softnot coralsshown). are Soft marine corals invertebrates are marine invertebrates regarded as aregarded holobiont as for a holobiontharboring manyfor harboring organism includingmany organism endosymbiotic including dinoflagellates endosymbiotic partner dinoflagellatesSymbiodinium partnerthat is Symbiodiniumcentral to the successthat is central of corals. to the Additionally, success of corals. an array Additionally, of other microorganisms an array of other associated microorganisms with coral associatedviz., bacteria, with archaea, coral viz., fungi, bacteria, and viruses archaea, that fungi, have and intricate viruses role that in maintaininghave intricate homeostasis role in maintaining between homeostasiscorals and Symbiodinium between corals[33 ].and Symbiodinium [33].

A Control PG u[1]

t[1] Chaetoglobosin 510

P (cor) (cor) [1] P Diepoxy-cembratriene

Unknown p[1] Figure 6. OPLS-DA score plot (A) derived from modelling Lobophyton pauciflorum corals elicited with Figure 6. OPLS-DA score plot (A) derived from modelling Lobophyton pauciﬂorum corals elicited with PG at a dose of 20 and 200 μM modelled versus control unelicited corals. The S-plots from PG (B) PG at a dose of 20 and 200 µM modelled versus control unelicited corals. The S-plots from PG (B) show show the covariance p[1] against the correlation p(cor)[1] of the variables of the discriminating the covariance p[1] against the correlation p(cor)[1] of the variables of the discriminating component componentof the OPLS-DA of the model. OPLS-DA Cutoff model. values Cutoff of p < 0.05values were of used; p < 0.05 selected were variables used; selected are highlighted variables in are the highlightedS-plot with m/zin theand S-plot retention with timem/z and in minutes retention and time identiﬁcations in minutes and are discussedidentifications in the are text. discussed in the text.

2.4. Relative Quantification of S. glaucum & L. pauciflorum in Response to Elicitation To further confirm whether these metabolites revealed from multivariate data analyses are significantly relevant, relative quantification of metabolites was attempted and displayed as bar plot

Molecules 2017, 22, 2195 8 of 15

2.4. Relative Quantification of S. glaucum & L. pauciflorum in Response to Elicitation To further confirm whether these metabolites revealed from multivariate data analyses are significantly relevant, relative quantification of metabolites was attempted and displayed as bar plot (Figure7) . Largest increases were observed in S. glaucum in response to PG, with + ca. 30- and 27-fold increase in campestenetriol, C28H49O3 , 433.36737 at a dose of 20 and 200 µM, respectively. Such massive increases were not observed in the case of L. pauciflorum post PG elicitation. A common metabolite response in S. glaucum and L. pauciflorum observed in case of MeJA and PG was a decline in m/z 510.39130 levels, annotated as chaetoglobosin 510 at ca. 0.3 to 0.5 fold decrease compared to un elicited control corals. The upregulating effect of MeJA on corals metabolism was only observed in S. glaucum at ca. 2-fold levels in the cembranoid diterpene suggesting that MeJA is not a general inducer of diterpenoids in corals as well observed in case of planta terpenoids [34,35]. Whether the origin of diterpenes formation in coral holobionts is the soft coral animal itself or the harbored symbiotic zooxanthellae and associated microorganisms inside is not fully determined.

3. Discussion Enhancement of secondary metabolites production by oxylipins i.e., jasmonates and fatty acids has been reported in numerous plant systems [36]. In planta, the signals between plant perception of the aggression, gene activation, and the subsequent biosynthesis of secondary compounds are assumed to be for oxylipin derivatives. JA and its methylated analogue MeJA are small signaling molecules induced in response to wounding or pathogens attack in plants [37]. The effect of jasmonates on eliciting plant secondary metabolism include the accumulation of terpenoids, flavanoids, alkaloids, and phenylpropanoids [38–40]. Such an effect for oxylipins in corals has yet to be demonstrated and this study provide the first report for contrasting the effect of an animal oxylipin viz. prostaglandin and plant oxylipin viz. methyl jasmonate on terpenes/sterol content in two soft coral species. In contrast to the specimens exposed to GGP, AA, wounding and the untreated control samples, application of MeJA and PG had notable effects on S. glaucum metabolite composition (PCA results in Figures3 and4), whereas in L. pauciflorum only PG, but not MeJA, that appeared to alter its secondary metabolism (Figures5 and6). Here the absence of response to MeJA treatment could be related to presumably lower zooxanthellae content of L. pauciflorum suggested by its slightly paler color. Under such circumstances, the plant specific elicitor MeJA would have less pronounced effects at low density zooxanthellae, although a previous experiment showed that the symbionts readily responded at the metabolic level in Sarcophyton ehrenbergi [21]. Incubation with MeJA in the culture seawater led to decreased photosynthetic efficiency in S. ehrenbergi, but at this experiment neither the coral host nor the separately analysed zooxanthellae increased their terpenoid levels [21]. Nevertheless MeJA has been shown to elicit stress response in plants [41]. The injection of elicitors directly into the corals tissue, as conducted herein, is assumed to be a better targeted application that reaches the symbionts within the coral cells and might account for the fact that no effect was observed in [21] in which MeJA was just added in salty water surrounding the coral. Dinoflagellate responsiveness to MeJA was not expected because of their evolutionary distance to the chlorophyta in which MeJA is known to be a signaling compound [42]. However, injected into the corals head, MeJA triggered a metabolomic response in S. glaucum (Figure3A,C) accompanied by the accumulation of unknown lipids (Figures3B and7) and diepoxy-cembratriene (Figures3D and7). Diepoxycembratriene might offer comparable cytotoxicity as cembratrienes viz., sinuflexolide, dihydrosinuflexolide, and sinuflexibilin from Sinularia (Octocorallia, Alcyonacea) [43] and the unknown lipids may refer to changes attributed to stress as known for the lipidome in fission yeast. There the membrane lipid composition is altered by newly synthesized lipids during heat stress [44]. Metabolomic responses were species specific in our case. On the whole, the elicitation process was less pronounced in L. pauciflorum. Aside from possible differences in zooxanthellae density among the two investigated coral species, such species specific pattern may refer to the different defense strategies to be employed by the different species being confronted with disparate predators despite their close Molecules 2017, 22, 2195 9 of 15

phylogenetic relationship [45]. In addition to chemical defense, octocorals have developed a set of physical defense strategies against predators [46]. Calcium carbonate sclerites with spine or needle like shape impede predation by fish and gastropods [47] and the zooxanthellae rich polyp colony is rapidly Moleculesretracted 2017, 22 from, 2195 the corals head immediately after initial wounding [48]. L. pauciflorum 9bears of 14 much less and considerable smaller retractable polyps than S. glaucum and thus may need less pronounced L. chemicalpauciflorum defense bears much strategies. less and Though considerable the most smaller effective retractable defense line polyps is terpenoids than S. glaucum upregulation, and thus which is mayof alsoneed potential less pronounced interest forchemical pharmaceutical defense strate applications.gies. Though Possible the most future effective demands defense of terpenoids line is have terpenoidsalready ledupregulation, to the development which is of of also mass potential production interest guidelines for pharmaceutical of soft corals applications. for closed Possible systems [49], futurethough demands it has beenof terpenoids reported have that already aquarium led cultured to the development coral specimens of mass frequently production possess guidelines lower of amounts softof corals defensive for closed bioactive systems compounds [49], though [12] having it has lostbeen their reported chemical that weaponaquarium strategies. cultured Tocoral elicit the specimens frequently possess lower amounts of defensive bioactive compounds [12] having lost production of bioactive compounds, it is of particular interest to gain insight of soft corals metabolic their chemical weapon strategies. To elicit the production of bioactive compounds, it is of particular response to the different elicitors. In this study, PG was found effective in upregulating terpene/sterol interest to gain insight of soft corals metabolic response to the different elicitors. In this study, PG levels in both examined species and thus may have the potential to modulate soft coral cultures in was found effective in upregulating terpene/sterol levels in both examined species and thus may future commercial applications. Our results give insights into the detailed metabolic response with have the potential to modulate soft coral cultures in future commercial applications. Our results give differences and concordance between the two species. insights into the detailed metabolic response with differences and concordance between the two species.PG and MeJA treated S. glaucum metabolome were clearly separated from all other samples postPG 24 and and MeJA 48 h oftreated elicitation S. glaucum (Figure metabolome4A,C), whereas were onlyclearly PG separated treatment from was all significant other samples ( p <0.05) in postL. pauciflorum24 and 48 h of(Figure elicitation6A). (Figures Post 24 h4A,C), most whereas prominent only terpenoids PG treatment are was detected significant at different (p < 0.05) quantities in L. dependingpauciflorum on(Figure the respective 6A). Post species24 h most as wellprominent as elicitor terp concentrationenoids are detected (Figure at7 different). S. glaucum quantitiesaccumulates dependingchaetoglobosin on the 510 respective though atspecies slightly as higherwell as quantities elicitor inconcentration response to (Figure 20 µM PG,7). concurrentS. glaucum with a accumulatesdecline in thechaetoglobosin unknown lipid 510 ofthough mass weightat slightlym/z 800.1431.higher quantities Chaetoglobosin in response is a to cytotoxic 20 μM compoundPG, concurrentisolated fromwith fungia decline [50 ,51in ].the The unknown PG treatment lipid of has mass also weight led to m/z increased 800.1431. sterol Chaetoglobosin (campestenetriol) is a level cytotoxicin S. glaucum compound. Sterols isolated are involved from fungi in plant [50,51]. stress The responses PG treatment and arehas essential also led componentsto increased ofsterol eukaryotic (campestenetriol)cell membranes level that in modulate S. glaucum their. Sterols physicochemical are involved properties in plant stress [52]. responses In general, and terpenoids are essential are known componentsas the most of widespread eukaryotic andcell me effectivembranes protective that modulate chemicals their against physicochemical pathogens properties and predation [52]. inIn various general,taxa including terpenoids soft are corals known [47 ,as53 ,the54]. most widespread and effective protective chemicals against pathogens and predation in various taxa including soft corals [47,53,54].

Figure 7. Relative quantification of major terpenoids and sterols in S. glaucum and L. pauciflorum soft coralsFigure 24 h 7. postRelative prostaglandin quantification (PG) and of majormethyl terpenoids jasmonate and(MeJA) sterols elicitation in S. glaucum at a doseand of 20L. pauciflorumand 200 soft μM.corals Asterisk 24 h postindicates prostaglandin significant (PG) differences and methyl between jasmonate treatments (MeJA) according elicitation atto aleast dose significant of 20 and 200 µM. differenceAsterisk (LSD) indicates at p =significant 0.05. differences between treatments according to least significant difference (LSD) at p = 0.05. 4. Materials and Methods

Molecules 2017, 22, 2195 10 of 15

4. Materials and Methods

4.1. Soft Coral Materials The soft coral of the family Alcyoniidae comprise the most common genera along several sea coasts. Specimen of Sarcophyton glaucum and Lobophytum pauciﬂorum were cultured in the aquarium facility of the Leibniz Center for Tropical Marine Research (Bremen, Germany). These corals were kept below 28 ◦C in a 2500 L recirculation system with 50 cm distance between coral nubbins and a blue/white combination of two 39 W ﬂuorescence light bulbs.

4.2. Chemicals and Reagents Acetonitrile and acetic acid (LC–MS grade) were obtained from J.T. Baker (Avantor, The Netherlands), MilliQ water was used for LC analysis. A Chromoband C18 (500 mg, 3 mL) cartridge from Macherey and Nagel (Düren, Germany). All chemicals and standards were acquired from Sigma Aldrich (St. Louis, MO, USA).

4.3. Elicitation For elicitation, corals were placed in glass jars of an average volume of 600 mL with a closed lid through which a Teflon tube provided aeration to corals. Elicitors were diluted in 50% ethanol deionized water mixture and injected to coral head using tight glass 1 mL syringe at a dose of 20 and 200 µM. Wounding was made by making two cuts into coral heads, of ca. 2 cm length and 0.5 cm depth using sterile scalpels. Control unelicited corals were injected with the same volume of 50% ethanol in water. Corals were kept at 28 ± 1 ◦C, with a 12 h photoperiod and specimens were harvested at 0, 24 and 48 h post elicitation. Harvested corals were immediately transferred and kept at −80 ◦C until further analysis. Corals were treated with each elicitor in triplicates by eliciting three independent corals placed in three different glass jars (Figure1).

4.4. Soft Coral Metabolites Extraction Procedure and Sample Preparation for UPLC-MS Analysis Approximately 20 mg tissue from the coral umbrella was cut with a clean scalpel and transferred to liquid nitrogen. The powdered freeze-dried soft coral tissue was ground with a pestle in a mortar under liquid nitrogen. The powder was then homogenized with 1.0 mL 100% ethanol 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 12,000 g for 5 min to remove debris. For UPLC-MS analyses, 500 µL were aliquoted and ﬁltered through 22 µm pore ﬁlter. Three µL were used for UPLC-MS analysis. For each specimen, three biological replicates were provided and extracted in parallel under the same conditions.

4.5. UPLC-MS Analysis Chromatographic separations were performed on an Acquity UPLC system (Waters, Milford, CT, USA) 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: 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). Eluted compounds were detected from m/z 100 to 1000 using a LCQ Deca XP ion trap MS (ThermoElectron, San Jose, CA, USA) equipped with an ESI source (electrospray voltage 4.0 kV, sheath gas: nitrogen; capillary temperature: 275 ◦C) in positive ionization 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, 5 eV; collision gas, argon; Molecules 2017, 22, 2195 11 of 15 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.

4.6. UPLC-MS Data Processing for Multivariate Analyses Relative quantification and comparison of soft corals 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/)[55]. This software approach employs peak alignment, matching and comparison, as described by [56] to produce a peak list. The resulting peak list was processed using the Microsoft Excel software (Microsoft Inc., Redmond, WA, USA), where the ion features were normalized to the total integrated area (1000) per sample. Principal component analysis (PCA) was 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 [57] that was applied to discriminate, e.g., between different elicitors and unelicited specimens. Models strength were determined using both R2 and Q2 values, where R2 represents the total amount of variance explained by the model whereas Q2 represents model accuracy.

5. Conclusions In summary, here we report on metabolomic perturbations of two soft coral species in response to oxylipins elicitation as analyzed via UPLC-MS. Four signiﬁcantly altered metabolites accounted for PG and MeJA segregation from control soft corals including diepoxycembratriene (a diterpene) and campestenetriol (a sterol) in addition to unknown lipid masses. The combination of metabolomic MS technique with multivariate data analysis appeared appropriate to reveal for changes that occur in the soft corals metabolism in response to elicitation. Our results show that manipulation of S. ehrenbergi soft coral terpenoid pool with MeJA was not as dramatic as in plants. We rather demonstrated that the animal oxylipin had a more pronounced effect. Compared to salicylic acid, another signaling molecule involved in defense responses, PG was found less active in triggering accumulation of terpenes in soft corals [21], though such comparison is approximate as different application methods of elicitors and soft coral species were used than that described herein. The present work provides the ﬁrst metabolic evidence for oxylipin function in soft corals secondary metabolism. In future work, monitoring enzymatic activity or gene expression levels related to the biosynthesis of the altered metabolites might provide a deeper understanding of its regulation in soft corals in response to abiotic elicitation. Further studies should be now pursued with oxylipins on coral-harbored organisms, i.e., zooxanthellae, to provide more evidence for their role in coral defense response and whether such an induction response is attributed to the coral itself or its harbored organisms. Analyzing biological structure-activity relationships between PG analogues may also help to identify more biologically active oxylipins and reveal for crucial structural motifs that elicit a metabolic response in soft corals.

Supplementary Materials: The Supplementary Materials are available online. Acknowledgments: Mohamed A. Farag wishes to thank the Alexander von Humboldt foundation, Germany and the Hanse Wissenschaftskolleg, Germany for the financial support. Author Contributions: M.A.F. and A.M. conceived and designed the experiments; M.A.F. performed the experiments and the measurements; T.F.E. performed the data analysis; M.A.F., A.M., L.A.W. and H.W. analyzed the data and wrote the paper. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results. Molecules 2017, 22, 2195 12 of 15

Abbreviations

UPLC-MS Ultrahigh performance coupled to mass spectrometry PG-E1 prostaglandin MeJA methyl jasmonate GGP geranylgeranlypyrophosphate AA arachidonic acid PCA Principal component analysis Orthogonal projection to latent OPLS-DA structures-discriminant analysis

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Sample Availability: Samples of the compounds are not available from the authors.

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