Phytochemistry 108 (2014) 67–76

Contents lists available at ScienceDirect

Phytochemistry

journal homepage: www.elsevier .com/locate /phytochem

Developmental changes in leaf phenolics composition from threeartichoke cvs. (Cynara scolymus) as determined via UHPLC–MSand chemometrics

http://dx.doi.org/10.1016/j.phytochem.2014.09.0040031-9422/� 2014 Elsevier Ltd. All rights reserved.

Abbreviations: GG, American Green Globe; FH, French Hyrious; EB, EgyptianBaladi.⇑ Corresponding author. Tel.: +20 2 2362245; fax: +20 2 25320005.

E-mail address: mfarag73@yahoo.com (M.A. Farag).

Amira S. El Senousy a, Mohamed A. Farag a,⇑, Dalia A. Al-Mahdy a, Ludger A. Wessjohann ba Pharmacognosy Department, College of Pharmacy, Cairo University, Cairo, Egyptb Leibniz Institute of Plant Biochemistry, Dept. Bioorganic Chemistry, Weinberg 3, D-06120 Halle (Saale), Germany

a r t i c l e i n f o a b s t r a c t

Article history:Received 25 May 2014Received in revised form 27 August 2014

Keywords:Cynara scolymus L. (Asteraceae)UHPLC–MSPrincipal component analysis (PCA)Orthogonal projection to latent structures-discriminant analysis (OPLS-DA)Developmental stagesCaffeoylquinic acids

The metabolomic differences in phenolics from leaves derived from 3 artichoke cultivars (Cynara scoly-mus): American Green Globe, French Hyrious and Egyptian Baladi, collected at different developmentalstages, were assessed using UHPLC–MS coupled to chemometrics. Ontogenic changes were consideredas leaves were collected at four different time intervals and positions (top and basal) during artichokedevelopment. Unsupervised principal component analysis (PCA) and supervised orthogonal projectionto latent structures-discriminant analysis (O2PLS-DA) were used for comparing and classification of sam-ples harvested from different cultivars at different time points and positions. A clear separation amongthe three investigated cultivars was revealed, with the American Green Globe samples found mostenriched in caffeic acid conjugates and flavonoids vs. other cultivars. Furthermore, these metabolites alsoshowed a marked effect on the discrimination between leaf samples from cultivars harvested at differentpositions, regardless of the plant age. Metabolite absolute quantifications further confirmed that discrim-ination was mostly influenced by phenolic compounds, namely caffeoylquinic acids and flavonoids. Thisstudy demonstrates an effect of artichoke leaf position, regardless of plant age, on its secondary metab-olites composition. To the best of our knowledge, this is the first report for compositional differencesamong artichoke leaves, based on their positions, via a metabolomic approach and suggesting that toppositioned artichoke leaves present a better source of caffeoylquinic acids, compared to basal ones.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

Globe artichoke (Cynara cardunculus var. scolymus Hayek), for-merly Cynara scolymus L. is an ancient perennial plant species ofthe Asteraceae family (Lattanzio et al., 2009), mostly cultivatedworldwide for its large immature inflorescences, called capitula,with edible fleshy leaves (bracts) and receptacle (Lattanzio et al.,2009). Apart from being consumed as a food, artichoke is recog-nized as herbal medicine (Lombardo et al., 2010; Schutz et al.,2006b). Leaves are mostly utilized for the production of commer-cial extracts in nutraceuticals, being enriched in polyphenols,whereas flower heads and roots, considered as a source of inulin,are used as prebiotic ingredient in functional foods (Raccuiaand Melilli, 2004). Several pharmacological experiments have

demonstrated the health promoting effects of artichoke extractsincluding its hepatoprotective (Gebhardt and Fausel, 1997;Mehmetcik et al., 2008), choleretic (Kirchhoff et al., 1994;Matuschowski et al., 2005; Saenz Rodriguez et al., 2002), antichole-static (Gebhardt, 2001, 2005), hypolipidemic (Shimoda et al.,2003), antioxidative (Gebhardt and Fausel, 1997), antimicrobial(Mossi and Echeverrigaray, 1999; Zhu et al., 2004) and antispas-modic effects (Emendorfer et al., 2005), as well as the antihyper-cholesterolemic effect (Gebhardt, 2002). In addition, artichokeextracts have shown anti-tumour (Noldin et al., 2003), apoptoticanalgesic and anti-inflammatory (Trouillas et al., 2003) activities.The chemical components of artichoke have been extensively stud-ied, found to be a rich source of caffeoylquinic acids and flavonoids(Adzet and Puigmacia, 1985; Hausler et al., 2002; Lattanzio et al.,2005; Pandino et al., 2011). Among hydroxycinnamates, chloro-genic and 1,5-dicaffeoylquinic acids are the predominant com-pounds (Coinu et al., 2007; Lombardo et al., 2010; Romani et al.,2006; Schutz et al., 2004). However, 1,3-dicaffeoylquinic acid wasthe major dicaffeoylquinic compound reported from other studies

http://crossmark.crossref.org/dialog/?doi=10.1016/j.phytochem.2014.09.004&domain=pdfhttp://dx.doi.org/10.1016/j.phytochem.2014.09.004mailto:mfarag73@yahoo.comhttp://dx.doi.org/10.1016/j.phytochem.2014.09.004http://www.sciencedirect.com/science/journal/00319422http://www.elsevier.com/locate/phytochem

68 A.S. El Senousy et al. / Phytochemistry 108 (2014) 67–76

(Schutz et al., 2006a; Wang et al., 2003), likely formed as an artifactfrom the isomerisation of 1,5-dicaffeoylquinic acid during aqueousextraction (Schutz et al., 2006a). On the other hand, main flavo-noids detected in artichoke leaves and heads were those of apige-nin and luteolin, as well as, their glycosides (Lombardo et al., 2010;Pandino et al., 2011). Other bioactive compounds include the anti-hyperlipedemic sesquiterpenes; cynaropicrin, as well as, the ses-quiterpene glycosides; cynarascolosides A, B, C (Shimoda et al.,2003) and anthocyanins, present only in capitula (Schutz et al.,2006b).

Among artichoke secondary metabolites, chlorogenic acid andcynarin have been proven to mediate for its hepatoprotective,choleretic and antimicrobial activities (Gebhardt and Fausel,1997; Matuschowski et al., 2005; Zhu et al., 2004). Nevertheless,lipid lowering and anti-cholestatic activities of artichoke, as wellas, its ability to inhibit cholesterol biosynthesis were mainlyattributed to flavonoids i.e., luteolin (Gebhardt, 1998, 2001,2005). A considerable variation in the phenolic content of arti-choke extracts has been shown, throughout the literature,depending on the part used, variety, maturity stage (harvest time)and the applied methodologies, which may ultimately affect itsbiological efficacy (Farag et al., 2013; Fratianni et al., 2007;Lombardo et al., 2010; Pandino et al., 2011; Romani et al., 2006;Wang et al., 2003).

The developmental stage, at which leaves from medicinal plantsare harvested, has been found to influence their phytochemicalcomposition. For example, in Mentha piperita, it was found thatthe A- and B-ring O-methylation patterns of flavonoid aglyconesdiffer according to leaf age (Voirin and Bayet, 1992). In sainfoinleaves, it was found that the number-average molecular weightand the degree of polymerization of proanthocyanidins increasewith leaf development (Koupai-Abyazani et al., 1993). Further-more, during leaf maturation, the influence of the developmentalstage on the pools of individual flavonols, as well as, procyanidinswas obvious in apple leaves (Mayr et al., 1995). Besides, in Arabid-opsis thaliana, older leaves had lower glucosinolate concentrationsthan younger ones (Brown et al., 2003). In addition, Mondolot et al.,2006, found that caffeoylquinic acids content varied throughoutCoffea canephora leaf development. In fruits, such as Loquat, highconcentrations of phenolic compounds were detected in youngfruits, which then decreased steadily during growth. In contrast,the concentration of chlorogenic acid increased during fruit ripen-ing, which appeared to be a characteristic of Loquat fruit ripening(Ding et al., 2001). Moreover, immature green peppers (Capsicumannum L.) revealed very high phenolic content (hydroxycinnamicacids and flavonoids), ca. 4–5 times higher than in red ripe ones(Marín et al., 2004). It was also found that harvesting broccoliheads of over-maturity stage resulted in maximal flavonol levels(quercetin and kaempferol aglycones), compared to other stagesof head development (Krumbein et al., 2007).

Regarding artichoke, young artichoke heads were reported tocontain more antioxidant phenolic compounds than mature ones(Wang et al., 2003); a higher concentration of phenolics was alsoobserved in artichoke head, as well as floral stem harvested inspring compared to the winter, revealing the influence of harvesttime on phenolics composition (Lombardo et al., 2010). Althoughthe complex network of metabolites is dramatically altered duringdevelopment, most researchers have approached the problem bystudying only target compounds. In this regard, metabolomics pro-vides a better tool for analysing developmental changes due to itsability to follow a relatively large number of metabolites in a singleanalysis (Farag, 2014). Metabolites profiling has been increasinglyapplied to study the developmental changes, e.g., in tomato(Roessner-Tunali et al., 2003), strawberry (Fait et al., 2008), grape(Zamboni et al., 2010) and peach (Lombardo et al., 2011) fruits,as well as, Vanilla planifolia leaves (Palama et al., 2010).

In a previous report, metabolites from three artichoke cultivars:American Green Globe (GG), French Hyrious (FH) and EgyptianBaladi (EB), together with different commercial preparations, weremeasured using UHPLC–q-TOF-MS to reveal secondary metabolitecompositional differences among cultivars and preparations (Faraget al., 2013).

Nevertheless, and to the best of our knowledge, the effect ofmaturity stage on artichoke leaves chemical composition has notbeen addressed, an issue of value for the artichoke nutraceuticalsindustry. In this report, we describe a simple and efficient methodto determine metabolite variation in artichoke leaves of differentcultivars and at different developmental stages, using UHPLC–ESI-ITMS and analyzed by chemometrics.

2. Results and discussion

2.1. Metabolite profiling of artichoke leaves using UHPLC–PDA-MS

Artichoke leaves from 3 different cultivars were harvested atdifferent time points and positions. For each cultivar, leaves werecollected at 4 different maturity stages: from 3, 5, 6 and 8 monthsold plants. Except for 3 months samples, two nodal positions (api-cal and basal) were selected for sample collections, at each timepoint (see Section 4.1), for a total of 21 harvested samples to pro-vide an overview of artichoke secondary metabolite accumulationpatterns in a holistic manner, using an UHPLC–MS based metabolo-mics approach (see Section 4.4). Different nodal positions repre-sented different plant ages, with apical (top) leaves being theyoungest ones and basal leaves the oldest, mature ones. In caseof the 3 months harvest, only one nodal position (apical or top)was available due to the small size of the plants and absence ofbasal leaves at this plant age. Biological replicates for each samplewere harvested in triplicate [(21 � 3), a total of 63 samples],extracted and analyzed under identical conditions via reverse-phase UHPLC coupled to electrospray negative ionization ion-trapmass spectrometry detection (UHPLC–ESI-ITMS). It should benoted that artichoke extract was initially analyzed in positiveand negative ion electrospray ionization (ESI) MS modes aschanges in ESI polarity can often circumvent or significantly altercompetitive ionization and suppression effects revealing otherwisesuppressed metabolite signals. Compared to the positive-ion ESImode, negative-ion MS spectra revealed better sensitivity andmore observable peaks than in positive mode, most notably inthe elution range of phenolics acids, flavonoids and saponins(Farag et al., 2013). Consequently, all samples and metabolitesidentifications were made in the negative ionization mode. Chro-matographic parameters described in the experimental sectionresulted in the separation of metabolites within ca. 10 min (Sup-plementary Fig. S1). The elution order of compounds correlatedwith decreasing polarity, whereby phenolic acids and flavonoiddi-glucosides eluted first, followed by mono-glucosides and sapo-nins, and finally fatty acids. Metabolites were identified basedupon UV and mass spectra. A detailed description of the artichokeleaves peak identifications and strategy has been published else-where (Farag et al., 2013). A complete list of identified peaks, alongwith their characteristic UV and mass spectral data, is provided inTable 1.

2.2. Multivariate PCA and O2PLS-DA analyses of UHPLC–MS data

PCA and OPLS-DA are often used to analyze large complex datasets. Principal component analysis (PCA) is the most widely usedmultivariate data analyses method for chemometrics, as it is thebasis of all multivariate analysis modeling regarded as an unsuper-vised clustering method that reduces the dimensionality of

Table 1UHPLC–UV–MS peak identifications for metabolites in artichoke leaf extracts (peaks are listed in order of retention time in minutes on RP-18 column).

Peak No. Rt (min) Identification UV (nm) M–H�(m/z)

1 5.90 Luteolin-O-glycoside 345 7052 6.03 5-O-Caffeoylquinic acid (chlorogenic acid) 295 shd, 325 3533 6.12 3-O-Feruloylquinic acid 290 shd, 325 3674 7.35 Luteolin-7-O-rutinoside (scolymoside) 271, 341 5935 7.63 Luteolin-7-O-glucoside (cynaroside) 270, 344 4476 7.70 Hesperetin glycoside nd 4637 7.87 Apigenin-7-O-rhamnosyl-glucoside nd 5778 7.91 Luteolin-7-O-glucuronide nd 4619 7.95 1,5-di-O-Caffeoylquinic acid 296 shd, 325 51510 8.02 1,5-di-O-Caffeoylquinic acid isomer 296 shd, 325 51511 8.09 Unknown caffeic acid conjugate 296 shd, 325 51912 8.15 Luteolin-7-O-acetyl-glucoside nd 48913 8.27 Apigenin-7-O-glucoside (cosmoside) 265, 330 43114 9.92 Luteolin 330 28515 10.32 Cynarasaponin E nd 80916 10.88 Trihydroxy octadecadienoic acid nd 32717 11.28 Cynarasaponin J nd 94118 11.42 Trihydroxy octadecenoic acid nd 32919 11.53 Cynarasaponin C nd 79320 15.62 Hydroxy octadecatrienoic acid nd 29321 16.43 Hydroxy octadecadienoic acid nd 295

A.S. El Senousy et al. / Phytochemistry 108 (2014) 67–76 69

multivariate data, while preserving most data information, withoutrequiring any knowledge of the data set (Goodacre et al., 2000).While the unsupervised nature of the PCA algorithm provides ameans to achieve unbiased dimensionality reduction, its applica-tion only reveals group structure when within-group variation issufficiently less than between-group variation (Worley andPowers, 2013). Consequently, supervised forms of discriminantdata analysis such as (OPLS-DA), that utilize class information tomaximize the separation between classes and minimize the sepa-ration between intra-group clustering, are also utilized (Xianget al., 2011).

Peaks detected by UHPLC–MS (Table 1) from all samplesderived from the 3 studied artichoke cultivars were subjected ini-tially to PCA analysis (Fig. 1) to outline the metabolomic differ-ences among them and to assess the overall experimentalvariation. The PCA score plot (Fig. 1A) reveals a clear separationbetween the Egyptian cultivar ‘‘Baladi’’ (EB), French cultivar ‘‘Hyri-ous’’ (FH), allocated at the left side of the vertical line representingPC1 (negative PC1 values) and the American cultivar ‘‘Green Globe’’(GG), positioned to the right of the plot, with the main principalcomponent to differentiate between samples, i.e. PC1, accountingfor 68% of the variance. Triplicate measurements from the samesample were found to be reproducible, as the scores of replicatemeasurements were more or less superimposed. The segregationobserved in PCA score plot can be explained in terms of the iden-tified compounds, using the loading plots for PC1 signals thatexpose those peaks exerting the largest effect on the respectiveprinciple component. Metabolites are represented by their(M–H)� m/z values/Rt (min) pairs. As shown in Fig. 1B, two majorgroups stand out in this plot. The first one corresponds to the MSsignals of caffeic acid conjugates (cynarin and chlorogenic acid)and luteolin-7-O-acetyl-glucoside, contributing positively to PC1,more enriched in the ‘‘Green Globe’’ (GG) cultivar. The secondgroup of MS signals was assigned for the luteolin flavonoidglycoside ‘‘scolymoside’’, contributing negatively to PC1 and foundto be enriched in the two other cultivars (EB and FH). These resultsare in agreement with previous PCA results for cultivars harvestedat one time point only, and confirm metabolite accumulationpatterns across these artichoke cultivars (Farag et al., 2013).In spite of the clear separation observed in PCA analysis for GGleaves, harvested at different maturity stages and from differentpositions, FH and EB samples cluster altogether. Consequently,supervised O2PLS-DA was carried out to enhance samples

separation observed in the PCA model (Fig. 1), as the latter hasgreater potential in the identification of markers by providing themost relevant variables for differentiation among sample groups.The score plot from O2PLS-DA analysis shows a clear separationbetween FH and EB samples (Fig. 2A), accounting for 98% of thetotal variance (R2 = 0.98) with the prediction goodness parameterQ2 = 0.96. The S-plot is a particularly useful tool that comparesthe variable magnitude against its reliability and is an easy wayto visualize an OPLS classification model, mainly used to filterout putative markers from ‘‘omics data’’. The axes plotted fromthe predictive component are the covariance p[1] against the cor-relation p(cor), at a cut-off value of P < 0.01. The S-plot results(Fig. 2B) shows that EB cultivar was particularly enriched in fattyacids, i.e. hydroxy octadecatrienoic (peak 20) and trihydroxy octa-decadienoic acids (peak 16), whereas the FH one is more enrichedin hydroxy octadecadienoic acid (peak 21) and cynarasaponin C(saponin), suggesting that the most relevant variables among bothcultivars are attributed to fatty acids and saponins. This is the firstapplication of supervised data analysis for artichoke cultivarclassification.

The artichoke variety seems to be more relevant for sample seg-regation in PCA, compared to leaf age and or leaf position. In orderto better reveal these latter differences, samples from each cultivarwere modelled separately. Separation could now be achievedbetween samples harvested at different time intervals and posi-tions (Fig. 3), less readily observed when all samples were pooledtogether, suggesting that multivariate data analysis can also beapplied to reveal for ontogenic effects on artichoke chemicalcomposition.

The UHPLC–MS datasets derived from ‘‘Green Globe’’ leaves,collected at different ages and leaf positions, was subjected toPCA analysis (Fig. 3). The PCA score plot (Fig. 3A) reveals two con-fined clusters, one corresponding to basal leaves (older, matureleaves), positioned to the left of the vertical line representing PC1(negative PC1 values) and the other one corresponding to top(younger) leaves, positioned to the right of the plot (positive PC1values), with the main principal component to differentiatebetween samples, i.e. PC1, accounting for 66% of the variance.Except for the leaves collected at 5 months, top leaves (apicalleaves) were clearly separated from basal ones. However, it shouldbe noted that no clear separation based on different plant agescould be observed from the PCA analysis, suggesting little influenceof plant age compared to leaf position. Loading plot analyses

-4000 -2000 0 2000 4000 6000 8000 10000

-2000

02000

4000

6000

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4

-0.6

-0.4

-0.2

0.0

0.2

705/5.9353/6.03367/6.12

593/7.35

463/7.68577/7.87461/7.9

515/7.95519/8.02

489/8.15

161/8.22

431/8.27285/9.92

809/10.32

327/10.88

941/11.28

329/11.42793/11.43

293/15.62295/16.43

Fig. 1. Principal component analyses of Cynara scolymus cultivars from American (Green Globe), France (Hyrious) & Egypt (Baladi) analyzed by UHPLC–MS (n = 3) harvested atdifferent age and leaf positions. The metabolome clusters are located at the distinct positions in two-dimensional space described by two vectors of principal component 1(PC1 = 68%) and principal component 2 (PC2 = 19%). (A) Score plot of PC1 vs. PC2 scores. (B) Loading plot for PC1 & PC2 with contributing mass peaks and selected mostdistinct assignments, with each metabolite denoted by its mass/Rt (min) pair. It should be noted that the ellipses do not denote statistical significance, but rather is for bettervisibility of the cluster as discussed. Gg_3 (j), GG_5b (�), GG_5t (N), GG_6b (�), GG_6t (d), GG_8b (j), GG_8t (s), FH_5b ( ), FH_3 ( ), FH_5b (�), FH_6b ( ), FH_8b ( ),FH_8t (5), EB_3 (s), EB_5b (4), EB_5t (+), EB_6b (X), EB_6t (}), (sample codes denotes: cultivar type_age/position).

70 A.S. El Senousy et al. / Phytochemistry 108 (2014) 67–76

(Fig. 3B) revealed that flavonoids and caffeic acid conjugates have amarked effect on leaf position sample discrimination. Top leaveswere more enriched in 1,5-di-O-caffeoylquinic acid (cynarin), lute-olin-7-O-glucoside (cynaroside) and luteolin-7-O-acetyl-glucoside,whereas basal ones contained more luteolin-7-O-rutinoside(scolymoside).

To confirm our hypothesis that top positioned leaves (youngleaves) accumulate more caffeic acid conjugates and flavonoidsthan old basal ones, young leaves of variety ‘‘Green Globe’’ weremodelled against old ones, using supervised O2PLS-DA. Thederived score plot shows a clear separation between both samples(Fig. 4A). The model explains 97% of the total variance (R2 = 0.97)with the prediction goodness parameter Q2 = 0.94. Potential bio-markers for discrimination, based on the effect of leaf position,were identified, using S-plot (Fig. 4B), confirming metaboliteresults derived from the PCA loading plot (Fig. 3B).

To confirm whether such a pattern is also observed among theother cultivars, FH and EB, leaves were modelled each separatelysimilarly, using O2PLS-DA model. Two new models were con-structed with EB and FH. The French Hyrious model shows oneorthogonal component with R2 = 0.71 and Q2 = 0.55, whereas forEgyptian Baladi R2 = 0.44 and Q2 = 0.29. The O2PLS-DA score plot(Supplementary Fig. S2) shows that FH leaves were also separatedaccording to their positions, regardless of the plant age, confirmingthe pattern observed in GG cultivars. The S-plot (SupplementaryFig. S2) reveals that younger leaves are more enriched in caffeicacid conjugates (chlorogenic acid and cynarin), whereas older onesare enriched in luteolin-7-O-rutinoside (scolymoside). This partly

confirms the hypothesis that younger top leaves accumulate morephenolics than old basal ones, albeit considerable inter-group met-abolomic differences are observed, based on the effect of leaf posi-tion. In contrast, no clear separation of EB leaves, could be observedbased on their position, in spite of their different morphologicalcharacters (i.e., size), see Supplementary Table S1. This suggeststhat contrary to other cultivars a leaf age and position related accu-mulation of phenolics is less pronounced in Egyptian Baladi culti-var. Nevertheless, and in terms of absolute quantification ofphenolic levels in relation to leaf age, the 3 months old artichokeleaves, derived from seedlings, mostly show the highest levels ofcaffeic acid conjugates and flavonoids among all examined culti-vars (Supplementary Fig. S3). High levels of phenolics in seedlings(and young soft leaves) are likely to function in leaf protectionagainst biotic and abiotic stresses, especially at this critical stageof artichoke development. Localization of caffeoylquinic acids inchlorenchyma cells, in the immature leaf blades of C. canephora,suggest their protective role against light damage (Mondolotet al., 2006). This active role was previously reported for hydroxy-cinnamate esters in tobacco and Arabidopsis leaves (Cerovic et al.,2002), as well as, for chlorogenic acid in Betula leaves (Tegelberget al., 2004). Chlorogenic acid also appeared to be involved in thedefense mechanism of plants against pests (Leiss et al., 2009;Szalma et al., 2005). The potent antioxidant and antimicrobialactivities of caffeoylquinic acids and flavonoids viz., cynarin, chlor-ogenic acid, cynaroside and scolymoside (Akroum et al., 2010;Mabeau et al., 2007; Wang et al., 2003; Zhu et al., 2004), rationalizetheir presence at high levels in seedlings. For the 3 months old

Fig. 2. O2PLS-DA (A) score plot and (B) loading S-plots obtained from UHPLC–MS analysis of leaves from cultivars: French (Hyrious, class 1, ) & Egyptian (Baladi, class 2, )modelled separately. The S-plot shows the covariance p[1] against the correlation p(cor)[1] of the variables of the discriminating component of the PLS-DA model. Cut-offvalues of P < 0.01 were used; variables selected are highlighted in the S-plot with m/z retention time in minutes and identifications are discussed in the text.

A.S. El Senousy et al. / Phytochemistry 108 (2014) 67–76 71

plants, cynarin showed 5-, 2- and 1.6-fold increase, compared tochlorogenic acid content in GG, FH and EB cultivars, respectively,a pattern not obvious at other investigated maturity stages. Thiscan be seen as support for the hypothesis of the protective roleof these phenolics in young plants, as cynarin (a diester) is a morepotent antioxidant than chlorogenic acid (a monoester) (Wanget al., 2003), coinciding with the fact that antioxidant activities ofphenolic compounds are largely determined by the number andpositioning of hydroxyl groups at the aromatic ring.

2.3. Quantification of metabolites among artichoke leaf samples

To confirm, that the discrimination between samples is mostlyinfluenced by variation in caffeoylquinic acids or flavonoid con-tents, absolute quantification was attempted for these metabolites.In agreement with PCA results, the highest concentration of 1,5-dicaffeoylquininc acid (62 mg/g) was found in the cultivar ‘‘GG’’which amounted to 6-fold and 17-fold amounts, compared to FH(9.7 mg/g) and EB (3.6 mg/g) cultivars, respectively, derived fromtop leaves of 6 months old plants. Similar results were obtainedfor cynaroside in GG (55 mg/g) which amounted to twice its con-centration vs. EB (30.4 mg/g) and three times that in FH(22.3 mg/g), as well as for luteolin-7-O-acetyl-glucoside (Supple-mentary Fig. S3). In contrast, for scolymoside highest levels were

detected in FH (49 mg/g) and EB (39 mg/g) cultivars, ca. 2–3 higherthan those for GG cultivar (17 mg/g) derived from basal, 6 monthsold leaves. This scolymoside accumulation pattern contrastingother phenolics has also been observed in other studies (de Beeret al., 2012). These showed that the cultivation effect on phenolicscomposition in Cyclopia subternata (Honeybush) using 64 seedlingsof the same age revealed that among monitored phenolics, scoly-moside was the most variable, suggesting that it might not func-tion as a predictable marker for examining cultivation effects onsecondary metabolite composition. An alternative source for scoly-moside production aside from the flavonoid biosynthetic pathwayis via the oxidation/degradation of eriocitrin (eriodictyol-7-O-ruti-noside), which might contribute to its levels in plants (Joubertet al., 2010). On the other hand, although chlorogenic acid was con-sidered as one of the markers for discrimination between GG andthe other two cultivars, as revealed from PCA results (Fig. 1), abso-lute quantification of this metabolite revealed higher concentra-tions in FH, ca. 1.3-fold those of GG, and 4-fold those of EB.Slight differences in the accumulation of mono- vs. di-caffeoylqui-nic acids in artichoke leaf suggest for a distinct acyl transferase,catalysing the attachment of second caffeoyl moiety, or that mono-and di- caffeoylquinic acids undergo different metabolic processesduring leaf ageing. In 1994 Aerts and Baumann reported a distinctdecrease in chlorogenic acid content, coinciding with an increase in

Fig. 3. Score plot (PC1 vs. PC2) of PCA results obtained from UHPLC–MS spectra of American Green Globe leaves collected from 3, 5, 6 and 8 months old plants. For samplescollected at 5, 6 and 8 months, top and basal leaves were harvested. (A) Score plot of PC1 vs. PC2 scores. (B) Loading plot for PC1 & PC2 with contributing mass peaks andselected most relevant assignments, with each metabolite denoted by its mass/Rt (min) pair. It should be noted that ellipses do not have statistical significance. Arrow in panel(A) points to the 5 months top old leaves not following the metabolites accumulation pattern observed in 6 and 8 months old top leaves.

72 A.S. El Senousy et al. / Phytochemistry 108 (2014) 67–76

the deposition of phenolic polymers in Coffea arabica cotyledonsduring development, suggesting that lignification may occur viathe utilization of the stored 5-O-caffeoylquinic acid. This hypothe-sis was further reinforced by the findings of Mondolot et al. (2006),in which chlorogenic acid was found to accumulate in the vascularsystem of older Coffea leaf tissues, confirming its involvement inthe lignification process in these tissues. This indicates that mono-and di-caffeoylquinic acids undergo different metabolic processesduring leaf ageing, the former being biosynthesized in the earlystage of organ development and stored, and the latter beingdegraded or exported as the organ matures.

In summary, among examined artichoke cultivars, GG wasfound the most enriched in caffeoylquinic acids and flavonoids(i.e., luteolin derivatives). Regarding leaf position effect, highestlevels of phenolics were found in top positioned leaves (youngleaves), compared to basal ones (older, mature leaves), and inaccordance with PCA, O2PLS-DA results and absolute quantifica-tion. Green Globe provided the best representative model in thatrespect, with high concentrations of caffeoylquinic acids and flavo-noids found in top positioned leaves than in basal ones, regardlessof the time of harvest (Fig. 5). Our results are in line with previousstudies on other species reporting on the decrease in phenoliccompounds levels with increasing age (Andreotti et al., 2006;Bhakta and Ganjewala, 2009; Mondolot et al., 2006). Comparedto basal leaves harvested from 6 months old artichoke plants, top

positioned ones showed 18-, 6-, 6-, and 8-fold increases in1,5-dicaffeoyl quinic acid, chlorogenic acid, cynaraoside, and lute-olin-7-O-acetyl-glucoside, respectively. An exceptional metaboliteis scolymoside which showed a 17-fold increase in basal leaves,compared to the top positioned ones (Supplementary Fig. S3).Additionally, top and basal leaves harvested from 5 months oldplants showed different pattern for metabolites, compared to thosecollected from 6 and 8 months old plants. It should be noted thatsaponins and fatty acids also exhibited variable accumulationpatterns among different leaf positions and different cultivars(Supplementary Fig. S4). However, such variation could not leadto a remarkable discrimination based on leaf position as observedin phenolics.

3. Conclusions

This study reveals a clear distinction between artichoke leavesderived from different cultivars, and within a cultivar betweenleaves of different maturation. The metabolomic approach usingUHPLC–PDA-ESI-MS data and multivariate data analysis can rap-idly reveal the major secondary metabolites that contribute tothe discrimination. These were caffeoylquinic acids and flavonoidsi.e., cynarin, chlorogenic acid, luteolin-7-O-acetyl-glucoside andscolymoside. Among examined cultivars, American ‘‘Green Globe’’was found to be most rich in the relevant metabolites known to

Fig. 4. O2PLS-DA (A) score plot and (B) loading S-plots derived from young (class 1, ) and mature leaf samples (class 2, ) of American Green Globe cultivar, modelledseparately. The S-plot shows the covariance p[1] against the correlation p(cor)[1] of the variables of the discriminating component of the PLS-DA model. Cut-off values ofP < 0.01 were used; variables selected are highlighted in the S-plot with m/z retention time in minutes and identifications are discussed in the text.

Fig. 5. Box plot showing cynaroside and 1,5-di-O-caffeoylquinic acid (cynarin)contents in ‘‘Green Globe’’ top and basal leaves, expressed as lg/g leaf dry wt. Thesesecondary metabolites were identified by quantitative UHPLC–MS and are respon-sible for the differentiation in PCA and OPLS analyses (line = mean; box = standarderror; whisker = standard deviation). For quantification of other metabolites amongall cultivars, see Supplementary Fig. S3.

A.S. El Senousy et al. / Phytochemistry 108 (2014) 67–76 73

mediate for artichoke broad therapeutic effects. Accordingly,among the selection studied, more focus should be directed onthe propagation of this cultivar to be used as raw material for arti-choke nutraceutical products and for the design of functionalfoods. To the best of our knowledge, this is the first report forthe effect of artichoke leaf position (leaf age) on the secondarymetabolite composition. Especially that caffeoylquinic acids andflavonoids patterns and content are dependent on leaf age ratherthan plant age. It remains to be examined whether such differentialmetabolite accumulation patterns among artichoke cultivars andleaf positions are due to precursor limitation i.e. aromatic aminoacids or more likely, to differences in regulation and enzymaticactivities. Probing enzymatic activity or gene expression levelscould provide a more conclusive understanding of metabolomicresults in artichoke. Although certified, official procedures includethe basal leaves (PharmaMed, 2004; British Herbal Pharmacopoeia,1996), our study reveals for the first time that harvesting youngleaves in seedlings, or top positioned leaves in older plants, presenta richer source of caffeoylquinic acids. Indeed, the use of top posi-tioned leaves of artichoke ought to be considered in the future asthe officially used part in nutraceuticals as the individual phenolicscontent of artichoke leaves is not only a function of the cultivar,but also of leaf age. Considering artichoke leaf potential healtheffects, it should be of interest to measure whether suchdifferences observed in phenolics composition in response to leaf

74 A.S. El Senousy et al. / Phytochemistry 108 (2014) 67–76

development (i.e. age) is reflected in terms of leaf anti-hepatotoxicor antioxidant effects.

4. Experimental

4.1. Plant material

Artichoke (C. scolymus) cultivars used in this study were Amer-ican cultivar ‘‘Green Globe’’, French cultivar ‘‘Hyrious’’ and Egyp-tian cultivar ‘‘Baladi’’; all grown side by side in the field of KahaResearch Station, Horticultural Research Institute, Kaha, Egypt.The three cultivars were identified by Dr. Fatma S. Elian and vou-cher specimens of American cultivar ‘‘Green Globe’’ (13-3-11A),French cultivar ‘‘Hyrious’’ (13-3-11B) and Egyptian cultivar ‘‘Bala-di’’ (13-3-11C) were deposited at the Herbarium of Faculty of Phar-macy, Cairo University, Egypt. Leaves were harvested from 3, 5, 6and 8 months old plants during the period of December 2012 tillAugust 2013. Leaf samples collected from 5, 6 and 8 months oldplants were separated into top and basal leaf samples. Threebiological replicates were measured for each sample to assess forbiological variance. All samples were collected at noon time. Thecollected material was kept at �80 �C until further analyzed.

4.2. Chemicals and reagents

Acetonitrile and formic acid (LCMS grade) were obtained fromJ.T. Baker (Deventer, The Netherlands), milliQ water was used forLC analysis. Luteolin-7-O-rutinoside, luteolin (P98%) and apigenin(P99%) were all purchased from Chromadex (Wesel, Germany).1,3-O-Dicaffeoylquinic acid and 1,5-O-dicaffeoylquinic acid werepurchased from Phytolab (Vestenbergsgreuth, Germany). umbellif-erone (P98%), chlorogenic acid (P95%) and linolenic acid (P97%)were bought from Sigma Aldrich (St. Louis, MO, U.S.A).

4.3. Extraction procedure and sample preparation of artichoke leafextracts

Freeze dried artichoke leaves were ground with a pestle in amortar using liquid nitrogen. The powder (20 mg) was thenhomogenized with 4 ml 60% MeOH containing 10 lg/ml umbellif-erone (as internal standard) using an ultrasonic bath for 30 min.Extracts were then vortexed and centrifuged at 10,000g for10 min to remove plant debris and filtered through 22 lm Milli-pore filter. Samples were analyzed in negative ionization mode.

4.4. UHPLC–PDA-MS analysis

Chromatographic separations were performed on an AcquityUHPLC system (Waters) equipped with a HSS T3 column(100 � 1.0 mm, particle size 1.8 lm; Waters) applying the follow-ing elution binary gradient at a flow rate of 150 lL min�1:0–1 min, isocratic 95% A (water/formic acid, 99.9/0.1 [v/v]), 5% B(acetonitrile/formic acid, 99.9/0.1 [v/v]); 1–16 min, linear from5% to 95% B; 16–18 min, isocratic 95% B; 18–20 min, isocratic 5%B. The injection volume was 3.1 lL (full loop injection). Elutedcompounds were detected from m/z 100 to 1000 using a LCQ DecaXP ion trap MS (ThermoElectron, San Jose, USA) equipped with anESI source (electrospray voltage 4.0 kV, sheath gas: nitrogen; cap-illary temperature: 275 �C) in negative ionization modes.

4.5. MS data processing for multivariate analysis

Relative quantification and comparison of artichoke metaboliteprofiles after UHPLC–MS was performed using XCMS data analysissoftware, which can be downloaded for free as an R package from

the Metlin Metabolite Database (http://137.131.20.83/download/)(Smith et al., 2006). This software approach employs peak align-ment, matching and comparison, as described in Farag andWessjohann (2012) and Farag et al. (2013). Orthogonal projectionsto latent structures-discriminant analysis (OPLS-DA) was per-formed with the program SIMCA-P Version 13.0 (Umetrics, Umeå,Sweden). Biomarkers for organs were subsequently identified byanalysing the S-plot, which was declared with covariance (p) andcorrelation (pcor). All variables were mean centred and scaled toPareto variance. The PCA was run for obtaining a general overviewof the variance of metabolites, and OPLS-DA was performed toobtain information on differences in the metabolite compositionamong samples. Distance to the model (DModX) test was used toverify the presence of outliers and to evaluate whether a submittedsample fell within the model applicability domain.

4.6. Identification and quantification of metabolites

UHPLC–MS files were converted to netcdf file format using theFile Converter tool in X-Calibur software and further processedusing AMDIS software to assist in adjacent peak deconvolutionand background subtraction (Halket et al., 1999). Metabolites werecharacterized by their UV–Vis spectra (220–600 nm), retentiontimes relative to external standards, mass spectra and comparisonto phytochemical dictionary of natural products database and ref-erence literature (Farag et al., 2013). Quantification of dicaffeoyl-quinic acids, luteolin conjugates and mono-caffeoylquinic acidswere calculated from the calibration curve of cynarin, luteolinand chlorogenic acid standards, respectively, detected using theMS detector. Standard calibration curves were constructed for eachstandard using three concentrations spanning from 0.1, 1, 10 and100 lg mL�1. Assays were carried out in triplicate. Recovery per-centages for flavonoids and caffeic acids were performed by stan-dard addition of chlorogenic acid, 1,5-O-dicaffeoylquinic acid andluteolin prior to the extraction of leaf samples. Recovery rates were93% for chlorogenic acid, 87% for 1,5-O-dicaffeoylquinic acid and79% for luteolin, respectively.

Acknowledgments

Dr. M.A. Farag thanks the Alexander von Humboldt Foundation,Germany, for financial support. We also thank Dr. Christoph Bött-cher for assistance with the UHPLC–MS. We are grateful to Dr. TiloLübken for providing R scripts for UHPLC–MS data analysis.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.phytochem.2014.09.004.

References

Adzet, T., Puigmacia, M., 1985. High-performance liquid chromatography ofcaffeoylquinic acid derivatives of Cynara scolymus L. leaves. J. Chromatogr. A348, 447–453.

Aerts, R.J., Baumann, T.W., 1994. Distribution and utilization of chlorogenic acid inCoffea seedlings. J. Exp. Bot. 45, 497–503.

Akroum, S., Bendjeddou, D., Satta, D., Lalaoui, K., 2010. Antibacterial, antioxidantand acute toxicity tests on flavonoids extracted from some medicinal plants. Int.J. Green Pharm. 4, 165–169.

Andreotti, C., Costa, G., Treutter, D., 2006. Composition of phenolic compounds inpear leaves as affected by genetics, ontogenesis and the environment. Sci.Hortic. 109, 130–137.

Bhakta, D., Ganjewala, D., 2009. Effect of leaf positions on total phenolics, flavonoidsand proanthocyanidins content and antioxidant activities in Lantana camara (L).J. Sci. Res. 1, 363–369.

British herbal pharmacopoeia. British Herbal Medicine Association, Exeter, 1996.

http://137.131.20.83/download/http://dx.doi.org/10.1016/j.phytochem.2014.09.004http://dx.doi.org/10.1016/j.phytochem.2014.09.004http://refhub.elsevier.com/S0031-9422(14)00367-7/h0005http://refhub.elsevier.com/S0031-9422(14)00367-7/h0005http://refhub.elsevier.com/S0031-9422(14)00367-7/h0005http://refhub.elsevier.com/S0031-9422(14)00367-7/h0010http://refhub.elsevier.com/S0031-9422(14)00367-7/h0010http://refhub.elsevier.com/S0031-9422(14)00367-7/h0015http://refhub.elsevier.com/S0031-9422(14)00367-7/h0015http://refhub.elsevier.com/S0031-9422(14)00367-7/h0015http://refhub.elsevier.com/S0031-9422(14)00367-7/h0020http://refhub.elsevier.com/S0031-9422(14)00367-7/h0020http://refhub.elsevier.com/S0031-9422(14)00367-7/h0020http://refhub.elsevier.com/S0031-9422(14)00367-7/h0025http://refhub.elsevier.com/S0031-9422(14)00367-7/h0025http://refhub.elsevier.com/S0031-9422(14)00367-7/h0025http://refhub.elsevier.com/S0031-9422(14)00367-7/h0030

A.S. El Senousy et al. / Phytochemistry 108 (2014) 67–76 75

Brown, P.D., Tokuhisa, J.G., Reichelt, M., Gershenzon, J., 2003. Variation ofglucosinolate accumulation among different organs and developmental stagesof Arabidopsis thaliana. Phytochemistry 62, 471–481.

Cerovic, Z.G., Ounis, A., Cartelat, A., Latouche, G., Goulas, Y., Meyer, S., Moya, I., 2002.The use of chlorophyll fluorescence excitation spectra for the non-destructivein situ assessment of UV-absorbing compounds in leaves. Plant, Cell Environ. 25,1663–1676.

Coinu, R., Stefania, C., Urgeghe, P.P., Mulinacci, N., Pinelli, P., Franconi, F., Romani, A.,2007. Dose-effect study on the antioxidant properties of leaves and outer bractsof extracts obtained from Violetto di Toscana artichoke. Food Chem. 101, 524–531.

de Beer, D., Schulze, A.E., Joubert, E., de Villiers, A., Malherbe, C.J., Stander, M.A.,2012. Food ingredient extracts of Cyclopia subternata (Honeybush): variation inphenolic composition and antioxidant capacity. Molecules 17, 14602–14624.

Ding, C.K., Chachin, K., Ueda, Y., Imahori, Y., Wang, C.Y., 2001. Metabolism ofphenolic compounds during Loquat fruit development. J. Agric. Food Chem. 49,2883–2888.

Emendorfer, F., Bellato, F., Noldin, V.F., Cechinel-Filho, V., Yunes, R.A., Monache, F.D.,Cardozo, A.M., 2005. Antispasmodic activity of fractions and cynaropicrin fromCynara scolymus on guinea-pig ileum. Biol. Pharm. Bull. 28, 902–904.

Fait, A., Hanhineva, K., Beleggia, R., Dai, N., Rogachev, I., Nikiforova, V.F., Fernie, A.R.,Aharoni, A., 2008. Reconfiguration of the achene and receptacle metabolicnetworks during strawberry fruit development. Plant Physiol. 148, 730–750.

Farag, M.A., 2014. Comparative mass spectrometry & nuclear magnetic resonancemetabolomic approaches for nutraceuticals quality control analysis: a briefreview. Recent Pat. Biotechnol. 8 (1), 17–24.

Farag, M.A., El-Ahmady, S., Alian, F., Wessjohann, L.A., 2013. Metabolomics drivenanalysis of artichoke leaf and its commercial products via UHPLC–q-TOF-MS.Phytochemistry 95, 177–187.

Farag, M.A., Wessjohann, L.A., 2012. Metabolome classification of commercialHypericum perforatum (St. John’s Wort) preparations via UPLC-qTOF-MS andchemometrics. Planta Med. 78, 488–496.

Fratianni, F., Tucci, M., Palma, M.D., Pepe, R., Nazzaro, F., 2007. Polyphenoliccomposition in different parts of some cultivars of globe artichoke (Cynaracardunculus L. var. scolymus (L.) Fiori). Food Chem. 104, 1282–1286.

Gebhardt, R., Fausel, M., 1997. Antioxidant and hepatoprotective effects of artichokeextracts and constituents in cultured rat hepatocytes. Toxicol. In Vitro 11, 669–672.

Gebhardt, R., 1998. Inhibition of cholesterol biosynthesis in primary cultured rathepatocytes by artichoke (Cynara Scolymus L.) extracts. J. Pharmacol. Exp. Ther.286, 1122–1128.

Gebhardt, R., 2001. Anticholestatic activity of flavonoids from artichoke (Cynarascolymus L.) and of their metabolites. Med. Sci. Monit. Suppl. 1, 316–320.

Gebhardt, R., 2002. Inhibition of cholesterol biosynthesis in HepG2 cells byartichoke extracts is reinforced by glucosidase pretreatment. Phytother. Res.16, 368–372.

Gebhardt, R., 2005. Choleretic and anti cholestatic activities of flavonoids ofartichoke (Cynara cardunculus L. subsp. Scolymus (L.) Hayek). Acta Hortic. 681,429–435.

Goodacre, R., Shann, B., Gilbert, R.J., Timmins, E.M., McGovern, A.C., Alsberg, B.K.,Kell, D.B., Logan, N.A., 2000. Detection of the dipicolinic acid biomarker inBacillus spores using Curie-point pyrolysis mass spectrometry and Fouriertransform infrared spectroscopy. Anal. Chem. 72, 119–127.

Halket, J.M., Pryzborowska, A., Stein, S.E., Mallard, W.G., Down, S., Chalmers, R.A.,1999. Deconvolution gas chromatography/mass spectrometry of urinaryorganic acids-potential for pattern recognition and automated identificationof metabolic disorders. Rapid Commun Mass Spectrom. 13, 279–284.

Hausler, M., Ganzera, M., Abel, G., Popp, M., Stuppner, H., 2002. Determination ofcaffeoylquinic acids and flavonoids in Cynara scolymus L. by high performanceliquid chromatography. Chromatographia 56, 407–411.

Joubert, E., Manley, M., Maicu, C., de Beer, D., 2010. Effect of pre-drying treatmentsand storage on color and phenolic composition of green honeybush (Cyclopiasubternata) herbal tea. J. Agric. Food Chem. 58, 388–444.

Kirchhoff, R., Beckers, C., Kirchhoff, G.M., Trinczek-Gärtner, H., Petrowicz, O.,Reimann, H.J., 1994. Increase in choleresis by means of artichoke extract.Phytomedicine 1, 107–115.

Koupai-Abyazani, M.R., Mccallum, J., Muir, A.D., Bohm, B.A., Towers, G.H.N., Gruber,M.Y., 1993. Developmental changes in the composition of proanthocyanidinsfrom leaves of Sainfoin (Onobrychis viciifolia Scop.) as determined by HPLCanalysis. J. Agric. Food Chem. 41, 1066–1070.

Krumbein, A., Saeger-Fink, H., Schonhof, I., 2007. Changes in quercetin andkaempferol concentrations during broccoli head ontogeny in three broccolicultivars. J. Appl. Bot. Food Qual. 81, 136–139.

Lattanzio, V., Cicco, N., Linsalata, V., 2005. Antioxidant activities of artichokephenolics. Acta Hortic. 681, 421–427.

Lattanzio, V., Kroon, P.A., Linsalata, V., Cardinali, A., 2009. Globe artichoke: afunctional food and source of nutraceutical ingredients. J. Funct. Foods, 131–144.

Leiss, K.A., Maltese, F., Choi, Y.H., Verpoorte, R., Klinkhamer, P.G.L., 2009.Identification of chlorogenic acid as a resistance factor for thrips inchrysanthemum. Plant Physiol. 150, 1567–1575.

Lombardo, S., Pandino, G., Mauromicale, G., Knodler, M., Carle, R., Schieber, A., 2010.Influence of genotype, harvest time and plant part on polyphenolic compositionof globe artichoke [Cynara cardunculus L. var. scolymus (L.) Fiori]. Food Chem.119, 1175–1181.

Lombardo, V.A., Osorio, S., Borsani, J., Lauxmann, M.A., Bustamante, C.A., Budde, C.O.,Andreo, C.S., Lara, M.V., Fernie, A.R., Drincovich, M.F., 2011. Metabolic profilingduring peach fruit development and ripening reveals the metabolic networksthat underpin each developmental stage. Plant Physiol. 157, 1696–1710.

Mabeau, S., Baty-Julien, C., Helias, A.B., Chodosas, O., Surbled, M., Metra, P., Durand,D., Morice, G., Chesne, C., Mekideche, K., 2007. Antioxidant activity of artichokeextracts and by-products. Acta Hortic. 630, 491–496.

Marín, A., Ferreres, F., Tomás-Barberán, F.A., Gil, M.I., 2004. Characterization andquantitation of antioxidant constituents of sweet pepper (Capsicum annuum L.).J. Agric. Food Chem. 52, 3861–3869.

Matuschowski, P., Nahrstedt, A., Winterhoff, H., 2005. Pharmacologicalinvestigations on the effect of fresh juice from Cynara scolymus on cholereticeffects. Zeitschrift-fur-Phytotherapie 26, 14–19.

Mayr, U., Treutter, D., Santos-Buelga, C., Bauer, H., Feucht, W., 1995. Developmentalchanges in the phenol concentrations of ‘golden delicious’ apple fruits andleaves. Phytochemistry 38, 1151–1155.

Mehmetcik, G., Ozdemirler, G., Koçak-Toker, N., Cevikbas�, U., Uysal, M., 2008. Effectof pretreatment with artichoke extract on carbon tetrachloride-induced liverinjury and oxidative stress. Exp. Toxicol. Pathol. 60, 475–480.

Mondolot, L., La Fisca, P., Buatois, B., Talansier, E., De Kochko, A., Campa, C., 2006.Evolution in caffeoylquinic acid content and histolocalization during Coffeacanephora leaf development. Ann. Bot. 98, 33–40.

Mossi, A.J., Echeverrigaray, S., 1999. Identification and characterization ofantimicrobial components in leaf extracts of globe artichoke Cynara scolymusL.. Acta Hortic. 501, 111–114.

Noldin, V.F., Cechinel Filho, V., Monache, F.D., Benassi, J.C., Christmann, I.L., Pedrosa,R.C., Yunes, R.A., 2003. Chemical composition and biological activities of theleaves of Cynara scolymus L. (artichoke) cultivated in Brazil. Quim. Nova 26,331–334.

Palama, T.L., Fock, I., Choi, Y.H., Verpoorte, R., Kodja, H., 2010. Biological variation ofVanilla planifolia leaf metabolome. Phytochemistry 71, 567–573.

Pandino, G., Lombardo, S., Mauromicale, G., Williamson, G., 2011. Phenolic acids andflavonoids in leaf and floral stem of cultivated and wild Cynara cardunculus L.genotypes. Food Chem. 126, 417–422.

PharmaMed: Aufbereitungsmonographien (Kommission E CD-ROM). DeutscherApotheker Verlag, Stuttgart, 2004.

Raccuia, S.A., Melilli, M.G., 2004. Cynara cardunculus L., a potential source of inulinin the Mediterranean environment: screening of genetic variability. Aust. J.Agric. Res. 55, 693–698.

Roessner-Tunali, U., Hegemann, A., Lytovchenko, F., Carrari, C., Bruedigam, D.,Granot, D., Fernie, A.R., 2003. Metabolic profiling of transgenic tomato plantsoverexpressing hexokinase reveals that the influence of hexosephosphorylation diminishes during fruit development. Plant Physiol. 133, 84–99.

Romani, A., Pinelli, P., Cantini, C., Cimato, A., Heimler, D., 2006. Characterization ofVioletto di Toscana, a typical Italian variety of artichoke (Cynara scolymus L.).Food Chem. 95, 221–225.

Saenz Rodriguez, T., Garcia Gimenez, D., de la Puerta Vazquez, R., 2002. Cholereticactivity and biliary elimination of lipids and bile acids induced by an artichokeleaf extract in rats. Phytomedicine 9, 687–693.

Schutz, K., Muks, E., Carle, R., Schieber, A., 2006a. Quantitative determination ofphenolic compounds in artichoke-based dietary supplements andpharmaceuticals by high-performance liquid chromatography. J. Agric. FoodChem. 54, 8812–8817.

Schutz, K., Persike, M., Carle, R., Schieber, A., 2006b. Characterization andquantification of anthocyanins in selected artichoke (Cynara scolymus L.)cultivars by HPLC–DAD–ESI-MSn. Anal. Bioanal. Chem. 384, 1511–1517.

Schutz, K., Kammerer, D., Carle, R., Schieber, A., 2004. Identification andquantification of caffeoylquinic acids and flavonoids from artichoke (Cynarascolymus L.) heads, juice, and pomace by HPLC–DAD-ESI/MSn. J. Agric. FoodChem. 52, 4090–4096.

Shimoda, H., Ninomiya, K., Nishida, N., Yoshino, T., Morikawa, T., Matsuda, H.,Yoshikawa, M., 2003. Anti-hyperlipidemic sesquiterpenes and newsesquiterpene glycosides from the leaves of artichoke (Cynara scolymus L.):structure requirement and mode of action. Bioorg. Med. Chem. Lett. 13, 223–228.

Smith, C.A., Want, E.J., O’Maille, G., Abagyan, R., Siuzdak, G., 2006. XCMS: processingmass spectrometry data for metabolite profiling using nonlinear peakalignment, matching, and identification. Anal. Chem. 78, 779–787.

Szalma, S.J., Buckler, E.S., Snook, M.E., McMullen, M.D., 2005. Association analysis ofcandidate genes for maysin and chlorogenic acid accumulation in maize silks.Theor. Appl. Genet. 110, 1324–1333.

Tegelberg, R., Julkunen-Tiitto, R., Aphalo, P.J., 2004. Red:far-red light ratio and UV-Bradiation: their effects on leaf phenolics and growth of silver birch seedlings.Plant, Cell Environ. 27, 1005–1013.

Trouillas, P., Calliste, C.A., Allais, D.P., Simon, A., Marfak, A., Delage, C., Duroux, J.-L.,2003. Antioxidant, anti-inflammatory and antiproliferative properties of sixteenwater plant extracts used in Limousin countryside as herbal teas. Food Chem.80, 399–407.

Voirin, B., Bayet, C., 1992. Developmental variations in leaf flavonoid aglycones ofMentha x piperita. Phytochemistry 31, 2299–2304.

Wang, M., Simon, J.E., Aviles, I.F., He, K., Zheng, Q.-Y., Tadmor, Y., 2003. Analysis ofantioxidative phenolic compounds in artichoke (Cynara scolymus L.). J. Agric.Food Chem. 51, 601–608.

Worley, B., Powers, R., 2013. Multivariate analysis in metabolomics. Curr.Metabolomics 1, 92–107.

http://refhub.elsevier.com/S0031-9422(14)00367-7/h0035http://refhub.elsevier.com/S0031-9422(14)00367-7/h0035http://refhub.elsevier.com/S0031-9422(14)00367-7/h0035http://refhub.elsevier.com/S0031-9422(14)00367-7/h0060http://refhub.elsevier.com/S0031-9422(14)00367-7/h0060http://refhub.elsevier.com/S0031-9422(14)00367-7/h0060http://refhub.elsevier.com/S0031-9422(14)00367-7/h0060http://refhub.elsevier.com/S0031-9422(14)00367-7/h0065http://refhub.elsevier.com/S0031-9422(14)00367-7/h0065http://refhub.elsevier.com/S0031-9422(14)00367-7/h0065http://refhub.elsevier.com/S0031-9422(14)00367-7/h0065http://refhub.elsevier.com/S0031-9422(14)00367-7/h0070http://refhub.elsevier.com/S0031-9422(14)00367-7/h0070http://refhub.elsevier.com/S0031-9422(14)00367-7/h0070http://refhub.elsevier.com/S0031-9422(14)00367-7/h0075http://refhub.elsevier.com/S0031-9422(14)00367-7/h0075http://refhub.elsevier.com/S0031-9422(14)00367-7/h0075http://refhub.elsevier.com/S0031-9422(14)00367-7/h0080http://refhub.elsevier.com/S0031-9422(14)00367-7/h0080http://refhub.elsevier.com/S0031-9422(14)00367-7/h0080http://refhub.elsevier.com/S0031-9422(14)00367-7/h0085http://refhub.elsevier.com/S0031-9422(14)00367-7/h0085http://refhub.elsevier.com/S0031-9422(14)00367-7/h0085http://refhub.elsevier.com/S0031-9422(14)00367-7/h0090http://refhub.elsevier.com/S0031-9422(14)00367-7/h0090http://refhub.elsevier.com/S0031-9422(14)00367-7/h0090http://refhub.elsevier.com/S0031-9422(14)00367-7/h0095http://refhub.elsevier.com/S0031-9422(14)00367-7/h0095http://refhub.elsevier.com/S0031-9422(14)00367-7/h0095http://refhub.elsevier.com/S0031-9422(14)00367-7/h0320http://refhub.elsevier.com/S0031-9422(14)00367-7/h0320http://refhub.elsevier.com/S0031-9422(14)00367-7/h0320http://refhub.elsevier.com/S0031-9422(14)00367-7/h0100http://refhub.elsevier.com/S0031-9422(14)00367-7/h0100http://refhub.elsevier.com/S0031-9422(14)00367-7/h0100http://refhub.elsevier.com/S0031-9422(14)00367-7/h0325http://refhub.elsevier.com/S0031-9422(14)00367-7/h0325http://refhub.elsevier.com/S0031-9422(14)00367-7/h0325http://refhub.elsevier.com/S0031-9422(14)00367-7/h0105http://refhub.elsevier.com/S0031-9422(14)00367-7/h0105http://refhub.elsevier.com/S0031-9422(14)00367-7/h0105http://refhub.elsevier.com/S0031-9422(14)00367-7/h0110http://refhub.elsevier.com/S0031-9422(14)00367-7/h0110http://refhub.elsevier.com/S0031-9422(14)00367-7/h0115http://refhub.elsevier.com/S0031-9422(14)00367-7/h0115http://refhub.elsevier.com/S0031-9422(14)00367-7/h0115http://refhub.elsevier.com/S0031-9422(14)00367-7/h0120http://refhub.elsevier.com/S0031-9422(14)00367-7/h0120http://refhub.elsevier.com/S0031-9422(14)00367-7/h0120http://refhub.elsevier.com/S0031-9422(14)00367-7/h0125http://refhub.elsevier.com/S0031-9422(14)00367-7/h0125http://refhub.elsevier.com/S0031-9422(14)00367-7/h0125http://refhub.elsevier.com/S0031-9422(14)00367-7/h0125http://refhub.elsevier.com/S0031-9422(14)00367-7/h0330http://refhub.elsevier.com/S0031-9422(14)00367-7/h0330http://refhub.elsevier.com/S0031-9422(14)00367-7/h0330http://refhub.elsevier.com/S0031-9422(14)00367-7/h0330http://refhub.elsevier.com/S0031-9422(14)00367-7/h0130http://refhub.elsevier.com/S0031-9422(14)00367-7/h0130http://refhub.elsevier.com/S0031-9422(14)00367-7/h0130http://refhub.elsevier.com/S0031-9422(14)00367-7/h0135http://refhub.elsevier.com/S0031-9422(14)00367-7/h0135http://refhub.elsevier.com/S0031-9422(14)00367-7/h0135http://refhub.elsevier.com/S0031-9422(14)00367-7/h0140http://refhub.elsevier.com/S0031-9422(14)00367-7/h0140http://refhub.elsevier.com/S0031-9422(14)00367-7/h0140http://refhub.elsevier.com/S0031-9422(14)00367-7/h0145http://refhub.elsevier.com/S0031-9422(14)00367-7/h0145http://refhub.elsevier.com/S0031-9422(14)00367-7/h0145http://refhub.elsevier.com/S0031-9422(14)00367-7/h0145http://refhub.elsevier.com/S0031-9422(14)00367-7/h0150http://refhub.elsevier.com/S0031-9422(14)00367-7/h0150http://refhub.elsevier.com/S0031-9422(14)00367-7/h0150http://refhub.elsevier.com/S0031-9422(14)00367-7/h0155http://refhub.elsevier.com/S0031-9422(14)00367-7/h0155http://refhub.elsevier.com/S0031-9422(14)00367-7/h0160http://refhub.elsevier.com/S0031-9422(14)00367-7/h0160http://refhub.elsevier.com/S0031-9422(14)00367-7/h0160http://refhub.elsevier.com/S0031-9422(14)00367-7/h0165http://refhub.elsevier.com/S0031-9422(14)00367-7/h0165http://refhub.elsevier.com/S0031-9422(14)00367-7/h0165http://refhub.elsevier.com/S0031-9422(14)00367-7/h0170http://refhub.elsevier.com/S0031-9422(14)00367-7/h0170http://refhub.elsevier.com/S0031-9422(14)00367-7/h0170http://refhub.elsevier.com/S0031-9422(14)00367-7/h0170http://refhub.elsevier.com/S0031-9422(14)00367-7/h0175http://refhub.elsevier.com/S0031-9422(14)00367-7/h0175http://refhub.elsevier.com/S0031-9422(14)00367-7/h0175http://refhub.elsevier.com/S0031-9422(14)00367-7/h0175http://refhub.elsevier.com/S0031-9422(14)00367-7/h0180http://refhub.elsevier.com/S0031-9422(14)00367-7/h0180http://refhub.elsevier.com/S0031-9422(14)00367-7/h0180http://refhub.elsevier.com/S0031-9422(14)00367-7/h0185http://refhub.elsevier.com/S0031-9422(14)00367-7/h0185http://refhub.elsevier.com/S0031-9422(14)00367-7/h0185http://refhub.elsevier.com/S0031-9422(14)00367-7/h0190http://refhub.elsevier.com/S0031-9422(14)00367-7/h0190http://refhub.elsevier.com/S0031-9422(14)00367-7/h0190http://refhub.elsevier.com/S0031-9422(14)00367-7/h0195http://refhub.elsevier.com/S0031-9422(14)00367-7/h0195http://refhub.elsevier.com/S0031-9422(14)00367-7/h0195http://refhub.elsevier.com/S0031-9422(14)00367-7/h0200http://refhub.elsevier.com/S0031-9422(14)00367-7/h0200http://refhub.elsevier.com/S0031-9422(14)00367-7/h0200http://refhub.elsevier.com/S0031-9422(14)00367-7/h0200http://refhub.elsevier.com/S0031-9422(14)00367-7/h0205http://refhub.elsevier.com/S0031-9422(14)00367-7/h0205http://refhub.elsevier.com/S0031-9422(14)00367-7/h0205http://refhub.elsevier.com/S0031-9422(14)00367-7/h0210http://refhub.elsevier.com/S0031-9422(14)00367-7/h0210http://refhub.elsevier.com/S0031-9422(14)00367-7/h0210http://refhub.elsevier.com/S0031-9422(14)00367-7/h0215http://refhub.elsevier.com/S0031-9422(14)00367-7/h0215http://refhub.elsevier.com/S0031-9422(14)00367-7/h0215http://refhub.elsevier.com/S0031-9422(14)00367-7/h0215http://refhub.elsevier.com/S0031-9422(14)00367-7/h0220http://refhub.elsevier.com/S0031-9422(14)00367-7/h0220http://refhub.elsevier.com/S0031-9422(14)00367-7/h0225http://refhub.elsevier.com/S0031-9422(14)00367-7/h0225http://refhub.elsevier.com/S0031-9422(14)00367-7/h0225http://refhub.elsevier.com/S0031-9422(14)00367-7/h0235http://refhub.elsevier.com/S0031-9422(14)00367-7/h0235http://refhub.elsevier.com/S0031-9422(14)00367-7/h0235http://refhub.elsevier.com/S0031-9422(14)00367-7/h0240http://refhub.elsevier.com/S0031-9422(14)00367-7/h0240http://refhub.elsevier.com/S0031-9422(14)00367-7/h0240http://refhub.elsevier.com/S0031-9422(14)00367-7/h0240http://refhub.elsevier.com/S0031-9422(14)00367-7/h0240http://refhub.elsevier.com/S0031-9422(14)00367-7/h0245http://refhub.elsevier.com/S0031-9422(14)00367-7/h0245http://refhub.elsevier.com/S0031-9422(14)00367-7/h0245http://refhub.elsevier.com/S0031-9422(14)00367-7/h0250http://refhub.elsevier.com/S0031-9422(14)00367-7/h0250http://refhub.elsevier.com/S0031-9422(14)00367-7/h0250http://refhub.elsevier.com/S0031-9422(14)00367-7/h0255http://refhub.elsevier.com/S0031-9422(14)00367-7/h0255http://refhub.elsevier.com/S0031-9422(14)00367-7/h0255http://refhub.elsevier.com/S0031-9422(14)00367-7/h0255http://refhub.elsevier.com/S0031-9422(14)00367-7/h0260http://refhub.elsevier.com/S0031-9422(14)00367-7/h0260http://refhub.elsevier.com/S0031-9422(14)00367-7/h0260http://refhub.elsevier.com/S0031-9422(14)00367-7/h0260http://refhub.elsevier.com/S0031-9422(14)00367-7/h0265http://refhub.elsevier.com/S0031-9422(14)00367-7/h0265http://refhub.elsevier.com/S0031-9422(14)00367-7/h0265http://refhub.elsevier.com/S0031-9422(14)00367-7/h0265http://refhub.elsevier.com/S0031-9422(14)00367-7/h0265http://refhub.elsevier.com/S0031-9422(14)00367-7/h0270http://refhub.elsevier.com/S0031-9422(14)00367-7/h0270http://refhub.elsevier.com/S0031-9422(14)00367-7/h0270http://refhub.elsevier.com/S0031-9422(14)00367-7/h0270http://refhub.elsevier.com/S0031-9422(14)00367-7/h0270http://refhub.elsevier.com/S0031-9422(14)00367-7/h0335http://refhub.elsevier.com/S0031-9422(14)00367-7/h0335http://refhub.elsevier.com/S0031-9422(14)00367-7/h0335http://refhub.elsevier.com/S0031-9422(14)00367-7/h0275http://refhub.elsevier.com/S0031-9422(14)00367-7/h0275http://refhub.elsevier.com/S0031-9422(14)00367-7/h0275http://refhub.elsevier.com/S0031-9422(14)00367-7/h0280http://refhub.elsevier.com/S0031-9422(14)00367-7/h0280http://refhub.elsevier.com/S0031-9422(14)00367-7/h0280http://refhub.elsevier.com/S0031-9422(14)00367-7/h0285http://refhub.elsevier.com/S0031-9422(14)00367-7/h0285http://refhub.elsevier.com/S0031-9422(14)00367-7/h0285http://refhub.elsevier.com/S0031-9422(14)00367-7/h0285http://refhub.elsevier.com/S0031-9422(14)00367-7/h0290http://refhub.elsevier.com/S0031-9422(14)00367-7/h0290http://refhub.elsevier.com/S0031-9422(14)00367-7/h0295http://refhub.elsevier.com/S0031-9422(14)00367-7/h0295http://refhub.elsevier.com/S0031-9422(14)00367-7/h0295http://refhub.elsevier.com/S0031-9422(14)00367-7/h0300http://refhub.elsevier.com/S0031-9422(14)00367-7/h0300

76 A.S. El Senousy et al. / Phytochemistry 108 (2014) 67–76

Xiang, Z., Wang, X.Q., Cai, X.J., Zeng, S., 2011. Metabolomics study on quality controland discrimination of three curcuma species based on gas chromatograph–massspectrometry. Phytochem. Anal. 22, 411–418.

Zamboni, A., Carli, M.D., Guzzo, F., Stocchero, M., Zenoni, S., Ferrarini, A., Tononi, P.,Toffali, K., Desiderio, A., Lilley, K.S., Pè, M.E., Benvenuto, E., 2010. Identification

of putative stage-specific grapevine berry biomarkers and omics dataintegration into networks. Plant Physiol. 154, 1439–1459.

Zhu, X., Zhang, H., Lo, R., 2004. Phenolic compounds from the leaf extract ofartichoke (Cynara scolymus L.) and their antimicrobial activities. J. Agric. FoodChem. 52, 7272–7278.

http://refhub.elsevier.com/S0031-9422(14)00367-7/h0305http://refhub.elsevier.com/S0031-9422(14)00367-7/h0305http://refhub.elsevier.com/S0031-9422(14)00367-7/h0305http://refhub.elsevier.com/S0031-9422(14)00367-7/h0310http://refhub.elsevier.com/S0031-9422(14)00367-7/h0310http://refhub.elsevier.com/S0031-9422(14)00367-7/h0310http://refhub.elsevier.com/S0031-9422(14)00367-7/h0310http://refhub.elsevier.com/S0031-9422(14)00367-7/h0315http://refhub.elsevier.com/S0031-9422(14)00367-7/h0315http://refhub.elsevier.com/S0031-9422(14)00367-7/h0315

Developmental changes in leaf phenolics composition from three artichoke cvs. (Cynara scolymus) as determined via UHPLC–MS and chemometrics1 Introduction2 Results and discussion2.1 Metabolite profiling of artichoke leaves using UHPLC–PDA-MS2.2 Multivariate PCA and O2PLS-DA analyses of UHPLC–MS data2.3 Quantification of metabolites among artichoke leaf samples

3 Conclusions4 Experimental4.1 Plant material4.2 Chemicals and reagents4.3 Extraction procedure and sample preparation of artichoke leaf extracts4.4 UHPLC–PDA-MS analysis4.5 MS data processing for multivariate analysis4.6 Identification and quantification of metabolites

AcknowledgmentsAppendix A Supplementary dataReferences