ORIGINAL ARTICLE

Seasonal changes in the metabolic fingerprint of Juniperuscommunis L. berry extracts by 1H NMR-based metabolomics

Antonio Falasca • Dominique Melck •

Debora Paris • Gabriella Saviano • Andrea Motta •

Maria Iorizzi

Received: 29 April 2013 / Accepted: 3 July 2013 / Published online: 12 July 2013

� Springer Science+Business Media New York 2013

Abstract The economically important crop Juniperus

communis (Cupressaceae) is a coniferous evergreen shrub

or tree distributed throughout the temperate zone of the

Northern hemisphere. Its dried bluish-black cones, known

as ‘‘juniper berries’’, are used as flavoring agent in food-

stuffs and spirits, while their extracts are widely employed

in medicine for their antimicrobial, antifungal, and anti-

cancerogenous activity. Despite their medicinal and com-

mercial values, no systematic studies on the metabolome of

J. communis berries have been carried out so far. We have

investigated the metabolic profiles of J. communis berries

by NMR spectroscopy and statistical analysis, aiming at

monitoring their seasonal variation. Principal components

analysis and partial least square discriminant analysis of

proton NMR spectra provided a clear class separation

according to the ripeness and the harvest season, with well-

defined metabolic profiles for each class. The results show

that the Juniperus metabolome is dominated by 26

metabolites including sugars, amino acids, organic acids

and diterpenes. The specificity of NMR profiles offers a

powerful tool for assessing the molecular determinants

characterizing the quality of this commercially important

crop.

Keywords Juniperus communis L. � NMR-based

metabolomics � Principal component analysis � Plant

secondary metabolites � Labdane derivatives

Abbreviations

1D One-dimensional

2D Two-dimensional

ASL Above sea level

HSQC Heteronuclear single quantum coherence

spectroscopy

NMR Nuclear magnetic resonance

PCA Principal component analysis

PLS-DA Partial least squares discriminant analysis

TOCSY Total correlation spectroscopy

1 Introduction

The genus Juniperus (Cupressaceae) comprises ca. 70

species principally growing in the Northern hemisphere.

Juniperus wood, essential oil and cones (false fruits,

improperly called berries) have been largely used in tra-

ditional medicine, and dried ripe berries of Juniperus

communis are registered in the European Pharmacopoeia

(European Pharmacopoeia 2008). Among the genus, J.

communis L. is an evergreen shrub or tree growing from

the mountains to the coastal areas in scrubs, pastures and

cliffs. J. communis flowers gather in axillary ament, and its

cones, ripening the 2nd or 3rd year, are green the 1st year,

then bluish-black or sometimes purplish-red when fully

Electronic supplementary material The online version of thisarticle (doi:10.1007/s11306-013-0566-1) contains supplementarymaterial, which is available to authorized users.

A. Falasca � G. Saviano � M. Iorizzi (&)

Dipartimento di Bioscienze e Territorio, Universita degli Studi

del Molise, Contrada Fonte Lappone, 86090 Pesche, Isernia,

Italy

e-mail: iorizzi@unimol.it

D. Melck � D. Paris � A. Motta (&)

Istituto di Chimica Biomolecolare del CNR, Comprensorio

Olivetti, Edificio A, Via Campi Flegrei 34, 80078 Pozzuoli,

Napoli, Italy

e-mail: andrea.motta@icb.cnr.it

123

Metabolomics (2014) 10:165–174

DOI 10.1007/s11306-013-0566-1

ripe (Miceli et al. 2009). Berries have a fragrant, spicy

aroma and a slightly bittersweet flavor. Mature, dark ber-

ries are used in cuisine to season sauces and stuffing, and in

pickling meats (Leung and Foster 1996), are also used to

flavor spirits like gin or grappa (Vichi et al. 2005).

According to the International Trade Centre UNCTAD/

WTO, in 2007, the harvested quantities around the world

added up to ca. 1,000 t [International Trade Centre UNC-

TAD/WTO 2007].

The composition of Juniper extracts is influenced by

several factors, such as growth site, plant age, bushes form,

and berries ripeness. Berries’ methanol extract contains

many secondary metabolites, namely monoterpene and

megastigmane glycosides (Nakanishi et al. 2005), labdane

diterpenes (San Feliciano et al. 1991; Martin et al. 2006),

and flavonoids and biflavonoids (Innocenti et al. 2007;

Miceli et al. 2009). Aromatic volatile components, which

are fundamental for gin characteristics whose classical

‘‘juniper’’ taste comes from Juniperus berries, have been

obtained by investigating different dry gins (Vichi et al.

2005).

Most studies focus on berry’s essential oil chemical

composition (Angioni et al. 2003; Filipowicz et al. 2003;

Barjaktarovic et al. 2005), which is used in the meat

industry as well as in the pharmaceutical industry to pro-

duce inhalators, disinfectants, fumigants etc. A number of

biological activities for the essential oil has been reported,

including antinflammatory, hypoglycaemic (Sanchez de

Medina et al. 1994), antioxidant (Miceli et al. 2009),

antimicrobial (Angioni et al. 2003), antifungal (Filipowicz

et al. 2003), and as diuretic remedy and carminative agent

(Stanic et al. 1998). Recently, the labdane derivative im-

bricatolic acid has been isolated from the methanolic

extract of fresh ripe berries, and was shown to prevent cell

cycle progression in p53-null CaLu-6 cells (De Marino

et al. 2011).

Currently, no systematic studies on the metabolome of

J. communis berries have been reported, despite their

medicinal and commercial values. In fact, the above

investigations center on selected metabolites without a

systematic evaluation of the environmental effects on

metabolite production, which is extremely important to

understand plant metabolism and for quality control. Dur-

ing recent years, high-resolution NMR spectroscopy and

multivariate data analysis have been widely used for a

holistic view of the chemical composition of plant extracts.

In the present study, we applied NMR-based metabolomics

to investigate the seasonal variation of metabolic profiles in

J. communis berries. We demonstrate that the specificity of

NMR profiles linked to ripeness and the harvest season

offers a powerful tool for assessing the molecular deter-

minants characterizing the quality of this commercially

important crop.

2 Experimental

2.1 Plant material

Unripe (green) and ripe berries of J. communis were har-

vested in May, August and November 2010, while fully

ripe (bluish-black) berries were harvested in February 2011

from three shrubs in the same area. Samples were collected

in Molise (a Region in Central-Southern Italy) at an alti-

tude of 620 m ASL, and were identified by Dr. Paola

Fortini.

A voucher specimen is deposited in the Herbarium of

DiBT, University of Molise, with the number JC-48-2010.

Fresh berries were frozen in liquid nitrogen and stored at

-80 �C until analysis.

2.2 Extraction procedure and sample preparation

for NMR

To extract the metabolites of interest (e.g., lipids, carbo-

hydrates, amino acids and other small metabolites), while

leaving others compounds (e.g., DNA, RNA, proteins) in

the tissue pellet, tissues were mechanically disrupted.

Combined extraction of polar and lipophilic metabolites

was carried out by using the methanol/chloroform protocol

suggested by the Standard Metabolic Reporting Structures

working group (Lindon et al. 2005). The vegetal tissue

(0.5 g) was frozen in liquid nitrogen and powdered with a

pestle in a ceramic mortar. The ground material was

transferred into a centrifuge tube and 4 ml of methanol,

1.70 ml of water and 4 ml of chloroform per g of wet tissue

(all solvents were cold) were added to the tube, and vor-

texed for 30 s. The sample was gently stirred and mixed,

on ice, for 10 min (the solution must be mono-phasic).

Four ml of chloroform and 4 ml of water per g of wet tissue

were then added, and the final mixture was vortexed and

centrifuged at 3,000 rpm for 15 min at 4 �C. This proce-

dure separates three phases: water/methanol at the top

(aqueous phase, with the polar metabolites), denatured

proteins and cellular debris in the middle, and chloroform

at the bottom (lipid phase, with lipophilic metabolites). The

methanol/water and chloroform fractions were collected

separately in a 50 ml round-bottomed flask, dried in a

rotary vacuum evaporator at RT and stored at -80 �C until

required. Five parallel experiments were conducted for

each sample.

Prior to NMR analyses, the methanol/water fractions

were resuspended in 630 ll of phosphate buffer saline

(PBS, pH 7.4), adding 70 ll of a 2H2O solution [containing

1 mM sodium 3-trimethylsilyl [2,2,3,3-2H4] propionate

(TSP) as a chemical shift reference for 1H spectra] to

provide a field frequency lock, reaching 700 ll of total

volume. The organic residues were resuspended in 700 ll

166 A. Falasca et al.

123

of C2HCl3. Then the samples were transferred in NMR

tubes.

2.3 NMR analysis

One-dimensional (1D) spectra were recorded on a Bruker

Avance III-600 MHz spectrometer (Bruker BioSpin GmbH,

Rheinstetten, Germany), equipped with a TCI CryoProbeTM

fitted with a gradient along the z-axis, at a probe tempera-

ture of 27 �C. 1D proton spectra were acquired at 600 MHz

by using the excitation sculpting sequence (Hwang and

Shaka 1995). A double-pulsed field gradient echo was used,

with a soft square pulse of 4 ms at the water resonance

frequency, with the gradient pulse of 1 ms each in duration,

adding 256 transients of 16,384 points with a spectral width

of 8,389.3 Hz. Time-domain data were all zero-filled to

32,768 points, and prior to Fourier transformation, an

exponential multiplication of 0.6 Hz was applied. For the

two-dimensional (2D) clean total correlation spectroscopy

(TOCSY) (Griesinger et al. 1988; Bax and Davis 1985)

spectra we used a standard pulse sequence with a spin-lock

period of 64 ms, achieved with the MLEV-17 pulse

sequence, and incorporating the excitation sculpting

sequence for water suppression. In general, 256 equally

spaced evolution-time period t1 values were acquired,

averaging 64 transients of 2,048 points, with 8,389.3 Hz of

spectral width. Time-domain data matrices were all zero-

filled to 4,096 points in both dimensions, thus yielding a

digital resolution of 2.04 Hz/pt. Prior to Fourier transfor-

mation, a Lorentian-to-Gauss window with different

parameters was applied for both t1 and t2 dimensions for all

the experiments. Spectra in water were referred to internal

0.1 mM TSP, assumed to resonate at d = 0.00 ppm; spectra

in chloroform were referred to the residual solvent line,

assumed to resonate at d = 7.26 ppm.

Natural abundance 2D 1H–13C heteronuclear single

quantum coherence (HSQC) spectra were recorded at

150.90 MHz for 13C, using an echo-antiecho phase sen-

sitive pulse sequence with adiabatic pulses for decoupling

(Palmer et al. 1991; Kay et al. 1992) and pre-saturation

for water suppression (Schleucher et al. 1994). 128

equally spaced evolution time period t1 values were

acquired, averaging 240 transients of 2,048 points and

using GARP4 for decoupling. The final data matrix was

zero-filled to 4,096 in both dimensions, and apodized

before Fourier transformation by a shifted cosine window

function in t2 and in t1. Linear prediction was also applied

to extend the data to twice its length in t1. HSQC spectra

in water were referred to the a-glucose doublet resonating

at 5.25 ppm for 1H and 95.10 ppm for 13C, while those in

chloroform were referred to 7.26 ppm for 1H and

77.70 ppm for 13C.

2.4 Data analysis

The 0.50–8.50 ppm proton region of aqueous extracts, and

the 0.50–7.00 ppm proton region of organic fractions were

automatically data reduced to integrated regions (buckets) of

0.02-ppm width using the AMIX 3.9.7 package (Bruker

Biospin GmbH, Rheinstetten, Germany). The residual water

resonance region (4.72–4.96 ppm) was excluded, and each

integrated region was normalized to the total spectrum area.

To differentiate samples through NMR spectra, we carried

out a multivariate statistical data analysis using projection

methods. The integrated data reduced format of the spectra

was imported into SIMCA-P? 12 package (Umetrics,

Umea, Sweden), and principal component analysis (PCA)

and partial least squares discriminant analysis (PLS-DA)

were performed. Mean-centering and Pareto scaling were

used as data pre-treatment for both PCA and PLS-DA. Each

spectral region was scaled to (1/sk)1/2, where sk is the stan-

dard deviation for variable k, increasing the contribution of

lower concentration metabolites in the generated models

compared with models where no scaling is used. In this case,

all variables are given equal weighting, so that the model is

not biased towards the higher magnitude variables (as these

generally have larger variances). This scaling effectively

increases the importance of low concentration metabolites in

the resultant models, but not to an extent where the noise

significantly contributes to the model (Llorach-Asuncion

et al. 2010).

PCA was first applied to extract and display the sys-

tematic variation in a data matrix to identify trends and

clusterings in an unsupervised manner (i.e., no prior group

knowledge is used in the calculation) (Eriksson et al.

2006). However, PLS-DA was also used to better define

clustering. This classification tool is based on the PLS

model in which the dependent variable is chosen to rep-

resent the class membership (Westerhuis et al. 2008).

Validation of the models was carried out using sevenfold

cross-validation and permutation tests (400 repeats) to test

possible model overfit. The quality of all PCA and PLS-DA

models was evaluated using the correlation coefficient R2

and a cross-validate correlation coefficient Q2.

Metabolites concentration was obtained by integrating

isolated NMR signals, referred to the internal TSP standard

of known concentration.

3 Results and discussion

3.1 Identification of metabolites in aqueous extracts

Supplementary Figure S1a–d, representing spring to win-

ter, shows the 1D 1H-NMR spectra of the aqueous extracts

Seasonal changes in Juniperus communis L. 167

123

in the four seasons. Resonances were identified by com-

parison with NMR data of authentic samples (De Marino

et al. 2011) with the help of 2D TOCSY and HSQC

experiments. Where space permits, identified resonances

are labeled according to supplementary Table S1. Visual

inspection of the aromatic (7.5–5.5 ppm), carbohydrate

(5.0–3.0 ppm), and aliphatic (3.0–1.0 ppm) regions in the

spring profile (Fig. S1a) indicates that the carbohydrate

region is rather complex because of the overlapping sig-

nals. The sucrose signals are clearly distinguishable at

5.43 ppm (label 16, H-1 of glucose) and 4.23 ppm (16, H-3

of fructose unit), associated with the characteristic signals

for the a-glucose at 5.25 ppm (label 15), and b-glucose at

4.66 ppm (label 14). The aromatic region is dominated by

signals from the anionic form of shikimic acid, and cate-

chin. Shikimate characteristic signals include an olefinic

proton at 6.50 ppm (label 9, H-2), two methylene protons

at 2.21 and 2.78 ppm (9, spectrum S1B, H2-6), and a

methine proton at 4.42 ppm (9, spectrum S1B, H-3). Res-

onances at 6.81, 6.73, 6.02 and 5.90 ppm were respectively

assigned to the H-20, overlapping H-50 and H-60, H-8 and

H-6 of the catechin moiety (label 11).

In the aliphatic region, the ring structure of quinate

(label 6, spectrum S1B) was established through the

extensive TOCSY coupling between two methylene groups

(CH2-2, 1.90/2.08 ppm; CH2-6, 2.01/2.08 ppm), and three

hydroxylated methine protons (H-3, 4.03 ppm; H-4,

3.56 ppm; H-5, 4.17 ppm). In addition, in the spring

extract (Fig. S1a), some amino acids were identified,

namely, alanine (label 4), glutamine (label 8), aspartate

(label 12), glycine (label 17) and arginine (label 5). The

presence of choline (label 13) was indicated by the char-

acteristic singlet at 3.19 ppm stemming from the N?(CH3)3

group. The assignment was supported by the HSQC cor-

relation with the methyl carbon at 56.6 ppm.

The summer (Fig. S1b) and autumn (Fig. S1c) profiles

show almost the same pattern of the spring profile,

although with different intensity: the concentration of shi-

kimate, quinate, catechin and choline increases in summer

to gradually decrease in autumn. Surprisingly, the winter

profile (Fig. S1d) only shows the carbohydrate signals from

a and b glucose (labels 15 and 14, respectively), and the

fructose (label 18). No sucrose signals were instead

detected.

3.2 Identification of metabolites in the chloroform

extracts

The 1H-NMR spectral regions of the chloroform extracts of

Juniperus berries are shown in supplementary Fig. S2a–d,

representing spring to winter. Assignment of metabolites is

reported in the supplementary Table S2. Three labdane-type

diterpenoids namely cis-communic acid, myrcecommunic

acid, and imbricatolic acid (supplementary Fig. S3) have

been detected in the proton spring spectrum (labeled 3, 1,

and 2, respectively, in Fig. S2), with cis-communic acid

being the major component. The cis-communic and myr-

cecommunic acids were identified starting from olefinic

proton signals, and recognizing the carbon resonances in

HSQC spectra. Imbricatolic acid, which bears a saturated

side chain with a terminal primary alcoholic group, was

identified from the multiplet at 3.67 ppm, labeled 2 in Fig.

S2d. An aldehyde, cis-communal, found as a minor com-

ponent in the spring extract, was identified by means of the

–CHO group characteristic signal at 9.75 ppm (inset Fig.

S2a, label 4); it correlated with the corresponding carbon at

206.2 ppm in the HSQC experiment.

The 1H-NMR profiles of summer (Fig. S2b) and autumn

(Fig. S2c) extracts present almost the same components,

although in lower concentration compared with spring. The

winter spectrum (Fig. S2d) clearly indicated an increase in

concentration of imbricatolic acid (labeled 2) with respect

to autumn, and traces of sandarocopimaric acid (label 6)

could be identified by the olefinic signal at 5.76 ppm (H-

15). Trans-communic acid (label 5 and t) was also identi-

fied from the signals at 5.40 ppm (H-12) and 6.31 ppm (H-

14), in the ratio 1:3 (inset in Fig. S2d) with the corre-

sponding isomer cis-communic acid (label 3 and c) (Fang

et al. 1993).

3.3 Statistical analysis

To obtain statistically relevant biochemical information

from NMR data, each spectrum (sample) was completely

analyzed, and pattern recognition based on multivariate data

analysis was applied. We first applied unsupervised PCA to

all pairs (spring–summer, summer–autumn, autumn–winter,

winter–spring), obtaining a sample classification of ca. 70 %

(samples classes correctly identified) (data not shown). We

next applied PLS-DA to all seasons pairs, obtaining a

stronger regression model (96 %, p \ 0.0001) between all

pairs.

The resulting supervised models were tested by itera-

tively predicting the class membership of every sample,

and the results were used to evaluate the goodness of fit

(R2) and the goodness of prediction (Q2), for which

acceptable values must be C0.5 (Eriksson et al. 2006). For

the models describing polar extracts (Fig. 1), the spring

versus summer scores plot (Fig. 1a) presented R2 = 0.884

and Q2 = 0.978; for summer versus autumn we obtained

R2 = 0.756 and Q2 = 0.963; for autumn versus winter we

recorded R2 = 0.922 and Q2 = 0.996; and for winter

versus spring we obtained R2 = 0.949 and Q2 = 0.999.

For the models describing chloroform extracts (scores plots

in Fig. 2), spring versus summer presented R2 = 0.634

and Q2 = 0.711; for summer versus autumn we obtained

168 A. Falasca et al.

123

R2 = 0.816 and Q2 = 0.971; for autumn versus winter we

recorded R2 = 0.768 and Q2 = 0.981; and for winter

versus spring we obtained R2 = 0.748 and Q2 = 0.956.

The above values indicate that data variation is well

explained by each 2-component model (R2), with a very

good predictivity (Q2). Therefore, each season separates

from the others by relying on both polar and apolar

extracts.

When the NMR profiles of all four seasons were ana-

lyzed in one model, it showed a cross validated accuracy

value of 96 % (p \ 0.0001). The PLS-DA statistical

analysis of data yielded a model for the four classes, which

resulted in three predictive and three orthogonal compo-

nents (Fig. 3), with R2 = 0.871 and Q2 = 0.912 for the

polar extracts, and R2 = 0.845 and Q2 = 0.744 for the

apolar extracts. Again, data variation is well explained by

the 3-component model (R2), with a very good predictivity

(Q2). Permutation tests, performed to validate the PLS-DA

model, gave R2 and Q2 values lower than the real model,

confirming the absence of data overfitting.

As observed in Fig. 3a, the aqueous extracts from the

four seasons showed clear inter-group separation along the

three components, revealing significant seasonal variations

in the chemical composition. The scores plot 3a shows that

marked differences are present between the winter samples

(green dots), corresponding to fully ripe berries, and the

samples harvested in the other seasons along the first least-

square component PC1 (labeled t[1]). Spring, summer and

autumn samples (blue, red and yellow dots, respectively)

showed positive t[1] values, but they differed in their t[2]

(PC2) and t[3] (PC3) values. Moreover, close clustering

within each class of samples indicates good reproducibility

in the extraction procedure and NMR measurements. PLS-

DA was also performed for the dataset derived from the

chloroform fractions. The scores plot (Fig. 3b) shows that

samples correctly cluster according to the season, and that

groups, with respect to Fig. 3a, are closer to each other

along the three components, suggesting that they share a

similar metabolic profile. This is in line with the findings of

Fig. S2, in which the depicted spectra are quite similar,

except for differences in the intensity of the resonances. In

contrast, the aqueous groups are more separated in the

score plot 3A, in particular, spring, summer, and autumn

from winter, recalling the fact that the 1D winter spectrum

is remarkably different from the others.

The variables responsible for between-class differences

observed in the scores plots of Figs. 1 and 2 can be iden-

tified from the loadings plots corresponding to spring–

summer, summer–autumn, autumn–winter, winter–spring

season pairs (not shown). The most important metabolites

are illustrated in the variables of importance plots (VIPs) of

supplementary Figs. S4 and S5, which show the first 15

buckets generating the models reported in Figs. 1 and 2,

respectively. On the x-axis, chemical shifts (in ppm)

identify the buckets, which can be assigned to corre-

sponding metabolites by referring to Tables S1 and S2. It is

important to notice that when more than one chemical

group belonging to the same metabolite is observable in the

NMR spectrum, the metabolite is reported more than once

on VIPs. For example, the buckets at 3.73, 6.51, 4.01, and

Fig. 1 PLS-DA scores plots for the methanol/water extracts of

J. communis berries harvested in spring, summer, autumn and winter.

Plots show the degree of separation of the model between consecutive

seasons. t[1] and t[2] represent the scores (the ‘first two partial least

squares components’) of the model, which are sufficient to build a

satisfactory classification model. The model showed a strong

regression (96 %). For R2 and Q2 of each model see text

Seasonal changes in Juniperus communis L. 169

123

4.43 ppm in supplementary Fig. S4a all identify shikimate,

which is one of the relevant metabolites separating spring

from summer berries using polar extracts.

3.4 Quantification of metabolites generating seasonal

variation

The NMR spectra reported in Fig. S2 indicate that lipo-

philic profiles can be divided in two pairs: spring and

winter (traces S2a, d) and summer and autumn (traces S2b,

c). Overall, the profiles are virtually identical, only show-

ing different metabolite concentration. The Cupressaceae

family is known to be a rich source of sesquiterpenoids

and diterpenoids, which are important chemotaxonomic

markers and possibly important ecophysiological metabo-

lites. Plant vacuoles can serve as storage organelles for

primary and secondary metabolites, sometimes reaching

high concentration (Taiz 1992; Wink 1993). Since many of

these compounds are toxic, deterrent or repellent against

microorganisms/viruses/herbivores, when needed they can

be retrieved from the vacuole and utilized. It can be

hypothesized that the constant presence of specific apolar

metabolites can be part of the vacuole reservoir and be used

in metabolic pathways involved in actions like growth,

defense, etc., used at variable concentration over the year

(Taiz 1992; Wink 1993).

Figure 4 illustrates seasonal concentration variation of

some of the most important polar metabolites. We focused

Fig. 2 PLS-DA scores plots for the chloroform extracts of J.

communis berries harvested in spring, summer, autumn and winter.

Plots show the degree of separation of the model between consecutive

seasons. t[1] and t[2] represent the scores (the ‘first two partial least

squares components’) of the model, which are sufficient to build a

satisfactory classification model. The model showed a strong

regression (96 %). For R2 and Q2 of each model see text

Fig. 3 Score plot of three-

component PLS-DA model of

methanol/water extracts (a) and

chloroform extracts (b). t[1],

t[2] and t[3] along the axes

represent the scores (the ‘first

three partial least squares

components’) of the model,

which are sufficient to build

satisfactory classification

models that separates seasons.

The model showed a cross

validated accuracy value of

96 %, with R2 = 0.871 and

Q2 = 0.912 for the polar

extracts, and R2 = 0.845 and

Q2 = 0.744 for the chloroform

extracts

170 A. Falasca et al.

123

on polar metabolites since they showed the largest seasonal

differences (Figs. S1, S3a) with respect to those observed

for apolar profiles (Figs. S2, S3b). Compared with summer,

spring berries’ extracts show higher levels of sucrose

(histogram 4c). Summer berries present higher concentra-

tions of choline, catechin, quinate and shikimate (panels

4a, b, e, f) in comparison with spring and autumn samples.

Finally, winter samples showed high levels of a-glucose,

fructose and b-glucose (histograms 4d, g, h) as the sole

metabolites present, while choline, catechin, sucrose, qui-

nate and shikimate were beyond NMR detection. Table 1

reports the concentration of polar metabolites as obtained

from isolated 1H-NMR resonances over the seasons, in mM

and in mg/g of berries.

Sucrose showed the maximum concentration in spring

and disappeared in winter, while a-, b-glucose and fruc-

tose, virtually absent during spring, summer and autumn,

maximized the concentrations in winter when sucrose

disappears. Sugar metabolism is a very dynamic process,

and metabolic fluxes and sugar concentrations vary dra-

matically both during development and in response to

environmental signals such as diurnal changes, and biotic

and abiotic stress (Rolland et al. 2006). Sugars are known

to act as signaling molecules regulating gene expression

during plant development, and play an important role in

regulating vegetative dormancy (Chao et al. 2007). The

high sucrose content in spring (Fig. 4c) could be explained

by a low or absent enzymatic activity during the growing

season, when glucose is required as an energy source

(Koch 2004; Lunn 2008). On the contrary, fructose and

glucose accumulate in winter during a dormancy period

when they act as storage compounds (Rolland et al. 2006).

A similar sugar pattern has been reported for the pollina-

tion drops from cones of J. communis collected in the

Fig. 4 Absolute quantification

of selected metabolites,

obtained by integrating isolated

peaks in the 1H-NMR spectra.

Concentrations are given in

mg/g of berries. Data are shown

as mean of concentration ± SD,

with n = 5

Seasonal changes in Juniperus communis L. 171

123

February–March period (Mugnaini et al. 2007). The lower

levels of sugars observed in summer period (panels 4c, d, g,

h) could relate to temperature increase since, under heat

stress, sucrose synthesis is greatly influenced by the

reduced activities of sucrose phosphate synthase (Chaita-

nya et al. 2001), ADP glucose pyrophosphorylase and

invertase, with a parallel increase of starch (Vu et al. 2001).

Sugars are involved in several other processes like photo-

synthesis, respiration, nitrogen metabolism and defense

mechanisms (Jang et al. 1997). Sucrose strongly up-regu-

lates the flavonoid and anthocyanin biosynthetic pathways

in Arabidopsis thaliana; this process is sucrose-specific

since glucose and fructose produced virtually no effect

(Solfanelli et al. 2006). Similar results have been reported

for phenylpropanoid biosynthesis in Vitis vinifera cell

cultures (Ferri et al. 2011).

Juniperus communis berries contain several compounds

belonging to the category of catechins (flavan-3-ols).

Flavonoids have a number of important physiological roles

in root development, seed germination, allelopathy, plant–

bacterium interaction, UV-B protection, and plant defense

against pathogens and environmental stress (Peer and

Murphy 2006; Liu et al. 2012, and references therein). The

J. communis berries begin life as small flowering cones,

with a flowering time in April–June. Each berry contains

3–6 seeds, but some of them are often infertile due to insect

infestations. Catechin, whose highest level is often found in

plant flowers (Kalinova and Vrchotova 2009), is generally

regarded as the most common basic unit of proanthocy-

anidins, which, together with monomeric flavan-3-ols and

their derivatives, are considered the first line of defense

against microbial pathogens and insect pests (Aziz et al.

2005; Dixon et al. 2005). Therefore, an early increase of

catechin concentration, as the growing presence of catechin

up to summer (Fig. 4b), could provide a hostile environ-

ment to potential microbial invaders to protect the plant

growth. In the seed coat of Sesbania virgata (?)-catechin

seems to operate as an allelochemical (Simoes et al. 2008),

although some doubts are cast on the protecting role of

catechin (Bais et al. 2010).

Choline increases from spring to its maximum concen-

tration in summer berries, to decrease in autumn and dis-

appear in winter time (Fig. 4a). Choline is the precursor of

the osmolyte glycine betaine, which plays an important

role in conferring resistance to abiotic stresses by stabi-

lizing highly ordered structures of complex proteins and

membranes (Sakamoto and Murata 2000). Furthermore,

increased osmolytes concentration in the cytosol may

increase cellular water content, resulting in greater turgor

pressure that favors cell expansion (Zhang et al. 2007).

This, together with the fact that choline production is also

vital for synthesizing the major plant membrane lipid

phosphatidylcholine (Moore 1990; Bolognese and McGraw

2000), suggests that the observed choline variation should

be related to Juniperus berries growth.

The seasonal variation of quinate and shikimate is strictly

related (Fig. 4e, f): these compounds, with highest concen-

tration in summer, are both found in the shikimate pathway

(Herrmann and Weaver 1999; Dewick 2009). In this path-

way, the presence of a quinate hydrolyase that reversibly

catalyzes the conversion of quinate to shikimate, indicates

that shikimate is a direct degradation product of quinate. The

end product of the pathway, chorismate, is the precursor of

tyrosine, phenylalanine and tryptophan, which, in turn, are

precursors of a large variety of plant secondary metabolites

like flavonoids, many phytoalexins, indole acetate, alkaloids

such as morphine, UV light protectants, and lignin (Herr-

mann and Weaver 1999; Dewick 2009).

A high demand for shikimate pathway intermediates for

both primary and secondary metabolism is reasonable in

the growing and flowering periods (Weaver and Herrmann

1997). For example, in spring, the presence of quinate and

shikimate seems to be important in developing coniferous

needles in the course of photosynthetic CO2 fixation

Table 1 Absolute quantification (mM, with respect to 100 lM internal TSP) of principal polar metabolites obtained by integrating isolated 1H

NMR resonances

Metabolite Spring Summer Autumn Winter

mM mg/g of

berries

mM mg/g of

berries

mM mg/g of

berries

mM mg/g of

berries

a-Glucose 5.12 ± 1.01 1.29 ± 0.25 7.55 ± 0.89 1.90 ± 0.22 6.60 ± 0.32 1.66 ± 0.08 207.62 ± 18.34 52.37 ± 4.62

b-Glucose 1.53 ± 0.40 0.38 ± 0.10 2.47 ± 0.32 0.62 ± 0.08 2.35 ± 0.15 0.59 ± 0.04 105.53 ± 10.37 26.62 ± 2.62

Sucrose 32.29 ± 4.16 15.47 ± 1.99 2.30 ± 1.44 1.10 ± 0.69 9.92 ± 3.08 4.75 ± 1.48 – –

Shikimate 9.24 ± 1.88 2.24 ± 0.46 29.32 ± 2.83 7.11 ± 0.69 13.42 ± 1.61 3.25 ± 0.39 – –

Catechin 2.39 ± 0.33 0.97 ± 0.14 6.94 ± 0.75 2.82 ± 0.30 6.19 ± 2.85 2.51 ± 1.16 – –

Fructose – – – – – – 217.35 ± 12.70 54.82 ± 3.20

Quinate 2.72 ± 0.37 0.73 ± 0.10 19.23 ± 1.19 5.15 ± 0.32 5.51 ± 0.62 1.47 ± 0.17 – –

Data are shown as mean concentration ± SD, with n = 5

172 A. Falasca et al.

123

(Weaver and Herrmann 1997). The metabolite pool is next

metabolized during the lignification process in the summer.

As mentioned above, the parallel behavior of quinate and

shikimate is most likely an answer to the Juniperus berries

requirement for growth and flowering, where processes

such as gametogenesis, pollen biogenesis, and pollen ger-

mination and tube growth would require high rates of

protein synthesis and high concentrations of phenylpropa-

noid precursors (Weaver and Herrmann 1997).

4 Concluding remarks

We have applied NMR-based metabolomics to investigate

the seasonal variation of metabolic profiles in J. communis

berries. Depending on seasonal chemical composition,

Juniperus berries are used worldwide for flavoring and

fragrance, as well as for their beneficial health properties.

The knowledge of metabolic profiling at different devel-

opment stages, and the quantitative determination of

component at different ripeness time, may have an

important role in determining the most suitable harvesting

time for this commercially important crop. We demon-

strated that NMR spectral profiles of polar and apolar

extracts are season specific, and found that molecular

composition is dominated by primary metabolites (namely,

sugars, amino acids, organic acids), and secondary

metabolites (namely, labdane diterpenes and polyphenolic

components). It is worth noting that some components

(chiefly apolar) are always present over the seasons,

although in different concentrations, whereas a few others

(predominantly polar) are specific. Statistical analysis

clearly clusters the samples according to the ripeness and

the harvest season, with well-defined metabolic profiles for

each class. It is concluded that NMR-based metabolomics

offers an excellent holistic method to monitor variations

resulting from environmental factors, harvesting time, and

quality control of pharmaceutical herbs and crops.

Acknowledgments We thank Dr. Paola Fortini for careful identi-

fication of J. communis berries.

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