Metabolomic investigation of Mytilus galloprovincialis (Lamarck 1819) caged in aquatic environments

Ecotoxicology and Environmental Safety 84 (2012) 139–146

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Ecotoxicology and Environmental Safety

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Metabolomic investigation of Mytilus galloprovincialis (Lamarck 1819) cagedin aquatic environments

Salvatore Fasulo a,b, Francesco Iacono c, Tiziana Cappello c, Carmelo Corsaro d,Maria Maisano a, Alessia D’Agata a, Alessia Giannetto a, Elena De Domenico a,Vincenzo Parrino a, Giuseppe Lo Paro a, Angela Mauceri a,b,n

a Department of Animal Biology and Marine Ecology, University of Messina, Viale F. Stagno D’Alcontres 31, 98166 Messina, Italyb Centro Universitario CUTGANA, Via Terzora 8, 95027 San Gregorio di Catania, Italyc Ph.D. in Biology and Cellular Biotechnologies, Department of Animal Biology and Marine Ecology, University of Messina, Viale F. Stagno D’Alcontres 31, 98166 Messina, Italyd Department of Physics, University of Messina, Viale F. Stagno D’Alcontres 31, 98166 Messina, Italy

a r t i c l e i n f o

Article history:

Received 28 December 2011

Received in revised form

29 June 2012

Accepted 2 July 2012Available online 20 July 2012

Keywords:

Caged mussels

Mytilus galloprovincialis

Digestive gland

PAHs

Metabolomics1H NMR

13/$ - see front matter & 2012 Elsevier Inc. A

x.doi.org/10.1016/j.ecoenv.2012.07.001

esponding author at: Department of Animal

ity of Messina, Viale F. Stagno D’Alcontre

9 090 6765556.

ail address: [email protected] (A. Mau

a b s t r a c t

Environmental metabolomics was applied to assess the metabolic responses in transplanted mussels to

environmental pollution. Specimens of Mytilus galloprovincialis, sedentary filter-feeders, were caged in

anthropogenic-impacted and reference sites along the Augusta coastline (Sicily, Italy). Chemical

analysis revealed increased levels of PAHs in the digestive gland of mussels from the industrial area

compared with control, and marked morphological changes were also observed. Digestive gland

metabolic profiles, obtained by 1H NMR spectroscopy and analyzed by multivariate statistics, showed

changes in metabolites involved in energy metabolism. Specifically, changes in lactate and acetoacetate

could indicate increased anaerobic fermentation and alteration in lipid metabolism, respectively,

suggesting that the mussels transplanted to the contaminated field site were suffering from adverse

environmental condition. The NMR-based environmental metabolomics applied in this study results

thus in it being a useful and effective tool for assessing environmental influences on the health status of

aquatic organisms.

& 2012 Elsevier Inc. All rights reserved.

1. Introduction

Metabolomics is an emerging approach to assessing the healthstatus of organisms based on the identification of low molecularweight metabolites, whose production and levels vary with thephysiological, developmental, or pathological state of cells, tis-sues, organs or whole organisms (Lin et al., 2006). Proton nuclearmagnetic resonance (1H NMR) spectroscopy-based metabolomics,when linked with pattern recognition techniques and data miningtools, can detect differences in the profile of metabolites (meta-bolic biomarkers) in response to environmental stressors, dis-eases or exposure to toxicants (Fiehn, 2002; Hines et al., 2007;Tuffnail et al., 2009; Viant et al., 2003), thus providing an over-view of the metabolic status of a biological system. Metaboliteprofiling, originally developed for human biomedical applications(Nicholson et al., 1988) has now been increasingly employed inseveral research areas, including plant science (Kim et al., 2010),

ll rights reserved.

Biology and Marine Ecology,

s 31, 98166 Messina, Italy.

ceri).

food quality (Tarachiwin et al., 2008), microbial metabolomics(Boroujerdi et al., 2009) and environmental metabolomics (Viant,2009). Because metabolomics can provide valuable informationon how xenobiotics influence physiological functions, this tech-nique has also been applied to experimental studies of selectiveexposure on various aquatic organisms, both invertebrates (Wuand Wang, 2010) and fish (Iacono et al., 2010; Santos et al., 2010).

Pollution of coastal areas may arise from various industrialand urban sources, such as shipping, loading and bunkeringoperations, shipyards, accidental spills, wastewater emissions(Bocchetti et al., 2008). This may result in elevated concentrationsof toxicants in the water column and sediments. In particular,harbours are generally enclosed areas characterized by poorwater quality, due to a low flushing rate and human activitieswithin or adjacent to the harbour (Yin et al., 2000). There areconcerns about risk to aquatic organisms residing in innerharbours, because these organisms are exposed to high concen-trations of environmental contaminants due to low hydrodyna-mism and intense anthropogenic impact. In this regard, the‘‘Augusta-Melilli-Priolo’’ industrial area has been considered forthis study. It extends approximately 20 km along the Augustacoastal area (eastern Sicily, Italy) and is one of the largest and

Fig. 1. Map depicting location of the mussel caging sites.

Table 1Mean (7S.D.) of water physico-chemical parameters of Vendicari and Priolo.

Sampling area Vendicari Priolo

Temperature (1C) 23.470.5 22.570.6

Salinity (PSU) 37.670.1 38.270.2

pH 8.070.1 7.970.1

Oxygen (mg/l) 4.870.2 3.770.3

S. Fasulo et al. / Ecotoxicology and Environmental Safety 84 (2012) 139–146140

most complex petrochemical sites in Europe, because manyindustrial installations can be found there, including oil refineries,chemical plants, mineral deposits, a military base and many otherindustrial installations (Ausili et al., 2008). Mercury (Hg) andpolycyclic aromatic hydrocarbons (PAHs) are found in excessiveconcentrations (ICRAM, 2005). Levels of these contaminantsexceed national and international regulatory guidelines, asreported in recent studies on sediments collected from the coastalzone of Augusta (Di Leonardo et al., 2008, 2007).

Such pollutant mixtures (heavy metals, drugs, PAHs, poly-chlorinated biphenyls PCBs) can induce toxic effects at differentbiological levels (e.g. molecular, cellular, biochemical, physiologi-cal). Because changes at the organism level lead to changes at thepopulation and community levels, a number of biomarkers arefrequently used as early warning signals of environmental dis-turbance (Walker et al., 2006).

In environmental monitoring studies mussels, particularly thegenus Mytilus, are widely used as sentinel organisms (Fasulo et al.,2008; Hellou and Law, 2003; Viarengo et al., 2007). This is becauseof their wide geographical distribution, ability to tolerate a range ofenvironmental conditions and accumulate toxic chemicals, andsuitability for caging experiments at field sites (Andral et al., 2004;Romeo et al., 2003; Tsangaris et al., 2010; Viarengo et al., 2007; Wuand Shin, 1998). The use of transplanted mussels originating from aclean area allows comparison of control organisms with those cagedin potentially polluted sites, and allows more control over theexperiment than collection of native individuals. In addition, usingcaged mussels from a single population minimizes confoundingfactors such as the age and reproductive status of the organisms thatinfluence both contaminant bioaccumulation and biomarkerresponses. Thus, a more accurate assessment of the real biologicaleffects of pollutant exposure is possible, providing an early sign ofimpaired health of the ecosystem (Andral et al., 2004; Regoli, 2000;Tsangaris et al., 2010; Viarengo et al., 2007).

The digestive gland is a target organ widely used in environ-mental toxicology because it accumulates pollutants and partici-pates actively in the xenobiotic metabolism (Rajalakshmi andMohandas, 2005). It is also involved in immune defense, detoxifica-tion and in homeostatic regulation (Marigomez et al., 2002; Mooreand Allen, 2002), and therefore exposure to contaminants may leadto its histopathological alterations (Garmendia et al., 2011).

Histopathology is a biomarker of effect for an overall assessmentof the general health status of animals, and provides valuableinformation concerning changes in the cellular as well as sub-cellularstructures of an organ or tissue much earlier than the externalmanifestations (Auffret, 1988; Fasulo et al., 2010a, 2010b; Ferrandoet al., 2005; Livingstone and Pipe, 1992; Mauceri et al., 2002).

The aim of this study was to assess biological effects ofenvironmental pollution, mainly related to the presence of PAHs,in the caged mussel Mytilus galloprovincialis, through the use ofmorphological and metabolite assays. In fact, although in recentyears several reports have suggested that NMR-based environ-mental metabolomics is a powerful tool in environmental tox-icology (Viant et al., 2003), there are few studies dealing withassessment of aquatic organism health through a metabolomicsbased approach.

2. Materials and methods

2.1. Sites and experimental design

The ‘‘Augusta-Melilli-Priolo’’ industrial area, chosen as polluted site for this

study, has been declared a ‘‘site of national interest’’ by the Italian Ministry of

Environment (Law No. 426/98; Ministerial Decree of 10.01.2000) owing to the

high level of pollution and subsequent risk for human health. By contrast, the

natural reserve of Vendicari, established in 1984 and representing a wildlife

reserve in the southernmost part of the east coast of Sicily, was chosen as a non-

impacted reference site. It covers an area of 1512 ha (575 ha of a integral reserve

and 937 ha of a pre-reserve) and its biological importance is due to the presence of

different biotopes, e.g. rocky and sandy coastlines, Mediterranean scrub, both salt

and fresh water marshes (Fig. 1). At both sampling sites, water physico-chemical

parameters (temperature, salinity, pH, dissolved oxygen) were measured by a

portable instrument (Multi 340i/SET, WTW Wissenschaftlich, Weilheim, Germany),

as reported in Table 1.

Mussels M. galloprovincialis (6.170.54 cm shell length) were purchased in

October 2009 from a consortium of fishermen in Goro (Ferrara, Italy), a reference

site in which physico-chemical parameters have been previously reported (Fasulo

et al., 2008). Mussels were maintained 1 week in aerated seawater in the

laboratory, and then transplanted in the two selected sites for 30 days in stainless

steel cages (about 200 specimens per cage) covered with a net to guarantee free

seawater circulation and protect mussels from fish predation. Cages were

deployed by scuba-diving at 8 m depth below the surface both in Priolo

(3711201000N; 1511304400E) and Vendicari (361 470 3500 N; 151 080 5200 E). The

mussels were retrieved after 4 weeks by diving and immediately conditioned after

collection on board of the experimental vessel. Fifteen male individuals from each

area were selected randomly and sacrificed. Body length and mass were recorded,

and digestive gland samples were rapidly excised and flash-frozen in liquid

nitrogen for chemical and metabolic measurements, then transferred to the

laboratory and stored at �80 1C prior to analysis. In addition, small pieces of

each dissected tissue were taken for histological analysis.

This study was conducted according to the guidelines for the protection of

animal welfare, in compliance with the Italian National Bioethics Committee

(INBC).

2.2. PAH concentration in digestive gland

For PAH analysis, the following solvents and reagents were used: acetonitrile

ACN (Romil), water and cyclohexane (Chromanorm BDH), acetone (Pestinorm

BDH), KOH, ethanol and exane (Carlo Erba), all of HPLC grade. The digestive glands

dissected from fifteen individuals were pooled in three samples (each with tissues

of five specimens) per each sampling area. Approximately 3 g of each pooled

sample were weighted with an analytical balance Mettler Toledo AT 104 and

homogenized in a glass vial using an Ultra-TURRAX IKA T10 basic. The homo-

genized samples were saponified with 10 ml of 1 M KOH in an ethanol solution for

Table 2PAH concentrations (mean7S.D.) in the digestive gland (mg/g). n.d.¼not

detectable.

PAHs Priolo Vendicari

Naphthalene 0.91270.038 n.d

Acenaphthylene 0.01170.007 n.d

Acenaphtehene 0.00770.002 n.d.

Fluorene 0.00970.005 n.d.

Phenanthrene o0.006 n.d.

Anthracene o0.006 n.d.

Fluranthene 0.18770.012 n.d.

Pyrene 0.00870.004 n.d.

Benz(a)anthracene o0.006 n.d.

Chrysene n.d. n.d.

Benzo(b)fluoranthene 0.01270.007 n.d.

Benzo(k)fluoranthene 0.00870.002 n.d.

Benzo(a)pyrene 0.06070.017 n.d.

Dibenz(a,b)anthracene 0.92570.028 n.d.

Benzo(g,h,i)perylene o0.006 n.d.

Indeno(1,2,3-cd)pyrene o0.006 n.d.

S. Fasulo et al. / Ecotoxicology and Environmental Safety 84 (2012) 139–146 141

3 h at 80 1C in a water bath. Then, 20 ml of cyclohexane was added and samples

mixed by an orbital agitator for 10 min using dark glassware (Dafflon et al., 1995).

The hexanic phase was recovered and the polar mixture washed once with

cyclohexane and then discharged. The extracts were filtered, concentrated under a

nitrogen gas stream to about 1 ml, and the concentrated extract was removed

with a pasteur pipette and loaded into a Varian Bond Elut C18 cartridge 12 ml,

previously conditioned. The eluates were dried under nitrogen flow and dissolved

with 1 ml of acetonitrile before the analysis.

The concentrations of the following sixteen PAHs identified by the EPA as

priority pollutants, naphthalene (NA), acenaphthylene (ACY), acenaphthene (AC),

fluorene (FL), phenanthrene (PHE), anthracene (AN), fluoranthene (FA), pyrene

(PY), benzo(a)anthracene (BaA), chrysene (CH), benzo(b)fluoranthene (BbF),

benzo(k)fluoranthene (BkF), benzo(a)pyrene (BaP), dibenz(a,h)anthracene (DahA),

benzo(g,h,i)perylene (Bghi) and indeno(1,2,3-cd)pyrene (IP), were determined.

Quantitative analysis of PAHs was carried out with a high-performance liquid

chromatography (HPLC) apparatus Pro-Star 363 (Varian, Palo Alto, CA) equipped

with a 20 ml loop and a fluorescence detector (FLD Pro-Star 363). The software

used was Star Chromatography Workstation version 5.2 (Varian, Palo Alto, CA).

The chromatographic separation was carried out using a Chromspher three

PAH Varian (100�4.6 mm2) coupled with a guard column ChromSep SS

10�2 mm2, Varian. The analytical method involved a mobile phase consisting

of H2O/ACN 50 percent for 5 min, which achieved 100 percent ACN in 5 min with a

flow of 1 ml/min. The UV determination was performed at 255 nm, while the FL

detection was conducted with six different excitation/emission wavelengths. The

National Institute of Standards and Technology (NIST) Standard Reference Mate-

rial SRM 1647c, consisting of an acetonitrile solution of sixteen PAHs (target

compounds), was used as a calibration mixture. Percent recovery and matrix

interference was assessed with reference to M. galloprovincialis tissue.

The external standard multipoint calibration technique was used to determine

the linear response interval of the detector and in all cases, regression coefficients

were higher than 0.996 for all the analytes detected by UV, and higher than 0.989

for all the analytes detected in FL.

2.3. Histological analysis

Digestive gland tissues of fifteen mussels from each sampling site were fixed

in four percent paraformaldehyde (Immunofix, Bio-Optica Milano, Italy) in 0.1 M

phosphate buffered solution (pH 7.4) at 4 1C for 3 h, dehydrated in a graded series

of ethanol and embedded in Paraplast (Bio-Optica Milano, Italy), according to

standard protocols (Mauceri et al., 1999). Histological sections, 5 mm thick, were

cut with a rotary automatic microtome (Leica Microsystems, Wetzlar, Germany),

mounted on glass slides and stained with Hematoxylin/Eosin (Bio-Optica Milano,

Italy) to assess morphological features.

All observations were made with a motorized Zeiss Axio Imager Z1 microscope

equipped with an AxioCam digital camera (Zeiss).

2.4. Tissue metabolite extraction

Polar metabolites were extracted from the digestive gland tissues of fifteen

mussels from each sampling site using a ‘‘two-step’’ methanol/chloroform proce-

dure (Wu et al., 2008). Briefly, a 100 mg subsample of each frozen gland was

homogenized in 4 ml/g of cold methanol and 0.85 ml/g of cold water by using an

Ultraturrax homogenizer. The homogenates were transferred to glass vials, and

4 ml/g chloroform and 2 ml/g water were added. Samples were vortexed for 60 s,

left on ice for 10 min for phase separation, and then centrifuged for 5 min at 2000g

at 4 1C. Four hundred microliter of the upper methanol layer with polar

metabolites were transferred to glass vials, dried in fume hood overnight and

stored at �80 1C. Immediately prior to NMR analysis, the dried polar extracts were

resuspended in 100 ml of D2O (Armar AG, Dottingen, Switzerland) buffered in

240 mM sodium phosphate, pH 7.0, containing 12.5 mM 2,2-dimethyl-2-silapen-

tane-5-sulfonate (DSS) (Sigma-Aldrich Co) and vortexed. The DSS acts as an

internal standard and also provides a chemical shift reference (d¼0.0 ppm) for

the NMR spectra, while the D2O provides a deuterium lock for the NMR spectro-

meter. Fifty microliter of each resuspended sample were then pipetted into a

4 mm-diameter zirconia rotors with a spherical insert and a Kel-F cap.

2.5. High resolution magic Angle spinning (HR-MAS) 1H NMR spectroscopy

Extracts of digestive gland tissue from mussels were analyzed on a Bruker

Avance-700 NMR spectrometer operated at a spin rate of 4000 Hz (at 300 K). One-

dimensional (1-D) 1H NMR spectra were obtained using a 7.0 ms (901) pulse,

11 kHz spectral width (15.94 ppm) and 2.0 s relaxation delay with pre-saturation

of the residual water resonance, with 128 transients collected into 32.768 data

points requiring a 10.5 min acquisition time. Exponential line-broadenings of

0.5 Hz were applied before Fourier transformation. All 1H NMR spectra were

manually phased, baseline-corrected, and calibrated (DSS at 0.0 ppm) using

XWIN-NMR (version 3.5; Bruker) software. Peaks within the 1H NMR spectra

were assigned with reference to known chemical shifts and peak multiplicities

(Wishart, 2007) and by use of Chenomx NMR Suite (version 5.1; Chenomx Inc.,

Edmonton, Canada) software.

2.6. Spectral processing and multivariate data analysis

NMR spectra were converted to a format for multivariate analysis using

custom-written ProMetab 3.3 software (Viant, 2003) in MATLAB (version R2009b;

The MathWorks, Natick, MA). Each spectrum was segmented into 0.005 ppm

chemical shift bins between 0.7 and 10.0 ppm, with bins from 1.12 to 1.22 and

3.62 to 3.67 ppm (ethanol for rotor cleaning), 4.70 to 5.15 ppm (water) and 7.19 to

7.28 ppm (chloroform) excluded from all the NMR spectra. Because some peaks

shifted due to slight variations of the sample pH, nine groups of bins (2.382–2.457,

2.612–2.657, 3.247–3.297, 3.537–3.557, 3.867–3.907, 4.342–4.367, 4.622–4.627,

5.212–5.217 and 8.887–8.927 ppm) were each compressed into single bins. The

area for each segmented region was calculated and normalized to the total

integrated area of the spectra. All the NMR spectra were generalized by log

transformation (with a transformation parameter, l¼3.6�10�6) to stabilize the

variance across the spectral bins and to increase the weightings of the less intense

peaks (Wu and Wang, 2010). Data were mean-centered before Principal Compo-

nents Analysis (PCA) using the Unscrambler X package (version 10.0.1; Camo

Software AS, Oslo, NO) and the singular value decomposition (SVD) algorithm was

applied to perform a PCA with cross validation. PCA, an unsupervised pattern

recognition technique, allowed the differences and similarities between NMR

metabolic fingerprints to be visualized in a score plot, where samples that are

metabolically similar cluster together. The corresponding PCA loadings plot was

used to identify the metabolic basis of the clustering. Representative proton peaks

were normalized to total spectral area, and Student’s t tests were used to indicate

the significant metabolic changes between mussel groups (Microsoft Excel).

3. Results

3.1. PAH concentration

For PAHs molecules containing from two to five condensedrings (NA, ACY, AC, FL, PHE, AN, FA, PY, BaA, CH, BbF, BkF, BaP,DahA) recovery was from 90 to 97 percent, while for theremaining (Bghi, IP), recovery was from 99 to 100 percent.

PAH concentrations in the digestive gland samples from thereference site were lower than the instrument detection limit. Bycontrast, the samples from Priolo had elevated levels of PAHs,especially naphthalene and fluoranthene among light PAHs,benzo(a)pyrene and dibenzo(a,b)anthracene among high molecu-lar weight PAHs (Table 2).

3.2. Histological analysis

The digestive gland of M. galloprovincialis caged in the refer-ence site (Fig. 2A) showed the typical organization of the digestive

Fig. 2. Hematoxylin and Eosin (H&E) staining in the digestive gland of Mytilus galloprovincialis caged in the reference site (A) compared with those transferred to the

polluted area (B), which displayed severe histopathological alterations and relevant aggregations of haemocytes (arrow) among digestive tubules. Scale bars, 20 mm.

S. Fasulo et al. / Ecotoxicology and Environmental Safety 84 (2012) 139–146142

diverticula of bivalves, as described by Owen (1970). On thecontrary, a rather irregular digestive gland morphology of mus-sels from the polluted area was noted (Fig. 2B). The tissue wasremarkably modified and damaged, and massive haemocyticinfiltration was observed among digestive tubules.

3.3. Metabolomics analysis

3.3.1. 1H NMR spectroscopy of digestive gland tissue extracts

Fig. 3 shows a representative 1H NMR spectrum of the musseldigestive gland tissue extracts. Although several metaboliteswere identified, all spectra were found to be dominated bybetaine, taurine, homarine and glycine, known to act as osmo-lytes. Other prominent classes of compounds included aminoacids (e.g. leucine, alanine, valine), carbohydrates (e.g. glucose),tricarboxylic acid cycle intermediates (e.g. succinate), organiccompounds (e.g. acetoacetate) and nucleotides (e.g. uracil).

3.3.2. Pattern recognition analysis of 1H NMR spectra

The PCA scores plot of the 1H NMR metabolic fingerprints ofM. galloprovincialis digestive gland (Fig. 4A) shows a clear separa-tion between the two mussel groups caged in the selected sitesalong PC2 (explaining seven percent of variance). The correspond-ing PC2 loadings plot, depicted in Fig. 4B, was used to determinewhich metabolites were important in the separation of thetwo groups and the direction of their changes. In particular,peaks with positive loadings correspond to metabolites that havehigher concentrations in ‘‘stressed’’ (specimens transplanted inthe polluted area) than in the control mussels, whereas negativeloadings correspond to metabolites whose concentration isdecreased in the stressed group relative to the control. From thePC2 loadings plot, the metabolic profiles of digestive glandextracts from stressed individuals were characterized by signifi-cantly elevated levels (metabolite changes were calculated via theratio between the averages of the stressed and control peak areas,Po0.05) of valine, lysine, phenylalanine, acetoacetate, nucleo-tides such as thymidine and adenine, and an unidentified meta-bolite at 4.15 ppm, together with a decreased concentration(not significant) of glucose, glutamine and glutamate, as reportedin Table 3.

4. Discussion

The use of caged mussels has been demonstrated to be aneffective and useful tool for assessing the environmental qualitystatus and the real biological effects induced by xenobiotics

(Andral et al., 2004; Nigro et al., 2006; Regoli, 2000; Romeoet al., 2003).

In the present study, digestive glands of mussels caged for30 days in Priolo displayed relevant histological lesions such asaltered diverticula morphology and conspicuous haemocyticinfiltration. This might result in impairment of its metabolicactivities. Previous studies have provided evidence of haemocyticinfiltration in response to exposure to hydrocarbons (Cajaravilleet al., 1990) that could be interpreted as a repair process followingtissue damage (Garmendia et al., 2011).

While water physico-chemical parameters showed no signifi-cant difference between the two investigated areas, chemicalanalysis revealed high concentrations of naphthalene and fluor-anthene, indicative of pyrolytic origin of the PAHs, and benzo(a)-pyrene and dibenzo(a,b)anthracene, which are commonly theconstituents of urban and industrial contamination, in digestivegland tissue of mussels from the polluted site. These findings areconsistent with the presence of PAHs in the industrial area ofPriolo.

The environmental metabolomics approach here reported,based on 1HNMR spectroscopy, allows the successful investiga-tion of the metabolic changes in response to various environ-mental insults (Tikunov et al., 2010). PCA analysis indicated thatthe mussels caged in the natural reserve of Vendicari clusteredseparately from those transplanted in the industrial area of Priolo,suggesting a differential metabolic profile between organisms.Specifically, the PC2 loadings plot indicated the key metabolicchanges occurring in individuals acclimatized in the industrialarea (relative to the control). This metabolic fingerprint is char-acterized by increased concentrations of branched chain aminoacids (BCCA) such as valine, free amino acids, energetic metabo-lites, nucleotides and an unidentified metabolite, and depletion(not significant) of glucose and glutamate.

Amino acid levels were markedly increased in the musselscaged at Priolo. Free amino acids represent a large fraction of themetabolome of marine invertebrates (Henry et al., 1980). It hasbeen reported that free amino acids and their catabolites are usedin marine molluscs, as well as in other marine invertebrates,as the major osmolytes to balance their intracellular osmolaritywith the environment (Yancey et al., 1982). Hence, the noticeablyelevated concentration of amino acids is consistent withperturbations in osmoregulatory mechanism due to exposure totoxic compounds. In addition, these pools of amino acids, exceptfor glycine, glutamine and aspartic acid that are necessaryin the biosynthesis of nitrogenous bases, are also extensivelyinvolved in cellular energy metabolism. In fact, a metabolomicstudy on M. edulis exposed to high dose of herbicide reportedincreases in leucine and isoleucine (Tuffnail et al., 2009), and this

Fig. 3. Representative 1-D 700 MHz 1H NMR spectrum of digestive gland from mussel (Mytilus galloprovincialis) caged in the reference site, with (A) representing the

aliphatic region and (B) a vertical expansion of the aromatic region. Keys: (1) DSS, (2) isoleucine, (3) leucine, (4) valine, (5) lactate, (6) alanine, (7) arginine, (8) lysine,

(9) glutamate, (10) glutamine, (11) acetocetate, (12) succinate, (13) hypotaurine, (14) aspartate, (15) malonate, (16) choline, (17) taurine, (18) betaine, (19) glucose, (20)

glycine, (21) homarine, (22) glycogen, (23) uracil, (24) inosine, (25) fumarate, (26) tyrosine, and (27) phenylalanine.

S. Fasulo et al. / Ecotoxicology and Environmental Safety 84 (2012) 139–146 143

observation was consistent with the stimulation of metabolicactivity.

Changes in metabolites involved in energy metabolism werealso observed. Specifically, levels of lactate increased in musselstransferred to the industrial area, indicating inhibition of aerobicmetabolism (Wu and Wang, 2010). The observed depletion inglucose accompanied by the concomitant increase in lactateindicates then an enhancement in anaerobic metabolism.

In addition to the metabolic changes associated with energeticpathways, increases in acetoacetate were found in digestive glandof mussels caged in Priolo. Acetoacetate is a compound categor-ized as ketone body, and synthesized from three molecules of

acetyl-coenzyme A (acetyl-CoA) as end product of fatty acidoxidation. The increase in acetoacetate is then consistent withan alteration in lipid metabolism. Alternatively, some aminoacids, such as phenylalanine, lysine, isoleucine, leucine andtyrosine, under certain metabolic conditions can be converted toketone bodies. As a matter of fact, acetoacetate reacts withsuccinil-CoA to form succinate and acetoacetyl-CoA. The reportedincrease of succinate and fumarate allows thus to hypothesizethat the Krebs cycle would proceed towards oxaloacetate, whichcan be used as precursor to biosynthesize amino acids, purinesand pyrimidines. This was consistent with the observed signifi-cant increase of the nitrogenous bases (adenine and thymidine).

Fig. 4. (A) PCA score plot from analysis of mussel digestive gland 1H NMR spectra showing separation of mussels (Mytilus galloprovincialis) caged in the reference site (blue

square) from those transferred to the polluted area (red triangle). The ellipse represents the 95 percent confidence limit (Hotelling T2). (B) PC2 loadings plot showing the

metabolic differences between individuals acclimatized for 30 days in the selected sites. Keys: (1) isoleucine, (2) leucine, (3) valine, (4) lactate, (5) arginine, (6) lysine,

(7) glutamate, (8) glutamine, (9) acetoacetate, (10) succinate, (11) aspartate, (12) malonate, (13) glucose, (14) glycine, (15) unknown metabolite, (16) uracil, (17)

thymidine, (18) fumarate, (19) tyrosine, (20) phenylalanine, and (21) adenine. (For interpretation of the references to color in this figure legend, the reader is referred to the

web version of this article.)

S. Fasulo et al. / Ecotoxicology and Environmental Safety 84 (2012) 139–146144

In particular, mussels caged at Priolo exhibited an elevatedamount of adenine in association with presence of arginine.Arginine is the end product of the reaction between phosphoar-ginine and ADP, in which phosphoarginine is the primary highenergy phosphagen used for ATP regeneration in invertebrates(Fan et al., 1991). Thus, these data are also consistent with analteration in ATP metabolism.

Furthermore, decreased concentrations of glutamate werenoticeable in mussels caged in the industrial area of Priolo, andthis is consistent with the increased glycolytic metabolism.Glutamate serves as the precursor for the synthesis of glutamine,and is a constituent of some oligopeptides such as glutathione,which plays a central role in protective mechanisms againstoxidative insult (Storey, 1996). Glutamate is involved in multiplemetabolic pathways and plays a key role in cellular metabolism(Newsholme et al., 2003). Therefore, changes in glutamate levelsmay be correlative with response to environmental disturbances,suggesting glutamate as suitable metabolic biomarker.

5. Conclusions

Data reported in this study revealed that the highlycontaminated ‘‘Augusta-Melilli-Priolo’’ industrial area induces

marked changes in the digestive gland morphology, as wellas metabolic disturbance, in caged M. galloprovincialis individuals.Therefore, the use of caged organisms and the novelNMR-based environmental metabolomics approach demon-strated to be sensitive and effective tools for site-specificassessment of pollutant toxicological mechanisms on musseldigestive gland, which has been re-confirmed as targetorgan for bioaccumulation of toxicants. Indeed, the metabolicbiomarkers detected in this study provide evidence of theeffects of environmental pollution on mussels at the cellularlevel.

Specifically, the digestive gland metabolic profile was char-acterized by changes in the metabolites involved in energymetabolism that may indicate anaerobic fermentation and berelated to the reduced use of metabolites in the citric acid cycle.Moreover, the increase in acetoacetate is consistent with altera-tion in lipid metabolism.

Overall, results from this work demonstrate the effectivenessand sensitivity of metabolomics in ecotoxicological studies inassessing environmental influences on the health status ofaquatic organisms. Hence, further metabolomic investigationon the selected sentinel organism is needed to gain a betterunderstanding of how environmental pollution influences otherorgans.

Table 3Key up- or down-regulated metabolites in Mytilus galloprovincialis digestive gland identified by PCA analysis and presented together

with their significance (Student’s t test).

Metabolites Chemical shift and peak shape (ppm) p-Value

Amino acids

Isoleucine 0.92 (t), 1.00 (d), 1.26 (m), 1.44 (m), 1.96 (m), 3.66 (d) m 0.104

Leucine 0.94 (d), 0.96 (d), 1.66 (m), 3.71 (t) m 0.136

Valine 0.98 (d), 1.03 (d), 2.25 (m), 3.59 (d) m 0.0203

Arginine 1.68 (m), 1.90 (m), 3.23 (t), 3.74 (t) m 0.951

Lysine 1.48 (m), 1.73 (m), 1.91 (m), 3.03 (t), 3.76 (t) m 0.012

Glutamate 2.08 (m), 2.34 (m), 3.74 (t) k 0.226

Glutamine 2.12 (m), 2.44 (m), 3.75 (t) k 0.206

Aspartate 2.66 (dd), 2.79 (dd), 3.87 (dd) k 0.103

Glycine 3.54 (s) k 0.693

Tyrosine 6.89 (d), 7.19 (d) m 0.692

Phenylalanine 3.13 (m), 3.28 (m), 3.98 (m), 7.31 (d), 7.36 (t), 7.41 (m) m 0.038

Energy metabolites

Lactate 1.33 (d), 4.12 (q) m 0.286

Acetoacetate 2.22 (s), 3.41 (m) m 0.006

Succinate 2.41 (s) m 0.803

Malonate 3.13 (s) k 0.203

Glucose 3.23 (m), 3.40 (m), 3.45 (m), 3.52 (dd), 3.73 (m), 3.82 (m),

3.88 (dd), 4.63 (d), 5.22 (d)

k 0.505

Fumarate 6.51 (s) m 0.494

Osmolytes

Choline 3.21 (s), 3.52 (s), 4.07 (m) � 0.847

Taurine 3.25 (s), 3.41 (t) � 0.504

Betaine 3.25 (s), 3.89 (s) � 0.709

Homarine 4.35 (s), 7.95 (dd), 8.02 (d), 8.53 (dd), 8.71 (d) � 0.922

Nucleotides

Uracil 5.81 (d), 7.54 (d) m 0.304

Thymidine 1.88 (s), 2.36 (m), 3.76 (dd), 3.83 (dd), 4.01 (q),

4.46 (q), 6.28 (t), 7.63 (s)

m 2.61E�06

Adenine 8.18 (s), 8.21 (s) m 0.047

Unknown resonances

Unknown 4.15 (s) m 0.0034

S. Fasulo et al. / Ecotoxicology and Environmental Safety 84 (2012) 139–146 145

Acknowledgments

The authors gratefully acknowledge Prof. Mark Viant (Univer-sity of Birmingham, UK) for reading the manuscript and his usefulsuggestions. This research was supported by a National InterestResearch Project (PRIN 2007-20079FELYB).

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