Science of the Total Environment 524–525 (2015) 136–147

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Metabolomic responses in caged clams, Ruditapes decussatus, exposed toagricultural and urban inputs in a Mediterranean coastal lagoon(Mar Menor, SE Spain)

Juan A. Campillo a,⁎,1, Angel Sevilla b,c,1, Marina Albentosa a, Cristina Bernal d, Ana B. Lozano d,Manuel Cánovas d, Víctor M. León a

a Instituto Español de Oceanografía, IEO, Centro Oceanográfico de Murcia, Varadero 1, E-30740 San Pedro del Pinatar, Murcia, Spainb Department of Biotechnology, Delft University of Technology, Julianalaan, 67, Delft 2628 BC, The Netherlandsc Inbionova Biotech S.L., Edif. CEEIM, University of Murcia, 30100 Murcia, Spaind Dept. of Biochemistry and Molecular Biology B and Immunology, Faculty of Chemistry, University of Murcia, E-30100 Murcia, Spain

H I G H L I G H T S

• Metabolomic responses were characterized in caged clams• Differences in metabolite profiles were observed between control and polluted sites• Two-phase temporal pattern was described in the metabolite response to pollution• Amino acids, osmotic protectants and nucleotides were affected by pollution

⁎ Corresponding author.E-mail address: juan.campillo@mu.ieo.es (J.A. Campill

1 These authors contributed equally to this work.

http://dx.doi.org/10.1016/j.scitotenv.2015.03.1360048-9697/© 2015 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 12 January 2015Received in revised form 26 March 2015Accepted 29 March 2015Available online xxxx

Editor: Mark Hanson

Keywords:MetabolomicsPollutionToxicityBiomarkers

The Mar Menor is a coastal lagoon affected by the growth of intensive agriculture and urban development in thesurrounding area. Large amounts of chemical pollutants from these areas are discharged into El Albujón, a perma-nentwater-course flowing into the lagoon. Biomarkers such as the activity of acetylcholinesterase or antioxidantenzymes have been previously tested in this lagoon demonstrating the presence of neurotoxicity and oxidativestress in clams transplanted in sites affected by the dispersion of the effluent from El Albujón. To complete thistraditional toxicology work, a metabolomic profiling of these transplanted organisms has been carried out forthe detection of metabolic biomarkers induced by agricultural/urban pollutants. More than 70 metaboliteshave been quantified using a targeting metabolomics platform based on HPLC–MS. The intracellular metabolicpattern was analyzed by PCA from the digestive gland of clams after 7 and 22 days of transplantation. Resultsshowed a different profile of metabolite between organisms collected from control and exposed sites. At theshorter exposure time, there was an increase in several metabolites in the latter when compared with thosefrom control sites, whereas metabolic profiling at 22 days showed that those metabolites were drasticallydiminished, with even lower levels than at control sites. These metabolites included: (i) 12 amino acids fromthe 21 proteogenic and HomoSer, (ii) osmotic protectants such as γ-butyrobetaine and taurine and (iii)nucleotides such as ITP. Regarding sulfur-containing molecules, taurine could be highlighted as a potentialbiomarker since its concentration was reduced by more than 30 times after 22 days of exposure, whereas theantioxidant glutathione remained constant in the organisms from both control and exposed sites. Althoughtargetedmetabolomics has been shown as an early technique of pollutant effect detection, the two-phase patterncould highlight a more complicated metabolite response to pollutants than classical biomarkers.

© 2015 Elsevier B.V. All rights reserved.

o).

1. Introduction

The assessment of the biological significance of the detected levels ofpollutants in marine environments and their deleterious effectsrequires, besides chemical quantification, the use of other indicatorsrelated with their biological effects on organisms. Nowadays, the use

137J.A. Campillo et al. / Science of the Total Environment 524–525 (2015) 136–147

of biomarkers (sublethal physiological or biochemical responses inorganisms exposed to contaminants) inmarine species looks promisingin pollution monitoring programs as they play a valuable role inassessing whether or not adverse health effects in individual organismsare occurring, and can be used to provide an early diagnosis of disorderscaused by anthropogenic contaminants (Lyons et al., 2010).

Coastal lagoon environments are characterized by being isolatedfrom the open sea, which makes them highly vulnerable to impacts.This is the case of the studied area, the Mar Menor lagoon (Murcia, SESpain), which receives a wide variety of chemical pollutants associatedwith anthropogenic activities. Its ecological equilibrium is threatenedby massive urban growth and intensive agricultural activity (Conesaand Jiménez-Cárceles, 2007). The lagoon receives water run-off fromthe coastal plain of Campo de Cartagena, which is one of the mostimportant intensive agricultural areas in Europe. At the present time,El Albujón watercourse constitutes the main collector in the Campo deCartagena drainage system (García-Pintado et al., 2007), maintaining aregular flux fed by groundwater (drainage of irrigated crops) that isonly continuous in the last 3–8 km, depending on the season (Velascoet al., 2006).

At present, metabolomics represents an emerging approach toassess the health status of organisms and the impact ofmarine pollutionon them. Metabolomics is the study of the complete set of metabolites/low molecular weight intermediates, which are context dependent,varying according to the physiology, development or pathologicalstate of the cell, tissue, organ or organism. Environmentalmetabolomicscharacterizes the metabolic responses of an organism to both naturaland anthropogenic stressors that can occur in its environment (Viant,2007), being a powerful approach for discovering biomarker profilesof toxicant exposure and disease, and for identifying the metabolicpathways involved in such processes (Robertson, 2005). This approachhas been proven to be highly sensitive for the detection of effects asso-ciated with both drugs and environmental toxicants, in that metabolicperturbations often present much earlier than other pollutant-inducedchanges (Jones et al., 2008a). A number of studies on marine bivalvesreported the application of the metabolomic to characterize themetabolic responses and toxicity of specific contaminants (Zhanget al., 2011a,b; Wu and Wang, 2010). These studies showed how theexposure to chemical pollutants such as lead, for example, can causeneurotoxicity, disturbances in energy metabolism, changes in osmoticregulation or alteration of lipid metabolism (Fasulo et al., 2012; Wuet al., 2013; Ji et al., 2015). However, the majority of these studieswere performed under controlled conditions in the laboratory, mainlyto study the metabolomic alterations as a result of stress conditions(salinity variations, pCO2, etc.) or specific contaminant exposure, e.g.to trace metals. Moreover, those studies were carried out using almostexclusively NMR, which presents some drawbacks such as low sensitiv-ity (Wishart, 2013), and may therefore have missed some importantmetabolites in low abundance. HPLC–MS could thus be the perfectpartner to validate the obtained results and to find new biomarkers ofinterest.

Different studies have shown the usefulness of marine clams assentinel organisms for the detection of the impact of environmentalpollution in coastal waters through the application of different bio-markers (Bebianno et al., 2004; Nasci et al., 1999, 2000). These biomon-itoring studies have employed native populations of bivalves ororganisms that have been transplanted from a reference site to apolluted area (Rank et al., 2007; Tsangaris et al., 2010, 2011). This latterstrategy, called active biomonitoring, is based on comparing chemicaland/or biological properties of samples collected from one populationthat, after randomization and translocation, has been exposed todifferent environmental conditions at monitoring sites (Roméo et al.,2003). This approach avoids bias related to the age and the reproductivestatus of the organisms and allows for better control of the accumula-tion and biological effects of contaminants over a predeterminedexposure period (Tsangaris et al., 2011). In the present study, a native

clam of the Mar Menor, Ruditapes decussatus, was selected as there isan important bed of this species located in the northern area of thelagoon. This bed is located far from agricultural, urban and industrialinfluences.

In a previous study (Campillo et al., 2013), the effect of the input ofpollutants through El Albujón watercourse on the water quality of theMar Menor was assessed by means of the biological effects in thesame clams R. decusssatus, as those used here. Thus, a multi-biomarkerapproachwas applied in transplanted clams caged near thewatercoursemouth. Biomarkers included biochemical measurements whichrepresent important endpoints of particular chemicals or mixturesexpected in the study area: acetylcholinesterase to test neurotoxicity,antioxidant enzyme levels to test for oxidative stress and bioenergeticssuch as SFG (scope for growth) used to detect general stress effectson the health status of clams. These biomarkers demonstrated thatthe transplanted organisms were exposed to a complex mixture oftoxicants which induced toxic effects in them at different biologicallevels (oxidative stress, neurotoxicity and physiological), whichcan be used as warning signals of environmental disturbance(Campillo et al., 2013).

In the present study, an HPLC–MS-based platform has been appliedto simultaneously measure more than 70 metabolites in order tocharacterize the metabolic responses to environmental pollution inclams caged in the Mar Menor lagoon under the influence of El Albujónwatercourse. In this area, clams were exposed to real environmentalconditions (agriculture and urban impacted area), which constitutes arelevant and new contribution, especially when exposure concentra-tions to more than 90 contaminants in seawater were simultaneouslydetermined. Moreover, this study compares the responses of clams todifferent grades of exposure to contaminants present in this lagoon(mainly pesticides and pharmaceuticals) and at different exposuretimes. The results obtained have been used to identify potentialbiomarkers and the effect of the contaminants on their metabolisms.Furthermore, they were compared with more conventional biomarkersin order to obtain a more detailed understanding of pollutant effects onthis species. The digestive gland was used as the target organ because itaccumulates pollutants and participates actively in xenobiotic metabo-lism. It is also involved in immune defense, detoxification and inhomeostatic regulation (Fasulo et al., 2012).

2. Materials and methods

2.1. Study area and experimental design

TheMarMenor lagoon is a shallow coastal basin located in the SE ofSpain and connected with the Mediterranean Sea principally throughthree sea channels (Fig. 1). The general circulatory pattern of the lagoonmakes it possible to differentiate three basins: the northern basin,which shows a higher Mediterranean influence and lower hydraulicresidence time than other basins; the southern basin, which is themost confined area; and the intermediate central basin, correspond-ing to the mixing area of Mediterranean and lagoon waters (Pérez-Ruzafa et al., 2005). The lagoon receives a wide variety of chemicalpollutants associated with anthropogenic activities by means of sev-eral wadis of which the most important is El Albujón, located in thecentral basin (references detailed in Table 1). This watercourse isthe main collector of residues from the agricultural products usedin the CampodeCartagena (García-Pintado et al., 2007), and of effluentsfrom the urban wastewater treatment plant of the nearby town of LosAlcázares.

Experimental design was the same as described in Campillo et al.(2013). In short, two thousand native R. decussatus clamswere collectedfrom a clean area in the northern basin. The clams were maintained for10 days in the laboratory using clean filtered seawater and feedingwiththe microalgae Isochrysis galbana (clone T-ISO). They were then placedin baskets used for oyster culture and put in stainless steel cages

Fig. 1. Locations in the Mar Menor coastal lagoon for clams field exposure and circulatory patterns inside the lagoon.

138 J.A. Campillo et al. / Science of the Total Environment 524–525 (2015) 136–147

immersed at 3 sites (Fig. 1), two of themwere used as reference sites, S1and S2. S1 was located in the northern basin, and S2 was locatedupstream of the El Albujón watercourse mouth, near the Los Alcázareswaterfront, sites which according to the lagoon's circulatory systemare not affected by the dispersion of the effluent from the wadi. Thethird site, S3, was located close to the El Albujón watercourse, 0.5 kmdownstream from the wadi mouth. According to the main currentsfound in this area, S3 is directly affected by the input from the said wa-tercoursemouth. Once the cageswere immersed, a systematic sampling

Table 1Maximum concentrations of the predominant organic pollutants found in El Albujónwatercourprevious studies.

Contaminant group Predominant contaminant Surface watercourse

Surfactants (μg L−1) Linear alkylbenzene sulfonates 42.1Alcohol polyethoxylates 5.4Nonylphenol polyethoxylates 1.7

PAHs (ng L−1) Naphthalene 27.91

Acenaphthene 20.81

Triazines (ng L−1) Simazine 8.01

Terbuthylazine 33.91

Terbuthylazine-desethyl 67.61

Terbumeton 9.51Other pesticides (ng L−1) Chlorpyrifos 59.51

Propyzamide 63.11

Pendimethalin 9.11

Flutolanil 20.51

Pharmaceuticals (ng L−1) Azythromycin 162.9Salicylic acid 156.3Hydrochlorotiazide 73.5Acetaminophen 71.4Ketoprofen 40.7Ibuprofen 36.7Valsartan 36.3

campaign was developed to characterize levels of pollutants in water,temperature, pH, dissolved oxygen and salinity at each sampling pointduring the first 7 days, the results having been published elsewhere(Campillo et al., 2013).

Clam samples were taken at 7 and 22 days for metabolomics analy-ses. Five clams were randomly collected from the baskets at each site ineach sampling. Immediately after collection, digestive gland organswere dissected, frozen in nitrogen liquid and stored at −80 °C untilfurther analysis was performed.

se water and in seawater close to this watercourse in autumn (2009 and 2010) obtained in

Seawater close to watercourse mouth Reference

110.0–140.0 Traverso-Soto et al. (2015)5.0–10.00–0.513.32 1Moreno-González et al. (2013a)

2Moreno-González et al. (2013b)1.42

16.52 1Moreno-González et al. (2013a)2Moreno-González et al. (2013b)13.02

84.22

7.22

45.82 1Moreno-González et al. (2013a)2Moreno-González et al. (2013b)42.92

1.22

4.02

Moreno-González et al. (2014)

139J.A. Campillo et al. / Science of the Total Environment 524–525 (2015) 136–147

2.2. Metabolomics analyses

2.2.1. ChemicalsStandard metabolites were generally supplied by Sigma Aldrich

(St Louis MO, USA), but glycine and histidine were provided by Merck(Madrid, Spain); phenylalanine, tryptophan, and chemicals for eluents(acetonitrile, acetic acid, ammonium acetate, ammonium hydroxide,and water) were obtained from Panreac (Barcelona, Spain). Allchemicals were at least HPLC grade quality.

2.2.2. Metabolic analysis of the clam digestive gland by HPLC–MSMetabolite extraction was based on the work of Bernal and collabo-

rators (Bernal et al., 2013). In brief, either samples from the previousstep or standard mixtures were re-suspended in 2 mL of extractionsolution (acetonitrile + 10mMKH2PO4 (3:1 v/v) at pH 7.4) and subse-quently incubated in a wheel for 30 min at 4 °C. This homogenate wasthen centrifuged at 15,000 ×g for 20min at 4 °C. Secondly, the superna-tant was split and added to 4mL of chloroform and centrifuged again at15,000 ×g for 5 min. This yielded a biphasic system, from which theaqueous phase was harvested. This process was carried out twicemore. The extraction procedure was finished by filtering through a ster-ile 0.2 μm filter before being analyzed. It is important to point out thatlyophilising or freezing during and after the extraction procedureshould be avoided. Therefore, sampleswere preparedwhen the analysisplatform was ready to avoid potential metabolic degradation, since

Table 2Quantified metabolites in digestive gland of caged clams.

Abbreviation Metabolite Abbrevi

2PG 2-Phosphogliceric acid HomoCy

3PG 3-Phosphogliceric acid HomoSe

8-Oxo-dG 8-Hydroxy-deoxyguanosine Hydroxy

AcCoA Acetyl-CoA HypoxanAcetyl-P Acetylphosphate Ileu

Ac-Gln N-acetyl-L-glutamine IMPADP Adenosine diphosphate ITPAla L-Alanine Leu

AMP Adenosine monophosphate Lys

Arg L-Arginine Met

Asn L-Asparagine NAD

Asp L-Aspartic acid NADH

ATP Adenosine triphosphate NADPBiotin Biotin NADPHCarnitine L-Carnitine OPE

CDP Cytidine diphosphate Orn

CDP-choline CDP-choline Phe

CIR L-Citrulline Pro

CMP Cytidine monophosphate P-serineCoA Coenzyme A Ser

CTP Cytidine triphosphate TaurineCys L-Cysteine THF

Cystin L-Cystine ThiaMP

DA 2′-Deoxyadenosine Thr

dATP Deoxyadenosine triphosphate ThymididCTP Deoxycytidine triphosphate ThymineFMN Riboflavin-5′-monophosphate TMPGBB γ-Butyrobetaine Trp

GDP Guanosine diphosphate TTPGln L-Glutamine Tyr

Glu L-Glutamic acid UDP

GlucN6P Glucosamine 6-phosphate UMPGlucosamine Glucosamine UTPGly Glycine Val

GMP Guanosine monophosphate XMPGSH L-GlutathioneGSSG L-Glutathione oxidized formGTP Guanosine triphosphateHis L-Histidine

lyophilisation has been proven to alter the composition of metabolicmixtures (Bernal et al., 2013; Oikawa et al., 2011).

Previous to the quantification process, unequivocal identification ofthe metabolites included in Table 2 was performed using the retentiontime and relative intensities of the diagnostic ions of a pool of samples,see Bernal et al. (2013) for details.

Measurements for quantification were conducted using single ionmonitoring (SIM) using the SIM mode for the m/z of each compound.The separation was carried out as previously described (Bernal et al.,2013) using a ZIC-HILIC column (Merck SeQuant, Marl, Germany) witha gradient method at flow rate of 0.7 mL/min. Mobile phases were20 mM ammonium acetate (adjusted to pH 7.5 with NH4OH) in H2O(solvent A) and 20 mM ammonium acetate in AcN (solvent B). Gradientelution was performed, starting with 0% A and increasing to 80% A over30 min, then back to starting conditions (80–0% A) for 1 min followedby a re-equilibration period (0% A) of 14 min (total run time, 45 min).Data were acquired by a PC using the Agilent Chemstation softwarepackage provided by the HPLC manufacturer. Afterwards, EasyLCMS(Fructuoso et al., 2012) was used for automated quantification.

2.2.3. Quality controlThe quality of the results was assessed by: (i) checking the extrac-

tionmethodwith standardmixtures; (ii) the use of an internal standard(IS); and (iii) quality control samples (QC). The extraction method wasvalidated by comparing the concentration of standard mixtures with

ation Metabolite

s L-Homocysteiner L-HomoserinePro L-Hydroxyproline

Hypoxanthine

L-IsoleucineInosine monophosphateInosine triphosphateL-Leucine

L-Lysine

L-MethionineNicotinamide adenine dinucleotide oxidized form

Nicotinamide adenine dinucleotide reduced form

Nicotinamide adenine dinucleotide phosphate oxidized formNicotinamide adenine dinucleotide phosphate reduced formO-phosphorylethanolamine

L-Ornithine

L-Phenylalanine

L-ProlineO-phospho-L-serine

L-SerineTaurineTetrahydrofolic acid

Thiamine monophosphate

L-Threoninene Thymidine

ThymineThymidine monophosphateL-TryptophanDeoxythymidine triphosphate

L-TyrosineUridine diphosphate

Uridine monophosphateUridine triphosphateL-ValineXanthosine monophosphate

140 J.A. Campillo et al. / Science of the Total Environment 524–525 (2015) 136–147

andwithout the extraction process. Recoveries were higher than 85% inall of the analyzed metabolites (results not shown). N-acetyl-L-glutamine (m/z 189) was added as IS since normalization withN-acetyl-glutamine gave similar results to isotope-labeled standardsfor several metabolic groups including nucleoside bases, nucleosides,nucleotides, amino acids, redox carriers (NAD+, NADP+), and vitamins,among others (Bajad et al., 2006), reaching a final concentration of50 μM in each analyzed sample, and the analysis was monitored bycontrolling that the internal standard area and retention time werealways within an acceptable range. Acceptable coefficient of variationwas set at 20% for the peak area and 2% for retention time.

With respect to quality control samples, two types of QCs wereincorporated in the analysis: (i) a pool of samples and (ii) a pool of stan-dards. QC analysis was performed in all of the analyzed metabolites inthe standard pool samples, and in all of those in which concentrationswere over the quantification limits in the sample pools. This was carriedout by comparing the corrected areas. For the standard pool, the theo-retical corrected area was calculated for the measured concentration.Regarding the pool of samples, the corrected areas were comparedamong all of the samples. An acceptable coefficient of variation wasset at 20% for the peak area and 2% for retention time. QC sampleswere included in the analysis of all 20 biological samples. Additionally,samples were randomly introduced into the analysis.

2.3. Statistical analysis

Concentrations of themetabolites were normalized by theweight ofthe tissue, scaled by mean subtraction, and divided by the standarddeviation of each metabolite (autoscaling), since metabolite concentra-tions were separated by several orders of magnitude and PCA is scaledependent. Moreover, this scaling method has been demonstrated toperform optimally with regard to biological expectations (van denBerg et al., 2006). The PCA Methods R package (Stacklies et al., 2007)was used to perform the PCA on the concentrations after 7 days and22 days of transplantation. The differences among the four groupswere checked by using a one-way ANOVA, followed by Tukey's HSDpost-hoc of the PC1 scores of the samples since normality and homosce-dasticitywere testedusing Shapiro–Wilk andBartlett tests, respectively.Metabolites found to be statistically significant for the PC1 by statisticalhypothesis test (Yamamoto et al., 2012) were selected for subsequentanalysis. Shapiro–Wilk was used to check for normality of thosemetabolites. Since some metabolites were not normally distributed,the Kruskal–Wallis test followed by the Mann–Whitney test was usedto determine significant statistical differences. The false discovery ratewas controlled using the Benjamini–Hochberg FDR correction(Benjamini and Hochberg, 1995). Additionally, metabolite levels werecompared between 7 days and 22 days of transplantation usingWilcoxon signed-rank tests as the normal distribution assumptionwas violated.

3. Results

3.1. Chemical pollution in the lagoon

For a better understanding of the metabolite fingerprints shown inthe present study, pollutant levels found in El Albujón watercourseand in Mar Menor surface waters, sediment and biota samples fromthe studied area are briefly summarized below. Data has already beenpublished (Moreno-González et al., 2013a, b; 2014; Traverso-Sotoet al., 2015; Campillo et al., 2013; León et al., 2013).

The main input of pollutants through El Albujon watercourse isassociated with the water run-off of the intensive agricultural activityfrom the coastal plain of Campo de Cartagena. The contaminant inputof this watercourse is characterized by insecticides during summerand herbicides during winter (Moreno-González et al., 2013a). Otherchemical pollutants such as pharmaceutical compounds (Moreno-

González et al., 2014) or surfactants (Traverso-Soto et al., 2015) werealso detected at high concentrations. These pollutants come mainlyfrom the effluent from a nearby urban wastewater treatment plant(Los Alcazares WWTP). In both cases, agricultural or urban inputs, thelevels of these compounds in the water bodies can vary over timedepending on factors such as sporadic discharges, seasonal use, waterflow and weather events.

A similar seasonal pattern of agricultural pollutants was observed inseawater fromdifferent points throughout the lagoon, themost ubiquitouspollutants in the lagoon waters being: chlorpyrifos, chlortal-dimethyl,terbuthylazine, naphthalene and propyzamide (Moreno-González et al.,2013b). A further study, restricted to the areawhere the cageswere placed(S1–S3) (Campillo et al., 2013), detected between 27 and 32 pollutants inthe 3 sampling areas, including PAHs, triazines and organophosphoruspesticides (OPs), among others. More than 8 compounds displayedmaximum concentrations higher than 20 ng L−1 in S2, and S3, butonly 3 in S1. Mean concentrations of PAHs and triazines were similarbetween all three areas, although notable differences were observedfor the OPs detected. In this case, chlorpyrifos and chlorpyrifos-methylwere the most commonly found. Mean concentrations of chlorpyrifos,pendimethalin and methyl-chlorpyrifos in S3 (52 ± 49, 24 ± 16 and13 ± 8 ng L−1, respectively) were higher than in sampling areas S1and S2 (Campillo et al., 2013). A wide range of chlorpyrifos concentra-tions was detected in S3, 3.0–199.3 ng L−1. Similar concentrationswere also detected for propyzamide, tributhylphosphate and chlortaldimethyl in all 3 sites. With regard to triazines, terbuthylazine-desethyl, terbuthylazine and propazine were detected in the majorityof water samples from all sites, with maximum concentrations rangingbetween 16.5–28.1, 7.0–12.5 and 8.2–12.4 ng L−1, respectively. Othertriazines (simazine, atraton, atrazine, prometryn, prometon) wereonly detected in a small percentage of water samples.

In the case of pollutants bioaccumulated in the caged clams, p,p′DDEand PCB concentration in S3 clams were double initial values at the endof exposure period (Campillo et al., 2013), indicating that althoughthese pollutants were not detected in the water samples near thecages, there must be an input via this route into the lagoon, as has alsobeen evidenced in other bivalve species (León et al., 2013).

3.2. Metabolic profiling at 7 days after transplantation

Fig. 2 depicts the PCA from clams after 7 days of transplantation inwhich PC1 and PC2 conglomeratedmore than 30% of the total variance.The PCA score plot showed that samples from the different sites couldbe separated according to the PC. The statistical significance of thedifference was confirmed by a one-way ANOVA followed by TukeyHSD post-hoc analysis using the mean of the PCA scores of the PC1.This analysis showed statistically significant (p b 0.05) differencesbetween the three sites. According to the loading plot in Fig. 3, almostall measured metabolites were involved in the PC1, which suggestedthat the site location extensively altered the global metabolic pattern.Moreover, the contribution of themajority of themetabolites is positive,and it therefore seems thatmetabolite levels in S2 and S1 (control)werelower than in S3, although differences between S2 and S1 were alsoobserved.

Those metabolites found to be significant for the PC1 by the statisti-cal hypothesis test are shown in Table 3. Since metabolite levels couldviolate the assumption of homogeneity of variance, non-parametrictests were applied in order to assess the statistical significance of theirdifferences, as detailed in the Materials and methods section. Indeed,all these metabolites were also found to be significant using theKruskal–Wallis test. In general, metabolite levels in S3 and S2 weresignificantly higher compared with those in S1. More particularly,numerous amino acids (Ala, Arg, Asp, Gln, Glu, Gly, His, Lys and Tyr),the amino acid intermediateHomoSer, GBB (an osmoprotectant derivedfrom L-carnitine) and the nucleotide ITP presented higher levels in S3than the control site S1 with statistical significance (p b 0.05 in S3–S1

Fig. 2. PCA score plot for the animals of the three location groups, namely S1, S2 and S3 (n=5) after 7 days of the transplantation (PC1 vs. PC2). Confidence regions (95%) aremarkedwithcontinuous ellipses. Locations are abbreviated as in Fig. 1.

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comparison Table 3). This group of amino acids includes Ala, Arg, Asp,Glu and Gly which are quantitatively the most important amino acidsin bivalves, and non-essential as they can be formed from carbohydrate(Gabbot, 1976). The clams collected from S2 showed a similar pattern ofmetabolites to those from S3, with concentrations of Ala, Arg, Asp, GBB,Gln, Glu, Gly, His, ITP and Lys significantly higher than found in thosefrom S1. However, the levels of some of these metabolites (Ala, Asp,Gln, Glu, Gly, and HomoSer) were significantly lower in S2 comparedwith S3.

3.3. Metabolic profiling at 22 days after transplantation

The PCA score plot from clams after 22 days of transplantation isrepresented in Fig. 4. Similarly as above, PC1 and PC2 conglomeratedmore than 30% of the total variance. The PCA score plot showed apotential separation along the PC1 confirmed by a one-way ANOVAand ulterior post-hoc analysis which showed significant (p b 0.01)differences between the three sites. In accordance with the results at7 days of transplantation, no clear metabolites were involved in thePC1 (Fig. 5), suggesting a global contribution from all themeasuredme-tabolites. Even though the contribution of the majority of the metabo-lites is positive, as before, significant differences were observed whencomparing the relative position of the sites. In particular, S3 sampleswere translated to the negative part of the PC1 axis, which highlighteda strong fall in the metabolite levels, as will be confirmed below.

Using the statistical hypothesis test, several metabolites were foundto be statistically significant for the PC1 (see Table 4) and, therefore,thesemetabolites should present notable differences among the groups,this being subsequently confirmed in the non-parametric test (Table 4).It is important to mention that no statistical significant differencewhatsoever was found in any of the metabolites of the control location

(S1) between 7 days and 22 days of transplantation using theWilcoxonsigned-rank test, as shown in Table 4. In contrast, S2 and S3 locationswere statistically affected by the time of exposure. Globally, S3 locationmetabolic levels were notably diminished 22 days after transplantation(several of them by evenmore than one order of magnitude) comparedwith those obtained at 7 days. Moreover, some amino acids (Ala, Gln,and Leu) and osmoprotectants (taurine and carnitine) presentedlower levels with statistical significance compared with the controlsites S1 and S2 (p b 0.05 in S3–S1 and S3–S2 comparisons, seeTable 4). This is a surprising result since themajority of them presentedhigher levels than S1 after 7 days of transplantation. Other amino acids(Arg, Asp, Glu, Gly, and Lys) showed decreased levels in S3 comparedwith those obtained at 7 days, but although these levels showed nosignificant differences compared with S1, their concentrations weresignificantly lower than those found in the control site S2. In contrastto these results found in S3, the metabolite levels found in S2 after22 days showed the same pattern as that detected after 7 days, charac-terized by significantly higher concentrations of the amino acids Ala,Gln, Glu, Gly, HomoSer, and Lys, as well as of the metabolites 2PG andITP, compared with S1 clams.

4. Discussion

Different studies have demonstrated that caged bivalves transplantedfrom a reference site to a polluted area (active biomonitoring) areeffective tools for the assessment of the biological effect of pollutantsand the quality of marine environments (Rank et al., 2007; Tsangariset al., 2010). Most of these studies focus on the application of severalbiomarkers recommended by different international pollutionmonitor-ing programs (OSPAR, 2010; ICES, 2013). Our previous study using thisactive biomonitoring approach has demonstrated the impact of the

Fig. 3. PCA loading plot for the animals of the three location groups after 7 days of the transplantation. This figure reveals metabolites which have strong influence in PC1 and PC2.

142 J.A. Campillo et al. / Science of the Total Environment 524–525 (2015) 136–147

main input of pollutants in the lagoon using these traditionalbiomarkers (Campillo et al., 2013), revealing the higher exposure ofthe S3 clams to a mixture of chemicals dissolved in their surroundingwaters, the concentrations of some compounds, such as chlorpyrifos,being higher than those specified by EQS (Directive 2008/105/EC) forsurface waters. Several works have evaluated the toxicity on aquaticorganism of a number of pesticides and herbicides detected in the

Table 3Concentrations and p-values after 7 d of transplantation for the selected metabolites foundKruskal–Wallis test followed by the Mann–Whitney test with the Benjamini–Hochberg FDRdivided into three groups corresponding to the three stations, namely, S1, S2, and S3. Abbrstandard error, n = 5).

Metabolites S1 S2 S3

Ala 282.1 ± 18.7 921.5 ± 86.3 2105.3 ±Arg 16.4 ± 0.7 39.1 ± 2.1 42.4 ±Asp 57.6 ± 3.9 150.3 ± 10.3 208.7 ±GBB 0.3 ± 0.2 2.8 ± 0.2 3.1 ±Gln 10.4 ± 0.4 15.8 ± 0.6 26.2 ±Glu 59.3 ± 3.2 106.0 ± 8.1 151.3 ±Gly 362.9 ± 29.7 4627.3 ± 634.1 13951.6 ±His 0.08 ± 0.08 6.88 ± 2.14 10.36 ±HomoSer 13.0 ± 1.4 16.0 ± 1.6 41.3 ±Ileu 178.4 ± 18.9 113.2 ± 13.1 239.9 ±ITP 98.4 ± 15.6 886.6 ± 212.1 1097.6 ±Leu 9.4 ± 1.9 7.7 ± 0.9 21.8 ±Lys 6.7 ± 0.7 26.1 ± 2.0 32.1 ±Trp 5.7 ± 1.2 3.7 ± 0.5 9.0 ±UMP 122.0 ± 13.0 65.4 ± 9.3 49.6 ±

⁎ p b 0.05.⁎⁎ p b 0.01.

Mar Menor lagoon (for example diazinon) (Moreno-González et al.,2013b). These studies identified significant metabolic perturbations tothe early life stages of marine organisms (Viant et al., 2006a,b). Otherhydrological parameters such as salinity, dissolved oxygen and temper-ature showed no great differences between sites (Campillo et al., 2013).In the present paper, the metabolomic response of digestive gland cellsfrom the same clams has been studied in order to identify themetabolic

statistically significant for the PC1 by statistical hypothesis test of factor loading in PCA.correction was used to analyze between-group differences. Experimental samples wereeviations are described in Table 2. Concentration values are given in nmol/g (mean ±

ANOVA S2–S1 S3–S1 S3–S2

221.9 0.003⁎⁎ 0.02⁎ 0.02⁎ 0.02⁎

3.9 0.018⁎ 0.03⁎ 0.03⁎

19.9 0.005⁎⁎ 0.03⁎ 0.03⁎ 0.037⁎

0.4 0.018⁎ 0.03⁎ 0.03⁎

3.6 0.005⁎⁎ 0.03⁎ 0.03⁎ 0.037⁎

10.1 0.003⁎⁎ 0.02⁎ 0.02⁎ 0.02⁎

461.6 0.003⁎⁎ 0.02⁎ 0.02⁎ 0.02⁎

1.31 0.012⁎ 0.027⁎ 0.027⁎

5.1 0.007⁎⁎ 0.03⁎ 0.03⁎

31.9 0.009⁎⁎ 0.037⁎

175.9 0.015⁎ 0.03⁎ 0.03⁎

3.6 0.015⁎ 0.037⁎

3.3 0.01⁎ 0.03⁎ 0.03⁎

1.3 0.02⁎ 0.037⁎

5.7 0.018⁎

Fig. 4.PCA score plot for the animals of the three location groups, namely S1, S2 and S3 (n=5) after 22 days of the transplantation (PC1 vs. PC2). Confidence regions (95%) aremarkedwithcontinuous ellipses. Locations are abbreviated as in Fig. 1.

143J.A. Campillo et al. / Science of the Total Environment 524–525 (2015) 136–147

pathways involved in the toxic responses observed. In this regard, itmust be noted that the majority of published studies on environmentalmetabolomics in bivalves have been carried out under laboratorycontrolled conditions (Bussell et al., 2008; Tuffnail et al., 2009; Liuet al., 2011), whereas in this case animals were maintained underfield conditions,whichmight give amore realistic view of themetabolicevolution in impacted areas.

4.1. Metabolic profiling at 7 days after transplantation

After PCA analysis at 7 days after transplantation, the PC score plot(PC1 vs. PC2) showed clear separations along the PC1 axis betweenthe three groups of clams (Fig. 2). There were significantly higher levelsof several metabolites in the S3 and S2 locations with regard to S1,mainly some of the 21 proteogenic amino acids, but also compoundssuch as GBB, an osmotic protectant, and nucleotides such as ITP. More-over, S2metabolite levelswere lower than those observed in S3.Most ofthe amino acids increased bymore than four times in S3 and, in the caseof Gly, by as much as one order of magnitude. Free amino acids (FAA)represent a large fraction of the metabolome of marine invertebrates(Cappello et al., 2013). The main pathways by which FAA may beincreased or decreased in bivalve tissues might operate at one of fourpoints: assimilation from particulate food or medium; the balancebetween synthesis and catabolism; the equilibrium between proteinsand FAA; and by control of excretion (Bayne et al., 1976). Althoughthe mechanism of control of the FAA pool remains to be clarified inthese species, it is clear that exposure to toxic compounds could alsoaffect their concentrations (Fasulo et al., 2012; Wu and Wang, 2010).

It has been reported that FAA and their catabolites are used inmarine mollusks as the major osmolytes to balance their intracellular

osmolarity due to salinity changes in the environment (Shumway andYoungson, 1979). Taurine, glycine and alanine constitute the mostrepresentative amino acids in cell volume regulation in bivalves(Livingstone et al., 1979). In fact, several studies showed that intra-cellular pool concentrations found in soft tissue of populations ofMacoma balthica and Mytilus spp. are significantly correlated with thehabitat osmolarity (Kube et al., 2007). In this study, clams were cagedin sites with similar salinity values, with mean values ranging from40.96 to 41.61 (Table 2 in Campillo et al., 2013), so it seems thatamino acid levels cannot be explained by inter-site differences in salin-ity. The elevated concentration of amino acids and detected in S3mighttherefore be related to perturbations in the osmoregulationmechanismof clams. These perturbations could be caused by exposure to toxiccompounds. In agreement with our results, several studies found anincrease in the concentration of several proteogenic amino acids afterthe addition or the presence of organic pollutants (Day et al., 1990;Wu andWang, 2010; Zhang et al., 2011b; Fasulo et al., 2012). Moreover,Cappello et al. (2013) found an increase of amino acid levels in gills inmussels caged in an anthropogenic-impacted area of the Siciliancoastline, as well as those of several osmolytes (hypotaurine, taurine,betaine and homarine), which may indicate disturbances in osmoticregulation due to the adverse environmental conditions.

The concentrations of each amino acid in the tissues could be regu-lated by the different processes in which they are involved. In the caseof Ala, which plays an important role in the osmoregulation of bivalves,it constitutes the greatest proportion of the end product of anaerobicglucose breakdown, together with the metabolite of succinate in inver-tebrates (Zhang et al., 2011a, 2011b). This amino acid could increase inthe tissue of bivalves under environmental conditions of anoxia(Sokolowski et al., 2003), and after exposure to different contaminants

Fig. 5. PCA loading plot for the animals of the three location groups after 22 days of the transplantation. This figure reveals metabolites which have strong influence in PC1 and PC2.

144 J.A. Campillo et al. / Science of the Total Environment 524–525 (2015) 136–147

such as benzo(a)pyrene (Zhang et al., 2011b) or the herbicide glypho-sate (Hanana et al., 2012), suggesting impairment of the oxidativemetabolism. In agreement with these works, the high accumulation ofAla in S3 clamsmight be relatedwith an alteration of the aerobicmetab-olism by chemical pollutants, which is also supported by the elevatedITP concentrations in clams from S3 and S2 cages. In marine bivalves,ITP is also a product of the phosphoenolpyruvate carboxykinase

Table 4Concentrations and p-values after 22 d of transplantation for the selected metabolites foundKruskal–Wallis test followed, by the Mann–Whitney test with the Benjamini–Hochberg FDR cowere compared between 7 d and 22 d of transplantation using Wilcoxon signed-rank tests. Enamely, S1, S2, and S3. Abbreviations are described in Table 2. Concentration values are given7 d and 22 d transplantation results.

Metabolites S1 S2 S3

2PG 118.3 ± 28.8 279.8 ± 26.3 44.4Ala 442.2 ± 73.4 1758.5 ± 137.8a 155.9Arg 28.0 ± 9.3 40.7 ± 1.8 10.3Asp 116.9 ± 42.9 213.6 ± 21.6a 93.3Carnitine 5.3 ± 1.6 7.5 ± 0.9a 0.1GBB 0.7 ± 0.4 3.6 ± 0.2a 0.2Gln 15.6 ± 2.5 23.3 ± 1.2a 7.9Glu 82.7 ± 20.8 177.3 ± 16.9a 23.4Gly 876.4 ± 332.4 11680.1 ± 825.1a 355.3HomoSer 17.5 ± 3.1 32.5 ± 2.3a 10.1ITP 142.4 ± 60.6 1100.3 ± 208.3 121.3Leu 13.5 ± 5.0 18.9 ± 2.1a 0.2Lys 8.8 ± 5.1 33.3 ± 2.4 2.3Taurine 11548.3 ± 1189.9 11076.2 ± 1667.6 360.2

⁎ p b 0.05.⁎⁎ p b 0.01.

catalyzed carboxylation of PEP (phosphoenolpyruvate) under anaerobicconditions (Gabbot, 1976). After 7 days, clams collected at S3 and S2also exhibited an elevated amount of Arg compared to S1. The concen-tration of Arg could also be related with its environmental presence inwater, as pointed out by Sokolowski et al. (2003), who related spatialvariations of Arg levels in clams to its level in the water. However,several studies have shown that exposure to pollutants could also affect

statistically significant for the PC1 by statistical hypothesis test of factor loading in PCA.rrection were used to analyze between-group differences. Additionally, metabolite levelsxperimental samples were divided into three groups corresponding to the three stations,in nmol/g (mean ± standard error, n = 5). a p b 0.05 Wilcoxon signed-rank test between

ANOVA S2–S1 S3–S1 S3–S2

± 11.4a 0.002⁎⁎ 0.012⁎ 0.012⁎ 0.012⁎

± 26.4a 0.002⁎⁎ 0.012⁎ 0.012⁎ 0.012⁎

± 4.9a 0.012⁎ 0.036⁎

± 11.4a 0.025⁎ 0.037⁎

± 0.1a 0.007⁎⁎ 0.017⁎ 0.017⁎

± 0.1a 0.004⁎⁎ 0.018⁎ 0.018⁎

± 1.9a 0.004⁎⁎ 0.032⁎ 0.037⁎ 0.032⁎

± 19.4a 0.004⁎⁎ 0.018⁎ 0.018⁎

± 196.6a 0.006⁎⁎ 0.018⁎ 0.018⁎

± 3.8a 0.006⁎⁎ 0.018⁎ 0.018⁎

± 19.9a 0.009⁎⁎ 0.018⁎ 0.018⁎

± 0.1a 0.006⁎⁎ 0.017⁎ 0.017⁎

± 1.6a 0.006⁎⁎ 0.018⁎ 0.018⁎

± 247.7a 0.009⁎⁎ 0.018⁎ 0.018⁎

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Arg levels. This amino acid is the end product of the reaction betweenphosphoarginine and ADP, in which phosphoarginine is the primary highenergy phosphagen used for ATP regeneration in invertebrates (Fasuloet al., 2012). Thus, Viant et al. (2001) reported an increase of Arg withdepletion of phosphoarginine in gill tissue of abalone exposed to Cu.

Metabolite pools could also be affected by endogenous factors ofstudied animals, such as nutritive condition, which directly correlateswith nutritional conditions in the environment and is thereby heavilydependent on food availability (Sokolowski et al., 2003; Cappello et al.,2013; Babarro et al., 2006, 2011). S2 and S3 sites are located in the cen-tral part of the lagoon characterized by a high load of nutrients throughsurface and ground water inputs (García-Pintado et al., 2007), incontrast to S1, located in the northern area. The high levels of FAAdetected in S2 and S3 compared with S1 might be related to the highertrophic conditions at these points, which will be later evidenced in thehigher clam condition index (CI) observed in the sampling 22 daysafter transplantation (Campillo et al., 2013). However, the breakdownof tissue to obtain FAA for metabolism cannot be discarded as it hasbeen previously reported (Jones et al., 2008b) to be a consequence ofthe presence of organic pollutants. The interpretation of themetabolomic data is complicated by the interactions between naturaland environmental stressors and the toxic effect due to environmentalpollutants. Therefore characterizing the metabolomic effects of naturalstressors such as hypoxia or food limitation will also be important forunraveling the effects of natural stressors, anthropogenic stressors andresidual biological noise (Viant, 2007). The interpretation of the meta-bolic responses could also be complicated by different biological factorsincluding sex, age and seasonal effects (Viant, 2007). However, the useof caged organismswith the same origin could help to avoid bias relatedto the age and reproductive status of the organisms and allow for bettercontrol of the biological effects of contaminants over a predeterminedexposure period (Tsangaris et al., 2011).

Since antioxidation and detoxification are the first line of defenseagainst environmental stress, Zhou et al. (2010) hypothesized thatwhen the antioxidation and detoxification systems break down othertoxicologically relevant responses, such asmetabolic disorder and tissuedamage, might be induced by the toxin. In this work the response of thebiochemical and physiological markers showed that there were nodifferences in physiological rates (SFG) after the first 7 days of exposurebetween clams from S1, S2 and S3, while biochemical markers began toshow some effects after the same period. Thus AChE, CAT, GR and GSTafter this exposure period did not show differences between clamsfrom S2 and S1, whereas the levels of GST and GR detected in clamsfrom S3 were significantly higher than those found in S1. However,LPO levels detected in both sites S2 and S3 (significantly higher thanthose fromS1) point to ROS overproductionwith a failure in antioxidantdefenses, and therefore oxidative stressmay also be involved in some ofthe metabolic changes detected in clams at this time.

4.2. Metabolic profiling at 22 days after transplantation

At this time of exposure, the content of some amino acids andderivativeswasgreatlydiminished at the S3 location,withAla,Gln, Leu, car-nitine and taurine levels being significantly lower than at the S1 location.Surprisingly, these metabolites, among others were considerably higher inS3 than S1 after 7 days of transplantation (see above) and, therefore,some had decreased by as much as one order of magnitude (Ala, Gly Leu,and Lys) from the values recorded at 7 days of transplantation.

Considering that the gills play an important role in the direct uptakeof solutes fromwater, the decrease in free amino acid levels in tissues ofbivalves exposed to contaminants might be related to the fact that theuptake of amino acids is reduced as a consequence of a decrease in thephysiological activity of gills (Viarengo et al., 1980), which in turnmight cause a reduction of protein metabolism. The exposure to an en-vironmental mixture of pesticides and pollutants resulted in reducedlevels of acetylcholinesterase (AchE) in gills, SFG and clearance

efficiency (CE) in S3 after 22 days (Campillo et al., 2013). Therefore,the feeding efficiency of these organisms and also, in all probability,gill activity to retain particulate matter might be reduced, which couldexplain the lower levels of amino acids found in S3 clams.

CI levels, proposed as an indicator of health in marine bivalves,showed a marked increase after 22 days in S2 and S3. The decrease offree amino acid levels detected in S3 at 22 days could be an earlybiomarker of the effect of pollutants, as itmightmean a future reductionof the protein metabolism and the growth rate. The decrease in the rateof protein synthesis may provide a biochemical index of stress exertinga detrimental effect on growth and survival (Viarengo et al., 1980).

Taurine and carnitine were also diminished in this study by evenmore than one order of magnitude in location S3 after long-termexposure to the toxic environment. Previous studies have demonstratedthat several molecules utilized as osmoprotectants such as taurine orbetaine, were diminished after exposure to toxic compounds such asmetals or pesticides (Ji et al., 2015; Liu et al., 2011; Tuffnail et al.,2009). These results, together with the decreased concentrations ofamino acids, suggest an osmotic stress in these clams after 22 days.

It has been emphasized that organic hydroperoxides, alkenals andepoxides resulting from oxidative metabolism may be regarded as‘natural’ substrates for the phase II detoxification enzyme GST. Theincreased levels of lipid peroxidation (LPO) and GST detected in clamsform S3 after 7 and 22 days (Campillo et al., 2013) might indicate anincreased utilization of glutathione (GSH) in conjugation reactionsinvolved in the metabolism of lipid hydroperoxides or xenobiotics.The increased levels of the antioxidant enzyme GR also showed theneed to restore the reduced pool of GSH (Campillo et al., 2013). Thus,GSH was tightly controlled, in agreement with the study by Antognelliet al. (2006), in which it was found that chlorpyrifos could not compro-mise the redox balance in the bivalve Scapharca inaequivalvis since ROStoxicity should be controlled in order to maintain the functionality ofthe digestive gland. They hypothesized that GSH levels should bemaintained after the addition of chlorpyrifos in order to maintain theredox status. Similar results were obtained in our study, where GSHlevels in S3 clams were not significantly different to S1 clams for boththe 7 and the 22-day samplings (Fig. 6). GSH can be maintained bytwo mechanisms, GSSG recycling or de novo biosynthesis. However,the conjugation of GSH to electrophilic compounds by GST leads todetoxification and excretion of the xenobiotics, but if this process isnot associated with GSH synthesis de novo by glutamate cysteine ligase(GCL) and glutathione synthase, GSH could be depleted (Peña-Llopiset al., 2002). Therefore, GSH de novo biosynthesis cannot be discardedin its maintenance. GSH is a tripeptide composed of Gly, Glu and Cys.Indeed, Glu and Gly levels increased by over 5 times in the short term(7 days), only to subsequently decline to even lower levels than thecontrol site after 22 days in S3. Taurine metabolism is mainly achievedby two pathways, namely synthesis from Cys and uptake (see a recentreview in Meng et al., 2013) and, therefore, its drastic depletion(30-times less in S3 than S1) could be explained as the result of bothpathways being affected. It is tempting to explain the former by the uti-lization of Cys in the GSH biosynthesis, although further experimentsshould be carried out to prove this point. The latter could be due tothe decrease in the physiological activity of gills, as previouslymentioned. Whatever the case, our results and those obtained in previ-ous works (Antognelli et al., 2006) suggest that GSH can present highregeneration and de novo synthesis rates in order to maintain its levelconstant in the digestive gland, even after a long exposure time to atoxic environment, whereas taurine does not. In consequence, the lattercould be considered a potential candidate as a biomarker althoughadditional research is needed to confirm this point.

5. Conclusions

Metabolomics of the digestive gland of R. decussatus has been shownto be an interesting approach in monitoring the toxicity of marine

Fig. 6. Bar graph of the reduced glutathione (GSH) level found in this study after 7 and 22 days of the transplantation. Bar height indicatesmean value of each sampling location and errorbars indicate the standard errors (n = 5). Locations are abbreviated as in Fig. 1.

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environments. After contact by individuals caged in the polluted envi-ronment, the FAA profile of this organ seemed to follow a two-phaseevolution, the first being, a compensation phase increasing the concen-tration of several proteogenic amino acids. This amino acid compositioncould depend on the physiological condition of the organism and thetrophic characteristics of the environmental site. However, tissue break-down in order to produce free amino acids to be used as energy sourcescould not be discarded, as has been previously described. Subsequently,a severe reduction of their concentration to levels, even lower thanthose at the control locations was found. Moreover, our results agreewith previous studies on bivalve environmental metabolomics, whichshow that the FAA profile is affected by exposure to pollution andcould thus be used as a biomarker of this exposure and its effects, asits change could affect important processes in the organism, such asenergy metabolism or osmotic regulation. Taurine in particular hasbeen shown as an interesting potential biomarker of polluted areasdue to its role as osmoprotectant as well as its biosynthesis needs andactive uptake or Cys availability. On the other hand, GSH levelsremained constant, even though several enzymes which are involvedin its control have been shown to undergo alterations in toxic environ-ments.Moreover, metabolomic responses in field studies should also beinterpreted taking into account environmental parameters such as tro-phic conditions and the fact that they could present a time-dependentpattern, as our findings indicate.

Acknowledgments

This work was supported by the Fundación Séneca (Region ofMurcia, Spain) through the BIOMARO project (15398/PI/10) and bythe Spanish Inter-Ministerial Science and Technology Commissionthrough the DECOMAR (CTM2008-01832-MAR) and the BIOCOMprojects (CTM2012-30737-MAR). This research was also partially sup-ported by the MICINN (Spain)/FEDER-EU Project BIO2011-29233-C02-01,MINECO (Spain) Project BIO2012-38103, Fundación Séneca (Murcia,Spain) projects 08660/PI/08 and 12031/PI/09, and Inbionova BiotechS.L. (NEOTEC IDI-20070429). C.B. acknowledge the FPU fellowships

from MICINN (Spain) and A.S. post-doctoral contract from MICINN(Juan de la Cierva program).

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