View Online / Journal Homepage / Table of Contents for this issue
PAPER
www.rsc.org/jem | Journal of Environmental Monitoring
A multibiomarker approach in Mytilus galloprovincialis to assess environmental quality ^ Alexandra Cravo, Belisandra Lopes, Angela Serafim, Rui Company, Luı´sa Barreira, T^ania Gomes * and Maria Joa˜o Bebianno
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
Received 19th May 2009, Accepted 24th July 2009 First published as an Advance Article on the web 7th August 2009 DOI: 10.1039/b909846a A multibiomarker approach was carried out for the first time in the South Portuguese Coast using Mytilus galloprovincialis, to assess environmental quality, establish if there are adverse biological responses associated to different sources of anthropogenic contamination and to determine spatial and seasonal trends. For this purpose the battery of biomarkers selected was: superoxide dismutase (SOD), catalase (CAT), glutathione peroxidases (GPx total and Se dependent), Cytochrome P450 component system, Glutathione-S-transferase (GST) and acetylcholinesterase (AChE), metallothionein (MT) and lead-d-aminolevulinic acid dehydratase (ALAD), lipid peroxidation (LPO) and Condition Index (CI) along with the determination of PAHs and metals (Cd, Cu, Ni, Pb and Zn). Results show that despite the levels of both organic and metallic contaminants in these eight spots in the South Coast of Portugal not being particularly high compared with other contaminated/polluted sites worldwide, the selected battery of biomarkers responded efficiently to the environmental changes and allowed an environmental assessment between seasons and sites. Different spatial and seasonal responses were evident along the South Coast of Portugal, meaning that the contamination is not homogeneous. This does not only reflect different competition, origin and intensity of contamination, but also different environmental conditions (e.g. temperature, salinity). Along the South Portuguese Coast site 8 was the most contaminated, while site 2 was considered the least contaminated. Despite environmental factors possibly causing difficulties in the general interpretation of biomarker data, those that better responded P to environmental contamination were CYP450, SOD-mit and T-GPx for the PAHs, MT (digestive gland) for metals (especially Cu), ALAD for Pb and LPO for both organic and metallic contamination. These biomarkers were also positively correlated with temperature in summer, revealing this as a more stressful/critical season. In future environmental contamination assessments there is no need to analyse the components b5, P418, NADH and NADPH of phase I MFO system, and MT in the gills, since their responses are not evident.
Introduction Coastal areas including estuaries are usually highly productive. However, over the years these ecosystems became increasingly affected by anthropogenic activities, mainly due to urban development, industrialization and tourism. In those areas, complex mixtures of contaminants including metallic, organo-
CIMA - Faculty of Marine and Environmental Sciences, University of Algarve, Campus de Gambelas, 8005-130 Faro, Portugal. E-mail:
[email protected]
metallic and persistent organic pollutants (POP; e.g. polycyclic aromatic hydrocarbons (PAHs), polychlorinated byphenyls (PCBs), fertilizers and pesticides) from different origins exist. Due to the ecological relevance of such areas new tools to evaluate the environmental quality became necessary.1 Analysing all the contaminants/pollutants present in the water is virtually impossible and does not directly reflect the effects upon the biota. Therefore, measuring the biological effects of pollutants as early warning signals became increasingly important to assess the ‘‘health status’’ of the environment.2 Bivalve mussels in particular Mytilus spp. have been widely used in environmental quality
Environmental impact The present paper describes a multibiomarker approach used for the first time in the south coast of Portugal with the mussel Mytilus galloprovincialis to assess the impact of contaminants in this European coastal area. Contrary to the single biomarker approach used commonly in ecotoxicology studies, this work integrates a battery of biomarkers to assess the effect of pollutants (metallic and organic) and environmental physico-chemical parameters. This represents a new tool to evaluate the quality of the coastal environment subjected to the impact of mixtures of contaminants and provides valuable information for environmental risk assessment that allows pollution effects to be distinguished from those induced by natural factors using the mussels as biological indicators. This journal is ª The Royal Society of Chemistry 2009
J. Environ. Monit., 2009, 11, 1673–1686 | 1673
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
View Online
assessment.3,4 These studies enable the evaluation of integrated biological effects in the environment and usually include the determination of one or several biomarkers in target species. The advantages of using biomarkers in the assessment of environmental contamination are also highlighted by van der Oost et al.5 The use of a battery of different biomarkers (a multibiomarker approach) is essential, particularly in environments where complex mixtures of contaminants are present, for the assessment of responses that reflect the environmental quality and for the identification of the sources of contaminants in the environment.6,7 Nevertheless, the use of biomarkers does not completely replace the more common approach of chemical analysis, but their integration provides a qualitative and/or semiquantitative approach to determine the effects and the possible nature of contaminants. In addition it provides a unique contribution to determine the synergistic effect of a mixture of pollutants, even in low amounts.8 Biological responses are also influenced by natural environmental factors. Studies using biomarkers pointed to the need to incorporate the effects of abiotic (temperature, salinity, diet, etc.) and biotic factors (reproduction cycle, growth, age, sex, etc.)9–11 to a correct interpretation of biomarker responses. In the South Coast of Portugal, a highly touristic region, the population increases markedly during the summer as well as the volume of sewage discharges and maritime activities. Consequently at some places which are already identified as hotspots of contamination, complex mixtures exist and are magnified during this season. In this region, the contaminants known to be present include metals,12–14 PAHs, PCBs15,16 and organotin compounds (mainly TBT).17–19 Nevertheless in this coastal area the biological effects of pollutants in mussels are scarce and only include MT and GST changes in M. galloprovincialis.12,20 Therefore, in order to assess if and how mussels M. galloprovincialis are affected by these contaminants, a multibiomarker approach was carried out to establish if there are adverse biological responses associated with different sources of anthropogenic contamination and to determine spatial and seasonal trends. Moreover, it was also aimed to discriminate between useful biomarkers and those less helpful for environmental assessement. The battery of biomarkers selected was: (i) oxidative stress biomarkers (antioxidant enzymes – superoxide dismutase (SOD), catalase (CAT), glutathione peroxidases (GPx total and Se dependent), (ii) biomarkers of exposure to organic compounds, including pesticides – Cytochrome P450 component system including the ‘418-peak’, Cytochrome b5, NAD(P)H cytochrome c reductase), Glutathione-S-transferase (GST) and acetylcholinesterase (AChE); (iii) biomarkers of metal exposure – metallothionein (MT) and lead – d-aminolevulinic acid dehydratase (ALAD) and (iv) biomarkers of effect – lipid peroxidation (LPO).
Material and methods Sampling sites and collection Eight sites were selected at the South Coast of Portugal (Fig. 1), and mussels Mytilus galloprovincialis collected at low tide, during summer (2005) and winter (2006) namely at: 1. (37 00,5880 N; 8 55,7770 W), 2. (37 06,4610 N; 8 40,2810 W), 3. (37 08,0870 N; 1674 | J. Environ. Monit., 2009, 11, 1673–1686
Fig. 1 Sampling sites along the South coast of Portugal.
008 32,0890 W), 4. (37 04,4060 N; 008 07,2950 W), 5. (37 00,1660 N; 7 54,9940 W), 6. (37 01,3670 N; 7 50,2120 W), 7. (37 06,9950 N; 7 37,7320 W) and 8. (37 10,6780 N0 ; 007 24,5080 W). Sites 1 and 6 are near fishing harbours, and sites 2 and 4 near recreational marinas, while sites 3, 7 and 8 are close to the mouth of the major rivers, whereas site 5 is close to a commercial harbour. From each sampling site about 100 mussels (5.2 0.8 cm shell length) were collected for both biochemical (biomarker measurements) and chemical (PAHs and metal) analyses. Mussels were transported alive (at 4 C) to the laboratory. To assess the physiological condition of native mussels, 10 animals per site were randomly collected for condition index determination. Following dissection, gills (for acetylcholinesterase and MT) and digestive gland of 40 mussels were immediately frozen in liquid nitrogen and kept at –80 C until analyses. The remaining mussels were maintained at 20 C for chemical analyses. Environmental parameters (temperature and salinity) were also measured at each site.
Condition index (CI) The condition index (CI) was determined as a percentage of the ratio between dry weight of the soft tissues and the dry weight of the shell. Biochemical analysis Antioxidant enzymes. Antioxidant enzymatic activities were determined in the digestive gland, the major site of organic xenobiotic metabolism. Three pools (5 digestive glands each) of M. galloprovincialis were used after homogenisation in 20 mM Tris buffer, pH 7.6, containing 1 mM of EDTA, 0.5 M of saccharose, 0.15 M of KCl and 1 mM of DTT. The homogenates were centrifuged at 500 g for 15 min at 4 C to precipitate large particles and recentrifuged at 12 000 g for 45 min at 4 C to precipitate the mitochondrial fraction. Gel filtration was used to eliminate low molecular weight impurities. Hence, all cytosolic fractions were chromatographed on a Sephadex G-25 column (PD10, Pharmacia) to remove small weight proteins. SOD activity (EC 1.15.1.1) was determined in the cytosolic (SOD-cyt) and mitochondrial (SOD-mit) fractions by measuring the reduction of cytochrome c by the xanthine oxidase/hypoxanthine system at 550 nm.21 One unit of SOD is defined as the This journal is ª The Royal Society of Chemistry 2009
View Online
amount of enzyme that inhibits the reduction of cytochrome c by 50%. SOD activity was expressed in U mg1 total protein concentrations. CAT activity (EC 1.11.1.6) was determined according to Greenwald22 by the decrease in absorbance at 240 nm due to H2O2 consumption. CAT activity was expressed as mmol min1 mg1 of total protein concentrations. GPx activities were measured following NADPH oxidation at 340 nm in the presence of excess glutathione reductase, reduced glutathione and corresponding peroxide.23 The Se-GPx (EC 1.11.1.9) and Total GPx activities were measured by using H2O2 and cumene hydroperoxide as substrates, respectively. GPx activities are expressed as mmol min1 mg1 of total protein concentrations.
Microsomal protein concentration was determined by the Lowry method using bovine serum albumin as standard.27 Protein yield of the microsomal fraction was measured and expressed as protein content per gram of weight of fresh digestive gland tissue. Glutathione-S-transferase activity was measured spectrophotometrically in the cytosolic fraction of the 100 000 g centrifuge, using 1-chloro 2,4 dinitrobenzene (CDNB) and reduced glutathione (GSH) as co-substrate, according to the method of Habig et al.28 Specific activity was expressed as nmol min1 mg1 protein, using a molar extinction coefficient of 49.6 mM1 cm1.
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
Acetylcolinesterase (AChE) CYP450 and GST enzymes Like for the antioxidant enzymes, for the analysis of CYP450, ‘‘418 peak’’, b5, NADPH, NADH and GST, 3 pools (5 digestive glands each) of mussels were used. Cytosolic and microsomal fractions were prepared at 4 C by differential centrifugation as described by Livingstone.24 Samples were homogenized in 1 : 3.5 (tissue weight : buffer volume) ratio, at 4 C with 10 mM Tris– HCl pH 7.6, containing 1 mM dithiothreitol, 0.15 M KCl and 0.5 M sucrose using an ultra-Turrax homogenizer. Following centrifugations of 500 g for 15 min, 10 000 g for 30 min and 100 000 g for 90 min, the resulting microsomal pellet was resuspended in 10 mM Tris–HCl, pH 7.6, containing 20% (w/v) glycerol to give a protein concentration of approximately 10 mg mL1. Biochemical measurements were carried out on microsomal samples either immediately (reductase activities), or after overnight storage at 80 C (CYP450, putative denatured CYP450, cytochrome b5). The cytosolic fraction (GST) was also stored at 80 C until analyses. CYP450 and reductase activities were assayed as described in Livingstone and Farrar.25 Cytochrome P450 concentration and ‘‘418 peak’’ was measured by the carbon monoxide difference spectrum of sodium dithionite reduced samples using an extinction coefficient of 91 mM1 cm1 (450–490 nm)26 as follows: 1 mL 100 mM Tris–HCl pH 7.6, 900 mL water and 100 mL of the mussels digestive gland microsomes were mixed and divided into two semi-micro cells. CO was bubbled (with a flow of 70 bubbles per minute) in the cell containing the sample and the baseline recorded. A few grains of sodium dithionite were added to both cells and the cytochrome P450 content was measured. CYP450 concentration was expressed in pmol mg1 proteins. Total ‘‘418 peak’’ was calculated in arbitrary units as described in Livingstone,24 i.e. peak height (lmax – 490 nm) 1000 mg1. Total cytochrome b5 was similarly analysed by difference spectroscopy using 50 mL of microsomal sample, 30 mM NADH and an extinction coefficient of 185 mM 1 cm 1 (426–409 nm). For the difference spectra, each sample was scanned from 3 to 4 times, and mean values were calculated from replicate spectra. NADPH-dependent cytochrome c and NADH-dependent cytochrome b5 reductase activities were measured, by the increase in absorbance at 550 nm (3 ¼ 19.6 mM1 cm1). Final assay conditions in a final volume of 1 mL were: 50 mM Tris–HCl pH 7.6, 1 mM KCN, 0.30 mM NADPH (or NADH), and 0.60 mM cytochrome c. Sample volumes were: 50 mL of microsomes for NADPH and 10 mL for NADH dependent reductases. This journal is ª The Royal Society of Chemistry 2009
The activity of AChE was assayed in the gills of the mussels. Measurements of AChE activity were performed using the colorometric method of Ellman.29 The absorbance at 405 nm was recorded for samples and blanks. AChE activity was expressed in nmol min1 mg1 protein using a molar extinction coefficient of 13.6 mM1 cm1. Metallothionein and ALAD Metallothionein concentrations were determined in the gills and digestive gland of M. galloprovincialis. Three replicates of tissues were weighed and homogenized in 3 volumes of 20 mM Tris-HCl (pH 8.6) in an ice bath (4 C). Subsamples of the total homogenate were used for wet/dry weight ratio determination and metal analysis. A further aliquot of the homogenate (3 mL) was centrifuged (30 000g for 1 h at 4 C) to separate the soluble and insoluble compounds. Two aliquots of the supernatant were further collected to be used in LPO and protein determinations. The supernatant was then heat treated at 80 C and centrifuged (30 000g for 1 h at 4 C) to precipitate the denatured proteins. Aliquots (50–250 mL) of the heat treated cytosol were used to quantify MT concentrations by differential pulse polarography, according to the method described by Bebianno and Langston.30 The standard addition method used for calibration of MT concentrations was rabbit liver MT (MT-I from Sigma) (working standard 10 mg L1 in distilled water). MT concentrations in the tissues of mussels were expressed as mg g1 protein. d-Aminolevulinic acid dehydratase activity (ALAD E.C.4.2.1.24) was determined according to the European standardized method for determination of d-ALAD activity in the blood.31 The whole soft tissues of mussels were homogenized with 0.1 M phosphate buffer (pH 6.6). The homogenates were centrifuged at 10 000 g for 15 minutes at 4 C. The resulting supernatants were then separated in 5 aliquots of 50 ml each and 200 mL of phosphate buffer was added to 2 aliquots and 200 mL of ALA-reagent (d-aminolevulinic acid) was added to the others. The mixture was incubated for 2 hours at room temperature and afterwards 750 mL of the precipitation reagent (containing trichloroacetic acid) was added, mixed for 30 minutes and then centrifuged at 2500 g for 5 minutes. 500 mL of the resulting supernatant was transferred to a plastic cell (1 mL) mixed with 500 mL of the Ehrlich chromogenic reagent (dimethylaminobenzaldehyde) and incubated for 15 minutes at 25 C. The UV absorbance of the amount of porphobilinogen (PBG) was determined in the bivalve samples and blanks at 550 nm. The J. Environ. Monit., 2009, 11, 1673–1686 | 1675
View Online
activity of ALAD was expressed in ng PBG min1 mg1 total protein.
Lipid peroxidation Lipid peroxidation was determined in the same homogenate as MT in the digestive gland. The method, described by Erdelmeier et al.,32 measures the amount of malondialdehyde (MDA) and 4hydroxyalkenals (4-HNE) produced in the peroxidation of membrane lipids. LPO was expressed as nmol MDA + 4-HNE g1 protein.
Table 1 Certified metal concentrations and analysed concentrations expressed by mean 95% confidence limits and mean standard deviation, respectively in TORT-2 reference materials (RM) (mg g1 dw) Metal
Certified
Analysed
Cadmium Copper Chromium Lead Nickel Zinc
26.7 0.6 106 10 0.77 0.15 0.35 0.13 2.50 0.19 180 6
25.3 1.1 106.5 1.8 0.80 0.21 0.30 0.02 2.32 0.12 186 3.1
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
Statistical analysis Total protein concentrations Total protein concentrations were determined by the method of Lowry.27 The quantification was performed using bovine serum albumin as standard. Protein concentrations are expressed as mg mL1.
PAHs analysis PAHs were determined in the whole soft tissues of M. galloprovincialis. Pools of mussels (n ¼ 5) were homogenised with Na2SO4 and Soxhlet extracted with n-hexane/dichloromethane (4 : 1) for 24 h, which was further separated by liquid chromatography (silica–alumina). The PAH containing fraction was eluted with n-hexane : dichloromethane, as described elsewhere.33 Individual PAHs (ng g1 dw) were identified and quantified by HPLC-UV, by comparison of retention times and library spectra of reference compounds. A standard mixture containing 16 individual PAHs, (EPA 610 PAH Mix, Sigma) namely naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benzo(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, dibenzo(a,h)anthracene, benzo(g,h,i)perylene and indeno-(1,2,3-cd) pyrene was used. Detection limit ranged from 0.01 to 0.24 ng g1 dw, for individual PAHs. PAH measurements were validated using a standard reference material of mussel tissue (SRM 2977; NIST, USA), that was extracted and analyzed as the samples. PAH recoveries from the analyses of certified material ranged between 73% and 112%. Blanks were performed extracting an amount of sodium sulfate equivalent to that used with the samples. Hydrocarbons were not detected in the blanks.
Results are expressed as means standard deviation (SD). The data were tested for normality and homogeneity and analyzed by analysis of variance (ANOVA). Duncan test was used to determine significant differences between variables. Pearson’s correlation analysis was also applied between all the biomarkers and between biomarkers and contaminants. Principal component analysis (PCA – ordination method) was used to discriminate the main variables responsible for the variance of biomarkers and environmental factors. Canonical Correspondence Analysis (CCA) was used to assess the influence of biomarkers associated with the contaminants on the different sites. Statistical analysis was carried out with Statistica 5.1 and CANOCO 4.5 package. A minimum significance level of 0.05 was used for all statistical analysis, i.e. a probability of p # 0.05 was considered significant.
Results Environmental factors Temperature and salinity data from each site are shown in Table 2. Temperature in summer increased eastward, i.e., 18.8 C (site 1) to 21.5 C (site 6). In winter, the range was smaller, from 13.7 C (sites 3 and 8) to 15.5 C (site 6) and lower than in summer (p < 0.05). Salinity ranged from 32.9 (site 8) to 36.9 (site 2) in summer and was significantly higher than in winter (ranged from 26.3 (site 3) to 35.7 (site 6); p < 0.05). The lowest salinity was measured at the sites close to the mouth of the rivers (sites 3, 7 and 8). Condition index No significant differences between the two seasons were observed in the condition index of the mussels (8.8% 2.5 and 9.0% 2.1, Table 2 Temperature ( C) and salinity (PSU) from the sites in the South Coast of Portugal
Metals Metals (Cd, Cu, Ni, Pb and Zn) were analysed in the whole soft tissues of M. galloprovincialis on dried subsamples by atomic absorption spectrophotometry (SpectrA-A10/20; Varian, Oxford, UK) after wet digestion with nitric acid. The accuracy of the analytical procedure was checked using certified reference material (TORT-2) from the National Research Council (Canada). The results were in good agreement with the certified values (Table 1). Metal levels were expressed as mg g1 dry weight of soft tissue. 1676 | J. Environ. Monit., 2009, 11, 1673–1686
Temperature ( C)
Salinity (PSU)
Sites
Summer
Winter
Summer
Winter
1 2 3 4 5 6 7 8
18.8 19.0 19.5 19.3 19.6 21.5 20.0 20.0
14.4 14.3 13.7 15.3 14.4 15.5 14.8 13.7
36.0 36.9 36.0 34.0 36.5 36.3 36.0 32.9
35.3 33.7 26.3 33 35.3 35.7 30.6 30.1
This journal is ª The Royal Society of Chemistry 2009
View Online
respectively; p > 0.05), with the exception of sites 2, 7 and 8. The lowest values, that indicate poor physiological condition were at sites 8 (4.8%) and 1 (6.5%) in summer and 2 (7.2% in winter). The highest CI was at sites 2 (14.6% in the summer) and 7 (12.1% in winter). Biomarker levels
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
The biomarkers analysed in the digestive gland of M. galloprovincialis are shown in Figs. 2–7. The antioxidant enzymes activity (SOD, CAT, GPx) are shown in Fig. 2. Each of the antioxidant
enzymes responded distinctively. The variation pattern of SODcyt was more similar to that of CAT and Se-GPx than to the SOD-mit. SOD activity in the cytosolic fraction (SOD-cyt; Fig. 2A) was higher than in the mitochondrial fraction (SOD-mit; Fig. 2B); (pv< 0.05). The variation of SOD-cyt activity (Fig. 2A) was similar between surveys except at sites 1 and 2. SOD-cyt activities were significantly higher and similar at sites 1 and 6 (summer) and 3 (winter) while a minimum was observed at site 4 for both seasons. SOD-mit activity (Fig. 2B), showed a different pattern with almost 2-fold higher activity in the summer (p < 0.05), than
Fig. 2 Cytosolic (A) and mitochondrial SOD (B), CAT (C), total (D) and Se- dependent GPx (E) activities (mean sd) in the digestive gland of mussels M. galloprovincialis collected in South coast of Portugal. Different letters mean significant differences. Activities of SOD are expressed in U/mg prot, CAT in mmol/min/mg prot, Total and Se- dependent GPx in nmol/min/mg prot.
This journal is ª The Royal Society of Chemistry 2009
J. Environ. Monit., 2009, 11, 1673–1686 | 1677
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
View Online
Fig. 3 CYP450 concentration (A), ‘‘peak 418’’ (B), b5 (C), NADPH (D) and NADH (E) activities (mean sd) in the digestive gland of mussels M. galloprovincialis collected in South coast of Portugal. Different letters mean significant differences. CYP450 is expressed in pmol/mg prot, ‘‘peak 418’’ in arbitrary units, b5 in pmol/mg prot and NADP(H) in nmol/min/mg prot.
in winter ( 0.05). With respect to T-GPx and Se-dependent GPx activities (Fig. 2D and E), both were of the same order of magnitude despite slightly higher for T-GPx but showed a distinct pattern. The maximum activity of T-GPx was at site 3 (9 nmol min1 mg1 prot in winter) but lower and similar at the other sites in both seasons (# 5.5 nmol min1 mg1 prot) (Fig. 3D). Se-GPx the 1678 | J. Environ. Monit., 2009, 11, 1673–1686
highest activities (5–6 nmol min1 mg1 prot) were measured at sites 1, 7 (summer) and 3 (winter), and minimum at site 4 (p < 0.05). The activity of the phase I enzymes (CYP450 and the related components ‘‘418 peak’’, cytochrome b5, NADPH cytochrome c reductase and NADH-cyt b5) and of phase II GST in the digestive gland of mussels M. galloprovincialis are shown in Fig. 3 and 4, respectively. In general, all biomarkers associated with phase I (Fig. 3) showed a similar trend, higher in summer than in winter. Levels generally increased from sites 1 to 6, decreasing progressively at sites 7 and 8. The CYP450 activity (Fig. 3A) was highest at site 6 (62 pmol mg1 prot in summer), followed by 3, 4 This journal is ª The Royal Society of Chemistry 2009
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
View Online
Fig. 4 GST activity (mean sd) in the digestive gland of mussels M. galloprovincialis collected in South coast of Portugal. Different letters mean significant differences. GST is expressed in nmol/min/mg prot.
Fig. 5 AChE activity (mean sd) in the gills of mussels M. galloprovincialis collected in South coast of Portugal. Different letters mean significant differences. AChE is expressed in nmol/min/mg prot.
and 2 (p < 0.05). The ‘418- peak’ (Fig. 3B) as representative of putative denatured cytochrome P450, followed a similar trend (Fig. 3A), with the highest value at site 6 (9.5 a.u; p < 0.05). Cytochrome b5 activity (Fig. 3C), as CYP450 (Fig. 3A) was also highest at site 6, although in summer the values were similar at all sites (p > 0.05). In winter cytochrome b5 activity decreased drastically, with the maximum at sites 3 and 8 (14 pmol mg1 prot; p < 0.05). NADPH cytochrome c reductase activity (Fig. 3D) like CYP450, ‘‘418 peak’’ and cytochrome b5 (Fig. 3A, B, C) was also significantly higher at site 6 (14 nmol min1 mg1 prot, p < 0.05) in summer. Only at site 8 could no significant difference between sites be detected. NADH-cyt b5 reductase activity (Fig. 3E), was also significantly higher at site 6 (85 nmol min1 mg1 prot in summer; p < 0.05), 4- to 6- fold higher than NADPH-cyt c reductase (Fig. 3D) and like for NADPH cyt c reductase activity levels were similar at site 8 between surveys. GST (Fig. 4), however, showed a different spatial and seasonal pattern of phase I enzymes, but followed that of SOD-cyt (Fig. 2A), with a maximum at sites 2 (13.5 nmol min1 mg1 prot in summer) and 3 (10.8 nmol min1 mg1 prot in winter). In winter, the GST activity decreased reaching a minimum at site 4 (6.5 nmol min1 mg1 prot, p < 0.05). The AChE activity in the gills of the M. galloprovincialis is shown in Fig. 5. For this biomarker, although levels were in general lower in summer than in winter, no seasonal variation This journal is ª The Royal Society of Chemistry 2009
was observed between sites (except for site 3 and 7). Interestingly, at site 5, AChE activity was lower than anywhere else and similar to that of site 7 in the winter (5.5–6.0 nmol min1 mg1 prot), suggesting an inhibition of this biomarker at these two sites. MT concentrations in the gills and digestive gland of M. galloprovincialis are shown in Fig. 6A, B, respectively. As expected, MT levels were higher in the digestive gland than in the gills and both tissues showed a distinct spatial and seasonal variation. MT concentrations in the gills (Fig. 6A) were significantly higher in summer than in winter, at sites 3, 4 and 8 (p < 0.05), while the opposite occurs at sites 5 and 7. The highest concentration was in winter at site 7 (12 mg g1 prot). Like for the gills, MT levels in the digestive gland were 2-fold higher (10–18 mg g1 prot; p < 0.05) in summer than in winter (Fig. 6B), particularly at site 4. Only at sites 7 and 8 no significant differences of MT exist between seasons (p > 0.05), with the minimum at site 8 (5 mg g1 prot in winter). In winter the highest concentration was at site 7 (11 mg g1 prot). The ALAD activity in the whole soft tissues of M. galloprovincialis is present in Fig. 6C. For this biomarker a marked seasonal pattern exists. ALAD activity was significantly lower in summer than in winter (p < 0.05) at all sites and varied in summer within a narrow range (0.1–0.3 ng PBG min1mg1 prot; p > 0.05). The minimum was at sites 3 and 4, which reveals that this biomarker is particularly inhibited at these sites. In winter, ALAD significantly increased particularly at sites 1 and 2 (1.6 ng PBG min1mg1 prot; p < 0.05). In general, ALAD activity showed an opposite variation of that of MT in the digestive gland (Fig. 6A). LPO concentrations in the digestive gland of M. galloprovincialis are shown in Fig. 7. This biomarker also showed a significant seasonal variation with levels significantly higher in summer than in winter for all sites, with a maximum at site 8 (13.5 mmol g1 prot; p < 0.05). In winter, LPO levels were similar at all the sites (1 mmol g1 prot; p > 0.05). Influence of biotic and abiotic factors on biomarkers The condition index varies with the reproductive cycle and the food availability, but also with environmental factors. Despite being similar between sites and seasons, a significant negative relationship exists between CI and LPO (r ¼ 0.566; p < 0.05). In order to minimize the influence of physiological conditions upon biomarker levels, when the data matrix is normalised by the condition index, GST was positively correlated with phase I biomarkers (except the ‘‘418 peak’’), SOD (cyt and mit), CAT and LPO (r > 0.580; p < 0.05). The influence of abiotic parameters temperature and salinity on biomarkers was also studied. Phase I (CYP450 and related components) and Phase II (GST) enzymes, SOD-mit, MT in the digestive gland and LPO increased when temperature increases (r > 0.680; p < 0.05) while ALAD activity decreases (r ¼ 0.720; p < 0.05). SOD-mit activity was also positively related to salinity while T-GPx activity was inversely related (r ¼ 0.640; p < 0.05). A PCA was applied to all biomarker data along with the environmental variables (temperature and salinity) and CI of mussels (Fig. 8). Biomarkers show a clear seasonal behaviour in most of the sites (except sites 3 and 5) reflecting the seasonal environmental changes associated with the bioavailability of contaminants and abiotic factors. J. Environ. Monit., 2009, 11, 1673–1686 | 1679
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
View Online
Fig. 6 MT concentrations (mean sd) in the gills (A and digestive gland (B) and ALAD activity in the whole soft tissues (C) of M. galloprovincialis collected in South coast of Portugal. Different letters mean significant differences. MT is expressed in mg/g prot and ALAD in ng/PBG/min/mg prot.
highest temperature and salinity values (and condition index) against ALAD. During winter, ALAD activity shows greater response than in the summer, indicating that this biomarker during summer was more inhibited. Moreover, sites in PCA are closer in the winter period than in summer. PC2 (that explains 26% of the variance), shows an increase of antioxidant enzymes activities SOD-cyt, CAT, T- and Se-GPx together with GST, AChE and MT in the gills against MT in the digestive gland. Contaminant levels
Fig. 7 LPO activity (mean sd) in the digestive gland of mussels M. galloprovincialis collected in South coast of Portugal. Different letters mean significant differences. LPO is expressed in MDA + 4-HNE mmol/g prot.
In Fig. 8, PC1 (explaining 55% of the variance) shows that the most evident biomarker response was in summer, for CYP450, SOD-mit, MT in the digestive gland and LPO associated with 1680 | J. Environ. Monit., 2009, 11, 1673–1686
Since in summer there was an evident seasonal influence on the biomarker levels with a general increase of SOD-mit, CYP450, MT in the digestive gland and LPO along with an evident decrease of ALAD, analyses of contaminants were carried out on the same samples to help to interpret the biomarker results. PAHs and metals (Cu, Cd, Cr, Ni, Pb and Zn) were analysed in the whole soft tissues of M. galloprovincialis (Tables 3 and 4, respectively). Total PAHs concentrations (Table 3), showed an increasing gradient from site 1 ( 0.05), as occurred for the MFO system and SOD-mit. For the 16 This journal is ª The Royal Society of Chemistry 2009
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
View Online
(Phen/Ant) and Fluoranthene/Pyrene (F/P) ratios, used to identify the origin of PAHs, showed that PAH sources are different along the South Coast of Portugal (Table 3). PAHs have a petrogenic origin (fuel), (Phen/Ant ratio > 10 and F/P ratio # 1) at sites 2, 4 and 8. At site 3, the origin is pyrogenic (pyrolysis) (Phen/ Ant and F/P ratios < 10 e > 1, respectively) while at sites 1, 6 and 7 the origin is both petrogenic and pyrolitic. Concerning metal levels accumulated in the mussel tissues (Table 4), different gradients were found. Cd, Ni and Pb levels increased from site 4 to 8, as for the antioxidant enzymes SODcyt, CAT and Se-GPx (Figs. 2A, C and E, respectively). Copper showed a different pattern, increasing from site 1 to 3, where the maximum occurred, and then decreased progressively from site 4 to 8. Cr and Zn were similar between sites (p > 0.05). Zn concentrations were, like for Cd, maximum at site 8 (380 mg g1) and similar between sites 7, 5, 3 and 1 (p > 0.05). Furthermore, a direct significant relationship exists between Cd and Zn (r ¼ 0.88, p < 0.01), Ni and Pb (r ¼ 0.80, p < 0.05) and Zn and Ni (r ¼ 0.74, p < 0.05) while Pb was only positively related with $ 5 ring PAHs (p < 0.01). Considering the effect of abiotic parameters (temperature, salinity) and CI with contaminants, it is observed that total PAH concentrations were directly related with temperature, Cd was inversely related with CI while LPO was negatively associated with salinity. Total PAH concentrations were maximum at the sites where temperature was highest (i.e. at site 6) and Cd was maximum where CI was minimum (i.e. at site 8).
Fig. 8 PCA of the biomarkers battery in mussels Mytilus galloprovincialis for both summer and winter surveys showing the data scores labeled as sites, temperature, salinity and condition index.
Relation between biomarkers and contaminant levels
individual PAH concentrations, 2 + 3 rings were predominant (>79%) at sites 4, 5, 6 and 8, while 4 ring PAHs (64–80%) were at the other sites. Five ring PAHs, which are usually more toxic, were only detected at site 8 (2%). The Phenanthrene/Anthracene
CCA was applied to biomarkers, contaminants, condition index and environmental factors, to provide insight into the factors that explain the variance of biomarkers in the summer (Fig. 9). In
Table 3 PAHs concentrations (ng g1 dw) in the soft tissues of mussels Mytilus galloprovincialis at the eight sites in summer 2005a Sites PAH compound
1
Naphthalene Acenaphthylene Acenaphthene Fluorene Phenanthrene Anthracene Fluoranthene Pyrene Benzo(a)anthracene Chrysene Benzo(b)fluoranthene Benzo(k)fluoranthene Benzo(a)pyrene Dibenzo(a,h)anthracene Benzo(g,h,i)perylene Indeno-(1,2,3-cd)pyrene SPAH P/A F/P BaA/Chrys 2 + 3 rings (%) 4 rings (%) 5 + 6 rings (%)
nd nd nd 11.3 10.1 0.63 7.2 2.5 37.6 5.3 nd nd nd nd nd nd 74.7 15.9 2.9 7.1 29.5 70.5
a
2 nd nd nd 2.0 6.1 1.5 0.8 14.4 3.9 0.05 0.94 0.36 1.4 1.4 0.27 0.03 7.7 0.17 7.1 26.5 6.7 1.2 2.9 0.01 nd nd nd nd nd nd 1.8 (b) 59.9 2.1 (b) 15.4 0.2 9.2 35.8 64.2
3
4
5
6
7
nd nd nd 6.3 1.9 6.7 0.6 2.6 0.03 28.7 4.0 15.1 2.9 15.8 3.3 3.6 0.08 nd nd nd nd nd nd 78.8 2.1 (b) 2.6 1.9 4.3 19.8 80.2
323 92 nd 106 8 6.8 1.3 20.3 5.4 0.65 0.17 9.0 1.5 10.1 0.02 29.3 7.8 5.5 0.73 nd nd nd nd nd nd 511 13.9 (a) 31.3 0.9 5.3 89.4 10.6
nd nd 128 44 6.6 0.7 8.6 1.8 0.38 0.07 nd 7.3 0.32 8.6 3.0 2.8 0.59 nd nd nd nd nd nd 162 7.2 (b) 22.6 0 3.1 88.5 11.5
nd nd 735 129 31.5 3.8 19.4 2.4 0.33 0.07 58.5 5.1 22.1 5.6 25.8 7.0 18.5 3.0 nd nd nd nd nd nd 911 19.5 (a) 65.3 2.7 1.4 86.3 13.7
nd nd nd 3.4 11.0 0.90 3.5 1.5 21.9 7.2 nd nd nd nd nd nd 49.3 12.2 2.4 3.0 30.9 69.1
8 nd nd 157 11 0.2 12.8 0.2 1.0 18.1 5.2 0.16 1.5 0.33 0.5 2.6 0.03 0.26 2.6 0.21 2.2 24.6 0.13 0.29 16.8 3.6 nd 4.8 0.50 nd nd nd nd 0.66 (b) 241 2.4 (b) 12.5 1.0 1.5 78.7 19.4 2.0
nd – not detected; () – Different letters mean significant differences.
This journal is ª The Royal Society of Chemistry 2009
J. Environ. Monit., 2009, 11, 1673–1686 | 1681
View Online Table 4 Metal concentrations of Cd, Cu, Cr, Ni, Pb and Zn (mg g1 dw) in the soft tissues of mussels Mytilus galloprovincialis at the eight sites in summer 2005a Sites Metal 1 Cd Cu Cr Ni Pb Zn
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
a
1.56 0.77 (b) 10.2 1.3 (b) 0.86 0.41 (a) 0.03 0.02 (c) 0.66 0.17 (bc) 238 73 (ab)
2
3
4
5
6
7
8
0.37 0.15 (c) 16.8 3.9 (ab) 0.80 0.58 (a) 0.05 0.02 (c) 0.40 0.27 (c) 143 60 (b)
0.58 0.14 (c) 24.9 13.0 (a) 0.67 0.13 (a) 0.26 0.07 (a) 6.91 4.61 (b) 243 141 (ab)
0.49 0.10 (c) 16.3 4.2 (ab) 0.64 0.21 (a) 0.05 0.02 (c) 2.69 1.33 (bc) 181 47 (b)
1.22 0.54 (bc) 12.5 7.3 (b) 0.50 0.12 (a) 0.21 0.12 (ab) 3.51 2.05 (bc) 268 99 (ab)
0.46 0.05 (c) 10.0 2.8 (b) 0.43 0.06 (a) 0.08 0.01 (bc) 5.62 3.23 (bc) 111 16 (b)
1.19 0.51 (bc) 10.9 1.7 (b) 0.66 0.30 (a) 0.16 0.04 (abc) 4.14 2.55 (bc) 287 91 (ab)
2.35 0.57 (a) 11.4 0.4 (b) 0.85 0.26 (a) 0.29 0.11 (a) 14.45 4.75 (a) 380 171 (a)
() – Different letters mean significant differences.
Fig. 9 CCA of the biomarkers battery in the mussels Mytilus galloprovincialis for the summer survey showing the data scores labeled as: (A) contaminants, temperature (T), salinity (S) and condition Index (CI) and (B) sites.
Fig. 9A is represented the association of biomarkers responding to contaminants while at Fig. 9B, the projection of the sites is associated with the same contaminant data. The two main axes explain 68% of the total variance, where only PC1 represents 50%. Axis 1 confirmed that the highest damage expressed by the increase of LPO was at site 8, associated with the presence of 5–6 P PAHs rings (2%), high levels of PAHs (240 ng g1 dw) and the maximum metal concentrations (Table 3 and 4). These metal concentrations also induce an increase of MT in the gills. At site 6, the increase of CYP450, SOD-mit and T-GPx may be assoP ciated with PAHs whose concentration was the maximum (900 ng g1 dw; Table 3) of the whole South Portuguese Coast while MT in the digestive gland with Cu. In axis 2, the increase of SOD-cit, CAT and Se-GPx at sites 1, 2 and 3 responded to the moderate levels of PAHs (50–80 ng g1 dw) mainly of 4 rings (65–80%) (Table 3). In opposition, the general inhibition of antioxidant enzymes at site 4 and 5 is mainly associated to 2 + 3 ring PAHs (90%) within the range 160–500 ng g1 dw (Table 3).
Discussion Mussels are suitable organisms for environmental quality assessment since they are able to provide cellular and 1682 | J. Environ. Monit., 2009, 11, 1673–1686
physiological responses to contamination.34 M. galloprovincialis is a very common species along the South Coast of Portugal, where it is subjected to different contamination loads. In environmental risk assessment, the use of a battery of biomarkers is strongly recommended7,8 because a multibiomarker approach gives a more comprehensive and integrated view of the biological responses, even where the levels of contaminants are not particularly high, like in the South Coast of Portugal. PAH levels are of the same order of magnitude of those found previously in the South Coast of Portugal,15,35 in the NW Portuguese coast,36 but lower than in mussels from areas affected by oil spills or tanker accidents, such as the Prestige37 and the Aegean Sea, at La Coru~ na38 and the Erika, along the Bay of 39 Biscay. Nevertheless, the highest PAH concentrations are similar to those in some polluted areas of the NW Mediterranean.40–43 Metal concentrations are similar to those early reported, for the South Coast of Portugal12,44 and in reference sites in the Adriatic Sea and Greece coast45,46 but lower when compared to contaminated coastal areas in the Atlantic and the Mediterranean (such as the Spanish,47 Italian,48 Moroccan49,50 and in the NW Mediterranean coasts).43 The overall biomarker data along with contaminants and environmental variables revealed that in the South Coast of Portugal, biomarkers in M. galloprovincialis varied spatially and This journal is ª The Royal Society of Chemistry 2009
This journal is ª The Royal Society of Chemistry 2009
77 6.5–28 0.6–4.9 10–102
10.7–88.5 0.13–2.14
3–15 2.5–10 40–70 7–32 24.5–40.8 28.6–176
2.7–3.8 6–9 3.3
8.6–12.8
39.8–81.3
20
1–3 (whole st)
This study 36 40 42 51 80 48 81 52 46 54 76 6–12.6 1.1–13.4 7–17
10–20 5–18 10–20 France, Italy, Spain –Mediterranean BIOMAR cruises –Mediterranean
37
Ebro delta –Spain Balearic Islands- Spain France Venice lagoon, Italy Mediterranean, Italy Adriatic Sea
7–18 2.5–7
1.7–5.9 4.0–8.8 7–35
8.8–24.4 30–70 6–13 5–17 12.6 South Portuguese Coast NW Portuguese Coast Meditteranean – Spain
33–62 15–50 2.5–4 1.7–8 3.4
References AChE (Gills) nmol/min/mg prot LPO (mmol g1 prot) Se-GPx Total GPx CAT (mmol min1 mg1 prot) SOD (U mg1 prot) Sites
GPx (nmol min1 mg1 prot)
seasonally depending upon both the origin and the magnitude of the contaminants present. Considering both summer and winter periods, PCA analyses demonstrated that the biomarkers response differs markedly between seasons and was more important in the summer (Fig. 8). In the winter, the major biomarker changes were in mussels from site 3, with an increase of all antioxidant enzymes (except SOD-mit), GST and AChE (Figs. 2, 4 and 5), which suggests that contamination at this site was more important in winter. Considering only the summer data, the response of the biomarkers (Fig. 9A) reflects a rise in the contamination levels during this period, leading to a clear separation of sites (Fig. 9B). There was an increase of most of the biomarkers responses, except for ALAD, which had an opposite trend. Moreover, the selected battery of biomarkers reflects different sources of contamination. The CCA analysis (Fig. 9) discriminate that the principal factor responsible for the primary biomarkers response, considered as the ‘‘contamination component’’, depends on the nature and intensity of contamination. At site 6, CYP450, SOD-mit and T-GPx, are responding P to maximum PAH concentrations, mainly of 2 + 3 rings and MT in the digestive gland responds particularly to Cu. At site 8, LPO responded primarily to high levels of PAH, including those of high molecular weight, and the highest metal concentrations. The secondary factor explaining the variability of the biomarkers data, elected as the ‘‘oxidative stress component’’, was mainly associated with the antioxidant enzymes Se-GPx and CAT, along with SOD-cyt, responding to 4-ring PAHs, that caused oxidative stress at sites 1 to 5. A very important feature related to the use of a multibiomarker approach in risk assessment is the need for having a detailed knowledge of basal levels of the biomarkers, and of its seasonal variation, in order to distinguish pollution induced effects from those induced by the natural biological cycle of mussels. From these results, most of the biomarkers at site 2 can be used as baseline levels. Despite the fact that the activity of antioxidant enzymes in the mussels from the South Coast of Portugal (Fig. 2) is similar to those of the same species of mussels from the Mediterranean sea, farmed in the Ebro Delta,51 Italian,52 French coast53 and in the Adriatic Sea46,54 (Table 5), spatially, their behaviour was interesting, varying in an inconsistent way. Mussels from site 1 revealed an increase of SOD-mit, SOD-cyt, CAT and Se-GPx, (Fig. 2 A, C, E) indicating a cascade of antioxidant enzymes working to counteract the presence of ROS. The levels of antioxidant enzymes at this site are associated with moderate levels of PAHs (mainly of 4-rings) (Table 3) or other organic compounds such as lindane, identified in the sediments as one of the highest concentrations in the South Coast of Portugal.55 At this site, the presence of PAHs and possibly other organochlorine compounds were also reflected by the increase of GST activity (Fig. 4). The levels of Cd (and Zn) are also important (Table 4) and can be responsible for stimulating the lipid peroxidation56 (Fig. 7). Similarly, the increase of MT levels in the gills (Fig. 6A) indicates the presence of bioavailable metals at this site, particularly Cd. This protein can, due to its high cysteine content, also participate in defence against oxidative stress, not only acting as oxyradical scavenger but also through metal binding/release dynamics.57 The increase in the activity of antioxidant enzymes in sites without significant differences in lipid peroxidation reflect an adaptation to a chronic exposure to contaminants at this
Table 5 Specific activities of antioxidant enzymes, LPO in the digestive gland and AChE in the gills in Mytilus galloprovincialis
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
View Online
J. Environ. Monit., 2009, 11, 1673–1686 | 1683
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
View Online
site.36,58 Opposed to what occurred at site 1, the inhibition of most antioxidant enzyme systems (SOD-cyt, CAT and Se-GPx) at site 4 (Fig. 9) points out the difficulty that mussels have at this site in defending themselves against oxidative stress. Moreover, CYP450 and MT were induced in the digestive gland suggesting the presence of organic compounds and metals that can compete to inhibit the antioxidant defence systems. CYP450 followed the trend of variation of the concentration of SPAH, expressed by a direct relationship between CYP450 and SPAHs, as previously reported and attained values similar to those previously found in the South Coast of Portugal,15,35 in the Spanish coast40,59 and in Venice lagoon.60 The highest CYP450 values were found, like for SOD-myt and SOD-cyt activities, in mussels from site 6. This suggests an increase of oxidative stress and subsequent activation of the biotransformation enzyme GST (Fig. 4) indicating the formation of superoxide anion due to the presence of organic compounds. At this P site, PAHs were highest (Table 3), pesticides such as fenitrothion and parathion-methyl were also identified,55 as well as Cu, Ni and Pb (Table 4) that also induced MT in the digestive gland. Moreover, at sites 3 (and 7) Se-GPx increases along with CYP450, which indicates the presence of organic peroxides, possibly due to the presence of low PAHs levels (Table 3). At site 2, CYP450 was induced along with of GST indicating the presence of organic pollutants (PAHs and possibly other organic contaminants). However, the highest SPAHs concentrations (sites 4 to 6 and 8) were not followed by the highest GST activity (at sites 2, 3 and 1). The apparent lack of response of GST activity in M. galloprovincialis to PAHs concentrations was also found either from natural field conditions61,62 or under experimental exposure.63 The activity of this biomarker in M. galloprovincialis was slightly lower than found previously for the same area15 and other Portuguese regions36 or at the Mediterranean and Adriatic Sea.46,52,54 Earlier results in the same area also showed that when PAH concentrations are highest (sites 4 and 5), GST activity was inhibited, particularly at site 4.15 At site 5, GST activity may be related to the high concentrations of total PCBs and PAHs in mussels from this site.20 MT responses in mussels were metal specific (see the review of Amiard et al.64). MT concentrations in the digestive gland of mussels are 1 order of magnitude lower than those in mussels from a heavily metal contaminated site in the NW Mediterranean.43 MTs (gills and digestive gland) (Fig. 6A, B) followed the trend of metals (Table 4). MTs in the gills were particularly related with Cr concentrations (Fig. 9A), in agreement with Porte et al.65 At site 3, MT levels increased in both tissues (gills and digestive gland) indicating the presence of metals, particularly Cu and Ni that were maximum at this site (Table 4). Bivalve gills are the primary site of metal uptake from the aqueous phase, suggesting that bioavailable metals are accumulated in the gills via water. In the digestive gland, MTs were mainly associated with Cu, which reflects the transport of this metal by the circulatory system from gills to the internal organs66 associated with food/diet (particulate phase). MT concentrations in the digestive gland, in summer, were relatively similar between sites, despite maximum at site 4, where none of the analysed metals reached the maximum concentration. One interesting feature is that, except at site 8 where metal levels were highest, MT was not induced and the general response of this biomarker reflects the low metal levels. 1684 | J. Environ. Monit., 2009, 11, 1673–1686
ALAD activity, which is a specific biomarker for Pb exposure,67,68 showed a marked seasonal signature. During summer, ALAD was inhibited at all sites suggesting an increase of Pb concentration during this season, particularly at sites 3 and 8 (Table 4) probably associated to boat traffic increase, since leaded petrol could still be used in some boat engines. In bivalves, ALAD data is scarce. Levels were only reported for the clam Chamelea gallina from the Southern Spanish coast69 and the freshwater clam Corbicula fluminea along the Guadiana estuary, within the range found in this study.70 Other ALAD levels in mussel M. galloprovincialis from site 8 71 are of the same order of magnitude as the present work and also negatively related to Pb, which confirms the enzymatic inhibition of ALAD by this metal. AChE has a fundamental role in the nervous system of both vertebrates and invertebrates, and is a suitable biomarker to detect environmental contamination caused by neurotoxic compounds either organic (organophosphates, carbamates), metals (Cd, Cu, and Pb)72 or surfactants.73,74 Compounds in complex mixtures may also inhibit the AChE activity of bivalve molluscs.75 The activity of this biomarker was similar to that reported in mussels collected from the French, Italian and Spanish coasts in the Mediterranean area (Table 5).53,76,77 Nevertheless, it is much higher than those found in species of mussels at the Adriatic Sea (Table 5).46 AChE levels do not change noticeably in the South Coast of Portugal between sites, however lowest levels were found at sites 5 and 7 (Fig. 5), due to the presence of neurotoxic organic compounds or even metals (Table 4). Pesticides such as lindane were identified in the sediments from the Ria Formosa lagoon.55 At site 5 the impact of the discharges from a small stream in the vicinity of a fruit crop area can possibly transport neurotoxic compounds including pesticides. Cu, Cd and Pb (Table 4) may in part contribute to the inhibition of AChE activity. Site 7 is located in the vicinity of intense agricultural activity where pesticides are used. Metals are known to produce peroxidation of membrane lipids.78,79 Lipid peroxidation in mussels from the South Portuguese Coast were similar to those found for the same species from the NW Portuguese coast36 and Balearic Islands80 and relatively higher than in some Mediterranean areas such as in the French, Italian and Spanish coasts.76 However, levels were lower than at the Venice lagoon, considered a highly polluted system81 (Table 5). LPO damage was maximum at site 8, the major estuarine area in the South Coast of Portugal, where Cd, Ni, Pb and Zn concentrations were the highest (Table 4). LPO may also be associated to the exposure of high molecular weight PAHs.82,83 and site 8 was also the only site where 5 + 6 rings PAHs were detected. Nevertheless it is important to identify where synergistic, addition or antagonistic effects exist (including environmental factors) that might complicate the general interpretation. Abiotic factors (temperature and salinity) may affect the response of several biomarkers. Antioxidant enzymes,84 MT85 ALAD, LPO86 and AChE87 are directly affected by temperature variability. The water temperature in summer (> 18.5 C) contributes to increase the environmental stress in M. galloprovincialis, reflected not only by the variation of biomarker levels but also by a slight decrease in CI in mussels at the eastern sites (4 to 8). In summer, the variation of AChE in particular is directly related to temperature (higher at site 6, while minimum at site 5). In fact, This journal is ª The Royal Society of Chemistry 2009
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
View Online
temperature is claimed to be the most important natural factor affecting AChE activity in mussels.75,88 Moreover, temperature also increases the toxicity of some metals, namely Cd, leading to elevated oxidative stress.66,89 This may represent a synergistic effect between temperature and contaminants, particularly in the summer at site 8 (one of highest temperature (Table 2) and maximal metal concentrations (Table 4, including Cd). Moreover, changes in salinity are directly related to the bioavailability of contaminants, either metals or POPs,90 due to modification of speciation. This may lead to an increase of bioaccumulation with decreasing salinity,91 changing the response of some biomarkers. At site 8 (and 3 to a certain extent), the relatively lower salinity together with a wider range of salinity variation between surveys (Table 2), increases the environmental stress of mussels and subsequently LPO levels (Fig. 7), that are negatively related to salinity. Some other organic or other emerging contaminants not analysed here may also affect the biomarker responses.
Conclusions This is the first multibiomarker approach in the South Coast of Portugal using M. galloprovincialis, while previous studies based on few biomarkers provided limited information. Despite the levels of contaminants in these eight hotspots of contamination in the South coast of Portugal were not particularly high, the selected battery of biomarkers responded efficiently to the environmental changes and allowed an environmental assessment between seasons and sites. Different responses in space and time were evident along the South Coast of Portugal, meaning that the contamination is not homogeneous, reflecting not only different competition, origin and intensity of contamination, but also different environmental conditions. Summer represents the most stressful/critical situation, due to higher environmental contamination and temperature. Despite environmental factors that may complicate the general interpretation of biomarkers response, the most suitable battery of biomarkers are: CYP450, P SOD-mit and T-GPx for PAHs concentrations (mainly of low molecular weight), MT in the digestive gland particularly for Cu, ALAD for Pb and LPO as a damage biomarker for both metals and high molecular weight PAHs. Based on the interpretation of PCA and CCA analysis, for further environmental contamination assessment studies it is possible to reduce the analytical effort of some biomarkers, by eliminating the components of phase I MFO system (b5, P418, NADH and NADPH), and MT in the gills, since their responses are not so evident.
Acknowledgements This study was financially supported by the FCT project ref: POCTI/CTA/48027/2002.
References 1 P. M. Chapman and F. Wang, Environ. Toxicol. Chem., 2001, 20, 3– 22. 2 M. P. Cajaraville, M. J. Bebianno, J. Blasco, C. Porte, C. Sarasquete and A. Viarengo, Sci. Total Environ., 2000, 247, 295–311. 3 C. Nasci, L. Da Ros, N. Nesto, L. Sperni, F. Passarini and B. Pavoni, Mar. Environ. Res., 2000, 50, 425–430.
This journal is ª The Royal Society of Chemistry 2009
4 N. Bodin, T. Burgeot, J. Y. Stanisiere, G. Bocquene, D. Menard, C. Minier, I. Boutet, A. Amat, Y. Cherel and H. Budzinski, Comp. Biochem. Physiol., Part C: Toxicol. Pharmacol., 2004, 138, 411–427. 5 R. Van der Oost, J. Beyer and N. P. E. Vermeulen, Environ. Toxicol. Pharmacol., 2003, 13, 57–149. 6 A. Viarengo, B. Burlando, A. Giordana, C. Bolognesi and G. P. Gabrielides, Mar. Environ. Res., 2000, 49, 483–486. 7 J. M. Monserrat, P. E. Martı´nez, L. A. Geracitano, L. L. Amado, C. M. G. Martins, G. Lopes, L. Pinho, I. S. Chaves, M. FerreiraCravo, J. Ventura-Lima and A. Bianchini, Comp. Biochem. Physiol., Part C: Toxicol. Pharmacol., 2007, 146, 221–234. 8 F. Donnini, E. Dinelli, F. Sangiorgi and E. Fabbri, Environ. Int., 2007, 33, 919–928. 9 R. Smolders, L. Bervoets, G. De Boeck and R. Burst, Environ. Toxicol. Chem., 2002, 21, 87–93. 10 D. Schiedek, K. Broeg, J. Barsien_e, K. K. Lehtonen, J. Gercken, S. Pfeifer, H. Vuontisj€arvi, P. J. Vuorinen, V. Dedonyte, A. Koehler, L. Balk and R. Schneider, Mar. Pollut. Bull., 2006, 53, 387–405. 11 G. Damiens, E. His, M. Gnassia-Barelli, F. Quiniou and M. Romeo, Comp. Biochem. Physiol., Part C: Toxicol. Pharmacol., 2004, 138, 121–128. 12 M. J. Bebianno and L. M. Machado, Mar. Pollut. Bull., 1997, 34, 666– 671. 13 M. J. Bebianno and A. Serafim, Sci. Total Environ., 1998, 214, 123–131. 14 M. A. Serafim and M. J. Bebianno, Environ. Toxicol. Chem., 2001, 20, 544–552. 15 M. J. Bebianno, B. Lopes, L. Guerra, P. Hoarau and A. M. Ferreira, Environ. Int., 2007, 33, 550–558. 16 L. A. Barreira, S. M. Mudge and M. J. Bebianno, J. Environ. Monit., 2007, 9, 187–198. 17 C. M. Barroso, S. Mendo and M. H. Moreira, Mar. Pollut. Bull., 2004, 48, 1145–1167. 18 M. R. Coelho, M. J. Bebianno and W. J. Langston, Appl. Organomet. Chem., 2002, 16, 384–8. 19 S. Diez, S. Lacorte, P. Viana, D. Barcelo and J. M. Bayona, Environ. Pollut., 2005, 136, 525–36. 20 P. Hoarau, G. Damiens, M. Romeo, M. Gnassia-Barelli and M. J. Bebianno, Comp. Biochem. Physiol., Part C: Toxicol. Pharmacol., 2006, 143, 196–203. 21 J. M. McCord and I. Fridovich, J. Biol. Chem., 1969, 244, 6049–6055. 22 R. A. Greenwald, CRC Press, Boca Raton, Florida, 1985, pp. 447. 23 R. A. Lawrence and R. F. Burk, Biochem. Biophys. Res. Commun., 1976, 71, 952–958. 24 D. R. Livingstone, Mar. Ecol.: Prog. Ser., 1988, 46, 37–43. 25 D. R. Livingstone and S. V. Farrar, Sci. Total Environ., 1984, 39, 209– 235. 26 R. W. Estabrook and J. Werringloer, Methods Enzymol., 1978, 52, 212–220. 27 O. H. Lowry, N. J. Rosebrough, A. L. Farr and R. J. Randall, J. Biol. Chem., 1951, 193, 265–275. 28 W. H. Habig, M. J. Pabst and W. B. Jakoby, J. Biol. Chem., 1974, 25, 7130–7139. 29 G. L. Ellman, K. O. Courtney, V. Anders and R. M. Featherstone, Biochem. Pharmacol., 1961, 7, 88–95. 30 M. J. Bebianno and W. J. Langston, Port. Electrochim Acta, 1989, 7, 511–524. 31 A. Berlin and K. N. Schaller, Z. Klin. Chem. Klin. Biochem., 1974, 12, 389–390. 32 I. Erdelmeier, D. Gerard-Monnier, J. C. Yadan and J. Acudiere, Chem. Res. Toxicol., 1998, 11, 1184–1194. 33 J. Albaiges, A. Fadn, M. Soler, A. Gallifa and P. Martin, Mar. Environ. Res., 1987, 22, 1–18. 34 P. S. Lau and H. L. Wong, Mar. Pollut. Bull., 2003, 46, 1563–1572. 35 A. Serafim, B. Lopes, R. Company, A. M. Ferreira and M. J. Bebianno, Mar. Pollut. Bull., 2008, 57, 529–537. 36 I. Lima, S. M. Moreira, J. Rend on-Von Osten, A. M. V. M. Soares and L. Guilhermino, Chemosphere, 2007, 66, 1230–1242. 37 J. A. Soriano, L. Vi~ nas, M. A. Franco, J. J. Gonzalez, L. Ortiz, J. M. Bayona and J. Albaiges, Sci. Total Environ., 2006, 370, 80–90. 38 C. Porte, X. Biosca, D. Pastor, M. Sole and J. Albaiges, Environ. Sci. Technol., 2000, 34, 5067–5075. 39 J. Tronczynski, C. Miunschy, K. Heas-Moisan, N. Guiot, I. Truquet, N. Olivier, S. Men and A. Furaut, Aquat. Living Resour., 2004, 17, 243–259.
J. Environ. Monit., 2009, 11, 1673–1686 | 1685
Downloaded by UNIVERSIDAD DEL PAIS VASCO on 15 October 2012 Published on 07 August 2009 on http://pubs.rsc.org | doi:10.1039/B909846A
View Online 40 M. Sole, C. Porte and J. Albaiges, Sci. Total Environ., 1995, 159, 147– 153. 41 C. Porte and J. Albaiges, Arch. Environ. Contam. Toxicol., 1993, 26, 273–281. 42 M. Sole, C. Porte and J. Albaiges, Environ. Toxicol. Chem., 1995, 14, 157–164. 43 I. Zorita, I. Apraiz, M. Ortiz-Zarragoitia, A. Orbea, I. Cancio, M. Soto, I. Marig omez and M. P. Cajaraville, Environ. Pollut., 2007, 148, 236–250. 44 M. E. Morgado and M. J. Bebianno, Cienc. Mar., 2005, 31, 231–241. 45 C. Tsangaris, E. Papathanasiou and E. Cotou, Ecotoxicol. Environ. Saf., 2007, 66, 232–243. 46 S. Gorbi, C. V. Lamberti, A. Notti, M. Benedetti, D. Fattorini, G. Moltedo and F. Regoli, Mar. Environ. Res., 2008, 65, 34–49. 47 V. Besada, J. Fumega and A. Vaamonde, Sci. Total Environ., 2002, 288, 239–253. 48 C. Locatelli, J. Phys. IV, 2003, 107, 785–788. 49 A. Chafik, M. Cheggour, D. Cossa, S. Benbrahim and M. Sifeddine, Aquat. Living Resour., 2001, 14, 239–249. 50 M. Maanan, Environ. Pollut., 2008, 153, 176–183. 51 M. Sole, C. Porte and J. Albaiges, Aquat. Toxicol., 1994, 30, 271– 283. 52 F. Regoli, Arch. Environ. Contam. Toxicol., 1998, 34, 48–63. 53 F. Akcha, C. Izuel, P. Venier, H. Budzinski, T. Burgeot and J. F. Narbonne, Aquat. Toxicol., 2000, 49, 269–287. 54 R. Bocchetti, C. V. Lamberti, B. Pisanelli, E. M. Razzetti, C. Maggi, B. Catalano, G. Sesta, G. Martuccio, M. Gabellini and F. Regoli, Mar. Environ. Res., 2008, 66, 24–26. 55 J. Villaverde, A. Hildebrandt, E. Martı´nez, S. Lacorte, E. Morillo, C. Maqueda, P. Viana and D. Barcel o, Sci. Total Environ., 2008, 390, 507–513. 56 F. Geret, A. Jouan, V. Turpin, M. J. Bebianno and R. P. Cosson, Aquat. Living Resour., 2002, 15, 61–66. 57 W. J. Langston, M. J. Bebianno and G. R. Burt, in Metal Metabolism in Aquatic Environments, ed. W. J. Langston and M. J. Bebianno, Chapman & Hall, London, 1998 ch 8, pp. 219–284. 58 C. C. C. Cheung, G. J. Zheng, A. M. Y. Li, B. J. Richardson and P. K. S. Lam, Aquat. Toxicol., 2001, 52, 189–203. 59 C. Porte, M. Biosca, M. Sole and J. Albaiges, Environ. Pollut., 2001, 112, 261–268. 60 M. Sole, C. Nasci and D. R. Livingstone, Biomarkers, 2000, 5, 129– 140. 61 D. R. Livingstone, P. Lemaire, A. Matthews, L. D. Peters, C. Porte, P. J. Fitzpatrick, L. Forlin, C. Nasci, V. Fossato, N. Wootton and P. Goldfarb, Mar. Environ. Res., 1995, 39, 235–240. 62 M. Sole, C. Porte, X. Biosca, C. L. Mitchelmore, J. K. Chipman, D. R. Livingstone and J Albaiges, Comp. Biochem. Physiol., 1996, 113, 157–265. 63 X. R. Michel, PhD Thesis, University of Bordeaux I, 1993. 64 J. C. Amiard, C. Amiard-Triquet, S. Barka, J. Pellerin and P. S. Rainbow, Aquat. Toxicol., 2006, 76, 160–202.
1686 | J. Environ. Monit., 2009, 11, 1673–1686
65 C. Porte, M. Solea, V. Borghi, M. Martinez, J. Chamorro, A. Torreblanca, M. Ortiz, A. Orbea, M. Soto and M. P. Cajaraville, Biomarkers, 2001, 6, 335–350. 66 A. A. Cherkasov, R. A. Overton, E. P. Sokolov Jr and I. M. Sokolova, J. Exp. Mar. Biol., 2007, 210, 46–55. 67 S. N. Kelada, E. Shelton, R. B. Kaufmann and M. J. Khoury, Am. J. Epidemiol., 2001, 154, 1–13. 68 J. Aisemberg, D. E. Nahabedian, E. A. Wider and N. R. V. Guerrero, Toxicology, 2005, 210, 45–53. 69 J. Kalman, I. Riba, J. Blasco and T. A. DelValls, Mar. Environ. Res., 2008, 66, 38–40. 70 R. Company, A. Serafim, B. Lopes, A. Cravo, T. J. Shepherd, G. Pearson and M. J. Bebianno, Sci. Total Environ., 2008, 405, 109–119. 71 R. Company, unpublished work. 72 M. G. Lionetto, R. Caricato, M. E. Giordano, M. F. Pascariello, L. Marinosci and T. Schettino, Mar. Pollut. Bull., 2003, 46, 324–330. 73 F. Regoli and G. Principato, Aquat. Toxicol., 1995, 31, 143–164. 74 L. Guilhermino, P. Barros, M. C. Silva and A. M. V. M. Soares, Biomarkers, 1998, 3, 157–63. 75 M. Dellali, M. G. Barelli, M. Romeo and P. Aissa, Comp. Biochem. Physiol., Part B: Biochem. Mol. Biol., 2001, 130, 227–235. 76 J. F. Narbonne, N. Aarab, C. Clerandeau, M. Daubeze, J. Narbonne, O. Champeau and P. Garrigues, Biomarkers, 2005, 10, 58–71. 77 M. Romeo, P. Hoarau, G. Garello, M. Gnassia-Barelli and J. P. Girard, Environ. Pollut., 2003, 122, 369–378. 78 A. Viarengo, Rev. Acq. Sci., 1989, 1, 295–317. 79 J. A. Knight and R. P. Voorhees, Ann. Clin. Lab. Sci., 1990, 20, 347–352. 80 A. Box, A. Sureda, F. Galgani, A. Pons and S. Deudero, Comp. Biochem. Physiol, 2007, 146, 531–539. 81 D. M. Pampanin, I. Marangon, E. Volpato, G. Campesan and C. Nasci, Environ. Pollut., 2005, 136, 103–107. 82 D. R. Livingstone, P. Lemaire, A. Matthews, L. Peters, D. Bucke and R. J. Law, Mar. Pollut. Bull., 1993, 26, 602–606. 83 C. Cossu, A. Doyotte, M. C. Jacquin, M. Babut, A. Exinger and P. Vasseur, Ecotoxicol. Environ. Saf., 1997, 38, 122–131. 84 M. L. Vidal, A. Basseres and J. F. Narbonne, Comp. Biochem. Physiol., Part A: Mol. Integr. Physiol., 2002, 132, S93–104. 85 M. A. Serafim, R. M. Company, M. J. Bebianno and W. J. Langston, Mar. Environ. Res., 2002, 54, 361–365. 86 A. Viarengo, L. Canesi, L. Pertica and D. R Livingstone, Comp. Biochem. Physiol., 1991, 100, 187–190. 87 S. Pfeifer, D. Schiedek and J. Dippner, J. Exp. Mar. Biol. Ecol., 2005, 320, 93–103. 88 M. F. Frasco, D. Fournier, F. Carvalho and L. Guilhermino, Biomarkers, 2005, 10, 360–375. 89 F. Geret, A. Serafim, L. Barreira and M. J. Bebianno, Biomarkers, 2002, 7, 242–256. 90 J. J. Johnston and M. D. Corbett, Comp. Biochem. Physiol., Part C: Comp. Pharmacol., 1985, 80, 145–149. 91 P. Bjerregaard and M. H. Depledge, Mar. Biol., 1994, 119, 385–395.
This journal is ª The Royal Society of Chemistry 2009