Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence of biotic and abiotic factors

MPB-07586; No of Pages 10 Marine Pollution Bulletin xxx (2016) xxx–xxx

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Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence of biotic and abiotic factors Ibtissem Louiz a,c,⁎, Oum Kalthoum Ben Hassine a, Olivier Palluel b, Mossadok Ben-Attia c, Sélim Aït-Aïssa b a b c

Université de Tunis-El-Manar, Faculté des Sciences de Tunis, UR11ES08 Biologie Intégrative et Écologie Évolutive et Fonctionnelle des Milieux Aquatiques, 2092 El Manar, Tunisia Institut National de l’Environnement Industriel et des Risques (INERIS), Unité d’Écotoxicologie in vitro et in vivo, f-60550 Verneuil-en-Halatte, France Université de Carthage, Faculté des Sciences de Bizerte, UR, Laboratoire de Biosurveillance de l'Environnement, 7021 Zarzouna, Tunisia

a r t i c l e

i n f o

Article history: Received 15 October 2015 Received in revised form 14 March 2016 Accepted 20 March 2016 Available online xxxx Keywords: Gobius niger Bizerta lagoon Biomarkers Environmental factors Pollution Sentinel organism

a b s t r a c t This study aims at evaluating both the influence of natural and some anthropogenic pressures on spatio-temporal variations on biomarker responses in sedentary benthic fish Gobius niger. For this purpose, variability of biotransformation enzymes and oxidative stress parameters response were studied in six stations from Bizerta lagoon as well as a reference station located in Ghar El Melh lagoon. Biomarker responses were shown to vary according to both physico-chemical parameters and anthropogenic pressures, but no influence of sex was reported. Based on multivariate analyses, the responses of biomarkers, obtained after covariate analysis in order to weigh the effect of physico-chemical parameters, allowed us to discriminate all stations, with a good classification rate for those that are highly contaminated. Altogether, this study shows the usefulness of G. niger as a sentinel species and stresses the necessity of integrating natural variables for data interpretation. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Of the various coastal ecosystems, the coastal lagoons rank among the most productive ecosystems on earth, as they provide a wide range of ecosystem services and resources (Kennish and Paerl, 2010). However, like all coastal ecosystems worldwide, they are globally among the most heavily used and thus threatened natural systems (Barbie et al., 2011). Indeed, the anthropogenic impacts are increasing in many coastal lagoons (Kennish and Paerl, 2010). Thanks to their high natural productivity, the coastal lagoon environments represent productivity one of the most productive humid zones in the Mediterranean basin (Diawara et al., 2008) and so play the role of a reservoir of biodiversity. Therefore, they are considered of high ecological and economical value. However, these ecosystems, which are intrinsically fragile and highly sensitive to external forces (Louiz et al., 2013), are very vulnerable and suffer from numerous pressures of human origin (Danovaro, 2003; Unep-Map Rac/Spa, 2010).

⁎ Corresponding author at: Université de Tunis-El-Manar, Faculté des Sciences de Tunis, UR11ES08 Biologie Intégrative et Écologie Évolutive et Fonctionnelle des Milieux Aquatiques, 2092 El Manar, Tunisia. E-mail address: [email protected] (I. Louiz).

The Bizerta lagoon, located on the coasts of Northern Tunisia, has many urban, industrial and agricultural activities. In fact, it has been exploited for shellfish since 1964 (Beji, 2000). However, the degradation of water and sediment quality has led to a decrease in annual fish productivity (ANPE, 1990). The direct and indirect discharges of urban and industrial wastes and runoff have led to chemical contamination of the lagoon by various toxic compounds such as heavy metals (Yoshida et al., 2002; Ben-Garali et al., 2010), organo-chlorinated pesticides (Cheikh et al., 2002), polychlorobiphenyls (PCBs) (Derouiche et al., 2004; Barhoumi et al., 2014a, 2014b), organotins (Mzoughi et al., 2005) and polycyclic aromatic hydrocarbons (PAHs) (Louiz et al., 2008; Barhoumi et al., 2014a, 2014b). The Bizerta lagoon connects with the Mediterranean Sea by a straight canal along seven kilometers. It is also connected to the Ichkeul lagoon by the narrow channel of Oued Tinja (Fig. 1). This ecosystem is subjected to high biotic and abiotic variations that make it a specific context in regard to various parameters, such as salinity, temperature, and temporal oxygen deficiency. Salinity is determined by water exchanges between the Mediterranean Sea and Ichkeul lagoon (Bejaoui et al., 2010). The measurement of biological effects of chemical contaminants has become of major importance for the assessment of environmental quality (Gray, 1992; Serafim et al., 2012; Altenburger et al., 2015). In the Bizerta lagoon, most biomonitoring studies performed so far have focused on molluscs and bivalves (Dellali et al., 2001; Khessiba et al.,

http://dx.doi.org/10.1016/j.marpolbul.2016.03.045 0025-326X/© 2016 Elsevier Ltd. All rights reserved.

Please cite this article as: Louiz, I., et al., Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence..., Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.03.045

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I. Louiz et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx

Fig. 1. Localisation of sampling sites in the Bizerta lagoon and the reference station in Ghar El Melh Lagoon. CA: the Channel, NJ: Njila, MB: Menzel Bourguiba, MR: Maghrawa, MJ: Menzel Jemil and ML: Menzel Abderrahmen.

2001). Despite their importance as bio-indicators, only few studies have used fish within this ecosystem (Louiz et al., 2009; Ben Ameur et al., 2012; Ben Ameur et al., 2015). In the present study, we aim at evaluating the potential of a gobiidea fish, Gobius niger, as a sentinel model species for a biomarker-based monitoring. This species is a sedentary benthic fish and well represented in this area. It is also widely distributed in the Mediterranean Sea and in the eastern Atlantic, northwards to Norway and the Baltic Sea (Miller, 1961). A previous study has shown that gobiidea fish are able to bio-accumulate toxic compounds, such as polychlorinated biphenyls (Kwon et al., 2006). Some studies have also reported the feasibility of measuring biochemical biomarkers in gobies (Fossi et al., 1989; Corsi et al., 2003a; Corsi et al., 2003b; Barhoumi et al., 2014a, 2014b). Previously, we showed that – at some contaminated sites of the Bizerta lagoon – G. niger health status was altered as indicated by substantial vertebral abnormalities and gonad histopathology (Louiz et al., 2007, 2009). The assessment of in situ biological effects is critical in pollution monitoring as it can be influenced by other factors than chemical pollutants themselves. For instance, biological parameters such as biomarkers are subjected to several biotic and abiotic natural factors that modulate their responses in surveyed populations (Sheehan and Power, 1999) and make difficult the interpretation of their variation levels between different sampling sites. It is known that an efficient measurement of field biomarker responses in a given species should imply a good knowledge of the potential confounding factors that may interfere with the studied responses. Therefore, the evaluation of biomarker responses should be associated with assessment of both biotic (e.g. sex, gonadosomatic index) and abiotic (e.g. temperature, salinity, ph, oxygen) environmental factors in order to assess the part of pollution exposure on observed changes. Thus, to better understand the observed variations in this water plan, a good knowledge of biotic and abiotic parameters, that represent potential confounding factors in this environment, is essential. Furthermore, data normalization could allow the reduction of biomarker variability as reported by Flammarion et al. (1998) in the case of gender differences in the cytochrome P450 1A-related EROD (7-ethoxyresorufin-O-deethylase) biomarker. Nonetheless, it is now widely admitted that a single biomarker cannot unambiguously measure the environmental deterioration and the use of a battery of complementary biomarkers is required to address multi-contamination exposure context (Aït-Aïssa et al., 2003; Sanchez et al., 2007). In the present study, the responses of a suite of biochemical biomarkers were investigated in black goby G. niger sampled at several differently impacted stations of the Bizerte and Ghar El Melh lagoons. The investigated endpoints were related to different biological processes including xenobiotic biotransformation capacities and oxidative stress biomarkers. The measured biomarker responses were interpreted in regard to both biotic and abiotic factors, including seasons, as well as to anthropogenic pressures at the stations.

2. Materials and methods 2.1. Study area and choice of stations Goby sampling stations were located in the Bizerta and Ghar el Melh lagoons (Fig.1). The Bizerta lagoon extends for about 150 km2 and is connected to the Mediterranean Sea by straight channels. A relatively undisturbed site (Louiz et al., 2008) located at the seawards entrance of Ghar el Melh lagoon (GH) was chosen as a reference station since it is free from chemical contamination (Mahmoudi, 2003; Louiz et al., 2008). Six sampling stations were selected in the Bizerta lagoon (Fig. 1). These stations were characterized by various anthropogenic pressures. Thus, Menzel Bourguiba (MB) is located near a heavily industrialized area (e.g. metallurgical industry, boatyard, tyre production factories); the Channel (CA) is affected by a naval and commercial shipping harbor as well as cement works and Menzel Abderrahmen (ML) station is influenced by urban, industrial and fishing activities. The Njila (NJ) and Maghrawa (MR) stations are affected by neighboring agricultural zones. Menzel Jemil (MJ) station is also impacted by an agricultural activity, shellfish farming and industrial zone.

2.2. Fish collection and tissue sampling At each station, adult male and female gobies were collected from autumn 2005 to summer 2006 using a small benthic trawl. The total number of fish per station is indicated in Table 1. After capture, fish were measured for standard length (SL) (± 0.01 mm) and eviscerate body wet weight (We) (± 0.01 g) and were immediately sacrificed. Each fish was sexed by an external examination of the urogenital papilla (Miller, 1984) and by macroscopic observation of the gonads and each Table 1 Number of fish collected in Bizerta lagoon per station. Saison/sites

N (males;females) Winter

Spring

Summer

Autumn

∑

GH

20 (14;6) 27 (21;6) 44 (32;12) 38 (33;5) 18 (12;6) 14 (9;5) 31 (27;4)

40 (33;7) 27 (14;13) 42 (32;10) 36 (29;7) 23 (17;6) 29 (23;6) 10 (8;2)

23 (20;3) 41 (15;26) 60 (43;17) 38 (34;4)

15 (10;5) 31 (10;21) 43 (29;14) 10 (7;3) 21 (12;9) 8 (6;2) 21 (16;5)

98

CA NJ MB MR MJ ML

0 18 (12;5) 14 (11;3)

126 189 122 62 69 76

Please cite this article as: Louiz, I., et al., Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence..., Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.03.045

I. Louiz et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx

gonad was weighed (Wg) (±0.001 g). The gonadosomatic index (GSI) was calculated as GSI = (Wg / We) ∗ 100. Fish ranging from 50 to 125 mm SL was used to measure biochemical parameters. The liver and muscles were rapidly dissected and stored in dry ice until arrival to the laboratory where they were homogenized individually in ice-cold phosphate buffer (100 mM, pH 7.8) with 20% glycerol and 0.2 mM phenylmethylsulfonyl fluoride as a serine protease inhibitor. The homogenates were centrifuged at 10,000 × g, 4 °C, for 15 min and the post-mitochondrial fractions were conserved at − 80 °C until analysis, immediately after the sampling period (2005– 2006). 2.3. Water physico-chemical parameters The physico-chemical quality of water was monitored in-situ over the sampling period at all stations. Salinity (PSU), conductivity, temperature (°C), dissolved oxygen (mg/L) and pH were measured with a portable conductivity-meter (WTW 315i/SET) and a portable pH-meter (WTW 315i/SET). The temporal variations of physico-chemical parameters at reference (GH) and all-combined Bizerta lagoon (LB) stations are presented in Table 2. 2.4. Biomarker analysis Hepatic and muscle biomarker microplate assays (Aït-Aïssa et al., 2003) were optimized for the goby to fit linearity of each assay. All measures were carried out in triplicates at room temperature, using a microplate reader (Power Wavex—Bio-Tek instruments). Total protein concentrations in the liver and muscles were determined using the Bradford (1976) with bovine serum albumin (SigmaAldrich Chemicals, France) as a standard. The ethoxyresorufin-O-deethylase (EROD) activity was measured in the liver according to the Flammarion et al. (1998) method. The reaction mixture contained S9 pure or diluted, phosphate buffer containing 8 μM of 7-ethoxyresorufin and NADPH. It was initiated by the addition of NADPH and allowed to proceed at room temperature for 15 min. The resorufin formed was measured fluorometrically using 530 nm (excitation) and 590 nm (emission) filters. EROD activities were determined by comparing the rate of the fluorescence change in the samples with the fluorescence of authentic resorufin standards. The enzyme activity (EROD) was expressed in nmol of resorufin/min/mg of

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proteins. 7-Ethoxyresorufin-O-deethylase activity was determined by a fluorimetric method in black microplates using a microplate spectrofluorimeter reader (Victor2 Wallac, Perkin-Elmer). Glutathione S-transferase (GST) activity was measured in the liver according to Habig et al. (1974) using 1-chloro-2,4-dinitrobenzene (CDNB) and GSH as substrates and purified GST from the equine liver (Sigma) as a standard. Absorbance was measured at 340 nm and the activities were expressed as a unit of conjugated product per gram of protein. The total glutathione (GSHtot) concentrations were measured in the liver by Vandeputte et al. (1994). Briefly, the homogenates were precipitated with TCA (trichloroacetic acid) and the precipitate was removed after centrifugation. The reaction mixture containing 0.1 mM DTNB and 0.34 mM NADPH dissolved in a stock buffer (NaH2PO4/EDTA, pH 7.4) was added to wells containing samples and the reaction was started by adding 20 μL of 8.5 IU m/L GR. The absorbance at 415 nm was monitored for 5 min and compared to a standard curve made with GSH. Lipid peroxidation was estimated by the formation of TBARS. This biomarker was measured in the muscle according to the method developed by Ohkawa et al. (1979). Briefly, the homogenates were mixed with phosphate buffer/EDTA containing 31 μM of BHT, 7.7% of trichloroacetic acid and 0.3% of thiobarbituric acid. The mixture was incubated at 80 °C for 40 min. After cooling and centrifugation at 600 g for 10 min TBARS were quantified by fluorimetric measurement with 520 nm wavelength excitation and 550 nm wavelength emission. Malonaldehyde (MDA) was used as standard and the results were expressed in nmol of MDA equivalent/g of proteins. 2.5. Statistical analysis All data are reported as mean ± one standard deviation (SD) or one standard error of the mean (SEM) as stated in the figure legends and the SPSS 18.0 software was used for data analysis. Firstly, the normal distribution and homoscedasticity of data were checked using Kolmogorov– Smirnov and Levene tests, respectively. Since data sets did not have a normal distribution and/or homogeny of variance, the data were log transformed using F(x) = log10 (1 + x) prior to parametric analysis. Secondly, a three-way analysis of variance (ANOVA) and analysis of covariance (ANCOVA) were performed for each biomarker using stations, sex and sampling season as factors and abiotic parameters previously

Table 2 Seasonal variation of water temperature, salinity, pH and dissolved oxygen at the reference station (GH), Bizerta lagoon stations (LB); mean (±Ety). GH

CA

BC

MB

MR

MJ

ML

Mean LB

23.6 (±6.8) 17.00 (±2.7) 23.6 (±1.3) 29.5 (±0.8)

24.5 (±7.4) 17.1 (±3.5) 23.0 (±1.1) 30.1 (±1.8)

23.0 (±6.6) 14.5 (±2.9) 23.2 (±1.4) 28.8 (±2.1)

23.4 (±5.5) 15.5 (±2.3) 22.8 (±1.7) 28.8 (±2.4)

8.18 (±0.13) 8.20 (±0.15) 8.20 (±0.21) 8.23 (±0.10)

8.18 (±0.17) 8.11 (±0.33) 8.22 (±0.09) 8.10 (±0.10)

8.18 (±0.17) 8.19 (±0.15) 8.21 (±0.09) 8.25 (±0.16)

8.17 (±0.13) 8.15 (±0.20) 8.20 (±0.20) 8.22 (±0.15)

35.5 (±1.8) 32.6 (±1.5) 31.1 (±1.0) 35.21 (±1.5)

35.8 (±0.2) 32.4 (±1.7) 30.7 (±2.0) 35.2 (±1.7)

35.7 (±1.5) 28.7 (±2.5) 29.9 (±4.0) 35.9 (±3.1)

36.7 (±0.6) 32.9 (±2.1) 30.3 (±1.7) 35.5 (±0.1)

35.9 (±1.4) 32.1 (±1.6) 31.0 (±2.1) 35.4 (±2.0)

8.3 (±0.3) 7.9 (±0.3) 9.3 (±0.9) 5.6 (±1.1)

8.0 (±0.0) 7.8 (±0.3) 9.8 (±0.8) 5.4 (±1.9)

7.5 (±0.2) 8.6 (±0.3) 8.9 (±1.0) 4.9 (±1.6)

8.7 (±1.1) 8.8 (±0.3) 7.6 (±0.5) 6.4 (±3.3)

8.2 (±1.2) 8.5 (±1.4) 8.8 (±0.9) 5.8 (±1.7)

Temperature (°C) Autumn 22.4 (±1.5) Winter 14.6 (±0.4) Spring 21.8 (±1.8) Summer 27.9 (±1.5 )

22.5 (±5.8) 14.6 (±1.5) 21.6 (±0.9) 27.5 (±1.9)

23.3 (±5.8) 14.6 (±1.4) 21.6 (±1.2) 28.3 (±2.1)

23.5 (±6.3) 14.9 (±1.8) 23.1 (±1.3) 29.0 (±2.5)

pH Autumn Winter Spring Summer

8.13 (±0.01) 8.10 (±0.01) 8.17 (±0.06) 8.14 (±0.10)

8.11 (±0.02) 8.12 (±0.11) 8.16 (±0.06) 8.14 (±0.04)

8.15 (±0.11) 8.09 (±0.24) 8.18 (±0.14) 8.25 (±0.15)

8.20 (±0.06) 8.19 (±0.05) 8.23 (±0.38) 8.32 (±0.19)

Salinity Autumn Winter Spring Summer

37.2 (±0.2) 36.0 (±0.2) 36.9 (±0.4) 37.1 (±0.5)

36.20 (±0.5) 33.6 (±0.1) 32.5 (±1.5) 35.4 (±1.1)

35.7 (±0.4) 32.4 (±0.5) 31.5 (±1.6) 32.2 (±2.0)

Dissolved oxygen (mg/L) Autumn 9.3 (±1.2) Winter 9.1 (±1.3) Spring 8.5 (±1.2) Summer 6.2 (±1.0)

8.5 (±1.8) 8.3 (±0.3) 8.9 (±1.0) 6.3 (±1.4)

8.6 (±2.3) 9.9 (±0.3) 7.9 (±0.8) 6.1 (±1.4)

Please cite this article as: Louiz, I., et al., Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence..., Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.03.045

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identified (temperature, salinity, conductivity, pH and dissolved oxygen) in the regression analysis as covariates. The final ANCOVA tests the difference between the factor level means adjusted for the covariate effect. When stations by sex and seasons by sex interactions were not significant (p N 0.05), male and female data were combined. To determine the differences between biomarker responses measured each month, a one-way ANOVA followed by Gabriel test was performed. A significant difference was assumed when p b 0.05. The relationship between biomarker responses and previously reported levels of DDT, PCBs, organotins and PAHs in sediment (Cheikh et al., 2002; Derouiche et al., 2004; Mzoughi et al., 2005; Louiz et al., 2008) was evaluated by Pearson linear correlation analysis of the predictive value of ANCOVA which is the adjusted average of factors obtained after covariate analysis. There is no contradiction between the magnitude of the correlation coefficient found and the fact that it is statistically significant. In a larger sample (our case) a smaller coefficient may turn out significant, while in a small sample even a relatively large coefficient may fail to reach significance. A discriminant analysis was carried out on the same data sets to get the spatial variability defined by the contaminant levels and biomarker responses. This minimizes the effect of the environmental parameters and highlights, within station and season, relationships for easier interpretation of biochemical responses.

3. Results 3.1. Physico-chemical parameter analysis The temporal variation of physico-chemical parameters at reference (GH) and all-combined Bizerta lagoon (LB) stations are presented in Table 2. The salinity at GH station was relatively constant over the sampling period. In Bizerta lagoon (LB) sites, we noticed a temporal variation of salinity, irrespective of sites with minimum values in winter 32.1‰ (±1.7) and spring 31‰ (±0.9) and maximum values recorded in autumn 35.9‰ (± 1.4) and summer 35.5‰ (± 1.9). This variation seemed to be related to the degree of pluviometry (Table 2). The variation of salinity recorded at each station showed that, during the rainy seasons (winter and spring), salinity was lower at the MJ station, while during summer, salinity value was higher at this same station (Table 2). Water temperature at all sampled stations exhibited comparable temporal fluctuations. The maximum values were observed in August reaching about 29 °C in Bizerta lagoon, irrespective of stations, and 28 °C at the reference station (GH) located under the marine influence (Table 2). The minimum values were recorded in January with values of about 15 °C at both Bizerta lagoon and the reference station. The differences of temperature, recorded at each station and compared to the average, showed that the stations located inside the lagoon (i.e. MJ and MR) and away from the channel that allows the water exchange with the sea had the highest temperature levels (Table 2). However, CA and NJ stations, located in the navigation channel, have the lowest temperatures. These values are very close to that of the reference station GH. The temperature was negatively correlated with dissolved oxygen (r = − 0.47, p b 0.001) and positively with pH (r = 0.23, p b 0.05). The monthly pH values remained relatively constant in all stations and generally alkaline throughout the sampling period with a slight increase during summer (mean 8.22) (Table 2). An examination of differences, between the pH values at each station showed that Menzel Bourguiba (MB) had the highest value in summer, autumn and spring (Table. 2). The dissolved oxygen ranged from a maximum of 8.8 mg/L in spring and a minimum of 5.8 mg/L in summer (Table 2). The deviation of dissolved oxygen showed that MB, MR and MJ stations were less oxygenated during winter, summer and autumn (Table 2).

3.2. Factors influencing biomarker variability In order to determine factors involved in the variability of the four analyzed biomarkers, we carried out an analysis of covariance with three factors (sex, station and season) and the abiotic parameters were used as covariates (Table 3). This analysis did not show any significant contribution of sex in biotransformation and oxidative stress biomarkers in G. niger. The effect of season on EROD and GSHtot levels was not significant (ANCOVA, p N 0.05) and had therefore no influence on the spatial variability of these biomarkers. Conversely, the interaction with both stations and season parameters presented a significant contribution in the observed variations of all biomarkers. The results of ANCOVA incorporating sex as a third co-factor reduced the contribution of station and season on the variance of all biomarkers (Table 3). Assessing environmental parameters as covariates showed that they influenced significantly but differently the different biomarkers. Temperature and dissolved oxygen contributed significantly to the observed differences in EROD and GSHtot activity between stations (Table 3). The ANCOVA results, incorporating salinity, conductivity and pH, as a covariate, showed a significant effect on the GST variance (Table 3). The covariance analysis revealed that pH and dissolved oxygen make a significant contribution in the TBARS variability (Table 3). No correlation was observed between GSI and EROD activity in females (R2 = −0.035; p = 0.094). 3.3. Annual variations of biochemical biomarkers As the present study has showed that the variability of biomarkers is not influenced by sex, male and female biomarker data were combined. Annual variations of biochemical biomarkers at the reference station are recorded in Fig. 2. The monthly monitoring of EROD activity, in reference station GH, shows a significant increase from August to October and in July (one-way ANOVA Gabriel post-hoc test, p b 0.05) values are between 10 and 5.7 pmol/min/mg prot, while from November to April EROD activity remains lower than the annual average (Fig. 2A). The GST activity was induced during the months from August to January and in July (ANOVA Gabriel post-hoc test, p b 0.05). The GST activity values are between 4 and 9 × 10−3 U/g prot (Fig. 2B). Oxidative stress parameters were surveyed in fish by using the total glutathione (GSH) content and lipoperoxidation level (as revealed by TBARS). A monthly monitoring of the GSHtot activity highlighted an inhibition of the activity from November to March (one-way ANOVA Gabriel posthoc test, p b 0.05) (Fig. 2C). The mean values of GSHtot activity are on the order of 5.2 and 19.5 mol/g prot. The annual evolution of TBARS allowed the observation of two periods. So, the lower values extended from August 2005 to March 2006 (one-way ANOVA Gabriel post-hoc test, p b 0.05), while a second period (April to July) had higher values (Fig. 2D). The mean TBARS values were on the order of 38 and 320 nmol MDA/g prot. 3.4. Spatio-temporal expression of biomarkers The variation of the measured biomarker values according to stations during each sampling season is reported in Fig. 3. This variation is due to local abiotic factors (natural and anthropogenic). At Bizerta lagoon (BL) stations, EROD levels were much more variable than the reference station, ranging from b 1 up to 10 pmol/min/mg prot during the four seasons. However, no general trend was evidenced as the observed effects were greatly dependent on station sampling. For instance, the significant EROD increases were noted at MB and MR (in winter) and MJ and ML (in summer) stations (Fig. 3A). Unlike EROD, GST activity levels at GH station were seasondependent, with a two-fold increase in summer as compared to winter. Only a few statistical differences were noted at some stations and for a given sampling period as compared to the reference station, i.e. NJ in

Please cite this article as: Louiz, I., et al., Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence..., Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.03.045

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Table 3 Results of variance with three factors (stations, sex and sampling season) and covariance analysis (abiotic parameters were used as covariates) (ANCOVA) of log10-transformed data from EROD, GST and GSHtot activities and TBARS level. T: temperature; Sal: salinity; Cond: conductivity; Oxy: dissolved oxygen.

EROD

GST

GSHtot

TBARS

Source of variation

Type III sum of squares

ddl

F

Sig.

T Oxy Sex Station Season Sex ∗ station Sex ∗ season Station ∗ season Sex ∗ station ∗ season pH Sal Cond Sex Station Season Sex ∗ station Sex ∗ season Station ∗ season Sex ∗ station ∗ season T Oxy Sex Station Saison Sex ∗ station Sex ∗ season Station ∗ season Sex ∗ station ∗ season pH Oxy Sex Station Season Sex ∗ station Sex ∗ season Station ∗ season Sex ∗ station ∗ season

0.592 0.763 0.259 1.830 0.083 0.364 0.393 5.198 2.362 0.094 0.062 0.070 0.005 0.308 0.646 0.016 0.046 0.555 0.215 0.489 0.944 0.173 0.694 0.314 0.042 0.157 2.208 0.738 0.657 0.769 0.098 0.156 3.874 0.172 0.084 2.504 0.677

1 1 1 6 3 5 3 14 12 1 1 1 1 6 3 5 3 14 12 1 1 1 6 3 5 3 14 12 1 1 1 6 3 5 3 14 9

5.414 6.985 2.371 3.350 0.252 0.666 1.199 3.398 1.802 7.321 4.854 5.465 .374 4.807 16.796 0.256 1.198 3.095 1.396 7.238 13.968 2.558 2.053 1.551 0.125 0.774 2.334 0.910 22.688 26.532 3.393 1.076 44.557 1.189 0.972 6.171 2.594

0.02⁎ 0.009⁎⁎ 0.124 0.006⁎⁎ 0.86 0.65 0.31 0.000⁎⁎⁎ 0.046⁎ 0.007⁎⁎ 0.03⁎ 0.02⁎ 0.54 0.000⁎⁎⁎ 0.000⁎⁎⁎ 0.94 0.31 0.000⁎⁎⁎ 0.16 0.007⁎⁎ 0.000⁎⁎⁎ 0.11 0.07 0.20 0.99 0.51 0.004⁎⁎ 0.54 0.000⁎⁎⁎ 0.000⁎⁎⁎ 0.066 0.373 0.000⁎⁎⁎ 0.31 0.41 0.000⁎⁎⁎ 0.006⁎⁎

⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

winter. There is no statistical difference between all sites in spring (Fig. 3B). Similar to GST, the total glutathione (GSHtot) varied according to the season at the GH reference station, with a peak in spring and summer periods. At the other stations, similar profiles were observed but with overall lower GSHtot levels in spring and summer (Fig. 3C). Reduced GSHtot levels, statistically different from the reference station, were noted at NJ and MJ stations in winter, at MJ and ML in spring and at CA in summer, while it increased at MB station in autumn. A statistically significant increase of TBARS was noted in MJ during winter and CA, NJ and MJ during spring, as compared to the reference station. In summer, the TBARS levels at NJ and MJ stations were below those of the reference station. Through autumn, all stations in the Bizerta lagoon did not differ from the reference station (Fig. 3D). 3.5. Relationship between biomarker responses and chemical contamination To carry out this work, we used predictive values (biomarker values minus the contribution of covariates, such as T, pH, Oxy and Cond) of biomarkers obtained after covariate analysis to weigh the effect of abiotic parameters. Most of the studied biomarkers were positively or negatively correlated to each other, but the correlation coefficients were generally low (Table 4) and would not yield strong predictive power when using linear regression models.

Fig. 2. Monthly monitoring of EROD (A), GST (B), GSHtot activities (C) and TBARS concentration (D), of Gobius niger from reference station (GH). Values annotated with different letters are significantly different (one-way ANOVA followed by the Gabriel test, p b 5%). Xa: annual average. Data are presented as mean ± SEM.

A possible relationship between biomarker (predictive values of biomarkers obtained after covariate analysis) variation and chemical contamination of sediments was assessed on the basis of previously reported data at the same stations, by our team and other groups (Table 5). It is noteworthy that the TBARS activity exhibited positive correlation with organotins and PAHs, which may reflect the anthropogenic pressures of the lagoon. A negative correlation between EROD and DDT was also observed, while PCB and PAHs were found to be positively correlated with GSHtot. GST was correlated with all sediment contaminants (Table 5). We performed a discriminant analysis with biomarkers, PAHs and organotins data to explore inter-station discriminating power of the studied variables. Data distribution and the position of the centroids in the first two discriminating functions (F1: 95.1%, F2: 3.3%) are shown in Fig. 4. The first axis is useful to distinguish stations characterized by a contamination by PAHs and organotins (MB and ML 92% and 100% of data are well classified) from the other stations. The standardized discriminating function coefficients (Table 6) show that this axis is primarily associated with GST activity in the positive side. The negative side of

Please cite this article as: Louiz, I., et al., Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence..., Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.03.045

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I. Louiz et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx

Fig. 3. Spatio-temporal monitoring of the EROD (A), GST (B) and GSHtot (C) activities and the TBARS (D) concentration among representatives of G. niger collected at the reference station (GH) and in Bizerta lagoon. CA: the Channel, NJ: Njila, MB: Menzel Bourguiba, MR: Maghrawa, MJ: Menzel Jemil and ML: Menzel Abderrahmen.

Please cite this article as: Louiz, I., et al., Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence..., Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.03.045

I. Louiz et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx

the axis is associated with TBARS. MR and CA stations are well discriminated in the negative side (97% and 61% of data are well classified). The other stations (MJ, NJ, GH) are not well discriminated and the percentage of well classified data in each station is rather low, i.e. 50% (Fig. 4). 4. Discussion In the present work, the reference sampling site retained was the seaward entrance of Ghar el Melh lagoon that has no evident source of contamination and then considered as a potential reference site. Thereby, the dosage of chemical contaminants (heavy metal and PAHs) carried out by Mahmoudi (2003) showed that this station had a very low level of pollutants. Furthermore, in a previous work (Louiz et al., 2008), where the sediment contamination was evaluated by endocrine disruptors and PAHs, the obtained data confirmed the use of this station as a reference-sampling site. Indeed, the framework of the identification of a sentinel model species for biomonitoring of aquatic environment, biomarker measurements and follow-up of their variations require, primarily, the selection of a reference-sampling site free of chemical contaminants as a reliable control area for better interpretation of biomarker data (Nixon et al., 1996), although some authors (Lindström-Seppa and Oikari, 1990) consider that the choice of reference sampling sites is difficult due to the extent of anthropic pressures which spare very few sites of chemical contamination. Thereafter, the spatio-temporal follow-up of biotic and abiotic environmental factors was carried out concomitantly with that of 4 biochemical biomarkers (EROD, GST, GSHtot, TBARS) in gobies from the reference sampling site and Bizerta lagoon in order to determine the effects of natural and anthropogenic factors on biomarkers. Indeed, it is well known that various factors (e.g. sex, age, sexual maturity, parasitism, temperature and bioavailability of food) can influence, in the study area, the physiological levels and biomarker responses (Bucheli and Fent, 1995; Eggens et al., 1995; Flammarion and Garric, 1997; Whyte et al., 2000; Martinez-Alvarez et al., 2005) by inducing a confused tangle in the responses generated by pollutant exposure and causing background noise (Sanchez et al., 2008). These factors can then bias the interpretation of the biomarker response. It should be noted that the results of our survey showed a more marked seasonality of physicochemical parameters in Bizerta lagoon than at the reference station (GH). In Bizerta lagoon, the observed large variability of salinity can be explained by the evaporation and rainfall amounts as well as exchanges with both Ichkeul lagoon and the sea. Indeed, high evaporation and marine water inflows during summer are increasing the salinity of the lagoon. However, during the rainy season the decline of salinity is explained by the large freshwater discharge (20 Mm3 year/L) from Ichkeul Lagoon through the Tinja River (Harzallah, 2002). We noted a high annual pH at MB station. This site is impacted by metallurgic industry and important macrophyte development is observed. An increase in the photosynthetic activity of macrophytes and microphytes tends to alkalize water pH (Buapet et al., 2013). Table 4 Pearson linear correlation coefficients between predicted values of biomarkers, calculated from ANCOVA analysis.

EROD

GST

GSHtot

⁎ p b 0.05. ⁎⁎ p b 0.01.

r Sig. N r Sig. N r Sig. N

GST

GSHtot

TBARS

−0.03 0.52 431

0.01 0.81 381 0.51⁎⁎ 0.00 453

−0.12⁎ 0.02 381 −0.40⁎⁎ 0.00 438 0.22⁎⁎ 0.00 385

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The relative decrease in dissolved oxygen observed in summer is associated with the increase in temperature, pH and salinity. This may similarly be associated with summer eutrophication in Bizerta lagoon and CO2 solubility decreases (Khessiba et al., 2005). The stations, situated inside the Bizerta lagoon, are less oxygenated than those located in the navigation channel (NJ and CA) and the reference station. This could be partly responsible for increased stress in living organisms. Indeed, the biochemical biomarker levels were significantly influenced by physico-chemical parameters. Based on the ANCOVA analysis, this physicochemical parameter variability may influence that of the studied biomarker responses. Thus, the EROD biomarker was found to be influenced by some abiotic factors such as temperature and dissolved oxygen. EROD activity is known to be influenced by both endogenous cycles, e.g. reproductive status (Arukwe and Goksøyr, 1997; Whyte et al., 2000) and environmental factors (Galgani et al., 1992). Similarly, Rotchell et al. (1999) observed the influence of temperature on this biomarker. They found a maximum of hepatic EROD activity in summer that was 6–8 times greater than that recorded in winter in Anguilla anguilla. Kammann et al. (2005) also reported high values of EROD activity during summer in Limanda limanda. A positive correlation between EROD and temperature was reported in sole Pleuronectes vetulus (Collier et al., 1995). As for the observed contribution of dissolved oxygen in the EROD activity variability, it can be the indirect influence of temperature. Nevertheless, we did not detect a significant contribution of sex in the observed variability by using threefactor analysis of variance (sex, station and season). Likewise, Eggens et al. (1995) did not report any sex differences in the sole. The maximum values of EROD activity that we noted were in MB (in autumn) and MJ (in summer) stations. This may reflect an activation of detoxification processes, probably due to the contamination of this station by PAHs (Louiz et al., 2008). Our findings also showed a positive correlation between Bizerta lagoon sediment concentration of 4 and 6 ring PAHs and EROD activity. Indeed, it is known that several industrial chemicals are involved in this induction such as planar halogenated biphenyls, polycyclic aromatic hydrocarbons and structurally related compounds (Whyte et al., 2000). In fact, not all PAHs induce EROD activity equally in fish, 4–6 ring PAHs and photo-oxidized products of PAHs being the most potent inducers (Aas et al., 2000). The negative correlation between EROD and DDT, observed in this study, could be due to indirect effect of one or more of its metabolites exerting sub-toxic or hepatotoxic impacts leading to inhibition on enzyme systems. Another hypothesis could rely on the ability of DDT and some of its metabolites to bind to and activate estrogen receptor (ER) (Wetterauer et al., 2012), thereby inhibiting the AhR pathway through cross-talk mechanisms (Navas and Segner, 2000). Nevertheless, our observation is in accordance with the study by Binelli et al. (2006) who reported an inhibition of basal EROD activity in Zebra mussel exposed to pp'DDT. Similarly, it is well known that this enzymatic activity can be inhibited by heavy metals (Viarengo et al., 1997) or xeno-estrogens (Arukwe et al., 2000; Hasselberg et al., 2004). Sediments of Bizerta lagoon are contaminated by heavy metals (Yoshida et al., 2002) and estrogen active chemicals (Louiz et al., 2008) and we suspect, in our case, the inhibition of EROD activity that masks the real induction caused by specific inducers such as PAHs or PCBs (Bucheli and Fen, 1995; Whyte et al., 2000). In this study, the three-factor analysis of variance (sex, station and season) revealed that the GST activity did not seem to be related to sex. However, there was a significant contribution of stations and season. In fact spatial differences in GST activity in black goby can be related to sediment contamination level. The increase in GST activity, observed in the summer and autumn, suggests an activation of detoxification processes probably due to the high salinity level. This feature was already reported by Rachel et al. (2006) who observed that GST activity increased significantly with elevated salinity in white sturgeon. This finding corroborates with that of Mathieu et al. (1991) which showed that hepatic GST in mullet Mullus barbatus decreases in winter as well. In Bizerta lagoon GST is correlated with PAHs, PCBs, DDT and organotins.

Please cite this article as: Louiz, I., et al., Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence..., Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.03.045

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I. Louiz et al. / Marine Pollution Bulletin xxx (2016) xxx–xxx

Table 5 Pearson linear correlation coefficients between sediment contaminants and predicted values of biomarkers calculated from ANCOVA analysis.

EROD

GST

GSHtot

TBARS

r Sig. N r Sig. N r Sig. N r Sig. N

∑ PCBsa

∑DDTsb

Organotinsc

4-Ring PAHsd

5-Ring PAHs d

6-Ring PAHsd

∑ PAHsd

−0.01 0.91 439 0.35⁎⁎ 0.00 515 0.15⁎⁎

−0.25⁎⁎ 0.00 439 0.43⁎⁎ 0.00 515 0.08 0.09 502 0.03 0.06 465

0.17 0.06 439 0.23⁎⁎ 0.00 515 0.04 0.34 502 0.13⁎⁎ 0.00 465

0.11⁎ 0.02 439 0.13⁎⁎ 0.003 515 0.09⁎

0.13⁎⁎ 0.009 439 0.03 0.533 515 0.10⁎

0.16⁎⁎ 0.001 439 0.13⁎⁎ 0.002 515 0.19⁎⁎⁎

0.17⁎⁎ 0.00 439 0.11⁎⁎ 0.01 515 0.205⁎⁎⁎

0.046 502 0.120⁎ 0.01 465

0.026 502 0.148⁎⁎ 0.001 465

0.000 502 0.058 0.214 465

0.000 502 0.19⁎⁎ 0.00 465

0.00 502 −0.07 0.15 465

a

Derouiche et al. (2004). Cheikh et al. (2002). c Mzoughi et al. (2005). d Louiz et al. (2008). ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001. b

The discriminant analysis, by using predictive values of ANCOVA, succeeded in distinguishing stations characterized by a contamination by PAHs and organotins and induction of GST (MB and ML 92% and 100% of data are well classified) from the other stations. In the literature, elevated GST activities in fish were related to PCBs- or PAH-containing sediments (Narbonne et al., 1991; López et al., 2002), but other laboratory studies failed to show any significant GST induction by PAHs and PCBs in eels (Fenet et al., 1996). Khessiba et al. (2001) found an increase of this enzyme activity in the mussel, Mytilus galloprovincialis, due to contamination by p,p'-DDE. In addition, some metals, such as copper and organometallic compounds, can inhibit the GST activity (Stien et al., 1997; Al-Ghais and Ali, 1999). Najimi et al. (1997) confirmed the induction of this enzyme in animals originating from polluted sites by metals. This biomarker has a relatively complex response to several pollutants in the environment and can be triggered by some pollutants (Goldberg and Bertine, 2000; Moreiro and Guilhmino, 2005). Our findings revealed that the sex of fish has no contribution in GSHtot and TBARS variation. However, station and season present a significant contribution. The normal seasonal base­line values of these biomarkers evaluated in the reference station show an increase in the hot season. However, fish from Bizerta lagoon have lower antioxidant defenses. The results show, as well, that pH and dissolved oxygen

contribute significantly to the variation of GSHtot activity in gobies collected in Bizerta lagoon. This response is probably related to the highest ambient temperature, an increase in oxygen consumption, and therefore to ROS generation and it seems that the occurrence of damage comes after a defense phase. These results are in accordance with the literature. In fact, the level of antioxidant enzyme variability is likely to be related to the change in metabolic status of individuals who depend on several factors such as food availability, temperature, photoperiod, dissolved oxygen content and salinity (Khessiba et al., 2005; Leiniö and Lehtonen, 2005; Santovito et al., 2005; Damiens et al., 2004). The GSHtot levels may increase during summer to counteract the increase of reactive oxygen species (ROS) production. Generally, thermo-dependent organisms such as fish are subjected to metabolic changes associated with the production of ROS, caused by water temperature change (Lushchak and Bagnyukova, 2006a, 2006b). Indeed, the production of ROS and the activation of antioxidant enzymes should theoretically increase when the temperature increases which may be considered as a natural prooxidant factor in summer. In fact, the increase in temperature allows an increase in metabolic activity and oxygen consumption (Storey, 1996) and a specific induction of ROS generating system, such as cytochrome P450. Low levels of dissolved oxygen can sometimes cause anoxic conditions in the shallow water of the lagoon and consequently reduce the possibility of ROS formation in the warm periods (Cooper et al., 2002; Lushchak et al., 2005; Storey, 1996). In the section spatio-temporal expression of biomarkers, we failed to clearly highlight stations potentially impacted by contaminants by all biomarkers simultaneously. That may reflect the strong influence of natural abiotic factors on biomarker levels. Then, we carried out the PCA analysis, by using predictive values of biomarkers obtained after covariate analysis to weigh the effect of natural abiotic parameters (T, Sal, pH an O2). Table 6 Standardized discriminant function coefficients calculated based on biomarkers and sediment contaminants descriptors.

Fig. 4. Data distribution of biomarkers and sediment contaminants on the two principal axes of the discriminant analysis.

Disciminant function

Eigenvalue

Relative percentage

Canonical correlation

F1 F2

19.42 0.67

95.1 3.3

0.98 0.63

Organotin PAH EROD GST GSHtot TBARS

F1 1.00 1.00 −0.05 0.73 0.28 −0.64

F2 0.79 −0.52 −0.58 0.74 0.14 0.02

Please cite this article as: Louiz, I., et al., Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence..., Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.03.045

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The comparison of station data by PCA showed that the responses of the two oxidant stress biomarkers, GSHtot and TBARS were reversed. Indeed, the level of GSHtot activity was induced in MB and ML sites, which are highly contaminated by heavy metals (Yoshida et al., 2002). Likewise, several studies showed that metal exposure can lead to an increased formation of ROS (Silva et al., 1999). However, TBARS, that generally occur when antioxidant systems are overwhelmed (Wu et al., 2005), were induced at MR and CA sites, indicating a higher impact on cell membranes (Cossu et al., 2000), than at MB and ML stations were lower values were recorded. In addition, changes in membrane permeability induced by MDA can decrease GSH cellular levels by allowing faster ROS intake and GSH loss (Ault and Lawrence, 2003). 5. Conclusion The aim of this study was to examine the seasonal variations of biomarkers in a polluted lagoon and identify the contribution of natural factors (sex, gonadosomatic index, physicochemical parameters) involved in these variations, in order to better clarify the conditions of their use as tools in biomonitoring programs of the aquatic environment. The results showed that the physicochemical parameters and the chemical contaminants have a significant effect on the EROD, GST, GSHtot and TBARS activities. Overall, our findings imply that the timing of sample collection is crucial for a correct interpretation of biomarkers in light of pollution exposure. Therefore, this study emphasized the need to standardize sampling procedures in close relation with environmental parameters for biomonitoring studies. Finally, our results demonstrate that G. niger constitutes a useful tool for biomonitoring aquatic pollution and can be used as a sentinel species in relatively small ecosystems. Acknowledgments The authors gratefully acknowledge the Ministry of Higher Education and Scientific Research, Tunisia (99/UR/09-04) and the National Institute for Industrial Environment and Risks INERIS France (P190), for their financial support. The authors thank Adel Rdissi an English teacher from the faculty of Medicine of Monastir for English revision. References Aas, E., Baussant, T., Balk, L., Liewenborg, B., Andersen, O.K., 2000. PAH metabolites in bile, cytochrome P4501A and DNA adducts as environmental risk parameters for chronic oil exposure: a laboratory experiment with Atlantic cod. Aquat. Toxicol. 51, 241–258. Aït-Aïssa, S., Ausseil, O., Palluel, O., Vindimian, E., Ganier-Laplace, J., Porcher, J.M., 2003. Biomarker responses in juvenile rainbow trout (Oncorhynchus mykiss) after single and combined exposure to low doses of cadmium, zinc, PCB77 and 17-β-estradiol. Biomarkers 8 (6), 491–508. Al-Ghais, S.M., Ali, B., 1999. Inhibition of glutathione S-transferase catalyzed xenobiotic detoxication by organotin compounds in tropical marine fish tissues. Bull. Environ. Contam. Toxicol. 62, 207–213. Altenburger, R., Aït-Aïssa, S., Antczak, P., Backhaus, T., Barcelo, D., Seiler, T.B., Brion, F., Busch, W., Chipman, K., López de Alda, M., de Aragão, Umbuzeiro G., Escher, B.I., Falciani, F., Faust, M., Focks, A., Hilscherova, K., Hollender, J., Hollert, H., Jäger, F., Jahnke, A., Kortenkamp, A., Krauss, M., Lemkine, G., Munthe, J., Neumann, S., Schymanski, E., Scrimshaw, M., Segner, H., Slobodnik, J., Smedes, F., Subramaniam, K., Teodorovic, I., Tindall, A.J., Tollefsen, K.E., Walz, K.H., Williams, T.D., Van den Brink, P.J., van Gils, J., Vrana, B., Zhang, X., 2015. Future water quality monitoring Adapting tools to deal with mixtures of pollutants in water resource management. Sci. Total Environ. 512-513, 540–551. ANPE, 1990. Etude préliminaire de l’écologie du lac de Bizerte. Rapport de l’Agence Nationale de Protection de l’Environnement, Tunis (100 pp.). Arukwe, A., Celius, T., Walther, B.T., Goksøyr, A., 2000. Effects of xenœstrogen treatment on zona radiata protein and vitellogenin expression in Atlantic salmon (Salmo salar). Aquat. Toxicol. 49, 159–170. Arukwe, A., Goksøyr, A., 1997. Changes in three hepatic cytochrome P450 subfamilies during a reproductive cycle in turbot (Scophthalmus maximus L.). J. Exp. Zool. 277, 313–325. Ault, J.G., Lawrence, D.A., 2003. Glutathione distribution in normal and oxidatively stressed cells. Exp. Cell Res. 285, 9–14. Barbie, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C., Silliman, B.R., 2011. The value of estuarine and coastal ecosystem services. Ecol. Monogr. 81 (2), 169–193. Barhoumi, B., LeMenach, K., Dévier, M.-H., El megdiche, Y., Hammami, B., Ben Ameur, W., Ben Hassine, S., Cachot, J., Budzinski, H., Driss, M.R., 2014b. Distribution and ecological

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Please cite this article as: Louiz, I., et al., Spatial and temporal variation of biochemical biomarkers in Gobius niger (Gobiidae) from a southern Mediterranean lagoon (Bizerta lagoon, Tunisia): Influence..., Marine Pollution Bulletin (2016), http://dx.doi.org/10.1016/j.marpolbul.2016.03.045