Ecotoxicology and Environmental Safety 112 (2015) 153–160
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Effect of different river flow rates on biomarker responses in common carp (Cyprinus carpio) Branimir K. Hackenberger *, Mirna Velki, Željka Lončarić, Davorka K. Hackenberger, Sandra Ečimović Department of Biology, Josip Juraj Strossmayer University of Osijek, Cara Hadrijana 8/A, 31000 Osijek, Croatia
art ic l e i nf o
a b s t r a c t
Article history: Received 10 January 2014 Received in revised form 13 October 2014 Accepted 16 October 2014
The present study investigated effects of different river flow rates on basal activities of selected biomarkers and the occurrence of oxidative stress in the common carp (Cyprinus carpio). Juvenile carp were exposed to different river flow rates (5–120 cm/s) by caging for 3 weeks. After this period, one half of the fish were sacrificed and used for analysis. The other half received a single intraperitoneal injection of 3-methylcholanthrene (3-MC) and after 6 days were sacrificed and used for analysis. In order to investigate whether the physical activity of carp in the environment will influence the condition status of carp, following biomarkers were measured – activities of glutathione S-transferase (GST), catalase (CAT) and ethoxyresorufin-O-deethylase (EROD) and concentration of protein carbonyls (PC). The results showed that different flow rates significantly influenced biochemical biomarkers. The basal activity of GST did not change significantly after exposure to different river flow rates, whereas the activity of CAT increased with increasing river flow rates. The application of 3-MC caused significant increases in GST and CAT activities, but there were no difference between 3-MC control and 3-MC different flow rates. The occurrence of oxidative stress as a result of exposure to increased physical activity, i.e. increased river flow rates, was confirmed by measurement of PC levels – the level of PC increased with increasing river flow rates. Measurement of EROD basal activity showed that at lower river flow rates the EROD activity increased and at higher river flow rates decreased towards control levels demonstrating a close relationship between oxidative stress, PC levels and EROD activity. Obviously, biomarker responses in carp of different condition status can differ substantially. It can be concluded that flow rate may be an important factor in biomonitoring of rivers using biomarkers and since at different locations river water flow rate can vary significantly, the site selection is extremely important for proper design of river biomonitoring studies involving caging. & Elsevier Inc. All rights reserved.
Keywords: Flow rate Condition status Oxidative stress Biomarkers Carp
1. Introduction Focusing only on chemical data (e.g. pollutant concentrations) in different environmental compartments is not sufficient to reliably assess potential risks, since different environmental factors influence the bioavailability of pollutants to organisms. Measurement of responses of organisms can serve as a functional measure of exposure to different stressors and can be used in environmental monitoring and assessment (van der Oost et al., 2003). Biomarkers at the molecular and biochemical level can provide insight into the mode of action of pollutants (Kammenga et al., *
Corresponding author. Fax: 385 31 399 921. E-mail addresses: [email protected] (B.K. Hackenberger), [email protected] (M. Velki), [email protected] (Ž. Lončarić), [email protected] (D.K. Hackenberger), [email protected] (S. Ečimović). http://dx.doi.org/10.1016/j.ecoenv.2014.10.021 0147-6513/& Elsevier Inc. All rights reserved.
2000) and can be used as early warning signals of pollutant exposure. Consequently, biomarkers have been often measured in the different groups of aquatic organisms, and in fish species various biomarkers have been used as a tool for ecotoxicological assessments (Kirby et al., 2007). The inclusion of the biomarker approach in biomonitoring studies is common, however it is necessary to be aware of the potential confounding factors (Forbes et al., 2006). The confounding factors can be biological (e.g., age, sex, condition) and environmental (e.g., temperature, pH, oxygen) and can significantly influence biomarker responses. Fish have proved to be good models for the evaluation of health status of aquatic ecosystems exposed to environmental pollution. Many fish species can be considered as top consumers in aquatic ecosystems (Dallinger et al., 1987) and it is likely that pollutants present in the aquatic environment will accumulate in fish and represent a potential risk for them and also to piscivorous birds and mammals (Adams et al., 1992). Monitoring of sentinel fish
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species has been widely used in order to assess the degree of accumulation of pollutants and the effects on the health status of aquatic environments (De la Torre et al., 2000; Bervoets and Blust, 2003). In studies addressing the impact of environmental contaminants on fish biomarkers, several experimental designs can be applied – laboratory studies, field studies using wild-caught fish, and in situ studies using caged fish. In laboratory studies it is not possible to duplicate true field conditions so the results obtained in such studies may provide misleading conclusions (Hanson and Larsson, 2007; Martin-Diaz et al., 2008). Field studies using wildcaught fish require investigation of the same species at each site in order to be able to compare different sites. Since fish community structure depends on different abiotic and biotic factors present in the water body under investigation, it is practically impossible to find a species that is present at all sites of interest. Also, in wildcaught fish the high inter-variability in biomarker responses is often present due to mobility of fish and varied exposure histories (Hanson and Larsson, 2007). To minimize this problem, it is possible to conduct transplant experiments and active biomonitoring using cages has already been implemented using different organisms, including fish (Bervoets et al., 2009; De la Torre et al., 2007; Oikari, 2006; Reynders et al., 2008). In order to reduce the variability and to more closely resemble true field exposure conditions, in the present study in situ caged fish exposure was applied. Environmental conditions present at different sites of interest can affect organisms and consequently cause differences in the biomarker responses which are not derived exclusively from exposure to pollutants. Both organism condition and an understanding of how changes in environmental conditions may change organism response to pollutant exposure are essential for successful biomonitoring. It is known that each species prefers certain environmental conditions, and changes in these conditions can possibly affect the responses of organisms. Regarding the river flow rate, it is also known that some fish species prefer slow moving waters whereas other prefer fast moving waters. Although the influence of river flow rates on fish biomarker responses has not been investigated so far, it can be assumed that higher rates will lead to greater physical activity and consequently lead to stress in organisms. Namely, in the laboratory study it has been shown that oxidative stress induced by intensive exercise and CdCl2 affects 7-ethoxyresorufin O-deethylase (EROD) inducibility, concentration of thiobarbituric acid reactive substances (TBARS) and protein carbonyls (PC) in common carp (Stepić et al., 2012). So in order to investigate whether the different river flow rates, i.e. the increased physical activity of carp under field conditions, will influence the condition status and the inducibility of EROD, the present study was undertaken. Fish were exposed in situ to different river flow rates and besides EROD activity, widely used biomarker of environmental pollution in aquatic environments, following biomarkers were also measured – concentration of PC and activities on enzymes catalase (CAT) and glutathione S-transferase (GST). Cytochrome P450 (CYP) is one of the most intensively studied biomarkers, in both laboratory and field conditions (Stegeman, 2000). The CYP450 enzymes belong to the large family of hepatic mixed-function oxidase enzymes of phase I xenobiotic biotransformation that are involved in reactions related to the biotransformation of many endogenous and exogenous substances, and this enzyme system has been detected in all organisms examined, from bacteria to mammals. Among them, one of the best studied parameters used as a biomarker of exposure of aquatic organisms to pollutants in the environment is the induction of CYP450 1A (Cheevaporn and Beamish, 2007; Flammarion et al., 1998; Flammarion et al., 2002; Mayon et al., 2006). Induction of CYP450 1A occurs after ligand-activation of the aryl hydrocarbon receptor (AhR). This receptor is typically, but not exclusively,
activated by planar, polycyclic and aromatic compounds including polyaromatic hydrocarbons (PAHs), dioxins, and polychlorinated biphenyls (PCBs) (Denison et al., 2002). However, there is an increasing evidence that activation of AhR is also triggered by other compounds, including some pharmaceuticals and personal care products and pesticides (Fernández-Cruz et al., 2011; Casado et al., 2006). The induction of EROD activity is a commonly used biomarker for exposure to CYP450 1A inducers and the advantages of using EROD activity as a biomarker are the specificity for CYP 1A in fish, high sensitivity, feasibility and simplicity of its measurement (Arinç and Sen, 1994; Arniç et al., 2000; Bucheli and Fent, 1995). Oxidative stress occurs when the generation of reactive oxygen species (ROS) in a system exceeds the system's ability to neutralize and eliminate them. In fish, antioxidant defenses are sensitive to exposure to environmental pollutants and can therefore be used as indicators of aquatic environmental health (Sturve et al., 2008). When antioxidant defenses are impaired or overcomed, oxidative stress can damage all types of biological molecules and the most common perturbation resulting from oxidative stress is protein carbonyl formation (Shacter, 2000a). Oxidative modifications of proteins can lead to diverse functional consequences, e.g. they can inhibit a wide array of enzyme activities (Stadtman, 1990) and can also lead to loss of function (Stadtman and Oliver, 1991). Enzyme CAT plays an important role in maintaining reactive oxygen homeostasis and also regulating pathophysiology of the organisms (Deisseroth and Dounce, 1970). This enzyme catalyzes the conversion of hydrogen peroxide to oxygen and water and is regarded as one of the most important specialized antioxidant enzymes for the detoxification of oxidative stress in the cellular antioxidant mechanism (Gravato et al., 2005; Michiels et al., 1994). Enzyme GST belongs to a phase II family of detoxifying enzymes and neutralizes a broad range of xenobiotics and endogenous metabolic by-products via enzymatic glutathione conjugation, glutathione-dependent peroxidase activity or isomerisation reactions (Hayes et al., 2005). This enzyme catalyzes the conjugation of reduced glutathione (GSH), an efficient scavenger against reactive oxygen species, with a group of compounds having electrophilic centers (Hayes et al., 2005). The aim of the present study was to investigate the changes in basal activities of selected biomarkers and the occurrence of oxidative stress in regard to different river flow rates and to assess the impact of condition status on the inducibility of EROD activity in common carp (Cyprinus carpio). Common carp, fish species commonly used in monitoring of freshwater systems, is usually found in still or slowly flowing waters at low altitudes and are capable of tolerating a range of environmental conditions. However, although carp is considered as tolerant species, there are no studies investigating the effects of different river flow rates on its physiology. To our knowledge, this is the first study investigating the response of biomarkers to current variation using caged carp, i.e. investigating the effects of river flow rate on fish biomarkers under field conditions.
2. Materials and methods 2.1. Animals Juvenile common carp (C. carpio, L.) (mean body weight 28.3975.98 g) were obtained from the fish-farm “Grudnjak” (Virovitica-Podravina County, Croatia). The fish were acclimatized for 1 week before experiments. They were kept unfed in tanks with aerated, filtered, dechlorinated tap water (hardness 380.3 mg/L as CaCO3, pH 7.1 70.2). The temperature of the water was maintained at 15 71 °C and light in the room followed a 12: 12 h photoperiod.
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2.2. Chemicals
2.4. Preparation of homogenates
All reagents used were of analytical grade. Phenylmethylsulfonyl fluoride (PMSF), trichloroacetic acid (TCA), 2.4-dinitrophenylhydrazine (DNPH), guanidine-HCl, ethylenediamine-tetraacetic acid (EDTA), nicotinamide adenine dinucleotide phosphate (NADPH), 7-ethoxyresorufin, 1-chloro-2.4-dinitrobenzene (CDNB), reduced glutathione (GSH), hydrogen peroxide (H2O2), bovine serum albumin (BSA), coomassie brilliant blue G-250 and 3-methylcholanthrene (3-MC) were purchased from Sigma-Aldrich (St. Louis, MO, USA).
Livers were washed in chilled 50 mM potassium phosphate buffer (pH 7.0, containing 0.5 mM EDTA and a few crystals of protease inhibitor PMSF) and homogenized. The homogenates were then centrifuged at 9000g for 15 min at 4 °C and the supernatant (S9) was used for EROD measurements. A 500 mL aliquot of S9 extracts was mixed with 1 mL of 30% TCA and then centrifuged for 10 min at 5000g. The pellet was used for a protein carbonyl assay.
2.3. Caging exposure of carp to different river flow rates and application of 3-methylcholanthrene Fish used for experiment were transferred from the fish-farm and conditioned in the laboratory for 2 weeks. The acclimatized fish were divided into six groups, five experimental and one control, with 30 carp in each group. Animals in the control group remained in the tanks without water flowing (0 cm/s) and with a continuous replacement of water (10% of the total volume was replaced daily). Animals in the experimental groups were transplanted to the selected sites in the riverbed of Drava (27–28 river kilometer (rkm), coordinates of the middle of the experimental area are 45° 35′ 47.32″ N, 18° 36i′ 29.47″ E). The experimental sites differed substantially only on the river flow rates – 5, 10, 30, 80 and 120 cm/s (Supplementary Table S1). The river flow rates were monitored throughout the exposure period using a digital water velocity meter (variations from nominal rates were up to 10%). Carp were placed in mesh plastic cages (50 cm in diameter and 120 cm height, app. 235.5 l) anchored with concrete blocks. Cages were set 1.5 m above the river bottom using a buoy-anchor system and within a diameter of 100 m (with maximum distance between cages of 30 m). The caging exposure (30 animals per cage) was conducted for 3 weeks in the spring season (last three weeks in May). During the experimental period there were no substantial deviations from the mean monthly temperatures and precipitation characteristic for that period (mean monthly temperature 16.7 °C, mean monthly precipitation 59.2 mm). The selected sites were 5 km upstream from possible sources of pollution (municipal water from nearby village). After 3 weeks, one half of the fish from each group (control and experimental) were sacrificed and used for analysis. The other half of experimental group was transferred to laboratory and received a single intraperitoneal injection of 3-methylcholanthrene (3-MC) previously dissolved in sterile corn oil at 50 mg/kg body weight. The other half of the control group received a single intraperitoneal injection of sterile corn oil. 3-MC belongs to the group of polycyclic aromatic hydrocarbons (PAHs) and is a known CYP 1 A inducer. In this study, 3-MC was applied in order to investigate the inducibility of CYP 1 A activity after pre-exposure to different river flow rates. Induction of CYP1A measured by EROD activity has been widely used as an indicator of exposure to planar halogenated hydrocarbons, PAHs and other structurally similar compounds (Fenet et al., 1998). All fish (control injected with corn oil and experimental injected with 3-MC) were kept in laboratory in tanks for 6 days and afterwards were sacrificed and used for analysis. During the experiment, in some experimental groups mortality occurred (20–73.3%) and measurements were taken from surviving fish. Surviving fish were weighed and decapitated, and their livers were carefully isolated, weighed, and immediately stored in liquid nitrogen for further processing. The animal care and treatment complied with the standards described in the Croatian Animal Protection Act (2006).
2.5. Biochemical analysis The activity of GST was determined according to the method of Habig et al. (1974). The assay mixture contained CDNB (1 mM), GSH (25 mM) and sample (S9). The kinetics was recorded at 340 nm for 1 min. The enzymatic activity was expressed as nmol of S-(2.4-dinitrophenyl) glutathione (DNPG) generated per min per mg of protein and for calculations the absorption coefficient of 9.6 103 M 1 cm 1. The activity of CAT was measured following the method of Claiborne (1985). The reaction medium contained sodium phosphate buffer (0.1 M, pH 7.2), H2O2 (0.019 M) and sample (S9). The kinetics was recorded at 240 nm for 1 min. To calculate H2O2 concentration, an absorption coefficient of 42.6 M 1 cm 1 was used and the activity was expressed as mmol of degraded H2O2 per min per mg of protein. Carbonyl derivatives of proteins were detected by reaction of 2,4-dinitrophenylhydrazine (DNPH) with protein carbonyl groups. Resulting 2,4-dinitrophenylhydrazones were quantified spectrophotometrically (Lenz et al., 1989). The pellet from TCA treated extract was mixed with 10 mM DNPH in 2 M HCl. Control samples contained only 2 M HCl. Samples were incubated for 1 h at room temperature, then centrifuged for 10 min at 5000g. Supernatants were discarded and pellets were washed 3 times with 1 mL of ethanol-butylacetate (1:1 v/v) mixture. Pellets were then dissolved in 6 M guanidine-HCl. The amount of protein carbonyls (PC) was measured at 370 nm and a molar extinction coefficient of 22 103 M 1 cm 1 was used to calculate PC concentration. The values were expressed as nmol of PC per mg of protein in the guanidine chloride solution. CYP1A was measured at the enzyme activity level as EROD activity by fluorimetric measurement of the conversion of 7-ethoxyresorufin into the fluorescent resorufin (Burke and Mayer, 1974; Flammarion and Garric, 1997; Grzebyk and Galgani, 1991). The reaction was performed in 96-well plates and a fluorescent plate reader at 4 °C using 20 mL of S9 in 200 mL of buffer (Tris 0.1 M, NaCl 0.1 M, pH 8.0) containing 2 mM 7-ethoxyresorufin and 0.25 mM NADPH. The progressive increase in fluorescence resulting from the resorufin formation was measured for 5 min at a wavelength of 554 nm excitation and 590 nm emission. Results were expressed as pmol of formed resorufin per min per mg of microsomal protein. Protein concentration was measured by the Bradford method with Coomassie Brilliant Blue G-250 (Bradford, 1976) and using BSA as a standard. Samples were diluted in buffer, mixed with a dye and measured at 595 nm. The amount of proteins was calculated based on the calibration curve with BSA and expressed as mg/mL. 2.6. Statistical analysis All data analyses were performed using the statistical software R version 3.0.2 (R Development Core Team, 2011). Prior to the analysis, the data were checked for normality using Shapiro–Wilks test and when necessary were log-transformed to obtain
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normality. Data were analyzed using one-way ANOVA followed by Dunnett's post-hoc test to determine the particular significant differences between control and experimental groups. In order to determine differences in responses between basal activity and activity after application of 3-MC for the same group (i.e. same flow rate exposure), unpaired t-test was used. The probability level for statistical significance was p o0.05 throughout the study. Data presented in figures are original, non-transformed data.
3. Results 3.1. Mortality and hepatosomatic index In control groups mortality did not occur during the experiment, however in some experimental groups mortality was recorded. After three weeks, mortality of 73.3% was recorded in the group exposed to 120 cm/s, the highest flow rate. After application of 3-MC, mortality of 20% was recorded in the group exposed to 80 cm/s flow, whereas in the group previously exposed to 120 cm/s, there were no surviving animals, so measurements could not be taken. Regarding the hepatosomatic index, i.e. the ratio of liver weight to body weight, there was no significant differences between groups. Also, the protein content of the liver did not differ significantly between groups. 3.2. Activity of GST in carp exposed to different river flow rates and after application of 3-MC The results of GST responses in carp are shown in Fig. 1. Significant differences in GST activity compared to controls were not recorded after exposure of carp for 3 weeks to different river flow rates. Basal activity in the control group was 19.15 7 5.48 nmol/min/mgPROT. After application of 3-MC, the activity of GST increased and in control group after 3-MC application was 35.03 78.65 nmol/min/mgPROT. Although GST activity after 3-MC application increased in all groups (control and experimental groups), there were no significant differences between activities in 3-MC control carp and 3-MC carp exposed to different river flow rates. Comparison of relative GST activities of experimental groups exposed to the same river flow rates (i.e. basal activity vs. activity after 3-MC application) showed no significant differences in GST responses.
3.3. Activity of CAT in carp exposed to different river flow rates and after application of 3-MC The results of CAT responses in carp are shown in Fig. 2. The activity of CAT increased with increasing river flow rates and after 3 weeks significant differences compared to control were recorded at river flow rates of 30, 80 and 120 cm/s. Basal activity in control group was 12.797 3.34 μmol/min/mgPROT and the highest CAT activity of 24.55 710.90 μmol/min/mgPROT was recorded at the highest river flow rate, i.e. 120 cm/s. After application of 3-MC, the activity of CAT increased and in control group after 3-MC application was 34.83 78.84 μmol/min/mgPROT. Although CAT activity after 3-MC application increased in all groups (control and experimental groups), there were no significant differences between activities in 3-MC control carp and 3-MC carp exposed to different river flow rates. However, the comparison of relative CAT activities of experimental groups exposed to the same river flow rates showed significant differences in responses between basal CAT activity and CAT activity after 3-MC application at river flow rates of 30 and 80 cm/s.
3.4. Level of PC in carp exposed to different river flow rates and after application of 3-MC The results of PC levels in carp are shown in Fig. 3. The level of PC increased with increasing river flow rates and after 3 weeks significant differences compared to control were recorded at river flow rates of 80 and 120 cm/s. PC level in control group was 6.057 1.22 nmol/mgPROT and the highest PC level of 8.59 7 1.58 nmol/mgPROT was recorded at the highest river flow rate, i.e. 120 cm/s. After application of 3-MC, the PC level in the control did not change significantly and was 5.78 71.50 nmol/mgPROT. However, application of 3-MC to carp in experimental groups led to an increase in PC levels and significant difference was obtained at river flow rates of 5, 10, 30 and 80 cm/s compared to control. The highest PC level after 3-MC application was recorded at a river flow rate of 10 cm/s and was 12.39 74.61 nmol//mgPROT. Also, the relative PC levels of experimental groups exposed to the same river flow rates were compared and the significant differences in responses were recorded between basal PC levels and PC levels after 3-MC application at river flow rates of 5, 10 and 30 cm/s.
Fig. 1. Activity of GST [nmol/min/mgPROT] measured in carp (Cyprinus carpio) exposed to different river flow rates. Bars represent mean values and vertical lines indicate standard deviation. White bars represent basal activities and dashed bars represent activities measured after application of 3-MC. Significant differences (p o 0.05) between control (0 cm/s) and basal activities at different flow rates are labelled with n and significant differences (po 0.05) between 3-MC control (0 cm/s) and activities after 3-MC application are labelled with @.
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Fig. 2. Activity of CAT [μmol/min/mgPROT] measured in carp (Cyprinus carpio) exposed to different river flow rates. Bars represent mean values and vertical lines indicate standard deviation. White bars represent basal activities and dashed bars represent activities measured after application of 3-MC. Significant differences (p o 0.05) between control (0 cm/s) and basal activities at different flow rates are labelled with n and significant differences (po 0.05) between 3-MC control (0 cm/s) and activities after 3-MC application are labelled with @.
3.5. Activity of EROD in carp exposed to different river flow rates and after application of 3-MC The results of EROD responses in carp are shown in Fig. 4. At lower river flow rates the EROD activity in carp increased and followed by a decrease towards control levels at higher river flow rates. Significant differences were recorded at river flow rates of 5 and 10 cm/s when the activity of EROD increased. The highest activity was recorded at river flow rate of 10 cm/s (6.7873.39 pmol/min/mgPROT) when activity increased up to 123.8% compared to the control basal activity (3.0371.68 pmol/min/mgPROT). After application of 3-MC, the activity of EROD increased up to 15.64β times compared to basal activity in control and was 47.40714.46 pmol/min/mgPROT. The application of 3-MC caused slight increase in EROD activity compared to 3-MC control at river flow rates of 5 and 10 cm/s, and decrease in EROD activity at river flow rates of 30 and 80 cm/s. The EROD activity at river flow rate of 80 cm/s was 31.60717.29 pmol/min/mgPROT and the decrease compared to 3-MC control was significant. Also, the increase in river flow rate caused the decrease in EROD inducibility. In control group, the application of 3-MC caused induction of EROD up to 15.64 times, whereas the EROD inducibility decreased to 8.86, 8.58, 8.30 and
10.69 times at 5, 10, 30 and 80 cm/s, respectively. The comparison of relative EROD activities of experimental groups exposed to the same river flow rates showed significant differences in responses between basal EROD activity and EROD activity after 3-MC application at river flow rates of 5, 10, 30 and 80 cm/s.
4. Discussion In environmental biomonitoring, the biomarker approach has been used both in vertebrates and invertebrates since it can supply an integrated response to pollutant exposition. However, the use of biomarkers requires the investigation of possible variations that can influence the biochemical response. In the present study impact of different river flow rates on biochemical biomarkers in common carp was investigated. Carp were exposed in situ under field conditions and the control group (inducibility control) was employed in laboratory tanks in order to determine whether the pre-exposure to different river flow rates will change the inducibility of certain biomarkers. The results indeed showed that river flow rates affected biomarker responses, as well as the
Fig. 3. Level of PC [nmol/mgPROT] measured in carp (Cyprinus carpio) exposed to different river flow rates. Bars represent mean values and vertical lines indicate standard deviation. White bars represent basal activities and dashed bars represent activities measured after application of 3-MC. Significant differences (p o 0.05) between control (0 cm/s) and basal concentrations at different flow rates are labelled with n and significant differences (p o 0.05) between 3-MC control (0 cm/s) and concentrations after 3-MC application are labelled with @.
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Fig. 4. Activity of EROD [pmol/min/mgPROT] measured in carp (Cyprinus carpio) exposed to different river flow rates. Bars represent mean values and vertical lines indicate standard deviation. White bars represent basal activities and dashed bars represent activities measured after application of 3-MC. Significant differences (p o 0.05) between control (0 cm/s) and basal activities at different flow rates are labelled with n and significant differences (po 0.05) between 3-MC control (0 cm/s) and activities after 3-MC application are labelled with @.
inducibility of biomarkers, indicating that flow rate is an important factor in biomonitoring of rivers using biomarkers. The activities of GST and CAT and concentration of PC were measured since these biomarkers indicate occurrence of oxidative stress and previous studies have showed that increased physical activity and exercise can lead to an increased production of ROS which consequently leads to occurrence of oxidative stress (Liu et al., 2000; Leeuwenburgh and Heinecke, 2001; Manna et al., 2003; Finaud et al., 2006; Fisher-Wellman and Bloomer, 2009; etc.). The activity of GST did not change significantly after exposure to different river flow rates and although the application of 3-MC caused significant increase compared to basal activities, there were no difference between 3-MC control and 3-MC different flow rates. The induction of GST activity after 3-MC exposure was previously recorded in different organisms including carp (e.g. Kumar et al., 1980; Derbel et al., 1993; Reynaud et al., 2002) and since GST is one of the major detoxification enzymes, the increase in its activity indicates the increased metabolization process due to 3-MC exposure. The lack of difference in GST activity between different river flow rates is probably due to the primary role of this enzyme in detoxification process. Enzymes superoxide dismutase (SOD) and CAT are the major enzymes in eliminating ROS formed during bioactivation of xenobiotics in the hepatic tissues (Sk and Bhattacharya, 2006) and SOD, CAT and glutathione peroxidase (GPx) provide the primary defence against ROS generated during exercise. Also, the increases in activities of these enzymes have been recorded in response to exercise (Ji, 1995). The role of CAT activity in the defence against ROS generated during exercise has been confirmed in the present study and the activity of CAT increased with increasing river flow rates. Also, the application of 3-MC caused significant increase in CAT activity and the increase was expected since it is known that PAHs induce oxidative stress indirectly, through cytochrome P450, epoxide hydrolase and dihydriodiol dehydrogenase, which results in the generation of quinones (Penning et al., 1998). These redox active quinones act as catalysts for free radical production, consequently causing oxidative stress. However, although the CAT activity increased after 3-MC application, there were no significant differences in 3-MC CAT activities at different river flow rates. Possibly, as a result of 3-MC application, the induction of CAT activity reached maximum and the exposure to increasing flow rates, i.e increasing physical stress, had no additional effect on CAT activity. This result is of great importance for biomonitoring studies using biomarkers
since it shows that if maximal CAT induction is reached by exposure to one stressor, the detection of exposure to other stressor could be overlooked. ROS can modify all types of biological molecules – carbohydrates, lipids, nucleic acid and proteins. Since proteins have many different and unique biological functions and are responsible for numerous processes in cells, modifications of proteins can lead to macromolecular damage and cell death. Most protein modifications are irreparable, and oxidative changes in protein structure can have a wide range of downstream functional consequences as they may inhibit enzymatic or binding activities, increase susceptibility to aggregation and proteolysis, increase or decrease cell uptake, and alter immunogenicity (Dalle-Donne et al., 2003). Protein oxidation is induced either directly by ROS or indirectly by reaction with secondary by-products of oxidative stress (Caner et al., 2006). The most common products of protein oxidation in are the protein carbonyl derivatives which are chemically stable and serve as markers of oxidative stress for most types of ROSinduced protein oxidation (Shacter, 2000b). The occurrence of oxidative stress as a result of exposure to increased physical activity, i.e. increased river flow rates, was confirmed by measurement of PC levels – the level of PC increased with increasing river flow rates. In addition, although the application of 3-MC to control did not affect PC levels in carp, when 3-MC was applied to carp exposed to different river low rates, the significant increase in PC levels was observed. Obviously, application of 3-MC together with increased physical activity of carp exposed to different river flow rates, caused oxidative stress which led to a significant increase in PC concentration in carp. As already mentioned, it is known that PAHs induce oxidative stress, so in combination with increased physical activity a stronger oxidative stress and thus increased concentration of PC in carp was observed. Similar result was obtained in study conducted under laboratory conditions (Stepić et al., 2012). The PC levels data obtained in the present study demonstrate that exposure of carp of different condition status to pollutants may yield different biomarker results and thus may lead to the misinterpretation of the data obtained in biomonitoring studies. Biochemical markers of the first (e.g. CYP) and second phase of detoxification (e.g. GST) are significant indicators of pollutant exposure. Biotransformation of a wide range of xenobiotics is performed by CYP1 A subfamily, whose catalytic activity is expressed as activity of EROD. The changes in EROD activity have been
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commonly used in fish as a biomarker of exposure to substances that bind to the aryl hydrocarbon receptor (Teles et al., 2005). In the present study, at exposure to lower river flow rates the EROD activity increased and at higher river flow rates decreased towards control levels. These results show that an increased physical activity caused an induction of basal EROD activity. Similar results were observed in the study of Lindström-Seppä et al. (1996) who recorded slightly elevated EROD activities in rainbow trout of different physical activity and suggested that the elevation of monooxygenase activity, together with recorded increase glutathione activities, in physical stress may indicate the increased production of reactive intermediates which are further metabolized through activated glutathione system. The results obtained in the present study also showed that the application of 3-MC caused significant increases in EROD basal activities in both control carp and carp exposed to different river flow rates. Similarly as in case with basal activities, EROD activity after 3-MC application slightly increased at lower river flow rates and then decreased at higher river flow rates and was significantly different compared to 3-MC control at flow rate of 80 cm/s. The trend of EROD activity increase at lower flow rates and decrease at higher flow rates can be explained with recorded PC levels in carp. Namely, as previously mentioned, oxidative modifications of proteins can lead to inhibition of enzymatic activities. Since at lower river flow rates the intensity of oxidative stress was lower, as well as PC levels in carp, the slight increase in EROD activity could be observed. However, the increase in river flow rate lead to more intense oxidative stress (higher PC levels in carp) – the greater amount of proteins was oxidized and enzymatic activity inhibited, so the decrease in EROD activity was recorded. Additionally, the exposure to all river flow rates caused the decrease in EROD inducibility. It is obvious that there is a close relationship between oxidative stress, PC levels and EROD activity, and that oxidative modification of proteins will directly affect the inducibility of enzymes. It is known that different abiotic and biotic factors, such as temperature, salinity, season, nutritional state, sex etc. influence the activity of enzymes, i.e. biomarker responses. Also, since responses of organisms can substantially differ under environmental conditions compared to laboratory controlled conditions, when investigating biomarker responses the conditions under which organisms are being exposed are of crucial value. In the environment fish are often found at sites where river water flow rate varies significantly, so it was important to investigate whether the flow rate will have influence on biomarker responses in fish species commonly used in freshwater biomonitoring. Results from caging exposure of common carp to different river flow rates showed that the flow rates significantly influenced measured biochemical biomarkers. Locations of fish caging sites can influence the biomarker responses and the inducibility of particular biomarkers, thus the site selection is extremely important for proper design of river biomonitoring studies.
Acknowledgments This work was supported by the Ministry for Science and Technology of the Republic of Croatia, Project No. 285-00000003484 and Research Council of Norway, CroWat project.
Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.ecoenv.2014. 10.021.
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Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv
Effect of different river flow rates on biomarker responses in common carp (Cyprinus carpio) Branimir K. Hackenberger *, Mirna Velki, Željka Lončarić, Davorka K. Hackenberger, Sandra Ečimović Department of Biology, Josip Juraj Strossmayer University of Osijek, Cara Hadrijana 8/A, 31000 Osijek, Croatia
art ic l e i nf o
a b s t r a c t
Article history: Received 10 January 2014 Received in revised form 13 October 2014 Accepted 16 October 2014
The present study investigated effects of different river flow rates on basal activities of selected biomarkers and the occurrence of oxidative stress in the common carp (Cyprinus carpio). Juvenile carp were exposed to different river flow rates (5–120 cm/s) by caging for 3 weeks. After this period, one half of the fish were sacrificed and used for analysis. The other half received a single intraperitoneal injection of 3-methylcholanthrene (3-MC) and after 6 days were sacrificed and used for analysis. In order to investigate whether the physical activity of carp in the environment will influence the condition status of carp, following biomarkers were measured – activities of glutathione S-transferase (GST), catalase (CAT) and ethoxyresorufin-O-deethylase (EROD) and concentration of protein carbonyls (PC). The results showed that different flow rates significantly influenced biochemical biomarkers. The basal activity of GST did not change significantly after exposure to different river flow rates, whereas the activity of CAT increased with increasing river flow rates. The application of 3-MC caused significant increases in GST and CAT activities, but there were no difference between 3-MC control and 3-MC different flow rates. The occurrence of oxidative stress as a result of exposure to increased physical activity, i.e. increased river flow rates, was confirmed by measurement of PC levels – the level of PC increased with increasing river flow rates. Measurement of EROD basal activity showed that at lower river flow rates the EROD activity increased and at higher river flow rates decreased towards control levels demonstrating a close relationship between oxidative stress, PC levels and EROD activity. Obviously, biomarker responses in carp of different condition status can differ substantially. It can be concluded that flow rate may be an important factor in biomonitoring of rivers using biomarkers and since at different locations river water flow rate can vary significantly, the site selection is extremely important for proper design of river biomonitoring studies involving caging. & Elsevier Inc. All rights reserved.
Keywords: Flow rate Condition status Oxidative stress Biomarkers Carp
1. Introduction Focusing only on chemical data (e.g. pollutant concentrations) in different environmental compartments is not sufficient to reliably assess potential risks, since different environmental factors influence the bioavailability of pollutants to organisms. Measurement of responses of organisms can serve as a functional measure of exposure to different stressors and can be used in environmental monitoring and assessment (van der Oost et al., 2003). Biomarkers at the molecular and biochemical level can provide insight into the mode of action of pollutants (Kammenga et al., *
Corresponding author. Fax: 385 31 399 921. E-mail addresses: [email protected] (B.K. Hackenberger), [email protected] (M. Velki), [email protected] (Ž. Lončarić), [email protected] (D.K. Hackenberger), [email protected] (S. Ečimović). http://dx.doi.org/10.1016/j.ecoenv.2014.10.021 0147-6513/& Elsevier Inc. All rights reserved.
2000) and can be used as early warning signals of pollutant exposure. Consequently, biomarkers have been often measured in the different groups of aquatic organisms, and in fish species various biomarkers have been used as a tool for ecotoxicological assessments (Kirby et al., 2007). The inclusion of the biomarker approach in biomonitoring studies is common, however it is necessary to be aware of the potential confounding factors (Forbes et al., 2006). The confounding factors can be biological (e.g., age, sex, condition) and environmental (e.g., temperature, pH, oxygen) and can significantly influence biomarker responses. Fish have proved to be good models for the evaluation of health status of aquatic ecosystems exposed to environmental pollution. Many fish species can be considered as top consumers in aquatic ecosystems (Dallinger et al., 1987) and it is likely that pollutants present in the aquatic environment will accumulate in fish and represent a potential risk for them and also to piscivorous birds and mammals (Adams et al., 1992). Monitoring of sentinel fish
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species has been widely used in order to assess the degree of accumulation of pollutants and the effects on the health status of aquatic environments (De la Torre et al., 2000; Bervoets and Blust, 2003). In studies addressing the impact of environmental contaminants on fish biomarkers, several experimental designs can be applied – laboratory studies, field studies using wild-caught fish, and in situ studies using caged fish. In laboratory studies it is not possible to duplicate true field conditions so the results obtained in such studies may provide misleading conclusions (Hanson and Larsson, 2007; Martin-Diaz et al., 2008). Field studies using wildcaught fish require investigation of the same species at each site in order to be able to compare different sites. Since fish community structure depends on different abiotic and biotic factors present in the water body under investigation, it is practically impossible to find a species that is present at all sites of interest. Also, in wildcaught fish the high inter-variability in biomarker responses is often present due to mobility of fish and varied exposure histories (Hanson and Larsson, 2007). To minimize this problem, it is possible to conduct transplant experiments and active biomonitoring using cages has already been implemented using different organisms, including fish (Bervoets et al., 2009; De la Torre et al., 2007; Oikari, 2006; Reynders et al., 2008). In order to reduce the variability and to more closely resemble true field exposure conditions, in the present study in situ caged fish exposure was applied. Environmental conditions present at different sites of interest can affect organisms and consequently cause differences in the biomarker responses which are not derived exclusively from exposure to pollutants. Both organism condition and an understanding of how changes in environmental conditions may change organism response to pollutant exposure are essential for successful biomonitoring. It is known that each species prefers certain environmental conditions, and changes in these conditions can possibly affect the responses of organisms. Regarding the river flow rate, it is also known that some fish species prefer slow moving waters whereas other prefer fast moving waters. Although the influence of river flow rates on fish biomarker responses has not been investigated so far, it can be assumed that higher rates will lead to greater physical activity and consequently lead to stress in organisms. Namely, in the laboratory study it has been shown that oxidative stress induced by intensive exercise and CdCl2 affects 7-ethoxyresorufin O-deethylase (EROD) inducibility, concentration of thiobarbituric acid reactive substances (TBARS) and protein carbonyls (PC) in common carp (Stepić et al., 2012). So in order to investigate whether the different river flow rates, i.e. the increased physical activity of carp under field conditions, will influence the condition status and the inducibility of EROD, the present study was undertaken. Fish were exposed in situ to different river flow rates and besides EROD activity, widely used biomarker of environmental pollution in aquatic environments, following biomarkers were also measured – concentration of PC and activities on enzymes catalase (CAT) and glutathione S-transferase (GST). Cytochrome P450 (CYP) is one of the most intensively studied biomarkers, in both laboratory and field conditions (Stegeman, 2000). The CYP450 enzymes belong to the large family of hepatic mixed-function oxidase enzymes of phase I xenobiotic biotransformation that are involved in reactions related to the biotransformation of many endogenous and exogenous substances, and this enzyme system has been detected in all organisms examined, from bacteria to mammals. Among them, one of the best studied parameters used as a biomarker of exposure of aquatic organisms to pollutants in the environment is the induction of CYP450 1A (Cheevaporn and Beamish, 2007; Flammarion et al., 1998; Flammarion et al., 2002; Mayon et al., 2006). Induction of CYP450 1A occurs after ligand-activation of the aryl hydrocarbon receptor (AhR). This receptor is typically, but not exclusively,
activated by planar, polycyclic and aromatic compounds including polyaromatic hydrocarbons (PAHs), dioxins, and polychlorinated biphenyls (PCBs) (Denison et al., 2002). However, there is an increasing evidence that activation of AhR is also triggered by other compounds, including some pharmaceuticals and personal care products and pesticides (Fernández-Cruz et al., 2011; Casado et al., 2006). The induction of EROD activity is a commonly used biomarker for exposure to CYP450 1A inducers and the advantages of using EROD activity as a biomarker are the specificity for CYP 1A in fish, high sensitivity, feasibility and simplicity of its measurement (Arinç and Sen, 1994; Arniç et al., 2000; Bucheli and Fent, 1995). Oxidative stress occurs when the generation of reactive oxygen species (ROS) in a system exceeds the system's ability to neutralize and eliminate them. In fish, antioxidant defenses are sensitive to exposure to environmental pollutants and can therefore be used as indicators of aquatic environmental health (Sturve et al., 2008). When antioxidant defenses are impaired or overcomed, oxidative stress can damage all types of biological molecules and the most common perturbation resulting from oxidative stress is protein carbonyl formation (Shacter, 2000a). Oxidative modifications of proteins can lead to diverse functional consequences, e.g. they can inhibit a wide array of enzyme activities (Stadtman, 1990) and can also lead to loss of function (Stadtman and Oliver, 1991). Enzyme CAT plays an important role in maintaining reactive oxygen homeostasis and also regulating pathophysiology of the organisms (Deisseroth and Dounce, 1970). This enzyme catalyzes the conversion of hydrogen peroxide to oxygen and water and is regarded as one of the most important specialized antioxidant enzymes for the detoxification of oxidative stress in the cellular antioxidant mechanism (Gravato et al., 2005; Michiels et al., 1994). Enzyme GST belongs to a phase II family of detoxifying enzymes and neutralizes a broad range of xenobiotics and endogenous metabolic by-products via enzymatic glutathione conjugation, glutathione-dependent peroxidase activity or isomerisation reactions (Hayes et al., 2005). This enzyme catalyzes the conjugation of reduced glutathione (GSH), an efficient scavenger against reactive oxygen species, with a group of compounds having electrophilic centers (Hayes et al., 2005). The aim of the present study was to investigate the changes in basal activities of selected biomarkers and the occurrence of oxidative stress in regard to different river flow rates and to assess the impact of condition status on the inducibility of EROD activity in common carp (Cyprinus carpio). Common carp, fish species commonly used in monitoring of freshwater systems, is usually found in still or slowly flowing waters at low altitudes and are capable of tolerating a range of environmental conditions. However, although carp is considered as tolerant species, there are no studies investigating the effects of different river flow rates on its physiology. To our knowledge, this is the first study investigating the response of biomarkers to current variation using caged carp, i.e. investigating the effects of river flow rate on fish biomarkers under field conditions.
2. Materials and methods 2.1. Animals Juvenile common carp (C. carpio, L.) (mean body weight 28.3975.98 g) were obtained from the fish-farm “Grudnjak” (Virovitica-Podravina County, Croatia). The fish were acclimatized for 1 week before experiments. They were kept unfed in tanks with aerated, filtered, dechlorinated tap water (hardness 380.3 mg/L as CaCO3, pH 7.1 70.2). The temperature of the water was maintained at 15 71 °C and light in the room followed a 12: 12 h photoperiod.
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2.2. Chemicals
2.4. Preparation of homogenates
All reagents used were of analytical grade. Phenylmethylsulfonyl fluoride (PMSF), trichloroacetic acid (TCA), 2.4-dinitrophenylhydrazine (DNPH), guanidine-HCl, ethylenediamine-tetraacetic acid (EDTA), nicotinamide adenine dinucleotide phosphate (NADPH), 7-ethoxyresorufin, 1-chloro-2.4-dinitrobenzene (CDNB), reduced glutathione (GSH), hydrogen peroxide (H2O2), bovine serum albumin (BSA), coomassie brilliant blue G-250 and 3-methylcholanthrene (3-MC) were purchased from Sigma-Aldrich (St. Louis, MO, USA).
Livers were washed in chilled 50 mM potassium phosphate buffer (pH 7.0, containing 0.5 mM EDTA and a few crystals of protease inhibitor PMSF) and homogenized. The homogenates were then centrifuged at 9000g for 15 min at 4 °C and the supernatant (S9) was used for EROD measurements. A 500 mL aliquot of S9 extracts was mixed with 1 mL of 30% TCA and then centrifuged for 10 min at 5000g. The pellet was used for a protein carbonyl assay.
2.3. Caging exposure of carp to different river flow rates and application of 3-methylcholanthrene Fish used for experiment were transferred from the fish-farm and conditioned in the laboratory for 2 weeks. The acclimatized fish were divided into six groups, five experimental and one control, with 30 carp in each group. Animals in the control group remained in the tanks without water flowing (0 cm/s) and with a continuous replacement of water (10% of the total volume was replaced daily). Animals in the experimental groups were transplanted to the selected sites in the riverbed of Drava (27–28 river kilometer (rkm), coordinates of the middle of the experimental area are 45° 35′ 47.32″ N, 18° 36i′ 29.47″ E). The experimental sites differed substantially only on the river flow rates – 5, 10, 30, 80 and 120 cm/s (Supplementary Table S1). The river flow rates were monitored throughout the exposure period using a digital water velocity meter (variations from nominal rates were up to 10%). Carp were placed in mesh plastic cages (50 cm in diameter and 120 cm height, app. 235.5 l) anchored with concrete blocks. Cages were set 1.5 m above the river bottom using a buoy-anchor system and within a diameter of 100 m (with maximum distance between cages of 30 m). The caging exposure (30 animals per cage) was conducted for 3 weeks in the spring season (last three weeks in May). During the experimental period there were no substantial deviations from the mean monthly temperatures and precipitation characteristic for that period (mean monthly temperature 16.7 °C, mean monthly precipitation 59.2 mm). The selected sites were 5 km upstream from possible sources of pollution (municipal water from nearby village). After 3 weeks, one half of the fish from each group (control and experimental) were sacrificed and used for analysis. The other half of experimental group was transferred to laboratory and received a single intraperitoneal injection of 3-methylcholanthrene (3-MC) previously dissolved in sterile corn oil at 50 mg/kg body weight. The other half of the control group received a single intraperitoneal injection of sterile corn oil. 3-MC belongs to the group of polycyclic aromatic hydrocarbons (PAHs) and is a known CYP 1 A inducer. In this study, 3-MC was applied in order to investigate the inducibility of CYP 1 A activity after pre-exposure to different river flow rates. Induction of CYP1A measured by EROD activity has been widely used as an indicator of exposure to planar halogenated hydrocarbons, PAHs and other structurally similar compounds (Fenet et al., 1998). All fish (control injected with corn oil and experimental injected with 3-MC) were kept in laboratory in tanks for 6 days and afterwards were sacrificed and used for analysis. During the experiment, in some experimental groups mortality occurred (20–73.3%) and measurements were taken from surviving fish. Surviving fish were weighed and decapitated, and their livers were carefully isolated, weighed, and immediately stored in liquid nitrogen for further processing. The animal care and treatment complied with the standards described in the Croatian Animal Protection Act (2006).
2.5. Biochemical analysis The activity of GST was determined according to the method of Habig et al. (1974). The assay mixture contained CDNB (1 mM), GSH (25 mM) and sample (S9). The kinetics was recorded at 340 nm for 1 min. The enzymatic activity was expressed as nmol of S-(2.4-dinitrophenyl) glutathione (DNPG) generated per min per mg of protein and for calculations the absorption coefficient of 9.6 103 M 1 cm 1. The activity of CAT was measured following the method of Claiborne (1985). The reaction medium contained sodium phosphate buffer (0.1 M, pH 7.2), H2O2 (0.019 M) and sample (S9). The kinetics was recorded at 240 nm for 1 min. To calculate H2O2 concentration, an absorption coefficient of 42.6 M 1 cm 1 was used and the activity was expressed as mmol of degraded H2O2 per min per mg of protein. Carbonyl derivatives of proteins were detected by reaction of 2,4-dinitrophenylhydrazine (DNPH) with protein carbonyl groups. Resulting 2,4-dinitrophenylhydrazones were quantified spectrophotometrically (Lenz et al., 1989). The pellet from TCA treated extract was mixed with 10 mM DNPH in 2 M HCl. Control samples contained only 2 M HCl. Samples were incubated for 1 h at room temperature, then centrifuged for 10 min at 5000g. Supernatants were discarded and pellets were washed 3 times with 1 mL of ethanol-butylacetate (1:1 v/v) mixture. Pellets were then dissolved in 6 M guanidine-HCl. The amount of protein carbonyls (PC) was measured at 370 nm and a molar extinction coefficient of 22 103 M 1 cm 1 was used to calculate PC concentration. The values were expressed as nmol of PC per mg of protein in the guanidine chloride solution. CYP1A was measured at the enzyme activity level as EROD activity by fluorimetric measurement of the conversion of 7-ethoxyresorufin into the fluorescent resorufin (Burke and Mayer, 1974; Flammarion and Garric, 1997; Grzebyk and Galgani, 1991). The reaction was performed in 96-well plates and a fluorescent plate reader at 4 °C using 20 mL of S9 in 200 mL of buffer (Tris 0.1 M, NaCl 0.1 M, pH 8.0) containing 2 mM 7-ethoxyresorufin and 0.25 mM NADPH. The progressive increase in fluorescence resulting from the resorufin formation was measured for 5 min at a wavelength of 554 nm excitation and 590 nm emission. Results were expressed as pmol of formed resorufin per min per mg of microsomal protein. Protein concentration was measured by the Bradford method with Coomassie Brilliant Blue G-250 (Bradford, 1976) and using BSA as a standard. Samples were diluted in buffer, mixed with a dye and measured at 595 nm. The amount of proteins was calculated based on the calibration curve with BSA and expressed as mg/mL. 2.6. Statistical analysis All data analyses were performed using the statistical software R version 3.0.2 (R Development Core Team, 2011). Prior to the analysis, the data were checked for normality using Shapiro–Wilks test and when necessary were log-transformed to obtain
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normality. Data were analyzed using one-way ANOVA followed by Dunnett's post-hoc test to determine the particular significant differences between control and experimental groups. In order to determine differences in responses between basal activity and activity after application of 3-MC for the same group (i.e. same flow rate exposure), unpaired t-test was used. The probability level for statistical significance was p o0.05 throughout the study. Data presented in figures are original, non-transformed data.
3. Results 3.1. Mortality and hepatosomatic index In control groups mortality did not occur during the experiment, however in some experimental groups mortality was recorded. After three weeks, mortality of 73.3% was recorded in the group exposed to 120 cm/s, the highest flow rate. After application of 3-MC, mortality of 20% was recorded in the group exposed to 80 cm/s flow, whereas in the group previously exposed to 120 cm/s, there were no surviving animals, so measurements could not be taken. Regarding the hepatosomatic index, i.e. the ratio of liver weight to body weight, there was no significant differences between groups. Also, the protein content of the liver did not differ significantly between groups. 3.2. Activity of GST in carp exposed to different river flow rates and after application of 3-MC The results of GST responses in carp are shown in Fig. 1. Significant differences in GST activity compared to controls were not recorded after exposure of carp for 3 weeks to different river flow rates. Basal activity in the control group was 19.15 7 5.48 nmol/min/mgPROT. After application of 3-MC, the activity of GST increased and in control group after 3-MC application was 35.03 78.65 nmol/min/mgPROT. Although GST activity after 3-MC application increased in all groups (control and experimental groups), there were no significant differences between activities in 3-MC control carp and 3-MC carp exposed to different river flow rates. Comparison of relative GST activities of experimental groups exposed to the same river flow rates (i.e. basal activity vs. activity after 3-MC application) showed no significant differences in GST responses.
3.3. Activity of CAT in carp exposed to different river flow rates and after application of 3-MC The results of CAT responses in carp are shown in Fig. 2. The activity of CAT increased with increasing river flow rates and after 3 weeks significant differences compared to control were recorded at river flow rates of 30, 80 and 120 cm/s. Basal activity in control group was 12.797 3.34 μmol/min/mgPROT and the highest CAT activity of 24.55 710.90 μmol/min/mgPROT was recorded at the highest river flow rate, i.e. 120 cm/s. After application of 3-MC, the activity of CAT increased and in control group after 3-MC application was 34.83 78.84 μmol/min/mgPROT. Although CAT activity after 3-MC application increased in all groups (control and experimental groups), there were no significant differences between activities in 3-MC control carp and 3-MC carp exposed to different river flow rates. However, the comparison of relative CAT activities of experimental groups exposed to the same river flow rates showed significant differences in responses between basal CAT activity and CAT activity after 3-MC application at river flow rates of 30 and 80 cm/s.
3.4. Level of PC in carp exposed to different river flow rates and after application of 3-MC The results of PC levels in carp are shown in Fig. 3. The level of PC increased with increasing river flow rates and after 3 weeks significant differences compared to control were recorded at river flow rates of 80 and 120 cm/s. PC level in control group was 6.057 1.22 nmol/mgPROT and the highest PC level of 8.59 7 1.58 nmol/mgPROT was recorded at the highest river flow rate, i.e. 120 cm/s. After application of 3-MC, the PC level in the control did not change significantly and was 5.78 71.50 nmol/mgPROT. However, application of 3-MC to carp in experimental groups led to an increase in PC levels and significant difference was obtained at river flow rates of 5, 10, 30 and 80 cm/s compared to control. The highest PC level after 3-MC application was recorded at a river flow rate of 10 cm/s and was 12.39 74.61 nmol//mgPROT. Also, the relative PC levels of experimental groups exposed to the same river flow rates were compared and the significant differences in responses were recorded between basal PC levels and PC levels after 3-MC application at river flow rates of 5, 10 and 30 cm/s.
Fig. 1. Activity of GST [nmol/min/mgPROT] measured in carp (Cyprinus carpio) exposed to different river flow rates. Bars represent mean values and vertical lines indicate standard deviation. White bars represent basal activities and dashed bars represent activities measured after application of 3-MC. Significant differences (p o 0.05) between control (0 cm/s) and basal activities at different flow rates are labelled with n and significant differences (po 0.05) between 3-MC control (0 cm/s) and activities after 3-MC application are labelled with @.
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Fig. 2. Activity of CAT [μmol/min/mgPROT] measured in carp (Cyprinus carpio) exposed to different river flow rates. Bars represent mean values and vertical lines indicate standard deviation. White bars represent basal activities and dashed bars represent activities measured after application of 3-MC. Significant differences (p o 0.05) between control (0 cm/s) and basal activities at different flow rates are labelled with n and significant differences (po 0.05) between 3-MC control (0 cm/s) and activities after 3-MC application are labelled with @.
3.5. Activity of EROD in carp exposed to different river flow rates and after application of 3-MC The results of EROD responses in carp are shown in Fig. 4. At lower river flow rates the EROD activity in carp increased and followed by a decrease towards control levels at higher river flow rates. Significant differences were recorded at river flow rates of 5 and 10 cm/s when the activity of EROD increased. The highest activity was recorded at river flow rate of 10 cm/s (6.7873.39 pmol/min/mgPROT) when activity increased up to 123.8% compared to the control basal activity (3.0371.68 pmol/min/mgPROT). After application of 3-MC, the activity of EROD increased up to 15.64β times compared to basal activity in control and was 47.40714.46 pmol/min/mgPROT. The application of 3-MC caused slight increase in EROD activity compared to 3-MC control at river flow rates of 5 and 10 cm/s, and decrease in EROD activity at river flow rates of 30 and 80 cm/s. The EROD activity at river flow rate of 80 cm/s was 31.60717.29 pmol/min/mgPROT and the decrease compared to 3-MC control was significant. Also, the increase in river flow rate caused the decrease in EROD inducibility. In control group, the application of 3-MC caused induction of EROD up to 15.64 times, whereas the EROD inducibility decreased to 8.86, 8.58, 8.30 and
10.69 times at 5, 10, 30 and 80 cm/s, respectively. The comparison of relative EROD activities of experimental groups exposed to the same river flow rates showed significant differences in responses between basal EROD activity and EROD activity after 3-MC application at river flow rates of 5, 10, 30 and 80 cm/s.
4. Discussion In environmental biomonitoring, the biomarker approach has been used both in vertebrates and invertebrates since it can supply an integrated response to pollutant exposition. However, the use of biomarkers requires the investigation of possible variations that can influence the biochemical response. In the present study impact of different river flow rates on biochemical biomarkers in common carp was investigated. Carp were exposed in situ under field conditions and the control group (inducibility control) was employed in laboratory tanks in order to determine whether the pre-exposure to different river flow rates will change the inducibility of certain biomarkers. The results indeed showed that river flow rates affected biomarker responses, as well as the
Fig. 3. Level of PC [nmol/mgPROT] measured in carp (Cyprinus carpio) exposed to different river flow rates. Bars represent mean values and vertical lines indicate standard deviation. White bars represent basal activities and dashed bars represent activities measured after application of 3-MC. Significant differences (p o 0.05) between control (0 cm/s) and basal concentrations at different flow rates are labelled with n and significant differences (p o 0.05) between 3-MC control (0 cm/s) and concentrations after 3-MC application are labelled with @.
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Fig. 4. Activity of EROD [pmol/min/mgPROT] measured in carp (Cyprinus carpio) exposed to different river flow rates. Bars represent mean values and vertical lines indicate standard deviation. White bars represent basal activities and dashed bars represent activities measured after application of 3-MC. Significant differences (p o 0.05) between control (0 cm/s) and basal activities at different flow rates are labelled with n and significant differences (po 0.05) between 3-MC control (0 cm/s) and activities after 3-MC application are labelled with @.
inducibility of biomarkers, indicating that flow rate is an important factor in biomonitoring of rivers using biomarkers. The activities of GST and CAT and concentration of PC were measured since these biomarkers indicate occurrence of oxidative stress and previous studies have showed that increased physical activity and exercise can lead to an increased production of ROS which consequently leads to occurrence of oxidative stress (Liu et al., 2000; Leeuwenburgh and Heinecke, 2001; Manna et al., 2003; Finaud et al., 2006; Fisher-Wellman and Bloomer, 2009; etc.). The activity of GST did not change significantly after exposure to different river flow rates and although the application of 3-MC caused significant increase compared to basal activities, there were no difference between 3-MC control and 3-MC different flow rates. The induction of GST activity after 3-MC exposure was previously recorded in different organisms including carp (e.g. Kumar et al., 1980; Derbel et al., 1993; Reynaud et al., 2002) and since GST is one of the major detoxification enzymes, the increase in its activity indicates the increased metabolization process due to 3-MC exposure. The lack of difference in GST activity between different river flow rates is probably due to the primary role of this enzyme in detoxification process. Enzymes superoxide dismutase (SOD) and CAT are the major enzymes in eliminating ROS formed during bioactivation of xenobiotics in the hepatic tissues (Sk and Bhattacharya, 2006) and SOD, CAT and glutathione peroxidase (GPx) provide the primary defence against ROS generated during exercise. Also, the increases in activities of these enzymes have been recorded in response to exercise (Ji, 1995). The role of CAT activity in the defence against ROS generated during exercise has been confirmed in the present study and the activity of CAT increased with increasing river flow rates. Also, the application of 3-MC caused significant increase in CAT activity and the increase was expected since it is known that PAHs induce oxidative stress indirectly, through cytochrome P450, epoxide hydrolase and dihydriodiol dehydrogenase, which results in the generation of quinones (Penning et al., 1998). These redox active quinones act as catalysts for free radical production, consequently causing oxidative stress. However, although the CAT activity increased after 3-MC application, there were no significant differences in 3-MC CAT activities at different river flow rates. Possibly, as a result of 3-MC application, the induction of CAT activity reached maximum and the exposure to increasing flow rates, i.e increasing physical stress, had no additional effect on CAT activity. This result is of great importance for biomonitoring studies using biomarkers
since it shows that if maximal CAT induction is reached by exposure to one stressor, the detection of exposure to other stressor could be overlooked. ROS can modify all types of biological molecules – carbohydrates, lipids, nucleic acid and proteins. Since proteins have many different and unique biological functions and are responsible for numerous processes in cells, modifications of proteins can lead to macromolecular damage and cell death. Most protein modifications are irreparable, and oxidative changes in protein structure can have a wide range of downstream functional consequences as they may inhibit enzymatic or binding activities, increase susceptibility to aggregation and proteolysis, increase or decrease cell uptake, and alter immunogenicity (Dalle-Donne et al., 2003). Protein oxidation is induced either directly by ROS or indirectly by reaction with secondary by-products of oxidative stress (Caner et al., 2006). The most common products of protein oxidation in are the protein carbonyl derivatives which are chemically stable and serve as markers of oxidative stress for most types of ROSinduced protein oxidation (Shacter, 2000b). The occurrence of oxidative stress as a result of exposure to increased physical activity, i.e. increased river flow rates, was confirmed by measurement of PC levels – the level of PC increased with increasing river flow rates. In addition, although the application of 3-MC to control did not affect PC levels in carp, when 3-MC was applied to carp exposed to different river low rates, the significant increase in PC levels was observed. Obviously, application of 3-MC together with increased physical activity of carp exposed to different river flow rates, caused oxidative stress which led to a significant increase in PC concentration in carp. As already mentioned, it is known that PAHs induce oxidative stress, so in combination with increased physical activity a stronger oxidative stress and thus increased concentration of PC in carp was observed. Similar result was obtained in study conducted under laboratory conditions (Stepić et al., 2012). The PC levels data obtained in the present study demonstrate that exposure of carp of different condition status to pollutants may yield different biomarker results and thus may lead to the misinterpretation of the data obtained in biomonitoring studies. Biochemical markers of the first (e.g. CYP) and second phase of detoxification (e.g. GST) are significant indicators of pollutant exposure. Biotransformation of a wide range of xenobiotics is performed by CYP1 A subfamily, whose catalytic activity is expressed as activity of EROD. The changes in EROD activity have been
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commonly used in fish as a biomarker of exposure to substances that bind to the aryl hydrocarbon receptor (Teles et al., 2005). In the present study, at exposure to lower river flow rates the EROD activity increased and at higher river flow rates decreased towards control levels. These results show that an increased physical activity caused an induction of basal EROD activity. Similar results were observed in the study of Lindström-Seppä et al. (1996) who recorded slightly elevated EROD activities in rainbow trout of different physical activity and suggested that the elevation of monooxygenase activity, together with recorded increase glutathione activities, in physical stress may indicate the increased production of reactive intermediates which are further metabolized through activated glutathione system. The results obtained in the present study also showed that the application of 3-MC caused significant increases in EROD basal activities in both control carp and carp exposed to different river flow rates. Similarly as in case with basal activities, EROD activity after 3-MC application slightly increased at lower river flow rates and then decreased at higher river flow rates and was significantly different compared to 3-MC control at flow rate of 80 cm/s. The trend of EROD activity increase at lower flow rates and decrease at higher flow rates can be explained with recorded PC levels in carp. Namely, as previously mentioned, oxidative modifications of proteins can lead to inhibition of enzymatic activities. Since at lower river flow rates the intensity of oxidative stress was lower, as well as PC levels in carp, the slight increase in EROD activity could be observed. However, the increase in river flow rate lead to more intense oxidative stress (higher PC levels in carp) – the greater amount of proteins was oxidized and enzymatic activity inhibited, so the decrease in EROD activity was recorded. Additionally, the exposure to all river flow rates caused the decrease in EROD inducibility. It is obvious that there is a close relationship between oxidative stress, PC levels and EROD activity, and that oxidative modification of proteins will directly affect the inducibility of enzymes. It is known that different abiotic and biotic factors, such as temperature, salinity, season, nutritional state, sex etc. influence the activity of enzymes, i.e. biomarker responses. Also, since responses of organisms can substantially differ under environmental conditions compared to laboratory controlled conditions, when investigating biomarker responses the conditions under which organisms are being exposed are of crucial value. In the environment fish are often found at sites where river water flow rate varies significantly, so it was important to investigate whether the flow rate will have influence on biomarker responses in fish species commonly used in freshwater biomonitoring. Results from caging exposure of common carp to different river flow rates showed that the flow rates significantly influenced measured biochemical biomarkers. Locations of fish caging sites can influence the biomarker responses and the inducibility of particular biomarkers, thus the site selection is extremely important for proper design of river biomonitoring studies.
Acknowledgments This work was supported by the Ministry for Science and Technology of the Republic of Croatia, Project No. 285-00000003484 and Research Council of Norway, CroWat project.
Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.ecoenv.2014. 10.021.
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