Seasonal variability in biomarkers in the bivalves Mytilus edulis and Macoma balthica from the northern Baltic Sea

Comparative Biochemistry and Physiology, Part C 140 (2005) 408 – 421 www.elsevier.com/locate/cbpc

Seasonal variability in biomarkers in the bivalves Mytilus edulis and Macoma balthica from the northern Baltic Sea Sari Leinio¨, Kari K. Lehtonen* Finnish Institute of Marine Research, P.O. Box 33, FI-00931, Helsinki, Finland Received 2 December 2004; received in revised form 12 April 2005; accepted 13 April 2005

Abstract Metallothionein level (MT), and acetylcholinesterase (AChE), catalase (CAT) and glutathione-S-transferase (GST) enzyme activities in the bivalves Mytilus edulis and Macoma balthica were investigated for seasonal variations from an inshore and an offshore site in the northern Baltic Sea. All the biomarkers showed variability, following mostly a similar pattern at both sites. Relationships between biomarkers and environmental factors and protein concentration and weight of target tissues were examined. In M. edulis, GST activity was related to Secchi depth, while in M. balthica a correlation with near-bottom oxygen saturation was observed. AChE activity correlated with the weight of the foot tissue of M. balthica. In both species, an integrated biomarker index indicated a stressed condition during the spring/early summer period. Strong seasonal variability in temperature and a concentrated period of food availability in spring – both governing the reproductive cycle of the bivalves – probably explains most of the observed natural variability in biomarkers in this sea area. D 2005 Elsevier Inc. All rights reserved. Keywords: Baltic Sea; Biomarker; Bivalve; Clam; Ecotoxicology; Macoma balthica; Mussel; Mytilus edulis; Seasonal variability

1. Introduction Applying biomarkers in aquatic organisms as indicators of pollution effects has been under strong development during the recent decades. Since in this way it is possible to express chemical stress in biological terms, biomarkers provide a valuable tool for environmental assessments. Prior to the correct use of biomarkers it is essential to know the ranges of natural variability in their levels in the studied species populations inhabiting a geographic area that possesses similar hydrographical and climatological features to the one investigated for pollution effects. Strong seasonality is a typical environmental characteristic of the northern Baltic Sea area and this – in addition to low salinity– requires physiological adaptations in organisms. Seasonal effects on biomarker levels in natural populations may therefore be expected. Water temperature

* Corresponding author. Tel.: +358 9 61394566; fax: +358 9 61394494. E-mail address: [email protected] (K.K. Lehtonen). 1532-0456/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.cca.2005.04.005

varies usually between 0 and ca. 20 -C during the year and the area is usually covered by ice from the end of January to early April although the duration of the ice season varies according to weather conditions (Seina¨ and Peltola, 1991). After the breakage of ice, phytoplankton production increases rapidly and the spring bloom in the Gulf of Finland (GOF) generally reaches a maximum in early May (Ha¨llfors et al., 1981). Sedimentation of organic material is the highest soon after the peak of the spring bloom, followed by very low sedimentation rates during the summer (Heiskanen and Kononen, 1994). The phytoplankton bloom and the ensuing sedimentation provide fresh organic matter of high nutritional value for benthic organisms, both for suspension and deposit feeders. Sessile benthic organisms are universally accepted as optimal bioindicators of contamination of the marine environment (e.g. Rainbow and Phillips, 1993). In the northern Baltic Sea, where the constantly low salinity strictly limits the distribution of marine organisms, the hard-bottom filter-feeding mussel Mytilus edulis and the infaunal deposit-feeding clam Macoma balthica are wide-

S. Leinio¨, K.K. Lehtonen / Comparative Biochemistry and Physiology, Part C 140 (2005) 408 – 421

spread and abundant. However, a salinity of <4.5 hinders the dispersal of M. edulis northwards along the Gulf of Bothnia and towards the eastern parts of the GOF (Segerstra˚le, 1944). M. balthica has a wider distribution with a salinity limit of ca. 2– 3 (Lassig, 1965). Depending on food availability, reproductive status, growth-dilution with season and some other factors, the levels of pollutants in tissues and biomarker responses may fluctuate extensively during the year. As a result, changes in biomarker levels may simply be a natural part of the annual physiological cycle of the species and thus quite unrelated to changes in exposure to chemical pollution (Sheehan and Power, 1999). Metallothionein (MT) consists of sulphur-rich metalloproteins with a major function related to the metabolism of essential trace metals, Cu and Zn. MT also binds Cd and Hg and is therefore involved in the detoxification of metals. Also hormones and other endogenous factors induce biosynthesis of MT. Basal levels of MT in organisms have been shown to alter with season, reproductive state, water temperature (George and Olsson, 1994), salinity and dissolved oxygen (O2) levels (Viarengo et al., 1999). Seasonal or otherwise temporal variability in MT concentrations in bivalves has been observed in Mytilus galloprovincialis (Viarengo et al., 1997; Rome´o et al., 2003), Ruditapes decussatus (Serafim and Bebianno, 2001), Corbicula fluminea (Baudrimont et al., 1997), and in North Sea populations of M. balthica (Bordin et al., 1997). Acetylcholinesterase (AChE) is an enzyme involved in the synaptic transmission of nerve impulses and is inhibited by neurotoxic compounds like organophosphate and carbamate pesticides targeted to cause this mode of toxicity. More recently, the responsiveness of AChE to other chemicals such as heavy metals, detergents (Guilhermino et al., 1998) and algal toxins (Lehtonen et al., 2003) has also been acknowledged. AChE may thus prove to be a useful biomarker of general physiological stress in aquatic organisms. According to Bocquene´ and Galgani (1998), the natural variability in the activity of AChE is not directly related to the age, sex or reproductive period of the organism, with temperature emerging as the most important regulating factor. In bivalves, seasonal variation in AChE has been found in R. decussatus and M. galloprovincialis (Dellali et al., 2001) and in the freshwater swan mussel Anodonta cygnea (Robillard et al., 2003). Glutathione-S-transferases (GST) are Phase II (conjugation) enzymes involved in the detoxification of organic xenobiotics, belonging to a versatile enzyme superfamily that possesses also a range of other functions (Sheehan et al., 2001). GST activity, measured traditionally using glutathione (GSH) and chlorodinitrobenzene (CDNB) as co-substrates (Habig et al., 1974), has widely been used as a biomarker of exposure to e.g. polycyclic aromatic hydrocarbons (PAH) and polychlorinated biphenyls (PCB) both in fish and invertebrates (Lee et al., 1988; Fitzpatrick et al., 1997). In regard to seasonal variation in GST activity

409

contradictory results have been obtained; Sheehan and Power (1999) found no seasonal variations in M. edulis whereas Wilhelm Filho et al. (2001) observed changes in the green mussel Perna perna correlating with the seasonal pattern in temperature and reproductive state. Catalase (CAT) is an antioxidant enzyme responsible for the breakdown of hydrogen peroxide. Increase in CAT activity signifies oxidative stress, often connected to excessive oxyradical formation during the catabolism of various organic compounds (Claiborne, 1985; Di Giulio et al., 1989). Its activity as an indicator of oxidative stress caused by exposure to e.g. trace metals has recently been studied in aquatic organisms including bivalves (e.g. Regoli, 1998; Regoli et al., 1997, 1998). Antioxidant enzyme levels are also linked to seasonal variability in the metabolic status of individuals, shown to follow changes in temperature, food availability and reproductive state (Viarengo et al., 1991; Power and Sheehan, 1996). The latter observed seasonal variation in CAT activity in M. edulis from the Atlantic waters, while similar patterns have also been recorded in M. galloprovincialis (Cancio et al., 1999; Orbea et al., 2002), Crassostrea sp. (Orbea et al., 2002) and C. fluminea (Vidal et al., 2002). All the previous studies on seasonal variations in biomarkers in bivalves have been performed in temperate or subtropical regions where variability in temperature, light intensity and food availability is much less pronounced than in the northern Baltic Sea. In the present study, the levels of MT, AChE, GST and CAT in M. edulis and M. balthica were studied on a monthly basis during 8 months (April – November) in order to set seasonal ‘‘baseline’’ levels and ranges of natural variability in bivalve populations living under environmental constraints typical for the northern Baltic Sea. The potential causes for the seasonal fluctuations in biomarkers are examined in relation to environmental and endogenous factors.

2. Material and methods 2.1. Sampling Sampling was performed at monthly intervals between April and November 2001 near Tva¨rminne Zoological Station (TZS; GOF, Baltic Sea). M. edulis were collected by scuba diving from the depth of 4– 5 m at two sampling sites, Sundholmen (Site 1) and Granbusken (Site 2; Fig. 1). M. balthica were collected using a Box Corer from the depth of 33 m at Storfja¨rden (Site 1) and from 36 m at La˚ngska¨r (Site 2). The Site 1 stations are more sheltered inshore areas whereas the Site 2 stations represent an exposed outer archipelago zone. The sampling area is considered relatively clean from organic pollution, but the concentrations of metals in sediments and biota are slightly elevated due to the presence of the nearby Koverhar steel factory since the 1950s (Korhonen

410

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Fig. 1. Map of the sampling area. (A) The Baltic Sea and the location of the study area, (B) study sites 1 and 2 near Tva¨rminne Zoological Station (TZS). >— M. edulis sampling station, —M. balthica sampling station.

et al., 2001; Voigt, 2004; Lehtonen et al., submitted for publication). However, Site 1 is located in the outer range of the effective area of the factory and the metal levels in the sediment are close to average (Pb, Cu, and Zn) or low (Cd) compared to those observed in the GOF (Leivuori, 2000; personal communication). During the monthly samplings each species was collected in successive days, always in the morning (between 10:00 and 12:00 h). M. edulis were placed in baskets containing ambient surface seawater. M. balthica were rapidly sieved upon a 5-mm mesh aboard the vessel and then placed to a seawater basket. The bivalves were then transported rapidly to a temperature-regulated room (8– 10 -C) at the TZS. In connection with M. balthica sampling, near-bottom (1 m) temperature and salinity were measured from a water sample taken with a Limnos sampler using YSI 63 temperature –conductivity – pH meter. O2 content was measured by Winkler titration. At the M. edulis stations, temperature and salinity were measured from the surface water and Secchi depth (water transparency) from the water column. Salinity is expressed as practical salinity units (PSU) and oxygen as saturation-%. Sampling for M. balthica in August and for M. edulis in April could not be performed at Site 2 due to heavy weather conditions. Dissection of the bivalves was performed in the afternoon (between 13:00 and 18:00 h). For the analysis of AChE, the gill tissue of M. edulis and foot tissue of M. balthica were carefully cut with a scalpel while for MT, GST and CAT the digestive gland (DG) of both species was obtained. The tissue samples were immediately frozen in

liquid nitrogen and stored at 80 -C. The length of the bivalves was measured at 1-mm accuracy. 2.2. Biomarker analyses In M. edulis, gill pairs obtained from five individuals were pooled for the analysis of AChE activity (six replicates). The foot tissue of eight M. balthica individuals was pooled (six replicates). Among the different tissue types in M. edulis the gills have the highest AChE activity (Bocquene´ et al., 1990), while for M. balthica that has small gills the foot tissue has been shown to be a good alternative (Lehtonen and Leinio¨, 2003; Lehtonen et al., 2003). The analyses were performed essentially as described in Bocquene´ and Galgani (1998). Briefly, the tissues were homogenised in 1:2 (w/v) 0.02 M phosphate buffer (pH 7.0) with 0.1% Triton X-100 and centrifuged at 10,000g at 4 -C for 20 min and the supernatants (S9) were used for the AChE measurements. A Bio-Rad Benchmark microplate reader was used for the spectrophotometric determination of the Ellman reaction (Ellman et al., 1961). Tissue protein concentration was determined using the Bradford (1976) method with bovine serum albumin (BSA) as standard. The activity values are expressed as equivalents of acetylthiocholine (ACTC) hydrolysed (nmol ACTC/min/mg protein), with 1 DO.D. corresponding to the hydrolysis of 75 nmol of ACTC. For MT, digestive gland tissues from 10 individuals were pooled, with five replicate samples per station. The analysis was carried out according to Viarengo et al. (1997). In short,

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(pH 7.0) and centrifuged at 10,000g at 4 -C for 20 min. The S9 obtained was diluted with the homogenisation buffer and analysed for both enzyme activities. The GST assay was performed using a modified CDNB method based on Habig et al. (1974) with S9 dilutions of 1:10 for M. edulis and 1:40 for M. balthica. CAT activity was measured according to Claiborne (1985), using S9 dilutions of 1:10 for M. edulis and 1:25 for M. balthica. The reaction speed measured between 0 and 60 s was afterwards evaluated between 10 and 40 s. Protein concentration in the S9 was determined on microplates using the Bradford (1976) method and a BSA standard.

Table 1 Seasonal variations in temperature, salinity, and/or oxygen saturation and Secchi depth in (A) surface water at the sampling sites of Mytilus edulis and (B) near bottom water at the sampling sites of Macoma balthica Month

Temperature (-C)

Salinity

Site 1

Site 2

Oxygen saturation (%)

Site 1

Site 2

Site 1

Site 2

(A) Mytilus edulis sampling Apr 0.5 nm May 7.6 8.1 June 10.3 8.6 July 19.5 18.5 Aug 16.1 16.4 Sept 15.8 15.8 Oct 12.5 12.5 Nov 5.1 5.7

5.4 5.1 5.5 5.3 5.4 5.6 5.4 6.0

nm 5.1 5.5 5.4 5.3 5.6 5.6 6.1

nm 4.0 5.0 2.8 4.5 3.0 6.0 3.0

nm 6.0 7.9 3.4 4.9 3.5 7.5 3.5

Month

Temperature (-C)

Salinity

Site 1

Site 1

Site 2

2.3. Statistical analyses Seasonal biomarker data were analysed by ANOVA (SYSTAT\ 9.0 software), with sampling time and site as factors and biomarkers as dependent variables. Seasonal variations in tissue weights and protein concentrations were examined separately. Relationships between biomarkers and environmental factors, body/tissue weight, and tissue protein concentration were examined by regression analysis using STATGRAPHICS\ Plus 3.0 software. A method for combining all the measured biomarker responses into one general ‘‘stress index’’ termed ‘‘Integrated Biomarker Response’’ (IBR; Beliaeff and Burgeot, 2002) was applied for each species. The basis of the calculation is described here briefly. For each biomarker: (1) Calculation of mean and S.D. for each station. (2) Standardisation of data for each station: x iV= (x i mean x) / s, where x iV-standardised value of the biomarker, x i -mean value of a biomarker from each station, mean x-mean of the biomarker calculated for all the stations, and s-standard deviation calculated for the station-specific values of each biomarker. Result: variance = 1, mean = 0. (3) Using standardised data, addition of the value obtained for each station to the absolute ( = nonnegative) value of the minimum value in the data set: B = x iV+)x min). Result: adjusts the lowest value in the set to zero. For all the biomarkers treated this way: calculation of starplot areas by multiplication of the obtained value of

Secchi depth (m)

(B) Macoma balthica sampling Apr 1.9 nm 6.4 May 4.4 4.4 4.9 June 4.1 3.8 6.3 July 10.8 9.1 5.7 Aug 6.4 6.0 6.3 Sept 15.8 nm 5.4 Oct 11.0 10.8 6.1 Nov 6.5 7.0 6.0

Site 2

Site 1

Site 2

nm 6.2 6.4 5.6 6.5 nm 6.2 6.3

50 67 62 64 49 85 65 80

nm 74 60 66 50 nm 70 71

411

The depths of M. balthica sampling sites were 36 m (Site 1) and 33 m (Site 2). nm—not measured.

the tissues were homogenised 1:3 (w/v) in reducing conditions (0.05 M sucrose TRIS buffer, pH 8.6, containing 0.01% h-mercaptoethanol). The homogenates were centrifuged at 30,000g at 4 -C for 20 min. The resulting supernatants were collected and ethanol/chloroform fractionation was used to obtain a partially purified metalloprotein fraction. Concentration of MT was measured by spectrophotometric determination of -SH groups using Ellman’s reagent (DTNB). GST and CAT activity measurements were performed on five individuals from each study station. Pieces of digestive gland (>15 mg) were homogenised in 1:3 (w/v) KPO4 buffer

Table 2 Compilation of statistical analyses (2-way ANOVA) of seasonal and between-site variability in biomarker responses in Mytilus edulis and Macoma balthica AChE

Mytilus edulis Site Season Site * Season Error

MT

GST

CAT

df

F

p

df

F

p

df

F

p

df

1 6 6 70

0.14 11.99 2.04

0.707 0.000*** 0.071

1 6 6 56

0.64 12.40 0.72

0.427 0.000*** 0.637

1 6 6 65

0.01 6.64 0.79

0.948 0.000** 0.584

1 6 6 65

0.48 3.31 1.31

0.492 0.007** 0.265

1.87 6.83 2.18

0.176 0.000*** 0.055

1 6 6 54

7.17 9.12 4.04

0.010** 0.000*** 0.002**

1 6 6 65

2.39 2.58 2.54

0.127 0.026* 0.029*

1 6 6 65

10.93 3.59 1.00

0.063 0.000*** 0.459

Macoma balthica Site 1 Season 6 Site * Season 6 Error 70

Significance levels (95% confidence limits): * = 0.05, ** = 0.01, *** = 0.001.

F

p

412

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each biomarker (B i ) with the value of the next biomarker, arranged as a set, dividing each calculation by 2 and summing-up of all values: {[(B 1  B 2) / 2] + [(B 2  B 3) / 2] + . . .[(B n 1  B n ) / 2]}. Result: IBR (average of several arrangements of biomarkers in the set).

3. Results 3.1. Seasonal variability in environmental factors In the surface water, elevations in temperature from 0.5 -C (April) up to 19.5 -C (July) were recorded during the

study period, while in the near-bottom water the range was from 1.9 (April) to 15.8 -C (September; Table 1). The post-winter (April) O2 saturation in the near-bottom water was only 50% but had elevated in May. From thereon the O2 levels started to decrease steadily with the warm-up of the water, reaching a minimum in August and elevating again after the breakdown of temperature stratification in September. Surface water salinity increased steadily from 5.2 in April to 6.0 in late November while in the nearbottom water no apparent pattern could be noted. The Secchi depth varied during the season, being always higher in the outer archipelago area. It should be noted that the low Secchi depth values observed in September and

Fig. 2. Seasonal variations in AChE activity (mean T S.E.) in the gill (M. edulis) or foot (M. balthica) tissue, and the levels of MT and activities of GST and CAT in the digestive gland tissue of the study species at the study sites.

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GST activity (nmol/min/mg protein)

600

A

500

2200 Mytilus edulis

2000

B

413

Macoma balthica

1800

400

1600

300

1400

200

1200

100

1000 800

0 2

3

4

5

6

7

50

8

55

60

65

70

75

Oxygen saturation (%)

Secchi depth (m)

Fig. 3. Relationships between (a) GST activity and Secchi depth in M. edulis, Sites 1 and 2 combined (r 2 = 0.56, p = 0.013, n = 10), and (b) GST and oxygen saturation-% in the near-bottom water in M. balthica at Site 2 (r 2 = 0.88, p = 0.006, n = 6). Regression lines with 95% confidence limits.

November do not indicate an increase in the biomass of living plankton but result from the resuspension of decaying particulate organic matter due to stormy weather prevailing in the study area at the time. Therefore, these Secchi depth data were omitted from all the regression analyses performed. 3.2. Seasonal variability in biomarkers In both species, significant seasonal variability could be observed in all the biomarkers studied (Table 2). In most cases no significant differences in the biomarker levels existed between the study sites, with the exception of a higher MT concentration in M. balthica at Site 1 compared to Site 2. Here also the 2-factor interaction was significant, indicating a differential effect of seasonality on the MT concentration at the two sites. The same interaction effect was noted in the activity of GST in M. balthica but in all the other cases the seasonal pattern in biomarker responses was similar at both sites. AChE activity was higher in the gill tissue of M. edulis compared to the foot of M. balthica (Fig. 2). The seasonal difference between minimum and maximum AChE activities was about two-fold within both species. In M.

balthica the lowest levels (mean T S.E.) were recorded in April (14.7 T 1.5 and 16.9 T 1.5 nmol/min/mg protein at Sites 1 and 2, respectively), while in M. edulis the activity was lowest in May (22.6 T 1.3 and 19.3 T 1.1 nmol/min/mg protein). In both species the activity of AChE increased during the summer along with elevations in water temperature, but in late summer and autumn changes in activity appeared independent of temperature. As a result, regression analyses on seasonal data showed no significant relationships between temperature and AChE activity of the bivalves. The effects of salinity and O2 concentration on AChE activity were also insignificant in both species. In M. balthica the highest activities were recorded in July – August [28.0 T 2.3 and 28.5 T 2.4 nmol/min/mg protein; Site 1 (August) and Site 2 (July), respectively], and in M. edulis about a month later (September: 38.0 T 4.9 and 43.4 T 4.1 nmol/min/mg protein). As shown by the high S.E.s a high inter-individual variability in AChE activity seems to co-occur with the maximum activities, especially in M. edulis. MT levels were systematically higher in M. balthica compared to M. edulis, but a similarity in the seasonal patterns between the species was evident (Fig. 2). The seasonal peak in MT levels was ca. 1.5 times higher than the

Mytilus edulis

Dec

Jan 6

Macoma balthica Feb

Dec

4 Mar

Mar

Nov

2

2

0

Apr

Sept

May Aug

June July

Feb

4

Nov

Oct

Jan 6

Oct

Apr

0

Sept

May Aug

June July

Fig. 4. Seasonal variations in the Integrated Biomarker Index (IBR) calculated for M. edulis and M. balthica at Site 1 using data from AChE, GST and CAT activities and MT levels for each species.

S. Leinio¨, K.K. Lehtonen / Comparative Biochemistry and Physiology, Part C 140 (2005) 408 – 421

correlation between CAT activity and the environmental variables could be observed. Due to the missing biomarker data from Site 2 (M. edulis in April, M. balthica in September), the IBR index was calculated using only the data sets from Site 1 (Fig. 4). For both species from this site the IBR values were clearly highest between April and June and homogeneously low towards late summer and autumn. This implies that the intergrated stress response in both species, assessed by combining the information obtained from all the four

mg wet wt/ind

Mytilus edulis 160 150 140 130 120 110 100 90 80

mg wet wt/ind

90

Gill

Site 1 Site 2

Apr May June July Aug Sept Oct Nov

DG

80 70 60 50 40

Apr May June July Aug Sep Oct Nov

Macoma balthica 35 mg wet wt/ind

lowest levels. In M. edulis, the maximum MT levels were observed in July (338 T 13 and 359 T 15 Ag/g wet wt., Sites 1 and 2 respectively). The levels of MT decreased towards late summer and autumn with minimum levels observed in October – November [241 T 9 and 239 T 8 Ag/g wet wt., Site 1 (October) and Site 2 (November), respectively]. Interindividual variability was high in spring and early summer compared to the late summer and autumn. In M. balthica, the levels of MT were significantly higher at Site 1 compared to Site 2. The highest MT levels were found in May –June [521 T 47 and 460 T 13 Ag/g wet wt. at Site 1 (June) and at Site 2 (May)], followed by a decrease during the summer. The levels were lowest in August – September [322 T 9 and 303 T 12 Ag/g wet wt. Site 1 (September) and Site 2 (August)] and increased again during October – November. No statistically significant relationships between MT levels and temperature, oxygen or salinity could be observed in either species. The level of GST activity was markedly higher in M. balthica compared to M. edulis (Fig. 2). In the latter, the level of GST activity and the overall variability between the individuals were higher in April – May and July compared to the autumn months. The seasonal range in GST activity was from 162 T 12 (October, Site 1) to 472 T 66 nmol min/mg protein (May, Site 1). Regression analysis showed that in M. edulis the GST activity decreased with increasing Secchi depth (i.e. less phytoplankton biomass in water; Fig. 3a). Temperature and salinity were not related to the observed seasonal fluctuations in GST activity. In M. balthica, the pattern in GST activity was more irregular, ranging from 784 T 68 (November, Site 2) to 1987 T 422 nmol min/mg protein (August, Site 1). Between April and July the variability was similar at both sites, but later in the summer (August) higher values were recorded at Site 1. In autumn the pattern was reversed with higher activities at Site 2, accompanied by a higher inter-individual variability. At Site 2, the GST activity was related to the near-bottom levels of O2 with higher rates occurring at increased O2 saturation (Fig. 3b). Seasonal changes in temperature and salinity were not related to the variability observed in the GST activity of M. balthica. Similar to GST, also CAT activity was clearly higher in M. balthica compared to M. edulis (Fig. 2). In mussels the seasonal range in CAT activity was between 55 T 16 (July, Site 1) and 144 T 24 (May, Site 2) Amol/min/mg protein. The activity was remarkably stable at both stations in autumn (August –November) compared to the spring and early summer when also the general activity levels were higher. In M. balthica the seasonal variability in CAT activity ranged between 53 T 9 (July, Site 1) and 379 T 44 (May, Site 2) Amol/min/mg protein. The mean activity levels measured in May were up to two-fold higher compared to those recorded in autumn (August –November). Also in this species CAT activity was relatively stable at both stations in autumn. In either of the species no

30

Site 1 Site 2

Foot

25 20 15 10 140

Apr May June July Aug Sept Oct Nov

120 mg wet wt/ind

414

DG

100 80 60 40 20 Apr May June July Aug Sept Oct Nov Month

Fig. 5. Seasonal variations in individual wet weight of the gill and the digestive gland tissues of M. edulis and the foot and digestive gland tissues of M. balthica at the study sites (mean T S.E.).

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Mytilus edulis Protein (mg/ml)

6

Site 1 Site 2

Gill

5 4 3

Protein (mg/ml)

2 16 15 14 13 12 11 10 9 8 7 6

Apr May June July Aug Sept Oct Nov

DG Apr May June July Aug Sept Oct Nov

Protein (mg/ml) Protein (mg/ml)

Macoma balthica 14 13 12 11 10 9 8 7 6 5 4 15 14 13 12 11 10 9 8 7 6 5

Site 1 Site 2

Foot

Apr May June July Aug Sept Oct Nov

DG

Apr May June July Aug Sept Oct Nov Month

relatively stable throughout the season while the weight of the biomarker target tissues, i.e. DG, gill and foot showed significant changes depended on the period of sampling (ANOVA: p < 0.05; Fig. 5). In M. balthica, weak positive linear correlations were found between the AChE activity and the weight of the foot (Site 1: r 2 = 0.12, p = 0.025, n = 42; Site 2: r 2 = 0.19, p = 0.004, n = 42). However, no significant relationship could be observed in M. edulis between the AChE activity and the weight of gill tissue. MT, GST and CAT levels did not correlate with seasonal changes observed in the weight of the DG used for these biomarker measurements. AChE, GST and CAT enzyme activities are expressed in moles of substrate hydrolysed per minute per weight of protein in a measured volume of the sample supernatant. Since the protein concentration of the samples expressed seasonal variability, its relationship with the proteinspecific enzyme activities calculated was examined. The protein contents of the foot tissue of M. balthica from both sites and that of the gill of M. edulis from Site 2 varied depending on the season (ANOVA: p < 0.005; Fig. 6). In addition, the protein levels of M. balthica were related to tissue weight. Protein content decreased with the increasing weight of the foot (Fig. 7) while in the gills of M. edulis no such relationship could be noted. The protein levels in the DG varied seasonally in M. edulis at Site 2 and in M. balthica at Site 1 (ANOVA: p < 0.01). However, no correlation between the weight of the DG and its protein level could be seen. The protein level of the gill tissue was more stable than that of the foot or DG, especially between July and November. In addition, the protein levels of the DG varied less in autumn than in summer. In M. edulis no significant differences between months or sites existed in the protein content of DG during the September –November period nor the protein content of the gills during the July –November period (ANOVA: p > 0.1). However, in M. balthica (at Site 1) variability was marked in September – November in the foot tissue ( p < 0.001) but not in the DG.

Fig. 6. Seasonal variations in protein concentration in enzymatic extracts prepared for AChE (M. edulis: gill tissue; M. balthica: foot tissue) and GST/CAT (digestive gland tissue) measurements (mean T S.E.).

3.3. Variability in body condition

14 Protein (mg/ml)

biomarkers selected for this study, shows a peak in spring – early summer in this sea area.

415

Macoma balthica

12

Site 1 Site 2

10 8 6 4

Likely due to the site-specific differences in environmental conditions the mean size of the bivalves inhabiting the outer archipelago was slightly smaller compared to the inshore areas [M. edulis: 25.9 T 2.4 (mean T S.D.) and 24.6 T 2.4 mm at Sites 1 and 2, respectively; M. balthica: 17.2 T 1.4 and 15.4 T 1.3 mm at Sites 1 and 2, respectively]. The shell length of the bivalves sampled at each site was

2 10

15

20 25 30 foot wet wt (mg/ind)

35

Fig. 7. Relationship between protein concentration in AChE enzymatic extract and mean individual wet weight of the foot of M. balthica. Site 1: r 2 = 0.21, p = 0.003, n = 42; Site 2: r 2 = 0.34, p = 0.000, n = 42. Regression lines with 95% confidence limits.

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4. Discussion Seasonal variability was observed in all the biomarkers studied. Temperature has been claimed to be the most important natural factor affecting AChE activity (Bocquene´ and Galgani, 1998). It has also been shown to affect the synthesis of MT in the M. galloprovincialis (Serafim et al., 2002). In the freshwater A. cygnea, the activities of AChE, CAT and GST have been observed to vary as a function of temperature and pH over the course of one year in France (Robillard et al., 2003). Because seawater temperature in the northern Baltic Sea varies from near-zero up to ca. 20 -C its effects on at least some of the biomarkers could be anticipated. The results obtained here indicate that the measured enzymatic biomarker activities and also MT levels of bivalves studied only occasionally follow the seasonal oscillations in seawater temperature under this environmental regime. In the Antarctic scallop Adamussium colbecki antioxidant enzymes (except CAT) have been noted to be generally active at low temperatures, although their activities do increase with rising temperature (0 – 34 -C; Regoli et al., 1997). In the same study, the activity of GST increased with elevating temperature until 19 -C and reduced thereafter, while the activity of CAT was not influenced by temperature at all. Similar results have previously been obtained from studies on Antarctic fishes (review by Macdonald et al., 1987) with the presence of enzyme forms very active at low temperatures, characterised by a reduced temperature-sensitivity as a sign of Fcold adaptation_. In the present study, other environmental factors apart from temperature appear to play an important role in regulating the physiological condition of the local bivalve populations and this is reflected in the ‘‘natural baseline levels’’ of biomarker responses, in some of them more strongly than in others. The seasonal patterns observed in the AChE activity and MT levels of the bivalves appear to follow their reproductive stage and main growth period. The build-up of glycogen and lipid reserves in M. balthica in the Baltic Sea starts immediately after the spring bloom (Graf et al., 1982; Pekkarinen, 1983). In M. edulis from the Baltic Proper, a sharp increase in the weight of the soft body is directly associated with the spring phytoplankton outburst (Kautsky, 1982). In the present study, the beginning of the active growth period in both bivalve species coincides with the gradual increase in AChE activity during the spring and early summer. In M. balthica the contemporary weight increases in the foot and DG reflect tissue growth. The maximum weights observed in July – August coincide with a peak in AChE activity and are followed by a decrease in August. In M. balthica collected from the same area, Pekkarinen (1983) observed a similar annual pattern in their body condition index. In M. edulis, the observed weight decrease in DG and gill tissues in June could be related to reproduction; a weight decrease of up to 52% in post-

spawning Baltic Sea mussels was recorded by Kautsky (1982; although most of this loss obviously consists of gametes). In bivalves, a close functional relationship exists between the digestive system and gonad development, and therefore the periods of food abundance and gonad development are often coincident (Mackie, 1984). In the Baltic Sea characterised by a strong pulse-like seasonal food abundance, gonad development of M. edulis and spawning of M. balthica might be regulated more by food availability than temperature (Kautsky, 1982; Pekkarinen, 1983). M. balthica spawns in April – May (Pekkarinen, 1983; Bonsdorff and Wenne, 1989) when the water temperature in the area is still low, while M. edulis has only one annual reproduction period, with spawning occurring mostly in July (Sunila, 1981; Antsulevich et al., 1999). The levels of MT have been shown to vary according to the reproductive state, since hormonal status, among with other endogenous factors, induce the biosynthesis of metalloproteins (George and Olsson, 1994). Therefore, the observed seasonal peaks in the levels of MT (May –June in M. balthica, July in M. edulis) are most likely related to spawning of these species in the area. In a study by Baudrimont et al. (1997) the freshwater clam C. fluminea showed fluctuations in the concentrations of MT similar to those recorded in the present study, especially in M. balthica. In C. fluminea the fluctuations in MT levels correlated with the reproductive cycle of the population, with an increase during the spring coincident with the period of gonadal maturation (but correlating also with a change in temperature), peaking in mid-May just before the spawning period. This was followed by a steep decrease in MT levels with the lowest seasonal values observed during the post-spawning period in August and a gradual increase again in September – November. Furthermore, it is noteworthy that the tissue concentrations of metals had remained at low and relatively constant levels throughout the year. Also Serafim and Bebianno (2001) found significant seasonal variation in MT concentrations in the DG of R. decussatus during the period of sexual development in June – August. Fluctuations in the weight of the soft parts have also been related to temporal and spatial changes in the levels of MT. In North Sea populations of M. balthica the concentration of MT varied significantly during the season (high in winter, low in summer) mainly due to fluctuations in body weight (Bordin et al., 1997). Usually, decreased MT concentrations have been associated with an increase in body weight (Bordin et al., 1997; Amiard-Triquet et al., 1998; Mouneyrac et al., 1998; Mouneyrac et al., 2000). However, the relationship seems to be tissue-dependent. In the oyster C. gigas the concentration of MT has been observed to correlate positively with the weight of the DG (Geffard et al., 2001). In R. decussatus the concentrations of metals and MT have been shown to correlate positively with the condition index of gills and DG, but negatively with the

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remaining tissues (Bebianno and Serafim, 2003). However, in the present study, no relationship could be observed between the weight of the DG and the concentration of MT. In conclusion, fluctuations in the weight of the DG cannot explain the seasonal variation in MT levels observed in these bivalve populations. The levels of MT were higher at Site 1 compared to Site 2. In May 2001, the tissue levels of Cu and Zn in M. balthica were recorded to be higher at Site 1 than at Site 2, while the concentrations of Cd and Hg were similar (Lehtonen et al., submitted for publication). The sediment at Site 2 was light grey clayish sand gravel, whereas at Site 1 dark soft sulphide mud existed under a 3 –4 cm oxygenated surface layer. Differences in sediment type have an effect on the concentration and bioavailability of metals for bivalves (Hummel et al., 1998) and may therefore have contributed to the levels of MT observed here in M. balthica. The activity of AChE was measured in the gill tissue of M. edulis and in the foot tissue of M. balthica. The weight and protein concentration of both tissues varied according to season with greater shifts in the foot compared to the gill. The protein content of the foot of M. balthica decreased in parallel with a slight elevation in the activity of AChE and an increase in the weight of the foot. Since AChE activities are usually expressed in relation to the protein content of the sample, the seasonal variability observed here is partly explained by fluctuations in total protein concentrations due to e.g. growth or reproduction instead of true seasonal changes in the enzyme activity itself (discussed also by Radenac et al., 1998). Therefore, the foot tissue of M. balthica cannot be considered as optimal tissue choice for the AChE measurements compared to e.g. the gill tissue of M. edulis which shows stable protein levels between July and November. However, in M. balthica the use of gills is impractical because of their very small size. In whole soft tissue homogenates significant seasonal changes in protein levels occur as well (e.g. Pekkarinen, 1983). Thus, the use of the foot tissue for the determination of AChE activity in M. balthica must be regarded as a compromise. In M. edulis, the activity of CAT was generally high in late spring. After a long period of poor nutritional conditions, fresh food in the water column has suddenly become abundant while the seawater temperature has elevated to 7– 8 -C from the near-zero winter values. From late summer on, the activity of CAT was remarkably stable, being ca. 35% lower compared to the spring levels despite that temperature of the seawater was much higher than in spring and remained relatively high (ca. 16 -C) until September. Similar seasonal patterns with a generally higher activity in CAT during the summer compared to the winter followed by a decrease in the autumn have been observed in M. edulis from temperate Atlantic areas (Viarengo et al., 1991; Power and Sheehan, 1996), and also from other bivalve species which displayed the highest CAT levels in summer and the lowest in winter (Cancio et al., 1999; Orbea

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et al., 2002; Vidal et al., 2002). Working in a cold sea area (St. Lawrence Gulf) Pellerin-Massicotte (1994, 1997) reported elevated CAT activity in mussels associated with increased lipid peroxidation at elevated temperatures and exposure to air. Variability in antioxidant enzyme activity levels is presumably linked with a changing metabolic status of the individuals which depends on factors like temperature, gonad ripening and food availability (Viarengo et al., 1991; Regoli, 1998; Vidal et al., 2002). This hypothesis agrees well with the results obtained here. The high CAT activity observed in spring is probably related to intensified overall metabolism induced by the availability of food and the ensuing start of the growth period after a long period of winter quiescence. A rapid shift to an efficient uptake of the available foodstuffs in spring causes increased oxyradical formation during the catabolism of various substances contained within the assimilated material, mainly fresh phytoplankton (diatoms and dinoflagellates). A similar phenomenon can be seen in M. balthica with the highest mean CAT activities occurring in May during the period of major sedimentation, the levels being up to two-fold compared to those recorded in autumn. Conclusively, the activity of CAT in both species expresses an almost immediate response to the increased availability of quality food in the spring. As noted earlier, the spawning of M. balthica also takes place in the spring, which may also be associated with elevated antioxidant enzyme levels in May. The seasonal patterns of GST activity appear rather erratic in both species, which has been observed also in other seasonal studies. In M. edulis collected from Ireland, GST activity measured in the gill tissue peaked during the winter months but showed little variability in the DG tissue (Power and Sheehan, 1996). In C. fluminea the activity of GST displayed no obvious seasonal trend (Vidal et al., 2002). In contrast, in P. perna, GST together with the activities of superoxide dismutase (SOD) and CAT showed seasonal variability that correlated with changes in temperature and reproductive cycle (Wilhelm Filho et al., 2001). As discussed in connection with CAT, the high GST activity observed here in May may also be related to increased oxyradical formation due to high metabolic activity during food abundance, vigorous growth and reproduction. In M. edulis, the highest GST activities coincided with the periods of high plankton biomass (indicated by low Secchi depth) which supports this hypothesis. The elevated GST activity levels observed in M. edulis in July may also be associated with the concurrent occurrence of intensive toxic cyanobacterial blooms which are common in the whole GOF during the late July –early August period (Sivonen et al., 1989; Kononen et al., 1993). Metabolism of cyanobacterial hepatotoxins involves Phase II detoxification reactions mediated by GST (Pflugmacher et al., 1998). Accumulation of hepatotoxic nodularin from

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Nodularia spumigena-cyanobacteria during and after the blooms has been recorded in M. edulis (Sipia¨ et al., 2001) with the maximum concentration coinciding with the peak of the blooms in the western GOF (Kankaanpa¨a¨ et al., 2001). The elevated GST levels observed in M. edulis in July may be associated with exposure to toxic cyanobacteria since in 2001 the most intensive blooms occurred in the latter part of July (The Baltic Sea Portal, Alg@line, Finnish Institute of Marine Research). The low Secchi depths recorded in July (compared to June and August) also indicate an elevated plankton biomass in July. Since M. edulis in general expressed low GST activity during times of high Secchi depth the question is whether the activity was actually induced more by increased food abundance than specifically the presence of cyanobacterial toxins in the water. In M. balthica, laboratory exposure studies have demonstrated a rapid accumulation of nodularin and simultaneous effects on AChE activity of the clams when exposed to pure nodularin and toxic N. spumigena (Lehtonen et al., 2003). In M. balthica, the activity of GST was decreased at lower O2 saturation of the near-bottom water, but only at Site 2. The between-site difference may, again, be caused by the sediment type, which may have an effect on biomarkers due to a differential exposure to pollutants. A reduction in general metabolic activity under low O2 conditions may be connected to the lowered GST activity but this hypothesis needs to be verified by further investigations. To conclude, the results obtained here underline the importance of identifying the potential interfering factors as well as the direction and magnitude of their impacts on the biomarker signals observed in wild populations. Contaminant levels in tissues are likely to change during the season, reflecting variability in the discharge and metabolism of contaminants and/or changes in tissue weight (metals) and lipid content (organic pollutants; Swaileh, 1996; Zhou et al., 1996; Livingstone et al., 1995; Regoli, 1998; Sheehan and Power, 1999). Thus, the possibility of the observed seasonal biomarker patterns being partly driven by changes in contaminant levels cannot be totally excluded. In a study by Viarengo et al. (1997) the MT content of M. galloprovincialis varied seasonally parallel to tissue concentrations of Zn, being highest in June –July. However, possible seasonal changes in trace metal concentrations are unlikely to entirely explain the observed patterns in the activities of GST and CAT. Environmental and biological factors, like food availability and reproductive stage, seem to have a marked influence on the seasonal variability in metal concentrations and antioxidant defences (Regoli, 1998). Orbea et al. (2002) investigated variability in antioxidant and peroxisomal enzymes, the structure of peroxisomes and their relation to changes in the bioavailability of organic contaminants in field conditions, coming up with a conclusion that under low pollution conditions seasonal

factors may affect biomarker responses to a greater extent than pollutant stress. This is likely to be the case also in the present study. By combining the different biomarker signals the IBR index provides a simple means for a general description of the ‘‘health status’’ of populations. Other methods to provide combined biomarker indices have also been developed recently (Adams et al., 1993; Narbonne et al., 1999; Che`vre et al., 2003a,b). Here, the IBR clearly indicates that both bivalve species show a high ‘‘stress syndrome’’ during the spring – early summer period in this sea area. As a consequence, in regard to environmental monitoring, it is obvious that because of the high natural ‘‘noise’’ the May – June period is sub-optimal for the detection of biomarker signals caused by exposure to pollutants. The December – March period not covered by this sampling scheme is mostly impractical for monitoring due to heavy weather conditions and ice coverage in this sea area. Therefore, basing on the results of this seasonal study it is recommended that monitoring of pollution effects in this part of the Baltic Sea should be carried out in autumn when the biomarker responses have stabilised to a ‘‘background’’ level. Nesto et al. (2004) studied spatial and temporal variation in several biomarkers in M. galloprovincialis from the Venice Lagoon. In this area the most relevant differences between polluted and reference samples were identified in spring when natural and endogenous factors were less effective. This example demonstrates that the optimal season for carrying out biomarker field studies or regular monitoring is highly regional. Moreover, the sensitivity of different biomarkers to toxicants may vary according to season (Bordin et al., 1997), which should also be investigated prior to including biomarkers to monitoring programmes. In the northern Baltic Sea, the littoral filter feeder M. edulis and the soft-bottom detritivore/suspension feeder M. balthica represent quite different habitats in the same geographical areas. By measuring biomarker responses in both of these species it is possible to obtain a more accurate and comprehensive picture of the nature, sources and level of pollution stress in the local environment. As demonstrated here, seasonal variability and natural ranges in the responses must be carefully taken into account when comparing biomarker responses in different areas. An ideal biomarker would show no seasonal variability in response to factors such as food supply or reproductive status and would vary only in response to pollutant exposure, but in practice this seldom is the case (Sheehan and Power, 1999). Despite the observed marked seasonality in MT level and AChE, GST and CAT activities in bivalves from this northern Baltic Sea area, all the biomarkers (together with tissue weight and protein level) showed relatively stable levels in autumn. The seasonal biomarker data set for bivalves obtained here is the first of its kind in the northern Baltic Sea and, together with the interpretation of the potential causes of the observed variability in responses,

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valuable for future biomarker studies on polluted sites in the area.

Acknowledgements Sari Leinio¨ was partly funded by the Maj and Tor Nessling Foundation. We wish to thank the Tva¨rminne Zoological Station for providing excellent sampling facilities and M.Sc. Malin Kronholm for assistance in field work.

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