Faculty Education, Health and Social work

Campus Vesalius

Seasonal variations in biochemical composition and metallothionein contents of digestive gland of Noah’s Ark shell (Arca noae Linnaeus, 1758) from the Adrriatic Sea

NELE DE CLERCQ

Academic year 2015-2016

Biomedical Laboratory Technology

Pharmaceutical and Biological Laboratory Technology

Promotor: Dr. Marijana Erk

Co-promotor: Dr. Brita Muyssen

Keramiekstraat 80 - 9000 Gent tel. 09 321 21 10 | fax 09 220 17 26 Acknowledgement

This thesis is the result of hard work from different people. My part was only a piece of the puzzle. I appreciate that the Ruđer Bošković Institute allowed me to do my internship there. Because of this I met a lot of new people who were very welcoming. I am very grateful for the work that the persons did from my university college HoGent, i.e. the teachers from the department Biomedical Laboratory technology for making the internship happen through a lot of effort and communication and the administrative staff who helped me with the paper work which was very welcome since I’m not very good at that.

I want to award some persons in particular and since the Nobel prizes aren’t awarded yet, I want to make some suggestions.

I nominate my promotor Dr.Sc. Marijana Erk for the Nobel prize in Chemistry. I am very glad that she accepted me to do my internship in her laboratory LBUM. I learned a lot from her during those 4 months. I want to thank her for the effort she put in helping me with my work and giving feedback on my thesis.

I would like to give the Nobel prize in Literature to my co-promotor Dr. Brita Muyssen. I am very grateful for the patience that she had with me for answering all my questions. I want to thank her for the time she spent in reading my thesis and guide me with the writing part of my thesis.

The Nobel Peace Prize should go to the colleagues from the Laboratory for Biological Effects of Metals (LBUM): Dr. Zrinka Dragun, Dr. Vlatka Filipović Marijić, Dr. Dušica Ivanković and Nesrete Krasnići. They and my promotor were very supportive and helped me where they could. They showered me with kindness, I felt at home and also a part of the team. I am happy as a clam that they learned me some Croatian, hvala najljepša.

The nomination for Nobel prize for Physics goes to the colleagues of IRB. This is for a different kind of physics, i.e. football. The games of football were a perfect outlet after a work day. Thanks, guys.

The Nobel Prize in Physiology or Medicine goes to Steffi Lemmens and Bronwen Serrgeant for the healthy and less healthy diners. You made the experience unforgettable.

The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel is for my family for supportingn me in mmy choice to do my internship iin Zagreb.

Special gratitude goes to Dr.Sc. Ivan Župan, assistant professsor at University of Zadar for SCUBA diving and collecting shell fish, to Dr.Sc. Jellena Čulin, assistant professor at University of Zadarr for logistics and dissection of shell fish, to Dr.Sc. Melita Peharda Uljević from the Institute for Oceanogo raphy and Fisheries in Split and Dr.Sc. Sanja Puljas from the University of Split for determination of reproductive status of shell fish.

I also want to thank Dr. Sc. Sanja Gottstein and Dr. Sc. Miirrela Sertić Perić for showing me the ropes of field work.

Table of contents

1 Introduction ...... 9 1.1 Noah’s ark shell ...... 9 1.1.1 Taxonomy ...... 9 1.1.2 Morphology ...... 9 1.1.3 Reproduction ...... 10 1.1.4 Habitat ...... 11 1.1.5 Consumption ...... 12 1.2 Pollution ...... 12 1.2.1 Bivalves as indicator organisms in environmental studies ...... 13 1.3 Sampling sites ...... 15 1.3.1 Telaščica ...... 16 1.3.2 Pašman kanal/channel ...... 16 1.4 Biomarkers ...... 17 1.4.1 Metallothionein ...... 20 1.4.2 Proteins, carbohydrates and lipids ...... 23 1.5 Biotic and abiotic factors that affect biomarker variability ...... 24 1.6 Aim of this study ...... 26 2 Materials and methods ...... 27 2.1 Homogenization ...... 27 2.1.1 Materials ...... 27 2.1.2 Protocol ...... 28 2.2 Quantification of metallothioneins and cytosolic proteins ...... 30 2.2.1 Materials ...... 30 2.2.2 Protocol ...... 31 2.3 Quantification of total tissue proteins ...... 35 2.3.1 Materials ...... 35 2.3.2 Protocol ...... 36 2.4 Quantification of total tissue carbohydrates ...... 37 2.4.1 Materials ...... 37 2.4.2 Protocol ...... 38 2.5 Quantification of total tissue lipids ...... 40 2.5.1 Materials ...... 40 2.5.2 Protocol ...... 40 2.6 Statistics ...... 42 3 Results ...... 43 3.1 Metallothionein ...... 43 3.2 Proteins ...... 46 3.3 Carbohydrates ...... 49 3.4 Lipids ...... 52 3.5 Correlations between measured parameters ...... 55 3.6 Seasonal and spatial variability of measured parameters ...... 60 3.6.1 Metallothionein ...... 60 3.6.2 Proteins ...... 62 3.6.3 Carbohydrates ...... 63 3.6.4 Lipids ...... 65

4 Discussion ...... 67 5 Conclusion ...... 74 Bibliography ...... 75

Abbreviations

MT Metallothionein

PK 1 Pašman kanal 1

PK 2 Pašman kanal 2

TE Telaščica d.w. dry weight w.w. wet weight

MGI Mean gonadal index

CI Condition index

ISQG Interim Marine Sediment Quality Guidelines

PEL Probable Effect Level

MCL Maximum contaminant level n.v. no value

Glossary Periostracum = an organic, outside layer that is found on shells of a mollusk. Protandric = having male sexual organs in young stadium of life, and female in the later stadia.

Abstract English Noah’s Ark shell (Arca noae) is a bivalve that is common in the Adriatic Sea, it is exploited for culinary purposes from natural populations. The aim of this study is to determine the concentrations of total lipids, proteins, carbohydrates and metallothionein to differentiate between the sampling sites and their respectively different environmental pressures and potential metal exposure. Three sampling sites were selected: one from nature park Telaščica and two from the Pašman kanal- the main harvesting area in the central part of Eastern Adriatic Sea in Croatia. From each site, 36 individuals were sampled monthly in the period from March 2013 to February 2014 . The digestive gland was used for measuring total lipids, total proteins, total carbohydrates and metallothionein concentration, all by spectrophotometric methods. Correlations between parameters were calculated and two-way ANOVA was used to investigate the seasonal and spational variability between the different sampling stations. Correlations were found for metallothionein concentrations and reproductive cycle as well as for temperature and total lipids. This study provided first data on metallotioneins in Arca noae. Levels of metallothionein showed the impact that nautical tourism has on nature parks. In summer the level of metallothionein in A. noae from Telaščica is higher than that for the sampling sites where anthropogenic stress is expected. From the results we can conclude that metallothionein is a possible biomarker for A. noae. More research with organisms from sampling sites of which pollution levels are more elevated is necessary to confirm this conclusion.

Keywords: Arca Noae, biochemical composition, seasonal variation, metallothionein, digestive gland

Abstract Nederlands Arca noae is een tweekleppige schelp die voorkomt in de Adriatische Zee. Schelpen uit de natuurlijke populatie worden gevangen genomen voor menselijke consumptie. Het doel van de studie is het achterhalen of de concentratie van alle lipiden, proteïnes, carbohydraten en metallothioneïne beïnvloed wordt door de verschillende staalafnameplaatsen en hun eigen verschillen in ecologische druk en eventuele metaal vervuiling. Er zijn drie staalafnameplaatsen waarvan één in het natuurpark Telaščica en twee uit het Pašman kanal wat het grootste oogstgebied is, in het centrale gedeelte van de Adriatische Zee van Kroatië. Voor elke plaats zijn er maandelijks 36 individuen genomen over een periode van maart 2013 tot februari 2014. Voor de metingen van alle lipiden, alle proteïnes, alle carbohydraten en metallothioneïne werd de spijsverteringsklier gebruikt en gemeten door spectrofotometrische bepalingen. De correlaties tussen de parameters werden onderzocht en two way ANOVA berekende de seizoens- en plaatsschommelingen voor de verschillende sites. Er zijn correlaties gevonden tussen metallothioneïne concentratie en de voortplantingscyclus alsook voor de temperatuur en de lipiden. Het is voor het eerst dat er concentraties van metallothioneïne zijn gemeten bij de Arca noae. De metallothioneïnewaarde in de zomer voor Telaščica ligt hoger dan de plaatsen waar er antropogenische invloeden werden verwacht. Dit toont de invloed van boottoerisme op natuurparken. Metallothioneïne zou een mogelijke biomarker kunnen zijn voor A. noae maar verder onderzoek is aangewezen, aangezien de concentraties van de verontreinigde stoffen niet hoog lagen.

Keywords: Arca Noae, biochemische samenstelling, seizoensvariatie, metallothioneïne, spijsverteringsklier

BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 9

1 Introduction

1.1 Noah’s arkk shell

1.1.1 Taxonomy Noah’s ark shell, Arca noae (Linnaeus, 1758) belongs to the phylum of the molluscs and the class of the bivalves. The subclass is Pteriomorphia and the order Arcida. It belongs to the superfamily of the Arcoidea and the family Arcidae. The genus is Arca and the species name is Arca noae (Gofas S. & Vandepitte L., 2004).

1.1.2 Morphology The shell of A. noae is extremely variable in overall form; this being reflected most obviously in general shape and coloration as such that a numbbeer of subspecies and forms have been described. The shell of A. noae is thick, transversely inflated, strongly radially ribbed and covered, especially marginally, in a thick, brown, fibrous and leafy periostracum. The colour of the shell is whitee or light with a dark brown zigzaagging pigmentation on top, see figure 1 below (Morton & Peharda, 2008).

Figure 1. The shell of Arca noae with its typical pigmentation (molluskaseeafood, 2012).

The largest length recorded was 69 mm in 1970s for this speciess. Later studies noted that it can grrow to a length of 90 mm. It is likely that the Noah’s ark shell can live lonnger than 16 years. In unexploited populations of Noah’s arrk shell species of 24 years are found (Puljas et al., 2015).It takes 3 to 7 years to reach a commercial length

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of 50 mm (Radić et al., 2014). Therefore, local fishing can threaten the existence of this slow-growing old population (Peharrda et al., 2002).

The growth of the species is slower in winter as a result of declining seawater temperatures and decreased food availability. Disturbance of the habitat, predator attacks and detrimental algal blooms can be recorded because it results in formation of surface rings in the shell. Since they estimate the age of the Noah’s ark shell of prismatic shell growth lines, surfface rings can cause an overestimation of an individual’s age (Peharda et al., 2002). A studyd by Morton & Peharda (2008) showed that males dominate small shell length categories with females becoming more predominant as size increases sugggesting that some individuals (at least) may undergo protandric development.

1.1.3 Reproduction Macroscopic observations from previous studies showed that male gonads are whitish, while sexually mature females have orange to red gonads. Bivalves undergo an annual reproductive cycle that involves a period of gametogenesis followed bby either a single, extended or several spawning events whichh, in turn, are often followed by a period of gonad reconstitution (Peharda et al., 2006). Female gametogenesis usually begins in October. Female gametes are in late development from February to May. They are fully mature in June and July. Spawning (i.e. releasing the gonads) occurs in July and August. Gametogeenic development begins almost immediatelly after spawning. Male gametogenesis and subsequent steps in development occur one month later (Peharda et al.., 2006). Hermaphroditic animals are recorded only during the colder part of the year (October to April) and usually undergo the same development phases as non- hermaphroditic individuals. It is interesting that hermaphrodites werre recorded in this period because it is around the same time when the gonadal preparation takes place for the next spawning. It is possiblle that sex change occurs durinng this period (Peharda et al., 2006). BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 11

Changes in the condition index, expressed as the ratio of tissue dry weighht and wet shell weight, appear to be a good indication of spawning since a statistically significant correlation was obtained between this and mean goonadal index (MGI). The value of MGI is obtained by multiplying the number of individuals from each development stage by the numerical ranking of that stage, and dividing the result by the total number of individuals that are analyzed (Gosling, 2003). Qualitative categories of reproductive stagees and correspondingn numeriical values used for calculation of mean goonadal index are the following: inactive (0), early active (3), late active (4), mature (5), partiallly spawned (2) and spent (1). (Peharda et al., 2006)

Differences between sampling yeears in relation to spawning periodicity in A. noae might be the result of differences in sample size, sampled shell lengths or temporal variability in environmental conditions. Temperature is considered to be one of the main exogenous factors influenccing bivalve reproductive cyccles (Peharda et al., 2006).

1.1.4 Habitat The shell has a solid byb ssus that can attach to the rocks. It is found on rocks or shells. In the Mediterranean species can be found up to depths of 119m, at the Croatian side of the Adriatic shells are found to depths of 60m. The Noah’s ark shell is more abundant in habitats which are characterized by lower salinities caused by terrestrial run-off, underwater springs or river inflows (Župan et al., 2012). The Noah’s ark shell is distributed in the eastern Atlantic Ocean, the Mediterranean and Black Seas and the west Indies (Župan et al., 2012). It is most common in the coastal waters of the eastern Adriatic Sea where it is commercially exploited (Peharda et al., 2002), see figure 2 below.

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Figure 2. The distribution of Arca noae in the Croatian Adriatic. Sighting locations are indicated by black dots. Main traditional harvesting areas are indicated by arrows (Morrtton & Peharda, 2008).

1.1.5 Consumption It is one of the most important edible bivalves in the eastern Adriatic Sea and is harvested from natural populations primarily byb scuba divers. The majority of harvested shells is sold on the black market during the tourist season or is consumed by the local population. Data on the biochemical and fatty acid composition of Noah’s Ark shell indicate that it is an excellent source of n-3 fatty acids, especially eicosapentaenoic (20:5n-3) and docosahexaenoic (22:6n-3) acid, and as such it represents a high quality seafood product (Radić et al., 2014).

1.2 Pollution There are different kinds of pollutants that can cause harm to orrganisms, e.g. inorganic ions, orgaanic pollutants, organometallic compounds, radioactive isotopes and gaseous pollutants. Metal cations, nitrates and phosphates are examples of inorganic ions. Nitrates and phosphates are not toxic, they only cause environmental problems when found in large quantities unlike some metals which are toxic in smaller amounts. Examples of organic pollutants are: hhydrocarbons, organochlorine insecticides, carbamate insecticides, phenoxy herbiciides. Most of BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 13 these products are used in agriculture. Methylated mercury iis an organometallic compound. A well-known case about this pollutant is the accident in Minimata bay,, Japan in 1950s. A paper factorry had released metallic mercury in the aquatic environment. Bacteria in the sediments then methylated the mercury which resulted in a form that is bioavailable to fish. 100 people died of eating the poiisonous fish (Walker et al., 2001).

Coastal areas are characterized bby a very high biodiversity and they include some of the richest and most fragile eccosystems on earth. Main sources of anthropogenic immpact in coastal areas of the Adriatic Sea are the residences in the coastal zone, fisheries and aquaculture, shipping, tourism and land-use practices (agriculture, industrial development). Organisms (e.g. bivalves, crustaceans and fish) that live in such an environment can accumulate metal pollutants in high cooncentrations. Metal polllution is a critical environmental issue, due to metal toxiicity for the aquatic organisms and also for humans, as their final consumers (Walker et al., 2001).

1.2.1 Bivalves as indicator organisms in environmental studies In 1980s it has been recognised that the disadvantage of the contaminant analysis of natural waters is the lack of correlation between the concentratiions of contaminants and their biological availabilitty. Consequently, it has been concluded that organisms should be preferred for most environmental monitoring tasks (Phillips & Rainbow, 1993). Bivalves have been used widely as sentinel or indicator organisms for environmental monitoring in order to detect either natural or anthropogeenic stress. Some of the characteristics of bivalves that make them suuitable bioindicator organism are:  wide geographical distribution  ecologically abundant orgaanism  sessile form of living (attached to the substrate)  direct contact with environmental media  filter-feeder  ability to accumulate pollutants in the organism

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Currently, monitoring of contaminants in marine biota in the Northh--east Atlantic Ocean is performed within the framework of OSPAR as the regional convention for the protection of the marine environment of this area. The objectives of monitoring and assesssment are described in the Joinnt Assessment and Monitoring Programme (JAMP) under the Hazardous Substances Strategye , providing the basis for the monitorinng programme of chemicals for priority action, and hazardous substances in general. Within JAMP, analysis of contaminants are performed in shellfish (marine bivalves and snails) as well ass in fish and seabird eggs. These groups of organisms as representatives of marine biota are suitable for monitorinng hazardous substances: trace metals and organic compounds including chlorinated compounds (such as chlorobiphenyls, DDT and metabolites, HCH isomers, HCB and dieldrin), parent and alkylated PAHs, brominated flame retardants such as pollybrominated diphenyl ethers (PBDEs) and hexabromocyclododecane (HBCD), perfluorinated compounds (PFCs), organotin compounds (TBT and its breakdown products), dioxins, furans and dioxin-like PCBs (OSPAR JAMP, 2012).

Concentrations of the various types of contaminants can differ in different types of tissue. Digestive gland is the main metabolic and storage tissue for metals, as well as important metabolic site for organic contaminants (Geffard et al., 2001) and therefore is has been often selected as the targeet tissue for the survey of metal exposure by means of MTs as a specific biomarker (e.g. within the framework of the Mediterranean Action Plan (UNEP/MAP, 1999). Within the OSPAR Joint Assessment Monitoring Programme the bioindicator organism is Mytilus galloprovincialis, the target tissue is the digestive glland (OSPAR JAMP, 2007). With regaard to the digestive gland, the breakdown of the digestive epithelium appears to be a generalized stress response, resulting not only from exposure to a wide range of contaminants, but also to physiologo ical extremes such as increased salinity and starvation (Livingstone & Pipe, 1992). It is known that pollutant exposure may induce alterations in cell-type ratios in the digestive epithelium (basophilic cells become more abundant than digestive cells), and therefore the cellular composition of the digi estive epithelium was examined as a marker of the general condition of the digestive gland (Cajaraville et al., 1990). BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 15

Furthermore, for detecting natural environmental stressors in the mussel M.galloprovincialis using cellular energy allocation (CEA) methoodology the digestive gland is preferred above the mantle (Erk et al., 2012). Thus generally speakingn , bivalvees that are basically sedentary are suitable indicator species of coastal pollution because their body contaminant burdens normally can be attributed to local pollution sources or locally polluted water and sediment. They give time-integrated information on the bioavailability of chemicals in the water column and sediments (Regoli, 1998).

1.3 Sampling sites The study area is located in the central Adriatic Sea in Croatia, where three sampling sites were selected, as shown in figi ure 3.

Figure 3. Study area in the central Adriatic Sea in Croatia with indicated sampling sites. PC1 and PC2 are sampling sites in the Pašman channel harvesting area and TE is a sampling site in the Telašćica Nature Park.

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1.3.1 Telaščica The sampling site was selected in the nature park Telašćica (TE; at a depth of 10-14 m) located on the island Dugii otok, and represented the site of low anthropogeenic impact. However the nature park is a popular place for tourists. There are a lot of vessels in the bay. During the touristic season (June, July, August) there are many daily visits and excursions. This leads to excessive crowds and pollution of the area, because of the fuel from the boats andd of the trash that the tourists leave behind (Favro & Gržetić, 2008). So we have to investigaate in this study if Telašćica can indeed be used as a reference site. Nickel and chromium concentrations in sediments of Telašćica are higher compared to the unpolluted Adriatic sediments. For nickel there are values of 87 µg/g dry weight while the unpolluted area gives values lower thann 52 µg/g dry weight. The levels of copper, lead and zinc are lower at TE than the values from other unpolluted areas (Mihelčić et al., 2010).

1.3.2 Pašman kanal/channel The Pašman channel includes one of the most important natural habitats of A. noae in the central Adriatic, (Figure 3). It will further on be referred to as Pašman kanal. This area represents the main harvestinng area for Noah’s Ark shells iin the central Adriatic. However, this sea region is under considerable anthropogeenic pressure and orgaanisms living there can be exxposed to contaminants originating from different sources. Potential contamination in the Pašman kanal is the result of municipal waste from the town Biograd (hospital and marina) in the southeast and industrial waste of the city of Zadar (international harbor - passengers and transport of chemicals) in the northwest.

Two sampling sites were selected in Pašman kanal (PK), which is spreading in the northwest–southeast direction between the coastline and the island Pašman. Sampling site PK 1 (at a depth of 12-15 m) is located in the proximity of the coast, while site PK 2 (at a depth of 5-7 m) is located close to the island Pašman. The potential influence of anthropogenic impact (municipal and industrial waste waters) is to be expected on both PK 1 and PK 2 sampling sites. BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 17

There are no data available of measured concentrations of any pollutants in Pašman kanal.

1.4 Biomarkers Biomarkers are biochemical, phhysiological, or histological iindicators of either exposure to or effects of xenobiotic chemicals. Biomarkers were first used by environmental scientists around 1981 (Walker et al., 2001). Before that time biomarkers were alreadyd known in human toxicologyg , in which they have proven to be very useful as measures of human exposure to specific chemicals or as early warning indicators of specific diseases or syndromes (Hill, 19655; Forbes et al., 2006).

Biological responses at the level of population and communitty are considered as bioindicators. Changes at individual level or below, i.e. cell and tissue, are called biomarkers (Walker et al., 20011). The first level of environmental stress causes changes at the level of cells and tissues (Figure 4).

Figure 4. Conceptual diagrram of different scales of ecosystem health caaused by environmental stress/ pollution (graphic: Michael Ahrens).

In the first level exposed to environmental stress, i.e. cell and tiissues, the structural integrity is affected, e.g. sister chromatid exchange is a biomarker for changes in genetics caused by chemicals that cause chromosomal aberrations e.g..

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dichlorophenol (Walker et al., 2001). The second level of the response affects the organism, it can lead to an increase of ddiseases or death. Changes in behaviour of the organism occur, e.g. avoidance behaviour has been used as a biomarker for heavy metals (Walker et al., 2001).

Nowadays, biomarkers are often used as monitors for quantifying expoosure and the effects of environmental pollutants.

In order to evaluate candidate biomarkers’ strengths and weaknnesses several criteria should be checked.

- If a biomarker responds to a variety of different chemicals, thus a general indicator, it is more useful in monitoringn (Huggeett, 1989). The enzyme aminolaevulinic acid dehydratase (ALAD) is only inhibited by lead whereas acetylcholinesterase geets inhibited by orgaanophosphorous and carbamate pesticides and a complex mixture of pollutants (Walker et al., 2001).

- Relative sensitivity is a criterion which looks at the sensiitivity of the biomarker compared to endpoints such as lethality or growth impairment and to other candidate biomarkers (Huggett, 1989).

- Biological specificity must be kept in mind since biomarkers can differ among different species (Huggett, 1989).

- Chemical specificity is also different for every biomarker, some biomarkers react differently to chemicals (Huggett, 1989), e.g. pollution of lead will inhibit ALAD which can be morrtal while lead also induces metallothionein which is a protective mechanism (Walker et al., 2001).

- It should be checked if the biomarker is a transient or permanennt occurrence as a reaction to exposure of pollutants (Huggg ett, 1989).

- Other criteria are applicability to field conditions and validation in the field. It is important to know if the biomarker is useful for field measurements and not only in the laboratory.

Knowing the limitations of a method should be considered beforee making any conclusions (Huggeett, 1989). BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 19

If biomarkers are measured in a dose- or time-dependent manner, it is easier to notice small changes and therefore biomarkers can allow for rapid assessment of the health of the organism. The health of an organism can be defined as the residual capacity to withstand stress. If an organism cannot endure a limited stressful situation without havingn negative effects, the organism is not healthy. A stressor is any condition or situation thatt causes a system to mobilizze its resources and increase its energy expenditure, e.g.. a drop in water temperaature. Stressors that occur in the environment can be natural (e.g. . salinity fluctuatioon inside the estuary,, seasonal fluctuation in the seawater and air temperature) andd anthropogenic (e.g.. chemical pollution) (Huggu ett, 1989).

In case of chemical pollution there is an interaction between the toxicant and a biochemical receptor in the organism (Huggett, 1989), e.g. the binding of tetrachlorodibenzodioxin on thee aryl hydrocarbon receptor (Walker et al., 2001). This biochemical response is expected to be the most immediiate, and faster than one of the whole organism. So yoou can detect pollution with biomarkers before any changes are visible in the organissm (Huggeett, 1989).

Before you can use a protein as a biomarker the following properties should be established:

- The assay to quantify the biomarker should be sensitive, reliable and easy.

- Baseline data for the connccentration of the biomarker should be known in order to be able to distinguish between natural variability and contaminant- induced stress.

- The basic physiology of the test organism should be known so that sources of uncontrolled variation can be minimized.

- All the factors that affect the biomarker should be known.

- It should be established whhether changes in biomarker concentration are due to physiological acclimation or to genetic adaption.

- And the increased levels of the biomarker should be correlated with the health of the organism (Huuggett, 1989).

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1.4.1 Metallothionein Metallothioneins (MT) constitute a family of low molecular weighht (6-7 kDa), cysteine-rich, metal binding proteins and oligopeptides. The cysteeinyl residues serve as liigands for metal chelation. The synthesis of MT can be induced by a wide variety of metal ions, such as cadmium, copper, zinc, mercury, cobalt, nickel, bismuth, and silver. Native MTs often contain mixed-metal clusters conntaining both essential metals zinc and copper. After exposure to cadmium the protein may contain cadmium, copper and zinc (Hugggett, 1989).

Figure 5. Metallothionein (MT) structure. Model of two binding sites of metallothiionein. Red big beads are metal atoms (e.g., Zn), and small yellow beads are sulfur atoms (Branislav et al., 2013).

MTs from marine crustaceans consist of two 9- cysteine domains that each bind three metals. There is a difference between MTs from non-mammalian vertebrates and inverrtebrates compared to the typical mammalian MT.

MTs are thought to be involved in innterrelated processes associated with metal metabolism, such as metal detoxification, reguulation of zinc and copper, and donation of metals to metalloproteins. Evidence presented byb several investigators is consistent with the hypothesis that MTs supply zinc, and possibllyy copper, for nucleic acid metabolism, protein synnthesis, and other metabolic processes in growing, injured or regenerating tissues. MT also plays a role in the response to stressors such as cold, heat, exercise, and injection of chemicals. The stress related increase of MT synthesis seems to be mediated by glucocorticoid hormones. This is until now only known for mammals, insects and crustaceans (Huggett,, 1989). BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 21

MT has also been shown to be an effective free-radical scavenger, which can be immportant in normal cellular metabolism and in modifying radiation sensitivity.. They discovered in blue crabs (Callinectes sapidus) that cadmium- and copper inducible MTs are different proteins. Such observations sugggest that MT gene expression may be metal specific. The rates of biodegradation of MT are dependent on the metal bound. When MT biodegrades copper and zinc will be lost. However cadmium remains bound to MT in the steady state of biosynthesis and biodegradation of MT.

In table I basal metallothionein concentrations are expressed in d.w. or w.w. for different species and different tissues with differential pulse pollarography as the method of determination. The method used for metallothionein quantification in Table I is a voltammetric method which is different than the one used in this study, i.e. spectrophotometric. According to the studyd of Ivankković et al. (2003) who compared two SH-based methods for quantification of metallothioneins spectrophotometric measurements were at least one order of magnnitude lower in comparison wiith the voltammetric measurements depending on the particular sample treatment as well as two specific reagents and reference standards applied. Therefore,, caution should be taken if the results of different studies are compared. Ass can be inferred from Table I, the values of MT concentrations are higher in the digestive gland than in the wholle organism, while the values iin the gills are lower than in the whole organism example.g. in M. edulis.

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Table I. Basal metallothionein concentrations determined by Differential Pulse Polarography in bivalves from sites considered to be uncontamiinated (Amiard et al., 2006).

In the study performed by (Ivanković et al., 2005) MT concentrations of digestive gland for M. galloprovincialis determined by spectrophotometric method ranged from 520–870, 360–760, 225–615 and 180–860 µg/g d.w. in winter, spring, summer and autumn (0.52-0.87, 0.36-0.76, 0.225-0.615 and 0.18-0.86 mg/g d.w.). Therefore, these results could be compared to the data obtained in this thesiis – althougu h interspecies differences would be expected - as longn as you convert w.w. to d.w.. BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 23

1.4.2 Proteins, carbohydrates and lipids Changes in the concentration of stored energy reserves in innvertebrates can be caused by phhysical and chemical stressors. Changes in stored energy reserves occur because of long-term, sublethal exposure to a stressor and are normally not manifested under acute exposure conditions (Huggett, 1989).

When the energy intake exceeds the maintenance, growth and reproductive requirements of the organism the energy will be stored as glycogen or lipids. The energy reserves are mobilized when there is an increased enerrgy demand such as stress or reproduction. Proteins can also be used as an energy source, although that is not the main role because theiir role is that of a structural uunit of an organism (Huggett, 1989).

Glycogen is a storage form of glucose. Depletion of glycogeen can occur due to toxicant-induced stress. Ansaldo et al. (2006) proved in their study that glycogen from the gonadal region can be used as a general biomarker for snails (Biomphalaria glabrata). Generally, there will be a depletion of glyccogen because of an increased energy demand. Glycogo en storage and mobilization is restricted to certain tissues, therefore observed alterations in glycogen concentrations are ooften tissue specific. For vertebrates the primary storaage location is the liver. For mussels the primary storage organs are the hepatopancreas (digestive gland) and thhe mantle (Huggett, 1989). Glycogen reserves are gennerally used during the gametogenetic processes when lipids are not available, since the reserves of lipids depletes when the gonads are released. This process is followed by the gametogenetic deevvelopment (Radić et al., 2014).

Lipids are the most important, readily available energy source for aquatic invertebrates, fish and birds. For most of the species lipids aree the primary energyy source. Although it can tend towards a secondary energy source among different seasons, species or stadia in life ccycle or reproductive cycle. The tissue distribution of llipids is variable in an organism. For instance there are more lipids present in the dark muscle than in the white muscle of a fish. A decrease in llipids can be caused by toxicant-induced stress but a decrease in total lipid concentrations might occur as well followed by an increase in specific components of the lipid constituents, e.g..

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an increase of triglycerids. Lipids are most useful as sublethal indicators of long- term stress, although it is possible that changes in specific lipid components can occur during acute stress (Huggett, 1989). Lipids are used as a biomarker in humans to detect prostate cancer (Zhou et al., 2012).

Structural proteins are abundant in an organism. These proteins are not intended as an energyy source. However under conditions of severe stress the organism can mobilize proteins as an energy source. Normally lipids and glycogen are used first but depending on the season, reproductive status and the length of the stress circumstances, proteins can be a quite important energy source. Proteins are not used as an individual biomarker, becauuse of the fact that geenerally prroteins will be the last source of energy used and therefore will not be a very sensitiivve biomarker (Huggett, 1989). Total protein concentration in the plasma of a fish can detect nutritional stress, however for chemical pollutants it is not sensitive (Heath, 1995).

1.5 Biotic and abiotic factors that affect biomarker variability In general the glycoogen content can be influenced by diet, reproductive condition and season (Huggeett, 1989). This is demonstrated in various species of bivalves such as the mussel M. galloprovincialis and the clams Venerupis philippiinarum and V. decussate (Radić et al., 2014). The glycogeen content of fish tissue is affected by acute and chronic exposure to metals. A decrease in glycogen usually occurs because of an increased energgy demand combined with chemical-induced stress (Hugggett, 1989). Similar results have been found for snails (Ansaldo et al., 2006) and the shell Ruditapes decussatus (Hamza-Chaffai et al., 2003). Carbohydrate concentration in A. noae is neegatively affected by seawater temperature, therefore the highest carbo- hydrate value was detected in December (Radić et al., 2014). In the studdy performed by Radić et al. (2014), a second carbohyddrate peak in A. noae was observed in April, coinciding with highh phytoplankton abundance. The seasonal variation in glycogen reserves is strongly influenced by food availability, in addition to reproductive demand (Radić et al., 2014). BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 25

In a study performed by Radić et al. (2014), protein and lipid levels in A. noae reached the maximum value in June, just before spawning, folllowed by a decrease in the concentration of proteins annd lipids. Protein is a major organic componnent of bivalve oocyttes. Therefore, protein maxima prior to spawning (June) may support this hyh pothesis. The decrease in protein level after spawning (from July to September) was similar to many other marine bivalves. In marine bivalves, lipids play a crucial role in maturring gonadal tissues and constitute a major component of reproductive material (Radić et al., 2014).

Lipid content of various species of bivalves (Radić et al., 20144) is affected by diet, growth, reproductive condition, salinity and temperature (Huggett, 1989). In general, lipid content increases before mass spawningn occurs, and then markedly decreases. In A. noae, lipid variations from February to June are inversely related to carbohydrate content. This relationship is usually attributed to the conversion of glycogen to lipids biosys nthesized during the formation of gaametes (Radić et al., 2014).

There is no proof of a significant correlation between gonadal index and temperature for Arca naoe in Mali Ston Bay. However, most of the times an increase in mean gonad index is followed by an increase in temperature. Lack of a significant correlation between these two variables is probaably due to values recorded in the period from July to December when spawning ccaused a decrease in gonad index during the period when temperature was still increasingn (July to Auugust) (Peharda et al., 2006).

For the mussel M. galloprovincialis different biotic and abiotic factors including mussel reproductive state, age and sex, sea temperature and salinity, as well as the season of sampling, contribute to variations in the MT ceellular concentration (Ivanković et al., 2005).

For oysters it is the reproductive cycle/season of the year and for blue crabs it is growth and the molt cycle that can affect MT concentration. Itt is thus evident that measurement of MT in organisms does not necessarily reflect the degree of exposure to metals (Huggett, 1989). For detecting exposure in the environment without the influence of the reproductive cycle on the level of MT, a method is used

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where theey transport species from an unpolluted area to the area of iinvestigation. The concentrations are measured at both sites. Then they compare the changn es between the two sites to conclude if there is any exposure in sitte of interest. (Pellerin & Amiard, 2009)

Therefore, when using biomarkers in environmental studies it is important to differentiate between the physiological changes caused by biotic influences (for example gonadal development and food abundance), abiotic innffluences (for example temperature and salinity) and the contribution to biomarker changn e caused by the anthropogenic stressor studied. In other words, the natural variations of biomarker response may interfere with the estimation of the level of changn es induced bby anthropogenic sources.

1.6 Aim of this study This thesis focused on analysses of the digestive gland of A. noae as one part of a much larger research that was done on this species. The other part focused on determination of metal concentrations on the whole soft tissue, as well as in the seawater and the sediments. The unpublished results showed accumulation of metals in the soft tissue. Accumulation was only measured in March 2013 since it was not the main aim of that research. The aim of this studdy was to determine the concentrations of total prrooteins, lipids, and carbohydrates in the tissue of digestive gland of Noah’s Ark shellls in order to differentiate between the sampling sites and their respectively different environmental pressures. The levels of metallothionein will be determined aiming to provide information about potential metal exposure in the environment. Based on the obtained results of this study we will evaluate the potential of A. noae as a bioindicator species and digeestive gland as the target orgaan in the assessment of environmental stress that could be caused by metal pollution. It is the ffirst time that the levels of metallothionein are measured for the species A. noae. Dr. Sc. Melita Peharda Uljević and Dr. Sc. Sanja Puljass were in charge of studying the histologyy of the gonads.

BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 27

2 Materials and methods The Noah’s Ark shells from natural populations were sampled monthly from March 2013 until February 2014 bby SCUBA diving done by Drr.Sc. Ivan Župan and transported in dark and cool containers to the laboratory within 6 hours, where theey were immediately washed with seawater and dissected by Dr.Sc. Jelena Čulin using stainless steel equipmentt to extract the digeestive gland. Four composite samples were collected for each sampling site and time. Each composite sample contained digestive glands dissected from 9 individuals.

Our samples consist of tissue of the digestive gland of Arca noae. For each sampling site there are four different pools; A, B, C and D. One pool contains 9 individuals, so for one sampling site there are 36 individuals. Preparation of the samples is necessary when measuring biochemical compositions such as liipids, carbohydrates, proteins and the level of metallothionein (MT). As a first step, all the samples need to be homoogenized. There is a difference in the homogeenization buffer for biochemical composition and MT measurements. After the homogenization process samples for analysis of biochemical composition are stored in several aliquots at - 80°C for later use.

2.1 Homogenization

2.1.1 Materials In Table II the products needed for homogeenization of the samples are listed.

Table II. Products used for homogenization. Product/solution Brand/firm

β- mercaptoethanol Sigma

Leupeptine Sigma

PMSF (phenylmethylsulfonyl fluoride) Sigma

Tris- HCl Sigma

Tris- base Sigma

Absolute ethanol Kemika

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Preparation of buffers Biochemical composition homogenization buffer:

100 mM Tris-HCl pH = 8.6 (at 5 °C)

MT homoogenization buffer (add fresh additives every day):

20 mM Tris-HCl pH = 8.6 (at 5 °C)

+ PMSF dissolved in ethanol (stock solution 58 mg/ml) - add 1.5 µl/ml homogenization buffer.

+ leupeptin dissolved in 20 mM Tris-HCl (stock solution 1 mmg/ml) – add 3µl/ml homoogenization buffer.

+ β- mercaptoethanol dissolved in MQ- H2O(stock solution 100 µl/ml)- addition 1 µl/ml homogenization buffer.

2.1.2 Protocool ‐ Transfer the tissues of the digestive gland from -80 °C to -20 °C.

‐ Switch on the ice maker.

‐ Turrn on the centrifuge to cool the rotor (4-9 °C).

‐ Label the sample tubes and put the tubes and the homogenizatiion buffers on ice.

‐ Weigh the tissue and cut with scissors until you get a pulp.

‐ Take 1 g of the tissue for MT measurement.

‐ Weigh about 0.4 g for biochemical composition measurement.

‐ Weigh the remaining tissue and put in a cryr ogenic vial of 1.5 or 1.8 ml and store them at -20 °C. These will be used for lyophilization .

Procedure for samples to measure MT: We use tthe method according to Viiarengo et al (1997). (Viarengo, Ponzano, Dondero, & Fabbri, 1997)

Add 3 ml homogenization buffer per 1 g of tissue and transfer to a gllass homogenization tube. BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 29

‐ Homoogenize on ice using a motor-driven homogo enizer with PTFE pestle.

‐ Rinse the pestle and otther equipment used with De-H2O in between samples.

‐ Transfer the homogenates into the centrifugation tubes and keep on ice until centrifuged, cover with parafilm.

‐ Before positioning the tubes in the rotor check their weight in order to balance the centrifuge rotor. A difference less than 0.5 g between opposite samples is allowed.

‐ Centrifuge for 120 min at 50000 ×g (this is equivalent to 1 h at 100000 ×g).

‐ Separate the supernatant using a Pasteur-pipette taking care to avoid pipetting fat (on top) and pellet (at the bottom); if there is a large fraction of fat, centrifuge for an additional 15 min.

‐ Vortex the supernatant and transfer it to labelled Eppendorf tubes (on ice); Make aliquots for all anallyses needed. Samples should never be repeatedly defrosted and frozen. Additionally, separate several parts of supernatant (SN) for determination of cytosolic proteins. We collected 2x250 µl for MT and 3 subsamples of 30 µl for ccytosolic proteins. These superrnnatant samples will further be referred to as S50.

‐ Store the aliquots of samples at -80 °C.

Procedure for samples to measure biochemical composition:

‐ Add 10 volumes of homoogenization buffer per volume of tissue to a glass homogenization tube, here the ratio for digestive gland is 0.4 g tissue and 4 ml buffer.

‐ Homoogenize the sampless on ice using a motor-driven homogenizer with PTFE pestle.

‐ Rinse the pestle and otther equipment used with De-H2O in between samples.

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‐ Transfer homogenates from the glass homogenization tube into PE tubes, cover with parafilm and keep on ice until you finish homogenization of all samples.

‐ Vortex before you make 12 aliquots of 200 µl homogenate iin Eppendorf tubes, use the blue pipette tips 100-1000 µl. 3 for determination oof proteins; 3 forr determination of lipids; 3 for determination of carbohydrates and 3 for determination of ETS activity.

‐ Transfer the remaining homogenate in a cryogenic vial of 1.8 mll.

‐ Store the aliquots and remaining homogenate on -80 °C.

2.2 Quantification of metallothioneins and cytosolic proteins

2.2.1 Materials The materials that were used for quantification of metallothioneins (MTs) and cytosolic proteins are listed below in Table III. Table III. Products used for MT and cytosolic proteins quantification. Product/solution Brand/firm

Chlorophorm Kemika

Ethanol, 95% Kemika

NaCl Merck and Kemika

EDTA Merck

HCl Merck

DTNB (5,5-dithio-bis-(2-nitrobenzoic Sigma acid)

GSH (glutathion) Serva

KH2PO4 Kemika

NaOH Kemika

BSA (bovine serum albumin) Sigma

H3PO4 Kemika

G- Coomassie brilliant blue 250 Serva BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 31

Tris-HCl Sigma

Tris-base Sigma

Prreparation of buffers and solutions For quantification of MTs

Washing solution: ethanol/chlorophorm/20 mM Tris-HCl, ratiio 87:1:12

Following solutions need to be sttored at 5 °C but before using them, they need to be at room temperature.

4 mM EDTA in 1 M HCl

0.25 M NaCl

0.2 M Phosphate buffer (KH2PO4) pH = 8.0

20 mM Tris-HCl pH = 8.6 (5 °C)

DTNB reagent, 50 ml:

Dissolve 8.55 mg DTNB in a few ml 0.2 M phosphate buffer pH = 8.0 until it is dissolved, then add 5.844 g NaCl (2M). Fill up to 50 ml with the phosphate buffer. (Always make fresh solution, keep it in the dark at room temperature. Wrap the flask in alu- foil.)

For quantification of cytosolic proteins

Bradford reagent 1 l:

A) 100 mg Coomassie brillliant blue + 50 ml 95%(V/V) ethanol

B) 100 ml 85%(V/V) H3PO4 + 500 ml De-H2O

Add B to A and fill up to 1 l with De-H2O

2.2.2 Protocol Procedure for MT: SEMIMICRO METHOD for 250 µl S50

We use the modification of method accordingn to Viarengo et al. (1997).

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All solutions and labware are kept on ice (turn on the ice maker, centrifuge and photometer!). Ethanol is used for precipittation of proteins. Precipitationn is performed in two steps. In the first step high molecular weight proteins are precipitated, and thus removed from the solution.

In the second step, with the addition of absolute ethanol MTs are precipitating during 1h at -20 °C and after centrifugaation the pellets are collected. These pellets contain MTs. The ethanol precipitation procedure is performed in Eppendorf tubes of 1.5 and 2 ml.

All following steps are performed on ice.

Figure 6. Procedure for removal of high molecular proteins and precipitation of MT fraction. Removal of high molecular weight proteins on 250 µl S50 (Eppendorf tubes of 1.5 ml)

The samples should be completely thawed.

‐ Add 262 µl cold (-20 ºC) absolute ethanol

‐ Add 20 µl cold (-20 ºC) chlorophorm BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 33

‐ Vortex.

‐ Centrifuge 6 minutes at 12000 rpm (around 14 000 x g), at 2 ºC (Biofugee Fresco, rotor radius 85 mm).

‐ Transfer the supernatant (S14) in new cooled Eppendorf tubes of 2 ml.

Precipitation of MT fraction on S14 (Eppendorf tubes of 2 ml)

‐ Add 1.35 ml cold (-20 ºC) absolute ethanol (~3× of S12 volume)

‐ Gently turn it upside down a few times.

‐ Leave for 1h at -20 °C (write down the time).

‐ Centrifuge 6 minutes at 12000 rpm (around 14 000 x g), at 2 ºC (Biofugee Fresco, rotor radius 85 mm).

‐ Discard the obtained superrnatant.

‐ Decant the tubes on filter paper.

Washing the pellet

‐ Add 700 µl washing solution to the pellet, gently mix without vortex so nothinng remains on the tube walls.

‐ Centrifuge 6 minutes at 12000 rpm (around 14 000 x g), at 2 ºC (Biofugee Fresco, rotor radius 85 mm).

‐ Remove the supernatant annd put the tubes with pellet on ice.

‐ Decant the tubes on filterr paper (in groups of 4, because there are 4 places for drying in the stream of nitrogen).

* Drying and subsequent steps are performed at room temperatture.

‐ Dry the pellets in the streaam of nitrogen and put the tubes with dry pellets into the stand at room temperature.

‐ Dissolve the pellets first in 35 µl 0.25 M NaCl and then add 35 µl solution of 4mM EDTA/1 M HCl, and vortex.

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‐ Set the centrifuge at 20 °C.

Determination of SH- groups with DTNB (Ellman’s reagent)

‐ Prepare stock solution of GSH 1 mmg/ml in 0.25 M NaCl (preparee fresh and keep it on ice)

‐ Make a series of GSH standard solutions in the rangn e of 2.5 to 30 µg/µL according to Table IV.

Table IV. Preparation of GSH standards and samples obtained by ethanol precipitation for determination of content of free –SH groups witth Ellman's reagent – MICROMETHOD. Stock solution of 1M HCl/ GSH in 0.25 M NaCl 0.25 M NaCl 4 mM EDTA DTNB Standard (GSH, µg) (µl) (µl) (µl) (µl) Blank 0 35 35 1055 2.5 2.5 32.5 35 1055 5 5 30 35 1055 10 10 25 35 1055 20 20 15 35 1055 25 25 10 35 1055 30 30 5 35 1055 Sample (pellet) 0 35 35 1055 ‐ Add 1055 µl of DTNB first to the samples, and then in the standaards.

‐ Voortex.

‐ Centrifuge the samples for 3 minnutes at 12000 rpm (around 14 000 x g), at 20 ºC (Biofuge Fresco, rotor radius 85 mm), standards do not need to be centrifuged.

‐ After centrifuugation transfer the samples to clean Eppendorf tubes of 1.5 ml.

‐ Transfer 3 replicates of 200 µl standard and sample to a 96-weell microplate. The DTNB reagent needs to incubate for 20 minutes at room temperature. (If adding the samples takes less than 20 minutes, wait until the time passes before reading the absorbance.)

‐ Read the absorbance at 412 nm in the photometer for micropplates (Tecan).

Procedure for cytosolic proteins We use the method according to Bradford (1976). ‐ Thaw the samples on ice. BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 35

‐ Make a stock of 2 ml bovine serum albumin (BSA) 5 mg//ml (dissolve it in the same buffer as the samples).

‐ Make a 1/2 serial dilution of the BSA stock solution staarting from 1 mg/ml to 0.0625 mg/ml in Eppendorf tubes. (Use end volume off 500 µl, replicates are not needed)

‐ Dilute the samples to a protein concentration below 0.5 mmg/ml. For digestive gland of A. noae dilute S50 34 times in the following way: 30 µl of S50 + 990 µl of 20 mM Tris buffer, pH 8.6).. (New types of tissue are tested with different dilution factors. The dilution with an absorbance lower than the absorbance of the 0.5 mg/ml standard is chosen.)

‐ Add in triplicate to a 96-well microplate 10 µl sample or 10 µl standard and 200 µl CBB reageent. (Vortex before you add volumes as smalll as 10 µl.)

‐ Shake the microplate so thhat sample and reagent mix well (Shaking: 5 sec; file Tecan reader).

‐ After 5 minutes shake again and read the absorbance at 595 nm.

2.3 Quantification of total tissue proteins

2.3.1 Materials The products that are needed for quantification are listed below in Table V. Table V. Products used for total tissue proteins quantification. Product/solution Brand/firm

Tris- HCl Sigma

Tris- base Sigma

BSA (bovine serum albumin) Sigma

Prreparation of buffers and solutions Buffer and solution need to be stored at 5 °C and before use on room temperature.

Bradford reagent 1 l:

See § 2.2.1 for the preparation of Bradford reagent.

100mM Tris-HCl pH = 8.6 (5 °C)

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2.3.2 Protocool We use the method according to Bradford (1976). (Bradford, 1976)

Preparation of the standard solutions

‐ Prepare the stock solution of protein standard (4 mgm /ml): dissolve 8 mg BSA in 2 ml homogenization buffer.

Do not make dilutions of the standard until the samples are also ready for the analysis.

‐ Dilute the standard stock solution with 100 mM Tris (pH = 8.6) to 0.4, 0.2, 0.1, 0.05, 0.025 mg/ml and a blank.

‐ From each standard concentration add 10 l in a 96-well microplate. Make 3 replicates. Also add three blanks in the microplate well  perform this step together with the samples

Preparation of the samples

‐ Thaw the samples on ice and centrifuge the samples for 10 miinutes at 2000 × g (5000 rpm) at 4°C (Biofuge Fresco, rotor radius 85 mm).

‐ Transfer the supernatant to new Eppendorf tubes and dilute them. Keep (diluted) samples on ice!

Dilution depends on the tissue type annd total amount of proteins in that tissue. If working with a “new” type of tissue, make a few test dilutions to check if the intensity of colour fits in the range of the calibration line. Use homogenization buffer (100 mM Tris, pH = 8.6) to make dilutions of samples.

 Digestive gland samples are diluted 41× (25 l sample + 1000 l 100 mM Tris).

‐ From each diluted sample transfer three replicates of 10 l tto microplate wells, and add 200 l Bradford reagent (add the reagent using a repeating pipette). (Vortex before you add volumes as small as 10 µl.) BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 37

‐ After the addition of reagent, incubate the plate in the dark at room temperature for 5 minutes. (Turn on the timer. Work as precisely as possible.)

‐ Absorbance measurement is performed at 592 nm using a microplate reader (Tecan).

2.4 Quantification of total tissue carbohydrates

2.4.1 Materials In Table VI the products that are used for quantificatiion of total tissue carbohydrates are listed. Table VI. Products used for total tissue carbohydrates quantification. Product/solution Brand/firm

Tris-HCl Sigma

Tris-base Sigma

TCA (trichloroacetic acid) Kemika

Phenol Bethesda research laboratories

H2SO4 Kemika

D-glucose Kemika

Prreparation of buffers and solutions Buffer and solutions are stored at 5 °C and put on room temperature before use.

15% (w/V)TCA

5%(w/V)TCA

5%(w/V) phenol 40 mM Tris-HCl pH = 8.6 (5 °C)

0.5%(w/V) glucose diluted in 40 mM Tris pH = 8.6

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2.4.2 Protocool We use the method according to Taylor (1994).(Taylor, 1994)

Preparation of the standard solutions ‐ All standard solutions (D(+)glucose) are prepared with the homogenization buffer (40 mM Tris pH = 8.6). ‐ From the initial 0.5% glucose solution prepare dilutions of the following concentrations: 0.75, 0.5, 0.25, 0.125, 0.062 and 0.031 mgm /ml. Since the samples were homogenized in 100 mM Tris (pH = 8.6), and then during the extraction of carbohydrates 300 l TCA was added per 200 l of homogenate (sample) a final concentration of 40 mM Tris is obtained in the samples. To make the conditions for measurement of standards and samples more similar, we also take 40 mM Tris, pH 8.6 for the preparation of standards.

Carbohhydrate measurements ‐ In llabelled 1.5 ml Eppendorf tubes put 100 l of each standard solution, and as a blank 100 l of 40 mM Tris pH = 8.6.

‐ Add 100 l of 5%(w/V) phenol. A repeating pipette can be used.

‐ Voortex before you add 0.4 mL of concentrated H2SO4  perform this step (addition of phenol and sulphuric acid) together with the sammples Use protection gloves when working with acid, and always add the aciid carefully. To develop the colour it is important how to add the acid! The acid should be added as fast as possible, and every time in the same way; mix immediattely after acid addition. The colouring should appear instantly. ‐ From each of the glucose standard solutions take 100 l and transfer it to a 96-well microplate. Make 3 replicates. ‐ Incubate the microplate in the dark at room temperature for 10 minutes. (When it takes too long to add everything into the plate and the collour is already formed, incubation in the dark is not necessary anymore.) ‐ Absorbance measurement is performed at 492 nm using a microplate reader (Tecan).

BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 39

Preparation of the samples ‐ Thaw the samples on ice. ‐ Add 100 l of 15% TCA to the sample, vortex and leave it on ice for 10 minutes ( a white precipitate of proteins is formed) ‐ Centrifugee the samples 10 min at 2000 × g (5000 rpm) at 4 °C (Biofugee Fresco, rotor radius 85mm). Make sure the centrifufuge is cooled at 4°C before starting to work with the samples. ‐ Separate the supernatant and keep it in labelled Eppendorf tubes of 1.5 ml for carbohydrate determination. ‐ Resuspend the pellet in 200 l of 5% TCA, vortex, and centrifuge again 10 min at 2000 × g (5000 rpm) at 4 °C (Biofuge Fresco, rotor radius 85mm). Separate the supernatant and add it to the previous one. Mix.

Carbohhydrate measurements ‐ Take 100 l of the sample and put it in a new tube. Add 100 l of 5%

phenol. Vortex before you add 0.4 ml of concentrated H2SO4.  perform this step together with the standards. ‐ Transfer 100 l of each sample in a 96-well microplate . Make 3 replicates of each sample. ‐ Incubate the microplate in dark at the room temperaature for 10 minutes. (When it takes too long too add everything into the plate and the colour is already formed, incubation in the dark is not necessary anymore.) ‐ Absorbance measurement is performed at 492 nm using a microplate reader (Tecan).

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2.5 Quantification of total tissue lipids

2.5.1 Materiaals The products that are needed for the quaantification of total tissue lipids are listed in Table VII. Table VII. Products used for total tissue lipids quantification. Product/solution Brand/firm

Tris-HCl Sigma

Tris-base Sigma

Methanol Kemika

Chlorophorm Kemika

H2SO4 Kemika and Fisher scientific

Tripalmitin Alfa Aesar

Preparation of buffers 100 mM Tris-HCl pH = 8.6 (5 °C) (needs to be at room temperature before using it)

2.5.2 Protocool We use the method according to Bligh and Dyer (1959) and Marsh annd Weinstein (1966). (Bligh & Dyeer, 1959; Marsh & Weinstein, 1966) Protocol for preparation of the standard solutions ‐ Prepare the stock solution of 6.25 mg/ml tripalmitin in a glass tube. Weigh 31.25 mg tripalmitin and dissollve in 5 ml chlorophorm. From this stock sollution prepare the following dilutions: 3.125, 1.563, 0.782, 0.391, 0.196,

0.098 mg/ml in CHCl3 and a blank (chloroform). (end volume of 1 ml) Take care that all labware used in contact with chlorophorm is made of glass (do not use parafilm). Glass tubes used for preparation of dilutions should have volume of more than 2 ml. To mix the solutions vortex strongly till the standard is turbid. (Increasing turbidity according to inncreasing concentration.) Lipid measurements ‐ Add 100 l of each concentration into a new glass tube (+100 l of blank

CHCl3), than add 500 l of H2SO4 and vortex slowly. BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 41

‐ Put the tubes with standard solutions for 15 minutes in thhe labdryeer at 200°C  perform this step togetthher with the samples ‐ Turn on the lab-dryer 1 hour before using it, use only metal tuube racks. ‐ Cool the standards to room temperature, and subsequenttly add 1.5 ml of De-

H2O ‐ Vortex. ‐ From each concentrationn of the tripalmitin standard solution take 250 µl and put it in a 96-well microplate. Make 3 replicates. ‐ Absorbance measurement is performed at 340 nm using a microplate reader (Tecan). Take care that standard solutions are cooled (at room temperature) before the measurements.

Protocol for preparation of the saamples ‐ Defrost the samples.

‐ Add 500 l CHCl3, 500 l CH3OH and 250 l De-H2O and mix the solution using vortex. ‐ Centrifuge the samples 10 min at 2000 × g (5000 rpm) at 4 °C (Biofugu e Fresco, rotor radius 85mm).afterwards take carefully 400 l of the bottom phase

with the pipette (top phase: H2O + CH3OH; bottom phase: chlorophorm + lipids) and transfer it to a new Eppendorf tube.

Lipid measurements ‐ Take 100 l from each sample, put it in a glass tube, and ddry it for 30 minutes at 60 °C (in lab-dryer). Make 2 replicates per sample. (2  100 l = 200 l)  2 glass tubes per sample

‐ Add 500 l of concentrated H2SO4 and mix the solution using soft vortex. Vortex at 1000 r/min. ‐ Put the tubes with samples for 15 minutes in the laab-dryer at 200°C  perform this step together with the standards. ‐ Cool the samples to room temperature and add to each sample 1.5 ml De-

H2O.

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‐ Voortex( at 1000 r/min.) ‐ Transfer 3 replicates of 250 l sample in a 96-well microplate well. ‐ After cooling to room temperaturre measure the absorbance at 340 nm using a microplate reader (Tecan).

2.6 Statistics SigmaPlot was used for making graphics. Various test were performed in SigmaPlot: the Spearman correlation looks for the correlation between the different parameters. P values less than 0.050 show a signnificant relationship between two variables. Two way ANOVA is a two waay analysis of variance. Here it was used for finding interactions between seasons and sampling stations (as independent variables or factors) as well the effect of seasons and the effect of sammppling sites on the tested dependent variable. If the p value was lower than 0.050 there was a statisticallly significant effect or interaction.

Before you can use two way ANOVA six assumptions have to be fulfiilled. The first assumption is that your dependent variable should be measured at a continuous level Since every concentration is expressed in mgm /g w.w. this is fulfillled for every variable. Secondly you should check if your two independent variables each consist of two or more categorical, independent groups. The two independent variables are sampling station and season. There are three sampling stations: Telaščica, Pašman kanal 1 and Pašman kanal 2. The foour seasons are also represented in our measurements, so the second assumptiion is also completed. The third one is that there should be an independence of observations, which means that there must be different participants in each group with no participant being in more than one group. For every month different shells were collected and dissected, so an independence of observations exists. The fourth assumptions is that thhere should be no significant outliers, this is not the caase anymore since we excluded the values from carbohydrates of the month March. The last two assumptions are a normal distribution and homogeneity of variances. These are tested by the pprogram two way ANOVA in SigmaPlot . (statistics)

BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 43

3 Results

We have samples of three sampliing stations. The first sampling station is Telaščica and this station is our referencee. The other two stations are Pašman kanal 1 and Pašman kanal 2. These two stations are expected to be influenced by anthropogenic stress. The obtained results are shown in a yearly graph for each parameter. The correlations between the parameters were also calculated. As a final test two way ANOVA was used for finding interactions between seasons and station as well as the single effects of season and sttation..

3.1 Metallothionein

In figure 7 to 9 the monthly values of MT concentration at Telaščica and Pašman kanal are presented.

Figure 7. Concentration of MT aat samplingn site Telaščica during the whole year. A. Mean and standard deviation (n=4). B. Median and 1st quartal (25%) and 3rd quartal (75%) are presented.

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The highest value of MT for sampling site TE is in the month March which is in the season of spring. Overall, summer has the highest values and winter has the lowest levels of MT. The range between the highest value (March, 120 mg/g w.w. ) and the lowest (December, 70 mg/g w.w. ) is arouund 50 mg/g w.w. . There is a 330% decrease between March & April and August & September. After the lowest value in

December an increase occurs to the value around 100 mg/g w.w.. This value is close to the one measured in October and the plateau in the beginning of the summer.

Figure 8. Concentration of MT at sampling site Pašman kanal 1 during the whole year. A. Mean and standard deviation (n=4). B. Mediian and 1st quartal (25%) and 3rd quartal (75%) are presented.

The difference between the highest value of PK 1 (March, 110 mg/g w.w.) and the lowest (December, 70 mg/g w.w) is 40 mg/g w.w.. Spring is the season with the highest concentrations of MT. Summer and autumn have low levels of MT except BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 45 for the month October. There is a general, 36% decrease from March to December with an exception in October and small peaks in May and Auguust.

Figure 9. Concentration of MT at sampling site Pašman kanal 2 duuring the whole year. A. Mean and standard deviation (n=4). B. Median and 1st quartal (25%) and 3rd quartal (75%) are presented.

The range between the highest value of PK 2 (March, 130 mg/g w.w.) and the lowest

(September, 70 mg/g w.w.) is 60 mg/g w.w.. The highhest values are seen in Spring.. The general trend for this sampling site is a 46% decrease from March until September.

For all three sampling sites there is a general trend: a decrease from October to December followed by an increase from December until March.. In sampling site PK 2 MT concentrations decrease more than in the other two sampling sites. The levels of MT concentrations are clearly hhigher in PK 2 than in PK 1 and TE. As a reference site, Telaščica has the lowest values.

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3.2 Proteins

In figure 10 to 12 the monthly values of total protein concentration at TTelaščica and Pašman kanal are presented.

Figure 10. Concentration of total proteins at sampling site Telaščica during tthe whole year. A. Mean and standard deviation (n=4). B. Mediian and 1st quartal (25%) and 3rd quartal (75%) are presented.

The rangee between the highest value of TTE (July, 70 mg/g w.w.) and the lowest value

(December, 55 mg/g w.w.) is about 15 mg/g w.w.. There is a 27% increase from March to July, affterwards there is a 27% decreaase to October. BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 47

Figure 11. Concentration of total proteins at sampling site Pašman kanal 1 during the whole year. A. Mean and standard deviation (n=4). B. Median and 1st quartal (25%) and 3rd quartal (75%) are presented.

The range between the highest value of PK 1 (June, 70 mg/g w.w.) and the lowest

(October, 50 mg/g w.w.) is 20 mg//g w.w.. There is a 40% decreasse from the month of June to October after which the total protein concentration does not significantly increase until the month of February.

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Figure 12. Concentration of total proteins at sammpling site Pašman kanal 2 during the whole year. A. Mean and standard deviation (n=4). B. Mediian and 1st quartal (25%) and 3rd quartal (75%) are presented.

The rangee for sampling site PK 2 between the highest (April, 62 mg/g w.w.) and lowest value (October, 52 mg/g w.w.) is 10 mg/g w.w.. A 16% decrease occurs from April to October.

The protein concentrations in A. noae are lower for sampling site PK 2 than TE and PK 1. The highest fluctuations in the levels of proteins happen in sampling station PK 1, i.e; 40%. For PK 2, the difference is only 16%. PK 1 and PK 2 have a similar trend throughout the year, a decrease that starts in spring and goes on until autumn. For TE there is first an increase from spring to summer and afterwards there is a decrease until autumn.

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3.3 Carbohydrates

In figure 13 to 15 the monthly values of total carbohydrates concentration at Telaščica and Pašman kanal are presented. The value of the month March is unrealisticallly high for every saampling station, so these will be excluded for the statistical analyses of carbohydraates.

Figure 13. Concentration of total carbohydrates at sampling site Telaščica during the whole year. A. Mean and standard deviation (n=4). B. Median and 1st quartal (25%) and 3rd quartal (75%) are presented.

The range between the highest value of TE (September, 14 mg/g w.w.) and the lowest (May, 11 mg/g w.w.) is 3 mg/g w.w.. There is a 27% increase from May to September.

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Figure 14. Concentration of total carbohydrates at sampling site Pašman kanal 1 during the whole year. A. Mean and standard deviation (n=4). B. Mediian and 1st quartal (25%) and 3rd quartal (75%) are presented.

The rangee between the highest value of PK 1 ( June, 14 mg/g w.w.) and the lowest

(February, circa 11 mmg/g w.w.) is around 3 mg/g w.w.. There is a 21% decrease from June to July. Afterwards there is a generral 27% increase from July to January.

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Figure 15. Concentration of total carbohhydrates at sampling site Pašman kaanal 2 duringn the whole year. A. Mean and standard deviation (n=4). B. Median and 1st quartal (25%) and 3rd quartal (75%) are presented.

The range between the highest value of PK 2 (December, 15 mg/g w.w.) and the lowest (January, circa 12 mg/g w..w.) is 3 mg/g w.w.. The carbohydrate concentrations are relatively stable throughout thhe year, but due a higi her value in December and a lower value in January,, differences can reach up to 15 or 20%.

For sampling site TE the highest values are in summer and autumn. PK 1 shows higher values in the season of autumn.

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3.4 Lipids

In figure 16 to 18 the monthly values of total carbohyddrates concentration at Telaščica and Pašman kanal are presented.

Figure 16. Concentration of total lipids at sampling site Telaščica during the whole year. A. Mean and standard deviation (n=4). B. Mediian and 1st quartal (25%) and 3rd quartal (75%) are presented.

The rangee between the highest value of TE (170 mg/g w.w.) and the lowest value

(September, 100 mg/g w.w.) is 70 mg/g w.w.. There is a 41% decrease from April to September. Then it increases byb 70% until the highhest level is reached again, with a peak in November. The lipid concentration in February 2014 is at the same level as in March in the previous yeear 2013. As for all three sampling sites, the lowest concentration of lipids were measured in the summer. BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 53

Figure 17. Concentration of total lipids at samplingn site Pašman kanal 1 during the whole year. A. Mean and standard deviation (n=4). B. Median and 1st quartal (25%) and 3rd quartal (75%) are presented.

The range between the highest value of PK 1 (February, 210 mg/g w.w.) and the lowest value (August, 140 mg/g w.w.) is 70 mg/g w.w.. There is a 33% decrease from February to August. Then it increases 50% until Februarry, with a peak in September.

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Figure 18. Concentration of total lipids at sampling site Pašman kanal 2 during tthe whole year. A. Mean and standard deviation (n=4). B. Mediian and 1st quartal (25%) and 3rd quartal (75%) are presented.

The rangee between the highest value of PK 2 (March, 210 mg/g w.w.) and the lowest value (June, 120 mg/g w.w.) is 90 mg/g w.w.. From March to June there is a 43% decrease, from September until November it increases 43 %.

The general trend for all three sampling sites is that summer has the lowest level of lipids. In winter and the begiinning of spring the values of lipids are at its highhest level. Overall PK 1 has the highest absolute values and TE the lowest. The maximum and minimum of PK 1 is 210 mg/g w.w. and 140 mgm /g w.w., for TE it is 170 mg/g w.w. and 100 mmg/g w.w.. The highhest fluctuations in lipid concentrations are measured at samplinng site TE.

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Sampling station TE has the highest fluctuations in llipid concentration. Sampling station PK 1 has the highest fluctuations in levels of proteins. The highest absolute values of lipids were also measured at PK 1. Sampling station PK 2 has the highest values and the most variance of the levels for MT. Compared to the other stations the level of total proteins are lower.

3.5 Correlations between measured parameters

In table VIII, IX and X the correlation matrices for sampling sites Telaščica and Pašman kanal are shown. There is a signnificant correlation between parameters when the p value is less than 0.050. The shellfish length and shellfish mass were measured by the colleagues from the university of Zadar. Condition index was calculated by dr. Ivanković from the Ruđer Bošković Institute , gonadal index were measured by colleagues from the university of Split and the Institute for Oceanogrraphy and Fisheries in Split. Temperature was measured at thhe sampling site from the watch of the scuba divers.

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Table VIII. Sampling site TELAŠĆICA – Correlation matrix for studied variables (cell contents: Spearman correlation coefficient and p value); p values marked with an asterisk indicate a significant correlation (p < 0.05).

1 2 3 4 5 6 7 8 9 10 11 1 – Mean shellfish 1.000 0.706 0.056 0.126 0.000 0.063 -0.007 -0,236 0.084 0.413 0.242 length (cm) 0.009* 0.852 0.683 0.989 0.834 0.974 0,467 0.783 0.173 0.429 2 – Mean shellfish 1.000 0.168 -0.053 -0.236 -0.448 0.098 -0,427 -0.315 -0.035 0.147 mass (g) 0.588 0.852 0.467 0.136 0.749 0,178 0.306 0.904 0.635 3 – Mean mass of 1.000 -0.291 -0.418 -0.685 0.189 0,218 0.007 0.357 -0.074 digestive gland (g) 0.340 0.188 0.013* 0.542 0,502 0.974 0.243 0.800 4 – Condition 1.000 0.731 0.305 -0.172 -0,521 0.382 0.382 -0.362 index (%) 0.009* 0.317 0.572 0,0948 0.206 0.206 0.233 5 – Mean gonadal 1.000 0.664 -0.127 -0,309 0.664 0.345 -0.032 index 0.023* 0.693 0,339 0.023* 0.283 0.903 6 – Total proteins 1.000 -0.371 0,136 0.552 0.224 0.256 (mg/gw.w.) 0.224 0,673 0.058 0.470 0.402 7 – Total lipids 1.000 -0,509 -0.161 -0.035 -0.438 (mg/gw.w.) 0,102 0.603 0.904 0.143 8 – Total 1.000 -0,0364 -0,0455 0,551 carbohydrates 0,903 0,881 0,0706 (mg/gw.w.) 9 – MT 1.000 0.832 0.035 (g/gw.w.) <0.000* 0.904 10 – MT 1.000 -0.018 (g/mmg cytosolic 0.939 proteins) 11 – Temperature 1.000 (°C)

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Table IX. Sampling site Pašman kanal 1 – Correlation matrix for studied variables (cell contents: Spearman correlation coefficient and p value); p values marked with an asterisk indicate a significant correlation (p < 0.05).

1 2 3 4 5 6 7 8 9 10 11 1 – Mean shellfish 1.000 0.809 0.207 0.293 0.137 0.091 0.018 -0,178 -0.361 -0.431 0.090 lengtth (cm) <0.000* 0.498 0.340 0.673 0.766 0.939 0,575 0.233 0.150 0.766

2 – Mean shellfish 1.000 0.490 0.403 0.196 0.049 0.091 -0,464 -0.119 -0.196 -0.056 mass (g) 0,0998 0.181 0.538 0.869 0.766 0,141 0.699 0.527 0.852

3 – Mean mass of 1.000 0.364 -0.219 -0.364 0.650 -0,182 0.434 0.552 -0.347 digestive gland (g) 0.233 0.502 0.233 0.020* 0,575 0.150 0.058 0.253 4 – Condition index 1.000 0.708 0.144 0.203 -0,178 0.511 0.389 -0.276 (%) 0.0127* 0.635 0.513 0,575 0.084 0.197 0.377 5 – Mean gonadal 1.000 0.565 -0.346 -0,314 0.433 0.178 -0.238 indeex 0.065 0.283 0,324 0.168 0.575 0.467 6 – Total proteins 1.000 -0.350 -0,518 0.063 -0.147 -0.411 (mg//gw.w.) 0.253 0,0948 0.834 0.635 0.173 7 – Total lipids 1.000 0,109 0.210 0.322 -0.628 (mg//gw.w.) 0,734 0.498 0.295 0.026* 8 – Total 1.000 -0,273 -0,309 0,429 carbohydrates 0,400 0,339 0,178 (mg//gw.w.) 9 – MT(g/gw.w.) 1.000 0.888 -0.284 <0.000* 0.352 10 – MT 1.000 -0.291 (g//mgcytosolic proteins) 0.340 11 – Temperature 1.000 (°C)

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Table X. Sampling site Pašman kanal 2 – Correlation matrix for studied variables (cell contents: Spearman correlation coefficient and p value); p values marked with an asterisk indicate a significant correlation (p < 0.05).

1 2 3 4 5 6 7 8 9 10 11 1 – Mean 1.000 0.280 0.280 0.297 0.373 0.126 -0.364 -0,273 0.014 0.056 0.049 shellfish length <0.000* 0.364 0.329 0.245 0.683 0.233 0,400 0.956 0.852 0.869 (cm) 2 – Mean 1.000 0.336 0.499 0.591 0.112 -0.322 -0,418 0.112 0.140 0.172 shellfish mass (g) 0.273 0.0944 0.051 0.716 0.295 0,188 0.716 0.651 0.572 3 – Mean mass of 1.000 0.538 0.755 0.364 0.629 0,0182 0.503 0.552 -0.667 digestive gland 0.0663 0.006* 0.233 0.026* 0,946 0.089 0.058 0.017* (g) 4 – Condition 1.000 0.908 0.471 0.117 0,0826 0.481 0.411 -0.059 indeex (%) <0.000* 0.117 0.699 0,797 0.105 0.173 0.852 5 – Mean 1.000 0.564 0.145 -0,0182 0.718 0.527 -0.151 gonadal index 0.065 0.653 0,946 0.011* 0.088 0.633 6 – Total proteins 1.000 0.035 -0,0545 0.580 0.189 -0.084 (mg//gw.w.) 0.904 0,860 0.045* 0.542 0.783 7 – Total lipids 1.000 0,155 0.287 0.497 -0.754 (mg//gw.w.) 0,633 0.352 0.094 0.003* 8 – Total 1.000 0,127 0,127 0,228 carbohydrates 0,693 0,693 0,484 (mg//gw.w.) 9 – MT (g/gw.w.) 1.000 0.860 -0.133 <0.000* 0.667 10 – MT 1.000 -0.337 (g//mgcytosolic 0.273 proteins) 11 – Temperature 1.000 (°C)

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For the sampling station Telaščica (TE) there are significant correlations between mean shellfish length & mean shellfish mass, mean mass of digestive gland & total proteins, condition index & mean gonadal index, mean gonadal index & total proteins, mean gonadal index & MT (g/gw.w.) and MT (g/gw.w.) & MT

(g/mgcytosolic proteins). All of them have positive correlation coefficients except for mean mass of digestive gland & total proteins which has a negative correlation coefficient. For a higher mass of digestive gland the concentration of total proteins will be lower.

For sampling site Pašman kanal 1 (PK 1), sigi nificant correlations are found between mean shellfish length & mean shellfish mass, mean mass of digestive gland & total lipids, condition index & mean gonadal index, total lipids & temperature, MT

(g/gw.w.) & MT (g//mgcytosolic proteins). All of them have a positive correlation coefficient except for total lipids & temperature which has a negative correlation coefficient. A higher temperature gives a lower concentration of total lipids.

For sampling site Pašman kanal 2 (PK 2), significant positive correlations are seen between mean shellfish length & mean shellfish mass, mean mass of digestive gland & mean gonadal index, mean mass of digestive gland & total lipids, mean mass of digeestive gland & temperature, condition index & mean gonadal index, mean gonadal index & MT (g/gw.w.), total proteins & MT (g//gw.w.), total lipids & temperature, MT (g/gw.w.) and MT (g/mgm cytosolic proteins). Mean mass of digestive gland & temperature and total lipids & temperature have a significant negative correlation coefficient.

General signnificant correlations for every sampling station are: mean shellfish lenngth & mean shellfish mass, ccondition index & mean gonnadal index and MT

(g/gw.w.) & MT (g/mgm cytosolic proteins).

Only for sampling station Telaščica there is a significant negative correlation between mean mass of digestive gland & total proteins and a positive correlation for the mean gonadal index & total proteins. There is no significant correlation between mean gonadal index & total proteins for the two sampling stations of Pašman kanal but the p values are also small, i.e. 0,065.

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Mean gonadal index & MT have a positive signnificant correlation for sampling stations TE and PK 2 but there is no correlation for PK 1 at all.

PK1 and PK2 have a common positive siignificant correlation between mean mass of digestive gland & total lipids and a negaative for total lipids & temperature.

Only for PK2 there are correlations between mean mass of digestive gland & mean gonadal inndex (positive), mean mass of digestive gland & temperatuure (negative), total proteeins & MT (positive). There is no correlation for total proteeiins & MT for sampling station Telaščica but its p value is 0.058 which is very close. No significant correlation can be found between total proteins & MT (g/mgm cytosolic proteins).

3.6 Seasonal and spatial variability of measured parameters Since we are interested in different levels of biochemical contents during one year and for different sampling sites, we performed two way ANOVA to see if there is any signiificant interaction between stations and season. If there is a significant interaction it means that it does matteer where you take your sample during a season or when yoou take your sample for comparing between sammppling station. When there is no siignificant interaction there will be always the same pattern. For instance a sampling station will always have the highest values throughout a whole year for every season. Significant differences between sampling sites within one season are indicated by letters in the graphs.

3.6.1 Metallothionein A two-way ANOVA analysis of metallothionein concentrations (Figure 19) indicates that there is a significant effecct of sampling station (p= 0.029), season (p< 0.001) and a significant interaction between season and sampling site (p= 0.011).

BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 61

Figure 19. Metallothionein concentrattions, comparison of sampling stations within a season. Mean values within one season sharinng the same letter are not significanntly different (two way ANOVA). The significant interaction between sampling site and season inndicates that it does matter when you take your sample for a sampling station. For spring PK2 has the highest value but for summer TE and PK2 both have the highest values. There are no significant differences betweeen sampling stations in the seasons autumn and winter.

Figure 20. Metallothionein concentrations, comparison of seasons within a sampling station. Mean values within one sampling sitee sharing the same letter are not signnificantly different (two way ANOVA).

The concentration of MT in a seaason depends at which samplinng station it has been taken (Figure 20). It does matter where you take your sample during a season. TE

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has the hiighest values for the spring and summer while the other samppling stations only have a peak in spring.

To convert the results for MT from w.w. to d.w., since most of the values in the literature are expressed as d.w.. The mass of wet tissue was measureed before the lyophilization, the d.w. mass after lyoophilization of the tissue. Afterwards we calculated the ratio of w.w/d.w.. For every subsample we calculated the ratio and took the average. The average of w.w./d.w. is here 3.8.

For TE the values are 380 µg/mg d.w. for spring and summer, in autumn and winter it is 304 µµg/mg d.w. PK 1 has a value off 380 µg/mgm d.w. in the summeer and around

304 µg/mmg for the other seasons. PK 2 has a value of MT around 420 µg/mg d.w. in spring, 340 µg/mg d.w. in summer and 300 µg/mg d.w. in autumn and winter.

3.6.2 Proteins A two-waay ANOVA analysis of total prootein concentrations indicates tthat there is a significant effect of sampling station (p< 0.001), season (p< 0.001) and a significant interaction between season and samplinng site (p< 0.001).

Figure 21. Total protein concentrations, comparison of sampling stations in a season. Mean values within one season sharing the same letter are not significantly different (two way AANOVA).

The concentration of proteins for a sampling station depends on iis the season (Figure 21). In sprinng, PK 1 has the highhest level of proteins. In summer it is TE and BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 63

PK 1 which have the higi hest values. For autumn and winter there are no significant differences between sampling stations.

Figure 22. Total protein concentrations, comparison of seasons in a sampliing station. Mean values within one sampling site sharing the same letter are not significanttly different (two way ANOVA).

The concentration of proteins in a season depends at which sampling station it has been taken (Figure 22). TE has the highest value in summer but the lowest in winter. The levels of spring and autumns are in between suummer and winter, although there is no significant difference between autumn and winter. PK 1 reaches its hiighest level of proteiins in springn and summer but iits lowest in autumn and winter. For samplingn station PK 2 the peak occurs in spring.

3.6.3 Carbohydrates A two-way ANOVA analysis of total protein concentrations indicates that there is a significant effect of sampling station (p= 0.041), season (p= 0.0144) and no significant interaction between season and saampling site (p= 0.108).

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Figure 23. Total carbohydrate concentrations, comparison of sampling stations in a season. Mean values within one season sharingn the same letter are not significantly different (two way ANOVA). The levels of carbohhydrates per station do not depend on what season it is (Figure 23). PK 2 has overall the highhest concentration of carbohyddrates. For summer and autumn there is no significant difference between the concentration of carbohydrates for different sampling stations.

Figure 24. Total carbohydrate concentrations, comparison of seasons in a samplinng station. Mean values within one sampling site sharing the same letter are not significantly different (two way ANOVA). In sampling station TE the highest leveels of carbohydrates occur in summer and autumn. For sampling sites PK 1 andd PK 2 there are no significant differences between the different seasons (Figure 24).

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3.6.4 Lipids A ttwo-way ANOVA analysis of total lipid concentrations indiicates that there is a significant effect of sampling station (p< 0.001), season (p< 0.0011) and no significant interaction between season and saampling site (p= 0.442).

Figure 25. Total lipid concentrations, comparison of sampling stations in a season. Mean values within one season sharing the same letter are not significantly different (two way ANOVA). PK1 and PK 2 have significantly higher values of total lipids than TE in all seasons except in summer. In summer it is only sampling station PK 1 that has the highest value.

Figure 26. Total lipid concentrations, comparison of seasons in a samplinng station. Mean values within one sampling site sharing the same letter are not significanttly different (two way ANOVA).

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As it is shown in figure 26 there is a general pattern for every samplinng station, i.e. all stations have the same increasing and decreasing trends during the seasons. For each sampling station summer has the lowest values, winter and sprinng the highhest. The concentrations in autumn are in between those of the summer and winter.

It can be concluded that for the concentration of MT and proteins the seasonal variation is different for each sampling station. The seasonal vvariation for carbohydrates and lipids, however, shows the same pattern for all the sampling stations.

For the concentration of MT and proteins there is only a significant difference between sampling stations for spring and summer. In case of MT PK 2 has a higher value than the other sampling stations in spring. In the summer PK 1 has a lower level of MT than the others. Comparison of seasons within a samplinng station for the concentration of MT shows that spring has a higher level than the oother seasons with an exception of sampling station TE.

Total proteins have more seasonal variation than MT. TE has a peak in summer and a minimum in winter. PK 1 has two high levels for spring and summer while autumn and winter are the minima. PK 2 has onlyl a peak in spring and the other seasons levels of proteins show no significant difference.

The concentrations of carbohyh drates only have a significant difference between sampling stations in the seasons spring and winter, where PK 2 is the ppeak for both seasons. The pattern for total lipids of the comparison of sampling stations within a season is that TE has the lowest values for all seasons except summer. In summer TE and PK 2 have a lower level of total lipids than PK 1. Seasonal variation in lipids has the same pattern for every sampling station: a significant miniimum for the summer and a peak in winter and spring except for sampling station PK 1 where the level of spring is lower than the one in winter.

BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 67

4 Discussion According to literature the reproductive cyccle influences the level of carbohydrates, total proteins and total lipids for A. noae (Radić et al., 2014). Proteins are a major component of gametes and lipids play a maja or role in the maturing process of the gonadal tissue. Since it is the first measurement of MT for this species, we have to investigate if the reproductive cyccle also influences MT concentrrations. The correlation between MT and the reproductive cycle is already prroven for the mussel M. galloprovincialis (Ivankković et al., 2005).

Figure 27 shows the gonadal index for every sampling site throughout the whole year except for the month March which has no available data. The gonad produces gametes, so it gives you information about the reproductive cyccle. A decrease in the gonadal index corresponds with the release of the gametes.

In this research we found a signnificant correlation for sampliing site TE between mean gonadal index & total protteins and mean gonadal index & MT (Table VIII). The drop in the values of MT from August to September (Figuure 7) corresponds to the release of the gametes at the same time. Sampling site TE has a peak of total proteins in July (Figure 10) which is just before the spawning.. The levels increase until the gametes are released and afterwards there is a decrrease of the protein values, this is similar to a study bby Radić et al. (2014).

Significant correlation between mean gonadal index and MT (Table X) was found also for sampling site PK 2. This is represented in figuure 9 and figure 27. A decrease from June to September in the gonadal index is also represented in a decrease of MT from June to September.

For all sampling sites there was a significant correlation between the mean gonadal index (MGI) & the condition index (CI). The condition index that we used was expressed as a ratio of dry weight of the whole soft tissue and length of the shell. The correlation between MGI and CI was also proven by previous studies e.g. Peharda et al (2006).

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6

5 P1 P2 T

4

3

2 Mean gonad index gonad Mean

1

0 Apr May Jun July Aug Sept Oct Nov Dec Jan Feb 2013 2014

Figure 27. Mean gonadal index, P1 – Pašman 1, P2- Pašman 2, T – Teleščica (figure made by Peharda). The carbohydrate peak in December for sampling site PK 1 and PK 2 corresponds to the peak that Radić et al. (2014) saw in their measurements of carbohydrates in A. noae from Mali Ston Bay. For sampliing site TE there is a small decrease of the concentration of carbohydrates which corresponds to the beginning of the gametogeenetic development.

Temperature influences the concentration of lipids and carbohydrattes negatively according to the literature (Radić et al. 2014). The reason is not mentioned in the articles but it is to be expected that the leevel of lipids are lower in the summer since the need of reserves in the summer is not as important as for the winter. In mussels the level of MT is influenced by temperature (Ivanković et al., 2005). In this study there was a significant negative correlation found between temperaature & total lipids for sampling site PK 1 (Table IX) and PK 2 (Table X). In figure 17 (PK 1) and 18 (PK 2) low levels are seen from May until October and high levels of lipids in the winter. Fiigure 26 shows the seasonal variation for lipids. The fluctuations are like the reverse trend of the temperature cycle. In figure 28 you can see that temperature for PK 2 increases faster in the summer compared to PK 1 and TE but also cools down faster in the autumn. The reason could be that the temperature was measured at a more shallow depth at PK 2 than at PK 1 and TE. BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 69

Figure 28. Sea water temperature for sampling sites Teleščica and Pašman kanal duringn the whole year (figure made by Erk).

Although there is no significant correlation between temperature & lipids for sampling station TE (Figure 16), lower levels of lipids are detected when the temperature is the highhest (Figure 28).

In figure 20 it is shown that for sampling sites PK 1 and PK 2 the levels of MT are higher in spring than in the other seasons. Just as was already found for the mussel (Ivanković et al., 2005). the levell of MT of A. noae is dependent of the reproductive state and the season of sampling. The seasonal variation does not correspond completely with the reproductive cycle for all sampling sites otherwise the value should be higher in the summer for PK 1.

The seasonal variation for total proteins (Figure 22) shows thhe same trend as the variation in the mean gonadal index. There is less variation for PK 2 but that is because the spawning occurred earlier than at PK 1 and PK 2.

Even thoughh there was no significant correlation found for samplingn site TE between carbohydrates & temperature. The seasonal variation for TE (Figure 24) shows the same trend as temperature cycle. The average of the summer and autumn is higher than of winter and spring. In figure 23 it is shown that the level of carbohydrates in springn and winter is highher for PK 2 than PK 1 and TE. In these seasons the temperature at PK 2 is lower than at TE and PK 1.

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Figure 26 shows the seasonal variatioon for lipids. The fluctuations are like the reverse trend of the temperature cycle.

There is a significant difference between the concentration of MT in summer and the seasons autumn and winter for sampling site TE, while there is no such difference for PK 1 and PK 2. It can be assumed that the high level of MT for sampling site TE in summer is because of the nautical tourism. In the summer there are more boats and tourists visiting the nature park. According to statistics there is a significant correlation between the level of MT &total proteins for ssampling site PK 2. The levels of MT for PK 2 (Figure 19) are higher than the other stations while the concentrations of total proteins foor PK 2 are lower. This can be a possible indication that the area is not as unpolluted as PK 1 and TE overall.

A lack of variation in the levels of carbohydrates in summer and autumn can be the result of sufficient food available for the organisms.

The same trend for the seasonal variation for lipids in each sampling site can indicate that there was not a big difference in pollutants.

In the other part of the studyd about A. noae the concentrations of metals were measured in the sediments at all three sampling sites in March 2013 and in the seawater for March, June, October 2013 and February 2014.

Table XI shows the metal concentrations in the sediment for Marrcch 2013. The concentrations are lower than those values previously reported for relatively unpolluted areas. The level of chromiuum differs more between the saampling sites than the other metals. Pollution of chromium can be caused by leaching of chromium from part of boats that are coated with chromate (Mihelčić et al.,2010).

The level of metals in the sea water (seeTable XII) are in the range of the values of unpolluted areas. The concentrations arre higher in June and October of 2013 which marks the beginninng and the end of the touristic season. The concentration of cadmium in summer is 1.5 times higher for every sampling site than the unpolluted area. In the seasons spring and winter the concentrations of metals in the sea water are generally slightlly higher for PK2 than for TE and PK 1. This may confirm the previous thought of that PK 2 has slightlly more pollutants than PK 1 and TE. BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 71

Table XI. Metal concentrations in surface sediment samples from the studied sites (mean ± S.D. of two analytical replicates) sampled in March 2013,. Concentrations are compared to previously reported metal concentrations in different relatively unpolluted Adriatic sediments and to the Interim Marine Sediment Quality Guidelines (ISQG) and Probable Effect Level (PEL) values for marine organisms adopted by Environment Canada are also given. (n.v. – no value) Metal concentrations (mg/kg d.w.) Sampling sites Cd Pb Cr Ni Cu Co Zn TE 0,03±0,01 10,51±0,52 36,10±9,71 7,23±0,44 2,31±0,02 0,92±0,06 5,16±0,93 PK1 0,05±0,01 10,12±0,80 12,93± 4,54 4,10±0,35 1,54±0,16 1,02±0,12 9,54±0,00 PK2 0,08±0,04 8,99±0,40 63,83±3,78 5,35±0,50 1,88±0,24 0,96±0,06 3,65±0,00 1Telašćica Bay, central Adriatic, Croatia ― < 0,6–13 60–208 14,5–87,2 6,6–21 ― 20–83,7 2Northern Adriatic < 1 7–51 40–129 19–86 4,1–33,4 4–12 29–167 3ISQG 0,7 30,2 52,3 15,9 18,7 n.v. 124 3PEL 4,2 112,0 160 42,8 108 n.v. 271 1Mihelčić et al. 2010; 2Dolenec et al. 1998; 3CCME (Canadian Council of Ministers of the Environment) 2002

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Table XII. Sea water concentration of metals in studied locations in March, June and October 2013 and February 2014 (samples of sea water were taken from the same depth where the shellfish were sampled). Mean values and 95% confidence interval for the method of standard addition with 5 analyzed points (voltammetric technique) are presented. Concentrations are compared to previously reported metal concentrations in unpolluted Adriatic sediments and to maximum contaminant level (MCL). (n.v. – no value) Metal concentration (µg/l) Cd Ni Cu Co Zn Sampling sites March 2013 TE 0,0088±0,0007 0,350±0,039 0,213±0,009 0,017±0,003 0,358±0,019 PK1 0,0067±0,0008 0,364±0,019 0,206±0,021 0,023±0,005 0,645±0,054 PK2 0,0078±0,0005 0,369±0,016 0,216±0,018 0,023±0,005 0,473±0,021 June 2013 TE 0,0113±0,0009 0,409±0,040 0,629±0,063 0,013±0,004 1,769±0,085 PK1 0,0130±0,0014 0,432±0,023 0,410±0,031 0,030±0,002 1,364±0,114 PK2 0,0101±0,0007 0,385±0,017 0,510±0,063 0,018±0,001 0,695±0,048 October 2013 TE 0,0091±0,0005 0,384±0,023 0,418±0,030 0,027±0,003 1,048±0,062 PK1 0,0089±0,0007 0,372±0,019 0,348±0,025 0,030±0,001 0,639±0,035 PK2 0,0105±0,0012 0,375±0,019 0,457±0,045 0,034±0,002 1,619±0,082 February

2014 TE 0,0077±0,0010 0,398±0,037 0,210±0,021 0,022±0,002 0,693±0,063 PK1 0,0094±0,0009 0,366±0,034 0,236±0,021 0,024±0,003 0,791±0,081 PK2 0,0089±0,0018 0,405±0,013 0,290±0,027 0,026±0,001 5,290±0,388 1Mljet Island, Adriatic Sea (open sea) 0,0075±0,0002 ― 0,250±0,077 ― 0,188±0,095 2MCL (µg//l) for aquatic life 8,8 8,2 3,1 n.v. 81 2MCL (µg//l) for human health for consumption: water+organism/organism n.v. 610/4600 1300/n.v. n.v. 7400/26000 only 1 2 Cuculić et al. 2009; USEPA 2009 BIOCHEMICAL COMPOSITION AND METALLOTHIONEIN CONTENTS IN ARCA NOAE 73

To compare our MT concentrations with the values from Ivanković et al. (2005) for the mussel we have converted our results to d.w. (see § 3.6.1). For TE the values are

300-380 µg/mmg d.w. , PK 1 has a range of 300-380 µg/mg d.w. , PK 2 has a range of

MT around 300-420 µg/mg d.w.. The highest values were meaasured in spring and the lowest in autumn and winter. The values for the mussel (M.. galloprovincialis) are 52–87, 36–76, 22.5–61.5 and 18–86 µg/mg d.w. in winter, sppring, summer and autumn. The values of MT in Arca noae are therefore 5 to 10 tiimmes higher than the concentration in M. galloprovincialis.

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5 Conclusion The results obtained in this study showw seasonal variation in analysed biochemical components and metallothioneins.

The level of total lipids depends on the sea temperature. The higher the temperature the lower the lipid concentrations.

Althoughh there was no significant relation found between carbohydrrates and sea temperature, two way ANOVA sugggests that the levels of carbohydrates are influenced by temperature.

For MT there is a significant correlation with the reproductive cycle. This should be kept in mind when taking a sample foor monitoring potential pollution. We can assume that MT can be used as a biomarker since we detected some pollution in sampling site TE. Therefore the sampling site Telaščica is not ideall as a control station. It can be used as longn as there are no measurements takeen during the summer, because than the area is polluted by the many tourists that visit the park.

The measured concentrations of metals in the environment at TE are higher in summer compared to another unpolluted area, however the levels are still below the maximum contaminant level.

In samplinng site PK 2 there are a few indications that point to more environmental pressure throughout the year than at PK 1.

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