Fulkerson Capstone Paper

A Comparison of Growth and Gene Expression in Two Species of Oysters Katie Fulkerson Capstone Project, Autumn Quarter (2007)-Spring Quarter (2008), University of Washington, Seattle, WA Received June 13, 2008

Abstract The Pacific oyster, Crassostrea gigas, is the most valuable commercial species in Puget Sound, in the state of Washington. While of less commercial value, the Olympia oyster, Ostrea conchaphila, still holds significant importance in being native to Washington State. To better understand the differences in these two species as it is relates to growth, this study 1) compared growth rates in both species grown at the same site, 2) identified genes likely involved in growth in C. gigas and O. conchaphila, and 3) characterized gene expression patterns from tissues extracted during two periods of juvenile oyster development. Oysters were purchased as seed and grown in Agate Pass in Kitsap County. Growth measurements were taken once a month beginning in August 2007 and ending in December 2007. Tissue samples of the mantle, muscle, and gills were taken during the months of September and November for gene expression analysis. Bioinformatic techniques were used to identify growth-related genes in C. gigas and O. conchaphila by mining expressed sequence tags (ESTs). Growth rates were significantly higher in C. gigas compared to O. conchaphila over the course of the experiment. Several genes putatively involved in growth were identified and quantified, including the Molluscan growth differential factor (mGDF), Kazal-type serine peptidase inhibitor domain 1 (KSPI), Protein kinase C inhibitor protein 1 (PKCIP), and Cytochrome P450 17hydroxylase/lyase (P450). Additionally, a fifth gene was studied, Insulin-induced gene 2 protein (INSIG2), which was not detected in either species. The differential expression patterns observed, based on quantitative PCR analysis, suggest some of these genes are involved in controlling growth in oysters. Obtaining a better understanding of the mechanisms involved in growth will provide further knowledge of the biology of oysters and has the potential to assist the aquaculture industry in selecting broodstock. Key Words: Molluscan growth differential factor, Kazal-type serine peptidase inhibitor domain 1, Protein kinase C inhibitor 1, Insulin-induced gene 2 protein, Cytochrome p450, gene expression

Introduction Oysters have been harvested and consumed throughout the world for scores of generations. Having gradually been integrated into traditional aquaculture, oysters have been cultivated for over 1,000 years in Japan (Patrick et al. 2006). Oyster trade was instigated in 1608 following the exploration by Samuel de Champlain in North America. This led to the depletion of natural stocks through overexploitation and then by habitat degradation and pollution as North America became colonized. Oyster culture was turned to in the mid 19th century as the solution to saving natural stocks while at the same time meeting the increasing consumer demand for oysters. On the Atlantic coast, the native oyster Crassostrea virginica enjoyed success in the aquaculture business. Unfortunately, Ostrea conchaphila, the oyster native to the Pacific coast, did not respond well to aquaculture. Presently, there are five species of oysters cultivated along the Pacific Coast of the United States: the native oyster Ostrea conchaphila, the Atlantic oyster Crassostrea virginica, the Kumamoto oyster Crassostrea sikamea, the European flat oyster Ostrea edulis, and the Pacific oyster Crassostrea gigas. In 2003, oyster production equaled approximately 4.5 million kg of meat, about 95% of the total oysters yielded on the Pacific Coast with 61% of the landed value coming from Washington State (Lavoie). Crassostrea gigas, which comprised the bulk of the harvest, has since established itself as the most dominant species in the Pacific Northwest (Lavoie). The ability of oysters to reach market size in a timely manner is of great concern to aquaculturists. Growth is a complex process as it is dependent on genetics as well as multiple environmental factors such as water temperature, food availability, placement in the water column, and density of the bed. The role of genetics in growth has been confirmed through successive successful selective breeding for increased growth (Kittel, 1999). Kittel (1999) documented a heritability estimate of 0.54 for whole weight in C. gigas. While genetic factors determine the rate and ultimate size of an individual, food and temperature are viewed as the primary influences of growth. Temperature regulates growth through physiological rates concerning metabolism and consumption as well as playing a role in the abundance and size of the available prey (Johnson et al. 2001). Laboratory studies have shown a positive correlation between metabolic rate and temperature and, as a result, seasonal variations in metabolic activity are often considered a function of temperature. Newer experimental designs show that food availability may, in fact, be more important than temperature. In one study by (Brockington and Clarke, 2001) on the urchin, Sterechinus neumayeri, only 15-20% of the summer increase in metabolism was found to be caused directly by the rise in temperature, while 80-85% was caused by the increase in physical activity associated with feeding, growth, and spawning (Brockington and Clarke, 2001). In this case, the extra oxygen consumption induced by feeding includes the handling costs of food and metabolic costs of growth. Together these two elements comprise the heat increment of feeding, or specific dynamic action (SDA) (Brockington and Clarke, 2001). Sediment type and seston concentration are also known to affect growth of bivalve species by impeding filtration and the digestive process (Cardoso et al, 2007). Cardoso et al (2007) also notes field studies have observed competition for food occurring in dense intertidal beds of C. gigas. Intense competition does not allow the oyster to achieve optimum foraging rates, resulting in slower growth due to low food availability (Villarroel et al. 2004). Villarroel et al. (2004) cites slow growth rates of the

Crassostrea rhizophorae as being due to low food availability, mainly of phytoplankton biomass (Villarroel et al. 2004). Furthermore, additional growth has been known to occur in other bivalve species, such as Macoma blathica and Cerastoderma, at the lowest tidal zone where submersion time and daily feeding periods are longer (Cardoso et al, 2007). The scarcity or even total absence of food during shorter or longer periods of time is a characteristic of marine ecosystems that affects the physiology of the animals that inhabit them (Malanga et al. 2007). Food resources for most animals are abundant during spring and summer and lacking during winter months (Malanga et al. 2007). This nutritional deprivation is a natural part of the life cycle of many aquatic organisms. It results in behavioral modifications known as winter torpor which reduces metabolic rates so as not to deplete the reserves of protein, glycogen, and lipids too rapidly (Vinagre et al. 2007). By slowing metabolic rates, the animal is able to maintain body mass per shell length during winter (Malanga et al. 2007). The decrease in metabolic rate is known as standard metabolism (Alberntosa et al. 2007). Disease and parasitism can also reduce growth rate potential by increasing energetic costs (Johnson et al. 2001). Understanding the metabolic processes and the response of the organism to the total absence of food reveals a greater wealth of information as to the ecology of a species (Alberntosa et al. 2007). A concept known as scope of growth (SFG) is used as a summation of energy acquisition and expenditure in bivalves (Kesarcodi-Watson et al, 2001). An energy budget equation is defined as the sum of energy from the food ingested divided into metabolizable, egested, and excreted energy. The amount of energy will vary according to the effects of extrinsic (fluctuations in the biotic and abiotic conditions within the water column) and intrinsic (body size, reproductive stage) factors. Animal production or growth is represented by the difference between the absorbed energy and the energy lost in respiration and excretion, taking age, sex and body type into account. Nutritional deficiencies will also affect production, and a satisfactory diet is needed to obtain optimal production. Feed composition and ingestion are the most important factors to consider in a balanced growth equation. Additionally, metabolic rate is a major component of the equation. It is considered a loss term that provides a measure of the energetic cost to the system of supporting the animal (Farias et al. 2003). SFG represents the total available energy for reproduction, somatic tissue growth, and shell production. An organism can only allocate net positive energy to SFG. Positive energy is obtained when the total energy absorbed is greater than total metabolic losses (Kesarcodi-Watson et al, 2001). 1. SFG = AE – (RE+EE) SFG = scope for growth AE = absorbed food energy RE = energy lost in respiration EE = energy lost as excretion Temperature and food availability also influence the annual cycle of accumulation and use of energy reserves associated with gametogenesis in bivalves. The simplest model consists of the buildup of energy during periods of prey abundance and releasing the energy in the form of genetic material during the spawning process (Alberntosa et al. 2007).

The mantle surface is responsible for shell deposition (Pauly et al, 1988). The growth of soft body parts and the shell of oysters is a continuous process. Soft body growth occurs mainly in spring (Gricourt et al. 2003) with shell growth occurring primarily in the summer due to the higher water temperatures which result in an increased food supply (Gricourt et al. 2003; Pauly et al. 1988). The increase of calcium in the diet is used for increasing the shell size (Pauly et al. 1988). The shell consists of three layers: the outermost layer (periostracum), the outer calcareous (prismatic) layer, and the inner calcareous (cross-lamellar) layer. Mantle edge cells are specifically involved in the formation of the periostracum. They allow for the synthesis and secretion of proteinaceous components as well as cellular calcium transport to the extrapallial space (Gricourt et al. 2003). In Washington State, the size of C. gigas following two years of growth is correlated with the month the oyster was planted as well as the size of the oyster at planting (Pauly et al, 1988). C. gigas reared from seed average a length of 4 to 5cm during their first year of growth. Growth in C. gigas tends to be more rapid when they are young and typically decreases when they reach 4 to 5 years of age (Pauly et al, 1988). In contrast, O. conchaphila experiences a much slower growth rate, taking 4 to 5 years to reach market size of approximately 50mm. In Washington State, it takes O. conchaphila an average of 3 to 4 years to reach shell heights of 35 to 45mm, with little growth occurring afterward (Gillespie, 1999). While there is general information on growth in both species, limited information is available on the internal mechanisms which regulate the growth process. It is generally thought that growth and related metabolisms in mollusks are controlled by the nervous ganglia. It is known that mollusks, in general, possess insulin-related peptides and, more specifically, insulin-like growth factor (IGF) (Gricourt et al, 2003). In mammals, insulin is an important regulator of numerous physiological processes such as glucose uptake and cellular growth and division (Hamano et al. 2005). A study by Gricourt et al (2003) observed the occurrence/amount of IGF-1 in the mantle of C. gigas during periods of elevated shell growth. In particular, this study ascertained that insulin-like peptides may participate in the control of growth in mollusks by stimulating protein synthesis in the edge of the mantle cells and, through the mantle, influence shell growth. Gricourt et al (2003) also observed IGF in other tissues such as the labial palps and gonad. In gastropods, the cauterization of the light green cells (LGCs) in juvenile snails resulted in the retardation of body and shell growth as well as a reduction in food consumption and changes in carbohydrate metabolism in various tissues (Hamano et al. 2005). Insulinrelated peptides also appear to be involved in the reproductive process of mollusks (Gricourt et al. 2006). From a broader perspective, it is likely that the genes involved in general metabolism are also involved in realized growth. Expression of those genes is likely to change in relation to the developmental stage, water temperature, feeding, and placement. It is known that the bivalve digestive gland has a substantial amount of alpha-amylase, an enzyme used to break down starch into glucose molecules (Pennec and Pennec, 2002). While the enzyme is scarce during the winter, its mRNA transcripts are abundant from the beginning of the phytoplankton bloom in March until September. It has been suggested that there may be a relationship between the presence of the enzyme and the phytoplankton bloom. Studies have shown a positive correlation with food inputs and

amylase activity in bivalves (Pennec and Pennec, 2002). Additionally, the presence of aldolase, which breaks down into glycerol-3-phosphate and dihydroxyacetone during digestion, may indicate a need for the direction of energy for metabolic purposes within the shell (Pennec and Pennec, 2002). The intent of this study is to gain a better understanding of growth rates and internal growth mechanisms in O. conchaphila and C. gigas. The specific objectives include 1) comparing the growth rates of C. gigas and O. conchaphila in the same environment, 2) identifying 1-5 genes involved in the growth of these two species, and 3) to compare the gene expression patterns from C. gigas and O. conchaphila tissues extracted during two periods of development. It was expected that the growth rates of the two species would differ but that the internal mechanisms regulating growth would be similar. Further insight of the mechanisms surrounding growth in oysters will be beneficial to aquaculturists in determining the length of time it will take oysters to reach market size as well as improving the hatchery production of these economically important animals. Additionally, it can aid shellfish managers in setting sustainable harvest rates so as not to overexploit the larger oysters which provide a greater contribution to recruitment. Methods Objective 1: Growth Rates Single C. gigas was grown in six purse bags with 130 oysters per purse. Single O. conchaphila were grown in six purse bags with 125 oysters per purse. The number of oysters per purse bag was determined via purchase packages and grower recommendation. Purses were attached to stakes at the 0.05 high tide mark at Agate Pass in Kitsap County. Measurements were taken at the beginning of August before placement on beach. A random sample of 30 oysters of each species was measured in millimeters, once a month, lengthwise from the umbo to the edge, from August to December 2007. The sample size was established via a statistical analysis based on determining the minimum significant sample size and an additional five individuals to strengthen results. Weather and precipitation data were5 collected from the Weather Underground station KWAKINGS1 located in Chris Lane, Kingston, WA. Ocean temperatures were collected from NOAA station ID: 9447130. Objective 2: Identifying genes regulating growth At the end of the first month’s growth period on the beach (August) and at the end of November 2007, ten oysters of each species were collected for tissue samples. Extracted tissues included the mantle, gills, and muscle as well as a conglomeration of tissues from O. conchaphila. Tissues were placed in small, capped tubes, kept on ice and placed in a freezer at -80°C. Bioinformatic techniques were used to identify genes related to growth in Crassostrea through the expressed sequence tags (ESTs). Specifically, genes known to be associated with growth in other taxa were compared to unannotated oyster sequences. Due to the limited sequences for Ostrea, at this time, degenerative primerbased PCR was performed in order to find the homologs (or similar genes) in Ostrea

samples. In addition, one previously described gene (mGDF) was examined as it was known to be involved in molluscan growth. RNA was extracted from all samples using 1000ul Tri-Reagent (Molecular Research Center). Samples were homogenized in Tri-Reagent, and 200ul of Chloroform was added. After a thorough mixing samples were centrifuged at 4°C for 15 min at 11,500rpm. The aqueous phase was removed and 500ul of Iso-2-propanol was added, to precipitate the RNA, and centrifuged again. The supernatant was removed and 1000ul of 75% EtOH in DEPC water was added and centrifuged for a third time. The EtOH was removed and the RNA pellet isolated. 50ul of DNASE free water was added before incubating the samples at 55°C for 10 min. Total RNA was quantified using NANODROP 1000. Samples were kept at -80°C. CDNA was made from reverse transcribing the Total RNA which was extracted from the tissue samples. cDNA reactions were carried out in 20ul reactions containing 4ul AMV RT buffer (Promega), 8ul dNTPs(2.5uM), 1ul oligo dt primer (Promega), 1ul AMV transcriptase (Promega), 1ul RNase free water, 5ul total RNA previously extracted. CDNA was PCRed and observed on 3% gels. PCR reactions were carried out in 25ul reactions containing 10.5ul water, 12.5ul 2x goTaq (Promega), 1ul cDNA, 0.5ul forward primer, and 0.5ul reverse primer (Table 1). The PCR was used to amplify the following five genes: mGDF, KSPI, KPCIP, INSIG-2, and P450. Amplification of C. gigas genes began at 95°C for the initial five minute denaturing, followed by 40 cycles of 95°C for 60 sec, 55°C for 60 sec, 72°C for 60 sec, and a final extension step at 70°C for 10 min. Amplification of O. conchaphila genes began at 95°C for the initial 5 min denaturing, followed by 40 cycles of 95°C for 60 sec, 50°C for 60 sec, 72°C for 60 sec, and a final extension step at 70°C for 10 min. The temperature was lowered for O. conchaphila to lessen the specificity of the primers and increase the likelihood of getting a match.

Table 1: Primer sequences used for identifying genes likely involved in the growth of C. gigas and O. conchaphila grown in Agate Pass, Kitsap County, WA in 2007. Gene Primer sequences Expected Actual Product Product length Length mGDF Forward: 388 ≈ 388 AAAGCCGTGGGTTGGAACGATT Reverse: TTCCGAACACACACCTGGAACA KSPI Forward: 250 ≈ 250 ACGCGCGACAGGTGTAAATGTT Reverse: TCACTTTGAGGTCACGCCCTTT KPCIP Forward: 599 ≈ 599 ATCATGGGCGACAGGGAAGAAT Reverse: TTGGCTAACTCGCAAGCAGTGT INSIG- Forward: 536 ≈ 536 2 TCGGCAACTTCTTTGCCGTGTT Reverse: TGCGAGCTGTCTTCCGATGTTT P450 Forward: 585 ≈ 585 AATTTCAAGTGGCCCGTGTGGT Reverse: ATGCCATGCGCAGAGTCTCTTT Objective 3: Gene Expression Quantitative RT-PCR was used to measure gene expression levels in the oysters collected from the field in August and November 2007. Real Time reactions were carried out in 25ul reactions containing 1ul cDNA, 0.1ul forward primer (10uM), .1ul reverse primer (uM), and either 12.5u 2x Brilliant II SYBER GREEN QPCR Master Mix (STRATAGENE) or 12.5ul 2x Immomix (Bioline) and 1ul Syto 13 (Invitrogen) from a 50ul stock (Table 2). Table 2: Quantitative RT-PCR reactions carried out to measure gene expression levels in C. gigas and O. conchaphila. Tissues Sampled Species mGDF KSPI PKCIP INSIG-2 P450 Pacific mantle mantle mantle x Gill Olympia muscle muscle muscle x x

Results Objective 1: Growth Rates C. gigas displayed a growth rate superior to that of O. conchaphila (Figure 1). The growth of this species increased steadily by approximately 10mm a month until leveling off between November and December. On average, these oysters grew about 30mm over this four-month time span. O. conchaphila did not show such high increases in growth. This species, on average, only grew approximately 10mm over the four-month period with the majority of growth occurring between August and September. The maximum length reached by C. gigas during the time period of this study was 84mm, while that of O. conchaphila was 56mm.

Figure 1: C. gigas and O. conchaphila growth rates in millimeters for August, September, November and December 2007, in Agate Pass, Kitsap County, WA.

Average air temperatures during August and September remained in the range of 60°F (Figure 2). The temperature dropped into the 40s during October and leveled off through December. High temperatures remained in the high 70s between August and October while low temperatures dropped to 0°C with the exception of September when the low temperature jumped to 40°F. Precipitation averaged 50mm a month from August to November (Figure 3). Average precipitation increased to 290.1mm in December. The ocean surface temperature in August averaged 55.26°C and decreased steadily to an average of 49°C in December (Figure 4).

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Figure 4: Ocean temperatures for August, September, November and December 2007, in Agate Pass, Kitsap County, WA.

Objective 2: Gene Identification mGDF, with a product size of 388 base pairs, was detected in all tissue samples (mantle, gill, and muscle) from C. gigas (Figure 5). KSPI and KPCIP were also detected in the same tissue samples with bands showing a product size of approximately 250 base pairs for KSPI and approximately 599 for KPCIP. mGDF was detected in the muscle tissue of O. conchaphila with a product size of approximately 388 base pairs (Figure 6). KSPI was detected in all the tissue samples of O. conchaphila with a product size of approximately 250 base pairs, while KPCIP was detected in the gill and muscles with

product sizes of approximately 599 base pairs. INSIG-2, with a product size of 536 base pairs, was not detected in either species. P450 was not detected in O. conchaphila, but it was detected in the gill and muscle tissue of C. gigas with a product size of 585 base pairs.

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Figure 5: PCR of genes possibly involved in growth in C. gigas. Tissue samples include the mantle (ma), gill (g), and muscle (m). Red labels indicate the detection of the gene.

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Figure 6: PCR of genes possibly involved in growth in O. conchaphila.. Tissue samples include the mantle (ma), gill (g), and muscle (m). Red labels indicate the detection of the gene.

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Objective 3: Gene Expression The expression level of mGDF in the mantle of C. gigas is higher in November than in September; however, the change is not significant (P = 0.107) (Table 1; Figure 7). The expression of mGDF in the muscle of O. conchaphila is similar to C. gigas with expression levels being higher in November, but the overall change does not show a significant difference (P = 0.166) (Figure 8). The expression level of KSPI in the mantle of C. gigas is higher in September, although the difference between September and November is very small (P = 0.409 (Figure 9). Expression of KSPI in the muscle of O. conchaphila shows a similar trend, the only difference being that KSPI is slightly more prevalent in November instead of September (P = 0.488) (Figure 10). The expression level of KPCIP in the mantle of C. gigas is higher in September in comparison to November (Figure 11). This difference, while not significant (P = 0.075), still shows a substantial change in expression. The expression level of KPCIP in the muscle of O. conchaphila displays the same trend as C. gigas, with higher expression in September (Figure 12). In O. conchaphila, however, KPCIP is either not expressed in November or the expression is so low it is undetectable (P = 0.000). The expression of P450 in the gill of C. gigas displayed a very low level of expression in September in comparison to the higher level of expression in November (P = 0.024) (Figure 13).

Figure 7: Expression of mGDF from C. gigas mantle samples between September and November 2007, in Agate Pass, Kitsap County, WA.

Figure 8: Expression of mGDF from O. conchaphila muscle samples, between September and November 2007, in Agate Pass, Kitsap County, WA.

Figure 9: Expression of KSPI from C. gigas mantle samples between September and November 2007, in Agate Pass, Kitsap County, WA.

Figure 10: Expression of KSPI from O. conchaphila muscle samples, between September and November 2007, in Agate Pass, Kitsap County, WA.

Figure 11: Expression of PKCIP from C. gigas mantle samples between September and November 2007, in Agate Pass, Kitsap County, WA.

Figure 12: Expression of PKCIP from O. conchaphila muscle samples, between September and November 2007, in Agate Pass, Kitsap County, WA.

Figure 13: Expression of P450from C. gigas gill samples between September and November 2007, in Agate Pass, Kitsap County, WA.

Discussion Objective 1: Growth Rates The results of this study, as supported by previous studies, reveal the superior growing ability of C. gigas in relation to O. conchaphila. This may be due to the superior filtering system of C. gigas. A study by (31) states that data collected from the annual growth measurements of C. gigas showed two periods of higher growth. The first, occurring in spring, is the soft tissue due to intense gonadal development. The second, occurring in summer and early autumn, concerns shell growth, which is what this study is measuring (Gricourt et al. 2003). The period of no growth in C. gigas between November and December is most likely due to the onset of winter. This corresponds to a decrease in phytoplankton in the system caused by cold temperatures and the shortening of days. The latter can also aid in the explanation of the lack of growth in O. conchaphila as well. Growth in this species may cease sooner than in C. gigas as it is a poorer filter feeder and, as the autumn progresses and food becomes scarce, the oysters obtain less and less of it. Objective 2: Gene Identification Four genes associated with growth were successfully identified in these oysters. They are also follows: mGDF The first gene identified as being associated with growth in mollusks was the transforming growth factor (TGF)-beta family of proteins. This family has been extensively studied and characterized at the molecular level in vertebrates. All the proteins in this superfamily share characteristic features and, on the basis of their extended homology, were classified into the following subgroups: TGF-beta, bone

morphogenetic proteins (BMP), activins, inhibins, and the growth differentiation factors (GDF). A study by Lelong et al 2000 shows that the mGDF protein in C. gigas is more closely related to the human proteins BMP2 and BMP4 than to the corresponding proteins DPP and 60A in Drosophila. An unrooted phylogenetic tree supports the relationship of mGDF with that of the BMP2-4 and DPP group with a high bootstrap value (98.3%). Expression of mGDF mRNA was not observed in the oocytes and embryos up to the gastrula stage in C. gigas followed by a very small amount in the trochophore stage, in the study by Lelong et al (2000). mGDF is not truly expressed in C. gigas until the adult stage when it is present in most tissues with higher levels appearing in the digestive gland, mantle, and gills (Lelong et al. 2000). It was expected that the molluscan growth factor (mGDF) would be detected in the tissues of C. gigas as the gene had been previously identified in this species. An alignment using Genious Basic 3.6.2 shows 100% similarity (Table 3) (Genious, Biomatters Ltd.). This gene is also known to be involved in growth in several other species such as the zebra fish (Danio rerio), and abalone (Haliotis asinine). KSPI Kazal-type serine peptidase inhibitor domain 1 (KSPI) belongs to a family of serine proteinase inhibitors also known as MEROPS inhibitor family I1, clan IA (Letunic, 2006). Serine proteinase inhibitors are classified into several protein families based on the primary sequence, structural motifs, and mechanism of binding. Kazal inhibitors are multi-domain proteins that share several common structural features, such as cysteine distribution patterns, VCG-x(4)-TY sequence motifs, and highly homologous threedimensional structures. Over 100 Kazal-type proteinase inhibitors have been discovered in vertebrates, arthropods, nematodes, and bacteria. In vertebrates, Kazal is usually found in blood plasma, saliva, secretions of pancreas, seminal vesicles, and submandibular glands. Kazal may also act as an insulin-like growth factor binding protein. The molecular characterization, gene cloning, and expression of the serine proteinase inhibitor in mollusks is not very well defined. However, a group of humoral factors was identified in hemolymph of C. virginica, C. gigas, the surface clam, Spinuls, and the softshell clam, Mya arenaria. KSPI is known to function in growth in other species with similarities of approximately 30% such as the trout (Oncorhynchus mykiss), mouse (Mus musculus), and Atlantic salmon (Salmo salar).

Table 3: Gene characterization and species with similar sequences. Sequence name Accession Top Blasted number Similar species Accession number Molluscan Growth AJ130967 Pinctada fucata BAE96291 Differential Factor (pearl oyster) Haliotis vulgate AAM33143 (limpet) Haliotis asinine ABC00191 (abalone) Crassostrea gigas CAA10268 (Pacific oyster) Kazal-type serine Mus musculus NP_849260 peptidase inhibitor (mouse) domain 1 Salmo salar ABO36539 (salmon) Oncorhynchus ABA33953 mykiss (trout) Biomphalaria ABL74453 glabrata (snail) Protein kinase C Mus musculus NP_849260 inhibitor protein 1 (mouse) Salmo salar ABO36539 (salmon) Rattus norvegicus NP_001028236 (rat) Oncorhynchus ABA33953 mykiss (trout) Xenopus laevis ABF71729 (frog) Homo sapiens EAW49782 (human) Cytochrome P450 17Danio rerio (zebra NP_997971 hydroxylase/lyase fish) Pleuronectes CAA52010 (flatfish) Liza aurata (grey AAB70307 mullet)

% similar 62.6% 36.7% 37.0% 100.0% 32.8% 36.8% 36.8% 38.4% 11.8% 14.8% 15.0% 14.1% 11.8% 11.3% 16.8% 28.7% 16.8%

KPCIP Protein kinase C inhibitor protein 1(PKCIP) is part of a family of conserved regulator proteins (Strochilic et al. 2004) composed of serine/threonne kinases which are present in the tissues of all animals. Mammalian PKC isoforms share similar domain structures and have been classified into three groups: classical PKCs which are calcium, phosphatidylserine (PS) and diacylgyceral (DAG) dependent; novel PKCs which are

calcium independent but are still regulated by DAB and PA; and atypical PKCs that are regulated by PS alone. PKCs play key regulatory roles in multiple cellular processes that include differentiation, cell growth, secretion and muscle contraction (Walker and Plows, 2003). 14-3-3 Gamma was identified as the adaptor protein for muscle specific kinase signaling at the neuromuscular junction (NMJ) through the forced expression of 14-3-3 y in myotubes in vitro and in muscle fibers in vivo induced both the specific perturbations of the NMJ (Strochilic et al. 2004). KPCIP shows an average 11% alignment similarity with the following species where it is also involved in growth: humans (Homo sapiens), frog (Xenopus laevis), and trout (O. mykiss). INSIG2 Insulin-induced gene 2 protein (INSIG2) is involved in metabolic activity, gene transcription, and cell growth. INSIG-2 is a close homolog to INSIG-1. They share a similarity of 59% (Yabe et al. 2002). INSIG-2 differs from INSIG-1 in two respects: 1) INSIG-1 depends on nuclear sterol regulatory element-binding proteins (SREBPs) for its expression and 2) the action of INSIG-2 shows an absolute requirement for sterols (Yabe et al. 2002). INSIG-2 is a second protein of the endoplasmic reticulum that blocks the processing of SREBPs (Yabe et al. 2002). INSIGs, in general, encode proteins that block proteolytic activation of sterol regulatory element-binding proteins and transcription factors that regulate lipogenic enzymes and adipocyte metabolism (Krapivner et al. 2008). The genes restrict the cholesterol biosynthetic pathway by preventing proteolytic activation of SREBPs and by enhancing degradation of HMG-CoA reductase (Engelking et al. 2006). P450 Cytochrome P450 17-hydroxylase/lyase (P450) monooxygenase enzymes (CYP) comprise an ancient and widely distributed protein superfamily. A recent published accounting lists more then 750 sequences belonging to more then 107 different families. P450 proteins are found in a diverse array of organisms, including bacteria, plants, fungi, and animals (Snyder, 2000). Cytochrome P450 is a dependent monooxygenase system composed of approximately 100 isozymes from 27 gene families of endogenous substances such as steroid biosynthesis, fatty acid metabolism, and also xenobiotics (Fisher et al. 2003). It is a coupled electron transport system in the endoplasmic reticulum of the cell where Cytochrome P450 binds and activates oxygen (Lee and Anderson, 2005). Functions of P450s in the metabolism of endogenous compounds and xenobiotics (i.e. dietary plant chemicals, various aromatic hydrocarbons (PAH, AH), polychlorinated biphenyls (PCB), insecticides, drugs) have been extensively studied in the last 30 years. Types of P450 mediated reactions include hydroxylation, epoxidation, oxidative deamination, S-, N-, and O-dealkylations, and dehalogenation. The results of these reactions tend to be hydrophilic, and presumably more excretible products (Snyder, 2000). Four of the Cytochrome P450 genes code for enzymes that degrade lipophilic xenobiotics to more water-soluble substances to facilitate their mobility and excretion (Fisher et al. 2003). In the marine environment, the best studied member of the Cytochrome P450 superfamily is CYP1A1, the major form induced by dioxins, PAHs and PCBs. P450-type enzymatic activities have been reported in arthropods (crustaceans), annelids (polecats), cnidarians, mollusks, porifera, platyhelminths, and echinoderms. In

mollusks, P450 is highest in the digestive gland, though it is also detectable in blood cells, gills, foot and gonads. In echinoderms, P450 is mainly detectable in the pyloric ceca, gonads and haemal plexus. Crustaceans have the highest total P450 protein in the hepatopancreas, but significant activity also occurs in the green gland, gonads, and stomach. Typically, total P450 protein and associated enzymatic activities are found to be ten-fold lower in mollusks than in mammals. The second mollusk CYP gene, CYP4Y1, was identified from the digestive gland of the mussel M. galloprovincialis, where its expression was inhibited by increasing concentrations of beta-NF added in static exposures (Snyder, 2000). P450 shows a 17% similarity in the zebra fish (D. rerio) and a 28% similarity with the flat fish (pleuronectes). Objective 3: Gene Expression The characterization of tissue expression patterns and quantifying expression level in relation to different periods of development met the third objective of this study. The expression of mGDF follows the same trend in both C. gigas and O. conchaphila. While there is definitely a difference in expression over time and an indication that the gene must be doing something, the preliminary data of this study will only allow for speculation. The elevated level of expression in November may indicate a higher level of activity in the gene. Another explanation could be that the lower expression levels in September could indicate a higher level of protein translation. This leads to a relationship between the growth and weather data as oysters are more likely to be creating proteins needed for growth during the summer and early autumn months when food is available. Varying levels of gene expression may not be observed with KSPI as it could be involved in a different aspect of growth in oysters not affected by seasonal changes. Since this gene is found in blood plasma, saliva, secretions of pancreas, seminal vesicles and submandibular glands, it may be that this gene is more involved in metabolism or growth of reproductive organs than in actual body growth. Additionally, it may only be the protein levels that are changing which this study would not reveal. PKCIP shows dramatic variances in gene expression in both C. gigas and O. conchaphila. This gene which is directly involved in cell growth is probably not expressed in O. conchaphila during November as O. conchaphila has ceased growing due to the change in temperature and corresponding lack of food. PKCIP is most likely still present in C. gigas during this period as C. gigas is a stronger filter feed and may still be able to strain the remaining food particles from the water. Tissue samples taken in December for this species may have yielded a significant difference in expression. However, this is all speculation as these genes have not been extensively studied in oysters. The change in the expression of P450 in C. gigas could possibly indicate one of two things. First, it could indicate a change in growth between September and November when the gene is actively translating the protein in September and not in November with the drop in the food supply. Second, it could indicate a change in the water quality as P450 is also involved in the metabolism of endogenous compounds. The change in water quality could have occurred with change in seasons and water temperature.

Concluding Statements It was expected that the growth rates of the two species would differ but that the internal mechanisms regulating growth would be similar. It was discovered that, in support of previous studies, the growth rates of the two species are different due to differences in their life history. The genes identified as being likely to be involved in regulating growth were not only the same (mGDF, KSPI, KPCIP), but similarly expressed as well. In addition, the expression of those genes showed consistent trends. It was speculated that the expression of genes involved in growth may be influenced by changes in relation to the environment, such as water temperature, feeding, and placement. While this study can merely add to this hypothesis, the trends of the expression patterns could potentially be reflections of a change in the environment. It would be a worthwhile objective for a future study, as it appears that available prey items which vary seasonally play a role in gene expression. The United States is the largest producer and consumer of oysters in the world. It produces 50 million pounds of oyster meats and imports over 20 million pounds annually. This is approximately 56% of the production in the world. As the largest oyster producer on the Pacific Coast, Washington State produces approximately 5 million pounds of oyster meat which is roughly five times the amount produced by British Columbia, Oregon, and California combined (Pauly et al. 1988). A greater understanding of basic growth mechanisms in oysters can lead to the ability to modify it through manipulations of water quality, temperature, food supply, or genetics. The ability to influence the growth of oysters would aid aquaculturists in establishing the length of time it will take oysters to reach market. Larger oysters could also be introduced into areas such as Chesapeake Bay which is in need of the water quality control services provided by the filter feeder. It can aid managers in setting sustainable harvest rates and in avoiding the overexploitation of larger oysters which provide a greater contribution to recruitment. Additionally, it can also aid in the broodstock selection of hatcheries. Future work suggested by this study could include a more in-depth study on the genes involved in growth. Measurements and tissue samples should also be taken at multiple points throughout the year. Data could also be collected during different life stages and age groups as well. This data provides insight into the processes involved in the growth of two important species of oysters in our region.

Acknowledgements I would like to thank all the people who have contributed to the completion of the this project: Lin Murdock and Greg Jensen for helping me get started; my advisor Steven Roberts for all the guidance and assistance along the way; Sam White for all the technical support in lab, Vivianne Barry and Debbie Kay from the Suquamish Tribe for the use of their oysters and facilities, my dad for helping me to collect measurement data once a month, and my mom for the grammatical corrections on my paper.

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