fulkerson capstone paper

8/14/2019 Fulkerson capstone paper

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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 andO. 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 17-

hydroxylase/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


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Introduction

Oysters have been harvested and consumed throughout the world for scores of

generations. Having gradually been integrated into traditional aquaculture, oysters havebeen 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 habitatdegradation and pollution as North America became colonized. Oyster culture was turnedto 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 nativeoysterCrassostrea virginica enjoyed success in the aquaculture business. Unfortunately,

Ostrea conchaphila, the oyster native to the Pacific coast, did not respond well toaquaculture. Presently, there are five species of oysters cultivated along the Pacific Coast

of the United States: the native oysterOstrea conchaphila, the Atlantic oysterCrassostrea virginica, the Kumamoto oysterCrassostrea sikamea, the European flat

oysterOstrea edulis, and the Pacific oysterCrassostrea gigas. In 2003, oyster productionequaled 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 asmultiple 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 beenconfirmed through successive successful selective breeding for increased growth (Kittel,

1999). Kittel (1999) documented a heritability estimate of 0.54 for whole weight inC. 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 regulatesgrowth 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 considereda 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% wascaused by the increase in physical activity associated with feeding, growth, and spawning

(Brockington and Clarke, 2001). In this case, the extra oxygen consumption induced byfeeding 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 ofbivalve species by impeding filtration and the digestive process (Cardoso et al, 2007).

Cardoso et al (2007) also notes field studies have observed competition for foodoccurring in dense intertidal beds ofC. gigas. Intense competition does not allow the

oyster to achieve optimum foraging rates, resulting in slower growth due to low foodavailability (Villarroel et al. 2004). Villarroel et al. (2004) cites slow growth rates of the


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Crassostrea rhizophorae as being due to low food availability, mainly of phytoplanktonbiomass (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 tidalzone 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 thatinhabit them (Malanga et al. 2007). Food resources for most animals are abundant duringspring 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. Itresults 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 etal. 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 asstandard metabolism (Alberntosa et al. 2007). Disease and parasitism can also reduce

growth rate potential by increasing energetic costs (Johnson et al. 2001). Understandingthe 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 energyacquisition 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 intometabolizable, egested, and excreted energy. The amount of energy will vary according

to the effects of extrinsic (fluctuations in the biotic and abiotic conditions within thewater column) and intrinsic (body size, reproductive stage) factors. Animal production or

growth is represented by the difference between the absorbed energy and the energy lostin 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 optimalproduction. Feed composition and ingestion are the most important factors to consider in

a balanced growth equation. Additionally, metabolic rate is a major component of theequation. 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 availableenergy for reproduction, somatic tissue growth, and shell production. An organism can

only allocate net positive energy to SFG. Positive energy is obtained when the totalenergy absorbed is greater than total metabolic losses (Kesarcodi-Watson et al, 2001).

1. SFG = AE (RE+EE)SFG = scope for growthAE = absorbed food energy

RE = energy lost in respirationEE = 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 simplestmodel 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).


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The mantle surface is responsible for shell deposition (Pauly et al, 1988). Thegrowth 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 occurringprimarily 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: theoutermost layer (periostracum), the outer calcareous (prismatic) layer, and the innercalcareous (cross-lamellar) layer. Mantle edge cells are specifically involved in the

formation of the periostracum. They allow for the synthesis and secretion ofproteinaceous components as well as cellular calcium transport to the extrapallial space

(Gricourt et al. 2003).In Washington State, the size ofC. gigas following two years of growth is

correlated with the month the oyster was planted as well as the size of the oyster atplanting (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 areyoung 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 toreach 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 growthoccurring afterward (Gillespie, 1999).

While there is general information on growth in both species, limited informationis 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 nervousganglia. 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, insulinis 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 ofC. gigas during periods of

elevated shell growth. In particular, this study ascertained that insulin-like peptides mayparticipate 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 inthe 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). Insulin-related 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 tochange 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, anenzyme 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 fromthe 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 thephytoplankton bloom. Studies have shown a positive correlation with food inputs and


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amylase activity in bivalves (Pennec and Pennec, 2002). Additionally, the presence ofaldolase, which breaks down into glycerol-3-phosphate and dihydroxyacetone during

digestion, may indicate a need for the direction of energy for metabolic purposes withinthe 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 objectivesinclude 1) comparing the growth rates ofC. gigas and O. conchaphila in the sameenvironment, 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 tissuesextracted 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 besimilar. Further insight of the mechanisms surrounding growth in oysters will

be beneficial to aquaculturists in determining the length of time it will take oysters toreach market size as well as improving the hatchery production of these economically

important animals. Additionally, it can aid shellfish managers in setting sustainableharvest rates so as not to overexploit the larger oysters which provide a greater

contribution to recruitment.

Methods

Objective 1: Growth RatesSingle 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 ofoysters 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 Passin 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 theminimum significant sample size and an additional five individuals to strengthen results.

Weather and precipitation data were5 collected from the Weather Underground stationKWAKINGS1 located in Chris Lane, Kingston, WA. Ocean temperatures were collected

from NOAA station ID: 9447130.

Objective 2: Identifying genes regulating growthAt the end of the first months 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 andplaced in a freezer at -80C.

Bioinformatic techniques were used to identify genes related to growthin Crassostrea through the expressed sequence tags (ESTs). Specifically, genes known to

be associated with growth in other taxa were compared to unannotated oystersequences. Due to the limited sequences forOstrea, at this time, degenerative primer-

based PCR was performed in order to find the homologs (or similar genes) in Ostrea


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samples. In addition, one previously described gene (mGDF) was examined as it wasknown to be involved in molluscan growth.

RNA was extracted from all samples using 1000ul Tri-Reagent (MolecularResearch Center). Samples were homogenized in Tri-Reagent, and 200ul of Chloroform

was added. After a thorough mixing samples were centrifuged at 4C for 15 min at11,500rpm. 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 of75% 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 beforeincubating the samples at 55C for 10 min. Total RNA was quantified using

NANODROP 1000. Samples were kept at -80C.

CDNA was made from reverse transcribing the Total RNA which was extracted

from the tissue samples. cDNA reactions were carried out in 20ul reactions containing4ul 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, 0.5ulforward 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 ofC. gigas genes began at 95C for the initial five minute denaturing, followed by 40 cycles

of 95C for 60 sec, 55C for 60 sec, 72C for 60 sec, and a final extension step at 70Cfor 10 min. Amplification ofO. conchaphila genes began at 95C for the initial 5 min

denaturing, followed by 40 cycles of 95C for 60 sec, 50C for 60 sec, 72C for 60 sec,and a final extension step at 70C 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.


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Table 1: Primer sequences used for identifying genes likely involved in the growth ofC. gigas and O. conchaphila grown in Agate Pass, Kitsap County, WA in 2007.

Gene Primer sequences ExpectedProduct

length

ActualProduct

Length

mGDF Forward:AAAGCCGTGGGTTGGAACGATT

Reverse:TTCCGAACACACACCTGGAACA

388

388

KSPI Forward:ACGCGCGACAGGTGTAAATGTT

Reverse:TCACTTTGAGGTCACGCCCTTT

250 250

KPCIP Forward:ATCATGGGCGACAGGGAAGAAT

Reverse:

TTGGCTAACTCGCAAGCAGTGT

599 599

INSIG-2

Forward:TCGGCAACTTCTTTGCCGTGTT

Reverse:TGCGAGCTGTCTTCCGATGTTT

536 536

P450 Forward:AATTTCAAGTGGCCCGTGTGGT

Reverse:ATGCCATGCGCAGAGTCTCTTT

585 585

Objective 3: Gene ExpressionQuantitative 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 carriedout 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 inC. gigas and O. conchaphila.

Tissues Sampled

Species mGDF KSPI PKCIP INSIG-2 P450Pacific mantle mantle mantle x Gill

Olympia muscle muscle muscle x x


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Results

Objective 1: Growth RatesC. gigas displayed a growth rate superior to that ofO. 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 about30mm over this four-month time span. O. conchaphila did not show such high increasesin growth. This species, on average, only grew approximately 10mm over the four-month

period with the majority of growth occurring between August and September. Themaximum length reached by C. gigas during the time period of this study was 84mm,

while that ofO. conchaphila was 56mm.

Figure 1: C. gigas andO. conchaphila growth

rates in millimeters forAugust, September,

November and December2007, in Agate Pass,

Kitsap County, WA.

Average air temperatures during August and September remained in the range of60F (Figure 2). The temperature dropped into the 40s during October and leveled off

through December. High temperatures remained in the high 70s between August andOctober while low temperatures dropped to 0C with the exception of September when

the low temperature jumped to 40F. Precipitation averaged 50mm a month from Augustto November (Figure 3). Average precipitation increased to 290.1mm in December. The

ocean surface temperature in August averaged 55.26C and decreased steadily to anaverage of 49C in December (Figure 4).


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Figure 2: Average, high,and low temperatures for

August, September,

November and December2007, in Agate Pass,Kitsap County, WA.

Figure 3: Precipitation forAugust, September,

November and December2007, in Agate Pass,

Kitsap County, WA.

Figure 4: Ocean

temperatures for August,September, November

and December 2007, inAgate 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 detectedin 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 muscletissue ofO. conchaphila with a product size of approximately 388 base pairs (Figure 6).

KSPI was detected in all the tissue samples ofO. conchaphila with a product size ofapproximately 250 base pairs, while KPCIP was detected in the gill and muscles with

0

20

40

60

80

100

Aug Sept Oct Nov Dec

Months

ave temphigh templow temp

0

50

100

150

200

250

300

350


Month

precip mm

45

4647

48

49

50

5152

53

54

5556


Months


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product sizes of approximately 599 base pairs. INSIG-2, with a product size of 536 basepairs, was not detected in either species. P450 was not detected in O. conchaphila, but it

was detected in the gill and muscle tissue ofC. gigas with a product size of 585 basepairs.

L ma g m ma g m ma g m

mGDF KSPI PKCIP

L w ma g m ma g m b w ma g m ma g m

Sept Nov Sept Nov

INSIG P450

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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Objective 3: Gene Expression

The expression level of mGDF in the mantle ofC. gigas is higher in Novemberthan in September; however, the change is not significant (P = 0.107) (Table 1; Figure 7).

The expression of mGDF in the muscle ofO. conchaphila is similar to C. gigas with

expression levels being higher in November, but the overall change does not show asignificant difference (P = 0.166) (Figure 8). The expression level of KSPI in the mantleofC. 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 ofO. 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). Theexpression level of KPCIP in the mantle ofC. gigas is higher in September in comparison

to November (Figure 11). This difference, while not significant (P = 0.075), still shows asubstantial 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 gillofC. 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 andNovember 2007, in Agate Pass, Kitsap County,

WA.

Figure 8: Expression of mGDF fromO. conchaphila muscle samples, between

September and November 2007, in AgatePass, Kitsap County, WA.


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Figure 11: Expression of PKCIP fromC. gigas mantle samples between

September and November 2007, in AgatePass, Kitsap County, WA.

Figure 12: Expression of PKCIP from

O. conchaphila muscle samples, betweenSeptember and November 2007, in Agate

Pass, Kitsap County, WA.


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Figure 13: Expression of P450from

C. gigas gill samples betweenSeptember 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 superiorgrowing ability ofC. gigas in relation to O. conchaphila. This may be due to the superior

filtering system ofC. gigas. A study by (31) states that data collected from the annualgrowth measurements ofC. 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 Novemberand 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. Thelatter 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 feederand, as the autumn progresses and food becomes scarce, the oysters obtain less and less

of it.

Objective 2: Gene IdentificationFour genes associated with growth were successfully identified in these oysters.

They are also follows:

mGDFThe first gene identified as being associated with growth in mollusks was the

transforming growth factor (TGF)-beta family of proteins. This family has beenextensively studied and characterized at the molecular level in vertebrates. All the

proteins in this superfamily share characteristic features and, on the basis of theirextended homology, were classified into the following subgroups: TGF-beta, bone


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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 correspondingproteins DPP and 60A inDrosophila. 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 andembryos up to the gastrula stage in C. gigas followed by a very small amount in thetrochophore 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 levelsappearing 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 ofC. gigas asthe 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 isalso 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 aremulti-domain proteins that share several common structural features, such as cysteine

distribution patterns, VCG-x(4)-TY sequence motifs, and highly homologous three-dimensional structures. Over 100 Kazal-type proteinase inhibitors have been discovered

in vertebrates, arthropods, nematodes, and bacteria. In vertebrates, Kazal is usually foundin blood plasma, saliva, secretions of pancreas, seminal vesicles, and submandibular

glands. Kazal may also act as an insulin-like growth factor binding protein. Themolecular characterization, gene cloning, and expression of the serine proteinase inhibitor

in mollusks is not very well defined. However, a group of humoral factors was identifiedin hemolymph ofC. 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 ofapproximately 30% such as the trout (Oncorhynchus mykiss), mouse (Mus musculus), and

Atlantic salmon (Salmo salar).


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Table 3: Gene characterization and species with similar sequences.

Top BlastedSequence name Accession

number Similar species Accession

number

%

similar

Pinctada fucata

(pearl oyster)

BAE96291 62.6%

Haliotis vulgate

(limpet)

AAM33143 36.7%

Haliotis asinine

(abalone)

ABC00191 37.0%

Molluscan Growth

Differential Factor

AJ130967

Crassostrea gigas

(Pacific oyster)

CAA10268 100.0%

Mus musculus

(mouse)

NP_849260 32.8%

Salmo salar(salmon)

ABO36539 36.8%

Oncorhynchusmykiss (trout)

ABA33953 36.8%

Kazal-type serine

peptidase inhibitordomain 1

Biomphalariaglabrata (snail)

ABL74453 38.4%

Mus musculus(mouse)

NP_849260 11.8%

Salmo salar(salmon)

ABO36539 14.8%

Rattus norvegicus(rat)

NP_001028236 15.0%

Oncorhynchus

mykiss (trout)

ABA33953 14.1%

Xenopus laevis(frog)

ABF71729 11.8%

Protein kinase Cinhibitor protein 1

Homo sapiens

(human)

EAW49782 11.3%

Danio rerio (zebra

fish)

NP_997971 16.8%

Pleuronectes

(flatfish)

CAA52010 28.7%

Cytochrome P450 17-

hydroxylase/lyase

Liza aurata (grey

mullet)

AAB70307 16.8%

KPCIP

Protein kinase C inhibitor protein 1(PKCIP) is part of a family of conservedregulator proteins (Strochilic et al. 2004) composed of serine/threonne kinases which are

present in the tissues of all animals. Mammalian PKC isoforms share similar domainstructures and have been classified into three groups: classical PKCs which are calcium,

phosphatidylserine (PS) and diacylgyceral (DAG) dependent; novel PKCs which are


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calcium independent but are still regulated by DAB and PA; and atypical PKCs that areregulated 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 perturbationsof the NMJ (Strochilic et al. 2004). KPCIP shows an average 11% alignment similaritywith the following species where it is also involved in growth: humans (Homo sapiens),

frog (Xenopus laevis), and trout (O. mykiss).

INSIG2Insulin-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 asimilarity 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 itsexpression 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 theprocessing of SREBPs (Yabe et al. 2002). INSIGs, in general, encode proteins that block

proteolytic activation of sterol regulatory element-binding proteins and transcriptionfactors that regulate lipogenic enzymes and adipocyte metabolism (Krapivner et al.

2008). The genes restrict the cholesterol biosynthetic pathway by preventing proteolyticactivation of SREBPs and by enhancing degradation of HMG-CoA reductase (Engelking

et al. 2006).

P450Cytochrome P450 17-hydroxylase/lyase (P450) monooxygenase enzymes (CYP)

comprise an ancient and widely distributed protein superfamily. A recent publishedaccounting 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 endogenoussubstances such as steroid biosynthesis, fatty acid metabolism, and also xenobiotics

(Fisher et al. 2003). It is a coupled electron transport system in the endoplasmic reticulumof 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 thesereactions tend to be hydrophilic, and presumably more excretible products (Snyder,

2000). Four of the Cytochrome P450 genes code for enzymes that degrade lipophilicxenobiotics to more water-soluble substances to facilitate their mobility and excretion

(Fisher et al. 2003). In the marine environment, the best studied member of theCytochrome 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


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fulkerson capstone paper

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