This document discusses a study investigating the effect of temperature and salinity on infection intensity of Bonamia ostreae, a parasite that infects European flat oysters (Ostrea edulis). The study found that infection intensity increased by 43% in oysters kept at 20°C compared to 12°C, and decreased by 69% in oysters kept at 28‰ salinity compared to 34‰ salinity. These results have implications for disease management, as culturing oysters at lower temperatures and salinities could help reduce parasite impact. The study also found the primary PCR method unreliable for detection and recommends using nested PCR.

1. 1
An investigation into the effect of
temperature and salinity on the
infection intensity of Bonamia
ostreae in Ostrea edulis in an
attempt to improve disease
management practices in light of
present climate change issues
24288543
Sam Kirby
MSci Advanced Independent Research Project
May 2015
Word count: 9817

2. 2
Abstract
B. ostreae is a haplosporidian parasite that primarily infects the haemocytes of the European
flat oyster, O. edulis. This parasite, along with M. refringens, has caused devastating
mortalities in oyster populations in Europe and has led to a serious decline in oyster
production. Production of O. edulis in Europe is now 10% of what it used to be. Sensitive
diagnostic techniques such as PCR assays, in situ hybridisation and monoclonal antibody
immunoassays have been developed to help prevent the spread of the parasite and
management practices such as reduced stocking density have been put in place to try to
control progression of the disease. Acquired resistance to the parasite has also been
demonstrated in oyster strains in France and Ireland, which could play an important role in
managing the disease in the future. Previous work has suggested that temperature and
salinity has an effect on parasite prevalence due to impacts on the parasite and/or host
defence mechanisms. The current study aimed to assess the impact of temperature and
salinity on parasite prevalence and infection intensity, by exposing O. edulis individuals to
various temperature-salinity combinations, and obtaining parasite prevalence estimates and
infection intensities using primary PCR, nested PCR and histological staining techniques.
The study found that infection intensity rose by 43% in oysters kept at 20°C compared with
oysters kept at 12°C (P<0.05). A 69% decrease in infection intensity was observed in
oysters kept at 28‰ salinity compared with individuals kept at 34‰ (P<0.05). This has
important implications in management practices as oysters could be cultured at lower
salinities in an attempt to reduce the impact of the parasite, as has been done in the past
with H. nelsoni Additionally, this data supports the seasonal peaks in prevalence and could
be related to life cycle work carried out on Bonamia spp. in T. chilensis. The study found the
primary PCR reaction to be unreliable and, if used, should be used in conjunction with a
second nested PCR reaction. However, plenty of other diagnostic techniques are available
and molecular techniques could play an important role in uncovering the life cycle of B.
ostreae and the mechanisms for resistance in O. edulis in the future.

3. 3
Table of contents
Acknowledgements 5
1. Introduction 6
1.1 Ostrea edulis 6
1.2 Bonamia ostreae 7
1.2.1 Bonamiasis andparasite distribution 7
1.2.2 Pathologyof Bonamia ostreae 9
1.2.3 The appearance of Bonamia ostreae and the decline of the Europeanflat oyster industry 10
1.3 Diagnostic techniques and disease management 11
1.3.1 Development of diagnostic techniques 11
1.3.2 Disease management:E.U. and OIE controls 12
1.4 The effect of temperature and salinityon parasite prevalence and infection intensity 13
1.4.1 The effect of temperature andsalinityon Bonamia ostreae andOstrea edulis 13
1.4.2 Project aims 13
2. Materials and methods 14
2.1 Sampling,husbandryand dissection 14
2.2 Histological analysis 15
2.3 PCR analysis 16
2.4 Statistical analysis 17
3. Results 18
3.1 Data visualisation 18
3.1.1 Boxplots 18
3.1.2 Q-Q plots and normality 19
3.2 Wet weightand infection intensity 20
3.2.1 Data visualisation 20
3.2.2 Effect of wet weight on meaninfectionintensity 20
3.3 Infection intensityfrom histological analysis 21
3.3.1 Tank infectionintensities 21
3.3.2 Treatment groupinfectionintensities 21
3.3.3 Effect of temperature andsalinityon meaninfectionintensity 22
3.3.4 Site comparison 22
3.4 Detection methods and prevalence 23
3.4.1 PCR results 23

4. 4
3.4.2 Detectionmethods andsuccess 23
3.4.3 Comparison ofdetectionsuccess of each methodbetween groups 24
4. Discussion 25
5. Conclusions and future work 29
References 30

5. 5
Acknowledgements
Firstly, I would like to thank both my project supervisors, Dr. Lawrence Hawkins and Dr.
Chris Hauton, for all the time and effort they have put in throughout this project, and all their
guidance at each step of the way. I would also like to thank all the aquarium staff at NOCS
for all their help and their patience and thank you to all the technical staff for their help with
materials and guidance on procedures.

6. 6
1. Introduction
1.1 Ostrea edulis
Ostrea edulis
(otherwise known as
the European flat
oyster) is a bivalve
mollusc with an oval,
irregular shell (FAO
2004). The shell
consists of two valves
that are hinged along
the mid-dorsal line. The
hinge ligament is
organised in
conjunction with the
sculpturing of the dorsal
valve margins. The
ligament is subjected to
compression when the
valves are closed and
elastic recoil causes the
valves to open upon
relaxation of the adductor muscles (Harrison & Kohn 1997). The two valves are different
shapes, the left valve concave and the right valve flat. The right valve sits inside the left
valve and acts as a lid. Individuals can grow to large sizes (>20 cm) and can live up to 20
years old. O. edulis can be found along the Western European coast from Norway to
Morocco and in the Mediterranean basin (figure 1). Populations can also be observed along
the eastern coast of the USA from Maine to Rhode Island (FAO 2004).
O. edulis populations can be found inhabiting muddy-sand, muddy-gravel and rock substrata
in shallow coastal waters at depths up to 20 m. Populations can also be found in estuaries
and can tolerate salinities as low as 23‰. Individuals feed by filtering phytoplankton and
other particulate material from the water column (Lapège et al. 2007).
The European flat oyster has an unusual reproductive biology (da Silva 2009). It is a
larviparous and protandric species, generally changing gender twice in a single reproductive
cycle (Lapège et al. 2007). Individuals first mature as a male before undergoing a regular
rhythm of alternating between female and male sexual phases (Sparck 1925; Orton 1927,
1933; Cole 1942). Gametogenesis of both sexes in a single follicle is common due to this
changing of sex (Galtsoff 1964; Pascual et al. 1989). Individuals can produce up to 1 million
eggs per spawning, which are released into the pallial cavity where they become fertilised by
externally released sperm. Embryos are then brooded for 8-10 days before being released
as pelagic larvae. This dispersal stage usually lasts 8-10 days before settlement (Lapège et
al. 2007).
Figure 1. Distribution of O. edulis in Europe (Jaziri 1990).

7. 7
1.2 Bonamia ostreae
1.2.1. Bonamiasis and parasite distribution
Bonamiasis is the parasitic disease of flat oyster haemocytes (OIE 2009) caused by
haplosporidian microcells belonging to the genus Bonamia and infect oysters around the
world. Haemocytes play an important role in the molluscan immune system and eliminate
foreign particles through phagocytosis (Cheng 1981). There are rarely signs of infection but
a yellow or black colouration of the mantle and gill lesions have been observed in heavily
infected individuals (Woolmer et al. 2011). The only usual sign of infection is mass mortality
but by this time it is too late for mitigation (Cao et al. 2009). Studies have also shown that,
once a site is infected, it is almost impossible to eradicate and the disease tends to reappear
in populations that are reintroduced to areas after a fallowing period of a number of years
(van Banning 1985).
The genus Bonamia is comprised of four species: B. ostreae that infects O. edulis in Europe,
USA, Canada and Morocco (Pichot et al. 1980; Bucke et al. 1984; Elston et al. 1986; Montes
& Melendez 1987; Friedman et al. 1989; McArdle et al. 1991; Friedman & Perkins 1994; OIE
2005; Marty et al. 2006), O. lurida, O. angasi, T. chilensis and O. puelchana (Argentina)
(Culloty & Mulcahy 2007); B. exitiosa which infects Tiostrea chilensis in New Zealand (Hine
et al. 2001; Berthe & Hine 2003) and O. angasi in Australia (Corbeil et al. 2006); B. roughleyi
that infects Saccostrea glomerata in Southeast Australia (Cochennec-Laureau et al. 2003a)
and B. perspora that infects O. lurida on the east coast of the USA (Carnegie et al. 2006).
B. ostreae is an intracellular protistan parasite belonging to the phylum Haplosporidia
(Sprague 1979), roughly 2-5 µm in diameter (Arzul et al. 2009), and was initially detected in
Brittany, France in 1979 (Comps et al. 1980; Grizel & Tige 1982), Spain & Denmark in 1980
(van Banning 1985; Figueras 1991), Fal and Helford rivers, UK in 1982 (Bannister & Key
1982) and Ireland in 1987 (McArdle et al. 1991). Since then the disease has spread and has
most recently been recorded in British Columbia, Canada in 2004 (Marty et al. 2006),
Morocco in 2005 and Scotland and Wales in 2006 (Culloty 2007). An ultrastructural study of
infected branchial epithelial tissue and haemocytes in individuals from Galicia, Spain found
that the parasite can be located intracellularly in both ciliated epithelial cells and in
haemocytes (Montes et al. 1994). B. ostreae exhibits a large nucleus containing dispersed
chromatin with a prominent nucleolus to one side of the nucleus. The cytoplasm is
moderately dense and contains mitochondria and haplosporosomes. In epithelial cells, the
parasite was visible as a spherical or ovoid cell contained within a vacuole formed by the
host-cell membrane (see figure 3) and structures which may be interpreted as the parasite
undergoing mitosis were observed (Montes et al. 1994).
The place that species of Bonamia hold within the phylum Haplosporidia is tenous, however.
Pathology and host range formed the initial basis for microcell taxonomy (Farley et al. 1988)
but early ultrastructural studies of B. ostreae found dense cytoplasmic structures resembling
haplosporosomes (Pichot et al. 1980), features present in the haplosporidia, myxozoa
(Perkins 1979) and Paramyxea (Morris et al 2000). The presence of these structures, in an
organism that does not display the cell-within-a-cell structure of the Paramyxea, supported
the argument for their inclusion into the Haplosporidia (Perkins 1987, 1988), although a
spore stage has never been observed in B. ostreae. Additionally, direct transmission is not
characteristic of Haplosporidium spp. except, perhaps, in Haplosporidium pickfordi (Barrow
1965). On the other hand, a spore stage has recently been described in a close relative of B.
ostreae, B. perspora, suggesting that other species of Bonamia may also produce spores,
but perhaps only under certain conditions that have not been observed yet (Carnegie et al.
2006).

8. 8
Despite 25 years of research, the life cycle of B. ostreae is poorly known. Regardless of the
date that naïve oysters are exposed to the parasite, the first known stages of the parasite
can be observed 3-5 months after (Tige & Grizel 1984; Montes 1991). The parasite can be
detected in oyster spat (Lynch et al. 2005) but mortalities mainly affect oysters that are 2
years old or more (Culloty & Mulcahy 1996) and can be transmitted directly between oysters
in a population or experimentally by cohabitation (Elston et al. 1986; Hervio et al. 1995),
meaning that it is unlikely that an intermediate host is required to complete the life cycle.
Culloty et al. (1999) found that a number of bivalve species (e.g. Mytilus edulis, Mytilus
galloprovincialis, Ruditapes philippinarum and Crassostrea gigas) cannot become infected
with B. ostreae nor act as a vector. However, some evidence does suggest that the brittle
star, Ophiothrix fragilis, may act as an intermediate carrier of the parasite as two naïve
oysters in a laboratory cohabitation study showed low levels of infection after cohabitating
with the brittle star (Lynch et al. 2007). In an attempt to contribute to our understanding of
the life cycle of this parasite, Montes et al. undertook an ultrastructural study of B. ostreae.
They observed parasites in the gill epithelium, however it could not be determined whether
they were leaving the host as none were observed crossing either the basal or apical
membrane. The parasite Haplosporidium nelsoni appear to invade gill epithelia and form
plasmodia in extracellular spaces. However, no observation of plasmodial phases of B.
ostreae were found although these types of cell can be seen in figure 2.. They also observed
phases of rapid proliferation of the parasite, not only in haemocytes but also within the gill
epithelium. Based on this evidence they proposed a life cycle for the parasite: 1) adhesion of
Bonamia to haemocyte, 2) subsequent phagocytosis, 3) proliferative stage, 4) eventual
destruction of haemocyte, 5) release of parasite into extracellular medium, thus reinitiating
the cycle through infection of new haemocytes or epithelial cells. In addition to this, an
ultrastructural study of Bonamia spp. in T. chilensis (Hine 1991a) was carried out and 5
stages of development were described. Stage 1 consisted of a dense, small cell with
haplosporosomes and dense ribosomes. Stage 3 was described as an intermediate, dense
form with an irregular cell shape and a golgi detached from the nucleus. Stage 5 appeared to
be a plasmodial stage containing multivesicular bodies a large smooth endoplasmic
reticulum. Stages 2 and 4 were described as transitional phases. Based on seasonal
observations, the following life cycle was proposed: An incubation phase from September to
November (spring), a proliferation phase from December to May (summer and autumn) and
a plasmodial phase from June to August (winter) (Hine 1991b). This seems to correlate well
with the peaks in parasite prevalence and infection intensity seen in B. ostreae populations
in late winter and autumn (Grizel 1985; Montes 1990) (i.e. just after the proliferation phase
and during the terminal stages when a larger plasmodial form occurs).

9. 9
1.2.2. Pathology of Bonamia ostreae
As previously
mentioned, B. ostreae
is a parasite of the
haemocytes of O.
edulis. This is
important because
haemocytes, as well
as playing an
important role in the
internal defence
mechanism of
molluscs, are also
involved in wound
repair, shell repair,
nutrient digestion and
transport and
excretion (Cochennec-
Laureau et al. 2003b).
Bonamiasis is
characterised by
branchial ulceration
accumulation of
haemocytes in
connective tissue
(Cochennec et al.
1992). The infection is
usually associated
with intense
haemocyte infiltration
of the connective
tissue of the gills,
mantle and digestive
gland. Although
described as a
haemocytic infection,
the parasite can also
be observed
extracellularly between
epithelial cells in the
gills, stomach and
necrotic connective
tissues. The
observation of free
parasites in the gill
epithelia supports the theory of the release of the parasite through these organs (Montes et
al. 1994). However, most are probably released through tissue lysis upon death of the host
organism and the infective form and routes of entry remain unclear (Arzul et al. 2009).
Sometimes, cells may appear more electron-dense and this is especially true in heavily
Figure 2. Micrograph showing binucleate (∆) and plasmodial (→)
stages of B. ostreae (Culloty & Mulcahy 2007).
Figure 3. Heart smearof O. edulis. Thick arrow indicates extracellular
B. ostreae cells. Thin arrow indicates B. ostreae within oyster
haemocytes (Culloty & Mulcahy 2007).

10. 10
infected organisms, which has led scientists to believe that this may be the infective form
(Carnegie & Cochennec-Laureau 2004).
Various studies have been undertaken to try to better our understanding of how the parasite
affects the haemocytes of oysters (Balouet et al. 1983; Cochennec et al. 1992; Montes et al.
1994; Cochennec 2001; Cochennec et al. 2003b; Comesaña et al. 2012). Cochennec et al.
(2003b) found no difference in the total haemocyte count (THC) between infected and naïve
individuals. They also found an increase in accumulation of tissue haemocytes that was
quantitatively associated with infection intensity. They also found that the balance between
granulocytes and agranular cells varied significantly between infected and naïve individuals,
with a significantly higher proportion of large agranular cells in infected oysters. A difference
in circulating haemocyte ratios was also found between susceptible and resistant strains of
O. edulis, suggesting that a lower number of granulocytes in the haemolymph of susceptible
oysters may explain the variation in susceptibility between individuals (Cochennec et al.
2003). This relative abundance of different types of haemocyte is termed the differential
haemocyte count (DHC) and is known to be influenced by some pathogens already (Allam &
Ford 2006; Oubella et al. 1996; Reid et al. 2003).
Phagocytosis is the principal cell defence mechanism of bivalves (Bachère et al. 1995;
Cheng 1981; Feng 1988). The phase of bonamiasis occurring within the haemocytes
appears to involve a, sometimes profound, alteration of the host cell (Montes et al. 1994).
Hervio (1988) provided histological and enzymatic evidence that the parasite occurs within a
phagolysosome (see figure 3) and possesses enzymatic machinery that interferes with the
host cell cytocidal mechanisms. Once the pathogen is internalised, a respiratory burst may
be triggered, leading to the generation of reactive oxygen species (ROS) with antimicrobial
properties (Adema et al. 1991; Babior 1997; Kimura et al. 2005). The superoxide anion (O2
-
)
is the first to be formed and is subsequently transformed into H2O2 (Comesaña et al. 2012).
The production of these oxygen species has been demonstrated by O. edulis and C. gigas
(Bachère et al. 1991; Chagot 1989; Hervio et al. 1989; Nakayama & Maruyama 1998). In
addition to this, two reactive nitrogen species (RNS) may be generated by nitric oxide
synthase that occurs in mollusc haemocytes, nitric oxide (NO) and peroxinitrite (ONOO-
)
(Comesaña et al. 2012). Nitric oxide participates in the elimination of pathogens
(Chakravortty & Hensel 2003) and production is stimulated by phagocytosis in bivalves and
shows cytotoxic properties (Romestand & Torreilles 2002). A few studies have found that the
ROS levels in the haemocytes of O. edulis individuals after in vitro phagocytosis of B.
ostreae were minimal (Cochennec & Garcia 2000; Hervio 1992; Morga et al. 2009) which
may be due to an acid phosphatase activity of the parasite, leading to the block of a
respiratory burst (Hervio et al. 1991).
Considering the parasite can be found in the branchial epithelium tissue, it cannot be
considered as a strictly haemocytic parasite. However, the only division that occurs in the
connective tissue is within haemocytes (Pichot et al. 1980) and the division occurs rapidly
(Grizel et al. 1988). However, it has been reported that the parasite does undergo rapid
proliferation within epithelial cells (Montes et al. 1994). In digestive gland tissue, the parasite
was found in haemocytes located between the digestive tubules and the parasite appears to
be contained within a vacuolar membrane. Montes et al. (1994) also observed that the
parasite cell was generally more dense in haemocytes than in the epithelium.
1.2.3. The appearance of Bonamia ostreae and the decline of the European oyster industry
Worldwide oyster production was almost 4,604 million tons in 2004 with aquaculture
supplying 97% of that (European Commission 2009). China is the main oyster producer
(83%). The second highest producer is Republic of Korea (6%), followed by Japan (4%).

11. 11
Over the last 40
years, the
production of O.
edulis has seen a
dramatic decline
producing 6000
tons a year
compared to a
peak output of
nearly 30,000 tons
in 1961. This
decline has been
caused by two
parasitic agents
Marteilia refringens
and B. ostreae
(Lallias et al. 2008)
(see figure 4). The
natural range of O.
edulis extends to Norway, Morocco, and into the Mediterranean basin (FAO 2012) but
natural beds are rare and are only surviving in a few areas such as the west coast of Ireland
(Flannery 2014). In 2002, O. edulis production contributed less than 0.2% of the total global
of all farmed oyster species, with 67% of production coming from Spain (4,565 tons), 24% in
France (1,600 tons) and only the UK and Ireland contributed more than 200 tons (FAO
2012). However, as the supply of oysters decreased, the average price has increased
substantially and O. edulis can be 3-5 times more expensive than C. gigas and now occupy
a niche market (Lapège et al. 2007). The total value of farmed O. edulis in 2002 was USD
24.3 million and therefore remains an important industry (European Commission 2009).
However, due to the on-going problems with O. edulis culture, C. gigas has become the
main focus of European oyster production. C. gigas production constitutes 80% of the global
oyster production from aquaculture, reaching 728,552 tons in 2007 (FAO 2008).
It is thought that the spread of B. ostreae throughout Europe can mainly be attributed to the
transfer of infected oysters to uninfected areas, but it has also been suggested that the use
of equipment in infected areas can contribute to the spread of the disease such as in Lake
Grevelingen, Netherlands (van Banning 1991). Other mechanisms have also been
suggested such as fouling on boat hulls (Howard 1994). The rapid spread of the disease and
its devastating effects on oyster production therefore necessitated the development of
sensitive detection techniques for effective early diagnosis in an attempt to control the
spread of the disease (Lynch et al. 2005).
1.3 Diagnostic techniques and disease management
1.3.1. Development of diagnostic techniques
Traditional methods for the detection of B. ostreae include histological staining procedures
for microscopic detection, heart and gill tissue imprints and Transmission Electron
Microscopy. However, it is thought that these methods lack necessary sensitivity for
detecting low-level infections (Lynch et al. 2005). Tissue imprints were described as having
low specificity, but higher sensitivity than histological staining (da Silva & Villalba 2004).
However, heart imprints are not considered unreliable for detecting latent infections.
Histology considered reliable for moderate-high intensity infections but not for low-level
Figure 4. Worldwide production (tonnes) of O.edulis from1950-2005.
Appearance of M. refringensandB. ostreaemarkedwith arrows
(Culloty).

12. 12
infections (OIE 2012). Additionally, histological techniques can be slow and require a trained
observer (Ramilo et al. 2013). Specifically, B. ostreae bears a resemblance to routine intra-
haemocytic inclusions which can lead to false positive identification (Carnegie et al. 2000).
This has led to the development of numerous diagnostic methods (Cochennec et al. 1992;
Carnegie et al. 2000; Cochennec et al. 2000; Carnegie et al. 2003; Carnegie et al. 2004;
Corbeil et al. 2006; Marty et al. 2006; Robert et al. 2009).
Due to the lack of suitable tissue culture systems for the culture of the parasite, Mialhe et al.
(1988) developed a purification protocol. The development of this protocol and therefore the
availability of purified B. ostreae suspensions allowed for quantification of parasite injection
allowing more precise experimental infections to be carried out (Hervio et al. 1995) and
allowed scientists to investigate in vitro interactions between parasites and haemocytes
(Chagot et al. 1992; Mourton et al. 1992).
In 1992, Cochennec et al. developed a monoclonal antibody sandwich immunoassay for the
detection of B. ostreae in haemolymph samples and the specificity and sensitivity of this
technique were 76.7% and 106%, respectively, compared with histology (Cochennec et al.
1992). However, this kit is no longer available on the market (OIE 2012). However, specific
primer pairs (BO & BOAS) were developed later by Cochennec et al. (2000). These primers
amplify a 300 base-pair (bp) product and successfully detected B. ostreae infection with in
situ hybridisation.
Around the same time, Carnegie et al. (2000) developed another PCR assay for the
detection of B. ostreae, involving two primer (CF and CR which amplify a 760 bp product. This
PCR assay proved to be more sensitive when compared with cytological analysis (Carnegie
et al. 2000).
In addition to these PCR assays, two TaqMan® PCR assays (Corbeil et al. 2006; Marty et
al. 2006) and a qPCR assay (Robert et al. 2009) have been developed. The latter is
considered extremely sensitive and semi-quantitative (OIE 2012).
These molecular techniques can be faster and more sensitive than traditional approaches,
have the benefit of only needing a small tissue sample (Traub et al. 2005) and are more
useful for detecting low-level infections. Not only this, they have given insight into the
phylogenetic relationships and has become an important tool in parasite systematics
(Gasser 2006). These techniques may require lengthy optimisation, however.
1.3.2 Disease management: E.U. controls and OIE
Considering it is almost impossible to eradicate B. ostreae once a site has been infected
(van Banning 1985 & 1987), control of the disease relies heavily on prophylactic measures
such as restricting the transfer of oysters and strict screening processes. Countries within
the EU are required to monitor for a number of notifiable diseases. Legislation dealing with
the movement of molluscs include Council Directive 95/70/EC (22 December 1995)
introducing minimum Community measures for the control of certain diseases affecting
bivalve molluscs. The OIE Aquatic Manual (2009) compares the specificity and sensitivity of
some diagnostic tests available and the advantages and disadvantages of these techniques
(OIE 2009).
In addition to this, management practice guidelines have been set out by the Centre for
Environment, Fisheries and Aquaculture Science (CEFAS) and these include: disposal of
unwanted oysters on land, avoiding unnecessarily stressful conditions (such as
overstocking) and do not accept undocumented batches of oyster spat Woolmer et al. 2011).

13. 13
Perhaps the most important area of research with regards to disease management is the
development of disease-resistant populations of O. edulis. By the mid-1980’s there was
strong evidence to suggest that O. edulis might be capable of developing resistance to B.
ostreae (Carnegie et al. 2004). A degree of resistance appears to have developed in a strain
of Rossmore oysters in Ireland. The site has been running a selective breeding programme
for 16 years. The Rossmore population performed significantly better compared with other
populations in terms of prevalence and intensity of infection (Culloty et al. 2004). Resistance
to B. ostreae infection is clearly heritable and appears to be mostly additive (Naciri-Graven
et al. 1998). A French O. edulis selection programme focused on improving growth as well
as improving disease resistance. The economic impact of the pathogen can be reduced by
improving survival and obtaining market-size oysters faster (Carnegie et al. 2004).
1.4 The effect of temperature and salinity on parasite prevalence and infection intensity
1.4.1. The effect of temperature and salinity on Bonamia ostreae and Ostrea edulis
Correlations between environmental parameters and development of bonamiasis have been
difficult to demonstrate thus far, though previous work has suggested an impact of
temperature on the parasite and/or the defence capacity of the host oyster (Arzul et al.
2009).
One study found that B. ostreae prevalence was higher at 10 °C than at 20 °C, suggesting
low temperatures may negatively affect the host defense mechanisms or positively affect the
ability of the parasite to infect its host (Cochennec & Auffret 2002). Audemard et al. (2008)
also found that increased temperature and salinity increased pathogenicity of Bonamia spp.
in C. ariakensis.
There have also been studies dedicated to quantifying the physiological response of O.
edulis to adverse environmental conditions. In a study by Hutchinson & Hawkins (1992), O.
edulis individuals were found to have a reduced Scope for Growth (SFG) at low salinities
(regardless of temperature) but showed a differential response to temperature at high
salinities. SFG was reduced at low temperatures. Additionally, another study concluded that
adverse temperature and salinity conditions can reduce the functionality of O. edulis
haemocytes. A Neutral Red Retention (NRR) assay was used to assess haemocyte
functionality of individuals that had been exposed to various temperature-salinity
combinations and found a reduced retention time at low salinities, potentially due to osmotic
imbalance. The retention time showed a peak at 15°C, slowly decreasing towards 25°C at a
salinity of 32‰ (Hauton et al. 1998).
1.4.2. Project aims
It is important to understand the effect of temperature and salinity on the development of
bonamiasis in O. edulis, especially in light of the global climate issues facing us. Highlighting
the relationship between environmental factors and infection may help to better improve our
understanding of the parasite biology, especially in relation to life cycle, which may
contribute to improving management practices and mitigating oyster losses. Perhaps most
importantly, forecasting of disease evolution is necessary in relation to climate change and
may help contribute to defining risky and non-risky geographic areas (Arzul et al. 2009).
The aim of this project is to determine whether there is a statistically significant difference in
infection intensity and prevalence between individuals subjected to varying temperature-
salinity combinations. Additionally, a brief site comparison between oysters obtained from
two different sites will be conducted as well as an analysis of the three diagnostic methods
employed.

14. 14
2. Materials and methods
2.1. Sampling, husbandry and dissection
On the 5th
of August 2014, 52 O. edulis individuals were taken from Ryde Middle (~50°46’N.,
1°14’W.) and transported back to the NOC in Southampton. These individuals were stored in
a net bag on the pontoon of the NOC from 8th
August 2014, and were shaken daily to
remove accumulated sediment and prevent clogging of the gills. These individuals were
removed from the pontoon and dissected on 14th
August for histology and PCR analysis. All
individuals were opened, their gills removed and their hepatopancreas halved. Half of each
hepatopancreas was fixed in Bouin’s solution for histological analysis and half was frozen at
-20°C to preserve DNA for PCR analysis. Each sample was also earmarked appropriately.
On Monday 9th
February 2015, a further 88 O. edulis individuals were obtained from Poole
Harbour (~50°41’N., 1°59’W.) for temperature and salinity manipulation under three
treatments. These organisms were originally stored in 4 tanks (2a, 2b, 3a and 3b) on a
stand-alone system set up by the aquarium staff in a constant temperature room of 12°C.
The organisms were kept in full strength seawater and were kept under these conditions for
a further 9 days to allow for their acclimation to the aquarium conditions. A sample size of 20
individuals was examined for each treatment group (treatment A: ~28‰ at 12°C; treatment
B: 34‰ at 12°C; treatment C: ~34‰ at 20°C). Individuals from treatment groups A and B
were kept in a 12°C constant temperature room to control the water temperature and
individuals from treatment group C were kept in the main aquarium in a stand-alone system
which was heated by a 200W Juwel™ aquarium heater. 28‰ water was prepared by mixing
full strength seawater with R.O. water and checked using a temperature/salinity probe. The
tank water was filtered using standard mesh filters and a UV filter. Treatment B acted as a
control group whilst samples from Ryde Middle were considered as a separate treatment
(D).
On 18th
February, 20 random oysters were transferred to treatment A (tanks 1a and 1b) and
20 random oysters were transferred to a water bath at 16°C and 33‰ salinity and kept
overnight to avoid shocking the organisms by moving them directly to water at 20°C. On 19th
February those organisms were transferred to treatment C (tanks 4a, 4b and 4c). The
remaining oysters were kept in the original four tanks at the same conditions as they
represent the control group (treatment B).
The individuals from Poole Harbour required water changing and feeding whilst being stored
in the aquarium and this was carried out every Monday, Wednesday and Friday. The tank
conditions were also recorded each day so that any variation between tanks in each
treatment group could be elucidated. Individuals were fed Isochrysis spp. and any water
required for the water changes was made up the day before and acclimated overnight to the
appropriate temperature for each treatment group. The algal concentration was obtained
each day by counting individual algal cells under a microscope using a Neubauer
0.0025mm² flow cytometer. The volume of algae was then adjusted each day to deliver a
consistent amount (concentration x volume = concentration x volume).
On 11th
March all oysters were removed from the aquarium, earmarked appropriately (e.g.
sample 2A1 – sample 2 from tank A1) and dissected for histology and PCR analysis. A
negligible mortality was observed over the acclimation period with two oysters from tank 2A
and one oyster from tank 4A dead. With such a minute mortality rate, a mortality index would
have been unnecessary. The dissection procedure for the individuals from Poole Harbour
was the same as for the individuals from Ryde Middle but the wet weight of each individual
was also recorded before dissection (mean wet weight=11.68g, ±4.61SD, n=85). No control

15. 15
for gender was made during this experiment, resulting in a mixture of males and females
being processed. 20 random oysters chosen from each treatment group, with an attempt to
take equal samples from each tank, and 20 random individuals from Ryde Middle were then
examined using histological and PCR analysis. A sample size of 20 was chosen in
accordance with research published on minimum sample sizes required for accurate
estimates of parasite prevalence (Jovani & Tella 2006).
2.2. Histological analysis
As mentioned before, half of
the hepatopancreas of all
individuals was fixed in
Bouin’s solution ready for
histological analysis. The
samples were then
dehydrated through an
ethanol series (30%, 50%,
70%, 90%, 100%, dry
ethanol). These ethanol
concentrations were prepared
using Fisher analytical grade
ethanol and were diluted
using R.O. water. Dry ethanol
was prepared by heating a
crucible of cupric sulphate
until it became discoloured
and adding it to 1 litre of stock
ethanol. All samples were
kept at each ethanol concentration for at least 24 hours and were finally kept in Fisher
analytical grade xylene for a further 24 hours to clear the tissue for histology. All samples
were then embedded in paraffin wax and allowed to set. 5 micrometre sections were then
taken from the front, middle and back of each embedded sample, using a microtome, so that
a prevalence and intensity estimate that was representative of the whole tissue sample could
be obtained. Approximately 3 sections were taken from the front, middle and back of the
tissue, giving a total of 9 sections per sample. Each slide was then stained using the
following adaptation of the method used by Austin & Austin (1989):
1. 5 minutes in Xylene
2. 1 minute in 100% ethanol
3. 1 minute in 70% ethanol
4. 1 minute in 50% ethanol
5. 1 minute in 30% ethanol
6. 3 minutes in haematoxylin
7. 15 minutes under running water
8. 15 minutes in Gomori triple stain
9. Rinse with water
10. 1 minute in acetic acid
11. First rinse in 100% ethanol (2 minutes)
12. Second rinse in 100% ethanol (2 minutes)
13. 3 minutes in Xylene
14. DPX and mounting
Figure 5. Micrograph O. edulis connective gill tissue (H&E
stain). Arrows indicate B. ostreae cells (Arzul et al. 2011).

16. 16
Slides were then observed under an Olympus BH2-RFCA microscope to obtain prevalence
and intensity estimates. Slides were scanned at 20x magnification and once a parasite had
been located it was bought to the centre of the field of view. The magnification was then
increased to 40x and all parasites located within that field of view were counted and an
average was obtained from the three counts for each individual.
Parasites were identified by reference to figures 2, 3 and 5 as there is a good representation
of different stages of the parasite over these figures.
2.3. DNA extraction and PCR analysis
DNA was extracted from 10mg of tissue of each sample using the Qiagen™ DNeasy Blood
and Tissue kit, following the Animal Tissues Spin-Column Protocol. The resulting DNA was
quantified using a NanoDrop™ ND-1000 Spectrophotometer.
Once the DNA was extracted, 25µl PCR reactions were carried out following an adaptation
of protocol C set out by Carnegie et al. (2000). These PCR reactions contained 0.5µl C FWD
and REV primers (Eurofins MGW Operon™) (see table 1), 1µl dNTP nucleotide mix
(QIAGEN™ 10mM), 7.75µl nuclease-free water, 4µl MgCl2 (Promega™, 25mM), 5µl 5X
green GoTaq® Flexi buffer (Promega™), 0.25 µl GoTaq® G2 Flexi DNA Polymerase
(Promega™) and 6 µl template DNA. Amplification was then carried out using a Bio-Rad™
MyCycler thermocycler following the following protocol: a 3-minute initial denaturing cycle at
94°C, followed by 40 one-minute cycles of denaturing, annealing and extension at 94°C,
59°C and 72°C, respectively. This protocol was designed to amplify a 760bp product of
putative Bonamia ostreae DNA.
In addition to this
PCR protocol, a
‘nested’ PCR
protocol was also
carried out on the
remaining primary
PCR products. The
first round PCR
products were
diluted to a concentration of 10% by combining 1µl of primary PCR product with 9µl of milliQ
water. 1 µl of diluted PCR product were combined with 0.5 µl B. ostreae nested F1 and R1
primers (Eurofins Genomics™), 1 µl dNTP nucleotide mix (QIAGEN™ 10mM), 12.75µl
nuclease-free water, 4µl MgCl2 (Promega™, 25mM), 5µl 5X green GoTaq® Flexi buffer
(Promega™) and 0.25 µl GoTaq® G2 Flexi DNA Polymerase (Promega™) to produce 25 µl
PCR reactions for each sample. These reactions were then amplified using the Bio-Rad™
MyCycler following the protocol: a 3-minute initial denaturing cycle at 95°C, followed by 35
one-minute cycles of denaturing, annealing and extension at 95°C, 55°C and 72°C,
respectively.
The final primary and nested PCR products were analysed using gel electrophoresis.
Agarose gel (Fisher, 1%) was made by adding 1g of Fisher Agarose to each 100ml of 1xTAE
buffer. 50 ml of gel was then combined with 4 µl ethidium bromide (Sigma) and then poured
into the casting tray. Two 15-well combs were added to allow 28 samples to be run each
time. The gel was allowed to set for approximately 30 minutes and the primary and nested
PCR products for each sample were loaded adjacent to each other. 5 µl of 100bp DNA
ladder (500 µg/ml, New England BioLabs Inc. ™) was combined with 1 µl blue Gel Loading
Dye (6X, New England BioLabs Inc. ™) and loaded onto the gel. The gels were then run for
Table 1. Forward andreverse primersequences,annealingtemperature
and amplicon size primary and nested PCR reactions.
PCR reaction Primer sequence Tm (°C) Amplicon size (bp)
C (FWD) CGGGGGCATAATTCAGGAAC
C (REV) CCATCTGCTGGAGACACAG
Nest (FWD) AAGGAATTGACGGAAGGGCAC
Nest (REV) TAAGAACGGCCATGCACCAC
59
55
760
150

17. 17
30 mins at 70V and the separated fragments were visualised using ultraviolet
transillumination in a Bio-Rad™ Gel Doc 2000 and analysed using Quantity One (Bio-Rad™)
software. The separated DNA fragments were then compared with the DNA ladder to
estimate fragment size.
2.4. Statistical analysis
All data obtained was stored using Microsoft Excel. The data was then imported into R and
all subsequent data manipulation, graphical representation and statistical analysis was
carried out there.
Firstly, all vectors and dataframes required for graphical and statistical analysis were
created. Boxplots were created for data visualisation and all normality was assessed using
Q-Q plots and Shapiro-Wilk Normality Tests. All Shapiro-Wilk outputs were indicated for
each Q-Q plot. The relationship between wet weight (g) and infection intensity was tested
using a General Linear Model and Spearman’s Rank Correlation Coefficient. Dunn’s Multiple
Comparison Test was used to assess differences in average infection intensity between
tanks within each treatment group and in arcsine-transformed detection success between
each treatment group for each detection method. Wilcoxon Rank Sum tests were used to
test for differences in average infection intensity between treatment groups, Kruskal-Wallis
Rank Sums tests to test for differences in average infection intensity between different
temperature and salinity treatments and Chi-Square Goodness of Fit tests to test for
differences in arcsine-transformed detection successes between different detection
methods. All statistical outputs can be found in the appendix. All statistical outputs were
denoted in the appropriate figures except in the case of non-significance and when the figure
would become over-crowded.

18. 18
3. Results
3.1. Visualisation of treatment data
3.1.1. Boxplots
The boxplots in figure 6 provide a useful tool for data visualisation. It can be seen that most
of the sample groups that they represent do not appear normally distributed with perhaps the
exception of figure 6b) (treatment A), although it does appear slightly skewed. The
remainder of the sample groups appear to be negatively skewed with figures 6a) and d) (all
samples and treatment group C) displaying a few outliers (supposedly shared). These
outliers represent a few exceptionally high parasite counts, apparently in treatment group C.
Considering the skew of the sample groups and the outliers in figures 6a) and d), it is
unlikely that the data is normal.
From figure 7(a-i) it can be seen that the majority of the data for the individual tanks is also
skewed one way or another (except for maybe 7f)).
Figure 6(a-e).Figure showing
boxplots of the mean
infection intensity for a) all
samples;b) treatment group
A; c) treatment group B; d)
treatment group C; e) Ryde
Middle samples.
Figure 7(a-i).Figure showingboxplotsof the mean infection intensity for a) tank A1; b) tank A2;
c) tank B1; d) tank B2; e) tank B3; f) tank B4; g) tank C1; h) tank C2; i) tank C3.

19. 19
3.1.2. Q-Q plots and normality
From figure 8(a-e), it can be seen from the Q-Q plots that the sample group data is close to
being normal as the majority of the points lie on the normal line of the plot. However, as the
boxplots appeared to portray, the data appears to be lightly tailed in treatment groups A and
B, and the rest of the data appears to be heavily tailed. The Shapiro-Wilk tests indicate
normality in all data groups except for all samples and treatment group C. All sample sizes
are equal (n=20) except for figure 8a) (n=80).
ail
Figures 9(a-i) indicate a mixture of normality and non-normality among the different tanks.
Figures 9(a-b) look normal from the Q-Q plot and the majority of the data points appear to fit
the normal line. However, the Shapiro-Wilk test indicates non-normality. Figure 9h) and i)
appears to be skewed to the right whilst the rest of the plots appear to show tailing to
different extents. None of the Shapiro-Wilk tests came back positive except for tank 4C,
which appears to heavily tailed. Additionally, as we break down the data into smaller groups,
Figure 9(a-i).Figure showingQ-Qplotsof meaninfectionintensitywithShapiro-Wilkstatisticand
p-value (n=10 for a and b, n=5 for c-f, n=7 for g and i and n=6 for h) for a) tank A1; b) tank A2; c)
tank B1; d) tank B2; e) tank B3; f) tank B4; g) tank C1; h) tank C2; i) tank C3.
Figure 8(a-e). Figure showing Q-Q plots of mean infection intensity with Shapiro-Wilk statistic
and p-value (n=80 for a and n=20 for b-e) for a) all samples; b) treatment group A; c) treatment
group B; d) treatment group C; e) Ryde Middle samples.

20. 20
the sample size decreases also with the lowest (n=5) for tanks B1-4 and the highest (n=10)
for tanks A1 and 2. With all this in mind, it is best to treat all data groups as non-normal and
apply non-parametric tests to test for significant differences.
3.2 Wet weight and infection intensity
3.2.1. Data visualisation
From figures 10(a-b), it can be seen that the intensity data appears to be skewed to the left,
whilst the Q-Q plot also reveals heavy tails in the data. In addition to this, the Shapiro-Wilk
test also indicates non-normality. However, the histogram and boxplot regarding the weight
data appears to show a normal distribution. The Q-Q plot and Shapiro-Wilk test also appears
to indicate normality. Square root transformation (recommended for count data) did not
result in normal intensity data. Therefore, the data should be treated as non-normal and non-
parametric tests should be applied.
3.2.2. Effect of wet weight on mean infection intensity
Figure 11 shows
that there
appears to be no
relationship
between weight
and infection
intensity. The
General Linear
Model applied to
the data appears
to show an
almost steady
infection rate
(slightly
Figure 10(a-f).Figure showinghistogram,boxplotandQ-QplotwithShapiro-Wilkstatisticandp-
value (n=60) of the meaninfectionintensityof Poole Harboursamples (a-c) andhistogram,
boxplotandQ-QplotwithShapiro-Wilkstatisticandp-value (n-60) of weight(g) of Poole
Harbour samples(d-f).
Figure 11. Figure showing mean infection intensity plotted against weight
(g) for Poole Harbour samples (n=60) with General Linear Model equation
and Spearman’s rank correlation coefficient.

21. 21
decreasing) as weight increases.
This apparent decrease in intensity is probably due to the high value outliers at lower
weights. The Spearman’s rank correlation coefficient indicates no correlation between wet
weight and infection intensity. As wet weight has no apparent effect on infection intensity, it
will be ruled out as a factor for the remainder of the analysis.
3.3. Infection intensity from histological analysis
3.3.1. Tank infection intensities
Firstly, it is
important to
understand
whether there
is any
variation
within
treatments
groups for
determining
whether there
is variations
between
treatment
groups. From
figure 12, it
can be seen
that the lowest mean infection intensities are associated with treatment group A
(approximately 5-6), whilst the highest mean infection intensities are associated with
treatment group C (approximately 16). Dunn’s multiple comparison indicated that only two
tanks within a treatment group were significantly different from each other (Tanks B3 and
B4). In addition to this, tanks from treatment group B also have the lowest sample size (n=5).
The standard deviations for group C and tanks B2 and B3 are also quite high.
3.3.2. Treatment group infection intensities
From figure 13, it can
be seen that treatment
group C had the
highest mean infection
intensity whilst
treatment group C had
the lowest mean
infection intensity.
Treatment groups B
and D appear to have
similar mean infection
intensities between
treatment groups A and
C. It can also be seen
that there are
numerous significant
Figure 12. Figure showing the mean (±1 S.D.) infection intensity for each
holding tank within each treatment group. Sample size (n) indicated within
bars, lower case letters denote statistically significant difference (P<0.05,
Dunn’s multiple comparison). All statistical outputs in appendix.
Figure 13. Figure showingmean(±1S.D.) infectionintensity for each
treatment group. Sample size (n) indicated within bars, lower case
letters denote statistically significant difference (P<0.05, Wilcoxon
rank sum test). All statistical outputs in appendix.

22. 22
differences between treatment groups. Firstly, treatment group A is significantly different
from all other treatment groups, as is treatment group C. In addition to this, treatment group
B is significantly different from treatment group C as well. The standard deviations
associated with treatments groups B-D also remain high.
3.3.3. Effect of temperature and salinity on mean infection intensity
Figure 14 can be used to visualise the differences in mean infection intensities between
different environmental conditions. Kruskal-Wallis rank sum test was used so that the effect
of temperature and salinity could be assessed instead of just testing between two
independent sample groups. At 34‰, the mean infection intensity increased from 9.42 at
12°C to 13.69 at 20°C (43% increase). At 12°C the mean infection intensity decreased to
5.57 at 28‰ salinity from 9.42 at 34‰ salinity (69% r).
3.3.4. Site comparison
Figure 15 shows the mean infection intensity for all Poole Harbour samples (n=60)
compared with the mean infection intensity of the Ryde Middle samples (n=20). The
Figure 14a) and 14b). Figure showing the mean (±1 S.D.) infection intensity of Poole Harbour
samples at a) 12 and 20 (°C) at 34‰ salinity and b) 28 and 34 (‰) at 12°C. Sample size (n)
indicated within bars, H-statistic and p-value of Kruskal-Wallis rank sum Test also supplied.
Figure 15. Comparison of mean (±1 S.D.) infection intensities between the two sample sites.
Wilcoxon rank sums test showed no significant difference.

23. 23
standard deviations remain large and the Wilcoxon rank sums test showed no significant
difference between the two sites as the mean infection intensities appear almost identical.
3.4. Detection methods and prevalence
3.4.1 PCR results
All PCR results were
obtained by
comparing
transilluminated DNA
fragments with a DNA
ladder. Figure 16
shows the gel
electrophoresis result
for the primary PCR
(P) and nested PCR
(N) for sample 3-10
from tank A1 and
compared with the
DNA ladder. This is
only 7 samples out of
80 and the rest of the
PCR results can be
found in the
appendix. The red
lines help to visualise
the 1000 and 500
base-pair bands of
the DNA ladder and the green lines help to visualise the 100 and 200 base-pair bands of the
DNA ladder. This helps us to determine whether the sample tests positive for the primary
PCR (760 base-pairs) and the nested PCR (150 base-pairs). From figure 16, we can see
that positive primary PCR results were obtained for samples 3A and 9A, although 9A is very
faint and questionable. The remainder of primary PCR results for this figure are negative,
however. The nested PCR appears to have tested positive for all samples in this figure.
However, faint bands were observed for some other samples and, depending on how faint,
may not have been convincing enough to warrant a positive result. Ideally, all DNA
fragments would need to be sequenced to determine whether they were truly positive.
3.4.2. Detection methods and success
Figure 17 was designed to compare prevalence estimates between three detection methods
(histological analysis, primary PCR and nested PCR). However, a prevalence of 100% was
obtained through histological analysis and it seems that comparing detection success
between the different methods would be more appropriate. Whilst histological analysis
revealed a 100% prevalence, PCR and nested PCR reactions only managed to successfully
detect 13.75 and 72.5%. These values indicate that there is a difference of 86.25% between
success rates of histology and PCR, whilst there is a difference of 58.75% between PCR
and nested PCR. According to figure 17, histology is the most successful method for
detection whilst PCR is the least effective.
Ladder
3A1(P)
3A1(N)
4A1(P)
4A1(N)
6A1(P)
6A1(N)
7A1(P)
7A1(N)
8A1(P)
8A1(N)
9A1(P)
9A1(N)
10A1(P)
10A1(N)
Figure 16. Photoof an example transilluminatedgel containing
primaryand nestedPCRproductsforsamples3A1-10A1 with100 bp
ladder.Redlinesindicateregionbetween1000 and 500 bp whilst
greenlinesindicateregionbetween100 and200 bp.All gel imagescan
be foundinthe appendix.

24. 24
Chi-squared
goodness of fit
tests revealed no
significant
difference
between the
arcsine-
transformed
proportions any
of the detection
methods.
3.4.3. Comparisons of detection success of each method between treatment groups
As can be seen from figure 18, the detection success of the primary and nested PCR
reactions varied between treatment groups. The highest primary PCR detection success
(25%) was for treatment group A, whilst the lowest (5%) was for treatments B and C. A 20%
detection success was found for treatment group D. The highest nested PCR detection
success (95%) was in treatment group A, whilst the lowest (40%) was in treatment group B.
Dunn’s multiple comparison’s test also revealed a significant difference between the arcsine-
transformed proportions of these two treatment groups. The detection successes for
treatment groups C and D were 85% and 70%, respectively. The highest detection success
for molecular methods appears to be in treatment group A, whilst the lowest appears to be in
treatment group B.
Figure 17. Figure showing the total detection success (%) for each
detection method (n=80).
Figure 18. Figure comparingthe detectionsuccess(%) foreachdetectionmethod for
each treatment group (n=20). Lower case letters denote a statistically significant
difference (P<0.05, Dunn’s multiple comparison’s Test)

25. 25
4. Discussion
Firstly, the data collected was visualised and tested for normality in figures 6-9. These plots
revealed a mixture of normality and non-normality between the treatment groups and a lot of
the data appeared to be skewed or tailed. However, Shapiro-Wilk normality tests revealed a
large proportion of normal treatment groups. However, the data for all samples was not
normal and nor was treatment C. The count data for treatment groups appears to display
great standard deviation as well as occasional outliers. Square root transformation
(recommended for count data) did not help to normalise non-normal groups of data. The
high standard deviations and some extreme outliers can be explained by the nature of this
experiment. Counts of B. ostreae cells were higher in tissues such as connective tissue and
digestive tubules. These odd, extremely high counts are probably the reason for the skew in
some of the data and the high standard deviations. Although sections were taken from the
front, middle and back of the tissue sample in an attempt to obtain an average
representative of the whole tissue samples, in future studies a higher number of technical
replicates should be examined. Due to the non-normality of some of the groups of data, the
high standard deviations across the board, numerous outliers and the fact that most of the
data was analysed according to discrete categories, all data was treated as non-normal and
the appropriate non-parametric tests were applied. This is especially true when comparing
mean tank intensities as some group sample sizes are greatly reduced (n=5).
The wet weight of all Poole samples were measured prior to dissection (mean=11.68g
±4.61S.D. n=85) in an attempt to quantify the effect of weight on mean infection intensity to
better understand the effect this parameter may have when analysing the mean infection
intensities between groups. From figure 11, it can be seen that there is no correlation
between wet weight (g) and mean infection intensity. The general linear model shows an
almost constant infection rate with increasing weight and the Spearman’s rank correlation
coefficient shows no sign of correlation between the two. Therefore, this parameter can be
ignored in the later analyses. Weight is not known to affect prevalence or infection intensity.
Older O. edulis individuals tend to show higher infection intensities although it is not entirely
clear why. Older and larger oysters are certainly more likely to have captured infectious
particles but a physiological change associated with age may affect the progression of the
disease (da Silva 2009). Reproduction in bivalve molluscs has been shown to be affected by
some protozoan parasites such as M. refringens in O. edulis (Robert et al. 1991) and M.
galloprovincialis (Villalba et al. 1993) and H. nelsoni in C. virginica, usually by inhibiting
gametogenesis. Van Banning (1990) suggested that B. ostreae has an incubation period
within the ovaries of O. edulis before developing to the infectious stage. Further investigation
showed no effect of gender on infection intensity (Caceres-Martinez et al. 1995; Culloty &
Mulcahy 1996). However, it has been suggested that oysters become weaker after
spawning, allowing the progression of bonamiasis (Hine 1991b; Culloty & Mulcahy 1996;
Jeffs & Hickman 2000). One study did obtain results that suggest an advancement of
infection associated with female gametogenesis (da Silva 2009). In any case, the Poole
Harbour oysters likely came from the same cohort, meaning that they were probably similar
in age too and therefore this effect of age is likely to be constant between individuals.
Another important factor to quantify before comparing infection intensities between treatment
groups is the variation within treatment groups. The only significant difference found within
treatments was between tanks three and four in treatment B. This may have also contributed
to the large standard deviations. Considering that there was no way of knowing how infected
the individuals stored in each tank were at the start of the incubation period, there is no way
to tell if this difference was due to different tank conditions, or to initial infection intensities. In
addition, this positive statistical result came from an extremely small sample size. Therefore,

26. 26
this apparent difference will be ignored. Future studies would benefit from the study of naïve
oysters and infect them through inoculation (Hervio et al. 1995). This way, each oyster can
receive the same dose of B. ostreae and proliferation can be measured more accurately.
Figures 13 indicates a statistically significant difference between all treatment groups except
for B and D, which appear similar. Figure 14 indicates a 43% increase in infection intensity
between oysters kept at 12°C and 20°C at 34‰ salinity and a reduction of 69% between
oysters maintained at 34‰ and 28‰ at 12°C. This suggests that a rise in temperature or a
rise in salinity can improve the parasites infectious abilities. One previous study did find
bonamiasis prevalence was higher at low temperature (10°C) than at high temperature
(20°C), suggesting that temperature may impact the abilities of the parasite and/or the
defensive capabilities of O. edulis (Cochennec & Auffret 2002). This impact of temperature
on prevalence may explain the peaks in parasite prevalence observed during late winter,
although transmission occurs year-round and peaks are also observed in Autumn (Grizel
1985). Arzul et al. (2009) found that 25°C did not appear suitable for parasite survival and
purified B. ostreae suspensions seemed to show preference for a hypersaline medium.
Additionally, Audemard et al. (2008) found that increased water temperature seemed to
increase the pathogenicity of Bonamia spp. in C. ariakensis. Finally, the environmental
conditions also would have affected oyster physiology, with the NRR time of haemocytes
reduced at low salinities (possibly due to osmotic imbalance). The retention time of
haemocytes in O. edulis has also been shown to be low around 10°C, with a peak at 15°C.
Retention time at 20°C also seems reasonable as a compromise between 15°C and 25°C at
high salinities (Hauton et al. 1998). The results of this study are particularly important in the
present context due to B. ostreae’s existence as a, predominantly, intrahaemocytic parasite.
These factors help to explain the apparent difference in infection intensity between treatment
groups in the Poole Harbour samples.
However, it is difficult to draw conclusions from the apparent difference in intensity between
treatment D and other groups due to the fact that the samples were taken from different sites
at a different time of year, resulting in the samples being drawn from sites with different
environmental conditions, as well as being at different life cycle stages. Additionally, no
control for age or size was made for treatment D. Figure 15 displays the average infection
intensities for all Poole samples compared with the average infection intensity of the Ryde
Middle (treatment D) samples processed. A Wilcoxon rank sums test showed no significant
difference between the two. Considering that the Ryde Middle samples were taken in early
autumn and the Poole samples in late winter (both approximately around the time of peak
prevalence) this is unsurprising.
The results presented here have many potential possible applications in management
practices, life cycle elucidation and in light of the current climate change issues facing us
(Audemard et al. 2008) as the global distribution of B. ostreae will surely be affected. O.
edulis can tolerate salinities as low as 23‰ (Beaumont) and, as the data suggests lower
parasite prevalence at lower salinities, management practices could be based around
holding individuals of susceptible populations at low salinities to mitigate oyster losses.
Similar management practices have been implemented, successfully, in an attempt to
mitigate infection of oysters with H. nelsoni in Delaware Bay (Haskin & Ford 1982).
Furthermore, a life cycle consisting of five stages, based on seasonal observations and
ultrastructural studies, has been proposed for Bonamia spp. in T. chilensis (Hine 1991b).
This life cycle correlates well with seasonal peaks in prevalence and is supported by
changes in ultrastructure throughout the cycle. Seasonal changes in temperature and
salinity, therefore, could possibly act as cues for transitions between development stages of

27. 27
the parasite. Although these studies are based on different species to B. ostreae, any
discoveries concerning B. ostreae may also be relevant to other haplosporidians, and vice
versa (Carnegie et al. 2004).
The implementation of these management practices in conjunction with other current
management practices may help to greatly reduce the impact of B. ostreae on the European
flat oyster industry. Perhaps one of the most important current areas of research with
regards to disease management is the development of resistance to B. ostreae in O. edulis.
Acquired resistance has been demonstrated previously in Ireland (Culloty 2001) and France
(Carnegie 2004). In both cases, this acquired resistance has led to improved survival.
Research has been conducted to try to better understand the mechanism for resistance in
flat oysters, comparing the circulating haemocyte ratios of resistance and susceptible
populations of O. edulis and C. gigas, which appears to resist infection by B. ostreae. The
study found that the proportion of agranular cells was significantly higher in infected oysters
than uninfected and significant differences in haemocyte ratios were observed between
susceptible and resistant oysters, suggesting that a lower number of granulocytes may
impact susceptibility (Cochennec et al. 2003b).
Figures 17 and 18 compare the total detection success of the three techniques employed in
this study and the success of these methods between treatment groups, respectively. From
figure 17, although no statistical difference was observed, it is quite clear that the nested
PCR protocol and histology returned a much higher prevalence estimate than the primary
PCR. This is a strange observation as the PCR protocol has previously been shown to be
more sensitive than histological techniques (Carnegie et al. 2000). However, this highlights
the need for optimisation of PCR protocols before reliable prevalence estimates can be
produced. Unfortunately, this optimisation could not be fully carried out in the present study
due to time and resource limitations. Additionally, in the case of PCR, there are a number of
steps involved, and no way of telling if a particular step has not worked properly. Therefore,
it is sensible to conclude that, although they are generally considered less sensitive (Lynch
et al. 2005), this PCR protocol should be used in conjunction with histological techniques.
The nested PCR returned a much higher prevalence estimate than the primary PCR.
Although, there are many methods recommended by OIE for the detection of B. ostreae
(OIE 2012). The results presented in this study suggest that protocol C (Carnegie et al.
2000) should be used in conjunction with a second round of nested PCR for higher
sensitivity. Detection through histology returned a prevalence of 100%, higher than either
molecular techniques employed here. For the Poole samples, this is unsurprising due to the
close proximity of infected oysters to each other in experimental tanks and both sites were
sampled during periods of peak prevalence. In addition to this, B. ostreae can resemble
intra-haemocytic inclusions, possibly resulting in false-positive microscopic identification
(Carnegie et al. 2000). However, the histological staining protocol recommended by the OIE
is staining in Haemotoxylin before counter-staining in Eosin. Using Gomori triple stain, no
difficulties were encountered in locating and identifying the parasite in histological sections in
the present study and is the protocol recommended by Austin & Austin (1989). Finally, the
unexpected PCR result may also be due to other factors. High counts of B. ostreae cells
were observed in the connective tissues and digestive tubules of O. edulis and some of the
negative PCR results may have been due to the type of tissue sampled. In any future study,
careful selection of the tissue used for PCR should be employed, preferably tissue from
deep within the animal.
Figure 18 indicates that there was no significant difference in detection success of any
method between treatment groups except between treatments A and B for the nested PCR.

28. 28
However, due to the apparent unreliability of the PCR results in this study, it would be
unwise to draw any conclusions on how temperature and salinity may affect detection.
Although the primary
molecular analysis
carried out in this study
has not provided reliable
results, there are
numerous other
detection methods
recommended by OIE for
the detection of B.
ostreae and molecular
diagnostic techniques
have great potential in
other areas of parasitic
research as well as
detection. The nested
PCR primers used in this
study show great
sensitivity, as well as
other PCR assays that
have been developed
(Corbeil et al. 2006;
Marty et al. 2006) as well
as a qPCR assay
(Robert et al. 2009) that
shows good sensitivity
and reliable prevalence
estimates when
compared with tissue
imprints (OIE 2012). The
reliability of the
prevalence estimates is
encouraging, as semi-
quantitative analysis of
B. ostreae infections has
traditionally been limited
to histological analysis.
These histological
methods have been
described as slow and require a trained observer for screening (Lynch et al. 2005), making
qPCR an attractive candidate for future analysis. The advantages and disadvantages of
histological techniques compared with molecular techniques, as well as the varying
sensitivities and specificities of certain techniques, are outlined in figures 19 and 20,
respectively. Molecular techniques such as laser-assisted microdissection could become
useful techniques for isolating individual parasites and may be used to more accurately
describe the life cycle of B. ostreae (Small et al. 2008). In addition to this, the use of crosses
between lines of selectively bred individuals may be useful in mapping quantitative trait loci
(QTL’s) and could be useful in elucidating the molecular basis of host-parasite interactions
(Carnegie et al. 2004).
Figure 19. Table comparingthe sensitivity(se) andspecificity(sp)
of some detectionmethods(OIE2009).
Figure 20. Figure summarisingthe advantagesanddisadvantages
of some detectionmethods(OIE2009).

29. 29
Conclusions and future work
In conclusion, the results presented here suggest that higher temperatures and salinities
result in an increased intensity of infection with B. ostreae in O. edulis, an important. This
has important implications in disease management practices and could be used in
conjunction with seasonal ultrastructural studies to shed some light on the life cycle of B.
ostreae. The results are also important in the context of climate change, as forecasting of
disease evolution is much needed.
Additionally, the primary PCR protocol used in this study proved unreliable. If used, results
should be verified with a second-round nested PCR reaction. Histological staining with
Gomori triple stain was a useful and easy protocol for identification of B. ostreae, but
molecular techniques as well as traditional techniques should both be employed for
detection as well as for physiological studies. Unfortunately, this study was limited by the
lack of technical replicates taken from each sample to give a fully representative rate of
infection due to high variation within tissue types. Additionally, time and resource constraints
did not allow for full optimisation of the primary PCR protocol.
Future work should now concentrate on seasonal ultrastructural studies that may help to
elucidate the life cycle of B. ostreae, as well as continued research into resistance
mechanisms in O. edulis. Both these areas of research have important implications in
disease management and industry practices. QTL mapping may play an important part in
unveiling the mechanism for resistance in O. edulis and life cycle studies (and the effect of
environmental conditions on development) may also help to reduce the economic impact
caused by this disease and contribute to the restoration of the European flat oyster industry.

30. 30
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