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Diversity and Dynamics of Algal Viruses
in the Bay of Quinte
By
Robin Marie Rozon
A thesis submitted in conformity with the requirements
for the degree of Master of Science
Ecology and Evolutionary Biology
University of Toronto
© Copyright by Robin Marie Rozon 2013
ii
Diversity and Dynamics of Algal Viruses in the Bay of Quinte
Robin Marie Rozon
Master of Science
Ecology and Evolutionary Biology
University of Toronto
2013
Abstract
To initiate algal virus research in the Bay of Quinte, three stations were sampled biweekly
throughout 2011. By targeting algal virus DNA polymerase, major capsid protein genes (MCP),
and a Microcystis aeruginosa cyanophage (Ma-LMM01) tail sheath protein gene, PCR
amplification revealed diverse and unique Phycodnaviruses (viruses of eukaryotic algae) and
cyanophage. When analysed statistically, patterns of virus abundance suggested that the
seasonality of any one virus cannot be generalised to predict that of other viruses, even among
closely related viruses. This study also demonstrated a strong relationship between algal virus
abundance and host biomass. It was found that despite the apparent heterogeneity of virus
abundance across the Bay, virus abundance patterns clustered by sampling date and geographic
location. By providing evidence for diverse algal viruses with complex seasonality, this work
highlights significant gaps in the current understanding of Bay of Quinte phytoplankton
ecology.
iii
Acknowledgements
First and foremost I would like to thank my supervisor Dr. Steven Short for making this
‘drive-by Masters’ the most rewarding and intellectually challenging experience of my life. It is
because of your enthusiasm that I came to UTM, it was with your encouragement that I learned
to learn from my mistakes, and without your guidance I would never had been able to finish this
thesis. Because of your passion for science I understand what it means to work hard and to truly
love what you do; it’s viral! I am forever grateful for your patience and commitment to my
education. Thank you for welcoming me into your lab family and will never forget what we’ve
accomplished here.
Thank you to the members of my supervisory committee, Dr. Peter Kotanen and Dr.
Nick Collins. Your insights, recommendations and comments throughout my graduate degree
have improved my thesis and my skills as a scientist. I hope I can be as beneficial to others in
the future as you have both been to me.
To Cindy Short, Michael Staniewski, and all the past and present members of the Short
lab. While my stay with all of you was ‘short’-er than other programs, you deserve more than a
‘short’ expression of gratitude. In all seriousness, I thank you from the bottom of my heart for
putting up with my endless questions and helping me at every step of my journey. You are the
heart and soul of our work; the lab would have been lonely without you. I will miss our lab
lunches and geeking out with other like-minded souls. I wish you all the best for your future
endeavours and expect many great things from all of you. Finally, thank you for teaching me
that “adversity builds character; if everything went right all the time you’d be as interesting as a
carrot”. People like you are rare in this world and I look forward to many lasting friendships.
A very big thank you goes to Dr. Mohi Munawar and the people at DFO-GLLFAS who
made this collaboration possible. Without the encouragement received from Mark Fitzpatrick
and Heather Niblock I would never have come this far. To the GLLFAS field and lab crew,
Robert Bonnell, Ashley Bedford and Michele Burley, the DFO samples were collected,
processed and analysed on your backs and I am forever grateful. To the smiling faces of Lisa
Elder, Jennifer Lorimer, Ron Dermott, Kelly Bowen, Marten Koops, and the staff at CCIW
Burlington, thank you for your encouragement throughout my undergraduate and graduate
degrees, and for making my time with you so unforgettable.
iv
Last but not least thank you to my family and friends who supported me through the
good times and the bad. Thank you for being a sympathetic ear when I needed it and a kick in
the pants when I needed that too. Special thanks to Nathaniel Costa for your understanding and
patience; I owe my current sanity to you. Also, thank you for use of your ‘workspace’ and
assorted computer programs. To my parents, thank you for tolerating my rants against physics,
statistics, traffic jams and incidences of personal electrical interference. Most of all, I truly
appreciate your support over the last six years; it’s the best feeling in the world when your
parents can tell other people, “my daughter is working on her Masters degree. Something about
the little dudes in the water… I think.”
Robin
v
Dedication
To my parents
Je t’aime toujours
vi
Table of Contents
Abstract ii
Acknowledgements iii
Table of Contents vi
List of Tables viii
List of Figures ix
List of Abbreviations x
Chapter 1: Introduction
The Bay of Quinte
A Historical Perspective 1
Environmental Degradation 1
Project Quinte 3
Phytoplankton in the Bay of Quinte
Research History 3
Historical Trends 5
Aquatic Viruses
Historical Perspective on Aquatic Virus Research 6
Virus Diversity and Dynamics in Aquatic Systems 9
Objectives and Research Questions 11
Chapter 2: Materials and Methods
Study Sites and Sample Collection 12
PCR, Cloning and Sequencing
AVS 13
MCP 14
Sheath 14
Sequence Analysis and Primer Design 16
Quantitative PCR 17
Analysis 18
vii
Chapter 3: Results
Algal Virus Diversity Study
Algal virus DNA polymerase genes 20
Algal virus major capsid protein genes 20
Microcystis phage sheath protein genes 21
Quantitative Analysis of Viral Dynamics
Virus Dynamics across Time and Space 21
Statistical Analysis of Viral Dynamics 23
Virus Abundance and Host Biomass 24
Clustering by Taxonomic Classification 25
Clustering by Spatial Distribution 26
Chapter 4: Discussion
Algal Virus Diversity Study 27
Quantitative Analysis of Viral Dynamics
Virus Dynamics across Time and Space 31
Statistical Analysis of Viral Dynamics 35
Virus Abundance and Host Biomass 37
Clustering by Taxonomic Classification 39
Clustering by Spatial Distribution 41
Summary 42
Future Directions 44
Literature Cited 45
viii
List of Tables
Table 1. Limnological Divisions of the Bay of Quinte 52
Table 2. Sample Locations 53
Table 3. Reagents used in PCR reactions 54
Table 4. Thermocycling parameters for PCR reactions 55
Table 5. Quantitative Primers and Probes 56
Table 6. Reagents used in quantitative PCR 58
Table 7. Test of Primer and Probe Specificity 59
Table 8. Friedman analysis of virus abundances between stations 60
ix
List of Figures
Figure 1. Map of the Bay of Quinte 61
Figure 2. Neighbor joining phylogeny polB 62
Figure 3. Neighbor joining phylogeny MCP 63
Figure 4. Abundances of individual virus genes plotted against time 64
Figure 5. Virus gene abundances at each station plotted against time 65
Figure 6. Virus gene abundance 66
Figure 7. Regression of the sum of virus gene abundances on chlorophyll a. 67
Figure 8. Cluster analysis of virus abundance 68
Figure 9. A comparison of different proximity measures 69
Figure 10. Cluster analysis of biweekly stations 70
Figure 11. Cluster analysis of spatial stations 71
x
List of Abbreviations
ºC – Degree(s) Celsius
µ - micro (10-6
)
µL – microliter(s)
µm – micrometers
µM – micromolar
AOC – Area of Concern
B – Belleville sampling station
BLAST – Basic Local Alignment Search Tool
bp – Base pairs
BQ – Bay of Quinte
cm – centimeters
Ct – Cycle threshold
DFO – Fisheries and Oceans Canada
DH5α – E. coli strain of competent cells
DNA – deoxyribonucleic acid
dNTP – deoxyribonucleotide triphosphate
dsDNA – Double stranded DNA
EDTA – ethylenediaminetetraacetic acid
FAM – 6-carboxyfluorescein
FQ – Fluorescein quencher
g – gram(s)
GLLFAS – Great Lakes Laboratory for
Fisheries and Aquatic Sciences
GLWQA – Great Lakes Water Quality
Agreement
GPS – Global Positioning System
HAB(s) – Harmful Algal Blooms
HB – Hay Bay sampling station
HCl – Hydrogen chloride
IBM SPSS – International Business
Machines’s Statistical Package
for the Social Sciences
IJC – International Joint Commission
IPTG - Isopropyl β-D-1-
thiogalactopyranoside
JTT – Jones-Taylor-Thornton
L – liter(s)
LB agar - Lysogeny broth agar
LE agarose – Low electroendosmosis agarose
LO – Lake Ontario
M – Molarity
m – meter(s)
Ma-LMM01 – Strain of Microcystis
aeruginosa cyanophage
MCP – Major capsid protein
xi
MEGA – Molecular Evolutionary Genetics
Analysis
mg – milligram
MgCl2 – Magnesium chloride
mM – milimolar
mm – millimeter(s)
mol – mole(s)
MUSCLE – Multiple Sequence Comparison
by Log-Expectation
n – nano (10-9
)
N – Napanee sampling station
NCLDV – Nucleocytoplasmic large DNA
virus
ng – nanogram(s)
NR – Napanee River
OTU(s) – Operational taxonomic units
PBCV-1 – Paramecium bursaria Chlorella
virus 1
PCR – Polymerase chain reaction
polB – DNA polymerase
PQ – Project Quinte
PVDF – Polyvinylidene fluoride
qPCR – Quantitative polymerase chain
reaction
RAP – Remedial Action Plan
RNA – Ribonucleic acid
ROX – 6-Carboxyl-X-Rhodamine
rpm – revolutions per minute
s – second(s)
SNPs – Single nucleotide ploymorphisms
SS – Spatial survey
ssDNA – Single stranded DNA
TAE buffer – TrisCl base, acetic acid and
EDTA
TEM – Transmission electron microscope
TP – Total phosphorus
TrisCl – Tris(hydroxymethyl)aminomethane
UPGMA – Unweighted pair group method
with arithmetic mean
UV – ultraviolet light
USA – United States of America
V – volt(s)
VC – viral concentrate
X g – acceleration due to gravity
X-Gal – Bromo-chloro-indolyl-
galactopyranoside
YGDTDS – amino acids encoding for
catalytical domain of a viral
DNA polymerase
1
Chapter 1: Introduction
The Bay of Quinte
A Historical Perspective
In the Late 1700s, empire loyalists fled north and settled along the northern shores of Lake
Ontario. They preferentially settled in a small, ecologically diverse area which could support
their logging and farming needs, now known as the Bay of Quinte. The Bay of Quinte is a 100
km long Z-shaped embayment on the northern shores of Lake Ontario, located 135 km east of
Toronto and 40 km west of Kingston. It has an area of 254 km² and acts as a watershed for
18,182 km², including four major rivers (Trent, Moira, Salmon, and Napanee) that empty into
the Bay. The Bay of Quinte is narrow and relatively shallow, deepening at the interface to Lake
Ontario (Figure 1). The Bay is subdivided into 3 bays; Upper, Middle and Lower Bay, each with
distinct widths and depths (Table 1). The Lower Bay has the only natural passages to Lake
Ontario (Upper and Lower Gap), however the Upper Bay has a navigational channel which
artificially joins the Bay of Quinte to Lake Ontario to allow for ease of access to the local towns
and cities (Johnson and Hurley, 1986).
There are 12 townships, 3 towns (Picton, Napanee, and Deseronto), 2 cities (Trenton and
Belleville) (Committee, 1990), and two historic Department of National Defence bases within
the Bay of Quinte area (Johnson and Hurley, 1986). Over the years, the uses for the Bay of
Quinte have ranged from agricultural to industrial, to commercial and sport fishery, and other
recreational pursuits (Committee, 1993). Belleville and Deseronto take water from the Bay for
domestic and industrial use, but all the aforementioned municipalities have discharged
wastewater into the Bay. Some of these activities were the cause of the excessive nutrient
enrichment and eutrophication of the Bay of Quinte (Johnson and Hurley, 1986).
Environmental Degradation
Since the arrival of settlers in the 1780s, land use in the area has had a great impact on
water quality in the Bay of Quinte. Increased logging and agriculture gave way to full-scale
urbanization in the late 1800s which included mining and deforestation (Committee, 1990).
Nutrient enrichment in the Bay of Quinte became apparent in 1904 when algal slime began
fouling fishing nets and again in 1938 causing taste and odour issues for the municipal water
2
treatment plants, all likely due to excess phosphorus from the introduction of untreated sewage
and detergents into the Bay (Minns et al., 2011).
The 1940s saw the collapse of the commercial herring fishery and a shift from pike and
bass (visual predators) to walleye (turbid water predators); all indications of more turbid, algal
prone waters. Eurasian milfoil (Myriophyllum spicatum) invaded the Bay in the 1950s,
outcompeting the native macrophytes. By the 1960s, algal blooms were so severe that other
macrophyte species were shaded and disappeared. This massive die-off caused low dissolved
oxygen at depths, loss of species diversity, repugnant odors and diminished water clarity. To
make matters worse, white perch invaded the Bay around this time, decimating the native
populations of whitefish, lake herring, walleye and northern pike (Committee, 1990).
Finally, in 1970, the low dissolved oxygen levels, loss of species diversity and diminished
water quality drove the International Joint Commission (IJC) to take action against the levels of
phosphorus being discharged into Lake Ontario and the Bay of Quinte by striving to achieve “at
least an 80 percent reduction by 1975” (Minns et al., 1986). Two years later, Project Quinte, a
long-term multi-agency ecosystem project lead by Fisheries and Oceans Canada was developed
to control eutrophication under the newly formed Great Lakes Water Quality Agreement
(GLWQA). In 1975, the Bay of Quinte was listed as a “problem area” mainly due to its
excessive nutrient enrichment, nuisance algal blooms, low dissolved oxygen and general
contamination. By the time the GLWQA was renewed in 1978, municipal phosphorus loadings
into the Bay of Quinte had decreased by 50 % and there were improvements in water quality.
However phosphorus levels in the Bay had only decreased by 35 %, trophic levels remained
unbalanced, ecosystems remained dominated by single algal species, and the Bay remained
unsafe for recreational use (Committee, 1990).
In 1985, the Bay of Quinte was listed as an Area of Concern (AOC) according to the
GLWQA. AOCs are locations where environmental quality and beneficial uses of the aquatic
environment are lessened. The Bay of Quinte had 10 of the 14 potential use impairments,
including wildlife consumption restrictions, degradation of benthos, drinking water restrictions,
and particularly, undesirable algae. Part of the process of being listed as an AOC is the
development of a Remedial Action Plan (RAP) which defines the actions and timetables to
restore the beneficial uses of the AOC through surveillance, monitoring and the control of
pollution sources (Minns et al., 1986).
3
Project Quinte
What began as a straightforward research objective from 1972, “does phosphorus
reduction to specific levels at point sources increase production of economically important fish
stocks and improve water quality in a reasonable amount of time?” became a multi-agency
project that brought together committed scientists and managers with expertise, interest and
concern (Minns et al., 1986). Project Quinte has become a pilot project for the experimental
management approach; with base line data from before phosphorus-loading restrictions came
into effect, to 40 seasons of annual research and monitoring activities. Such multi-level
ecosystem studies are rare and unique in the world (Minns et al., 2011).
With the successful completion of RAP stage one (Committee, 1990), and stage two
(Committee, 1993), local organisations, alongside federal and provincial governments, are
focusing on the implementation of the 80 recommendations set out in the stage two report and
monitoring of the improvements from these remediation efforts to ensure that the goals are
accomplished. Goals presented in 2000 include: fewer beach closures, healthy primary
producers and protection for habitats restored to acceptable levels. Successful completion of
stage three is followed by a delisting process where the RAP participants decide if all the
remediation goals have been met. At this time, the Area of Concern becomes an Area in
Recovery, where continued diligence is necessary to maintain the health of the ecosystem
(Committee, 1990).
The Bay of Quinte is targeted for delisting in 2013; therefore the current focus is on
meeting those targets, reporting the science and preserving the knowledge as successors carry
on. Project Quinte has become a leader in eutrophication research and ecosystem-wide
modeling, with the goal of promoting whole ecosystem wellbeing (Minns et al., 2011). Because
of Project Quinte, localized phytoplankton research is ongoing and far-reaching.
Phytoplankton in the Bay of Quinte
Research History
Prior to Project Quinte, phytoplankton research in the Bay of Quinte was intermittent. The
first investigation of phytoplankton in the Bay of Quinte was accomplished by Tucker (1948).
4
By sampling in the Lower Bay during the summer of 1945, the research examined the
relationship between wind and mixing depth, and determined the viability of phytoplankton
enumeration as a measure of productivity (Tucker, 1948). This marked the first examination of
phytoplankton trends and abundances in the Bay. Since then, phytoplankton in the Bay of
Quinte have been the focus of numerous studies (McCombie, 1967; Nicholls and Carney, 1979;
1986; Nicholls and Heintsch, 1986; Nicholls et al., 1986; Nicholls et al., 2002; 2004; Nicholls
and Carney, 2011; Munawar et al., 2011).
Project Quinte, as mentioned, was developed as a case study to assess the response of the
Bay to a reduction in phosphorus loadings. Fourteen years after its inception, a special
publication detailing ten years’ worth of research in the Bay of Quinte was released to detail
how the experimental management approach was progressing. Included in this publication was a
paper by Nicholls and Carney (1986) which demonstrated that nitrogen was limiting to
phytoplankton growth in the highly eutrophic Upper Bay while phosphorus was limiting in the
mesotrophic Lower Bay. By studying the impact of reduced phosphorus loading in the Bay of
Quinte, it was found that periods of nitrogen limitation were much less frequent after the
implementation of the phosphorus abatement program, which coincided with a reduction in
biomass of N-fixing phytoplankton, such as blue-green algae like Anabaena and
Aphanizomenon. In the same special publication, Nicholls et al., (1986) described specific
phytoplankton biomass from 1972 to 1981, precisely during the period of reduced phosphorus
loading. It was found that total phytoplankton biomass and bloom durations decreased
significantly, so much so that odor problems and municipal filter clogging issues had lessened.
It was also found that the phytoplankton communities within the natural divisions of the Bay
were no longer as distinct after 1977 (Nicholls et al., 1986). In addition, Nicholls and Heintsch
compared total phytoplankton levels in the Lower Bay from 1945 to 1981 by duplicating the
methods and analysis of Tucker (1948). The results showed that by 1945, the Bay of Quinte was
already becoming eutrophic (although not as eutrophic as the 1950s and 60s) and that there was
no significant difference between the total biomass in the Lower Bay between these studies
(Nicholls and Heintsch, 1986). Nicholls continues to research phytoplankton in the Bay of
Quinte, focusing on statistical assessments of the impact of reduced phosphorus loading and the
Dreissena spp. invasion on phytoplankton and how it might be possible to guide future
management objectives (Nicholls and Carney, 2011).
5
Historical Trends
Since Tucker (1948) indexed biological productivity in the Bay of Quinte, Nicholls
repeatedly analyzed biomass and community composition trends of phytoplankton species over
the past three decades (Nicholls et al., 2002; Nicholls, 2010; Nicholls and Carney, 2011). Since
1970, the Bay of Quinte has been subjected to two large environmental alterations; point-source
phosphorus loading reduction in the late 1970s and the Dreissena invasion around 1995.
Following reduced phosphorus loading, there was a 51 % decline in total phytoplankton
biomass. There were significant declines in total biomass of Chlorophyceae (–66 %),
Dinophyceae (–58 %), Bacillariophyceae (–56 %), Cryptophyceae (–52 %), and non-nitrogen
fixing species of Cyanophyceae (–26 %). The dominant diatom species Aulacoseira and
Stephanodiscus also showed biomass declines by 58 and 78 % respectively.
The arrival of zebra mussels 15 years later (~1995), influenced some phytoplankton
populations differently than others; total Bacillariophyceae, total Chrysophyceae, and total
Cryptophyceae biomass did not change significantly, but there was a 42, 51 and 55 % decline in
biomass in Chlorophyceae, Cyanophyceae, and Dinophyceae, respectively. Although total
diatom biomass did not change, there was a shift in diatom communities with certain taxa such
as Stephanodiscus, Synedra, and Tabellaria declining by 82, 84, and 98 %, respectively. Of
particular relevance, there was a 13-fold increase in the biomass of bloom forming, toxin-
producing Microcyctis (Nicholls et al., 2002). Overall, the most important primary producers in
the Bay of Quinte have consistently been Bacillariophyceae and Cyanophyceae, which make up
between 75 and 95 % of the total phytoplankton biomass in the Upper Bay, less so in the Middle
and Lower Bay. Within cyanophyta, Anabeana, Aphanizomenon, and Gloeotrichia (all nitrogen-
fixing species) were abundant prior to the establishment of dreissenids. After the arrival of zebra
mussels, non-nitrogen fixing species, such as Microcystis became more abundant, particularly in
the Middle and Lower Bays (Nicholls and Carney, 2011).
Multi-year patterns in phytoplankton biomass have emerged since the origin of Project
Quinte. In the Upper and Middle Bays, the period 1972-1977 showed very high total biomass
(7-16 mm3/L), followed by a year of low biomass associated with point source phosphorus
control. Biomass steadily increased from 1979 to 1984, then stabilized around 5-10 mm3/L until
the arrival of dreissenids in 1995. For the three years following the invasion of zebra mussels
6
phytoplankton biomass values were the lowest recorded, but then they rapidly increased in 1998
to pre-phosphorus control levels. A sharp decline followed, so much so that 2000 had the lowest
biomasses since 1970. Currently, phytoplankton levels have stabilized around the Upper Bay
RAP objective levels of 4-5 mm3/L (Nicholls and Millard, 2005; Nicholls and Carney, 2011).
Although these changes in phytoplankton growth in the bay over the last few decades have been
somewhat unpredictable, it is clear that the phosphorus abatement program and the invasion of
zebra mussels have had a major impact on algal growth in the Bay of Quinte. While it is
reassuring that algal biomass has been reduced in recent decades, the observed shifts in
community composition with increased abundance of harmful species like Microcystis represent
a new and unpredicted management challenge. Despite the uniqueness and thoroughness of
Project Quinte’s 40 seasons of field data, it is notable that important constituents in Bay of
Quinte food webs, such as algal viruses, have not been studied. Inclusion of these microbes in
Bay of Quinte research will certainly provide a more complete picture of the Bay’s algal
ecology, and may highlight unforeseen complexity helping explain why shifts in Bay of Quinte
algal communities have been unpredictable.
Aquatic Viruses
Historical Perspective on Aquatic Virus Research
Forty years ago, R. M. Brown wrote a seminal review on little known, little understood
algal viruses (Brown, 1972). Brown summarized previous studies describing the isolation of
viruses that infect the blue-green algae Lyngbya, Plectonema and Phormidium (Safferman and
Morris, 1963), as well as other studies that provided the first evidence that viruses could infect
eukaryotic algae such as Chlorella pyrenoidosa (Zavarzina and Protsenko, 1958). With the
discovery of the bacteriophage nearly 50 years prior, Safferman and Morris (1963) approached
their discovery of a blue-green algae virus with some skepticism. They provided evidence for
viruses of cyanobacteria by showing that filtered material could lyse growing cultures, phage-
like particles were present in electron micrographs, and by demonstrating the virus’s host
specificity (Safferman and Morris, 1963; 1964). Thus, at the time of Brown’s review the idea of
viruses being the cause of rapid algal population changes was not new; a number of prokaryotic
viruses had been isolated and virus-like particles had been observed for a number of eukaryotic
hosts. While these observations of viruses infecting algae were profound, at the time the
7
possibility that algal viruses were highly abundant and widespread in marine and freshwater
environments was not recognized.
Later, the observation of 5 x 106
to 2.5 x 108 virus-like particles per milliliter of water in
transmission electron micrographs of a variety of seawater samples represented a major
paradigm shift in marine microbiology (Bergh et al., 1989). Bergh’s discovery of abundant and
widespread viruses has since been supported by complementary methods such as
epifluorescence microscopy and flow cytometry which also estimate that all seawater samples
contain from 106
to 108 viruses/mL; there are roughly 10
30 viruses in the ocean making them the
most abundant biological entity in the ocean (Suttle, 2005). A study by Proctor and Fuhrman
(1990) also supported the observation that marine viruses were highly abundant, but also
emphasized that they were ecologically relevant. Similar observations of highly abundant
viruses have been noted for freshwater ecosystems, particularly in samples from numerous lakes
in Quebec, Canada that had on average 1.1x108 virus-like particles/mL (Maranger and Bird,
1995), and Lake Ontario that had 2.6x107 virus-like particles/mL (Gouvêa et al., 2006). With the
discovery of the most abundant biological entity in the ocean came the realisation that little was
known about the ecology of algal viruses; counts provided total virus estimates, but gave no
information on virus diversity or potential hosts.
Eukaryotic algal virus research prior to 1979 was strictly observation and speculation. It
wasn’t until after viruses were successfully isolated that viral infection of eukaryotic algae was
demonstrated. The first eukaryotic algal virus isolated was a lytic virus infecting Micromonas
pusilla, a small marine eukaryotic member of Prasinophyceae (Mayer and Taylor, 1979). Mayer
and Taylor observed small polyhedral virus-like particles within M. pusilla under transmission
electron microscope (TEM) and subsequently lysed otherwise healthy M. pusilla cultures using
filtered medium from an infected culture. Through TEM, they successfully observed the
infection and lysing of a host cell by this algal virus. The first freshwater eukaryotic algal isolate
was obtained shortly after when PBCV-1, a large double-stranded DNA virus which infects
Chlorella-like algae, was isolated from Chlorella NC64A, obtained from Lake Muscatine, USA
(Van Etten et al., 1983). A particularly interesting viral isolate was the Acanthamoeba
polyphaga mimivirus (La Scola et al., 2003). This virus contained a record sized genome (1.2
megabase pairs) and was the largest known virus at the time (Raoult et al., 2004). This
mimivirus has been classified into its own group, Mimiviridae, as part of the nucleocytoplasmic
8
large DNA virus (NCLDV) family as the closest relative of Phycodnaviridae (viruses that infect
eukaryotic algae). From studies of these model virus systems as well as many others, advances
in algal virus ecology related to knowledge of virus burst sizes (range 102 to 10
5), host
specificities (with the exception of viruses of brown algae, there are no known algal viruses that
infect more than one species), persistence in the environment (thought to range from days to
weeks), and genome composition (RNA, ssDNA, dsDNA, etc) have highlighted the biological
complexity of these algal parasites (Short, 2012). While these concepts are important to the
understanding of virus ecology, they don’t demonstrate the ecological significance of algal
viruses in aquatic food webs.
By observing algal viruses in the laboratory and in situ, the impact of viruses on the
phytoplankton community can be inferred. For example, an early study by Suttle at al. (1990) on
primary productivity of phytoplankton infected by viruses suggested that a wide range of
primary producers, including diatoms, cryptophytes, prasinophytes and cyanobacteria could be
susceptible to viral infection. The infection of a phytoplankton community by viruses obtained
from the same seawater sample resulted in a 78 % reduction of primary production indicating
that viruses and their specific hosts occur in close spatial and temporal proximity. The in situ
observations of Emiliania huxleyi, a marine coccolithophorid, and large viruses-like particles by
Bratbak et al. (1993) suggested that host blooms are succeeded by increased virus abundances
and that viruses accounted for 25-100 % of the net mortality of E. huxleyi under non-limiting
nutrient and low nitrate conditions. It is interesting to note that these studies were repeated over
four years in the same Norwegian Fjords, and different virus and phytoplankton abundance
trends were observed each year (Bratbak et al., 1993). The studies by Suttle at al. (1990) and
Bratbak et al. (1993) suggest that viral infection is an important regulator of phytoplankton
community structure.
Phytoplankton community succession is a well-studied area of research; however, the role
that viruses play in host succession is still unresolved. When host species which are differently
susceptible to different strains of virus coexist, it creates the opportunity for host succession.
Viral infection of the most abundant or most productive host species could lead to the rapid
decline of that specific algae species by “killing the winner”, thereby making way for other
hosts to take advantage of the newly available resources (Thingstad, 2000; Winter et al., 2010).
As host densities decline, the probability of a virus encountering its host also declines, leaving
9
scientists to speculate which host population will be the next ‘winner’. While these theories of
virus ecology are compelling, empirical support is lacking and viral dynamics are still, for the
most part, unresolved; it is still unknown if some virus species are ‘drivers’ in host blooms
dynamics while some species are ‘passengers’ along for the ride (Short, 2012). Therefore it is
crucial to determine what viruses are present in the environment and in what abundance.
Virus Diversity and Dynamics in Aquatic Systems
With advancements in molecular biology, tools to amplify and examine environmental
DNA became available to scientists in 1983 with the invention of the polymerase chain reaction
(Mullis et al., 1986). With the development of the polymerase chain reaction (PCR), a
cultivation-free method of virus identification became possible. The first efforts to uncover the
diversity of eukaryotic algal viruses came from Chen and Suttle (1995) with the development of
primers specific to the DNA polymerase gene (polB) of Phycodnaviruses. Not only did Chen
and Suttle amplify DNA from the aforementioned virus isolates (Chloroviridae and
Prasinoviridae), but they were also able to amplify DNA from natural virus communities,
creating the first technique to rapidly detect eukaryotic viruses in the environment (Chen et al.,
1996). This work paved the way for advancements in quantitative molecular studies of
eukaryotic algal viruses in both marine and freshwater environments. Of particular significance
to this study are the diversity studies by Short and Short (2008) and Short et al. (2011b). In
2008, Short and Short examined the diversity of Phycodnaviruses using the polB gene by
sampling four sites in two distinct geographic locations. They found and sequenced a large
number of unique gene sequences and through phylogenetic analysis determined that cultured
marine viruses were not genetically distinct from the freshwater viruses identified during their
study. Three years later, Short et al. (2011b) used new and extant polB PCR primers to amplify
and identify gene fragments from Lake Ontario that were related to genes from cultivated
prasinoviruses, chloroviruses, and prymnesioviruses; interestingly this first report of freshwater
prymnesioviruses demonstrated the existence of viruses that had never before been observed in
freshwater. While the polB primers allow an examination of algal virus diversity, they may
provide a limited picture of diversity since they are biased towards prasinoviruses and
chloroviruses (Short 2012). Fortunately these primers are not the only molecular approach
available to examine algal virus diversity.
10
Sequencing genomes of viruses that infect marine prymnesiophytes and haptophytes
revealed conservation among major capsid protein (MCP) genes. By developing universal algal
virus PCR primers targeting MCP gene fragments, it was possible to infer the genetic
relatedness of MCP sequences from cultured viruses and environmental clones (Larsen et al.,
2008). A recent study by Park et al. (2011) demonstrated that the use of both DNA polymerase
and MCP PCR methods to study the diversity of prasinoviruses, chloroviruses, and
prymnesioviruses simultaneously enabled the discovery of wide genetic diversity within similar
virus groups across different geographic locations.
The isolation of cyanophage infecting the toxic bloom forming cyanobacteria Microcystis
aeruginosa from a freshwater lake in Japan (Yoshida et al., 2006) is particularly important to
HAB (Harmful Algal Bloom) research in general, and this study of viruses in the Bay of Quinte
specifically. By developing PCR primers that specifically target genes for Microcystis
aeruginosa phage tail sheath proteins, it was possible to identify the cyanophage in the
environment and assess its potential impact on M. aeruginosa blooms (Takashima et al., 2007).
Since Chen and Suttle (1995), a number of algal virus genes have been studied and numerous
PCR primers have been developed creating tools that can be used to study the environmental
diversity of a broad spectrum of algal virus (Short et al., 2010). Following the implementation of
methods to examine algal virus diversity in marine and freshwater environments, the dynamics
of these viruses can be explored via the development of real-time, quantitative molecular
techniques.
Quantitative studies of algal virus dynamics are still relatively rare in the literature, but
several studies have used quantitative polymerase chain reaction (qPCR) to investigate algal
virus dynamics. Of particular relevance to this Bay of Quinte study is research on the dynamics
of algal viruses in Lake Ontario. Short and Short (2009) used qPCR to track the abundance of
three viruses over 13 months in weekly samples. Using these seasonal abundance patterns, Short
and Short demonstrated that some algal viruses persisted at low abundances during the winter,
while others were more transient, possibly being brought in by riverine flow. Further, continuing
research over longer monitoring periods demonstrated that many viruses are persistent in the
environment suggesting that seed-bank virus populations may be an ecologically important
factor in phytoplankton community succession (Short et al., 2011a). Although Gouvêa (2006)
sampled one station in the Bay of Quinte for total virus abundance (2.09 x 107 particles/mL), no
11
research has specifically examined algal viruses in the Bay of Quinte. However, if the paradigm
that diverse phytoplankton and viruses are present in all freshwater environments holds true,
then one would expect to find a diverse and dynamic community of viruses in the Bay of
Quinte.
Objectives and Research Questions
The Bay of Quinte and its rich history of seasonally occurring algal blooms present a
unique opportunity to investigate the roles that viruses play in complex phytoplankton
dynamics. Groundbreaking research into algal viruses in the Bay of Quinte could contribute to
understanding freshwater viruses and their impact on phytoplankton communities, as well as the
ecology of the hazardous algal blooms that have plagued the Bay of Quinte for many years. This
study extended previous research on freshwater viruses by increasing the scope of diversity
studies based on algal virus DNA polymerase genes (Short & Short 2008, Short et al. 2011b) by
including other algal virus marker genes such as MCP (Phycodnaviruses) and sheath protein
(Microcystis phage) to examine diversity in the Bay of Quinte. This study then examined the
dynamics of several distinct algal virus taxa across space and time to uncover patterns in algal
virus seasonality. The outcomes of this project were realized through a series of molecular
approaches designed to address the following questions: What virus populations are present in
the Bay of Quinte at a given location and time? Can the use of complementary PCR methods
targeting several different marker genes reveal greater diversity than methods based on any one
marker alone? How do virus abundances in the Bay of Quinte fluctuate temporally over the
sampling season, and spatially between stations? Does the seasonal timing of increased virus
abundance coincide with increased phytoplankton biomass? Do taxonomically related virus
populations show similar seasonality? And finally, are viruses patchily distributed throughout
the Bay such that trends in virus abundances are similar in proximal locations?
12
Chapter 2: Materials and Methods
Bay of Quinte algal virus communities were characterized using a variety of approaches.
Algal virus diversity was examined through sequencing clone libraries of marker gene
fragments amplified via PCR. Virus population dynamics were examined using qPCR with
primers and probes that were designed to target ten different virus genes observed in this study
as well as previous studies (Short and Short, 2009; Short et al., 2011a). Individual virus
abundances were compared to examine trends within and between taxa, and a variety of cluster
analyses were used to examine temporal and spatial patterns of virus abundance.
Study Sites and Sample collection
In collaboration with the Great Lakes Laboratory of Fisheries and Aquatics Sciences
(GLLFAS) of Fisheries and Oceans Canada (DFO), samples were collected approximately
biweekly during the 2011 sampling season, from early May to late October, from three long-
term study sites (Belleville, Napanee, and Hay Bay) in the Bay of Quinte. An additional 9
samples were also collected from various stations in the Bay of Quinte during an intensive
spatial survey conducted from September 13th
to 15th
(Table 2). Each water sample was an
integrated epilimnetic sample collected from the surface to one meter above the thermocline, or
in the absence of stratification, to twice secchi depth to a maximum of 1 meter from the bottom,
as determined by a Hydrolab Minisonde 5 (Hach Hydromet; Colorado, USA). As per the Quinte
RAP, complementary field data and laboratory analyses conducted by Fisheries and Oceans
Canada will be made available in the Project Quinte Annual Report for 2011. Of particular
importance here are chlorophyll a concentration estimates which were used as a proxy for host
abundance.
Following collection, water samples were transported on ice back to the Canada Center for
Inland Waters in Burlington, Ontario, for processing within 12 hours of field sampling. As
described in Short and Short (2008), each virus sample was filtered using a 0.45 µm pore-size
Durapore PVDF membrane filter (Millipore; Billerica, USA) and the filtrate was stored at 4°C
until it could be transported to the University of Toronto Mississauga for further processing.
From each sample, 72 mL of the filtrate was centrifuged in a SW32-Ti rotor at 25°C for 3.5
hours at 118,000 X g. Following centrifugation, the supernatant was decanted and 300 μL of 10
mM TrisCl (pH 8.0) was added to each centrifuge tube. All tubes were soaked overnight at 4°C,
13
vortexed for 30 seconds and pelleted material was resuspended using a pipettor. The
resuspended material, henceforth called a virus concentrate, was transferred into a screw-cap
microcentrifuge tube and stored at -20°C.
PCR, cloning and Sequencing
As described in Short and Short (2008), to prepare samples for PCR, 50 µL of each viral
concentrate was subjected to a freeze-thaw treatment to release viral DNA. This procedure
involved heating the concentrate to 95°C for 2 minutes then freezing at -20°C until solid; this
hot-cold cycling was repeated 3 times for each sample. In an attempt to address past concerns
about target biases of individual algal virus PCR primer sets and obtain a broader perspective on
algal virus diversity in the Bay of Quinte, three different sets of primers were used in this study.
Although the AVS and MCP primer sets described below both amplify Phycodnavirus gene
fragments, past work suggests that they are biased towards different subsets of the
Phycodnaviridae and can be used in a complementary manner (Park et al., 2011). Furthermore,
because there is particular interest in the ecology of toxic bloom-forming cyanobacteria, such as
Microcystis aeruginosa, sheath primers targeting M. aeruginosa phage were also used.
AVS:
The AVS primers specifically target a ≈ 700 bp fragment of virus DNA polymerase
genes (polB) in Phycodnaviruses, viruses that infect eukaryotic algae (Chen and Suttle,
1995). DNA polymerase polB genes were amplified from each sample in two rounds of
PCR. First round PCR was made up of reagents in quantities listed in Table 3 (AVS)
with a negative control where nuclease-free water substituted viral template. First round
PCR thermal cycling followed the conditions as in AVS-40 (Table 4). To increase the
yield of amplified DNA from the AVS primers, a second round of PCR was conducted
using the same quantities of reagents, with the exception of viral template (2 μL). For
second round PCR, viral template was obtained by excising, with disposable glass
Pasteur pipettes, agarose plugs from bands visualized in a gel of first-round PCR
products. The excised plugs were placed in tubes with 100 μL of 10 mM TrisCl and
were heated (65°C for 20 minutes) to elute the DNA from the agarose. Two microlitres
14
of the resulting eluant was used as template for the second round PCR (Short and Suttle,
2003), and thermal cycling followed AVS-25 (Table 4).
MCP:
The MCP primers target ≈ 400 bp to ≈ 500 bp gene fragments in the conserved region of
the major capsid protein in Phycodnaviruses that infect some prymnesiophytes,
haptophytes and Chlorella-like algae (Larsen et al., 2008). Major capsid protein genes
were amplified from select samples in two rounds of PCR. Quantities of reagents used in
first and second round PCR are listed as “MCP” in Table 3, with a negative control
where nuclease-free water substituted viral template. Second round PCR was identical to
first round PCR with the exception that plugs of gel bands from the first round PCR
electrophoresis (as described above for AVS reactions) were the source of template
DNA. Thermal cycling followed MCP50-35 for both first and second round PCR (Table
4).
Sheath:
Sheath primers target a sheath protein gene fragment ≈ 950 bp long in cyanophages
which specifically infect a toxin producing strain of Microcystis aeruginosa (Yoshida et
al., 2006). Sheath protein genes were amplified from select samples in a single round of
PCR using the primers Sheath F2 & R2 (Takashima et al., 2007). Quantities of reagents
used in PCR are listed as “Sheath” in Table 3, with a negative control where nuclease-
free water substituted viral template. Thermal cycling followed the Sheath profile (Table
4).
After thermal cycling, all of the PCR products were mixed with loading dye and loaded
into a 1.5 % LE agarose gel (Promega; Fitchberg, USA) and were electrophoresed at 6.6 V/cm
for 60 minutes in 1x TAE (40mM TrisCl, 20mM Acetic acid, 1 mM EDTA; pH 8.0).
GeneRuler® 100 bp Plus ladder (ThermoFisher Scientific; Waltham, USA) was used as a
molecular weight marker. The gel was stained with 0.5 μg/ml ethidium bromide for 30 minutes
followed by 15 minutes of destaining in water. PolB products from samples collected on June
7th
, 2011 were an exception; following first and second round PCR, PCR products were
15
electrophoresed for 90 minutes, but first round PCR products were stained for 35 minutes and
destained for 35 minutes while second round followed the aforementioned staining times. The
gels were visualised using a Molecular Imager Gel Doc XR (Bio-Rad Laboratories; Hercules,
USA). In all cases, agarose gels were not exposed to UV light for more than five seconds.
Sheath PCR bands from Hay Bay on June 7th
and July 19th
, 2011 were excised from the
gel and purified using a QIAquick Gel Purification Kit (QIAGEN; Valencia, USA) following
the manufacturer’s recommendations, with the exception of a three minute spin instead of the
recommended one minute following the ethanol wash, and DNA was eluted in 30 μl of 10 mM
TrisCl and let stand for two minutes as opposed to one minute. The purified PCR product was
quantified using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies;
Wilmington, USA) before being sent for direct sequencing from the forward primer at the
Center for Applied Genomics at the Hospital for Sick Children (Toronto, Ontario, Canada).
With regard to AVS and MCP, bands from second round PCR of samples collected from
Belleville on June 7th
, August 16th
(MCP only) and October 12th
, 2011 were excised from the gel
and DNA was extracted using a QIAquick Gel Extraction Kit (QIAGEN) following the
manufacturer’s recommendations, with the exception of a three minute spin instead of the
recommended one minute following the ethanol wash, and DNA was eluted in 30 μl of 10 mM
TrisCl and let stand for two minutes as opposed to one minute. Once purified, the PCR products
from the excised bands were cloned using pGEM-T Vector Systems (Promega) according to the
manufacturer’s recommendations for ligation (three μL of purified DNA fragments from the
PCR reactions were used in each ligation). Using Max efficiency DH5α competent cells
(Invitrogen; Carlsbad, USA), the transformation process followed the manufacturer’s
recommendations for heat shock and recovery. Two hundred microlitres of transformed bacteria
were spread on LB agar (10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl and 15 g/L agar)
bacteriological plates that were prepared with 100 μg/mL carbenicillin, and were smeared with
100 μL 0.1M IPTG (isopropyl β-D-1-thiogalactopyranoside) and 20 μL 50 mg/mL of X-Gal
(bromo-chloro-indolyl-galactopyranoside) immediately before use. Plates were incubated at
37°C overnight.
Transformants were screened for recombinant plasmids using a single round of PCR as
per the above conditions for the respective primers, with the exception that bacterial cells were
used as templates for the reactions by transferring cells from the edge of a colony by scraping
16
with a sterile pipette tip and stirring the pipette tip directly into the PCR reaction mixture. From
each transformation, 18 colonies that were confirmed to contain recombinant plasmids were
picked off the plate and incubated overnight at 37°C, 250 RPM for 16 hours in 5 mL of LB
broth with 100 μg/mL carbenicillin. Following the overnight incubation, plasmid DNA was
purified from the colonies using a QIAprep Spin Miniprep Kit (QIAGEN), and quantified using
a NanoDrop ND-1000 spectrophotometer and sent for automated sequencing from the M13f
priming site on the plasmid backbone at the Center for Applied Genomics at the Hospital for
Sick Children (Short and Short, 2008). Only full-length sequences AVS (≈ 700 bp), MCP (≈ 400
bp or ≈ 500 bp), Sheath (≈ 950 bp), were used for further analysis.
Sequence Analysis and Primer Design
Sequence identity matrices were generated in BioEdit 7.0.9.0 (Hall, 1999) and allowed for
nucleic acid sequences greater than 97 % identical to be grouped together and hereafter referred
to as operational taxonomic units (OTUs). Separate alignments for polB, MCP and Sheath were
created using inferred amino acid sequences using MUSCLE (Multiple Sequence Comparison
by Log-Expectation) with the default parameters in MEGA 5.0 (Molecular Evolutionary
Genetics Analysis; Tamura et al., 2011). Aligned polB sequences and MCP sequences were
compared phylogenetically using MEGA 5.0 by constructing independent neighbor-joining trees
based on the Jones-Taylor-Thornton (JTT) model of amino acid substitution (Jones et al., 1992)
and bootstrap from 500 replicates with complete deletion options. It is important to note that due
to the small number of unique Sheath sequences obtained from this study and available in
GenBank, only BLAST analyses were used to characterize the Bay of Quinte sequences from
Sheath PCR. For the polB, MCP and Sheath clone libraries developed in this study, percent
coverage was determined using the calculation C=1-(N/n) where C was the homologous
coverage, N was the number of singleton sequences and OTUs, and n was the total number of
sequences in the sample. Bay of Quinte OTUs in the polB and MCP phylogenies as well as
sequences from previously generated polB clone libraries (Short and Short, 2009; Short et al.,
2011a) were used to choose targets for qPCR primer and probe design. MEGA 5.0 and Adobe
Illustrator CS (Adobe Systems, USA) were used for tree viewing and drawing.
As previously described in Short and Short (2009), using default parameters in Beacon
Designer 7.0 (Premier Biosoft International, USA), TaqMan probes were designed for four
17
MCP sequences; two of which were from the Mimivirus infecting prasinophytes group (365-
M5.3; 252-M5.13) and the other two from the Mimivirus infecting prymnesiophytes group (399-
M5.4; 356-M5.14rc), and both Sheath OTUs (Sheath 253; Sheath 282). Each qPCR assay was
optimized for highest quality and greatest number of mismatches with non-target sequences.
Closest non-target sequences were determined by creating alignments of nucleic acid sequences
using MUSCLE in MEGA 5.0, and sequence identity matrices generated in BioEdit 7.0.9.0. The
number of base pair mismatches was counted for the most closely related sequences to each
qPCR target. In addition to the six qPCR assays developed for this study, four extant primer and
probe pairs were used to expand the breadth of virus communities available for quantitative
analysis (Table 5). These assays included two primer and probe sets previously described in
Short and Short (2009), LO1b-49 and LO1a-68, as well as two primer and probe pairs
previously described in Short et al. (2011a), LO.20May09.33 and LO Jul.16.20.
Quantitative PCR
The newly designed quantitative assays included probes which were 5’ labelled with FAM
(6-carboxyfluorescein) as a fluorescent reporter and 3’ labelled with Zen Internal Quencher
(Integrated DNA technologies; Coralville, USA) as a quencher. Of the extant primer and probes
pairs, only LO Jul.16.20 was 3’ labelled with Zen Internal Quencher (Integrated DNA
technologies) as a quencher; the other three assays were 3’ labeled with Iowa Black FQ
(Integrated DNA technologies).
As described in Short and Short (2009), quantitative PCR via the 5’ nuclease assay was
conducted on every Bay of Quinte virus concentrate with each primer and probe set using a
MX3000P qPCR system (Stratagene; Cedar Creek, USA). Each sample measurement was
replicated three times and every set of reactions included eight 10-fold serially diluted standards
(on average, ranging from 3.0 x 100 to 3.0 x 10
7 molecules per reaction) run in duplicate, along
with three no-template controls where template was substituted with nuclease-free water. Each
of the ten standards were created from cloned fragments of the target sequence that were
linearized by restriction digest, purified by agarose gel electrophoresis, extracted via QIAquick
gel extraction kit (QIAGEN), and quantified using a NanoDrop ND-1000 spectrophotometer
(NanoDrop Technologies). Experiments to test the efficacy and specificity of the newly
designed qPCR primers and probes were conducted as described in Short and Short (2009)
18
where Ct (cycle threshold) values of target and closest non-target at ca. 3 x 107 molecules were
compared. The extant primer and probe pairs had previously undergone validity testing in Short
and Short (2009) and Short et al. (2011a) respectively. Reaction conditions for the newly
designed primer and probe pairs (Mimivirus-Pras 252, Mimivirus-Pras 356, Mimivirus-Prym
399, Mimivirus-Prym 356, Sheath 253 and Sheath 282) follow assay group “A” in Table 6,
while the extant assays Chlorovirus and Prasinovirus49 follow group “B”. The other extant
assays, Prasinovirus16.20 and Prasinovirus68, follow groups “C” and “D”, respectively. The
thermal cycling conditions for each primer and probe pair consisted of an initial denaturation
step of five minutes at 95°C followed by 40 cycles of 15 seconds at 95°C and one minute at
60°C.
Analysis
To illustrate the dynamics of the ten individual viruses examined in this study, the virus
gene abundance was plotted in line graphs, smoothed with a modified cubic spline (Liengme,
2008), by virus and by sampling site. Error bars were calculated based on the standard deviation
of qPCR determined gene abundance, and where the error bars are not visible they were smaller
than the plotted line. A box and whiskers plot was created to illustrate and summarize seasonal
abundances for each virus gene at each station. To illustrate the relationship between the sum of
the gene abundance of all ten viruses quantified in this study and chlorophyll a concentrations
(µg/L) as a proxy for phytoplankton host abundance, linear regression analysis was performed
for each biweekly station. The line graphs, box and whiskers plot as well as the linear regression
were created using Microsoft Excel 2007 (Microsoft Corporation; Redmond, USA).
Tests for normality and sphericity were conducted and since gene abundances were not
normally distributed and were not homoscedastic, non-parameteric tests were used for statistical
analyses. Friedman analysis of variance by ranks was used to compare biweekly virus
abundances from each individual virus gene to each other individual virus gene to determine if
there were significant differences between gene abundances within a station (all ten viruses at
each biweekly station) and between stations (each individual virus between all three biweekly
stations). Results with a significance value of p < 0.05 were subjected to post-hoc analysis with
Wilcoxon Signed-Rank Test with a Bonferroni correction applied. The statistical tests were
completed using IBM SPSS Statistics 19 (IBM; Armonk, USA).
19
To resolve if taxonomically related virus populations cluster together based on abundance
data, distance measures were created using pair-wise Pearson Correlation and Bray-Curtis
dissimilarity matrices based on the biweekly abundance data from each virus gene. The Pearson
Correlation values were converted into distance measures by 1 – r = D, where r was the Pearson
Correlation coefficient and D was the dissimilarity between two data points. The abundances of
each virus gene were compared to the abundances of every other virus gene at all stations in a
pairwise manner, and similar discrete pair-wise comparisons were conducted for the abundances
at each individual station. In an additional discrete pair-wise comparison, the abundances of
each virus gene were compared to the abundance of each other virus gene at each station
sampled during the September spatial survey. The dissimilarity matrices were transposable so
that separate dendrograms of virus genes and sampling events were created from the same data
set. Using four different clustering methods [average (unweighted pair group method with
arithmetic mean; UPGMA), furthest neighbor (complete-linkage), nearest neighbor (single-
linkage) and Ward’s method], dendrograms were created for each cluster analysis. Matrix
calculations were performed using R Statistical Computing ver. 2.15.1 (R Core Team; Vienna,
Austria) and cluster analysis was done using R Statistical Computing and MEGA (UPGMA
only).
20
Chapter 3: Results
Algal Virus Diversity Study
Algal virus DNA polymerase genes
PCR with algal virus specific polB primers (AVS) was used to generate clone libraries of
amplified fragments from samples collected from Belleville on June 7th
and October 12th
, 2011.
Sequencing generated 29 usable sequences; 25 sequences were discarded because of incomplete
sequencing of the gene fragments, vague base calling or did not display the highly conserved
polB amino acid motif YGDTDS (Short and Suttle, 2002). Based on a sequence identity matrix
nine unique sequence types were identified from the 29 usable sequences. Of these nine unique
sequences types, five were singletons and four were OTUs, or groups of sequences more than 97
% identical. Representative sequences for each unique sequence type were compared to
cultivated and environmental virus polB gene fragments and all polB gene fragments obtained
from the Bay of Quinte were more closely related to genes from cultivated prasinoviruses than
they were to polB genes from other virus genera (Figure 2). Using C=1-(N/n), the percent
coverage of the polB clone libraries was calculated to be 69.0 %.
Algal virus major capsid protein genes
The Phycodnavirus-specific MCP clone libraries were generated from PCR of samples
collected from Belleville on June 7th
, August 16th
and October 12th
, 2011. Sequencing generated
61 usable sequences; nine were discarded for vague base calling and incomplete gene
sequencing. Based on a sequence identity matrix, 24 unique sequences were identified of which
16 were singletons and eight were OTUs. Representative sequences were compared
phylogenetically to cultivated and environmental virus MCP gene fragments (Figure 3). The
majority of Bay of Quinte MCP sequences were ~500 bp sequences that were most closely
related to one of two types of mimivirus-like viruses that are classified based on the types of
hosts they infect; one group are mimiviruses that infect prymnesiophytes, the other are
mimiviruses that infect prasinophytes. The other MCP sequences (~400 bp) were most closely
related to cultivated viruses belonging to the genus Prasinovirus. The percent coverage of the
MCP clone library was calculated to be 60.7 %.
21
Microcystis phage sheath protein genes
Microcystis sheath protein clone libraries were created from samples collected from Hay
Bay on June 7th
, and July 19th
, 2011 and generated 13 sequences, that formed two distinct OTUs
comprised of ten (Sheath 282) and three (Sheath 253) sequences. BLAST and a sequence
identity matrix revealed that representatives of the two sheath OTUs, Sheath 253 and Sheath
282, were 95 % and 93 % identical to the cultured Microcystis phage Ma-LMM01 (Yoshida et
al., 2006) over 864 and 873 nucleotides, respectively. However, Sheath 253 and Sheath 282
were only 92 % identical to each other. The percent coverage of the Sheath clone library was
calculated to be 84.6 %.
Quantitative Analysis of Viral Dynamics
Pure DNA of the most closely related non-target gene fragments, based on information
from the sequence identity matrices and phylogenetic analyses, were used to test the primer and
probe specificities for each qPCR assay. QPCR detection of 107 non-target molecules ranged
from a Ct value of 22.95 to below the limit of detection, while qPCR detection of 107 target
molecules ranged from a Ct value of 12.07 to 16.91 (Table 7). This demonstrates that the
detection limits for the most closely related genes were at least three orders of magnitude higher
than the target genes (Sheath 253), and for some assays non-targets were not even detected
(Sheath 282). Amplification efficiencies of all qPCR reactions were consistently between 90-
100 %, as per recommendations by Dorak (2006) for accurate quantification via the 5’ nuclease
assay. It is important to note that this technique of viral quantification required that virus
abundances be inferred based on quantified gene abundances (Short and Short, 2008).
Virus Dynamics across Time and Space
Using qPCR, gene abundances were quantified and compared for water samples collected
from three Bay of Quinte stations throughout the 2011 growing season (May to October). Plots
of virus gene abundance over the year revealed complex and unique patterns for each of the
genes studied (Figure 4). Seasonal abundance patterns of some virus genes were relatively
consistent between stations, some had distinct single peaks in abundance, while others seemed
to experience several boom and bust cycles throughout the year. Virus gene abundance also
22
varied with virus groups, most notably between the two Microcystis phage genes, but also
between Prasinoviruses and mimiviruses infecting prasinophytes and prymnesiophytes.
Quantitative analysis of one of the putative M. aeruginosa phage genes, Sheath 282,
revealed oscillations that varied dramatically across the Bay of Quinte. At Hay Bay, Sheath 282
abundance peaked in July at around 256,000 gene copies/mL, whereas at Napanee it peaked
near the end of September (~22,800 gene copies/mL). In contrast, Sheath 282 abundances at
Belleville never exceeded ten gene copies/mL. Sheath 253 abundances on the other hand were
much more consistent among the three sampling sites, with peak abundance occurring at
Napanee (5,300 gene copies/mL) in the spring. Chlorovirus abundances were also consistent
between stations, with abundances at both Napanee and Hay Bay peaking in early August three
times higher than at Belleville. Analysis of Prasinovirus49 revealed virus abundances below 500
gene copies/mL at all stations until October where gene copies at Belleville peaked at
approximately 2,500 gene copies/mL. A particularly interesting trend was revealed in the gene
abundances of Prasinovirus68 and Prasinovirus16.20; virus abundances at Belleville remained
below detection for the entire 2011 sampling season and gene detection was decreasingly patchy
with stations further out in the Bay, to where both prasinoviruses were regularly detected at Hay
Bay.
Another algal virus that was regularly detected at consistent levels at all three biweekly
Bay of Quinte stations was Mimivirus-Pras 356, peaking in the late summer at Belleville at
approximately 1,600 gene copies/mL. Contrary to Mimivirus-Pras 356 and historical Bay of
Quinte algae blooms, Mimivirus-Pras 252 was most abundant during the spring and early
summer (never more than 300 gene copies/mL), then in July, all Mimivirus-Pras 252 viral
activity dropped below four gene copies/mL for the rest of the season. Gene abundances of
Mimivirus-Prym 399 also revealed consistently high abundances in the spring and early summer
which fell below detection in July. However, unlike Mimivirus-Pras 252, abundances of
Mimivirus-Prym 399 returned to high levels in the early fall, where Hay Bay diverged from the
Upper Bay stations which peaked in late September (Belleville; 2,250 gene copies/mL). Of the
ten gene fragments examined in this study, the abundances of Mimivirus-Prym 356 were the
most consistent between the historical Bay of Quinte stations over the sampling season; virus
abundances at all three stations peaked from July through August, with the highest abundance
(16,000 gene copies/mL) observed at Belleville.
23
When the abundances of all ten virus genes are plotted in a single graph for each station,
the highly dynamic oscillations of these viruses over a single growing season become apparent
(Figure 5). With all ten viruses plotted in each graph, virus succession is also more evident. For
example, at Belleville, as Mimivirus-Pras 252 drops to below detection, Prasinovirus49 becomes
more abundant. Some viruses are more consistent throughout the sampling season, particularly
Mimivirus-Prym 356 and Chlorovirus, while some are only present at certain times (i.e.,
Mimivirus-Pras 252). Of particular interest are incidences of repeated boom and bust, as seen in
Sheath 282. The uniqueness and complexity of virus abundance oscillations within the Bay of
Quinte is fascinating; the dramatic seasonality differences, particularly within virus groups were
unexpected. However, figures 4 and 5 illustrate the qualitative differences between virus
abundances only; no statistical significance can be inferred. Therefore, further analyses were
conducted to determine if the abundance trends seen in Figure 4 were significantly different.
Statistical Analysis of Viral Dynamics
Seeing that the virus gene abundance data set was obtained from environmental samples
where abundances can vary over several orders of magnitude, it was not surprising that the
values were non-normally distributed. In order to compare the variability of virus abundances
between viruses within a station and virus abundance across stations, a box and whiskers plot
was created (Figure 6). This was complemented by regression analysis (Figure 7) and a
Friedman non-parametric analysis of variance by ranks.
There was a statistically significant difference between each individual virus gene assay at
each station based on virus abundances from all sampling events. That is to say that abundances
of all ten viruses are significantly different from each other at Belleville (χ²(9) = 68.146, p <<
0.01), Hay Bay (χ²(9) = 59.847, p << 0.01) and Napanee (χ²(9) = 58.003, p << 0.01). Post-hoc
analysis with Wilcoxon Signed-Rank Test with a Bonferroni correction applied was conducted,
resulting in a significance level set at p = 0.001, but did not reveal any significant differences
between the abundances of any individual virus and any other virus, at any station. This was
likely due to the high number of multiple comparisons required, as well as being conservative
for possible type I errors. Nonetheless, inspection of the box and whisker plots reveals apparent
patterns in the abundance data (Figure 6). For instance, at all stations, Chlorovirus and
Mimivirus-Pras 356 are more similar with respect to their median abundance throughout the
24
Bay of Quinte than to any other virus group. In Belleville, Prasinovirus49 and Mimivirus-Prym
399 have similar range and median abundance, as do Mimivirus-Pras 252 and Sheath 253. In
Hay Bay, both Sheath 282 (which has a largest range) and Mimivirus-Pras 356 (smallest values)
have distinctly different abundance medians and ranges than other viruses. Prasinovirus49,
Prasinovirus16.20, Mimivirus-Pras 252, Sheath 253 and Mimivirus-Prym 399 were all detected
at similar ranges of abundance. In Napanee, Sheath 282 and Mimivirus-Prym 399 have similar
abundance means, as do Prasinovirus68, Prasinovirus16.20 and Mimivirus-Pras 356. The other
viruses, Sheath 253, Mimivirus-Pras 252 and Prasinovirus49 also share similar median
abundances.
By comparing the variance of virus abundance of individual viruses between stations, that
is to say, comparing the abundance of a given virus gene at one biweekly station to its
abundance at the other two biweekly stations, it was determined that there were statistically
significant differences for only four of the quantitative assays: Prasinovirus49, Prasinovirus68,
Prasinovirus16.20 and Sheath 282 (Table 8). Follow up with a post-hoc analysis with Wilcoxon
Signed-Rank Test with a Bonferroni correction applied was conducted, resulting in a
significance level set at p = 0.017. The post-hoc analysis examined specific pair-wise
relationships to determine if there were significant differences between any two stations. With
regard to Prasinovirus49, despite being significantly different overall, there were no significant
differences between any of the stations on a pair-wise basis. Conversely, analysis of the other
three virus groups between stations revealed significant differences between Hay Bay and the
other two stations for both Prasinoviruses, and between Hay Bay and Belleville only for Sheath
282 (Table 8).
Virus Abundance and Host Biomass
As previously discussed, the algae virus abundance reported in this study are inferred from
gene abundances obtained through qPCR and therefore gene abundance was a proxy for virus
abundance. The phytoplankton host abundances in this study were also measured via a proxy;
chlorophyll a. As a pigment found in the chloroplasts of algae, chlorophyll a has historically
been used as a measure of phytoplankton biomass in the Bay of Quinte (Minns et al., 2011).
Chlorophyll a concentrations from every sample from each biweekly station during the 2011
sampling season were plotted against the sum of all ten virus abundances at each station (Figure
25
7). Regression analysis of the sum of virus abundance versus chlorophyll a at Belleville
revealed a strong positive relationship between these proxies for host biomass and virus
abundance (R2
= 0.5316) with a significantly non-zero slope (p = 0.01). Close inspection of virus
and chlorophyll a data revealed that in Belleville, increases in host biomass and virus abundance
were in phase and peaked at the same time. However in the later portion of the sampling season,
the chlorophyll a and virus gene abundances were not as tightly coupled and ended the season
slightly out of phase (data not shown). Regression analysis of the same data from the Hay Bay
location revealed that the relationship of chlorophyll a and virus gene abundance was not
statistically significant (R2 = 0.036, p = 0.576). However, this station included a virus abundance
data point (Sheath 282 from July 19th
) that was at least an order of magnitude higher than all
others that was responsible for this weak relationship. When this exceptionally high Sheath 282
abundance value was removed from the data set, the relationship between virus and chlorophyll
a was strong (R2 = 0.6048) and statistically significant (p < 0.01). The sharp increase in virus
abundance due to Sheath 282 is not reflected in host biomass, nevertheless, fluctuations in host
biomass and virus abundance remain tightly coupled prior to and after the peak in virus
abundance (data not shown). Regression analysis at Napanee revealed a weak positive
correlation between virus abundance and chlorophyll a (R2 = 0.2591), however the slope of the
regression was not significantly non-zero (p = 0.133), which was likely due to a decoupling
between host biomass and virus abundances in the later part of the growing season (data not
shown).
Clustering by Taxonomic Classification
To explore the patterns of virus gene abundance qualitatively, cluster analysis was
conducted in several ways to determine if there were any robust conclusions that could be
reached with regards to fluctuations of virus gene abundances in the Bay of Quinte. After
matrices were generated through pair-wise comparisons of virus gene abundances at all stations,
as well as at each individual station, cluster analysis of each virus type in the Bay demonstrated
that genetically related viruses very rarely clustered together based on virus abundance, but that
the clustering patterns were very sensitive to sample location; i.e., the branching pattern of the
dendrograms created for all stations and each station independently were distinct (Figure 8).
26
Including the average linkage (UPGMA) method (Figure 8), four different clustering
methods (average linkage, nearest neighbour, furthest neighbour and Ward’s method) were used
to create dendrograms from each proximity (i.e., distance) measure. Using identical clustering
methods, the proximity measures (Pearson Correlation and Bray-Curtis dissimilarity) produced
different branching patterns from one another (Figure 9), but within a proximity measure, each
clustering method produced comparable or virtually identical branching patterns. Despite
different stations and proximity measures, each clustering method supported the observed
pattern that related viruses did not cluster together based on seasonal abundances.
Clustering by Spatial Distribution
Using the same clustering technique as above, virus abundances for all biweekly stations
were used to cluster sampling events as opposed to clustering of the virus groups. Regardless of
proximity measure or clustering method, cluster analysis revealed that the biweekly stations did
cluster together based on location, but rather more by sampling date (Figure 10). Once again
using the same clustering technique, virus abundances from the intensive, three day September
spatial survey were used to cluster sample sites (Figure 11). All proximity measures and
clustering methods revealed that, for the most part, the spatial survey stations cluster together
based on their spatial distribution (Figure 1; Table 2), with most of the Middle Bay stations
(HB4, HB, and P) clustering together and apart from the other Upper Bay stations (NR1, N,
BQ8, BQ9, BQ6, BQ7, B, and B2). It is also interesting to note that many of the samples
collected from nearby stations (i.e., BQ6, BQ7, and B) also clustered together based on virus
abundance.
27
Chapter 4: Discussion
Through molecular techniques, this seminal examination of algal virus diversity in the Bay
of Quinte has confirmed the presence of diverse Phycodnaviruses and Microcystis cyanophage
in the Bay. In addition, this study has provided the first evidence that PCR methods based on
Phycodnavirus MCP genes could be used to examine the diversity of freshwater algal viruses.
Quantitative molecular techniques were used to determine that the seasonality of certain virus
taxa differed in abundance across both time and space, regardless of their genetic similarity. By
comparing proxies for virus abundance and host biomass, this study supported the current
paradigm that that viruses are dependent on their hosts which, in turn, are subject to
environmental factors, both biotic and abiotic, that drive cycles of phytoplankton bloom and
decay, hereafter referred to as ecological drivers. Finally, via cluster analysis, this study
demonstrated that taxonomic classification or genetic relatedness cannot be used to predict virus
seasonality, and that the abundance of algal virus communities in the Bay of Quinte may be
more similar seasonally than geographically.
Algal Virus Diversity Study
Using four different sample dates, phylogenetic trees were constructed to examine the
diversity of viruses in the Bay of Quinte. Using the three clone libraries created from these
samples, phylogenetic trees were created (Figures 2 and 3) and percent coverage was calculated.
As the first investigation into algal viruses in the Bay of Quinte, the percent coverage values for
this study were lower than those reported in previous Lake Ontario Phycodnavirus diversity
studies (85 %; Short and Short, 2008; Short et al., 2011a) indicating that there remains the
potential for yet undiscoved algal virus diversity in the Bay. However, to provide a first glimpse
into algal virus diversity in this unique environment, higher clone library coverage through deep
sequencing efforts was not necessary.
As expected, the environmental polB sequences obtained from the Bay of Quinte were
biased towards prasinoviruses (Figure 2), a single genus within the Phycodnaviridae. While this
bias is likely the effect of a PCR primer bias, there is at least some evidence that the observed
bias of the AVS PCR primers towards prasinoviruses could actually be the result of their
predominance in natural environments (Clasen and Suttle, 2009). Whatever the case, almost all
of the environmental polB sequences from the Bay of Quinte form a single group with other
28
environmental sequences from Lake Ontario (Short and Short, 2008; Short et al., 2011a) that is a
sister clade to sequences from cultivated viruses that infect marine phytoplankton. This could
indicate that these freshwater sequences belong to a distinct group of viruses that are common in
Lake Ontario but have not yet been cultivated. The closest cultured relative of these Bay of
Quinte sequences is an Ostreococcus virus (Derelle et al., 2008) however since viruses infecting
the same types of hosts generally cluster together (Chen and Suttle, 1996) and this host genera is
made up of many closely related prasinophytes, it is not possible to speculate on the actual
identity of the host of these Bay of Quinte viruses. It is interesting to remark that all Bay of
Quinte polB samples cluster with prasinoviruses, yet prasinophytes have never been reported in
Lake Ontario (Munawar and Munawar, 1982). This is likely due to the fact that all past Lake
Ontario phycological studies that have been based on light-microscopy, and it is possible that
prasinophytes are too small (< 3 μm) to be readily differentiated from other picoplankton even
at the highest magnification (Short et al., 2011a). Altogether, studies of algal virus polB
diversity suggest that, through molecular surveys, there are great opportunities for
advancements in Lake Ontario and Bay of Quinte phycology and virology.
This study provided the first evidence that PCR methods targeting MCP genes from
viruses of eukaryotic algae can be used to examine freshwater virus communities (Figure 3).
Most of the environmental MCP sequences obtained during this study were most closely related
to two types of mimivirus-like viruses as defined by Monier et al. (2008a) and Monier et al.
(2008b); one group of sequences were closely related mimivirus-like viruses that infect the
prymnesiophytes Phaeocystis pouchetti and Chrysochromulina ericinia, while others were
related to mimivirus-like viruses that infect the prasinophyte Pyramimonas orientalis (Figure 3).
Another set of shorter (i.e., ~400 bp) MCP sequences from the Bay of Quinte (399-M4.18, 399-
M4.1, and 399-M4.2) were most closely related to viruses belonging to the genus Prasinovirus
(Figure 3). While the presence of introns has been shown in the polB gene of some
Phycodnaviruses (Nagasaki et al., 2005), none have been identified for the MCP gene.
According to Larsen et al. (2008), the different amplicon sizes from various MCP genes are
likely due to structural differences in the proteins between different viruses, particularly
mimivirus-like viruses and prasinoviruses.
Although hosts for Bay of Quinte prasinoviruses have not yet been identified, there are
many potential hosts for mimivirus-like viruses of prymnesiophytes. For example, according to
29
the clustering of MCP sequences, clone 356-M5.14 (qPCR target Mimivirus-Prym 356) is likely
from a virus that infects a Chrysochromulina ericina-like host (Figure 3). This is intriguing
since C. ericina is a marine phytoplankton species. However, since related freshwater species
such as C. parva are known in Lake Ontario and the Bay of Quinte and have been observed
throughout the year (Munawar and Munawar, 1982), it is reasonable to speculate that C. parva
is the host of this virus. On the other hand, it is much more difficult to speculate about the
identity of the host of other types of viruses. The Bay of Quinte sequences 252-M5.5 and 356-
M5.3 (qPCR target Mimivirus-Pras 356) cluster with sequences from Pyramimonas orientalis
virus, a virus that infects a marine prasinophyte. Again, the argument can be made that these
sequences originate from viruses of a freshwater prasinophyte that has not yet been observed in
Lake Ontario or elsewhere. Ultimately, the identity of these and other Bay of Quinte mimivirus-
like viruses will remain uncertain until they are cultivated. Unfortunately this will first require
that the phytoplankton hosts themselves are isolated and grown in the laboratory and this in
itself is a considerable challenge.
By utilising both AVS and MCP primer sets that are specific for a variety of
Phycodnaviruses, these complementary methods have succeeded in providing a broader, more
complete picture of the viral diversity within the Bay of Quinte. Either technique alone would
have led to an incomplete view of algal virus diversity, but used together they mitigate their
apparent biases. Granted that some Phycodnaviridae taxa were not represented in the clone
libraries generated for this study, but given that relatively few samples were amplified, cloned
and sequenced, and only a superficial sequencing effort was conducted for this study, it appears
that the complementary PCR methods used here were an improvement with respect to capturing
a broader range of Phycodnavirus diversity compared to previous studies based on single primer
sets (Short and Short, 2008; Clasen and Suttle, 2009).
To examine an additional dimension of diversity, a clone library for a specific cyanophage
was created. Using primers that target a sheath protein gene sequence of a Microcystis phage
isolated from Japanese freshwater environments (Yoshida et al., 2006), two unique sheath
sequences were found in the Bay of Quinte. While genetically similar algal viruses have been
detected in distantly separated oceans (Short and Suttle, 2002; Bellec et al., 2010), and
freshwater environments (Short and Short, 2008; Roux et al., 2012), the observation of these
phage sequences in the Bay of Quinte was fascinating nonetheless. The Bay of Quinte sequence
30
Sheath 253 was more closely related to the only known target for these PCR primers, the sheath
protein gene from the Japanese phage Ma-LMM01, than to the other Bay of Quinte sequence,
Sheath 282. Multiple Sheath sequences in the Bay of Quinte are possible if there are multiple
strains of Microcystis phage for a single host stain, or multiple phage strains for different strains
of M. aeruginosa; the Ma-LMM01 sequence and Sheath 253 could be derived from one phage
strain while Sheath 282 is from a different strain, or it is even possible that all three sequences
represent different phage strains. Unlike the Phycodnaviridae which are extremely host specific,
some freshwater cyanophage isolates have been shown to infect a range of bloom-forming hosts,
including Microcystis, and a range of Anabaena and Planktothrix species (Deng and Hayes,
2008). However, Ma-LMM01 is exceptionally host specific with respect to species strains as it
infects only 1 of 11 screened strains of M. aeruginosa, and did not infect any other blue-green
algae species screened (Yoshida et al., 2006). Therefore, it is likely that the Bay of Quinte is
home to an unidentified strain (or strains) of M. aeruginosa that is susceptible to Ma-LMM01,
but until M. aeruginosa in the Bay of Quinte are isolated and sequenced, multiple strains are
impossible to differentiate.
The sequences obtained from each of the clone libraries generated in this study add
credence to the statement that algal viruses are ubiquitous (Breitbart and Rohwer, 2005) and that
the Bay of Quinte is no exception. It has been remarked that “variation within populations is
often greater than variation between populations” (Wilson et al., 2009), which is an important
argument when comparing diversity between locations; it is common to find more genetic
variation within populations than between them. This could be interpreted for differences
between stations within the Bay of Quinte or between sites within Lake Ontario, such as the Bay
of Quinte and Port Credit. A brief comparison of the DNA polymerase gene phylogeny from the
Bay of Quinte with the polB phylogeny from Short and Short (2009) showed a similar clustering
of environmental samples within the genus Prasinovirus, and the Bay of Quinte and Lake
Ontario sequences often cluster together. Deeper sequencing efforts and an expanded study will
be needed to determine if Bay of Quinte viruses are distinguishable from Lake Ontario viruses,
or if either location is home to unique endogenous viruses. The discovery of such algal virus
diversity in the Bay of Quinte has reinforced the effectiveness of complementary PCR methods
in novel environments and has benefited continued diversity studies in freshwater environments.
31
Quantitative Analysis of Viral Dynamics
All qPCR assays were performed within acceptable amplification efficiencies after having
been subjected to specificity testing. Each of the qPCR assays had percent nucleotide identities
with non-target sequences less than 94 %, most of which ranged between 69 and 83 % (Table
7), and yet all of the non-target fragments used for each qPCR assay verification were the
environmental sequence most closely related to the target. It is interesting to note that as the
only two OTUs available, the sheath protein gene sequences were each other’s closest non-
target sequence, yet the two assays were remarkably different with respect to the differences in
Ct between target and non-target molecules; with Sheath 253 having the smallest and Sheath
282 having the largest of all ten quantitative assays. This might indicate that the nucleotide
differences (8 %) between the sequences are localized to particular areas of the gene, however
that was not observed; crude inspection of the sequence alignment suggests that the single
nucleotide polymorphisms (SNPs) were distributed throughout the entire ~950 bp fragment.
Since the detection limits for non-target sequences were minimally three orders of magnitude
higher than for target sequences (e.g., for Sheath 253), it was unlikely that any non-target genes
contributed to the estimated target abundance, as argued by Short and Short (2009). It is
important to note that the qPCR assays developed and used in this study were used to quantify
gene abundances, virus abundances were merely inferred. As polB is a single-copy gene in
known viral genomes (Delaroque et al., 2001), MCP is a single-copy gene in PBCV-1 (Graves
and Meints, 1992), and the sheath protein is a single-copy gene in Ma-LMM01 (Yoshida et al.,
2006), it is reasonable to infer virus abundance from gene copy abundances. It is also important
to note that quantitative numeration of virus gene copies does not give an indication of
infectious potential nor does it infer the infectious proportion of a virus population (Short,
2012).
Virus Dynamics across Time and Space
By examining the dynamics of several different virus taxa observed in the Bay of Quinte,
the first and most obvious conclusion is that each virus’ seasonality differs from the others.
Even within a genus (i.e., Prasinovirus) the dynamics of different viruses differed in the Bay
reinforcing the results of previous studies of a just a few different Phycodnavirus genes in Lake
Ontario (Short and Short, 2009; Short et al., 2011a). As obligate parasites of phytoplankton,
32
Phycodnaviruses and cyanophages are both dependent on hosts that photosynthesise and
flourish during seasonal periods to which they are best adapted. In most temperate waters,
phytoplankton biomass is highest during the spring and declines during nutrient limited summer
months. However, historical biomass trends in the Bay of Quinte repeatedly show late-summer
total biomass peaks (Nicholls, 2001) and blue-green algae blooms (Minns et al., 2011). While
most of the viruses monitored were at peak abundance during the late summer and early fall, the
abundance of some, like Mimivirus-Prym 399 and Mimivirus-Pras 252, were higher in the
spring and declined into the summer, succeeded by potentially faster growing, more abundant
virus populations, like Sheath 282 (Figure 4). Phytoplankton succession from spring species to
summer species has been observed in the Bay of Quinte (Munawar and Munawar, 1982;
Munawar et al., 2011; Project Quinte, 2012) and patterns of succession are often attributed to
changing environmental conditions alone (Reynolds, 2006), but the coexisitance of hosts and
viruses with different susceptibilities could lead to even more complex patterns of succession
than could be driven by changing environmental conditions alone (Thyrhaug et al., 2003; Short,
2012). As the hosts go through species succession, it is the associated oscillations in virus
populations that are captured through qPCR, yet it is still unknown if viruses are tracking or
driving host populations (Short, 2012).
The dynamics of Chlorovirus and Mimivirus-Prym 356 were unique among the viruses
examined in this study; they remained consistently detectable at high levels throughout the
sampling season and peaked as per historical Bay of Quinte late-summer bloom abundance
trends. This is likely an effect of consistent host availability where the host species remained
sufficiently abundant/dense to permit continued viral infection throughout the year, peaking
during the late-summer, early-fall (Munawar et al., 2011). Many freshwater phytoplankton
species, including Chlamydomonas globosa, Scenedesmus bijuga, Chrysochromulina parva and
Chroococcus dispersus, have been shown to remain detectable in Lake Ontario (Munawar and
Munawar, 1982) and the Bay of Quinte (Nicholls and Carney, 2011; Project Quinte, 2012)
throughout the spring, summer and fall. With regards to algal virus phenology (Figures 4 and 5),
distinct seasonal patterns over the sampling season would be expected as phytoplankton
community composition in the Bay of Quinte is not consistent over the growing season
(Nicholls and Carney, 2011; Project Quinte, 2012). An intriguing observation of Chlorovirus
and Mimivirus-Prym 356 oscillation patterns is the absence of expected boom and bust
33
oscillations. A typical boom/bust oscillation implies that the host-parasite interactions reach
high levels then crash out. However, these viruses appeared stable throughout the sampling
season in comparison to the other viruses in this study; they never crashed and were consistent
between stations (Figure 4). This could be due to a stable host-virus interaction created by the
coexistence of multiple strains of hosts and viruses that could not be discriminated, or these
algae hosts could be expressing some form of phenotypic plasticity, altering their susceptibility
to viral infection, thereby coexisting with viruses in the environment (Thyrhaug et al., 2003). On
a broader scale, some viral oscillations are not even consistent between stations, notably the
rapid spring-time boom and bust of Mimivirus-Pras 252 at Belleville, or the slow growth and
eventual crash later in the year of Prasinovirus16.20 at Hay Bay. These observed abundance
patterns are far more complex than what might have been speculated based on typical, yet
simplistic view of virus-host ecology.
The seasonal patterns revealed by qPCR showed a much more dynamic and variable virus
community than what was expected based on previous virus abundance studies in Lake Ontario
where seasonal virus trends were relatively similar in peak timing and growing season duration
(Short and Short, 2009; Short et al., 2011a). It would also be reasonable to expect virus group
abundances to oscillate in a similar fashion, particularly the closely related Microcystis phage
sequences, Sheath 253 and Sheath 282, but that was not observed. Quantitative PCR revealed
that the Microcystis phage sequences had different seasonal patterns and spatial distributions.
Different seasonalities were also observed for the prasinoviruses, particularly Prasinovirus49;
the abundance of this virus varied over the 2011 sampling season as well as between sampling
stations. Both mimiviruses that infect prasinophytes and both mimiviruses that infect
prymnesiophytes were different in with respect to the timing and duration of their peak
abundances. Therefore, the observations of this study suggest that it is not possible to predict the
seasonal trends of a virus based trends of related viruses.
A number of previous studies have examined several distinct aspect of algae virus
ecology, from host succession due to viruses (Thyrhaug et al., 2003), to estimates of host
density dependence (e.g., Cottrell and Suttle, 1995), to determining how viruses persist in the
environment (Cottrell and Suttle, 1995; Short and Short, 2009; Thomas et al., 2011). While
some aspects of algal virus ecology may seem predictable (Short 2012), such as the reoccurance
of a late-summer blue-green algae bloom in the Bay of Quinte, individual virus-host systems
34
may have to be considered unique. This viral dynamics study was conducted as a primary
examination of ten species/strains of Phycodnaviruses and cyanophage in the Bay of Quinte
over a relatively short period of 24 weeks during the 2011 sampling season; it was not meant to
be all encompassing, but rather ‘snapshots’ of the progression of particular virus communities
over a single field season. By sampling biweekly, smaller virus abundances fluctuations may
have been missed, but even by looking at these ten different viruses with this sampling regime,
the uniqueness of the dynamics observed at the same locations over the same period is
astounding.
By comparing the Bay of Quinte results using the four previously designed quantitative
assays (Table 5) to identical assays previously performed on water samples from Port Credit,
Lake Ontario (Short and Short, 2009; Short et al., 2011a), intriguing differences and similarities
become apparent. Comparing the results from the Prasinovirus49 assay conducted on Port
Credit samples from summer 2008 and the Bay of Quinte samples from summer 2011 revealed a
similar timing of peak abundance (mid-October), but also demonstrated that Prasinovirus49 was
twice as abundant in Port Credit compared to the Bay of Quinte. The Chlorovirus gene
monitored in Port Credit over a two-year period peaked in July 2008 and in June 2009, while the
same gene fragment peaked at approximately 6.5 times higher abundance, but later in the year
(August) in the Bay of Quinte in 2011. Prasinovirus16.20 and Prasinovirus68 peaked in the Bay
of Quinte only a few weeks earlier than they did previously in Port Credit, but for these gene
fragments the abundances were 20 and 50 times larger, respectfully, in Port Credit than the Bay
of Quinte. It is interesting to note that abundances of Prasinovirus16.20 and Prasinovirus68 in
the Bay of Quinte remained low at Hay Bay, nearly below detection at Napanee, and were not
detected at Belleville. The apparent decrease in abundance of these prasinoviruses with
increasing distance into the Bay could be due to host exclusion through competition with algae
species better adapted to the Upper and Middle Bays of the Bay of Quinte. Since these virus
genes were originally detected in Lake Ontario (Short et al., 2011a), their hosts could be better
adapted to open Lake Ontario water. With riverine flushing in the Upper Bay (Nicholls, 1999),
the dominant exchange flows between the Bay of Quinte and Lake Ontario occur in the Lower
and Middle Bays (Freeman and Prinsenberg, 1986), which corresponds to where these
prasinoviruses were most abundant. Past research has found distinct phytoplankton communities
at each of the historical Bay of Quinte monitoring stations (Nicholls et al., 2002; Munawar et al.,
35
2011), indicating patchy phytoplankton distribution in the Bay. Efforts to document station
specific Bay of Quinte phytoplankton communities are ongoing (Project Quinte, 2012).
The 2011 sampling year in the Bay of Quinte could be considered an atypical season for a
number of reasons. Historically, the Upper Bay has undergone a late-summer single species
algal bloom (Committee, 1990), but in 2011 late-summer algal growth was co-dominated by
several different species (Munawar, unpublished data). In addition, the highest chlorophyll a
and total phosphorus levels historically observed at Belleville (Nicholls, 1999; Nicholls and
Carney, 2011; Project Quinte, 2012) were highest in Hay Bay in 2011. Therefore it is highly
likely that results from future viral dynamics studies in the Bay of Quinte will differ from the
observations of 2011. Nevertheless, it is very unlikely that the observed differences in
seasonality patterns during 2011 (Figures 4 and 5) could be attributed to a unique growing
season. Therefore, future studies would likely arrive at the same conclusion; each virus is unique
and displays different dynamics over time and space. Of particular interest would be the impact
of a historically typical Bay of Quinte season on Prasinovirus68, Prasinovirus16.20 and Sheath
282 trends as all three viruses were the most abundant at Hay Bay over the majority of the
sampling period; their hosts may be particularly sensitive to total phosphorus levels. As the
effects of climate change are becoming more evident on a global scale, it is possible that this
‘atypical’ sampling season could represent a new norm for the Bay of Quinte. Moreover, the
development of additional prasinovirus quantitative assays specific to the Bay of Quinte and
additional freshwater sampling locations within Lake Ontario would allow for the comparison of
genetically related, geographically separated prasinovirus species.
Statistical Analysis of Virus Dynamics
The results of the non-parametric Friedman analysis show that the abundance of different
algal viruses within a given station are statistically very different from each other (p << 0.001)
even though some of the viruses are within the same family (Prasinoviridae) or represent related
strains of a virus (e.g., Microcystis phage). This supports the statement that abundance and trend
generalizations are simply not possible (Short, 2012). Because of the large number of viruses
examined at each station, post-hoc analysis was not informative; the statistical power of non-
parametric tests is already less than that of parametric tests and in order to limit possible type I
errors the Bonferroni correction was applied further increasing the likelihood of a type II error
36
(failing to observe a difference when one actually exists), an effect of the conservative nature of
the Bonferroni correction. Nevertheless, boxplots of virus abundance at each biweekly station
highlight the variability in the data (Figure 6). Overall, Belleville appears the most variable of
all of the sample sites with respect virus gene abundance, and virus genes such as Microcystis
phage sheath proteins, Prasinovirus49 polB, and all of the mimivirus-like MCP genes were
highly variable across locations with distinct median values and ranges. On the other hand, some
types of viruses at Hay Bay were very similar with respect to their abundance and variability.
Two prasinovirus genes had similar median values and ranges, and while the sheath proteins
genes were similar in median, their ranges were distinct. Of the closely related viruses at
Napanee, only the sheaths were similar with respect to median abundance, but as with Hay Bay,
their range of abundance was dramatically different. Based on the result of the Friedman
analysis, visual inspection of the seasonality patterns of different virus genes at the different
stations (Figure 5), and the obvious variability of virus gene abundances (Figure 6), it is clear
that there is considerable ecological variability within Bay of Quinte algal virus communities,
regardless of sampling location or virus taxonomic classification. This is very likely due to the
variable ecology of the different hosts of these viruses and the different ecological drivers that
influence their growth.
By using the Friedman analysis to compare the abundances of individual viruses between
stations (e.g., comparing the abundance of Chlorovirus at Belleville to Hay Bay and to
Napanee), only four of the ten viruses were statistically significantly different between biweekly
sampling stations (Prasinovirus49, Prasinovirus68, Prasinovirus16.20, and Sheath 282). A
Wilcoxon Signed-Rank post-hoc analysis with a Bonferroni correction applied demonstrated
that of those four, three had significant differences between specific stations (Prasinovirus68,
Prasinovirus16.20 and Sheath 282). By examining these particular viral seasonal trends (Figure
4) and viral variability across stations (Figure 6), the results of the statistical tests are fairly
obvious. For both Prasinovirus68 and Prasinovirus16.20, abundance at Hay Bay was
significantly different than the other two stations, and for Sheath 282, Belleville was
significantly different from Hay Bay, but not Napanee. Since Hay Bay was the station most
often statistically different than other stations based on variances of virus gene abundances, it
appears to be an atypical Bay of Quinte station compared to Belleville and Napanee. As
mentioned above, the 2011 sampling season was not a ‘typical’ Bay of Quinte summer; it would
37
appear that the ecological drivers unique to Hay Bay had an impact on viral dynamics and
abundance. Therefore is it possible that future algal virus studies in the Bay of Quinte would
produce different results.
The data in Figures 4, 5 and 6 demonstrate the very large range of virus gene abundances
that were observed during this study of different viruses, from below detection limits to over
256,000 gene copies/mL. Previous quantitative studies of freshwater algal viruses have also
found a wide range of gene copies. For example, Short et al. (2011a) observed prasinovirus
abundance from near detection limits to 11,000 gene copies/mL, and studies from Lake Erie in
2009 found up to 3.1 x 106 gene copies/mL of a single cyanomyovirus gene (Matteson et al.,
2011). These past studies support the validity of this work’s abundance values; it reasonable that
a single strain of Microcystis phage was so highly abundant in certain samples (> 105 gene
copies/mL) while other viruses peaked at a much lower abundance (e.g., Prasinovirus68 peaked
at < 20 gene copies/mL). While these statistical tests have demonstrated that algal viruses in the
Bay of Quinte are significantly different from each other across time, and a few are significantly
different between stations, host abundance dynamics were only part of the analysis indirectly. A
possible avenue of future research into Bay of Quinte algal ecology could be the development of
a multivariate analysis based on environmental parameters and phytoplankton population trends
particularly bloom events. Using this framework, future work can determine which
environmental parameters best predict host abundances, and therefore virus abundances. A
particular goal of algal ecology is the prediction and forecasting of harmful algal events, which
could prevent illnesses to livestock and humans alike. Furthermore, predicting an algal bloom
would enable algal virus ecologists to track a lytic event through host growth, bloom and decay.
Virus Abundance and Host Biomass
To determine if phytoplankton biomass predicted algal virus abundance in the Bay of
Quinte, a regression analysis was conducted to compare the sum of the abundance of all ten
virus genes, a proxy for virus abundance, to chlorophyll a concentration, a proxy for host
biomass (Figure 7). The initial clone libraries of this study captured the most abundant and most
common viral sequences in the environment from specific days, at specific locations, and the
quantitative abundance values reflect this (Figure 5). While this ‘sum of viruses’ does not reflect
the abundance of all algal viruses present in the Bay of Quinte, it does reflect the most abundant
38
viruses from Belleville on June 7th
, August 16th
and October 12th
and Hay Bay on June 7th
and
July 19th
. Of the ten virus sequences used in this study, Chlorovirus and Mimivirus-Prym 356
were consistent, major contributors to the sum of virus abundances at Belleville (together
averaging 75 % of the sum of viruses over the sampling season), but less so at Hay Bay (51 %)
and Napanee (70 %), where Sheath 282 was another major contributor. Sheath 282 accounted
for, on average, 29 % and 16 % of the sum of viruses at Hay Bay and Napanee respectfully, but
only averaged 0.05 % of the sum of viruses at Belleville. Altogether, these three viruses
accounted for over 75 % of the sum of viruses for each station respectively; therefore it was
these three viruses that were driving relationship patterns with host abundances (Figure 7),
which is interesting as chlorophyll a concentration is the measure of all hosts, not of select
species.
Clearly, this study did not estimate total virus abundances. Assuming the total virus counts
(~2.09 x107 virus-like particles) from a single sample from the Bay of Quinte made in 2003 by
Gouvêa (2006) have not changed and represent year round average abundances this study would
have examined at most 1.3 % of the total virus community (Hay Bay, July 19th
, 2011). For any
given sample, the average percentage of the total virus community (by averaging the sum of all
viruses from the biweekly stations) was only 0.09 %. Put into perspective, a quantitative study
in Lake Erie described up to 4.6 % of the total virus community using the target gene g20
present in Cyanomyoviridae (Matteson et al., 2011). Total virus counts from freshwater
(Wilhelm and Smith, 2000; Gouvêa et al., 2006; Wilhelm et al., 2006; Matteson et al., 2011) and
marine ecosystems (Suttle, 2005) report roughly 107 viruses/mL, indicating that a large
proportion of viruses and virus-like particles are not included in quantification studies. Despite
the large proportion of virus-like particles in the Bay of Quinte not accounted for, virus
abundances and chlorophyll a show strong positive statistically significant relationships at
Belleville and Hay Bay, but not at Napanee (Figure 7). This indicates that the algal viruses
studied track strongly with their hosts and that even a small percentage of the total virus
community is enough to see this trend. This supports Clasen (2008) who also found significant
positive relationships between chlorophyll a and virus abundance in freshwater. It is understood
that the sum of viruses used in Figure 7 is not total virus community, but it is possible that the
chlorophyll a values are not a fully accurate representation of actual phytoplankton biomass and
either could be the cause of the disassociation between hosts and viruses seen at Napanee.
39
By examining the correlation between the sum of virus abundance to chlorophyll a
concentration (Figure 7), the relationship between host and viruses in the Bay of Quinte
supports the current notion that these algal viruses are dependent on their hosts. Almost all
Phycodnaviruses display extreme host specificity; therefore, it is very likely that the ten
different viruses observed in this study each have unique algal/cyanobacteria strains or sub-
stains as their hosts (Short, 2012). However, these hosts could also be susceptible to other
viruses present in the Bay of Quinte. Viral regulated host succession remains largely unresolved
(Thyrhaug et al., 2003), and in situ “killing the winner” studies are limited (Winter et al., 2010).
The few studies that have examined the “killing the winner” viral succession hypothesis
(Thingstad, 2000) in the environment have not provided convincing evidence that viruses
regulate host community composition (Schwalbach et al., 2004) or cause bloom termination
(Schroeder et al., 2003).
This study confirms the relationship between host and virus abundances, and while it
shows that host biomass can predict algal virus abundance it does not provide evidence that
viruses regulated host bloom dynamics. Establishing viruses as causal agents in bloom
termination would require quantification of host and virus abundances over the course of an
algal bloom’s growth and decline. Quantitative studies of both host and viral isolate are
complicated and comprehensive, limited in the literature and often exclusive to a certain
location, so researchers settle for site specific host proxies and total virus counts. Future studies
in the Bay of Quinte could include total virus counts (Noble and Fuhrman, 1998), more
intensive sampling (increased sample number and field season duration) and interpreting the
impact of different year to year environmental drivers on the virus-host system. This could be
done by culturing host algae and isolating viruses, then tracking each through quantitative
techniques to provide a much better understanding of the interaction between viruses, hosts and
environmental conditions.
Clustering by Taxonomic Classification
Distance measures were created through pair-wise Pearson Correlation and Bray-Curtis
dissimilarity comparisons of virus abundance from each virus gene studied to every other virus
gene for all stations, as well as for each station individually to resolve if related virus
populations cluster together based on virus abundances (Figure 8). In an attempt to converge on
40
a single consensus cluster analysis dendrogram, multiple clustering techniques were used
(Figure 9). While a consensus regarding absolute clustering was not achieved, almost every one
of these analytical techniques produced the same result: Taxonomic groups do not cluster
together based on virus abundance. The only exception is the clustering of Sheath 282 and
Sheath 253 observed in the Belleville dendrograms, which could be due to Prasinovirus16.20
and Prasinovirus68 being excluded as these viruses were consistently below detection limits at
this station, and therefore it is not given much credence. It would appear that each of the viruses
studied have unique seasonal dynamics which greatly limits the potential for overarching
ecological generalizations to be derived solely from virus taxonomic classification. As new
viruses continue to be isolated, more viral taxa are being recognised each year (almost 200
viruses between 2009 and 2011; King et al., 2012). Current taxonomic classification within the
Phycodnaviridae is based partially on the taxonomic affiliation of the hosts (Van Etten et al.,
2002) and the presence of ‘core-genes’ (i.e., polB, MCP) since it can be difficult to infer genetic
relationship due to horizontal gene transfer between viruses. However, based on current
information, the relatedness of many of the established Phycodnavirus genera is relatively
robust. With regards to this study, although the genes targeted for quantitative analysis were
from the same genera, respectfully (or species in the case of the sheath protein gene), each virus
infects a different host. For instance, the cultivated Chrysochromulina ericina virus is closest to
Mimivirus-Prym 356, so it is possible that freshwater species of Chrysochromulina (e.g., C.
parva) is the host for this virus. However it is probably not the host of the other mimivirus-like
virus of prymnesiophytes, Mimivirus-Prym 399, since it clusters separately among similar Bay
of Quinte environmental clones. As with the differences in Phycodnavirus abundance and
seasonal patterns observed by Short et al. (2011a), this study supports the speculation that even
genetically related viruses can infect hosts which have different seasonality themselves. Using
the prasinoviruses from this study as one example, the very unique dynamics between related
virus types will prevent these viruses from being binned together and treated as a single
population in ecosystem models. Based on this conclusion, it is apparent that virus dynamics
cannot be predicted based solely on virus type. That is to say that viral dynamics determined by
quantitative techniques such as these can only be applied to the single virus type containing the
target gene fragment of interest.
41
As previously mentioned, the 2011 Bay of Quinte sampling season was atypical with
regard to some environmental traits, which would presumably impact virus-host dynamics and
influence the way in which virus abundances cluster together in the dendrograms. Repeated
cluster analysis in a more historically typical sampling season could produce different results.
There is an interesting question raised by this cluster analysis; how could two phage sequences
originating from a single Microcystis phage strain not show more similar temporal abundance
patterns and therefore cluster together more often. It can be hypothesized that the observed
different patterns of Microcystis phage seasonality were due to the presence of two different
host strains of M. aeruginosa in the Bay, but thorough examination of the dynamics of different
Microcystis species, or even strains of M. aeruginosa would be needed to confirm or refute this
hypothesis. Currently, only bulk data on M. aeruginosa is available so considerations of intra-
species population dynamics are highly speculative.
The conclusion of this cluster analysis, that viral dynamic predictions cannot be based on
virus type, represents an important and fundamental hurdle for continued aquatic viral studies.
With regard to viral ecology, it may not be possible for “observed patterns [to] be scaled up
through statistical analysis and modeling to describe the structure and function of marine
ecosystems” (Cullen et al., 2007). To properly understand virus ecology, the dynamics of each
virus of interest may need to be examined individually, and not extrapolated from seasonality
data from related viruses.
Clustering by Spatial Distribution
Using virus abundances from each virus gene for each sample date at each biweekly
stations, as well as virus abundances from each virus gene for each station sampled during the
September spatial survey, distance measures were created through pair-wise Pearson Correlation
and Bray-Curtis dissimilarity comparisons to resolve if sampling events (Figure 10) or spatially
similar stations (Figure 11) cluster together based on abundances. The different cluster analysis
dendrograms revealed the same pattern regardless of clustering technique used; the biweekly
stations clustered more often by sample date than by station, while the spatial stations clustered,
for the most part, by their location in the Bay. The results from the biweekly stations suggest
that for viruses in the Bay of Quinte, patterns of abundance are more similar at certain times of
the year regardless of the sampling location. This makes sense since phytoplankton biomass (as
42
estimated through chlorophyll a concentration) was low during the early part of the sampling
season and algal virus abundances were also low. Similarly, samples from the later sampling
dates also tended to cluster together. Thus, virus abundances tended to cluster by date rather
than location (Figure 10). Nonetheless, there remains several nodes where sample location,
particularly from Belleville station, cluster. This is likely due to the uniqueness of
Prasinovirus16.20, Prasinovirus68 and Sheath 282 abundances at Belleville; Belleville is the
only location where several viruses were below detection or present at very low abundance.
With HB5 as the only exception, the virus abundance across spatial stations clustered into
groups consistent with the Upper and Middle Bay delineations (Figure 1; Robinson, 1986).
Based on the geographical location of HB5, far into Hay Bay, it may have been subjected to
similar environmental trends as the Upper Bay stations. Particularly with regards to station
depth, HB5 is shallower (4.7 m) than other Middle Bay stations (averaging 8.2 m), and is closer
in depth to the average depth of the Upper Bay stations (4.9 m; Table 1). Also, as mentioned, the
highest levels of total phosphorus in the Bay were found in Hay Bay, however, total phosphorus
levels at HB5 were more comparable to Upper Bay levels than to other Middle Bay stations
(Quinte, unpublished data). Despite increased exceptions (HB5, B2, and N), three divisions of
the sample locations are visible in Figure 11; the Belleville area group (B, BQ7, BQ6, and B2),
the Hay Bay area group (HB, HB4, HB5, and P) and the Napanee area group (N, BQ8, NR1,
and BQ9). While this could be a result of the spatial distribution of the virus communities in the
Bay of Quinte, owing to sampling and laboratory processing constraints, the spatial sampling
itself was done over three days which coincides exactly with the three aforementioned divisions
of the spatial stations. Therefore, the speculation that virus abundances in the Bay of Quinte are
more similar in time than in space may hold true for the spatial survey cluster analysis as well,
albeit over a very short time scale. Repeated sampling with a mix of each area on different days,
and with samples from all three bays (Upper, Middle and Lower) would help resolve if virus
abundance in the Bay of Quinte samples truly cluster with respect to location.
Summary
Broadly speaking and based on the patterns of virus abundance revealed in the
aforementioned analyses, it is clear that virus communities are heterogeneously distributed
across the Bay and over the phytoplankton growing season. Previous marine research has
43
demonstrated the microscale patchiness of virus abundance at spatial resolutions of 1 and 5 cm
with ‘hotspot’ concentrations varying up to 17-fold (Seymour et al., 2006) as well as significant
changes in virus abundances with 20-minute temporal resolutions (Wommack and Colwell,
2000). When virus abundances can differ between the tips of a multi-channel pipettor (Steven
Wilhelm, personal communication, October 5th
, 2012) and host distributions can be equally
heterogeneous (Long and Azam, 2001), differences across the Bay may seem inconsequential.
However, given that significant virus heterogeneity can occur over centimeters, a better
understanding of the spatial and temporal microscale processes will elucidate a relevant scale
for future sampling (Breitbart, 2012). Obviously, attempting to sample everything, everywhere,
at all times is not feasible for any environmental study, but rather taking small steps towards
better understanding the ecological role of algal viruses is a worthy goal for research on aquatic
ecosystems. This seminal Bay of Quinte algal virus study supports past observations suggesting
that viruses are omnipresent in freshwater ecosystems, and that genetically similar algal viruses
(e.g., based on polB, MCP and sheath protein genes) can be detected in oceans and freshwater
lakes all over the world. This study brings into sharp relief the apparent complexities in the
ecology of algal viruses, and the potential intricacies of Bay of Quinte phytoplankton ecology.
Predicting and modeling phytoplankton dynamics in the Bay and determining the factors that
are contributing to the modern emergence of harmful algal blooms is not trivial. Efforts such as
this to dive below the surface of phytoplankton ecology are returning more questions than
answers and are finding these waters deeper than were ever imagined.
44
Future Directions
With the confirmation of the presence of algal viruses in the Bay of Quinte, this study
demonstrated the surprisingly unique seasonality of individual virus types, suggesting that
predictions of virus seasonality cannot be based on virus identity, or taxonomic affiliation.
Future algal studies in the Bay of Quinte should compare results from a more ‘typical’ sampling
season to these results, as well as to other freshwater viruses from the same and different water
basins. Increasing the number of sequences in a clone library would provide a more in depth
examination of the algal virus diversity in the Bay of Quinte. Quantitative analysis of other
viruses as well as confirmed host-virus pairs from the Bay of Quinte would help better
understand virus-host interactions during periods of algal blooms and long term ‘seed-bank’
persistence (Short and Short, 2009). Continued modeling efforts to predict and forecast harmful
algal blooms (in the Bay of Quinte and around the world) must take into account the effect
viruses have on their host species by looking at virus-algae interactions. Questions related to
how viruses affect the ecology of their host algae, or which environmental parameters are the
best predictor of algae population growth need to be addressed. Future work with multivariate
statistics could make use of the abundant environmental and phytoplankton data from the past
40 years of Project Quinte and compare observations to hypothesize which environmental
parameters best predict hosts population oscillations, and eventually virus population growth
and decline. Ecology of the microscale is on the cusp of being properly understood, and with
conclusions like the ones from this study limiting the scope of overarching statements related to
viral dynamics, proper modeling of algal virus communities will be a laborious yet rewarding
campaign.
45
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52
Tables
Table 1. Limnological divisions of the Bay of Quinte.
Delineationa Length Width Depth Range
Upper Bay Trenton – Deseronto 35 km Various 4-8 m
Middle Bay Deseronto – Glenora 13 km 0.8-5.6 km 6-17 m
Lower Bay Glenora – Lake Ontario 16 km 3 km 17-52 m
a Map with the delineations indicated is shown in Figure 1 (Minns et al., 1986; Leisti and Doka, 2008).
53
Table 2. Sample locations.
Site Latitude Longitude Notes
Belleville (B) 44° 09’ 13.201” N 77° 20” 44.002” W PQ-U
B2 44° 09’ 15.480” N 77° 14’ 59.748” W SS-U
BQ6 44° 09’ 20.376” N 77° 20’ 00.744” W SS-U
BQ7 44° 08’ 35.844” N 77° 21’ 33.552” W SS-U
BQ8 44° 11’ 42.792” N 77° 01’ 46.056” W SS-U
BQ9 44° 09’ 56.772” N 77° 03’ 32.112” W SS-U
Hay Bay (HB) 44° 05’ 35.999” N 77° 04’ 18.001” W PQ-M
HB4 44° 06’ 52.549” N 77° 01’ 08.764” W SS-M
HB5 44° 08’ 58.560” N 76° 58’ 06.593” W SS-M
Napanee (N) 44° 10’ 49.001” N 77° 02’ 22.801” W PQ-U
NR1 44° 11’ 58.524” N 77° 00’ 56.556” W SS-U
P 44° 02’ 42.000” N 77° 07’ 00.001” W SS-M
Table adapted from data provided by DFO-GLLFAS (2012), with the GPS coordinates of the
historical Project Quinte (PQ) stations and spatial survey (SS) stations used in this research
project, each with limnological classification (U – Upper Bay, M – Middle Bay; No Lower Bay
stations were included in this study).
54
Table 3. Reagents used in PCR reactions.
Primer Total
vol.
10x
buffer MgCl2
dNTP
each Forward Reverse
pTaq
Invitrogen Viral
Template
AVS 50 µl 5 µl 1.5 mM 0.2 mM AVS1
a
400 nM
AVS2a
600 nM 1 unit 5 µl
MCP 25 µl 2.5 µl 1.5 mM 0.2 mM MCPf
b
500 nM
MCPrb
500 nM 0.5 units 2 µl
Sheath 25 µl 2.5 µl 1.5 mM 0.2 mM SheathF2
c
400 nM
SheathR2c
400 nM 0.5 units 2 µl
a Sequence described in Chen and Suttle (1995)
b Sequence described in Larsen et al. (2008)
c Sequence described in Takashima et al. (2007)
55
Table 4. Thermocycling parameters for PCR reactions.
Cycle name Denaturation
step
# of
cycles Step 1 Step 2 Step 3
Final
Extension Hold
AVS-40 2m @ 95°C 40 30s @
95°C
1m @
50°C
45s @
72°C
30m @
72°C
∞ @
8°C
AVS-25 2m @ 95°C 30 30s @
95°C
1m @
50°C
45s @
72°C
30m @
72°C
∞ @
8°C
MCP50-35 2m @ 94°C 35 30s @
95°C
30s @
50°C
30s @
72°C
20m @
72°C
∞ @
4°C
Sheath01 2m @ 94°C 35 30s @
94°C
30s @
56°C
1m @
72°C
20m @
72°C
∞ @
4°C
56
Table 5. Quantitative Primers and Probes.
Continued...
Target Name Probe Primer
252-M5.13
(Mimivirus-Pras 252)
ACT GTC TCA TGC GTG
CCT CG
F CTT GGT CTA GAT GCG
ACC
R CTC GAA GTC CAG GTT
AAT G
356-M5.3
(Mimivirus-Pras 356)
CGT AAC CTC GCC TGG
TCC AA
F CAC GAA GTC AAG ATC
AAC
R CAG TGT CAA GGT AGA
TGT A
399-M5.4
(Mimivirus-Prym 399)
CTT CCG CTT ACG CTC
AGT CTC T
F TCC GGT GAT ACA AAG
GTA
R CGT AGT CAA CAT AGA
GAG AAG
356-M5.14
(Mimivirus-Prym 356)
CAC ATC TGG AAC AAC
TTG ACT CTC C
F GCG TAT TGA TCG CCA
GTA
R GCC AAC CAT AGCATA
GTA AC
253-SH
(Sheath 253)
AAA CCT CTA TTA GCA
GTG TTA TCG TTT
F TCT CGT TTT ACG TAA
CGG
R GTG ACT AAT GCA GGA
CTA G
282-SH
(Sheath 282)
CGT AGA GGC AAC TGA
TAA CAC CAC TA
F CTG GGT CTA TCA GCA
ATC
R CAG AGT AGT AGT GAC
TAA TG
LO.20May09.33b
(Chlorovirus)
TGT CCA CAG TTC CGT
CCT CT
F GAT ACA GAT TCC GTT
ATG GT
R CAT CTT GAA GTG TGC
CTC
57
Table 5. Quantitative Primers and Probes. (continued)
a Previously described by Short and Short (2009)
b Previously described by Short et al (2011a)
Target Name Probe Primer
LO1b-49a
(Prasinovirus49)
CGA CAA TCT TCC AGG
TG
F TGT TAC TCA ACT CTG TA
CTT G
R GCG AAC TTG TAA GTC
CTA CC
LO Jul.16.20b
(Prasinovirus16.20)
TGG AAC GCA AGG
CAA CAT ACC
F CAG TTG GCC TAC AAG
ATT
R CCT TCA TGG TGA CAG
TTG
LO1a-68a
(Prasinovirus68)
TTG CTA CTC ATC CCT
CG
F GTT GTA TCC ATC TAT
TAT GAT TGC
R GAA AGT CTC ATA GGT
GAT GC
58
Table 6. Reagents used in quantitative PCR.
Total
Volume
10X
Buffer MgCl2
dNTP
each Forward Reverse Probe
ROX
dye
pTaq
Invitrogen Template
Group A 20 µL 2 µL 5 mM 0.2 mM 0.25 µM 0.25 µM 0.1 µM 30 nM 0.5 units 2 µL
Group B 25 µL 2.5 µL 5 mM 0.2 mM 0.4 µM 0.4 µM 0.2 µM 30 nM 0.625 units 2 µL
Group C 20 µL 2 µL 5 mM 0.2 mM 0.5 µM 0.5 µM 0.25 µM 30 nM 0.5 units 2 µL
Group D 20 µL 2 µL 1.8 mM 0.2 mM 0.4 µM 0.4 µM 0.2 µM 30 nM 0.5 units 2 µL
59
Table 7. Test of Primer and Probe Specificity.
Target
Sequence
Closest
non-target
relative
Percent
nucleotide
identity
Mismatches Ct at 10
7
target
Ct at 107
non-target Forward
primer
Reverse
primer Probe
252-M5.13 (Mimivirus-Pras 252)
252-M5.1 78 6 2 4 16.9 28.7
356-M5.3 (Mimivirus-Pras 356)
252-M5.5 73 1 14 9 14.8 31.5
399-M5.4 (Mimivirus-Prym 399)
399-M5.1 94 3 3 3 15.5 31.2
356-M5.14 (Mimivirus-Prym 356)
399-M5.14 74 5 6 6 12.8 28.2
253-SH (Sheath 253)
282-SH 92 3 1 5 12.7 23.0
282-SH (Sheath 282)
253-SH 92 1 2 6 13.0 No Ct
LO.20May09.33b
(Chlorovirus) UTM.09jun09.47 69 6 7 13 12.3 33.1
LO1b-49a
(Prasinovirus49) LO1a-42 73 6 4 4 14.9 33.3
LO Jul.16.20b
(Prasinovirus16.20) LO1a-68 81 2 4 4 13.9 31.5
LO1a-68a
(Prasinovirus68) LO1b-53 83 6 3 4 14.3 39.6
a Previously described by Short and Short (2009)
b Previously described by Short et al (2011a)
60
Table 8. Friedman analysis of virus abundances between stations.
Virus abundance
between stations χ²(2) p-value
Post-hoc: Significant
differences between..
Sheath 282 12.667 < .01**
HB-B
Sheath 253 5.600 .06 --
Chlorovirus 0.600 .74 --
Prasinovirus49 7.789 .02* No significant differences
Prasinovirus68 15.440 < .01**
HB-N; HB-B
Prasinovirus16.20 18.727 < .01**
HB-N; HB-B
Mimivirus-Pras 356 4.667 .10 --
Mimivirus-Pras 252 0.444 .80 --
Mimivirus-Prym 399 2.387 .30 --
Mimivirus-Prym 356 0.000 1.0 --
* Indicates significance at p < .05
** Indicates significance at p < .01
61
Figures
Figure 1. Map of the Bay of Quinte. Note the locations of the stations sampled during the 2011 sampling season. The historical
Project Quinte stations (B, HB, N) are marked in larger font than the spatial stations. Note the approximate delineations between the
Upper Bay, Middle Bay and Lower Bay. Image taken at an altitude of 53.6 km (Google Earth; USA, 2012).
62
Figure 2. Neighbor joining phylogeny of inferred amino acid sequences from DNA polymerase
gene fragments (polB) of NCLDVs. Virus sequences obtained from the Bay of Quinte are
shown in bold, whereas sequences from GenBank are shown in italicized font with accession
numbers in parentheses (Short et al., 2011a). For Bay of Quinte sequences, OTUs are
represented by a single sequence and are followed in parentheses by the number of redundant
sequences observed in clone libraries; singleton sequences are those without numbers in
parentheses. Values at nodes indicate bootstrap support as a percent of 500 replicates. The scale
bar represents the number of substitutions per site. **Indicates sequences used for primer and
probe design (see Table 5 for quantitative nomenclature).
63
Figure 3. Neighbor joining phylogeny of inferred amino acid sequences from major capsid
protein (MCP) gene fragments of NCLDVs. Virus sequences obtained from the Bay of Quinte
are shown in bold, whereas sequences from GenBank are shown in italicized font with accession
numbers in parentheses (Larsen et al., 2008; Park et al., 2011). For Bay of Quinte sequences,
OTUs are represented by a single sequence and are followed in parentheses by the number of
redundant sequences observed in clone libraries; Singleton sequences are those without numbers
in parentheses. Values at nodes indicate bootstrap support as a percent of 500 replicates. The
scale bar represents the number of substitutions per site. **Indicates sequences used for primer
and probe design (see Table 5 for quantitative nomenclature).
64
Figure 4. Abundances of individual virus genes plotted against time. Note that the y-axis is
drawn in log scale to highlight the range of values, and the X-axis intercept, “bd”, indicates that
the genes were below detection limits of the assay. Also note that Napanee abundances begin
one sampling event after Belleville and Hay Bay.
65
Figure 5. Virus gene abundances at each station plotted against time. Note the log scale of the Y
axis, and the X-axis intercept, “bd”, indicates that the genes were below detection limits of the
assay.
66
Figure 6. Virus gene abundance. The plot shows the median, first and third quartiles of each
individual virus’ abundance, with whiskers showing the range of values. Note the log scale of
the Y axis, and the X-axis intercept, “bd”, to illustrate a range from below detection limits of the
assay.
67
Figure 7. Regression of the sum of virus gene abundances on chlorophyll a. Note that at Hay
Bay on July 19th
, the total abundance peaked at 15x higher than any other abundance from that
station, therefore that point was included above the axis, labeled with sum of virus abundance in
parentheses. The dotted regression line at Hay Bay is the regression including July 19th
, while
the solid regression line is the regression excluding July 19th
.
68
Figure 8. Cluster analysis of virus abundance. Dendrograms created by UPGMA clustering of a distance matrix created from pair-
wise Pearson Correlations of abundance (i.e., 1 – the correlation coefficient). The upper left dendrogram was created using all the
biweekly samples for all ten quantitative assays. Note that Prasinovirus16.20 and Prasinovirus68 were excluded from the Belleville
dendrogram as these viruses were consistently below the detection limit at this location.
69
Figure 9. A comparison of different proximity measures. A) Dendrogram based on a distance
matrix calculated from a Pearson Correlation, and grouping patterns created by the UPGMA
clustering method. B) Dendrogram based on a distance matrix calculated using a Bray-Curtis
dissimilarity matrix, clustered using the same UPGMA method.
70
Figure 10. Cluster analysis of biweekly stations. Dendrogram based on a distance matrix
created from a Pearson Correlation, and an UPGMA clustering method using a composite of all
quantitative abundance data. Each station designation is followed by the sample date during the
2011 Bay of Quinte sampling season.
71
Figure 11. Cluster analysis of spatial stations. Dendrogram created based on a distance matrix
created from a Pearson Correlation, and an UPGMA clustering method using the abundance
data from all ten virus assays during the spatial survey. The limnological divisions of each
station are indicated in parentheses.
