Persistence of Algal Viruses and Cyanophages in Freshwater ... · Persistence of Algal Viruses and Cyanophages in Freshwater Environments Andrew Milam Long Doctor of Philosophy Ecology

Persistence of Algal Viruses and Cyanophages in Freshwater Environments

by

Andrew Milam Long

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Ecology and Evolutionary Biology University of Toronto

© Copyright by Andrew Milam Long 2017

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Persistence of Algal Viruses and Cyanophages in Freshwater

Environments

Andrew Milam Long

Doctor of Philosophy

Ecology and Evolutionary Biology

University of Toronto

2017

Abstract

Algal viruses and cyanophages exert top-down population controls upon primary producers in

aquatic environments. Despite their clear importance, many ecological phenomena related to

viruses are poorly understood. For instance, several studies suggest that phytoplankton viruses

often exist at stable abundances, even when their hosts are absent. However, estimates of algal

virus and cyanophage decay suggest that they decay too swiftly for these stable abundance

patterns to occur. This paradox is the primary impetus for my research. In order to begin to

address this knowledge gap, the seasonality of algal virus decay was assessed using decay

incubation experiments across all four seasons using infectivity assays with cultivated viruses to

estimate decay rates, which found high decay rates in the summer and spring and low decay rates

in the winter. This seasonal study found that the low algal virus decay rates during winter

allowed for survival after 126 days under ice cover in a seasonally frozen freshwater pond. This

work was expanded upon by developing and validating molecular assays to estimate decay of

environmental viruses with either unknown or uncultivated hosts, which represent the majority

of viruses in nature. Upon validation of molecular assays for estimating decay rates,

environmental algal virus and cyanophage decay rates were found to vary seasonally in the same

way that cultivated algal virus decay rates did. Further, environmental algal viruses were found

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to have lower decay rates than cyanophages. In the molecular study, viruses were also found to

persist in the winter under/within the ice cover for 126 days. However, the spring and summer

decay rates estimated in both studies were often too high to permit virus population maintenance

for long periods without ongoing production, which would require the presence of host cells at

relatively high abundances. As such, the ability of freshwater sediment to serve as an

environmental refugium for phytoplankton viruses was assessed using molecular methods.

Freshwater sediments from Lake Erie were found to harbor diverse assemblages of both algal

viruses and cyanophages. Some algal virus and cyanophage genotypes were found at high

abundances in putatively 50 year old sediments, suggesting that sediments may aid in the

persistence of viruses. In conclusion, over-wintering of algal viruses in the water column appears

to be one mechanism that maintains a viral ‘seed-bank,’ and the sediments of aquatic

environments may be an environmental refugium for algal viruses and cyanophages alike.

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Acknowledgments

First, I would like to acknowledge my advisor, Dr. Steven Short, for his counsel and support

throughout my degree. Steve helped me through the twists and turns that my research took,

allowed me to take on interesting problems, and was always there to help find interesting

solutions to them. I would also like to acknowledge my committee members, Drs. George Espie

and Linda Kohn, for their advice during my research program and the guidance they both gave

me that allowed me to become a better scientist in general and helped to shape my research

specifically. I would also like to thank my fellow Short Squad members, Mike Staniewski, Robin

Rozon, Samia Mirza, Cindy Short, Ankita Virdi, Amna Alam, Yuri Chaban, Lyndsey Ogden,

Dylan Shea, Nikhil George, Alex Paquette, and Donglin Wang, all of whom made the lab a lot

more fun to be in and provided interesting topics of conversation.

In addition, I would to acknowledge my father-in-law, Dr. Jeff Velten, for providing feedback on

the introduction and discussion chapters and helping me improve the document.

I would also like to thank my wife, Dr. Brandy Velten, for not only helping me stay relatively

sane throughout this experience and helping proofread my thesis, but for providing me insight on

how to be a better student, a better scientist, and a better person in general. In addition, I must

acknowledge my dog, Rufus, for letting me know when to take him outside and forget about my

worries.

Last but definitely not least, I would like to thank my parents, James and Colleen Long, for their

emotional and financial support.

Without the help of those mentioned above, I would have surely not produced this document.

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Table of Contents

ACKNOWLEDGMENTS IV

TABLE OF CONTENTS V

LIST OF TABLES IX

LIST OF FIGURES X

LIST OF ABBREVIATIONS XI

CHAPTER 1 GENERAL INTRODUCTION 1

AQUATIC VIRAL ECOLOGY 1

1.1 Biology of Phytoplankton Viruses 3

1.1.1 Diversity of Phytoplankton Viruses 3

1.1.2 Overview of Algal Virus Diversity 3

1.1.3 Overview of Cyanophage Diversity 6

1.2 Population Dynamics of Phytoplankton Viruses 9

1.2.1 Abundances of Algal Viruses and Cyanophages 9

1.2.2 Seasonal Variation in Phytoplankton Virus Abundances 13

1.2.3 Environmental Persistence of Phytoplankton Viruses 15

1.3 Thesis Focus and Objectives 21

CHAPTER 2 SEASONAL DETERMINATIONS OF ALGAL VIRUS DECAY RATES REVEAL OVERWINTERING

IN A TEMPERATE FRESHWATER POND 23

ABSTRACT 23

2.1 Introduction 24

2.2 Materials and Methods 27

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2.2.1 In situ Incubations to Estimate Virus Decay 27

2.2.2 Cell Culture Conditions and Estimating Virus Titres 28

2.2.3 Decay Rate Calculations and Statistical Analyses 29

2.3 Results 31

2.3.1 Environmental Parameters 31

2.3.2 Environmental Decay 31

2.3.3 Statistical Comparisons of Decay Rates 31

2.4 Discussion 40

2.4.1 Decay of Aquatic Viruses 40

2.4.2 Seasonality and Variability in Rates of Decay 42

2.4.3 Algal Virus Overwintering 44

2.4.4 Conclusions 45

2.4.5 Acknowledgements 46

CHAPTER 3 QUANTITATIVE PCR REVEALS ENVIRONMENTAL PHYTOPLANKTON VIRUS DECAY RATES

VARY SEASONALLY 47

ABSTRACT 47

3.1 Introduction 48


3.2.1 In situ decay incubation experiments 52

3.2.2 Algal cell culture conditions and viral infectious titre estimations 53

3.2.3 PCR conditions and sequence analysis 53

3.2.4 Quantitative PCR primer and probe design and conditions 55

3.2.5 Decay calculations and statistical analyses 57

3.3 Results 59

3.3.1 Environmental data 59

3.3.2 Algal virus and cyanomyovirus sequence analysis 59

3.3.3 Algal virus and cyanophage decay 63

3.3.4 Statistical comparisons of estimated algal virus and cyanophage decay rates 68

3.4 Discussion 73

3.4.1 Methodological considerations 73

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3.4.2 Diversity of algal viruses and cyanophages in a freshwater pond 74

3.4.3 Environmental decay of algal viruses and cyanophages 74

3.4.4 Treatment effects, seasonality and virus-to-virus variability 76



CHAPTER 4 DIVERSE AND ABUNDANT ALGAL VIRUSES AND CYANOPHAGES OBSERVED IN LAKE ERIE

SEDIMENTS 80

ABSTRACT 80

4.1 Introduction 81


4.2.1 Sample collection and DNA extraction 85

4.2.2 Analysis of algal virus and cyanophage communities 85

4.2.3 Quantitative PCR of viral genotypes in Lake Erie sediment 88

4.3 Results 91

4.3.1 Diversity of algal viruses and cyanophages in Lake Erie sediment 91

4.3.2 Abundance of algal virus and cyanophage genotypes in Lake Erie sediment 98

4.4 Discussion 104

4.4.1 Diversity of phytoplankton viruses in freshwater sediment 104

4.4.2 Phytoplankton virus gene abundance in Lake Erie sediment 106

4.4.3 Sediments as environmental refugia or geological record? 108



CHAPTER 5 GENERAL CONCLUSIONS AND FUTURE DIRECTIONS 111

PHYTOPLANKTON VIRUS SURVIVAL 111

5.1 Seasonality of Algal Virus Decay Rates 112

5.2 Seasonality of Phytoplankton Virus Decay Rates Estimated with Molecular Methods 113

5.3 Diversity and Abundance of Phytoplankton Viruses in Sediment 115

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5.4 Potential Fates of Phytoplankton Viruses in Freshwater Environments 117

5.5 Future Directions 123

REFERENCES 125

APPENDICES 142

APPENDIX 1 142

APPENDIX 2 145

APPENDIX 3 157

COPYRIGHT ACKNOWLEDGEMENTS 161

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List of Tables

Table 2.1 Environmental parameters for seasonal decay experiments ..............32

Table 2.2 Linear regression analysis of decay curves ........................................34

Table 3.1 Algal virus and cyanophage targeting quantitative PCR primers and

probes designed for this study............................................................56

Table 3.2 Environmental parameters for autumn 2014 experiment ...................60

Table 4.1 Primer and probe sets of detected algal virus and cyanophage

genes in Lake Erie sediment ..............................................................90

Table 4.2 Species richness and diversity of polB and g20 genes in Lake Erie

sediment .............................................................................................95

Appendix Table 1.1 Pairwise statistical comparisons of the slopes from decay incubations

using the same viruses within the same treatment in different

seasons ...............................................................................................140


using the same viruses within the in same season using different

treatments ...........................................................................................141


using different viruses within the same season and treatment ...........142

Appendix Table 2.1 Linear regression analysis of decay curves ........................................143

Appendix Table 2.2 ANCOVA comparing slopes from qPCR assays versus infectivity

assays of the same viruses..................................................................146

Appendix Table 2.3 ANCOVA of regression slopes calculated in the same season from

the same viruses with different treatments.........................................148

Appendix Table 2.4 ANCOVA of regression slopes from the same virus and treatment

in different seasons ............................................................................149

Appendix Table 2.5 ANCOVA of regression slopes from the same treatment in the same

season with different viruses ..............................................................152

Appendix Table 3.1 Search results from blastp for polB OTU representative sequences ..155

Appendix Table 3.2 Search results from blastp for g20 OTU representative sequences ...156

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List of Figures

Figure 2.1 Seasonal decay rates of algal viruses. ............................................................33

Figure 2.2 Percentage of statistically significant differences in the comparisons

between seasons, filtration treatment, or viruses ...........................................36

Figure 2.3 Over-wintering of algal viruses in a seasonally frozen freshwater pond .......37

Figure 3.1 Phylogenetic tree of inferred amino acid sequences of algal virus polB

fragments........................................................................................................61

Figure 3.2 Phylogenetic tree of inferred amino acid sequences of algal virus MCP

fragments........................................................................................................62

Figure 3.3 Phylogenetic tree of inferred amino acid sequences of cyanomyovirus g20

genes ..............................................................................................................64

Figure 3.4 Seasonal decay rates of (A) cultivated algal viruses, (B) environmental algal

viruses, and (C) environmental cyanophages estimated using qPCR ............66

Figure 3.5 Polynomial regression of infectious titre estimates against qPCR estimates

of ATCV-1, CVM-1, and CpV-BQ1 .............................................................69

Figure 4.1 Map of Lake Erie denoting sediment sampling sites. ....................................86

Figure 4.2 Maximum likelihood phylogenetic tree of putative algal virus polB gene

sequences from Lake Erie sediment ..............................................................93

Figure 4.3 Maximum likelihood phylogenetic tree of putative cyanomyovirus

g20 gene sequences from Lake Erie sediment ...............................................96

Figure 4.4 Abundances of individual algal virus genes at stations 1326 (A),

452 (B), 882 (C), and 973 (D)........................................................................100

Figure 4.5 Abundances of individual cyanophage genes at stations 1326 (A),

452 (B), 882 (C), and 973 (D)........................................................................101

Figure 5.1 Diagram of potential fates for phytoplankton viruses in aquatic

environments. .................................................................................................116

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List of Abbreviations

λ decay constant

% h-1 percent lost per hour

μm micrometer

ANCOVA analysis of covariance

ATCV-1 Acanthocystis turfacea Chlorella virus-1

AFC analytical flow-through cytometry

blastp basic local alignment search tool for proteins

C carbon

cm centimeter

CO2 carbon dioxide

CpV-BQ1 Chrysochromulina parva virus - Bay of Quinte 1

CVM-1 Chlorella virus Marburg-1

DNA deoxyribonucleic acid

DOM dissolved organic matter

DY-V Do it yourself media five

EhV-86 Emiliania huxleyi virus-86

EsV-1 Ectocarpus siliculosus virus-1

g20 portal protein encoding homolog gene 20

kb kilobase pairs

h hour

LPP Lyngbya, Plectonema, and Phormidium

MBBM modified Bold’s Basal Medium

MCP major capsid protein

mL milliliter

MPN most probably number

N nitrogen

NCBI National Center for Biotechnology Information

NCLDV nucleocytoplasmic large DNA viruses

MEGA molecular evolutionary genetics analysis

MpV-SP1 Micromonas pusilla virus-SP1

OTU operational taxonomic unit

PA plaque assay

PAR photosynthetically active radiation

PC polycarbonate

PCR polymerase chain reaction

PFU plaque forming units

polB DNA polymerase B gene

POM particulate organic matter

PVC polyvinyl chloride

qPCR quantitative PCR

dsDNA double-stranded DNA

dsRNA double-stranded RNA

ssDNA single-stranded DNA

ssRNA single-stranded RNA

RNA ribonucleic acid

TCAG The Center for Applied Genomics

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UTM University of Toronto Mississauga

UV ultraviolet

VLP virus-like particles

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Chapter 1 General Introduction

Aquatic Viral Ecology

Viruses are important players in aquatic ecosystems. They can exert top-down control on specific

populations and, more generally, alter primary production and the cycling of nutrients through

ecosystems (Fuhrman, 1999; Wommack and Colwell, 2000; Suttle, 2007; Short, 2012; Breitbart,

2012). One example of how viruses may alter the population dynamics of their hosts is through

the mechanism proposed in the ‘killing the winner’ hypothesis (Thingstad, 2000; Winter et al.,

2010). In the simplest case of this theoretical model, two prokaryotes, one competition specialist

and one defense specialist, compete for the same resource. The competition specialist has a

higher growth rate and is typically more abundant than the defense specialist, which relies on

attributes that increase its survivability in the environment. Thus, because of its higher

abundance, the competition specialist is more likely to be infected by its specific virus or be

preyed upon by non-selective, predatory protozoans than the defense specialist. In the simplest

version of this model, as the competition specialist population crashes due to viral lysis and

predation, resources are then freed up for the defense specialist to utilize. In more complex

models, these newly freed resources are now available for other competition specialists and

defense specialists to utilize. The population dynamics presented by the ‘killing the winner’

hypothesis result in continual replacements of the most active or abundant member of a

community by other less abundant/active populations through the actions of both viruses and

predators. This continual replacement of the most abundant population may drive diversity in

host populations and requires at least one virus able to exploit each individual host.

While the ‘killing the winner’ hypothesis cannot be explicitly tested in natural systems due to

methodological limitations, a number of studies have clearly demonstrated that viruses occupy

key roles in the population control of cellular organisms within aquatic environments (reviewed

in: Winter et al., 2010). For example, viruses are linked to the cessation of algal blooms (e.g.,

Bratbak et al., 1993; Wilson et al., 2002a; Castberg et al., 2001; Brussaard et al., 2005), have

caused population crashes of heterotrophic flagellates (Massana et al., 2007), can account for up

to 70 % of the mortality of cyanobacteria (Proctor and Fuhrman, 1990), and 5 - 66 % of the

mortality of the marine algae Phaeocystis globosa during blooms (Baudoux et al., 2006).

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The documented role of viruses as agents of mortality might indicate that viruses only have an

antagonistic relationship with bacteria and primary producers, however, recent evidence suggests

that viral lysis can also stimulate the production of a variety of organisms by altering the flow of

nutrients and energy through aquatic food webs. In what has been termed the ‘viral shunt,’ viral

lysis diverts nutrients away from primary producers and, thus, higher trophic levels into

dissolved and particulate organic matter (DOM and POM) pools (Fuhrman, 1999; Wilhelm and

Suttle, 1999). One of the key predictions of the ‘viral-shunt’ is that carbon that would otherwise

be utilized by higher trophic levels is lost from the system via CO2 production by heterotrophic

bacteria. However, carbon and nutrients taken up by bacteria can re-enter the DOM and POM

pools by further viral lysis of these organisms. After nutrients enter the DOM and POM pools,

bacteria or primary producers (Shelford et al., 2012) can consume the released nutrients, as

demonstrated experimentally for nutrients released by both lysed bacterial cells (Middelboe et

al., 2003) and algal cells (Haaber and Middelboe, 2009). Furthermore, the ‘viral shunt’ does not

always require complete lysis, as infected cells of the alga Phaeocystis globosa were documented

to leak nutrients even before the lysis event and these nutrients (C and N) were then utilized by

bacteria (Sheik et al., 2014). The consumption of nutrients released through viral lysis by

phytoplankton provides a mechanism that may explain the observed increase of primary

productivity due to the presence of viruses reported in marine (Weinbauer et al., 2011) and

freshwater systems (Staniewski and Short, 2014). Thus, through viral lysis and leakage of

nutrients during infection cycles, viruses have the ability to directly alter biogeochemical cycles.

Because many of the known mechanisms by which viruses alter biogeochemical cycles are in

relation to either causing the mortality of primary producers or by stimulating primary

production, the biology of viruses that infect phytoplankton requires special attention.

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1.1 Biology of Phytoplankton Viruses

Diversity of Phytoplankton Viruses

The two major groupings of viruses that infect phytoplankton are algal viruses and cyanophages.

Algal viruses infect eukaryotic algae, while cyanophages infect prokaryotic algae (i.e.,

cyanobacteria). While single-stranded DNA (ssDNA), single-stranded RNA (ssRNA), and

double-stranded RNA (dsRNA) viruses that infect algae have been observed, the majority of

isolated algal viruses have double-stranded DNA (dsDNA) genomes and belong to family

Phycodnaviridae. In contrast, all characterized cyanophages belong to order Caudovirales, an

order of tailed phages with dsDNA genomes (Ackermann and DuBow, 1987). Within

Caudovirales, the three families that contain cyanophages are Myoviridae, Podoviridae, and

Siphoviridae.

Overview of Algal Virus Diversity

Phycodnaviridae, which is one of the families of nucleocytoplasmic large DNA viruses

(NCLDV), contains six genera, Chlorovirus, Coccolithovirus, Phaeovirus, Prasinovirus,

Prymnesiovirus and Raphidovirus (International Committee on Taxonomy of Viruses, Viral

Taxonomy 2015 release). The genera are named for the types of algae members of each specific

genus generally infect: i.e., chloroviruses infect Chlorella and Chlorella-like green algae,

coccolithoviruses infect coccolithophores belonging to Prymnesiophycaea, phaeoviruses infect

brown algae belonging to Phaeophycaea, prasinoviruses infect green algae belonging to

Prasinophycaea, prymnesioviruses infect haptophyte algae belonging to Prymnesiophycaea, and

raphidoviruses infect raphidophytes belonging to Raphidophycaea (Nagasaki and Bratbak,

2010). Members of family Phycodnaviridae have large genomes (160 - 560 kb) encapsulated by

icosahedral capsids and almost all are obligate lytic viruses, such that infection leads to viral

lysis of the cell (Dunigan et al., 2006).

Remarkably, viruses that infect the brown alga Ectocarpus siliculosus are the only

phycodnaviruses known to date to be temperate, meaning that lysis and cell death does not

immediately follow infection. The genetic material of the temperate phaeoviruses can be

integrated into the hosts’ genomes and can even be inherited by gametophytes (Bräutigam et al.,

1995; Delaroque et al., 1999). Furthermore, these phaeoviruses, along with Chrysochromulina

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brevifilum virus PW1, are also the only members of Phycodnaviridae currently known to infect

multiple hosts (Suttle and Chan, 1994; Müller et al., 1996). Additionally, Haptolina ericina virus

RF02 and Prymnesium kappa virus RF01, algal viruses that putatively belong to the NCLDV

family Mimiviridae, infect strains of both Haptolina ericina and Prymnesium kappa

(Johannessen et al., 2015). Observations of algal viruses belonging to either Phycodnaviridae or

Mimiviridae that infect multiple hosts challenge the previously held notion that most algal

viruses have a single host as more viruses are isolated and characterized.

As noted before, members of family Phycodnaviridae make up the majority of algal viruses

cultured to date. However, many other types of algal viruses are beginning to be isolated. Several

dsDNA algal viruses that are closely related to Mimiviridae have been isolated, including two

viruses that infect both Haptolina ericina and Prymnesium kappa (Johannessen et al., 2015).

Additionally, the same study isolated an additional algal virus, Prymnesium kappa virus RF02,

which infects two strains of Prymnesium kappa (Johannessen et al., 2015). Moreover, other

studies have isolated algal viruses related to Mimiviridae that infect green algae belonging to

Prasinophycaea (e.g., Pyramimonas orientalis virus-01B; Sandaa et al., 2001), and haptophyte

algae belonging to Prymnesiophycaea (e.g., Chrysochromulina ericina virus-01B; Sandaa et al.,

2001). In addition to other types of dsDNA viruses which infect eukaryotic algae, there are

several reports of viruses with ssDNA, ssRNA, and dsRNA genomes. For instance, there are

several types of ssRNA algal viruses which infect diatoms (e.g., Rhizosolenia setigera RNA

virus; Nagasaki et al., 2004; Chaetoceros tenuissimus RNA virus; Shirai et al., 2008;

Chaetoceros tenuissimus RNA virus type II; Kimura and Tomaru, 2015). Single-stranded RNA

viruses have also been isolated which infect raphidophyte algae (e.g., Heterosigma akashiwo

RNA virus; Tai et al., 2003) and dinoflagellates (e.g., Heterocapsa circularisquama RNA virus;

Tomaru et al., 2004a). Single-stranded DNA algal viruses which infect diatoms have also been

described (e.g., Chaetoceros salsugineum nuclear inclusion virus; Nagasaki et al., 2005;

Chaetoceros tenuissimus DNA virus type II; Tomaru et al., 2011; Chaetoceros tenuissimus DNA

virus type II; Kimura and Tomaru, 2015). Finally, a single dsRNA virus, Micromonas pusilla

RNA virus-01B, has been fully characterized that infects a green alga belonging to

Prasinophycaea (Brussaard et al., 2004). Even though several types of algal viruses have been

isolated, the diversity observed in environmental surveys using molecular tools far exceeds the

cultured diversity.

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In addition to the algal viruses identified through isolation from environmental samples, various

molecular tools have been used to assess the diversity of uncultivated algal viruses in aquatic

environments. For instance, the use of DNA polymerase B (polB hereafter) as a signature gene

for algal viruses has been well established since the development of the universal algal virus

polB PCR primers AVS1 and AVS2 (Chen and Suttle, 1995). More recently, an additional

universal algal virus polB PCR primer set has been developed and successfully used to study the

diversity of environmental algal viruses (Clerissi et al., 2014a). Furthermore, major capsid

protein (MCP) gene sequences have been used to distinguish between strains of Emiliania

huxleyi viruses (Schroeder et al., 2002) and more recently, universal algal virus MCP PCR

primers have been developed (Larsen et al., 2008; Clerissi et al., 2014a). The majority of

diversity studies using these algal virus primer sets have been in marine systems and have

provided evidence that similar genotypes are widespread geographically and that many different

algal virus genotypes co-exist within the same environment (e.g., Chen et al., 1996; Short and

Suttle, 2002; Schroeder et al., 2002, 2003; Park et al., 2011; Clerissi et al., 2014b, 2015).

While the majority of algal virus diversity studies have been conducted in marine systems,

several studies using algal virus polB and/or MCP PCR primers have recently assessed the

diversity of these viruses in various freshwater systems, including rivers (Short and Short, 2008;

Gimenes et al., 2012), reservoirs (Short and Short, 2008), and several lakes (Short and Short,

2008; Clasen and Suttle, 2009; Gimenes et al., 2012; Short et al., 2011a, 2011b; Rozon and

Short, 2013; Zhong and Jacquet, 2014) One of the prevailing observations from the diversity

surveys using algal virus polB primers is that viruses related to Prasinovirus are the dominant

genotypes in aquatic systems (e.g., Short and Short, 2008; Clasen and Suttle, 2009; Gimenes et

al., 2012; Rozon and Short, 2013; Zhong and Jacquet, 2014; Wang et al., 2015). These

observations may be due to inherent primer biases within polB, however, metagenomic studies of

lakes often find Prasinovirus-like sequences to be the dominant algal virus sequences in

freshwater systems (e.g., López-Bueno et al., 2009; Zhang et al., 2015). While studies using

universal polB primers have also obtained sequences closely related to Prymnesiovirus and

Chlorovirus in freshwater environments (Short et al., 2011b; Wang et al., 2015), the

development of primers biased towards specific cultivated Chlorovirus species yielded more

Chlorovirus-like sequences than the universal algal virus polB PCR primers in Lake Ontario

(Short et al., 2011b). Furthermore, studies utilizing universal algal virus MCP PCR primers have

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also yielded Prasinovirus-like sequences in freshwater environments, but the same studies also

obtained sequences related to Prymnesiovirus, Mimivirus-like prasinoviruses, and Mimivirus-like

prymnesioviruses (Rozon and Short, 2013; Zhong and Jacquet, 2014; Wang et al., 2015). In

addition to the putative algal virus sequences identified with polB and MCP PCR primers,

metagenomic studies have found Prasinovirus-like sequences in Antarctic lakes (López-Bueno et

al., 2009) and Yellowstone Lake, USA (Zhang et al., 2015), a Mimivirus-like prymnesiovirus in

Yellowstone Lake, USA (Zhang et al., 2015), and a Chlorovirus-like algal virus in Cayuga Lake

and Fayetteville Green Lake, USA (Hewson et al., 2012). The large diversity of putative algal

viruses in aquatic environments, which far exceeds the diversity of cultivated algal viruses,

suggests that the isolation of new algal viruses from aquatic environments is still a critical task in

aquatic viral ecology.

Overview of Cyanophage Diversity

Cyanophages that belong to the three families Myoviridae, Podoviridae, and Siphoviridae all

have dsDNA genomes ranging in size from 80 to 100 kb (McDaniel, 2011). Myoviridae,

Podoviridae, and Siphoviridae are morphologically distinct. While all three have icosahedral

capsids, Myoviridae have contractile tails separated by a neck protein, Podoviridae have non-

contractile tails that are much shorter than the tails of either Myoviridae or Siphoviridae, and

Siphoviridae have long non-contractile tails (Safferman et al., 1983). While these terms lack

taxonomic backing, cyanophages belonging to Myoviridae, Podoviridae, and Siphoviridae are

often referred to as cyanomyoviruses, cyanopodoviruses, and cyanosiphoviruses, respectively.

Cyanophages have been isolated that infect a wide range of cyanobacteria, including the coccoid

Synechococcus, which is widespread and ecologically important in both marine (Barsanti and

Gualtieri, 2006) and some freshwater systems (e.g., Wilhelm et al., 2006b). While all three

morphologies of cyanophages have been shown to infect Synechococcus species, the majority of

the isolated cyanophages that infect Synechococcus are cyanomyoviruses (Mann, 2003;

McDaniel, 2011). Additionally, many of the cyanophages that infect the freshwater toxin-

producing and bloom-forming Microcystis aeruginosa are myoviruses, though cyanopodoviruses

have been found that infect Microcystis species as well (e.g., Yoshida et al., 2006; Deng and

Hayes, 2008). Cyanophages have also been isolated that infect cyanobacteria of genera Lyngbya,

Plectonema, and Phormidium (the LPP group, Safferman and Morris, 1963), Prochlorococcus

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(Sullivan et al., 2003), Anabaena (Khudyakov and Gromov, 1973; Hu et al., 1981; Franche,

1987), Nostoc (Hu et al., 1981), and Nodularia (Jenkins and Hayes, 2006).

Even though many earlier isolated cyanophages have been found to infect multiple hosts

(Safferman et al., 1983), it has been suggested that their multi-host status reflects the

dysfunctional state of cyanobacteria taxonomy rather than a true ability to infect vastly different

hosts (Suttle, 2000b). However, cyanophages have been shown to infect multiple types of

Synechococcus (e.g., Suttle and Chan, 1993) and some cyanophages even infect both

Synechococcus and Prochlorococcus species (Sullivan et al., 2003). Additionally, several

cyanophage isolates have been shown to infect cyanobacteria stains that are unambiguous

members of the genera Microcystis, Planktothrix, and Anabaena (Deng and Hayes, 2008). As

such, the ability to infect multiple hosts appears to be common among several types of

cyanophages. Surprisingly, some of the isolated cyanophages known to infect Microcystis,

Planktothrix, and Anabaena showed a novel filamentous morphology (Deng and Hayes, 2008).

These filamentous viruses suggest that some cyanophages may not belong to Myoviridae,

Podoviridae, or Siphoviridae.

As with algal viruses, molecular methods of detection have been used to assess the diversity of

cyanophages in aquatic environments. The environmental diversity of cyanomyoviruses has been

assessed through the use of PCR primers targeting the portal protein encoding homolog gene 20

(g20) of cyanomyoviruses infecting Synechococcus and Prochlorococcus (Fuller et al., 1998;

Zhong et al., 2002; Sullivan et al., 2008). Additionally, the diversity of cyanomyoviruses and

some cyanopodoviruses has been studied using PCR primers targeting the photosystem II protein

D1 gene psbA (Sullivan et al., 2006). This gene was likely acquired from host organisms and

models have suggested that viral photosynthetic genes may increase the fitness of host

organisms, especially during periods with intense light conditions (Bragg and Chisholm, 2008;

Hellweger, 2009). More recently, various PCR primers targeting the DNA polymerase gene,

polA, of cyanopodoviruses (Chen et al., 2009) and the ribonucleotide reductase, large terminase

subunit, and major capsid protein genes of cyanosiphoviruses (Wang et al., 2015) have been

developed and utilized to study the diversity of these cyanophage morphotypes in the

environment.

8

As was the case for algal viruses, the majority of cyanophage diversity studies were conducted in

marine systems (e.g., Zhong et al., 2002; Frederickson et al., 2003; Marston and Sallee, 2003;

Wang and Chen, 2004; Sandaa and Larsen, 2006; Sandaa et al., 2008; Huang et al., 2010;

Jameson et al., 2011). Nonetheless, the diversity of g20 of cyanomyoviruses and psbA genes of

cyanomyoviruses and cyanopodoviruses infecting Synechococcus have been studied in a number

of freshwater lakes (Dorigo et al., 2004; Short and Suttle, 2005; Wilhelm et al., 2006b; Chénard

and Suttle, 2008; Wilhelm and Matteson, 2008; Wang et al., 2009; Zhong and Jacquet, 2014;

Wang et al., 2015) and ponds (Short and Suttle, 2005), and the diversity of cyanosiphoviruses

infecting Synechococcus has been studied using multiple targeted genes in a single lake in China

(Wang et al., 2015). The most common observation from these studies is that, like algal viruses

and other microorganisms, the diversity of uncultivated environmental cyanophages far exceeds

the current diversity of cultured cyanophages. Furthermore, in the case of g20, several studies

have obtained sequences more closely related to other uncultured, environmental sequences than

sequences related to those from cultivated cyanophages (e.g., Short and Suttle, 2005; Wang et

al., 2009; Wang et al., 2015). This observation has led to the suggestion that the g20 primer sets

amplify other myoviruses that are not necessarily cyanomyoviruses (Short and Suttle, 2005).

However, when Sullivan and colleagues (2008) screened their redesigned g20 PCR primers with

a multitude of isolated phages, amplification was obtained for every Synechococcus and

Prochlorococcus cyanomyovirus tested, but none of the myoviruses that infect other types of

bacteria yielded PCR products. Therefore, the majority of sequences more closely related to

environmental sequences than cultivated cyanophages may be cyanomyoviruses.

In freshwater systems, additional PCR primers targeting cyanophages that infect the filamentous

cyanobacteria Anabaena and Nostoc (Baker et al., 2006), and for cyanophages that infect

Microcystis (Takashima et al., 2007; Kimura et al., 2013; Nakamura et al., 2014) have been

developed. The PCR primers developed by Takashima et al. (2007) to target Microcystis

cyanophage sheath proteins may be less suitable for diversity studies as only two genotypes were

obtained in an embayment of Lake Ontario in Canada (Rozon and Short, 2013), while many

genotypes were found in Hirosawanoike Pond and Lake Shinji in Japan using more recently

designed primers (Nakamura et al., 2014). Whether this discrepancy in observed genotypes

between these two locations is due to primer biases or differences in viral diversity at these sites

remains to be explored.

9

The isolation of diverse environmental viruses that infect phytoplankton suggests that numerous

viruses must be present in the environment. If the phytoplankton is able to be cultured, then

infectivity assays may be used to assess the abundance of its virus in environmental samples.

However, as stated above, the majority of phytoplankton viruses are only known from molecular

evidence. The molecular data for these phytoplankton viruses without known hosts has been used

to estimate the abundance of these viruses in nature. The use of infectivity and molecular assays

to estimate the abundance of phytoplankton viruses has begun to yield insights on the

distribution of these viruses in the environment and what factors might influence the observed

patterns.

1.2 Population Dynamics of Phytoplankton Viruses

Due to the wide ranging effects they have on food webs and biogeochemistry, the population

dynamics of algal viruses and cyanophages are of particular importance in viral ecology. The

abundance of phytoplankton viruses, their seasonality, their dependence upon host cell densities,

and their mechanisms for survival in the environment are all factors which directly influence

their population dynamics.

Abundances of Algal Viruses and Cyanophages

In order to give context to the abundances of individual phytoplankton viruses, the abundances

of total viruses in aquatic systems is summarized first. Total virus abundance is typically

measured with transmission electron microscopy or epifluorescence microscopy and is reported

as virus-like particles (VLP) per mL. As reviewed by Wilhelm and Matteson (2008), viruses in

water samples often have abundances between 106 and 108 VLP mL-1 in freshwater

environments and 104 and 108 VLP mL-1 in marine environments. While viral abundances are

typically higher in freshwater, they appear to be subject to greater seasonal variations in these

environments (Wilhelm and Matteson, 2008). Unsurprisingly, phytoplankton virus abundances

are estimated to be lower than the higher ranges of total virus abundance.

Before the abundance of phytoplankton viruses can be discussed, it is important to take note of

the limitations of the methods used to measure algal virus and cyanophage populations.

Individual phytoplankton virus populations have been enumerated through several methods.

10

Infectivity assays such as plaque assays and most probable number (MPN) assays rely on

cultured host organisms, while analytical flow-through cytometry (AFC) or quantitative PCR

(qPCR) do not. Quantifying algal viruses and cyanophages with measures of infectivity (i.e.,

plaque assays or MPN) is perhaps the most ecologically relevant method. However, it relies upon

the availability of host organisms capable of growth in culture, which represents a small minority

of single-celled organisms. Further, infectivity measures can only be used to deduce the total

community of viruses that infect specific algae or cyanobacteria. This can include several

different strains of viruses and/or even different viruses with varying genetic material. For

instance, both dsDNA and dsRNA viruses are known to infect the same host, Micromonas

pusilla (Brussaard et al., 2004). While AFC and qPCR can enumerate total viruses of specific

types, they likely represent overestimates of ecologically viable virus particles as only a portion

of algal virus progeny in culture are infectious (e.g., Van Etten et al., 1983b; Cottrell and Suttle,

1995; Bratbak et al., 1998). However, AFC and qPCR have a distinct advantage in their ability to

enumerate viruses of algae and cyanobacteria that are currently unable to be cultured. In

particular, qPCR has the advantage of being able to enumerate specific strains of environmental

phytoplankton viruses, as discussed above.

In marine systems, the abundances of several types of algal viruses have been estimated using

infectivity assays. The number of infectious units have been estimated for viruses that infect

Micromonas pusilla (Cottrell and Suttle, 1995; Sahlsten, 1998; Zingone et al., 1999),

Phaeocystis globosa (Baudoux et al., 2006), Heterosigma akashiwo (Tomaru et al., 2004b),

Heterocapsa circularsquama (Nagasaki et al., 2004), Ostreococcus tauri (Bellec et al., 2010),

and Chaetoceros spp. (Tomaru et al., 2011a) in several different marine environments. These

studies have found the range of abundances for these algal viruses to be 0.02 to 104 infectious

units mL-1. In addition to studies that estimated abundances with infectivity assays, several

studies have estimated the abundance of putative algal viruses using AFC. As these studies

typically estimated virus abundance during algal blooms, most of the large viruses estimated

with AFC were assumed to infect the prevailing algae during the bloom. AFC has been used to

estimate abundances of viruses that putatively infect Emiliania huxleyi (Wilson et al., 2002b;

Jacquet et al., 2002; Sorensen et al., 2009) and Phaeocystis globosa (Baudoux et al., 2006)

during algal blooms of these species. The maxima (up to 107 viruses mL-1) for abundance studies

using AFC were higher than those using infectivity assays. This may be due to the AFC studies

11

being conducted during blooms of host species, however, the abundance of viruses infecting

Phaeocystis globosa were simultaneously measured with AFC and MPNs, which found that

AFC-derived abundances were 20x that of the MPN-derived abundances (Baudoux et al., 2006).

It cannot be currently elucidated which method provides the more realistic measurement as AFC

likely overestimates the number of viruses due to its non-specific count of viruses of the same

size and MPN may underestimate the number of viruses if the strains present in the environment

do not infect the strain of algae used in the assay.

While the majority of phytoplankton virus abundance studies using infectivity assays have been

in marine systems, there have been several investigations of phytoplankton virus abundances

using these methods in freshwater environments. For example, the use of plaque assays to

enumerate viruses infecting Chlorella algae species have found abundances of up to 4.0 x 104

plaque-forming units (PFU) mL-1 in the Waccamaw River, NC, USA (Van Etten et al., 1985a),

3.2 x 103 PFU mL-1 in a drainage ditch in IL, USA (Van Etten et al., 1985b), 8.0 103 PFU mL-1 in

a pond in Seisei, Japan (Yamada et al., 1991), 1.4 x 103 PFU mL-1 in Holmes Lake, NE, USA

(Quispe et al., 2016), and up to 105 PFU mL-1 in an undescribed natural water sample (Kang et

al., 2005). However, for many of the environments tested in these studies, the PFU mL-1 of

Chlorella-infecting viruses appear to be lower than these maxima, ranging from below detection

in several sites in the surveys conducted in the USA, to less than one PFU mL-1 in other sites, up

to the maxima stated above (Van Etten et al., 1985a, 1985b). Additionally, plaque assays and

MPNs have been used to enumerate infectious units of cyanophages infecting Microcystis

aeruginosa in a hypereutrophic pond in Japan (Manage et al., 1999) and in Lake Baroon,

Australia (Tucker and Pollard, 2005). The abundances of Microcystis aeruginosa cyanophages

were estimated over time in a hypereutrophic pond in Japan using plaque assays, which found a

range of 2.0 x 102 to 4.2 x 104 PFU mL-1 (Manage et al., 1999). When estimated at a single time

point in Lake Baroon using MPNs, the abundance of Microcystis aeruginosa cyanophages were

found at a slightly higher abundance of 5.6 x 104 infectious units mL-1 (Tucker and Pollard,

2005). Like Chlorella-infecting algal viruses and Microcystis aeruginosa-infecting cyanophages,

cyanophage infecting the LPP group of cyanobacteria have also been shown to have a global

distribution (e.g., Safferman and Morris, 1963; Singh, 1973). However, these studies found the

LPP-infecting cyanophages to have infectious titres up to only a few thousand per mL. In

contrast, a group of cyanophage that infect Nostoc and Plectonema cyanobacteria were reported

12

to reach infectious titres of up to 104 mL-1 in fish farms and waste stabilization ponds throughout

Russia (Muradov et al., 1990). Overall, the abundance of infectious phytoplankton viruses varies

considerably, from below detection to less than 1 infectious particle mL-1 to 105 mL-1. Both

Chlorella-infecting viruses (105 mL-1) and cyanophages (104 mL-1) have similar maxima, with

the exception of LPP-infecting cyanophages, which are known to only reach 103 infectious units

mL-1. As only relatively few algae and cyanobacteria are in culture, several recent efforts have

looked at phytoplankton virus abundances using culture-free techniques.

Several recent studies have used qPCR to enumerate algal viruses and cyanophages in freshwater

environments. In order to enumerate specific types of algal viruses and cyanophages present

within freshwater systems, these studies have used sequences obtained using universal algal

virus or cyanophage PCR primers, as discussed above, to develop qPCR primers and probes. For

algal viruses, the abundances of Chlorovirus-like genotypes, Prasinovirus-like genotypes,

Mimivirus-like prasinovirus-like genotypes, and Mimivirus-like prymnesiovirus-like genotypes

have been enumerated in several locations in Lake Ontario, Canada across three studies (Short

and Short, 2009; Short et al., 2011a; Rozon and Short, 2013). Abundances of up to 104 gene

copies mL-1 were found for Chlorovirus-like genotypes, up to 105 gene copies mL-1 for

Prasinovirus-like genotypes, 103 gene copies mL-1 for Mimivirus-like prasinovirus-like

genotypes, and 104 gene copies mL-1 for Mimivirus-like prymnesiovirus-like genotypes (Short

and Short, 2009; Short et al., 2011a; Rozon and Short, 2013). Furthermore, the abundance of

Microcystis aeruginosa-infecting cyanophages related to the cyanomyovirus, Ma-LMM01, has

been studied using qPCR in several lakes ponds in Japan (Yoshida et al., 2008b, 2010; Kimura-

Sakai et al., 2015), in an embayment of Lake Ontario (Rozon and Short, 2013), and in East Lake,

China (Xia et al., 2013). These abundance surveys have often found cyanophage densities of up

to 105 gene copies mL-1. Finally, some studies have used universal cyanomyovirus PCR primers

in qPCR assays to enumerate the total cyanomyovirus community present in the environmental

samples, such as in the case of cyanomyovirus-like genotypes related to cyanomyoviruses that

infect Synechococcus in two lakes in France (Zhong et al., 2013) and in Lake Erie, USA

(Matteson et al., 2011). The abundance survey in the two French lakes found cyanomyovirus

genotype densities of up to 105 gene copies mL-1, while cyanomyovirus genotype densities had a

maximum of 106 gene copies mL-1 in Lake Erie. The overall range of phytoplankton virus

abundances estimated with qPCR is from below the detection limit for some viruses, to tens of

13

gene copies mL-1 to 106 gene copies mL-1. In addition to finding widespread and abundant algal

viruses and cyanophages in environments across the globe, many of these abundance surveys

have followed the abundance of phytoplankton viruses across seasons.

Seasonal Variation in Phytoplankton Virus Abundances

Upon estimating phytoplankton virus abundances across seasons and years, several studies have

found seasonal variations in both marine and freshwater systems (e.g., Van Etten et al., 1985b;

Yamada et al., 1991; Manage et al., 1999; Zingone et al., 1999; Tomaru et al., 2004b; Yoshida et

al., 2008a; Short and Short, 2009; Bellec et al., 2010; Short et al., 2011a; Rozon and Short,

2013; Zhong et al., 2013; Quispe et al., 2016). For instance, infectious titres of Chlorella

infecting viruses were monitored in several different ponds in Illinois, USA from April until

November. One pond had no infectious viruses over the sampling period. However, the overall

trend was peak abundances in May, followed by a drop in abundance by one or two orders of

magnitude, then relatively stable abundances at 5 - 90 PFU mL-1 throughout the rest of the time

points (Van Etten et al., 1985b). Similar patterns were observed in five ponds in Japan for

Chlorella-infecting viruses, sampled from June 1990 to March 1991 with peaks in abundance in

either April, May, or June, depending on the pond (Yamada et al., 1991). These peaks were then

routinely followed by a drop in abundance by one to two orders of magnitude to low, yet

relatively stable abundances throughout the rest of the year, except for two ponds, which both

had at least one month in which no viruses were detected. Furthermore, one pond in Japan had

stable, although very low abundances throughout the year with less than one PFU mL-1 for much

of the year, reaching a maximum of only 1 PFU mL-1. More recently, the seasonal abundance

patterns of algal viruses that infect four types of Chlorella-like algae were studied across three

years in Holmes Lake, NE, USA (Quispe et al., 2016). The strains used in this study were

Chlorella variabilis NC64A, Chlorella variabilis Syngen 2-3, Chlorella heliozoae SAG 3.83,

and Micractinium conductrix Pbi. There were no infectious viruses of M. conductrix Pbi detected

throughout the three year sampling period. In contrast, viruses were detected in every sampling

time for the other three types. The abundance patterns observed were unique for each virus, each

year, and even between the two sites sampled within Holmes Lake. ‘Boom-and-bust’ patterns, in

which periods of high abundance were followed by population crashes, were observed for all

14

three algal virus types. Typically, the periods of high abundance were in the summer, but there

were two peaks in autumn. Throughout the rest of the year, the most common pattern was a low,

stable abundance.

In addition to the three seasonal studies of Chlorella-infecting viruses, infectivity assays have

also been used to study the seasonal abundance of cyanophages infecting Microcystis aeruginosa

in a freshwater pond in Japan. This study found abundances in cyanophages peaked in the

summer and late autumn, after the peak of Microcystis aeruginosa, and was proceeded by a

sharp decline in both host and cyanophage abundances following their autumn maxima (Manage

et al., 1999). Like the Chlorella-infecting viruses, the Microcystis-infecting cyanophages were

present throughout the entire sampling period. The seasonality of cyanophages infecting the LPP

group of cyanobacteria have also been assessed with abundances ranging from single digits in

February and March to tens of infectious units mL-1 from May to June in waste stabilization

ponds in Arkansas, USA (Safferman and Morris, 1967). However, peak abundances of thousands

of infectious units mL-1 occurred during blooms of cyanobacteria in a fish pond in Israel (Padan

and Shilo, 1969).

Not only has the seasonality of algal viruses and cyanophages been studied using infectivity

assays, several studies have examined the seasonality of phytoplankton viruses using qPCR.

Using qPCR, the seasonality of putative of chloroviruses, prasinoviruses Mimivirus-like

prasinoviruses, and Mimivirus-like prymnesioviruses have been assessed in several sites in Lake

Ontario (Short and Short, 2009; Short et al., 2011a; Rozon and Short, 2013). Although some

genotypes had similar abundance patterns, there were complex population dynamics such that

many of the genotypes had unique patterns of abundances. Some genotypes even had different

patterns depending on the location. The two prevalent abundance patterns across the three studies

were that of ‘boom-and-bust’ and the pattern of low, but stable, abundances throughout the entire

sampling periods. Furthermore, the seasonality of Microcystis aeruginosa cyanophages related to

Ma-LMM01 were also estimated in ponds and lakes in Japan (Yoshida et al., 2008b, 2010;

Kimura-Sakai et al., 2015), in Lake Ontario (Rozon and Short, 2013), and in East Lake, China

(Xia et al., 2013). The abundance patterns of Microcystis aeruginosa cyanophages were similar

to those reported for the algal viruses in Lake Ontario, with all but one population of

cyanophages experiencing ‘boom-and-bust’ population dynamics. Only a single cyanophage

population maintained relatively stable abundances throughout the year in the Bay of Quinte in

15

Lake Ontario (Rozon and Short, 2013). Finally, the seasonal abundances of Synechococcus-

infecting cyanomyovirus genotypes have been estimated in Lake Bourget and Lake Annecy,

France (Zhong et al., 2013) and were enumerated in two seasons in Lake Erie, USA (Matteson et

al., 2011). In the two French lakes, there were large fluctuations between most time points, with

an overall maxima in Lake Bourget in September with 106 gene copies mL-1 and a minimum of

~104 gene copies mL-1 in March, while Lake Annecy had an overall maxima in September with

105 gene copies mL-1 and a minimum of ~103 gene copies mL-1 in January (Zhong et al., 2013).

The seasonality of phytoplankton viruses as estimated by both infectivity assays and qPCR raise

interesting questions on how these viruses persist from season-to-season and from year-to-year.

Many phytoplankton viruses, as mentioned above, have seasonal patterns of abundance in which

they exist in the water column in freshwater environments throughout the year. This year-long

persistence can be maintained at low levels following patterns of ‘boom-and-bust,’ at higher,

relatively stable densities, or even at stable low, but detectable, abundances. These patterns

suggest that either hosts remain at densities necessary for viral infection or that algal viruses and

cyanophages have low decay rates.

Environmental Persistence of Phytoplankton Viruses

The observations of stable phytoplankton virus populations in freshwater environments, coupled

with those reported for viruses in marine environments, such as Micromonas pusilla viruses

being present at detectable levels when their hosts were not (e.g., Zingone et al., 1999), provide

support for the ‘Bank’ model of viral ecology. The Bank model was first described by Breitbart

and Rohwer (2005) utilizing metagenomic data of viruses in a marine environment . It states that,

through a rank-abundance curve of viral genotypes, two fractions of viruses exist: one that is

highly abundant and active, but low in diversity, and one that contains the vast majority of

individual types of viruses that exist at low abundance. The viruses that survive in the

environment form a ‘seed-bank.’ Viruses within this ‘bank’ remain at low population levels until

their hosts reach an appropriate abundance such that contact between virus and host is likely to

occur. The threshold that host organisms must reach for phytoplankton viruses to infect and

produce new virions has been estimated to be 103 - 104 cells mL-1 for a number of algal viruses

and cyanophages (Suttle and Chan, 1994; Cottrell and Suttle, 1995; Jacquet et al., 2002).

However, the density of many types of algae drop below this threshold abundance at various

16

times throughout the year in many freshwater environments (e.g., Munawar and Munawar, 1986;

Reynolds, 2006). It would therefore be expected that phytoplankton virus decay rates would

allow for these viruses to persist during the times in which their hosts are below the threshold

necessary for viral infection and replication. However, in the few studies that have assessed the

environmental decay rates of phytoplankton viruses, this has not been the case. To illustrate this,

consider a population of viruses with an abundance equal to the highest reported for algal viruses

and cyanophages, 105 mL-1. Upon its host dropping below densities necessary for virus

production, the virus population will be subject to decay. The half-life for this virus population

will be ~5 days at the lowest decay of infectivity detailed below. This means only 1 virus particle

will remain from a starting population of 105 after 80 days with the lowest report decay rate of

infectivity. Even in this extreme case, the theoretical phytoplankton virus would require the

reoccurrence of its host 3 months after its initial drop below the threshold necessary for viral

production. However, most phytoplankton virus populations have not been observed to reach 105

mL-1 and the majority of phytoplankton viruses have been estimated to have lower half-lives than

5 days. This paradox of high viral decay rates for seemingly stable populations of algal viruses

and cyanophages in the absence of their hosts remains unresolved in aquatic environments.

Despite its clear importance in viral ecology and the necessity of phytoplankton virus particles to

survive outside host cells in order to reproduce, the number of studies that focus upon algal virus

and cyanophage decay rates is extremely limited. Only three studies provide estimates of algal

virus decay rates: two with infectivity measurements for marine algal viruses (Cottrell and Suttle,

1995; Frada et al., 2014) and one with qPCR for freshwater algal viruses (Hewson et al., 2012).

The decay rates of the marine Prasinovirus, Micromonas pusilla virus-SP1, was estimated to be

28 percent infectivity lost per hour (% h-1) in March in the Gulf of Mexico and 30 % h-1 in April,

which translate to half-lives of 2.5 and 2.3 hours (Cottrell and Suttle, 1995). The decay rate of

the marine Coccolithovirus, Emiliania huxleyi virus, was estimated to be 2 - 3 % h-1 in the North

Atlantic, with half-lives between 23 and 35 hours (Frada et al., 2014). Using qPCR, the decay

rate of an algal virus genotype related to the freshwater Chlorovirus, Acanthocystis turfacea

Chlorella virus-1 (ATCV-1), was estimated to be 0.13 % gene copies lost per hour (gene copy

half-life of 22 days) in a freshwater pond on the campus of Cornell University in Ithaca, NY,

USA (Hewson et al., 2012). To date, there have been no seasonal reports of algal virus decay

rates.

17

Likewise, estimates of cyanophage decay rates have been reported in five studies: four with

infectivity measures (Suttle and Chan, 1994; Garza and Suttle, 1998; Cheng et al., 2007; Liu et

al., 2011) and one with qPCR (Hewson et al., 2012). The decay rates of marine cyanophages

infecting Synechococcus have been estimated in two studies in the Gulf of Mexico using

infectivity assays, the first reporting a range of 0.5 - 17 % h-1 (half-lives between 4 hours and

5.75 days), depending on the location and depth in the water column (Suttle and Chan, 1994),

while the second study reported decay rates that ranged from ~ 5 to ~ 70 % h-1 (half-lives

between 1 and 13 hours), in both cultured isolates and naturally occurring cyanophages (Garza

and Suttle, 1998). Garza and Suttle (1998) also assessed the seasonality of Synechococcus

cyanophage decay, finding a maximum of ~ 40 - 70 % h-1 (half-lives between 1 and 1.7 hours) in

June and a minimum of ~ 5 - 20 % h-1 (half-lives between 3.5 and 13 hours) in November. Decay

rates have also been estimated for the freshwater cyanphage PP, which infect the filamentous

cyanobacterium Plectonema boryanum, in two studies in Donghu Lake, China. These studies

found a range of 60 - 230 % h-1 (half-lives between 0.3 and 1.2 hours; Cheng et al., 2007; Liu et

al., 2011). The decay rates of cyanophage PP had a clear seasonality, with a maximum of 230 %

h-1 (half-life of 0.3 hours) in summer and a minimum of 80 % h-1 (half-life of 0.9 hours) in

autumn (Cheng et al., 2007). Furthermore, the decay rate of a putative cyanomyovirus genotype

related to Prochlorococcus phage P-SSM-4 has been estimated to be 1.3 % h-1 (half-life of 2

days) in a freshwater pond on the campus of Cornell University in Ithaca, NY, USA (Hewson et

al., 2012). As such, the range of estimated decay rates varies across several environments from

approximately 0.13 - 30 % h-1 for algal viruses and 1.3 - 230 % h-1 for cyanophages. It is

important to note that the decay rates estimated with qPCR are, as the authors acknowledged,

likely to be underestimates of algal virus and cyanophage decay. This discrepancy is likely due,

in part, to only 20 - 60 % of algal virus progeny in culture being infectious (e.g., Van Etten et al.,

1983b; Cottrell and Suttle, 1995; Bratbak et al., 1998) as stated above. Additionally, many of the

decay processes that render a virus non-infective may not alter the amplifiability of the specific

regions of DNA necessary for enumeration via qPCR.

Virus particles in general can be removed from the system in a number of ways, including:

inactivation by solar radiation by either ultra-violet (UV) or photosynthetically active radiation

(PAR) (e.g., Wommack et al., 1996; Baudoux et al., 2012), heat-labile organic matter such as

nucleases and proteases (Gerba, 2005; Dell’Anno et al., 2015), consumption of viruses by

18

heterotrophic nanoflagellates (González and Suttle, 1993), attachment to non-host cells, and

adsorption to particles and subsequent sinking (Hewson and Fuhrman, 2003). These factors are

variable throughout aquatic environments. For example, cyanophages infecting Synechococcus

in the Gulf of Mexico were found to have lower decay rates at deeper depths, likely due to lower

levels of solar radiation, and lower enzyme activities stemming from lower temperatures at depth

(Garza and Suttle, 1998). It is easy to understand why some of these decay mechanisms would

destroy the infectivity of a virus, which requires a full complement of unaltered proteins and

genes in order replicate, before compromising the ability to amplify the DNA of a single gene

fragment necessary for qPCR enumeration. It is thus necessary for the relationship between the

loss of infectivity and the loss of amplifiable DNA to be established before further decay rates

can be estimated using qPCR.

Despite the harsh environment viruses experience outside of host cells and the sometimes rapid

turnover of viruses in the environment, algal viruses and cyanophages have several physiological

and life history traits that may aid in persistence outside of host cells. These characteristics

include thick capsids, UV-specific and other DNA repair mechanisms, host-mediated repair

mechanisms, lysogeny and pseudolysogeny. In bacteriophages that infect Escherichia coli,

survivability was found to be inversely proportional to the burst size (De Paepe and Taddei,

2006). Additionally, E. coli bacteriophages with small burst sizes have thick capsids and densely

packaged genomes, which may account for their increased survivability relative to the

bacteriophages with large burst sizes, which generally have thinner capsids and less densely

packed genomes (De Paepe and Taddei, 2006). Similar mechanisms may be involved in the

persistence of phytoplankton viruses as many algal viruses have burst sizes in line with the more

persistent bacteriophages (e.g., Chlorovirus burst sizes range from 200 to 350 PFU per cell,

Dunigan et al., 2006) and cyanophages are related to many of the persistent bacteriophage types,

such as Enterobacteria phage T4, studied by De Paepe and Taddei (2006).

Another mechanism in which algal viruses, in particular, may persist in the environment is

through the use of various DNA repair mechanisms. For instance, many algal viruses of the

family Phycodnaviridae, including many chloroviruses and at least one coccolithovirus, EhV-86,

possess genes for a UV-specific DNA glycosylase-pyrimidine lyase (Furuta et al., 1997;

Dunigan et al., 2006; Fitzgerald et al., 2007; Jeanniard et al., 2013). Other genes that algal

viruses possess include: DNA ligase, DNA polymerase δ, proliferating cell nuclear antigen, as

19

well genes involved in base incision repair and nucleotide incision repair (Dunigan et al., 2006;

Redrejo-Rodríguez and Salas, 2014). Although many algal viruses possess these genes, the

presence of several of the DNA repair genes are strain specific. For instance, some chlorovirus

strains such as KS1B, and the phaeovirus, EsV-1, lack the gene for UV-specific DNA

glycosylase-pyrimidine lyases (Dunigan et al., 2006; Jeanniard et al., 2013). This genetic

variation within algal viruses may partially explain the highly variable decay rates observed to

date.

Cyanophages have additional mechanisms that may aid in survival. For instance, host-mediated

photoreactivation has been shown to repair up to 59 % of the infectivity of cyanophage PP in

Donghu Lake, China (Cheng et al., 2007). Additionally, cyanophages are much more likely to

infect their hosts lysogenically (or temperately) than algal viruses, of which, as stated above,

only algal viruses that infect brown algae have been described to do. Studies have shown that

77.8 % of Synechococcus cyanophage in the Gulf of Mexico and Mississippi River plumes were

infecting their hosts lysogenically (Long et al., 2008). In addition to lysogeny, psuedolysogeny

has been exhibited by several cyanophages, including typically lytic cyanomyoviruses (e.g.,

Wilson et al., 1996; McDaniel and Paul, 2005). Pseudolysogeny occurs when a virus attaches to

the host, enters the host cell, but does not enter the lytic cycle and does not insert its genome into

that of the host cell. Lysogenic and pseudolysogenic infections tie their persistence with that of

their host cells until some environmental or chemical cue causes the virus to enter the lytic cycle

and produce more virions. Even though there are several mechanisms that may aid in the

persistence of phytoplankton viruses, their environmental decay rates can be quite high. Thus,

environmental refugia must also be considered in the maintenance of the viral ‘seed-bank.’

One potential environmental refugium for algal viruses and cyanophages in aquatic ecosystems

is the sediments of these environments; sediments may serve as a habitat that prolongs the

survival of viruses in general in aquatic systems. For example, estuarine sediments have shown

to increase the survival time of enteric viruses in laboratory decay experiments by up to 4 times

the survival time in water from the same sites (e.g., De Flora et al., 1975; Smith et al., 1978;

LaBelle and Gerba, 1980). Additionally, the abundance of viruses in the sediment often exceeds

the abundance of viruses in the overlaying water column (e.g., Paul et al., 1993; Maranger and

Bird, 1996). Furthermore, there is direct evidence that viable phytoplankton viruses can be found

in the sediments of several aquatic environments. In marine systems, sediments have been found

20

to harbor viruses that infect the raphidophyte Heterosigma akashiwo (Lawrence et al., 2002),

viruses that infect diatoms of Chaetoceros spp (e.g., Tomaru et al., 2011b; Kimura and Tomaru,

2015), viruses that infect the dinoflagellate Heterocapsa circularisquama (Nagasaki et al., 2004;

Tomaru et al., 2007), and cyanophages that infect the cyanobacterium Synechococcus strain DC2

(Suttle, 2000a). In the case of the Synechococcus cyanophages, these viruses remained viable

within sediments up to 100 years old (Suttle, 2000a). Further, qPCR has found algal virus

genotypes related to viruses that infect the coccolithophore Emiliania huxleyi in sediments up to

7000 years old in the Black Sea (Coolen, 2011). In freshwater systems, cyanophages infecting

Microcystis strains PPC 7820 and BC 84/1 have been recovered from lake sediments up to 50

years old (Hargreaves et al., 2013) and qPCR has been used to detect genotypes of a putative

chlorovirus and a putative cyanomyovirus in lake sediments (Hewson et al., 2012).

Although sediments may enhance the survivability of viruses, the hosts of phytoplankton viruses

are most active and abundant in the water column; therefore, phytoplankton must be capable of

re-entering the water column to successfully infect their hosts. One possible mechanism for re-

entry from sediment comes from natural disturbances due to storms, seasonal turnovers, and

even human activity such as dredging and high motorboat activity (Rao et al., 1984; Bosch et al.,

1988). These potential mechanisms for re-entry to the water column from the sediment are more

likely to occur in shallow areas and thus sinking of viruses into deep sediments may constitute an

irretrievable loss from the system. While there is clear evidence that sediments harbor

phytoplankton viruses, the diversity of algal viruses and cyanophages within aquatic sediments

has yet to be explored and the abundances of viruses within sediments have been estimated in

relatively few environments and for only some of the viral strains listed above.

The paradox of stable phytoplankton virus abundances in aquatic environments throughout the

year despite the estimations of decay rates that would not allow for this to occur drives my

research questions. In the following, I will discuss the overall and specific research questions of

my thesis and how the remaining chapters of my thesis seek to address these questions.

21

1.3 Thesis Focus and Objectives

Given the paradox of the constant abundances of many types of algal viruses and cyanophages in

freshwater systems, the variable abundances of their hosts, and the reported decay rates of

phytoplankton viruses that are sometimes too high to support long-term survival, the overall

research question of my thesis is: how do algal viruses persist throughout the year in freshwater

environments?

Chapter 2 seeks to answer the specific research question: does viral decay in the water column

proceed in a way that allows for environmental persistence? To do this, seasonal decay rates

were estimated via infectivity assays of three cultivated algal viruses, two chloroviruses and one

newly isolated virus that infects Chrysochromulina parva, in the water column of a freshwater

pond. The seasonal decay incubation experiments were designed in order to compare the

differences between seasons, viruses, and the two treatments: one which included a full

microbial community and one that contained particles less than 0.45 μm in diameter. Further, the

overwintering of these viruses under or within the ice that covered the pond for the entire winter

sampling period was assessed.

Chapter 3 seeks to answer the same specific research question as Chapter 2. Chapter 3 builds off

the framework of Chapter 2 by exploring the seasonal decay rates estimated via qPCR of not

only the three cultivated viruses in Chapter 2, but also for environmental algal viruses and

cyanomyoviruses detected via molecular methods. The viability of qPCR as a measure of

phytoplankton decay was addressed through comparing the environmental decay of the three

cultivated viruses estimated by infectivity assays and by qPCR. This allowed estimation of the

decay rates of uncultivated algal viruses and cyanophages, which represent the vast majority of

viruses in aquatic systems. Further, seasonal, virus, and treatment differences were examined as

in Chapter 2 as well as the overwintering of environmental, uncultivated phytoplankton viruses.

Chapter 4 examines the specific research question: do freshwater sediments provide an

environmental refugium that favors viral persistence? In order to accomplish this, the diversity

of algal viruses and cyanomyoviruses was examined in four discrete locations in Lake Erie in

four depth profiles ranging from the sediment surface to a depth of 8 cm. Further, the abundances

of 11 phytoplankton viral genotypes, 7 algal virus-like genotypes and 4 cyanophage-like

genotypes, were estimated at all four depths and at all four stations via qPCR. Whether the

22

results from this chapter hint at the potential for environmental refugia or provide historical

records of phytoplankton virus abundances in the water column through DNA preservation in the

sediment is discussed.

Chapter 5 will provide an explanation of how the results from Chapters 2 - 4 fit into the overall

research question of my thesis as well as highlight avenues for future research related to the

overall research question.

23

Chapter 2 Seasonal Determinations of Algal Virus Decay Rates Reveal

Overwintering in a Temperate Freshwater Pond

Abstract

To address questions about algal virus persistence (i.e. continued existence) in the environment,

rates of decay of infectivity for two viruses that infect Chlorella-like algae, ATCV-1 and CVM-

1, and a virus that infects the prymnesiophyte Chrysochromulina parva, CpV-BQ1, were

estimated from in situ incubations in a temperate, seasonally frozen pond. A series of

experiments were conducted to estimate rates of decay of infectivity in all four seasons with

incubations lasting 21 days in spring, summer, and autumn, and 126 days in winter. Decay rates

observed across this study were relatively low compared to previous estimates obtained for other

algal viruses, and ranged from 0.012 to 11 % h-1. Overall, the virus CpV-BQ1 decayed most

rapidly whereas ATCV-1 decayed most slowly, but for all viruses the highest decay rates were

observed during the summer and the lowest were observed during the winter. Furthermore, the

winter incubations revealed the ability of each virus to over-winter under ice as ATCV-1, CVM-

1, and CpV-BQ1 retained up to 48 %, 19 %, and 9 % of their infectivity after 126 days,

respectively. The observed resilience of algal viruses in a seasonally frozen freshwater pond

provides a mechanism that can support the maintenance of viral seed-banks in nature. However,

the high rates of decay observed in the summer demonstrates that virus survival and therefore

environmental persistence can be subject to seasonal bottlenecks.

A version of this chapter has been published in The ISME Journal

Long AM, Short SM. (2016). Seasonal determinations of algal virus decay rates reveal

overwintering in a temperate freshwater pond. ISME J 10: 1602–1612.

24

2.1 Introduction

Since the revelation that viruses are the numerically dominant component of aquatic

environments, the burgeoning field of viral ecology has begun to illuminate the roles that viruses

play in these ecosystems (Bergh et al., 1989; Wommack and Colwell, 2000; Brussaard et al.,

2004; Suttle, 2007; Short, 2012). Their high abundance and obligate parasitic lifestyle allow

viruses to exert top-down control of cellular organism populations, which is illustrated most

dramatically through the implication that viruses are involved in the termination of some algal

blooms (e.g., Bratbak et al., 1993; Tarutani et al., 2000; Wilson et al., 2002a; Brussaard et al.,

2005; Gobler et al., 2007; Tomaru et al., 2007). More subtly, viruses contribute to the mortality

of bacteria, phytoplankton, and higher trophic levels of the aquatic food web (Proctor and

Fuhrman, 1990; Suttle, 1994; Baudoux et al., 2006). Viral lysis of algae (Haaber and Middelboe,

2009) and bacteria (Middelboe et al., 2003) can cause the release of particulate and dissolved

organic matter (POM and DOM), and DOM can also be leaked from algal cells currently

infected with viruses (Sheik et al., 2014). Liberated POM and DOM due to viral lysis or leakage

from infected cells can be utilized by bacteria (Bratbak et al., 1998; Middelboe et al., 2003;

Haaber and Middelboe, 2009; Sheik et al., 2014) or by primary producers (Shelford et al., 2012).

Together, these observations demonstrate that viruses can have direct effects on ecosystems via

viral lysis of host cells and altered population dynamics, and indirect effects such as enhanced

nutrient recycling (Fuhrman, 1999). Further, by altering the flow of nutrients, viruses can even

stimulate primary production (Weinbauer et al., 2011; Staniewski and Short, 2014).

Historically, aquatic virus ecology has focused on marine environments, but high viral

abundances have been observed in both the water column and the sediments of freshwater

systems (Maranger and Bird, 1996; Filippini and Middelboe, 2007). The importance of viruses as

agents of freshwater phytoplankton mortality has also been established through a number of

modified dilution experiments, (Gobler et al., 2007; Tijdens et al., 2008; Staniewski et al., 2012),

and algal virus diversity surveys of various lakes and rivers have been conducted (e.g., Short and

Short, 2008; Clasen and Suttle, 2009; Gimenes et al., 2012; Zhong and Jacquet, 2014). Seasonal

studies of algal virus abundance in lakes (e.g., Short and Short, 2009; Short et al., 2011a;

Hewson et al., 2012; Rozon and Short, 2013) have revealed distinct seasonality with patterns of

25

‘boom or bust’ oscillations, or constant abundance depending on the particular virus examined.

Similar abundance patterns have been observed for VLPs (virus-like particles) and phages in

rivers (e.g., Mathias et al., 1995; Farnell-Jackson and Ward, 2003) and lakes (Hofer and

Sommaruga, 2001; Bettarel et al., 2004; Hewson et al., 2012; Zhong et al., 2015). Additionally,

observations of thousands of viral genomes in metagenomic studies of aquatic environments

(Breitbart et al., 2002), and of persistent viruses that exist at low but detectable abundances

throughout much of the year (Waterbury and Valois, 1993; Short and Short, 2009; Short et al.,

2011a; Zhong et al., 2013) have provided evidence for an environmental ‘seed-bank.’ In the

context of aquatic viruses, the concept of a seed bank is borrowed from terrestrial plant ecology

and implies that an inactive pool of viruses persist in the environment waiting for appropriate

conditions for ‘germination’, or replication (Short et al., 2011a). In turn, this idea is based on the

‘Bank model’ hypothesis of Breitbart & Rohwer (2005). Their metagenomics study

demonstrated that only a few virus genomes are highly abundant, and most are rare and part of a

‘bank’ fraction maintained at low abundances resisting destruction until their hosts reach

abundances high enough to promote their replication.

In contrast to observations of ‘seed-bank’ viruses, experimentally derived decay rates of aquatic

viruses are variable, but can be high, ranging from 0.13 - 54 % particles every hour (% h-1;

Heldal and Bratbak, 1991; Cottrell and Suttle, 1995; Noble and Fuhrman, 1997; Garza and

Suttle, 1998; Hewson et al., 2012; Frada et al., 2014). To date, there are few environmental

decay rate estimates for algal viruses. For marine algal viruses, decay rates of MpV-SP1 that

infects the prasinophyte Micromonas pusilla (Cottrell and Suttle, 1995) and the virus EhV that

infects the coccolithophore Emiliania huxleyi (Frada et al., 2014) have been determined via loss

of infectivity and were also variable, but suggested relatively high turnover (2 - 3 % h-1 and 28 -

30 % h-1 for EhV and MpV-SP1, respectively); a decay rate of 2 % h-1 is equal to a half-life of

only 34 h. Interestingly, decay rates of freshwater algal viruses most closely related to

Acanthocystis turfacea Chlorella virus 1 (ATCV-1) were estimated by tracking loss of viral

DNA in experimental incubations of water samples from a lake in New York, and were much

lower than previous reports of algal virus decay with an estimated half-life of 22 days (0.13 % h-

1; Hewson et al., 2012).

Since virus decay rates are potentially high, the seasonality of algal hosts of many viruses

presents an obstacle to the continued production, and thus the persistence (i.e., continued

26

existence), of virus particles throughout the year. It is well established that many freshwater

algae species drop below detectable levels throughout much of the year (e.g., Munawar and

Munawar, 1986; Reynolds, 2006). Because estimated host abundance thresholds for virus

transmission range from 103 - 104 host cells ml-1 for both cyanophages (e.g., Wiggins and

Alexander, 1985; Suttle and Chan, 1994) and algal viruses (e.g., Cottrell and Suttle, 1995;

Jacquet et al., 2002), it is clear that both viral infection and production is dependent upon host

availability. Given the apparently contradictory observations of viral ‘seed-banks’ and seemingly

high decay rates for the few virus taxa that have been studied, it is vital to further explore

viruses’ ability to persist in the environment when their hosts are absent.

The purpose of our study was three-fold: (1) to gain information about the environmental

persistence of algal viruses in freshwater, (2) to test if virus decay rates, and hence their ability to

survive outside of host cells, varies seasonally, and (3) to determine if algal viruses survive

during the winter months in a temperate freshwater habitat. With these goals in mind, decay rates

of three strains of freshwater algal viruses including two chloroviruses, ATCV-1 and Chlorella

virus Marburg-1 (CVM-1), and a newly isolated virus (CpV-BQ1; Mirza et al., 2015) which

infects the prymnesiophyte Chrysochromulina parva were estimated from seasonal in-situ

incubations. These particular viruses were used in this study because their hosts can be grown in

the laboratory and therefore, titres of infectious viruses can be estimated. Furthermore, close

relatives of each of these viruses are known in local freshwaters; CpV-BQ1 was isolated from

Ontario waters (Mirza et al., 2015), and DNA polymerase gene sequences closely related to

ATCV-1, CVM-1, and CpV-BQ1 have been amplified from the study site (Short et al., 2011b).

27

2.2 Materials and Methods

In situ Incubations to Estimate Virus Decay

To assess the seasonality of algal virus decay in a freshwater environment, decay rates of ATCV-

1, CVM-1, and CpV-BQ1 were estimated via experiments conducted throughout the year in a

storm water management pond on the University of Toronto Mississauga (UTM) campus.

Seasonal decay rates were estimated by incubating 500 mL natural water samples with known

concentrations of infectious viruses in situ during incubation experiments initiated within a few

days of the spring and fall equinoxes and the summer solstice, and within a month of the winter

solstice on May 23, 2013, June 21, 2013, October 3, 2013, and December 2, 2013. The infectious

titers in incubation bottles were determined before and after incubation to provide estimates of

loss of infectivity (i.e., decay). Additionally, to compare biotic and abiotic components of decay,

each incubation experiment involved two treatments; infectious viruses were incubated with

either unfiltered water, or water filtered to remove microorganisms larger than viruses.

At the beginning of each decay incubation, ~15 L water samples collected from the UTM pond

were passed through a 210 μm pore-size Nitex mesh to remove large particulates and floating

debris before being split for the two different treatments. The so-called unfiltered water (i.e.,

whole water) treatment used filtrate from the Nitex mesh as medium for in situ virus incubations

while the other treatment used water filtered through 142 mm dia., 0.45μm pore-size HVLP

membrane filter (EMD Millipore, Etobicoke, Canada). For each experiment and treatment

(whole water and filtered water), triplicate 500 mL polycarbonate (PC) bottles (VWR

International, Mississauga, Canada) were filled with the appropriate natural water to which final

concentrations of 1.40 - 4.31 x 106 infectious viruses mL-1 for ATCV-1, 7.02 x 105 - 2.96 x 106

for CVM-1, and 2.20 - 3.53 x 104 for CpV-BQ1 were added. CpV-BQ1 had not been isolated at

the time of the spring incubation and thus was not used in the May 2013 experiment. PC bottles,

although UV opaque, were used for this study because of their durability and because they are

not known to have deleterious effects on algal viruses. Given the lengthy incubations that were

conducted, using incubations bottles that could maintain sample integrity by withstanding

potential disturbances from local fauna as well as a wide range of environmental conditions was

deemed essential. For the spring, summer, and autumn decay experiments triplicate bottles for

each treatment were destructively sampled after incubating in situ for 1, 4, 7, and 21 days.

28

Winter incubations were sampled on days 1, 4, and 7, and again after 126 days to test the

survivability of algal viruses when frozen in the pond over the winter months. During

incubations, the bottles were secured in an arbitrary order to a PVC frame tethered to float

unshaded at the surface of the UTM pond. Ice thickness was monitored for the first 7 days of the

winter experiment.

Water temperatures were measured at each time point with a digital thermometer (VWR

International). Upon destructive sampling of triplicate incubation bottles at each time point and

for each treatment, 100 mL of H2O from each bottle was sequentially filtered through a 47 mm

dia., GC50 glass filter (0.5 μm nominal rating, Advantec AMD Manufacturing Inc. Mississauga,

Canada) followed by a 47 mm dia., 0.45 μm pore-size HVLP membrane filter (EMD Millipore).

The infectious titers of each virus in the resultant 0.45 μm filtrates were determined and used to

calculate environmental decay rates for each sample. Infectious titers were estimated within 2

weeks after destructive sampling at each time point; an earlier experiment demonstrated that 4 °C

storage of a 0.45 μm filtrated lysate of ATCV-1 retained 100 % of its infectivity even after 86

days.

Cell Culture Conditions and Estimating Virus Titres

Cell cultures of Micractinium conductrix strain Pbi (formerly Chlorella strain Pbi) were grown

in FES medium (Reisser et al., 1986), while cultures of Chlorella heliozoae strain SAG 3.83

were grown in modified Bold’s Basal Medium (MBBM) (Van Etten et al., 1983a), and

Chrysochromulina parva were grown in DY-V medium (Andersen et al., 2005). Viral lysates

were generated for ATCV-1, CVM-1, and CpV-BQ1 by inoculating 1 mL of infectious viruses

(i.e., 0.45 μm-filtered viral lysates) into 150 mL cultures of the appropriate host. After the cell

cultures cleared, the resulting viral lysates were filtered with a 47 mm dia. 0.45μm pore-size

HVLP membrane filter (Merck Millipore, Billerica, MA). Filtered viral lysates were stored at

4°C until utilization in environmental decay experiments. Infectious titers of the viral lysates

were estimated using plaque assays for ATCV-1 and CVM-1, and a most probable number assay

(MPN) for CpV-BQ1. Infectious titers remaining in each bottle after in situ incubation were

determined using the same methods.

Plaque assays for ATCV-1 used the host C. heliozoae grown on MBBM-agar medium (Van

Etten et al., 1983a), whereas plaque assays for CVM-1 were carried out using the same protocol,

29

but with M. conductrix grown on FES-agar medium. Plaque assays were performed in triplicate

for every sample. At the time of this study C. parva had not been successfully cultivated on solid

medium, so MPN assays were used to titer CpV-BQ1. MPNs were carried out in 96-well

microtiter plates with 100 μL of C. parva cells (107 cells mL-1) and 100 μL of the 0.45 μm

filtered samples from each incubation bottle diluted serially from 100 to 10-10; each column of 8

wells in the plates were replicates of a single dilution level. A column of wells with 100 μL of

cells and 100 μL of DY-V was used as a control in each plate. MPNs were calculated as

described in Jarvis et al., (2010).

Decay Rate Calculations and Statistical Analyses

Decay rates were estimated as previously described (Noble and Fuhrman, 1997). Briefly, linear

regressions were calculated for natural log transformed infectious titers plotted against time, as

decay of infectivity follows the exponential model 𝑁(𝑡) = 𝑁0𝑒−𝜆𝑡, where N0 is the infectious

titer at time zero, 𝑁(𝑡) is the infectious titer at time t, and λ is the decay constant. The slope of

the regressions represent the decay constants (units are h-1), the reciprocal of the decay constants

are turnover times, and decay rates expressed as percentage infectivity lost per hour were

calculated by multiplying λ by 100 and half-lives were calculated by dividing ln(2) by λ. In the

summer incubation, time points 7 and 21 days were excluded from the CpV-BQ1 calculations in

both treatments due to the absence of detectable infectious CpV-BQ1 viruses. Statistical

comparisons using summer CpV-BQ1 data also exclude these time points.

Linear regression analyses and analysis of covariance (ANCOVA) statistical tests were

conducted using GraphPad Prism 6 (GraphPad Software, La Jolla, CA). To be consistent with

the decay rate calculations, statistical tests were calculated using natural log transformed data. A

significance level of 0.05 was used for linear regression. To determine if decay rates of the three

viruses were significantly different, ANCOVA was used to compare the slopes of linear

regressions of different viruses incubated during the same season and in the same treatments.

Similarly, to determine if the two treatments (whole water incubation versus incubation in 0.45

μm filtrate) had a significant effect on viral decay rates, ANCOVA was used to compare slopes

of two treatments for the same viruses within the same season, while tests for the effect of

seasons on viral decay rates were based on comparisons of the same viruses in the same

treatment incubated during different times of the year. To account for the multiple pairwise

30

comparisons, Bonferroni corrections were applied to adjust the significance levels for seasonal (α

= 0.00167), treatment (α = 0.0045), and virus-to-virus (α = 0.0025) comparisons.

31

2.3 Results

Environmental Parameters

The average number of daylight hours over the course of the spring, summer, autumn, and winter

experiments were 15.25, 15.36, 11.05, and 10.22 h, respectively, with cumulative daylight hours

and irradiation greatest during the summer and lowest in winter (Table 2.1). The average water

temperature over the five time points was 21.85, 27.08, 17.36, and 3.18°C during the spring,

summer, autumn, and the first four time points of winter, respectively. The temperature at the

final time point of the winter experiment after the pond thawed was 5.9°C. During the winter

incubation, ice covered the pond for all 126 days with a thickness of at least 1.5 cm and

snowpack up to 25 cm according to data from Environment Canada for nearby Toronto

International Airport (approx. 14 km from the study site). During this winter incubation, snow

began to accumulate on the 7th day of the incubation and stayed until the 109th day of the 126 day

incubation. The daily average snow cover thickness was 5, 6, 15, and 5 cm, during the months of

December, January, February, and March, respectively.

Environmental Decay

All three algal viruses experienced lowest decay rates in the winter filtered water treatment and

the highest in the summer whole water treatment (Figure 2.1). ATCV-1 infectivity decayed at

rates ranging from 0.012 - 1.10 % h-1 with the lowest rates in the winter and the highest in the

summer (Figure 1). Half-lives for ATCV-1 ranged from 2.6 - 240 days. CVM-1 decay rates

ranged from 0.047 - 1.2 % h-1 with half-lives from 2.4 – 61 days, and for CpV-BQ1, decay rates

ranged from 0.077 - 11 % h-1, with half-lives from 0.26 - 38 days.

Statistical Comparisons of Decay Rates

Complete statistical comparisons of slopes from the regressions of the natural logarithm of virus

abundance versus time (i.e., decay rates) for each virus during every season and each treatment

are compiled in Table 2.2 and Appendices 1.1 - 1.3. Except for the regression slope for ATCV-1

during the winter decay incubation in the whole water treatment, the regression slopes for all

decay incubations were significantly non-zero, with p-values < 0.05 (Table 2.2). Pairwise

comparisons of the regression slopes were conducted to detect differences in decays rates

32

Table 2.1 Environmental Parameters for Seasonal Decay Experiments

Season Sampling

Times (hours) Water Temperature

(°C) Cumulative

Daylight Hours Ice Thickness

(cm)

Spring 0 20.6 0 -

24 15.3 15.03 -

96 20.7 60.31 -

168 27.1 105.78 -

504 25.6 320.05 -

Summer 0 25.1 0 -

24 26.7 15.45 -

96 29.4 61.78 -

168 27.1 108.07 -

504 27.1 322.63 -

Fall 0 21.2 0 -

24 18.5 11.07 -

96 18.1 45.45 -

168 18.3 79.38 -

504 10.8 232.13 -

Winter* 0 3.5 0 3

24 4.0 10.12 4.5

96 2.8 37.37 1.5

168 2.4 64.37 5.5

3072 5.9 1288.53 0

*Cumulative daylight hours do not necessarily reflect the exposure of viruses to daylight during this incubation due to persistent ice and snow cover.

33

Figure 2.1. Seasonal decay rates of algal viruses. Each data point is the average of three

calculated decay rates from triplicate incubations for each time point and treatment. Error bars

represent standard deviation. Note the split Y axis with different scaling above and below the

split.

4

5

6

7

8

9

10

11

12

ATCV-1

CVM-1

CpV-BQ1

0

0.5

1

1.5

2

Filt

ere

d

Wh

ole

wa

ter

Filt

ere

d

Wh

ole

wa

ter

Filt

ere

d

Wh

ole

wa

ter

Filt

ere

d

Wh

ole

wa

ter

Spring Summer Fall Winter

De

ca

y R

ate

(%

h-1)

34

Table 2.2. Linear Regression analysis of decay curves

Season Virus Treatment Slope 95% Confidence Interval Slope significantly

non-zero? F DFn, DFd p value

Spring ATCV-1 Filtered Water -0.0028 ± 0.00044 -0.0038 to -0.0019 Yes 41.03 1, 13 < 0.0001

Whole water -0.0093 ± 0.00043 -0.0102 to -0.0084 Yes 474.4 1, 13 < 0.0001

CVM-1 Filtered Water -0.0068 ± 0.00034 -0.0075 to -0.0061 Yes 410.4 1, 13 < 0.0001


Summer ATCV-1 Filtered Water -0.0047 ± 0.00056 -0.0059 to -0.0035 Yes 70.8 1, 13 < 0.0001

Whole water -0.011 ± 0.00019 -0.011 to -0.0105 Yes 3144 1, 13 < 0.0001



CpV-BQ1 Filtered Water -0.096 ± 0.0042 -0.11 to -0.087 Yes 530.5 1, 7 < 0.0001


Fall ATCV-1 Filtered Water -0.0029 ± 0.00022 -0.0034 to -0.0025 Yes 178 1, 13 < 0.0001





Whole water -0.016 ± 0.0035 -0.024 to -0.0083 Yes 20.6 1, 13 0.00061

Winter ATCV-1 Filtered Water -0.00014 ± 4.8e-005 -0.00025 to -3.7e-005 Yes 8.5 1, 13 0.012

Whole water -0.00012 ± 6.8e-005 -0.00027 to 2.7e-005 No 3.1 1, 13 0.10

CVM-1 Filtered Water -0.00047 ± 6.1e-005 -0.00059 to -0.00034 Yes 61.04 1, 13 < 0.0001

Whole water -0.00086 ± 6.5e-005 -0.001001 to -0.00072 Yes 178.4 1, 13 < 0.0001



35

between seasons, between filtered or whole water treatments, and between the different viruses

themselves, and significant differences were observed among all three sets of comparisons.

As noted above, for each of the three algal viruses studied slopes were most negative in the summer

and least negative in the winter; i.e., decay rates were highest in the summer and lowest in the winter

(Table 2.2). In general, regression slopes in different seasons were significantly different from each

other (p-value < 0.00167, Appendix 1.1) but there were some exceptions. For both ATCV-1 and

CVM-1, certain comparisons of spring and summer, spring and autumn, and summer and autumn

slopes were not significantly different. On the other hand, for CpV-BQ1 all seasonal comparisons

produced significant differences. Overall, seasonal comparisons were statistically significant for

73% of all slopes compared (Figure 2.2).

During all seasons and for every virus, regression slopes were more negative in the whole water

treatments compared to filtered water. The regression slopes of the two treatments were significantly

different (p-value < 0.0045, Appendix 1.2) except for viruses in the autumn incubation experiment,

as well as ACTV-1 in the winter, and CpV-BQ1 in the summer. For both treatments in every

seasonal incubation experiment, ATCV-1 decayed most slowly (least negative regression slopes),

while CpV-BQ1 decayed most rapidly (most negative slope). Treatment effects were significant in

55% of the statistical comparisons (Figure 2.2). The differences of the decay rates between the three

algal viruses were generally statistically significant, with p-values < 0.0025 (Appendix 1.3). Notable

exceptions include comparisons of the regression slopes of CVM-1 and ATCV-1 in the summer and

spring whole water treatments, and the spring filtered treatment, and CVM-1 and CpV-BQ1

comparisons during the autumn and in the winter filtered water treatment. Overall, virus-to-virus

comparisons were statistically significant for 70% of the time (Figure 2.2).

After 126 days in a frozen freshwater pond, infectious viruses were detected for all three of the algal

viruses and in both treatments. ATCV-1 retained 47.82 % of its original infectivity in the filtered

water treatment and 45.58 % in the whole water treatment (Figure 2.3A), while CVM-1 retained

18.82 % in filtered water and 5 % in whole water (Figure 2.3B), and CpV-BQ1 retained 9.22 % in

filtered water and 0.79 % in whole water (Figure 2.3C). Statistical comparisons demonstrated that

36

Figure 2.2. Percentage of statistically significant differences in the comparisons between seasons,

filtration treatment, or viruses. Values were derived from the season to season, whole water to

filtered water, and virus to virus comparisons using the statistical tests from Supplemental Tables 1,

2, and 3, respectively. The numbers above each bar show the number of significantly different

comparisons and the total number of comparisons.

22/30

6/11

14/20

0

25

50

75

100

Season to Season Filtered to Whole water Virus to Virus

Pe

rce

nt

Sig

nific

an

t D

iffe

recnces

Statisical Comparisons

37

38

Figure 2.3. Over-wintering of algal viruses in a seasonally frozen freshwater pond. Mean percent

infectivity remaining in triplicate bottles incubated through the winter months was plotted against

time in days for the viruses ACTV-1 (A), CVM-1 (B), and CpV-BQ1 (C). Filled circles are data

points from the filtered-water treatment while open triangles are data points from the whole water

treatment. Error bars represent standard deviation. The inset figure in panel C shows a close-up view

of the percent infectivity remaining for CpV-BQ1 at the final time point.

39

there was no difference in decay rates for the filtered versus whole water treatments for ATCV-1 in

the winter, but rates for these treatments were significantly different in the winter for CVM-1 and

CpV-BQ1 (Appendix 1.2).

40

2.4 Discussion

Decay of Aquatic Viruses

Viruses can be destroyed, inactivated, or removed from aquatic environments by exposure to

sunlight (both UV and PAR), extreme temperatures, heat-labile organic matter such as nucleases,

consumption by heterotrophic nanoflagellates, attachment to non-host cells, and adsorption to

detritus and subsequent sinking (reviewed in: Wommack and Colwell, 2000). The role of other

microbes in virus destruction has also been demonstrated in several studies showing increased

survival of viruses when other microbes were inactivated through antibiotics, autoclaving, or

filtration, and many microorganisms are known to produce enzymes with antiviral properties

(reviewed in: Gerba, 2005). These observations, together with the knowledge of threshold

abundances for virus production and succession in phytoplankton assemblages, have led to questions

about the survival and environmental persistence of aquatic viruses. In general, the results of our

study corroborate past work on aquatic virus decay since rates were variable among different

viruses, and the highest decay rates were observed during summer incubations, which received the

greatest irradiation, and whole water incubations, which included microbes < 210 μm in size. Most

significantly, the results of our study also demonstrate the ability of freshwater algal viruses to

overwinter and remain viable after freezing, supporting the hypothesis that some viruses, even in the

absence of ongoing production, can form a persistent seed-bank in aquatic environments.

Using cultivation-based techniques to estimate numbers of infectious viruses, environmental decay

rates of the algal viruses ATCV-1, CpV-BQ1, and CVM-1 were estimated from in-situ incubations.

The ranges of decay rates estimated in this study for ATCV-1 (0.012 - 1.10 % h-1), CVM-1 (0.047 -

1.12 % h-1), and CpV-BQ1 (0.077 - 11.26 % h-1) were relatively low compared to decay rates

determined using a similar experimental approach, but for bacteriophages incubated in direct

sunlight using water from Santa Monica Bay, USA (4.1 - 11 % h-1; Noble and Fuhrman, 1997).

Given the inclusion of seasonal estimates of decay for the study reported here, and structural

differences between different types of viruses, and the fact that the Santa Monica study included UV

exposure, it is not surprising that decay rates estimated for bacteriophages and algal viruses are not

directly comparable. However, even when compared to other algal viruses, ATCV-1 and CVM-1

41

decay rates were relatively low compared to rates reported in other studies (Cottrell and Suttle, 1995;

Hewson et al., 2012; Frada et al., 2014). On the other hand, the decay rates estimated for CpV-BQ1

were the highest of the three algal viruses used in this study and, at least for the summer

experiments, were within the range of previously reported values for algal viruses. That the rates

estimated in this study were generally low compared to previous studies of algal viruses is likely due

to the different experimental approaches that were used, and more importantly because previous

studies estimated decay in only a single season. It is important to note that the decay rates estimated

in this study likely represent underestimates, especially for the summer incubations, due to the use of

polycarbonate bottles which are essentially UV opaque. UV radiation, and UV-B in particular, is an

important factor for bacteriophage decay, and decay rates estimated in the absence of UV-B can be

much lower (e.g., 20 %) than the values estimated from incubations in full sunlight (Suttle and Chen,

1992). However, in some cases, PAR, or photosynthetically active radiation, can be responsible for

more viral decay than UV radiation (e.g., Wommack et al., 1996; Baudoux et al., 2012). Since the

PC bottles used in this study are PAR transparent it is certain that the algal viruses in this study were

subjected to some photochemical, or sunlight-mediated decay, but relative contributions of UV and

PAR to algal virus decay cannot be resolved. Additionally, as is the case with any microcosm study

conducted using closed incubation bottles, attachment to detritus and sinking is not a mechanism of

decay that can be estimated in this study. Therefore, true environmental decay rates are likely higher

than the estimates presented in this, or indeed any, study of viral decay.

The decay rate of 0.13 % h-1 for the ATCV-1-like environmental virus reported in Hewson et al.,

(2012) was, as the authors acknowledged, not an estimate of decay of infectivity but rather an

estimate of decay of genomic DNA based on qPCR. Infectivity decays more rapidly than virus

particles or genomes because virion damage can compromise attachment to host cells, or other

critical steps such as cell entry and unpackaging despite the fact that the virus particle and even

genome can remain intact (Suttle and Chen, 1992; Wommack et al., 1996; Noble and Fuhrman,

1997). MpV-SP1 infectivity was estimated to decay at a rate of 28 - 30 % h-1 during incubations

carried out in March and April in unattenuated sunlight using water from the Gulf of Mexico

(Cottrell and Suttle, 1995). Overall, decay rates determined during our study were often low

compared to the few literature values available for algal viruses, but the rates we observed during the

42

spring, summer, and autumn were similar to estimates determined for Emiliania huxleyi viruses in

the North Atlantic (i.e., 2 - 3 % h-1; Frada et al., 2014). Furthermore, as expected, the lowest rates

estimated during this study came from the winter incubation, and for ATCV-1 were 10 times lower

than the lowest estimate previously reported for any algal virus. However, if the algal viruses in this

study followed the pattern observed for marine bacteriophage where the decay rate in the absence of

UV-B is only 20% of the decay rate in the presence of UV-B (Suttle and Chen, 1992), the decay

estimates obtained in this study can be normalized to account for UV-B –mediated decay by

multiplying the rates by a factor of 5. This yields decay estimates ranging from 0.06 – 5.5 % h-1 for

ACTV-1, 0.235 – 5.6 % h-1 for CVM-1, and 0.385 – 56.3 % h-1 for CpV-BQ1. As noted above, this

normalization may be conservative for the summer incubations and exaggerated for the winter

incubations, but the highest corrected decay rate observed in this study actually exceeds the decay

estimate obtained from the Gulf of Mexico for the algal virus MpV-SP1 (Cottrell and Suttle, 1995).

Seasonality and Variability in Rates of Decay

Although other more general studies of aquatic virus decay have noted the lowest rates in the winter

(Thomas et al., 2011), the winter incubation experiment with the freshwater algal viruses ATCV-1,

CVM-1, and CpV-BQ1 produced the lowest estimated decay rates observed for any algal viruses in

nature. It is likely that these unprecedented low rates of decay were due to the fact that the

freshwater pond was frozen over during the winter incubation, which would dramatically reduce

exposure to sunlight, especially when ice is covered by snow (Perovich et al., 1993; Bertilsson et al.,

2013). As expected, for all three algal viruses decay rates were highest in the summer and

intermediate for the spring and autumn. Similarly, decay rates of cyanophage in the Gulf of Mexico,

Texas (Garza and Suttle, 1998) and Lake Donghu, China (Cheng et al., 2007) were also shown to be

highest during the summer. Seasonality in virus decay is likely due to seasonal fluctuations in

temperature and sunlight (both UV and PAR), which have both been implicated in virus inactivation

(Lo et al., 1976; Suttle and Chen, 1992; Garza and Suttle, 1998; Baudoux et al., 2012). Sunlight

exposure can cause photochemical damage of viruses directly, while increased temperatures may act

indirectly through anti-viral increased microbial and enzymatic activity (e.g., Yates et al., 1985;

Gersberg et al., 1987; Garza and Suttle, 1998).

43

Exposure to UV radiation and PAR can deactivate viruses (Cottrell and Suttle, 1995; Furuta et al.,

1997; Jacquet and Bratbak, 2003; Baudoux et al., 2012), yet some algal viruses encode genetic

machinery to repair light-induced DNA damage. Many, but not all, genomes of chloroviruses,

including ATCV-1 and CVM-1, contain homologs of a UV repair gene, denV, which encodes a UV-

specific DNA glycosylase-pyrimidine lyase (Fitzgerald et al., 2007; Jeanniard et al., 2013) known to

be functional in the strain PBCV-1 (Furuta et al., 1997). Moreover, other nucleic acid metabolism

genes encoded by algal viruses that could aid in environmental persistence include DNA ligase,

DNA polymerase δ, proliferating cell nuclear antigen, as well genes involved in base incision repair

and nucleotide incision repair (Dunigan et al., 2006; Redrejo-Rodríguez and Salas, 2014). The

absence of denV homologues in some chlorovirus strains such as KS1B, and other DNA repair genes

in other algal virus genomes demonstrates that these capabilities are not universally present among

algal viruses (Jeanniard et al., 2013). Furthermore, the genome of EhV-86, a coccolithovirus,

contains a pyrimidine dimer-specific glycosylase, while the genome of EsV-1, a phaeovirus, does

not (Dunigan et al., 2006). While photo-induced pyrimidine dimers were likely not a major source of

DNA damage in this study, the variability of denV is illustrative of the variability in the genetic

potential of specific algal virus strains to repair DNA damage.

Even though UV-B and much of UV-A were attenuated in this study, the fact that ATCV-1 and

CVM-1 encode pyrimidine dimer-specific glycosylases as well as other DNA repair genes and had

10 fold lower decay rates than CpV-BQ1 in summer months suggest that CpV-BQ1 might not

encode similar DNA repair machinery. Genome sequence information from CpV-BQ1 could resolve

this hypothesis and generate other interesting questions about the genetic basis for the environmental

stability of aquatic viruses. Furthermore, the results presented here reveal both intra- and inter-genus

variability as the chloroviruses ATCV-1 and CVM-1, and the newly isolated, putative

prymnesiovirus CpV-BQ1 all decayed at different rates. Differential rates of decay could drive

differences in virus-host dynamics among different algal viruses supporting the notion that

individual virus-host pairs are ecologically unique (Rozon and Short, 2013). It is worth noting that

decay rates of CVM-1 were estimated via MPNs versus PAs to compare these different approaches

but were not significantly different when compared with ANCOVA (F = 0.392, DFn, DFd = 1, 11,

44

p-value = 0.54), suggesting that differences in decay rates between the chloroviruses and

prymnesiovirus were not due to experimental methods alone.

Differences in decay rates between the filtered and whole water treatments implicate the anti-viral

effect of microbes as the filtered water treatment produced decay rates that were often lower than did

the whole water treatment. An active microbial community is known to accelerate rates of viral

decay through adsorption to non-host cells, consumption of viruses by nanoflagellates, or the activity

of extracellular nucleases and other enzymes (Gerba, 2005). This effect was most significant in the

spring and summer incubations for ATCV-1 and CVM-1 when temperatures, and presumably

microbial activity, were highest. On the other hand, at least one study has demonstrated that

particulate material that was presumably removed during our filtration could actually reduce algal

virus decay rates. The viruses ATCV-1 and CVM-1 can dynamically attach and detach to host cells

and host cell debris, and remain infectious, and this process has been implicated in increased virus

survival (Agarkova et al., 2014). As such, depending on the particular samples there may have been

particulate material filtered out of the whole water that could have enhanced virus decay, or

enhanced virus survival. These contrasting effects of different particulate materials on virus decay

may explain why only a little more than half of the filtered water treatments yielded decay rates that

were significantly different than rates from the corresponding whole water treatment. The highest

estimated decay rates observed in this study coincided with the maxima for water temperature,

sunlight, and daylight hours, providing further evidence that temperature and sunlight are major

factors in the inactivation of virus particles in aquatic environments and drove the seasonality

observed in the decay of ATCV-1, CVM-1, and CpV-BQ1.

Algal Virus Overwintering

This study presents the first experimental evidence that algal viruses can persist in ice-covered,

freshwater environments. For the winter incubation, samples bottles were initially placed underneath

the existing thin ice cover and remained in the water column, unfrozen, for the first 7 days of the

experiment. After 7 days, the ice was too thick to break and the bottles were left in situ for the rest of

the season. During this time, the exact date that the samples froze is unknown, but before the ice

became covered in snow the bottles were clearly stuck in the thick ice cover and the liquid in the

45

bottles was visibly frozen. Following their winter-long incubation, samples were recovered after the

ice on the pond had thawed and every incubation bottle contained infectious viruses. Furthermore, in

a lab study conducted to simulate freezing in the environment, ATCV-1, CVM-1, and CpV-BQ1

retained approximately 92, 85, and 89 % of their infectivity after they were chilled in ice water for 3

h, stored at -20 °C for 15 h, and were subsequent thawed in ice water. Thus it is apparent that some

algal viruses can survive overwinter in the ice of seasonally frozen ponds and lakes. The survival of

ATCV-1 and CVM-1 after freezing is perhaps surprising as PBCV-1, a close relative, can be

inactivated by freezing (Van Etten et al., 1991). Although we have provided the first direct evidence

that some algal viruses can survive in frozen environments, other viruses have been previously

shown to tolerate similar conditions. For example, human enteric viruses persisted for several

months when incubated in situ in dialysis bags filled with autoclaved marine water, and were most

stable during winter months (Lo et al., 1976). Also, viruses have been shown to exist in high

abundances in frozen Antarctic lakes (Foreman et al., 2011), viral genomes have been detected in

~700 year old frozen caribou feces (Ng et al., 2014), and even more incredible, a putatively 30,000

year old giant virus of amoebas has been recovered from Siberian permafrost (Legendre et al.,

2014). Hence, it is plausible, even likely, that reduced decay rates experienced during winter months

may constitute a major mechanism for algal virus survival and the establishment of viral seed-banks

in many aquatic environments.

Conclusions

Over the year, algal virus decay rates were highly variable, but the winter decay rates observed here

are among the lowest reported for aquatic viruses. The winter half-life of the least resilient virus

examined, CpV-BQ1, was 38 days, long enough for a substantial fraction of the virus population to

survive as a seed-bank for the subsequent ice-free growing season. The observation that decay rates

were greatly reduced in the winter and viruses maintained infectivity after freezing provides direct

evidence that algal viruses can persist in the environment for many months. However, during the

summer when decay rates peaked the half-life of even the most resilient virus, ATCV-1, was only 2

days suggesting that even this population would be rapidly destroyed; hypothetically, starting with

105 viruses mL-1 only 6 viruses mL-1 would remain after 30 days. This suggests that the summer

46

represents a seasonal bottleneck for virus survival, and ongoing virus production or some other

means of escaping destruction such as mixing into deeper waters is necessary to maintain virus

populations through these months; the decay experiments described here were conducted at the

surface of the pond where sunlight was maximal, and it is known that algal virus decay rates

attenuate rapidly with depth (Cottrell and Suttle, 1995). Although virus survival when experiencing

high decay rates necessitates constant production, environmental refugia, or non-lytic infections,

viruses that withstand the summer in temperate aquatic environments should be able to overwinter

until the subsequent growing season of their hosts. Therefore, the results of this study clearly

demonstrate the importance of seasonality in the environmental persistence of algal viruses.

In future studies, qPCR methods that were established to monitor the abundance of diverse,

uncultivated phycodnaviruses (Short and Short, 2009; Short et al., 2011a) could be combined with

cultivation approaches to determine relationships between decay of infectivity and decay of viral

nucleic acids. In turn, these relationships could be used to infer decay rates for viruses that have not

yet been cultivated, which constitutes the vast majority of environmental viruses. Knowing true,

functional rates of decay for a range of algal viruses is essential to establish boundaries related to

their resilience and environmental persistence. Constraining estimates of virus environmental

persistence is a vital step towards realistic models of virus-host dynamics in aquatic environments.

Acknowledgements

Special thanks to Dr. James Van Etten and colleagues at University of Nebraska, Lincoln for

providing cell cultures of M. conductrix and C. heliozoae as well as isolates of ATCV-1 and CVM-1.

We are also grateful to Cindy Short and Samia Mirza for their support in maintaining cell culture

lines. This research was supported in part by the Canadian Foundation for Innovation Leaders

Opportunity Fund and NSERC Discovery grants awarded to S.M.S.

47

Chapter 3 Quantitative PCR reveals environmental phytoplankton virus

decay rates vary seasonally

Abstract

Algal viruses and cyanophages are implicated in the top-down control of eukaryotic algae and

cyanobacteria. Despite this clear importance, many questions about how viral populations are

maintained when host cells drop below abundances necessary for viral infection. In order to

answer questions on the persistence of naturally occurring algal viruses and cyanophages,

seasonal decay incubations were conducted and molecular assays were developed to monitor the

decay of uncultivated viruses, which constitute the majority of viruses. Assays for estimating

viral decay rates were validated by comparing loss of infectivity and loss of amplifiable DNA in

field incubation experiments with the cultivated algal viruses ATCV-1, CVM-1, and CpV-BQ1.

The range of decay rates for cultivated viruses based on molecular assays of seasonal incubations

was 0.0056 to 1.23 % h-1, while decay rates for uncultivated algal viruses and cyanophages

ranged from 0.007 to 1.30 % h-1 and 0.27 to 14.81 % h-1, respectively. For every virus, the lowest

decay rates were observed in the winter, while the highest decay rates were typically recorded in

the spring and summer. The winter decay incubation experiment, which lasted 126 days,

demonstrated that all viruses remained detectable throughout the season, with 20 - 62 % of the

amplifiable DNA remaining for cultivated algal viruses, 19 % for an uncultivated algal virus, and

0.008 % for an uncultivated cyanophage in the whole water treatment. These results demonstrate

that environmental algal viruses can successfully over-winter in temperate, seasonally frozen

aquatic environments. However, the low percentage of amplifiable DNA remaining for

cyanophages in winter suggests that other mechanisms may be necessary for the maintenance of

the viral ‘seed-bank’ for these virus types.

48

3.1 Introduction

Algal viruses and cyanophages infect aquatic primary producers, exert top-down controls on the

population dynamics of algae and cyanobacteria, and by extension, alter biogeochemical cycles

(Wommack and Colwell, 2000; Brussaard, 2004; Suttle, 2007; Wilhelm and Matteson, 2008).

Algal viruses and cyanophages have both been implicated in algal bloom termination and more

generally, are involved in algal population turnover via viral lysis (e.g., Bratbak et al., 1993;

Wilson et al., 2002a; Brussaard et al., 2005; Deng and Hayes, 2008). Furthermore, the mortality

of algae and cyanobacteria by algal viruses and cyanophages can alter the flow of nutrients to

higher trophic levels by ‘shunting’ organic material to the particulate and dissolved fractions

where they can be utilized by both bacteria and other algae (Wilhelm and Suttle, 1999; Suttle,

2007; Shelford et al., 2012). Being a direct source of mortality intuitively implies that viruses

can decrease primary productivity in aquatic systems, yet there is mounting evidence that in

some cases viral lysis may actually stimulate primary productivity, perhaps by enhancing rates of

nutrient recycling (Weinbauer et al., 2011; Shelford et al., 2012; Staniewski and Short, 2014).

Though it is clear that algal viruses and cyanophages play an important role in the population

dynamics of their hosts, basic questions on the ecology of algal viruses and cyanophages remain.

The majority of ecological studies of algal viruses and cyanophages have been in marine

environments and only relatively recently have viruses in freshwater environments been studied

in detail. Studies using molecular markers and metagenomics have found diverse communities of

algal viruses and cyanophages with widespread distributions in various freshwater environments,

and some genotypes that exist in marine environments have also been observed in freshwater

systems (e.g., Dorigo et al., 2004; Short and Suttle, 2005; Wilhelm et al., 2006b; Chénard and

Suttle, 2008; Short and Short, 2008; Short et al., 2011b; Hewson et al., 2012). Additionally,

sequence information obtained through molecular studies of aquatic viruses has been recently

used to develop tools such as quantitative PCR (qPCR) primers and probes to monitor seasonal

changes in the abundance of specific virus genes in freshwater environments (e.g., Short and

Short, 2009; Zhong et al., 2013). Increasingly, viral abundance studies based on molecular tools

have revealed a variety of population patterns, including ‘boom and bust’ ecologies where

populations oscillate between periods of high abundance followed by rapid decline, or

conversely, where populations are maintained at relatively constant levels, but low abundance

(Short et al., 2011a; Rozon and Short, 2013; Zhong et al., 2013).

49

Observations of viral populations persisting at constant, low abundances stemming from qPCR-

based studies, metagenomics, microscopy counts of virus-like particles, or counts of infectious

viruses all support the ‘Bank model’ of viral ecology (Waterbury and Valois, 1993; Breitbart et

al., 2002; Short and Short, 2009; Short et al., 2011a; Quispe et al., 2016). The Bank model, as

proposed by Breitbart and Rohwer (2005), suggests that there exists a community of highly

abundant viruses that actively infect their hosts, as well as a diverse community of viruses with

low abundances that remain viable in a ‘seed-bank’ while their hosts are below abundance levels

that could support ongoing viral reproduction. The population dynamics of many eukaryotic

algae and cyanobacteria in the environment are such that they are either below threshold

abundance levels necessary for viral infection (103 - 104 host cells mL-1; Wiggins and Alexander,

1985; Suttle and Chan, 1994; Cottrell and Suttle, 1995; Jacquet et al., 2002), or remain at

undetectable levels for many months of the year (Munawar and Munawar, 1986; Reynolds,

2006). Observations of constant, low abundances for some viruses even when their hosts may

fall below population thresholds needed for virus replication suggest that viruses have the ability

to survive long periods without replication, supporting the Bank model. However, many of the

reported viral decay rates in both marine and freshwater systems are too high to permit

sustainable viral populations without ongoing production. This paradox drives many of the

research questions in this study.

Contrary to a widespread, general phenomenon of viral seed-banks, estimated rates of decay for

algal viruses are variable and can be quite high, ranging from 0.012 to 30 % lost per hour (% h-1)

in some aquatic environments (Cottrell and Suttle, 1995; Hewson et al., 2012; Frada et al., 2014;

Long and Short, 2016). Furthermore, the decay rates of cyanophages can be even higher, ranging

from 1.50 to 230 % h-1 (Suttle and Chan, 1994; Garza and Suttle, 1998; Cheng et al., 2007; Liu

et al., 2011; Hewson et al., 2012). However it should be noted that for some bacteriophages and

cyanophages photoreactivation may counteract high decay rates through host-mediated virus

repair (Weinbauer et al., 1997; Wilhelm et al., 1998; Cheng et al., 2007). The variability in

decay rates between algal viruses and cyanophages may be due to differences in the structure and

physiology of the two viral groups, or it may be, in part, due the different physical environments

in which the viruses were studied. Differences in genetic content, such as the presence of genes

that code for UV repair mechanisms, and/or differences in the thickness of the virus capsid have

been suggested to change the ability of individual virus strains to withstand the environment

50

outside their hosts (Furuta et al., 1997; De Paepe and Taddei, 2006; Dunigan et al., 2006).

Additionally, seasonal decay rates of algal viruses and cyanophages described in aquatic

environments highlight marked seasonal differences in the environmental conditions experienced

by viruses, especially between winter and all other seasons (Garza and Suttle, 1998; Cheng et al.,

2007; Long and Short, 2016). The especially low decay rates observed in the winter in a

seasonally frozen freshwater pond could provide a potential mechanism through which the viral

‘seed-bank’ might be maintained (Long and Short, 2016). However, the much higher decay rates

observed in the spring and summer months in that same study suggest that unanswered questions

on how a viral ‘seed-bank’ could be maintained year-round yet remain.

In particular, estimating decay rates for individual viruses with hosts that have not been cultured

is not possible using common approaches for estimating viral decay, such as microscopy or

infectivity-based assays. Recently, qPCR has been used to assess the decay rates of uncultured

viruses, including algal viruses and a specific group of cyanophages commonly referred to as

cyanomyoviruses (Hewson et al., 2012). However, the presence of amplifiable DNA does not

confirm the presence of viable, infectious viruses, especially as known algal viruses produce

many non-viable virions upon replication. For example, only 25 - 50 % of the particles produced

by the algal virus Paramecium bursaria Chlorella virus 1 are infectious (Van Etten et al.,

1983b). Additionally, decay of infectivity is likely much faster than decay of amplifiable DNA

due to the relatively fragile nature of infectivity, which requires an intact virion in its native

conformation with a suite of intact genes. In contrast, amplification of a gene fragment requires

only the specific fragment to be intact across the targeted region. As such, any molecular

methods for estimating viral decay for must be compared and validated against infectivity-based

estimates of decay to obtain a biologically-relevant picture of viral decay. Thus, development of

qPCR methods for estimating viral decay remains an important task in viral ecology.

The goals of this study are threefold: 1) to determine if qPCR can successfully be used to track

the decay of uncultured viruses, 2) to estimate the decay of several algal viruses and

cyanophages across all four seasons and from year to year, and 3) to determine the over-

wintering ability of uncultured algal viruses and cyanophages. In order to accomplish these

goals, five in situ incubation experiments, including a 126 day long winter incubation, were

employed with two cultivated chloroviruses, Acanthocystis turfacea Chlorella virus 1 (ATCV-1)

and Chlorella virus Marburg-1 (CVM-1), as well as a cultivated algal virus that infects the

51

prymnesiophyte Chrysochromulina parva (CpV-BQ1). The decay rates of these viruses were

estimated via both infectivity and molecular assays. Molecular markers for algal viruses and

cyanomyoviruses were used to design qPCR primer and probe sets, which were subsequently

utilized to estimate the decay of several naturally-occurring, uncultivated algal virus and

cyanomyoviruses during five incubation experiments, spanning all four seasons.

52


In order to test questions on the persistence of uncultivated algal viruses and cyanophages, the

following experiments were conducted. First, cultivated viruses were used in seasonal decay

experiments in a freshwater pond where both the loss of infectivity and the loss of amplifiable

DNA were measured. Then, molecular probes were developed for several uncultivated algal

viruses and cyanophages and were utilized to estimate the loss rates of their amplifiable DNA.

In situ decay incubation experiments

Complete details for the first four seasonal decay incubation experiments are presented in Long

and Short (2016). Briefly, the decay incubation experiments were conducted in a freshwater

pond on the University of Toronto Mississauga (UTM) campus in Mississauga, Ontario, Canada

and were initiated May 23, 2013, June 21, 2013, October 3, 2013, and December 2, 2013. 500

mL polycarbonate incubation bottles were destructively sampled at five time points, starting at

time zero and ending at 21 days for the spring, summer, and autumn incubation experiments, or

ending at 126 days for the winter incubation. Additionally, two treatments were conducted for

every incubation: unfiltered natural water was used as the medium in one set of bottles (whole

water hereafter), while the other treatment used natural water filtered through 47 mm dia. 0.45

μm pore-size HVLP membrane filters (Merck Millipore, Billerica, MA) in order to remove

microorganisms and other particulates (filtered water hereafter). For both treatments, ATCV-1

and CVM-1 were added to the spring decay incubation experiment while ATCV-1, CVM-1, and

CpV-BQ1 were added to the summer, autumn, and winter decay incubation experiments.

Immediately after sampling, 100 mL of subsamples were filtered with 0.45 μm filters, 50 mL of

which was stored at 4 °C for infectivity assays, and 1 mL was stored at -20 °C for molecular

assays.

A fifth decay incubation was conducted in autumn 2014 using the same procedure. However,

during time zero of the 21 day time series, which commenced on October 16, 2014, an additional

100 mL water sample was taken from the pond for molecular analysis of the naturally occurring

algal virus community. This 100 mL sample was filtered through a 0.45 μm filter and then

concentrated via centrifugation at 118,000 x g for 3.5 h using a SW32-Ti rotor (Beckham

Coulter, Inc., Indianapolis, IN). Concentrated samples were stored at -20 °C. For the other

sampling periods, bottles were destructively sampled at each time point, filtered, and stored at 4

53

°C for infectivity assays, while 36 mL was concentrated via centrifugation and stored at -20 °C

for molecular assays as before. To release encapsidated nucleic acids, all samples for molecular

analysis from every decay incubation experiment were subjected to three freeze-thaw cycles as

per Short and Short (2008). Water and air temperature were measured at each time point with a

digital thermometer (VWR International) and daylight hours were obtained from Environment

Canada for the closest monitoring station (Toronto, Canada).

Algal cell culture conditions and viral infectious titre estimations

Cell cultures of Chlorella heliozoae strain SAG 3.83, Micractinium conductrix strain Pbi and

Chrysochromulina parva were used to generate viral lysates of ATCV-1, CVM-1, and CpV-

BQ1, respectively, which were then filtered through 0.45 μm filters and stored at 4°C as

described in Long and Short (2016). For the first four seasonal decay incubations, infectious

titres were published in Long and Short (2016). The infectious titre of ATCV-1, CVM-1, and

CpV-BQ1 in the samples from the autumn 2014 decay incubation experiment were estimated

using a most probable number assay (MPN) designed for CpV-BQ1, which is fully described in

Long and Short (2016). The protocol was modified for ATCV-1 and CVM-1 by replacing C.

parva and its growth medium (DY-V; Andersen et al., 2005) with the hosts for ATCV-1 (C.

heliozoae) and CVM-1 (M. conductrix) and their respective growth media, modified Bold’s basal

medium (Van Etten et al., 1983a) and FES medium (Reisser et al., 1986). MPNs were calculated

using the methods of Jarvis and colleagues (2010).

PCR conditions and sequence analysis

Previously published primer sets for the major capsid protein gene (MCP; mcp Fwd and mcp

Rev; Larsen et al., 2008) and for the DNA polymerase gene of algal viruses (polB; VpolAS4 and

VpolAAS1; Clerissi et al., 2014a) were used to create clone libraries of PCR-amplified, naturally

occurring algal viruses from the autumn 2014 decay incubation experiment. No samples from the

first four decay incubations were taken without the addition of the three cultivated algal viruses,

which prevented the generation of clone libraries of environmental algal virus genes for those

incubations. For cyanophages, a previously described marker for the cyanophage portal-protein-

encoding gene 20 (g20; CPS1.1 and CPS8.1; Sullivan et al., 2008) was used to create clone

libraries of naturally occurring cyanophages in each of the 5 decay incubation experiments.

54

For algal virus MCP and polB, only one round of PCR was required for amplification, while for

cyanophage g20 genes, two rounds of PCR were required. The PCRs for polB were as follows:

50 μL total volume with 25 μL of GoTaq G2 Green Master Mix (Promega Corporation,

Madison, WI), 200 nM of VpolAS4, 800 nM of VpolAAS1, and 5 μL of the template, while the

PCRs for MCP used the same reaction and GoTaq G2 Green Master Mix volumes, but 400 nM

of each primer was used with 2 μL of template. The PCR cycling conditions for MCP genes used

the methods of Larsen et al., (2008). The PCR cycling conditions for polB used an initial 180 s

denaturation step at 95 °C, followed by 40 cycles of 95 °C for 30 s, 50 °C for 50 s, and 72 °C for

90 s, and terminating with an elongation step of 72 °C for 240 s. For g20 genes, the following

PCR cycling conditions were used: a 95 °C step for 180 s, then 35 cycles for round 1 and 25

cycles for round 2 of 95 °C for 30 s, 45 °C for 60 s, and 72 °C for 60 s, and a final step of 72 °C

for 300 s. The PCR product from the initial g20 PCR was purified using a BioBasic PCR

purification kit following the manufacturer’s protocol (BioBasic, Toronto, Canada). The purified

PCR product (2 μL) was then used as the template for the second round of PCR. First round PCR

of g20 gene fragments used 50 μL total reaction volumes consisting of: 5 μL of 10x PCR Buffer,

1.5 mM of MgCl2, 0.2 mM of each dNTP, 400 nM of each primer, 1 unit of Platinum Taq DNA

Polymerase (Life Technologies Corporation, Carlsbad, CA), and 5 μL of template, while the

second round used the same reagent concentrations with 2 μL of template.

All final PCR products were visualized using gel electrophoresis. Appropriately sized DNA

bands were excised from the gels for each marker gene and purified using a BioBasic Gel

purification kit using the manufacturer’s protocol (BioBasic, Toronto, Canada). Purified PCR

products were cloned using a pGem-T Vector System II kit using the manufacturer’s protocol

(Promega Corporation, Madison, WI). A PCR using T7/SP6 primers was used on white colonies

to confirm the presence of an appropriately sized insert fragment, and appropriate amplification

products were purified with the BioBasic PCR clean-up kit and were sequenced at the Center for

Applied Genomics (TCAG) in Toronto, Canada. Only full-length sequences were used in further

analyses (~500 bp for MCP, ~350 bp for polB, and ~592 bp for g20).

For all three genes, DNA sequences were separated into OTUs at a 97% cut-off level using the

computer program mothur (Schloss et al., 2009). Amino acid sequences were inferred from the

sequences of the OTUs, and representative sequences from each OTU were aligned with selected

sequences from NCBI Genbank using MUSCLE with default parameters in MEGA 6.0 (Tamura

55

et al., 2013). After alignment, maximum likelihood phylogenies were constructed using the

Jones-Taylor-Thornton amino acid substitution model in MEGA. All sequences used for

phylogenetic analysis were submitted to NCBI Genbank (accession numbers: KY082068 -

KY082085 for g20, KY082086 - KY082088 for polB, KY082089 for MCP).

Quantitative PCR primer and probe design and conditions

Using the approach described by Short and Short (2009), TaqMan® primers and probes were

designed for putative polB sequences from the cultivated algal viruses ACTV-1 and CVM-1, a

putative algal virus MCP gene sequence related to viruses that infect prymnesiophytes

(F2MCP1), a putative algal virus polB gene sequence related to viruses that infect prasinophytes

(F2VPOL1), and two putative cyanophage g20 gene sequences, one related to cyanophage P-

TIM40 (IZCPS1) and one related to Synechococcus phage S-SM1 (WZCPS8). Previously

described primers and probes were used for the cultivated algal virus CVM-1 (Short et al.,

2011a), CpV-BQ1 (Mirza et al., 2015) and for a putative algal virus polB gene fragment related

to viruses that infect chlorophytes (LO.20May09.33; Short et al., 2011a). The newly designed

primer and probe sequences are detailed in Table 3.1. All TaqMan ® probes used in this study

were 5’ labeled with FAM (6-carboxyfluorescein) and were 3’ labeled with a Zen Internal

Quencher (Integrated DNA Technologies, Coralville, IA), except for LO.20May09.33.

The conditions for quantitative PCR for every primer and probe set in this study are as follows:

20 μL reactions with 0.5 units of Platinum Taq DNA polymerase (Life Technologies

Corporation, Carlsbad, CA), 1X Platinum Taq PCR Buffer, 5 mM MgCl2, 200 μM each dNTP,

250 nM forward and reverse primers each, 100 nM TaqMan probe, 30 nM ROX reference dye,

and 2 μL of template. For every primer and probe set, the thermal cycling conditions used a

denaturation step at 95 °C for 5 min, followed by 40 cycles of 95 °C for 15 s and 60 °C for 1

min, during which, the fluorescence was measured using an Mx3000P QPCR System

(Stratagene, La Jolla, CA). Quantitative PCR standards for each primer and probe set were

generated from clones as previously described in Short and Short (2009), and ranged from 1.0 x

56

Table 3.1. Algal virus and cyanophage targeting quantitative PCR primers and probes designed for this study

Target Closest cultivated blastp match to target* Probe (5' - 3') Primers (5' - 3')

ATCV-1 Acanthocystis turfacea chlorella virus 1(100%) CGA GCC ACT TCG CAA CTT CAA Fwd: GTC TGT AGT GTA TGG TG

Rev: GAG CTT TGT ACT CCT TTG TG

F2VPOL1 Bathycoccus virus BpV178 (78%) CGC ACA ATC TCT GTT ATT CAA CCC T Fwd: GTC TGT ACC CAT CGA TCA

Rev: CGC TTT GAG TTC TCT GAG

F2MCP1 Chrysochromulina parva virus CpV-BQ1 (89%) TCT TGC TCT TCC TCT CAT TGC TCT T Fwd: TCT CAA TTT CTG GTT CTG

Rev: GCC GTA AAT CAA TGT TAA TAC

IZCPS1 Cyanophage P-TIM40 (64%) TTC TGG ATG CCT CGC CGT GA Fwd: CGC CAT CTA TCA ATG ATG

Rev: CGG CTA ATT GGT ACA TTC

WZCPS8 Synechococcus phage S-SM1 (72%) AGT TCT CAC CAC CTG GCA ATG Fwd: GTG ACA TTA TGG CAC GTT A

Rev: GGA AGT AGA GAA TGT CTT CAA

*Parenthetical percentage denotes the percent identity to the top blastp search match using inferred amino acid sequences

57

100 to 1.0 x 107 molecules per reaction. Every standard curve had an efficiency between 91.1 and

104.5 % with R-squared values above 0.99. All standards were run in duplicate and all samples

and negative controls were run in triplicate.

Decay calculations and statistical analyses

Decay rates were estimated as described in Noble and Fuhrman (1997) and Long and Short

(2016). Briefly, linear regressions were calculated for the natural log of infectious titers, or of

gene abundances, against incubation time points. Regression slopes were used as decay constants

(units are h-1), and the percent infectivity or genotype abundance lost per hour was calculated by

multiplying the decay constant by 100. Time points that did not have detectable viruses or had

detectable but unquantifiable viruses (i.e. one or two, but not all, triplicate qPCRs amplified in a

sample) were excluded from decay calculations as well as the subsequent statistical analyses,

resulting in several decay rates being estimated with less than the total five time points.

Additionally, the final time points during the summer incubation for both treatments were

excluded due to the presence of PCR inhibitors that reduced the observed gene copies of known

standards by 40 %, indicating that reliable gene quantification was not possible in these samples.

A second-order polynomial regression was used to assess the relationship between the loss of

infectivity and the loss of amplifiable DNA and calculated in Excel (Microsoft, Redmond, WA).

The quantity obtained via infectivity estimated either in Long and Short, (2016), or in this study

for the autumn 2014 incubations, was plotted against the quantity obtained via qPCR in the same

time point for all cultivated viruses, treatments, and seasons. Spearman correlation analysis was

conducted on these same values. Linear regression analyses and analysis of covariance

(ANCOVA) statistical tests were conducted for decay constants as described in Long and Short

(2016). The Spearman correlation, linear regressions, and ANCOVAs were calculated in

GraphPad Prism 7 (GraphPad Software, La Jolla, CA, USA) using natural log transformed data,

which is consistent with decay rate calculations. To determine if decay rates of the three

cultivated viruses were significantly different when estimated via infectivity assays versus

molecular assays, ANCOVA was used to compare the slopes of linear regressions of the

cultivated viruses incubated during the same season and in the same treatments but with these

different enumeration techniques. Additionally, ANCOVA was used to compare treatments,

seasonal, and virus-to-virus differences as previously described (Long and Short, 2016).

58

Bonferroni corrections were applied to account for the multiple pairwise comparisons. Thus,

significance levels were adjusted for comparisons of enumeration technique (α = 0.0018),

treatment (α = 0.002), seasons (α = 0.0025 for ATCV-1, α = 0.0025 for CVM-1, α = 0.0042 for

CpV-BQ1, α = 0.0083 for F2VPOL1, α = 0.013 for WZCPS8), and viruses (α = 0.0031 for

spring 2013, α = 0.00031 for summer 2013, α = 0.0083 for autumn 2013, α = 0.0025 for winter

2013-2014, and α = 0.0025 for autumn 2014).

59

3.3 Results

Environmental data

The environmental data from the four 2013 seasonal decay incubation experiments was

published and fully discussed in Long and Short (2016). For autumn 2014, the average water

temperature was 13.8 °C, while the average daylight hours per day were 10.5 h. The autumn

2014 decay incubation experiment started later in the year than in autumn 2013 and had lower

overall temperatures and shorter day lengths (Table 3.2). Overall, the highest temperatures and

longest day lengths were in the summer and spring incubations while the lowest temperatures

and shortest day lengths were in the winter incubation (Long and Short, 2016). Ice cover existed

over the winter incubation for the entirety of the experiment (Long and Short, 2016).

Algal virus and cyanomyovirus sequence analysis

Environmental sequences of algal virus polB and MCP genes were obtained from the autumn

2014 samples. Samples from the previous four incubations were not taken before the addition of

cultivated algal viruses, which prevented sequences from environmental algal viruses to be

obtained. For polB, three OTUs were obtained from 70 total sequences using a 97 % nucleotide

identity cut-off, two of which were 95 % identical to each other with respect to nucleotides

(F2VPOL1 & F2VPOL2), while the third OTU shared only 79 % identity with the other OTUs

(F2VPOL43). All three OTUs were most closely related to uncultivated algal viruses (Figure

3.1), and had the prasinovirus BpV178 as their closest cultivated relative according to blastp (78

%, 77 %, and 77 % amino acid sequence identity). A primer and probe set (F2VPOL1) was

designed to amplify two of these OTUs, which accounted for all but one of the sequences

obtained. For MCP, one OTU was obtained from 61 total sequences using a 97 % cut-off. The

MCP OTU was most closely related to the cultured prymnesiovirus CpV-BQ1 (Figure 3.2; 87 %

amino acid sequence identity). A qPCR primer and probe set (F2MCP1) was designed for this

OTU.

Environmental sequences of cyanomyovirus g20 genes were obtained from all five decay

incubation experiments. Overall, 18 OTUs were obtained from 78 sequences across the five

sampling periods. Autumn 2014 contained the most OTUs while autumn 2013 contained the

least. The OTUs used for qPCR primer and probe design, IZCPS1 and WZCPS8, were both most

60

Table 3.2. Environmental Parameters for Autumn 2014 Experiment

Season Time point (hours) Water Temperature (°C) Daylight Hours

Autumn 0 17.9 0

2014 24 16.3 11.07

96 12.3 43.73

168 13.4 75.65

504 9.1 220.5

61

Figure 3.1. Maximum likelihood phylogenetic tree of inferred amino acid sequences of algal

virus polB fragments using the Jones-Taylor-Thornton amino acid substitution model with 1000

bootstrap iterations. Bolded sequence names indicate OTUs obtained in this study, and asterisks

indicate sequences targeted by qPCR primers and probes designed for this study. Sequences from

cultivated viruses or from other environmental studies are shown with their Genbank accession

number in parentheses. The names of the sequences obtained in this study indicate autumn 2014

(F2), the primers used (Vpol for VpolAAS4/VpolAS1) and the clone number of the

representative OTU sequence.

62

Figure 3.2. Maximum likelihood phylogenetic tree of inferred amino acid sequences of algal

virus MCP fragments using a Jones-Taylor-Thornton amino acid substitution model with 1000

bootstrap iterations. Bolded sequences indicate OTUs obtained in this study, and asterisks

indicate sequences targeted by qPCR primers and probes designed for this study. Sequences from

cultivated viruses or from other environmental studies are shown with their Genbank accession

number in parentheses. The names of the sequences from this study indicates autumn 2014 (F2),

the primers used (MCP for mcp Fwd/mcp Rev) and the clone number of the representative OTU

sequence.

63

closely related to putative cyanophage sequences obtained from environmental samples (Figure

3.3). Additionally, IZCPS1 had Cyanophage P-TIM40 (64 % amino acid sequence identity) and

WZCPS8 had Synechococcus phage S-SM1 (72 % amino acid sequence identity) as their closest

cultivated relatives according to blastp. Sequences belonging to the IZCPS1 and WZCPS8 OTUs

were obtained from samples in multiple seasons: spring and summer for IZCPS1 and spring and

winter for WZCPS8. The occurrence in multiple seasons provided the impetus for designing

probes for these two targets.

Algal virus and cyanophage decay

The decay of infectivity for ATCV-1, CVM-1, and CpV-BQ1 in the spring, summer, autumn,

and winter 2013 was previously reported in Long and Short (2016). In autumn 2014, ATCV-1

had infectivity decay rates of 0.59 ± 0.12 % h-1 for the filtered water treatment and 0.85 ± 0.24 %

h-1 for the whole water treatment, while CVM-1 had infectivity decay rates of 0.81 ± 0.15 % h-1

and 1.42 ± 0.47 % h-1, and CpV-BQ1 had infectivity decay rates of 0.75 ± 0.04 % h-1 and 1.01 ±

0.05 % h-1 for these same treatments, respectively.

For each incubation experiment, qPCR was used to estimate decay rates for the three cultivated

viruses as well as naturally occurring, uncultivated algal viruses and cyanophages (Figure 3.4).

When estimated via qPCR assays, ATCV-1 had decay rates that ranged from 0.0056 to 0.75 % h-

1, CVM-1 had decay rates that ranged from 0.034 to 0.84 % h-1, and CpV-BQ1 had decay rates

that ranged from 0.047 to 1.25 % h-1. Overall, the highest decay rates were during the spring and

summer, while the lowest decay rates were in the winter. ATCV-1 exhibited the lowest decay

rates, while CpV-BQ1 had the highest decay rates. Generally, filtered water treatments had lower

estimated rates of decay than whole water treatments.

Estimates for the decay rates of uncultivated algal viruses and cyanophages were not obtained for

each season simply because some viral genes were not detected or quantifiable (i.e. amplified in

one or two but not all triplicate qPCRs) in all seasons (Figure 3.4). F2VPOL1, a sequence related

to prasinoviruses, was detected in summer 2013, winter 2013-2014, and autumn 2014. F2MCP1,

related to viruses that infect prymnesiophyte algae, was detected in autumn 2013 and 2014, but

was only quantifiable in autumn 2014. IZCPS1, related to cyanomyoviruses, was detected in

spring 2013 and summer 2013, but decayed so rapidly that it was no longer detectable after 24

hours rendering a decay rate estimate unreliable, except in the spring filtered

64

65

Figure 3.3. Maximum likelihood phylogenetic tree of inferred amino acid sequences of

cyanomyovirus g20 genes using a Jones-Taylor-Thornton amino acid substitution model with

1000 bootstrap iterations. Bolded sequence names indicate OTUs obtained in this study, and

asterisks indicate sequences targeted by qPCR primers and probes designed for this study.

Sequences from cultivated viruses or from other environmental studies are shown with their

Genbank accession number in parentheses. The names of the sequences from this study indicates

spring 2013 (IZ), summer 2013 (SZ), autumn 2013 (FZ), winter 2013-14 (WZ), and autumn

2014 (F2), the primers used (CPS for CPS1.1/8.1) and the clone number of the representative

OTU sequence.

66

0

0.5

1

1.5

2

FW WW FW WW FW WW FW WW FW WW

Spring Summer Autumn 2013 Winter Autumn 2014

ATCV-1

CVM-1

CpV-BQ1

0

0.5

1

1.5

2



LO.20May09.33

F2VPOL1

F2MCP1

0

5

10

15

20



IZCPS1

WZCPS8

B

A

C

% G

en

e c

opie

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er

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ur

% G

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op

ies lost p

er

hour

% G

ene c

op

ies lo

st p

er

hou

r

t.f.

t.e.

t.f.

t.e.

t.f.

t.e

.

t.f.

t.e.

n.d

.

n.d

.

n.d

.

n.d

.

n.d

.

n.d

.

n.d

.

n.d

.

n.d

.

n.d

.

n.d

.n

.d.

n.d

.n

.d.

n.d

.

n.d

.

n.d

.

n.a

.

n.d

.n.d

.n.d

.

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.n.d

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.d.

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.

n.d

.

n.d

.

n.d

.

n.d

.

n.d

.

n.d

.

n.a

.

67

Figure 3.4. Seasonal decay rates of (A) cultivated algal viruses, (B) environmental algal viruses,

and (C) environmental cyanophages estimated using qPCR. Each data point is the average of

triplicate incubations of decay and the error bars represent standard deviation. Note that

cyanophages have different scaling on the Y-axis. FW indicates the filtered water treatment and

WW indicates the whole water treatment. The abbreviation n.a. indicates the virus was not added

to the incubation during that season, n.d. indicates the decay rate was not determined because it

was not detected during that season, and t.f.t.e. indicates the virus decayed too fast to be

estimated for that treatment (see text for further details).

68

water treatment. WZCPS8, also related to cyanomyoviruses, was detected in spring and summer

of 2013, and winter 2013-2014. As with IZCPS1, WZCPS8 decayed too rapidly in the summer

whole water treatment for reliable decay rate estimation. Finally, LO.20May09.33, a previously

described sequence related to chloroviruses, was detected only in spring 2013.

Like the cultivated algal viruses, decay rate estimates for the uncultivated virus genes were

highest in the summer incubations and lowest in the winter, and were higher in the whole water

treatments compared to the filtered water treatments. Algal virus genes had lower decay rates

than cyanomyovirus genes in the seasonal incubations in which they were both detected. Decay

rate estimates for the prasinovirus-like gene, F2VPOL1, ranged from 0.007 to 1.30 % h-1; the

prymnesiovirus-like gene, F2MCP1, ranged from 0.048 to 0.99 % h-1; the chlorovirus-like gene,

LO.20May09.33, ranged from 0.44 to 0.76 % h-1; the cyanomyovirus-like gene, WZCPS8,

ranged from 0.27 to 14.81 % h-1. A decay rate was only able to be estimated for the IZCPS1

cyanomyovirus-like gene in the spring filtered incubation (2.60 % h-1).

Statistical comparisons of estimated algal virus and cyanophage decay rates

A second-order polynomial regression between natural log transformed infectivity measurements

and natural log transformed qPCR measurements at every time point used for the decay rate

calculations found a close relationship between the loss of infectivity and the loss of amplifiable

DNA (R-squared = 0.648; Figure 3.5). Spearman correlation was strong and highly significant (ρ

= 0.79, n = 375, p-value < 0.0001).

Linear regression analyses of the natural-log transformed qPCR abundances revealed that the

slopes (i.e., decay constants) for the whole water treatments were statistically significant (i.e.,

non-zero) at α = 0.05, except for ATCV-1 in the autumn 2013 (Appendix 2.1). The slopes of the

filtered treatments were non-significant for 61 % of the slopes tested. The patterns for the decay

constants were inverse from the patterns for the estimated decay rates above as more negative

decay constants produce higher decay rates.

ANCOVA was used to test if the decay constants based on infectivity assays were significantly

different than the decay constants based on molecular assays for the same virus. In every

69

Figure 3.5. Polynomial regression of infectious titre estimates from either Long and Short, 2016

or this study in the case of autumn 2014, against qPCR estimates of ATCV-1, CVM-1, and CpV-

BQ1 in all time points used for qPCR decay rate calculations in both treatments and every

season.

y = 0.0801x2 - 0.5275x + 9.4036R² = 0.648

0

5

10

15

20

25

0 5 10 15 20

ln G

ene

co

pie

s p

er

mL

ln Infectious units per mL

70

case, the decay constants estimated from infectivity assays were more negative (i.e., higher

decay) than decay constants derived from qPCR. Overall, 13 of 28 of the comparisons between

infectivity and qPCR measures were significantly different (Appendix 2.2). For ATCV-1, CVM-

1, and CpV-BQ1, 4 of 10, 5 of 10, and 4 of 8 of the decay constants were significantly different,

respectively. For filtered water treatments, 6 of 14 were significantly different, while 7 of 14

differed significantly for the whole water treatments. For spring, 2 of 4 comparisons were

significantly different, while 2 of 6 were significantly different in the summer, 7 of 12 were

significantly different in the autumn, and 2 of 6 were significantly different in the winter. In

summation, the number of significant differences between decay constants estimated with

infectivity assays and those estimated with qPCR did not vary much between the three viruses,

the two treatments, or seasonally, although over half of the decay constants were significantly

different in the two autumn incubations.

Similarly, ANCOVA was also used to compare the decay constants from the two different

treatments for each virus tested within the same season. Generally, decay constants from the

filtered water treatments were less negative than decay constants from the whole water

treatments. However, only 5 of 21 of the decay constants from the filtered treatments were

significantly different than those estimated from the whole water treatments (Appendix 2.3). In

spring, most (3 of 4) of the decay constants from the two treatments were significantly different

than each other, while no statistical differences were found in the summer, and only 1 of 7 and 1

of 5 comparisons were significantly different in autumn and winter, respectively. There was a

clear seasonality in the number of significant differences between treatments as over half were

significantly different in spring and no other season had more than one significant difference.

Additionally, comparisons were made between the decay constants from different seasons for the

same virus and treatment using ANCOVA, where overall, decay constants were more negative in

summer than the other seasons and less negative in winter than the other seasons. While these

patterns were observed, only 18 of the 62 decay constant comparisons were significantly

different (Appendix 2.4). Significant seasonal differences were found for 12 of 52 of the decay

constant comparisons for the cultivated viruses and in 3 of 10 comparisons for the uncultivated

viruses (2 of 4 for cyanophage genotypes, 1 of 6 for algal virus genotypes). For spring, 7 of 19

decay constant comparisons were significantly different from the other seasons (1 of 10 in

filtered, 6 of 9 in whole water), while 4 of 27 comparisons (1 of 14 in filtered, 3 of 13 in whole

71

water) in summer, 3 of 22 comparisons (0 of 11 in filtered, 3 of 11 in whole water) in autumn

2013, 13 of 29 comparisons (3 of 15 in filtered, 10 of 14 in whole water) in winter, and 7 of 26

comparisons (1 of 13 in filtered, 6 of 13 in whole water) autumn 2014 were significantly

different from other seasons. For all viruses, significant seasonal differences in decay rate

estimates were exhibited in 3 of 32 comparisons for the filtered water treatments and 15 of 30

comparisons for the whole water treatments. Additionally, decay constants from autumn 2013

were significantly different than the decay constants from autumn 2014 for 1 of 6 comparisons.

Overall, decay constants from the whole water treatments were much more likely to have

significant seasonal differences than decay constants from the filtered treatments and the spring

and winter experiments were more significantly different from other seasons than either summer

or autumn experiments.

Further, ANCOVA was used to test for significant differences between the decay constants

estimated for different viruses in the same season and treatment. Generally, in these

comparisons, cultivated algal viruses had less negative decay constants than uncultivated algal

viruses, and cyanomyoviruses had the most negative decay constants. Despite these observed

differences, only 29 of 78 decay constant comparisons between viruses were significantly

different from each other within the same season and treatment type (Appendix 2.5). The most

striking differences observed were between cyanophages, which had the highest decay rates, and

all other viruses. When the cyanophage decay constants were compared to all other viruses, 15 of

21 of the comparisons were significantly different. Of the two cyanophages, IZCPS1 was only

significantly different in 1 of 3 comparisons with algal viruses and WZCPS8 was significantly

different in 14 of 18. Due to the high decay observed in cyanophages, only one statistical

comparison between IZCPS1 and WZCPS8 could be made, which found WZCPS8 to have a

significantly different and more negative decay constant in the spring filtered treatment. While

WZCPS8 had a higher decay rate in this instance, IZCPS1 was more likely to decay too quickly

to be quantified and thus was likely to be the less stable cyanophage.

For the algal viruses, which had similar rates of decay, only 20 of the 80 comparisons between

viruses in the same treatment and season were significantly different, with 14 of 52 significantly

different between cultivated algal viruses, 5 of 26 significantly different between cultivated and

uncultivated algal viruses, and 1 of 2 significantly different between cultivated algal viruses.

Throughout most of the seasons, ATCV-1 had the lowest rate of decay, but only 9 of 28 of

72

comparisons between ATCV-1 and other algal viruses were significantly different. Similarly,

while CpV-BQ1 had the highest rate of decay for algal viruses for most seasons, only 7 of 22

comparisons were significantly different.

73

3.4 Discussion

Methodological considerations

Most phytoplankton viruses have not been cultivated and have unknown environmental hosts.

Therefore, cultivation-free techniques are vital to study viruses in their natural environments.

Molecular tools have been used to measure the diversity, abundance, and population dynamics of

algal viruses and cyanophages (e.g., Dorigo et al., 2004; Short and Suttle, 2005; Wilhelm et al.,

2006b; Chénard and Suttle, 2008; Short et al., 2011a; Hewson et al., 2012; Zhong and Jacquet,

2014). While one other study has estimated decay rates of uncultivated viruses via qPCR

(Hewson et al., 2012), the technique requires further validation to make inferences about the

decay of virus infectivity. The decay of infectivity is ecologically important because once a virus

is rendered non-infectious, it can no longer influence the mortality of its host populations. Since

decay rates of uncultivated viruses can only be assessed using culture-free methods, development

and validation of molecular techniques to estimate the decay of viruses is vital in viral ecology.

Validation of qPCR as a means of estimating virus decay is necessary as only some virions are

infectious (e.g., Van Etten et al., 1983b) and some mechanisms of decay may affect infectivity

without altering PCR amplification of viral genes. For instance, photochemical, chemical, and

enzymatic damage can render a virion non-infectious by compromising proteins involved in host

cell attachment and entry without damaging the small stretch of DNA required for qPCR

amplification. However, several mechanisms of decay will eliminate both infectivity and the

presence of amplifiable DNA at the same rate in filtered water samples, such as adsorption to

non-host cells and detritus or consumption by nanoflagellates (reviewed in: Gerba, 2005). It is

thus vital to resolve the relationship between the loss of infectivity and the loss of amplifiable

DNA in order to ascertain the usefulness of qPCR enumeration to estimate viral decay rates. In

order to establish the relationship between the loss of infectivity and the loss of amplifiable DNA

due to environmental factors in this study, the remaining infectious titre at each time point for

each treatment in each season was estimated with plaque assays or most probable number assays

and the remaining amplifiable DNA in the same samples was estimated with qPCR. The close

relationship between the loss of infectivity and the loss of amplifiable DNA, as evidenced by the

R-squared value in the polynomial regression (Figure 3.5) and high and significant Spearman

correlation values, suggests that qPCR measurements can be used as an effective proxy for

74

infectivity assays in decay experiments. Despite this, as the decay rates estimated with qPCR

were universally lower than those estimated with infectivity assays, molecular-based estimations

of virus decay should be interpreted cautiously.

Diversity of algal viruses and cyanophages in a freshwater pond

While the main focus of this study is the persistence of algal viruses and cyanophages, the

sequence data obtained revealed interesting patterns of diversity. Both polB and MCP sequences

obtained in autumn 2014 were dominated by either a single OTU or a few closely related OTUs,

suggesting the algal virus community during this season was not diverse. For both polB and

MCP, the sequences from autumn 2014 had fewer OTUs than previous studies in the same

environment (Short et al., 2011b) or in nearby Lake Ontario (Short and Short, 2008) and the Bay

of Quinte (Rozon and Short, 2013). Primer biases or shallow sequencing depth may have

influenced these results, however high host abundances may have contributed to the high relative

abundances of the observed genotypes. For instance, the dominance of the MCP prymnesiovirus-

like genotype may be explained by the population dynamics of the locally present

Chrysochromulina parva, which occurs in high abundances during autumn in nearby waters and

is infected by this OTU’s closest cultivated relative, CpV-BQ1 (Munawar and Munawar, 1982;

Mirza et al., 2015).

Though putative cyanophage g20 gene fragment sequences were obtained from every season, the

sequencing depth varied between the seasons, and thus, definitive statements cannot be made.

However, cyanophage OTU richness was highest in autumn 2014 and lowest in autumn 2013.

This study suggests highly similar cyanomyovirus-like genotypes have a global distribution as 16

of the 18 OTUs in this study were most closely related to sequences (65 - 95 % amino acid

sequence identity, with the majority over 85 %) obtained from freshwater environments in Asia

(Wang et al., 2011; Wang et al., 2015).

Environmental decay of algal viruses and cyanophages

Infectivity assays were used to estimate decay rates in autumn 2014. As in the previous four

incubations, ATCV-1 was the most resilient virus (Long and Short, 2016). Contrary to the

previous incubations, which found CpV-BQ1 to the most fragile virus (Long and Short, 2016),

CVM-1 had the highest decay rate in autumn 2014. However, CVM-1 and CpV-BQ1 did not

75

have statistically different decay rates when compared with ANCOVA (F = 0.0541, DFn, DFd =

1, 26, p-value = 0.817 for the filtered treatment; F = 1.802, DFn, DFd = 1, 26, p-value = 0.197

for the whole water treatment).

Molecular assays were utilized to enumerate specific genes throughout in situ decay incubation

experiments. These abundance estimates were then used to estimate the decay rates of ATCV-1,

CVM-1, CpV-BQ1, a chlorovirus-like gene, a prymnesiovirus-like gene, a prasinovirus-like

gene, and two cyanomyovirus-like genes. Overall, the decay rate estimates (0.0056 - 14.81 % h-

1) varied considerably, but the lower ranges are comparable to the decay rates estimated via

infectivity in these same incubations (0.012 - 11.26 % h-1; Long and Short, 2016). To our

knowledge, the only other decay incubation experiments which utilized qPCR to estimate decay

rates focused on a freshwater pond in Ithaca, NY, USA (Hewson et al., 2012) and presented rates

for a chlorovirus-like gene (0.13 % h-1) and a cyanomyovirus-like gene (1.50 % h-1) that were

within the range we obtained for uncultivated algal viruses (0.007 - 1.30 % h-1) and

cyanomyoviruses (0.27 - 14.81 % h-1). In the spring and summer incubations, two of the

cyanophage genes experienced decay that was too fast to be estimated with our experimental set-

up as they were no longer detectable after 24 hours (IZCPS1 in the spring whole water and both

summer treatments; WZCPS8 in the summer whole water treatment). If the final abundance at 24

hours is assumed to be one copy per mL, minimum decay rates can be estimated. The decay rate

estimates using this assumption range from 44 to 58 % h-1, which is near the range of

cyanophage PP decay as estimated with infectivity assays in Wuhan Lake, China (60 – 90 % h-1;

Liu et al., 2011).

The decay rates of ATCV-1, CVM-1, and CpV-BQ1 were estimated with both qPCR and

infectivity assays. In every case, the decay rate estimated with infectivity assays exceeded the

decay rate estimated with qPCR. While significant statistical differences were not always found,

this means that decay rates determined with qPCR should be treated as minimum estimates. It

should also be noted that ultraviolet (UV) irradiation was largely excluded from these

experiments and therefore these decay rates may have been underestimated by either

enumeration method. However, as some viruses are more damaged by photosynthetically active

radiation (PAR) than UV (e.g., Baudoux et al., 2012), further experiments utilizing UV-

transparent and UV-opaque vessels may be necessary to resolve whether the estimates of decay

in this study were underestimated.

76

Treatment effects, seasonality and virus-to-virus variability

The decay rates in the whole water treatments were higher than the filtered treatments but, less

than half of these differences were significant statistically. Interestingly, most of the treatment

differences were significantly different in the spring, while few were significantly different in

any other season, which suggests a clear seasonality in treatment effects. Additionally, while no

statistical inferences can be made, the decay was so high in the spring whole water for IZCPS1

and the summer whole water for WZCPS8 that they were no longer detected after 24 hours, but

the same viruses remained quantifiable in the filtered treatments, further suggesting a treatment

effect in the spring and summer incubations. The treatment effects were also greater in the spring

and summer when decay rates were estimated via infectivity during the same incubations (Long

and Short, 2016). Greater concentrations of non-host cells and other large particles, as expected

in whole water treatments, could increase the rate of absorption of viruses to these cell and

particles resulting in these viruses being filtered out during the sample preparation for qPCR.

Further, biomass is generally greater in the late spring and summer than in the autumn and winter

and, thus, viral absorption to non-host cells, the enzymatic breakdown of viruses, and the

presence of oxidizing and reducing agents of bacterial origin are more likely to occur in spring

and summer. These biological factors have been implicated in the decay of viruses in previous

studies (reviewed in: Gerba, 2005). It should also be noted that potential host cells were likely

present in the whole water treatment and thus whole water decay rates may have been lessened

by some amount of viral production during the incubations.

Seasonality in general was observed, viruses decayed fastest in spring and summer and lowest in

winter. The low decay rates in the winter were likely due to the ice cover that remained over the

incubation bottles throughout the experiment, which reduced the level of sunlight capable of

reaching the incubation bottles (Bertilsson et al., 2013). To our knowledge, this study constitutes

only the second reported experimental evidence that algal viruses persist over-winter within the

ice column of temperate aquatic systems and is the first study to provide evidence that

cyanomyoviruses may also persist in these conditions. The cultivated algal viruses, ATCV-1,

CVM-1, and CpV-BQ1, were shown to persist via infectivity assays previously (Long and Short,

2016). In this study, qPCR found ACTV-1 retained up to 62 % of its initial gene copies, CVM-1

up to 44 %, and CpV-BQ1 up to 20 %. In every case, a higher percentage of gene copies

remained than a percentage of infectivity. Despite this, the decay rates estimated with infectivity

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assays were significantly different than the decay rates estimated with qPCR for only 2 of 6

comparisons in the winter.

In addition, both uncultivated viruses tested were found to retain gene copies throughout the

winter. F2VPOL1, the prasinovirus-like genotype, retained 19 % of its original gene copies in

the whole water treatment while WZCPS8, a cyanomyovirus-like genotype, retained only 0.0077

%. As less infectivity remained for each of the three cultivated viruses than gene copies, it is

reasonable to assume that F2VPOL1 will have remained infectious, but as only a very low

percentage of WZCPS8 gene copies remained, it is less likely that an appreciable amount of the

virus with this gene remained infectious at the end of winter. This marked contrast between algal

viruses and cyanophages suggest that they may employ different life histories in temperate

aquatic systems. However, high abundances of cyanomyoviruses have been reported during the

winter in Lake Erie, USA during which ice cover was present (Matteson et al., 2011). Therefore,

while the much lower decay rates of algal viruses in winter compared to the other seasons may

constitute a vital mechanism for the maintenance of viral ‘seed-banks’ in aquatic systems, further

questions on the stability of cyanophages during winter remain.

Estimated decay rates also had distinct differences between individual viral types. Overall,

cultivated algal viruses had the lowest decay rates and uncultivated cyanomyovirus-like genes

had the highest decay rates, which parallels a study that found lower decay rates for uncultivated

algal viruses than for uncultivated cyanomyoviruses (Hewson et al., 2012). Similarly, decay

patterns for the cultivated algal viruses monitored in this study using qPCR mirrored those from

the previous study of infectivity decay (Long and Short, 2016).

The generally high decay rates of cyanomyoviruses throughout the year compared to algal

viruses may be explained by differences in life history. For instance, several cyanomyoviruses

that infect Synechococcus infect multiple strains of Synechococcus, and even some strains of

Prochlorococcus (Sullivan et al., 2003), which may allow for more opportunities for infection of

different hosts throughout the year and continued production to counteract rapid rates of decay.

The differences in decay rate between algal viruses and cyanomyoviruses may also be due to

differences in genetic potential and physical structures. For instance, cyanophages and other

bacteriophages can exhibit extraordinary rates of photoreactivation (up to 78 %), whereby host

cell machinery repairs the phage, enabling it to regain infectivity (e.g., Weinbauer et al., 1997;

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Wilhelm et al., 1998; Cheng et al., 2007). Similarly, many algal viruses contain the genetic

potential for DNA repair that, while independent of host genes, relies upon host cell machinery

(e.g., Dunigan et al., 2006). Furthermore, a strong relationship between capsid thickness

(represented by surfacic mass) and the mortality of phage has been reported in bacteriophages,

including myoviruses, whereby a greater capsid surfacic mass increased the survival of the

individual phage strain (De Paepe and Taddei, 2006).

Conclusions

The application of qPCR to enumerate specific viral genotypes allows estimates of decay for

viruses, whereas previous methods only allowed for the estimation of decay rates for total virus-

like particles in the case of microscopy (e.g., Heldal and Bratbak, 1991), or for infectious

particles with known and cultivated hosts (e.g., Noble and Fuhrman, 1997). The application of

qPCR, which was first validated in this study, is particularly important, as the vast majority of

viruses are uncultivated. While the decay rates of the majority of viruses can be estimated using

qPCR, it is important to note that the qPCR decay estimates represent minimum estimates of

decay and, as viruses are most ecologically important when infectious, infectivity decay

estimates remain the preferred method when host-virus pairs are in culture. Additionally,

metagenomics and deep-amplicon sequencing may offer more potential gene targets, which

would allow for future studies to estimate the decay rates of many more types of viruses than

were estimates in the current study.

The results of this study show clear seasonality in the decay rates of both algal viruses and

cyanophages. The high decay rates in the summer and spring suggest that other mechanisms,

such as environmental refugia, may be necessary to maintain the viral ‘seed-bank’ during these

periods of rapid decay. One such refugium may be in sediments as decay rates in the sediments

of aquatic systems are generally lower than decay rates in the water column and are less likely to

show extreme seasonal differences (Middelboe et al., 2011). While seasonal bottlenecks in virus

survival seem to occur in the spring and summer, the exceptionally low decay rates of

uncultivated algal viruses and cyanophages in winter suggest these viruses may overwinter

frozen within the ice cover of a freshwater pond. However, the decay rates estimated for an

uncultivated cyanophage genotype during winter suggests that the survival of cyanophages

throughout winter still remains unresolved.

79

Acknowledgements

I am appreciative of the constructive comments on data analysis by Michael Staniewski and on

analysis in general by Brandy Velten. This research was supported in part by the Canadian

Foundation for Innovation Leaders Opportunity Fund and NSERC Discovery grants awarded to

S.M.S.

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Chapter 4 Diverse and abundant algal viruses and cyanophages observed in

Lake Erie sediments

.

Abstract Algal viruses and cyanophages infect important primary producers in aquatic systems and have

wide-ranging effects upon the food web and biogeochemical cycles. However, little is known

about these obligate pathogens within aquatic sediments. To address this information gap,

sediment core samples were taken from Lake Erie at four distinct sites: two in the western basin,

one in the central basin, and one in the eastern basin. Molecular probes targeting the polB gene

of algal viruses and the viral capsid assembly gene (g20) of cyanophages were used to examine

the diversity of environmental phytoplankton virus sequences. Additionally, quantitative PCR

primers and probes were utilized to estimate the abundances of select algal virus and cyanophage

genes in Lake Erie sediment. PCR and sequencing of polB and g20 genes revealed diverse

assemblages of putative algal viruses and cyanomyoviruses, uncovering many viral gene

sequences that had previously only been described from water column samples. Wide abundance

ranges of certain algal virus (below detection to 2.97 x 106 gene copies per gram of wet

sediment) and cyanophage (below detection to 9.42 x 104 gene copies per gram of wet sediment)

genes were found using qPCR. Abundance patterns were variable between viruses and were

often specific to the virus gene and sampling site. The diversity of viruses coupled with the high

abundances of several virus genes, suggest that aquatic sediments are an important

environmental refugia for phytoplankton viruses

A version of this chapter has been submitted to Applied and Environmental Microbiology

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4.1 Introduction

Viruses have high abundances relative to cellular life in both the water column (Bergh et al.,

1989) and sediments (Maranger and Bird, 1996) of aquatic ecosystems. These highly abundant

particles are thought to exert top-down population control upon cellular organisms, a process

which has widespread implications for aquatic food webs and biogeochemical cycles (Brussaard,

2004; Wilhelm and Suttle, 1999; Suttle, 2007; Short, 2012). Additionally, evidence suggests

viruses can drive succession in both eukaryotic and prokaryotic algal communities, and that they

are responsible for cessation of certain algal blooms (e.g., Bratbak et al., 1993; Tarutani et al.,

2000; Wilson et al., 2002a; Brussaard et al., 2005; Gobler et al., 2007; Tomaru et al., 2007). Not

only do viruses actively decrease populations via lytic mortality, their activity has increasingly

been linked to increased productivity of both heterotrophic bacteria and primary producers via

liberation of nutrients following cell lysis (Haaber and Middelboe, 2009; Weinbauer et al., 2011;

Shelford et al., 2012; Staniewski and Short, 2014). Furthermore, it has been suggested that the

destruction of virus particles themselves contribute to the productivity of certain environments,

such as anoxic sediments (Dell’Anno et al., 2015). While viruses that infect eukaryotic or

prokaryotic algae are assumed to have important roles in aquatic ecosystems, the population

dynamics of these viruses and their effects upon hosts are only just beginning to be understood.

In addition to reports of high viral abundance in aquatic environments at discrete time points,

many studies have sought to analyze the diversity and seasonal patterns of viruses in aquatic

environments. The use of PCR targeting of hallmark genes for specific groups of viruses (e.g.,

Dorigo et al., 2004; Short and Suttle, 2005; Wilhelm et al., 2006b; Chénard and Suttle, 2008;

Short and Short, 2008; Clasen and Suttle, 2009) and metagenomics (Mohiuddin and Schellhorn,

2015) has allowed the diversity of uncultivated viruses to be studied in a number of freshwater

environments. The use of these molecular tools for algal viruses and cyanophages have often

revealed closely related gene sequences with widespread occurrences in marine and freshwater

environments (Short and Suttle, 2005; Wilhelm et al., 2006b; Short and Short, 2008). Despite

this, many diversity studies based on marker gene analysis have pointed to the existence of

sequences unique to specific environmental samples (Short et al., 2011b; Rozon and Short,

2013).

82

Moreover, sequence information obtained via PCR or metagenomics have recently been

extended to develop quantitative PCR (qPCR) assays that can assess the abundance of putative

algal virus and cyanophage genes in aquatic environments (e.g., Short and Short, 2009; Short et

al., 2011a; Matteson et al., 2011; Hewson et al., 2012; Zhong et al., 2013). Studies using qPCR

or infectivity assays to monitor the abundance of algal viruses or cyanophages have

demonstrated that some populations of algal viruses and cyanophages exhibit ‘boom and bust’

patterns, whereas other populations can be maintained at lower, but stable, abundances

throughout several seasons (e.g., Short and Short, 2009; Short et al., 2011a; Hewson et al., 2012;

Rozon and Short, 2013; Quispe et al., 2016). Indeed, observations of stable virus abundances

throughout the year, coupled with metagenomic studies, have revealed aquatic virus

communities with a few dominant virus genotypes but many more less abundant, yet detectable

genotypes (Breitbart et al., 2002; Breitbart and Rohwer, 2005). These findings provide evidence

for a ‘bank model’ of viral ecology, which proposes the presence of two pools of viruses: one of

highly abundant, less diverse viruses that actively produce more viruses while their hosts are

present, and another pool of highly diverse viruses that exist at relatively low abundances

(Breitbart and Rohwer, 2005; Waterbury and Valois, 1993). This pool of highly diverse, low

abundance viruses constitutes a ‘seed-bank’ that persists in the environment until hosts reach

‘threshold’ abundances necessary to promote viral production. The host-cell ‘threshold’

abundance for algal viruses and cyanophages typically ranges from 103 - 104 host cells per mL

(Suttle and Chan, 1994; Cottrell and Suttle, 1995; Jacquet et al., 2002), concentrations that both

eukaryotic and prokaryotic algae generally drop below during certain times of the year (e.g.,

Munawar and Munawar, 1986). After decreases in Synechococcus populations in the South

Pacific Ocean, total virus populations decreased in the 2 days immediately after, further

suggesting that phytoplankton virus populations are dependent on the availability of their hosts

(Matteson et al., 2012).

Despite this clear pressure on the continued existence of algal viruses and cyanophages in

aquatic environments, water column decay rates for these viruses are often highly variable (0.012

- 230 % lost per hour) and subject to seasonal variations (e.g., Heldal and Bratbak, 1991; Cottrell

and Suttle, 1995; Noble and Fuhrman, 1997; Garza and Suttle, 1998; Cheng et al., 2007; Hewson

et al., 2012; Frada et al., 2014; Long and Short, 2016). Recently, three different cultivated algal

viruses were shown to retain much of their infectivity after in situ incubation for 126 days under

83

ice in a freshwater pond (Long and Short, 2016). These observations provide evidence for one

mechanism of persistence within the water column that may maintain the viral ‘seed-bank.’

However, in other seasons, as well as in other studies examining the rate of decay of algal

viruses or cyanophages, the reported rates of decay were high enough to prevent continued

persistence of viruses in the absence of their hosts (e.g., 11 - 230 % lost per hour; Cottrell and

Suttle, 1995; Cheng et al., 2007; Long and Short, 2016).

To this end, other mechanisms must aid in the maintenance of algal virus and cyanophage ‘seed-

banks.’ These may include the DNA repair mechanisms encoded in several algal virus genomes

(e.g., Jeanniard et al., 2013), host- or virus-mediated photo-reactivation (e.g., Cheng et al., 2007;

Moniruzzaman et al., 2014), and even temperate life cycles for cyanophages (e.g., Sode et al.,

1994; Long et al., 2008). Further, environmental refugia may play a vital role in the maintenance

of the viral ‘seed-bank.’ One obvious potential refugium for algal viruses and cyanophages is the

sediment of aquatic environments, especially considering that early studies on sediment viruses

suggested that they experience reduced decay rates relative to viruses in the water column (Smith

et al., 1978; LaBelle and Gerba, 1980). Additionally, viable cyanophages have been recovered

from marine sediments up to 100 years old (Suttle, 2000a) and from freshwater sediments up to

50 years old (Hargreaves et al., 2013). Infectious algal viruses of the raphidophyte Heterosigma

akashiwo have also been recovered from sediment depths of up to 40 cm in coastal British

Colombia, Canada (Lawrence et al., 2002). The single stranded DNA (ssDNA) algal viruses of

Chaetoceros spp. and single stranded RNA (ssRNA) viruses of Heterocapsa circularisquama

have also been found in Japanese coastal sediments (Nagasaki et al., 2004; Tomaru et al., 2007,

2011b; Kimura and Tomaru, 2015). Furthermore, algal virus (Coolen, 2011; Hewson et al.,

2012), cyanomyovirus (Hewson et al., 2012), and potential host (Coolen, 2011; Rinta-Kanto et

al., 2009b) genes have been detected in sediments using qPCR. As such, marine and freshwater

sediments alike may harbor viable viruses that could be reintroduced into the water column,

acting as a source of viruses for the overlying waters.

Even though sediments are a likely refuge for aquatic viruses, viral ecology in aquatic sediments

remains poorly understood relative to the overlying waters. Studies of reef environments (Paul et

al., 1993), freshwater lakes (Maranger and Bird, 1996), and estuaries (Hewson et al., 2001) have

all found that viruses, enumerated via infectivity assays with specific hosts or via microscopy of

virus-like particles, can be up to 1,000 times more numerous in sediments than in the overlying

84

waters. Similarly, infectious algal viruses were found to be several times more abundant in the

coastal sediments of British Columbia compared with the overlying water column (Lawrence et

al., 2002). These observations have stimulated studies of virus diversity within aquatic sediment

using different experimental approaches, including metagenomics (Breitbart et al., 2004), pulsed

field gel electrophoresis (Filippini and Middelboe, 2007), and random amplification of

polymorphic DNA (Helton and Wommack, 2009; Borrell et al., 2012). Virus diversity in both

marine and freshwater sediments have been found to be quite high when compared to the water

column, but with large variations between sampling sites (Helton and Wommack, 2009). Despite

the clear importance of algal viruses and cyanophages in freshwater ecosystems and the presence

of specific algal viruses and cyanophages in sediments, the diversity of these viruses in aquatic

sediments has not been fully explored, and the abundances of only one algal virus and one

cyanophage have been estimated from freshwater sediments (Hewson et al., 2012).

Lake Erie is an important socioeconomic resource that is experiencing harmful algal blooms,

particularly of the toxic cyanobacterium Microcystis, at an increased rate and intensity (Rinta-

Kanto et al., 2009a; Michalak et al., 2013; Harke et al., 2016). As such, understanding

phytoplankton viral ecology in Lake Erie is of particular importance, including an examination

of sediments as a viral refugium. Therefore, the goals of this study were two-fold: 1) to assess

the potential for the freshwater sediments of Lake Erie to harbor diverse assemblages of algal

viruses and cyanophages and 2) to quantify the abundance of specific algal virus and cyanophage

genes across multiple depths in these same sediments. In order to accomplish these goals,

sediment samples were collected from four different locations and PCR of specific algal virus

and cyanophage hallmark genes was used to obtain gene sequences to assess the diversity of

these groups. Subsequently, qPCR was used to estimate the abundance of specific algal virus and

cyanophage genes at discrete depth profiles within the sediment samples.

85


Sample collection and DNA extraction

Lake Erie sediment sampling was conducted during a research cruise aboard CCGS Limnos

during the summer of 2013. Three sediment cores were taken at stations 452, 882, and 973 and

two were taken at station 1326 using clean, graduated acrylic coring tubes with a 2.5 cm

diameter (Figure 4.1). Sediment cores were immediately subsampled on board into 0 - 2 cm, 2 -

4 cm, 4 - 6 cm, and 6 - 8 cm depth profiles. Samples from each depth profile were placed in 4

ounce Whirl-Pak bags (Nasco, Fort Atkinson, WI) and stored at -20 °C until further processing.

Sediment samples were thawed in the lab, wet weight was measured, and DNA was extracted

with a PowerSoil DNA isolation kit (MO BIO Laboratories, Carlsbad, CA) using the

manufacturers’ protocol. The starting wet weight of sediment averaged 0.5 grams (standard

deviation = 0.1, n = 44). Triplicate (or duplicate in the case of station 1326) DNA extracts were

analyzed with a NanoDrop 1000 (Thermo Scientific, Wilmington, DE) for quantity and purity,

and were pooled to reduce sample variability creating composite samples for each depth at each

station, and then were stored at -20 °C.

Analysis of algal virus and cyanophage communities

PCR of DNA polymerase B genes (polB) was conducted to examine the community composition

of algal viruses in Lake Erie sediment using the primer set VpolAS4/VpolAAS1 (Clerissi et al.,

2014a), while the primers CPS1.1/CPS8.1 (Sullivan et al., 2008) were used for PCR of the

portal-protein-encoding gene 20 (g20) of cyanomyoviruses. PCR amplification of algal virus

polB gene fragments required one round of PCR using 50 μL reactions with 25 μL of GoTaq G2

Green Master Mix (Promega Corporation, Madison, WI), 200 nM of VpolAS4, 800 nM of

VpolAAS1, and 5 μL of template. Cycling conditions for polB PCR reactions were: 180 s at 95

°C, 40 cycles of 95 °C for 30 s, 50 °C for 50 s, and 72 °C for 90 s, and 240 s at 72 °C. PCR of

cyanophage g20 gene fragments required two rounds of PCR where products from the first round

of g20 PCR (180 s at 95 °C, 35 cycles of 95 °C for 30 s, 44 °C for 60 s, and 72 °C for 60 s, and

300 s at 72 °C) were purified with a Biobasic PCR clean-up kit (Biobasic, Markham, Canada)

and were used as templates for a second round of PCR (180 s at 95 °C, 25 cycles of 95 °C for 30

s, 45 °C for 60 s, and 72 °C for 60 s, and 300 s at 72 °C). The first round PCR of g20 used 50 μL

reactions with 5 μL of 10x PCR Buffer, 1.5 mM of MgCl2, 0.2 mM of each dNTP, 400 nM of

86

Figure 4.1. Map of Lake Erie denoting sediment sampling sites.

87

CPS1.1, 400 nM of CPS8.1, 1 unit of Platinum Taq DNA Polymerase (Life Technologies

Corporation, Carlsbad, CA), and 5 μL of template, while the second round of g20 PCR used the

same reagent concentrations, but only 1 μL of template. Final PCR products for both polB and

g20 gene fragments were visualized via gel electrophoresis and DNA bands of approximately the

correct size for each primer set were excised. Excised DNA bands were purified with a BioBasic

Gel Purification kit (Biobasic, Toronto, Canada) using the manufacturers’ protocol. Purified PCR

products were cloned using a pGem-T Vector System II kit (Promega Corporation, Madison,

WI). PCR was then conducted on portions of individual bacterial colonies using SP6/T7 primers

to verify the presence of the correct inserts, and PCR products of appropriately sized inserts were

purified using a Biobasic PCR clean-up kit as before. These purified PCR products were Sanger

sequenced at the Center for Applied Genomics at Sick Kids Hospital in Toronto, Canada. Only

sequences of the correct length were used for subsequent analysis, i.e., ~350 bp for polB and

~592 bp for g20.

For both polB and g20 gene fragment sequences, NCBI BLAST was utilized to verify that

amplicons were related to sequences from cultivated algal viruses and cyanophages. Sequences

that did not match the targeted genes were removed from the data set and not used in further

analysis. Nucleotide sequences of polB or g20 gene fragments were aligned using MUSCLE in

MEGA 6 (Tamura et al., 2013) and mothur was used to check for chimeric sequences using the

bellerophon approach (Schloss et al., 2009). Operational Taxonomic Units (OTUs), chao1, and

inverse Simpson indices were calculated in mothur with a 97 % identity cut-off for OTUs

(Schloss et al., 2009). Amino acid sequences were inferred from representative sequences of

each OTU, aligned with MUSCLE along with closely related sequences obtained using NCBI

blastp, and were used to construct maximum likelihood phylogenetic trees based on the Jones-

Taylor-Thornton amino acid substitution model (JTT) with 1000 bootstrap iterations in MEGA

6. All alignments, blastp searches, and phylogenetic reconstructions were conducted using

default parameters. Representative nucleotide sequences from each polB and g20 OTU were

submitted to NCBI Genbank (accession numbers: KY082090 - KY082166 for g20 and

KY082167 - KY082185 for polB).

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Quantitative PCR of viral genotypes in Lake Erie sediment

To estimate the abundance of various algal virus and cyanophage genes, qPCR was used.

Numerous extant qPCR primer and probe sets for algal virus and cyanophage genes were tested

on DNA extracted from the sediment core samples. Eleven qPCR primer and probe sets, seven

targeting algal virus genes and four targeting cyanophage genes, reliably produced amplification

signals from sediment DNA and thus were used to estimate the abundance of these genes at all

stations and depths. The primer and probe sets used in this study and their targets are detailed in

Table 4.1.

For qPCR with all primer and probe sets, 20 μL reaction mixtures were used with 1x Platinum

Taq PCR Buffer, 0.5 units of Platinum Taq DNA polymerase (Life Technologies Corporation,

Carlsbad, CA), 5 mM of MgCl2, 200 μM of each dNTP, 250 nM of the respective forward

primers, 250 nM of the respective reverse primers, 100 nM of the respective TaqMan probe, 30

nM of ROX reference dye, and 2 μL of template. All reactions had an initial denaturation step for

300 s at 95 °C which was followed by 40 cycles of 95 °C for 15 s and 60 °C for 60 s. The qPCRs

were conducted and fluorescence was measured on an Mx3000P QPCR System (Stratagene, La

Jolla, CA). The efficiencies for the standard curves ranged from 93.7 to 104.2 %, and for all

primer and probe sets, the R-squared values of Ct vs. gene copies for the standards were above

0.99. As per Short et al., (2004), when only one or two of the three triplicate qPCRs amplified,

the gene was considered detectable but not quantifiable. Final gene abundances were estimated

as gene copies per gram of wet sediment.

In order to account for variability in DNA extraction efficiency, known quantities of plasmid

DNA (pGem®-T vector, Promega Corporation, Madison, WI) containing a synthetic gene

fragment (gBlocks®, Integrated DNA Technologies, Coralville, IA) insert were added to the

sediment samples before DNA extraction was conducted. The insert sequence, a fragment of the

Suricata suricatta (meerkat) mitochondrial gene for cytochrome b (accession number D28906;

Masuda et al., 1994), was selected based on the improbability of its existence in the

environmental samples examined in this study. A qPCR primer and probe set amplifying a 109

bp region of a 209 bp gBlocks® synthetic meerkat cytochrome b gene fragment was designed

using Beacon Designer 7 (Premier Biosoft International, Palo Alto, CA) under default

parameters for TaqMan® probe design. Nucleotide sequences for the primers and probe (5’-3’)

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are as follows: forward primer GCCTTTTCATCAGTAACTC, reverse primer

CGTGTATGAATAAGCAGATAA, and probe CAACTATGGCTGAATCATCCGATATGC.

The probe was 5’ labelled with FAM (6-carboxyfluorescein), 3’ labelled with Iowa Black® FQ,

and incorporated an internal ZEN™ quencher. The meerkat gene insert was cloned using an A-

tailing procedure as described in Kobs (1997), and ligation, transformation, overnight

incubations, plasmid harvesting, and DNA quantification were performed as previously

described (Short and Short, 2008). Plasmids containing the desired insert DNA sequence

(verified by Sanger sequencing performed by the Centre for Applied Genomics, at the Hospital

for Sick Children, Toronto, ON, Canada) were linearized using the ApaI restriction endonuclease

(New England Biolabs, Ipswich, MA) and were purified using a QIAquick PCR purification kit

(Qiagen, Hilden, Germany). Eight, 10-fold serial dilutions of the linearized cloned fragments

were used to create qPCR standard curves. Quantitative PCRs utilizing the Suricata suricatta

primers and probe set, following the same reaction conditions as described above, were

conducted to quantify the number of meerkat gene copies in the stock solution that was added to

each sediment samples as well as in extracted the DNA samples. Percent recovery of amplifiable

meerkat cytochrome b gene copies was then determined (range: 31 - 53 %), and was used to

correct environmental gene copy estimates for DNA extraction efficiencies.

.

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Table 4.1. Primer and probe sets of detected algal virus and cyanophage genotypes in Lake Erie sediment

Group Target name Targeted gene Closest cultivated relative to target* Primer design study

Algal Viruses 356-M5.3 MCP gene Pyramimonas orientalis virus isolate M05-01 (56%) Rozon and Short, 2013

356-M5.14 MCP gene Chrysochromulina parva virus-Bay of Quinte 1 (81%) Rozon and Short, 2013

F2MCP1 MCP gene Chrysochromulina parva virus-Bay of Quinte 1 (89%) Chapter 3

CpV-BQ1 polB gene Chrysochromulina parva virus-Bay of Quinte 1 (100%) Mirza et al., 2015

LO.08may08.08 polB gene Chlorella Marburg virus-1 (99%) Short et al., 2011a

F2VPOL1 polB gene Bathycoccus virus BpV178 (78%) Chapter 3

LO1b-49 polB gene Ostreococcus virus isolate OtV343 (26%) Short et al., 2011a

Cyanophages IZCPS1 g20 gene Cyanophage P-TIM40 (64%) Chapter 3

WZCPS8 g20 gene Synechococcus phage S-SM1 (72%) Chapter 3

252.SH Sheath protein gene Microcystis phage Ma-LMM01 (95%) Rozon and Short, 2013

282.SH Sheath protein gene Microcystis phage Ma-LMM01 (93%) Rozon and Short, 2013

*Percent sequence identity to closest cultivated nucleotide BLAST match in parentheses

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4.3 Results

Diversity of algal viruses and cyanophages in Lake Erie sediment

178 putative polB sequences were obtained from the four stations in Lake Erie, representing 20

OTUs (Table 4.2). Putative polB sequences were obtained from all four depths at station 882 in

the western basin of Lake Erie and station 1326 in the central basin, the 2 - 4 cm sample from

station 452 (eastern basin), and the 4 - 6 cm sample from station 973 (western basin). The

highest number of OTUs were in the 0 - 2 cm depth profile of station 1326, while the lowest

number of OTUs were in the 6 - 8 cm depth profile of station 1326. The 6 - 8 cm depth profile of

station 1326 had both the lowest chao1 and inverse Simpson index scores, while the station 1326

0 - 2 cm and the 2 - 4 cm depth profiles had the highest inverse Simpson and chao1 index scores,

respectively. For station 1326, the offshore station near Cleveland, OH in the central basin, the

general trend was more OTUs and higher index scores in the shallower depth profiles and fewer

OTUS and lower index scores in the deeper depth profiles. For station 882, the station near the

mouth of the Maumee River in the western basin, the trend was the opposite, with more OTUs

and higher index scores in the deeper sediment depth profiles.

A maximum likelihood JTT amino acid phylogenetic tree comparing representative sequences

from each polB OTU to reference sequences of cultivated algal viruses and other environments

was used to provide a crude identity of sediment sequences (Figure 4.2). The majority of polB

OTUs (75 %) clustered within a clade of cultivated prasinoviruses (Figure 4.2), and according to

NCBI blastp, had at least 76 % identity with cultivated prasinoviruses (Appendix Table 3.1).

These prasinovirus-like OTUs contain sequences obtained from every station and every depth

sampled. In addition to prasinovirus-like OTUs, one polB OTU (88268VPOLCC1) clustered

with mimivirus-like prymnesioviruses and had 99 % amino acid sequence identity with the polB

sequence from the recently isolated freshwater algal virus Chrysochromulina parva virus BQ1

(Mirza et al., 2015). This prymnesiovirus-like polB gene was only amplified from the 6 - 8 cm

depth sample from station 882 and the 0 - 2 cm and 4 - 6 cm depths at station 1326. Another

polB OTU (132624VPOLCC19) clustered with Yellowstone Phycodnavirus 1 and 2 and had 86

% identity with these putative phycodnavirus polB sequences. This OTU was a singleton, and

was only observed in the 2 - 4 cm depth at station 1326. The remaining three OTUs were in a

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clade by themselves (Figure 4.2), and shared either 51 % identity to Yellowstone Phycodnavirus

1 in the case of 88202VPOLCC1 and 88246VPOLCC5, or 49 % identity with Phaeocystis

globosa virus PgV-03T in the case of 45224VPOLCC1. Other OTU sequences

(88202VPOLCC1 and 88246VPOLCC5) were found in all sediment depths at station 882, while

sequences in the 45224VPOLCC1 OTU were only found in the 2 - 4 cm depth profile of station

452.

From the four stations in Lake Erie, 143 putative g20 sequences were obtained, which

represented 76 OTUs based on a 97 % nucleotide identity cut-off (Table 4.2). Putative g20

sequences were obtained for all depths in station 882 in the western basin, the top three depths in

station 1326 in the central basin, and the 0 - 2 cm depths for station 452 in the eastern basin and

station 973 in the western basin. The highest number of OTUs was observed in the 6 - 8 cm

depth profile of station 882, while the fewest number of OTUs was observed in the 0 - 2 cm

depth profile of station 1326. The 6 - 8 cm depth of station 882 also had the highest chao1 and

inverse Simpson index values, while the 0 - 2 cm depth of station 1326 had the lowest chao1 and

inverse Simpson index values. The general trend observed in both station 882 and station 1326

showed higher numbers of OTUs and higher diversity and richness values in the deeper sediment

depth profiles as compared to the more shallow sediment depth profiles.

A total of 20 of the 76 g20 OTUs obtained when using a 97 % cut-off clustered with g20

sequences from cultivated cyanomyoviruses (Figure 4.3). These 20 OTUs shared 81 - 94 %

amino acid sequence identity with cultivated Synechococcus-infecting and Prochlorococcus-

infecting cyanomyoviruses and were present in all the stations and depths analyzed (Appendix

Table 3.2). The majority of the g20 OTUs (~72 %) obtained in this study were more closely

related to sequences obtained from previous environmental studies than to cultivated

cyanomyoviruses. These OTUs shared between 72 - 100 % sequence identities with g20

sequences obtained from environmental sequences and had only 59 - 72 % sequence identities

with cultivated cyanomyoviruses (Figure 4.3). One OTU obtained in this study

(88246CPSCC11) was in a clade with only itself and shared 52 % sequence identity with

Synechococcus phage S-CBM2 and 58 % sequence identity with an environmental sequence

from East Lake, China (Figure 4.3). This OTU was only present in the 4 - 6 cm depth profile of

station 882.

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94

Figure 4.2. Maximum likelihood phylogenetic tree of amino acid sequences inferred from

putative algal virus polB gene sequences using a Jones-Taylor-Thornton amino acid substitution

model with 1000 bootstrap iterations. Bolded sequences are OTUs obtained from Lake Erie

sediment in this study. The first three or four numbers denote station (first three for 452, 882, and

973; first four for 1326), the next two numbers indicated depth (02 for 0 - 2 cm, etc.), Vpol

indicates primer used (VpolAAS4/VpolAS1) and CC and number after indicate clone number.

Sequences obtained in this study were compared to polB gene sequences from cultivated algal

viruses and environmental sequences from previous studies to provide phylogenetic and

environmental context. The environmental reference sequences are color-coded based on their

isolation source.

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Table 4.2. Species richness and diversity of polB and g20 genes in Lake Erie sediment

Gene Station Depth profile Total number of sequences OTUs* chao1* Inverse Simpson*

polB 452 2 - 4 cm 20 5 5.5 2.88

882 0 - 2 cm 18 3 4 1.28

2 - 4 cm 18 3 2 1.13

4 - 6 cm 20 4 4.5 1.88

6 - 8 cm 17 6 9.5 5.67

all 73 13 17.5 2.33

973 4 - 6 cm 19 3 3 1.8

1326 0 - 2 cm 14 9 11 9.23

2 - 4 cm 16 7 12 2.96

4 - 6 cm 15 4 4.5 1.88

6 - 8 cm 21 2 2 1.11

all 66 13 20.5 3.35

all all 178 20 41 6.84

g20 452 0 - 2 cm 16 7 12 3.24

882 0 - 2 cm 15 13 31.33 52.5

2 - 4 cm 18 13 46.33 76.5

4 - 6 cm 15 13 21 35

6 - 8 cm 19 17 86 171

all 67 55 235.17 130.06

973 0 - 2 cm 15 8 9.5 8.08

1326 0 - 2 cm 15 4 4 3.39

2 - 4 cm 11 6 7 6.88

4 - 6 cm 3 3 6 1

all 29 9 10.5 4.72

all all 143 76 241.3 30.04

*All OTU, chao1, and inverse Simpson indices were calculated with a 97% cut-off

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Figure 4.3. Maximum likelihood phylogenetic tree of putative cyanophage g20 gene sequences

using a Jones-Taylor-Thornton amino acid substitution model with 1000 bootstrap iterations.

Bolded sequences are OTUs obtained from Lake Erie sediment in this study. The first three or

four numbers denote station (first three for 452, 882, and 973; first four for 1326), the next two

numbers indicated depth (02 for 0 - 2 cm, etc.), CPS indicates primer used (CPS1.1/CPS8.1) and

CC and number after indicate clone number. Sequences obtained in this study were compared to

g20 gene sequences from cultivated algal viruses and environmental sequences from previous

studies to provide phylogenetic and environmental context. The environmental cyanomyovirus

group are putatively cyanophage sequences and are color-coded according to isolation source.

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Abundance of algal virus and cyanophage genotypes in Lake Erie sediment

The abundances of the seven algal virus genes ranged from below detection or detectable but not

quantifiable (i.e., only one or two of the triplicate qPCRs amplified) to 3.78 x 102 - 2.97 x 106

gene copies per gram of wet sediment in the samples (Figure 4.4). The limit of detection varied

from sample to sample and from virus to virus (5.40 x 101 - 3.76 x 103 gene copies per gram of

sediment) due to small differences in the lowest detectable dilution of the standards used for each

qPCR assay, the total weights of sediment used for each DNA extraction, and the efficiencies of

the DNA extractions as inferred from meerkat gene amplification. Overall, the most abundant

algal virus gene at every station and depth was 356-M5.14, a putative mimivirus-like

prymnesiovirus major capsid protein gene first identified in the Bay of Quinte in Lake Ontario

(Rozon and Short, 2013). The least abundant genes all had at least one depth profile in which

they were below the limit of detection, and included the putative polB gene from CpV-BQ1,

F2VPOL1, a putative Prasinovirus-like polB gene first observed in a storm-water pond in

Mississauga, Ontario, and LO1b-49, another putative Prasinovirus-like polB gene from Lake

Ontario. Overall, the least abundant algal virus was likely Chlorella Marburg virus 1 (CVM-1),

as it was present in detectable but unquantifiable levels in only one depth profile from every

station.

The abundances of the four cyanophage genes ranged from below detection to detectable but not

quantifiable to 7.32 x 102 - 9.42 x 104 gene copies per gram of wet sediment (Figure 4.5).

IZCPS1, a putative cyanomyovirus-like g20 gene, first found in a storm-water pond in

Mississauga, Ontario, had the highest maxima of the four cyanophage-like genes. While each of

the four genes had depths at various stations in which they were below the limit of detection,

252.SH, an M. aeruginosa phage-like sheath protein gene first found in the Bay of Quinte, Lake

Ontario, was the least abundant overall, as it was not quantifiable at any of the depth profiles in

which it was detectable.

As Figures 4.4 and 4.5 illustrate, there are a number of abundance patterns for both algal virus

and cyanophage genes with depth. For instance, some algal virus genes were generally most

abundant in the more shallow depth profiles and were less abundant in the deeper sediments

(e.g., 356-M5.14 at stations 452 and 1326), whereas other algal viruses and cyanophages were

more abundant in the deeper sediment than in the more shallow depth profiles (e.g., LO1b-49 at

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station 1326). Further, several phytoplankton virus genes had relatively similar abundances from

depth profile to depth profile (e.g., 356-M5.14 at stations 882 and 973), while others had no

discernable pattern with varying degrees of abundance from depth to depth. Abundance patterns

differed between sampling stations, with the same virus gene having different abundance

patterns at different stations. For example, 356-M5.14 had higher abundances in the shallower

depth profile at stations 1326 and 452, in the central and eastern basins of Lake Erie, respectively

while exhibiting relatively constant abundances at each depth profile in the two western basin

stations, 882 and 973. Additionally, all four cyanophage genes tested were either detectable but

not quantifiable, or not detectable at most of the depths in both central and eastern basin stations,

but were either quantifiable, with abundances of up to 9.42 x 104 gene copies per gram of wet

sediment, or detectable but not quantifiable at most depths in the two western basin stations.

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101

Figure 4.4. Abundances of individual algal virus genotypes at stations 1326 (A), 452 (B), 882 (C), and 973 (D). Each individual bar

represents the average of triplicate quantitative PCRs and the error bars represent standard deviation. A filled circle represents a genotype

that was detectable but not quantifiable at that depth profile. Shades of blue represent putative algal virus MCP gene abundances while

shades of green represent putative algal virus polB gene abundances.

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Figure 4.5. Abundances of individual cyanophage genotypes at stations 1326 (A), 452 (B), 882 (C), and 973 (D). Each individual point

represents the average of triplicate quantitative PCRs and the error bars represent standard deviation. A filled circle represents a genotype

that was detectable but not quantifiable at that depth profile. Shades of blue represent putative Microcystis phage sheath protein gene

abundances while shades of green represent putative cyanomyovirus g20 gene abundances.

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4.4 Discussion

The presence of diverse algal virus and cyanomyovirus communities in sediment from Lake Erie

was confirmed through the use of molecular techniques targeting hallmark genes distinguishing

these virus groups. To our knowledge, this was the first attempt to examine the diversity of algal

viruses and cyanomyoviruses in freshwater sediments using these techniques. To ensure

amplification of the most abundant taxa at each station, and to reduce within site heterogeneity

(Goyer and Dandie, 2012), composite sediment samples were analyzed. Additionally, qPCR was

used to assess the abundance patterns of specific algal virus and cyanophage genes, which

revealed distinct patterns of abundance over the four discrete depth profiles sampled and at the

four sampling sites. The qPCR results also demonstrated the presence of Microcystis phage-like

genes in Lake Erie sediment. This finding suggests that sediments may be a reservoir for phages

that infect Microcystis aeruginosa, a harmful algal bloom-forming species that has caused

serious water quality issues in Lake Erie in recent years (e.g., Rinta-Kanto et al., 2009a;

Michalak et al., 2013; Harke et al., 2016).

Diversity of phytoplankton viruses in freshwater sediment

The majority of algal virus-like polB OTUs from Lake Erie sediments were closely related to

Prasinovirus sequences. While prasinophyte algae have never been reported in the Laurentian

Great Lakes (Munawar and Munawar, 1986), algal virus diversity surveys have often found

Prasinovirus-like sequences to be the dominant algal virus in the Great Lakes. It has been

suggested that these viruses could have hosts other than prasinophytes, such as other closely

related chlorophyte algae (Short and Short, 2008; Rozon and Short, 2013). While the polB

primers used in this study were designed to target Prasinovirus genes (Clerissi et al., 2014a),

other observations have suggested that Prasinovirus is the most abundant algal virus type in

environmental samples (Clasen and Suttle, 2009).

Even though the majority of algal virus polB OTUs were most closely related to Prasinovirus

sequences, there was also an OTU obtained that was closely related to CpV-BQ1, which infects

the haptophyte alga Chrysochromulina parva (Mirza et al., 2015). Additionally, three OTUs

(45224VPOLCC1, 88202VPOLCC1 and 88246VPOLCC5) formed their own clade within

Phycodnaviridae. These OTUs were only distantly related to their closest blastp matches

(Appendix Table 3.1). Additionally, all three of the OTU sequences were checked for chimeras

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using the bellerophon approach, and none were detected as chimeric sequences. The finding of

algal virus polB that branch with only environmental sequences is not unprecedented as similar

results have been reported for a group of algal virus polB sequences from Lake Ontario (Short

and Short, 2008).

While algal viruses have been largely unexplored in Lake Erie, a previous study using water

samples from Lake Erie found several cyanomyoviruses via screening cyanobacterial isolates

and PCR of g20 genes (Wilhelm et al., 2006b). Several cyanobacterial isolates were used in that

study but only Synechococcus sp. strain WH 7803, a marine isolate, was found to be infected by

the isolated viruses. Additionally, several of the OTUs obtained from the water column study

were more closely related to marine cyanomyovirus isolates than to freshwater isolates. Even

though a different primer set was used in the previous study (CPS1/CPS8; Zhong et al., 2002),

several sequences from that study, including those related to both marine and freshwater isolates,

were closely related to sequences obtained from the sediments during the current study. The

presence of similar sequences in both the water column and the sediments suggests that

cyanomyoviruses from the water column may be deposited into the sediment of Lake Erie.

Furthermore, the majority of cyanophage-like g20 OTUs obtained from Lake Erie sediments

were closely related to environmental sequences obtained from East Lake, China (Wang et al.,

2015). The presence of freshwater and marine cyanophage OTUs, as well as the presence of

OTUs closely related to those found on multiple continents, underpins the idea that cyanophages

may have global distributions of closely related OTUs (Short and Suttle, 2005).

The clade of cyanophage-like g20 OTUs most closely related to cultivated phytoplankton viruses

are likely to infect the cyanobacterium Synechococcus, which have been found to exist in high

abundances in Lake Erie (e.g., Wilhelm et al., 2006a). While many of the closely related

cultivated OTUs are from cyanophages that infect Prochlorococcus species, reports of several

cyanomyoviruses being able to infect both Prochlorococcus and Synechococcus species

(Sullivan et al., 2003), coupled with the absence of Prochlorococcus in Lake Erie (Loar, 2009),

suggests that the natural hosts of these cyanophage-like OTUs in Lake Erie are Synechococcus

species. However, the majority of g20 OTUs obtained from Lake Erie sediment were more

closely related to sequences of environmental origin than of sequences generated from cultivated

cyanophages. These OTUs may be from currently undescribed cyanophages or, despite the

recent redesign of the CPS1.1/CPS8.1 primer set to exclude myoviruses that do not infect

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cyanobacteria (Sullivan et al., 2008), may be amplified from myoviruses that infect other

bacteria. It is impossible to know the true hosts of these virus OTUs until the virus-host pairings

are known in culture. It is thus still a primary goal in viral ecology to isolate and characterize

viruses that infecting novel host organisms.

Phytoplankton virus gene abundance in Lake Erie sediment

In order to obtain qPCR-derived gene abundances more representative of nature, sample to

sample variability in DNA extraction efficiency must be taken into account. In this study, known

quantities of exogenous DNA (a fragment of the Suricata suricatta mitochondrial cytochrome b

gene ligated into a plasmid) were added to the sediment samples prior to DNA extraction and

were used to assess percent recovery of DNA. The main assumption of this approach is that both

exogenous and environmental DNA will be affected equally by the DNA extraction procedure

and so, the proportion of exogenous DNA lost during extraction will mirror the proportion of

environmental DNA lost. The application of this technique permits absolute quantification of

gene copies, a more ecologically relevant estimate compared to relative quantification. Species

abundances derived from gene copies per gram of sediment (either wet or dry weight) would

underestimate natural abundances if they were not corrected for variable DNA extraction

efficiencies and the resulting DNA losses.

Using qPCR with corrections for DNA extraction efficiency, the abundance of 11 different

phytoplankton virus genes were quantified in two western basin stations, 882 and 973, one

central basin station, 1326, and one eastern basin station, 452. The overall range of algal virus

gene abundance fell within the reported ranges of coccolithovirus gene abundance in western

Black Sea sediments (from below detection to over 106 gene copies per gram of total organic

carbon; Coolen, 2011), while the upper range was higher than the range of abundance of a

chlorovirus-like gene in Fayetteville Green Lake, NY sediments (~1.0 x 101 - 50 x 101 gene

copies per gram of sediment; Hewson et al., 2012).

While algal virus genes have been detected in metagenomic studies (Mohiuddin and Schellhorn,

2015) and the effect of viral lysis has been studied on eukaryotic algae in Lake Erie (Gobler et

al., 2008), algal viruses have not been quantified using qPCR or indeed, any method of

numeration, in the water column of Lake Erie and thus, the overlaying abundances of algal

viruses cannot be compared to those in the sediment. However, several studies have quantified

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algal virus genes in nearby Lake Ontario (e.g., Short and Short, 2009; Rozon and Short, 2013;

Mirza et al., 2015), some of which included the same genes quantified in Lake Erie sediment in

this study. Overall, the most abundant algal virus in Lake Erie sediment, the putative mimivirus-

like prymnesiovirus 356-M5.14, was present at greater abundances in the sediment of station

1326 in the central basin of Lake Erie than any of the reported abundances for any viral gene in

the water column of Lake Ontario. While this comparison must be interpreted carefully due to

the differences between quantifying gene copies per mL of water and gene copies per gram of

sediment (wet weight), many of the other algal virus gene abundances in Lake Erie sediment

exceeded the range of abundances reported for the same genes in Lake Ontario water samples. It

may be that Lake Erie has higher abundances of these algal virus genes than Lake Ontario.

However, the water samples from Lake Ontario were discrete time points, while the sediment

samples were from 2 cm depth profiles that likely contained viruses sedimented throughout

entire growing seasons, or even over multiple years, thereby reflecting an accumulation of

viruses rather than viruses present at a discrete time point.

Similarly, the upper range of cyanophage gene abundances in Lake Erie sediment exceeds the

range of abundance of a cyanomyovirus gene Fayetteville Green Lake, NY sediments (~5.0 x 101

- 4.0 x 102 gene copies per gram of sediment; Hewson et al., 2012). Unlike algal viruses, total

cyanomyovirus abundances using qPCR of g20 genes have been estimated in Lake Erie

(Matteson et al., 2011). In that study of Lake Erie water column cyanomyoviruses the

CPS1/CPS2 qPCR primers, which amplify many different cyanomyoviruses g20 genes (Fuller et

al., 1998), were used so it is not surprising that most gene-specific g20 abundances were much

lower in this current study of Lake Erie sediments. In addition to the two cyanomyovirus g20

genes, two putative Microcystis aeruginosa phage sheath protein genes were quantified in Lake

Erie sediments via qPCR. The putative Microcystis aeruginosa phage sheath protein genes were

both detected at station 882, which is located at the outflow of Maumee River, the putative

starting point of many Microcystis aeruginosa blooms (e.g., Rinta-Kanto et al., 2005) and 973,

which is in the western basin and is much closer to the Maumee River than the other two

stations. One of the two putative Microcystis aeruginosa phage sheath protein genes was also

detected at stations 1326, in the central basin, and 452, in the eastern basin. The presence of

these two genes in the stations where Microcystis aeruginosa blooms are known to initiate

compliments the recent discovery of Microcystis-specific cyanophage sequences found in the

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water column at these same stations (Steffen et al., 2015) and further suggests that these harmful

algal bloom species may be subject to top-down population control in Lake Erie.

Sediments as environmental refugia or geological record?

The presence of diverse and abundant phytoplankton viruses in Lake Erie sediments from all

three basins of the lake suggests two complementary hypotheses: sediments serve as an

environmental refugium for phytoplankton viruses, and sediments can preserve molecular signals

of phytoplankton virus infections on decadal and longer timescales. For this refugium to be

ecological important, phytoplankton viruses must be able to re-enter the water column. A

possible mechanism for the re-entry of viruses to the water column from the sediment could be

that the virus attaches to its host in the sediment. The host itself may re-enter the water column,

infection may then occur and then new virus particles may be produced. In the case of

Microcystis aeruginosa, cells are known to overwinter in benthic environments (Reynolds et al.,

1981) and up to 20 % of these benthic colonies can re-enter the water column (Xie et al., 2003)

aided by gas vesicle buoyancy. More generally, viruses attached to sediment particles may be re-

suspended into the water column upon a disturbance, such as storms, seasonal turnovers, and

human activity like dredging and motorboat activity, as has been suggested for human

enteroviruses in marine sediment (Bosch et al., 1988). While certain mechanisms may exist to

aid in the re-entry of phytoplankton to the water column from the sediment, it is certainly

possible that many of the viruses that enter sediments do not return to the water column and their

infectivity is irreversibly lost.

One of the phytoplankton viral abundance patterns found in this study was decreasing abundance

with increasing sediment depth. If it is assumed that the year-to-year production and subsequent

sedimentation of these viruses is constant, genes that decrease in abundance with sediment depth

could be used to provide a rough estimate of virus decay in sediments based on previous

estimates of sedimentation rates. For instance, the most abundant virus observed, the putative

mimivirus-like prymnesiovirus 356-M5.14, had its peak gene abundance in the surface sediments

of station 1326, and nearby sedimentation rates have been estimated at be 1.5 mm yr-1 (Kemp et

al., 1977). If this sedimentation rate is constant and compaction is minimal, then the 6 - 8 cm

depth profile at station 1326 can be estimated to be 40 to 53 years old. Using the qPCR

abundances of 356-M5.14, the sediment age estimates and the decay calculations of Noble and

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Fuhrman (Noble and Fuhrman, 1997), the decay rate of the most abundant viral gene can be

estimated to be 4.59 % gene copies lost per year with a half-life of 15 years. It is important to

note that even if these assumptions are met in nature, this decay rate is representative of

amplifiable viral DNA persisting and not necessarily infectious particles. While this estimate and

the recovery of viable algal viruses and cyanophages from potentially decade- (Suttle, 2000a;

Lawrence et al., 2002) and century-old sediments (Hargreaves et al., 2013) hints at long-term

persistence of phytoplankton viruses in both marine and freshwater sediments, qPCR abundances

in sediments can also be useful in paleoecological studies.

As sedimentation rate data estimates place the potential ages of the deepest sediments in this

study anywhere from 12 to 53 years old, several of the abundance patterns found in this study

may point to historical abundance patterns of phytoplankton viruses in the water column. For

instance, several of the viral genes had peak abundances below the surface sediments, which

suggests that they had higher abundances several years before the present. Others showed

differences in abundance from depth profile to depth profile within the same sample site, which

may be indicative of year-to-year variations in phytoplankton virus abundances. Variations in

virus abundance may also suggest differences in host population levels. For instance, fluctuations

in coccolithophore virus and potential host populations have been observed throughout 7000

years of sediments in the Black Sea (Coolen, 2011). Additionally, Microcystis abundances using

qPCR have been estimated in Lake Erie sediment (Rinta-Kanto et al., 2009b). However, the

Microcystis phage genes quantified in this study cannot be directly compared to the previously

reported Microcystis abundances because sediment samples were collected from different cores

in a different part of the lake. The results from qPCR quantification in Lake Erie sediment

suggest that historical phytoplankton virus population dynamics may be accessed through the use

of molecular enumeration techniques.

Conclusions

In summary, this study presents a glimpse into the diversity and abundance of several types of

phytoplankton viruses in freshwater sediments. The results suggest that the sediments of Lake

Erie harbor diverse types of algal viruses and cyanophages that could re-enter the water column

and reinitiate infection of their hosts. Additionally, this study presents further evidence for the

presence of Microcystis aeruginosa phages in Lake Erie, where blooms of Microcystis

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aeruginosa are known to occur, suggesting that these harmful algae are subject to top-down

population control via viral lysis. Finally, while the sediments may be an environmental

refugium for algal viruses and cyanophages, the qPCR abundances show patterns that may also

reflect historical abundance patterns of phytoplankton viruses in the water column.

This study provides evidence supporting the hypothesis that sediments are an important

environmental refugium for algal viruses and cyanophages, but vital questions remain about the

persistence of phytoplankton viruses in the environment. For example, future studies should

address, through the use of decay incubation experiments with cultivated algal viruses and/or

cyanophages, whether phytoplankton virus decay rates are lower in sediments than the water

column. Additionally, this and previous studies have found qPCR to be a valuable tool to

observe historical algal virus and cyanophage populations across both freshwater and marine

sediments, opening up the possibility to study the paleoecology of phytoplankton viruses with a

wide variety of hosts and in very different ecosystems.

Acknowledgements

I am very grateful to the captain, crew, and scientific staff on the CCGS Limnos. This research

was supported in part by the Canadian Foundation for Innovation Leaders Opportunity Fund and

NSERC Discovery grants awarded to S.M.S.

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Chapter 5 General Conclusions and Future Directions

Phytoplankton Virus Survival

My research results relate directly to how algal viruses and cyanophages survive in the

environment. My thesis provides the first reports of in situ algal virus decay using infectivity in

freshwater environments. In addition, the first reports of the seasonal differences throughout the

entire year for algal virus decay rates in any system are described in my thesis. Furthermore, the

use of qPCR to estimate virus decay rates was validated, but estimates using this technique must

be cautiously interpreted. Using this method, the decay rates of viruses that were previously

intractable (i.e., viruses that infect either currently unknown hosts or hosts that cannot presently

be cultured, which represent the vast majority of viruses in nature) were estimated. Additionally,

qPCR estimated decay rates were used to further describe the seasonality of viral decay in both

cultivated viruses and environmental algal virus and cyanomyovirus populations. The seasonality

of decay was such that algal viruses have the ability to survive under or within the ice cover of

temperate freshwater ponds over the entirety of the winter season. This observation of algal virus

overwintering provides a possible mechanism for maintaining the viral ‘seed-bank.’ While

extremely low decay rates were estimated during over-wintering of cultivated algal viruses, the

higher decay rates observed in the spring and summer incubation experiments and for

cyanomyoviruses in every incubation suggest alternative mechanisms are involved in

maintaining the viral ‘seed-bank’ at these times of the year.

One mechanism that may help to sustain the viral ‘seed-bank’ in aquatic systems are

environmental refugia, such as the sediment. My thesis contains the first glimpse of the diversity

of algal viruses and cyanophages within the sediment of any aquatic environment. Additionally,

the abundance of eleven distinct algal virus and cyanophage genes in Lake Erie sediments were

estimated for the first time. The findings of diverse algal viruses and cyanophages, along with

sometimes high abundances of these viruses in sediments up to 53 years old, suggest that when

disturbed, freshwater sediments may be a source of many types of phytoplankton viruses that can

be re-introduced into the water column. What follows will provide further detail on the key

findings of my thesis, how they fit into hypothetical framework of viral persistence in freshwater

environments, and how future research directions on this topic should proceed.

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5.1 Seasonality of Algal Virus Decay Rates

Chapter 2 addresses the specific research question, does viral decay in the water column proceed

in a way that allows for environmental persistence? In order to accomplish this, the decay rates

of three cultivated algal viruses were estimated across four seasonal incubation experiments. The

algal virus decay rates were found to be highest in spring or summer and lowest in winter.

Furthermore, all three algal virus types remained infectious after the 126 day winter incubation.

In multiple studies of algal virus abundance in freshwater systems, several of the viruses

examined have been found to occur at relatively constant numbers, representing a viral ‘seed-

bank’ (e.g., Van Etten et al., 1985b; Yamada et al., 1991; Zingone et al., 1999; Short and Short,

2009; Short et al., 2011a; Rozon and Short, 2013). The observed seasonal variations in virus

decay rates and over-wintering of cultivated algal viruses described in Chapter 2 provides one

possible mechanism likely to contribute to sustaining the viral ‘seed-bank.’

In addition to finding a mechanism that may aid in the maintenance of the viral ‘seed-bank,’

other findings in Chapter 2 show that there were clear differences in the decay rates of the two

chloroviruses and of a newly isolated algal virus which infects C. parva. Specifically, the C.

parva-infecting virus had higher decay rates than the two chloroviruses and these differences

were often statistically significant. While this needs to be further explored to find the root cause

of the differences in decay rates, it can be speculated that it may be due to differences in genetic

potential. For instance, there is precedence for specific algal virus strains possessing genes for

several DNA repair mechanisms (e.g., Redrejo-Rodríguez and Salas, 2014). Further, other algal

virus strains, sometimes even closely algal viruses within the same genus, do not possess some

of these DNA repair genes (e.g., Dunigan et al., 2006; Jeanniard et al., 2013; Redrejo-Rodríguez

and Salas, 2014). When the genome of the C. parva-infecting algal virus, CpV-BQ1, is

sequenced and annotated, it will be possible to directly assess the differences in its genetic

potential and how those differences relate to the decay of this virus in the environment.

Expression studies of algal viruses with varying decay rates may also help to find the underlying

cause of the observed decay rate differences.

Furthermore, there were decay rate differences between the two treatments. Without exception,

the whole water treatment had higher decay rates than the filtered treatment. One possible

explanation for the observed differences is that the microbial flora present in the whole water

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produced heat-labile substances, such as nucleases and proteases, which are known to inactivate

viruses (reviewed in: Gerba, 2005).

While the methods used allowed for observations of over-wintering and the differences in decay

rates across different types of algal viruses and treatments, the limitations of these results should

be discussed. One such limitation stems from the use of polycarbonate bottles. Polycarbonate

greatly attenuated UV radiation, which is often found to be the most damaging wavelength of

light to viruses (e.g., Suttle and Chen, 1992). However, other studies estimating the decay rates

of specific viruses found the PAR range to be more damaging to the viruses than UV (e.g.:

Baudoux et al., 2012). Thus, the decay rates from this study may be underestimated in the spring

and summer incubation experiments, when the day lengths were longer and samples were

subjected to more solar radiation. However, for the winter decay incubation experiments, the

decay rates are less likely to be underestimates as the persistent ice cover and intermittent snow

pack greatly reduced the amount of solar radiation, both UV and PAR, experienced by the

viruses. Further, polycarbonate bottles are more sturdy than UV-penetrable alternates and over-

wintering may not have been observed if another vessel was used that might have been destroyed

during the 126 incubation. Another limitation is that, while the cultivated algal viruses chosen

were either isolated from nearby waters or were chosen based upon evidence of close relatives

being present within local ecosystems, the decay rates in Chapter 2 were estimated from

cultivated algal viruses, and may not necessarily have the same characteristics of the endogenous

algal virus community. This limitation was addressed in Chapter 3 through the use of qPCR of

environmental algal virus and cyanophage genotypes.

5.2 Seasonality of Phytoplankton Virus Decay Rates Estimated with Molecular Methods

Chapter 3 further addresses the specific research question first explored by Chapter 2 by

assessing the seasonality of environmental algal viruses and cyanophage decay rates with qPCR.

Before this could be accomplished, the relationship between the loss of infectivity and the loss of

gene copies estimated by qPCR had to be established. In order to achieve this, the cultivated

virus abundance estimates from infectivity assays were compared to the abundance estimates of

these same viruses using qPCR. The two measures were found to have a close relationship that

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could be described by a second-order polynomial equation and had a strong and significant

Spearman correlation. In addition, the decay rates estimated using the two methods were

compared using ANCOVA. In the majority of cases, the decay rates estimated using infectivity

measurements were not significantly different from the decay rates of the same virus (in the

same season and treatment) estimated with qPCR. The close and significant relationship between

the loss of infectivity and the loss of gene copies, coupled with the results from the ANCOVA

comparisons, suggests that qPCR can be used as an effective proxy for infectivity assays for

estimating the decay of environmental viruses. Thus, the first key finding of Chapter 3 was that

the decay rates of uncultivated environmental phytoplankton viruses may be estimated with

qPCR. It should be noted that these qPCR derived decay rates represent minimum estimates and

might exaggerate survival, necessitating cautious interpretation. The primary limitation of this

study was therefore the likely underestimated decay rates of environmental viruses.

One of the key findings in Chapter 3 that compliments the findings of Chapter 2 was the distinct

seasonality of environmental phytoplankton virus decay rates. As seen for the cultivated algal

viruses, both the environmental algal viruses and cyanophages had their highest decay rates in

the spring and the summer and their lowest decay rates in the winter. The seasonality of decay as

estimated by both infectivity and qPCR is likely due to the seasonal differences in the causes of

viral decay, including: solar radiation, temperature, non-host cell abundance and the activity of

extracellular enzymes such as nucleases. In addition, there was a clear seasonality of treatment

effects. While whole water treatments again had higher rates of decay than the filtered treatment,

these differences were statistically different more often in spring than in any other season. For

cyanophages, decay rates were too high to estimate accurately for some whole water treatments

and yet, the filtered treatment for the same virus and season were able to be calculated. One

reason for the seasonality observed in treatment effects may be that microbial biomass has a

clear seasonality. In addition to the reasons stated above, an increased biomass in the whole

water treatment is likely to increase the adsorption of viruses to non-host cells. Viruses attached

to non-hosts would then be removed by the subsequent sample processing (0.45 μm filtration)

before qPCR.

Another key finding of Chapter 3 was that cyanomyovirus-like genotypes had much higher

decay rates than algal viruses, further complimenting the previous findings that cyanophages

often have higher decay rates than algal viruses (e.g., Cottrell and Suttle, 1995; Cheng et al.,

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2007; Hewson et al., 2012). Previous studies exploring the decay of cyanophage PP in Donghu

Lake, China, found similarly high decay rates, but photoreactivation of this population reached

up to 59 %, mitigating the high decay rates of these cyanophages (Cheng et al., 2007). While this

was not directly tested, it is reasonable to speculate that at least some of the decay experienced

by the cyanomyovirus-like genotypes would be repaired via host-mediated photoreactivation.

Further, like the cultivated algal viruses, algal virus and cyanophage genotypes were observed

with qPCR to over-winter under, or within, the ice cover across a 126 day period. However, very

few of the initial gene copies for the cyanomyovirus (0.0077 % remained in the whole water

treatment), there was likely little to no infectivity remaining for these viruses. While the low

decay rates in the winter decay incubation experiments may propagate the viral ‘seed-bank’ for

algal viruses, other mechanisms may be needed to explain how some cyanophages could persist

in these same conditions. One such mechanism may be through the persistence of phytoplankton

viruses in environmental refugia, which was explored in Chapter 4.

5.3 Diversity and Abundance of Phytoplankton Viruses in Sediment

Chapter 4 sought to address the specific research question: does viral decay in the water column

proceed in a way that allows for environmental persistence? In order to accomplish this, PCR

primers targeting signature genes of algal viruses and cyanomyoviruses were used to assess the

diversity of phytoplankton viruses in four distinct sites in Lake Erie and across four sediment

depth profiles. Additionally, previously designed qPCR primer and probe sets were utilized to

quantify the abundance of specific algal virus and cyanophage genotypes in Lake Erie sediments.

This work is, to my knowledge, the first study that utilized phytoplankton virus signature gene

PCR primers on samples from the benthic environment of any aquatic system. While several

previous studies have found algal viruses or cyanophages in sediments using either infectivity

assays (Suttle, 2000a; Lawrence et al., 2002; Hargreaves et al., 2013) or qPCR (Coolen, 2011;

Hewson et al., 2012), the observations of diversity of algal viruses and cyanophages in Lake Erie

sediment are the first attempt at assessing the diversity of phytoplankton viruses present within

any aquatic sediment.

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For algal viruses, the polB gene diversity observed was limited to putative prasinoviruses, a

prymnesiovirus, and three Operational Taxonomic Units (OTUs) more closely related to putative

algal virus polB gene sequences, known only from environmental sequences as opposed to

sequences from previously cultivated algal viruses. While the polB primer set used in Chapter 4

was designed to target prasinoviruses (Clerissi et al., 2014a), previous studies have suggest that

Prasinovirus-like genotypes are the dominant algal virus type in freshwater systems (e.g., Clasen

and Suttle, 2009). This is perhaps surprising for samples from Lake Erie as prasinophyte algae

have never been identified in samples from the Laurentian Great Lakes (Munawar and Munawar,

1986). However, several studies in Lake Ontario have found Prasinovirus-like genotypes to be

the dominant algal virus present and have suggested that the host for these viruses might be a

green alga related to prasinophyte algae (e.g., Short and Short, 2008; Rozon and Short, 2013).

The putative cyanomyovirus sequences in Lake Erie sediment were far more diverse than the

putative algal virus polB sequences from the same samples. While many different putative

cyanomyovirus g20 sequences were obtained, the g20 PCR primer set was designed to amplify

only genotypes that infect Prochlorococcus and Synechococcus species of cyanobacteria

(Sullivan et al., 2008). The OTUs obtained from Lake Erie sediment samples closely related to

cultivated cyanomyoviruses are more likely to infect Synechococcus cyanobacteria, which have

been found to exist at high abundances in Lake Erie (Wilhelm et al., 2006a). In addition to the

sequences closely related to cultivated cyanomyoviruses, the majority of putative cyanomyovirus

g20 sequences obtained from Lake Eire sediment samples were more closely related to

sequences only known from environmental samples. This observation is not without precedent as

many studies using samples from the water column have had similar results (e.g., Short and

Suttle, 2005; Zhong and Jacquet, 2014). While previous versions of the cyanomyovirus g20

primers have been suggested to amplify g20 genes from bacteriophages that do not infect

cyanobacteria (Short and Suttle, 2005), the redesigned CPS1.1/CPS8.1 primers amplify g20

sequences from cyanomyovirus isolates but fail to amplify the g20 sequences present in non-

cyanophage myovirus isolates (Sullivan et al., 2008). However, the possibility that the

CPS1.1/CPS8.1 PCR primer set may amplify sequences from non-cyanobacteria-infecting

myoviruses cannot be excluded and thus more data is required before the hosts can be identified.

Ascertaining the hosts for the observed cyanomyoviruses can best be accomplished by the

isolation and identification of more freshwater cyanomyoviruses and myoviruses in general.

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Overall, the diversity of phytoplankton viruses obtained from Lake Erie sediments suggest that

freshwater sediments can harbor many different types of phytoplankton viruses including both

cyanophages and virus of eukaryotic algae.

The other key observation of Chapter 4 was that specific algal virus and cyanophage genotypes

can exist at high abundances in freshwater sediments. Algal viruses had abundances up to 2.97 x

106 gene copies per gram of wet sediment and cyanophages had abundances up to 9.42 x 104

gene copies per gram of wet sediment. These high abundances observed for several distinct algal

viruses and cyanophage genes suggest that freshwater sediment may be an important

environmental refugium for phytoplankton viruses. Furthermore, the observed changes in

abundances through the sediment depth profile may reflect historical infection events in the

water column as suggested by Coolen when estimating the abundances of coccolithoviruses in

the Black Sea in sediments up to 7000 years old (2011).

It is also important to discuss the limitations of the study in Chapter 4. The primary limitation is

that molecular evidence cannot provide concrete information on the hosts of the diverse

phytoplankton viruses observed. However, as limited phytoplankton hosts are currently available

in culture, surveys of phytoplankton virus diversity using only culture-based methods will likely

miss most of the phytoplankton viruses in the environment. Furthermore, the abundances

observed may constitute over-estimates of the ecological importance as molecular measures can

enumerate viruses that may not be infectious (as shown in Chapter 3). Nevertheless, as several

previous studies have identified infectious algal viruses and cyanophages in sediments up to 100

years old (Suttle, 2000a; Lawrence et al., 2002; Hargreaves et al., 2013), it is likely that there are

infectious viruses within the populations that were estimated using qPCR.

5.4 Potential Fates of Phytoplankton Viruses in Freshwater Environments

Using the findings in Chapters 2 - 4 that provide new insights on the persistence of algal viruses

and cyanophages in aquatic environment as a framework, it is possible to speculate on the

potential fates of phytoplankton in freshwater systems (Figure 5.1). Starting at infection,

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Figure 5.1. Diagram of potential fates for phytoplankton viruses in aquatic environments.

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phytoplankton viruses can either enter the lytic cycle, rapidly producing more viruses and

ultimately resulting in cell lysis, or the virus can proceed through the lysogenic cycle (or

temperate infection in algal viruses) in which the viral genome inserts into the host genome,

directly tying the fate of the virus to its host's vigor. A third viral infection pathway is

pseudolysogeny, in which the virus genome enters the host cells but does not enter either the

lytic or lysogenic cycles. It is important to note that lysogeny and pseudolysogeny are thus far

only known for cyanophages (e.g., Wilson et al., 1996; McDaniel and Paul, 2005; Long et al.,

2008) and phaeoviruses that infect macrophytic brown algae are the only known temperate algal

viruses (Bräutigam et al., 1995; Delaroque et al., 1999). Furthermore, unless the hosts have

mechanisms to resist virus production, temperate, lysogenic, and pseudolysogenic viruses will

eventually, after some environmental stimuli, enter the lytic cycle and lyse the host cell. After

cell lysis, the newly produced virions may or may not be infectious as up to 20 - 60% of algal

viruses produced are not infectious (e.g., Van Etten et al., 1983b; Cottrell and Suttle, 1995;

Bratbak et al., 1998). Infectious virions will then be subject to a number of decay inducing

mechanisms, including: solar radiation (both UV and PAR; Suttle and Chen, 1992; Baudoux et

al., 2012), high temperatures (Garza and Suttle, 1998), consumption by heterotrophic

nanoflagellates (González and Suttle, 1993), adsorption to non-host cells and detritus (Hewson

and Fuhrman, 2003), and inactivation by heat-labile substances such as nucleases (Noble and

Fuhrman, 1997). The magnitude of these mechanisms vary seasonally. As reported in Chapters 2

and 3, the decay rates of phytoplankton viruses vary seasonally as well, with high decay rates in

the summer and low decay rates in the winter.

Consider a population of phytoplankton viruses with a peak abundance in the summer. Using the

lowest decay rate observed in the summer (ATCV-1, Chapter 2), a viral population would drop

from 105 mL-1 to 6 mL-1 within 30 days. In order to counter-act this high decay rate, virus

production must either match or exceed the decay rate, or the viruses must employ additional

mechanism(s) to insure the survival of sufficient phytoplankton viruses for continued viability.

Mechanisms such as host-mediated photoreactivation for cyanophages (e.g., Cheng et al., 2007)

or DNA repair in algal viruses may aid in viral persistence (e.g., Redrejo-Rodríguez and Salas,

2014). However, based on the high estimated decay rates, the decay processes are likely to

exceed repair mechanisms in summer. It may then be necessary for phytoplankton viruses to rely

upon environmental refugia. In the case of the raphidophyte algae, Heterosigma akashiwo, viral

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infection enhances algae sinking rates (Lawrence and Suttle, 2004), which may lead to viruses

being deposited into the sediment during times in which algae are above the threshold necessary

for viral production. As sediments have shown to decrease the decay rates of viruses (e.g.,

LaBelle and Gerba, 1980), sediments may serve as an environmental refugium for phytoplankton

viruses. In Chapter 4, many types of phytoplankton viruses were observed in Lake Erie sediment,

some of which had abundances estimated up to 106 gene copies per gram of wet sediment. Given

this, the ultimate fate of algal viruses in the summer seems likely to be destruction or

sedimentation if the population of host cells drops below the threshold for viral infection.

Virus particles may remain in the sediment for years, as evidenced by the recovery of infectious

algal viruses and cyanophages from sediments with ages as old as 100 years (e.g., Suttle, 2000a;

Lawrence et al., 2002; Hargreaves et al., 2013). Despite this, viruses are still subject to decay in

the sediment, largely from the action of proteases (Dell’Anno et al., 2015). The active

phytoplankton viruses that remain in the sediment must re-enter the water column in order to be

ecologically relevant. While sediments may be disturbed by benthic organisms throughout the

year, phytoplankton viruses that re-enter the water column during periods of lake stratification

are unlikely to return to the surface waters. During thermal stratification, which often occurs in

the summer and winter, currents are unlikely to reach the bottom waters of the pelagic zone

(hypolimnon) due to the differences in water density. However, if the sediment is in the littoral

zone, currents may still reach the benthos and viruses deposited in littoral sediment may re-enter

the water column, even in times of stratification. If re-entry occurs in the summer, the virus will

most likely decay in the water column unless its host is present.

During times of stratification, a possible mechanism to return to the upper layers of the water

from pelagic sediment may lay in the vertical migration of host cells, which can occur for algae

that alter their buoyancy or actively swim. Additionally, zooplankton that feed on phytoplankton

and migrate throughout the water column may aid in the transport of viruses from deeper waters

to the surface where hosts are most abundant. Recent studies in marine environments have

reported that 80 % of copepods in the North Atlantic contained traces of algal virus DNA (Frada

and Vardi, 2015) and that decay rates of algal viruses are lower in the gut of large zooplankton

than they are in free water (Frada et al., 2014). This finding suggests that zooplankton may act as

transmission vectors for phytoplankton viruses in aquatic systems.

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In the autumn of dimictic lakes, the water column has a relatively constant temperature from top

to bottom. This constant temperature allows wind to mix the entirety of the water column.

During such times, the diverse and abundant community of phytoplankton viruses found in the

sediments in Chapter 4 may be constantly replenished from the sediments into the water column.

If the host abundance is above the threshold needed for virus production, the individual virus

population that infects this abundant host may increase, however, if the host is below this

threshold, virus concentration will decay. During times of mixing, the viruses may only be

subjected to the peak decay forces of the surface waters for only part of the day, which may

decrease the decay of phytoplankton viruses in a fully mixed aquatic environment. However, the

highest decay rates in autumn reported in either Chapter 2 or 3 (CpV-BQ1 in whole water) had

an equivalent half-life as the lowest decay rate in summer and thus a population of 105 viruses

mL-1 may still drop to 6 viruses mL-1 in 30 days. This suggests that the least stable viruses in

autumn still rely upon high abundances or other mechanisms of persistence such as

sedimentation and subsequent re-entry into the water column via the previous described

mechanisms. However, viruses with lower decay rates in the autumn, such as ACTV-1 (Chapters

2 and 3) or F2MCP1 (Chapter 3), have half-lives between 3 and 7 days, which means a starting

population of 105 viruses mL-1 is reduced to 6 viruses mL-1 in 42 to 98 days. While viruses are

likely to survive longer in the water column in autumn, it is still reasonable to speculate that the

other mechanisms of survival discussed above are important during this season.

As the temperature cools and winter begins, lakes will once again stratify. As mentioned above,

this makes re-entry from the sediment less likely. However, if the water column develops ice

cover, the decay rates of phytoplankton viruses can be reduced dramatically, as seen in Chapters

2 and 3. The survival of both cultivated and uncultivated phytoplankton viruses over a 126 day

period (Chapters 2 and 3), suggest that algal viruses in general may be able to persist in the

winter, even if the abundances of their hosts are below the threshold necessary for virus

production. For instance, even the highest algal virus decay rate in the winter (CpV-BQ1 in

Chapter 2), has a half-life of 16 days. This half-life would the delay drop from 105 viruses mL-1

to 6 viruses mL-1 until 224 days had passed. As such, algal viruses that are abundant in winter are

likely to survive at least until the ice thaws. However, cyanophage may require further

environmental refugia during this same time. For the cyanophage decay rate estimated in the

winter whole water treatment (Chapter 3), the half-life is 10 days, meaning 105 viruses mL-1

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would be reduced to 6 viruses mL-1 after 140 days. As the ice cover only lasted for 126 days and

that this rate is a likely underestimate as it was estimated with qPCR, only highly abundant

cyanophages would be able to persist in the water column. As such, cyanophages are likely to

rely upon other environmental refugia unless their hosts are present.

During spring in a dimictic lake, the water temperature of the water column will eventually

become relatively constant and mixing will occur. As in the autumn, the act of mixing may

protect the viruses by limiting the time at the surface and may recharge aqueous virus

populations from those that were in the sediment. However, decay rates were estimated to be

high in the spring, with the lowest decay rates (LO.20May09.33, Chapter 3) equating to half-

lives on the order of 3 to 4 days. Again, these half-lives would reduce a population of 105 viruses

mL-1 to 6 viruses mL-1 after 42 to 56 days. Further, as stated above, this qPCR-derived estimate

of decay is likely an underestimate. As such, spring virus populations are likely to be reliant

upon viral production and the re-entry of viruses from the sediment if the environment is

completely mixed.

Given this framework, it seems reasonable that phytoplankton viruses persist throughout the year

in freshwater ecosystems. The mechanisms in which viruses rely upon to persist vary seasonally.

During times in which their hosts are present, the most likely mechanism is continual production

of viruses that either equals or overcomes decay rates. However, once host populations drop

below the threshold for viral production, phytoplankton viruses are likely to either have infected

their host lysogenically/temperately, sedimented into the benthos, or are subject to decay forces.

During such times, one likely source of new viruses that maintained populations in the water

column is the sediment. The only time of the year in which survival of phytoplankton viruses in

the water column is likely sufficient to maintain virus populations is during the winter, under the

ice. Despite this, as reported in Chapter 3, cyanophages are not as likely to persist in the water

column in the winter without some source of exogenous viruses, which, if the ice covers the

entire environment, can only be the sediment. Even with this framework, many questions remain

on the persistence of phytoplankton viruses in aquatic environments.

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5.5 Future Directions

Even though the key findings of Chapters 2 and 3 indicate that algal viruses are likely to survive

during the winter in temperate freshwater environments with seasonal ice cover and the results

reported in Chapter 4 found that the sediments of freshwater environments can contain diverse

and abundant phytoplankton viruses, several questions remain about the persistence of algal

viruses and cyanophages in freshwater environments. For instance, the effect of UV radiation on

algal viruses in the environment has yet to be fully explored, as was done for cyanophages

infecting Synechococcus in the Gulf of Mexico (Garza and Suttle, 1998). While long-term

incubations may not be feasible due to the nature of the required UV-permeable experimental

vessels, short-term decay incubation experiments incorporating reaction vessels composed of

materials that pass specific wavelengths, such as polycarbonate and polyethylene, could be

undertaken and the decay rate estimates between vessel type could be compared. These short-

term incubation experiments may allow the major wavelengths causing algal virus decay to be

deduced. Furthermore, while it seems intuitive that deeper depths with lower temperatures and

greater light attenuation would be protective, the effect of depth in the water column on the

decay rates of freshwater algal viruses has not been fully explored.

In addition, as the use of qPCR to estimate decay rates was validated (Chapter 3), the decay rates

of many more virus types in different aquatic environments may be explored, providing vital

information regarding the persistence of viruses in the environment. One of the limitations of

Chapter 3 was that decay rates were estimated for only a few, five total, environmental

phytoplankton virus genes. While time zero samples without added algal viruses for all five

incubations may have provided more potential targets for qPCR assays, the one incubation that

clone libraries could be made had algal virus communities that were not very diverse. To obtain

a more diverse set of sequences, several methods could be undertaken. First, metagenomic

sequencing of environmental samples could provide targets for qPCR assays, as used by Hewson

and colleagues (2012). Second, deep-amplicon sequencing may also provide more targets for

qPCR, which may necessitate the development of new primers for high-throughput sequencing

as the only currently available algal virus primers for this are purposefully biased to amplify

Prasinovirus sequences (Clerissi et al., 2014a). Finally, as cyanophages appear to be more

diverse in the tested environments, the relationship between the loss of infectivity and the loss of

amplifiable DNA should be established for these virus types.

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For phytoplankton viruses found in the sediment of freshwater systems, further studies should be

undertaken to confirm their viability, and thus further confirm the findings that sediments can act

as a refugium for diverse and abundant algal viruses and cyanophages. Additionally, even though

studies have found that sediments can enhance the survival of some viruses (e.g., LaBelle and

Gerba, 1980), the decay rates of algal viruses and cyanophages, using either infectivity assays,

qPCR, or both, in the sediment should be explored and compared to the decay rates of these

same viruses in the water column. Furthermore, as viruses in the sediment require some

disturbance in order to re-enter the water column, possible mechanisms for dispersal of

phytoplankton viruses should be examined, a research topic that has been explored very sparsely

to date. Research on these matters will allow for a more thorough understanding of the

mechanisms that allow for the maintenance of the viral ‘seed-bank’ in aquatic systems.

125

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Appendices

Appendix 1

Appendix Table 1.1. Pairwise statistical comparisons of the slopes from decay incubations using the same viruses within the same treatment in different seasons

Virus Slope 1† Slope 2†

Slopes Significantly Different?* F

DFn, DFd p-value

ACTV-1 Spring Filtered Summer Filtered No 7.07 1, 26 0.013

Spring Filtered Fall Filtered No 0.063 1, 26 0.804

Spring Filtered Winter Filtered Yes 48.098 1, 26 <0.0001

Spring Wholewater Summer Wholewater No 12.43 1, 26 0.0016

Spring Wholewater Fall Wholewater Yes 106.59 1, 26 <0.0001

Spring Wholewater Winter Wholewater Yes 441.18 1, 26 <0.0001

Summer Filtered Fall Filtered No 8.65 1, 26 0.0068

Summer Filtered Winter Filtered Yes 99.23 1, 26 <0.0001

Summer Wholewater Fall Wholewater Yes 441.51 1, 26 <0.0001

Summer Wholewater Winter Wholewater Yes 1001.29 1, 26 <0.0001

Fall Filtered Winter Filtered Yes 104.8 1, 26 <0.0001

Fall Wholewater Winter Wholewater Yes 110.85 1, 26 <0.0001

CVM-1 Spring Filtered Summer Filtered No 0.301 1, 26 0.501

Spring Filtered Fall Filtered No 3.096 1, 26 0.0902



Spring Wholewater Fall Wholewater Yes 14.53 1, 26 0.00076


Summer Filtered Fall Filtered No 0.26 1, 26 0.62


Summer Wholewater Fall Wholewater Yes 13.95 1, 26 0.00093




CpV-BQ1 Summer Filtered Fall Filtered Yes 32.804 1, 20 <0.0001


Summer Wholewater Fall Wholewater Yes 33.61 1, 20 <0.0001




*α level = 0.00167 with Bonferroni correction †Slopes in bold and italics are more negative and have higher rate of decay, but bolds fonts indicate statistically significant differences.

143

Appendix Table 1.2. Pairwise statistical comparisons of the slopes from decay incubations using the same viruses within the in same season using different treatments

Virus Slope 1† Slope 2†


DFn, DFd p-value

ATCV-1 Spring Filtered Spring Wholewater Yes 111.73 1, 26 <0.0001

Summer Filtered Summer Wholewater Yes 110.72 1, 26 <0.0001

Fall Filtered Fall Wholewater No 9.29 1, 26 0.0052

Winter Filtered Winter Wholewater No 0.066 1, 26 0.79

CVM-1 Spring Filtered Spring Wholewater Yes 66.45 1, 26 <0.0001

Summer Filtered Summer Wholewater Yes 25.65 1, 26 <0.0001


Winter Filtered Winter Wholewater Yes 19.85 1, 26 0.00014

CpV-BQ1 Summer Filtered Summer Wholewater No 7.602 1, 14 0.015


Winter Filtered Winter Wholewater Yes 21.091 1, 26 <0.0001

*α level = 0.0045 with Bonferroni correction †Slopes in bold and italics are more negative and have higher rate of decay, but bolds fonts indicate statistically significant differences.

144

Appendix Table 1.3. Pairwise statistical comparisons of the slopes from decay incubations using different viruses within the same season and treatment

Season Slope 1 Slope 2


DFn, DFd p-value

Spring ACTV-1 Filtered CVM-1 Filtered Yes 51.53 1, 26 <0.0001

ACTV-1 Wholewater CVM-1 Wholewater No 9.31 1, 26 0.0052

Summer ACTV-1 Filtered CVM-1 Filtered No 8.65 1, 26 0.0068

ACTV-1 Wholewater CVM-1 Wholewater No 1.09 1, 26 0.31

ACTV-1 Filtered CpV-BQ1 Filtered Yes 627.82 1, 20 <0.0001

ACTV-1 Wholewater CpV-BQ1 Wholewater Yes 1504.26 1, 20 <0.0001

CVM-1 Filtered CpV-BQ1 Filtered Yes 527.22 1, 20 <0.0001

CVM-1 Wholewater CpV-BQ1 Wholewater Yes 693.08 1, 20 <0.0001

Fall ACTV-1 Filtered CVM-1 Filtered Yes 194.41 1, 26 <0.0001

ACTV-1 Wholewater CVM-1 Wholewater Yes 74.45 1, 26 <0.0001

ACTV-1 Filtered CpV-BQ1 Filtered Yes 21.23 1, 26 <0.0001

ACTV-1 Wholewater CpV-BQ1 Wholewater Yes 11.35 1, 26 0.0024

CVM-1 Filtered CpV-BQ1 Filtered No 9.18 1, 26 0.0056

CVM-1 Wholewater CpV-BQ1 Wholewater No 4.03 1, 26 0.055

Winter ACTV-1 Filtered CVM-1 Filtered Yes 18.06 1, 26 0.00024

ACTV-1 Wholewater CVM-1 Wholewater Yes 62.89 1, 26 <0.0001

ACTV-1 Filtered CpV-BQ1 Filtered Yes 19.98 1, 26 0.0014

ACTV-1 Wholewater CpV-BQ1 Wholewater Yes 78.25 1, 26 <0.0001


CVM-1 Wholewater CpV-BQ1 Wholewater Yes 23.95 1, 26 <0.0001

*α level = 0.0025 with Bonferroni correction †Bolded slope is more negative and thus has higher rate of decay

145

Appendix 2

Appendix Table 2.1. Linear Regression analysis of decay curves

Season Virus Treatment Slope ± Standard Error* Slope significantly

non-zero? F DFn, DFd p value

Spring ATCV-1 Filtered -0.00066 ± 0.00070 No 0.897 1, 13 0.36

2013 Wholewater -0.0071 ± 0.00090 Yes 61.29 1, 13 < 0.0001

CVM-1 Filtered -0.00046 ± 0.010 No 0.197 1, 13 0.67

Wholewater -0.0062 ± 0.00083 Yes 55.68 1, 13 < 0.0001

CpV-BQ1 Filtered n.a. - - - -

Wholewater n.a. - - - -

LO.20May09.33 Filtered -0.0044 ± 0.00074 Yes 35.27 1, 13 < 0.0001

Wholewater -0.0076 ± 0.0013 Yes 35.47 1, 10 0.0001

F2Vpol1 Filtered n.d. - - - -

Wholewater n.d. - - - -

F2MCP1 Filtered n.d. - - - -


IZCPS1 Filtered -0.026 ± 0.0049 Yes 10.72 1, 7 0.014

Wholewater t.h.t.e. - - - -

WZCPS8 Filtered -0.043 ± 0.0049 Yes 76.36 1, 7 < 0.0001

Wholewater -0.15 ± 0.025 Yes 34.22 1, 2 0.028

Summer ATCV-1 Filtered -0.0046 ± 0.0062 No 0.57 1, 9 0.47

2013 Wholewater -0.0075 ± 0.0023 Yes 10.52 1, 7 0.014

CVM-1 Filtered -0.0047 ± 0.0019 Yes 6.171 1, 10 0.032

Wholewater -0.0071 ± 0.0027 Yes 6.817 1, 7 0.034

CpV-BQ1 Filtered -0.0037 ± 0.0045 No 0.658 1, 10 0.44

Wholewater -0.012 ± 0.0024 Yes 13.59 1, 10 0.0042

LO.20May09.33 Filtered n.d. - - - -


146

F2Vpol1 Filtered -0.00091 ± 0.0026 No 0.126 1, 9 0.73

Wholewater -0.0074 ± 0.0015 Yes 23.73 1, 10 < 0.0001



IZCPS1 Filtered t.h.t.e. - - - -


WZCPS8 Filtered -0.029 ± 0.022 No 1.641 1, 4 0.27


Autumn ATCV-1 Filtered -0.00089 ± 0.00042 No 4.495 1, 13 0.054

2013 Wholewater -0.0010 ± 0.00047 No 4.606 1, 13 0.051

CVM-1 Filtered -0.00050 ± 0.00046 No 1.189 1, 13 0.30

Wholewater -0.0013 ± 0.00046 Yes 7.918 1, 13 0.015

CpV-BQ1 Filtered -0.0065 ± 0.0023 Yes 7.946 1, 13 0.018

Wholewater -0.0074 ± 0.0011 Yes 43.55 1, 13 0.022



F2Vpol1 Filtered n.d. - - - -




IZCPS1 Filtered n.d. - - - -


WZCPS8 Filtered n.d. - - - -


Winter ATCV-1 Filtered -0.00012 ± 0.000047 Yes 5.933 1, 13 0.03

2013-14 Wholewater -0.000056 ± 0.000061 No 0.824 1, 13 0.38

CVM-1 Filtered -0.00034 ± 0.000049 Yes 47.83 1, 13 < 0.0001

Wholewater -0.00042 ± 0.000060 Yes 50.23 1, 13 < 0.0001

CpV-BQ1 Filtered -0.00047 ± 0.000038 Yes 157.6 1, 13 < 0.0001

Wholewater -0.00053 ± 0.000049 Yes 116.6 1, 13 < 0.0001


147


F2Vpol1 Filtered -0.000069 ± 0.000054 No 1.654 1, 13 0.22

Wholewater -0.00065 ± 0.00014 Yes 21.57 1, 13 0.0005



WZCPS8 Filtered -0.0027 ± 0.00025 Yes 121.2 1, 13 < 0.0001

Wholewater -0.0029 ± 0.00029 Yes 98.92 1, 13 < 0.0001



Autumn ATCV-1 Filtered -0.00085 ± 0.00085 No 1.002 1, 13 0.34

2014 Wholewater 0.0034 ± 0.00060 Yes 32.32 1, 13 < 0.0001

CVM-1 Filtered -0.00068 ± 0.0022 No 0.092 1, 13 0.76

Wholewater -0.0084 ± 0.0011 Yes 58.36 1, 13 < 0.0001

CpV-BQ1 Filtered -0.00099 ± 0.0024 No 0.169 1, 13 0.69

Wholewater -0.0096 ± 0.0011 Yes 74.9 1, 13 < 0.0001



F2Vpol1 Filtered -0.0080 ± 0.0012 Yes 43.23 1, 13 < 0.0001

Wholewater -0.013 ± 0.0028 Yes 22.39 1, 13 0.0004

F2MCP1 Filtered -0.00048 ± 0.00090 No 0.292 1, 13 0.60

Wholewater -0.0099 ± 0.0020 Yes 23.85 1, 13 0.0003

WZCPS8 Filtered n.d. - - - -




*n.a. = not added, n.d. = not detected, t.h.t.e. = too high to estimate

148

Appendix Table 2.2. ANCOVA comparing slopes from qPCR assays versus infectivity assays of the same viruses

Virus qPCR Slope† Infectivity Slope† Slopes Significantly

Different?* F DFn, DFd p-value

ATCV-1 Spring Filtered Spring Filtered No 6.855 1, 26 0.015

Spring Wholewater Spring Wholewater No 5.261 1, 26 0.030

Summer Filtered Summer Filtered No 0.850 1, 18 0.37

Summer Wholewater Summer Wholewater No 7.357 1, 14 0.017

Autumn 2013 Filtered Autumn 2013 Filtered Yes 19.04 1, 26 0.0002

Autumn 2013 Wholewater Autumn 2013 Wholewater Yes 31.07 1, 26 <0.0001

Winter Filtered Winter Filtered No 1.344 1, 26 0.26

Winter Wholewater Winter Wholewater No 0.007 1, 26 0.94

Autumn 2014 Filtered Autumn 2014 Filtered Yes 19.94 1, 26 0.0001


CVM-1 Spring Filtered Spring Filtered Yes 40.79 1, 23 <0.0001

Spring Wholewater Spring Wholewater Yes 29.06 1, 26 <0.0001

Summer Filtered Summer Filtered No 11.87 1, 20 0.0026

Summer Wholewater Summer Wholewater No 4.654 1, 20 0.049

Autumn 2013 Filtered Autumn 2013 Filtered Yes 183.1 1, 26 <0.0001



Winter Wholewater Winter Wholewater Yes 26.34 1, 26 <0.0001

Autumn 2014 Filtered Autumn 2014 Filtered No 8.107 1, 26 0.0085

Autumn 2014 Wholewater Autumn 2014 Wholewater No 6.771 1, 26 0.015

CpV-BQ1 Summer Filtered Summer Filtered Yes 179.5 1, 17 <0.0001

Summer Wholewater Summer Wholewater Yes 192.5 1, 17 <0.0001

Autumn 2013 Filtered Autumn 2013 Filtered Yes 39.21 1, 26 <0.0001



Winter Wholewater Winter Wholewater Yes 48.35 1, 26 <0.0001

149



*α level = 0.0018 with Bonferroni correction †Bolded slope is significantly more negative and thus has higher rate of decay while italicized slope is more negative, but not significantly different

150

Appendix Table 2.3. ANCOVA of regression slopes calculated in the same season from the same viruses with different treatments

Virus Slope 1† Slope 2† Slopes Significantly


ATCV-1 Spring Filtered Spring Wholewater Yes 31.36 1, 26 <0.0001

Summer Filtered Summer Wholewater No 0.099 1, 16 0.76

Autumn 2013 Filtered Autumn 2013 Wholewater No 0.034 1, 26 0.85



CVM-1 Spring Filtered Spring Wholewater Yes 19.06 1, 23 0.0002

Summer Filtered Summer Wholewater No 0.418 1, 17 0.53




CpV-BQ1 Summer Filtered Summer Wholewater No 2.467 1, 20 0.13




F2 Vpol 1 Summer Filtered Summer Wholewater No 2.385 1, 20 0.14

Winter Filtered Winter Wholewater Yes 14.92 1, 26 0.0007


F2 MCP 1 Autumn 2014 Filtered Autumn 2014 Wholewater Yes 18.02 1, 26 0.0002

WZ CPS 8 Spring Filtered Spring Wholewater Yes 12.55 1, 14 <0.0001


LO.20May09.33 Spring Filtered Spring Wholewater No 2.619 1, 23

0.12

*α level = 0.0024 with Bonferroni correction

†Bolded slope is significantly more negative and thus has higher rate of decay. Italicized slope is more negative, but not significantly different.

151

Appendix Table 2.4. ANCOVA of regression slopes from the same virus and treatment in different seasons

Virus Slope 1† Slope 2† Slopes Significantly


ATCV-1 Spring Filtered Summer Filtered No 0.756 1, 22 0.39

Spring Filtered Autumn 2013 Filtered No 0.076 1, 26 0.79

Spring Filtered Winter Filtered No 1.109 1, 26 0.30



Spring Wholewater Autumn 2013 Wholewater Yes 35.51 1, 26 <0.0001


Spring Wholewater Autumn 2014 Wholewater Yes 11.44 1, 26 0.0023

Summer Filtered Autumn 2013 Filtered No 0.762 1, 22 0.39

Summer Filtered Winter Filtered No 1.241 1, 22 0.28


Summer Wholewater Autumn 2013 Wholewater No 6.202 1, 20 0.021

Summer Wholewater Winter Wholewater Yes 13.95 1, 20 0.0013


Autumn 2013 Filtered Winter Filtered No 4.05 1, 26 0.055


Autumn 2013 Wholewater Winter Wholewater No 4.932 1, 26 0.035

Autumn 2013 Wholewater Autumn 2014 Wholewater Yes 9.945 1, 26 0.004

Winter Filtered Autumn 2014 Filtered No 1.398 1, 26 0.25

Winter Wholewater Autumn 2014 Wholewater Yes 46.65 1, 26 <0.0001

CVM-1 Spring Filtered Summer Filtered No 2.677 1, 20 0.12


Spring Filtered Winter Filtered No 0.026 1, 23 0.87



Spring Wholewater Autumn 2013 Wholewater Yes 26.54 1, 26 <0.0001


Spring Wholewater Autumn 2014 Wholewater No 2.654 1, 26 0.12


152




Summer Wholewater Winter Wholewater No 9.485 1, 20 0.0059




Autumn 2013 Wholewater Winter Wholewater No 4.136 1, 26 0.052




CpV- BQ1 Summer Filtered Autumn 2013 Filtered No 2.058 1, 20 0.17








Autumn 2013 Wholewater Winter Wholewater Yes 12.51 1, 26 0.0018

Autumn 2013 Wholewater Autumn 2014 Wholewater Yes 0.898 1, 23 0.35



F2 Vpol 1 Summer Filtered Winter Filtered No 0.172 1, 19 0.68




Winter Filtered Autumn 2014 Filtered Yes 61.27 1, 26 <0.0001


WZ CPS 8 Spring Filtered Summer Filtered No 0.410 1, 11 0.54

153


Spring Wholewater Winter Wholewater Yes 10.19 1, 15 0.0061


*α level = 0.0025 for ATCV-1, 0.0025 for CVM-1, 0.0042 for CpV-BQ1, 0.0083 for F2VPOL1, 0.013 for WZCPS8 with Bonferroni corrections †Bolded slope is significantly more negative and thus has higher rate of decay while italicized slope is more negative, but not significantly different

154

Appendix Table.2 5. ANCOVA of regression slopes from the same treatment in the same season with different viruses

Season Slope 1† Slope 2† Slopes Significantly


Spring 2013 ATCV-1 Filtered CVM-1 Filtered No 0.028 1, 23 0.87

ATCV-1 Filtered LO.20May09.33 Filtered Yes 13.33 1, 26 0.0012

ATCV-1 Filtered IZ CPS 1 Filtered Yes 12.10 1, 20 0.0024

ATCV-1 Filtered WZ CPS 8 Filtered Yes 90.12 1, 20 <0.0001

ATCV-1 Wholewater CVM-1 Wholewater No 0.517 1, 26 0.48

ATCV-1 Wholewater LO.20May09.33 Wholewater No 0.059 1, 23 0.81

ATCV-1 Wholewater WZ CPS 8 Wholewater Yes 28.56 1, 15 <0.0001

CVM-1 Filtered LO.20May09.33 Filtered No 9.992 1, 23 0.0044

CVM-1 Filtered IZ CPS 1 Filtered No 8.609 1, 17 0.0093

CVM-1 Filtered WZ CPS 8 Filtered Yes 63.58 1, 17 <0.0001

CVM-1 Wholewater LO.20May09.33 Wholewater No 0.434 1, 23 0.517

CVM-1 Wholewater WZ CPS 8 Wholewater Yes 33.52 1, 15 <0.0001

LO.20May09.33 Filtered IZ CPS 1 Filtered No 6.501 1, 20 0.019

LO.20May09.33 Filtered WZ CPS 8 Filtered Yes 70.67 1, 20 <0.0001

LO.20May09.33 Wholewater WZ CPS 8 Wholewater Yes 84.85 1, 12 <0.0001

IZ CPS 1 Filtered WZ CPS 8 Filtered Yes 14.92 1, 14 0.0017

Summer 2013 ATCV-1 Filtered CVM-1 Filtered No 0.0003 1, 19 0.99

ATCV-1 Filtered CpV-BQ1 Filtered No 1.331 1, 19 0.26

ATCV-1 Filtered F2 Vpol 1 Filtered No 0.326 1, 18 0.58

ATCV-1 Filtered WZ CPS 8 Filtered No 0.397 1, 13 0.54


ATCV-1 Wholewater CpV-BQ1 Wholewater No 0.269 1, 17 0.61

ATCV-1 Wholewater F2 Vpol 1 Wholewater No 0.0001 1, 20 0.99


CVM-1 Filtered F2 Vpol 1 Filtered No 1.435 1, 19 0.25

CVM-1 Filtered WZ CPS 8 Filtered No 1.897 1, 14 0.19


CVM-1 Wholewater F2 Vpol 1 Wholewater No 0.002 1, 20 0.96

CpV-BQ1 Filtered F2 Vpol 1 Filtered No 7.249 1, 19 0.014

155

CpV-BQ1 Filtered WZ CPS 8 Filtered No 0.411 1, 14 0.53

CpV-BQ1 Wholewater F2 Vpol 1 Wholewater No 0.609 1, 20 0.44

F2 Vpol 1 Filtered WZ CPS 8 Filtered No 1.776 1, 13 0.21

Autumn 2013 ATCV-1 Filtered CVM-1 Filtered No 0.384 1, 26 0.54

ATCV-1 Filtered CpV-BQ1 Filtered Yes 8.816 1, 23 0.0069


ATCV-1 Wholewater CpV-BQ1 Wholewater No 0.243 1, 15 0.63

CVM-1 Filtered CpV-BQ1 Filtered Yes 9.483 1, 23 0.0053


Winter 2013 ATCV-1 Filtered CVM-1 Filtered No 10.94 1, 26 0.0028

ATCV-1 Filtered CpV-BQ1 Filtered Yes 34.9 1, 26 <0.0001

ATCV-1 Filtered F2 Vpol 1 Filtered No 0.408 1, 26 0.53

ATCV-1 Filtered WZ CPS 8 Filtered Yes 163.1 1, 26 <0.0001

ATCV-1 Wholewater CVM-1 Wholewater Yes 18.27 1, 26 0.0002

ATCV-1 Wholewater CpV-BQ1 Wholewater Yes 36.24 1, 26 <0.0001

ATCV-1 Wholewater F2 Vpol 1 Wholewater No 3.479 1, 26 0.073

ATCV-1 Wholewater WZ CPS 8 Wholewater Yes 137.1 1, 26 <0.0001


CVM-1 Filtered F2 Vpol 1 Filtered Yes 13.64 1, 26 0.001

CVM-1 Filtered WZ CPS 8 Filtered Yes 169.4 1, 26 <0.0001



CVM-1 Wholewater WZ CPS 8 Wholewater Yes 104.7 1, 26 <0.0001

CpV-BQ1 Filtered F2 Vpol 1 Filtered Yes 41.42 1, 26 <0.0001

CpV-BQ1 Filtered WZ CPS 8 Filtered Yes 125.2 1, 26 <0.0001

CpV-BQ1 Wholewater F2 Vpol 1 Whole water No 0.881 1, 26 0.36

CpV-BQ1 Whole water WZ CPS 8 Whole water Yes 98.63 1, 26 <0.0001

F2 Vpol 1 Filtered WZ CPS 8 Filtered Yes 109.9 1, 26 <0.0001

F2 Vpol 1 Wholewater WZ CPS 8 Wholewater Yes 48.67 1, 26 <0.0001

Autumn 2014 ATCV-1 Filtered CVM-1 Filtered No 0.005 1, 26 0.94

156

ATCV-1 Filtered CpV-BQ1 Filtered No 0.003 1, 26 0.95

ATCV-1 Filtered F2 Vpol 1 Filtered Yes 23.31 1, 26 <0.0001

ATCV-1 Filtered F2 MCP 1 Filtered No 0.087 1, 26 0.77

ATCV-1 Wholewater CVM-1 Wholewater Yes 16.04 1, 26 0.0005

ATCV-1 Wholewater CpV-BQ1 Wholewater Yes 24.06 1, 26 <0.0001

ATCV-1 Wholewater F2 Vpol 1 Wholewater Yes 11.71 1, 26 0.0021

ATCV-1 Wholewater F2 MCP 1 Wholewater No 9.431 1, 26 0.005


CVM-1 Filtered F2 Vpol 1 Filtered No 8.331 1, 26 0.0077

CVM-1 Filtered F2 MCP 1 Filtered No 0.006 1, 26 0.94



CVM-1 Wholewater F2 MCP 1 Wholewater No 0.396 1, 26 0.53

CpV-BQ1 Filtered F2 Vpol 1 Filtered No 6.704 1, 26 0.016

CpV-BQ1 Filtered F2 MCP 1 Filtered No 0.019 1, 26 0.89

CpV-BQ1 Wholewater F2 Vpol 1 Wholewater No 3.69 1, 26 0.066

CpV-BQ1 Wholewater F2 MCP 1 Wholewater No 0.74 1, 26 0.42

F2 Vpol 1 Filtered F2 MCP 1 Filtered Yes 24.77 1, 26 <0.0001

F2 Vpol 1 Wholewater F2 MCP 1 Wholewater No 0.863 1, 26 0.36

*α level = 0.0031 for spring, 0.00031 for summer, 0.0083 for autumn 2013, 0.0025 for winter, and 0.0025 for autumn 2014 with Bonferroni corrections

†Bolded slope is significantly more negative and thus has higher rate of decay while italicized slope is more negative, but not significantly different

157

Appendix 3

Appendix Table 3.1. Search results from blastp for polB OTU representative sequences.

OTU Stations and depths (cm) present First Cultivated blastp match (#, name) Percent

Identity (%)

45224VPOLCC1 452 2-4 AAR05084.1 Phaeocystis globosa virus PgV-03T 49

45224VPOLCC2 452 2-4; 882 6-8; 1326 0-2 ACP44143.1 Bathycoccus virus BpV178 76

45224VPOLCC7 452 2-4; 1326 0-2, 2-4, 6-8 ACP44143.1 Bathycoccus virus BpV178 80

88202VPOLCC1 882 0-2, 2-4, 4-6, 6-8 YP_009174732.1 Yellowstone lake phycodnavirus 1 51

88202VPOLCC15 882 0-2, 4-6, 6-8; 973 4-6; 1326 2-4 ACP44143.1 Bathycoccus virus BpV178 97

88224VPOLCC1 882 2-4, 6-8 ACP44143.1 Bathycoccus virus BpV178 79

88246VPOLCC5 882 4-6 YP_009174732.1 Yellowstone lake phycodnavirus 1 51

88246VPOLCC13 882 4-6 ACP44143.1 Bathycoccus virus BpV178 93

88268VPOLCC1 882 6-8; 1326 0-2, 4-6 ALH45659.1 Chrysochromulina parva virus BQ1 99


88268VPOLCC9 882 6-8 ADA81909.1 Ostreococcus lucimarinus virus OlV158 69

97346VPOLCC2 452 2-4; 882 0-2; 973 4-6; 1326 2-4 AKR54192.1 Micromonas virus RCC:4266 78

97346VPOLCC14 882 4-6; 973 4-6; 1326 2-4 AKR54181.1 Micromonas virus RCC:4236 86

132602VPOLCC4 882 6-8; 1326 0-2 ACP44143.1 Bathycoccus virus BpV178 79

132602VPOLCC8 1326 0-2, 2-4, 4-6, 6-8; 452 2-4 ACP44143.1 Bathycoccus virus BpV178 79

132602VPOLCC11 1326 0-2 AKR54192.1 Micromonas virus RCC:4266 82


132624VPOLCC19 1326 2-4 YP_009174598.1 Yellowstone lake phycodnavirus 2 86

132646VPOLCC3 1326 0-2, 4-6 AKR54192.1 Micromonas virus RCC:4266 80

132646VPOLCC15 1326 4-6 ACP44120.1 Micromonas virus MiV130 80

158

Appendix Table 3.2. Search results from blastp for g20 OTU representative sequences.

OTU Stations and depths (cm)

present First Cultivated blastp match (#, name)

Percent Identity

(%)

45202CPSCC1 452 0-2; 1326 0-2, 2-4 YP_214363.1 Prochlorococcus phage P-SSM2 90

45202CPSCC2 452 0-2 YP_007001618.1 Synechococcus phage metaG-MbCM1 63

45202CPSCC3 452 0-2 AAC23540.1 Cyanophage S-BnM1 63

45202CPSCC12 452 0-2; 1326 0-2, 4-6 ABB17262.1 Synechococcus phage S-CBM2 66

45202CPSCC14 452 0-2 YP_007674507.1 Synechococcus phage S-SKS1 88

45202CPSCC19 452 0-2 ABB17262.1 Synechococcus phage S-CBM2 65

88202CPSCC2 882 0-2 YP_003097343.1 Synechococcus phage S-RSM4 92


88202CPSCC5 882 0-2, 2-4 YP_007674507.1 Synechococcus phage S-SKS1 89

88202CPSCC6 882 0-2 YP_004323487.1 Prochlorococcus phage P-HM2 62

88202CPSCC9 882 0-2 YP_009133666.1 Synechococcus phage ACG-2014g 68

88202CPSCC11 882 0-2 YP_004322786.1 Synechococcus phage S-ShM2 62




88202CPSCC20 882 0-2 YP_004323727.1 Prochlorococcus phage Syn33 69

88224CPSCC1 882 2-4 YP_007673103.1 Synechococcus phage S-CAM1 94

88224CPSCC2 882 2-4 YP_195138.1 Synechococcus phage S-PM2 67


88224CPSCC5 882 2-4 YP_214665.1 Prochlorococcus phage P-SSM4 65


88224CPSCC7 882 2-4; 1326 0-2 YP_007674507.1 Synechococcus phage S-SKS1 83

88224CPSCC8 882 2-4 AAK31670.1 Synechococcus phage S-PWM1 68


88224CPSCC14 882 2-4 YP_004324197.1 Synechococcus phage S-SSM7 85

88224CPSCC15 882 2-4 ACD93434.1 Cyanophage P-RSM5 90


159

88224CPSCC18 882 2-4 AMO43137.1 Cyanophage S-RIM32 66


88246CPSCC2 882 0-2, 4-6; 973 0-2 YP_007673103.1 Synechococcus phage S-CAM1 94


88246CPSCC4 882 4-6 YP_009188207.1 Cyanophage P-TIM40 67

88246CPSCC7 882 4-6 YP_009213616.1 Prochlorococcus phage P-TIM68 68

88246CPSCC9 882 4-6 YP_717798.1 Synechococcus phage syn9 67









88268CPSCC3 882 4-6, 6-8 YP_009188207.1 Cyanophage P-TIM40 64


88268CPSCC5 882 6-8 AIX46593.1 Synechococcus phage ACG-2014a 63




88268CPSCC10 882 6-8 YP_004323020.1 Synechococcus phage S-SM1 70

88268CPSCC11 882 6-8 YP_717798.1 Synechococcus phage syn9 66








88268CPSCC20 882 6-8 YP_007518198.1 Synechococcus phage S-RIM8 A.HR1 66

97302CPSCC1 882 0-2; 973 0-2 ABB17262.1 Synechococcus phage S-CBM2 62

97302CPSCC4 882 0-2, 2-4; 973 0-2 YP_004322270.1 Synechococcus phage S-SM2 66

160


97302CPSCC6 973 0-2; 882 2-4 YP_004324197.1 Synechococcus phage S-SSM7 82



97302CPSCC11 973 0-2 YP_007674507.1 Synechococcus phage S-SKS1 93

132602CPSCC1 1326 0-2 YP_004324197.1 Synechococcus phage S-SSM7 83


132602CPSCC9 1326 0-2 YP_009140894.1 Synechococcus phage ACG-2014i 59

132602CPSCC10 1326 0-2 YP_004323264.1 Prochlorococcus phage P-RSM4 63



132624CPSCC4 1326 2-4 YP_009213616.1 Prochlorococcus phage P-TIM68 81




132646CPSCC2 452 0-2; 1326 0-2, 2-4, 4-6 YP_214363.1 Prochlorococcus phage P-SSM2 90

161

Copyright Acknowledgements

Chapter 2 is reprinted with permission from The International Society for Microbial Ecology

Journal.

The original reference for this paper is as follows:



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