Pseudomonas Bacteriophage Phi6 As a Model for Virus Emergence

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Pseudomonas Bacteriophage Phi6 as a Model for Virus Emergence

Brian Elwood Ford Graduate Center, City University of New York

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PSEUDOMONAS BACTERIOPHAGE Φ6 AS A MODEL FOR VIRUS EMERGENCE

BRIAN E. FORD

Academic dissertation submitted to the Graduate Faculty in Biology in partial fulfillment of the requirements for the degree of Doctor of Philosophy, The City University of New York

2015

This manuscript has been read and accepted for the Graduate Faculty in Biology in satisfaction of the dissertation requirement for the degree of Doctor of Philosophy.

Date Chair of Examining Committee Dr. John J. Dennehy

Date Executive Officer Dr. Laurel Eckhardt

Dr. John J. Dennehy, Queens College

Dr. Stéphane Boissinot, NYU Abu Dhabi

Dr. Nathalia Glickman Holtzman, Queens College

Dr. Paul Gottlieb, City College of New York

Dr. José A. Fernández-‐Romero, Population Council Supervisory Committee

THE CITY UNIVERSITY OF NEW YORK

iii

Abstract

Bacteriophage Φ6 a s a Model f or Virus Emergence

Brian E. Ford

Mentor: John J. Dennehy

The Pseudomonas bacteriophage Φ6 has a long and well-‐established history as a model organism. Here we describe a set of experiments to extend this model system in to concepts previously unclaimed. Chapter 1 presents a brief background of the ecology of viruses that infect microorganisms. Chapter 2 examines genetic mutations allowing for host range expansion. Chapter 3 presents a novel paired strain assay to study how a non genetic host acquired factor affects fitness of these enveloped viruses on subsequent hosts. Chapter 4 is an extension of this system to include how the bacteria host is affected in virus-‐host coevolution.

iv ABSTRACT ...... IV

LIST OF TABLES ...... XI

LIST OF FIGURES ...... XII

INTRODUCTION ...... XIV

CHAPTER 1: EVOLUTIONARY ECOLOGY OF THE VIRUSES OF MICROORGANISMS ...... 1

ABSTRACT ...... 2

INTRODUCTION ...... 3

VOM ABUNDANCE, BIODIVERSITY, AND B IOGEOGRAPHY ...... 3

POPULATION D YNAMICS OF V OMS ...... 6

Factors affecting rates of VoM population growth...... 7

Predator-‐prey dynamics...... 11

VOM IMPACT ON HOST ATIONS POPUL AND COMMUNITIES...... 12

VoM-‐host coevolution and its limitations...... 12

VoMs as drivers of host biodiversity...... 15

VOM ECOLOGICAL A DAPTATION ...... 16

Generation time as an optimizable trait...... 16

Coinfection, competition and cheating...... 18

Productivity versus latency...... 19

VOM GENOME E VOLUTION ...... 21

VoM genomes...... 22

Horizontal gene transfer and genome mosaicism...... 23

v VOM IMPACT ON H OST G ENETICS ...... 24

VOMS AS E COSYSTEM M ODIFIERS ...... 26

CLOSING R EMARKS ...... 28

ACKNOWLEDGEMENTS ...... 28

FIGURE L EGENDS ...... 29

REFERENCES ...... 34

CHAPTER 2: FREQUENCY AND FITNESS CONSEQUENCES OF BACTERIOPHAGE Φ6 HOST RANGE

MUTATIONS ...... 49

ABSTRACT ...... 50

INTRODUCTION ...... 51

METHODS AND M ATERIALS ...... 53

Study Organisms and Culture Conditions ...... 53

Virus Stock Preparation ...... 54

Host Range Mutant Frequency ...... 54

Host Range Mutant Isolation ...... 55

RNA Extraction and Sequencing ...... 55

Mutant Characterization ...... 56

Plaque Size Estimates ...... 56

Mutant Fitness on Native and Novel Hosts ...... 57

Attachment Rate Assays ...... 5 7

RESULTS ...... 58

Mutation Frequency ...... 58

vi Mutation Substitution Frequency ...... 59

Phenotypic Change Analysis ...... 60

P3 3D Structure Prediction ...... 61

Plaque Size ...... 62

Mutant Fitness on Original and Novel Hosts ...... 63

Attachment Rate ...... 64

Data Availability ...... 65

DISCUSSION ...... 65

Φ6 Host Range Mutation Frequency ...... 65

Mutation Hotspots ...... 69

Structural Speculations ...... 70

Fitness on Native and Novel Hosts ...... 71

Attachment to Native and Novel Hosts ...... 72

Implications for Disease Emergence ...... 73

ACKNOWLEDGEMENTS ...... 74

FIGURE L EGENDS ...... 75

FIGURES ...... 77

REFERENCES ...... 84

CHAPTER 3: HOST-‐ACQUIRED FACTOR IMPACTS FITNESS ON QUENT SUBSE HOSTS IN A AN RN

VIRUS ...... 88

ABSTRACT ...... 89

INTRODUCTION ...... 90

vii MATERIALS AND M ETHODS ...... 91

Bacterial Host Strain Determination and Selection ...... 91

Paired Bacteriophage Strain Preparation ...... 92

Fitness Assay Conditions ...... 93

Statistical Analysis ...... 94

RESULTS ...... 94

Φ6 ...... 94

Φ9 ...... 95

Φ13 ...... 95

Φ14 ...... 95

DISCUSSION ...... 97

AUTHOR C ONTRIBUTIONS ...... 101

CONFLICT OF I NTEREST S TATEMENT ...... 101

ACKNOWLEDGEMENTS ...... 101

CAPTIONS ...... 102

SUPPLEMENTAL F IGURES ...... 107

REFERENCES ...... 112

CHAPTER 4: BENEFITS OF STICKING TOGETHER: FITNESS REDUCED RUS IN VI -‐CHALLENGED

PSEUDOMONAS SPECIES FOLLOWING SHIFT TO PLANKTONIC PHENOTYPE ...... 114

ABSTRACT ...... 115

INTRODUCTION ...... 116

MATERIALS AND M ETHODS ...... 117

viii Bacteria Culture Preparation ...... 117

Selection for planktonic (non clumping) strain ...... 117

Isolation and Verification of Non Clumping Phenotype ...... 117

Heat Shock Buoyancy Test ...... 118

Determination of Cell Number in Clumps ...... 118

Colony Size Determination ...... 119

TEM Analysis of Cell Morphology ...... 119

Bacteriophage Strain Preparation ...... 120

Growth Curve Optical Density to Cell Number Correlation ...... 120

Flask Fitness Analysis ...... 121

High-‐Throughput Microplate Fitness Analysis ...... 122

Statistics ...... 122

DNA Preparation for Next Generation Sequencing ...... 123

RESULTS ...... 123

1.1 Growth ...... 123

1.2 Morphology ...... 125

1.3 Virus Susceptibility ...... 126

1.4 Comparisons ...... 127

1.5 Genotyping ...... 129

DISCUSSION ...... 129

AUTHOR C ONTRIBUTIONS ...... 131

ACKNOWLEDGEMENTS ...... 132

ix CAPTIONS ...... 132

FIGURES ...... 134

SUPPLEMENTARY T ABLES AND F IGURES ...... 139

REFERENCES ...... 140

CONCLUSION ...... 143

(INTRODUCTION) REFERENCES ...... 145

x List of Tables

Chapter 2

TABLE 1. PHAGE Φ6 MUTATIONS A LLOWING I NFECTION OF N OVEL H OST ERA ...... 80

TABLE 2. HOST R ANGE S UBSTITUTION H OTSPOTS IN Φ 6 P3 PROTEIN ...... 81

TABLE 3. AMINO A CID SUBSTITUTIONS A SSOCIATED WITH Φ 6 HOST R ANGE E XPANSION ON ERA ...... 82

TABLE 4 . PHENOTYPIC C HARACTERISTICS OF B ACTERIOPHAGE M Φ UTANTS...... 83

Chapter 3

TABLE 1. BACTERIAL S TRAINS ...... 92

TABLE 2. RELATIVE V IRUS P RODUCTIVITY O N M ISMATCHED H OSTS ...... 96

SUPPLEMENTARY T ABLE ST1. CYSTOVIRIDAE H OST R ANGE ON S ELECT H OST S TRAINS ...... 108

SUPPLEMENTARY T ABLE ST2. ONE-‐WAY ANOVA TABLES FOR THE T EFFEC OF VIRUS LIPID MEMBRANE COMPOSITION

ON FITNESS 15 AT HOURS AS MEASURED A BY REA U NDER C URVE (AUC), TIME TO INFECTION ( X-‐MAX) AND

MAXIMUM BACTERIAL OWTH GR ( Y-‐MAX). FITNESS IS MEASURED OR F EACH PAIRED N STRAI ON BOTH BACTERIAL

HOSTS...... 109

SUPPLEMENTARY T ABLE ST3. ANOVA POST-‐HOC T UKEY’S M ULTIPLE C OMPARISON T EST ...... 110

Chapter 4

TABLE 1. HEAT S HOCK G ROWTH V ERIFICATION ...... 124

SUPPLEMENTARY T ABLE 1. MICROPLATE F ITNESS (B ACTERIA VS B ACTERIA + PHAGE) UNPAIRED T -‐TEST S UMMARY

...... 139

xi List of Figures

Introduction

FIGURE 1. PSEUDOMONAS BACTERIOPHAGE Φ6...... XIV

FIGURE 2: SCHEMATIC OF P THE SEUDOMONAS PHAGE Φ6 LIFE CYCLE...... XV

Chapter 1

FIG. 1 POPULATION GROWTH R OVE TIME...... 31

FIG. 2 VIRUSES OF MICROORGANISMS SIGNIFICANTLY IMPACT THE CYCLING OF ORGANIC MATTER AND NUTRIENTS IN

THE OCEAN...... 32

FIG. 3 HORIZONTAL G ENE T RANSFER A CROSS D OMAINS...... 33

Chapter 2

FIG. 1 – SPATIAL MODELS OF Φ6 P3 PROTEIN MUTANTS ...... 77

FIG. 2 – MUTANT ABSOLUTE FITNESS ON CANONICAL NOVEL AND HOSTS ...... 78

FIG. 3 – MEAN ERA ATTACHMENT RATE ( K) IS PLOTTED AGAINST HAGE P Φ 6 FITNESS ON ERA ...... 79

Chapter 3

FIGURE 1. EXPERIMENTAL D ESIGN ...... 104

FIGURE 2. PAIRED F ITNESS A SSAYS ...... 105

FIGURE 3. HOST L EGACY E FFECT I NFECTION D YNAMICS ...... 106

SUPPLEMENTARY F IGURE 1. PAIRED F ITNESS A SSAY S UMMARY 6H (~TWO I NFECTION C YCLES) ...... 107

Chapter 4

FIGURE 1. PLANKTONIC S ELECTION S CHEME ...... 134

FIGURE 2. PHENOTYPE, CELLULAR AND C OLONY M ORPHOLOGIES ...... 135

xii FIGURE 3. BACTERIAL G ROWTH WITH S URFACTANT ...... 136

FIGURE 4. COLONY F ORMING U NIT AREA C OMPARISON ...... 137

FIGURE 5. SUSCEPTIBILITY TO BACTERIOPHAGE I NFECTION ...... 138

SUPPLEMENTARY F IGURE 1. GROWTH C URVE AND P LATING N UMBER ...... 13 9

Conclusion

FIGURE 1. PSEUDOMONAS PSEUDOALCALIGENES E AST R IVER I SOLATE A (ERA)...... 143

xiii Introduction

Isolated in [1] 1973 , the Pseudomonas bacteriophage Φ6 has an extremely established and well

characterized history as a model system. The Cystoviridae family reference strain Φ6 (Figure 1)

has many properties making it an ideal candidate to carry out the following studies.

A B

cba B rian E . Ford, T he D ennehy L ab

Figure 1. Pseudomonas bacteriophage Φ6. Electron micrographs of (A) Φ6 (magnification = 30K x) and (B) Φ6 attached to pilus of PP (magnification = 40K x). Samples were negatively stained with a 1% UA solution.

Φ6 has a segmented dsRNA linear tripartite genome with its segments designated small (2.9kb),

medium (4.0kb) and large (6.4kb). [2,3] Studies have demonstrated a lack of recombination between the segments. [4,5] The Cystoviridae family has nine (Φ6, Φ7, Φ8, Φ9, Φ10, Φ11,

Φ12, Φ13 and Φ14*) canonical members and one member (Φ2954) awaiting classification.

Family members Φ7, Φ9, Φ10 and Φ11 are classified as close relatives while Φ8, Φ12, Φ13 and

Φ14 are considered distant relatives to Φ6 based on the latter group’s adsorption and infection

via the lipopolysaccharide (LPS) rather than the [2,6] pilus (Figure 2). Previous studies have

* Φ14 appears as a member of the Cystoviridae family in the 2011 9th report of the International Committee on Taxonomy of Viruses, but not in the online NCBI database.

xiv characterized Φ8 as the most distantly related relative of Φ6 in the Cystovirus family[7,8] and recent evidence suggests that segmented RNA genomes are packaged into capsids more efficiently than are nonsegmented versions. [4,9]

Figure 2: Schematic of the Pseudomonas phage Φ6 life cycle. (A) Adsorption. (B) Envelope fusion. (C) Peptidoglycan digestion. (D–E) Endocytotic uptake of nucleocapsid. (F) Early transcription. (G) Polymerase complex assembly. (H) ssRNA packaging and replication. (I) Late transcription. (J) Nucleocapsid shell assembly. (K) Translocation of the viral envelope and assembly of spikes. (L) Host cell lysis and release of mature virions.

From Virus Taxonomy: Ninth Report of the International Committee on Taxonomy of Viruses (Elsevier: 2011). Reprinted with permission.

While the Pseudomonas bacteriophage Φ6 is most closely related to the Reoviridae (family in which the medically relevant rotaviruses fall), it is also related to both the hepatitis C virus

(HCV) and the flaviviruses. [6,10] Well known flaviviruses include West Nile virus (WNV),

Dengue virus (DENV) and Yellow fever virus. A similarly segmented virus, the influenza virus, is carefully monitored for reassortment as that results in antigenic shift and opens the potential

xv for pandemic outbreaks. Understanding of the niche breadth of a pathogen can help to accurately predict its potential for host emergence. [9,11]

Here, we extend this study system to include: 1) genetic mutations in the P3 attachment protein permitting host range expansion to a novel , host supported by a predicted 3D model

(Chapter 2); 2) a novel paired strain assay examining a non genetic host factor’s impact on subsequent infections (Chapter 3); and 3) characterization of a lab-‐evolved host strain phenotype with improved growth characteristics but increased susceptibility to bacteriophage infection.

xvi

Chapter 1: Evolutionary Ecology of the Viruses of Microorganisms

Brian E. Ford1,2, Marko Baloh1 and John J. Dennehy1,2,3

1Department of Biology, Queens College, Queens, NY, 11367

2The Graduate Center of the City University of New York, New York City, NY, 10016

3Corresponding Author: [email protected]

Running title: Virus Evolutionary Ecology

Accepted for publication January 2014 in Viruses of Microorganisms: Diversity, Molecular

Biology and Applications. Hyman P & Abedon ST (eds.). Poole, UK: Caister Academic Press.

Abstract

With estimated numbers greater 31 than 10 , viruses are the most abundant organisms on the planet, and are found in all habitats: aquatic, atmospheric and terrestrial. No cellular organisms – whether animal, plant or microbe – are free from viral parasitism. Consequently, the effects and influences of viruses are pervasive, directly or indirectly impacting all organisms, populations, communities and ecosystems. Here we consider the evolutionary ecology of the viruses of microorganisms (hereafter VoMs) which, due to the abundance of their hosts, outnumber all other types of viruses. Subfields of evolutionary ecology include life history evolution, population biology, biogeography, and community ecology. Like blind men describing an elephant, each approach only describes a facet of VoM evolutionary ecology. Here we describe some of the approaches used to describe VoM evolutionary ecology in hopes that a synthesis will allow some perception of the whole.

Introduction

Whether or not one describes viruses as organisms, living entities, or instead simply as infectious agents, viruses nonetheless both evolve (evolutionary biology) and interact with other organisms (ecology). In this chapter we consider the evolutionary biology and ecology of the viruses of microorganisms or VoMs, which include bacteriophages (the viruses of bacteria), archaeal viruses, viruses of protists, and various mycoviruses (viruses of fungi). In addition, we emphasize the “hybrid” discipline of evolutionary ecology, which covers the subdisciplines of life history evolution, population biology, biogeography, and community ecology. To our knowledge, this is the first publication to review the evolutionary ecology of VoMs as a group, though previous reviews consider these subjects as they apply to bacteriophages or archaeal viruses (Brussow and Kutter 2005; Abedon, 2008; Abedon 2009; Prangishvili 2013). For further discussion especially of VoM ecology, see the chapters (this volume) by Weinbauer on aquatic

VoMs and Williamson on soil VoMs.

VoM Abundance, Biodiversity, and Biogeography

The viruses of microorganisms may be the most numerous organisms on Earth (Bergh et al., 1989; Suttle, 2005; Angly et al., 2006). In aquatic and soil habitats, viral concentrations higher than 108 per milliliter or gram have been reported (Fuhrman, 1999; Wommack and

Colwell, 2000; Ashelford et al., 2003; Suttle, 2005; Williamson et al., 2005) [see also Williamson, this volume]. Even the Earth's atmosphere has been shown to harbor relatively high concentrations of viruses (107 -‐ 108 m -‐3) (Whon et al., 2012).

Given that VoMs exist in almost unimaginable numbers, we might expect a

correspondingly high level of diversity. Studying VoM diversity, however, is complicated by the fact that, unlike cellular organisms, VoMs do not have universal ribosomal DNA sequences that facilitate the identification of discrete species and determine the phylogenetic relationships

between these species (Rowher and Edwards, 2002). Thus, sampling VoM biodiversity was

approached in a piecemeal fashion (i.e., laboratory culturing or direct observation) until the

advent of direct environmental genomic sampling (i.e., metagenomics [see Vega Thurber, this

volume]). Metagenomic estimates of VoM diversity entail the isolation and high-‐throughput

sequencing of all viral nucleic acids in an environmental isolate (e.g., water, soil, tissue, feces).

To ensure that only viral genetic material is sequenced, environmental isolates are ultra-‐filtered

and nucleases are used to digest non-‐capsid protected prokaryotic and eukaryotic nucleic acids

(Breitbart et al., 2004a; Delwart, 2007; Thurber et al., 2009). Partial genetic fragments obtained

from random sequence reads are computationally aligned and assembled into contigs

(consensus sequences based on overlapping partial fragments). A contig spectrum is generated

for a virome by counting the number of sequences that fall into each contig (Allen et al., 2013).

Abundant VoM types are inferred by the presence of a large number of sequences mapping to

any particular contig.

By comparing the genomic sequences newly obtained from a community with those in

existing genetic sequence databases, VoM genetic richness and diversity can be estimated.

Previously unknown VoM sequences (i.e., sequences not matching those in existing genetic

databases) can be identified and compared among different communities (Edwards and

Rohwer, 2005; Suttle, 2007). The percentage of unknown VoM sequences in recent studies has

ranged from 60% to 97% (Angly et al., 2006; Desnues et al., 2008; Hurwitz and Sullivan, 2013).

As in population mark-‐recapture studies (Pradel, 1996), a high proportion of novel types recovered from population resampling indicates that a large number of new types remain undiscovered.

Some of the first metagenomic estimates of VoM diversity were conducted on marine and human virus communities by Rohwer, Breitbart, and colleagues (Breitbart et al., 2003;

Breitbart et al., 2002). For the marine communities, mathematical models predicted the existence of 374 to 7,114 viral types in the oceans off the California coast (Breitbart et al.,

2002). The human virome has been estimated to contain at least 1,250 distinct virus types, most of which are VoMs (Breitbart et al., 2003). In retrospect, large as they may be, these numbers may be significant underestimates (Allen et al., 2011; Angly et al., 2006; Culley et al.,

2006; Hurwitz and Sullivan, 2013; Kristensen et al., 2010; Mokili et al., 2012; Rosario and

Breitbart, 2011; Rosario et al., 2009). For one, these estimates do not include RNA virus diversity, which is harder to analyze due to the difficulties in sequencing RNA. Distinct VoM types in the biosphere may number in the millions (Allen et al., 2013).

Interestingly, analyses of VoM genetic material collected from geographically-‐distinct environments (i.e., freshwater, marine, terrestrial) show that similar VoM genetic sequences can be found in widely separated ecosystems, indicating that VoMs (or at least their genes) are in constant motion through the biosphere (Breitbart et al., 2004a; Breitbart et al., 2004b;

Breitbart and Rohwer, 2005; Hambly and Suttle, 2005; Kunin et al., 2008; O'Keefe et al., 2010;

Sano et al., 2004; Short and Suttle, 2005). Nonetheless, host habitat requirements may play a strong role in the distribution of VoMs. For example, an investigation of hot spring microbial

communities found that genetically distinct viral populations were associated with each local geothermal region (Held and Whitaker, 2009). This ties into Baas Becking's idea that

"everything is everywhere, but, the environment selects" (Baas Becking, 1934). In this context, the presence of hosts is the key aspect of the “environment” that determines whether a virus is present in a specific habitat (Angly et al., 2006).

Habitat specialization and isolation can have far-‐reaching effects on VoM evolution because the distribution of populations across space, and the connections between them, govern population diversification. Since population genetic differentiation is usually a function of gene flow between populations, connected populations will tend to follow more similar evolutionary trajectories, whereas isolated populations will tend to diverge as they become locally adapted (Kawecki and Ebert, 2004). We expect that VoM distributions will be highly contingent on chance historical events and the specific details of their ecology. Overall, there appears to be a tremendous diversity of VoMs, and they tend to be broadly, but unevenly, distributed across the biosphere (Thurber, 2009).

Population Dynamics of VoMs

The number of VoMs present in a habitat ultimately depends on the conditions that assist or impede VoM reproduction. Since most VoMs move between hosts as particles (except fungal viruses that are transmitted intracellularly during cell division (Ghabrial and Suzuki

2009)), their macroscale movement depends on the physical properties of the medium and forces acting upon it (i.e., bulk flow of water/air). At the microscale, the laws of random diffusion by Brownian motion govern VoM movement. Infection is contingent on chance

collisions between VoMs and hosts, the probabilities of which may be quite low on a virus-‐by-‐ virus basis. Abedon estimated that if a virus were the size of the Titanic, then a milliliter of fluid would be analogous to the volume of the Earth (Abedon, 2011). If it were not for the enormous population sizes of VoMs and their hosts, then infection events would be improbable.

There is a growing appreciation of how VoM morphology may affect mobility in different environments, thus increasing the chances of encountering a host. For example, rod-‐ shaped viruses may diffuse faster than spherical viruses in tissues or gels (Lee et al., 2013). We would expect also that very large virus particles such as those of protists that have been discovered [see La Scola, this volume] would diffuse at slower rates than small, icosahedral single-‐stranded bacteriophages [see Lehman, this volume]. In addition, Barr et al. (2013) have documented the potential of bacteriophage particles to adhere reversibly to mucus. We expect that further investigations of the relationship between the ecological milieu of VoM's and their morphologies would be profitable.

Factors affecting rates of VoM population growth. Because of VoM random diffusion, most models assume that VoM population dynamics follow mass action principles where host infection rates are directly proportional to the concentrations of VoMs and their hosts. That is, the greater the concentration of VoMs and hosts in a fluid, the greater probability of collisions leading to infection. Growth of a population of size N can be described by a differential equation that accounts for births (b) and deaths (d) over time:

(1) !" !# = 𝑏𝑏 − 𝑑𝑑 𝑁𝑁 In evolutionary biology, births minus deaths, b – d, over short intervals is termed the per capita growth rate, r, which often is used as an estimate of absolute fitness, a.k.a., reproductive

capacity (Fisher, 1930). Population growth in this model shows a characteristic concave-‐up curve, commonly termed exponential growth (Fig. 1A).

Each of the major VoM life history patterns—lytic, lysogenic (or equivalent), and chronic

[see Hyman and Abedon, this volume]—will exhibit exponential growth, but at varying rates.

Lytic VoMs, given sufficient environmental densities of host cells, will have growth rates greatly exceeding hosts. By contrast, chronic and especially lysogenic VoMs (existing as prophages or proviruses) will grow at rates more closely matching those of their hosts. The major distinction between these modes of life is the degree of host exploitation effected by VoMs. Lytic viruses are parasitic, while proviruses and chronic viruses have the potential to be mutualistic with their hosts. For instance, it has long been known that bacteriophage lysogens can carry genes that in many cases are likely to be beneficial to host fitness (Andersson and Banfield, 2008).

Examples include the CTXφ phage-‐encoded cholera toxin (Waldor and Mekalanos, 1996) and the bor gene of phage λ, which provides serum resistance to Escherichia coli (Barondess and

Beckwith, 1990; 1995). In addition, prophages often confer immunity to coinfection to their hosts, and may enable hosts to survive environmentally unfavorable periods (Brussow et al.,

2004; Paul, 2008).

The exponential growth model shown in Equation (1) is deterministic since the only variables are initial population size and reproduction rate. In reality, VoM population dynamics depend on many other factors, including stochastic influences. An additional complication not addressed in Equation (1) is that VoM per capita growth rate can depend on the ratio of adsorbed VoMs to susceptible hosts found within a system (the multiplicity of infection or

MOI). For many bacteriophage, growth rates are lower at high MOIs (i.e., negative density-‐

dependence) because infected bacteria cannot support additional phage infections while

bacteria that are not yet phage infected are not a rapidly renewed resource. Specifically, at

MOIs << 1, uninfected hosts can readily replenish themselves via replication, whereas at MOIs

>> 1 all hosts will become infected which, for lytic infections, results in a block on ability of a system to replenish uninfected cells. Thus, the total number of hosts available for infection over time can be greater when MOIs are low, but ultimately more host infections over the same period means higher phage growth rates along with higher resulting MOIs.

Surprisingly, Pseudomonas bacteriophage ɸ6 shows positive density-‐dependence –

greater phage growth rates at higher MOIs under certain conditions (Dennehy et al., 2006).

While the mechanism for this growth pattern is not entirely clear, it seems to stem from an

enhancement of infection when multiple viruses attack the same cell (Joseph et al., 2009). Allee first described positive density-‐dependence of population growth with reference to growth limitations in small populations due to limited access to mates (Allee, 1931). Several theoretical studies suggest that positive density-‐dependence may be an important factor in host-‐parasite evolutionary ecology (Gerla et al., 2013; Regoes et al., 2000; Weitz and Dushoff, 2008).

However, while positive density-‐dependent growth has been experimentally observed in

several host-‐parasite systems, including bacteria-‐Daphnia (Ebert et al., 2000; Little and Ebert,

2000), microsporidian-‐mosquito (Agnew and Koella, 1999), prion-‐mouse (McLean and Bostock,

2000), -‐ ɸ6 Pseudomonas remains the only virus-‐host system where positive density-‐dependent

growth has been observed (Dennehy et al., 2006). Whether this is due to an intrinsic property

of this particular phage, or simply due to multiplicity reactivation or its genome being split into

different virion particles, is unknown.

Another complication is that host numbers are finite for reasons that are independent of virus infection. Limited environmental resources ultimately circumscribe the number of hosts that a habitat can support, which, more formally, is referred to as the carrying capacity. The number of hosts in a habitat changes over time also due to factors such as host reproduction, environmentally-‐induced host death (including VoM-‐induced host lysis), and acquisition of resistance mechanisms to environmental antagonists, such as chemical and physical antagonists but also predators and viruses, which can have the effect of reducing, for example, host replication efficiency. As such, VoM population dynamics can be described by Verhulst-‐

Pearl logistic models, where the population size over time follows -‐ a sigmoid or S shaped function (Gause, 1934; Pearl, 1927; Pearl and Reed, 1920). This model can be formalized using the differential equation:

, (2) !" ,-" !# = 𝑟𝑟𝑟𝑟( , ) where N is population size and K represents the maximal possible value of N (Fig. 1B).

Closer inspection of growth curves derived from this model reveals an inherent asymmetry between host and VoM growth rates. This asymmetry results from the fact that from hosts reproduce by binary fission while VoMs produce, e.g., 10-‐100 progeny per cycle of reproduction over similar intervals. It is clear that only a few rounds of reproduction are required for VoMs to outstrip their hosts. This observation supports reports of VoM superabundance (~10:1) in marine (Chibani -‐Chennoufi et al., 2004) [see also Weinbauer, this volume] and terrestrial (Ashelford et al., 2003) [see also Williamson, this volume] habitats.

Alternatively, the VoM growth advantage is dependent on host density, with a high VoM advantage given high host densities (as a result of greater similarity between VoM and host

generation times) and a low or lower VoM advantage at low host densities (where VoM

generation times can be potentially much greater than those of hosts due to the substantial

extensions of the virion search stages of VoM life cycles). A major consequence of VoM

superabundance, when it occurs, can be extreme competition among VoMs for limited hosts,

exerting strong evolutionary pressure on VoMs to optimize infection mechanics and host

utilization.

Predator-‐prey dynamics. While interactions among VoMs play a role in shaping VoM life history traits, the influence of interactions with their hosts are essential, both molecularly and ecologically. In terms of ecology, a standard means of considering -‐ these virus host interactions is in terms of predator-‐prey dynamics. The first explicit models of predator-‐prey dynamics were introduced independently by Lotka and by Volterra (Lotka, 1925; Volterra, 1926). To apply

Lotka and Volterra's model to VoMs, assume that in the absence of VoMs, hosts increase exponentially as described in Eq. 1. By contrast, VoMs will decline in the absence of prey:

(3) !/ !# = −𝑟𝑟0𝑃𝑃 where P is the number of VoMs and r2 is the VoM instantaneous death rate. If VoMs and hosts are introduced into a limited space, the host growth rate, r1, will be reduced by a factor

dependent on VoM density:

, (4) !" !# = 𝑟𝑟2 − 𝛼𝛼𝛼𝛼 𝑁𝑁 where α is the adsorption rate N and is the number of hosts. The VoM population will increase

at a rate dependent on host density:

(5) !/ !# = 𝛼𝛼𝛼𝛼𝛼𝛼 − 𝑟𝑟0 𝑃𝑃

where β is the burst size or number of progeny produced per infection. Under these simplistic

conditions, VoM and host populations will oscillate in a systematic manner, showing a high

degree of stability (Fig. 1C ). Extensions of the basic Lotka-‐Volterra model have included

resource limitations on host density, the time lag due to VoM latent period and the acquisition

of host resistance (Bohannan and Lenski, 1997; Campbell, 1961; Kerr et al., 2008; Lenski, 1988;

Lenski and Levin, 1985; Levin et al., 1977; Stopar and Abedon, 2008).

VoM impact on host populations and communities.

Darwin suggested that “mutual relations” among organisms drives biological diversification

(Darwin, 1859). It is easy to imagine that the evolutionary arms race among VoMs and hosts can

splinter populations into myriad resistant and counter-‐resistant variants. Indeed, studies have

shown that diversification is a frequent occurrence in both static microcosms (Buckling and

Rainey, 2002; Buckling et al., 2006; Morgan et al., 2009) and chemostat communities (Chao et

al., 1977; Marston et al., 2012). For example, at least 4 cycles of adaptation-‐counteradaptation

between the cyanobacterium Synechococcus and the VoM RIM8 were observed during 6-‐month

chemostat experiments (Fig. 1D) (Marston et al., 2012). In these experiments, up to 13 newly-‐ evolved RIM8 and 11 Synechococcus variants appeared in coevolving populations. In this section, we consider two associated phenomena stemming from these mutual relations. The first is that of VoM-‐host coevolution—including limitations often seen on that coevolution,

particularly in experiments involving bacteriophages—and the second is the result that this

coevolution can have on host and indeed community diversity.

VoM-‐host coevolution and its limitations. The general findings of predator-‐prey models

are that 1) VoMs will limit host populations below the carrying capacity provided by available

resources, 2) host populations can fluctuate in a cyclic manner and 3) coexistence can occur over long periods. Experimental support from coevolution studies for these theoretical predictions is mixed. Frequently hosts have tended to stabilize at a density similar to that of controls, and VoMs persisted at a relatively low density (see Dennehy, 2012, for review). In other cases, VoMs were driven to extinction outright. The most likely interpretation of the former results is that, although resistant hosts came to dominate the habitat, some VoMs were able to persist due to the continued presence of a small number of sensitive hosts in a spatial or numerical refuge (Chao et al., Schrag 1977; and Mittler, 1996). The main point here is that, due to the rise of resistant hosts, demonstrations of repeated cycles of host-‐parasite coevolution in experimental microcosms have been somewhat rare.

This lack of repeated cycles implies a lack of repeated VoM adaptation and host counter-‐adaptation during VoM-‐host co-‐cultivations, or at least that such dynamics typically are not dominating during VoM growth within microcosms. One reason for the relative lack of such arms races between co-‐cultured VoMs and hosts may be due to a potential asymmetry in VoM and host evolutionary potential. Because of their high mutation rates and large population sizes, microbial host organisms are able to search the VoM-‐resistance sequence space relatively rapidly. While VoMs can have even greater population sizes and rates of mutation, the larger genomes of hosts versus VoMs may provide hosts with greater means to evolve VoM resistance. By contrast, the relatively small VoM genomes may be less well equipped to respond to challenges of host resistance. This is particularly acute when hosts can modify their resistance to VoMs via mutational loss of information (e.g., gene inactivation). Due to their limited genomic repertoire, VoMs often must mutationally change without substantially

impacting gene function, though VoMs may be more effective during VoM-‐host coevolution if they target, for virion adsorption, host molecules that the host is unable to do without.

VoM genome sizes can be limited by the capacities of their capsids. Hosts, by contrast, often can easily assimilate even relatively large blocks of new genetic material (e.g., as genomic islands). Study of this asymmetry may be exacerbated by the long history of laboratory culturing of the hosts and parasites typically used in experimental coevolution studies (e.g. E. coli and its phages λ, T4, T7). These laboratory-‐adapted strains may lack evolutionary potential for extensive coevolution because they have been selected under constrained conditions for hundreds of generations. This idea is supported by observations of repeated rounds of coevolution among hosts and parasites recently isolated from the wild (Barnet et al., 1981;

Buckling and Rainey, 2002; Marston et al., 2012).

How these laboratory studies relate to host-‐VoM evolutionary dynamics in the wild is poorly understood. Wild populations are subject to much greater spatial, environmental, and biotic heterogeneity, significantly impacting community structure. Moreover, VoM resistance usually incurs fitness costs (Bohannan and Lenski, 2000; Buckling and Rainey, 2002; Chao et al.,

1977; Lenski, 1988; Levin et al., 1977; Schrag and Mittler, 1996). As a consequence, while resistant hosts may predominate in the presence of VoMs, they will be outcompeted by VoM-‐ sensitive hosts in the absence of VoMs. This can ensure that any shifts in host susceptibility to

VoMs may be transient. As long as some VoMs can persist during the fallow period, including non-‐locally, their local populations may be able to recover following a precipitous drop in population size due to host resistance. VoM-‐host coevolution may be an important aspect of

VoM ecology where extrapolating from laboratory studies to dynamics in the wild may require careful consideration.

VoMs as drivers of host biodiversity. A widely held notion in ecological theory is that there is an inherent tradeoff between maximizing reproduction and minimizing predation

(Ludwig and Rowe, 1990; Pianka, 1976). This tension should promote the diversification of prey populations into competitive and defensive specialists (Winter et al., 2010). Such frequency-‐ dependent selection among alternative variants is commonly referred to as “killing the winner”

(Thingstad, 2000; Weinbauer and Rassoulzadegan, 2004). When predators are absent, competition specialists should dominate as they have a reproductive advantage over defensive specialists. However, their burgeoning numbers will allow for the propagation of predators, freeing up resources for survival-‐maximizing defensive specialists. Predators under these conditions will consequently tend to decline in number, allowing the return of competition specialists. These dynamics should encourage an increase in the microbial diversity of both prey and predator species.

Given that chemostats are used to create minimally varying microhabitats, we might expect coevolution in the wild to result in much greater diversification given the much higher degree of environmental heterogeneity. This notion is supported by the geographic mosaic theory of coevolution, which holds that populations respond to ever-‐changing patterns of interactions across space and time (Thompson, 1994; Thompson, 2012). Here fragmentation of the natural landscape allows local populations of one species to adapt to local populations of other species in a genotype-‐by-‐genotype-‐by-‐environment interaction. As these interactions

play out across larger spatial and temporal scales, and as different subpopulations interact or

fail to interact, coevolutionary changes can result in speciation and increased biodiversity.

VoM Ecological Adaptation

Like all organisms, VoMs are a collection of features and traits shaped by natural selection, but the story of evolution is not simply one of continual small improvements in each trait. Rather, selective forces on organismal traits often can be in direct opposition.

Improvement in one trait, that is, can entail a reduction in performance of another. For example, capsid thickness can be correlated with virus survival, but inversely correlated with the virus multiplication rate (De Paepe and Taddei, 2006; García-‐Villada and Drake, 2013).

Natural selection, then, will favor a capsid thickness that represents the optimal tradeoff between survival and reproduction such that the maximum number of surviving offspring are

produced over time for a given set of environmental circumstances. Since environments are

often changing, natural selection is continuously recalibrating the optimal balance between

specific traits.

Generation time as an optimizable trait. Nowhere is this issue of tradeoffs in the

optimization of different traits more apparent than in the fine-‐tuning of lysis timing. A general

assumption of VoM biology is that progeny are assembled at a linear rate (Wang et al., 1996).

Consequently, an early lysis time will result in fewer offspring produced than a later lysis time,

but the progeny of early lysers will have a head start in infecting new host cells. The optimal

lysis time therefore depends on the number and quality of hosts available for infection

(Abedon, 1989; Wang et al., 1996). If the number or quality of available hosts is low, then it may

be advantageous to delay lysis and produce more progeny. By contrast, high host densities will favor shorter latent periods and faster lysis. Experimental work on bacteriophage lysis timing has generally confirmed the predictions of optimality models (Abedon et al., 2003; Heineman and Bull, 2007; Shao and Wang, 2008), but has revealed that the evolution of optimal phenotypes is subject to genetic and physical constraints (Chantranupong and Heineman, 2012;

Heineman and Bull, 2007). Optimality theory provides a starting point to develop hypotheses regarding the evolution of organismal traits, but consideration should be paid to the genetic details and physiological constraints of the system under study.

A salient question is, when conditions change, how will VoMs adjust lysis timing, and by extension, generation time, to optimize reproduction in the new environment? Natural selection has several potential targets that could be manipulated to alter generation time: 1) the adsorption rate could be adjusted, 2) the eclipse period (i.e., time from genome penetration to production of first mature progeny) could be extended, or 3) the rise period (i.e., time from first mature progeny until lysis) could be altered (Chantranupong and Heineman,

2012). Time spent in the first period is a function of the attachment rate constant and host concentration. Naively, one might expect this rate to be maximized. However, high attachments rates may be deleterious because they may limit the number of hosts encountered per burst, and therefore productivity per host (Gallet et al., 2011; Gallet et al., 2009). Furthermore, mutations increasing attachment rates could have negative pleiotropic effects (Pepin et al.,

2006). Finally, high attachment rates could be disadvantageous when there are high levels of stationary phase cells or cellular debris to which too effectively adsorbing viruses might non-‐ productively adsorb (Gallet et al. 2012). On the other hand, low attachment rates would

prolong search times and expose phages to increased risks of environmentally-‐induced inactivation. These constraints suggest that attachment rate is probably not adjusted to optimize lysis time.

During the eclipse period, the phage genome is being replicated, transcribed and translated, and consequently the phage is in its most vulnerable state. Death of the host cell would entail destruction of the phage thus we expect this period to be minimized across all conditions. This leaves the rise period which we expect to be the most evolutionarily malleable.

Evidence from evolution experiments manipulating host density is consistent with this conjecture. In these studies, mutations tend to occur in regions of genome associated with genome replication and lysis, but not with attachment (Abedon et al., 2003; Heineman and Bull,

2007, but see Shao and Wang 2008).

Coinfection, competition and cheating. Describing the outcomes of competition between organisms is an essential feature of evolutionary ecology. Similar to other organisms,

VoMs compete for the resources required to replicate additional copies of themselves. As with any competitive interaction, natural selection favors VoMs that are more efficient at acquiring and using host resources. Notably for VoMs, this competition occurs not only extracellularly as

VoMs compete for hosts, but also intracellularly as VoMs compete for host resources (Dennehy et al., 2013; Preisig et al., 1998; Turner, 2005; Yau et al., 2011). In the case of coinfection or a multiple/sequential infection (superinfection), or even reactivation of a latent infection, after a

VoM introduces its genome into a host cell, proteins translated from a VoM's genome enter common pool and are therefore susceptible to co-‐option by competing genotypes of VoMs already present in the cell. That is, some virus types can package their genomes into capsids

containing proteins that they themselves did not cause to be produced. This parasitism enables

the "cheating" virus to specialize on other facets of their own reproduction, such as increasing

genome replication speed and packaging efficiency. An example of this type of competition

comes from coliphage P2 and its satellite P4 (Six and Klug, 1973). P4 lacks genes for head, tail

and lysis proteins, but is able to package its genome in the P2 capsid (Deho and Ghisotti, 2006).

As such, P4 is able to concentrate its resources on genome replication while parasitizing capsids

from P2.

Viruses or virus-‐like elements that require other "helper" viruses in order to reproduce

include satellite viruses (Deho and Ghisotti, 2006; Six and Klug, 1973), viroids, virusoids

(Symons, 1991), virophages (Fischer and Suttle, 2011; La Scola et al., 2008) [see Fischer, this

volume], and defective-‐interfering (DI) particles (Horiuchi, 1983; Huang, 1973; Roux et al.,

1991). As a rule, these types of parasitic viruses only arise when coinfection is common.

Consequently, there should be strong selection on the potentially parasitized virus to sequester a host and limit coinfection. The outcome of intense ecological competition is reflected in the numerous mechanisms of resistance to superinfection (Hyman and Abedon, 2010). For example, some lambdoid phages bind outer membrane protein FhuA to infect E. coli (Uc -‐Mass et al., 2004). Competing phages express a protein, Cor, that blocks FhuA-‐mediated ferrichrome uptake, and prevents FhuA-‐mediated phage infection (Uc-‐Mass et al., 2004).

Productivity versus latency. Some VoMs can enter long-‐term associations with host cells such as by integrating their genome into that of the host, which for bacteriophages has been described as lysogeny but more generally can be described as a form of latent infections.

These VoMs, now existing as proviruses (and/or prophages in the case of bacteriophages) only

“replicate” when the host cell replicates. As a consequence, and if uninfected host cells are

otherwise fairly prevalent within an environment , then this life history strategy entails a

significant reduction in reproductive output compared to the lytic strategy (Abedon et al.,

2001). Since natural selection typically will maximize reproduction, lysogeny is likely the product of a specific selective condition, namely where the probability of locating a new host is relatively low (Abedon et al., 2001; Wang et al., 1996).

The best-‐studied lysis-‐lysogeny system is that of the bacteriophage λ [see Lehman, this

volume]. When growth conditions are favorable, an important regulator of λ gene expression,

CII, is rapidly degraded by host proteases, and the likelihood of the lytic pathway dominating is

high. By contrast, when growth conditions are poor, CII accumulates, increasing the probability

of entering the lysogenic state (Herskowitz and Hagen, 1980; Oppenheim et al., 2005 ).

Ecologically, this decision process is intuitive because the essential "life decision" a lysogen

must make is whether to accept a low rate of replication in exchange for shelter or instead to

potentially risk everything by infecting lytically for the potential payoff of greater replication in

new hosts.

There are at least two sources of information by which a lysogen can base this

"decision": host physiology and the presence of coinfecting viruses. A favorable host

physiological state may signal the likely presence of available hosts for progeny, whereas the

opposite condition may imply that suitable hosts are in short supply. Alternatively, the presence

of competing VoMs within a host implies that VoMs are common relative to hosts and

uninfected hosts may not be available. Indeed, coinfection can reinforce the lysis inhibition

pathway, another bacteriophage-‐associated lysis-‐delaying phenomenon, prolonging infection

state (Abedon, 1990; Abedon et al., 2003; Weitz et al., 2008).

Interestingly, a greater penalty probably arises from not retaining infections if no new

host cells are available or if all the other host cells are occupied versus choosing to stay in place

(retaining infections) when permissive hosts are available. In the former, a bad decision results

in VoM destruction, whereas in the latter, the cost is a reduced reproduction rate compared to

competing genotypes. This asymmetry should have interesting implications for the evolution of

control mechanisms dictating the lysis-‐lysogeny decision that occurs at the start of infections

phages (or, more generally, viruses) that are able to choose between latent or productive

infection. Related to this, exit from lysogeny is often predicated by damage to the host DNA

agents like UV light and mitomycin C (Cochran et al., 1998; Weinbauer and Suttle, 1999). As an

analogy, if one’s house were on fire, one would attempt escape regardless of the external

conditions. Since death in the former is assured, lysis pays off regardless of the probability

survival outside the host.

VoM Genome Evolution

Microorganisms span the three domains of life and their VoMs reflect this breadth in

the variety of their genomes. Out of the seven virus classes within the Baltimore virus

classification system, six classes are known to parasitize all major phyla of microorganisms

while only Group one ( 7, or -‐ non retroviral dsDNA viruses that replicate through a ssRNA intermediate) does not contain exemplars that infect microorganisms. Despite this great diversity, not all virus classes are represented equally. Almost all of the nearly 100

characterized viruses infecting archaea hold their genetic information as dsDNA (Mochizuki et

al., 2012; Prangishvili 2013), while viruses infecting algae have genomes ranging from ssRNA to

dsDNA (Van Etten et al., 1991; Short, 2012). Viruses of fungi tend to be dsRNA (Göker et al.,

2011; Vainio et al., 2011), while representatives of at least four types can be found among the

bacteriophages (Calendar and Abedon, 2006). In this section we provide primers on VoM

genomics and genome evolution. See [Klumpp, this volume] for further discussion of VoM

genomics.

VoM genomes. Virus nucleic acid characteristics can have a large effect on genomic

attributes such as overall genome size and mutation rates. Exemplifying the upper limit of VoM

genome size are the recently discovered amoeba phages, such as Mimiviridae, Marseilleviridae

and Pandovirideae, which have dsDNA genomes larger than 2.5 Mb, surpassing even some

bacteria (La Scola et al., 2003; Monier et al., 2008; Colson et al., 2013; Philippe et 2013) [See

also La Scola, this volume]. By contrast, some ssRNA phages have genomes as small 3.3 Kb

(Hatfull and Hendrix, 2011), a 758-‐fold difference. Mutation rates show tremendous variation

as well, with values ranging from 1.1 × 10–3 nucleotide substitutions per site per cell infection

(s/n/c) in the ssRNA phage Qβ to 9.8 × 10–8 s/n/c for the bacteriophage T2 (Sanjuán et al.,

2010). Generally, VoMs that store their genetic information as RNA or ssDNA tend to have

smaller genomes and higher mutation rates, while dsDNA VoMs have larger genomes and lower

mutation rates. These differences are believed to reflect differences in polymerase fidelity and

nucleic acid stability (Duffy et al., 2008).

In addition to size and stability, VoM genomes show considerable structural variation.

Both circular and linear permutations are common, and do not seem to correlate with any

obvious factors. In addition, ssDNA, ssRNA and dsRNA VoM genomes can be segmented (Iranzo and Manrubia, 2012; Ojosnegros et al., 2011). Numerous hypotheses have been proposed to explain viral segmentation, including increased capsid stability (Iranzo and Manrubia, 2012;

Ojosnegros et al., 2011), virus sex (Chao et al., 1992), and copying fidelity (Pressing and

Reanney, 1984). Though a consensus is not available, observations show that the reassortment of virus segments can be an important factor in the generation of virus genetic diversity

(Marshall et al., 2013; O'Keefe et al., 2010).

Horizontal gene transfer and genome mosaicism. VoMs are extremely successful mediators of gene shuffling (Fig. 3). Regardless of reproductive strategy (lytic vs. latent vs. chronic) or type of genetic material (RNA versus DNA), many VoM genomes contain an amalgam of genetic information from a variety of sources, commonly referred to genomic mosaicism (Casjens and Thuman-‐Commike, 2011). For example, the tail genes of phages HK97 and λ show a high degree of sequence similarity, but the head genes are wholly dissimilar

(Juhala et al., 2000). The most parsimonious explanation for this observation is that the head genes of HK97 and λ, but not the tail genes, were acquired from two evolutionarily divergent sources.

The life history circumstances of VoMs may predispose them to genomic mosaicism since this is generated through recombination or reassortment with foreign DNA. As obligate parasites, VoMs reproduce inside cells containing separate and distinct genomes as well coinfecting viruses, transposons, plasmids and even transformed foreign DNA (Desnues et al.,

2012). Given this proximity, the accidental integration of foreign DNA into VoM genomes is not surprising. While capsid size can limit the extent of integration of new genetic material,

recombination and then retention of similar sized genetic fragments may be relatively common.

In fact, it is frequently suggested that the modular architecture of VoM genomes is specifically evolved to allow suites of genes dedicated to a common function to be traded among VoM populations, creating a vast meta-‐gene pool (Campbell and Botstein, 1983; Hendrix et al.,

2000). Some authors even go so far as to suggest that VoMs and their hosts constitute a superorganism, with access to a global gene pool (Weinbauer and Rassoulzadegan, 2004).

Consistent with expectations, this genomic mixing is more pronounced in generalists – that is,

VoMs capable of infecting greater diversities of cellular organisms – as they are more promiscuous in host range than specialists (Born et al., A 2011). non-‐genetic form of mosaicism may also arise in the form of phenotypic mixing, when coinfecting VoMs produce proteins that interact (Loverdo and Lloyd-‐Smith, 2013).

VoM Impact on Host Genetics

Horizontal gene transfer and genome mosaicism is common among hosts of VoMs as well. It has been best studied among bacteria (Baltrus, 2013; McDaniel, 2012), but is known to occur in all domains (Bock, 2010; Hotopp, 2011). A majority of all sequenced bacterial genomes contain prophages (Canchaya et al., 2003). These latent viruses comprise up to 20% of some bacterial genomes (Klumpp et al., 2013) and a significant portion of the total planetary phage genes is contained by prophages (Forterre and Prangishvili, 2013). There are, in addition, numerous examples of phage-‐mediated gene transfer with significant implications for public health and other fields (Brussow et al., 2004; Klumpp and Fuchs, 2007; Chen and Novick, 2009;

Imamovic et al., 2009; Saunders et al., 2001). For example, lysogenic conversion of

Corynebacterium diphtheriae by the Corynebacteriophage β renders the bacterium toxigenic

(Freeman, 1951). Antibiotic resistance genes are widespread in phage DNA obtained from

environmental isolates, and Colomer-‐Lluch and colleages generated resistant clones after

transfection with phage DNA (Colomer-‐Lluch et al., 2011). Clearly VoMs are important

components of microbial horizontal gene transfer, resulting in the gain, loss or reshuffling host

genes (Andersson, 2009; Filée et al., 2002; Rowher and Thurber, 2009; . Krupovic et al., 2011)

A general category of virus-‐ and especially phage-‐encoded genes that appear to be new

additions to virus genomes and which often affect lysogenic conversion, that is, the expression

of genes by prophages during lysogenic cycles, have been termed morons. Morons, intriguingly

so named because they add “more DNA” to phage genomes, often contain their own separately

regulated promoters and terminators. Although nonessential, they may allow prophages to

escape deletion from host populations by providing to hosts beneficial genes such as virulence

factors, restriction endonucleases, superinfection exclusion proteins and toxin-‐antitoxin pairs

(Cumby et al., 2012). For example, morons encoding superoxide dismutases confer resistance

to Salmonella against reactive oxygen species produced by the mammalian immune system

(Figueroa-‐Bossi and Bossi, 1999).

Morons, once acquired by a bacterial host, can also form pathogenicity Islands (PAIs),

encoding virulence factors (Hensel and Schmidt, 2008). Present in pathogenic, but not related

non-‐pathogenic strains, these presumptive phage-‐ or plasmid-‐mediated (pre-‐)virulence factors increase bacterial fitness by providing adhesins, toxins, invasins and protein secretion systems used in host infection (Hacker and Kaper, 2000). Recent theoretical modeling predicts that genes contributed by viruses can increase the evolvability of their hosts, allowing them to

better explore their adaptive landscape (Williams, 2013). In doing so, there is no doubt that

VoMs play a role in shaping microbial communities over time (Koskella and Meaden, 2013). In some cases, coevolution with VoM can even induce a host to change its genetic code. For example, Taylor and colleagues reported several yeast strains using a modified genetic code where CUG encoded serine (Taylor et al., 2013). Interestingly these authors also found a

Totivirus which possessed only one CUG codon and was able to infect these yeast strains.

Presumably this Totivirus adapted to the yeast by avoiding the modified codon (Taylor et al.,

2013).

VoMs as Ecosystem Modifiers

Microorganisms, such as heterotrophic bacteria and phytoplankton, form the foundation of the food web as they convert solar energy and dissolved nutrients into particulate organic matter usable by higher organisms. Although research in this area is nascent, recent work indicates that more than 90% of marine biomass is composed of microorganisms. Amazingly, VoM lysis reportedly liberates 20-‐55% of this biomass each day

(Suttle, 1994; Suttle, 2007; Winget et al., 2011; Wommack and Colwell, 2000). The destruction of primary producers has the effect of reducing the total available resources to grazers at the next trophic level, reducing their growth, with cascading effects at all higher trophic levels.

Through host cell lysis, however, VoMs also can convert organic matter from a particulate form to a dissolved form consumable only by osmotrophs (organisms that acquire nutrition by taking advantage of an osmotic gradient, that is by direct uptake of dissolved organic molecules) at the very bottom of the food chain (Thingstad et al., 2008; Weinbauer et al., 2011). This semi-‐

closed “viral shunt” may reduce the flow of energy between the primary producers and

osmotrophs and the higher trophic levels (Bratbak et al., 1992; Fuhrman, 1999; Suttle, 2007).

Indeed, as much as 25% of the carbon fixed through photosynthesis may cycle through the viral

shunt (Wilhelm and Suttle, 1999).

Due to the viral shunt, VoMs may significantly impact 'the biological pump', a process

that sequesters CO2 and other nutrients into the deep ocean as a result of the death and sinking of surface water microorganisms (Suttle, 2007). This effect plays a role in the global carbon cycle and any alterations of this mechanism could influence climate change via a modification of the efficiency of this sequestration process, though the exact nature of this impact is still unclear (Danovaro et al., 2011). The current debate on the ultimate role of VoM-‐mediated lysis of marine biomass on a global scale centers on several possible outcomes (Brussaard et al.,

2008). First, VoMs may short-‐circuit the biological pump by returning nutrients back to the dissolved phase, which is hypothesized to limit their transport to the deep ocean (Poorvin et al.,

2004). Second, VoMs might accelerate the biological pump cycling by promoting the sinking of infected host cells to the deep ocean (Lawrence and Suttle, 2004). One way this might occur is the failure of flagella-‐driven motility in some infected microbes, disabling their ability to prevent sinking (Lawrence and Suttle, 2004). Finally, VoM-‐mediated lysis may drive the flow of nutrients to the deep sea via the aggregation and sinking of lysed cellular debris (e.g., marine snow) (Danovaro et al., 2008; Mari et al., 2005). These nutrients may form the basis of the food web at depths where the available sunlight is insufficient to allow photosynthesis.

We expect that, due to the high degrees of organismal and environmental heterogeneity, the activity of the viral shunt and the biological pump will exhibit considerable

temporal and spatial variation. Net movement of nutrients among trophic levels and habitats

thereby should show significant context dependency. A major challenge for microbial ecologists

will be to parse major trends in biogeochemical dynamics across widespread ecosystems.

Closing Remarks

Viruses of microorganisms are a spectacularly diverse, influential and pervasive group of

organisms, but our understanding of their biology is not proportionate to their impact. They

thus may be described as the dark matter of the biosphere (Filée et al., 2005); the hidden world

that directly or indirectly influences every living organism. Much of the world's genetic

information perhaps also is contained within their genomes. VoMs too are masters of shuttling

genes, manipulating genomes, and either generating or otherwise increasing biodiversity. They

alter biogeographical cycles, communities, ecosystems, and may even influence global climate

and its change. Understanding how these influences interact to produce our living world

requires the application of ecology in the context of evolutionary history. Further study of the

evolution and ecology of VoMs can only increase our appreciation of the rich and wonderful

fabric of life.

Acknowledgements

The authors are grateful for rewarding discussions with James Carpino, Gregory Lallos

and past and present members of the Dennehy Laboratory. Funding was provided by the

National Science Foundation Faculty Early Career Award (#1148879) to JJD.

Figure Legends

Fig. 1 – Population growth over time is depicted for three functions (Panels A-‐C) and for

experimental data (Panel D). In panel A, the characteristic concave-‐up curve of

exponential growth is shown (solid line). The shape of this function changes with

increasing (dotted line) or decreasing (dashed line) growth rates. In Panel B, the

incorporation of resource limitation into the population growth model tempers growth

rates at higher densities, giving a distinctive S-‐shaped curve (solid line). Increasing

(dotted line) or decreasing dashed line) growth rate will shift the curve to right or

left, but they both approach the same maxima, the carrying capacity. Reducing

resources will have the effect of reducing maximal carrying capacity (dash-‐dot line).

Panel C shows Lotka-‐Volterra style predator (dashed line) and prey (solid line). Predator

numbers usually correspond to prey numbers with a slight lag and a reduced magnitude.

VoMs behave differently in that their numbers usually exceed that of their “prey.” Panel

D (modified from Marston et al., 2012) shows the population dynamics of a bacterium,

Synechococcus (black line), and a virus, RIM8 (grey line), in a chemostat (top third). For

reference, the dashed line is bacterial abundance in the control, virus-‐free chemostat.

The middle and bottom panels show host and virus phenotypes found at six time points.

Host range mutants are numbered in their order of infectivity (e.g., ϕ1–ϕ12), with

higher numbers indicating the ability to infect a greater number of host phenotypes.

Host phenotypes are labeled by their ability to resist infection by each host range

mutant. For example, S (sensitive to RIM8) is the ancestral host, and R0–2 is resistant to

ϕ0, ϕ1, and ϕ2. Dashed lines are hypothetical evolutionary histories based on the most

parsimonious interpretation of the data. Reprinted with permission from Marston et al.,

2012.

Fig. 2 – Viruses of microorganisms significantly impact the rganic cycling of o matter and

nutrients in the ocean. The shuttling of resources among trophic levels is interrupted by

viruses (virus shunt). In addition, viruses impact the biological pump between the

surface and deep ocean.

Fig. 3 –Horizontal Gene Transfer Across Domains. Phage-‐dominate (dotted lines) and phage-‐

influenced (dashed lines) are emphasized. Reprinted with permission from Abedon,

2009.

Fig. 1 Population growth over time.

Fig. 2 Viruses of microorganisms significantly impact the cycling of organic matter and nutrients in the ocean.

Fig. 3 Horizontal Gene Transfer Across Domains.

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Chapter 2: Frequency and Fitness Consequences of Bacteriophage Φ6 Host Range Mutations

Brian E. Ford1,2,3, Bruce Sun1,2,4, James Carpino1,3, Elizabeth S. Chapler1, Jane Ching1,5, Yoon Choi1,6, Kevin Jhun1,7, Jung D. Kim1,8, Gregory G. Lallos1, Rachelle Morgenstern1,9, Shalini Singh1, Sai Theja1, and John J. Dennehy1,3,10

1Biology Department, Queens College of the City University of New York, Queens, New 11367

2Co-‐first authors

3The Graduate Center of the City University of New York, New York, NY, 10016

4Current affiliation: Department of Surgery, Columbia University Medical Center, New York, NY 10032

5Current affiliation: University of Maryland School of Pharmacy, Baltimore, Maryland 21201

6Current affiliation: Smilow Research Center, New York University Medical Center, 522 First Avenue, New York, NY 10016

7Current affiliation: Icahn School of Medicine at Mount Sinai, New York, NY 10029

8Current affiliation: Epidemiology and Public Health, CUNY School of Public Health, New York, NY 10065

9Current affiliation: Mailman School of Public Health, Columbia University, New York, NY 10032

10Corresponding author: [email protected]

Running Head: Phage Host Range Mutation Frequency

Key Words: emerging infectious disease, virus emergence, host range expansion, antagonistic pleiotropy, host attachment protein

Research Article publish in PLoS ONE

Abstract

Viruses readily mutate and gain the ability infect novel hosts, but few data are available regarding the number of possible host range-‐expanding mutations allowing infection of any given novel host, and the fitness consequences of these mutations on original and novel hosts.

To gain insight into the process of host range expansion, we isolated and sequenced 69 independent mutants of the dsRNA bacteriophage φ6 able to infect the novel host,

Pseudomonas pseudoalcaligenes. In total, we found at least 17 unique suites of mutations among these 69 mutants. We assayed fitness for 13 of 17 mutant genotypes on P. pseudoalcaligenes and the standard laboratory host, P. phaseolicola. Mutants exhibited significantly lower fitnesses on P. pseudoalcaligenes compared to P. phaseolicola. Furthermore,

12 of the 13 assayed mutants showed reduced fitness on P. phaseolicola compared to wildtype

φ6, confirming the prevalence of antagonistic pleiotropy during host range expansion. Further experiments revealed that the mechanistic basis of these fitness differences was likely riation va in host attachment ability. In addition, using computational protein modeling, we show that host-‐range expanding mutations occurred in hotspots on the surface of the phage's host attachment protein opposite a putative hydrophobic anchoring domain.

Introduction

After a long period of steady decline, mortality due to infectious disease increased over

the past several decades, largely because of the emergence of new ectious inf diseases including

HIV [1,2] . Of these new diseases, a disproportionate number have been viruses [3,4] . Because

of their high mutation rates and vast population sizes, viruses have higher probabilities of

acquiring the requisite mutation(s) allowing infection of novel hosts than do other types of

pathogens [5]. A common fear is that a highly transmissible and virulent virus will spread

pandemically among humans, causing widespread mortality and economic damage. Thus, there

is a strong motivation to understand and predict virus emergence.

Virus emergence is a two-‐step process. A virus first mutates to gain the ability to infect a

new host, and then fully emerges by achieving positive population growth on that host via

adaptation [6]. Theoretical modeling has shown that emergence probabilities are highly

sensitive towards the type of mutation(s) required to productively ect inf a novel host [7].

Emergence events requiring single nucleotide substitutions are far more likely to occur than

those that require several simultaneous point mutations or recombination [8]. While mutations

altering virus host specificity can involve large-‐scale genomic rearrangements, most virus host shifts likely entail the modification of a small number of virus receptor amino acid residues [9].

In fact, single nucleotide substitutions are often sufficient to expand a virus’s host range [10]. If

this mechanism of host range expansion were common, the number of host range expanding

mutations and their frequency of appearance would be important parameters governing the

probability of emergence of a potential human pathogen.

Few studies have systematically determined the type, number, frequency and fitness consequences of host range expanding mutations for any particular virus-‐host combination

[11]. Such data can aid the parameterization of evolutionary ecological model of virus emergence. Factoring in other parameters, such as s transmission rate and population densities, may allow quantitative predictions of the likelihood a particular virus is able to emerge on a new host. This type of prioritization is critical before allocating resources to interdict potential pathogenic viruses before they emerge.

Here we use an experimental model system, the bacteriophage (phage) φ6, to determine number, frequency, fitness and structural consequences of mutations allowing infection of a novel host. Phage φ6 (family Cystoviridae) is a dsRNA virus with a tripartite genome divided into Small (2,948 bp), Medium (4,061 bp) and Large (6,374 bp) segments [12-‐

14]. Mutations allowing φ6 to infect novel hosts have been localized to the gene encoding

P3 protein on the Medium segment [11,15].

Two previous studies have systematically examined φ6 host range expansion [11,15].

Duffy et al. isolated 10 φ6 host range mutants on each of three different Pseudomonas host strains including Pseudomonas pseudoalcaligenes [15] . Genetic sequencing revealed that all mutations occurred in the P3 attachment protein. Moreover, the authors reported that 7 of the

9 host range mutations imposed a fitness cost on the canonical host. Ferris et al. isolated 40 φ6 host range mutants, of which 16 contained novel mutations [11]. The authors used a statistical approach to predict the existence of a further 39 mutations that were missed by their screen. In addition, they observed broad fitness costs on the canonical host in agreement with Duffy et al.

[11,15].

Our study builds on each of these earlier studies in order to present a more complete picture of host range expansion in φ6. We isolated 69 independent φ6 mutants able to infect the novel host, P. pseudoalcaligenes, and sequenced the entire P3 gene for each of them in order to determine the number, location and frequency of host range expanding genotypes. For a subset of unique mutant genotype, we quantified plaque size on P. pseudoalcaligenes, and reproductive capacity (fitness) and host attachment rate on canonical host P. phaseolicola and

P. pseudoalcaligenes. Finally, we used protein structural modeling to predict the effects of host range mutations on P3 attachment protein structure.

Methods and Materials

Study Organisms and Culture Conditions

Cystovirus φ6 used in these experiments is a descendant of the strain originally isolated from bean straw in 1973 [16]. φ6’s host of isolation was the Gram-‐negative bacterium, P. syringae p athovar phaseolicola (ATCC # 21781; hereafter PP) [16]. In our study, we used a nonpermissive host, P. pseudoalcaligenes E ast R iver isolate A (hereafter ERA), to isolate φ6 host range mutants (HRMs). The ERA receptor to which φ6 binds has not been determined, but is likely the ERA pili. Two other nonpermissive hosts, syringae P. pv. tomato (hereafter TOM) and

P. syringae pv. atrofaciens (hereafter ATRO), were used in some assays. All bacteria and virus stocks were obtained from Paul Turner, Yale University, New Haven, CT.

All phages and bacteria were propagated in lysogeny broth (LB: 10 g NaCl, 10 g Bacto™ tryptone, and 5 g Bacto™ yeast extract per . liter of water) at pH 7 Bacterial cultures were initiated by transferring a single colony from a streak plate into 10 mL LB in a sterile 50 mL flask

capped with a 20 mL beaker. Culture flasks were incubated with shaking (120 rpm) at 25 °C for

18 hours, allowing bacteria to attain stationary-‐phase density (~6 x 109 cells mL -‐1).

Virus Stock Preparation

High-‐titer phage lysates were prepared by adding 1 μL stock lysate and bacteria (200 μL of PP/ATRO/TOM or 20 μL ERA) to 3 mL top agar (LB with 0.7% Bacto™ agar; stored as liquid at

45 °C, solidifies at 25 °C), and pouring onto 35 mL bottom agar (LB with 1.5% Bacto™ agar) in a sterile Petri dish. After 24 hours at 25 °C, the resulting plaques were harvested and resuspended in 4 mL of LB, followed centrifugation by 10 min at RCF = 1400 x g to pellet agar and bacterial debris. Bacteria-‐free lysates were obtained by filtering the supernatant through a

0.22 μm filter (Durapore®; Millipore, Bedford, MA). Phage particles per mL in the lysates were

quantified via serial dilution and titering. Plaques were counted on plates where 30 -‐ 500

plaques were visible. The number of plaque forming units per mL (pfu mL-‐1) in the original lysate was obtained by multiplying the number of plaques times the dilution factor. Lysates were mixed 1:1 (v/v) with 80% glycerol and were stored -‐ at 20 °C.

Host Range Mutant Frequency

The frequency of HRMs in a phage population was estimated by plating a known number of wildtype φ6 on a lawn of a nonpermissive host and counting the resulting number of plaques. Each plaque represents the descendants of a single HRM in the parent population. To perform this assay, a single plaque was picked off a lawn of PP and placed in 1 mL LB. This mixture was serially diluted and plated on PP to estimate phage pfu mL-‐1. Subsequently, 107 or

(108 for TOM) phages were plated on lawns of the nonpermissive hosts, ERA, TOM . and ATRO

Typically, a φ6 plaque contains ~ 5 x 108 pfu/mL so sufficient phage for plating were easily

obtained [17]. Following 48 hrs growth, plaques were counted to estimate the number of

spontaneous HRMs among the descendants of a single phage. This assay was repeated at least

twenty times per nonpermissive host strain. Mutant frequency was calculated by dividing the

number of plaques observed by the initial inocula. The resulting data were analyzed using a

one-‐way analysis of variance (ANOVA) model. A Tukey-‐Kramer honest significant difference test

was applied post-‐hoc to ascertain significant differences mean among mutant frequencies on

each host type.

Host Range Mutant Isolation

Each HRM was isolated independently to minimize bias due a “jackpot effect” where

multiple descendants of the same mutational event appear in a population [18]. A φ6 lysate

was serially diluted and plated on a PP lawn such that only a few widely spaced plaques

appeared on the bacterial lawn. A single plaque was picked at random and placed in 1 mL LB.

After vortexing, 100 µL of the plaque suspension was added to 20 µL ERA and 3 mL top agar

plated. Following 48 hrs growth, a single HRM plaque was picked from the ERA lawn and

suspended in 500 µL LB. 10 µL from this solution was plated on an ERA lawn to obtain phage

lysate for RNA extraction. 500 µL 80% glycerol was added to the remainder which was then

stored at -‐20 °C. This protocol was repeated 69 times to obtain 69 independent HRM isolates.

RNA Extraction and Sequencing

To sequence the region of the Medium segment encoding the P3 protein, 3 mL phage lysate from each mutant was concentrated by centrifuging at RCF = 100,000 x g for 3 hrs at 4 °C

using a Beckman TL-‐100 ultracentrifuge. The supernatant was discarded and the pellet was

resuspended in 150 µL nuclease-‐free water. RNA was extracted ® using a QIAamp Viral RNA Mini

Kit (QIAGEN, Valencia, CA). Phage RNA was reverse transcribed using random hexamer primers

and Superscript® III reverse transcriptase (Life Technologies, Grand Island, NY), and the resulting cDNA was used as template for PCR. Three sets of oligonucleotide primers corresponding to bases 1298–2142, 2042–3052, and 2877–3873 of φ6’s Medium segment were used for the PCR amplification of the region encoding the P3 host attachment protein. PCR product was purified for sequencing using ExoSAP-‐It® (Affymetrix, Santa Clara, CA). PCR product was sequenced in

both directions with a minimum of 3-‐fold replication (6-‐fold coverage). Sequencing was

performed at the DNA Analysis Facility on Science Hill at Yale University. Sequence data were

analyzed using Geneious Pro Ver. [19] 5.4 and MEGA Ver. 5.05 [20]. Chromatograms were

verified via MacVector Ver. 12.5.1 bioinformatics software.

Mutant Characterization

We phenotypically characterized HRM genotypes by determining plaque size on ERA,

and by assaying reproductive capacity (fitness) and attachment rates on ERA and PP. Of our

unique mutant genotypes, we did not assay the three mutants whose mutations were not

identified (see Table 1). Furthermore, we only assayed one mutant in situations where

differences between genotypes were attributable to synonymous amino acid substitutions.

Finally, for one mutant, stored frozen lysate degraded due to a freezer failure following

sequencing, and viable phage could not be recovered for phenotypic characterization. In sum,

we phenotypically characterized 13 of our 17 unique HRM genotypes.

Plaque Size Estimates

Plaque sizes for 13 HRM genotypes were estimated from digital photographs of plaques

formed on ERA. All LB plates used for plaque assays were poured at the same time and were

weighed to maintain consistency. Each mutant’s lysate was diluted and plated such that

between 20 to 100 plaques formed on the ERA lawn after 48 hrs growth. Digital photographs

were taken using a Kodak Gel Logic® 440 digital imaging system. ImageJ software (NIH,

Bethesda, MD; http://rsb.info.nih.gov/ij/) was used to estimate the total area of the plaque. For

each genotype, at 5 least 3 plaque size estimates were made across 3 plates.

Mutant Fitness on Native and Novel Hosts

We assayed absolute fitness for 13 of our φ6 mutants and the wildtype on native and

novel hosts using traditional plating methods. Here 105 phages were added to 8 3 x 10 host cells

in 10 mL LB and incubated at 25 °C with 1 rotary shaking ( 20 rpm) for 24 hrs. All assays were

replicated 5x. Bacteria-‐free lysates were obtained by centrifuging 3 mL culture at RCF = 2.75 x g

for 10 min to pellet bacterial debris, then passing the supernatant through a 0.22 µm filter.

Phage particles per mL in the lysates were quantified via serial dilution and titering on host

lawns. For each assayed mutant genotype, we estimated absolute fitness using the

equation, , where Ni is the starting number of phage and Nt is the total number of "8 𝑊𝑊 = 𝑙𝑙𝑙𝑙 "9 progeny phage produced during the infection period.

Attachment Rate Assays

The rate of attachment to native and novel hosts of 13 mutant HRM genotypes was

measured using a centrifugation method. This method relies on the fact that, following

centrifugation, attached phages are pelleted with host cells, while unattached phages remain in

the supernatant. The decline of unattached phage over time is quantified to give the rate of

phage attachment to host cells. In this assay, 103 phages, which were titered on the same host as the assay host, were mixed with 5 × 109 exponentially growing host cells in 10 mL LB with 3-‐

fold replication. The mixture was incubated with orbital shaking for 40 min. Immediately after

mixing and every 10 min thereafter, a 1 mL sample from the mixture was centrifuged (RCF =

1,700 x g) for 1 min to pellet the cells then 100 µL from the supernatant was plated on a PP

lawn. The attachment rate constant (k) is calculated as

Nf ln( ) k = Ni Ct where i N is the total number of phage added, Nf is the number of unattached phage, C is the

concentration of bacteria, and t is the incubation time in minutes.

Attachment Protein P3 3D Structure Prediction

Three-‐dimensional structures of canonical bacteriophage φ6 ancestral strain P3

attachment protein was predicted by homologue modeling based on nucleotide sequences

submitted to the online I-‐TASSER server [21,22]. I-‐TASSER generates three-‐dimensional (3D)

atomic models from multiple threading alignments and iterative structural assembly

simulations.

Results

Mutation Frequency

φ6 HRMs were readily isolated by plating wildtype phages on lawns of the

nonpermissive hosts, ERA, TOM and ATRO. The frequency in which ERA -‐infective HRMs

appeared in populations of φ6 phages was 1.15 x 10-‐6 (n = 21, SD ± 5.219 x 10-‐7). This value is only slightly lower than φ6’s spontaneous mutation rate, 2.7 x 10-‐6 [23]. Rates on TOM and

were 1.39 x 10-‐7 (n =21, SD ± 1.489 x -‐ 10 8) and 4.45 x 10-‐7 (n =21, SD± 1.001 x 10-‐7) respectively.

We conducted a one-‐way ANOVA on log 10 mutant frequency with bacterial host strain as a

factor, and found significant differences in mutant frequency across different host strains (F =

198.54, DF = 2, P < 0.0001). A Tukey-‐Kramer post hoc test with α = 0.05 revealed that all

compared means were significantly different from each other. Interestingly, HRMs appeared

more readily on the phylogenetic outgroup P. pseudoalcaligenes ERA than they do on other

conspecific P. syringae pathovars such as P. syringae atrofaciens and P. syringae tomato [24] .

We found at least 17 unique genotypes among the 69 ERA HRMs isolated and partially

sequenced (Table 1). Three HRMs had no mutations in the sequenced region of the genome, thus we count them as, at a minimum, one unique genotype. Out of a combined 78 identified mutations from three studies, the majority resulted in nonsynonymous substitutions in the P3 amino acid sequence. Only 2 synonymous substitutions were identified (Table 1). This result conforms to Duffy et al.’s report of only 1 synonymous substitution among 31 mutations [15].

Synonymous substitution frequencies were similar among the two studies (2.5% vs. 3.2%).

Mutation Substitution Frequency

Among all nucleotide substitutions identified by our screen, the estimated

transition/transversion bias R was 1.90. At the 8th residue, at least 5 possible substitutions

(G22A, A23G, A23C, G24T and G24C) allow infection of ERA. However, of all substitutions

observed, the majority were transitions (51 vs. 9 ). These results suggest that host range

mutations allowing infection of ERA are heavily biased towards transitional substitutions. The

significance of this finding is not clear, and may simply be a consequence of spontaneous

deamination.

87% (60 of 69) of all mutants possessed a mutation at the 8th amino acid residue in the

P3 protein. Only 9 mutants (4 single, 1 double, 1 triple and 3 unknowns) did not show a

mutation at the 8th residue. This imbalance is higher than was observed in Duffy et al.’s study, where only 14 of 30 mutants isolated on ERA possessed mutations at the 8th residue [15] .

However, we note that Duffy et al.’s study did not control for the “jackpot effect” and 20 of 30 mutants were isolated from hosts other than ERA. The dominance of a single residue is not unprecedented. Ferris et al. reported that 12 of 40 mutants isolated on P. glycinea showed a mutation at the 554th residue [11] . Across all 3 studies and 5 different hosts, there seems to be

3 “hotspots” for host range mutations in φ6. 85.4% of all amino acid substitutions occurred close to the 8th (54.7%), 138th (16.7%) and 544th (14%) residues (Fig. 1A; Table 2).

Phenotypic Change Analysis

Changes in mass, electrical charge and hydrophobicity presumably can alter host

receptor binding by changing the protein’s tertiary structure and altering protein-‐protein

interactions. In Table 3, we compiled the phenotypic characteristics of all amino acid

substitutions allowing infection of ERA observed in this study and in Duffy et al. [15]. Using this

data, we performed a paired t-‐test on amino acid mass for each mutation with strain type

(wildtype or mutant) as a factor. Both factors had significant effects on amino acid mass.

Substituted amino acids in mutants had significantly less mass than the original amino acids in

the wildtype strain (t = 6.73, DF = 77, P < 0.0001). This effect was most pronounced in mutation

hotspots (F = 7.25, DF = 71, P < 0.0001). Perhaps lower mass substitutions permit greater

flexibility at the host binding site.

Electrostatic interactions between host and phage proteins are most likely the basis of

phage attachment. If so, we expect that charge changes incurred by host range mutations

should be consistently in the same direction. A Χ2 test was used to determine whether chemical

properties of substituted amino acids differed significantly from the random expectation based

on the amino acid composition of the P3: 9.16% acidic, 8.69% basic, 24.53% hydrophilic, and

57.45% hydrophobic. We found that mutant amino acids were significantly more likely to be

basic or hydrophilic than expected by chance (Χ2 = 110.008, DF = 3, P < 0.0001). Furthermore, the frequency of mutations occurring at acidic residues was disproportionately high (81/106 or

76%). Ferris et al. also observed a greater than expected number of loss of charge mutations

[11]. We speculate that these chemical changes make the P3 protein's host-‐binding site more permissive for binding host receptors.

P3 3D Structure Prediction

Little is known regarding how host range expanding amino acid substitutions affect phage attachment protein structure. We used -‐ I TASSER

(http://zhanglab.ccmb.med.umich.edu/I-‐TASSER) [21,22] and DAS modeling software

(http://www.sbc.su.se/~miklos/DAS/maindas.html) [25] to predict structural features of the P3

protein. I-‐TASSER generates three-‐dimensional atomic models from multiple threading

alignments and iterative structural assembly simulations based on homology to solved

structures (Fig. 1B). The predicted model’s confidence score (C-‐score) was -‐2.12, which is intermediate confidence where scores range from high (2) to low (-‐5) confidence. When predicting known structures, and using a C-‐score cutoff > -‐1.5 for the models of correct topology, both false positive and false negative rates are below 0.1 [21]. While our -‐ C score did

not meet this threshold, we are confident that the probability of an incorrect structure is still

low. Our view is supported by the ability of the predicted structure to provide a biologically

plausible interpretation of the mechanistic basis of host range expansion.

DAS modeling software predicts transmembrane protein segments based on low-‐ stringency dot-‐plots of query sequences against a collection of non-‐homologous membrane proteins using a previously derived, scoring matrix. Although P3 is soluble [26], DAS predicted a

21 amino acid hydrophobic membrane-‐interactive domain at residues 271 Based to 291. on the fact that, on the predicted structure, this domain extends out from the P3 core (Fig. 1B), we venture that domain likely anchors the P3 protein to the integral membrane protein P6 [27], thus we will refer to it as the hydrophobic anchoring domain, or HAD. All host range mutations occurred on the face opposite the HAD, suggesting that the opposite surface binds the host receptor, and that mutations in this region allow infection of novel hosts. However, this hypothesis assumes that amino acid substitutions do not substantially alter the protein shape, and that residues on this face in the ancestor would remain on this face in the mutant. Figure

1C suggests our conjecture is valid as the E8G mutant’s predicted structure does not show major structural rearrangements compared to the wildtype. Interestingly, the most common host range mutations found in our study alter the surface charge at this location from negative to neutral, hinting at a proximate mechanism for host range expansion (Fig. 1C).

Plaque Size

We isolated HRMs by visually identifying and picking plaques off lawns of the nonpermissive host, ERA. Our results showed that host range mutations were heavily biased towards the 8th residue. One possible criticism of our mutant isolation process is that it may have been biased towards certain mutations simply because these mutants formed larger plaques that were more likely to be spotted by the sampler. To test this hypothesis, we determined from digital photographs the average plaque size for 13 of 17 of our identified

HRMs. Mean plaque size for our mutants ranged from 3.5 to 10.3 mm2 (Table 4). We performed

an ANOVA of mean plaque size with mutant frequency as a factor, and the results confirmed

that plaque size did not predict mutant frequency. While we did find significant differences in

plaque size among genotypes, the two most frequent genotypes found by our study (E8K and

E8G) ranked th 4 and 9th respectively in mean plaque size. These data imply that mutant sampling was not biased. Furthermore, we did not observe any correlation between fitness and plaque size.

Mutant Fitness on Original and Novel Hosts

The fitness consequences of host range expanding mutations will play a large role in the ability of these mutants to persist in host populations [28]. With this in mind, we estimated the absolute fitness of 13 of our mutant genotypes on the canonical host, PP, and the novel host,

ERA (Table 4). A one-‐way ANOVA of absolute fitness with strain as a factor revealed significant differences among strain fitness on both ERA (Fig. 2A; F = 40.64, DF = 12, P < 0.0001 ) and PP

(Fig. 2B; F = 3.515, DF = 12, P 0.0008), but mean fitness on ERA was not correlated with mean fitness on PP nor was fitness on ERA correlated with the number of mutations a mutant possessed. In fact, genotypes containing multiple mutations tended to be less fit than those with single mutations, although this trend was not significant. Matching previous results, fitness on PP was, in all but once case, less than that of the ancestor [11,15]. These results are indicative of antagonistic pleiotropy, implying a tradeoff in fitness between infection of PP and

ERA. In addition, the coefficient of variation (i.e. standard deviation/mean; CV) in mutant fitness was considerably greater on ERA as opposed to PP (CV: 0.402 versus 0.015). This

suggests that mutations expanding the host range have a much wider range of fitness effects

on the novel host.

Attachment Rate

Bacteriophages initiate infections of host cells by binding to receptors on the surface of

the bacterial outer membrane. As such, the host attachment rate is a critical factor in the

ecological success of a phage. We measured the rate of phage attachment to the original and

novel hosts for 13 mutant genotypes (Table 4). A one-‐way ANOVA of the rate of attachment to

ERA with mutant genotype as a factor revealed ignificant s differences among the strains (F =

10.17, DF = 12, P < 0.0001). In addition, we regressed attachment rate against mutant fitness on

ERA to determine if the two were correlated. Since our HRMs most likely differ only by

mutations in the P3 host attachment protein, we expected that improved attachment would

lead to increased fitness. Indeed, for 13 mutant strains whose fitnesses and attachment rates were estimated, fitnesses on ERA were correlated with ERA 3 attachment rates (Fig. ; F = 11.91,

DF = 1, P = 0.0062). However, the linear regression model accounted for roughly half of the

variance in attachment rate (R2 = 0.54). These results are not surprising given the difficulty of precisely estimating the φ6 attachment rate. Nevertheless, the results conform to our expectation of a positive correlation between fitness and host binding ability. By contrast, attachment rates of the various mutants to PP were not significantly different, nor were they correlated with mutant fitnesses on this host. These results might be expected given the relatively narrow range of fitness differences on PP (Fig. 2; Table 4).

The rates of attachment to PP were significantly greater than attachment rates to ERA

(One-‐way ANOVA: F = 216.7, DF = 1, P <0.0001). This latter result matches expectations since

mutant fitness on PP is approximately an order of magnitude greater than that on ERA [17].

Presumably, the switching of receptor types and the lack of adaptation to an ERA receptor may

account for the significant differences in mutant fitness on the different host types. However,

attachment rates to PP and ERA were not correlated, implying that mutations that increase

binding to ERA do not necessarily increase or decrease binding to PP.

Data Availability

All genetic sequences were deposited with Genbank (Accession numbers KF027227 -‐

KF027297). Data files are available via datadryad.org .

Discussion

Φ6 Host Range Mutation Frequency

Understanding the genetic basis of virus host range expansion is critical to predicting the

emergence of potentially dangerous viruses. The genetic distance a virus must cross to gain the ability to infect a novel host may be a dominant factor determining the probability of

emergence. Not all viruses readily infect novel hosts [29]. For example, many

mycobacteriophages isolated on Mycobacterium smegmatis are unable to infect M.

tuberculosis, even when large numbers of phage are plated [30] . Presumably infection of M.

tuberculosis requires several simultaneous mutations or even the recombination of whole

genes or gene systems. By contrast, many viruses are able to infect novel hosts via single

nucleotide substitutions [10,31-‐36]. This minimal genetic distance can easily be traversed

because viral population sizes and mutation rates allow them to search available sequence

space rapidly. The phage φ6 is an excellent model to study virus emergence via single

nucleotide substitutions because such HRMs are easily isolated, sequenced, and characterized

in the laboratory [11,15].

In this study, we found that φ6 HRMs appear on ERA at a rate (1.17 x 10-‐6) slightly lower the estimated φ6 mutation rate of 2.7 x 10-‐6 per nucleotide per generation. Thus our figure

seems somewhat low given that there are multiple possible mutations allowing host range

expansion in the φ6 genome (Table 1). However, Chao et al.’s estimate was derived from the

frequency of revertants from an amber mutation (sus297), and it was assumed that there was

only one way to revert [23]. If there are multiple ways to revert from Chao’s et al.’s amber

mutation, then theirs is an overestimate of the mutation rate. Moreover, Chao et al. estimated

the mutation rate at a single locus, but the mutation rate may vary across the genome [37,38].

At any rate, it is clear that, given their potentially enormous population sizes, φ6 HRMs can be

isolated relatively easily.

Our results indicate that there is considerable variation in the ability of φ6 to mutate to

infect nonpermissive host strains. While there are certainly strong coarse-‐grained trends in

infectivity, e.g., φ6 seems mainly restricted to the pseudomonads [39], infectivity within this

group is currently unpredictable. Phage φ6 is better able to mutate to infect P.

pseudoalcaligenes ERA, a distant relative of P. syringae pv phaseolicola [24] , than two

pathovars from the same species, P. syringae pv tomato and P. syringae pv atrofaciens [40] .

Duffy et al. and Cuppels et al. found many examples of other P.syringae pathovars

nonpermissive for φ6 even at high plating densities [15,39]. For example, Duffy et al. were

unable to isolate HRMs on at least 8 P. syringae pathovars despite plating 10 over 10 φ6 phages on each pathovar [15]. Similar results were obtained by Cuppels et al. [39]. It would appear

that phylogeny is a poor predictor of infectivity at least at the fine scale level within the pseudomonads. φ6’s ability to expand host its range appears to be somewhat idiosyncratic, which is to be expected given myriad possible outcomes for parasite-‐host coevolution [41] . It

may be that the P. syringae strains have experience recent coevolution with φ6 or its close

relatives, and thus have acquired resistance to infection to these phages.

The frequency of mutants lacking mutations in the P3 (4.3%) was similar to that found in

Ferris et al.’s study (2.5%) [11] . These results provide strong evidence that the P3 sequence is

the primary, but not exclusive, determinant of host range among phage φ6 [42]. While it is

tempting to speculate that additional host range mutations might be found in membrane fusion

protein P6, Duffy et al. sequenced the P6 for 30 φ6 HRMs and found no mutations [15]. As of

publication, no other candidate genes for host range expansion on ERA have been explicitly

identified in φ6; however one study has reported that a mutation allowing infection of ERA was

localized to the large segment [43]. This segment contains a gene encoding an RNA-‐dependent

RNA polymerase and genes associated with RNA packaging and procapsid assembly [12].

The number of ways a virus can mutate to infect a novel host is an important parameter in predicting its potential for emergence [44]. Using a method based on the coupon collector’s problem of statistical theory, Ferris et al. estimated the total number of possible mutations that allow φ6 to infect a novel host, P. glycinea [11] . The coupon collector’s problem can be informally stated as: Given n coupons, how many coupons will need to be sampled before each coupon is observed at least once [45]? One assumption of the coupon collector’s problem is that all coupons are equally likely. This assumption does not hold for genetic mutations as some types are more likely than . others are Ferris et al. accommodate this simplification by adjusting

the equation to account for differences in the probabilities of transitions and transversions.

Since they found 19 distinct genotypes among their 40 independent samples, they estimated that further sampling would uncover an additional 36 mutations [11]. If Ferris et al.’s estimates are correct, it would mean that 1.3% of all possible nonsynonymous substitutions in P3 confer the ability to infect ERA (i.e., 55 of 4,380 potential nonsynonymous changes expand host range).

Although their HRMs were isolated on a different host, P. glycinea, both their study and ours found similar frequencies of transitions among all mutations (90% in Ferris et al., 84% in our study). However, out of 69 HRMs, we found only 17 distinct genotypes. Ferris et al. isolated almost the same number of distinct genotypes in half as many samples [11], which may be a consequence of the different hosts of isolation. Since Duffy et al. observed 10 unique genotypes out of 30 isolates (33% unique) [15] and we observed at least 17 unique genotypes out of 69

(26% unique), the implication is that more unique genotypes would be found with further sampling. However, a closer inspection of our data suggests otherwise. 8 of 17 of our unique genotypes were only unique because of second-‐ or third-‐site mutations. If we consider only those mutations that are sensu stricto necessary for infection of ERA, we only find a combined

13/99 (13%) unique genotypes among our and Duffy et al.’s study [15]. In fact, we only found 3 unique sensu stricto substitutions not found by Duffy et al. study and they found 6 not identified in ours.

If 1.3% of all possible nonsynonymous substitutions allowed φ6 to infect ERA, we would expect to see more unique genotypes among our isolates. Our results also indicate that some mutations occur far more frequently than expected by chance even if differences in transitions

and transversions are accounted for. One possibility is that low fitness HRMs are eliminated by within plaque selection and consequently are not represented in the mutant collection sampled. We have no means to ascertain the validity of this hypothesis at this point, but it could be an interesting question to approach by deep sequencing of single HRM plaques.

However, at the same time, it seems likely that additional factors that are not currently well understood, such as RNA structure, codon bias and variation in the mutation rate across the genome, influence the probability of mutation at any particular locus. Nonetheless, Ferris et al.’s method is a valuable step forward towards the estimation of an important parameter relevant to virus emergence.

Mutation Hotspots

We found that mutations expanding the host range of phage φ6 were more likely to appear in certain regions of the P3 gene than others. Such mutation hotspots are have been observed among virus drug resistance [46-‐48], host range [49,50], hemagglutinin [51], capsid

[52], and core antigen genes [53] among others. Mutation hotspots are evidence of strong positive selection for substitutions that provide an adaptive advantage in a particular environment [54,55]. Growth on a novel host should impose strong positive selection for nonsynonymous substitutions at loci associated with host range expansion. Thus, we can use the frequency of mutations found in our survey to identify regions of the P3 protein that are important in attachment to a host receptor. 85.4% of all mutations identified by our study and by Duffy et al. [15] were found in just three regions (near 8th, 133rd and 554th residues) of the P3 gene (Fig. 1A; Table 2). We venture that these hotspots on the P3 protein are important in host range determination among φ6 phages.

Structural Speculations

We used the structural modeling software I-‐TASSER [21,22] to predict the structure of

the P3 protein from its amino acid sequence. The resulting structure showed homology to

bacterial alcohol dehydrogenase quinoproteins [56-‐58]. Interestingly, in the best-‐fit model, our

putative mutation hotspots were located close together on one face of the ~60 Å diameter P3

protein (Fig. 1B). Residues 8 and 130 were located at the surface 18 Å from each other, and

residue 554 was located subsurface about 15 Å from residue 8 and 23 Å from residue 130.

Other less frequently observed mutations also occur near this region (Fig. 1B). We propose that

this region of the P3 protein is a host-‐binding domain and directly interacts with host receptors.

This supposition is supported by the fact that the host binding domain is diametrically opposite

the hydrophobic anchoring domain (residues 271-‐291) predicted by DAS (Fig. 1B). The most

parsimonious explanation is that this domain serves to anchor the P3 to the integral membrane

protein P6 [27], which leaves the putative host binding domain exposed to the environment.

Mutations allowing infection of ERA may not significantly alter the tertiary structure of the P3 protein. I-‐TASSER structural modeling did not show any major structural rearrangements in predicted structures for mutant strains. Rather mutations may alter the host-‐binding domain’s electrical charge from negative to positive or neutral (Fig. 1C). This difference in electrical charge may allow mutant φ6 to bind the ERA host receptor. The presumptive ERA receptor is its pilus, but this has not been definitively determined. If the ERA receptor were indeed the pilus, it would be interesting to know if its electrical properties are appreciably different than those of the pilus of PP. Moreover, it is plausible that neutral or positive electric charges and smaller mass amino acids confer more flexibility to the binding region, allowing a

greater variety of structures to be bound [59] It would be interesting to determine if host range

expanding mutations more frequently result in the substitution of small for large amino acids or

alter the charge of the binding site.

Fitness on Native and Novel Hosts

Fitness on native and novel hosts was assessed using standard flask productivity assays.

Phage φ6 HRMs showed a broad range of fitness values on ERA, some of which were

significantly different from the others (Fig. 2A). Mutant fitnesses on the native host, PP, were much greater than those on ERA (Fig. 2B). Since φ6 is presumably well adapted to native but not novel hosts, these results meet our expectations. Supporting these results, we found that the coefficient of variation (CV) of mutant fitnesses on PP was much lower than CV of mutant fitnesses on ERA. These results conform to theoretical expectations that there should be less variation in fitness values close to a fitness peak on an adaptive landscape [60]. Directional selection should erode the variation in fitness as a population increases in fitness a particular environment. Thus, a virus that is adapted to a particular host should have lower variation in fitness on that host as opposed to a host to which it is not well adapted.

We found that, in concert with previous studies [11,15], mutations expanding the φ6 host range usually reduced fitness on the original host, PP. On average, HRM fitness on PP was reduced about 2.5% compared to the wildtype. Negative genetic associations between host types is an example of antagonistic pleiotropy [61,62]. The adage that “a jack of all trades is a master of none” is well supported, at least among φ6 host infections. However, the ultimate cause of host specialism or generalism remains opaque. Intuitively one would imagine that a broader host range would produce greater returns than a narrow one as long as the reduction

in productivity on a single host was offset by an increase in overall productivity [63]. With

regard to the present system, it seems unlikely that the relatively minor cost in fitness on the

original host imposed by host range expansion should outweigh the benefits of an expanded

host range. Moreoever, we isolated one mutant (S28) whose fitness on the canonical host

actually increased following the acquisition of a mutation permitting infection of ERA. Why then

are broad host range phages relatively rare? The rarity of generalism may be a result of the

interaction of widespread habitat patchiness, reduced dispersal and the ubiquity of local

adaptation [64]. If these general trends hold, competition within a patch should favor the

evolution of specialism. This hypothesis should be amenable to testing via experimental

evolution studies.

As a general rule, we might expect that novel hosts will present a greater challenge to

virus reproduction than native hosts, a conclusion that is supported by many examples in the

literature [65-‐67]. Novel hosts may represent ecological sinks, defined as habitats where the

basic reproductive rate is < 1. Our fitness results support this conjecture, and suggest that φ6

probably experiences a broader range of sink conditions on ERA than it does on PP.

Consequently, φ6 population extinction is more likely in a habitat populated by ERA than one populated by PP [17]. Given the many HRM genotypes over a broad alues, range of fitness v φ6 should be a valuable system to test hypotheses regarding virus emergence [44].

Attachment to Native and Novel Hosts

With the exception of the three non-‐P3 mutants, the mutant strains are most likely

isogenic outside the host attachment protein region. The differences in fitness are expected to result mainly from differences in binding efficiency to the host receptor. Our results indicate

that different suites of mutations had highly divergent attachment rates and fitnesses on the

novel host (Fig. 2 and 3). Nonetheless, a regression of phage fitness on ERA against attachment

rate to ERA revealed a significant positive correlation. Ferris et al. reported a similar result for

φ6 infecting P. glycinea [11] . These results make intuitive sense as mutants that are better able

to bind to the host are expected to reproduce at a higher rate. Moreover, attachment to ERA

was significantly lower than to PP, which is also reflected in the large differences in fitness.

Implications for Disease Emergence

This study and other recent studies of φ6 host range expansion suggest several generalizations. First, phylogeny may only allow relatively -‐ coarse grained predictions of virus host range. Phage φ6’s ability to mutate to infect close relatives was frequently worse than its ability to infect distant relatives. Second, nonsynonymous substitutions allowing host range expansion may occur at hotspots in the host attachment protein. This prediction makes intuitive sense as host attachment relies on binding affinity between host and virus proteins. In addition, many host range-‐expanding mutations may not result in large structural rearrangements in host attachment proteins. Rather, amino acid substitutions may result in more subtle changes in protein surface charges, allowing binding to different host proteins.

Furthermore, the number of nonsynonymous substitutions allowing host range expansion is probably relatively small considering the number of possible substitutions. Nonetheless, the relatively high virus mutation rate allows viruses to rapidly acquire host range expanding mutations despite their relative rarity. Finally, initial fitness on a novel host is usually much less than that on the original host, and antagonistic pleiotropy among host range mutations is

common. This generalization conforms to our expectations since evolutionary tradeoffs in different habitats are anticipated to be ubiquitous.

Acknowledgements

We thank Paul Turner for providing phage and bacterial strains and Tim Short for technical advice. Constructive criticism from Paul Gottlieb and three anonymous reviewers was much appreciated. This work was completed in part using equipment in the Core Facility for Imaging,

Cellular and Molecular Biology at Queens College.

Figure Legends

Fig. 1 – Spatial models of φ6 P3 protein mutants

Panel A: Three host range mutation hotspots (accounting for 86% of all mutations) are

highlighted in this linear representation of the 648 amino acid sequence of the φ6 P3 gene. The

remaining 14% of mutations are not shown. Panel B: Space-‐filling representations the of φ6 P3

protein are shown as predicted by I-‐TASSER. Colored regions correspond to the mutation

hotspots depicted in Panel A. A putative hydrophobic anchoring domain (HAD) is shown in

orange. In our model, the hydrophobic anchoring domain penetrates φ6’s outer lipid

membrane to bind inner membrane protein P6. Panel C: Surface electrical charges of E8G

mutant contrasted with ancestor. Space-‐filling representations showing predicted surface

electrical charges for the φ6 E8G host range mutant and its ancestor were estimated using I-‐

TASSER. Positively-‐ and negatively-‐charged regions are depicted in blue and red respectively.

Arrows indicate the predicted location of the mutated 8th residue. The most prominent difference between the mutant and the ancestor is the greater surface positive charge at the presumed host binding domain.

Fig. 2 – Mutant absolute fitness on canonical and novel hosts

Panel A: Absolute fitness of 13 φ6 host range mutants on the novel host, ERA. Each point is the mean of 5 replicate measurements of fitness. Bars are ±1SE. Panel B: Absolute fitness of 13 φ6 host range mutants on the canonical host, PP. Each point is the mean of 5 replicate

measurements of fitness. Fitness of wildtype φ6 is shown by the dotted line for comparison.

Bars are ±1SE.

Fig. 3 – Mean ERA attachment rate (k) is plotted against phage φ 6 fitness on ERA

Attachment to ERA was correlated with fitness on ERA for φ6 host range mutants. Each point is the mean of 3 replicate measurements. Dotted lines show 95% confidence intervals.

Figures

Fig. 1 – Spatial models of φ6 P3 protein mutants

Fig. 2 – Mutant absolute fitness on canonical and novel hosts

Fig. 3 – Mean ERA attachment rate (k) is plotted against phage φ 6 fitness on ERA

Table 1. Phage φ6 Mutations Allowing Infection of Novel Host ERA In this table, the columns show the nucleotide and amino acid substitutions found it all 69 ERA host range mutants. Rows show unique genotypes. N is the number of mutants with a particular genotype. The penultimate row shows number of times a given mutation appears among all mutants. The last row shows the frequency in which one amino acid residue is replaced by others in amino-‐acid sequences of the same protein from different organisms [59]. Mutations at the same amino acid residue are shaded together. Transitions are indicated by closed squares, transversions with open squares, synonymous substitutions with open circles and unknown changes with a filled triangle.

Nucleic

Acid

N P3 E8K E8A E8D E8D E8G D59D Non L530L F46L T177C F46S A23C G24T G24C G22A A23G L555F C1 588T S161P T137C T481C T138A C896G

Amino A389G D554A Q130R D554G S299W C1663T A1661C A1661G Acid 20 n 24 n 4 ☐ Single 3 ☐ (n=56) 1 ☐ 1 n 2 n 1 ☐ 1 n n 1 n n Double 1 n n (n=8) 1 ☐ ☐ 3 n n 1 n ☐ Triple 1 n n ¢ (n=2) 1 ¢ n n Unknown 3 ▲ Total # 69 23 28 5 3 1 1 1 1 7 1 1 1 3 1 1 3 Observed Petsko — 10 11 27 83 83 4 17 — 12 27 2 — 16 15 17 — Frequency

Table 2. Host Range Substitution Hotspots in φ6 P3 Proteina Amino acid substitutions close together in the primary sequence are grouped together. We combine data from this study with two other studies of φ6 host range expansion. N is total number of times a substitution was observed across all studies. Frequency is percentage of total substitutions a particular substitution was observed. Combined frequency is percentage of total substitutions constituted by substitutions in a particular region of the primary sequence. Others category includes substitutions found outside substitution hotspots.

This b Combined Substitution Duffy Ferris N Frequency b Study Frequency G5S 2 2 1.3% 54.7% E8K 23 4 1 28 18.7% E8G 28 9 5 42 28% E8D 4 4 2.7%

E8A 5 1 6 4% Q130R 7 7 4.7% A133V 9 9 6.0% 16.7% D145G 3 3 2.0% N146S 6 6 3.9% D533A 1 1 0.7% D535N 1 1 0.7% D554G 3 1 8 12 8.0% D554A 1 1 2 1.3% 14.0% D554V 1 1 0.7% D554N 2 2 1.3% L555F 1 1 2 1.3% Others 9 6 7 22 14.7% 14.7% Total 81 30 39 150 100% 100%

aData compiled from this study, Duffy et 2006 al. and Ferris et 2007 al. [11,15] . bSome frequencies rounded off to nearest tenth percent.

Table 3. Amino Acid Substitutions Associated with φ6 Host Range Expansion on ERA Amino acid substitutions found to allow φ6 infection of Pseudomonas pseudoalcaligenes ERA from data obtained by two separate studies. N is number of times substitution was observed across the two studies. Average mass is residue weight of the original and substituted amino acid [68]. Hydrophobicity index was obtained from Kyte and Doolittle [69]. Negative numbers are more hydrophilic; positive numbers are more hydrophobic. Electrochemical Hydrophobicity Amino Acid Average Mass a b N Properties Index Substitution TS Du Wildtype Mutant Wildtype Mutant Wildtype Mutant Glutamic Acid to 2 Acidic (-‐) Basic (+) 129.1 128.2 -‐3.5 -‐3.9 Lysine (E8K) 23 4 7 Polar Polar Glutamic Acid to 3 Acidic (-‐) Neutral 129.1 57.1 -‐3.5 -‐0.4 Glycine (E8G) 28 9 7 Polar Nonpolar Glutamic Acid to Acidic (-‐) Acidic (-‐) 4 129.1 115.1 -‐3.5 -‐3.5 Aspartic Acid (E8D) 4 Polar Polar Glutamic Acid to Acidic (-‐) Neutral 6 129.1 71.1 -‐3.5 1.8 Alanine (E8A) 5 1 Polar Nonpolar Aspartic Acid to Acidic (-‐) Neutral 2 115.1 71.1 -‐3.5 1.8 Alanine (D35A) 2 Polar Nonpolar Phenylalanine to Neutral Neutral 1 147.2 87.1 2.8 -‐0.8 Serine (F46S) 1 Nonpolar Polar Phenylalanine to Neutral Neutral 1 147.2 113.2 2.8 3.8 Leucine (F46L) 1 Nonpolar Nonpolar Glutamine to Neutral Basic (+) 7 128.1 156.2 -‐3.5 -‐4.5 Arginine (Q130R) 7 Polar Polar Alanine to Valine Neutral Neutral 9 71.1 99.1 1.8 4.2 (A133V) 9 Nonpolar Nonpolar Serine to Proline Neutral Neutral 1 87.1 97.1 -‐0.8 -‐1.6 (S161P) 1 Polar Nonpolar Serine to Threonine Neutral Neutral 2 87.1 101.0 -‐0.8 -‐0.7 (S246T) 2 Polar Polar Serine to Neutral Neutral Tryptophan 1 87.1 186.2 Slightly -‐0.8 -‐0.9 1 Polar (S299W) Polar Lysine to Basic (+) Neutral 1 128.2 101.0 -‐3.9 -‐0.7 Threonine(K311T) 1 Polar Polar Glycine to Serine Neutral Neutral 1 57.1 87.1 -‐0.4 -‐0.8 (G515S) 1 Nonpolar Polar Aspartic Acid to Acidic (-‐) Neutral 4 115.1 57.1 -‐3.5 -‐0.4 Glycine (D554G) 3 1 Polar Nonpolar Aspartic Acid to Acidic (-‐) Neutral 1 115.1 71.1 -‐3.5 1.8 Alanine (D554A) 1 Polar Nonpolar Leucine to Neutral Neutral Phenylalanine 1 113.1 147.2 3.8 2.8 1 Nonpolar nonpolar (L555F) a = This study; b = Duffy et al. [15] 2006 .

Table 4 . Phenotypic Characteristics of Bacteriophage φ Mutants. Strain Mutation Frequency Fitness Fitness Plaque Attachment Attachment of Mutant ERA PP Size ERA ERA PP S8 D554G 2 3.31 14.73 6.89 2.58 x -‐ 10 11 4.79 x -‐ 10 11 S68 E8D 4 1.97 14.61 5.69 2.22 x -‐ 10 11 4.72 x -‐ 10 11 S53 E8A 4 3.97 14.57 7.8 4.80 x -‐ 10 11 4.68 x -‐ 10 11 S46 Q130R/S299W 1 0.98 14.80 9.26 1.68 x 10-‐11 5.05 x -‐ 10 11 S42 S161P/L555F 1 2.20 14.93 9.17 2.40 x -‐ 10 11 5.31 x -‐ 10 11 S4 Q130R 1 1.27 14.74 6.28 2.05 x -‐ 10 11 5.41 x -‐ 10 11 S30 E8K/Q130R 1 2.51 14.62 3.54 2.49 x -‐ 10 11 5.71 x -‐ 10 11 S28 E8A/F46L 1 2.47 15.21 5.94 2.53 x -‐ 10 11 5.63 x 10-‐11 S26 D554A 1 4.47 14.77 7.85 2.39 x -‐ 10 11 5.63 x -‐ 10 11 S154 E8G 24 4.12 14.92 6.29 2.59 x -‐ 10 11 5.33 x -‐ 10 11 S14 E8K/D554G 1 2.08 14.79 6.56 2.09 x -‐ 10 11 4.50 x -‐ 10 11 S13 E8K/F46S 1 3.16 14.85 10.25 2.24 x -‐ 10 11 4.60 x -‐ 10 11 S117 E8K 20 3.91 14.66 8.93 2.51 x -‐ 10 11 4.39 x -‐ 10 11 φ6 WT n/a n/a n/a 15.07 n/a n/a 4.91 x -‐ 10 11

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Chapter 3: Host-‐Acquired Factor Impacts Fitness on Subsequent Hosts in an RNA Virus

Brian E. Ford1,2, Kevin Mu1,3 and John J. Dennehy*,1,2

1 De partment of Biology, Queens College of the City University of New York, Queens, NY 2 Molecular, Cellular and Developmental Biology Program, The Graduate Center of the City University of New York, New York, NY 3 Macaulay Honors College of the City University of New York, New York, NY

*Corresponding author: [email protected]

Correspondence:

Prof. John J. Dennehy Department of Biology, Queens College 65-‐30 Kissena Blvd. Queens, NY -‐ 11367 1597

Keywords: host legacy effect, bacteriophage, , Cystoviridae Φ6, Φ9, Φ13, Φ14, enveloped virus, RNA virus, maternal effect, epigenetics, phenotype, Pseudomonas syringae

Research Article to be submitted to Axios Review (http://axiosreview.org). Target journals:

1. eLife 2. PLoS iology B 3. BMC Biology 4. American Naturalist

Abstract

Many viruses are able to infect multiple hosts, often across species and genus barriers. The ability to predict how well a virus will fare on a new host would be invaluable to epidemiologists. Here we describe our findings that a virus’ lipid envelope—notable for being both host derived and of non genetic origin—plays a significant role in virus fitness in subsequent infections. We demonstrate significant fitness tradeoffs for host shifts between numerous bacteriophage with different host ranges and across different mechanisms of infection. Our results demonstrate that for some viruses, passage through a novel host may hinder that virus’ ability to reinfect the original native host. However, in other cases, passage through a novel host actually improves reproductive fitness on both native and novel hosts, suggesting a host range expansion. This expansion is likely facilitated by a host-‐derived factor.

Our study demonstrates that other factors besides evolutionary adaptation may influence how successfully viruses reproduce on their hosts.

Introduction

Most viruses are able to infect multiple host types even when host cell physiologies are different [1] . Although permissive for infection, hosts may vary in intrinsic factors, such as receptor structure and number, codon usage patterns, circuitry of genetic interactions, metabolic capacities and pathways, immune system activity [2] , etc. These intrinsic physiological differences result in antagonistic pleiotropy, epistasis and other tradeoffs across hosts, giving rise to the common observation of differential virus reproductive output across different hosts [3 -10]. While these intrinsic factors are major causes of fitness differences across hosts, they are not the sole causes. Here we describe a new class of extrinsic factors that can affect virus fitness in a specific host, the genotype and phenotype of the previous host.

This effect, which we term the host legacy effect, occurs when the reproductive output of a virus infecting a particular host differs according to the genotype or phenotype of the previous host that produced the virus. Host legacy effect was first observed in the bacteriophage Φ6, which can infect its native host Pseudomonas syringae pv phaseolicola (hereafter PP) or a novel host Pseudomonas pseudoalcaligenes East R iver Isolate A (hereafter ERA). Interestingly, it was shown that Φ6 viruses acquired from infections of PP were more productive on subsequent hosts than Φ6 viruses acquired from tions infec of [4,11] ERA . In other words, some factor acquired from the previous host affects Φ6's ability to infect and reproduce in ERA. The goal of our study was to experimentally determine whether a host legacy effect is present in other bacteriophages in the Cystoviridae family, including Φ9, Φ13 and Φ14.

Materials and Methods

Bacterial Host Strain Determination and Selection

Members of Cystoviridae family (Φ6, Φ7, Φ8, Φ9, Φ10, Φ11, Φ12, Φ13 and Φ14) were screened via spot titer against a panel of bacterial strains for viability (supplementary table

ST1). To ensure that fitness assays would be representative for the whole bacteriophage family, two members of the “close relatives”—to the canonical member Φ6—[12], Φ6 and Φ9, and two members of the “distant relatives”, Φ13 and Φ14, were selected for testing. All bacterial host pairings consisted of the canonical bacterial host, Pseudomonas syringae pv phaseolicola

(PP) and a secondary bacterial host. Secondary bacterial hosts were different for each virus strain-‐ Φ6: LM2489; Φ9: Pseudomonas syringae pv atrofaciens (hereafter Atro); Φ13: LM2509;

Φ14: Pseudomonas syringae pv tomato (hereafter Tomato). LM2489 [13] is a derivative of PP susceptible to infection of all members of the Cystoviridae (Φ12 inclusive). LM2509 is another derivative of PP that [13] lacks pili . Secondary bacteria hosts were chosen to be different as possible, both from each other and for unique sensitivity. Bacteria host sensitivities are summarized in Table 1.

Table 1. Bacterial Strains Bacteria AKA Sensitive to Used with Pseudomonas syringae pv PP, HB Φ6, Φ9, Φ6, Φ9, phaseolicola Φ13, Φ14 Φ13, Φ14 Pseudomonas syringae pv Atro Φ9 Φ9 atrofaciens Pseudomonas syringae pv Tomato Φ14 Φ14 tomato “Common host” LM2489 Φ6, Φ9, Φ6 Φ13, Φ14 “Non-‐piliated” LM2509 Φ13 Φ13 Susceptibility to infection by bacteriophage strains used in this study.

Paired Bacteriophage Strain Preparation

Bacteria were propagated in lysogeny broth (LB: 10g NaCl, 10g Bacto tryptone and 5g yeast extract per liter of water) at pH 7. Bacterial cultures were initiated from a single colony from a streak plate in 10 ml of LB in a sterile 50 ml flask capped by a 20 ml beaker. Cultures were incubated for 18 H at 25°C with shaking at 120 rpm. Viruses were propagated in 3 ml top agar (0.7% agar) containing diluted virus lysate and bacteria (200 μl PP or 100 μl for all other hosts). Top agar was poured over LB agar plates (containing 1.5% agar) and incubated overnight at 25

°C. Top agar from four replicate plates was scraped into 4 ml of LB broth and centrifuged (RCF = 1,400x g) for ysates 40 min. Viral l were passed through 0.22 μm filters to remove bacteria and stored at 4°C for the duration of the experiments. Paired strains were made in tandem; virus stock propagated on PP was passed through either PP to create a base strain or its erial secondary bact host to create a differentially -‐‑ lipid enveloped test strain (Figure 1A). Bacteriophage lysates were titered on both PP and the secondary

strain and the highest titer values were used in multiplicity of infection (MOI) calculations.

Fitness Assay Conditions

A single colony of both Pseudomonas syringae pv phaseolicola and one of the secondary hosts

(either LM2489, Pseudomonas syringae pv atrofaciens, LM2509 or Pseudomonas syringae pv tomato) was used to inoculate 10 ml of LB broth each and incubated overnight (18H) at 25°C with shaking at 110 rpm. 150 μl of this 18H culture was used to inoculate 15 ml LB and grown at

°C to an optical density (OD) of 0.2. 30 μl of PP and each secondary host strain was serially diluted and plated to verify equivalent starting ny bacterial colo forming units (CFU) for the different strains.

Both bacterial hosts, as well as both of the differentially enveloped paired virus strains, were measured concurrently in a 96-‐‑well flat-‐‑bottom plate (Figure 1B). Virus concentration was titered for train each s and chosen to maximize bacterial growth before complete host lysis. In order to keep the phage infection curves consistent between strains, an inoculation concentration of 100 plaque forming units (pfu) for Φ6 or 10,000 pfu for Φ9,

Φ13 and Φ14 was used. Appropriate virus concentrations were diluted in a final volume of

100 μl. 100 μl of 0.2 OD bacteria was added to 100 μl of virus dilution (initial MOI

1:40,000 for Φ6 and 1:400 for the three remaining virus strains) following Figure 1C.

Subsequent optical density OD ( ) was measured in a Tecan Infinite Pro plate reader at 600 nm. ODs were measured every 25 5 minutes, 200 sec of orbital shaking, 5 flashes, with the lid on over the course of 18H at °C. Each sample was plated in octuplicate and each experiment repeated in triplicate (final n=24 for each treatment). Due to lack of change in

the readings from 15H to 18H for all strains, all results are only reported until 15H.

Statistical Analysis

Statistical analyses were carried out using GraphPad Prism for Macintosh (6.0c) (GraphPad

Software, La Jolla, CA). Optical density (OD) was plotted over time (15 hours total) for each

paired strain. Column statistics was used to determine the Area Under the Curve (AUC), the time to maximal infection (x-‐maximum) and the maximal growth (OD-‐max, or y-‐maximum) for each replicate and experiment (8 replicates per treatment times repeats 3 of each experiment equals 24 replicates total per treatment). One-‐way analysis of variance (ANOVA) with multiple comparisons was used to compare differences between groups (bacteria growth alone, bacteria growth with phage-‐canonical coat and bacterial growth with phage-‐secondary coat) using each of the three variables (AUC, x-‐max and y-‐max) on each bacterial host (canonical or secondary).

Due to close agreement between the results for the three measures (AUC, -‐ x max and -‐ y max, see ST2. and ST3.), only results from the AUC measures were used in the results and discussion.

Results

Φ6

For Φ6 strains passed through either PP or LM2489 (a PP derivative susceptible to infection by all members of the Cystoviridae), there is no difference in number of progeny produced, indicating that previous host identity does not change growth dynamics for either of the paired strains grown on PP (Figure 2A). However, for those same paired strains grown on LM2489,

there is a slight but still significant advantage (p = 0.0028, Supplementary Table ST3) to virus grown previously on PP (Figure 2B).

Φ9

For the paired Φ9 strains assayed on PP, virus that had been grown first on Atro demonstrated a significantly increased fitness in comparison to virus that had been grown on PP (figure 2C).

This trend of Φ9-‐atro growing better than Φ9-‐PP was also observed for the two strains when assayed on Atro (Figure 2D). The difference in progeny production between these paired strains was more pronounced on Atro than PP (Figure 2C,D).

Φ13

Φ13 was passaged through either PP or LM2509 (a non-‐piliated PP derivative) to make paired strains. Previous host exposure (i.e. those virus strains that had been previously passaged through and possessed the same lipid envelope) leads to an increased fitness on both hosts and a significant decrease in fitness for both strains lacking prior exposure (Figure 2E-‐F; p < 0.0001, supplementary figure ST3).

Φ14

Distant Φ6 relative Φ14 was passaged through either PP or Tomato to make paired strains.

Without pre-‐exposure to Tomato, Φ14 was unable to infect Tomato at the tested MOI (Figure

2H). However, Φ14 -‐ pre exposed to Tomato was even better at infecting PP than Φ14 that had been only exposed to PP (Figure 2G).

These results are summarized below in Table 2.

Table 2. Relative Virus Productivity On Mismatched Hosts Strain Previous Host Current Host Productivity Relative Change

PP LM2489 Increased Φ6 LM2489 PP No change

PP Atro Decreased Φ9 Atro PP Increased

PP LM2509 Decreased Φ13 LM2509 PP Decreased

PP Tomato Decreased Φ14 Tomato PP Increased

Summary of the relative productivity of each virus strain on the bacterial host not matching the previous passage (i.e. unmatched lipid membrane). Outcomes are relative to productivity measured for each strain on the bacterial strain matching the previous passage (i.e. matching lipid membrane). An increased productivity indicates higher progeny production (greater reduction in OD600). Relative change column indicates the difference in growth between paired strains (red = decrease relative to concordant envelope strain; green = increase relative to concordant envelope strain; blue = no significance; more color indicates greater difference).

Discussion

Considered the type strain for the Cystoviridae family, Φ6 is the member against which other canonical members are classified. Close family members are (Φ6) Φ7, Φ9, Φ10 and Φ11, while distant family members are Φ8, Φ12, [13] Φ13 and Φ14 . This close and distant classification is based on infection dynamics; for close family members the host bacteria pilus plays a crucial role in the infection [14] cycle while distant relatives infect via the lipopolysaccharide (LPS) [15] present on the cell membrane.

It is worth noting that—of the novel bacterial hosts tested—two are naturally occurring strains

(Pseudomonas syringae pathovars atrofaciens and tomato) and two are mutant derivatives of

PP. The two mutant derivatives are notable for their susceptibility dynamics. LM248 is a PP derivative allowing infection by any of the Cystoviridae, while LM2509 is a PP mutant that does not produce pili (hence the non-‐piliated designation). As pili are essential components for producing a productive infection for the Cystoviridae type strain Φ6 and its close relatives (Φ7,

Φ9, Φ10, Φ11) but not its distant relatives (Φ8, Φ12, Φ13, Φ14) we would not expect LM2509 to be susceptible to infection by any of the close Φ6 relatives. This is the exact infection pattern that we observe with spot titers (Supplementary Table ST1). LM2509 is only susceptible to infection by Φ12 and Φ13 in our studies. In other words, the two mutant derivatives can be considered special cases almost opposite in their infection outcomes; LM2489 in an

exceptionally permissive host and LM2509 is an exceptionally non permissive host. This

uniqueness is again apparent in their effect on Cystovirus infection patterns.

Passage through LM2489, the extremely permissive host, is the only case where there was no

difference in fitness between the paired strains on the canonical host PP. Stated differently,

jumping to an extremely permissive host carries very little cost in switching back to the original

host (Figure 2A). This is not to imply that there is no cost moving to a new host species as the

data demonstrate that following a jump to the new LM2489 host, subsequent Φ6 infection in

that same host (LM2489) is slightly less than that of unexposed Φ6 (Figure 2B). This seems in

direct opposition to the case of LM2509, the non-‐piliated or extremely non-‐permissive host.

Here, without prior passage through LM2509, Φ13 is unable to infect the host at tested

MOI (Figure 2F). Once the virus has made the jump to this mutant host, it is extremely impaired

at infecting the original host, PP (Figure 2E). Unlike in the previous case of LM2489 an easier jump to a permissive host, in LM2509, the shift to a relatively inhospitable host carries a significant fitness cost on the original host (Figure 2E). Once the virus successfully replicates on

an exceptionally impervious host, it has higher reproductive fitness on subsequent infections of

the same host, but is also much worse on the original host.

The two naturally occurring strains (Atro and Tomato) demonstrate a slightly different infection

dynamic. Here, in both cases, a jump to the novel host is greatly facilitated by previous

exposure to that host (Figure 2D,H) and passage through the secondary novel host significantly

increases fitness on the canonical host (Figure 2C,G). These results are interesting in that they

are consistent across both Φ9 and Φ14 (a close and distant relative of Φ6 respectively). That these data for these two viruses—although related—are consistent, is noteworthy given that both viruses display different host ranges and follow different infection dynamics (targeting the pilus vs the LPS respectively) [16] . These data would suggest that certain host range expansions could improve a generalist host recognition mechanism or that there are other factors responsible for bacteria host recognition in addition to recognition sites on either the pilus or

LPS. A prime candidate for the responsible factor is the lipid membrane.

The Cystoviridae are enveloped viruses. They consist of about 10% tripartite dsRNA, 70% protein and 20% [17] lipids . Their lipid membrane is derived from the host’s cytoplasmic membrane [18] . In our paired strain experiments, each virus is passaged once though PP or a novel host. We kept the passage cycles to a minimum to reduce genetic evolution as a confounding factor. Each paired strain should significantly differ only in their host-‐derived lipid membrane (Figure 1C). In our test scenario, resultant differences in fitness between each paired strain should be attributable to the composition of the host-‐derived membrane. Our data suggest the host-‐derived lipid membrane is a significant factor in subsequent virus fitness.

Further experimentation is required in order to determine if it is the composition of lipid membrane itself [19] or that of the host-‐derived proteins embedded in the [20] membrane as the responsible party for the subsequent fitness tradeoffs.

For organisms—such as viruses—that multiply exponentially, given a finite amount of both time and resources, initial conditions can play a large role in the final reproductive outcome. The

host legacy effect plays a direct role only in the initial infection cycle (Figure 3). In the first scenario, having a concordant lipid envelope—where the lipid envelope composition of the virus matches that of the bacteria—virus amplification for each round of infection is relatively equivalent to each other, differing only in the ratio of virus to bacterial hosts (Figure 3A). In the second case, having a discordant virus envelope—where the lipid envelope composition matches a different bacterial host—virus production can either be impaired or facilitated, leading either to little or great amplification of the virus (Figure 3B). For the bacteriophage Φ6, one infection cycle takes [21,22] roughly 3H . For the majority of the paired strain results

(Supplementary Figure 1A,B,C,E,F,G), differences in virus amplification are readily apparent following the second round of infection, at 6H (Supplementary Figure 1, Figure 2), indicating the importance of the host legacy effect and initial infection conditions.

Viruses have very defined host ranges and niches. Many factors have been identified either allowing or restricting expansion of these—usually narrow—host ranges [23] . Here we identify a new factor important for the success or failure of of these segmented dsRNA viruses in encountering a new host: the host-‐derived lipid membrane. These data are suggestive of a

“host legacy effect”—analogous to the eukaryotic maternal effect concept—of bacterial host identity impacting virus fitness in subsequent infection. While we do not see a generality in all tested infection scenarios, we do see that in all cases save one—in an extremely permissive host where the exception proves the rule—that the lipid membrane plays a significant role in subsequent virus fitness. Further experimentation will be required to determine both the identity—whether the effect is a function of differing proteins embedded in the envelope, the

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lipid composition of the envelope itself, or some other factor—and the mechanism of action responsible for this effect.

Author Contributions

BEF & JJD designed the study and analyzed the data, BEF & KM conducted the assays, and all three authors wrote the manuscript.

Conflict of Interest Statement

None of the authors have affiliations with, or involvement in any organization or entity with any financial interest, or non-‐financial interest in the subject matter or materials discussed in this manuscript.

Acknowledgements

We would like to thank Leonard Mindich and Paul E. Turner for their kind donations of bacteriophage and bacteria strains. We appreciate assistance from Marko Baloh and Elizabeth

S. Chapler and other members of the Dennehy Lab. This work was funded by National Science

Foundation Early Career Award to JJD (DEB #1148879). The Macaulay Honors College provided financial support to KM.

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Captions

Figure 1. Experimental Design

General experimental overview. (A) Reciprocal fitness assay setup for the four paired strains: each virus pair is concurrently assayed on both bacteria host strains along with a bacteria control. Close relatives (Φ6, Φ9) in top row; distant relatives (Φ13, Φ14) on bottom row. (B)

Fitness assay design. Each paired strain is assayed simultaneously on both bacterial hosts in octuplicate. Each experiment was repeated three times. White: not used; Red: blank; Blue: Φ9-‐ canonical on canonical strain; Purple: canonical alone; Cyan: Φ9-‐secondary on canonical; Dark green: -‐ Φ9 canonical on secondary; Bright green: secondary een: alone; Army gr -‐ Φ9 secondary on secondary. (C) Paired strain preparation. Each of the four strains (Φ6, Φ9, Φ13, Φ14) was passaged through one round of infection on both the canonical host (PP) and a secondary bacterial host (LM2489, Atro, LM2509 and Tomato) resulting in genetically identical viruses differing only in the host-‐derived lipid membrane.

Figure 2. Paired Fitness Assays

Growth curves for the four paired-‐strain reciprocal fitness assays. (A) Φ6 on Pseudomonas syringae pv phaseolicola (PP); (B) Φ6 on LM2489, a bacterial strain determined to be susceptible to the entire Cystoviridae family. (C) Φ9 on PP; (D) Φ9 on Pseudomonas syringae pv atrofaciens (Atro). (E) Φ13 on PP; (F) Φ13 on 2509, a non-‐piliated bacteria. (G) Φ14 on PP; (H)

Φ14 on Pseudomonas syringae pv tomato (Tomato). (A,C,E,G) represent growth on the

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canonical host Pseudomonas syringae pv phaseolicola. (B,D,F,H) bacteriophage fitness on secondary bacteria hosts; each bacterial host is different for each virus. N=24 for all experiments. Error bars represent the mean ± standard error of the mean (SEM).

Figure 3. Host Legacy Effect Infection Dynamics

The host legacy effect can change after each round of infection. (A) In a concordant infection— virus envelope matches bacteria type—infection dynamics and virus amplification proceed as normal. (B) For discordant infections—virus envelope matches a different bacteria type—the first round of infection can greatly differ from subsequent rounds of infection, leading to greater or reduced virus amplification.

Supplementary Figure 1. Paired Fitness Assay Summary at 6H (~2 Infection Cycles)

Paired strain fitness data from Figure 2 reformatted to emphasis differences at 6H, about two infectious cycles for Φ6.

Supplementary Table ST1. Host range of Cystoviridae on selected strains

Summary of selected bacteria strain susceptibility to members of the Cystoviridae.

Supplementary Table 2 ST . One-‐way ANOVA tables for the effect of virus lipid membrane composition on fitness at 15 hours, measured by Area Under Curve (AUC), time to infection

(x-‐max) and maximum bacterial growth (y-‐max).

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Fitness is measured for each paired strain on both bacterial hosts. Degrees of freedom (df) for all analyses is 2. Rows with discordant significance values between measures are boxed in red.

AUC, area under curve.

Supplementary Table ST3. ANOVA -‐ post hoc Tukey’s multiple comparison test.

Degrees of freedom (df) for all analyses is 23. Control, bacteria alone; AUC, area under curve;

Mean diff., mean difference; Summ., summary; Adj. P, adjusted P; ns, not significant. Rows with discordant significance values between measures are boxed in red.

Figures

Figure 1. Experimental Design

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Figure 2. Paired Fitness Assays

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Figure 3. Host Legacy Effect Infection Dynamics

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Supplemental Figures

Supplementary Figure 1. Paired Fitness Assay Summary 6H (~Two Infection Cycles)

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1 Supplementary Table ST1. Cystoviridae Host Range on Select Host Strains Name Lab code Designation Description Susceptible Current Study Reference Eschericha coli JJD1 E. coli None Pseudomonas JJD33 ERA Novel Φ6 host Φ8, Φ 10, Φ 11, pseudoalcaligenes Φ12, Φ13, East River Isolate Φ14* A Pseudomonas JJD34 HB, PP Host of Φ6, Φ 7, Φ 8, Φ6, Φ 9, Φ 13, syringae pv isolation for Φ9, Φ10, Φ11, Φ14 phaseolicola close relatives Φ13, Φ 14 JJD50 LM2333 Permissive Φ6, Φ 7, Φ 8*, Mindich 1999 host Φ9, Φ10, Φ11, Φ12*, Φ13*, Φ14* “Non-‐piliated” JJD53 LM2509 Φ8*, Φ 12, Φ 13 Φ13 Mindich 1999 Pseudomonas JJD98 Tomato Φ8, Φ 10*, Φ14 syringae pv Φ11*, Φ13, tomato Φ14* Pseudomonas JJD99 Atro Φ7, Φ 9, Φ 10, Φ9 syringae pv Φ11 atrofaciens JJD138 S4M Φ8 Mindich 1999 “Common host” JJD139 LM2489 Extremely Φ6, Φ 7, Φ 8, Φ6 Mindich 1999 permissive Φ9, Φ10, Φ11, host Φ12, Φ 13, Φ 14 JJD140 LM2572 Φ6, Φ 7, Φ 8, Φ9, Φ10, Φ11, Φ12, Φ 13, Φ 14 2 * infectivity only at high titer 108

3 Supplementary Table ST2. One-‐way ANOVA tables for the effect of virus lipid membrane composition on fitness at 15 hours as measured by Area Under Curve (AUC), time to infection (x-‐max) and maximum bacterial growth (y-‐max). Fitness is measured for each paired strain on both bacterial hosts. Factor AUC X-‐Max Y-‐Max SS F P SS F P SS F P Φ6 PP 361.1 2718 <0.0001 671.0 1104 <0.0001 0.9734 610.1 <0.0001 LM2489 191.6 3097 <0.0001 1093 2641 <0.0001 1.109 1196 <0.0001 Φ9 PP 5531.7 8621 <0.0001 1320 4345 <0.0001 2.796 5079 <0.0001 atro 177.6 974.4 <0.0001 792.8 836.7 <0.0001 1.894 712.6 <0.0001 Φ13 PP 498.7 2969 <0.0001 939.8 669.1 <0.0001 2.525 1462 <0.0001 LM2509 362.7 7202 <0.0001 1374 1646 <0.0001 3.107 5733 <0.0001 Φ14 PP 657.7 12443 <0.0001 1033 1444 <0.0001 3.147 1670 <0.0001 tomato 99.13 335.1 <0.0001 658.4 1611 <0.0001 1.668 506.0 <0.0001 4 AUC, area under curve; Mean diff., mean difference; Summ., summary; Adj. P, adjusted P; ns, not significant. 5 Degrees of freedom df ( ) for all analyses equals 2.

6 109

Supplementary Table T3 S . ANOVA Post-‐hoc Tukey’s Multiple Comparison Test

Factor AUC X-‐Max Y-‐Max Mean diff Summ. Adj. P Mean diff Summ. Adj. P Mean diff Summ. Adj. P Φ6 PP Control vs Φ6-‐PP 4.712 **** <0.0001 6.430 **** <0.0001 0.2455 **** <0.0001 Control vs Φ6-‐LM2489 4.788 **** <0.0001 6.521 **** <0.0001 0.2478 **** <0.0001 Φ6-‐PP vs Φ6 -‐LM2489 0.07608 ns 0.5079 0.09025 ns 0.3633 0.002379 ns 0.9315 LM2489 Control vs Φ6-‐PP 3.519 **** <0.0001 8.371 **** <0.0001 0.2798 **** <0.0001 Control vs Φ6-‐LM2489 3.399 **** <0.0001 8.159 **** <0.0001 0.2429 **** <0.0001 Φ6-‐PP vs Φ6 -‐LM2489 -‐0.1195 ** 0.0028 -‐0.2119 *** 0.0003 -‐0.03688 **** <0.0001 Φ9 PP Control vs Φ9-‐PP 5.642 **** <0.0001 8.768 **** <0.0001 0.3840 **** <0.0001 Control vs Φ9-‐atro 5.880 **** <0.0001 9.365 **** <0.0001 0.4453 **** <0.0001 Φ9-‐PP vs -‐ Φ9 atro 0.2381 *** 0.0004 0.5979 **** <0.0001 0.06127 **** <0.0001 atro Control vs Φ9-‐PP 1.473 **** <0.0001 5.002 **** <0.0001 0.2090 **** <0.0001 Control vs Φ9-‐atro 3.814 **** <0.0001 8.050 **** <0.0001 0.3971 **** <0.0001 Φ9-‐PP vs -‐ Φ9 atro 2.341 **** <0.0001 3.048 **** <0.0001 0.1881 **** <0.0001 Φ13 PP Control vs Φ13-‐PP 5.696 **** <0.0001 7.608 **** <0.0001 0.4083 **** <0.0001 Control vs Φ13-‐LM2509 0.2335 * 0.0118 -‐0.1104 ns 0.9097 0.02309 * 0.0189 Φ13-‐PP vs 13 Φ -‐LM2509 -‐5.462 **** <0.0001 -‐7.719 **** <0.0001 -‐0.3852 **** <0.0001 LM2509 Control vs Φ13-‐PP 0.05833 ns 0.4187 0.1992 ns 0.4644 0.01618 * 0.0268 Control vs Φ13-‐LM2509 4.790 **** <0.0001 9.365 **** <0.0001 0.4485 **** <0.0001 Φ13-‐PP vs -‐ Φ13 LM2509 4.732 **** <0.0001 9.166 **** <0.0001 0.4323 **** <0.0001 Φ14 PP Control vs Φ14-‐PP 6.140 **** <0.0001 7.857 **** <0.0001 0.3985 **** <0.0001 Control vs Φ14-‐tomato 6.653 **** <0.0001 8.200 **** <0.0001 0.4778 **** <0.0001 110

Φ14-‐PP vs -‐ Φ14 tomato 0.5126 **** <0.0001 0.3429 **** <0.0001 0.07930 **** <0.0001 tomato Control vs Φ14-‐PP 0.01917 ** 0.8708 0.004167 ns 0.8990 0.02136 ** 0.0055 Control vs Φ14-‐tomato 2.499 **** <0.0001 6.417 **** <0.0001 0.3330 **** <0.0001 Φ14-‐PP vs -‐ Φ14 tomato 2.479 **** <0.0001 6.413 **** <0.0001 0.3116 **** <0.0001 7 Control, bacteria alone; AUC, area under curve; Mean diff., mean difference; Summ., summary; Adj. P, adjusted P; ns, not 8 significant. 9 Degrees of freedom (df) for all comparisons equals 23. 10 Rows with discordant significance values between measures are boxed in red. 11 12 111

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Chapter 4: Benefits of Sticking Together: Fitness Reduced in Virus-‐Challenged Pseudomonas Species Following Shift to Planktonic Phenotype

Emily Y. Lin,1,2, David Toubiyan1, Marko Baloh1, Glennon V. Bythrow2,3, Brian E. Ford1,2 and John J. Dennehy1,2,*

1Department of Biology, Queens College of the City University of New York, Queens, NY 2Molecular, Cellular and Developmental Biology Program, The Graduate Center of the City University of New York, New York, NY 3Department of Biology, Brooklyn College of the City University of New York, Brooklyn, NY

*Corresponding author: [email protected]

Correspondence:

Prof. John J. Dennehy Department of Biology, Queens College 65-‐30 Kissena Blvd. Queens, NY -‐ 11367 1597

Keywords: Clumping, Pseudomonas, ERA, Phenotypic Shift, Planktonic, Sessile, bacteriophage

Original Research Article to be submitted to Axios Review (http://axiosreview.org). Target journals: 1. Molecular Ecology 2. American Naturalist 3. BMC Evolutionary Biology 4. Evolution

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Abstract

Many bacteria can switch between planktonic and sessile states. Single, motile, flagellated cells characterize the planktonic state, whereas cells in the sessile state are not motile and aggregate into clumps. Canonical Pseudomonas pseudoalcaligenes (ERA) predominantly exhibits the sessile phenotype. Here we describe the selection and isolation of a planktonic strain of P. pseudoalcaligenes through serial passage evolution. The planktonic strain has both longer and more numerous flagella than its ancestor, and demonstrates a buoyancy that is not movement dependent. Although the planktonic strain generates smaller colony forming units (CFU) than the sessile strain, total cell mass was not significantly different, suggesting that cell growth rates were similar. However, phage growth data suggested that cell aggregation decreases susceptibility to phage infection. These data imply a scenario where an increase in bacteria motility—a factor associated with virulence—in response to environmental stress (our selection), is tempered by an increase in susceptibility to bacteriophage infection. This model system can provide insights into a possible regulatory mechanism of bacterial phenotype in the host-‐virus coevolutionary arms race.

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Introduction

Pseudomonas pseudoalcaligenes East River Isolate A (hereafter ERA) as a model system to study virus host shifts has a long and well-‐established history [1 -6]. While it is a non-‐permissive host for the Pseudomonas bacteriophage Φ6 (hereafter Φ6). However, several different labs have isolated Φ6 host range mutants (HRMs) capable of infecting ERA [6-9]. These HRMs are able to infect both ERA and the canonical host for Φ6, Pseudomonas syringae pathovar phaseolicola (hereafter PP). Previous work with these HRMs has provided anecdotal evidence that HRM grown on PP have better fitness on ERA [2,8] than HRMs grown only on ERA . Direct, simultaneous comparison of fitness of these HRMs on both hosts is made challenging by the different growth characteristics between the two strains. Here, we describe a set of experiments designed to extend this model system to study new concepts. In order to be able to compare fitness of HRMs on their original host, we selected, through serial passage, an ERA strain with growth dynamics closer to PP. Standard ERA—isolated from the East River in New

York [9]—exhibits a sessile phenotype, grows quickly to stationary phase, and forms clumps.

We selected for -‐ a free floating, non-‐clumping, planktonic ERA that reaches stationary phase much later. Characterization of this new resultant planktonic phenotype of ERA has revealed a number of properties that allow for an even greater number of new types of assays than originally planned.

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Materials and Methods

Bacteria Culture Preparation

All bacteria were propagated in lysogeny broth (LB: 10 g NaCl, 10 g Bacto tryptone, and 5

Bacto yeast extract per liter of water) at pH 7. Bacterial cultures were initiated by transferring a single colony from a streak plate into 10 mL LB. Culture flasks were incubated with shaking (120 rpm) at 25 C for 18 hours.

Selection for planktonic (non clumping) strain

Figure 1 illustrates our selection scheme. Single colonies of ERA were transferred to liquid culture to establish two lineages (each in triplicate): the first with selection and the second without. The first was lineage briefly centrifuged following overnight incubation with only supernatant (bacterial cells that did not sediment) used to inoculate subsequent passages. The second lineage—the passage control— was not centrifuged prior to inoculating subsequent passages. Lineages were tested on days 7 and 14. By passage 14, all three selected lineages had achieved a planktonic phenotype. For consistency, the starting, unevolved lab ERA strain is labeled “sessile” in all graphs (in contrast to the evolved planktonic phenotype).

Isolation and Verification of Non Clumping Phenotype

As the selection scheme has resulted in a mixed bacterial population, passage 14 lineages were streaked out for single colonies and assayed for planktonic phenotype via 18H growth curves on a Tecan Infinite 200 Plate Reader (Tecan, Morrisville, NC). Single colonies were grown over

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night (O/N) at 25 °C with shaking at 120 rpm. At 18H, 200 μl of this stationary phase bacteria were used to inoculate 10 ml LB at 25 °C with shaking at 120 rpm. Liquid cultures were grown to an optical density of 0.2 at 600 nm (OD600). 200 μl of these exponential bacteria cultures were then plated in quadruplicate (starting OD600 = 0.02) along with their appropriate controls

(Day 0, corresponding passage control, unevolved sessile ERA).

Heat Shock Buoyancy Test

A single colony from each strain was used to inoculate 30 ml LB and grown O/N at 25 °C with shaking at 120 rpm. At 24H, 30 ml stationary phase cultures were divided in to three 10 ml aliquots and moved to three different temperatures: 25 °C, 37 ° C and 55 °C and incubated O/N with shaking at 120 rpm. At 18H, cultures were plated on LB agar for growth. Experiment was repeated in duplicate.

Determination of Cell Number in Clumps

Six single colonies from each strain were used to inoculate in triplicate either 10 ml LB or 10 ml

LB containing 1%-‐tween-‐80 and grown O/N at 25 °C with shaking at 120 rpm. ples At 18H, sam from each treatment were diluted to give approximately 20 – 200 CFUs and plated on LB agar.

Following an O/N incubation at 25 °C, colonies on the plates were counted. Original culture concentration was determined by dividing the number of counted CFUs by the dilution factor.

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Colony Size Determination

A single colony from each strain was used to inoculate 10 ml LB at grown O/N at 25 °C with shaking at 120 rpm. At 18 H, 200 μl of these stationary phase cultures was used to inoculate 10 ml LB broth (= exponential phase culture). Aliquots were taken from each of these three exponential phase cultures at 1-‐hour intervals up to 6 hours, serially diluted and plated on LB agar to give 20 – 200 colonies per plate. Plates were incubated O/N at 25 °C. Three photos— each containing ≥5 colonies—from each plate were taken for each time point (1H – 6H) and treatment (with or without 1% tween). Colony sizes were measured using the ImageJ (version

1.49) software (NIH, Bethesda, MD). ImageJ workflow: 1) Image: Adjust -‐> Threshold to black and white (B&W); 2) Process: Binary -‐> Fill in holes; 3) Process: Binary -‐> Convert to masks; 4)

Process: Binary -‐> Watershed; 5) Analyze: Analyze Particles.

TEM Analysis of Cell Morphology

Cell morphology for the three strains was analyzed via Transmission Electron Microscopy (TEM) using a JEOL 1200 EX Transmission Electron Microscope, (JOEL USA, Columbia, MD). Bacteria was incubated for 20 min at room temperature (RT) on Carbon Type-‐B, 200 mesh, opper c grids

(Ted Pella, Redding, CA), washed twice with deionized water and negatively stained using a 1% uranyl acetate solution for 2 min at room RT. Multiple micrographs for each strain were taken at 10k, 15k and 30k x magnifications.

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Bacteriophage Strain Preparation

Φ6-‐derived host range mutant (HRM) [6] S154 was propagated on Pseudomonas syringae

pathovar phaseolicola (hereafter PP). Lysates were obtained by adding 1 μl of frozen S154 stock

and 200 μl PP to 3 ml top agar (LB with 0.7% Bacto agar, liquid at 45 °C, solid at 25 °C) poured

over 35 ml bottom agar (LB with 1.5% Bacto agar) in a sterile petri dish. Following O/N

incubation at 25 °C, top agar is scraped off and suspended in 3 ml LB and centrifuged at RCF =

1400xg to pellet agar and cellular debris. Cell-‐free lysate is obtained by passing the supernatant

through a 0.22 μm filter (Durapore; Millipore, Bedford, MA). Phage particles per ml were

quantified via serial dilution and titering. Plaques were counted on plates where 30–500

plaques were visible. The number of plaque forming units (PFU) per ml in the original lysate was

obtained by multiplying the number of plaques counted by the dilution factor.

Growth Curve Optical Density to Cell Number Correlation

Because we were using multiple strains—of which at least two are clump-‐forming—the

relationship between OD600 and bacteria cell number (assessed via direct plate count) needed to be determined. To account for cell clumping, all growth curve and cell plating experiments were carried out both in the presence and absence of the surfactant Tween-‐80 (1% by v/v). A single colony from each of the three bacterial strains was used to inoculate 10 ml LB and grown

O/N at 25 °C with shaking at 120 rpm. At 18H, 200 μl from each stationary-‐phase overnight culture was used to inoculate both 20 ml LB and 20 ml LB with 1%-‐Tween. The relation between

OD and plating number was determined for all three bacteria over the course of 6 hours. At 1H

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intervals, 1 ml aliquots were taken from each liquid culture, measured in an Implen

Spectrophotometer P 330 (Implen, Westlake Village, ) CA at 600 nm and diluted (10-‐3, 10-‐4, 10-‐5 for the two control strains and 10-‐5, 10-‐6, 10-‐7 for the planktonic strain) to give plates with

between 20 – 200 CFU. Each experiment was repeated in triplicate. Results were plotted on

two graphs: 1) OD—in the presence or absence of Tween— over time and 2) number of cells—

with or without Tween—plotted over time. For the OD over time graphs, R-‐square values were

calculated using the non-‐linear curve fitting function of GraphPad Prism Macintosh v6.0f

(GraphPad, La Jolla, CA). For number of cells over time graphs, calculated CFU was plotted over

the course of the 6H experiment. To calculate the original CFU per time point, the total number

of CFUs were divided by the dilution factor. Note that the fitted curves for these experiments

are most accurate during the first four hours of the experiment (Supplementary Figure 1),

which was, fortunately, also the relevant time period for our assays. 1%-‐Tween CFU graphs

were used to determine cell number for all assays.

Flask Fitness Analysis

A single colony of each bacteria strain was used to inoculate 10 ml LB, the incubated O/N at 25

°C with shaking at 120 rpm. Following 18H of incubation, 109 bacteria was withdrawn from the

O/N cultures and added along with 104 bacteriophages to inoculate 10 LB in quintuplicate for a

multiplicity of infection (MOI) of 1:100,000. Bacteria and phage are incubated O/N at 25 °C with

shaking at 120 rpm. After another 18H, bacteria and phage cultures are centrifuged for 10 min

at RCF = 1400xg to pellet bacteria and debris then passed through a 0.22 μm filter to obtain a

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bacteria-‐free lysate. Lysates are subsequently titered on PP. Experiment was repeated in

triplicate (n = 15).

High-‐Throughput Microplate Fitness Analysis

Three flasks with 10 ml LB were inoculated each from a single colony of the three bacteria

strains and grown O/N at 25 °C with shaking at 120 rpm. At 18H, 200 μl of the stationary

cultures were used to inoculate 20 ml LB and grown at 25 °C with shaking at 120 rpm. OD600

9 was measured for each culture until they reached an OD600 corresponding to 10 bacteria per

100 μl (Supplemental Figure 1). Once each culture reached the target OD600, those cultures were incubated at 4 °C on ice until all three cultures reached the desired OD. ODs were verified before plating on a 96-‐well microplate. Each strain was plated in 12 replicates, either bacteria alone (109), or bacteria plus 104 phages (HRM S154). OD was measured in a Tecan Infinite Pro

(Tecan; Morrisville, NC) microplate reader at 600 nm. ODs were measure every 5 minutes, 200 sec of orbital shaking, 5 flashes, with the lid on over the course of 15H at 25°C. Each sample was plated in replicates of 12 and each experiment repeated in triplicate (n=36).

Statistics

All statistical analyses were calculated using GraphPad Prism Macintosh v6.0f (GraphPad, La

Jolla, CA). Bacterial growth with surfactant (Figure 3) and Colony Forming Unit Area Comparison

(Figure 4) were both analyzed via two-‐way analysis of variance (ANOVA) followed by a post-‐hoc

Tukey’s multiple comparison test (column effect = surfactant; row effect = strain). Flask fitness

(Figure 5A) was analyzed by one-‐way ANOVA. The column statistics function was used to

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calculate the area under the curve um (AUC), maxim time to infection (x-‐max) and maximum bacteria growth (y-‐max) for microplate fitness data, which in turn were analyzed by an unpaired two-‐tailed t-‐test (Figure 5B) and summarized in Supplementary Table 1. The non-‐linear curve fitting function was used to determine the formula to correlate OD with time (Supplementary

Figure 1).

DNA Preparation for Next Generation Sequencing

A single colony of each of the three bacterial strains was used to inoculate 10 ml LB and grown

O/N at 25 °C with shaking at 120 rpm. At 24H, cultures were processed using a PowerSoil DNA

Isolation Kit (Mo Bio, Carlsbad, CA) and sent out for Illumina Next Generation Sequencing (NGS) at the Genomic Sciences Lab at North Carolina State University.

Results

1. Characterization

1.1 Growth

Growth parameters of each strain were characterized by growth parameters, cell morphology, colony morphology and virus susceptibility.

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1.1.1 Phenotype Screening

As the original selection protocol resulted in a mixed population (Figure 1), strains were tested

for phenotypic heterogeneity. In order to distinguish between phenotypes, we screened their

growth parameters using a 96-‐well microplate reader. At 15H, colonies with the planktonic

phenotype reached an OD600 of ~0.9 while all control strains only reached ~0.2 (data not shown). Colonies displaying the planktonic phenotype were mixed 1:1 (v/v) with 80%-‐glycerol and frozen at -‐20 °C. These selected homogeneous strains were used in all subsequent assays.

1.1.2 Buoyancy

The planktonic phenotype is characterized as an unattached, free-‐floating and motile form of the bacteria [10] . To test whether the buoyancy demonstrated by our lab-‐evolved strain is motility dependent, we grew them at a non permissive temperature and observed resultant buoyancy. Results are summarized below in Table 1.

Table 1. Heat Shock Growth Verification Strain 25 ° C 37 ° C 55 ° C Sessile + + -‐ Passage Control + + -‐ Planktonic + + -‐ Samples from 24H replicate strains were divided and moved to three temperatures: 25°C, 37°C and 55°C for 24H and subsequently assayed for growth. + = growth, -‐ = no growth.

Despite the death of the bacteria grown O/N at 55 °C, no settling was observed for the

planktonic strain (Figure 2C) but observed in the two control strains (Figure 2A-‐B), even after 96

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1.1.3 Clumping

To test for the presence of clumping, bacteria strains were grown in the presence or absence of a surfactant (1%-‐Tween-‐80) and subsequently plated. There was a significant difference in number of observed colonies for the two control strains but not the planktonic strain (Figure 3) suggesting the planktonic strain does not clump. The surfactant Triton-‐X-‐100 was also tested but did not give as good a separation of cells as Tween-‐80 (data not shown).

1.2 Morphology

1.2.1 Cell Morphology

ERA, whether the unevolved, sessile phenotype or the planktonic phenotype, is a gram negative bacillus. Representative images for each strain: Sessile, Passage Control and Planktonic are presented in Figure 2. The two control strains, Sessile and Passage Control both present similar morphologies and observed frequencies of pili and flagella (Figure 2D-‐F). The planktonic strain, by contrast, consistently presented both more numerous and longer pili and flagella (Figure 2F).

1.2.2 Colony Morphology

Colony morphology was measured over the course of 6 hours. Aliquots were taken from cultures in exponential phase at six time points both in the presence and absence of the surfactant tween-‐80 (1% by volume). Representative images of colonies (grown in the absence of tween) for each strain are presented in Figure 2G-‐I. As we did not measure any significant

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difference between colony sizes based on time point (data not shown), data were aggregated across time points for comparison of grown with and without surfactant for a final n ≥ 90 colonies per strain (3 photos x ≥5 colonies x 6 time points = ≥90) and summarized in Figure 4.

Results demonstrate that addition of surfactant does not affect CFU area. However, we do see a significant difference in CFU area size between the planktonic strain and its two control strains

(Figure 4).

1.3 Virus Susceptibility

1.3.1 Flask Fitness

Flask fitness is a standard measure of bacteriophage fitness on a given host. We measured the fitness of the previously isolated Φ6-‐derived host range mutant (HRM) S154 [6] on the three strains. S154 was significantly better (1-‐way ANOVA; F = 37.97, DF = 2, p < 0.0001) at infecting the planktonic strain over the two control strains (Figure 5A). We measured no significant difference between S154 fitness on the two control strains. The fitness of S154 on the— canonical bacteriophage Φ6—host PP is again an order of magnitude larger than for the planktonic phenotype (data not shown).

1.3.2 High Throughput Fitness Assays

HRM S154 fitness was measured on all three strains via a 96-‐well Tecan Infinite 200 microplate reader. Fitness, for the purpose of our assay, is the number of progeny a phage is able to produce on a particular host. The higher the number of progeny (meaning the greater

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number of new virus) produced, the more the phage is able to infect, leading to a decrease in

measured 600 OD (as infection leads to lysis and subsequent clearing of the host cells). Fitness of

Φ6 on its canonical host PP is relatively easy to measure in a high-‐throughput manner. PP’s OD over 15H increases from 0.02 to over 9 thereby taking advantage of the full resolution of the spectrophotometer. By contrast, sessile ERA grows to stationary phase much quicker and forms clumps thereby reducing the reproducibility of of the assay. Unlike sessile ERA phenotype, the planktonic phenotype OD increases from 0.02 to more than 9 over the course of 15H, thereby increasing the resolution of this bacteria in this assay and adding sensitivity (as shifts in growth patterns were be more easily visualized) (Figure 5B). In our assay, the addition of S154 significantly (unpaired t-‐test; p < 0.0001, Supplementary Table 1) alters the growth of planktonic phenotype and to a lesser degree the passage control strain (unpaired t-‐test; p =

0.0132) but not the sessile strain (Figure 5B).

1.4 Comparisons

1.4.1 Planktonic vs Passage Control

In a microplate reader rowth setting, g of the passage control was significantly (Supplementary

Table 1) different from that of the planktonic strain (Figure 5B) indicating that simple serial passage of sessile ERA is insufficient to induce a planktonic phenotype. Interestingly, both the passage control strain and the planktonic strain demonstrated a significant susceptibility to bacteriophage infection (Figure 5B; p = 0.01 and p < 0.0001 respectively), unlike the sessile strain.

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1.4.2 Planktonic versus Sessile

Growth of the sessile strain is significantly different from that of the planktonic phenotype

(Figure 5B). In a microplate reader setting, planktonic growth parameters are ideal for the spectrophotometer's range (0 to 1) with an OD increase of 0.02 to >9 over the course of 15H.

This growth of ~0 to 9 is much different from that for the sessile phenotype, ~0 to ~2, indicating an increase in sensitivity for assays which perturb ERA growth (e.g. bacteriophage fitness assays). The planktonic phenotype produces longer and more abundant pili and flagella compared to the sessile strain.

1.4.3 Planktonic versus PP

Just as for its two control strains, the planktonic phenotype produces longer and more abundant pili and flagella compared to the sessile strain compared to PP. However, in a plate reader setting, both the planktonic phenotype and PP demonstrate similar OD increases over the same amount of time (data not shown). Although S154 fitness on PP is still greater than that on the planktonic phenotype ERA, the fitness difference between those two is much less than it is between PP and either the ERA sessile phenotype or the passage control strain. These data suggest the planktonic ERA as a better candidate to study phage fitness consequences from shifts back and forth between hosts than the sessile phenotype.

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1.5 Genotyping

We have received the sequences from the Illumina NGS and coverage for the three strains is high: sessile (~130 x), passage control (~146 x) and planktonic (~154 x). Preliminary contigs and raw reads have been deposited to the National Center for Biotechnology Information (NCBI)

Whole Genome Sequencing (WGS) portal under the BioProject number PRJNA287670, with individual accession numbers LFMA00000000 (sessile phenotype), LFMB00000000 (passage control) and LFMC00000000 (planktonic phenotype). Sequences have been sent to a collaborator at Hunter College for assembly.

Discussion

Starting with a sessile phenotype, serial passage emphasizing phenotypes that evade sedimentation results in a population with an ability to grow to a higher density (Figure 3) and that does not form clumps (Figure 2C). This increased growth and loss of clumping comes at a cost: a heightened susceptibility to predation by phage (Figure 5A). Whether the increased susceptibility to phage is a result of the loss of the ability to clump or due to the increased production of flagella and pili requires further experimentation. Different sets of pilins have been shown to be expressed in sessile growth compared to planktonic growth [11,12] suggesting another possible route for increased predation by phage, keeping in mind that the first step in a Φ6 infection is adsorption to the pilus [13,14]. We observed a marked increase in

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both the length and abundance of flagella in the planktonic strain. Flagella have been

demonstrated to be important for adhesion [15,16] to surfaces ; perhaps they play a

mechanical role by increasing the chances of phage and pilus encounters. Previous studies have

demonstrated that motility is regulated by the envelope stress response in Pseudomonas [17]

opening the possibility that the observed increase in flagella may not be in response to the

sedimentation per se but rather the stressful conditions of centrifugation. However, the sessile

phenotype has previously been demonstrated to be the precursor for biofilm formation

[18,19], which in turn is a well established defense against predation by phage [20-22]. These

studies would seem to support the theory that the increased predation by phage is a result of

the loss of clumping. It is well established that bacteriophage alter their host’s environment

[23], for example by affecting the bacterial quorum sensing, genome, motility, etc. [24]. The observation that the addition of low MOI phage affects the growth dynamics of the planktonic phenotype but not the sessile phenotype (Figure 5B) suggests that at one point in time the original ERA strain may have been subject to predation.

We have observed this change in phenotype, but as of yet the source of variation is unknown. In order to determine the source of the planktonic phenotype—whether it is a

reversible phenotypic switch [25 -27], normal phenotype heterogeneity [28] , or a genetic

mutation [29,30] —we sent out the three strains for next generation sequencing (NGS) with the

goal of identifying gene candidates for the switch. Although we lack a reference strain, we will

look for homologs of pilus and flagella genes as a starting point.

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One area where the planktonic phenotype presents a particular advantage is in a high-‐

throughput—or microplate reader—setting. High-‐throughput fitness screening has been

previously validated for PP[31] but not yet ERA. Flask fitness is reagent, space and labor

intensive; being able to assay more strains with less reagents offers both cost and time savings

advantages. It also opens the way to testing more combinations and iterations in a more

reproducible manner, increase replication and thereby increase the power of the assays. Under

our initial assay conditions, a concentration of phage that significantly altered the growth of

the planktonic phenotype, had no measurable effect on the sessile phenotype (Figure 5B). This

novel assay is of particular interest as we previously isolated 69 host range mutants [6] of Φ6

and will now be able to simultaneously test combinations of these mutants on ERA and PP

together. Previously it was not possible to compare phage fitness on host strains with

dramatically different growth differences. This assay also opens the door to testing how well

these phages—which have genetically mutated, often with single point mutations but also double and triple mutations—fare when put back in the context of their original host of isolation, PP.

Author Contributions

BEF & JJD designed the study and analyzed the data, EYL & GVB isolated the non clumping strains and performed initial fitness assays, BEF & DT conducted phenotype isolation, fitness assays, and physical characterization, EYL isolated the DNA for NGS; BEF & EYL assembled the

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NGS; BEF carried out TEM analysis, MB prepared the reagents, and all authors wrote the manuscript.

Acknowledgements

This work was funded by the National Science Foundation Early Career Award to JJD (DEB

#1148879).

Captions

Figure 1. Selection

Triplicate overnight colonies of ERA were grown each from a single colony, briefly centrifuged to sediment the bacteria and supernatant was transferred to fresh media daily for 14 days.

Colony morphology was assayed on days 7 and 14. Planktonic colonies were streaked out for single colonies, with each colony assayed for desired phenotype.

Icons from thenounproject.com.

Figure 2. Phenotype, Cellular and Colony Morphologies

(A-‐C) Bacterial buoyancy following 96H incubation at 4. (D-‐F) Transmission electron micrographs (magnification = 15,000 x) of cell morphology. Multiple and longer flagella and pili are visible on the planktonic form. (G-‐H) Planktonic colony forming units are significantly smaller than the control strains.

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Figure 3. Bacterial Growth with Surfactant

Bacteria strains were grown in triplicate (n=3) in the presence or absence of tween-‐20 in flasks.

Error bars represent SEM.

Figure 4. Colony Forming Unit Area Comparison

Strains were grown in the presence or absence of the surfactant Tween-‐80 at 1%. Cell areas (n ≥

90) were measured using built-‐in functions of the ImageJ software. Error bars represent SEM.

Figure 5. Susceptibility to Bacteriophage Infection

(A) Flask fitness. Strains were assayed in quintuplicate and repeated in triplicate (n = 15).

Colored bars indicate the mean. PP fitness is significantly higher than all three treatments (data not shown). (B) Plate fitness. Each strain had 12 replicates per experiment and each experiment was repeated in triplicate (n = 36). Error bars representing ±SEM are smaller than icons for all strains except “Passage Control + S154”. ns = not significant; **** indicates a p < 0.0001.

Supplementary Figure 1. Growth Curve and Plating Number

Determination of relationship between OD600 and cell number. This relationship was somewhat complicated by the clumping of cells in the control strains. To rectify this discrepancy, each of the three strains was grown in both the presence and absence of 1%-‐Tween-‐80 allowing calculation of true cell number for each measured OD. Error bars represent ±SEM. Experiments were repeated in triplicate.

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Supplementary Table 1. Microplate Fitness Statistics Summary

Summary of the statistical results of an unpaired two-‐tailed t-‐test (assuming a Gaussian distribution) using three measures for each bacteria strain: area under curve (AUC), time to maximum infection (x-‐max) and maximum bacteria growth (y-‐max).

Figures

Figure 1. Planktonic Selection Scheme

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Figure 2. Phenotype, Cellular and Colony Morphologies

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Figure 3. Bacterial Growth with Surfactant

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Figure 4. Colony Forming Unit Area Comparison

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Figure 5. Susceptibility to Bacteriophage Infection

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Supplementary Tables and Figures

Supplementary Figure 1. Growth Curve and Plating Number

Supplementary Table 1. Microplate Fitness (Bacteria vs Bacteria + Phage) Unpaired T-‐Test Summary AUC x-‐max y-‐max t P Sig. t P Sig. t P Sig. Sessile 1.092 0.2788 ns 1.484 0.1423 ns 1.057 0.2940 ns Passage 2.544 0.0132 * 2.609 0.0111 * 3.002 0.0037 ** Control Planktonic 11.63 <0.0001 **** 10.69 <0.0001 **** 17.46 <0.0001 **** AUC = area under curve; x-‐max = time at highest -‐ OD; y max = highest OD; t = t ratio; Sig. = significance; ns = not significant. All reported P values are from two-‐tailed t-‐tests.

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Conclusion

Three regions on the host attachment P3 gene—located on the middle segment—appear to be important for host range expansion in Φ6 (Chapter 2, Figure 1A). That these three regions all lie on the same face of the molecule (Chapter 2, Figure 1B), opposite the putative P6 binding domain, lends credence to the validity of the predicted 3D structure. Though all of the 69 host range mutants are able to infect the novel host ERA (Figure 1), they are not able to do so equally (Chapter 2, Figure 2A). In all save one case, the Φ6 host range mutants had reduced fitness on their canonical host PP in relation to their ancestor (Chapter 2, Figure 2B), an example of antagonistic pleiotropy (gaining a new host comes with costs on the old host).

cba Brian E. Ford, The Dennehy Lab

Figure 1. Pseudomonas pseudoalcaligenes East River Isolate A (ERA). Electron micrograph of ERA—the novel host of Φ6 mutants—at 20,000 x magnification. Cells were grown overnight at 25 °C and negatively stained with a 1% uranyl acetate (UA) solution.

A non genetic host-‐acquired factor—most likely either the lipid envelope or proteins contained therein—was demonstrated to have a role in subsequent bacteriophage fitness (Chapter 3,

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Figure 2). Similar trends were observed across different viruses with differing niches, host ranges and infection dynamics. These finding suggest that the host factor impacts virus emergence: they e ar able to gain an ability from their host that changes how well they are able to infect subsequent hosts.

Finally, the last study demonstrates how bacteriophage can affect their bacteria host phenotype. The planktonic strain shows an almost 2-‐fold increase in susceptibility to bacteriophage infection in relation to sessile form (Chapter 4, Figure 5A). The planktonic strain displays superior growth dynamics in plate fitness assays in comparison to sessile strain

(Chapter 4, Figure 5B) suggesting this lab-‐evolved strain to be a valuable tool for testing the fitness of host range mutants switching between hosts. These combined findings also emphasize a bacteria’s growth options: increased growth (with an increased susceptibility to infection) or protected growth (improved protection but to a lower density).

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