1

Name: Alexander S. Rose 1

Student #: 580013456 2

Candidate #: 025029 3

Title: The dual bacterial duel: Fighting primed plants under phage pressure 4

Journal choice: The ISME Journal 5

Justification of choice: The ISME journal is the microbial ecology niche subdivision of the 6

prestigious journal Nature. My project meets the following criteria: It advances knowledge on 7

scales relevant to microbial interactivities, namely phages and bacteria. I extensively 8

investigate plant-microbe interactions, including feedback and response pathways, 9

underlying mechanisms, unique traits, evolution, adaptation and fitness. I also address the 10

threat of host-parasite interactions and disease. 11

2

The dual bacterial duel: 12

Fighting primed plants under phage pressure 13

14

Alexander S. Rose1 & Britt Koskella

1 15

2014 16

1Environment and Sustainability Institute 17

College of Life and Environmental Sciences 18

School of Biological Sciences 19

University of Exeter 20

Penryn Campus 21

TR11 9FE 22

23

The ISME Journal: Microbial Ecology 24

[Subject Category: Microbe-microbe and microbe-host interactions] 25

3

ABSTRACT 26

The suitability of phage therapy as a bio-control tool to fight plant pathogens is often debated 27

with regards to efficacy and emergence of resistance. Resistance of bacteria to phages may 28

be gained by alterations in surface receptors to which phages bind, a mechanism that is 29

potentially costly to the fitness of bacteria invading the plant host as these molecules share a 30

function in virulence. Therefore, phages are candidates for the indirect influence of plant 31

phenotype. We propose tipping the balance in favour of phage so resistance is hindered and 32

costly, combining innate crop resistance against pathogens with phage therapy. We 33

compare population density and resistance to phage for the bacterium Pseudomonas 34

syringae pv tomato under phage selection in varying levels of plant defence, experimentally 35

elicited by exogenous application of lipopolysaccharides (immune inductive) or coronatine 36

(immune suppressive), to assess the feedback to bacterial phenotype and genotype after 37

these combined challenges. We show that bacteria in the phyllosphere are unlikely to evolve 38

resistance to phage in a scenario of elevated plant defence responses, due to stronger 39

plant-mediated selection. In an opposing scenario, we demonstrate that reduced plant 40

pressure led to a greater proportion of resistance to phage, but bacteria did not pay a fitness 41

cost of resistance in terms of reduced growth in the leaf. Thus, we underscore a cautionary 42

tale of cost-free resistance if phages are applied in too low a dose compared to their 43

bacterial hosts, particularly in a plant environment of suppressed defence. 44

KEYWORDS 45

Coevolution / host-parasite / Pseudomonas syringae / phage therapy / plant priming / 46

resistance / Solanum lycopersicon 47

4

INTRODUCTION 48

Phytopathogenic bacteria threaten plant communities by causing many serious diseases 49

worldwide. Aggressively multiplying in plant tissue (Hirano & Upper, 2000), the scope of their 50

impact manifests in agronomically devastating crop losses and damage to wild populations 51

of conservation importance (Strange & Scott, 2005; Webber et al., 2008). Historically, 52

management of pathogenic bacteria has depended on antimicrobial pesticides (McManus et 53

al., 2002) but emerging concerns over the advent of antibiotic resistance coupled with 54

modern public demand for sustainable food security has driven exploration of alternative 55

strategies to fight bacterial infection (Balogh et al., 2010). 56

Interest in the use of bacteriophage viruses (hereafter, ‘phages’) as a bio-control tool has 57

resurfaced in recent years (Goodridge, 2004), due to their ubiquity and abundance in natural 58

ecosystems (Srinivasiah et al., 2008). Readily isolated from terrestrial environments such as 59

the rhizosphere and phyllosphere (Griffiths et al., 2011; Lindow & Brandl, 2003), virulent 60

phages are obligate parasites of their prokaryotic hosts. Transmission occurs upon a lytic 61

burst of host cells following intracellular replication (Lenski, 1988) so phages are therefore 62

capable of controlling bacterial population density (Middelboe et al., 2001). Phage host-63

range may be influenced by a suite of factors (Koskella & Meaden, 2013), though many 64

phages are highly specific (Flores et al., 2011) and are capable of infecting only a subset of 65

bacteria within the local environment (Vos et al., 2009; Koskella, Thompson, et al., 2011). It 66

is their specificity, guided by recognition of bacterial surface receptors (Tétart et al., 1996), 67

that contends their suitability as a bio-control tool. 68

Phages bind to the external receptors of bacterial cells by adsorption with their tail fibre 69

(Lindberg, 1973), reliant on structures such as flagella, pili and the exo-lipopolysaccharide 70

matrix to gain access to the cell surface (Rakhuba et al., 2010). For example, flagellatropic 71

phages have been demonstrated to exploit the helical rotation of a bacterial flagellum to 72

approach bacterial cells (Samuel et al., 1999), whilst retraction of the pilus appendage has 73

5

been shown to play a role of translocating ‘piliphilic’ phages in a functionally similar manner 74

(Skerker & Berg, 2001). The lipopolysaccharide matrix is not necessarily a primary 75

adsorption site but it incorporates and stabilizes cell membrane components that contain the 76

ultimate receptors (Petty, Toribio, et al., 2007; Petty, Evans, et al., 2007). It follows that loss 77

or remodel of these outer target structures represents a bacterium’s primary line of defence 78

in resisting their phage parasites (Skurray et al., 1974). 79

Bacterial populations are under constant pressure by local phages to evolve resistance in an 80

ongoing coevolutionary arms race (Weitz et al., 2005), not unlike parasite-host dynamics 81

between bacteria and plants. Nevertheless, gaining resistance may confer a fitness cost to 82

bacteria (Richard E. Lenski, 1988; Brendan J. M. Bohannan, 1999), especially through 83

deleterious mutations in genes coding for highly conserved external structures required in 84

pathogenic life history strategy (Buckling et al., 2006). Expression of defective flagella 85

incapable of rotation (Icho & Iino, 1978) or an abnormally piliated cell wall (Brockhurst et al., 86

2005) may obstruct phage adsorption but accordingly compromise bacterial motility. These 87

fitness trade-offs have been shown to signify a decrease in components of virulence for 88

many leaf-colonizing bacteria invading the apoplast (Tans-Kersten et al., 2001). 89

Phages shape bacterial populations ecologically by altering interspecific microbial 90

competition and community composition (Brockhurst et al., 2006), directly through density-91

dependent regulation by lysis (Levin & Bull, 2004) and evolutionarily by selection for 92

attenuated fitness and virulence through resistance (Wagner & Waldor, 2002). These 93

attributes make them strong candidates for indirectly influencing plant fitness, which is why 94

the disease management strategy known as ‘phage therapy’ shows promise for controlling 95

bacterial pathogens. However, therapeutic manipulation of phages is met with mixed results 96

(Payne et al., 2000; Levin & Bull, 2004). If resistance were to arise without associated fitness 97

costs (Koskella, Taylor, et al., 2011) then a solution could become a problem, but this is 98

tackled by novel tactics such as applying a suite of different phages known as a ‘phage 99

cocktail’ (Chan et al., 2013). Given the environmental damage caused by pesticides and the 100

6

promise of alternate methods such as phage therapy, integrated disease control strategies 101

that encompass phage application complementary to other bio-control approaches should be 102

favoured. 103

For phage therapy to work, we need to tip the balance in favour of phage – both so that 104

bacterial resistance to phage is hindered and any resistance that evolves is more costly. To 105

do this, researchers are now looking into evolving phages themselves (Betts et al., 2013) 106

and also linking phage therapy with other control strategies such as low-dose antibiotics (Lu 107

& Koeris, 2011). One possibility is to combine innate crop resistance against pathogens with 108

phage therapy. In this case, phages would act to slow the adaptation of bacteria to the more 109

resistant hosts, and perhaps act to manage the disease rather than eliminate it completely. 110

Analogous to innate immunity in animals (Nürnberger & Brunner, 2002) and conceptually 111

akin to a vaccination, one can conceivably trigger plant immunity by activating a ‘primed’ 112

phenotypic state of defence (Paré et al., 2005). 113

As sessile organisms, plants display a remarkable degree of phenotypic plasticity. An 114

adaptive strategy to cope with conditions of varying hostility involves ‘priming’ of defences 115

after initial exposure to microbes, resulting in a faster and stronger induction of basal 116

resistance mechanisms upon subsequent pathogen attack (Conrath et al., 2006, 2002). 117

Basal resistance to pathogens is predominantly orchestrated via transduction of endogenous 118

signalling molecules such as salicylic acid (SA) and jasmonates (JA) (Halim et al., 2006). 119

Accumulation of these compounds is observed in leaves following invasion; pathogen 120

triggered immunity (PTI) is induced when pathogen recognition receptors (PRRs) perceive 121

microbe-associated-molecular-patterns (MAMPs) (Boller & Felix, 2009), none other than the 122

highly conserved yet indispensable bacterial surface structures that phages seek out. 123

The archetypal MAMP elicitor is the flagellin peptide flg22 (Zipfel et al., 2004), but others 124

present on pili and lipopolysaccharide endotoxins (LPS) induce similar responses (Chisholm 125

et al., 2006); treatment of plant leaves with LPS has been demonstrated to potentiate 126

7

expression of antimicrobial conjugates that fight pathogenic bacteria (Newman et al., 2002a). 127

LPS from any Gram-negative prokaryote will trigger innate immunity (Dow et al., 2000) and 128

thus represents a potential tool for achieving a primed defensive state of systemic acquired 129

resistance (SAR) across all plant tissue via SA-dependent signalling cascades (Durrant & 130

Dong, 2004). 131

Defence signal pathways are directed by lifestyle of the invading pathogen (Spoel et al., 132

2007). Biotrophs induce transcription of genes regulating the SA pathway whereas 133

resistance to necrotrophs is mediated by JA signalling (Halim et al., 2006). Moreover, signal 134

cross-talk within the plant system tailors the appropriate response, an example being the 135

negative regulation of SA by JA and vice versa (Kunkel & Brooks, 2002). Biotrophic bacterial 136

pathogens such as Pseudomonas syringae proliferate in the leaf rapidly because they 137

exploit this feedback mechanism by secreting multifunctional virulence factors such as 138

coronatine (COR) after invading the apoplast (Uppalapati et al., 2007). Coronatine is a 139

polyketide phytotoxin and, as a structural and functional mimic of the jasmonate derivative 140

jasmonic acid-isoleucine (JA-IL) (Katsir et al., 2008), it helps P. syringae overcome PTI upon 141

invasion by suppressing SA accumulation (Zheng et al., 2012), thereby retaliating against 142

plant resistance mechanisms and re-establishing disease by effector-triggered-susceptibility 143

(ETS) (Jones & Dangl, 2006). 144

In a simplified model, the accumulations of SA or JA elicited by LPS or COR represent 145

elevated or suppressed plant immune response scenarios respectively (Figure 1). We 146

exogenously applied these compounds to tomato plants (Solanum lycopersicon) to 147

reproduce PTI or ETS, delineating counterpart treatments in an experimental study system 148

featuring P. syringae pv tomato (Pst). Predicated on the rationale that features of the 149

bacterial surface crucial for phage infection are also likely to play a role in plant-pathogen 150

interactions, by introducing a phage into the equation we posed the general research 151

question: How do bacterial phenotype (population density) and genotype (resistance) 152

change as a function of combined phage- and plant-mediated selection? Explicitly, we 153

8

wanted to manipulate plant defence without altering the plant genotype to specifically test 154

whether phage therapy works differently in plants of varying levels of resistance, and 155

whether bacteria are less likely to evolve resistance to phages if they are also under strong 156

selection to evade plant defence. 157

We hypothesised that applying LPS and phage would bring about in lower bacterial density 158

and minimal resistance due to SA-dependent induction of plant defence and stronger 159

selection. Contrary to our expectations, neither elevating plant immunity nor phage presence 160

regulated bacterial density, though resistance was indeed minimal. Alternatively, we 161

predicted that COR and phage would give rise to a higher bacterial density and proportion of 162

resistance due to JA-mediated defence suppression. As we expected, eased plant pressure 163

led to proliferation of bacteria in the absence of phage and resistance was most prominent. 164

Here, a combination of experimental infection and evolution addresses these relationships, 165

and most intriguingly, the bacteria paid no cost of resistance under relaxed plant defensive 166

selection pressure. 167

9

METHODS 168

Plant material 169

Tomato, Solanum lycopersium cv “Moneymaker” (D.M. Brown, UK), was greenhouse-grown 170

from seed between the months of April and July 2014. Seeds were surface sterilised in 171

0.02% Tween 20 (Sigma), 1% sodium hypochlorite solution (Sigma). Seeds were sown in 172

Levington Seed & Modular Compost + Sand, of pH 5.3-6.0 and soil conductivity 210-290µs 173

(F2+S, Everris Ltd), with vermiculite and perlite (Sinclair Ltd) at a 5:1:1 ratio. Seedlings were 174

re-potted after 14 days; 4-week-old tomato plants were transferred to acclimate in a growth 175

chamber controlled at 24ºC, with a photoperiod of 15h day, 9h night (1h dawn/dusk) and 176

80% relative humidity (RH). 177

Leaf spray pre-treatments 178

5-week-old tomato leaves were sprayed with an atomizer containing either 179

lipopolysaccharide suspension or coronatine solution prior to bacterial inoculation. Purified 180

extracts were diluted at 100ng/µl lipopolysaccharide (LPS, isolated from P. aeuriginosa, 181

Sigma) and 0.5ng/µl coronatine (COR, isolated from P. syringae pv glycinea, Sigma); these 182

concentrations have been shown to elicit ecologically relevant plant-pathogen interactions 183

(Zhao et al., 2003; Zeidler et al., 2004; Melotto et al., 2006). Compounds were dissolved in 184

MES (2-(N-morpholino)-ethanesulfonic acid) buffer, consisting of 25mM MES and 10mM 185

KCl, adjusted to pH 6.15 with 1M KOH. Additional 0.25mM MgCl2 and 0.1mM CaCl2 salts 186

were included in LPS solution (Melotto et al., 2006). Leaves were marked and left 20h before 187

pathogen inoculation (Newman et al., 2002a). 188

Bacterial culture and phage titre 189

A preliminary experiment assayed the virulence of four strains of Pseudomonas syringae pv 190

tomato with regards to the ‘Moneymaker’ cultivar of tomato by perforating leaves three times 191

with cocktail sticks dipped in bacterial suspension: “803d” was chosen for this study. 250µl 192

10

overnight culture of “803d” was pelleted down (centrifuged 12,000rpm for 4 mins) and 193

resuspended in 25ml 10mM MgCl2. This 1 in 100 dilution was confirmed at 9.8x106 CFU ml-1 194

by plate counts. 195

Phage “WIL5” was chosen for Experiment 2 based on the previous finding (Meaden et al., in 196

prep) of a point mutation in the rfbD gene (polyketide sugar unit biosynthesis, KEGG 197

Orthology: (Kanehisa & Goto, 2000)) of the closely related strain DC3000 which led to a high 198

cost of resistance under WIL5 selection in tomato plants. 2ml of WIL5 was purified from 199

enumerated stock using a 0.45µm sterile filter and diluted in 20ml 10mM MgCl2. Filtration 200

avoids chloroform isolation methods which are known to destroy lipid-based phages and 201

thus limit phage coat composition (Koskella, Taylor, et al., 2011). The 1 in 10 dilution was 202

confirmed at 7x106 P.F.U. ml-1 by soft agar overlay plaque counts. 203

Initial inoculations in planta 204

Pre-treated tomato leaves were inoculated with either bacterial inoculum or bacteria-phage 205

co-inoculum via pressure infiltration using a 1ml flat syringe on the abaxial leaf surface. Co-206

inoculum was mixed at a 50:50 ratio (500µl bacteria + 500µl phage) immediately before leaf 207

infiltration; a separate tube of 1 in 10 diluted King’s medium B broth (KB, 10 g l−1 glycerol, 208

20 g l−1 proteose peptone (no. 3; Becton Dickinson UK Ltd, Oxford, UK), 2 g l−1K2HPO4·3H20, 209

2 g l−1 MgSO4·7H2O) supplemented the bacterial inoculum in order to mimic the co-inoculum 210

ratio. Three leaves per plant received approximately 50µl of (co-) inoculum each. Single 211

leaves were chosen as biological replicates in Experiment 1 but replication was carried out 212

at the plant level in Experiment 2 (four. Control plants (one per cohort) were treated 213

identically with phage-only solution. 214

11

Virulence assay 215

Population density in colony forming units (CFU) was used as a proxy for bacterial fitness, 216

encompassing virulence and thus growth in planta. Inoculated tomato leaves were assayed 217

after 3 days incubation using a 3mm hole-punch. Two discs were cut from each of three 218

leaves per plant, pooled into one tube of phosphate buffer with ceramic beads and 219

homogenised using a Fast-Prep tissue lysing machine. Samples were serially diluted and 220

plated onto King’s medium B (KB + 12 g l−1 agar) with 0.25g l-1 Nystatin selective antifungal 221

agent (Sigma; solubilised in 1M DMSO, Sigma]) .The number of viable bacteria in leaves 222

was determined by plate counts; phage-only control plants were plated out in the same way 223

to check for endophytic contaminants. 224

Resistance to phage 225

After quantification of bacterial density, 12 individual CFUs were randomly selected with a 226

sterile toothpick, transferred to 200μl KB broth in a 96-well plate and grown overnight before 227

performing a resistance streaking assay. Two 15μl drips of ancestral WILS phage were run 228

down a square plate of hard KB agar, and bacteria were streaked across left to right using a 229

12-pin replicator (Buckling & Rainey, 2002). Resistance was calculated as a proportion of 230

streaked colonies that grew through the phage drips. 231

Experimental evolution 232

As outlined above, plants initially received a reanimated freezer stock culture of P. syringae 233

pv tomato “803d”. After bacteria had been recovered and assayed from the first set of plants 234

and plates, 100 colonies were randomly selected from each sample with a sterile toothpick 235

and suspended in tubes of 1ml MgCl2 buffer to formulate the inoculum for the next time 236

point. Thus, every plant/plate had its own evolutionary lineage created and these were 237

transferred three times by serially passaging through plant cohorts (Guan et al., 2013); see 238

Figure 2 for experimental design schematic. 239

12

Statistical analysis 240

Analyses and figures were produced in R 3.0.1 (R Core Team, Vienna, Austria). Data 241

collected on bacterial density was non-Gaussian distributed, thus Generalized Linear Models 242

(GzLM) were created for each experiment, with a Poisson error structure for count data 243

(adjusted to quasi-Poisson to correct for overdispersion) (Crawley, 2012). A GzLM with 244

binomial error structure was used to analyse variation in the proportion of resistance 245

(adjusted to quasi-binomial to correct for overdispersion). 246

A two-way Analysis of Variance (ANOVA) was used to compare bacterial density across 247

plant pre-treatments and between presence/absence of phage, computed with an F-test in 248

line with the model error structure. Planned pairwise linear contrasts between the control and 249

each plant pre-treatment were conducted using multiple comparisons of means (Tukey-250

Kramer) within the model. To reduce the chance of Type-1 errors (false positives), no more 251

than two comparisons were made as this was equal to the experiment-wise degrees of 252

freedom at d.f.=2 (Ruxton & Beauchamp, 2008), whilst the tested p-values were adjusted for 253

the false discovery rate using the FDR method (Benjamini & Hochberg, 1995). 254

13

RESULTS 255

Pathovar-cultivar compatibility 256

We conducted a preliminary experiment to ascertain the most virulent pathovar strain of 257

Pseudomonas syringae pv tomato (Pst) with regards to the ‘Moneymaker’ cultivar of tomato 258

plant to be studied. After 72 hours, plants inoculated with 803 strains appeared most infected 259

compared to the control by the manifestation of chlorotic (yellowed) tissue and deformed 260

(curled) edges; we chose 803d as the study strain as it was judged to display the most 261

severe symptoms (Fig. 3). 262

Leaf spray pre-treatments 263

In Experiment 1 we tested whether exogenously pre-treating tomato plants with solutions of 264

different bacteria-derived elicitors 20h prior to inoculation affected the growth of Pst803d in 265

the leaf apoplast. Figure 4 offers a visualisation of these symptoms; most striking is the 266

chlorotic (yellowed) appearance of COR-treated leaves. Experimental replication was carried 267

out at the leaf level – mean density values per plant and treatment are summarised in Table 268

1. The lowest individual leaf density was found in an LPS-treated leaf (6.66 x 104 CFU ml-1, 269

Table 1), and overall, application of LPS resulted in the lowest mean treatment density (4.81 270

x 106 CFU ml-1, Table 1). Conversely, application of COR resulted in the highest mean 271

treatment density (1.65 x 107 CFU ml-1, Table 1) inclusive of the highest individual leaf 272

density recovered (4.37 x 107, Table 1). Figure 5 displays the raw data, highlighting these 273

trends. To test for our prediction regarding bacterial elicitors, we compared bacterial 274

densities across treatments with ANOVA. We found no significant effect of pre-treatment on 275

density (GLM: F2,12=2.94, p=0.09). 276

14

Plant priming and phage therapy 277

In Experiment 2 we investigated whether leaf density of P. syringae was affected by plant 278

pre-treatment with bacterial elicitors in the presence and absence of phage. Again, we 279

compared bacterial densities across treatments and found a significant effect of pre-280

treatment on density (GLM: F2,21=17.94, p<0.001, Fig. 5). To test our prediction that COR 281

acts to suppress plant defences and LPS acts to induce plant defences, a planned multiple 282

comparison of means revealed that, compared to the MES control treatment, COR had a 283

significantly different density whereas LPS did not (Tukey linear contrasts: MES-LPS, 284

p=0.17; MES-COR, p<0.001). This is highlighted by an order of magnitude higher CFU ml-1 285

in COR treatments at 108 compared to densities of 107 in MES-treated plants (Table 2). 286

To test for our prediction that phage presence acts to reduce bacterial density we compared 287

bacterial densities between all phage-present versus all phage-absent treatments. We found 288

a significant effect of phage presence; co-inoculation of phage with bacteria resulted in a 289

lower leaf bacterial density than inoculation with the bacteria alone (F1,20=6.65, p=0.018, Fig. 290

5). Despite significant responses of bacterial populations to the main effects of plant pre-291

treatment and phage presence, there was no evidence that one effect depended on the 292

other (interaction: F2,18=0.55, p=0.58). 293

A resistance assay was also performed in Experiment 2 to determine whether bacteria in the 294

leaf had evolved resistance under phage selection. As expected, all colonies tested from the 295

bacteria-only inoculations remained susceptible to phage (proportion resistant = 0 for all 296

lines in all three plant pre-treatments; MB1-4, LB1-4 and CB1-4, Table 2). Of the bacteria 297

under phage selection, all lines in MES-treated plants maintained complete susceptibility 298

(MP1-4: proportion resistant = 0, Table 1, Fig. 6). On average, 2% of bacteria in LPS-treated 299

plants gained resistance; this was equivalent to 1 of 12 colony streaks from 1 of 4 lines 300

growing through a phage barrier on KB agar (LP1, proportion resistant = 0.08, Table 2, Fig. 301

6) when the other three lines remained completely susceptible (LP2-4, proportion resistant = 302

15

0, Table 2, Fig. 6). Bacteria in COR-treated plants exhibited the greatest degree of 303

resistance as 25% on average evolved to resist phage (mean proportion resistant 0.25, 304

Table 2, Fig. 6); this was equivalent to 12 of 12 colonies from 1 of 4 lines exhibiting 305

resistance (CP1, proportion resistant = 1, Table 2, Fig. 6) when the other three lines 306

remained completely susceptible (CP2-4, proportion resistant = 0, Table 2, Fig. 6). To test 307

for our prediction regarding the effect of plant pre-treatment on phage resistance, we 308

compared proportion of resistant bacteria in phage-selected lines across treatments. We 309

found that the proportion of resistant bacteria did not depend on pre-treatment (GLM: 310

F2,9=2.08, p=0.18) 311

Experimental evolution 312

Experiment 3 aimed to repeat Experiment 2 in subsequent plants with recovered bacteria 313

from the previous passage. It proved difficult to obtain uncontaminated samples from lines in 314

presence of phage after the first transfer. Hence, only the bacteria-only lines were passaged 315

over three sets of plants and we just investigated the effects of treatment over time. A 316

significant interaction indicates that the effect of time on bacterial density depended on the 317

plant treatment (treatment*time: F2,30=8.17, p=0.0015, Fig. 7). 318

16

DISCUSSION 319

Phytopathogenic bacteria face a broad spectrum of challenges in the phyllosphere as 320

invading parasites of their plant hosts and as hosts themselves under attack from virulent 321

phages. Over ecological time, pathogen population density is subject to temperance under 322

defensive pressure from plants as aspects of host immune response restrict bacterial 323

proliferation, driving increased selective pressures on the pathogen for compensatory 324

changes in its ability to surpass these defences over evolutionary time (Anderson et al., 325

2010). Phage infectivity of bacterial hosts follows a similar pattern of oscillatory population 326

dynamics due to direct lytic pressure or selection for resistant mutants in a co-evolutionary 327

arms race (Weitz et al., 2005). However, the interface at which plant defences and phage 328

infectivity relate to pathogenic microbes remains to be fully elucidated due to large gaps in 329

our knowledge of this tri-trophic system, particularly pertaining to how bacteria resist phages 330

under varying phenotypic degrees of plant defence. 331

We wanted to manipulate plant defence without altering the plant genotype to specifically 332

test whether phage therapy works differently in plants of varying levels of resistance, and 333

whether bacteria are less likely to evolve resistance to phages if they are also under strong 334

selection to evade plant defence. We did this by exogenous application of bacterial elicitors 335

to reproduce effector-triggered susceptibility (ETS) via coronatine (COR) or pathogen 336

triggered immunity (PTI) via lipopolysaccharide (LPS) in leaves of host tomato plants. Our 337

expectations were met by more successful growth of P.syringae pv tomato “803d” in COR 338

treatments, but contrary to our predictions, LPS treatments did not significantly reduce leaf 339

bacterial populations of the pathogen. However, additional results from experimental 340

evolution suggest these effects were not consistent over time. Coherent with the 341

fundamental basis of phage therapy, presence of phage resulted in a lower bacterial density 342

overall, but this did not depend on treatment. Finally, we present evidence for rapid microbial 343

evolution by way of spontaneous mutations conferring resistance to phage that occurred 344

17

within the plant host after one passage, but the proportion of resistance did not depend on 345

treatment and there was no sign of bacterial fitness cost. 346

Suppressed plant defences 347

Experiments 1 (non-significant) and 2 (highly significant) both present data in line with 348

existing literature that coronatine acts to suppress plant defences as the greatest bacterial 349

density is seen in COR treatments (Uppalapati et al., 2007). Furthermore, host phenotype 350

photos agree with existing reports of coronatine-induced chlorosis (yellowing of leaves) 351

(Bender, 1999) and thus the promotion of susceptibility through ETS (Jones & Dangl, 2006). 352

A substantial degree of error was found between plants in COR treatments of Experiment 2, 353

but this may be clarified by natural differences in sensitivity to coronatine, possibly due to 354

allelic variation in the nrt2 gene family (Camañes et al., 2012). A lower bacterial density 355

found in phage-present COR treatments compared to phage-absent treatments of 356

Experiment 2 supports proponents of density-dependent parasite-host regulation (Anderson 357

& May, 1981), but without ongoing monitoring of bacteria-phage population dynamics it is 358

unclear whether this is a short-lived effect preceding recovery of density. 359

The highest proportion of resistance arising in a COR treatment may be explained simply by 360

an order of magnitude greater leaf C.F.U.s (108) in contrast to LPS treatments or MES 361

controls (107). At a glance, the sheer chance of mutation is greater in a large population, 362

assuming a high mutation rate, and natural selection dictates advantageous traits spread 363

rapidly. The question remains, however, as to why this occurred in only one of four 364

experimental lines (CP1), particularly as the highest density grew in this line. A hypothetical 365

explanation is this: all four bacterial lines entered respective plants with near-identical 366

genotype and phenotype, being cultured under the same conditions. Just as bacteria 367

respond to phage attack by altered expression of target structures, bacteria are constantly 368

changing by a process called ‘phase variation’ in the face of their plant hosts. This form of 369

phenotypic variation may manifest in skewed growth patterns, cell-size distortion, improved 370

18

motility, or the more familiar modification of surface apparatus, all governed by molecular 371

switches (Henderson et al., 1999). Albeit initially suppressed by COR, subsequent natural 372

variation in strength of the plant immune response may have been responsible for driving 373

phase variation as has been demonstrated previously for Pseudomonads (van den Broek et 374

al., 2003). We therefore speculate that phase variation may be accountable for increased 375

growth within the plant and proportion of resistance by up-rating host-phage encounters as 376

bacterial cells express increased motility and lower receptor density (39). 377

Resistance arising from phage therapy is theoretically minimised when the concentration of 378

applied phage can be kept above its inundation threshold (IT) - the phage titre at which rate 379

of bacterial growth = rate of phage infection (Payne & Jansen, 2001) – and remain so until 380

all susceptible bacteria are infected (Cairns & Payne, 2008). As the IT is dependent on 381

bacterial growth rate and adsorption rate of phage particles but not explicitly bacterial density 382

(Cairns & Payne, 2008), the danger of phase variation leading to resistance in response to 383

plant priming may be reduced by ensuring sufficiently high phage concentrations are used. 384

Experiment 2 used phage particles and bacterial cells at equal orders of magnitude (both 385

nominal at ~106), so in the future, a higher phage titre or even a ‘phage cocktail’ consisting 386

of multiple infective phage is recommended to mitigate against resistance (Meaden & 387

Koskella, 2013). 388

Despite evidence from previous work that resistance mutations to phage are often pleiotropic 389

and affect multiple aspects of fitness (Mulholland et al., 1993; Bohannan & Lenski, 2000; 390

Friman & Buckling, 2014), the resistance we observe here does not appear to have a cost. 391

In order to understand mutations coding for the resistance arising in our results, the next 392

step would be characterization of mutant by genetic sequencing to generate bioinformatics 393

data and determine how bacterial genotype has changed in the resistant line. 394

19

Elevated plant defences 395

Effects of lipopolysaccharide on proliferation of Pst803d within this study are contradictory. A 396

trend of lowest bacterial density in Experiment 1 LPS treatments, albeit non-significant, 397

implied the action of LPS in helping the plant fight disease. Although replication at the leaf 398

level should result in less error and noise, power of the statistical test is reduced in the 399

analysis of Experiment 1 as replicates were unbalanced; only three leaves were used for the 400

MES control compared to six for LPS. Experiment 2 found no difference between the control 401

and LPS treatments in terms of density. There are various possible explanations for these 402

ambiguities. A previous study investigating the effects of systemic acquired resistance (SAR) 403

observed a decline in bacterial growth of Pseudomonas syringae pv maculicola between 404

three and five days post-inoculation in Arabidopsis thaliana with a peak C.F.U. recorded at 405

three days (Kiefer & Slusarenko, 2003). Samples were taken in our study on the third day of 406

incubation so an argument can be made that allowing more time for bacterial populations to 407

diminish from SA-dependent restrictions would resolve the lack of distinction we saw 408

between LPS-treated plants and the controls. 409

Despite this potential time-lag effect, Kiefer and Slusarenko’s study pertained to acquired 410

resistance across distal leaves within the same plant, whereas we were more interested in 411

the primed induction of a faster and stronger immune response. We followed methods of 412

(Newman et al., 2002b) by inoculating plants 20hrs after spray-pretreatment with LPS, but 413

knowing the peak expression of SA-regulated defence genes with the specific tomato 414

cultivar ‘Moneymaker’ would shed light on the optimal timing of exogenous application. 415

Certain tobacco genes are induced by SA within 30mins, peak after 3hrs then proceed to 416

decay rapidly (Horvath & Chua, 1996), although examples such as this may represent early 417

kinetics upstream of further antimicrobial conjugates. We may have missed a key window in 418

defence signal transduction, so propose performing quantitative real-time PCR to 419

20

accompany data such as ours in order to achieve transcript profiles of SA-upregulated 420

defence genes as outlined in (Lee et al., 2013). 421

There was no difference between phage-present and phage-absent bacterial density in the 422

LPS treatment of Experiment 2. Theoretically, this is due to density-dependent feedbacks 423

between host density and parasite abundance, models of which predict a directly 424

proportional relationship between host population size and the impact of said parasite on 425

host density (Anderson & May, 1981) – the lower the bacterial density, the less effect 426

phages will have. This is concordant with our results with COR, as bacterial density may not 427

have reached a high enough threshold to support ongoing phage lytic bursts. Equally, low 428

proportion of resistance we saw in LPS treatments may rely on bacterial density, but also 429

phage titre. The quantity of phage applied may not have been sufficient to provide strong 430

enough selection to drive evolution of resistance, suggesting that under circumstances of 431

elevated plant defence, mutations arises dependent on phage titre, and at a low enough 432

concentration such as 106 in our study, there exists only a small chance of resistance. 433

An unexplored hypothetical explanation as to lack of therapeutic phage is toxic effects of 434

plant defence compounds on phage action. After recognition of MAMPs such as LPS, the 435

earliest cellular events are ion fluxes across plant plasma membranes followed by a burst 436

production of reactive oxygen species (ROS e.g. O2− & H2O2) (Nürnberger & Scheel, 2001). 437

It is known that phages are susceptible to DNA damage from UV exposure (Clark et al., 438

2012) and that ROS are also associated with photo-oxidative stress (Apel & Hirt, 2004), 439

therefore it cannot be ruled out that elements of plant innate immunity may interfere with 440

phage action and/or titre by oxidative DNA damage, reminiscent of reported T7 phage 441

inactivation in animal systems exposed to ROS (Hargreaves et al., 2007). Indeed, reductions 442

in bacteriophage λ concentration have been demonstrated at ≥7 log(10) by UV and 443

hydroxides (Clark et al., 2012). This rationale could easily be tested by conducting an in vitro 444

experiment to test the effects of a suite of known plant secondary metabolites on phage 445

abundance in a microcosm. 446

21

Time-dependent trends 447

Despite a significant interaction for the effect of time-point (i.e. transfer) on treatment over 448

three plant passages, we were not convinced that this deserved significant attention. The 449

aim of Experiment 3 was to experimentally evolve bacteria through new plants each time 450

with the original phage applied in half of the lines. Unfortunately we encountered serious 451

contaminations in phage-selected lines after Experiment 2 so were only able to transfer 452

bacteria-only lines - the source of contamination remains unresolved as phage-only controls 453

were included in the methods. 454

The trend over time indicates inconsistency in response of bacteria to the pre-treatments. 455

The most rational explanation for the changes in effect is that we created an artificial 456

bottleneck in the population. The original inoculum was confirmed at 106 CFU ml-1, whereas 457

the second two comprised of 100 CFUs only. We reasoned that this would be sufficient, it 458

may have been necessary to start with 100 CFUs and maintain this standard across 459

transfers. Moreover, the solutions used in the second two transfers were arguably degraded 460

compared the initial experiment. However, our storage protocol does not merit criticism as 461

we undertook preventative measures to preserve bacterial elicitor solutions LPS and COR 462

over the months of our study; concentrated stocks were prepared and divided into 2ml 463

aliquots, kept at -20°C, diluted to appropriate concentrations an hour before use and never 464

re-used once thawed. 465

For greater confidence in observed effects over multiple passages in future experiments, we 466

propose that inoculating an ancestral bacterial strain with the same cohort of plants would 467

provide a reference for non-evolved virulence and circumvent this problem. Preferably, this 468

experiment would be conducted with in vitro parallel lines as a reference. 469

22

Limitations and improvements 470

Although we strived to design a robust experiment, certain confounding factors may be 471

responsible for noise in the data. It is worth emphasizing that the results we obtained by 472

homogenising tomato leaves are likely to represent a subsample of the culturable 473

community; interactions are taking place within the phyllospheric community rather than a 474

sterile test tube. Thus, it is difficult to disentangle the effects of phage and plant defences 475

from the additional complexity of phage-plant-epiphytic microbe relationships, potentially 476

affected by altered competition regimes (Koskella & Meaden, 2013; Brockhurst et al., 2006). 477

With regards to technical shortcomings, we recommend revising the inoculation procedure if 478

this experiment is to be repeated. A principal effect of COR is to re-open guard cells, 479

facilitating further pathogen entry through stomata (Melotto et al., 2006). Conversely, 480

perception of MAMPS including LPS induces guard cell closure in Arabidopsis epidermal 481

peels (Melotto et al., 2006). The method of syringed pressure infiltration ignore these 482

responses which arguably may have affected density and inhibited ecologically relevant 483

effects; spray or dip inoculation procedures imitate natural colonization more closely (Zeng & 484

He, 2010; Zeng et al., 2011). In addition, although maximum care was taken to ensure a 485

standard 50µl volume of inoculum entered the leaves each time, occasionally more inoculum 486

shot into the leaf apoplast. This phenomenon could derive from the pretreatments 487

themselves as discussed above, but regardless, even a few microliters of surplus inoculum 488

has the potential to influence population density, possibly by augmenting virulence through 489

increased cell-cell communication (quorum-sensing) based mechanisms (Von Bodman et 490

al., 2003). At the very least, an increase in treatment group sample size from four to six 491

plants per determination would increase confidence in observed effect across all tests. 492

The most logical follow-up study would be to repeat the same overall design, improved by 493

the above propositions with increased sample sizes, but to experiment with phage cocktails 494

and aim to resolve short-term temporal trends throughout course of infection/colonization. 495

23

This could be easily be done by inoculating three leaves then sampling different leaves at 496

24-hour time intervals (Kim et al., 2008) for the purpose of constructing bacterial growth 497

curves that align with expression profiles of plant defence genes as discussed earlier. 498

Furthermore, it would be of interest to recover phages from the leaf too, to determine 499

whether the leaf is a stable spatial refuge for bacteria-phage coexistence (Heilmann et al., 500

2012), thereby determining the utility of phages as an in vivo replicating bio-control tool. 501

In the context of up-scaled agricultural application, we imagine tailoring elicitor specificity 502

and a more conservative application method than spraying. For general consideration, 503

airborne LPS would be prone to wind-dispersal and will induce an immune response in 504

animals (Janeway & Medzhitov, 2002) so would not be a wise choice with environmental 505

preservation in mind, and may indeed act to destabilize epidemiological population dynamics 506

and increase persistence of pathogens (Tidbury et al., 2012). Avoiding exposure to non-507

target organisms can be achieved with compounds such as acibenzolar-S-methyl, a putative 508

mimic of the SAR response (Cole, 1999). By directly upregulating genes of the class ‘Host 509

Plant Defence Induction P1’, it prepares a range of plants to protect themselves successfully 510

against attack with a less circuitous signal and therefore lower metabolic cost (Romero et al., 511

2007; Ishii et al., 1999; Brisset et al., 2000). 512

Concluding remarks 513

Overall, our results provide evidence that pathogenic bacteria in the phyllosphere are 514

unlikely to evolve resistance to phage in a scenario of elevated plant defence responses, 515

due to stronger plant-mediated selection. In an opposing scenario, we demonstrated that 516

reduced plant pressure led to a greater proportion of resistance to phage, but bacteria did 517

not pay a fitness cost of resistance in terms of reduced growth in the leaf. Our work also 518

demonstrates the potential utility of phage therapy as a reactive tool to reduce bacterial 519

density as phage therapy develops into a more common method for controlling plant 520

pathogens (Goodridge, 2004; Levin & Bull, 2004), but we find no evidence for attenuated 521

24

virulence in phage-resistant bacterial strains as has been demonstrated in the past (Evans et 522

al., 2010; Toth et al., 1999). Thus, we underscore a cautionary tale of cost-free resistance 523

when phages are applied in too low a dose compared to their bacterial hosts in a plant 524

environment of suppressed defence. This does not rule out an important role of phage-525

imposed selection and therapeutic benefits of working with the plant defence pathways, as 526

associated costs are likely to be dependent on a plethora of other factors such as timing of 527

plant defence induction or suppression. Finally, we highlight that, unlike experimental 528

evolution in a sterile microcosm, rigorous fine-tuning of experimental design is paramount to 529

obtaining a clear, multiple-passage signal without confounding noise in plant-pathogen-530

phage study systems. 531

There still remain many open questions about drivers and costs of resistance in complex 532

plant-pathogen-phage networks. Further work in scope of this combined method of defence 533

pathways is echoed in the traditional proverb: “The enemy of my enemy is my friend”. 534

Deploying the ancient plant immune system alongside primitive foes may hold the key to 535

fighting bacterial infection in a contemporary age, so furthering understanding of this tri-536

trophic evolutionary and ecological interaction will contribute to predicting and managing 537

disease outbreaks in natural and agricultural systems. 538

ACKNOWLEDGEMENTS 539

The authors would like to thank members of the microbiology laboratory at the Environment 540

& Sustainability Institute, University of Exeter, for their assistance with experimental 541

techniques and useful discussion, namely; Sean Meaden, Reinier van-Velzen and Nicole 542

Parr. 543

25

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Figure 1 – Diagram to show the model of interactions in the study system. Orange arrows indicate direct actions. Purple arrows indicate directional effects; dashed arrows represent indirect actions. Plant host = tomato (Solanum lycopersicon), bacterial pathogen = Pseudomonas syringae pv tomato. COR=coronatine, LPS=lipopolysaccharide.

33

+

Bacteria Phage

MES (-) LPS COR

1. Microbial inoculum

2. Pre-primed plant

3. Assay & transfer

100 CFU

+ ancestral

phage

100 CFU

+ ancestral

phage

100 CFU

+ ancestral

phage

Bacteria only

No phage selection

MES (-) LPS COR

4. Subsequent inoculum

Repeat steps 2-4 in replicate plant sets

100 CFU

+ buffer

100 CFU

+ buffer

100 CFU

+ buffer

5. Passage

Figure 2 – Experimental design used in this study. Leaves of tomato (Solanum lycopersicon) were pre-treated by spraying with bacterial elicitors of plant immune responses (LPS=lipopolysaccharides, 100ng/µl; COR=coronatine, MES (-) =control buffer) 20h prior to co-inoculation with bacteria (Pseudomonas syringae pv tomato “803d”) or bacteria plus phage (WIL5). Recovered bacteria were assayed for population density and resistance to ancestral phage, then 100 CFU from each treatment group were randomly selected to form the subsequent inoculum. These experimental lineages then were monitored over time, passaging between different plant cohorts.

34

a

20mm

Inoculation sites

Chlorosis

Curling

Figure 3 – Results from preliminary experiment testing pathovar-cultivar compatibility. Photographs show leaves of tomato cv ‘Moneymaker’ three days post-inoculation. A range of bacterial strains (each diluted 1 in 100 from an overnight culture) belonging to the pathovar Pseudomonas syringae pv tomato were tested: [a] 803d, [b] 803a, [c] DC3000, [d] 1310c and [e] control (MgCl2 buffer). Images captured with Apple iPad (1

st gen) and enhanced +20% brightness, +40% contrast with Microsoft Powerpoint correction tools in

order to visualise symptoms more clearly.

b

c d

e

35

Figure 4 – Results from Experiment 1. Photographs show tomato cv ‘Moneymaker’ leaves three days

post-inoculation with Pseudomonas syringae p.v. tomato “803d” (Pst803d). Plants were spray pre-treated

20h prior to inoculation with [a] MES (control buffer) + MgCl2 (buffer), [b] MES, [c] coronatine solution

(COR, 0.5ng/µl) or [d] lipopolysaccharide suspension, 100ng/µl (LPS). Images captured with Apple iPad

(1st

gen) and enhanced (+20% brightness, +40% contrast) with Microsoft Powerpoint correction tools in

order to visualise symptoms more clearly.

36

Figure 5 – Results from Experiment 1. A box and whisker plot shows bacterial populations of

Pseudomonas syringae p.v. tomato “803d” recovered from tomato leaves (Solanum lycopersicon) three days post-inoculation. Plants were spray pre-treated 20h prior to inoculation with either coronatine solution (COR, n=6), lipopolysaccharide suspension (LPS, 100ng/µl, n=6) or a control buffer (MES, n=3). Bacterial density values are log-transformed colony forming units (CFU) per ml.

37

0e+00

1e+08

2e+08

3e+08

4e+08

COR LPS MES (-)

Plant pretreatment

Ba

cte

ria

l d

en

sity (

C.F

.U. m

l -1

)

Phage

No

Yes

Figure 6 – Results from Experiment 2. A barplot shows bacterial populations of Pseudomonas syringae p.v. tomato “803d” recovered from tomato leaves (Solanum lycopersicon) three days post-inoculation. Plants were spray pre-treated with either coronatine solution (COR, 0.5ng/), lipopolysaccharide suspension (LPS, 100ng/µl) or MES (control buffer). Leaves were inoculated in the presence (blue) or absence (red) of phage. Bacterial density values are mean colony forming units per ml (CFU ml

-1) ± S.E.M. (n=4 per treatment, n=24 total).

Absent

Present

38

CP2, 3, 4 LP2, 3, 4 MP1, 2, 3, 4

CP1

LP1

Figure 7 – Results from Experiment 2. Schematic shows resistance of pathogen Pseudomonas syringae p.v. tomato “803d” to ancestral phage 3 days post-co-inoculation in tomato leaves (Solanum lycopersicon). Plants were spray pre-treated with either control solution (MES), lipopolysaccharide suspension (LPS, 100ng/µl) or coronatine solution (COR, 0.5ng/µl). Proportion resistant represents the proportion of 12 randomly selected colonies per co-inoculum line (n=4, n=12 total) that exhibited resistance to phage.

39

Figure 8 – Results from Experiment 3. Line graph shows bacterial populations of Pseudomonas syringae p.v. tomato “803d” recovered from tomato leaves (Solanum lycopersicon) in response to different pre-treatments in three instances of plant passage. Each plot point represents mean colony forming units (CFU) per ml ± S.E.M. (n=4 per treatment, n=32 total). For each time point, a fresh cohort of plants were spray pre-treated prior to inoculation with either coronatine solution (COR, 0.5ngµl), lipopolysaccharide suspension (LPS, 100ng/µl) or control buffer (MES). Bacteria were recovered between time points, assayed and 100 CFUs were randomly selected to form the subsequent inoculum.

40

Table 1 Bacterial populations of Pseudomonas syringae p.v. tomato “803d” extracted from tomato leaves (Solanum lycopersicon) 3 days post-inoculation, after plants were spray pre-treated with either MES (control buffer), lipopolysaccharide suspension (LPS. 100ng/µl) or coronatine solution (COR). Leaf density values are colony forming unit (CFU) means of six 10µl drop plate counts of a dilution series converted to CFU per ml; plant density values are means of these leaf scores. Treatment density values are averaged across all leaves of a particular treatment and shown as means ± 95% confidence intervals (95% C.I.)

Plant pretreatment

Plant Bacterial density (CFU ml-1)

Leaf Plant Treatment

COR

C1

2.16 x 107

2.56 x 107

1.65 x 107

±1.31 x 107

1.70 x 107

3.83 x 107

C2

4.37 x 107

7.34 x 106 1.10 x 107

6.66 x 106

LPS

L1

1.60 x 107

5.48 x 106

4.81 x 106

±6.35 x 106

3.80 x 105

6.66 x 104

L2

7.16 x 106

4.14 x 106 3.18 x 106

2.08 x 106

MES (-) M1

4.66 x 106

6.38 x 106 6.38 x 106

±1.24 x 107 1.20 x 106

2.40 x 106

41

Table 2 – Results from Experiment 2. Bacterial populations of Pseudomonas syringae p.v. tomato “803d” extracted from tomato leaves (Solanum lycopersicon) 3 days post-inoculation. Plants were spray pre-treated 20h prior to inoculation with either control buffer (MES), lipopolysaccharide suspension (LPS, 100ng/µl) or coronatine solution (COR 0.5ng/µl). Leaves were co-inoculated with a control buffer (Absent) or phage WIL5 (Present). Bacterial density values are colony forming units (CFU) per ml, treatment value are means ± 95% Confidence Interval (95% C.I., n=4 per treatment, n=24 total). Proportion resistant represents the proportion of 12 randomly selected colonies per line that exhibited resistance to phage. ‘Experimental line’ codes thus: B=bacteria only, P=bacteria + phage.

Plant pretreatment

Phage Experimental

line

Bacterial density (CFU ml-1

) Proportion resistant Plant Treatment

MES (-)

Absent

MB1 8.40 x 10 7

7.15 x 107

± 1.69 x 10

7

0

MB2 7.20 x 10 7 0

MB3 5.80 x 10 7 0

MB4 7.20 x 10 7 0

Present

MP1 2.40 x 10 7

3.25 x 107

± 9.50 x 10

6

0

MP2 3.40 x 10 7 0

MP3 3.80 x 10 7 0

MP4 3.40 x 10 7 0

LPS

Absent

LB1 5.00 x 10 7

8.50 x 107

± 6.48 x 10

7

0

LB2 5.00 x 10 7 0

LB3 1.14 x 10 8 0

LB4 1.26 x 10 8 0

Present

LP1 1.08 x 10 8

7.40 x 107

± 3.83 x 10

7

0.08

LP2 5.80 x 10 7 0

LP3 7.40 x 10 8

0

LP4 5.60 x 10 7

0

COR

Absent

CB1 3.22 x 10 8

2.98 x 108

± 2.92 x 10

8

0

CB2 7.20 x 10 7 0

CB3 2.78 x 10 8 0

CB4 5.20 x 10 8 0

Present

CP1 1.98 x 10 8

1.77 x 108

± 5.45 x 10

7

1

CP2 1.88 x 10 8 0

CP3 1.96 x 10 8 0

CP4 1.26 x 10 8 0