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
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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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774
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