bacterial persisters in long-term infection: emergence and ... · 5 jennifer a. bartell 1*, david...

1

Bacterial persisters in long-term infection: emergence and fitness in a complex host 1

environment 2

3

Authors 4

Jennifer A. Bartell1*, David R. Cameron2,3*, Biljana Mojsoska4,6*, Janus Anders Juul 5

Haagensen1, Lea M. Sommer4, Kim Lewis2§, Søren Molin1, Helle Krogh Johansen4,5§ 6

7

Affiliations: 8

1 The Novo Nordisk Foundation Center for Biosustainability, Technical University of 9

Denmark, Lyngby, Denmark 10

2 Antimicrobial Discovery Center, Northeastern University, Boston, USA 11

3 Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of 12

Bern, Switzerland 13

4 Department of Clinical Microbiology, Rigshospitalet, Copenhagen Ø, Denmark 14

5 Department of Clinical Medicine, University of Copenhagen, Copenhagen N, Denmark 15

6 Present address: Department of Science and Environment, Roskilde University, Roskilde, 16

Denmark 17

18

*Contributed equally to the work and listed alphabetically 19

§Corresponding authors 20

21

BIOLOGICAL SCIENCES: Microbiology 22

Keywords: persister cells, persistent infection, Pseudomonas aeruginosa, cystic fibrosis 23

24

25

26

27

28

29

30

31

32

33

34

was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (whichthis version posted December 17, 2019. ; https://doi.org/10.1101/561589doi: bioRxiv preprint

https://doi.org/10.1101/561589

2

Abstract 35

Despite intensive antibiotic treatment, Pseudomonas aeruginosa often persists in the airways 36

of cystic fibrosis (CF) patients for decades, and can do so without antibiotic resistance 37

development. Using high-throughput screening assays of bacterial survival after treatment with 38

high concentrations of ciprofloxacin and tobramycin, we have determined the prevalence of 39

persisters in a large patient cohort consisting of 460 longitudinal isolates of P. aeruginosa from 40

39 CF patients. Thirty patients exhibited high persister variants (Hip, defined by survival of at 41

least 75% of replicates) in at least one of the two antibiotic screens (25% of isolates in total). 42

Few bacterial lineages were dominated by Hip, but Hip emergence increased over lineage 43

colonization time. Furthermore, transient lineages were significantly less likely to exhibit Hips 44

than non-transient lineages, suggesting that the Hip phenotype is decisive for long-term 45

establishment of a lineage. While we observed no strong signal of adaptive genetic 46

convergence across all lineages with Hip emergence, Hip+ lineages were significantly 47

correlated with lineages with slow growing isolates. Finally, we evaluated Hips in a model CF 48

structured environment by testing the fitness properties of otherwise genotypically and 49

phenotypically similar low-persister (Lop) and Hip isolates in co-culture using a flow-cell 50

biofilm system with antibiotic dosing modelled on in vivo dynamics. Hip survived 51

ciprofloxacin treatment better than Lop. Our results strongly argue against the existence of a 52

single dominant molecular mechanism underlying bacterial antibiotic persistence. We instead 53

show that many routes, both phenotypic and genetic, are available for persister formation and 54

consequent increases in strain fitness in CF airways. 55

56

57

58

59

60

61

62

63

64

65

66

67

68


https://doi.org/10.1101/561589

3

Introduction 69

Antibiotic-tolerant persister cells are suspected to be a significant clinical problem that has 70

been seriously neglected in favor of combating antibiotic-resistant bacteria, though persisters 71

were in fact described shortly after the clinical introduction of antibiotics (1). Persisters are 72

distinct from antibiotic-resistant mutants, as they do not grow in the presence of antibiotics. 73

Instead, they remain dormant during antibiotic exposure but retain the capacity to resuscitate 74

and restore the population when antibiotic concentrations drop (2–4). However, our 75

understanding of the physiology and clinical relevance of persister cells is limited, given the 76

difficulty in reliably isolating what is theorized to be a stochastic phenotype in vitro, much less 77

monitoring this phenotype in routine clinical care. Thus, while a few characterizations of small 78

environmental isolate collections have shown that formation of persisters varies across strains 79

(5, 6), few studies have assayed persister formation in clinical or other complex environmental 80

scenarios. One study of oral carriage (0-19 weeks) of Candida albicans isolates from 22 cancer 81

patients undergoing chemotherapy found that patients with carriage of greater than 8 weeks 82

had significantly higher persister levels than those with less than 8 weeks of carriage, but did 83

not address the underlying mechanisms of persistence in this pathogen (7). To examine the 84

underpinnings and long-term impact of the persister phenotype in a clinical scenario, both a 85

large, aligned patient cohort that places the bacteria under similar environmental stresses as 86

well as isolate sampling at a resolution that captures the emergence and longevity of the 87

phenotype are needed. 88

89

P. aeruginosa is the most frequent cause of chronic airway infections in patients with CF (8, 90

9). Mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene often 91

result in inefficient mucociliary clearance of bacteria from the airways, creating opportunities 92

for bacterial colonization (10, 11). Upon entering the host, environmental P. aeruginosa adapts 93

to the CF lung environment, ultimately establishing an incurable airway infection (12, 13). 94

Despite intensive antibiotic treatment from the first discovery of the bacterium in the lung, 95

resistance emergence in the first years of infection is surprisingly low (14, 15). In the absence 96

of clinically defined antibiotic resistance, survival of the bacteria is likely enabled by diverse 97

and co-occurring traits including slowed growth rate, biofilm formation, and the production of 98

small fractions of antibiotic tolerant subpopulations (16–18). How persister cells interrelate 99

with these complex and co-selected changes in vivo is rarely accounted for in in vitro persister 100

studies, but is likely clinically important. 101

102


https://doi.org/10.1101/561589

4

These factors also complicate the search for genetic mechanisms of the persister phenotype 103

that are clinically impactful. While persister cells are stochastic phenotypic variants in any 104

bacterial population, genetic changes in bacterial populations have been shown to produce a 105

high persister state, producing increased numbers of antibiotic tolerant cells following exposure 106

to antibiotics in in vitro studies of pathogenic species (19, 20). Some of these genetic changes 107

have also been observed in clinical isolates; within a set of 477 commensal or urinary tract 108

infection isolates of Escherichia coli, 24 exhibited a mutation in the canonical persister gene 109

hipA, and the causality between a hipA7 mutation and a Hip phenotype confirmed by deleting 110

this allele from one of the clinical isolates (21). An investigation in young CF patients showed 111

an increase in persister phenotype in early/late infection isolate pairs from 14 patients. In this 112

study, 35 longitudinal P. aeruginosa isolates taken from one child over a 96-month period 113

showed increased levels of persister cells over time as well as an accumulation of 68 mutations 114

between the first and last isolate (16). However, the mutations in the single patient resembled 115

those known to accumulate in other CF patients over infection rather than any mutations 116

previously associated with the Hip phenotype in persister-focused in vitro studies. 117

118

To acquire a high-resolution pan-cohort perspective of persister emergence, genetic 119

mechanism, and impact in long-term infections, we have screened 460 longitudinal isolates of 120

P. aeruginosa collected from 39 young CF patients over a 10-year period from early 121

colonization onward for high persister variants (Hip, defined by survival of at least 75% of 122

replicates) tolerant to two unrelated antibiotics (ciprofloxacin and tobramycin). This unique 123

isolate collection allows us to determine Hip prevalence and dynamics during each colonizing 124

strain’s transition from environmental isolate to persistent pathogen. We describe relationships 125

between the Hip phenotype and response to each drug, the age of the isolate, and other adaptive 126

traits in longitudinal infections. We show that the Hip phenotype, defined in this study as a 127

strong and reliable recovery from antibiotic challenge that is a serious concern for the clinic, is 128

an independent and widespread trait. We further search for genetic and phenotypic changes 129

associated with the Hip phenotype in independent clonal lineages within distinct patients, 130

which may suggest adaptive routes to producing this phenotype. Finally, we show that the Hip 131

phenotype generally accumulates over time in patients via several archetypal patterns, appears 132

to contribute to long-term persistence of lineages, and increases the fitness of colonizing 133

populations of P. aeruginosa in antibiotic-treated CF patient lungs. 134

135

136


https://doi.org/10.1101/561589

5

Results 137

The isolate collection 138

We examined a collection of 460 P. aeruginosa airway isolates obtained from 39 young CF 139

patients over a 10 year period while they were treated at the Copenhagen CF Centre at 140

Rigshospitalet (22). These patients represent a cohort aligned at the early infection stage and 141

undergoing similar treatment regimens per CF Centre guidelines, with repeated culture of P. 142

aeruginosa from their monthly sputum sampling within a time frame of 2-10 years (patient 143

inclusion was on a rolling basis over the study period in order to capture all early colonization 144

cases). Early isolates therefore represent bacteria that have not been exposed to substantial 145

antibiotic treatment before the study start excepting rare cases of strain transmission from 146

another patient. The bacterial CF isolates have been grouped into 52 genetically distinct clone 147

types (22), and while many patients retained a monoclonal infection during the entire course 148

of infection, half (n=20, 51.3 %) were infected at least transiently with another clone type. To 149

effectively account for these multi-clonal infections, clinical isolates are described by their 150

patient-specific lineage combining the clone type and the patient of origin (74 lineages in total). 151

Throughout this paper, we will also refer to ‘Time since first detection’ for each isolate, which 152

represents the length of time between first detection and subsequent isolations of the same 153

patient-specific lineage. 154

155

Identification of high persister (Hip) isolates by high-throughput screening 156

We screened the collection of P. aeruginosa isolates for the propensity to survive in the 157

presence of high concentrations of antibiotics. We chose two distinct antibiotics that matched 158

the following criteria; (i) they are frequently used to treat early P. aeruginosa infections in CF 159

patients; (ii) have contrasting targets and mechanisms of action; (iii) drive resistance 160

development with different dynamics in patients; and (iv) are each bactericidal toward 161

stationary phase P. aeruginosa (23, 24). On this basis, we performed two independent screens 162

using either the fluoroquinolone ciprofloxacin, which interacts with DNA gyrase, or the 163

aminoglycoside tobramycin, which acts upon bacterial ribosomes. Briefly, P. aeruginosa 164

subcultures in micro-titer plates were grown for 48 hours until they reached stationary phase, 165

after which they were challenged with antibiotics (100 μg/ml) for 24 hours before survival was 166

assessed (Fig. 1A). A standardised antibiotic concentration was used that was at least 25-times 167

higher than the European Committee on Antimicrobial Susceptibility Testing (EUCAST) 168

resistance breakpoint for each drug, minimising the chance that the screen selected for isolates 169

with modestly elevated minimum inhibitory concentrations (MICs). Within each antibiotic 170


https://doi.org/10.1101/561589

6

screen, isolates were assayed eight times (technical quadruplicates performed in duplicate 171

biological experiments with a positive growth control for at least 3 of 4 replicates in each 172

experiment) and scored based on the capacity to re-grow after antibiotic treatment. An isolate 173

was given a score of 0 if it failed to re-grow in any replicate of an experiment, a score of 1 if it 174

grew once in both biological duplicates, a score of 2 if it grew in half of the technical replicates 175

in each experiment, a score of 3 if it grew in at least three replicates in each experiment, and a 176

score of 4 if it grew in all replicates (Fig. 1B, scores for the ciprofloxacin screen). To validate 177

our high-throughput screening approach, we selected 25 isolates, and enumerated CFUs 178

following 24 hours of ciprofloxacin treatment using standard survival assays. We observed a 179

significant positive correlation between persister score and CFU/ml following treatment (r2 180

0.5719, p < 0.0001, SI Appendix, Fig. S1), thus demonstrating the validity of our experimental 181

approach. 182

183

184

We defined high persister (Hip) isolates as those scoring either 3 or 4 (i.e. at least six of eight 185

technical replicates re-grew) and low persister (Lop) isolates as those scoring between 0 and 2. 186

This stringent scoring system was used to minimise the mis-classification of false Hips and 187

focus our analysis on isolates reliably producing persister cells, representing the most 188

concerning phenotype in a clinical environment. Isolates with a score of 4 made up the largest 189

Hip group, while the largest Lop group consisted of isolates scored as 0, failing to grow in any 190

replicate (Fig 1B). To validate this classification system, we selected six isolates from the same 191

patients, three of which were putative Hips, and three of which were classed as Lops and 192

performed time-dependent killing assays. The isolates displayed typical, biphasic killing (Fig. 193

1C). Each of the three putative Hip strains harboured a greater subpopulation of surviving 194

persister cells, thus they represent true Hips as defined by recent guidelines (25). In total, 195

24.8% of screened isolates (114 isolates) exhibited a persister phenotype in at least one of the 196

screens (Fig. 1D, AnyHip dataset). In Figure 2, we show the distribution of Hips across our 197

isolates with respect to (A) ciprofloxacin persistence (87 Cip Hip isolates), (B) tobramycin 198

persistence (60 Tob Hip isolates), and (C) multi-drug persistence (33 MD Hip isolates), where 199

an isolate scored 3 or more in both screens (Dataset 1). The bottom panel of Figure 2C shows 200

an explicit breakdown of dataset overlap. 201

202

The persister phenotype is antibiotic-specific and is dissociated from antibiotic resistance 203


https://doi.org/10.1101/561589

7

As we were using antibiotics used in patient treatment to select for persister isolates, we 204

evaluated if we were simply selecting for resistant isolates as opposed to true Hips. 205

Ciprofloxacin and tobramycin MICs were determined using E-tests for isolates within the 206

collection. Most of the isolates were characterized as susceptible based on the EUCAST 207

breakpoints (Fig. 2A, B). Twenty-four ciprofloxacin susceptible isolates were Hip while 113 208

Lop variants were resistant. Similarly, 10 tobramycin resistant isolates were Lop while 50 Hip 209

isolates were tobramycin susceptible. We then contrasted the emergence of antibiotic resistance 210

versus Hip for all study lineages (Table S1). Of the 74 lineages assessed, resistance emerged 211

without Hip detection in 14 lineages (40%), while Hips emerged without resistance in 16 212

lineages. Resistance and persistence emerged simultaneously in 11 lineages. Resistance 213

preceded Hip in 9 lineages, while Hip preceded resistance in 5 lineages. If we used a less 214

stringent persister classification (persister score greater than 0 instead of 2), we observed that 215

resistance precedes persistence in 5 lineages, emerges simultaneously in 7 lineages, and follows 216

persistence in 15 lineages. Taken together, these data confirm that our screening approach 217

identified a persister phenotype separate from an antibiotic-resistant phenotype. 218

219

The persister phenotype is enriched in patient-specific lineages with slow-growing isolates 220

Along with changes in antibiotic susceptibility, the persister phenotype in our collection is not 221

arising in isolation from other adaptations. We and others have previously observed that CF 222

isolates adapt towards slow growth rates, increased resistance to antibiotics, and preference for 223

a biofilm lifestyle (18, 26, 27). A specific association between slowing growth rate and the Hip 224

phenotype has also been proposed (28). To probe interrelationships with other phenotypes, we 225

used a principle component analysis to evaluate the distribution of Hip (blue diamonds) versus 226

Lop (grey circles) variants by multiple traits under selection pressure in the CF lung. We see 227

that Any Hip variants group with isolates exhibiting more adapted traits (increased antibiotic 228

MICs and slowing growth), but they also appear across the full phenotypic space alongside 229

Lop isolates (Fig. 3A). 230

231

When comparing the ciprofloxacin versus tobramycin screen data (Fig. 3B-C), the data ellipses 232

enclosing the approximated majority of each population (68% of the population, t distribution) 233

show that Hip variants (blue ellipse) only partially separate from Lop variants (grey ellipse). If 234

we assess the first Hip variant of each lineage (FirstHip – blue ellipse with yellow fill), we see 235

FirstHips overlap substantially with both Lops and Hips. This variation of initial adaptive state 236

could be due to different adaptive trajectories with patients as well as lapses of time between 237


https://doi.org/10.1101/561589

8

Hip emergence and isolation. The MD FirstHips show a more distinctive localization away 238

from Lops, which suggests further benefits of serious growth defects but could also be the 239

result of increased colonization time necessary to evolve both persister traits (Fig. 3D). If we 240

look at the likelihood of each lineage containing both Hips and slow growing isolates (where 241

the minimum growth rate of the lineage is less than 75% of the growth rate of PAO1), we see 242

a significant relationship between incidence of slow growing isolates and AnyHip isolates (Fig. 243

3E). In total, these results emphasize the complexity of selection pressures at play, resulting in 244

concurrent adaptation of distinct traits that may both influence each other and have related 245

genetic underpinnings. 246

247

Evolution of the persister phenotype is not genetically convergent across patient-specific 248

lineages 249

Sequencing and identification of genetic variations accumulating within each clone type have 250

been previously performed for most of the isolates used in this study (403 isolates, 46 lineages). 251

Genes targeted in convergent evolution were identified by the significant enrichment of 252

observed lineages with mutations in those genes compared to the number of lineages expected 253

to have mutations in the same genes according to genetic drift (derived from a simulated 254

evolution where lineages accumulate an equivalent number of mutations randomly for 1000 255

independent evolution simulations) (22). We split our dataset into Hip and Lop variants, and 256

then performed this same observed versus expected lineage enrichment analysis for each 257

population (see Materials and Methods for further details). The analysis was performed using 258

each of four datasets (ciprofloxacin Hip, tobramycin Hip, MD Hip and Any Hip), and the ratio 259

of lineage enrichment of mutated genes for Hip versus Lop variants allowed us to identify 260

candidate ‘hip’ genes for each set (Table 1 and Dataset 2). For completeness, we also performed 261

an additional genetic analysis focusing on mutations in non-coding sequences (Dataset 2). 262

263

In general, searching for hip genes accumulating non-synonymous mutations in Hip+ lineages 264

revealed only a weak signal for convergent evolution. Only one lineage assessed in the genetic 265

screen had only Hips present (the only isolate of the lineage assessed in our screen), so 266

practically all lineages with Hip isolates (Hip+ lineages, 29 included in the genetic study) also 267

contained Lops. Thus, mutated genes that were enriched 2-3 fold in independently evolved 268

Hip+ lineages were also frequently present in Lop isolates of the same lineage. Our lineage 269

enrichment ratio ultimately identified 12 mutated genes enriched in ciprofloxacin Hip+ 270

lineages (SI Appendix, Dataset 2), 13 mutated genes in tobramycin Hip+ lineages (SI 271


https://doi.org/10.1101/561589

9

Appendix, Dataset 2), a solitary gene for MD Hip+ lineages (SI Appendix, Dataset 2), and 7 272

mutated genes for Any Hip+ lineages (Table 1). 273

274

Of note, there was a surprising lack of the most prominent ‘hip’ genes previously identified in 275

in vitro studies and screens of P. aeruginosa (SI Appendix, Table S2). None of the lineage 276

enrichment data pointed toward RNA endonuclease-type toxin-antitoxin systems under 277

adaptive selection, which supports recent research that has questioned the contribution of these 278

systems to persistence in numerous bacterial pathogens (29–31). Instead, they belonged to 279

diverse functional categories including transcriptional regulation/two component regulatory 280

systems (4 genes), energy metabolism (3 genes), and DNA replication and repair (2 genes). In 281

the Any Hip analysis as well as the MD Hip analysis, the shared top gene target, aceF, encodes 282

a component of the pyruvate dehydrogenase complex, a key player in central metabolism, for 283

which functional mutations are known to reduce growth rate (32–34). Other genes in Table 1 284

include major regulators rpoN, known to induce a growth defect when functionally mutated 285

(35), and retS, which when functionally mutated induces an array of phenotypic changes linked 286

to chronic infection such as a non-motile biofilm lifestyle (36, 37). These genes, as well as 287

hypothetical protein PA4311, also overlap with the ‘pathoadaptive’ mutationally enriched gene 288

list identified in our prior study of convergent evolution across all lineages (22). 289

290

Hip variants emerge via diverse incidence patterns 291

The lack of strong genetic signatures differentiating Hip from Lop isolates motivated us to 292

examine the temporal dynamics of high persister incidence using our comprehensive Any Hip 293

dataset. In half of the patients, the earliest bacterial isolate is also the first-ever identified P. 294

aeruginosa in the clinic and the other patients’ isolates also cover most of the initial 295

colonization phase. We can thus estimate the emergence of the Hip phenotype as P. aeruginosa 296

adapts from a wild type-similar naïve state into an adapted persistent pathogen. Previous 297

findings have indicated that the number of Hip variants from a lineage may increase over time 298

as the bacteria adapt to the antibiotic pressure in the host, and that once a Hip isolate is 299

observed, it is assumed to persist in the infecting population of the patient (7, 16). 300

301

To illustrate the range of persister dynamics we observe, we grouped each lineage by an array 302

of descriptors. The lineage descriptors include Hip presence versus absence (Hip+ vs Hip-), 303

transience of the lineage (whether it appears for less than 2 years, less than half the length of a 304

patient’s infection and is afterwards replaced by another lineage), continuity of Hip variants 305


https://doi.org/10.1101/561589

10

(whether Hips are present for at least 3 sampling dates in a row), dominance of Hip variants 306

(whether Hips make up at least 2/3 of all isolates of a lineage), and whether a Hip variant 307

initiates the lineage. Figure 4A shows the ordered distribution of the lineages in 10 different 308

groups based on descriptor sets, illustrating the diversity of lineage Hip dynamics. We see that: 309

1) 34 of 74 lineages are Hip+, 2) 28 of 40 Hip- lineages are transient, while 4 Hip+ lineages 310

are transient, 3) 21 Hip+ lineages have Hip variants appear irregularly versus 7 lineages that 311

exhibit continuous periods and/or dominance of Hip variants, and 4) 12 lineages have initiating 312

Hip+ variants. Thus, the fraction (24.8%, Fig. 1D) of total isolates with a Hip phenotype 313

appears to be distributed over a subset of lineages (45.9%) in both stable (continuous/dominant) 314

and stochastic patterns of incidence, rather than present in every evolving lineage. 315

316

Hip variants accumulate over colonization time and occur rarely in transient lineages 317

We summarized the incidence of Any Hip variants over each lineage’s time of colonization in 318

Figure 4B. Here we plot the continuous counts of patients exhibiting the Hip phenotype (Hip+) 319

versus no Hip presence (Hip-) within the previous year (dashed lines) as well as the 320

accumulation of lineages that have exhibited a Hip variant at least once by a certain age of 321

colonization (solid line). The former illustrates both the number of lineages assessed at a given 322

colonization age and the increasing fraction of Hip+ versus Hip- lineages over time. The latter 323

shows how the likelihood of Hip emergence increases over time. Interestingly, when we plot 324

Hip accumulation for all four datasets (Fig. 4C), we also observe twice as many lineages with 325

initiating Hips in the Tob Hip dataset than the Cip Hip dataset, but Cip Hip incidence quickly 326

catches up with and eventually exceeds Tob Hip incidence over time. Overall, Any Hip variants 327

affect 34 lineages (Figure 4B-C) and 77% of the patients in our study cohort (Figure S2) by the 328

end of the study period. 329

330

Next, we evaluated the relationship between lineage transience and Hip presence. We first 331

mark the lineages present for less than 2 years in a patient at the end of their monitoring period 332

as ‘New’ lineages since we cannot determine transience without additional samples. Of the 56 333

remaining lineages, non-transient lineages are significantly associated with the Hip+ lineage 334

status, while transient lineages are significantly associated with the Hip- lineage status (Fig. 335

4D). Thus, a given patient often has multiple infecting lineages, but the Hip- lineages are much 336

more likely to disappear over the course of infection. In summary, we find that despite variable 337

incidence patterns, a clear majority of patients are infected by Hip+ lineages, and these lineages 338


https://doi.org/10.1101/561589

11

have a significant persistence advantage in comparison to Hip- lineages over time, suggesting 339

that the Hip phenotype contributes to a fitness increase in antibiotic-treated patients. 340

341

Hip variants show increased fitness in patient-similar biofilms 342

We next asked if Hip isolates are able to survive antibiotic treatment better than Lop isolates 343

with similar antibiotic susceptibilities and growth properties in more complex conditions. We 344

simulated antibiotic treatment of CF patients in a recently developed biofilm 345

Pharmacokinetic/Pharmacodynamic (PK/PD) system, in which the bacteria are challenged 346

with antibiotics in much the same way as in patients (38). We chose this model because P. 347

aeruginosa often appears as biofilms in lungs of CF patients (39), because biofilms have been 348

shown to harbor increased levels of persister cells (40), and because our model mimics the 349

bacterial exposure to ciprofloxacin treatment as described for CF patients (38, 41). The isolates 350

that were chosen shared similar 1) time since their first detection in the CF lungs were similar, 351

2) MIC values for ciprofloxacin, 3) growth rates, and 4) belong to the same clone type. The 352

Hip/Lop pair was differentially tagged with yellow fluorescent protein, YFP (Hip), or cyan 353

fluorescent protein, CFP (Lop). Both strains formed biofilms with comparable biomasses in 354

the flow-cell system. Hip and Lop cells were then mixed 1:1 and allowed to form a mixed 355

biofilm. Representative images of the Hip/Lop biofilms are shown before and after treatment 356

with ciprofloxacin (Fig. 5A). 357

358

The majority of Lop bacteria were located close to the glass substratum with the Hip population 359

proliferating at the external surface of the biofilm, facing the liquid flow. The addition of 360

ciprofloxacin preferentially killed the Lop population leaving the Hip population relatively 361

unaffected by the antibiotic. COMSTAT analysis confirmed this changed population structure 362

after ciprofloxacin addition (Fig. 5B). This documentation of a Hip associated fitness increase 363

in an antibiotic containing environment is all the more striking, as it has been shown previously 364

that ciprofloxacin treatment of flow-cell biofilms preferentially kills the surface sub-365

populations of micro-colonies (42) – yet the Hip cells on the colony surfaces survive much 366

better than the internal Lop bacteria under treatment with ciprofloxacin. 367

368

Discussion 369

We have mapped the prevalence of persisters in a large, aligned cohort of patients under 370

intensive antibiotic treatment for a 10 year period (22, 41). Of 460 P. aeruginosa isolates from 371

the airways of 39 young CF patients (74 lineages in total), 24.8% of the isolates were scored 372


https://doi.org/10.1101/561589

12

as robustly persisting Hip using a high-throughput screening approach to assay persistence 373

against ciprofloxacin or tobramycin (Fig. 1). We show that the isolates display different levels 374

of persisters, in accordance with the variance previously found between species and within 375

strains (5, 43, 44). Most adaptive changes occur during the first few years of colonization (18, 376

45), which matches our objective of searching for signs of increased fitness of Hip variants in 377

patients treated continuously with antibiotics. We show that in a young CF patient cohort 378

impacted by early longitudinal colonization by P. aeruginosa strains, Hip variants were 379

sampled from 77% of the patients (N=30) during a 10-year observation window. Our analysis 380

is a new and important comparative baseline for developing effective surveillance, impact 381

assessment, and eventual control of the persister phenotype in the clinic. 382

383

In the early years of infection after first detection of P. aeruginosa clone types, the Hip 384

phenotype appeared and disappeared over time in our routine clinical sampling (Fig. 4A). 385

While we see only partial overlap in Hip phenotype between our tobramycin and ciprofloxacin 386

screens, the number of lineages and patients that exhibit Hip variants increases over time for 387

all datasets (Fig. 4B-C, SI Appendix, Fig. S2), suggesting a selective advantage of this 388

phenotype during the continuation of antibiotic therapy. In general, the majority of lineages 389

that showed short-term colonization were made up of only Lop variants, which may partly 390

explain why they were unable to establish a persistent infection (Fig. 4D). These in-patient data 391

support the hypothesis that the Hip phenotype may generally have increased fitness in the 392

antibiotic-containing lung environment. It is, however, important to note that neither 393

dominance nor continuous presence of Hip variants is observed frequently (Fig. 4A). It is likely 394

that fitness trade-offs and clonal interference impact on the fitness properties and the 395

persistence level of Hip variants (14). 396

397

Multiple relationships between the Hip phenotype and other phenotypic traits such as growth 398

rate and antibiotic resistance have been suggested in the literature. While some studies point 399

out that there is no correlation between the mean growth rates of isolates and the observed Hip 400

phenotype (46–48), reduced growth rates have been associated with high persister phenotypes 401

in E. coli (28). A recent study in Salmonella enterica further supports that slow growth 402

(regardless of mechanism) promotes the persister phenotype (31). We see that lineages which 403

produce isolates with reduced growth rate are significantly more likely to also produce Hips. 404

Furthermore, multiple genes targeted for mutation at a higher rate in Hip+ lineages are known 405

to induce a growth defect (Table 1, SI Appendix, Dataset 2). However, we also observed the 406


https://doi.org/10.1101/561589

13

Hip phenotype among naïve, fast growing clinical isolates of P. aeruginosa (Fig. 3), supporting 407

that other factors also influence the phenotype. Drug-tolerant cells have also been proposed to 408

facilitate evolution of true antibiotic resistance in E. coli in vitro (49). Intermittent antibiotic 409

exposure of a batch culture of E. coli selected for mutant clones harboring tolerance mutations 410

that increased the growth lag-time, during which tolerance to killing by ampicillin selected for 411

MIC-increasing mutations. Though P. aeruginosa in the CF lung is also exposed to fluctuating 412

concentrations of antibiotic, our stringently defined Hip phenotype emerges simultaneously or 413

after resistance in a majority of cases in contrast to these findings. We also observe more or 414

less an equal number of lineages where Hip variants and resistant clones evolve independently 415

in patients under antibiotic selection pressure, which has previously been suggested by 416

comparative studies of lab strains (50). In summary, our results suggest that the Hip phenotype 417

may be an early advantageous adaptation (18) arising stochastically in infected patients treated 418

with antibiotics. 419

420

In many ways, investigations of the genetic underpinnings of persisters have been performed 421

analogously to studies of antibiotic resistance, i.e. it was expected that a relatively limited set 422

of genes defines the phenotype. In a study of urinary tract infection E. coli isolates, a gain-of-423

function mutation in the HipA toxin was commonly observed (21). In contrast, a lack of 424

common targeted genes in a small collection of clinical Hip strains of Mycobacterium 425

tuberculosis suggested utilization of multiple genetic pathways (51). Working at a much larger 426

collection scale in a faster adapting organism, we do not see enrichment of mutations which 427

previously have been associated with Hip phenotypes in vitro. A role for the top proposed 428

persister target aceF has yet to be described, likely owing to its important role in growth; 429

mutants with severe growth defects are often overlooked in genome-wide analyses in vitro. In 430

support of this, an aceF transposon mutant is not available from the widely used PAO1 two-431

allele mutant library, and aceF did not appear in a recent persister screen of a pool of 100,000 432

unique PAO1 transposon mutants (20, 52). The aceF gene is mutated in 20% of the Hip+ 433

lineages, which have been genotypically evaluated in our large, longitudinal collection, but in 434

three of six lineages, it is also mutated in Lops. Meanwhile, no other enriched mutated genes 435

are affected in as many Hip+ lineages alone (where only Hip isolates are affected). We 436

therefore conclude that a Hip phenotype may derive from a diverse array of accumulating 437

genetic changes, and it is likely that more than one mutation often determines the persister level 438

in the respective bacterial populations. There are certainly many adaptive routes to slowed 439

growth rate, which we have previously demonstrated is a convergent adaptive outcome in this 440


https://doi.org/10.1101/561589

14

early isolate collection (18). Our results likely reflect the multiple and dynamic selection 441

pressures in vivo, which challenge Hip variants in antibiotic-treated populations very 442

differently than those assessed in steady state in vitro conditions with only one selective force. 443

444

Many studies have shown the increased survival of persister cells under antibiotic treatment 445

(28, 53) and then screened for genetic determinants of persistence, but few have evaluated the 446

fitness of Hip versus Lop variants in direct competition experiments (43). In our study, we 447

tested a Lop/Hip pair of isolates matched by genotype, phenotype, and colonization age in 448

order to characterize the selective advantage of the Hip phenotype in a biofilm under treatment-449

replicating antibiotic exposure (38). We show that Hip cells survived ciprofloxacin treatment 450

far better than Lop isolates and this survival is potentially reliant on biofilm architecture. 451

Additionally, while homogeneous monoclonal P. aeruginosa biofilms treated with 452

ciprofloxacin show preferential killing of bacteria in the top layers (14, 42, 54), Lop bacteria 453

are preferentially killed in the deeper layers of the biofilm, showing an unexpected phenotype 454

worthy of further study. It is also striking that the in vitro biofilm fitness assessment shows 455

efficient elimination of the Lop strain in the presence of ciprofloxacin, whereas Hip variants 456

often coexist with Lop variants in vivo (Fig. 4A). This suggests that in the patient, direct 457

competition is likely limited by the large lung volume, many separate regional niches, and 458

influence of the host (55). 459

460

In summary, we have shown that Hip variants of P. aeruginosa emerge frequently in young 461

CF patients, and our results provide the first window into the evolving landscape of persistence 462

across a whole patient cohort. As pathogens increase their fitness in patients over time, they 463

clearly deploy the high persister phenotype as an important component in their survival 464

repertoire and can do so from the earliest stages of infection. It is still premature to conclude 465

that the high persister phenotype described here differs from what has been identified as Hip 466

in in vitro experimental conditions, but we consistently find a much broader bacterial repertoire 467

for survival in patient lungs. Hip variants do not seem to be mutated in genes previously found 468

from in vitro experiments to associate with Hip or in any strongly conserved genetic route. We 469

suggest that the difference in complexity of selection pressures when comparing in vitro and 470

in vivo environmental conditions results in highly different evolutionary trajectories. With our 471

investigation, we provide an important platform for broader clinically based studies and 472

contribute important new context for monitoring and one day hopefully preventing the high 473

persister phenotype in the clinic. 474


https://doi.org/10.1101/561589

15

475

Materials and Methods 476

Strain collection. In total, we analyzed 460 P. aeruginosa airway isolates from young CF 477

patients followed at the Copenhagen CF-clinic at Rigshospitalet (Dataset 1). The local ethics 478

committee at the Capital Region of Denmark (Region Hovedstaden) approved the use of the 479

stored P. aeruginosa isolates: registration number H-4-2015-FSP. Phenotyping data for 434 480

isolates of this strain collection (growth rate in LB, adhesion in LB, and ciprofloxacin MIC) 481

have been previously published (18). We include additional tobramycin MIC measurements 482

and expand the complete trait dataset to 446 isolates. All available trait data is provided in 483

Dataset 1 along with the persister classification of each isolate and descriptive data. 484

485

Of the 460 isolates examined in the study, 403 isolates from 32 patients were described 486

previously in Marvig et. al. (22) and the remaining isolates were taken from seven previously 487

undescribed patients. The isolates were collected and stored at the Department of Clinical 488

Microbiology at Rigshospitalet, Copenhagen, Denmark, between 2002 and 2014. Of the 489

patients included in this study, 35.9% were diagnosed as chronically infected with P. 490

aeruginosa by the end of the study period. We defined chronicity based on the Copenhagen 491

CF Centre definition, whereby either P. aeruginosa has been detected in six consecutive 492

monthly sputum samples or fewer consecutive sputum samples combined with observation of 493

two or more P. aeruginosa-specific precipitating antibodies (41, 56). Intermittently colonized 494

patients were defined as patients where at least one isolate of P. aeruginosa is detected, and 495

normal levels of precipitating antibiotics against P. aeruginosa were observed. 496

497

High-throughput screening for Hip mutants. To determine the frequency at which P. 498

aeruginosa Hip mutants emerge in CF patients, we screened 460 isolates for the ‘persister’ 499

phenotype against either ciprofloxacin or tobramycin. Stock 96-well microtiter plates 500

containing 4 technical replicates of each isolate stored in glycerol (25 % v/v) were prepared 501

and stored at -80°C. Using a 96-well spot replicator, bacteria were transferred from the stock 502

plates into sterile 96-well microtiter plates containing 150 μl of Lysogeny Broth (LB) media. 503

Plates were incubated statically for 48 hours at 37°C until the bacteria reached the stationary 504

phase of growth. To determine the initial viability of bacteria in each well, the replicator was 505

used to spot bacteria onto LB agar plates. Subsequently, 100 μg/ml of either ciprofloxacin or 506

tobramycin was added to each well and the microtiter plates were incubated statically for a 507

further 20-24 hours at 37°C. Serial dilutions were performed in 96 well microtiter plates 508


https://doi.org/10.1101/561589

16

containing 0.9 % NaCl using an automated fluid handling robot (Viaflo3844/ Integra 509

Biosciences AG). Each dilution was spotted onto LB agar plates using the replicator and plates 510

were incubated at 37°C for at least 24 hours. The growth of the bacteria was compared by 511

counting colonies whenever possible and visually inspecting growth on the plates before and 512

after antibiotic treatment. Experiments were performed in duplicate for each antibiotic. 513

514

Persister assay validation. Time-kill experiments were performed for six isolates from the 515

same lineage (3 Hip and 3 Lop). P. aeruginosa were inoculated in 3 ml of LB media in 14 ml 516

culture tubes and incubated for 48 hours at 37°C with shaking at 250 rpm. Following 517

incubation, each culture was serially diluted using sterile 0.9 % NaCl, plated onto LB agar and 518

incubated at 37°C to determine the initial colony forming units (CFU). The remaining culture 519

was treated with 100 μg/ml of ciprofloxacin and incubated at 37°C with shaking. Cultures were 520

washed and diluted in sterile 0.9 % NaCl, then spot plated onto LB agar 6 and 24 hours after 521

the addition of antibiotic. Plates were incubated for 24 hours at 37°C. Bacteria survival was 522

measured by counting CFU per ml. For an additional 19 isolates, the same validation 523

experiment was performed, however cultures were only plated out after 24 hours, which was 524

within the ‘persister plateau’. 525

526

Phenotype screening. The same frozen library of isolates used in the persister screening was 527

also replicated for assay of minimum inhibitory concentrations (MICs), bacterial growth, and 528

adhesion as described below and in Bartell et al (18). MICs for ciprofloxacin and tobramycin 529

were determined using E-test methodology according to the manufacturer’s recommendations 530

(Liofilchem®, Italy). To assay growth rate, bacteria were replicated from frozen plates into 531

96 well plates containing 150µL of LB medium, and incubated for 20 hours at 37°C with 532

constant shaking. OD 630 nm measurements were taken every 20 minutes using a microplate 533

reader (Holm & Halby, Copenhagen, Denmark/Synergy H1). Generation times (Td) were 534

determined on the best-fit line of a minimum of 3 points during exponential growth of the 535

bacterial isolate. Growth rates (hr-1) were calculated using the formula log (2)/ Td x 60 using 536

semi-automated code described in Bartell et al (18). Adhesion was measured via attachment 537

assays in 96-well plates using NUNC peg lids and 96 well plates with 150µl Luria broth 538

medium. OD600nm was measured after incubation for 20 hours at 37°C and subsequently, a 539

“washing microtiter plate” with 180µl PBS was used to wash the peg lids and remove non-540

adhering cells. After transfer of the peg lids to a microtiter plate containing 160µl 0.01% 541

crystal violet (CV), they were left to stain for 15 min. To remove unbound crystal violet, the 542


https://doi.org/10.1101/561589

17

lids were then washed again three times in three individual “washing microtiter plates” with 543

180µl PBS. Adhesion was measured by detaching adhering CV stained cells through washing 544

the peg lids in a microtiter plate containing 180µl 99% ethanol. An ELISA reader was then 545

used to measure the CV density at OD590nm. (Microtiter plates were bought at Fisher 546

Scientific, NUNC Cat no. 167008, peg lids cat no. 445497). 547

548

Pharmacokinetic/Pharmacodynamic (PK/PD) flow chamber biofilm model. For fitness 549

experiments, we used a PK/PD biofilm model system combined with confocal laser-scanning 550

microscopy. This system simulates the changing antibiotic concentrations in CF patients during 551

intravenous dosing in addition to retaining a similar profile of antibiotic decay as the one taking 552

place in CF patients (38). First, Hip and Lop isolates were differentially tagged with a yellow 553

fluorescent protein (YFP) or cyan fluorescent protein (CFP) respectively (14). Flow chambers 554

were inoculated with a 1:1 mixture of Hip and Lop bacteria (each isolate had an initial OD600 555

of 0.5). Bacteria were incubated for one hour at 30 °C, then nutrient flow was applied to each 556

chamber (40x diluted LB at a rate of 20 ml/h using a Watson Marlow 205S peristaltic pump). 557

Biofilms were allowed to form for 72 hours, at which point flow was stopped and medium 558

containing ciprofloxacin was added. Peak ciprofloxacin concentrations were calculated to be 4 559

mg/L based on PK parameters generated from healthy patients and CF patients (57). The 560

medium was pumped from the dilution flask through the antibiotic flask to the flow chambers 561

at a constant rate calculated to mimic the elimination rate constant of the antibiotic for 24 hrs. 562

A confocal laser-scanning microscope (Zeiss LSM 510) equipped with an argon/krypton laser 563

and detectors was used to monitor YFP (excitation 514 nm, emission 530 nm), CFP (excitation 564

458 nm, emission 490 nm), and dead cells (propidium iodine, excitation 543 nm, emission 565 565

nm). Multichannel simulated fluorescent projections (SFPs) and sections through the biofilms 566

were generated using Imaris software (Bitplane AG, Switzerland). The images were later 567

analyzed using COMSTAT (58). The PK/PD biofilm experiments were performed using two 568

independent Hip/Lop isolate pairs. Pairs were taken from the same patient at a similar time 569

since first detection and had similar growth rates and ciprofloxacin MICs (Table S2). The data 570

presented are from 2 biological experiments with 4 independent images taken from each 571

experiment. 572

573

Lineage-based genetic analysis. To generate a list of mutated genes associated with the Hip 574

phenotype, we used previously generated whole-genome sequencing data and variant calling 575

filtered to obtain nonsynonymous mutations that had accumulated within a lineage after the 576


https://doi.org/10.1101/561589

18

first isolate (22) to evaluate differential mutation patterns for Lop and Hip variants for 403 577

sequenced isolates. In this filtering process, we also removed mutations associated with any 578

known ‘hypermutator’ isolates based on a mutation in mutS or mutL to avoid the influence of 579

high random mutation in these isolates on the analysis. To identify genes that were mutated 580

more than would have been expected by drift/random mutation while accounting for lineage-581

based mutation accumulation over time, we adapted a statistical analysis of the relative 582

mutation enrichment by lineage. After separating Lop and Hip variants, we compared the 583

mutated-gene lineage enrichment ratios for each group - the number of lineages with observed 584

mutation(s) in a given gene divided by the number of lineages expected to have mutations in 585

that gene according to random mutation. This enrichment metric was obtained as follows for 586

each group: we determined the observed number of lineages mutated (sum-obs) in each gene. 587

Then we estimated the average number of lineages (avg-exp) that would have been mutated in 588

each gene if mutations were spread out randomly over the PAO1 genome. Using a random-589

roulette algorithm, the number of genes that were observed to be mutated in a given lineage 590

was spread out over the PAO1 genome for 1000 iterations, providing a mgene by niteration matrix 591

of randomly mutated gene profiles for each lineage. For the same iteration n across all lineages, 592

it was noted whether a given gene was mutated. This allowed us to determine an average 593

number of lineages expected to be mutated over 1000 iterations. If a gene was hit by chance 594

more than once in a single iteration, this would still only be denoted as one hit; this is in 595

alignment with our observed mutation assessment, where multiple isolates could be hit in the 596

same gene but we only noted whether or not the lineage was hit by unique mutations in the 597

specific gene. After obtaining the relative enrichment by lineage, a Poisson distribution was 598

used to calculate the probability of the observed given random drift (expected). We also divided 599

the lineage enrichment metric for genes mutated in Hip variants by that for Lop variants to 600

obtain a lineage enrichment ratio to identify targeted genes particularly impactful in the 601

evolution of the Hip population. 602

603

Data analysis and statistics. Analyses were conducted in RStudio v. 1.0.143 and R v. 3.4.0 604

with visualization package ggplot2 v. 3.0.0. Lineage set analysis was performed using UpSetR 605

v. 1.3.3 in R (59). Principal component analysis was performed in R using ‘prcomp’ with 606

centered and scaled phenotype data (Dataset 1). Mosaic plots (visualizing multi-way 607

contingency tables) showing the association between two variables via the conditional relative 608

frequency and significant associations based on a Pearson X2 test were created using vcd v. 609

1.4-4 in R (60). 610


https://doi.org/10.1101/561589

19

611

Data availability. Screen data for the persister phenotype is provided as a supplemental 612

dataset (Dataset 1) including trait data, which partially overlaps with trait data published 613

previously (18). Code in R for mosaic plots, PCA analysis, and set intersection analysis of 614

lineage characteristics is available on request. Lineage enrichment analysis was performed in 615

Anaconda3 v. 4.0.0 using custom scripts available on request. 616

617

Acknowledgments 618

This work was supported by Cystic Fibrosis Foundation Pilot and Feasibility Award to KL. 619

HKJ was supported by The Novo Nordisk Foundation (NNF12OC1015920 and 620

NNF15OC0017444), by Rigshospitalets Rammebevilling 2015-17 (R88-A3537), by 621

Lundbeckfonden (R167-2013-15229), by RegionH Rammebevilling (R144-A5287) and by 622

Independent Research Fund Denmark (DFF-4183-00051). JAB was supported by 623

postdoctoral fellowships from the Whitaker Foundation and the Cystic Fibrosis Foundation 624

(BARTEL18F0). We thank Katja Bloksted, Ulla Rydahl Johansen, Helle Nordbjerg 625

Andersen, Sarah Buhr Bendixen, Camilla Thranow, Pia Poss, Bonnie Horsted Erichsen and 626

Rakel Schiøtt for excellent technical assistance at Rigshospitalet. We thank Prof. Vasili 627

Hauryliuk for helpful comments. 628

629

Conflict of interest 630

The authors declare no competing financial interests. 631

632

Author Contributions 633

SM and KL designed the study. HKJ collected all the bacterial isolates. BM, DRC, JAB, and 634

JAJH performed all experiments. JAB and LMS performed genetic and lineage-based data 635

analysis. All authors contributed to the writing of the manuscript. All authors approved the 636

final version. 637

638

Ethics approval 639

The local ethics committee at the Capital Region of Denmark (Region Hovedstaden) 640

approved the use of the stored P. aeruginosa isolates: registration number H-4-2015-FSP. We 641

confirm that all methods were performed in accordance with the relevant guidelines and 642

regulations. 643

644


https://doi.org/10.1101/561589

20

Supplementary information 645

Supplementary methods. Description of phenotyping methods, biofilm flow chamber 646

experiments, and lineage enrichment analysis. 647

Figure S1. Correlation between persister score and colony forming units following treatment 648

with ciprofloxacin. 649

Figure S2. Hip accumulation in patients. 650

Table S1. Emergence of resistance versus persistence across 74 lineages. 651

Table S2. Comparison of persister genes identified in previous P. aeruginosa studies to 652

mutated genes highlighted by our lineage analysis. 653

Dataset 1. Phenotypic dataset for all isolates. 654

Dataset 2. Lineage-based mutation enrichment analysis results for coding genes and noncoding 655

regions (separately) for all datasets. 656

657

References 658

1. Bigger JW (1944) Treatment of staphylococcal infections with penicillin. Lancet 659

2(6320):497–500. 660

2. Wood TK, Knabel SJ, Kwan BW (2013) Bacterial persister cell formation and 661

dormancy. Appl Environ Microbiol 79(23):7116–7121. 662

3. Rowe SE, Conlon BP, Keren I, Lewis K (2016) Persisters: Methods for Isolation and 663

Identifying Contributing Factors—A Review. Methods Mol Biol 1333:17–28. 664

4. Lewis K (2010) Persister Cells. Annu Rev Microbiol. 665

doi:10.1146/annurev.micro.112408.134306. 666

5. Stewart B, Rozen DE (2011) Genetic variation for antibiotic persistence in Escherichia 667

coli. Evolution (N Y) 66(3):933–939. 668

6. Hofsteenge N, van Nimwegen E, Silander OK (2013) Quantitative analysis of persister 669

fractions suggests different mechanisms of formation among environmental isolates of 670

E. coli. BMC Microbiol 13(1):25. 671

7. LaFleur MD, Qi Q, Lewis K (2010) Patients with long-term oral carriage harbor high-672

persister mutants of Candida albicans. Antimicrob Agents Chemother 54(1):39–44. 673

8. Salsgiver EL, et al. (2016) Changing Epidemiology of the Respiratory Bacteriology of 674

Patients With Cystic Fibrosis. Chest 149(2):390–400. 675

9. Parkins MD, Somayaji R, Waters VJ (2018) Epidemiology, Biology, and Impact of 676

Clonal Pseudomonas aeruginosa Infections in Cystic Fibrosis. Clin Microbiol Rev 677

31(4):1–38. 678

10. Dalemans W, et al. (1991) Altered chloride ion channel kinetics associated with the 679

ΔF508 cystic fibrosis mutation. Nature 354(6354):526–528. 680

11. Boucher RC (2007) Cystic fibrosis: a disease of vulnerability to airway surface 681

dehydration. Trends Mol Med 13(6):231–240. 682

12. Folkesson A, et al. (2012) Adaptation of Pseudomonas aeruginosa to the cystic fibrosis 683

airway: an evolutionary perspective. Nat Rev Microbiol 10(12):841–51. 684

13. Gibson RL, Burns JL, Ramsey BW (2003) Pathophysiology and management of 685

pulmonary infections in cystic fibrosis. Am J Respir Crit Care Med 168(8):918–951. 686

14. Frimodt-Møller J, et al. (2018) Mutations causing low level antibiotic resistance ensure 687


https://doi.org/10.1101/561589

21

bacterial survival in antibiotic-treated hosts. Sci Rep 8(1):1–13. 688

15. Burns JL, et al. (1999) Effect of Chronic Intermittent Administration of Inhaled 689

Tobramycin on Respiratory Microbial Flora in Patients with Cystic Fibrosis. J Infect 690

Dis 179(5):1190–1196. 691

16. Mulcahy LR, Burns JL, Lory S, Lewis K (2010) Emergence of Pseudomonas 692

aeruginosa strains producing high levels of persister cells in patients with cystic 693

fibrosis. J Bacteriol 192(23):6191–9. 694

17. van Gestel J, Vlamakis H, Kolter R (2015) Division of labor in biofilms: the ecology 695

of cell differentiation. Microbiol Spectr 3(2):1–24. 696

18. Bartell JA, et al. (2019) Evolutionary highways to persistent bacterial infection. Nat 697

Commun 10(1):629. 698

19. Moyed HS, Bertrand KP (1983) hipA, a newly recognized gene of Escherichia coli K-699

12 that affects frequency of persistence after inhibition of murein synthesis. J Bacteriol 700

155(2):768–75. 701

20. Cameron DR, Shan Y, Zalis EA, Isabella V, Lewis K (2018) A Genetic Determinant 702

of Persister Cell Formation in Bacterial Pathogens. J Bacteriol 200(17):1–11. 703

21. Schumacher MA, et al. (2015) HipBA-promoter structures reveal the basis of heritable 704

multidrug tolerance. Nature 524(7563):59–66. 705

22. Marvig RL, Sommer LM, Molin S, Johansen HK (2015) Convergent evolution and 706

adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis. Nat Genet 707

47(1):57–64. 708

23. Eliopoulos GM, Gardella A, Moellering RC (1984) In vitro activity of ciprofloxacin, a 709

new carboxyquinoline antimicrobial agent. Antimicrob Agents Chemother 25(3):331–710

335. 711

24. Su HC, et al. (2010) The development of ciprofloxacin resistance in Pseudomonas 712

aeruginosa involves multiple response stages and multiple proteins. Antimicrob Agents 713

Chemother 54(11):4626–4635. 714

25. Balaban NQ, et al. (2019) Definitions and guidelines for research on antibiotic 715

persistence. Nat Rev Microbiol 17(July):441–448. 716

26. La Rosa R, Johansen HK, Molin S (2018) Convergent Metabolic Specialization 717

through Distinct Evolutionary Paths in Pseudomonas aeruginosa. MBio 9(2):e00269-718

18. 719

27. Winstanley C, O’Brien S, Brockhurst MA (2016) Pseudomonas aeruginosa 720

Evolutionary Adaptation and Diversification in Cystic Fibrosis Chronic Lung 721

Infections. Trends Microbiol 24(5):327–337. 722

28. Balaban NQ, Merrin JM, Chait R, Kowalik L, Leibler S (2004) Bacterial Persistence 723

as a Phenotypic Switch. Science (80- ) 305(5690):1622–1625. 724

29. Goormaghtigh F, et al. (2018) Reassessing the Role of Type II Toxin-Antitoxin 725

Systems in Formation of Escherichia coli Type II Persister Cells. MBio 9(3):1–14. 726

30. Conlon BP, et al. (2016) Persister formation in Staphylococcus aureus is associated 727

with ATP depletion. Nat Microbiol 1(5):16051. 728

31. Pontes MH, Groisman EA (2019) Slow growth determines nonheritable antibiotic 729

resistance in Salmonella enterica. Sci Signal 12(592):eaax3938. 730

32. Schutte KM, et al. (2016) Escherichia coli Pyruvate Dehydrogenase Complex Is an 731

Important Component of CXCL10-Mediated Antimicrobial Activity. Infect Immun 732

84(1):320–328. 733

33. Kim Y, Ingram LO, Shanmugam KT (2007) Construction of an Escherichia coli K-12 734

Mutant for Homoethanologenic Fermentation of Glucose or Xylose without Foreign 735

Genes. Appl Environ Microbiol 73(6):1766–1771. 736

34. Chung JCS, Rzhepishevska O, Ramstedt M, Welch M (2013) Type III secretion 737


https://doi.org/10.1101/561589

22

system expression in oxygen-limited Pseudomonas aeruginosa cultures is stimulated 738

by isocitrate lyase activity. Open Biol 3(1):120131. 739

35. Viducic D, Murakami K, Amoh T, Ono T, Miyake Y (2017) RpoN Promotes 740

Pseudomonas aeruginosa Survival in the Presence of Tobramycin. Front Microbiol 741

8(MAY):839. 742

36. Goodman AL, et al. (2004) A signaling network reciprocally regulates genes 743

associated with acute infection and chronic persistence in Pseudomonas aeruginosa. 744

Dev Cell 7(5):745–54. 745

37. Ventre I, et al. (2006) Multiple sensors control reciprocal expression of Pseudomonas 746

aeruginosa regulatory RNA and virulence genes. Proc Natl Acad Sci 103(1):171–176. 747

38. Haagensen JAJ, Verotta D, Huang L, Spormann A, Yang K (2015) New in vitro model 748

to study the effect of human simulated antibiotic concentrations on bacterial biofilms. 749

Antimicrob Agents Chemother 59(7):4074–4081. 750

39. Singh PK, et al. (2000) Quorum-sensing signals indicate that cystic fibrosis lungs are 751

infected with bacterial biofilms. Nature 407:762–764. 752

40. Spoering AL, Lewis K (2001) Biofilms and planktonic cells of Pseudomonas 753

aeruginosa have similar resistance to killing by antimicrobials. J Bacteriol 754

183(23):6746–6751. 755

41. Johansen HK, et al. (2004) Antibody response to Pseudomonas aeruginosa in cystic 756

fibrosis patients: a marker of therapeutic success? - A 30-year cohort study of survival 757

in Danish CF patients after onset of chronic P. aeruginosa lung infection. Pediatr 758

Pulmonol 37(5):427–432. 759

42. Pamp SJ, Gjermansen M, Johansen HK, Tolker-Nielsen T (2008) Tolerance to the 760

antimicrobial peptide colistin in Pseudomonas aeruginosa biofilms is linked to 761

metabolically active cells, and depends on the pmr and mexAB-oprM genes. Mol 762

Microbiol 68(1):223–240. 763

43. Stepanyan K, et al. (2015) Fitness trade-offs explain low levels of persister cells in the 764

opportunistic pathogen Pseudomonas aeruginosa. Mol Ecol 24(7):1572–1583. 765

44. Bink A, et al. (2011) Superoxide dismutases are involved in Candida albicans biofilm 766

persistence against miconazole. Antimicrob Agents Chemother 55(9):4033–4037. 767

45. Yang L, et al. (2011) Evolutionary dynamics of bacteria in a human host environment. 768

Proc Natl Acad Sci 108(18):7481–7486. 769

46. Fung DKC, Chan EWC, Chin ML, Chan RCY (2010) Delineation of a bacterial 770

starvation stress response network which can mediate antibiotic tolerance 771

development. Antimicrob Agents Chemother 54(3):1082–1093. 772

47. Keren I, Minami S, Rubin E, Lewis K (2011) Characterization and transcriptome 773

analysis of Mycobacterium tuberculosis persisters. MBio 2(3):3–12. 774

48. Wakamoto Y, et al. (2013) Dynamic persistence of antibiotic-stressed Mycobacteria. 775

Science (80- ) 339(6115):91–95. 776

49. Levin-Reisman I, et al. (2017) Antibiotic tolerance facilitates the evolution of 777

resistance. Science (80- ) 355(6327):826–830. 778

50. Vogwill T, Comfort AC, Furió V, MacLean RC (2016) Persistence and resistance as 779

complementary bacterial adaptations to antibiotics. J Evol Biol 29(6):1223–1233. 780

51. Torrey HL, Keren I, Via LE, Lee JS, Lewis K (2016) High Persister Mutants in 781

Mycobacterium tuberculosis. PLoS One 11(5):e0155127. 782

52. Held K, Ramage E, Jacobs M, Gallagher L, Manoil C (2012) Sequence-verified two-783

allele transposon mutant library for Pseudomonas aeruginosa PAO1. J Bacteriol 784

194(23):6387–6389. 785

53. Spoering AL, Vulić M, Lewis K (2006) GlpD and PlsB participate in persister cell 786

formation in Escherichia coli. J Bacteriol 188(14):5136–5144. 787


https://doi.org/10.1101/561589

23

54. Haagensen JAJ, et al. (2007) Differentiation and distribution of colistin- and sodium 788

dodecyl sulfate-tolerant cells in Pseudomonas aeruginosa biofilms. J Bacteriol 789

189(1):28–37. 790

55. Jorth P, et al. (2015) Regional Isolation Drives Bacterial Diversification within Cystic 791

Fibrosis Lungs. Cell Host Microbe 18(3):307–319. 792

56. Høiby N, et al. (1977) Pseudomonas aeruginosa infection in cystic fibrosis - 793

Diagnostic and prognostic significance of Pseudomonas aeruginosa precipitins 794

determined by means of crossed immunoelectrophoresis. Scand J Respir Dis 58(2):65–795

79. 796

57. Touw DJ, Knox AJ, Smyth A (2007) Population pharmacokinetics of tobramycin 797

administered thrice daily and once daily in children and adults with cystic fibrosis. J 798

Cyst Fibros 6(5):327–333. 799

58. Heydorn A, et al. (2002) Statistical Analysis of Pseudomonas aeruginosa Biofilm 800

Development: Impact of Mutations in Genes Involved in Twitching Motility, Cell-to-801

Cell Signaling, and Stationary-Phase Sigma Factor Expression. Appl Environ 802

Microbiol 68(4):2008–2017. 803

59. Conway JR, Lex A, Gehlenborg N (2017) UpSetR: An R package for the visualization 804

of intersecting sets and their properties. Bioinformatics 33(18):2938–2940. 805

60. Meyer D, Zeileis A, Hornik K (2006) The Strucplot Framework: Visualizing Multi-806

way Contingency Tables with vcd. J Stat Softw 17(3):1–48. 807

808

809

810

811

812

813

814

815

816

817

818

819

820

821

822

823

824

825

826

827

828


https://doi.org/10.1101/561589

24

Figures 829

830

831

Figure 1. High-throughput screening approach for isolates with a high persister (Hip) 832

phenotype. (A) A large collection of Pseudomonas aeruginosa clinical isolates were grown to 833

stationary phase in quadruplicate wells for two biological replicate (BR) experiments (each 834

isolate tested 8 times in total). The screen was performed twice, using either ciprofloxacin or 835

tobramycin. Each isolate was treated with 100 g/ml of antibiotic for 24 hours, while growth 836

was assessed by plating on LB agar. Following antibiotic treatment, cultures were diluted then 837

plated on agar, at which point survival was assessed. Each isolate was given a persister score 838

based on consistent replicate survival following treatment. Isolates for which 3-4 replicates 839

survived for each BR were given a score of 3-4, respectively, and were considered high 840

persisters (Hip). Isolates with a respective score of 0-2 were considered low persisters (Lop). 841

(B) Score distribution of P. aeruginosa Hip (blue) and Lop (grey) isolates against ciprofloxacin. 842

(C) Traditional time-kill assays were performed for three Hip (HipIso 1, 2, 3) and three Lop 843

isolates (LopIso 1, 2, 3) from the same patient to validate the high throughput screen. Colony 844

forming units (CFU) per ml were determined following treatment with 100 g/ml of 845

ciprofloxacin. Data are the mean of 6 independent cultures, bars represent SEM. (D) 846

Distribution of P. aeruginosa Hip (blue) and Lop (grey) isolates for the AnyHip dataset, where 847

Hips were given a score of 3 or 4 in at least one of the antibiotic screens. 848

849

850


https://doi.org/10.1101/561589

25

851

Figure 2. Persister screening results. (A) Distribution of Hip (blue) and Lop (grey) isolates 852

following ciprofloxacin (CipHip,) or (B) tobramycin treatment (TobHip), and (C) the overlap 853

between these datasets (MDHip - multi-drug Hip). For A and B, the bottom panel shows a 854

mosaic plot (multi-way contingency table) for the comparison of isolate persister class versus 855

resistance class for each antibiotic. Hip and Lop persister isolates were classified as susceptible 856

(S) or resistant (R) according to EUCAST breakpoints (cip: S≤0.5µg/ml, tob: S≤4µg/ml) based 857

on their MIC obtained via E-test. The area of each cell is proportional to the frequency of 858

isolates with the indicated combination of Hip and resistance classification, and resistance-859

associated cells are further highlighted by orange borders. The bottom panel of C shows the 860

overlap of Hip and Lop isolates under ciprofloxacin versus tobramycin treatment. MDHip are 861

highlighted by a black border. 862

863

864

865

866

867

868

869

870

871


https://doi.org/10.1101/561589

26

872

Figure 3. High persisters in the multi-trait landscape. (A) Lop (grey circle), Cip Hip (blue 873

squares), Tob Hip (blue triangles) and MD Hip isolates (black diamonds) were analyzed via 874

principle component analysis with respect to their similarity with other infection-linked traits: 875

growth rate (GR_LB), adhesion, ciprofloxacin MIC (cip) and tobramycin MIC (tob). 446 876

isolates with complete trait sets were included. Hip isolates do not consistently cluster with any 877

one additional trait. For (B) Cip Hip, (C) Tob Hip and (D) MDHip, the first Hip isolates from 878

a lineage (FirstHip, yellow triangles) were highlighted as Hip variants with mitigated effects 879

of other accumulating mutations within the lineage to improve cross-lineage comparison. In 880

each case, FirstHip and the remaining Hip isolates shift to various degrees from ‘naïve’ towards 881

‘adapted’ levels given the particular Hip dataset. We illustrate this using data ellipse enclosing 882

samples approximately within the first standard deviation (t distribution, 68% of the set) for 883

isolate sets characterized as FirstHip (yellow ellipse), and the remaining Hips (blue ellipse). 884

(E) We visualized the association between lineages that produced Hips versus slow growing 885

isolates (identified by the minimum growth rate of lineage isolates falling below 75% of the P. 886

aeruginosa PAO1 growth rate based on a 45 minute generation time in LB). Association 887


https://doi.org/10.1101/561589

27

between variables is illustrated by a mosaic plot (multi-way contingency table visualization) 888

where color indicates significant deviation from the expected frequency of lineages in each cell 889

under trait independence using Pearson’s chi-squared test. 890

891

892

893

894

895

896

897

898

899

900

901

902

903

904

905

906

907

908

909

910

911

912

913

914

915

916

917

918

919

920

921


https://doi.org/10.1101/561589

28

922

Figure 4. Hip Persister incidence patterns from a lineage-based perspective. (A) Lineages 923

were classed according to several nested characteristics: transient versus non-transient 924

lineages, Hip presence (AnyHip, meaning Cip Hip, Tob Hip or MD Hip), continuous periods 925

of isolated Hips, lineage-initiating Hips, and Hips dominating a lineage. Lineages representing 926

each combination of traits are shown on the left (Hip blue diamonds, Cip grey circles), while 927

characteristic sets are identified and enumerated for the entire collection on the right. 928

(B) For the AnyHip dataset, the continuous patient count of Hip- patients (grey circles) versus 929

Hip+ patients (blue diamonds) for the prior year of colonization is plotted, while the 930

accumulating count of Hip+ patients from time 0 is shown by black diamonds. (C) The 931

accumulating count of Hip+ patients is shown for all four datasets (AnyHip as black circles, 932

CipHip as transparent blue squares, TobHip as transparent blue triangles, and MDHip as dark 933

blue diamonds). (D) Transient lineages (lineages of shorter than 2 years duration, less than 934

50% of total patient infection length, and which are followed by the appearance of a new 935

lineage) are significantly associated with lineages lacking Hips (Hip-), while non-transient 936


https://doi.org/10.1101/561589

29

lineages are associated with the presence of Hips based on Pearson's chi-squared test (via a 937

mosaic plot visualizing a multi-way contingency table). Transience-unclassifiable lineages of 938

shorter than 2 years’ duration at the end of a patient’s collection period are shown for context 939

(‘New’). 940

941

942

943

944

945

946

947

948

949

950

951

952

953

954

955

956

957

958

959

960

961

962

963

964

965

966

967

968

969

970


https://doi.org/10.1101/561589

30

971

Figure 5. Fitness comparison of Lop and Hip isolates in biofilm conditions. (A) A 972

representative Lop and Hip isolate with similar characteristics (Lop: cip MIC 1.0 µg/ml; 973

growth rate 0.27 hr-1; time since first detection 4.28 years. Hip: cip MIC 0.75 µg/ml; growth 974

rate 0.25 hr-1; time since first detection 5.49 years) were differentially tagged with CFP (Lop) 975

or YFP (Hip). Tagged isolates were cocultured and allowed to form biofilms in a flow-cell 976

model for 72 hours. Mixed biofilms were treated for 24 hours with ciprofloxacin (4 µg/ml). 977

Propidium iodine (PI) was added to visualise dead cells (red). (B) Biomass was quantified for 978

each population. Significant differences in biomass following treatment were determined using 979

unpaired t-test (*** p <0.001). 980

981

982

983

984

985

986

987

988

989

990

991

992

993

994

995

996

997

998

999

1000

1001

1002

1003

1004

1005


https://doi.org/10.1101/561589

31

Tables 1006

1007

Table 1. Lineage-based mutation enrichment analysis. Mutated genes enriched in AnyHip 1008

versus Lop dataset as assessed from a convergent evolution perspective accounting for 1009

lineage adaptation. Lineage enrichment ratio was calculated by dividing lineage-based gene 1010

mutation enrichment within Hip variants by that within Lop variants for each gene. Top Hip-1011

linked genes were selected via the following criteria: greater than 2 lineages presenting 1012

mutations in that gene in the Hip population and a lineage enrichment ratio greater than 2. 1013

The only gene also enriched in MDHip lineages, aceF, is in boldface. 1014

1015

Locus Gene Product Hip+ Lineage Count

Hip- Lineage Count

Lineage Enrichment

Ratio

Total Lineages

Hit

PA5016 aceF dihydrolipoamide acetyltransferase

6 3 3.65 6

PA0339 hypothetical protein 3 2 2.75 3

PA4462 rpoN RNA polymerase sigma-54 factor

3 2 2.58 4

PA3640 dnaE DNA polymerase III, alpha chain

4 3 2.41 4

PA4311 conserved hypothetical protein

4 3 2.15 5

PA4856 retS RetS (Regulator of Exopolysaccharide and Type III Secretion)

6 5 2.06 6

PA2894 hypothetical protein 4 3 2.01 4

1016

1017

1018

1019

1020

1021

1022

1023

1024

1025

1026

1027

1028

1029

1030

1031

1032

1033

1034

1035

1036


https://doi.org/10.1101/561589

32

Supplementary Information for 1037

1038

Bacterial persisters in long-term infection: emergence and fitness in a complex host 1039

environment 1040

1041

Authors 1042

Jennifer A. Bartell1*, David R. Cameron2,3*, Biljana Mojsoska4,6*, Janus Anders Juul 1043

Haagensen1, Lea M. Sommer4, Kim Lewis2§, Søren Molin1, Helle Krogh Johansen4,5§ 1044

1045

Affiliations: 1046

1 The Novo Nordisk Foundation Center for Biosustainability, Technical University of 1047

Denmark, Lyngby, Denmark 1048

2 Antimicrobial Discovery Center, Northeastern University, Boston, USA 1049

3 Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of 1050

Bern, Switzerland 1051

4 Department of Clinical Microbiology, Rigshospitalet, Copenhagen, Denmark 1052

5 Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark 1053

6 Present address: Department of Science and Environment, Roskilde University, Roskilde, 1054

Denmark 1055

1056

*Contributed equally to the work and listed alphabetically 1057

§Corresponding authors 1058

1059

1060

This PDF file includes: 1061

Supplementary Figures 1 and 2 1062

Tables S1-S2 1063

Captions for Datasets 1 and 2 1064

References for SI reference citations 1065

1066

Other supplementary materials for this manuscript include the following: 1067

Datasets 1 and 2 1068

1069

1070

1071

1072

1073

1074 1075


https://doi.org/10.1101/561589

33

Supplementary Figures 1076

1077 1078 Figure S1. Correlation between persister score and colony forming units following treatment 1079 with ciprofloxacin. P. aeruginosa isolates representing each of the scores possible from the high-1080 throughput screen (0-4) were treated with 100 µg/ml of ciprofloxacin for 24 hours, then plated on agar 1081 for surviving CFU determination. Each isolate was tested independently at least 4 times. The data are 1082 represented by the mean and SEM. 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107

0 1 2 3 4

2

4

6

8

Persister score

log

(CF

U/m

l)

r2= 0.5719p < 0.0001


https://doi.org/10.1101/561589

34

1108 1109 Figure S2. Hip accumulation in patients. (A) Hip- (gray squares) and Hip+ (blue diamonds) show 1110 the continuous count of patients with Hip- lineage(s) versus Hip+ lineage(s) for the prior year of 1111 colonization, while the accumulating count of patients with Hip+ lineages from time 0 is shown by 1112 black circles (AnyHip). (B) Accumulated patients with Hip+ lineages are shown for all persister 1113 datasets. 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147


https://doi.org/10.1101/561589

35

Supplementary Tables 1148 1149

Table S1. Emergence of resistant versus persister isolates across 74 lineages via assessment 1150

of AnyHip isolates (persister score greater than 2) versus AnyHip plus transition isolates 1151

(persister score greater than 0). 1152

1153

Persister score

Category # Lineages affected Cip Tob Any

AnyHips (Persister score > 2)

Persisters alone 2 16 16

Resistance alone 14 4 14 Simultaneous emergence 11 1 11

Persisters before Resistance 2 3 5 Resistance before Persisters 8 2 9

AnyHips plus

transition variants

(Persister score > 0)

Persisters alone 10 45 48

Resistance alone 8 1 8 Simultaneous emergence 7 2 7

Persisters before Resistance 15 7 15

Resistance before Persisters 5 0 5

1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184


https://doi.org/10.1101/561589

36

Table S2. Persister genes identified in previous P. aeruginosa studies which we did not observe as 1185 targets in our lineage enrichment analysis. 1186

Locus Gene Product Function Reference

PA5338 spoT Guanosine-3',5'-

bis(diphosphate) 3'-

phyrophosphohydrolase

Adaptation, protection;

Nucleotide

biosynthesis and

metabolism

(8)

PA0934 relA GTP pyrophokinase Adaptation, protection (8)

PA3622 rpoS Sigma factor RpoS Transcription (9, 10)

PA4723 dksA DksA

Transcription; DNA

replication

(8)

PA5002 dnpA de-N-acetylase Membrane proteins (11)

PA1045 Hypothetical protein Unknown (11)

PA0299 spuC Polyamine:pyruvate

transaminase

Carbon compound

catabolism

(11)

PA3589 Probable acyl-CoA

thiolase

Carbon compound

catabolism

(11)

PA3883 Probable short-chain

dehydrogenase

Putative enzyme (11)

PA5261 algR AlgR Two-component

regulatory system;

secreted factors

(11)

PA0318 Hypothetical protein Putative enzymes (11)

PA0409 pilH PilH Motility, chemotaxis;

Two-component

regulatory system

(11)

PA3166 pheA Chorismate mutase Amino acid

biosynthesis and

metabolism

(11)

PA5332 crc Catabolite repression

control protein

Carbon compound

catabolism; energy

metabolism

(12)

PA4756 carB Carbamoylphosphate

synthase

Nucleotide

metabolism; Amino

(13)


https://doi.org/10.1101/561589

37

acid biosynthesis and

metabolism


https://doi.org/10.1101/561589

38

1187

1188

1189

1190

1191

1192

1193

1194

1195

1196

1197

1198

1199

1200

1201

1202

1203

1204

1205

1206

1207

1208

1209

1210

1211

1212

1213

1214


https://doi.org/10.1101/561589

39

Dataset Descriptions 1215

1216 Dataset 1. Isolate collection data, metadata, and screen results. Description of all isolates 1217 including age, genotypic, and phenotypic information used in the analysis. Identification of Hips in 1218 CipHip, TobHip, MDHip, and AnyHip datasets as well as Persister Scores in each antibiotic screen. 1219 Specific legend included below and in dataset file. 1220 1221 ID: The id of the isolate. 1222 Sequence_ID: Sequence id of isolate for internal use and can be referenced to the isolate collection 1223 published and analyzed in Marvig et al.: "Convergent evolution and adaptation of Pseudomonas 1224 aeruginosa within patients with cystic fibrosis", Nature Genetics 47, (2015). 1225 SRA_accNo: Accession number for sequences published previously in Marvig et al.: "Convergent 1226 evolution and adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis", Nature 1227 Genetics 47, (2015). 1228 Genotype: The clone type/genotype of the isolate. 1229 Patient: The patient wherefrom the isolate has been sampled. 1230 Lineage: Patient-Genotype combination distinguishing specific clone types evolving in a given 1231 patient. 1232 Date_no: Date of sampling, using fraction of year. 1233 IageCT: The "Infection age of the Clone Type", the time (in years) since the clone type of the specific 1234 isolate was first detected in the patient from which the specific isolate was sampled. This is referred to 1235 in the study as "the time since first detection" or "colonization time" or "age of the lineage". 1236 Length1PA: The time (in years) since P. aeruginosa was first positively cultured from the patient's 1237 lungs, regardless of clone type. 1238 PersisterScore: Score of 0-4 according to minimum number of spotted cultures that grew after 1239 antibiotic exposure (minimum in 2 biological replicates of 4 spots, 8 total spots). 1240 cip: MIC of Ciprofloxacin. 1241 tob: MIC of Tobramycin. 1242 AdhesionN: OD of crystal violet normalised against 20h of growth. 1243 GR_LB: Growth rate in LB, h-1. 1244 cipR: Isolate classification as ciprofloxacin sensitive or resistent based on EUCAST breakpoint of .5. 1245 tobR: Isolate classification as tobramycin sensitive or resistent based on EUCAST breakpoint of 4. 1246 hypermutator: designation of known hypermutator isolates based on mutations in mutL or mutS. 1247 Persister: Hip versus Lop classification as 1 versus 0 using a cutoff of at least 3 spots growth in each 1248 biological replicate. 1249 Persister_CIP: persister class from ciprofloxacin screen alone. 1250 Persister_TOB: persister class from tobramycin screen alone. 1251 Persister_MD: persister class from ciprofloxacin screen and tobramycin screen. Hips must have 1252 scored as 3 or greater in both screens, representing a multidrug persister. 1253 Persister_ANY: persister class from ciprofloxacin screen and tobramycin screen. Hips must have 1254 scored as 3 or greater in at least one screen. 1255 1256 1257 1258 Dataset 2. Lineage-based mutation enrichment analysis for coding genes and noncoding regions 1259 for each dataset (CipHip, TobHip, MDHip, AnyHip). Mutated genes enriched in Hip versus Lop 1260 isolates as assessed from a convergent evolution perspective accounting for lineage adaptation. 1261 Lineage enrichment ratio was calculated by dividing lineage-based gene mutation enrichment within 1262 Hip variants by that within Lop variants for each gene. Top Hip-linked genes were selected via the 1263 following criteria: greater than 2 lineages presenting mutations in that gene in the Hip population and 1264 a lineage enrichment ratio greater than 2. 1265

1266

1267


https://doi.org/10.1101/561589

40

Supplemental Information References 1268

1. Marvig RL, Sommer LM, Molin S, Johansen HK (2015) Convergent evolution and 1269

adaptation of Pseudomonas aeruginosa within patients with cystic fibrosis. Nat Genet 1270

47(1):57–64. 1271

2. Johansen HK, et al. (2004) Antibody response to Pseudomonas aeruginosa in cystic 1272

fibrosis patients: a marker of therapeutic success? - A 30-year cohort study of survival 1273

in Danish CF patients after onset of chronic P. aeruginosa lung infection. Pediatr 1274

Pulmonol 37(5):427–432. 1275

3. Høiby N, et al. (1977) Pseudomonas aeruginosa infection in cystic fibrosis - 1276

Diagnostic and prognostic significance of Pseudomonas aeruginosa precipitins 1277

determined by means of crossed immunoelectrophoresis. Scand J Respir Dis 58(2):65–1278

79. 1279

4. Haagensen JAJ, Verotta D, Huang L, Spormann A, Yang K (2015) New in vitro model 1280

to study the effect of human simulated antibiotic concentrations on bacterial biofilms. 1281

Antimicrob Agents Chemother 59(7):4074–4081. 1282

5. Frimodt-Møller J, et al. (2018) Mutations causing low level antibiotic resistance ensure 1283

bacterial survival in antibiotic-treated hosts. Sci Rep 8(1):1–13. 1284

6. Touw DJ, Knox AJ, Smyth A (2007) Population pharmacokinetics of tobramycin 1285

administered thrice daily and once daily in children and adults with cystic fibrosis. J 1286

Cyst Fibros 6(5):327–333. 1287

7. Heydorn A, et al. (2002) Statistical Analysis of Pseudomonas aeruginosa Biofilm 1288

Development: Impact of Mutations in Genes Involved in Twitching Motility, Cell-to-1289

Cell Signaling, and Stationary-Phase Sigma Factor Expression. Appl Environ 1290

Microbiol 68(4):2008–2017. 1291

8. Darija V, et al. (2013) Functional Analysis of spoT, relA and dksA Genes on 1292

Quinolone Tolerance in Pseudomonas aeruginosa under Nongrowing Condition. 1293

Microbiol Immunol 50(4):349–357. 1294

9. Murakami K, et al. (2005) Role for rpoS gene of Pseudomonas aeruginosa in antibiotic 1295

tolerance. FEMS Microbiol Lett 242(1):161–167. 1296

10. Nguyen D, et al. (2011) Active Starvation Responses Mediate Antibiotic Tolerance in 1297

Biofilms and Nutrient-Limited Bacteria. Science (80- ) 334(6058):982–986. 1298

11. De Groote VN, et al. (2009) Novel persistence genes in Pseudomonas aeruginosa 1299

identified by high-throughput screening. FEMS Microbiol Lett 297(1):73–79. 1300

12. Zhang L, et al. (2012) The catabolite repression control protein Crc plays a role in the 1301

development of antimicrobial-tolerant subpopulations in Pseudomonas aeruginosa 1302

biofilms. Microbiology 158(12):3014–3019. 1303

13. Cameron DR, Shan Y, Zalis EA, Isabella V, Lewis K (2018) A Genetic Determinant 1304

of Persister Cell Formation in Bacterial Pathogens. J Bacteriol 200(17):1–11. 1305

1306

1307


https://doi.org/10.1101/561589

bacterial persisters in long-term infection: emergence and ... · 5 jennifer a. bartell 1*, david...

Documents