Extracellular Polymeric Substance Arch itecture...

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Extracellular Polymeric Substance Architecture Influences Natural Genetic 1

Transformation of Acinetobacter baylyi in Biofilms 2

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Robin T. Merod and Stefan Wuertz* 5

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Department of Civil and Environmental Engineering, University of California, Davis, 8

One Shields Avenue, Davis, CA 95616, U.S.A. Phone: (530) 754 6407, Fax: (530) 752 9

7872 10

* Corresponding author, E-mail: [email protected] 11

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Running title: EPS architecture influences natural genetic transformation 14

Keywords: Biofilm architecture; EPS; Transformation; Acinetobacter 15

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AEM Accepts, published online ahead of print on 10 October 2014Appl. Environ. Microbiol. doi:10.1128/AEM.01984-14Copyright © 2014, American Society for Microbiology. All Rights Reserved.

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ABSTRACT 18

Genetic exchange by natural transformation is an important mechanism of 19

horizontal gene transfer in biofilms. Thirty-two biofilm metrics were quantified in a 20

Acinetobacter baylyi heavily encapsulated strain and a miniencapsulated mutant strain 21

accounting for cellular architecture, extracellular polymeric substances (EPS) architecture 22

and their combined biofilm architecture. In general, transformation location, abundance 23

and frequency were more correlated to EPS architecture than to cellular or their 24

combined architecture. Transformation frequency and transformant location had the 25

greatest correlation with the EPS metric surface area to biovolume ratio. Transformation 26

frequency peaked when EPS surface area to biovolume ratio was greater than 3 µm2/µm3 27

and less than 5 µm2/µm3. Transformant location shifted towards the biofilm-bulk fluid 28

interface as EPS surface area to biovolume ratio increased. Transformant biovolume was 29

most correlated with EPS biovolume and peaked when transformation occurred in close 30

proximity of the substratum. This study demonstrates that biofilm architecture influences 31

A. baylyi transformation frequency and transformant location. The major role of EPS may 32

be to facilitate the binding and stabilization of plasmid DNA for cellular uptake. 33


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INTRODUCTION 34

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Biofilms provide an optimal environment for the occurrence of horizontal gene 36

transfer such as natural genetic transformation, the active cellular uptake of free DNA. 37

Free DNA, naturally originating from cell lysis or active cellular secretion (7), is a central 38

component of biofilms (36) and has even been found in higher quantities than 39

intercellular genomic DNA (33). The capacity of biofilms to incorporate DNA into their 40

architecture prevents free DNA from escaping into the surrounding environment and 41

potentially protects free DNA from degradation. Subsequently, the high microbial density 42

of biofilms increases the probability that transformation competent cells come into 43

contact with free DNA. The microorganism Acinetobacter baylyi strain ADP1 is a good 44

experimental model for studying natural genetic transformation due to its naturally high 45

competence (5). Apposed to using planktonic cultures, its ability to form stable biofilms 46

makes it the organism of choice to investigate genetic transformation as it may occur in 47

the natural environment. 48

While nutrient conditions, biofilm maturity and mode of growth (10, 23, 26) have 49

been shown to effect natural genetic transformation in A. baylyi biofilms, the influence of 50

biofilm architecture (that is, the biofilm’s physical structure) has not been fully 51

established. Biofilm architecture is integral to microbial life and influences the functional 52

characteristics of the biofilm. For example, biofilm thickness, porosity, surface 53

roughness and cell cluster density contribute to mass transport limitations (16, 30), while 54

interconnecting channels facilitate mass transport throughout the biofilm’s depth (18, 25). 55

In turn, mass transport affects localized microbial diversity in multi-species biofilms (1, 56


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34), which subsequently determines the extent of metabolic capabilities. We 57

hypothesized that biofilm architecture would also influence transformation frequency and 58

transformant location and abundance. 59

Many studies investigating biofilm architecture structure-function relationships 60

focus on the microbial distribution but ignore extracellular polymeric substances (EPS), 61

which exhibit a critical function in biofilms (2, 4). In this study, different architectures 62

were induced in monoculture A. baylyi biofilms by varying the number of biofilm 63

establishing cells and biofilm developmental nutrient conditions. Additionally, the 64

influence of EPS architecture was determined by using the heavily encapsulated strain 65

BD4 and the miniencapsulated mutant strain ADP1 (12). Various metrics of cellular, EPS 66

and overall biofilm architecture were quantified in situ to elucidate their effect on natural 67

genetic transformation. 68

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MATERIALS AND METHODS 71

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Bacterial strains, plasmids and media. Acinetobacter baylyi strain BD4 and A. 73

baylyi strain ADP1 (35), previously referenced as Acinetobacter sp. strain ADP1, were 74

used as model biofilm forming organisms. The plasmid pGAR38, a Mob+ Tra- 75

gentamicin and tetracycline resistant IncQ plasmid, was used as the model DNA element 76

for transformation. pGAR38 is pML10 (15) containing the wild-type gfp (green 77

fluorescent protein) gene (Clontech, Palo Alto, California) under regulation of a PpsbA 78

promoter (8). Induction of the promoter was not necessary. Brain and heart infusion 79


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(BHI) medium, mineral medium M9 (31) and 0.01 M MgSO4 were used during 80

transformation experiments. 81

Biofilm growth and transformation. Biofilms were grown in stainless steel, 82

multi-lane flow cells previously described by Hendrickx et al. (9) under continuous flow 83

conditions. The entire system was autoclaved prior to experimentation. Prior to flow cell 84

inoculation, all cultures were washed three times in 1x PBS after having been grown 85

overnight in BHI at 30°C. A 250-μL volume of washed cells containing either 107 or 109 86

cells was inoculated into flow cell lanes and allowed to settle for 2 h before starting 87

continuous flow of the growth media indicated in TABLE 1. All biofilms were grown 88

with a flow rate of 2.5 ml/h in a constant temperature chamber at 30°C. 89

Each strain was subjected to four different growth conditions (see TABLE 1) in 90

order to manipulate the formation of architectural characteristics. For each growth 91

condition, two biofilms were grown in the same flow cell to obtain replicate biofilm 92

architectures prior to transformation (6). Biofilms were allowed to grow for 3 days prior 93

to being washed of growth media for 12 hrs with M9 containing no carbon source. 94

Following the wash step, one of the two biofilms underwent transformation with 95

2.5 μg/ml of pGAR38 in M9 containing 0.2% pyruvate. The remaining biofilm was 96

sacrificed to visualize the initial EPS and cellular architecture. For transformation, a 97

volume of 500 μl was pumped into flow channels equating to 1.25 μg pGAR38 applied 98

over the time course of 12 minutes. The plasmid containing medium was then switched to 99

M9 containing 0.2% pyruvate without DNA to allow GFP expression and maturation. 100

After 3 days, the biofilm was visualized for cells, EPS and transformants using confocal 101

laser scanning microscopy (CLSM). 102


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Biofilm staining and CLSM observation and image acquisition. Biofilms were stained 103

under continuous flow for 1 hour and then washed with 0.01M MgSO4 for 30 minutes. 104

Cells were stained with 5 μM Syto60 (Molecular Probes, Eugene, Oregon) suspended in 105

0.01 M MgSO4. EPS was stained with 0.05% (by weight) SOLOPHENYL® flavine 106

(SPF; Huntsman Int. LLC, High Point, North Carolina) suspended in 0.01 M MgSO4 and 107

filtered using a 0.2 μm sterilization filter (11). 108

Flow cells were mounted on a Zeiss 510 META CLSM (Carl Zeiss, Jena, 109

Germany) motorized stage and visualized using a 63 x /1.2 NA (C-Apochromat) water 110

immersion objective lens. Syto60 stained cells were scanned using a 633 nm helium laser 111

with a 650 nm long pass filter. EPS stained with SPF and GFP expressing transformants 112

were scanned using a 488 nm argon laser with a 505 nm long pass filter. EPS was stained 113

and visualized after transformant detection. 114

Twenty-one CLSM image stacks were acquired from each biofilm to obtain a 115

representative sample of architectural variation (14). Automatic acquisition was 116

accomplished using the MultiTime Series (MTS) macro supplemental to Zeiss’s CLSM 117

interface software. Scanning locations were limited to the central 20 mm region of the 118

flow lane to exclude entrance and exiting flow effects on biofilm architecture occurring 119

10 mm from the inlet and outlet. Scanning was also limited to 1.5 mm from the channel 120

walls to exclude excessive biofilm accumulation. Z-stacks scanned from a single biofilm 121

contained the same number of images. The number of images was set to capture the 122

thickest part of the biofilm. Images had a pixel resolution of 0.2856 µm/pixel. The z-step 123

for images in a z-stack was 0.75 µm. In accordance with optimal settings described by 124

Sekar et al. (32), images were acquired utilizing a 1x digital magnification, a pinhole 125


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setting of 1 airy unit and a scan average of 2; the detector gain (500 to 550 arbitrary 126

units), amplifier offset (0 to 0.05 arbitrary units) and laser intensity (10% to 25%) were 127

set to obtain adequately contrasted grey-scale images based on the brightest region of the 128

biofilm that was scanned. 129

Image analysis. Semi-automated image analysis was performed utilizing the 130

programs Auto PHLIP-ML (19) and PHLIP (22). Auto PHLIP-ML (available at 131

http://sourceforge.net/projects/auto-phlip-ml/) calculates an Otsu threshold for image 132

stacks not biased by extraneous images (images without pixels of biological 133

significance). Extraneous images are identified and removed based on their area coverage 134

of biomass as described by Merod et al. (19). The percent area coverage value used for 135

extraneous image removal (PACVEIR) identifying the substratum was set at 1%. The 136

bulk-media interface was defined by the limit of EPS and iteratively determined to be a 137

PACVEIR of either 0% or 0.005%. PHLIP version 0.7, a MatLab-based image analysis 138

toolbox freely available from the PHLIP Website 139

(http://www.itqb.unl.pt:1141/~webpages/phlip/), was used to quantify architectural 140

metrics for each z-stack. The Auto PHLIP-ML calculated Otsu threshold for transformant 141

images was overridden with manually set thresholds due to under thresholding. 142

The following biofilm architectural metrics are included in PHLIP v0.7 and 143

described by Mueller et al. (22): 144

• Biovolume - the volume of cellular biomass, transformant biomass, EPS 145

biomass, or cellular-and-EPS (overall) biomass; 146

• Surface area to biovolume ratio – the surface area of the biomass divided by 147

the biovolume; 148


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• Mean thickness – the average thickness of the biomass; 149

• Roughness – a measure of how much the biomass thickness varies; and 150

• Horizontal, vertical, and total spatial spreading – a characterization of the 151

spreading of the biomass in space. 152

Scripts for the following architectural metrics were added to the single-channel and all-153

channel image processing operations of PHLIP: 154

• Porosity (μm3/ μm3) - calculated as the difference between total volume and 155

biovolume divided by the total volume, where total volume was calculated as 156

the biomass mean thickness multiplied by the z-stack x, y area; 157

• Maximum thickness (μm) - calculated as the number of images in the z-stack 158

multiplied by the z-step (the μm distance between each image); 159

• Surface area (μm2) - calculated as the product of biovolume and surface area 160

to biovolume ratio; and 161

• Normalized mean location (NML) of biomass - calculated as described by 162

Hendrickx et al. (10). Normalized mean location ranges from 0 (substratum) 163

to 1 (biofilm bulk-media interface) and provides a normalized z-location for 164

the majority of biomass. 165

( )

k

k

iiiii

zV

AzzzNML

×

×−×=

=+

01

166

where, 167

=iz distance from the substratum at image i (μm) 168

=kz maximum thickness of biomass (μm) 169


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=iA area coverage of biomass at image i (μm2) 170

=V biovolume of biomass in z-stack (μm3) 171

Architectural metrics were calculated for both the cellular component and EPS 172

component of the biofilm using PHLIP’s single-channel processing. The overall 173

architecture for each metric was calculated using PHLIP’s all-channel image processing, 174

which superimposed the cellular channel and EPS channel. The 3D colocalization of 175

cells-and-transformants was defined to equal the transformation frequency (i.e., 176

biovolume of transformants per biovolume of cells). The biovolume of transformants was 177

calculated by multiplying the transformation frequency by the biovolume of cells. 178

Statistical analysis. The architecture of sacrificed biofilms was used to replicate 179

the initial architecture (i.e. the architecture prior to transformation) of the biofilm used for 180

transformation that developed under identical growth conditions. Three-way ANOVA 181

was used on the initial architecture characteristics to determine if biofilms displayed 182

significantly different architectures when grown under different media conditions. The 183

factors for the three-way ANOVA were biofilm-type (eight levels - two A. baylyi strains, 184

each with four treatments (Table 1)), architectural component (three levels - cellular, 185

EPS, and overall), and architecture metric (ten levels - biovolume, NML, porosity, mean 186

thickness, roughness, horizontal spreading, vertical spreading, total spreading, surface 187

area, and surface area to biovolume ratio). Each architectural metric had a sample number 188

of 21. Based on this design, each biofilm-type has three architectural components, and 189

each architectural component has 10 architecture metrics. Therefore, the architecture of 190

each biofilm-type was quantified by 30 levels of measurement. 191


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Spearmen rank order analysis was used to determine which initial architecture 192

metrics correlated with transformation frequency, transformant biovolume, or 193

transformant NML. Each architectural metric had a sample number of 168, corresponding 194

to 8 biofilms each with 21 z-stacks. The Spearmen Rank Order Correlation coefficient 195

(rs) has a value range from +1 to -1. Coefficient values were defined to have the 196

following level of correlation: 0.75 ≤ rs < 1 and -0.75 ≥ rs > -1 were strong; 0.50 ≤ rs < 197

0.75 and -0.50 ≥ rs > -0.75 were intermediate; 0.25 ≤ rs < 0.50 and -0.25 ≥ rs > -0.50 were 198

weak; and -0.25 < rs < 0.25 had no correlation. Only correlations with a p-value less than 199

0.05 were considered to be significant. Positive values had a positive correlation and 200

negative values had a negative correlation. SigmaStat version 3.5 (Systat Software, Inc.) 201

was used for all statistical analyses. 202

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RESULTS 205

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Effect of growth conditions on biofilm architecture. To determine the role that 207

biofilm architecture plays in natural genetic transformation, different architectures were 208

induced. The wild-type strain A. baylyi strain BD4 and the miniencapsulated EPS mutant 209

A. baylyi strain ADP1 were used to investigate the effect of differences in EPS. 210

Additionally, different growth conditions were utilized during biofilm development to 211

promote architectural differences between biofilms (Table 1). Brain heart infusion 212

medium was chosen to provide a rich, unlimiting growth environment and promote a 213


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“fluffy” architecture, while M9 medium with different concentrations of gluconate was 214

used to induce more dense biofilm architectures. 215

The architecture of each biofilm was quantified by 30 levels of measurement. 216

Three-way ANOVA allowed us evaluate if the eight biofilms had significant differences 217

in architecture. The Tukey test on three-way ANOVA results showed a significant 218

difference between all biofilm pairwise comparisons (P < 0.05) except for two: 219

ADP1_Gluc_109 vs. BD4_BHI_109 and ADP1_BHI_107 vs. BD4_Gluc_109 (biofilm 220

names have the notation of ‘strain_growth media_inoculation cell number.’). Based on 221

these results we concluded that sufficient architectural variation exists to evaluate the 222

effect of architecture on transformation frequency and location. 223

Effect of biofilm architecture on transformation frequency. The average 224

transformation frequency ranged from 0.019 to 0.00042 measured as transformant 225

biovolume per cellular biovolume (Table 1). As expected, the miniencapsulated mutant 226

strain ADP1, which is known to be highly competent, exhibited the greatest 227

transformation frequency. Cellular, EPS and overall biofilm architecture were 228

investigated to determine their influence on transformation frequency. Biofilm 229

architecture was measured using ten architectural metrics (see Materials and Methods – 230

Image Analysis). Additionally, 3D colocalization of cells-and-EPS and maximum 231

thickness were examined, totaling a comparison of thirty-two architectural metrics. Five 232

out of the thirty-two metrics correlated weakly with transformation frequency (Table 2). 233

No comparisons resulted in a strong or intermediate correlation. EPS surface area to 234

biovolume ratio was the most correlated, with an rs value of -0.42 and a p-value less than 235


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0.001. Transformation frequency peaked when EPS surface area to biovolume ratio was 236

greater than 3 µm2/ µm3 but less than 5 µm2/ µm3 (Fig. 1). 237

Effect of biofilm architecture on transformant biovolume. Transformant 238

biovolume underwent the same Spearman Rank Order correlation analysis performed on 239

transformation frequency. As with transformation frequency, transformant biovolume did 240

not strongly correlate with any of the thirty-two architectural metrics. However, 241

transformant biovolume had an intermediate correlation with EPS biovolume and EPS 242

porosity. Nine other architectural metrics had a weak correlation (Table 3). In general, the 243

metrics biovolume, porosity and surface area for each aspect of biofilm architecture (i.e., 244

cellular, EPS and overall) were correlated with transformant biovolume. Transformant 245

biovolume peaked when transformation occurred in close proximity of the substratum. 246

Effect of biofilm architecture on transformant location. Similar to 247

transformation frequency and transformant biovolume, the same biofilm architecture 248

metrics were investigated to determine their influence on transformant normalized mean 249

location (NML). Twenty-one out of thirty-two metrics correlated with transformant NML 250

(Table 4). The top five correlated metrics were all a measure of EPS architecture. The 251

metric EPS surface area to biovolume ratio was strongly positively correlated (Fig. 2), 252

while EPS NML (Fig. 3), porosity, total spreading and vertical spreading were 253

intermediately positively correlated. Of the remaining sixteen comparisons, fourteen 254

(five cellular, two EPS, six overall architectural metrics, and maximum thickness) were 255

weakly positively correlated to transformant NML and two were weakly negatively 256

correlated (EPS biovolume and 3D colocalization of cellular-and-EPS). 257

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DISCUSSION 260

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We investigated whether monoculture A. baylyi biofilm architecture – in terms of 262

cellular, EPS and their combined characteristics – influences the efficiency of gene 263

transfer by transformation. Previous studies have implicated the high cell density of 264

biofilms as a major influence on transformation efficiency (9, 17, 20). Li et al. (17) 265

showed that natural genetic transformation was 10 to 600 times more frequent in 266

Streptococcus mutans biofilms than in planktonic cultures. Additionally, Hendrickx et al. 267

(9) showed that transformation is detectable in A. baylyi biofilms with a concentration as 268

low as 1 fg of plasmid DNA per mL. The present study demonstrates that A. baylyi 269

biofilm architecture has a varying degree of influence on transformation frequency, 270

transformant volume and transformant location. 271

Because nutrient conditions are known to influence natural competence (23, 24, 272

27, 28), biofilms in this study were washed prior to transformation to avoid growth media 273

induced competence variability. Perumbakkam et al. (29) and Hendrickx et al. (9), 274

previously showed that transformants were most abundant in areas with high cellular 275

density. Corresponding with results from Hendrickx et al., transformation biovolume in 276

this study peaked near the biofilm substratum (Fig. 4), which typically had high cell 277

densities (Fig. 5). However, transformant biovolume had a greater positive correlation 278

with EPS and overall biovolume than with cellular biovolume (Table 3), suggesting that 279

cell density is not the major factor governing the occurrence of transformation and that 280

transformants may not be randomly distributed in the biofilm. Unlike in planktonic 281


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cultures, development of competency will be affected by a cell’s location in a biofilm, 282

which may be at a different growth stage than other cells in the biofilm or may be 283

experiencing different substrate concentrations. Notably, EPS architecture was not 284

measured in the investigations performed by Perumbakkam et al. (29) and Hendrickx et 285

al. (9). Specifically, competency may not be uniform throughout the biofilm but may 286

develop in some subpopulations but not others leading to the phenomenon of bistability 287

(3). This phenomenon may have played a role in the biofilms that did not have 288

significantly different architectures but did have different transformation frequencies. 289

Of the three biovolume metrics, EPS biovolume had the greatest Spearman Rank 290

Order correlation with transformant biovolume (Table 3). Additionally, although 291

transformant biovolume was not strongly correlated with any architectural metrics, the 292

two metrics with the greatest Spearman Rank Order correlation were a measure of EPS 293

architecture. The importance of EPS architecture is also apparent in its influence on 294

transformant NML, where the top five correlated metrics were also measures of EPS 295

architecture (Table 4). In particular, EPS surface area to biovolume ratio had the greatest 296

Spearman Rank Order correlation to both transformation frequency (Table 2) and 297

transformation NML (Table 4). 298

While BD4 has been described as being highly competent (12), to our knowledge 299

the transformation efficiency of BD4 planktonic cultures or biofilms has not previously 300

been quantified. Historically, the miniencapsulated ADP1 has been used for 301

experimentation due to its ease of use in liquid cultures; the copiously capsulated BD4 302

can be problematic in protocols requiring centrifugation because it does not pellet 303

sufficiently. Although it has been shown that the capsule of BD4 does not prevent 304


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transformation (12), we hypothesized that its capsulization would reduce transformation 305

frequency. Kaplan et al. (13) showed that BD4 produces four times the amount of the 306

primary capsular polysaccharide component, L-rhamnose, compared to ADP1. While 307

these results were obtained using liquid cultures, similar differences in EPS production 308

were also observed in biofilms of this study, where BD4 EPS biovolume was typically 309

twice that of ADP1 biofilms). However, despite increased EPS, a BD4 biofilm had the 310

second greatest average transformation frequency (Table 1). 311

The major role of EPS may be facilitating the binding and stabilization of plasmid 312

DNA for cellular uptake. Specifically, an optimized ratio of EPS surface area to 313

biovolume was necessary to enhance transformation frequency (Fig. 1). A high ratio 314

value increases interconnecting channels, which can facilitate transport to deeper biofilm 315

regions (18, 25). However, a high surface area also increases the chances of plasmid 316

DNA adhesion close to the bulk media interface before the DNA can penetrate into the 317

biofilm. As a result, when the ratio increases, transformants are found closer to the bulk 318

media interface (Fig. 2). The lower observed volume of transformants at the biofilm-bulk 319

liquid interface may be a result of cellular erosion (21). 320

In conclusion, the main effect of EPS on natural transformation in biofilms is due 321

to its architectural characteristics, and not the relative amount of EPS present. This effect 322

of EPS architecture on transformation may also be apparent in biofilms of other A. baylyi 323

strains, which have been characterized as being highly competent (35). Future research 324

quantifying the amount of retained DNA based on biofilm architecture would further 325

benefit this area of study. 326

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ACKNOWLEDGEMENTS 329

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The publication of this manuscript is dedicated to the memory of Larissa 331

Hendrickx. RM was partially funded by an industry-supported fellowship under the 332

Training Program in Biomolecular Technology at the University of California, Davis and 333

from the NEAT-IGERT program sponsored by the National Science Foundation (IGERT 334

Grant DGE-9972741). 335

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TABLE 1. Biofilm developmental growth conditions and subsequent transformation 452

frequency results 453

Growth Media Cell Numbera Strain Transformation Frequency ± SDb

BD4 (× 10-3) ADP1 (× 10-3)

0.01% gluconate in M9 109 2.4 ± 1.6 0.42 ± .06

0.3% gluconate in M9 107 0.98 ± 0.15 0.96 ± 1.16

Brain heart infusion 109 2.8 ± 3.1 2.9 ± 2.6

Brain heart infusion 107 5.5 ± 5.1 19 ± 9

a Number of cells inoculated into flow chamber to initiate biofilm growth. 454

b Standard deviation calculated from 21 scanned locations 455

456


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TABLE 2. Transformation frequency Spearmen Rank Order Correlation results for 457

significant architectural metricsa 458

Correlation

Levelb Positive Negative

Strong None None

Intermediate None None

Weak 3D Coloc Cellular-EPS (0.38; <0.001)c EPS SA:BVe (-0.42; <0.001)

EPS Biovolume (0.28; <0.001) EPS Porosity (-0.27; <0.001)

Overall NML (-0.25; 0.001)

a Correlations with a p-value less than 0.05 were considered to be significant. 459

b rs value were defined to have the following level of correlation: 0.75 ≤ rs < 1 and -0.75 460

≥ rs > -1 were strong; 0.50 ≤ rs < 0.75 and -0.50 ≥ rs > -0.75 were intermediate; 0.25 ≤ 461

rs < 0.50 and -0.25 ≥ rs > -0.50 were weak; and -0.25 < rs < 0.25 had no correlation. 462

c rs; p-value 463

Abbreviations: 464

3D Coloc Cellular-EPS – Three dimensional colocalization of cellular-and-EPS. 465

EPS – Extracellular polymeric substances. 466

NML – Normalized mean location. 467

SA:BV – Surface area to biovolume ratio. 468

469


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TABLE 3. Transformant biovolume Spearmen Rank Order Correlation results for 470

significant architectural metricsa 471

Correlation

Levelb Positive Negative

Strong None None

Intermediate EPS Biovolume (0.57; <0.001)c EPS Porosity (-0.53; <0.001)

Weak Overall Biovolume (0.47; <0.001) Overall Porosity (-0.40; <0.001)

EPS Surface Area (0.44; <0.001) Cell Porosity (-0.36; <0.001)

Overall Surface Area (0.44; <0.001) EPS SA:BV (-0.29; <0.001)

Cellular Biovolume (0.43; <0.001)

Cellular Surface Area (0.42; <0.001)

EPS Roughness (0.33; <0.001)

a Correlations with a p-value less than 0.05 were considered to be significant 472

b rs value were defined to have the following level of correlation: 0.75 ≤ rs < 1 and -0.75 473


rs < 0.50 and -0.25 ≥ rs > -0.50 were weak; and -0.25 < rs < 0.25 had no correlation 475

c rs; p-value 476

Abbreviations 477




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TABLE 4. Transformant NML Spearmen Rank Order Correlation results for significant 480

architectural metrics 481

Correlation

Levela Positive Negative

Strong EPS SA:BV (0.75; <0.001)b None

Intermediate EPS Porosity (0.63; <0.001) None

EPS NML (0.55; <0.001)

EPS Total Spreading (0.54; <0.001)

EPS Vertical Spreading (0.50; <0.001)

Weak Overall Vertical Spreading (0.45; <0.001) EPS Biovolume (-0.40; <0.001)

Max Thickness (0.45; <0.001) 3D Coloc Cellular-EPS (-0.26; <0.001)

Cellular Vertical Spreading (0.44; <0.001)

Overall SA:BVa (0.43; <0.001)

Overall NML (0.43; <0.001)

Cellular Mean Thickness (0.41; <0.001)

Cellular NML (0.40; <0.001)

EPS Horizontal Spreading (0.39; <0.001)

Overall Mean Thickness (0.38; <0.001)

Overall Porosity (0.38; <0.001)

Overall Total Spreading (0.28; <0.001)

Cellular Porosity (0.28; <0.001)

EPS Mean Thickness (0.27; <0.001)

Cellular Total Spreading (0.26; <0.001)


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a rs value were defined to have the following level of correlation: 0.75 ≤ rs < 1 and -0.75 482


rs < 0.50 and -0.25 ≥ rs > -0.50 were weak; and -0.25 < rs < 0.25 had no correlation 484

b rs;p-value 485

Abbreviations: 486

3D Coloc Cellular-EPS – Three dimensional colocalization of cellular-and-EPS. 487


NML – Normalized mean location. 489



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FIG. 1. Correlation of transformation frequency and EPS surface area to 491

biovolume ratio. The Spearman Rank Order correlation coefficient, rs, is -0.42 with a p-492

value of <0.001. The reference line at 0.01 shows that transformation frequency peaked 493

when EPS surface area to biovolume ratio was greater than 3 µm2/µm3 and less than 5 494

µm2/µm3. Each data point represents the result from one scanned location. 495

FIG. 2. The distribution of transformant normalized mean location as a function 496

of EPS surface area to biovolume ratio. The Spearman Rank Order correlation 497

coefficient, rs, is 0.75 with a p-value <0.001. Each data point represents the result from 498

one scanned location. The regression line has the equation xy 082.0176.0 +−= with a 499

R2 value of 0.62. NML values near zero indicate biomass near the substratum, while 500

values close to one indicate biomass close to the bulk-media interface. 501

FIG. 3. Correlation of transformant normalized mean location and EPS 502

normalized mean location. The Spearman Rank Order correlation coefficient, rs, is 0.55 503

with a p-value <0.001. Each point represents the result from one scanned location. NML 504

values near zero indicate biomass near the substratum, while values close to one indicate 505

biomass close to the bulk-media interface. 506

FIG. 4. The distribution of transformant biovolume as a function of transformant 507

normalized mean location. Most transformants were found close to the substratum. Each 508

point represents the result from one scanned location. The regression line has the 509

equation y = 1395.6e -10.4 with a R2 value of 0.59. The insert contains the same data but 510

on a linear scale to emphasize the peak in transfomant biovolume when the NML was 511

between 0.1 and 0.2. Normalized mean location values near zero indicate biomass near 512


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the substratum, while values close to one indicate biomass close to the bulk-media 513

interface. 514

FIG. 5. A confocal laser scanning microscope image looking up from below the 515

substratum of an ADP1 strain biofilm grown in brain heart fusion medium. This image 516

shows that transformants (red) are primarily localized at the substratum of the biofilm. 517

Cellular biomass is shown in green. 518


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