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Algal aggregation by bacteria 1 1 Mechanism of algal aggregation by Bacillus sp. strain RP1137 2 Ryan J. Powell and Russell T. Hill * 3 Institute of Marine and Environmental Technology 4 University of Maryland Center for Environmental Science 5 701 E. Pratt St. Baltimore MD, 21202 USA 6 * Corresponding author. 7 Mailing Address: Institute of Marine and Environmental Technology 8 University of Maryland Center for Environmental Science 9 701 E. Pratt St. Suite 326 Baltimore MD, 21202 10 11 Phone: (410) 234-8802 12 Fax: (410) 234-8818 13 E-mail: [email protected] 14 Running title: Mechanism of aggregation 15 Keywords: Nannochloropsis, aggregate, algal harvesting, teichoic acid, biofuel, flocculation 16 17 AEM Accepts, published online ahead of print on 25 April 2014 Appl. Environ. Microbiol. doi:10.1128/AEM.00887-14 Copyright © 2014, American Society for Microbiology. All Rights Reserved. on September 6, 2018 by guest http://aem.asm.org/ Downloaded from

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Algal aggregation by bacteria

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1

Mechanism of algal aggregation by Bacillus sp. strain RP1137 2

Ryan J. Powell and Russell T. Hill* 3

Institute of Marine and Environmental Technology 4

University of Maryland Center for Environmental Science 5

701 E. Pratt St. Baltimore MD, 21202 USA 6

*Corresponding author. 7

Mailing Address: Institute of Marine and Environmental Technology 8

University of Maryland Center for Environmental Science 9

701 E. Pratt St. Suite 326 Baltimore MD, 21202 10

11

Phone: (410) 234-8802 12

Fax: (410) 234-8818 13

E-mail: [email protected] 14

Running title: Mechanism of aggregation 15

Keywords: Nannochloropsis, aggregate, algal harvesting, teichoic acid, biofuel, flocculation 16

17

AEM Accepts, published online ahead of print on 25 April 2014Appl. Environ. Microbiol. doi:10.1128/AEM.00887-14Copyright © 2014, American Society for Microbiology. All Rights Reserved.

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ABSTRACT 18

Algal derived biofuels are one of the best alternatives for economically replacing liquid 19

fossil fuels with a fungible renewable energy source. Production of fuel from algae is technically 20

feasible but not yet economically viable. Harvest of dilute algal biomass from the surrounding 21

water remains one of the largest barriers to economic production of algal biofuel. We identified 22

Bacillus sp. strain RP1137 in a previous study and showed this strain can rapidly aggregate 23

several biofuel producing algae in a pH and divalent cation dependent manner. In this study, we 24

further characterize the mechanism of algal aggregation by RP1137. We show aggregation of 25

both algae and bacteria is optimal in the exponential phase of growth and that the density of 26

ionizable residues on the RP1137 cell surface changes with growth stage. Aggregation is likely 27

via charge neutralization with calcium ions at the cell surface of both algae and bacteria. We 28

show charge neutralization occurs at least in part through binding of calcium to negatively 29

charged teichoic acid residues. The addition of calcium also renders both algae and bacteria more 30

able to bind to hydrophobic beads, suggesting aggregation may be occurring through 31

hydrophobic type interactions. Knowledge of the aggregation mechanism may enable 32

engineering of RP1137 to obtain more efficient algal harvesting. 33

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INTRODUCTION 39

Energy underlies economies and is the largest single market in the world. However, most 40

energy systems are based on finite nonrenewable resources that increasingly have higher direct 41

and indirect costs. A growing research effort focuses on developing and deploying renewable 42

energy sources to supplement fossil fuels. Research into renewable liquid fuels is of particular 43

interest in the US because transportation is almost exclusively powered by petroleum. 44

Algal biofuels represent one of the best alternatives to sustainably produce fungible liquid 45

fuels. Algae act as self-replicating bioreactors that use light energy to chemically reduce CO2 46

into useful energy storage molecules. Unlike traditional crops, algae can be grown on land not 47

suitable for agriculture and can be grown in wastewater or saltwater (1, 2). Algae have rapid 48

growth rates, sometimes doubling their biomass in several hours, and can be harvested multiple 49

times per year (3). Algal biomass is ideally suited for conversion to crude oil via hydrothermal 50

liquefaction, which produces an oil that can be refined in existing refineries and also allows the 51

recovery of limiting nutrients such as nitrogen and phosphorous (4). 52

While technologically feasible, studies have shown algal biofuels are not yet 53

economically viable (5). Furthermore, to our knowledge no company has yet successfully 54

produced algal biofuel at a profit. Only when profitability is achieved will algal biofuels become 55

a self-sustaining venture that can make a significant impact on the production of renewable fuels. 56

Harvest of the algal biomass has been identified as one of the key hurdles to economically 57

producing fuel from algae (5). Algal biomass must be concentrated and most of the water 58

removed before the biomass can be converted to fuel. Mature technologies for algal harvest 59

include filtration, centrifugation, sedimentation, electrocoagulation, dissolved air floatation, 60

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chemical flocculation and bio-aggregation (6). Uduman et al. provide an excellent review of 61

different algal harvest methods including the advantages and disadvantages of each (6). Bio-62

aggregation uses biological agents such as extracellular polymeric substances, chitosan or whole 63

cells to form easily harvestable aggregates (7-11). Several algal aggregating bacterial strains are 64

known and have been proposed for use in harvesting algae (12-17). 65

In previous work we described the algae aggregating bacterium Bacillus sp. RP1137 (17). 66

This bacterium can rapidly aggregate multiple algae that are candidates for biofuel production. 67

Aggregation is pH and divalent cation dependent (17). Fixed cells were also shown to be as 68

effective as live cells at aggregating algae. However, the detailed mechanism of aggregation of 69

algae by RP1137 was unknown. Knowledge of the mechanism may be useful for understanding 70

why it is able to aggregate some algae but not others and for applying the strain to large scale 71

algal harvest. 72

In this study we define the mechanism of algal aggregation by Bacillus sp. RP1137. The 73

purpose of this research is to understand the aggregation mechanism of Bacillus sp. RP1137 to 74

determine its suitability for harvest of algae and to understand how harvest might be improved. 75

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METHODS 83

Strains and culture conditions. Liquid cultures of Bacillus sp. strain RP1137 were grown in 84

marine broth 2216 (BD, Franklin Lakes, NJ) at 30°C in 125 ml Erlenmeyer flasks with shaking 85

at 180 rpm. Marine broth 2216 plus 15 g/l Difco technical agar (BD) was used for solid medium. 86

Nannochloropsis oceanica IMET1 was grown as described before (17). Briefly, N. oceanica 87

was grown in 20 ppt salinity f/2 medium (18) in 500 ml ported photo-bioreactors at 25°C with a 88

light/dark photoperiod of 14/10. 89

Filtration aggregation assay. A filtration aggregation assay was used to quantitate the amount 90

of algae that were aggregated under a given condition. This assay has been described in detail 91

(17). Briefly, the assay involves carrying out aggregation reactions with N. oceanica IMET1 and 92

Bacillus sp. strain RP1137 in a 96 well plate. The entire volume of the reaction is then passed 93

through a 50 µm mesh, aggregates that are larger than the mesh are retained and smaller particles 94

pass through. Chlorophyll fluorescence is measured in the flow-through and compared to control 95

samples without bacteria added to determine the percent of algae that are aggregated upon 96

addition of the bacteria. Unless noted otherwise, aggregation assays were carried out in 97

deionized water where pH was adjusted to 10.5 with NaOH and 10 mM CaCl2 had been added. 98

Bacterial aggregation efficiency time course. RP1137 cells were streaked from cryo-stocks and 99

a single colony was used to start a 10 ml culture in marine broth medium. The culture was 100

incubated at 30°C in a 125 ml flask with 180 rpm shaking. From the initial culture three 101

subcultures were started at a calculated optical density (OD) of 0.01 in 200 ml of marine broth. 102

Cultures were grown in 1 L flasks at 30°C with 180 rpm shaking. Time points were taken every 103

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one to two hours for 24 hours. At each time point cells were collected, concentrated by 104

centrifugation at 5580 x g for 5 minutes, supernatant was aspirated, the cell pellet was suspended 105

in 4% PFA in 1x PBS pH 7.4 and incubated for one hour at room temperature. Cells were then 106

concentrated by centrifugation, the supernatant was aspirated and cells were suspended in 1x 107

PBS to wash the cells. The cells were then again concentrated by centrifugation and suspended in 108

a fresh aliquot of 1x PBS. Fixed cells were used to preserve the surface chemistry of the cell and 109

ensure that chemistry was not altered due to stress responses by the cell. Filtration aggregation 110

assays were carried out using bacteria from each time point with algae from +2 days after 111

subculturing. The samples were normalized by cell surface area to 3 x108 µm2/ml (described 112

below) so each sample had the same surface area available for interacting with algal cells. 113

Algal aggregation efficiency time course. N. oceanica IMET1 cultures were grown as 114

described above. Samples of algae were taken at two, five and 17 days after being subcultured, 115

which represents early exponential, exponential and stationary phases of growth respectively. 116

Cells were fixed following the protocol used for the bacterial cells. Samples were normalized by 117

cell surface area per ml (described below) so each sample had the same available surface area for 118

interacting with bacterial cells. Aggregation assays were carried out using bacterial cells from 119

exponential phase (OD = 0.7). 120

Determining cell size and surface area. Cells from the time course were stained in 1X SYBR 121

green I nucleic acid stain for 10 minutes in the dark. SYBR green staining was used to illuminate 122

the cell body and provide crisp cell margins that were amenable to automated image analysis. 123

Cells were then visualized on a Zeiss Axioplan microscope with excitation from a Zeiss X-Cite 124

120Q Iris FL light source using a filter cube with a 470/40 BP excitation filter, a FT 495 dichroic 125

mirror and a 525/50 BP emission filter. Cells were diluted or concentrated as needed to obtain 126

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well separated cells. The volume of each field of view was determined using the known depth of 127

the bacterial hemacytometer and the height and width of the field of view. For each time point 128

20-30 fields of view were captured and saved as TIFF files. Image processing was done in Cell 129

Profiler (19) with the following series of commands in a custom pipeline: LoadImage, 130

ColorToGrey, IdentifyPrimaryObjects, ReassignObjectNumbers, MeasureObjectSizeShape and 131

ExportToSpreadsheet. LoadImage imports the images. ColorToGrey converts the image to 132

greyscale to reduce processor time. IdentifyPrimaryObjects was used to find and identify objects 133

using the Otsu global algorithm, a 4-40 pixel cutoff and a 0.02 – 1 threshold cutoff. 134

ReassignObjectNumbers was used to join cells within a filament into one object using a six pixel 135

cutoff. MeasureObjectSizeShape was used to measure the perimeter and area of the identified 136

objects. ExportToSpreadsheet was used to export the data as a .cvs file for import into Microsoft 137

Excel for further analysis. Data were converted from pixels to micrometers using data gathered 138

from a stage micrometer. Cell length was approximated by dividing cell perimeter by two; this 139

provides a good estimate of cell length for filamentous bacilli though it does introduce a slight 140

overestimate of the absolute size of the cells. To obtain cell surface area for normalization both 141

perimeter and area data are used. The key parameter needed, but unavailable directly in Cell 142

Profiler, for calculating surface area of a cell is the radius of the cell. To derive the radius of 143

individual cells the 2D images of cells were used and the bacilli were modeled as a rectangle 144

with half circles on each end. The resulting equation for area is then sum of the area of a circle 145

and the area of a rectangle or A = πr2 + 2r((P/2) – 2r) where A is the area of the cell, r is the 146

radius of the cell and P is the perimeter of the cell. Since A and P are measured values the 147

equation can be solved for r using the quadratic equation which yields two solutions, one of 148

which is the real radius of the cell. Derived radius values were checked against the manually 149

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measured average radius along the length of individual cells. Calculated values are very close to 150

measured values indicating the method can be used to accurately calculate cell radius in an 151

automated format. Cell radius was used to calculate surface area of a three dimensional cell by 152

modeling the cell as two halves of a sphere plus the surface area of a cylinder minus the ends. 153

Surface area of individual cells was calculated for 900-1600 cells per time point. Cell numbers 154

per ml were then used to calculate available surface area per unit volume. Surface area per ml of 155

individual sample were used to normalize available surface area for interaction with algal cells 156

between samples at different time points. The available surface area of N. oceanica IMET1 time 157

points was determined using a similar pipeline to that used for RP1137 cells with the following 158

modifications. Images were captured using chlorophyll autofluorescence. Nannochloropsis cells 159

are spherical so the measured area of the 2D images could be used to directly derive radius using 160

the equation for the area of a circle (A = πr2). Radius could then be used to calculate 3D surface 161

area of a sphere (A = 4πr2). Surface area per unit volume was determined by combining surface 162

area data with cell concentration data. All experiments were normalized by surface area using the 163

cells prepared above. The OD of the culture is provided to make clear which growth phase is 164

being used in each experiment. 165

LiCl treatment of RP1137 cells. Lithium chloride treatment was done according the protocol of 166

Lortal et al. (20). RP1137 cells were concentrated by centrifugation at 20,000 g for 3 min and the 167

cell pellet was suspended in either distilled water, 5 M LiCl, 7.5 M LiCl or 10 M LiCl. Cells 168

were incubated at these conditions for 15, 45 and 120 minutes at room temperature and then 169

concentrated by centrifugation. The cell pellets were suspended in pH 10.5 deionized water with 170

10 mM CaCl2 and used for aggregation assays. 171

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Base titration of whole bacterial cells. Live RP1137 cells were used for base titration 172

experiments. Cells were taken in exponential phase (OD = 0.7, 3.4 x 106cells/ml) and stationary 173

phase (OD = 1.6, 7.2 x 106cells/ml). Culture volumes were normalized by surface area to ensure 174

the same amount of bacterial cell surface was being titrated in each sample. Cells were 175

concentrated by centrifugation at 15,000 g for 5 minutes and the cell pellet was suspended in pH 176

5 deionized water. This washing step was repeated twice more to ensure salts had been removed 177

and the cells were equilibrated to pH 5. Base in the form of 0.25 M NaOH was added to the cell 178

suspension and pH was recorded after each addition when the value stabilized. 179

Calcium binding assay. Calcium binding was evaluated by measuring the concentration of 180

calcium remaining after 1 ml of cells had been added. Calcium binding assays were performed 181

with fixed RP1137 cells from the exponential phase (OD = 0.7) of growth. Known 182

concentrations of CaCl2 were added to cells in pH 10 deionized water. Cells were then removed 183

by centrifugation at 20,000 g for 3 minutes. The calcium concentration in the supernatant was 184

measured using the LaMotte Calcium Hardness colormetric kit (Chestertown, MD). The kit was 185

adapted for use in a 96 well format and measurement in a Spectro Max M5 plate reader. The 186

readout for the assay was absorbance at 635 nm. Absorbance at this wavelength is linear for 187

calcium concentrations between 0-160 µM. Samples were diluted to ensure they were within the 188

linear range of the assay. Absorbance values were compared to a CaCl2 standard curve to 189

determine the concentration of calcium remaining. 190

Calcium coordination experiment. RP1137 and Nannochloropsis cells were separately 191

suspended in pH 10.5 water with 10 mM CaCl2. To ensure the cells were pre-loaded with 192

calcium both bacteria and algae were concentrated by centrifugation at 20,000 g and the cell 193

pellets were suspended in the same solution. This pre-loading step was repeated once. The cells 194

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were then used in filtration aggregation assays compared to controls where only the algal cells 195

were pre-loaded with calcium. 196

C18 binding assay. Binding of cells to C18 resin was performed with fixed RP1137 cells from 197

the exponential phase of growth (OD = 0.7) and with fixed Nannochloropsis cells from +5 days 198

post inoculation. Dry C18 beads with a 10 µm diameter were purchased from Hamilton (Reno, 199

Nevada). Beads were reconstituted in methanol overnight. The beads were concentrated by 200

centrifugation at 20,000 g for 1 minute. The beads were then suspended in pH 10.5 deionized 201

water with 10 mM CaCl2. This process of concentration and suspension in pH 10.5 deionized 202

water with 10 mM CaCl2 was repeated twice more to ensure methanol was removed and the 203

beads were equilibrated in the test solution. The equilibration process was repeated without 204

CaCl2 for a separate aliquot of beads to obtain beads for the “no calcium” samples. For each 205

experiment 200 beads were used per cell as this was found to give maximal binding with the 206

minimum number of beads. Equal numbers of algal or bacterial cells were incubated with C18 207

beads (200:1 bead to cell ratio) in the presence or absence of 10 mM CaCl2. The mixtures were 208

analyzed with an Accuri C6 flow cytometer to count the number of unbound algal or bacterial 209

cells. The beads were distinguished from cells by their larger forward scatter area with an upper 210

forward scatter area cutoff of 1,270,000. A lower cutoff of 44,000 was used to remove 211

background particles found within the medium. RP1137 cells fell between these two cutoffs. 212

Algal chlorophyll autofluorescence was used to distinguish Nannochloropsis cells from their 213

associated bacterial cells using the FL3 channel (excitation 488 nm, emission 670 LP filter) on 214

the flow cytometer. Only particles that were between the two forward scatter cutoffs and had a 215

chlorophyll autofluorescence of greater than 10,000 were counted as algal cells. These settings 216

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counted the unbound bacterial or algal cells which allowed comparison of the number of bound 217

cells in the presence or absence of calcium. 218

Zeta potential. Measurement of cell surface charge or zeta potential was done on a dynamic 219

light scattering instrument (Malvern Instruments Ltd, Worcestershire, UK). Measurements were 220

done on fixed algal cells from two, five and 17 days after being subcultured and on fixed RP1137 221

cells that were taken in exponential phase (OD = 0.7) and stationary phase (OD = 1.6). Cells 222

were concentrated by centrifugation at 20,000 g for 1 min. The supernatant was aspirated and the 223

cells were suspended in pH 10.5 deionized water. To ensure removal of trace salts the cells were 224

again concentrated by centrifugation, supernatant was aspirated and the cells were suspended in 225

pH 10.5 deionized water. For each sample zeta potential was measured at 0, 0.156, 0.313, 0.625, 226

1.25, 2.5, 5, 10, 20 and 40 mM CaCl2. 227

SDS inhibition of aggregation. Inhibition of aggregation by sodium dodecyl sulfate (SDS) was 228

tested using the filtration aggregation assay. Fixed algae from two days after subculture were 229

used. Fixed RP1137 cells from exponential phase (OD = 0.7) were used. Both algae and bacteria 230

were concentrated by centrifugation at 20,000 g for 1 min. The supernatant was aspirated and the 231

cells were suspended in pH 10.5 deionized water with 10 mM CaCl2. This washing step was 232

repeated once. In the treatment samples SDS was added to algae to a final concentration of 1 %. 233

Filtration aggregation assays were carried out as described for quantitation of the percentage of 234

algae aggregated in the SDS treated cells compared to the untreated controls. 235

Calcium displacement of pinacyanol. To determine if calcium displaces pinacyanol bound to 236

the bacterial cell surface, fixed RP1137 cells from exponential phase were concentrated by 237

centrifugation at 20,000 g for 1 min, supernatant was aspirated and the cells were suspended in 238

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pH 10.5 deionized water. This washing step was repeated once. RP1137 cells were then stained 239

with 20 µM pinacyanol chloride (Sigma). The stained cells were added to an equal volume of 240

water or water with CaCl2 resulting in a final pinacyanol concentration of 10 µM. The final 241

concentration of CaCl2 solutions tested were 0, 0.15, 0.6, 2.5 and 10 mM. The samples were 242

mixed by vortexing and then centrifuged at 20,000 g for 3 minutes. The supernatant was 243

aspirated to remove unbound dye and the cells were suspended in a fresh aliquot of water with 244

the same calcium concentration. Absorbance of the dyed cell solutions were measured at 485 nm 245

in an M5 Spectromax plate reader. Absorbance spectra from 450-650 nm were gathered at 5 nm 246

increments for unstained RP1137 cells, stained cells, water + pinacyanol and water + pinacyanol 247

+ 10 mM CaCl2. 248

Algal aggregation by different Bacillus strains. To determine if other Bacillus strains share the 249

aggregation phenotype found in RP1137 we tested the aggregation ability of Bacillus 250

megaterium QM B1551 and Bacillus subtilus SMY. Cells were grown in LB medium (BD, 251

Franklin Lakes, NJ) at 37°C with 180 rpm shaking. The cells were harvested in exponential 252

phase and fixed using the protocol listed above. Cell density normalized filtration aggregation 253

assays were then carried out. 254

Effect of higher pH and calcium concentrations on algal self-aggregation. To determine if 255

Nannochloropsis cells will self-aggregate under the conditions tested and at higher pH value and 256

calcium concentration we perform filtration aggregation assays with only the algae, no bacteria 257

added. The pH of the algae culture was adjusted with NaOH. Calcium concentrations were 258

adjusted with CaCl2. Once the algal culture was at the desired condition the filtration aggregation 259

assay was performed to quantitate the amount of algae that was found in aggregates. 260

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RESULTS AND DISCUSSION 261

Characterization of cell length over a growth period. In this study, we aimed to characterize 262

the mechanism by which Bacillus sp. strain RP1137 aggregates algae. A better understanding the 263

mechanism may help to improve the efficiency of the system. In previous, work we showed that 264

divalent cations are important for aggregation and that fixed cells were just as effective at 265

aggregating algae as live cells, pointing to the cell surface as the important cell structure for 266

investigation of the aggregation mechanism (17). Our previous work also ruled out filament 267

length as an important factor for aggregation but, initial observations suggested aggregation 268

ability changed over the growth cycle of a culture. Since the cell morphology changes over the 269

growth period we cannot accurately normalize by optical density or cell counts. To accurately 270

normalize between time points we first needed to characterize this change in morphology. Single 271

cells and filaments of cells are present in RP1137 cultures. In this paper we define cell length as 272

the total length of either a lone single cell or the length of a chain of cells in a filament. Changes 273

in cell length are shown in Fig. 1 where cell length was measured over time through a 274

combination of fluorescent microscopy and automated image analysis of thousands of cells. The 275

results show cell length changed over a growth period. 276

Change in aggregation ability of RP1137 over a growth period. With the cell length data we 277

were able to calculate cell surface area data which were used to normalize the cell surface area 278

between different time points. Normalization of cell surface area allowed us to isolate changes in 279

the cell surface composition from changes in the amount of cells or amount of cell surface area 280

in a sample. Using normalization, aggregation potential changes over a growth cycle was 281

determined. In Fig. 2, the percent algae aggregated is plotted with the growth curve of the 282

bacterium. Aggregation is most effective in the exponential phase of bacterial growth where 80% 283

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of the algae are found in the aggregates. As the cells enter stationary phase the aggregation 284

potential per normalized cell surface area decreases and reaches a minimum value of 40% 285

aggregated algae at 20 hours. These data show the aggregation potential of the bacterial cell 286

surface decreases when the cells enter stationary phase, indicating the surface chemistry of the 287

cell was likely changing. From the perspective of applying RP1137 for algal harvest these results 288

show it is important to use bacteria from exponential phase to obtain the best aggregation ability. 289

Change in aggregation ability of N. oceanica IMET1 over time. Next we measured the 290

aggregation potential of N. oceanica IMET1 algal cultures at different growth stages. The same 291

bacterial sample was used for the experiments and the algal cells were normalized between 292

samples using the cell surface area. Unlike the bacteria, algal cell morphology does not change 293

significantly over a growth cycle (data not shown). Algal aggregation was tested at two, five and 294

17 days after subculturing. Cells in the +2 days samples are just beginning to grow while the 295

algae are fully into exponential phase by day five. At +17 days the algae are in stationary phase. 296

Aggregation is most efficient with cells at the +5 day time point (93.4 ± 0.7 %) and is 297

significantly more efficient than cells at +2 days (88.4 ± 2.3 %, p = 0.0005) and +17 days (68.3 ± 298

10.2 %, p = 0.0003). The data show the algae are most effectively aggregated during exponential 299

phase and have decreased aggregation ability when they enter stationary phase. Little is known 300

about the cell surface of Nannochloropsis; however these results suggests the surface chemistry 301

of the cell is changing with the growth phase of the alga. Changes in surface chemistry will be 302

investigated further in subsequent sections. The change in aggregation efficiency indicates that 303

when harvesting algae with RP1137 cells it is best to harvest the algae when the algae are in 304

exponential growth phase. However the harvest time must be balanced according to the 305

production system that is being used. For example if high lipid algae are desired then it may not 306

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be advantageous to harvest in exponential phase. However there are several reasons why 307

harvesting in exponential phase would be attractive. First, while oil content can be higher in the 308

starved stationary phase cells, the increased oil content is typically counterbalanced by a loss in 309

total biomass yield over the same time period. A second consideration is that continuously 310

harvesting in exponential phase would keep the cells in a rapidly- growing state. This removes 311

the non-productive lag period at the beginning of growth as well as the decrease or cessation of 312

growth that occurs in stationary phase. Thus harvesting in exponential phase may produce more 313

total biomass and more fuel per unit area in a given time period. For some conversion methods 314

such as hydrothermal liquefaction large amounts of high protein biomass are desirable 315

and therefore harvesting within exponential phase is optimal. 316

LiCl treatment and S-layer proteins. Our previous work showed that proteinase K treatment of 317

the cell surface did not significantly decrease aggregation, suggesting surface proteins were 318

unlikely to be involved in aggregation (17). While proteinase K can cleave surface proteins (21), 319

it may not cleave proteins that do not have an exposed cleavage site. S-layer proteins are often 320

involved in adhesion of Gram-positive bacteria to either biotic or abiotic surfaces and can be 321

involved in aggregation (22). S-layer proteins are also typically attached to peptidoglycan via 322

electrostatic interactions and can be removed by LiCl treatment (20). To test if the S-layer is 323

involved in aggregation RP1137 cells were treated with 5, 7.5 and 10 M LiCl for 15, 45 and 120 324

minutes. The treated cells did not have a significant decrease in their aggregation ability under 325

any of the conditions tested (Fig. S1) suggesting the S-layer proteins are not involved in the 326

aggregation phenotype. 327

Base titration of the RP1137 cell surface. The data we have collected show that specific or 328

non-specific protein-protein interactions at the cell surface are inconsistent with available data. 329

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We next hypothesized that perhaps a more general property of the cell surface is involved in the 330

aggregation phenotype of RP1137. Since the bacterial cells show a significant difference in 331

aggregation ability between exponential and stationary phase we decided to test if the surface 332

chemistry of the cells at these growth stages is different, specifically if the density of 333

deprotonatable residues at the cell surface was different. To test this we used base titration of 334

whole live cells. The results of base titration of RP1137 cells, in Fig. 3, show that cells in 335

exponential phase have a lower number of deprotonatable residues compared to the cells in 336

stationary phase because pH increases more quickly as base is added relative to stationary phase 337

cells. Since these experiments were normalized by cell surface area this translates to more 338

positive or neutral residues per unit area of cell surface. The data show there is a measurable 339

difference in surface chemistry between these two populations of cells. 340

Binding of calcium to the cell surface. Cell surface chemistry can also be affected by the ions 341

that are present. Previously, we showed that aggregation is dependent on divalent cations, and 342

we hypothesized these ions reduce or neutralize negative charge at the cell surface (17). To 343

determine if calcium ions bind to the surface of the bacterium we measured the amount of 344

calcium removed by the cells at increasing calcium concentrations. Fig. 4 shows that calcium 345

binds to the bacteria and that bound calcium increases with increasing calcium concentration up 346

to a concentration of 0.625 mM. A saturating concentration of surface bound calcium ions could 347

allow cells to aggregate in at least two ways. Charge neutralization eliminates electrostatic 348

repulsion between cells and is commonly cited as a method in which cells can get close enough 349

to adhere via other attractive forces such as hydrophobic or Van der Waals type interactions (23). 350

Another method of interaction cited in the literature is coordination of ions bound to one cell by 351

another cell (24). In this method of aggregation two cells interact via bridged ions. This is similar 352

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to the commonly used nickel-NTA affinity chromatography system which binds proteins via the 353

common coordination of a nickel ion by the 6x-His tag on a protein and a nitrilotriacetic acid 354

residue attached to a solid substrate. The ion coordination model of aggregation predicts that 355

algal and bacterial cells that have been preloaded with calcium separately before combination 356

should not interact efficiently because they are both already binding ions at their cell surface and 357

thus are less likely to bind via common ions. However, the charge neutralization model predicts 358

a different outcome. It predicts cells preloaded with calcium before combination should interact 359

and lead to aggregation. We tested the hypothesis that aggregation occurs by coordination of 360

common ions by preloading algal and bacterial cells with calcium separately before mixing them. 361

The results showed that the cells still aggregated with an average value of 74.6 ± 1.1 % algae 362

aggregated. These results point toward the charge neutralization model of aggregation rather than 363

the coordination model. This result is further supported by the finding that, unlike RP1137 cells, 364

the Nannochloropsis cells do not tightly bind calcium at their cell surface. 365

Measurement of zeta potential. To test the hypothesis that calcium ions are causing charge 366

neutralization, we used dynamic light scattering to measure apparent cell surface charge or zeta 367

potential at different calcium concentrations. Zeta potential was measured for both bacterial and 368

algal cells at different stages of growth. The data shown in Fig. 5 demonstrate that surface charge 369

in both bacteria and algae decreases with increasing calcium concentration. In the absence of salt 370

the exponential phase bacterial cells have a more negative charge at – 110 ± 6 mV compared to 371

the stationary phase cell with a zeta potential of -72 ± 6 mV. As calcium concentration increases 372

the zeta potential of both exponential and stationary phase bacterial cells becomes similar, with 373

the charge curves overlapping at 10 mM calcium which is the optimal concentration for 374

aggregation. At this concentration, charge is - 18.6 ± 1.05 mV for exponential cells and – 19.8 ± 375

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1.05 mV for stationary phase cells, both of which are at or below -20 mV which is often 376

considered as the threshold where charge is no longer strong enough to separate cells by 377

electrostatic repulsion. The surface charge of algal cells also becomes less negative as calcium 378

concentrations increase, but compared to the bacterial cells the algae require a higher calcium 379

concentration to get below the -20 mV threshold. This result suggests the bacterial cells have a 380

higher affinity for calcium ions than the algal cells. The calcium binding and zeta potential 381

measurements show bacterial cells bind calcium which results in charge neutralization. Next we 382

aimed to determine what forces were likely mediating the binding of the RP1137 and 383

Nannochloropsis cells. 384

RP1137 binding to C18 resin. From previous work we knew that surface proteins, filament 385

length and lectin-carbohydrate type interactions were not involved in the underlying mechanism 386

of aggregation (17). Aggregation of multiple and distinct algae species by RP1137 also pointed 387

toward a general instead of specific mechanism of aggregation (17). Since the cell surface charge 388

decreased upon addition of calcium, we hypothesized that bacterial and algal cells interact via 389

hydrophobic type interactions and thus should become more able to interact with a hydrophobic 390

surface under these conditions. We tested this by measuring the number of individual cells that 391

were not bound to hydrophobic C18 beads in the presence or absence of calcium. Unbound cells 392

were measured because it is easier to get accurate data on free cells as compared to the number 393

of cells bound to the beads. Fig. 6A shows that there are fewer unbound bacterial cells in the 394

presence of calcium. This shows that the bacterial cells are more able to bind to a hydrophobic 395

surface when calcium is added. The algal cells show a similar trend with fewer unbound cells 396

present in the presence of calcium (Fig. 6B). These data demonstrate that the algal cells are also 397

more able to interact with a hydrophobic surface in the presence of calcium. Together these data 398

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support the hypothesis that bacterial and algal cells interact at least in part through hydrophobic 399

interactions. Hydrophobic interactions are often involved in aggregation (23), so this result fits 400

with what others have found. If both bacterial and algal cells can interact with a defined 401

hydrophobic surface then we propose they could interact with each other. This mechanism of 402

interaction suggests that we should be able to inhibit the interaction by coating the cells with an 403

anionic detergent prior to the aggregation process. 404

SDS inhibition of aggregation. To test the hypothesis that an anionic detergent will disrupt an 405

aggregation process that occurs via hydrophobic interactions we pre-coated the bacterial and 406

algal cells with the anionic detergent sodium dodecylsulfate (SDS). SDS has a hydrophobic tail 407

attached to an anionic sulfate group. If the bacterial and algal surface is more hydrophobic then 408

the SDS should orient with the hydrophobic tail oriented toward the cell and the anionic sulfate 409

residue facing the solvent (water). The cells in this setup now have an external negative charge 410

which should inhibit aggregation. We observed that addition of SDS results in a significant 411

decrease in aggregation (p = 5.19E-05), these data are shown in Fig. 7. Visually, aggregation 412

appears to be completely inhibited (Fig. 7A), however quantitation using the aggregation assay 413

shows that small aggregates are still formed (Fig. 7B). The aggregation assay works by removing 414

aggregates that do not pass through a mesh with 50 µm by 50 µm square holes. Individual 415

Nannochloropsis cells are spherical and have a diameter of about 3 µm (17). The results indicate 416

the formation of large aggregates is inhibited but that smaller aggregates (>50 µm) are still being 417

formed. These smaller aggregates incorporated less of the algae into the aggregates. These 418

results indicate that the interaction between bacteria and algal cells was not completely disrupted 419

under the conditions tested. It is possible other forms of bonding are important in the interaction 420

between these cell types. Finally, the cells may have gained sufficient momentum when mixed 421

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by vortexing to overcome the electrostatic repulsion and allow the some of the cells to get close 422

enough to interact. The idea that the bacterial and algal cells bind each other via hydrophobic 423

interactions implies the cells should also self-aggregate in the absence of the other cell type; this 424

is observed for RP1137 cells at high pH in the presence of divalent cations. Nannochloropsis 425

cells do form small self-aggregates of 5-10 cells but they do not form large aggregates. The 426

reason Nannochloropsis cells do not form large aggregates is unknown, however in the C18 bead 427

assays more of the bacterial population is bound to the beads than the algae population, 428

suggesting there are less algal cells whose cell surface is hydrophobic enough to bind the beads 429

within the population. We speculate that the smaller the population of hydrophobic cells, the 430

lower the chance of finding other hydrophobic cells in which they can interact. This explanation 431

must tempered with the knowledge that up to 95% of the algal population can be harvested with 432

RP1137, which may imply the interaction between algae and bacteria is different than the 433

interaction of algae with other algae. Further study is needed to clarify what other forces besides 434

hydrophobicity may be at play. 435

Determining a binding site of calcium at cell surface. The calcium binding data indicated the 436

ions are binding to the bacterial cells and result in charge neutralization. Teichoic acids are 437

negatively charged and bind various cations including calcium (25), making them a plausible 438

target for charge neutralization by calcium ions. Previous studies have shown that teichoic acids 439

have a higher affinity for calcium relative to magnesium (25), matching data we have gathered in 440

a previous study that showed the RP1137 has a higher affinity for calcium than magnesium (17). 441

We tested whether calcium binds the teichoic acid residues of RP1137 using the dye pinacyanol 442

chloride. Pinacyanol chloride binds purified teichoic acids and upon binding undergoes an 443

absorbance shift which results in a new absorbance band centered at 485 nm (26). Interestingly, 444

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calcium competes for binding of teichoic acid with the dye. When calcium is present the dye is 445

removed from teichoic acid and the absorbance band at 485 nm is no longer present (26). Here 446

we use this property to determine if calcium is binding teichoic acids on RP1137 cells. The 447

absorbance spectra of the dye alone, dye with calcium, cells and cells with dye are shown in Fig. 448

8A. The cells in the presence of the dye show the characteristic peak in absorbance at 485 nm as 449

was observed for purified teichoic acid. The peak is not present in the cells alone, dye alone or 450

dye with calcium. Next, the cells were stained with pinacyanol and then exposed to different 451

concentrations of calcium. The cells are then washed to remove unbound dye and the absorbance 452

of the cells at 485 nm is recorded. If calcium is binding teichoic acids then it should displace the 453

dye and result in a decreased absorbance at 485 nm with increasing calcium concentration, which 454

is what we observe in Fig. 8B. We must be cautious with our interpretation as the original work 455

cited was with purified and not cell bound teichoic acids. However, this result supports the 456

hypothesis that calcium binds to teichoic acids of RP1137 cells. The result does not rule out the 457

possibility that calcium is also binding to other parts of the cell. Interestingly, when the 458

pinacyanol displacement data is compared to the zeta potential and calcium binding data we see 459

a discrepancy in calcium binding kinetics. Both zeta potential and calcium binding data suggest 460

the RP11137 cells saturate below 1 mM calcium while the pinacyanol displacement data 461

suggests saturation does not occur until higher concentrations. This seeming contradiction can be 462

explained by the positive shift in the calcium dissociation that would be predicted when both 463

calcium and pinacyanol compete for the same substrate. 464

Similar mechanisms of aggregation have been proposed for other aggregating bacteria, 465

where charge neutralization with divalent cations is implicated in the mechanism of aggregation 466

(27). Divalent cations have also been implicated in the autofloculation of some algae (28). The 467

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mechanism described here is similar to algal aggregation by Paenibacillus kribbensis where both 468

high pH and calcium ions were implicated in the aggregation mechanism (14). However, in the 469

study on Paenibacillus kribbensis only pH and calcium were investigated in terms of the 470

mechanism of aggregation, whereas here we conduct a more detailed investigation. If the 471

mechanism of aggregation in RP1137 is based on charge neutralization via calcium binding to 472

teichoic acids then it would be expected that other related Gram-positive bacteria may 473

demonstrate a similar aggregation phenotype. RP1137 is most closely related to Bacillus 474

megaterium species so to test this hypothesis we compared the aggregation ability of B. 475

megaterium QM B1551 to RP1137. We also compared the aggregation ability of the more 476

distantly related Bacillus subtilus SMY strain to RP1137. The results show that RP1137 and B. 477

megaterium QM B1551 have very similar algal aggregation efficiencies while Bacillus subtilus 478

SMY does not aggregate algae (Fig. S2). These results suggest that not all Gram-positives have 479

the algal aggregation phenotype but that it is likely bacteria closely related to RP1137 will share 480

the phenotype. A final question that arises from this research is whether the bacterium is needed 481

for aggregation or whether aggregation of the algae alone can occur, particularly at higher 482

calcium concentrations and higher pH values. To test this we performed aggregation assays with 483

only the Nannochloropsis cells at different combinations of pH and calcium concentration. The 484

results shown in Fig. S3 demonstrate that at pH 10 and 10 mM calcium, the optimal conditions 485

for algal aggregation by RP1137, 5.3 ± 3.3% of aggregation can be attributed to algal self-486

aggregation. Self-aggregation increases with increasing pH and calcium concentration to a 487

maximum of 34.6 ± 2.3% at pH 12 and 25 mM calcium. The conditions needed for aggregation 488

of algal by RP1137 can be achieved in most algal production systems by simply stopping 489

bubbling of air or CO2 through the culture. The pH of the dense cultures used for biofuel 490

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production can easily achieve pH values greater than the minimum of pH 9 that is required for 491

aggregation. Calcium concentrations are also usually high enough, though in cases of low 492

salinity the bacterial cells can be charged with calcium prior to use. Higher pH and calcium 493

concentrations can cause self-aggregation, however to achieve the 35% harvest efficiency would 494

require addition of calcium to 2.5-fold higher concentrations than seawater and increasing pH 495

beyond what the algae alone can achieve. 496

It is important to point out that the current system for harvesting algae is not 497

economically feasible without further development. However, we speculate the chemical 498

characteristics of the RP1137 cell surface could be used to design means of attaching the cells to 499

solid substrates such as hydrophobic or magnetic beads to aid in recovering the cells after 500

aggregation. In a previous study, we show aggregation is pH dependent and reversible. If fixed 501

RP1137 cells can be attached to beads and maintain their aggregation phenotype, then there is 502

the possibility of reusing the cells multiple times. In this scheme the fixed cells would be 503

attached to beads and then used to aggregate algae. The aggregates could be recovered and then 504

pH would be lowered by either adding acid or by allowing the concentrated and thus light 505

limited, algae to lower pH via respiration. Lowering pH reverses the aggregation process and 506

separates the algae from the bacterium-bead complex. The bacterium-bead complex can then be 507

separated from the algae and reused for another round or harvest. A final implication of the 508

results presented in this study is that if the interaction between the algae and the bacteria is a 509

hydrophobic interaction then it may be possible to simply use hydrophobic beads in place of the 510

bacteria for harvest of algae. 511

CONCLUSION 512

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Information about the mechanism of aggregation primarily is useful for predicting which 513

algae RP1137 can harvest. The mechanistic data presented here suggests that RP1137 will be 514

able to harvest algae where charge neutralization occurs in either sea water or fresh water with 515

between 2-20 mM calcium ions at a pH of greater than or equal to 9. RP1137 will not likely 516

aggregate algae that have significant negative charge (> - 30 mV) under these conditions. The 517

data also point to charge neutralization of teichoic acids as the site where calcium binds to 518

RP1137 cells. 519

520

521

522

ACKNOWLEDGEMENTS 523

This work was supported by a Maryland Industrial Partnerships grant “Mass cultivation of algae 524

on wastewaters for fuels and products (P.I.: Feng Chen). We thank Leah Blasiak for critical 525

reading of this manuscript. We thank Jacques Ravel for the gift of the B. megaterium QM B1551 526

strain and Harold Schreier for the B. subtilis SMY strain. This work was supported by IMET 527

contribution number XXXX and UMCES contribution number YYYY. 528

529

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27. Lee, J., D. H. Cho, R. Ramanan, B. H. Kim, H. M. Oh, and H. S. Kim. 2013. 600

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FIGURE LEGENDS 606

607

FIG 1 Bacillus sp. RP1137 cell length changes over a growth period. Time points match the 608

growth curve shown in Fig. 2. Bar and error bars represent the mean and standard error 609

respectively of cell length measurements of between 900 – 1600 individual cells per time point. 610

(*) indicates statistically significant difference where p < 0.0005. 611

612

FIG 2 Normalized aggregation efficiency of RP1137 cells is highest in exponential phase. 613

Samples are normalized by cell surface area so the same surface area is available for aggregating 614

algae at each time point. Bar and error bars represent the mean and standard error respectively of 615

eight independent aggregation reactions. A value of 100% is aggregation of all algal cells. 616

617

FIG 3 Base titration curves of live RP1137 cells. Curves represent individual trials from RP1137 618

cells from either exponential phase (Exp) or stationary phase (Stat). 619

620

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FIG 4 Binding of calcium to RP1137 cells increases with increasing calcium concentrations. 621

Points and error bars represent the mean and standard error respectively of three independent 622

calcium binding reactions. 623

624

FIG 5 Surface charge of RP1137 cells (A) and Nannochloropsis cells (B) at different stages of 625

growth and different calcium concentrations. Charge decreases with increasing calcium 626

concentration. Points and error bars represent the mean and standard error respectively of three 627

biological replicates each with three technical replicates. 628

629

FIG 6 Binding of RP1137 cells (A) and Nannochloropsis cells (B) to hydrophobic C18 beads in 630

the presence or absence of 10 mM CaCl2. Both RP1137 and Nannochloropsis cells bind more 631

effectively to the beads in the presence of calcium. Bar and error bars represent the mean and 632

standard error respectively of three biological replicates with three technical replicates each. 633

634

FIG 7 Aggregation is inhibited by the presence of SDS. (A) Visual result showing the no bacteria 635

controls, algae with bacteria and algae with bacteria and SDS. (B) Quantitation of the percent of 636

algae aggregated with and without SDS using the filtration aggregation assay. The percent algae 637

aggregation value is calculated relative to the “no bacteria” control. Bar and error bars represent 638

the mean and standard error respectively of four independent aggregation reactions. A value of 639

100% is aggregation of all algal cells. 640

641

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Algal aggregation by bacteria

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FIG 8 Pinacyanol dye binding to RP1137 cells is disrupted by the addition of calcium. (A) 642

Absorbance spectra of (1) Water + dye, (2) Water + dye + 10 mM CaCl2, (3) RP1137 cells alone 643

and (4) RP1137 cells + dye. Arrows indicate position of the 485 nm absorbance band indicative 644

of pinacyanol binding teichoic acid. (B) Absorbance of pinacyanol stained cells at 485 nm with 645

increasing calcium concentration. Points and error bars represent the mean and standard error 646

respectively of three biological replicates each with two technical replicates. 647

648

649

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Algal aggregation by bacteria

31

650

651

652

653

FIG 1 Bacillus sp. RP1137 cell length changes over a growth period. Time points match the 654

growth curve shown in Fig. 2. Bar and error bars represent the mean and standard error 655

respectively of cell length measurements of between 900 – 1600 individual cells per time point. 656

(*) indicates statistically significant difference where p < 0.0005. 657

658

659

660

661

662

663

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Algal aggregation by bacteria

32

664

665

666

667

668

FIG 2 Normalized aggregation efficiency of RP1137 cells is highest in exponential phase. 669

Samples are normalized by cell surface area so the same surface area is available for aggregating 670

algae at each time point. Bar and error bars represent the mean and standard error respectively of 671

eight independent aggregation reactions. A value of 100% is aggregation of all algal cells. 672

673

674

675

676

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Algal aggregation by bacteria

33

677

678

679

680

681

682

683

FIG 3 Base titration curves of live RP1137 cells. Curves represent individual trials from RP1137 684

cells from either exponential phase (Exp) or stationary phase (Stat). 685

686

687

688

689

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Algal aggregation by bacteria

34

690

691

692

693

FIG 4 Binding of calcium to RP1137 cells increases with increasing calcium concentrations. 694

Points and error bars represent the mean and standard error respectively of three independent 695

calcium binding reactions. 696

697

698

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Algal aggregation by bacteria

35

699

FIG 5 Surface charge of RP1137 cells (A) and Nannochloropsis cells (B) at different stages of 700

growth and different calcium concentrations. Charge decreases with increasing calcium 701

concentration. Points and error bars represent the mean and standard error respectively of three 702

biological replicates each with three technical replicates. 703

704

705

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Algal aggregation by bacteria

36

706

707

708

FIG 6 Binding of RP1137 cells (A) and Nannochloropsis cells (B) to hydrophobic C18 beads in 709

the presence or absence of 10 mM CaCl2. Both RP1137 and Nannochloropsis cells bind more 710

effectively to the beads in the presence of calcium. Bar and error bars represent the mean and 711

standard error respectively of three biological replicates with three technical replicates each. 712

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Algal aggregation by bacteria

37

713

714

FIG 7 Aggregation is inhibited by the presence of SDS. (A) Visual result showing the no bacteria 715

controls, algae with bacteria and algae with bacteria and SDS. (B) Quantitation of the percent of 716

algae aggregated with and without SDS using the filtration aggregation assay. The percent algae 717

aggregation value is calculated relative to the “no bacteria” control. Bar and error bars represent 718

the mean and standard error respectively of four independent aggregation reactions. A value of 719

100% is aggregation of all algal cells. 720

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Algal aggregation by bacteria

38

721

FIG 8 Pinacyanol dye binding to RP1137 cells is disrupted by the addition of calcium. (A) 722

Absorbance spectra of (1) Water + dye, (2) Water + dye + 10 mM CaCl2, (3) RP1137 cells alone 723

and (4) RP1137 cells + dye. Arrows indicate position of the 485 nm absorbance band indicative 724

of pinacyanol binding teichoic acid. (B) Absorbance of pinacyanol stained cells at 485 nm with 725

increasing calcium concentration. Points and error bars represent the mean and standard error 726

respectively of three biological replicates each with two technical replicates. 727

728

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