algal aggregation by bacteria - applied and...
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Algal aggregation by bacteria
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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
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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
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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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5. Richardson, J. W., M. D. Johnson, and J. L. Outlaw. 2012. Economic comparison of 540
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6. Uduman, N., Y. Qi, M. K. Danquah, G. M. Forde, and A. Hoadley. 2010. Dewatering 543
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2:017201. 545
7. Sirin, S., R. Trobajo, C. Ibanez, and J. Salvado. 2012. Harvesting the microalgae 546
Phaeodactylum tricornutum with polyaluminum chloride, aluminium sulphate, chitosan 547
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8. Lavoie, A., and J. Noüe. 1983. Harvesting microalgae with chitosan. J. World Maricult. 549
Soc. 14:685-694. 550
9. Divakaran, R., and V. Sivasankara Pillai. 2002. Flocculation of algae using chitosan. J. 551
Appl. Phycol. 14:419-422. 552
10. Pavoni, J. L., Echelber.Wf, and M. W. Tenney. 1972. Bacterial exocellular polymers 553
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12. Nontembiso, P., C. Sekelwa, M. V. Leonard, and O. I. Anthony. 2011. Assessment of 559
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13. Gardes, A., M. H. Iversen, H. P. Grossart, U. Passow, and M. S. Ullrich. 2011. 562
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Isme J. 5:436-445. 564
14. Oh, H. M., S. J. Lee, M. H. Park, H. S. Kim, H. C. Kim, J. H. Yoon, G. S. Kwon, and 565
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Paenibacillus sp AM49. Biotechnol. Lett. 23:1229-1234. 567
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Place, O. Zmora, Y. Zohar, T. Zheng, and R. T. Hill. 2012. Novel bacterial isolate 569
from Permian groundwater, capable of aggregating potential biofuel-producing microalga 570
Nannochloropsis oceanica IMET1. Appl. Environ. Microbiol. 78:1445-1453. 571
16. Yoon, J. H., H. M. Oh, B. D. Yoon, K. H. Kang, and Y. H. Park. 2003. Paenibacillus 572
kribbensis sp nov and Paenibacillus terrae sp nov., bioflocculants for efficient harvesting 573
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18. Guillard, R. R. L. 1975. Culture of phytoplankton for feeding marine invertebrates. 577
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19. Kamentsky, L., T. R. Jones, A. Fraser, M. A. Bray, D. J. Logan, K. L. Madden, V. 579
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and D. Bumann. 2005. Identification of Helicobacter pylori surface proteins by selective 587
proteinase K digestion and antibody phage display. J. Microbiol. Meth. 62:345-349. 588
22. Garrote, G. L., L. Delfederico, R. Bibiloni, A. G. Abraham, P. F. Perez, L. Semorile, 589
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23. Hermansson, M. 1999. The DLVO theory in microbial adhesion. Colloid Surface B 592
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24. Sobeck, D. C., and M. J. Higgins. 2002. Examination of three theories for mechanisms 594
of cation-induced bioflocculation. Wat. Res. 36:527-538. 595
25. Marquis, R. E., K. Mayzel, and E. L. Carstensen. 1976. Cation exchange in cell walls 596
of gram-positive bacteria. Can. J. Microbiol. 22:975-982. 597
26. Pal, M. K., T. C. Ghosh, and J. K. Ghosh. 1990. Studies on the conformation of and 598
metal ion binding by teichoic acid of Staphylococcus aureus. Biopolymers 30:273-277. 599
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27. Lee, J., D. H. Cho, R. Ramanan, B. H. Kim, H. M. Oh, and H. S. Kim. 2013. 600
Microalgae-associated bacteria play a key role in the flocculation of Chlorella vulgaris. 601
Bioresour. Technol. 131:195-201. 602
28. Schlesinger, A., D. Eisenstadt, A. Bar-Gil, H. Carmely, S. Einbinder, and J. Gressel. 603
2012. Inexpensive non-toxic flocculation of microalgae contradicts theories; overcoming 604
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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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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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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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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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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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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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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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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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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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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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