Mechanism of Algal Aggregation by Bacillus sp. Strain RP1137

Ryan J. Powell, Russell T. Hill

Institute of Marine and Environmental Technology, University of Maryland Center for Environmental Science, Baltimore, Maryland, USA

Alga-derived biofuels are one of the best alternatives for economically replacing liquid fossil fuels with a fungible renewable en-ergy source. Production of fuel from algae is technically feasible but not yet economically viable. Harvest of dilute algal biomassfrom the surrounding water remains one of the largest barriers to economic production of algal biofuel. We identified Bacillussp. strain RP1137 in a previous study and showed that this strain can rapidly aggregate several biofuel-producing algae in a pH-and divalent-cation-dependent manner. In this study, we further characterized the mechanism of algal aggregation by RP1137.We show that aggregation of both algae and bacteria is optimal in the exponential phase of growth and that the density of ioniz-able residues on the RP1137 cell surface changes with growth stage. Aggregation likely occurs via charge neutralization with cal-cium ions at the cell surface of both algae and bacteria. We show that charge neutralization occurs at least in part through bind-ing of calcium to negatively charged teichoic acid residues. The addition of calcium also renders both algae and bacteria moreable to bind to hydrophobic beads, suggesting that aggregation may occur through hydrophobic interactions. Knowledge of theaggregation mechanism may enable engineering of RP1137 to obtain more efficient algal harvesting.

Energy underlies economies and is the largest single market inthe world. However, most energy systems are based on finite

nonrenewable resources that increasingly have higher direct andindirect costs. A growing research effort focuses on developingand deploying renewable energy sources to supplement fossil fu-els. Research into renewable liquid fuels is of particular interest inthe United States, because transportation is almost exclusivelypowered by petroleum.

Algal biofuels represent one of the best alternatives to sustain-ably produce fungible liquid fuels. Algae act as self-replicatingbioreactors that use light energy to chemically reduce CO2 intouseful energy storage molecules. Unlike traditional crops, algaecan be grown on land not suitable for agriculture and can begrown in wastewater or saltwater (1, 2). Algae have high growthrates, sometimes doubling their biomass in several hours, and canbe harvested multiple times per year (3). Algal biomass is ideallysuited for conversion to crude oil via hydrothermal liquefaction,which produces an oil that can be refined in existing refineries andalso allows the recovery of limiting nutrients such as nitrogen andphosphorous (4).

While they are technologically feasible, studies have shown al-gal biofuels are not yet economically viable (5). Furthermore, toour knowledge no company has yet successfully produced algalbiofuel at a profit. Only when profitability is achieved will algalbiofuels become a self-sustaining venture that can make a signifi-cant impact on the production of renewable fuels. Harvest of thealgal biomass has been identified as one of the key hurdles toeconomically producing fuel from algae (5). Algal biomass mustbe concentrated and most of the water removed before thebiomass can be converted to fuel. Mature technologies for algalharvest include filtration, centrifugation, sedimentation, electro-coagulation, dissolved air flotation, chemical flocculation, andbio-aggregation (6). Uduman et al. provide an excellent review ofdifferent algal harvest methods, including the advantages and dis-advantages of each (6). Bio-aggregation uses biological agentssuch as extracellular polymeric substances, chitosan, or wholecells to form easily harvestable aggregates (7–11). Several alga-aggregating bacterial strains are known and have been proposedfor use in harvesting algae (12–17).

In previous work we described the alga-aggregating bacteriumBacillus sp. strain RP1137 (17). This bacterium can rapidly aggre-gate multiple algae that are candidates for biofuel production.Aggregation is pH and divalent-cation dependent (17). Fixed cellswere also shown to be as effective as live cells at aggregating algae.However, the detailed mechanism of aggregation of algae byRP1137 was unknown. Knowledge of the mechanism may be use-ful for understanding why it is able to aggregate some algae but notothers and for applying the strain to large-scale algal harvest.

In this study, we define the mechanism of algal aggregation byBacillus sp. strain RP1137. The purpose of this research was tounderstand the aggregation mechanism of Bacillus sp. strainRP1137 to determine its suitability for harvest of algae and tounderstand how harvest might be improved.

MATERIALS AND METHODSStrains and culture conditions. Liquid cultures of Bacillus sp. strainRP1137 were grown in marine broth 2216 (BD, Franklin Lakes, NJ) at30°C in 125-ml Erlenmeyer flasks with shaking at 180 rpm. Marine broth2216 plus 15 g/liter Difco technical agar (BD) was used for solid medium.Nannochloropsis oceanica IMET1 was grown as described before (17).Briefly, N. oceanica was grown in 20-ppt-salinity f/2 medium (18) in500-ml ported photobioreactors at 25°C with a light/dark photoperiod of14 h/10 h.

Filtration aggregation assay. A filtration aggregation assay was usedto quantitate the amount of algae that were aggregated under a givencondition. This assay has been described in detail (17). Briefly, the assayinvolves carrying out aggregation reactions with N. oceanica IMET1 andBacillus sp. strain RP1137 in a 96-well plate. The entire volume of the

Received 13 March 2014 Accepted 20 April 2014

Published ahead of print 25 April 2014

Editor: G. Voordouw

Address correspondence to Russell T. Hill, hill@umces.edu.

Supplemental material for this article may be found at http://dx.doi.org/10.1128/AEM.00887-14.

Copyright © 2014, American Society for Microbiology. All Rights Reserved.

doi:10.1128/AEM.00887-14

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reaction mixture is then passed through a 50-�m mesh; aggregates thatare larger than the mesh are retained, and smaller particles pass through.Chlorophyll fluorescence is measured in the flowthrough and comparedto that in control samples without bacteria added to determine the per-centage of algae that are aggregated upon addition of the bacteria. Unlessnoted otherwise, aggregation assays were carried out in deionized waterwhere the pH had been adjusted to 10.5 with NaOH and 10 mM CaCl2 hadbeen added.

Bacterial aggregation efficiency time course. RP1137 cells werestreaked from cryostocks, and a single colony was used to start a 10-mlculture in marine broth medium. The culture was incubated at 30°C in a125-ml flask with 180-rpm shaking. From the initial culture, three sub-cultures were started at a calculated optical density (OD) of 0.01 in 200 mlof marine broth. Cultures were grown in 1-liter flasks at 30°C with 180rpm shaking. Time points were taken every 1 to 2 h for 24 h. At each timepoint, cells were collected and concentrated by centrifugation at 5,580 � gfor 5 min, supernatant was aspirated, and the cell pellet was suspended in4% paraformaldehyde (PFA) in 1� phosphate-buffered saline (PBS) (pH7.4) and incubated for 1 h at room temperature. Cells were then concen-trated by centrifugation, the supernatant was aspirated, and cells weresuspended in 1� PBS to wash the cells. The cells were then again concen-trated by centrifugation and suspended in a fresh aliquot of 1� PBS. Fixedcells were used to preserve the surface chemistry of the cell and ensure thatchemistry was not altered due to stress responses by the cell. Filtrationaggregation assays were carried out using bacteria from each time pointwith algae from 2 days after subculturing. The samples were normalizedby cell surface area to 3 � 108 �m2/ml (described below) so each samplehad the same surface area available for interacting with algal cells.

Algal aggregation efficiency time course. N. oceanica IMET1 cultureswere grown as described above. Samples of algae were taken at 2, 5, and 17days after subculturing, which represent the early exponential, exponen-tial, and stationary phases of growth, respectively. Cells were fixed follow-ing the protocol used for the bacterial cells. Samples were normalized bycell surface area per ml (described below) so that each sample had thesame available surface area for interacting with bacterial cells. Aggregationassays were carried out using bacterial cells from the exponential phase(OD � 0.7).

Determining cell size and surface area. Cells from the time coursewere stained in 1� SYBR green I nucleic acid stain for 10 min in the dark.SYBR green staining was used to illuminate the cell body and provide crispcell margins that were amenable to automated image analysis. Cells werethen visualized on a Zeiss Axioplan microscope with excitation from aZeiss X-Cite 120Q Iris FL light source using a filter cube with a 470/40 BPexcitation filter, an FT 495 dichroic mirror, and a 525/50 BP emissionfilter. Cells were diluted or concentrated as needed to obtain well-sepa-rated cells. The volume of each field of view was determined using theknown depth of the bacterial hemacytometer and the height and width ofthe field of view. For each time point, 20 to 30 fields of view were capturedand saved as TIFF files. Image processing was done in Cell Profiler (19)with the following series of commands in a custom pipeline: LoadImage,ColorToGrey, IdentifyPrimaryObjects, ReassignObjectNumbers,MeasureObjectSizeShape, and ExportToSpreadsheet. LoadImage im-ports the images. ColorToGrey converts the image to greyscale to reduceprocessor time. IdentifyPrimaryObjects was used to find and identify ob-jects using the Otsu global algorithm, a 4- to 40-pixel cutoff, and a 0.02 to1 threshold cutoff. ReassignObjectNumbers was used to join cells within afilament into one object using a six-pixel cutoff. MeasureObjectSizeShapewas used to measure the perimeter and area of the identified objects.ExportToSpreadsheet was used to export the data as a .cvs file for importinto Microsoft Excel for further analysis. Data were converted from pixelsto micrometers using data gathered from a stage micrometer. Cell lengthwas approximated by dividing cell perimeter by 2; this provides a goodestimate of cell length for filamentous bacilli, though it does introduce aslight overestimate of the absolute size of the cells. To obtain cell surfacearea for normalization, both perimeter and area data are used. The key

parameter needed, but unavailable directly in Cell Profiler, for calculatingsurface area of a cell is the radius of the cell. To derive the radius ofindividual cells, the two-dimensional (2D) images of cells were used, andthe bacilli were modeled as rectangles with half circles on each end. Theresulting equation for area is the sum of the area of a circle and the area ofa rectangle: A � �r2 � 2r[(P/2) � 2r], where A is the area of the cell, r is theradius of the cell, and P is the perimeter of the cell. Since A and P aremeasured values, the equation can be solved for r using the quadraticequation, which yields two solutions, one of which is the real radius of thecell. Derived radius values were checked against the manually measuredaverage radius along the length of individual cells. Calculated values arevery close to measured values, indicating that the method can be used toaccurately calculate cell radius in an automated format. Cell radius wasused to calculate surface area of a three-dimensional cell by modeling thecell as two halves of a sphere plus the surface area of a cylinder minus theends. The surface area of individual cells was calculated for 900 to 1,600cells per time point. Cell numbers per ml were then used to calculateavailable surface area per unit volume. Surface area per ml of individualsample was used to normalize available surface area for interaction withalgal cells between samples at different time points. The available surfacearea of N. oceanica IMET1 time points was determined using a pipelinesimilar to that used for RP1137 cells with the following modifications.Images were captured using chlorophyll autofluorescence. Nannochlorop-sis cells are spherical, so the measured area of the 2D images could be usedto directly derive the radius using the equation for the area of a circle (A ��r2). The radius could then be used to calculate the 3D surface area of asphere (A � 4�r2). Surface area per unit volume was determined by com-bining surface area data with cell concentration data. All experimentswere normalized by surface area using the cells prepared as describedabove. The OD of the culture is provided to make clear which growthphase was used in each experiment.

LiCl treatment of RP1137 cells. Lithium chloride treatment was doneaccording the protocol of Lortal et al. (20). RP1137 cells were concen-trated by centrifugation at 20,000 � g for 3 min, and the cell pellet wassuspended in either distilled water, 5 M LiCl, 7.5 M LiCl, or 10 M LiCl.Cells were incubated at these conditions for 15, 45, and 120 min at roomtemperature and then concentrated by centrifugation. The cell pelletswere suspended in pH 10.5 deionized water with 10 mM CaCl2 and usedfor aggregation assays.

Base titration of whole bacterial cells. Live RP1137 cells were used forbase titration experiments. Cells were taken in the exponential phase(OD � 0.7; 3.4 � 106 cells/ml) and stationary phase (OD � 1.6; 7.2 � 106

cells/ml). Culture volumes were normalized by surface area to ensure thatthe same amount of bacterial cell surface was being titrated in each sam-ple. Cells were concentrated by centrifugation at 15,000 � g for 5 min, andthe cell pellet was suspended in pH 5 deionized water. This washing stepwas repeated twice more to ensure that salts had been removed, and thecells were equilibrated to pH 5. Base in the form of 0.25 M NaOH wasadded to the cell suspension, and pH was recorded after each additionwhen the value stabilized.

Calcium binding assay. Calcium binding was evaluated by measuringthe concentration of calcium remaining after 1 ml of cells had been added.Calcium binding assays were performed with fixed RP1137 cells from theexponential phase (OD � 0.7) of growth. Known concentrations of CaCl2were added to cells in pH 10 deionized water. Cells were then removed bycentrifugation at 20,000 � g for 3 min. The calcium concentration in thesupernatant was measured using the LaMotte calcium hardness colori-metric kit (LaMotte, Chestertown, MD). The kit was adapted for use in a96-well format and measurement in a Spectramax M5 plate reader. Thereadout for the assay was absorbance at 635 nm. Absorbance at this wave-length is linear for calcium concentrations between 0 and 160 �M. Sam-ples were diluted to ensure that they were within the linear range of theassay. Absorbance values were compared to a CaCl2 standard curve todetermine the concentration of calcium remaining.

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Calcium coordination experiment. RP1137 and Nannochloropsiscells were separately suspended in pH 10.5 water with 10 mM CaCl2. Toensure that the cells were preloaded with calcium, both bacteria and algaewere concentrated by centrifugation at 20,000 � g, and the cell pelletswere suspended in the same solution. This preloading step was repeatedonce. The cells were then used in filtration aggregation assays, in contrastto controls, where only the algal cells were preloaded with calcium.

C18 binding assay. Binding of cells to C18 resin was performed withfixed RP1137 cells from the exponential phase of growth (OD � 0.7) andwith fixed Nannochloropsis cells from 5 days postinoculation. Dry C18

beads with a 10-�m diameter were purchased from Hamilton (Reno,NV). Beads were reconstituted in methanol overnight. The beads wereconcentrated by centrifugation at 20,000 � g for 1 min. The beads werethen suspended in pH 10.5 deionized water with 10 mM CaCl2. Thisprocess of concentration and suspension in pH 10.5 deionized water with10 mM CaCl2 was repeated twice more to ensure that methanol was re-moved, and the beads were equilibrated in the test solution. The equili-bration process was repeated without CaCl2 for a separate aliquot of beadsto obtain beads for the no-calcium samples. For each experiment, 200beads were used per cell, as this was found to give maximal binding withthe minimum number of beads. Equal numbers of algal or bacterial cellswere incubated with C18 beads (200:1 bead-to-cell ratio) in the presenceor absence of 10 mM CaCl2. The mixtures were analyzed with an AccuriC6 flow cytometer to count the number of unbound algal or bacterialcells. The beads were distinguished from cells by their larger forward scat-ter area with an upper forward scatter area cutoff of 1,270,000. A lowercutoff of 44,000 was used to remove background particles found withinthe medium. RP1137 cells fell between these two cutoffs. Algal chlorophyllautofluorescence was used to distinguish Nannochloropsis cells from theirassociated bacterial cells using the FL3 channel (LP filter; excitation, 488nm; emission, 670 nm) on the flow cytometer. Only particles that werebetween the two forward-scatter cutoffs and had a chlorophyll autofluo-rescence greater than 10,000 were counted as algal cells. These settingscounted the unbound bacterial or algal cells, which allowed comparisonof the number of bound cells in the presence and absence of calcium.

Zeta potential. Measurement of cell surface charge or zeta potentialwas done on a dynamic light scattering instrument (Malvern InstrumentsLtd., Worcestershire, United Kingdom). Measurements were done onfixed algal cells from 2, 5, and 17 days after being subcultured and on fixedRP1137 cells that were taken in the exponential phase (OD � 0.7) andstationary phase (OD � 1.6). Cells were concentrated by centrifugation at20,000 � g for 1 min. The supernatant was aspirated, and the cells weresuspended in pH 10.5 deionized water. To ensure removal of trace salts,the cells were again concentrated by centrifugation, supernatant was as-pirated, and the cells were suspended in pH 10.5 deionized water. For eachsample, zeta potential was measured at 0, 0.156, 0.313, 0.625, 1.25, 2.5, 5,10, 20, and 40 mM CaCl2.

SDS inhibition of aggregation. Inhibition of aggregation by sodiumdodecyl sulfate (SDS) was tested using the filtration aggregation assay.Fixed algae from 2 days after subculture were used. Fixed RP1137 cellsfrom the exponential phase (OD � 0.7) were used. Both algae and bacteriawere concentrated by centrifugation at 20,000 � g for 1 min. The super-natant was aspirated and the cells were suspended in pH 10.5 deionizedwater with 10 mM CaCl2. This washing step was repeated once. In thetreatment samples, SDS was added to algae to a final concentration of 1%.Filtration aggregation assays were carried out as described for quantita-tion of the percentage of algae aggregated in the SDS-treated cells com-pared to the untreated controls.

Calcium displacement of pinacyanol. To determine if calcium dis-places pinacyanol bound to the bacterial cell surface, fixed RP1137 cellsfrom the exponential phase were concentrated by centrifugation at 20,000 �g for 1 min, supernatant was aspirated, and the cells were suspended in pH10.5 deionized water. This washing step was repeated once. RP1137 cellswere then stained with 20 �M pinacyanol chloride (Sigma). The stainedcells were added to an equal volume of water or water with CaCl2, result-

ing in a final pinacyanol concentration of 10 �M. The final concentrationsof CaCl2 solutions tested were 0, 0.15, 0.6, 2.5, and 10 mM. The sampleswere mixed by vortexing and then centrifuged at 20,000 � g for 3 min. Thesupernatant was aspirated to remove unbound dye, and the cells weresuspended in a fresh aliquot of water with the same calcium concentra-tion. Absorbance of the dyed cell solutions was measured at 485 nm in anM5 Spectromax plate reader. Absorbance spectra from 450 to 650 nmwere gathered at 5-nm increments for unstained RP1137 cells, stainedcells, water plus pinacyanol, and water plus pinacyanol plus 10 mM CaCl2.

Algal aggregation by different Bacillus strains. To determine if otherBacillus strains share the aggregation phenotype found in RP1137, wetested the aggregation ability of Bacillus megaterium QM B1551 andBacillus subtilis SMY. Cells were grown in LB medium (BD, FranklinLakes, NJ) at 37°C with 180-rpm shaking. The cells were harvested in theexponential phase and fixed using the protocol listed above. Cell density-normalized filtration aggregation assays were then carried out.

Effect of higher pH and calcium concentrations on algal self-aggre-gation. To determine if Nannochloropsis cells self-aggregate under theconditions tested and at higher pHs and calcium concentrations, we per-formed filtration aggregation assays with only the algae (no bacteriaadded). The pH of the algae culture was adjusted with NaOH. Calciumconcentrations were adjusted with CaCl2. Once the algal culture was at thedesired condition, the filtration aggregation assay was performed to quan-titate the amount of algae in aggregates.

RESULTS AND DISCUSSIONCharacterization of cell length over a growth period. In thisstudy, we aimed to characterize the mechanism by which Bacillussp. strain RP1137 aggregates algae. A better understanding of themechanism may help to improve the efficiency of the system. Inprevious work, we showed that divalent cations are important foraggregation and that fixed cells are just as effective at aggregatingalgae as live cells, pointing to the cell surface as the important cellstructure for investigation of the aggregation mechanism (17).Our previous work also ruled out filament length as an importantfactor for aggregation, but initial observations suggested that ag-gregation ability changes over the growth cycle of a culture. Sincethe cell morphology changes over the growth period, we cannotaccurately normalize by optical density or cell counts. To accu-rately normalize between time points, we first needed to charac-terize this change in morphology. Single cells and filaments of cellsare present in RP1137 cultures. In this paper, we define cell lengthas the total length of either a lone single cell or the length of a chainof cells in a filament. Changes in cell length are shown in Fig. 1, forwhich cell length was measured over time through a combinationof fluorescence microscopy and automated image analysis ofthousands of cells. The results show that cell length changed overa growth period.

Change in aggregation ability of RP1137 over a growth pe-riod. With the cell length data, we were able to calculate cell sur-face area data, which were used to normalize the cell surface areabetween time points. Normalization of cell surface area allowed usto isolate changes in the cell surface composition from changes inthe amount of cells or amount of cell surface area in a sample.Using normalization, aggregation potential changes over a growthcycle were determined. In Fig. 2, the percentage of algae aggre-gated is plotted with the growth curve of the bacterium. Aggrega-tion is most effective in the exponential phase of bacterial growth,where 80% of the algae are found in the aggregates. As the cellsenter stationary phase, the aggregation potential per normalizedcell surface area decreases and reaches a minimum value of 40%aggregated algae at 20 h. These data show that the aggregation

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potential of the bacterial cell surface decreases when the cells enterstationary phase, indicating that the surface chemistry of the celllikely changes. From the perspective of applying RP1137 for algalharvest, these results show that it is important to use bacteria fromthe exponential phase to obtain the best aggregation ability.

Change in aggregation ability of N. oceanica IMET1 overtime. Next, we measured the aggregation potential of N. oceanicaIMET1 cultures at different growth stages. The same bacterialsample was used for the experiments, and the algal cells were nor-malized between samples using the cell surface area. Unlike bac-terial cell morphology, algal cell morphology does not change sig-nificantly over a growth cycle (data not shown). Algal aggregationwas tested at 2, 5, and 17 days after subculturing. Cells in the 2-daysamples were just beginning to grow, while the algae were fullyinto the exponential phase by day 5. At 17 days, the algae were instationary phase. Aggregation was most efficient with cells at the5-day time point (93.4% � 0.7%) and significantly more efficientthan with cells at 2 days (88.4% � 2.3%; P � 0.0005) and 17 days(68.3% � 10.2%, P � 0.0003). The data show that the algae aremost effectively aggregated during the exponential phase and havedecreased aggregation ability when they enter stationary phase.Little is known about the cell surface of Nannochloropsis; however,these results suggest that the surface chemistry of the cell changeswith the growth phase of the alga. Changes in surface chemistryare discussed below. The change in aggregation efficiency indi-cates that algae are harvested with RP1137 cells, it is best to harvestthe algae when the algae are in the exponential growth phase.However, the harvest time must be balanced according to the pro-duction system that is being used. For example, if high-lipid algaeare desired, then it may not be advantageous to harvest in theexponential phase. However, there are several reasons why har-vesting in the exponential phase would be attractive. First, whileoil content can be higher in the starved stationary-phase cells, theincreased oil content is typically counterbalanced by a loss in totalbiomass yield over the same time period. A second considerationis that continuously harvesting in the exponential phase wouldkeep the cells in a rapidly growing state. This removes the nonpro-ductive lag period at the beginning of growth as well as the de-

crease or cessation of growth that occurs in stationary phase. Thus,harvesting in the exponential phase may produce more total bio-mass and more fuel per unit area in a given time period. For someconversion methods, such as hydrothermal liquefaction, largeamounts of high-protein biomass are desirable, and therefore har-vesting within the exponential phase is optimal.

LiCl treatment and S-layer proteins. Our previous workshowed that proteinase K treatment of the cell surface did notsignificantly decrease aggregation, suggesting that surface pro-teins were unlikely to be involved in aggregation (17). Whileproteinase K can cleave surface proteins (21), it may not cleaveproteins that do not have an exposed cleavage site. S-layer proteinsare often involved in adhesion of Gram-positive bacteria to eitherbiotic or abiotic surfaces and can be involved in aggregation (22).S-layer proteins are also typically attached to peptidoglycan viaelectrostatic interactions and can be removed by LiCl treatment(20). To test if the S-layer is involved in aggregation, RP1137 cellswere treated with 5, 7.5, and 10 M LiCl for 15, 45, and 120 min.The treated cells did not have a significant decrease in their aggre-gation ability under any of the conditions tested (see Fig. S1 in thesupplemental material), suggesting that the S-layer proteins arenot involved in the aggregation phenotype.

Base titration of the RP1137 cell surface. The data we havecollected show that specific or nonspecific protein-protein inter-actions at the cell surface are inconsistent with available data. Wenext hypothesized that perhaps a more general property of the cellsurface is involved in the aggregation phenotype of RP1137. Sincethe bacterial cells show a significant difference in aggregation abil-ity between the exponential and stationary phase, we decided totest if the surface chemistry of the cells at these growth stages isdifferent and, specifically, if the density of deprotonatable residuesat the cell surface is different. To test this, we used base titration ofwhole live cells. The results of base titration of RP1137 cells (Fig. 3)show that cells in exponential phase have a lower number of de-

FIG 1 Bacillus sp. strain RP1137 cell length changes over a growth period.Time points match the growth curve shown in Fig. 2. Data are means andstandard errors of cell length measurements of 900 to 1,600 individual cells pertime point. *, P 0.0005. FIG 2 Normalized aggregation efficiency of RP1137 cells is highest in the

exponential phase. Samples are normalized by cell surface area so that the samesurface area is available for aggregating algae at each time point. Data aremeans and standard errors for eight independent aggregation reactions. Avalue of 100% indicates aggregation of all algal cells.

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protonatable residues than cells in stationary phase, because pHincreases more quickly as base is added in exponential-phase cellsthan in stationary-phase cells. Since these experiments were nor-malized by cell surface area, this translates to more positive orneutral residues per unit area of cell surface. The data show thereis a measurable difference in surface chemistry between these twopopulations of cells.

Binding of calcium to the cell surface. Cell surface chemistrycan also be affected by the ions that are present. Previously, weshowed that aggregation is dependent on divalent cations, and wehypothesized that these ions reduce or neutralize negative chargeat the cell surface (17). To determine if calcium ions bind to thesurface of the bacterium, we measured the amount of calciumremoved by the cells at increasing calcium concentrations. Figure4 shows that calcium binds to the bacteria and that bound calciumincreases with increasing calcium concentration up to 0.625 mM.A saturating concentration of surface-bound calcium ions couldallow cells to aggregate in at least two ways. Charge neutralizationeliminates electrostatic repulsion between cells and is commonlycited as a method in which cells can get close enough to adhere viaother attractive forces such as hydrophobic or Van der Waals typeinteractions (23). Another method of interaction cited in the lit-erature is coordination of ions bound to one cell by another cell(24). In this method of aggregation, two cells interact via bridgedions. This is similar to the commonly used nickel-NTA affinitychromatography system, which binds proteins via the commoncoordination of a nickel ion by the 6�His tag on a protein and anitrilotriacetic acid residue attached to a solid substrate. The ioncoordination model of aggregation predicts that algal and bacte-rial cells that have been preloaded with calcium separately beforecombination will not interact efficiently because they both alreadybind ions at their cell surface and thus are less likely to bind viacommon ions. However, the charge neutralization model predictsa different outcome. It predicts that cells preloaded with calciumbefore combination will interact, which will lead to aggregation.We tested the hypothesis that aggregation occurs by coordinationof common ions by preloading algal and bacterial cells with cal-

cium separately before mixing them. The results showed that thecells still aggregated, with an average value of 74.6% � 1.1% algaeaggregated. These results point toward the charge neutralizationmodel of aggregation rather than the coordination model. Thisresult is further supported by the finding that, unlike RP1137 cells,the Nannochloropsis cells do not tightly bind calcium at their cellsurface.

Measurement of zeta potential. To test the hypothesis thatcalcium ions cause charge neutralization, we used dynamic lightscattering to measure apparent cell surface charge or zeta potentialat different calcium concentrations. Zeta potential was measuredfor both bacterial and algal cells at different stages of growth. Thedata shown in Fig. 5 demonstrate that surface charge in both bac-teria and algae decreases with increasing calcium concentration.In the absence of salt, the exponential-phase bacterial cells had amore negative charge, �110 � 6 mV, than the stationary-phasecells, which had a zeta potential of �72 � 6 mV. As calciumconcentration increased, the zeta potential of both exponential-and stationary-phase bacterial cells became similar, with thecharge curves overlapping at 10 mM calcium, which is the optimalconcentration for aggregation. At this concentration, the chargewas �18.6 � 1.05 mV for exponential-phase cells and �19.8 �1.05 mV for stationary-phase cells, both of which are at or below�20 mV, which is often considered the threshold where charge isno longer strong enough to separate cells by electrostatic repul-sion. The surface charge of algal cells also becomes less negative ascalcium concentrations increase, but compared to the bacterialcells, the algae require a higher calcium concentration to get belowthe �20 mV threshold. This result suggests that the bacterial cellshave a higher affinity for calcium ions than the algal cells. Thecalcium binding and zeta potential measurements show that bac-terial cells bind calcium, which results in charge neutralization.Next, we aimed to determine what forces likely mediated the bind-ing of the RP1137 and Nannochloropsis cells.

RP1137 binding to C18 resin. From previous work we knewthat surface proteins, filament length, and lectin-carbohydratetype interactions were not involved in the underlying mechanism

FIG 3 Base titration curves of live RP1137 cells. Curves represent individualtrials from RP1137 cells from either exponential phase (Exp) or stationaryphase (Stat).

FIG 4 Binding of calcium to RP1137 cells increases with increasing calciumconcentrations. Data are means and standard errors from three independentcalcium binding reactions.

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of aggregation (17). Aggregation of multiple and distinct algalspecies by RP1137 also pointed toward a general instead of a spe-cific mechanism of aggregation (17). Since the cell surface chargedecreased upon addition of calcium, we hypothesized that bacte-rial and algal cells interact via hydrophobic type interactions andthus should become more able to interact with a hydrophobicsurface under these conditions. We tested this by measuring thenumber of individual cells that were not bound to hydrophobicC18 beads in the presence or absence of calcium. Unbound cellswere measured because it is easier to get accurate data on free cellsthan on cells bound to the beads. Figure 6A shows that there werefewer unbound bacterial cells in the presence of calcium. Thisshows that the bacterial cells are more able to bind to a hydropho-bic surface when calcium is added. The algal cells show a similartrend, with fewer unbound cells present in the presence of calcium

(Fig. 6B). These data demonstrate that the algal cells are also moreable to interact with a hydrophobic surface in the presence ofcalcium. Together these data support the hypothesis that bacterialand algal cells interact at least in part through hydrophobic inter-actions. Hydrophobic interactions are often involved in aggrega-tion (23), so this result fits with what others have found. If bothbacterial and algal cells can interact with a defined hydrophobicsurface, then we propose they could interact with each other. Thismechanism of interaction suggests that we should be able to in-hibit the interaction by coating the cells with an anionic detergentprior to the aggregation process.

SDS inhibition of aggregation. To test the hypothesis that ananionic detergent would disrupt an aggregation process that oc-curs via hydrophobic interactions, we precoated the bacterial andalgal cells with the anionic detergent sodium dodecyl sulfate(SDS). SDS has a hydrophobic tail attached to an anionic sulfategroup. If the bacterial and algal surfaces are more hydrophobic,then the SDS should be oriented with the hydrophobic tail towardthe cell and the anionic sulfate residue facing the solvent (water).The cells in this setup now have an external negative charge, whichshould inhibit aggregation. We observed that addition of SDS re-sulted in a significant decrease in aggregation (P � 5.19E-05) (Fig.7). Visually, aggregation appeared to be completely inhibited

FIG 5 Surface charge of RP1137 cells (A) and Nannochloropsis cells (B) atdifferent stages of growth and different calcium concentrations. Charge de-creases with increasing calcium concentration. Data are means and standarderrors for three biological replicates each with three technical replicates.

FIG 6 Binding of RP1137 cells (A) and Nannochloropsis cells (B) to hydro-phobic C18 beads in the presence or absence of 10 mM CaCl2. Both RP1137 andNannochloropsis cells bind more effectively to the beads in the presence ofcalcium. Data are means and standard errors for three biological replicateswith three technical replicates each.

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(Fig. 7A); however, quantitation using the aggregation assayshowed that small aggregates were still formed (Fig. 7B). The ag-gregation assay works by removing aggregates that do not passthrough a mesh with 50-�m by 50-�m square holes. IndividualNannochloropsis cells are spherical and have a diameter of about 3�m (17). The results indicate that the formation of large aggre-gates is inhibited but that smaller aggregates (50 �m) are stillformed. These smaller aggregates incorporated less of the algae.These results indicate that the interaction between bacteria andalgal cells was not completely disrupted under the conditionstested. It is possible that other forms of bonding are important inthe interaction between these cell types. Finally, the cells may havegained sufficient momentum when mixed by vortexing to over-come the electrostatic repulsion and allow the some of them to getclose enough to interact. The idea that the bacterial and algal cellsbind each other via hydrophobic interactions implies that the cellsshould also self-aggregate in the absence of the other cell type; thiswas observed for RP1137 cells at high pH in the presence of diva-lent cations. Nannochloropsis cells do form small self-aggregates of5 to 10 cells, but they do not form large aggregates. The reason

Nannochloropsis cells do not form large aggregates is unknown;however, in the C18 bead assays, more of the bacterial populationwas bound to the beads than the algal population, suggesting thatthere are fewer algal cells whose cell surface is hydrophobicenough to bind the beads within the population. We speculate thatthe smaller the population of hydrophobic cells, the lower thechance of finding other hydrophobic cells with which they caninteract. This explanation must be tempered with the knowledgethat up to 95% of the algal population can be harvested withRP1137, which may imply that the interaction between algae andbacteria is different than the interaction of algae with other algae.Further study is needed to clarify what other forces besides hydro-phobicity may be at play.

Determining a binding site for calcium at the cell surface.The calcium binding data indicated that the ions bind to the bac-terial cells, which results in charge neutralization. Teichoic acidsare negatively charged and bind various cations, including cal-cium (25), making them a plausible target for charge neutraliza-tion by calcium ions. Previous studies have shown that teichoicacids have a higher affinity for calcium than magnesium (25),matching data we gathered in a previous study that showed thatRP1137 has a higher affinity for calcium than magnesium (17).We tested whether calcium binds the teichoic acid residues ofRP1137 using the dye pinacyanol chloride. Pinacyanol chloridebinds purified teichoic acids and upon binding undergoes an ab-sorbance shift which results in a new absorbance band centered at485 nm (26). Interestingly, calcium competes for binding ofteichoic acid with the dye. When calcium is present, the dye isremoved from teichoic acid and the absorbance band at 485 nm isno longer present (26). Here we used this property to determine ifcalcium binds teichoic acids on RP1137 cells. The absorbancespectra of the dye alone, dye with calcium, cells, and cells with dyeare shown in Fig. 8A. The cells in the presence of the dye showedthe characteristic peak in absorbance at 485 nm, as was observedfor purified teichoic acid. The peak is not present in the cells alone,dye alone, or dye with calcium. Next, the cells were stained withpinacyanol and then exposed to different concentrations of cal-cium. The cells were then washed to remove unbound dye, and theabsorbance of the cells at 485 nm was recorded. If calcium bindsteichoic acids, then it should displace the dye and result in a de-creased absorbance at 485 nm with increasing calcium concentra-tion, which is what we observed (Fig. 8B). We must be cautiouswith our interpretation, as the original work cited was with puri-fied and not cell-bound teichoic acids. However, this result sup-ports the hypothesis that calcium binds to teichoic acids ofRP1137 cells. The result does not rule out the possibility that cal-cium also binds to other parts of the cell. Interestingly, when thepinacyanol displacement data are compared to the zeta potentialand calcium binding data, we see a discrepancy in calcium bindingkinetics. Both zeta potential and calcium binding data suggest thatthe RP11137 cells saturate below 1 mM calcium, while the pina-cyanol displacement data suggest that saturation does not occuruntil higher concentrations. This seeming contradiction can beexplained by the positive shift in the calcium dissociation thatwould be predicted when both calcium and pinacyanol competefor the same substrate.

Similar mechanisms of aggregation have been proposed forother aggregating bacteria, where charge neutralization with diva-lent cations is implicated in the mechanism of aggregation (27).Divalent cations have also been implicated in the autoflocculation

FIG 7 Aggregation is inhibited by the presence of SDS. (A) Visual resultshowing the no-bacteria controls, algae with bacteria, and algae with bacteriaand SDS. (B) Quantitation of the percentage of algae aggregated with andwithout SDS using the filtration aggregation assay. The percent alga aggrega-tion is calculated relative to the value for the no-bacteria control. Data aremeans and standard errors for four independent aggregation reactions. A valueof 100% indicates aggregation of all algal cells.

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of some algae (28). The mechanism described here is similar toalgal aggregation by Paenibacillus kribbensis, where both high pHand calcium ions were implicated in the aggregation mechanism(14). However, in the study on Paenibacillus kribbensis, only pHand calcium were investigated in terms of the mechanism of ag-gregation, whereas here we conducted a more detailed investiga-tion. If the mechanism of aggregation in RP1137 is based oncharge neutralization via calcium binding to teichoic acids, then itwould be expected that other related Gram-positive bacteriamight demonstrate a similar aggregation phenotype. RP1137 ismost closely related to Bacillus megaterium, so to test this hypoth-esis, we compared the aggregation ability of B. megaterium QM

B1551 to that of RP1137. We also compared the aggregation abil-ity of the more distantly related Bacillus subtilis strain SMY to thatof RP1137. The results show that RP1137 and B. megaterium QMB1551 have very similar algal aggregation efficiencies, while Bacil-lus subtilis SMY does not aggregate algae (see Fig. S2 in the sup-plemental material). These results suggest that not all Gram-pos-itive organisms have the algal aggregation phenotype but that it islikely that bacteria closely related to RP1137 share the phenotype.A final question that arises from this research is whether the bac-terium is needed for aggregation or whether aggregation of thealgae alone can occur, particularly at higher calcium concentra-tions and higher pHs. To test this, we performed aggregation as-says with only the Nannochloropsis cells at different combinationsof pH and calcium concentration. The results shown in Fig. S3 inthe supplemental material demonstrate that at pH 10 and 10 mMcalcium, the optimal conditions for algal aggregation by RP1137,5.3% � 3.3% of aggregation can be attributed to algal self-aggre-gation. Self-aggregation increases with increasing pH and calciumconcentration to a maximum of 34.6% � 2.3% at pH 12 and 25mM calcium. The conditions needed for aggregation of algal byRP1137 can be achieved in most algal production systems simplyby stopping bubbling of air or CO2 through the culture. The pH ofthe dense cultures used for biofuel production can easily achievepHs greater than the minimum of pH 9 that is required for aggre-gation. Calcium concentrations are also usually high enough,though in cases of low salinity the bacterial cells can be chargedwith calcium prior to use. Higher pH and calcium concentrationscan cause self-aggregation; however, to achieve the 35% harvestefficiency would require addition of calcium to concentrations2.5-fold higher than those in seawater and increasing pH beyondwhat the algae alone can achieve.

It is important to point out that the current system for harvest-ing algae is not economically feasible without further develop-ment. However, we speculate that the chemical characteristics ofthe RP1137 cell surface could be used to design means of attachingthe cells to solid substrates, such as hydrophobic or magneticbeads, to aid in recovering the cells after aggregation. In a previousstudy, we showed that aggregation is pH dependent and revers-ible. If fixed RP1137 cells can be attached to beads and maintaintheir aggregation phenotype, then there is the possibility of reus-ing the cells multiple times. In this scheme, the fixed cells would beattached to beads and then used to aggregate algae. The aggregatescould be recovered, and then pH would be lowered either by add-ing acid or by allowing the concentrated, and thus light-limited,algae to lower pH via respiration. Lowering pH reverses the aggre-gation process and separates the algae from the bacterium-beadcomplex. The bacterium-bead complex can then be separatedfrom the algae and reused for another round of harvest. A finalimplication of the results presented in this study is that if theinteraction between the algae and the bacteria is a hydrophobicinteraction, then it may be possible to simply use hydrophobicbeads in place of the bacteria for harvest of algae.

Conclusion. Information about the mechanism of aggregationis useful primarily for predicting which algae RP1137 can harvest.The mechanistic data presented here suggest that RP1137 is able toharvest algae where charge neutralization occurs in either seawa-ter or fresh water with 2 to 20 mM calcium ions at a pH greaterthan or equal to 9. RP1137 will not likely aggregate algae that havesignificant negative charge (�30 mV) under these conditions.

FIG 8 Pinacyanol dye binding to RP1137 cells is disrupted by the addition ofcalcium. (A) Absorbance spectra of water plus dye (curve 1), water plus dyeplus 10 mM CaCl2 (curve 2), RP1137 cells alone (curve 3), and RP1137 cellsplus dye (curve 4). Arrows indicate the position of the 485-nm absorbanceband indicative of pinacyanol binding of teichoic acid. (B) Absorbance ofpinacyanol-stained cells at 485 nm with increasing calcium concentration.Data are means and standard errors for three biological replicates with twotechnical replicates each.

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The data also point to charge neutralization of teichoic acids as thesite where calcium binds to RP1137 cells.

ACKNOWLEDGMENTS

This work was supported by the Maryland Industrial Partnerships grant“Mass cultivation of algae on wastewaters for fuels and products” (PI,Feng Chen). This work was supported by IMET contribution number14-125 and UMCES contribution number 4907.

We thank Leah Blasiak for critical reading of the manuscript. Wethank Jacques Ravel for the gift of the B. megaterium QM B1551 strain andHarold Schreier for the B. subtilis SMY strain.

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