AEM Accepts, published online ahead of print on 25 April 2014 Appl. Environ. Microbiol. doi:10.1128/AEM.00887-14 Copyright © 2014, American Society for Microbiology. All Rights Reserved. Algal aggregation by bacteria 1 2 Mechanism of algal aggregation by Bacillus sp. strain RP1137 3 Ryan J. Powell and Russell T. Hill* 4 Institute of Marine and Environmental Technology D o w n 5 University of Maryland Center for Environmental Science lo a d e 6 701 E. Pratt St. Baltimore MD, 21202 USA d f r o m 7 *Corresponding author. h t t p : 8 Mailing Address: Institute of Marine and Environmental Technology // a e m 9 University of Maryland Center for Environmental Science .a s m . o 10 701 E. Pratt St. Suite 326 Baltimore MD, 21202 r g / o n 11 A p r il 12 Phone: (410) 234-8802 1 1 , 2 0 13 Fax: (410) 234-8818 1 9 b y 14 E-mail: [email protected] g u e s 15 Running title: Mechanism of aggregation t 16 Keywords: Nannochloropsis, aggregate, algal harvesting, teichoic acid, biofuel, flocculation 17 1 Algal aggregation by bacteria 18 ABSTRACT 19 Algal derived biofuels are one of the best alternatives for economically replacing liquid 20 fossil fuels with a fungible renewable energy source. Production of fuel from algae is technically 21 feasible but not yet economically viable. Harvest of dilute algal biomass from the surrounding 22 water remains one of the largest barriers to economic production of algal biofuel. We identified D o w 23 Bacillus sp. strain RP1137 in a previous study and showed this strain can rapidly aggregate n lo a 24 several biofuel producing algae in a pH and divalent cation dependent manner. In this study, we d e d 25 further characterize the mechanism of algal aggregation by RP1137. We show aggregation of fr o m 26 both algae and bacteria is optimal in the exponential phase of growth and that the density of h t t p 27 ionizable residues on the RP1137 cell surface changes with growth stage. Aggregation is likely :/ / a e 28 via charge neutralization with calcium ions at the cell surface of both algae and bacteria. We m . a s 29 show charge neutralization occurs at least in part through binding of calcium to negatively m . o 30 charged teichoic acid residues. The addition of calcium also renders both algae and bacteria more rg / o 31 able to bind to hydrophobic beads, suggesting aggregation may be occurring through n A p 32 hydrophobic type interactions. Knowledge of the aggregation mechanism may enable r il 1 33 engineering of RP1137 to obtain more efficient algal harvesting. 1 , 2 0 1 34 9 b y 35 g u e s t 36 37 38 2 Algal aggregation by bacteria 39 INTRODUCTION 40 Energy underlies economies and is the largest single market in the world. However, most 41 energy systems are based on finite nonrenewable resources that increasingly have higher direct 42 and indirect costs. A growing research effort focuses on developing and deploying renewable 43 energy sources to supplement fossil fuels. Research into renewable liquid fuels is of particular D o w 44 interest in the US because transportation is almost exclusively powered by petroleum. n lo a d e 45 Algal biofuels represent one of the best alternatives to sustainably produce fungible liquid d f r o 46 fuels. Algae act as self-replicating bioreactors that use light energy to chemically reduce CO2 m h 47 into useful energy storage molecules. Unlike traditional crops, algae can be grown on land not tt p : / 48 suitable for agriculture and can be grown in wastewater or saltwater (1, 2). Algae have rapid /a e m 49 growth rates, sometimes doubling their biomass in several hours, and can be harvested multiple . a s m 50 times per year (3). Algal biomass is ideally suited for conversion to crude oil via hydrothermal . o r g 51 liquefaction, which produces an oil that can be refined in existing refineries and also allows the / o n 52 recovery of limiting nutrients such as nitrogen and phosphorous (4). A p r il 53 While technologically feasible, studies have shown algal biofuels are not yet 1 1 , 2 54 economically viable (5). Furthermore, to our knowledge no company has yet successfully 0 1 9 55 produced algal biofuel at a profit. Only when profitability is achieved will algal biofuels become b y g 56 a self-sustaining venture that can make a significant impact on the production of renewable fuels. u e s 57 Harvest of the algal biomass has been identified as one of the key hurdles to economically t 58 producing fuel from algae (5). Algal biomass must be concentrated and most of the water 59 removed before the biomass can be converted to fuel. Mature technologies for algal harvest 60 include filtration, centrifugation, sedimentation, electrocoagulation, dissolved air floatation, 3 Algal aggregation by bacteria 61 chemical flocculation and bio-aggregation (6). Uduman et al. provide an excellent review of 62 different algal harvest methods including the advantages and disadvantages of each (6). Bio- 63 aggregation uses biological agents such as extracellular polymeric substances, chitosan or whole 64 cells to form easily harvestable aggregates (7-11). Several algal aggregating bacterial strains are 65 known and have been proposed for use in harvesting algae (12-17). D o w 66 In previous work we described the algae aggregating bacterium Bacillus sp. RP1137 (17). n lo a 67 This bacterium can rapidly aggregate multiple algae that are candidates for biofuel production. d e d 68 Aggregation is pH and divalent cation dependent (17). Fixed cells were also shown to be as fr o m 69 effective as live cells at aggregating algae. However, the detailed mechanism of aggregation of h t t p 70 algae by RP1137 was unknown. Knowledge of the mechanism may be useful for understanding :/ / a e 71 why it is able to aggregate some algae but not others and for applying the strain to large scale m . a s 72 algal harvest. m . o r g 73 In this study we define the mechanism of algal aggregation by Bacillus sp. RP1137. The / o n 74 purpose of this research is to understand the aggregation mechanism of Bacillus sp. RP1137 to A p r 75 determine its suitability for harvest of algae and to understand how harvest might be improved. il 1 1 , 2 76 0 1 9 b 77 y g u e 78 st 79 80 81 4 Algal aggregation by bacteria 82 83 METHODS 84 Strains and culture conditions. Liquid cultures of Bacillus sp. strain RP1137 were grown in 85 marine broth 2216 (BD, Franklin Lakes, NJ) at 30°C in 125 ml Erlenmeyer flasks with shaking D 86 at 180 rpm. Marine broth 2216 plus 15 g/l Difco technical agar (BD) was used for solid medium. o w n 87 Nannochloropsis oceanica IMET1 was grown as described before (17). Briefly, N. oceanica lo a d e 88 was grown in 20 ppt salinity f/2 medium (18) in 500 ml ported photo-bioreactors at 25°C with a d f r o 89 light/dark photoperiod of 14/10. m h t t 90 Filtration aggregation assay. A filtration aggregation assay was used to quantitate the amount p : / / a 91 of algae that were aggregated under a given condition. This assay has been described in detail e m . a 92 (17). Briefly, the assay involves carrying out aggregation reactions with N. oceanica IMET1 and s m . 93 Bacillus sp. strain RP1137 in a 96 well plate. The entire volume of the reaction is then passed o r g / 94 through a 50 µm mesh, aggregates that are larger than the mesh are retained and smaller particles o n A 95 pass through. Chlorophyll fluorescence is measured in the flow-through and compared to control p r il 96 samples without bacteria added to determine the percent of algae that are aggregated upon 1 1 , 2 97 addition of the bacteria. Unless noted otherwise, aggregation assays were carried out in 0 1 9 98 deionized water where pH was adjusted to 10.5 with NaOH and 10 mM CaCl2 had been added. b y g u 99 Bacterial aggregation efficiency time course. RP1137 cells were streaked from cryo-stocks and e s t 100 a single colony was used to start a 10 ml culture in marine broth medium. The culture was 101 incubated at 30°C in a 125 ml flask with 180 rpm shaking. From the initial culture three 102 subcultures were started at a calculated optical density (OD) of 0.01 in 200 ml of marine broth. 103 Cultures were grown in 1 L flasks at 30°C with 180 rpm shaking. Time points were taken every 5 Algal aggregation by bacteria 104 one to two hours for 24 hours. At each time point cells were collected, concentrated by 105 centrifugation at 5580 x g for 5 minutes, supernatant was aspirated, the cell pellet was suspended 106 in 4% PFA in 1x PBS pH 7.4 and incubated for one hour at room temperature. Cells were then 107 concentrated by centrifugation, the supernatant was aspirated and cells were suspended in 1x 108 PBS to wash the cells. The cells were then again concentrated by centrifugation and suspended in D o 109 a fresh aliquot of 1x PBS. Fixed cells were used to preserve the surface chemistry of the cell and w n lo 110 ensure that chemistry was not altered due to stress responses by the cell. Filtration aggregation a d e d 111 assays were carried out using bacteria from each time point with algae from +2 days after f r o 112 subculturing. The samples were normalized by cell surface area to 3 x108 µm2/ml (described m h t 113 below) so each sample had the same surface area available for interacting with algal cells. tp : / / a e 114 Algal aggregation efficiency time course. N. oceanica IMET1 cultures were grown as m . a s 115 described above. Samples of algae were taken at two, five and 17 days after being subcultured, m . o 116 which represents early exponential, exponential and stationary phases of growth respectively. rg / o 117 Cells were fixed following the protocol used for the bacterial cells. Samples were normalized by n A p 118 cell surface area per ml (described below) so each sample had the same available surface area for r il 1 119 interacting with bacterial cells. Aggregation assays were carried out using bacterial cells from 1 , 2 0 120 exponential phase (OD = 0.7). 1 9 b y 121 Determining cell size and surface area. Cells from the time course were stained in 1X SYBR g u e 122 green I nucleic acid stain for 10 minutes in the dark. SYBR green staining was used to illuminate s t 123 the cell body and provide crisp cell margins that were amenable to automated image analysis. 124 Cells were then visualized on a Zeiss Axioplan microscope with excitation from a Zeiss X-Cite 125 120Q Iris FL light source using a filter cube with a 470/40 BP excitation filter, a FT 495 dichroic 126 mirror and a 525/50 BP emission filter. Cells were diluted or concentrated as needed to obtain 6 Algal aggregation by bacteria 127 well separated cells. The volume of each field of view was determined using the known depth of 128 the bacterial hemacytometer and the height and width of the field of view. For each time point 129 20-30 fields of view were captured and saved as TIFF files. Image processing was done in Cell 130 Profiler (19) with the following series of commands in a custom pipeline: LoadImage, 131 ColorToGrey, IdentifyPrimaryObjects, ReassignObjectNumbers, MeasureObjectSizeShape and D o 132 ExportToSpreadsheet. LoadImage imports the images. ColorToGrey converts the image to w n lo 133 greyscale to reduce processor time. IdentifyPrimaryObjects was used to find and identify objects a d e d 134 using the Otsu global algorithm, a 4-40 pixel cutoff and a 0.02 – 1 threshold cutoff. f r o 135 ReassignObjectNumbers was used to join cells within a filament into one object using a six pixel m h t 136 cutoff. MeasureObjectSizeShape was used to measure the perimeter and area of the identified tp : / / a 137 objects. ExportToSpreadsheet was used to export the data as a .cvs file for import into Microsoft e m . 138 Excel for further analysis. Data were converted from pixels to micrometers using data gathered a s m 139 from a stage micrometer. Cell length was approximated by dividing cell perimeter by two; this .o r g / 140 provides a good estimate of cell length for filamentous bacilli though it does introduce a slight o n A 141 overestimate of the absolute size of the cells. To obtain cell surface area for normalization both p r il 142 perimeter and area data are used. The key parameter needed, but unavailable directly in Cell 1 1 , 2 143 Profiler, for calculating surface area of a cell is the radius of the cell. To derive the radius of 0 1 9 144 individual cells the 2D images of cells were used and the bacilli were modeled as a rectangle b y g 145 with half circles on each end. The resulting equation for area is then sum of the area of a circle u e s 146 and the area of a rectangle or A = πr2 + 2r((P/2) – 2r) where A is the area of the cell, r is the t 147 radius of the cell and P is the perimeter of the cell. Since A and P are measured values the 148 equation can be solved for r using the quadratic equation which yields two solutions, one of 149 which is the real radius of the cell. Derived radius values were checked against the manually 7 Algal aggregation by bacteria 150 measured average radius along the length of individual cells. Calculated values are very close to 151 measured values indicating the method can be used to accurately calculate cell radius in an 152 automated format. Cell radius was used to calculate surface area of a three dimensional cell by 153 modeling the cell as two halves of a sphere plus the surface area of a cylinder minus the ends. 154 Surface area of individual cells was calculated for 900-1600 cells per time point. Cell numbers D o 155 per ml were then used to calculate available surface area per unit volume. Surface area per ml of w n lo 156 individual sample were used to normalize available surface area for interaction with algal cells a d e d 157 between samples at different time points. The available surface area of N. oceanica IMET1 time f r o 158 points was determined using a similar pipeline to that used for RP1137 cells with the following m h t 159 modifications. Images were captured using chlorophyll autofluorescence. Nannochloropsis cells tp : / / a 160 are spherical so the measured area of the 2D images could be used to directly derive radius using e m . 161 the equation for the area of a circle (A = πr2). Radius could then be used to calculate 3D surface a s m 162 area of a sphere (A = 4πr2). Surface area per unit volume was determined by combining surface .o r g / 163 area data with cell concentration data. All experiments were normalized by surface area using the o n A 164 cells prepared above. The OD of the culture is provided to make clear which growth phase is p r il 165 being used in each experiment. 1 1 , 2 0 166 LiCl treatment of RP1137 cells. Lithium chloride treatment was done according the protocol of 1 9 b 167 Lortal et al. (20). RP1137 cells were concentrated by centrifugation at 20,000 g for 3 min and the y g u 168 cell pellet was suspended in either distilled water, 5 M LiCl, 7.5 M LiCl or 10 M LiCl. Cells e s t 169 were incubated at these conditions for 15, 45 and 120 minutes at room temperature and then 170 concentrated by centrifugation. The cell pellets were suspended in pH 10.5 deionized water with 171 10 mM CaCl2 and used for aggregation assays. 8 Algal aggregation by bacteria 172 Base titration of whole bacterial cells. Live RP1137 cells were used for base titration 173 experiments. Cells were taken in exponential phase (OD = 0.7, 3.4 x 106cells/ml) and stationary 174 phase (OD = 1.6, 7.2 x 106cells/ml). Culture volumes were normalized by surface area to ensure 175 the same amount of bacterial cell surface was being titrated in each sample. Cells were 176 concentrated by centrifugation at 15,000 g for 5 minutes and the cell pellet was suspended in pH D o 177 5 deionized water. This washing step was repeated twice more to ensure salts had been removed w n lo 178 and the cells were equilibrated to pH 5. Base in the form of 0.25 M NaOH was added to the cell a d e d 179 suspension and pH was recorded after each addition when the value stabilized. f r o m 180 Calcium binding assay. Calcium binding was evaluated by measuring the concentration of h t t p 181 calcium remaining after 1 ml of cells had been added. Calcium binding assays were performed :/ / a e 182 with fixed RP1137 cells from the exponential phase (OD = 0.7) of growth. Known m . a s 183 concentrations of CaCl2 were added to cells in pH 10 deionized water. Cells were then removed m . o 184 by centrifugation at 20,000 g for 3 minutes. The calcium concentration in the supernatant was rg / o 185 measured using the LaMotte Calcium Hardness colormetric kit (Chestertown, MD). The kit was n A p 186 adapted for use in a 96 well format and measurement in a Spectro Max M5 plate reader. The r il 1 187 readout for the assay was absorbance at 635 nm. Absorbance at this wavelength is linear for 1 , 2 0 188 calcium concentrations between 0-160 µM. Samples were diluted to ensure they were within the 1 9 b 189 linear range of the assay. Absorbance values were compared to a CaCl2 standard curve to y g u 190 determine the concentration of calcium remaining. e s t 191 Calcium coordination experiment. RP1137 and Nannochloropsis cells were separately 192 suspended in pH 10.5 water with 10 mM CaCl2. To ensure the cells were pre-loaded with 193 calcium both bacteria and algae were concentrated by centrifugation at 20,000 g and the cell 194 pellets were suspended in the same solution. This pre-loading step was repeated once. The cells 9 Algal aggregation by bacteria 195 were then used in filtration aggregation assays compared to controls where only the algal cells 196 were pre-loaded with calcium. 197 C18 binding assay. Binding of cells to C18 resin was performed with fixed RP1137 cells from 198 the exponential phase of growth (OD = 0.7) and with fixed Nannochloropsis cells from +5 days 199 post inoculation. Dry C18 beads with a 10 µm diameter were purchased from Hamilton (Reno, D o w 200 Nevada). Beads were reconstituted in methanol overnight. The beads were concentrated by n lo a 201 centrifugation at 20,000 g for 1 minute. The beads were then suspended in pH 10.5 deionized d e d 202 water with 10 mM CaCl2. This process of concentration and suspension in pH 10.5 deionized fro m 203 water with 10 mM CaCl2 was repeated twice more to ensure methanol was removed and the h t t p 204 beads were equilibrated in the test solution. The equilibration process was repeated without :/ / a e 205 CaCl2 for a separate aliquot of beads to obtain beads for the “no calcium” samples. For each m . a s 206 experiment 200 beads were used per cell as this was found to give maximal binding with the m . o 207 minimum number of beads. Equal numbers of algal or bacterial cells were incubated with C18 rg / o 208 beads (200:1 bead to cell ratio) in the presence or absence of 10 mM CaCl2. The mixtures were n A p 209 analyzed with an Accuri C6 flow cytometer to count the number of unbound algal or bacterial r il 1 210 cells. The beads were distinguished from cells by their larger forward scatter area with an upper 1 , 2 0 211 forward scatter area cutoff of 1,270,000. A lower cutoff of 44,000 was used to remove 1 9 b 212 background particles found within the medium. RP1137 cells fell between these two cutoffs. y g u 213 Algal chlorophyll autofluorescence was used to distinguish Nannochloropsis cells from their e s t 214 associated bacterial cells using the FL3 channel (excitation 488 nm, emission 670 LP filter) on 215 the flow cytometer. Only particles that were between the two forward scatter cutoffs and had a 216 chlorophyll autofluorescence of greater than 10,000 were counted as algal cells. These settings 10