Methods:
At the start of the experiment (time zero), oil concentrations were measured using a Horiba Scientific Aqualog Fluorometer (excitation 254nm; emission 365nm) and expressed as estimated oil equivalent (EOE). At the start of each experiment (M2, M3, M4, and M5), mean EOE concentrations in the water accommodated fraction (WAF) were 0.26, 0.74, 0.29 and 2.15 mg/L, respectively. Mean EOE concentration in the Si-WAF was 0.13 mg/L.
Mesocosm and bottle experiments: Three mesocosm experiments (M2, M3 and M4) were conducted in October 2015 (M2) and July 2016 (M3 and M4) respectively. For each, controls (only seawater, no oil added) and oil treatments, as WAF of Macondo oil surrogate, were prepared in triplicate. For M2, seawater was collected near-shore (~0.5 km) south of Galveston, Texas in the Gulf of Mexico at 29.27°N, 94.81°W and settled in large tanks to remove large particles and debris before collection. WAF was prepared by mixing 25 ml oil with 130 L seawater in stirring baffled recirculating borosilicate glass tanks of 170 L capacity and allowed to equilibrate over 24 hours (specific details are in Wade et al. 2017). After that, only the aqueous phase in the bottom layer was added to the mesocosm tanks. A plankton concentrate was collected nearby in Galveston Bay using a 63 µm plankton net, in order to obtain the natural phytoplankton and their associated bacterial consortia. 2 L of this “concentrate” was added to both the WAF and Control to make a final volume of ~ 80 L. For M3, seawater was collected ~174 km off the coast of Texas near the Flower Garden Banks National Marine Preserve in the Gulf of Mexico (27.88°N, 94.03°W, salinity: 30.77 ppt; pH: 8.38, temperature: 30.8 ˚C), a site representing an open ocean location. For M4, a coastal site was chosen (~20 km from shore) at 29.37°N, 93.38°W (Salinity: 31.13 ppt, pH: 8.02 Temperature: 30.5○C) in the Gulf of Mexico. Unlike from M2, nutrients (N, P and Si) were added at f/20 concentrations to all tanks of M3 and M4, but not the natural microbial “concentrate”. In M4, an additional treatment was adopted in which a silicon tube was filled with crude oil and inserted into the mesocosm tank (see details in Bera et al. 2018). The oil was allowed to passively diffuse into the mesocosm tank 24 hrs prior to the start of the experiment (“Si-WAF” treatment). M5 was conducted on May 22 to June 8, 2017, using the same conditions that had been used in M2, with the difference being the duration of the experiment (M5, 16-day and M2, 4-day). For the bottle experiment, seawater was collected from the same location as the M2 experiment (see above) and filtered through 3 µm polycarbonate filters (Millipore) to remove most of the phytoplankton (Sun et al. 2018). This also filtered out any bacteria that were attached to phytoplankton but most of the free bacteria remained in the filtrate. Glucose (70.67 µM) was added as a carbon source, ammonium chloride (64 µM NH4Cl) as a nitrogen source and sodium dihydrogen phosphate as a phosphorus source, in a Redfield ratio. The bottles were gently swirled on an orbital shaker and incubated at 20 ˚C for three days in the dark. Bacterial growth was monitored by measuring OD 600 nm on a daily basis and EPS was harvested during the transition from exponential to stationary phase.
Transparent exopolymeric particles (TEP) and Coomassie stainable particles (CSP) measurement: Suspended particulate matter (SPM) samples for measuring TEP were collected daily during the M2, M3, and M4 mesocosm experiments and only on Day 1, 4 and 16 of M5, and measured according to Passow and Alldredge (1995) with consideration to updated protocols in (Bittar et al. 2018). Triplicate samples were taken from each tank and gently filtered onto a 0.4 µm polycarbonate filter. The volume of the filtered sample was adjusted between samples, depending on the SPM concentration, to achieve adequate amounts of material captured on the membrane while minimizing filter clogging. After filtration, small amounts of deionized water were used to rinse the salts off the membrane. The filter was then homogeneously stained with a pre-filtered and calibrated Alcian blue solution (0.2 g/L in 0.06% v/v acetic acid) and rinsed quickly with deionized water to remove any excess of dye. The filter was either frozen (-20 ˚C) for further processing or immediately extracted in a glass vial containing 5 mL 80% H2SO4 and gently vortexed for 4-hour at room temperature. The absorbance of the H2SO4 leachate was measured on a spectrophotometer at 787 nm. The particle concentration was measured by filtering the sample onto another pre-weighed 0.4 µm polycarbonate filter. The filter, with the particle, was briefly rinsed with deionized water and then dried in a 60 ˚C oven until a constant weight was achieved (error < 0.1%). SPM particle concentration was calculated from the difference in filter weight before and after filtration. TEP was quantified based on the correlation between the amount of Gum Xanthan standard retained on the 0.4 µm polycarbonate membrane and its corresponding absorbance determined by the same procedure as the samples. Thus the concentration of TEP was expressed as mg-GX equivalent/L. CSP was measured based on a method described by Cisternas-Novoa et al. (2014) with only a few selected samples. Bovine Serum albumin (BSA) was used as the standard and the CSP concentration was expressed as mg-BSA equivalent/L.
Extracellular polymeric substances (EPS) measurement: To extract EPS from SPM, another 0.4 µm polycarbonate membrane with SPM was re-suspended in 10 mL of 1% EDTA solution for EPS extraction (Xu et al. 2018a). The EDTA solution was supposed to dissociate metal ions-complexed EPS from particles. The mixtures were then incubated at 4 °C for 3 hours on an orbital shaker at 150 rpm. After the incubation, the particles were removed using a FlipMate 100 System (0.45 µm PES Environmental Express, USA). Excessive EDTA and particle-associated oil were removed by ultrafiltering the resulting filtrate (< 0.45 µm) via Amicon Ultra-4 Centrifugal Filter Unit with a 3 kDa cut-off membrane (Millipore, USA). The retentate (3 kDa- 0.45 µm) was concentrated to 2 mL for determination of protein, uronic acid and neutral sugars, which were assumed to represent the major components of EPS. Protein content in EPS was measured based on a modified bicinchoninic acid method (Smith et al. 1985) using the Pierce protein assay kit, with bovine serum albumin as the standard. Carbohydrate concentration was determined using the anthrone method (Morris 1948), with glucose as the standard. Uronic acid was estimated by adding sodium borate (75 mM) in concentrated sulfuric acid and m-hydroxydiphenyl, with glucuronic acid as the standard (Hung and Santschi 2001). Alternatively, total carbohydrates, presumably as the sum of neutral sugars, acidic polysaccharides (e.g., uronic acid) and amino sugars, was released into solution by hydrolyzing the EDTA extractant with 0.1 N HCl overnight and then measured by TPTZ method (Hung et al. 2003; Hung et al. 2001). Alternatively, the SPM collected on the filter, without any pre-extraction step, was analyzed directly for neutral sugars, uronic acid, protein, as well as total carbohydrates. A blank filter was included in each analytical process in order to subtract the absorbance caused by the filter. Additionally, the marine snow and marine oil snow (visible aggregates) that had settled to the bottom of the control and WAF tanks respectively, was collected for the purpose of comparing the pre-solvent extraction of the sinking aggregates and successive analysis of the extract, versus the whole particle analysis (Xu et al. 2018b). The marine snow slurry was gently filtered onto a 0.4 µm polycarbonate membrane and rinsed three times with 15 mL of nanopure water (18.2 MΩ). The material retained on the filter (> 0.4 µm) was then re-suspended in nanopure water and the filter quickly removed. The samples were then freeze-dried for later analysis (Xu et al. 2018b).
Gel counting: 10 mL of the unfiltered water from the bottle experiment was collected and amended with 0.02% NaN3 to inhibit bacterial growth. The LSR II flow cytometer (BD Biosciences) was set to collect 5 min events and record signals from forward-scattering and side-scattering from the EPS of Gulf of Mexico natural microbial consortia. The collected data was analyzed with FlowJo data analysis software package (TreeStar, USA) (Chiu et al. 2017; Chiu et al. 2018; Verdugo et al. 2008).
Nuclear magnetic resonance (NMR): To meet the demand of NMR for greater mass production of EPS, the bottle experiment (1 L) was scaled up to 20 L using the same conditions as the bottle experiment. Colloidal EPS was harvested at the transition from exponential to stationary phase, after pre-filtration through 0.4 µm AcroPakTM Capsule with Supor® Membrane (Pall Inc., USA). Cross-flow ultrafiltration was used to partly desalt and concentrate EPS (Zhang and Santschi 2009). When there was 200-300 mL retentate remaining, the solution was transferred into a 200 mL Amicon® stirred-cell, and extensively diafiltered against deionized water and concentrated to ~ 50 mL for lyophilisation and subsequent NMR analysis. The NMR spectrum was acquired with a 400MHz Bruker AVANCE II instrument using a multiple cross polarization magic angle spinning (multiCPMAS) pulse sequences according to Hatcher et al. (2018). The sample was placed in a 2.5mm rotor, covered with a Vessel cap, and spun at a frequency of 18kHz at the magic angle (54.7°). The optimum value for the time between CP pulses was found to be 0.5 s and the recycle delay was set to 2 s. The spectrum was externally calibrated to a glycine standard (176 ppm). Additional details about the pulse sequence and conditions are described in Johnson and Schmidt-Rohr (2014). A molecular mixing model (MMM) was applied to quantify the major organic biopolymer components (carbohydrate, protein, lignin, and lipid) (Baldock et al. 2004). This model requires two main data array inputs: 1) percent signal intensity for each chemical shift region as measured by solid-state 13C NMR for the sample, and 2) the solid-state 13C NMR percent signal intensities for each chemical shift region in each of the desired components. The marine ecosystems distributions used by Baldock et al. (2004) were applied. The proportion of each component was then varied by using the Generalized Reduced Gradient nonlinear optimization code in the Solver add-on in Microsoft Excel. The optimum value was found when the minimum sum of the squares of the difference between the predicted and the measured value was reached. The model was constrained by forcing the sum of all the components to be equal to 100% and the value of each component to be greater than or equal to zero (Hatcher et al. 2018).
Provenance and Historical References:
Baldock, J. A., Masiello, C. A., Gélinas, Y., & Hedges, J. I. (2004). Cycling and composition of organic matter in terrestrial and marine ecosystems. Marine Chemistry, 92(1-4), 39–64. doi:10.1016/j.marchem.2004.06.016
Bera, G., Parkerton, T., Redman, A., Turner, N. R., Renegar, D. A., Sericano, J. L., & Knap, A. H. (2018). Passive dosing yields dissolved aqueous exposures of crude oil comparable to the CROSERF (Chemical Response to Oil Spill: Ecological Effects Research Forum) water accommodated fraction method. Environmental Toxicology and Chemistry. doi:10.1002/etc.4263
Bittar, T. B., Passow, U., Hamaraty, L., Bidle, K. D., & Harvey, E. L. (2018). An updated method for the calibration of transparent exopolymer particle measurements. Limnology and Oceanography: Methods, 16(10), 621–628. doi:10.1002/lom3.10268
Chiu, M.H., Garcia, S.G., Hwang, B., Claiche, D., Sanchez, G., Aldayafleh, R., Tsai, S.M., Santschi, P.H., Quigg, A. and Chin, W.C. (2017). Corexit, oil and marine microgels. Marine Pollution Bulletin. doi:10.1016/j.marpolbul.2017.06.077
Chiu, M.H., Vazquez, C.I., Shiu, R.F., Le, C., Sanchez, N.R., Kagiri, A., Garcia, C.A., Nguyen, C.H., Tsai, S.M., Zhang, S. and Xu, C. (2019). Impact of exposure of crude oil and dispersant (Corexit) on aggregation of extracellular polymeric substances. Science of The Total Environment, 657, 1535–1542. doi:10.1016/j.scitotenv.2018.12.147
Cisternas-Novoa, C., Lee, C., & Engel, A. (2014). A semi-quantitative spectrophotometric, dye-binding assay for determination of Coomassie Blue stainable particles. Limnology and Oceanography: Methods, 12(8), 604–616. doi:10.4319/lom.2014.12.604
Hatcher, P. G., Obeid, W., Wozniak, A. S., Xu, C., Zhang, S., Santschi, P. H., & Quigg, A. (2018). Identifying oil/marine snow associations in mesocosm simulations of the Deepwater Horizon oil spill event using solid-state 13 C NMR spectroscopy. Marine Pollution Bulletin, 126, 159–165. doi:10.1016/j.marpolbul.2017.11.004
Hung, C.-C., & Santschi, P. H. (2001). Spectrophotometric determination of total uronic acids in seawater using cation-exchange separation and pre-concentration by lyophilization. Analytica Chimica Acta, 427(1), 111–117. doi:10.1016/s0003-2670(00)01196-x
Hung, C.-C., Tang, D., Warnken, K. W., & Santschi, P. H. (2001). Distributions of carbohydrates, including uronic acids, in estuarine waters of Galveston Bay. Marine Chemistry, 73(3-4), 305–318. doi:10.1016/s0304-4203(00)00114-6
Hung, C. C., Guo, L., Schultz, G. E., Pinckney, J. L., & Santschi, P. H. (2003). Production and flux of carbohydrate species in the Gulf of Mexico. Global Biogeochemical Cycles, 17(2).
Johnson, R. L., & Schmidt-Rohr, K. (2014). Quantitative solid-state 13C NMR with signal enhancement by multiple cross polarization. Journal of Magnetic Resonance, 239, 44–49. doi:10.1016/j.jmr.2013.11.009
Morris, D. L. (1948). Quantitative Determination of Carbohydrates With Dreywood’s Anthrone Reagent. Science, 107(2775), 254–255. doi:10.1126/science.107.2775.254
Passow, U., & Alldredge, A. L. (1995). A dye-binding assay for the spectrophotometric measurement of transparent exopolymer particles (TEP). Limnology and Oceanography, 40(7), 1326–1335. doi:10.4319/lo.1995.40.7.1326
Smith, P.K., Krohn, R.I., Hermanson, G.T., Mallia, A.K., Gartner, F.H., Provenzano, M., Fujimoto, E.K., Goeke, N.M., Olson, B.J. and Klenk, D.C. (1985). Measurement of protein using bicinchoninic acid. Analytical Biochemistry, 150(1), 76–85. doi:10.1016/0003-2697(85)90442-7
Sun, L., Chiu, M.H., Xu, C., Lin, P., Schwehr, K.A., Bacosa, H., Kamalanathan, M., Quigg, A., Chin, W.C. and Santschi, P.H. (2018). The effects of sunlight on the composition of exopolymeric substances and subsequent aggregate formation during oil spills. Marine Chemistry. doi:10.1016/j.marchem.2018.04.006
Verdugo, P., Alldredge, A. L., Azam, F., Kirchman, D. L., Passow, U., & Santschi, P. H. (2004). The oceanic gel phase: a bridge in the DOM–POM continuum. Marine Chemistry, 92(1-4), 67–85. doi:10.1016/j.marchem.2004.06.017
Wade, T.L., Morales-McDevitt, M., Bera, G., Shi, D., Sweet, S., Wang, B., Gold-Bouchot, G., Quigg, A. and Knap, A.H. (2017). A method for the production of large volumes of WAF and CEWAF for dosing mesocosms to understand marine oil snow formation. Heliyon, 3(10), e00419. doi:10.1016/j.heliyon.2017.e00419
Xu, C., Zhang, S., Beaver, M., Lin, P., Sun, L., Doyle, S.M., Sylvan, J.B., Wozniak, A., Hatcher, P.G., Kaiser, K. and Yan, G. (2018a). The role of microbially-mediated exopolymeric substances (EPS) in regulating Macondo oil transport in a mesocosm experiment. Marine Chemistry, 206, 52–61. doi:10.1016/j.marchem.2018.09.005
Xu, C., Zhang, S., Beaver, M., Wozniak, A., Obeid, W., Lin, Y., Wade, T.L., Schwehr, K.A., Lin, P., Sun, L. and Hatcher, P.G. (2018b). Decreased sedimentation efficiency of petro- and non-petro-carbon caused by a dispersant for Macondo surrogate oil in a mesocosm simulating a coastal microbial community. Marine Chemistry, 206, 34–43. doi:10.1016/j.marchem.2018.09.002
Zhang, S., & Santschi, P. H. (2009). Application of cross-flow ultrafiltration for isolating exopolymeric substances from a marine diatom (Amphorasp.). Limnology and Oceanography: Methods, 7(6), 419–429. doi:10.4319/lom.2009.7.419
View Metadata