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Combined Effect of Antibiotics and Ocean Acidification on Marine Bacterial Communities during winter and spring bloom conditions Triranta Sircar Degree Project for Master of Science in Ecotoxicology, 60 ECTS Department of Biological and Environmental Sciences University of Gothenburg September 2014 Combined Effect of Antibiotics and Ocean Acidification on Marine Bacterial Communities during winter and spring bloom conditions ABSTRACT As the atmospheric CO2 concentration increases, more CO2 will dissolve in the ocean resulting in a reduction of pH and changes in the carbonate system affecting biogeochemical processes consequently affecting marine microbial ecosystems. Contamination of the ocean with antibiotics, its input, fate and effect on marine microbial communities has also been reported in recent studies. Change in physico-chemical properties of antibiotics with change in environmental pH is being studied extensively in the field of pharmaceuticals. This change in physico-chemical properties in antibiotics increases or decreases its toxicity and bioaccumulation within a cell. The change in oceanic pH will induce change physico-chemical properties of the antibiotics being released into the ocean leading to increase or decrease in its toxicity towards marine bacterial communities. To investigate the combined effect of antibiotics and ocean acidification on marine bacteria, a series of ocean acidification experiments were conducted with natural pelagic bacterial communities from the Gullmar fjord. Seasonal variability in bacterial diversity was accounted for by repeating the experiment twice (during autumn and spring). The pH levels investigated were in situ seawater pH 8.1 and pH 7.7 representing the present day situation and acidification scenario predicted for the year 2100. Amoxicillin and Ciprofloxacin were the two antibiotics tested due their extensive use, traces in marine environment and amphoteric nature under varied pH levels. Change in bacterial number and diversity were observed under epifluorescence microscope using DAPI (4',6- diamidino2-phenyindole dilactate) stain and bacterial activity was recorded by measuring incorporation of radio-labeled amino acid (leucine) into protein. Both DAPI staining and leucine incorporation revealed change in total bacteria number and shift in bacteria community composition due to combined stress of antibiotics and ocean acidification. Bacteria cell numbers declined, while bacteria protein incorporation increased, at low pH amoxicillin and high pH ciprofloxacin treatment, respectively during autumn bloom. Less diverse bacterial community in autumn were more sensitive to the combined stress compared to the more diverse bacterial community in spring. From our study we can conclude pH actually moderates the effects of antibiotics in a natural setting with season specific marine bacterial communities. As predicted, amoxicillin became more toxic at low pH than at high pH and Ciprofloxacin more toxic at high pH that at low pH. 1 TABLE OF CONTENTS Abstract…………………………………………………………………………………………………………………………...1 Introduction………………………………………………………………………………………………………………….….3 Materials and Methods……………………………………………………………………………………………….……6 Sample Collection…………………………………………………………………………………………………….…….6 Bacterial growth, numbers and diversity………………………………………………………………………..8 Cell enumeration using DAPI staining……………………………………………………………………….….8 Bacteria community diversity……………………………………………………………………………………….8 Leucine incorporation…………………………………………………………………………………………………..9 Statistics and calculations………………………………………………………………………………………..…..9 Results……………………………………………………………………………………………………………………………11 Water Chemistry…………………………………………………………………………………………………………..11 Effects of season and time on bacterial numbers………………………………………………………….12 Effects of season and time on bacteria community diversity………………………………………..14 Effects of season and time on bacterial protein production………………………………………….19 Discussion……………………………………………………………………………………………………………………….22 Conclusion………………………………………………………………………………………………………………………25 References………………………………………………………………………………………………………………………26 2 INTRODUCTION One third of the anthropogenic carbon dioxide released into the atmosphere each year is absorbed by the ocean water (Sabine et al, 2004). According to the Intergovernmental Panel on Climate Change (IPCC) (2007) the carbon dioxide concentration in ocean surface water will increase almost three fold by the end of this century. This increase will result in a reduction in the ocean pH by around 0.35 units by the end of 2100 (Wolf-Gladrow et al. 1999). Most of the dissolved carbon dioxide will initially be stored in the upper 200 meters of the seas, and potentially affect the pelagic ecosystem in surface waters (Grossart et al. 2006). Marine heterotrophy is a process by which autotrophically synthesized organic compounds are transformed and respired by animals and microorganisms. Following phytoplankton blooms, the aggregation and sinking flux of organic matter is an important aspect of material transport out of the euphotic zone (Takahashi, 1986). Heterotrophic bacteria utilize a large fraction of sinking particulate organic matter (Weibinga et al., 1998). Interaction between sinking organic matter and heterotrophic bacteria in pelagic ecosystems is important in supporting life below the euphotic zone (Fuhrman et al., 1989). Dissolved and particulate organic carbon (DOC/POC) are the principle energy sources for heterotrophic bacteria in the pelagic environment (Azam et al. 1983). Changes in pCO2 will results in qualitative and quantitative changes in POC, which in turn can influence the activity of free-living and particle-associated bacteria. Changes in bacterial action on POC can affect energy and nutrient fluxes in the microbial loop and consequently have a strong influence on global oceanic carbon cycle (Azam, 1998). Slight changes in pH show direct effects on bacterial community composition as observed in a microcosm experiment performed in darkness with water samples from North sea during spring summer, autumn and winter (Krause et al. 2012). Shifts in community structure were also observed at pH 7.82, which indicates that a slight difference in ocean pH is as crucial changes in the phosphate, silicate and ammonia concentrations. Bacterial protein production (BPP) is likely to be affected by seasonal variations and pH alterations as observed in a mesocosm experiment showing temporal variability with BPP increase during an induced algal bloom (Grossart et al. 2006). Marine microorganisms play a crucial role in the global biogeochemical cycles and functioning of marine ecosystems (Azam and Malfatti, 2007). If these processes are disturbed by ocean acidification it will likely cause changes in the functioning of the global ocean and marine ecosystems. Different theories have been presented on how lowered ocean pH will affect marine microbes. One theory predicts little or no effects on microbial community structure, since microbes as a group appear to be resilient to biogeochemical change (Joint et al. 2011). Gates (2002) however, warns that this could be a misleading statement underestimating the threat of ocean acidification to marine microbes. 3 Pharmaceutical products, particularly antibiotics were extensively used in human and veterinary medicine as well as in aquaculture as prophylaxis or for treating microbial infections (Lutzhoft et al. 1998). 100,000 to 200,000 tons of antibiotics were estimated to be used worldwide in 2002 (Wise 2002). 3350 tons of antimicrobials including antibiotics were consumed in 29 countries of Europe in 2007 (EFSA J 2009). This extensive use of antibiotics has led to concerns about possible impacts, not only on terrestrial ecosystems but also on marine ecosystems. Monitoring studies have detected antibiotic concentrations in the ng- µg/l range in European waste water (Larsson et al 2007). In addition, runoff from farm lands also contributes to the risk of maintaining and inducing antibiotic resistance in bacteria in the marine environment (Kim and Aga 2007, Kummerer 2009b). In Sweden, penicillins; macrolides; sulfonamides; trimethoprim; fluoroquinolones; cephalosporins; tetracyclines are the groups of antibiotics most widely used. The main route for emission of household pharmaceuticals into the environment is through sewage treatment plants (STPs). The wastewater treatment has no specific cleaning step for pharmaceuticals, and only partly eliminates the antibiotics and thus allows their outlet into coastal environments (Lindberg et al. 2005). The different groups of antibiotics contain both acidic and alkaline functional groups sometimes (?) within the same molecule. The functional groups in a molecule influence its physiochemical and biological properties such as pKa, sorption behavior, photo-reactivity, antibiotic activity and most importantly its bioavailability. The bioavailability and toxicity of such amphoteric antibiotics may thus alter with change in pH. Depending on pH conditions antibiotics may be cationic, anionic or neutral (zwitter ionic) (Cunningham, 2008). Approximately 4 tons of ciprofloxacin and amoxicillin were consumed as household pharmaceuticals in 2002 (Swedish Medical Product Agency 2011, Qiang and Adams 2004). Their extensive use in combination with change in molecular behavior with change in pH makes them the focus of our study in relation to seawater pCO2 or ocean acidification. Both antibiotics change their charge precisely in the pH span relevant to current and predicted pCO2 conditions in 100 years due to ocean acidification. Ciprofloxacin (Cip) inhibits bacterial replication by blocking the DNA replication pathway through binding to the A-subunit of DNA-gyrase (Hooper 1999). The Cip molecule contains one carboxylic and three basic nitrogen sites. It dissociates into three different active species Cip2+, Cip+, Cip in the pH 6-8 span (Qiang and Adams 2004). At the iso-electric point of Cip (pH 7.4) the molecule carries both positive and negative charges and the molecule becomes a neutral zwitter ion. It is then considered more toxic since it more easily passes through the cell membrane as an uncharged molecule (Manallack 2007). During pH change, Cip zwitterions can either interact with H+/OH- ions and become polar, or with carboxylate oxygen atoms to form bidentate chelate bridges and precipitate as colloidal iron oxide-ciprofloxacin in marine environment (Gu and Karthikeyan 2005). Amoxicillin (Amox) inhibits the cross-linkage between the linear peptidoglycan polymer chains that 4 make up a major component of the cell walls of both Gram-positive and Gram-negative bacteria. The incorrect formation of the bacterial wall produces an osmotic imbalance that affects bacteria in their growth phase leading to lysis of the bacterial cell wall (Weber et al. 2012). Amox contains one carboxylic, one amine and one phenol group (Alekseev 2010) and thus has three dissociation equilibria. In a slightly basic aqueous media Amox is an anion, in neutral media it acts as a zwitter ion and acidic media it behaves as a cation (Alekseev 2010). At high pH the toxicity is high for bases and at low pH the toxicity is high for acids. The toxicity and bioavailabilty of amphoteric antibiotics depend on the pH of the solvent (Rendal et al 2011). Consequently, Amox will be more bioavailable and toxic at low pH and Cip will be more bioavailable and toxic at high pH conditions. These features are key to predict the effects of these antibiotics in the marine environment of present and future oceans. The adaptive capabilities or resistance development in bacterial populations when exposed to antibiotics in marine environment determine the diversity of the future microbial community. The change in the POC quality and quantity due to pH reduction will determine the abundance of heterotrophic bacteria in the future marine environment. The aim of this study was to investigate the combined effects of pH and antibiotics on marine bacteria communities, as part of natural plankton communities, collected during autumn and spring bloom conditions. The antibiotics investigated were amoxicillin and ciprofloxacin, both occurring in the marine environment as a result of human activities. The choice of antibiotics is based on their typical behavior and change in bioavailability in marine water at different pCO2. 5 MATERIALS AND METHODS Sample collection Autumn: Water was collected on November 08, 2011 in the Gullmar fjord (58°15.6'N 11°25.87' E) from the chlorophyll maximum depth (water depth of 23 meters) more than 1 km off shore to avoid organic matter run off. Water depth, temperature and chlorophyll content were obtained from a ADMConductivity – Temperature- Depth sensor (CTD) and salinity was measured with a conductivity meter. Around 250 l of water from the chlorophyll maximum depth was pumped using a submergible pump (JSpump RS-750) into 10 l, 25 l and 50 l light-proof thermos containers with air tight lids. The collected water was transferred into a larger thermos tank (capacity around 1000 l) and continuously mixed to homogenize the water mass with a sterile rod prior to transfer to 5 l Erlenmeyer flasks. 4.5 l of the homogenized sea water was siphoned into 5 l conical flask and placed in the water bath at 13 °C corresponding to ambient temperature conditions. The water temperature was maintained by circulating deep sea water in the water bath. The air temperature in the experiment room was maintained at 10 °C. The experiment was run in darkness to mimic the ambient scanty light source with occasional light availability during the calibration of pH computers. Mixing of the water in the flasks was maintained by slow and continuous bubbling of air, filtered through GF/F filter using sterile silicon tubing and diffusers. The flasks were differentiated by pH 8.1 (the current pH of sea water) and 7.7 (the predicted pH of sea water in 2100), the two toxicant treatments (amoxicillin and ciprofloxacin) and the two sampling times T1 (three days of exposure to toxicants) and T2 (six days of exposure to toxicants). The low pH of 7.7 was achieved by dynamically measuring and correcting the pH in the respective flasks using pH computers (AquaMedic) and allowing slow bubbling of pure gaseous CO2 whenever the pH exceeded 7.75 units. The pH meter was recalibrated once every day during the experiment. Stock solutions of Amox and Cip (purity >98%, Sigma Aldrich) were prepared in sterile milliQ water 24 hours prior to use, at concentration 100 times higher than the test concentration. Cip stock was made by slightly increasing the pH of the milliQ with four drops of 0.25 M sodium hydroxide (Merck) to make Cip dissolve completely. Amox and Cip were added in respective treatment flasks once in the beginning of the experiment, when the pH had become stable, to a nominal concentration of 20 ng l1 . This concentration of Cip was chosen based on the screening of human antibiotic substance and weekly mass flow in different STP's in Sweden (Lindberg et al. 2005). The final effluent released into the sea had Cip concentration of 13 ng l-1 in 2002 and 32 ng l-1 in 2003. We used 20 ng l-1 as concentrations of antibiotics close to the average concentration of both the years. The concentration of Amox was chosen equal to the concentration of Cip used. The limit of quantification 6 for Amox in effluent in STP is as high as 74 ng l-1 while the recovery is as low as 5 ng l-1 (Lindberg et al. 2005) as the beta-lactam ring is easily degraded by base reagents, metal ions and oxidizing agents (Deshpande et al. 2004). The use of much higher concentrations of Amox than the recovery concentration was to ensure the presence of the molecule during the entire span of experiment duration. Spring: Prior to sampling, the progress of the spring bloom was monitored twice a week from the third week of January in the Gullmar fjord from the dock of SLC-Kristineberg. The water samples for monitoring chlorophyll a and phaeopigments were collected from the surface and at depths of 1 m, 2 m, 3 m and 5 m. The experimental sampling day was chosen just after the peak of the phytoplankton bloom where a sharp decline in the chlorophyll a content in the water was observed. Experimental water was collected on 2012-03-08 in the Gullmar fjord (58°15.41'N 11°27.12'E) from the chlorophyll maximum depth (11 meters). The water depth, temperature and cholorophyll A contents were obtained with AMD CTD and salinity was measured with conductivity meter. Around 250 l of water from the chlorophyll maximum depth was pumped through the submergable pump (JSpump RS-750) and the water was subsequently filtered through a 90 µm plankton net into 10 l, 25 l and 50 l lightproof thermos containers with air tight lids. The 90 µm plankton net was used to filter out larger zooplankton that due to (?) an uneven distribution could have a strong top down impact on the system in specific experimental flasks. This procedure is justified since investigating top-down effects on marine bacterial communities is beyond the scope of this investigation. The fraction of organisms removed after filtering was noted. The collected water was transferred into a larger thermos tank (capacity around 1000l), mixed and poured into 5 l Erlenmeyer flasks as described above. The flasks were placed a water bath with a temperature of 3.5 °C corresponding to ambient conditions. The temperature was maintained in the tank water by circulating it through a cooling unit by use of a pump. The thermo-constant experiment room maintained an air temperature of 4 °C. Lighting was maintained at an intensity of 90 - 100 µmol s-1 and regulated according to the ambient day-night cycle (from 8:00 to 17:00). Water was mixed in the flasks through constant air bubbling, and pH was regulated in the test systems as described above. The pH was recorded twice daily and the pH meter was recalibrated accordingly. The toxicants Cip and Amox were added in respective treatment flasks once in the beginning of the experiment when the pH was stable in each of the low pH test flask. The nominal concentration of the Cip and Amox was 40 ng l-1, twice as high as the concentration used in the autumn experiment. The nominal concentration of Cip used was slightly higher than that found in the final effluent in different STPs in Sweden (Lindberg et al. 2005). The abundance and diversity of bacterial community 7 was expected to be higher in spring experiment (just after spring bloom) than in autumn (Pinhassi et al. 2004). Bacterial growth, numbers and diversity Cell enumeration using DAPI-staining Forty ml of a well-mixed sea-water sample was fixed with 2 ml of 37% Formaldehyde (Merck) reaching a final concentration of 5% (v/v) in 50 ml sterile Falcon tubes. A concentrated stock solution (1 mg ml-1) of 4',6- diamidino-2-phenylindole dilactate DAPI (Sigma Aldrich) was freshly made in sterile MilliQ water and stored at -20°C in the dark. The stock was brought to room temperature and the DAPI stain was diluted to 0.1 µg ml-1 using sterile MilliQ water. 500 µl of the diluted DAPI stain solution was added to 5 ml of the well-mixed sea-water sample and incubated for 15 to 20 minutes at 6°C in darkness. One ml of the stained sample was filtered through 0,2 µm 25 mm black polycarbonate filter (FRISENETTE ApS) with the shiny side up, and supported from beneath by a cellulose acetate filter (0.45 µm 25 mm, FRISENETTE ApS). The underpressure was not exceeding -15 kPa. The damp black polycarbonate filter was placed on a glass slide (76 mm x 26 mm), a drop of 70% glycerol (7:3 glycerol:1% PBS) was added on the filter and covered with coverslip 21 mm x 26 mm. The prepared slide was observed under (Leica) microscope and the bacterial cells in a grid field (L Plan 10 x, i.e. 10 mm x 10 mm) were counted under 1000 x magnification using ultraviolet light, blue emission filter at excitation wavelength 350 and emission wavelength 470. 300 cells on the grid were counted assuming uniform spread of cells on the filter. Bacteria community diversity Bacteria community diversity was measured differently during the November and March experiments. In November, bacteria where observed under UV light microscope revealing the presence of six morphologically different attached and free living bacteria. Their presence and absence were recorded for each sample, while counting the bacteria cell using DAPI staining technique. A color code was established for the abundance of each cell type in grids examined under the microscope. A similar approach for bacteria community diversity was taken during the experiment in March. However, in addition abundances of each of the different morphological types of bacteria cells were recorded. 8 Leucine incorporation Leucine incorporation was measured on bacterial communities according to the method by Smith and Azam (1992). This measurement gives information on bacterial protein production rate and can be converted into bacterial carbon production. Stock solutions of non-radioactive leucine (L-leucine) and radioactive leucine (L-[4,5-3H]) were prepared. 1 l of L- leucine (Acro Organics) stock solution was made in sterile milliQ water at a concentration of 8mg l-1. New diluted L-leucine was made fresh each day in sterile milliQ at a concentration of 176 nM. The combination of diluted L-leucine and L-[4,5-3H]leucine (Perkin Elmer) was freshly prepared for each test. Prior to collecting experimental water, equal volumes of 3H-leucine (1 mCi ml-1, SA≈160 Ci mmol-1,Perkin Elmer) and L-leucin 8 mg l-1 (Acros Organics) were mixed. 10 µl of L-leucine and L-[4,5-3H] leucine mixture was added to 2.0 ml sterile screw cap microcentrifuge tubes to obtain a final concentration of 40 nM upon addition of 1.7 ml sample water. To achieve a final concentration of 5% 89 µl of 100% (w/v) trichloroacetic acid (TCA) (Sigma-Aldrich) was added to half of the total number of tubes serving as corresponding blanks. All tubes were stored at 5°C and brought to incubation temperature of 13°C and 5°C during autumn and spring experiments respectively (in accordance with the ambient water temperature) just prior to incubation. The incubations were started by addition of 1.7 ml of homogenized sample water to each tube. Two replicates and two blanks (killed controls) from each flask were incubated for 60 minutes in darkness. The incubations were terminated by the addition of 89 µl of 100% TCA (5% final concentration) followed by a 30 min continued incubation. The tubes were then centrifuged (Eppendorf Centrifuge 5430R) for 10 minutes at 16000 x g and the radioactive supernatant was discarded. The pellet was washed by addition of 1.5 ml of 5% TCA and vortex mixing. The samples were centrifuged again for 10 minutes at 16000 x g and the radioactive supernatant was discarded. The washing step was repeated three times. The pellet obtained after the three washing steps was dissolved in 0.5 ml of Scintillation cocktail (Ultima Gold, Perkin Elmer) and placed upside down in scintillation vials overnight and radio-assayed in a liquid scintillation counter (Beckman LS 6500). Calculations and statistics Bacterial cell numbers were counted in grid cells under the 1000 x objective lens of microscope and then on the entire filter having a surface area of 4910714 mm2 resulting in number of cells ml-1 of the sample water. The equation used is (Porter and Feig, 1980): Number of bacterial cells ml-1= (Tot n/Tot N)* Tot fil Tot n = Total number of bacteria cells observed in the grid under 1000 x microscope Tot N = Total number of grids counted under 1000 x microscope Tot fil = Total number of grids on the surface area of the filter= 4910714 9 Bacterial protein production was calculated from 3H-leucine incorporation using the following equation: BPP (g) = moles leucine inc * (100/7.3)* 131.2 * ID = (DPM inc/ SA) *131.2 Where moles leucine inc = moles of exogenous leucine incorporated; 100/7.3 = 100/percentage of leucine in protein; ID= intracellular isotope dilution of 3H-leucine. BPP can also be calculated directly from intracellular pool specific activity (SA). 131.2 = formula weight of leucine (Simon and Azam 1989). Bacterial protein production was calculated per number of cells in each treatment using the equation: BPP per cell (g) = BPP/ Tot cells Where BPP is Bacterial protein production as calculated from the previous equation; Totcells = total number of bacterial cells observed in each treatment. Two-way orthogonal ANOVAs (Sigma Plot 11.2, Systat software, inc.) with pH and antibiotics as fixed factors were used to calculate the effect of pH and antibiotics and their interaction on total bacteria cell numbers and protein production rates for each treatment during November T1, T2 and March experiments. The presence and absence of morphologically different bacteria and the community diversity for each experiment in November and March were analyzed using MDS and ANOSIM (PRIMER v6, PRIMER-E Ltd). 10 RESULTS Water Chemistry The pH, temperature and salinity of natural condition were measured to be around 8.1, 12.7 °C and 28.2 respectively. The high pH treatments where maintained at the natural conditions. The pH, temperature and salinity of the acidification treatments were stable and maintained at 7.7, 12.7 °C and 28.2 respectively (Table 1). Experiment Time November T1 High No Ab Cip Amox 8 8 8 2309.82 2403.38 2330.36 Measured pH 7.99 8.01 7.99 Low No Ab Cip Amox 7 7 6 2282.22 2308.42 2294.13 7.69 7.77 7.73 12.8 12.7 12.8 28.2 28.2 28.2 1110.71 934.88 1046.82 High No Ab Cip Amox 4 4 4 2189.54 2243.36 2170.12 8.04 8.05 8.05 12.1 12.3 12.1 28.2 28.2 28.2 410.00 411.25 396.99 Low No Ab Cip Amox 3 4 3 2228.75 2107.32 2157.24 7.72 7.76 7.79 12.3 12.0 12.3 28.2 28.2 28.2 926.16 931.24 856.83 March High No Ab Cip Amox 5 5 5 2590.36 2607.07 2589.34 8.05 8.05 8.05 3.7 3.7 3.7 20.2 20.2 20.2 665.90 668.79 664.65 March Low No Ab Cip Amox 5 5 5 2593.18 2544.23 2637.08 7.70 7.69 7.69 3.7 3.7 3.7 20.2 20.2 20.2 1537.02 1545.03 1596.65 November T2 pH Treatment n Alkalinity Temperature Salinity pCO2 (°C) 12.7 28.2 554.21 12.8 28.2 507.66 12.6 28.2 534.17 Table 1. Summary of water chemistry analyses for the experiments in November and March. 11 Effects of season and time on bacterial numbers T1- three days In high pH (8.1) the number of cells ml-1 was generally lower in the antibiotics treatments compared to the no antibiotics (No Ab) treatment. No such difference was observed at the low pH (7.7) (Figure 1A). The number of cells ml-1 was lower in No Ab treatment at low pH (No Ab- Low) compared to the high pH treatment (No Ab- High). Similar effect was observed within the Amox treatments indicating increase in Amox toxicity at low pH (Tukey p<0.05). In contrast, the number of cells ml-1 in the Cip treatment was higher at low pH (Cip- Low) compared to high pH (Cip- High) indicating counteracting effects of pH and Cip. The combination effect depended significantly on the interaction between the Average number of bacteria cells 106 ml-1 antibiotics and pH (Two-way ANOVA, F2,18=10.426, p < 0.001). 0.7 November T1 0.6 0.5 0.4 0.3 0.2 0.1 0 No Ab High No Ab Low Cip High Cip Low Amox High Amox Low Treatments -1 . Figure 1A. Average number of bacterial cells ml after three days T1. The error bars represent standard deviation (SD). n=4. ** indicates p< 0.001, level of significance from two factor ANOVAs or post hoc Tukey tests. No Ab High: No antibiotics pH 8.1; No Ab Low: No antibiotics pH 7.7; Cip High: Ciprofloxacin pH 8.1: Cip Low: Ciprofloxacin pH 7.7; Amox High: Amoxicillin pH 8.1; Amox Low: Amoxicillin pH 7.7. T2- six days In the high pH (8.1) treatment a decline in the cells ml-1 was observed in the Amoxicillin (Amox) and Ciprofloxacin (Cip) treatments compared to the no Antibiotics (No Ab) treatment, similar to T1. No such difference was observed in the low pH (7.7) (Figure 1B). 12 A sharp decline in the numbers of cells ml-1 was observed in Amox- Low compared to Amox- High indicating enhanced toxicity of Amox at low pH (Tukey p<0.05). A decline in the number of cells ml-1 was also observed in No Ab- Low compared to No Ab- High. However, in the Cip treatment no statistically significant difference was observed in the number of bacterial cells ml-1 between pH treatments. The total number of cells ml-1, significantly depended on the interaction between the pH Average number of bacteria cells 106 ml-1 and antibiotics even at low concentrations of 20ng/l (Two-way ANOVA, F2,12=10.324, p< 0.002). November T2 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 No Ab High No Ab Low Cip High Cip Low Amox High Amox Low Treatments Figure 1B. Average number of bacterial cells ml-1 after six days T2 of exposure in November. The error bars represent standard deviation (SD). n=4. * indicates p<0.05 level of significance from two factor ANOVAs or post hoc Tukey tests. No Ab High: No antibiotics pH 8.1; No Ab Low: No antibiotics pH 7.7; Cip High: Ciprofloxacin pH 8.1: Cip Low: Ciprofloxacin pH 7.7; Amox High: Amoxicillin pH 8.1; Amox Low: Amoxicillin pH 7.7. March- four days In all treatments, the number of bacterial cells ml-1 was found to be nearly ten times higher in November than in March (two- way ANOVA). The cells ml-1 were significantly higher in the No Ab- High treatments compared to No Ab- Low treatments (Tukey, p< 0,05) with an average of 905690 cells ml-1 compared to 848053 cells ml-1. Decline in cells ml-1 was most pronounced in Amox- Low compared to Amox- High and least pronounced both in Cip- Low and Cip- High (Figure 1C). The antibiotic treatment grouped significantly in terms of cells ml-1 as No AB> Cip=Amox (Tukey,p= 0,05), indicating an effect of the antibiotics at concentration of 40 ng l-1 (Lindberg et al. 2005) regardless of pH.. 13 Significant independent effects were observed for both pH and antibiotics (Two-way ANOVAs, pH: F1,24=5.216, p<0,032 and Antibiotics: F2,24=7.889, p<0,002 respectively). There was no significant Average number of bacteria cells 106 ml-1 interaction between the two factors. March 0.12 0.1 0.08 0.06 0.04 0.02 0 No Ab High No Ab Low Cip High Cip Low Amox High Amox Low Treatments Figure 1C. Average number of bacterial cells ml-1 after four days of exposure in March. The error bars represents standard deviation (SD). n=5, except for Cip low where n=4. * indicates p<0.05, level of significance from two factor ANOVAs or post hoc Tukey tests. No Ab High: No antibiotics pH 8.1; No Ab Low: No antibiotics pH 7.7; Cip High: Ciprofloxacin pH 8.1: Cip Low: Ciprofloxacin pH 7.7; Amox High: Amoxicillin pH 8.1; Amox Low: Amoxicillin pH 7.7. Effects of season and time bacteria community diversity The diversity of the bacterial community in the March experiment was found to be higher than in the experiment in November. Six morphologically different bacterial cells were observed in the samples in November. These include small cocci, large cocci, small rods, thick rods and C-shaped bacterial cells. The samples obtained in March contained eight morphologically different bacterial cells. These include small cocci, large cocci, small rods, thick rods, C, spirelli and oval-shaped bacterial cells. The abundance of morphologically different bacteria are grouped as most abundant consisting of 40-60 %, intermediately abundant consisting of 10-40% and least abundant consisting of 1-10 % of the total number of bacterial cells ml -1 (Table 2). 14 Treatment|Type of cell Small Cocci Large Cocci Small Rods Long Rods Thick Rods Cshaped Spirilli Nov T1 No Ab High pH N/A Oval Sparkles N/A Nov T1 No Ab Low pH N/A N/A Nov T1 Amoxicillin High pH N/A N/A Nov T1 Amoxicillin Low pH N/A N/A Nov T1 Ciprofloxacin High pH N/A N/A Nov T1 Ciprofloxacin Low pH N/A N/A Nov T2 No Ab High pH N/A N/A Nov T2 No Ab Low pH N/A N/A Nov T2 Amoxicillin High pH N/A N/A Nov T2 Amoxicillin Low pH N/A N/A Nov T2 Ciprofloxacin High pH N/A N/A Nov T2 Ciprofloxacin Low pH N/A N/A March No Ab High pH March No Ab Low pH March Amoxicillin High pH March Amoxicillin Low pH March Ciprofloxacin High pH March Ciprofloxacin Low pH Table 2. Community sketch for the presence of morphologically different bacteria mainly free living for November and March experiment. 40-60% (most abundant), 10-40% (intermediately abundant), 1-10% (least abundant) of the total number of cells ml-1 observed under microscope. N/A no individual of these types of cell were observed. Table 2. November Bacterial community composition differed in response to all these factors and grouped generally according to time T1 and T2. Time T1- three days After three days of exposure (T1) No Ab at low pH (No Ab-Lo) made a distinct group with 13% dissimilarity to other groups (Figure 2A). The bacterial diversity differed significantly both between pH groups and among antibiotic groups (Two-way crossed ANOSIM, pH: global R=0.598, p= 0.001; antibiotics: global R=0.6, p=0.001 respectively). All antibiotics pair wise comparisons were 15 significantly different from each other, with most differences found between No Ab treatment and Amox (Figure. 2A). T2- Six days After six days of exposure (T2) Cip at low pH (Cip Lo) made a distinct group with 6% dissimilarity from the other groups. The bacterial diversity only differed significantly among antibiotic groups (Two-way crossed ANOSIM, global R=0.375, p= 0.034), and the difference between pH across antibiotic groups observed at T1 had now vanished (Figure 2B). Antibiotics At T1 bacterial communities of the No Ab high pH (No Ab-Hi) treatment and No Ab Low pH (No AbLow) treatment differed while at T2 they formed one single cluster (Figure C). No Ab-Lo at T1 showed a dissimilarity of 10% from the No Ab-Hi treatment at T1. The No Ab treatments at T1 on the whole showed 5% dissimilarity from the No Ab treatments at T2. There was a significant effect of pH and a highly significant effect of time on bacterial community diversity in the No AB treatments (Two-way crossed ANOSIM, pH: global R=0.613, p= 0.029; time: global R=1, p= 0.003). (Two-way crossed SIMPER using Bray-Curtis similarity) (Figure 2C). The T1 the Amox at low pH treatment (Amox-Lo) differed from the Amox at high pH treatment (Amox-Hi) by 6%. At T2 Amox-Hi and Amox-Lo formed clusters close to Amox-Hi at T1. Both pH and time significantly affected bacterial diversity of the Amox treatment (Two-way crossed ANOSIM, pH and time: global R=0.706, p= 0.029)(Two-way crossed SIMPER using Bray-Curtis similarity) (Figure 2D). The T1 Cip treatments at both low pH (Cip-Lo) and high pH (Cip-Hi) formed close clusters with Cip Lo treatments at T2. This group differed with 6% from the Cip Hi treatment at T2. There was no difference in diversity due to pH but diversity changed significantly with time (Two-way crossed ANOSIM, time: global R=0,5, p= 0,026), showing a distinct grouping of Cip-Hi at T2. This distinct grouping of Cip Hi at T2 was attributed to absence in the group “oval sparkles” (Figure 2E). 16 Figure 2.Multi dimentional scaling plots of bacteria community diversity based on presence and absence of morphologically different bacteria for experiment in November, A: T1 all pH and antibiotic treatments; B: T2 all pH and antibiotic treatments; C: T1 and T2 for No Ab treatment; D: T1 and T2 for Amox treatment; E: T1 and T2 for Cip treatment. Resemblance: Bray Curtis similarity. March In March, the average dissimilarity in species abundance among antibiotic groups and between pH groups did not exceed 13%. Community differences were mainly explained by abundance differences in the bacterial group “long rods” (Two-way crossed SIMPER using Bray-Curtis similarity). There was a significant difference in bacterial diversity among antibiotic groups across the high and low pH (Two-way crossed ANOSIM, global R=0.179, p= 0.016). This was attributed to the No AB 17 treatment differing significantly from both Cip and marginally from Amox (ANOSIM pairwise tests, No AB-Cip: R=0.4, p=0.004; No AB-Amox: R=0.128, p=0.099). The Cip and Amox treatment did not differ from each other Amox-Cip (ANOSIM pairwise test, R=0.011, p=0.43 )(Figure 3A). Bacterial diversity differed between the high and low pH treatments for all antibiotic treatments (Two-way crossed ANOSIM, global R=0,187, p= 0,031) (Figure 3B). Antibiotics There were no significant effects of pH on bacterial diversity in any of the antibiotic treatments except for Cip (ANOSIM, global R=0,531, p= 0,016) (Fig 3C). There was a difference of 30% between the Cip-Hi and Cip-Lo treatments. This difference was mediated by a reduced abundance in the group “long rods” in Cip-Lo treatment (Two-way crossed SIMPER using Bray-Curtis similarity). Figure 3. Multi dimentional scaling plots of bacteria community diversity based on presence and absence of morphologically different bacteria for experiment in March. A: Grouping highlighting Antibiotics treatment (Two-way crossed ANOSIM, global R=0.179, p= 0.016); B: Grouping highlighting pH treatment (Two-way crossed ANOSIM, global R=0.187, p= 0.031); C: Cip treatment at the two pHs (ANOSIM, global R=0,531, p= 0,016). Resemblance: Bray Curtis similarity. 18 Effects of season and time on bacterial protein production rate The number of cells ml-1 differed among treatments and consequently cell specific bacterial protein production (BPP) rate was calculated (g protein/l*h) to normalize rates among treatments and times. T1- three days Cell specific bacterial protein production (csBPP) rate depended significantly on the interaction between antibiotics and pH (Two-way ANOVA, F2,18=10.376, p< 0.001). The csBPP rate was higher at low pH compared to high pH for both Cip and Amox treatments while only a similar trend was observed for No AB (Figure 4). The csBPP rates were nearly three times higher in Cip-Low compared to Cip-High (Tukey p<0.05) and double in Amox-Low compared to Amox-High indicating a combined effect of pH and antibiotics. Bacterial protein production rate * 10-7 (g/l*h) November T1 1.2 1 0.8 0.6 0.4 0.2 0 No Ab High No Ab Low Cip High Cip Low Amox High Amox Low Treatments Figure 4. Average cell specific bacterial protein production rate after three days T1 in November. The error bars represents standard deviation (SD). n=4. ** indicates p< 0.001, level of significance from two factor ANOVAs or Tukey post hoc tests. No Ab High: No antibiotics pH 8.1; No Ab Low: No antibiotics pH 7.7; Cip High: Ciprofloxacin pH 8.1: Cip Low: Ciprofloxacin pH 7.7; Amo xHigh: Amoxicillin pH 8.1; Amox Low: Amoxicillin pH 7.7. T2- six days At T2 the csBPP rate still depended on interaction between the antibiotic and pH (Two-way ANOVA, F2,12=4.894, p=0.028), and was marginally higher at low pH compared to in high pH except for in Cip where no difference in csBPP rate was detected between pH levels (Figure 5). 19 In the Amox treatment, csBPP rate was nearly double at low pH compared to high pH indicating a combined effect of reduced pH and Amoxicillin (Tukey p<0,05). Similar trends were observed in No Ab-High and No Ab-Low treatments without any statistical significance. On the contrary, csBPP rates Bacterial protein production rate * 10-7 (g/l*h) were found to be higher in Cip-Hi compared to Cip-Lo (without statistical significance) (Figure 5). November T2 0.6 0.5 0.4 0.3 0.2 0.1 0 No Ab High No Ab Low Cip High Cip Low Amox High Amox Low Treatments Figure 5. Average cell specific bacterial protein production rate after six days T2 of exposure in November. The error bars represents standard deviation (SD). n=4. * indicates p< 0.001, level of significance from two factor ANOVAs or Tukey post hoc tests. No Ab High: No antibiotics pH 8.1; No Ab Low: No antibiotics pH 7.7; Cip High: Ciprofloxacin pH 8.1: Cip Low: Ciprofloxacin pH 7.7; Amox High: Amoxicillin pH 8.1; Amox Low: Amoxicillin pH 7.7. March- four days There was no significant effect of either pH or antibiotics on the csBPP rate (Figure 6). However, csBPP rate followed a similar trend as at T1 in November. The csBPP rate was generally higher at low pH than at high pH except for the Cip treatment where no difference was observed. Results indicate that there is a combined effect of pH and antibiotics, yet variances are too high or replicates too few to rule out any statistical differences. 20 Bacterial protein production rate * 10-7 (g/l*h) March 0.2 0.16 0.12 0.08 0.04 0 No Ab High No Ab Low Cip High Cip Low Amox High Amox Low Treatments Figure 6. Average cell specific bacterial protein production rate after four days of exposure in March. The error bars represents standard deviation (SD). n=5, except for Cip low where n=4. No Ab High: No antibiotics pH 8.1; No Ab Low: No antibiotics pH 7.7; Cip High: Ciprofloxacin pH 8.1: Cip Low: Ciprofloxacin pH 7.7; Amox High: Amoxicillin pH 8.1; Amox Low: Amoxicillin pH 7.7. 21 DISCUSSION Effects of season The experiments during November and March were both conducted under laboratory conditions mimicking the ambient physical conditions in terms of light and temperature. The total free-living bacteria observed using DAPI staining revealed nearly six times higher abundance of bacteria in November than in March. Flagellate grazing on bacteria regulate the bacterial population numbers during the spring bloom (Azam et al., 1983), and might be one reason explaining the lower number of free-living bacteria during spring bloom than in the autumn. The annual changes in abiotic factors such as light regime, temperature, and/or other physical disturbance can influence the biotic factors like competition, predation, and infection in-turn regulating response of different populations (Pinhassi and Hagström, 1997). The abiotic changes are also responsible for the succession of bacterial species in which those species present in low concentrations represent the potential of the bacterial community to respond to future changes (Harris, 1986). During the spring bloom more bacteria were observed attached to algal detritus than to free-living bacteria. This reduces the total number of free-living bacteria during spring compared to autumn conditions. Similar results were observed in a mesocosm study where the free living and attached bacteria where fractionated before being observed under microscope (Grossart et al., 2007). Brussard et al., (2005) also report an increase in numbers of attached bacteria during a spring bloom as associated with algal- derived organic matter in a mesocosm experiment. Rise in water temperature from spring to autumn might be responsible for succession of free living bacterioplankton as also observed by Pinhassi and Hagström, 2000. The ambient water temperature during autumn was 13°C, which was followed by spring with ambient water temperature was 3.5°C. This rise in water temperature might explain the observed higher abundance of free-living bacteria during autumn compared to spring. A decline in nutrients after the summer season and grazing by higher zooplankton are both known to control total bacterial count before the recurring subsequent spring bloom (Rivkin and Anderson, 1997), which supports our finding of free living bacteria number being associated with seasonal changes and spring bloom. Bacterial community diversity was found to be greater in March after the spring bloom than in November during the autumn. During autumn small cocci, large cocci, small rods, thick rods and Cshaped bacterial cells were observed while additional spirilli and oval sparkles were observed immediately after the spring bloom. Community composition and activity of both free living and attached bacteria is closely linked with seasonal variation and nutrient availability (Pinhassi and Hagström 2000). Shift in bacterial community structure has been reported as a result of change in phytoplankton and diatom community structure (Sapp et al., 2007). In our study the phytoplankton, 22 zooplankton and diatom community was found to differ between autumn and spring (Russ, 2011 and Stengren 2012, Bachelor theses). This supports our observation regarding change in bacteria community diversity from autumn to spring. Cell specific bacterial protein production (cs BPP) was slightly higher after the spring bloom than in autumn with high variances in the treatment. Bacteria production was shown to be directly correlated with changes in temperature, nutrient concentration and species composition of the bacterial community from spring to autumn (Pinhassi and Hagström, 2000). In our study we see the differences in temperature, species composition and bacterial abundance from autumn bloom to spring bloom. Effects of ocean acidification The effect of ocean acidification was found to be dependent both on exposure time and season. The pH in our experimental system was well regulated and stable throughout the time of exposure. Prominent effects of reduced pH and exposure times were observed on bacterial abundance and community structure in the autumn bloom but not during in the spring bloom. Low pH can also affect phytoplankton community by influencing primary production leading to changes in quality and quantity of dissolved organic matter (DOM) released consequently influencing bacterial growth (Allgaier, et al., 2008). We have not particularly analyzed the quality and quantity of DOM however the change in the bacterial number and the shift in community structure might be the result of changes in DOM. Similar results with initial decrease in bacteria number upon short exposure to low pH, increase in bacteria numbers upon longer exposure and changes in bacteria community with changes in phytoplankton community had been seen in microcosm experiments (Krause 2012). Cell specific bacterial protein production (cs BPP) was much higher after the spring bloom at low pH than in autumn showing a positive co-relation with the increase in Cholorophyll A in the phytoplankton (Stengren, 2012, independent study) as observed in an experiment where cs BPP was studied in heterotrophic bacteria couple with algal bloom at low pH (Allagaier et al., 2008). The decrease in pH consequently resulted in enhanced protein production in bacterial cells instead of in inhibition. Similar results have been found when bacterial communities have been exposed to gradients of low pH in independent treatments (Coffin et al. 2004). Bacteria cells switch biochemical processes for metabolic cycling of acids and bases at altered pHs. A low pH facilitates acidophilic bacterial growth and prevalence in relation to non-acidophilic bacteria (Wise et al. 1997). Change in bacteria community composition over time may profoundly affect csBPP rate. In a mesocosm study, csBPP rate was observed to increase much in a free living bacteria population following an algal bloom decline, while attached bacteria showed a decline in csBPP rate during the spring bloom and an 23 increase immediately after the spring bloom. The total csBPP rate (including both free living and attached bacteria) was observed to increase immediately after the spring bloom when the particulate organic carbon (POC) was highest (Grossart et al. 2006). In our study, the observed higher csBPP rate in spring after four days of exposure to low pH might be due to the indirect relationship between pCO2 induced shifts in particle quality and activity of the more abundant attached bacteria. At lower pH csBPP rate during autumn showed a similar trend as in spring however the effect observed was not very prominent. Combined effects of OA and antibiotics The combined effect of ocean acidification and antibiotics pH was found to be both season and exposure-time dependent. Effects of low pH and antibiotics at different exposure times were prominently observed in bacterial abundance and community structure in autumn bloom compared to in spring bloom. Ciprofloxacin is more bioavailable and toxic at high pH and Amoxicillin at low pH (Qiang and Adams 2004, Alekseev et al., 2005) leading to lowered bacterial abundance and change in community structure at the respective most toxic pHs. The effect of Ciprofloxacin at high pH is more distinguished after three days of exposure compared to after six days of exposure in autumn might be because Ciprofloxacin gets strongly sorbed to organic matter and its effect is lost over time (Kummerer 2009a). Season dependent shift in community structure in bacteria might have resulted in higher prevalence of species more tolerant to the combined stress of ocean acidification and antibiotics as observed in a microcosm experiment (Krause et al 2012). Cell specific bacterial protein production (csBPP) rate was lower after three days than after six days of combined exposure to ocean acidification and Ciprofloxacin, indicating reduced effects after longer exposures. Higher csBPP rate was observed in Amox low pH treatment than high pH treatment both after three days and six days of exposure indicating the presence of amoxicillin resistant bacteria out-competing the sensitive species and changing the community structure (Krause et al. 2012). We have not isolated the resistant bacteria in particular however the change in community structure might be the result of sensitive species being out competed by the resistant species. High variability in csBPP rate was observed in the post spring bloom bacterial communities. No clear differences were observed amongst Cip or Amox treatments at low and high pH. This indicates either the post spring bloom bacteria communities were more resistant to these antibiotics or the effects of antibiotics were shielded by the presence of different phytoplankton communities. Perhaps this shielding was moderated by adsorption of antibiotics to algal debris thus reducing exposure concentrations and bioavailavility. Studies show that leucine also can be incorporated by phytoplankton like Akashiwo sanguinea, Prorocentrum minimum and Nitzschia sp. (Mulholland et al. 24 2011). An independent study showed the presence of Nitzschia sp. during the spring bloom in our samples (Stengren, 2012). The variation in csBPP rate measured in this study can be related to uptake of externally supplied leucine by algae, having unexpected impact on the csBPP rate measurements and our conclusions on the combined effect of antibiotics at high and low pH levels. This method has however, been widely used to measure bacterial function in experiments and field monitoring programs since it was suggested by Smith and Azam (1989) and evaluating its validity in relation to bacterial productivity is beyond the scope of this study. Problems or concerns with the experimental setup Our experiment set up was closely but not perfectly mimicking the ambient physical conditions of the bacteria communities sampled at 23 meters and 11 meters of depth during autumn and spring respectively. The water temperature of 13°C during autumn was well maintained but the water temperature of 2°C was not practically possible to maintain in laboratory conditions. Water temperature and air temperature was kept at 4°C for the experiment in spring, which might provide an opportunity for the phytoplankton and bacteria to enhance their cellular activities. The pH regulation system was fairly stable throughout the experiment. The initial calibration of pH computers and stabilization of pH was gradual leading to sudden fluctuations in pH in our test systems for a very short period of time. The challenge of establishing the test system affected our test flasks uniformly and the variability in our results was mostly due to different treatments and probably not caused by physical variability amongst flasks. CONCLUSIONS This investigation has shown change in total bacteria number, cell-specific bacterial production and shift in bacteria community composition due to combined stress of antibiotics and ocean acidification. The changes in bacterial numbers and community composition were both exposure time and season dependent. The autumn bacterial community was less diverse and likely therefore more sensitive to combined stress of antibiotics and ocean acidification compared to the more diverse spring bacterial community. 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