Algicidal and denitrification characterization of Acinetobacter sp. J25 against Microcystis aeruginosa and microbial community in eutrophic landscape water

Marine Pollution Bulletin 107 (2016) 233–239

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Algicidal and denitrification characterization of Acinetobacter sp. J25 against Microcystis aeruginosa and microbial community in eutrophic landscape water Jun feng Su a,b,⁎, Min Ma a, Li Wei b,⁎, Fang Ma b, Jin suo Lu a, Si cheng Shao a a b

School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, People's Republic of China State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, People's Republic of China

a r t i c l e

i n f o

Article history: Received 1 March 2016 Received in revised form 24 March 2016 Accepted 30 March 2016 Available online 25 April 2016 Keywords: Response surface methodology (RSM) Algicidal bacteria Algae-lysing characteristics Denitrification High-throughput sequencing

a b s t r a c t Acinetobacter sp. J25 exhibited good denitrification and high algicidal activity against toxic Microcystis aeruginosa. Response surface methodology (RSM) experiments showed that the maximum algicidal ratio occurred under the following conditions: temperature, 30.46 °C; M. aeruginosa density, 960,000 cells mL−1; and inoculum, 23.75% (v/ v). Of these, inoculum produced the maximum effect. In the eutrophic landscape water experiment, 10% bacterial culture was infected with M. aeruginosa cells in the landscape water. After 24 days, the removal ratios of nitrate and chlorophyll-a were high, 100% and 87.86%, respectively. The denitrification rate was approximately −1 ·h−1. Moreover, the high-throughput sequencing result showed that Acinetobacter sp. 0.118 mg NO− 3 –N·L J25 was obviously beneficial for chlorophyll-a and nitrate removal performance in the eutrophic landscape water treatment. Therefore, strain J25 is promising for the simultaneous removal of chlorophyll-a and nitrate in the eutrophic landscape water treatment. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Cyanobacterial blooms, which could pose a threat to aquatic ecosystems and human health, are a frequent and harmful phenomenon in freshwater lakes and estuaries worldwide (Carey et al., 2012). In particular, blooms caused by toxic cyanobacteria, such as Microcystis, Anabaena, and Cylindrospermopsis, produce microcystin, which affects ecosystem functioning and creates a significant water quality problem (Yang et al., 2014a, 2014b; Zhu et al., 2014). As a fast and efficient method, chemical agents such as copper sulfate, potassium permanganate, hydrogen peroxide, and ozone are used for eutrophication control (Fan et al., 2013; Matthijs et al., 2012). However, chemical methods induce secondary pollution, which is potentially dangerous in aquatic ecosystems (Qu and Fan, 2010; Tang et al., 2012). Meanwhile, physical methods will also more easily result in secondary pollution to water (Paul and Pohnert, 2011). Thus, there is a need to explore ecologically safe ways to control harmful cyanobacterial blooms. At present, although many algicidal bacteria, such as Pseudoalteromonas, Sphingomonas, Staphylococcus, Bacillus amyloliquefaciens, and Cytophaga, have been reported and exploited by researchers, each algicidal bacterium has its specific host algae (Kang et al., 2008; Kim and Lee, 2006; Yang et al., 2014a, 2014b). ⁎ Corresponding authors at: State Key Laboratory of Urban Water Resource and Environment, School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin 150090, People's Republic of China. E-mail addresses: [email protected] (J. Su), [email protected] (L. Wei).

http://dx.doi.org/10.1016/j.marpolbul.2016.03.066 0025-326X/© 2016 Elsevier Ltd. All rights reserved.

Therefore, use of algicidal bacteria to control algal bloom and red tide is more effective, ecological, and environmentally friendly (Bährs et al., 2012; Choi et al., 2005). Recently, close associations between bacteria and microalgae have been reported, and bacteria living in the phycosphere of microalgae have been suggested to affect algal population dynamics and toxicity (Rooney-Varga et al., 2005). However, several environmental factors have been associated with algicidal effects involved in the termination and decomposition of algal blooms, including low temperature, starvation, salinity, visible and ultraviolet (UV) light, and air pressure (Oliver, 2010). Zhou et al. (2013) indicated that potassium release is the main cause of cell breakage resulting from exposure to copper sulfate, hydrogen peroxide, diuron, and ethyl 2-methylacetoacetate. Besides, highresolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) brings a major fraction of dissolved organic matter (DOM) into our analytical window, providing high-accuracy molecular-level information regarding elemental and inferred structural composition that can be related to source (Stubbins and Dittmar, 2014) and photochemical transformations of DOM (Gonsior et al., 2009; Stubbins et al., 2010). Furthermore, the presence of algae may also strongly affect denitrification. For example, readily degradable benthic algae, serving as a nitrate and organic carbon source, can facilitate denitrification (McMillan et al., 2010; Sirivedhin and Gray, 2006). Some algae (e.g., diatoms) even have a synergistic effect on denitrifying bacteria (Ishida et al., 2008). Pyrosequencing, developed by Roche 454 Life Science (Branford, CT, USA), is a high-throughput analytical method that can produce a large amount of DNA data through parallel sequencing-by-synthesis

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approach (Margulies et al., 2005). More than thousands of operational taxonomic units (OTUs) could be identified to investigate the microbial diversity in various environmental samples (Hu et al., 2012; Lu et al., 2012; Zhang et al., 2012). Pyrosequencing can provide better insights into the evolution of microbial community. In this study, Acinetobacter sp. J25 was inoculated in the landscape water to evaluate the efficiency of algicidal and denitrification characteristics. Meanwhile, response surface methodology (RSM) analysis was then used to determine the optimum conditions (Microcystis aeruginosa density, temperature, and inoculum) of the strain J25. Factors affecting the performance of Acinetobacter sp. J25 with algae lysing were also comprehensively evaluated based on RSM analysis under the optimum conditions. 454 high-throughput pyrosequencing was used to analyze the bacterial communities and investigate the algae-lysing and denitrification performance and their community structure. Furthermore, this study would reveal the relationship between bacterial community structure, chlorophyll-a, and nitrate removal performance in a reactor. 2. Materials and methods 2.1. Algal and bacterial culture M. aeruginosa was obtained from the Freshwater Algae Culture Collection of Institute of Hydrobiology (FACHB), Chinese Academy of Sciences (Wuhan, China). Before being used as an inoculant, it was cultured for 7 days to reach the log phase under the following conditions: sterilized BG11 media (Rippka et al., 1979); 3300 lx white light, light:dark = 12:12 h; 28 °C. The BG11 media used in this study comprised the following reagents in the amounts given per liter: NaNO3 1.5 g; K2HPO4 0.04 g; MgSO4·7H2O 0.075 g; CaCl2·2H2O 0.036 g; citric acid 0.006 g; ferric ammonium citrate 0.006 g; ethylenediaminetetraacetic acid (EDTA) 0.001 g; Na2CO3 0.02 g; and A5 (trace element) solution 1 mL. The ingredients of A5 solution per liter were as follows: H3BO3 2.86 g, MnCl2·4H2O 1.81 g, ZnSO4·7H2O 0.22 g, CuSO4·5H2O 0.079 g, Na2MoO4·2H2O 0.039 g, and Co(NO3)2·6H2O 0.049 g. Strain J25 (GenBank accession number KT023013) (Su et al., 2016) was isolated from a eutrophic Qu Jiang lake, Xi'an, China. In order to analyze algicidal characteristics, the strain J25 was grown in sterilized Luria– Bertani (LB; 10 g·L−1 of tryptone, 5 g·L−1 of yeast extract, 10 g·L−1 of NaCl, pH 7.2) medium at 30 °C and reached logarithmic growth phase. 2.2. Box–Behnken design for optimizing the environmental factors The RSM is a statistical experimental design for the optimization of biological processes. This method can build models, evaluating the effects of factors and searching for optimum condition of factors for desirable responses. RSM was used to investigate the algae-lytic effect of J25 bacteria on M. aeruginosa at the given conditions of temperature, inoculum, M. aeruginosa density. The RSM was used to optimize and evaluate the main effects, interaction effects, and quadratic effects of the independent variables. The experimental design was performed with three factors at three levels (+1, −1, 0): temperature (25, 30, and 35 °C); inoculum (v/v: 10%, 20%, and 30%); and M. aeruginosa density (4 × 105, 12 × 105, and 20 × 105 cells mL−1). Levels of these three independent variables were defined according to the Box–Behnken design, and 17 experiments were required for the procedure (Table 1). Design expert software was designed using the Minitab program (version 16, Minitab Inc., USA). In order to predict the optimal point, a second-order polynomial function was fitted to correlate the relationship between three factors and the relatively algicidal ratio. The function for the three factors is Y ¼ c0 þ a1 X1 þ a2 X2 þ a3 X3 þ β11 X1 2 þ β22 X2 2 þ β33 X3 2 þ X1 X2 β12 þ X1 X3 β13 þ X2 X3 β23 ;

Table 1 Box–Behnken experimental design and the corresponding responses. Treatment

Temperature (°C)

M. aeruginosa density (cells mL−1)

Inoculum (v/v, %)

Algicidal ratio (%)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

30 35 30 30 25 30 30 30 30 35 35 35 25 30 30 25 25

12 × 105 4 × 105 20 × 105 12 × 105 12 × 105 4 × 105 12 × 105 12 × 105 4 × 105 20 × 105 12 × 105 12 × 105 12 × 105 12 × 105 20 × 105 20 × 105 4 × 105

20 20 10 20 10 30 20 20 10 20 30 10 30 20 30 20 20

84.74 68.4 36.85 85.7 30.6 70.01 83 83.22 27.9 36.28 70.52 35.44 48.69 83.77 62.59 43.84 46.8

where Y is the predicted response; c0 is constant; X1, X2, and X3 are independent factors; a1, a2, and a3 are linear coefficients; β12, β13, and β23 are cross-product coefficients; and β11, β22, and β33 are quadratic coefficients. 2.3. Algicidal efficiency assessment In order to evaluate the efficiency of algicidal and denitrification characteristics, the 10% (v/v) inoculum of sample J25 was added to 5 L of eutrophic landscape water obtained from a eutrophic Qu Jiang lake (34.202534° to 34.211416° N, 108.989245° to 108.993611° E) in Xi'an, China. Meanwhile, the same volume of sterilized LB medium was also added to eutrophic landscape water as a control. The same M. aeruginosa cultures were added to eutrophic landscape water in both treatment and control, and the initial M. aeruginosa density was 300,000 cells mL−1. This experiment was conducted at a certain temperature range (25–35 °C). Nitrate and chlorophyll-a concentrations of the samples were measured from days 0–24 every 2 days. 2.4. Microbial community analysis 2.4.1. DNA extraction, PCR amplification, and high-throughput sequencing Both samples Z1 and Z2 were collected after 24 days in treatment and control (Section 2.3). Bacterial genomic DNA was extracted using the PowerSoil DNA Isolation Kit (MO BIO Laboratories, Inc., Carlsbad, CA, USA) following the manufacturer's protocols. For construction of gene libraries, the PCR amplification (Supplementary materials) used the universal bacteria primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′), which were targeting the V1 and V3 hypervariable regions (Liao et al., 2013). Then, the pyrosequencing procedure was performed similarly to our previous approach (Hao et al., 2013). 2.4.2. Sequence analysis and phylogenetic classification The sequence analysis followed the method (Supplementary materials) described in our previous study (Hao et al., 2013). The OTU, rarefaction curves, and diversity indices (Ace and Chao 1) were determined by Mothur ver. 1.17.0. Taxonomic classification of the sequences was performed using the RDP Classifier (Version 2.2) with a set confidence threshold of 80%. 2.5. Analytical measurements Briefly, culture samples were centrifuged at 5000 rpm for 8 min for chemical analysis. Chlorophyll-a was extracted with 90% alcohol, and the whole procedure was carried out in darkness at − 20 and − 4 °C.

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values of the variables for chlorophyll-a degradation. The final model was as follows:

Table 2 Least-squares fit and parameter estimates. Term

Regression coefficient

F-value

p-Value

Constant X1 X2 X3 X1X1 X2X2 X3X3 X1X2 X1X3 X2X3

84.09 5.09 −4.19 15.13 −7.29 4.25 −4.09 −19.14 −16.12 −18.63

25.185 6.607 4.488 58.39 6.78 2.302 2.137 49.199 34.877 46.624

0.0002⁎⁎⁎** 0.0370⁎ 0.0719 0.0001⁎⁎⁎ 0.0352⁎ 0.173 0.1872 0.0002⁎⁎⁎ 0.0006⁎⁎⁎ 0.0002⁎⁎⁎

Coefficient of correlation (R2) = 0.9700; coefficient of determination (adj R2) = 0.9315; coefficient of variation = 9.53%. ⁎⁎⁎ Vitally significant for p b 0.001. ⁎⁎ Very significant (0.001 b p b 0.01). ⁎ Significant (0.01 b p b 0.05).

The concentration of chlorophyll-a was determined using a spectrophotometer at 665 and 750 nm, according to the method described by Yang et al. (2007). Nitrate concentration was determined by a UV spectrophotometric screening method and by calculating the difference between OD220 and 2 × OD275 according to the guidelines of the State Environmental Protection Administration of China (2002). 2.6. Statistical analysis The formula for nitrate removal ratio and algicidal ratio is (C0 − Cn) / C0 × 100%, where C0 is the initial concentration and Cn is the final concentration of NO− 3 –N and chlorophyll-a. The data in this experiment were analyzed by Microsoft Excel and Origin 9.0 software. 3. Results and discussion 3.1. Box–Behnken design and statistical analysis The RSM with Box–Behnken design was used to analyze the interactive effects of important variables that significantly affected the algicidal ratio of M. aeruginosa by strain J25, including temperature, inoculum, and M. aeruginosa density. The response surface curves and contours provided an easy and convenient visualization to understand the interaction between two independent variables and determine the optimum

Y ¼ 84:09 þ 5:09X1 −4:19X2 þ 15:13X3 −7:29X1 X2 þ 4:25X1 X3 −4:09X2 X3 −19:14X1 2 −16:12X2 2 −18:63X3 2 ;

where Y is the predicted response of chlorophyll-a removal ratio and X1, X2, and X3 represent the coded values of temperature, M. aeruginosa density, and inoculum, respectively. Table 2 shows that the inoculum had the largest effect on the algicidal ratio. The F-value of 25.185 indicated that the overall model was vitally significant, as the probability of occurrence of such a large F-value due to noise is only 0.02%. In view of the main and interactive effects of the three factors, the optimal conditions were determined to be temperature of 30.46 °C, M. aeruginosa density of 960,000 cells mL−1, and inoculum of 23.75% (v/v) by ridge analysis using SAS program (version 9.1.3, SAS). The maximum algicidal ratio (88.06%) could be achieved, according to the model prediction under the optimal conditions. Three-dimensional response surface graphs were plotted to evaluate the interaction of temperature, M. aeruginosa density, and inoculum and the optimization conditions of algicidal ratio. From the response surface curves and contours, it was easy and convenient to understand the interaction effects between two independent variables and locate the optimum levels. Fig. 1 shows the response surface and contours of the algicidal ratio as a function of temperature and M. aeruginosa density as independent variables. The semi-spherical response surface of the algicidal ratio gradually increased when the temperature was increased from 25.00 to 32.03 °C, and then gradually decreased at temperatures above 32.03 °C. At the same time, the algicidal ratio increased with increasing M. aeruginosa density from 400,000 to 1,080,000 cells mL−1, and a further increase in M. aeruginosa density did not result in further improvement of algicidal ratio. Therefore, the optimum temperature and M. aeruginosa density were determined to be 32.03 °C and 1,080,000 cells mL− 1, respectively. The optimum algicidal ratio was determined to be 85.97%. Temperature is one of the external factors that play a significant role in bacterial growth, and can influence the growth and metabolic activity of microbes. High temperatures result in decreased growth and sometimes cessation of bacterial growth. Therefore, the optimal temperature of 32.03 °C was in good agreement with the data reported in this study. The optimum algae-lytic effect of strain s7 on Anabaena flos-aquae was achieved at a culture temperature of 33.06 °C (Wang et al., 2012).

Fig. 1. 3D surface plot and contour lines showing the effect of temperature and M. aeruginosa density on algicidal ratio.

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Fig. 2. 3D-surface plot and contour lines showing the effect of M. aeruginosa density and inoculum on algicidal ratio.

Response surfaces in Fig. 2 show that a maximum algicidal ratio of 83.21% could be obtained at an initial M. aeruginosa density range of 52,000–1,600,000 cells mL−1 and inoculum range of 19.10–25.10% (v/v). The optimization values for these factors were found to be 1,160,000 cells mL−1 for initial M. aeruginosa density and 22.02% (v/v) for inoculum, with the maximum algicidal ratio of 85.74%. These results are similar to those of the previous studies. The initial bacterial and M. aeruginosa densities significantly affected the phytoplankton-lytic activity. When the 15% (150 μL mL−1) concentration of bacterial cultures was infected, the highest phytoplankton-lytic activity reached 98.8% after 7 days (Zhang et al., 2011). 3.2. Application in landscape water The efficiency of removal of algae and nitrates is shown in Fig. 3. During strain J25 incubation in treatment group, the concentration of nitrate decreased slightly between 0 and 6 days, probably because of its adaptability to the environment of the eutrophic landscape water. Then, the decrease of nitrate was significant from 6 to 10 days, which was likely because the predominant strain J25 reached logarithmic growth phase and exhibited a strong growth and metabolism activity. Meanwhile, the strain J25 for growth could use nitrate as the nitrogen source to denitrification. Furthermore, the living M. aeruginosa were

also of use for nitrate in the growth process. Therefore, a large amount of nitrates for strain J25 was consumed from 6 to 10 days. Finally, it reached a stationary phase between 10 and 24 days. However, in the control group, nitrate decreased slightly in the whole process of algicidal activity, and the concentration of nitrate decreased significantly after 2 days. The maximum removal efficiency of nitrate was 100% at 10 days in the treatment group, and the denitrification rate was approximately −1 0.118 mg NO− ·h− 1. The change of nitrate concentration be3 –N·L came insignificant after 10 days. In the control group, the concentration of nitrate decreased slightly and that of chlorophyll-a significantly increased within 24 days. After 24 days, the change in chlorophyll-a had opposite trends in the treatment and control groups. In the former, the chlorophyll-a concentration increased between 0 and 2 days and then significantly decreased from 2 to 24 days. The maximum removal efficiency of chlorophyll-a was 87.76% at 24 days. However, in the control group, the concentration of chlorophyll-a increased obviously in the whole process, which was likely because the decrease of nitrate concentration was due to the growth of M. aeruginosa in the control group. On the one hand, it was suggested that the strain J25 can control the growth of algae and kill the M. aeruginosa cells. On the other hand, simultaneous reduction of nitrate and chlorophyll-a concentrations occurred in the treatment group. It showed that the strain J25 can simultaneously lyse M. aeruginosa and

Fig. 3. Changes of chlorophyll-a concentration and NO3–N in the eutrophic landscape water by the strain J25 with 10% of inoculum.

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Table 3 Different bacterial diversity indices of samples Z1 and Z2. Sample

Sample description

No. of sequences

Coverage

Ace

Chao1

Shannon

Simpson

Z1 Z2

Treatment Control

42,793 38,887

0.999471 0.999332

136.444 175.5153

138 169.5385

1.986484 1.877284

0.252792 0.376793

denitrification. Kong et al. (2012) found that a novel isolated algae-lysing strain Streptomyces sp. HJC-D1 exhibited an average chlorophyll-a removal efficiency of 80.94 ± 4.36% in the micro-polluted water by harmful algal blooms.

3.3. Bacterial diversity analysis in landscape water treatment Pyrosequencing of the samples Z1 and Z2 yielded 42,793 and 38,887 bacterial 16S rRNA gene sequences, respectively. The sequence information of the samples and calculated bacterial diversity indices are listed in Table 3. Rarefaction analyses based on OTUs at 3% dissimilarity are shown in Fig. 4. The numbers of Ace, Chao1, Shannon, Simpson (Table 3), and OUT index were similar and had no clear changes among the two samples, and the bacterial diversity of Z2 was higher than that of Z1, because the addition of strain J25 reduced species richness. BDE209 exposure in all the samples indicated a more similar community structure than the control (Zhang et al., 2015).

Fig. 4. Diversity of bacterial communities in the landscape reactor. Rarefaction curves based on bacterial OTUs at a dissimilarity level of 3%.

3.4. Bacterial community structure in landscape water treatment As showed in Fig. 5(a), the total number of identified phyla for samples Z1 and Z2 was 15. Sample Z1 was mainly composed of Proteobacteria (99.89%), Firmicutes (2.65%), and Chlamydiae (1.54%), whereas sample Z2 comprised Proteobacteria (82.35%), Firmicutes (1.65%), and Chlamydiae (1.54%). The phyla of samples Z1 and Z2 showed that approximately 90% of the bacteria were composed of Proteobacteria, Firmicutes, and Chlamydiae; however, the predominant phyla had been changed in the whole experiment. The composition of Proteobacteria in the treatment and control groups were 99.89% and 82.35%, respectively, suggesting that the phylum was distinctly enriched in the treatment. In addition, the composition of phylum Firmicutes increased from 1.65% to 2.65%. The Acinetobacter sp. J25 belonging to phylum Proteobacteria showed that the strain J25 could better adapt to other bacteria of eutrophic landscape water. Park et al. (2006) showed that Proteobacteria and Flavobacteria are the major populations in the denitrification biofilm reactor. The phylum Proteobacteria was the most abundant group in freshwater (Huang et al., 2011). The total number of identified classes for samples Z1 and Z2 was 34 (Fig. 5(b)). The predominant classes in sample Z1 were Gammaproteobacteria (9.37%), Deltaproteobacteria (41.38%), Betaproteobacteria (10.02%), and Alphaproteobacteria (34.86%). Sample Z2 was mainly composed of vadin HA17 (9.88%), Actinobacteria (4.92%), and Alphaproteobacteria (80.07%). The predominant classes in samples Z1 and Z2 were α-, β-, γ-, and δ-Proteobacteria. Furthermore, these classes are higher in sample Z1 than Z2, because of the addition of the strain J25. In several studies, the classes Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria were most abundantly found in freshwater (Kwon et al., 2011), which is in accordance with our finding that the Acinetobacter sp. J25 belonged to Gammaproteobacteria. As shown in Fig. 6, microbial community heatmap analysis and the multiple samples similarity tree were used to identify the similarity and differences between the two bacterial community structures. The largest genus in sample Z1 was Roseomonas (49.32%), Acinetobacter (6.32%), and Rhodobacter (13.25%), whose compositions were 0.10%, 0.05%, and 2.49%, respectively, in sample Z2. This might be due to the

Fig. 5. Relative read abundance of different bacterial community structures at phylum level (a) and class level (b) in sample Z1 and sample Z2 treatments using RDP Classifier with a confidence threshold of 80%.

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Fig. 6. Microbial community heatmap analysis and multiple samples similarity tree in two samples.

prime interactions between microorganisms, which always involved competition and synergy. Competition refers to the process that strain J25 and other various microorganisms competed for nitrate and carbon sources for self-growth and the removal of chlorophyll-a and nitrate. Besides, synergy refers to the use of the strain J25 and other microorganisms. Furthermore, it can be observed from Fig. 6 that the percentage of Acinetobacter in the treatment group was much higher than that in the control group, indicating that the strain J25 has a relatively high biomass in the treatment, and plays a key role in algae lysing and denitrification.

4. Conclusions In this study, RSM analysis showed that the maximum algicidal ratio can be achieved with a temperature of 30.46 °C, M. aeruginosa density of 960,000 cells mL − 1 , and inoculum of 23.75% (v/v) by ridge analysis when 10% bacterial culture was infected with the M. aeruginosa cells in the eutrophic landscape water (M. aeruginosa density = 300,000 cells mL−1). After 24 days, the removal ratios of nitrate and chlorophyll-a were 100% and 87.86%, respectively, and the −1 denitrification rate was approximately 0.118 mg NO− ·h−1. 3 –N·L

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High-throughput sequencing data showed that the Acinetobacter sp. J25 was the major contributor to the removal of chlorophyll-a and nitrate in the treatment. Therefore, the strain J25 is a promising candidate in the extensive application of eutrophic landscape water. Acknowledgments This study was partly supported by the National Key Technology Research and Development Program of the Ministry of Science and Technology of the People's Republic China (No. 2012BAC04B02), supported by Open Project of State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology (No. QA201518), and the Foundation for Creative Research Groups of China (50821002). References Bährs, H., Menzel, R., Kubsch, G., Stösser, R., Putschew, A., Heinze, T., Steinberg, C.E.W., 2012. Does quinone or phenol enrichment of humic substances alter the primary compound from a non-algicidal to an algicidal preparation? Chemosphere 87 (11), 1193–1200. Carey, C.C., Ibelings, B.W., Hoffmann, E.P., Hamilton, D.P., Broookes, J.D., 2012. Ecophysiological adaptations that favor freshwater cyanobacteria in a changing climate. Water Res. 46 (5), 1394–1407. Choi, H.J., Kim, B.H., Kim, J.D., Han, M.S., 2005. Streptomyces neyagawaensis as a control for the hazardous biomass of Microcystis aeruginosa (Cyanobacteria) in eutrophic freshwaters. Biol. Control 33, 335–343. Fan, J., Ho, L., Hobson, P., Brookes, J., 2013. Evaluating the effectiveness of copper sulphate, chlorine, potassium permanganate, hydrogen peroxide and ozone on cyanobacterial cell integrity. Water Res. 47 (14), 5153–5164. Gonsior, M., Peake, B.M., Cooper, W.T., Podgorski, D., D'andrilli, J., Cooper, W.J., 2009. Photochemically induced changes in dissolved organic matter identified by ultrahigh resolution Fourier transform ion cyclotron resonance mass, spectrometry. Environ. Sci. Technol. 43 (3), 698–703. Hao, T.W., Wei, L., Lu, H., Chui, H.K., Mackey, H.R., van Loosdrecht, M.C.M., Chen, G.H., 2013. Characterization of sulfate-reducing granular sludge in the SANI® process. Water Res. 47 (19), 7042–7052. Hu, M., Wang, X.H., Wen, X.H., Xia, Y., 2012. Microbial community structures in different wastewater treatment plants as revealed by 454-pyrosequencing analysis. Bioresour. Technol. 117 (10), 72–79. Huang, Y., Zou, L., Zhang, S., Xie, S., 2011. Comparison of bacterioplankton communities in three heavily polluted streams in China. Biomed. Environ. Sci. 2 (2), 140–150. Ishida, C.K., Arnon, S., Peterson, C.G., Kelly, J.J., Gray, K.A., 2008. Influence of algal community structure on denitrification rates in periphyton cultivated on artificial substrata. Microb. Ecol. 56 (1), 140–152. Kang, Y.K., Cho, S.Y., Kang, Y.H., Katano, T., Jin, E.S., Kong, D.S., Han, M.S., 2008. Isolation, Identification and Characterization of Algicidal Bacteria Against Stephanodiscus hantzschii and Peridinium bipes for the Control of Freshwater Winter. Kim, J.D., Lee, C.G., 2006. Antialgal effect of a novel polysaccharolytic Sinorhizobium kostiense AFK-13 on Anabaena flos-aquae causing water bloom. J. Microbiol. Biotechnol. 16, 1613–1621. Kong, Y., Zhu, L., Qi, J.Q., Yu, Y.W., Xu, X.Y., 2012. Characteristics of algae removal and nitrogen removal from micro-polluted source water by algae-lysing bacterium. Ecol. Environ. Sci. 21 (8), 1440–1446. Kwon, S., Moon, E., Kim, T.S., Hong, S., Park, H.D., 2011. Pyrosequencing demonstrated complex microbial communities in a membrane filtration system for a drinking water treatment plant. Microbes Environ. 26, 149–155. Liao, X.B., Chen, C., Wang, Z., Wan, R., Chang, C.H., Zhang, X.J., Xie, S.G., 2013. Pyrosequencing analysis of bacterial communities in drinking water biofilters receiving influents of different types. Process Biochem. 48 (4), 703–707. Lu, L., Xing, D.F., Ren, N.Q., 2012. Pyrosequencing reveals highly diverse microbial communities in microbial electrolysis cells involved in enhanced H2 production from waste activated sludge. Water Res. 46 (7), 2425–2434. Margulies, M., Egholm, M., Altman, W.E., Attiya, S., Bader, J.S., Bemben, L.A., Berka, J., Braverman, M.S., Chen, Y.J., Chen, Z.T., Dewell, S.B., Du, L., Fierro, J.M., Gomes, X.V., Godwin, B.C., He, W., Helgesen, S., Ho, C.H., Irzyk, G.P., Jando, S.C., Alenquer, M.L.I.,

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