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The dynamics of the water bloom-forming Microcystis aeruginosa and its relationship with biotic and abiotic factors in Lake Taihu, China
Ecological Engineering 47 (2012) 274–277
Contents lists available at SciVerse ScienceDirect
Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Short communication
The dynamics of the water bloom-forming Microcystis aeruginosa and its relationship with biotic and abiotic factors in Lake Taihu, China Ping Zhang a , Chunmei Zhai a , Ruoqi Chen a , Changhong Liu a,∗ , Yarong Xue a , Jihong Jiang b a b
State Key Laboratory of Pharmaceutical Biotechnology, School of Life Science, Nanjing University, Nanjing 210093, China The Key Laboratory of Biotechnology for Medicinal Plants of Jiangsu Province, Xuzhou 221006, China
a r t i c l e
i n f o
Article history: Received 17 February 2012 Received in revised form 29 June 2012 Accepted 16 July 2012 Available online 9 August 2012 Keywords: M. aeruginosa Algicidal Pseudomonas aeruginosa R219 Bacteria Environmental factors
a b s t r a c t The dynamics of the bloom-forming cyanobacterium Microcystis aeruginosa (MA), total bacteria (TB), and the algicidal bacterium Pseudomonas aeruginosa R219 (PaR) in a eutrophic lake was followed from December 2007 to November 2008 by measuring the copy numbers of 16s rRNA genes (CNrG) of each species using a real-time fluorescence quantitative PCR (FQ-PCR) technique. The highest CNrG of MA was observed in July (3.4 × 105 copies mL−1 ), while those of TB and PaR peaked in May (2.1 × 106 copies mL−1 ) and August (3.2 × 104 copies mL−1 ), respectively. A significant relationship was found between the CNrG of MA and biological factors such as the CNrG of PaR, TB, and the ratio of PaR to TB, as well as environmental factors including dissolved nitrogen (DN) and surface water temperature (T) (r2 = 0.955, p < 0.001), suggesting that the dynamics of the algicidal bacterium PaR and TB, particularly the ratio of PaR to TB, may regulate the abundance of M. aeruginosa. Thus, we suggest that the algicidal bacterium PaR together with T and DN might play important roles in MA bloom formation and outbreaks in freshwater environments. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Algal blooms are widespread in many lakes around the world, but the dominant species in the harmful blooms of phytoplankton vary greatly among different lakes. In Lake Taihu, one of the largest shallow eutrophic freshwater lakes in China, cyanobacteria and Microcystis represent 90% and 60% of the total algae during bloom periods (Chen et al., 2009). Based on its morphological features, the dominant individual species within Microcystis group was characterized as Microcystis aeruginosa (MA) (Yoshida et al., 2007). MA is a common unicellular colonial cyanobacterium and is one of the most ecologically damaging species due to its harmful effects on water quality and human health in contaminated water ecosystems (Duan et al., 2009). Many studies have been conducted to determine how MA becomes the dominant species in a phytoplankton community and to identify environmental factors such as nitrogen, phosphorus, iron, temperature, light, and biotic factors such as algicidal bacteria, viruses, protozoans, fungi, and aquatic macrophytes that may be responsible for bloom development (Chen et al., 2012; Jia et al., 2010; Paerl and Huisman, 2008; Ren et al., 2009). A number of algicidal bacteria have been tested for their algicidal effects on Anabaena cylindrical, Synechococcus cedrorum,
∗ Corresponding author. Tel.: +86 2583685469; fax: +86 2583685469. E-mail address: [email protected] (C. Liu). 0925-8574/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ecoleng.2012.07.004
Phormidium tadzschicicum, Aphanizomenon flos-aquae, Nostoc ellipsosporum, Oscillatoria spp., and Microcystis sp. (Manage et al., 2001), and some of them have been found to regulate the abundance of MA and bloom formation in the freshwater ecosystem (Manage et al., 2001; Mayali and Azam, 2004). However, the real influence of algicidal bacteria on the dynamics of bloom-forming cyanobacteria in the field remains unclear due to a lack of effective techniques for measuring the densities of cyanobacterial and bacterial populations in the water. With the development of molecular techniques, it has become possible to characterize variations in bacterial and algal communities in natural ecosystems over time with PCR using primers targeting a specific gene directly from environmental samples, i.e., without cultivation (Yoshida et al., 2007). Saker et al. (2007) used a genus-specific polymerase chain reaction (PCR) to detect the abundance of Microcystis in 26 environmental water samples collected from freshwater lakes, rivers, and reservoirs in Portugal. Recently, a real-time fluorescent quantitative PCR (FQ-PCR) method has been used to quantitatively detect the abundance of cyanobacteria and algicidal Pseudomonas in environmental samples (Peng et al., 2011; Zhao et al., 2006). However, these techniques have not been used to investigate the internal dynamics of cyanobacteria and algicidal bacteria in a natural eutrophic freshwater ecosystem. This study was undertaken to investigate the seasonal changes in MA, total bacteria (TB), and the algicidal bacterium Pseudomonas aeruginosa R219 (PaR) (Ren et al., 2009) in Lake Taihu by measuring
P. Zhang et al. / Ecological Engineering 47 (2012) 274–277
the copy numbers of the 16s rRNA genes (CNrG) of each species using the FQ-PCR technique. The effects of TB, PaR, and environmental factors such as dissolved phosphorus (DP) and nitrogen (DN) and surface water temperature (T) on the dynamics of the MA population were analyzed by a stepwise regression model (Chen et al., 2001). Moreover, the ecological significance of algicidal bacteria on dynamic changes in MA in the eutrophic lake is discussed. 2. Materials and methods 2.1. Sampling Water from the surface layer of the lake (0.4 m) was sampled monthly at 5 fixed points at intervals of 500 m in Meiliang Bay (31◦ 47 N, 12◦ 19 E), Lake Taihu from December 2007 to November 2008. A 100 mL sample of water was taken from each point, and the samples were mixed thoroughly in a 1-L glass flask. The concentrations of dissolved phosphorus (DP) and nitrogen (DN) in the water were first quantified by passing through a glass fiber filter and analyzed using the ammonium molybdate (Menzel and Corwin, 1965) and alkaline potassium persulfate digestion-UV (D’Elia et al., 1977) spectrophotometric method, respectively. T was determined using a digital thermometer in situ. 2.2. DNA extraction Total DNA was extracted from the water samples. Briefly, a 1L water sample was passed through a filter (0.45 m) to obtain a small pellet of concentrated microorganisms on the filter, and the passing solution was centrifuged at 2900 × g for 30 min to obtain an additional pellet. The genomic DNA was extracted from the combined pellets and the reference strains using the previously described method (Saker et al., 2007). 2.3. Plasmid construction The Micr184F/Micr431R primer pair was used to amplify a 220 bp fragment of the 16s rRNA gene from the reference strain M. aeruginosa PCC7820, which is specific for MA (Saker et al., 2007). Tf/Tr and Pf/Pr primer pairs were used to amplify 370 bp and 137 bp specific fragments of the 16s rRNA gene from the reference strain of PaR (CGMCC, No2754), which are specific for general bacteria and P. aeruginosa, respectively (Xue et al., 2006). The PCR products were cloned into the pMD18-T vector according to the manufacturer’s instructions. The recombined plasmids containing the given 16s rRNA gene fragments were named pMDT-TB (370 bp), pMDT-PaR (137 bp), and pMDT-MA (220 bp), respectively, transformed into DH5␣ competent cells, re-isolated from culture broth with a Mini Plasmid Purification Kit (Axygen) and stored at −20 ◦ C for FQ-PCR analysis. 2.4. FQ-PCR The CNrG values were calculated based on the molecular weight and concentrations of the given plasmids (Xue et al., 2006). The FQ-PCR assay was performed in a volume of 25 L containing 12.5 L SYBR Premix Ex Taq (Takara Biotechnology, Dalian, Co., Ltd.), 10 pmol of each primer, 1 L DNA from either a plasmid or a lake water sample, and 9.5 L of sterile ultra-pure water. The PCR was carried out in a TaKaRa PCR Thermal Cycler Dice DP600 using Thermal Cycler Dice Real Time SYSTEM DP800 Software (Takara Biotechnology, Dalian, Co., Ltd.). The thermal cycling conditions consisted of an initial cycle of 95 ◦ C for 30 s, 40 cycles of 95 ◦ C for 30 s, 60 ◦ C for 30 s, and 84 ◦ C for 30 s. A titration experiment with serial dilutions of the given plasmids (pMDT-TB, pMDT-PaR,
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Table 1 The concentrations of dissolved phosphorus (DP) and dissolved nitrogen (DN) and the surface water temperature (T) in Lake Taihu from December 2007 to November 2008. Month
T (◦ C)
DP (g mL−1 )
D J F M A M J J A S O N
11 4 8 15 18 23 26 32 32 27 20 10
0.05 0.12 0.08 0.03 0.08 0.02 0.10 0.05 0.10 0.12 0.15 0.04
± ± ± ± ± ± ± ± ± ± ± ±
0.001 0.003 0.002 0.002 0.002 0.002 0.011 0.011 0.060 0.023 0.004 0.003
DN (g mL−1 ) 0.64 0.69 0.66 0.51 1.32 1.01 0.89 0.71 1.41 0.97 0.17 0.13
± ± ± ± ± ± ± ± ± ± ± ±
0.004 0.035 0.006 0.017 0.006 0.026 0.010 0.002 0.027 0.017 0.110 0.021
DN/DP 13.2 5.6 8.2 15.7 16.5 41.8 8.7 13.5 14.4 7.8 1.1 3.3
and pMDT-MA) was used to create a standard curve of the change in Ct (threshold cycles) with each dilution. Standard curves were made by plotting the Ct values obtained as a linear function of the base 10 logarithm of the initial CNrG in the given plasmids (107 –104 copies L−1 ). The CNrG values for the TB, PaR, and MA in the water samples were determined by comparing the Ct values obtained to the standard curves. DNA extracts from the different samples were analyzed in triplicate in the PCR assay. The amplification efficiency (E) was estimated by using the slope of the standard curve and the formula E = (10−1/slope − 1). 2.5. Data analysis A stepwise regression analysis model was used to test the relationship between the dependent and independent variances using SPSS software version 13.0 (Chen et al., 2001). The dependent variance was the CNrG value of MA, and the independent variances were the CNrG values of TB and PaR; the ratio of PaR to TB; the concentration of DN, DP; the ratio of DN to DP; and T. All independent variances that made a significant contribution (p < 0.05) to the dependent variance were used in the final regression equation as described in Section 3. Data in the figures were presented as the mean ± SD. 3. Results 3.1. Concentrations of DN and DP, and T in the lake water During the study period, T varied between 4 and 32 ◦ C with an average of 18.8 ◦ C (Table 1). The concentrations of DP and DN fluctuated from approximately 0.02–0.15 g mL−1 and 0.13–1.41 g mL−1 , respectively. The ratio of DN to DP changed seasonally; this value was lowest in the winter, with an average of 5.8, but increased rapidly in the spring and summer (approximately 26 fold) from 1.6 in January to 41.8 in May (Table 1). 3.2. Dynamics of the MA population The amplification efficiency (E) of the FQ-PCR assay was 85.4% and the melting curves of the samples showed a peak at approximately 89 ◦ C, corresponding to the melting temperature of the reference strain of M. aeruginosa PCC7820. The CNrG of MA amplified with the Micr184F and Micr431R primer pair varied between 5.9 × 103 and 3.4 × 105 copies mL−1 (Fig. 1). Except in December 2007, the CNrG was relatively low from January to June with the average of 3.1 × 104 copies mL−1 , but increased significantly by approximately 18 times from May to July.
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P. Zhang et al. / Ecological Engineering 47 (2012) 274–277
ranged from 0.001 to 0.060. The highest ratios were approximately 0.060, 0.020, and 0.024 in August, July, and November, respectively. 3.4. Factors related to the dynamics of the MA population in Lake Taihu
Fig. 1. The copy numbers of the 16s rRNA gene (CNrG) for M. aeruginosa (MA) over a year-long study from December 2007 to November 2008 (D to N on the x axis).
After that, the CNrG gradually decreased to the normal level from September to November, with an average of 3.3 × 104 copies mL−1 . 3.3. Dynamics of the PaR and TB populations The amplification efficiency values (E) of the FQ-PCR assay for TB and PaR were 106.4% and 105.5%, respectively. Melting curves of the environmental 16s rRNA gene FQ-PCR products showed peaks at approximately 88.4 ◦ C and 86 ◦ C, respectively, corresponding to the melting temperatures of the product from reference strain of P. aeruginosa R219 generated using Tf/Tr and Pf/Pr primer pairs, respectively. The CNrG of TB and PaR varied from 6.7 × 104 to 2.1 × 106 copies mL−1 and from 7.0 × 102 to 3.2 × 104 copies mL−1 , respectively (Fig. 2). The CNrG of the TB in the lake was relatively low and quite steady from the winter to spring but changed considerably in other seasons, forming peaks in May (2.1 × 106 copies mL−1 ) and September (7.5 × 105 copies mL−1 ) and decreased to its lowest number in July (3.6 × 105 copies mL−1 ). The CNrG from April to June accounted for 56% of the TB population in the whole year. However, the distribution of PaR subpopulation in Lake Taihu was quite different from that of the TB population. The CNrG of PaR increased approximately 12% from March to May and reached the peak in August (3.2 × 104 copies mL−1 ), approximately 16.0, 14.3, and 4.4 times as high as that in March, May, and July, respectively. In winter, the numbers of all the bacteria including PaR decreased to the lowest densities (Fig. 2). Moreover, the ratio of PaR to TB
Fig. 2. The copy numbers of the 16s rRNA gene (CNrG) for the total bacteria (TB) and the algicidal bacterium Pseudomonas aeruginosa R219 (PaR) in the water samples collected from December 2007 to November 2008 (D to N on the x axis).
Stepwise regression analysis indicated that, except for DP and the ratio of DN to DP, the biotic factors such as PaR, TB, the ratio of PaR to TB, and the environmental factors including DN and T had significant influences on the seasonal change of the MA population in Lake Taihu. The relationship between the MA population and the factors could be described by the regression equation: Y = −38,364.8 + 23,278.937X1 − 23.13X2 + 6999.512X3 − 0.064X4 + 67,582.633X5 (r2 = 0.955, p < 0.001), where Y represents the CNrG of MA; X1 is the CNrG ratio of PaR to TB; X2 is the CNrG of PaR; X3 T; X4 is the CNrG of TB; and X5 DN. According to the equation, the ratio of PaR to TB, T, and DN all had a positive influence on the increase of MA population, whereas TB and PaR had negative effects on it. Specifically, the ratio of PaR to TB had the most significant effect on the development of the MA population among the tested factors. 4. Discussion MA bloom occurs almost every year in the eutrophic Lake Taihu. The highest abundance usually appears in July and the lowest abundance is observed in January (Song et al., 2007). The present study showed the same dynamic pattern. The MA population increased rapidly from May and declined in August in 2008 (Fig. 1). It has been reported that many environmental factors influence the dynamics or succession of MA; however, no consistent results have been achieved to date due to the varied conditions in different lakes. For instance, the main factors affecting MA population changes in Lake Dianchi, China are pH, T, and chemical oxygen demand (COD) (Li et al., 2007), but in Lake Kasumigaura, Japan, the major factors are pH, DN, and T (Wei et al., 2001). In Lake Taihu, Chen et al. (2001) reported that T, NO3 -N, and DN rather than Secchi depth (SD), pH, COD, dissolved oxygen, NO2 -N, and DP had a significant influence on the dynamic changes of MA. In this study, we also found that T and DN played a significant role in regulating the dynamics of MA in the lake. In addition to the abiotic factors, the bacterioplankton also show a major influence on the community compositions and the abundance of phytoplankton in Lake Taihu (Xing et al., 2007). Some bacteria enhance the bloom formation of MA by decomposing the unavailable organic P into dissolved organic P (Zhao et al., 2012), but others, such as algicidal bacteria, strongly or selectively inhibit algal growth (Manage et al., 2001). The effect of bacterioplankton on MA depends on both the specific species of bacteria and the environmental conditions. For instance, the lysis of algae by bacteria is rapid at water temperatures of 25–37 ◦ C and pH values of 8.0–9.5 (Daft et al., 1975). PaR, one of the algicidal bacteria that we have isolated from Lake Taihu, exhibits strong algicidal effect on MA (Ren et al., 2009). According to CNrG, the highest abundance of PaR occurred in August, one month later than the highest population of MA (Figs. 1 and 2). A similar phenomenon has been observed in other eutrophic lakes (Fukami et al., 1996; Mayali and Doucette, 2002). Thus, we suggest that PaR may act as a decomposer, decomposing blooms of MA in freshwater environments. Although many factors affect the dynamic changes of MA in the lake, the abundance of TB, PaR, and the ratio of PaR to TB together with T and DN have significant contributions to the seasonal changes of MA in Lake Taihu based on stepwise regression analysis. In particular, the ratio of PaR to TB, T, and DN exert positive
P. Zhang et al. / Ecological Engineering 47 (2012) 274–277
influences, whereas TB and PaR negatively regulate the increase in the MA population. To the best of our knowledge, this was the first study to describe the dynamic relationship between the MA population and the biotic/abiotic factors in the freshwater ecosystem. However, the relative contribution of algicidal bacteria to the dynamics of MA in Lake Taihu is still unclear. Further work will be conducted to study the ecology and physiology of the algicidal bacterium PaR and its regulation on the dynamics of MA to better understand the mechanism of MA bloom formation and provide a new strategy for bloom control. Acknowledgments This work was supported by the National Basic Research Program of China (2008CB418004), the Jiangsu Science and Technology Support Program (BE2011355), the Special Fund for the Public Service Sector of the National Environmental Protection Ministry (201009023), the Fundamental Research Funds for the Central Universities (1082020803, 1092020804), and the National Training Program for Fundamental Scientists (J1103512). References Chen, F.Z., Song, X.L., Hu, Y.H., Liu, Z.W., Qin, B.Q., 2009. Water quality improvement and phytoplankton response in the drinking water source in Meiliang Bay of Lake Taihu, China. Ecol. Eng. 35, 1637–1645. Chen, J.Z., Zhang, H.Y., Han, Z.P., Ye, J.Y., Liu, Z.L., 2012. The influence of aquatic macrophytes on Microcystis aeruginosa growth. Ecol. Eng. 42, 130–133. Chen, Y.W., Qin, B.Q., Gao, X.Y., 2001. Prediction of blue-green algae bloom using stepwise multiple regression between algae and related environmental factors in Meiliang Bay, Lake Taihu. J. Lake Sci. 13, 63–70. Daft, M.J., Susan, M., McCord, B., Stewart, W.D.P., 1975. Ecological studies on algallysing bacteria in fresh waters. Freshwater Biol. 5, 577–596. D’Elia, C.F., Steudler, P.A., Corwin, N., 1977. Determination of total nitrogen in aqueous samples using persulfate digestion. Limnol. Oceanogr. 22, 760–764. Duan, H.T., Ma, R.H., Xu, X.F., Kong, F.X., Zhang, S.X., Kong, W.J., Hao, J.Y., Shang, L.L., 2009. Two-decade reconstruction of algal blooms in China’s Lake Taihu. Environ. Sci. Technol. 43, 3522–3528. Fukami, K., Murata, N., Morio, Y., Nishijima, T., 1996. Distribution of heterotrophic nanoflagellates and their importance as the bacterial consumer in a eutrophic coastal seawater. J. Oceanogr. 52, 399–407.
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Jia, Y., Wang, Q., Chen, Z.H., Jiang, W.X., Zhang, P., Tian, X.J., 2010. Inhibition of phytoplankton species by co-culture with a fungus. Ecol. Eng. 36, 1389–1391. Li, H.B., Hou, G.X., Dakui, F., Xiao, B.D., Song, L.R., Liu, Y.D., 2007. Prediction and elucidation of the population dynamics of Microcystis spp. in Lake Dianchi (China) by means of artificial neural networks. Ecol. Inform. 2, 184–192. Manage, P.M., Kawabata, Z., Nakano, S., 2001. Dynamics of cyanophage-like particles and algicidal bacteria causing Microcystis aeruginosa mortality. Limnology 2, 73–78. Mayali, X., Azam, F., 2004. Algicidal bacteria in the sea and their impact on algal blooms. J. Eukaryot. Microbiol. 51, 139–144. Mayali, X., Doucette, G.J., 2002. Microbial community interactions and population dynamics of an algicidal bacterium active against Karenia brevis (Dinophyceae). Harmful Algae 1, 277–293. Menzel, D.W., Corwin, N., 1965. The measurement of total phosphorus in seawater based on the liberation of organically bound fractions by persulfate oxidation. Limnol. Oceanogr. 10, 280–282. Paerl, H.W., Huisman, J., 2008. Blooms like it hot. Science 320, 57–58. Peng, Y.K., Yue, D.M., Wu, J., Xiao, L., Yang, L.Y., 2011. Comparison of RT-qPCR approaches for quantification of cyanobacteria in Lake Taihu. Microbiol. China 38, 460–467. Ren, H.Q., Zhang, P., Liu, C.H., Xue, Y.R., Lian, B., 2009. The potential use of bacterium strain R219 for controlling of the bloom-forming cyanobacteria in freshwater lake. World J. Microbiol. Biotechnol. 26, 465–472. Saker, M.L., Vale, M., Kramer, D., Vasconcelos, V.M., 2007. Molecular techniques for the early warning of toxic cyanobacteria blooms in freshwaters lakes and rivers. Appl. Microbiol. Biotechnol. 75, 441–449. Song, L.R., Chen, W., Peng, L., Wan, N., Gan, N.Q., Zhang, X.M., 2007. Distribution and bioaccumulation of microcystins in water columns: a systematic investigation into the environmental fate and the risks associates with microcystins in Meiliang Bay, Lake Taihu. Water Res. 41, 2853–2864. Wei, B., Sugiura, N., Maekawa, T., 2001. Use of artificial neural network in the prediction of algal blooms. Water Res. 35, 2022–2028. Xing, P., Kong, F.X., Cao, H.S., Zhang, M., 2007. Relationship between bacterioplankton and phytoplankton community dynamics during late spring and early summer in Lake Taihu, China. Acta Ecol. Sin. 27, 1696–1702. Xue, L.J., Wang, Y.Z., Ren, H., Tong, Y.M., Zhao, P., Zhu, S.Y., Qi, Z.T., 2006. Rapid detection of Pseudomonas aeruginosa by the fluorescence quantitative PCR assay targeting 16S rDNA. Chin. J. Biotechnol. 22, 789–794. Yoshida, M., Yoshida, T., Takashima, Y., Hosoda, N., Hiroishi, S., 2007. Dynamics of microcystin-producing and non-microcystin producing Microcystis populations is correlated with nitrate concentration in a Japanese lake. FEMS Microbiol. Lett. 266, 49–53. Zhao, C.P., Pu, Y.P., Yin, L.H., Liang, G.Y., Lu, X., Li, X.N., 2006. Detection of abundance of Pseudomonas in environmental samples by real-time quantitative PCR. J. Southeast Univ. 36, 143–146. Zhao, G.Y., Du, J.J., Jia, Y., Lv, Y.N., Han, G.M., Tian, X.J., 2012. The importance of bacteria in promoting algal growth in eutrophic lakes with limited available phosphorus. Ecol. Eng. 42, 107–111.
Contents lists available at SciVerse ScienceDirect
Ecological Engineering journal homepage: www.elsevier.com/locate/ecoleng
Short communication
The dynamics of the water bloom-forming Microcystis aeruginosa and its relationship with biotic and abiotic factors in Lake Taihu, China Ping Zhang a , Chunmei Zhai a , Ruoqi Chen a , Changhong Liu a,∗ , Yarong Xue a , Jihong Jiang b a b
State Key Laboratory of Pharmaceutical Biotechnology, School of Life Science, Nanjing University, Nanjing 210093, China The Key Laboratory of Biotechnology for Medicinal Plants of Jiangsu Province, Xuzhou 221006, China
a r t i c l e
i n f o
Article history: Received 17 February 2012 Received in revised form 29 June 2012 Accepted 16 July 2012 Available online 9 August 2012 Keywords: M. aeruginosa Algicidal Pseudomonas aeruginosa R219 Bacteria Environmental factors
a b s t r a c t The dynamics of the bloom-forming cyanobacterium Microcystis aeruginosa (MA), total bacteria (TB), and the algicidal bacterium Pseudomonas aeruginosa R219 (PaR) in a eutrophic lake was followed from December 2007 to November 2008 by measuring the copy numbers of 16s rRNA genes (CNrG) of each species using a real-time fluorescence quantitative PCR (FQ-PCR) technique. The highest CNrG of MA was observed in July (3.4 × 105 copies mL−1 ), while those of TB and PaR peaked in May (2.1 × 106 copies mL−1 ) and August (3.2 × 104 copies mL−1 ), respectively. A significant relationship was found between the CNrG of MA and biological factors such as the CNrG of PaR, TB, and the ratio of PaR to TB, as well as environmental factors including dissolved nitrogen (DN) and surface water temperature (T) (r2 = 0.955, p < 0.001), suggesting that the dynamics of the algicidal bacterium PaR and TB, particularly the ratio of PaR to TB, may regulate the abundance of M. aeruginosa. Thus, we suggest that the algicidal bacterium PaR together with T and DN might play important roles in MA bloom formation and outbreaks in freshwater environments. © 2012 Elsevier B.V. All rights reserved.
1. Introduction Algal blooms are widespread in many lakes around the world, but the dominant species in the harmful blooms of phytoplankton vary greatly among different lakes. In Lake Taihu, one of the largest shallow eutrophic freshwater lakes in China, cyanobacteria and Microcystis represent 90% and 60% of the total algae during bloom periods (Chen et al., 2009). Based on its morphological features, the dominant individual species within Microcystis group was characterized as Microcystis aeruginosa (MA) (Yoshida et al., 2007). MA is a common unicellular colonial cyanobacterium and is one of the most ecologically damaging species due to its harmful effects on water quality and human health in contaminated water ecosystems (Duan et al., 2009). Many studies have been conducted to determine how MA becomes the dominant species in a phytoplankton community and to identify environmental factors such as nitrogen, phosphorus, iron, temperature, light, and biotic factors such as algicidal bacteria, viruses, protozoans, fungi, and aquatic macrophytes that may be responsible for bloom development (Chen et al., 2012; Jia et al., 2010; Paerl and Huisman, 2008; Ren et al., 2009). A number of algicidal bacteria have been tested for their algicidal effects on Anabaena cylindrical, Synechococcus cedrorum,
∗ Corresponding author. Tel.: +86 2583685469; fax: +86 2583685469. E-mail address: [email protected] (C. Liu). 0925-8574/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ecoleng.2012.07.004
Phormidium tadzschicicum, Aphanizomenon flos-aquae, Nostoc ellipsosporum, Oscillatoria spp., and Microcystis sp. (Manage et al., 2001), and some of them have been found to regulate the abundance of MA and bloom formation in the freshwater ecosystem (Manage et al., 2001; Mayali and Azam, 2004). However, the real influence of algicidal bacteria on the dynamics of bloom-forming cyanobacteria in the field remains unclear due to a lack of effective techniques for measuring the densities of cyanobacterial and bacterial populations in the water. With the development of molecular techniques, it has become possible to characterize variations in bacterial and algal communities in natural ecosystems over time with PCR using primers targeting a specific gene directly from environmental samples, i.e., without cultivation (Yoshida et al., 2007). Saker et al. (2007) used a genus-specific polymerase chain reaction (PCR) to detect the abundance of Microcystis in 26 environmental water samples collected from freshwater lakes, rivers, and reservoirs in Portugal. Recently, a real-time fluorescent quantitative PCR (FQ-PCR) method has been used to quantitatively detect the abundance of cyanobacteria and algicidal Pseudomonas in environmental samples (Peng et al., 2011; Zhao et al., 2006). However, these techniques have not been used to investigate the internal dynamics of cyanobacteria and algicidal bacteria in a natural eutrophic freshwater ecosystem. This study was undertaken to investigate the seasonal changes in MA, total bacteria (TB), and the algicidal bacterium Pseudomonas aeruginosa R219 (PaR) (Ren et al., 2009) in Lake Taihu by measuring
P. Zhang et al. / Ecological Engineering 47 (2012) 274–277
the copy numbers of the 16s rRNA genes (CNrG) of each species using the FQ-PCR technique. The effects of TB, PaR, and environmental factors such as dissolved phosphorus (DP) and nitrogen (DN) and surface water temperature (T) on the dynamics of the MA population were analyzed by a stepwise regression model (Chen et al., 2001). Moreover, the ecological significance of algicidal bacteria on dynamic changes in MA in the eutrophic lake is discussed. 2. Materials and methods 2.1. Sampling Water from the surface layer of the lake (0.4 m) was sampled monthly at 5 fixed points at intervals of 500 m in Meiliang Bay (31◦ 47 N, 12◦ 19 E), Lake Taihu from December 2007 to November 2008. A 100 mL sample of water was taken from each point, and the samples were mixed thoroughly in a 1-L glass flask. The concentrations of dissolved phosphorus (DP) and nitrogen (DN) in the water were first quantified by passing through a glass fiber filter and analyzed using the ammonium molybdate (Menzel and Corwin, 1965) and alkaline potassium persulfate digestion-UV (D’Elia et al., 1977) spectrophotometric method, respectively. T was determined using a digital thermometer in situ. 2.2. DNA extraction Total DNA was extracted from the water samples. Briefly, a 1L water sample was passed through a filter (0.45 m) to obtain a small pellet of concentrated microorganisms on the filter, and the passing solution was centrifuged at 2900 × g for 30 min to obtain an additional pellet. The genomic DNA was extracted from the combined pellets and the reference strains using the previously described method (Saker et al., 2007). 2.3. Plasmid construction The Micr184F/Micr431R primer pair was used to amplify a 220 bp fragment of the 16s rRNA gene from the reference strain M. aeruginosa PCC7820, which is specific for MA (Saker et al., 2007). Tf/Tr and Pf/Pr primer pairs were used to amplify 370 bp and 137 bp specific fragments of the 16s rRNA gene from the reference strain of PaR (CGMCC, No2754), which are specific for general bacteria and P. aeruginosa, respectively (Xue et al., 2006). The PCR products were cloned into the pMD18-T vector according to the manufacturer’s instructions. The recombined plasmids containing the given 16s rRNA gene fragments were named pMDT-TB (370 bp), pMDT-PaR (137 bp), and pMDT-MA (220 bp), respectively, transformed into DH5␣ competent cells, re-isolated from culture broth with a Mini Plasmid Purification Kit (Axygen) and stored at −20 ◦ C for FQ-PCR analysis. 2.4. FQ-PCR The CNrG values were calculated based on the molecular weight and concentrations of the given plasmids (Xue et al., 2006). The FQ-PCR assay was performed in a volume of 25 L containing 12.5 L SYBR Premix Ex Taq (Takara Biotechnology, Dalian, Co., Ltd.), 10 pmol of each primer, 1 L DNA from either a plasmid or a lake water sample, and 9.5 L of sterile ultra-pure water. The PCR was carried out in a TaKaRa PCR Thermal Cycler Dice DP600 using Thermal Cycler Dice Real Time SYSTEM DP800 Software (Takara Biotechnology, Dalian, Co., Ltd.). The thermal cycling conditions consisted of an initial cycle of 95 ◦ C for 30 s, 40 cycles of 95 ◦ C for 30 s, 60 ◦ C for 30 s, and 84 ◦ C for 30 s. A titration experiment with serial dilutions of the given plasmids (pMDT-TB, pMDT-PaR,
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Table 1 The concentrations of dissolved phosphorus (DP) and dissolved nitrogen (DN) and the surface water temperature (T) in Lake Taihu from December 2007 to November 2008. Month
T (◦ C)
DP (g mL−1 )
D J F M A M J J A S O N
11 4 8 15 18 23 26 32 32 27 20 10
0.05 0.12 0.08 0.03 0.08 0.02 0.10 0.05 0.10 0.12 0.15 0.04
± ± ± ± ± ± ± ± ± ± ± ±
0.001 0.003 0.002 0.002 0.002 0.002 0.011 0.011 0.060 0.023 0.004 0.003
DN (g mL−1 ) 0.64 0.69 0.66 0.51 1.32 1.01 0.89 0.71 1.41 0.97 0.17 0.13
± ± ± ± ± ± ± ± ± ± ± ±
0.004 0.035 0.006 0.017 0.006 0.026 0.010 0.002 0.027 0.017 0.110 0.021
DN/DP 13.2 5.6 8.2 15.7 16.5 41.8 8.7 13.5 14.4 7.8 1.1 3.3
and pMDT-MA) was used to create a standard curve of the change in Ct (threshold cycles) with each dilution. Standard curves were made by plotting the Ct values obtained as a linear function of the base 10 logarithm of the initial CNrG in the given plasmids (107 –104 copies L−1 ). The CNrG values for the TB, PaR, and MA in the water samples were determined by comparing the Ct values obtained to the standard curves. DNA extracts from the different samples were analyzed in triplicate in the PCR assay. The amplification efficiency (E) was estimated by using the slope of the standard curve and the formula E = (10−1/slope − 1). 2.5. Data analysis A stepwise regression analysis model was used to test the relationship between the dependent and independent variances using SPSS software version 13.0 (Chen et al., 2001). The dependent variance was the CNrG value of MA, and the independent variances were the CNrG values of TB and PaR; the ratio of PaR to TB; the concentration of DN, DP; the ratio of DN to DP; and T. All independent variances that made a significant contribution (p < 0.05) to the dependent variance were used in the final regression equation as described in Section 3. Data in the figures were presented as the mean ± SD. 3. Results 3.1. Concentrations of DN and DP, and T in the lake water During the study period, T varied between 4 and 32 ◦ C with an average of 18.8 ◦ C (Table 1). The concentrations of DP and DN fluctuated from approximately 0.02–0.15 g mL−1 and 0.13–1.41 g mL−1 , respectively. The ratio of DN to DP changed seasonally; this value was lowest in the winter, with an average of 5.8, but increased rapidly in the spring and summer (approximately 26 fold) from 1.6 in January to 41.8 in May (Table 1). 3.2. Dynamics of the MA population The amplification efficiency (E) of the FQ-PCR assay was 85.4% and the melting curves of the samples showed a peak at approximately 89 ◦ C, corresponding to the melting temperature of the reference strain of M. aeruginosa PCC7820. The CNrG of MA amplified with the Micr184F and Micr431R primer pair varied between 5.9 × 103 and 3.4 × 105 copies mL−1 (Fig. 1). Except in December 2007, the CNrG was relatively low from January to June with the average of 3.1 × 104 copies mL−1 , but increased significantly by approximately 18 times from May to July.
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ranged from 0.001 to 0.060. The highest ratios were approximately 0.060, 0.020, and 0.024 in August, July, and November, respectively. 3.4. Factors related to the dynamics of the MA population in Lake Taihu
Fig. 1. The copy numbers of the 16s rRNA gene (CNrG) for M. aeruginosa (MA) over a year-long study from December 2007 to November 2008 (D to N on the x axis).
After that, the CNrG gradually decreased to the normal level from September to November, with an average of 3.3 × 104 copies mL−1 . 3.3. Dynamics of the PaR and TB populations The amplification efficiency values (E) of the FQ-PCR assay for TB and PaR were 106.4% and 105.5%, respectively. Melting curves of the environmental 16s rRNA gene FQ-PCR products showed peaks at approximately 88.4 ◦ C and 86 ◦ C, respectively, corresponding to the melting temperatures of the product from reference strain of P. aeruginosa R219 generated using Tf/Tr and Pf/Pr primer pairs, respectively. The CNrG of TB and PaR varied from 6.7 × 104 to 2.1 × 106 copies mL−1 and from 7.0 × 102 to 3.2 × 104 copies mL−1 , respectively (Fig. 2). The CNrG of the TB in the lake was relatively low and quite steady from the winter to spring but changed considerably in other seasons, forming peaks in May (2.1 × 106 copies mL−1 ) and September (7.5 × 105 copies mL−1 ) and decreased to its lowest number in July (3.6 × 105 copies mL−1 ). The CNrG from April to June accounted for 56% of the TB population in the whole year. However, the distribution of PaR subpopulation in Lake Taihu was quite different from that of the TB population. The CNrG of PaR increased approximately 12% from March to May and reached the peak in August (3.2 × 104 copies mL−1 ), approximately 16.0, 14.3, and 4.4 times as high as that in March, May, and July, respectively. In winter, the numbers of all the bacteria including PaR decreased to the lowest densities (Fig. 2). Moreover, the ratio of PaR to TB
Fig. 2. The copy numbers of the 16s rRNA gene (CNrG) for the total bacteria (TB) and the algicidal bacterium Pseudomonas aeruginosa R219 (PaR) in the water samples collected from December 2007 to November 2008 (D to N on the x axis).
Stepwise regression analysis indicated that, except for DP and the ratio of DN to DP, the biotic factors such as PaR, TB, the ratio of PaR to TB, and the environmental factors including DN and T had significant influences on the seasonal change of the MA population in Lake Taihu. The relationship between the MA population and the factors could be described by the regression equation: Y = −38,364.8 + 23,278.937X1 − 23.13X2 + 6999.512X3 − 0.064X4 + 67,582.633X5 (r2 = 0.955, p < 0.001), where Y represents the CNrG of MA; X1 is the CNrG ratio of PaR to TB; X2 is the CNrG of PaR; X3 T; X4 is the CNrG of TB; and X5 DN. According to the equation, the ratio of PaR to TB, T, and DN all had a positive influence on the increase of MA population, whereas TB and PaR had negative effects on it. Specifically, the ratio of PaR to TB had the most significant effect on the development of the MA population among the tested factors. 4. Discussion MA bloom occurs almost every year in the eutrophic Lake Taihu. The highest abundance usually appears in July and the lowest abundance is observed in January (Song et al., 2007). The present study showed the same dynamic pattern. The MA population increased rapidly from May and declined in August in 2008 (Fig. 1). It has been reported that many environmental factors influence the dynamics or succession of MA; however, no consistent results have been achieved to date due to the varied conditions in different lakes. For instance, the main factors affecting MA population changes in Lake Dianchi, China are pH, T, and chemical oxygen demand (COD) (Li et al., 2007), but in Lake Kasumigaura, Japan, the major factors are pH, DN, and T (Wei et al., 2001). In Lake Taihu, Chen et al. (2001) reported that T, NO3 -N, and DN rather than Secchi depth (SD), pH, COD, dissolved oxygen, NO2 -N, and DP had a significant influence on the dynamic changes of MA. In this study, we also found that T and DN played a significant role in regulating the dynamics of MA in the lake. In addition to the abiotic factors, the bacterioplankton also show a major influence on the community compositions and the abundance of phytoplankton in Lake Taihu (Xing et al., 2007). Some bacteria enhance the bloom formation of MA by decomposing the unavailable organic P into dissolved organic P (Zhao et al., 2012), but others, such as algicidal bacteria, strongly or selectively inhibit algal growth (Manage et al., 2001). The effect of bacterioplankton on MA depends on both the specific species of bacteria and the environmental conditions. For instance, the lysis of algae by bacteria is rapid at water temperatures of 25–37 ◦ C and pH values of 8.0–9.5 (Daft et al., 1975). PaR, one of the algicidal bacteria that we have isolated from Lake Taihu, exhibits strong algicidal effect on MA (Ren et al., 2009). According to CNrG, the highest abundance of PaR occurred in August, one month later than the highest population of MA (Figs. 1 and 2). A similar phenomenon has been observed in other eutrophic lakes (Fukami et al., 1996; Mayali and Doucette, 2002). Thus, we suggest that PaR may act as a decomposer, decomposing blooms of MA in freshwater environments. Although many factors affect the dynamic changes of MA in the lake, the abundance of TB, PaR, and the ratio of PaR to TB together with T and DN have significant contributions to the seasonal changes of MA in Lake Taihu based on stepwise regression analysis. In particular, the ratio of PaR to TB, T, and DN exert positive
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influences, whereas TB and PaR negatively regulate the increase in the MA population. To the best of our knowledge, this was the first study to describe the dynamic relationship between the MA population and the biotic/abiotic factors in the freshwater ecosystem. However, the relative contribution of algicidal bacteria to the dynamics of MA in Lake Taihu is still unclear. Further work will be conducted to study the ecology and physiology of the algicidal bacterium PaR and its regulation on the dynamics of MA to better understand the mechanism of MA bloom formation and provide a new strategy for bloom control. Acknowledgments This work was supported by the National Basic Research Program of China (2008CB418004), the Jiangsu Science and Technology Support Program (BE2011355), the Special Fund for the Public Service Sector of the National Environmental Protection Ministry (201009023), the Fundamental Research Funds for the Central Universities (1082020803, 1092020804), and the National Training Program for Fundamental Scientists (J1103512). References Chen, F.Z., Song, X.L., Hu, Y.H., Liu, Z.W., Qin, B.Q., 2009. Water quality improvement and phytoplankton response in the drinking water source in Meiliang Bay of Lake Taihu, China. Ecol. Eng. 35, 1637–1645. Chen, J.Z., Zhang, H.Y., Han, Z.P., Ye, J.Y., Liu, Z.L., 2012. The influence of aquatic macrophytes on Microcystis aeruginosa growth. Ecol. Eng. 42, 130–133. Chen, Y.W., Qin, B.Q., Gao, X.Y., 2001. Prediction of blue-green algae bloom using stepwise multiple regression between algae and related environmental factors in Meiliang Bay, Lake Taihu. J. Lake Sci. 13, 63–70. Daft, M.J., Susan, M., McCord, B., Stewart, W.D.P., 1975. Ecological studies on algallysing bacteria in fresh waters. Freshwater Biol. 5, 577–596. D’Elia, C.F., Steudler, P.A., Corwin, N., 1977. Determination of total nitrogen in aqueous samples using persulfate digestion. Limnol. Oceanogr. 22, 760–764. Duan, H.T., Ma, R.H., Xu, X.F., Kong, F.X., Zhang, S.X., Kong, W.J., Hao, J.Y., Shang, L.L., 2009. Two-decade reconstruction of algal blooms in China’s Lake Taihu. Environ. Sci. Technol. 43, 3522–3528. Fukami, K., Murata, N., Morio, Y., Nishijima, T., 1996. Distribution of heterotrophic nanoflagellates and their importance as the bacterial consumer in a eutrophic coastal seawater. J. Oceanogr. 52, 399–407.
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