w a t e r r e s e a r c h 5 2 ( 2 0 1 4 ) 2 1 8 e2 3 0
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/watres
Diversity and dynamics of microbial communities at each step of treatment plant for potable water generation Wenfang Lin a, Zhisheng Yu a,b,*, Hongxun Zhang a, Ian P. Thompson b a
College of Resources and Environment, University of Chinese Academy of Sciences, 19 A Yuquan Road, Shijingshan District, Beijing 100049, PR China b Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
article info
abstract
Article history:
The dynamics of bacterial and eukaryotic community associated with each step of a water
Received 13 July 2013
purification plant in China was investigated using 454 pyrosequencing and qPCR based
Received in revised form
approaches. Analysis of pyrosequencing revealed that a high degree diversity of bacterial
27 October 2013
and eukaryotic communities is present in the drinking water treatment process before
Accepted 29 October 2013
sand filtration. In addition, the microbial compositions of the biofilm in the sand filters and
Available online 7 November 2013
those of the water of the putatively clear tanks were distinct, suggesting that sand filtration and chlorination treatments played primary roles in removing exposed microbial com-
Keywords:
munities. Potential pathogens including Acinetobacter, Clostridium, Legionella, and Mycobac-
454 pyrosequencing
terium, co-occurred with protozoa such as Rhizopoda (Hartmannellidae), and fungi such as
Biofilm
Penicillium and Aspergillus. Furthermore, this study supported the ideas based on molecular
Drinking water treatment process
level that biofilm communities were different from those in corresponding water samples,
Microbial diversity
and that the concentrations of Mycobacterium spp., Legionella spp., and Naegleria spp. in the
qPCR
water samples declined with each step of the water treatment process by qPCR. Overall, this study provides the first detailed evaluation of bacterial and eukaryotic diversity at each step of an individual potable water treatment process located in China. ª 2013 Elsevier Ltd. All rights reserved.
1.
Introduction
The supply of safe drinking water to the public is one of the great technological advancements of the 19th century, as well as a major technological challenge (Berry et al., 2006; Kormas et al., 2010; Poitelon et al., 2010). It is well known that microorganisms are widely present in the drinking water treatment system, e.g. in the storage tanks, filter systems, and the interior of pipe walls (Bonadonna et al., 2009). Uncontrolled and
excessive microbial growth not only leads to the deterioration in water quality and the associated undesirable tastes, odors, and visual turbidity, but can also cause process malfunctioning such as clogging of filters, bio-fouling and bio-corrosion (Hammes et al., 2008). In particular, the occurrence of pathogens such as enteropathogenic Escherichia coli O157, Helicobacter pylori, Legionella pneumophila, and Mycobacterium avium may cause waterborne illnesses (Aw and Rose, 2012). Among these, Legionella and Mycobacterium were most commonly
* Corresponding author. College of Resources and Environment, University of Chinese Academy of Sciences, 19 A Yuquan Road, Shijingshan District, Beijing 100049, PR China. Tel./fax: þ86 10 88256462. E-mail address: [email protected] (Z. Yu). 0043-1354/$ e see front matter ª 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.watres.2013.10.071
w a t e r r e s e a r c h 5 2 ( 2 0 1 4 ) 2 1 8 e2 3 0
detected in the drinking water systems, although typically present in trace quantities (Vaerewijck et al., 2005; Feazel et al., 2009; Felfo¨ldi et al., 2010; Marciano-Cabral et al., 2010). For instance, sequences related to Legionella spp. were detected in our previous studies of the biofilms developing on household taps (Lin et al., 2013). Eukaryotic microorganisms such as protozoa, algae, and fungi are also ubiquitous in freshwater environments. The presence of free-living pathogenic amoebae such as Acanthamoeba, Hartmannella, and Naegleria in potable water, increases the possibility of infections (Poitelon et al., 2009b; Marciano-Cabral et al., 2010; Koubar et al., 2011). Surprisingly, the diversity of eukaryotes in drinking water treatment system has received limited attention based on molecular technology. In general, conventional water treatment system includes coagulationeflocculation, sedimentation, sand filtration, and disinfection processes (Abbaszadegan et al., 1997). However, still comparatively little is known of how each process impacts of the water microbial community and specifically how each step impacts on the species composition and relative abundance particular with regards biofilms. Hence, a thorough understanding of the community structure in biofilms may facilitate the management of drinking water production system and the control of biofilm (Simoes et al., 2007; Elhariry et al., 2012). To date most of what we know with regards microbial community compositions and their dynamics in the drinking water treatment system has come from studies employing traditional culture methods, community fingerprinting techniques, and low-throughput molecular approaches (Eichler et al., 2006; Roeder et al., 2010). Recent advances in sequencing technology have enabled high-throughput sequencing of microbial communities, e.g. 454 pyrosequencing, and this now enables microbial detailed community structure analysis to be achieved at high taxonomic resolution (Andersson et al., 2010). Pyrosequencing is an ideal tool for investigating microorganisms that constitute the bulk of the community (Gobet et al., 2012). However, it is not well suited for quantitatively tracking key functional members of the community (Zhang et al., 2011). Conversely, quantitative realtime PCR (qPCR) has been demonstrated to be a useful tool for quantitative analysis of specific microorganisms in environmental samples (Brinkman et al., 2003). By integrating highthroughput pyrosequencing with qPCR, the composition of whole microbial communities can be assessed whilst quantifying key functionally important members. This study is the first to unveil the dynamics of complex microbial communities in water samples undergoing different stages of treatment as well as in biofilms associated with key treatment steps including coagulationeflocculation, sedimentation, sand filtration, and disinfection by 454 pyrosequencing. In addition, qPCR has been used for quantitative analysis of potentially pathogenic microbes and free-living amoebae.
Mingfeng River (Source 1, supplying about 10%) and the East Trunk Canal (Source 2, supplying about 90%), both of which are branches of Yangtze River. This drinking water treatment plant produces nearly 8000 m3/day of water, and serves almost 40,000 people. In brief, the surface water is first subjected to coagulation in reaction tanks, which then flows into settling ponds, resulting in sedimentation. Water is successively filtered through the sand. To ensure of the treated drinking water is safe to drink, ClO2 (free residual chlorine: >0.1 mg L1 after 30 min contact time) is added at a minimal residual concentration (>0.02 mg L1) prior to distribution to households (Fig. 1). Biofilms can grow on the walls of treatment facilities which are composed of cement, resulting in some nuisance, such as increasing the opportunity for pathogens to survive and persist. Hence, the walls of the reaction tanks and settling ponds are regularly cleaned at intervals of 15 days. In addition, the sand filtration is backwashed every 24 h to maintain downstream water quality.
2.2.
Materials and methods
2.1.
Drinking water treatment process
The drinking water treatment plant is located in a small town of Hubei province, China. The water source came from the
Sampling and nucleic acid extraction
Biofilm and water samples were taken from each of the water treatment steps, including two raw water source areas, reaction tank, settling pond, sand filtration, and the clear water tank (Fig. 1). Each original sample was a mixture of three independent samples (Zhang et al., 2012). Water samples were collected at each treatment step and processed within 12 h. Microbial biomass was harvested from approximately 3 L water samples using 0.22 mm polycarbonate membranes (47 mm diameter, Millipore, USA) (Poitelon et al., 2009a). Meanwhile, the water in the reaction tanks, settling ponds, and clear water tanks was ordinarily drained in order to provide access to biofilms growing on the walls which were mechanically removed with a sterile scraper (Joh and Lee, 2011). To obtain the biofilm samples from the sand filters, backwashed water sample was collected and then treated as detailed above. For each sampling point, the physico-chemical and microbiological water quality parameters were determined. In situ water quality parameters (temperature and pH) were measured using a multi-parameters water quality monitoring sonde (YSI Professional Plus, USA). Other analyses included assessment of turbidity, CODMn, nitrate, nitrite, ammonia and total plate count which were performed according to Chinese standards (GB: 5749-2006) by Pony Testing International Group (Beijing, China). Samples were stored in an ice box before being transported to the laboratory, and then kept at 70 C for further usage. Filtered membranes with microbes were cut into pieces with a sterilized cutter, and total DNA extracted using FastDNA SPIN Kit (MP Biomedicals, USA), following the manufacturer’s protocol. For biofilm samples (w0.5 g), the same kit was used for extraction of total DNA.
2.3.
2.
219
PCR amplification and 454 Pyrosequencing
Bacterial V1eV3 region of the 16S rRNA gene was amplified using forward primer 8F: 50 -AGAGTTTGATCCTGGCTCAG-30 and reverse primer 533R: 50 - TTACCGCGGCTGCTGGCAC-30 . For eukaryotes, a PCR strategy was designed to amplify the variable V4 region of the 18S rRNA gene. The universal
220
w a t e r r e s e a r c h 5 2 ( 2 0 1 4 ) 2 1 8 e2 3 0
Fig. 1 e Schematic diagram of the drinking water treatment process.
forward primer 3NDf: 50 -GGCAAGTCTGGTGCCAG-30 and the reverse primer V4_euk_R2: 50 -ACGGTATCT(AG)ATC(AG) TCTTCG-30 were added into the reaction. The PCR program for both bacterial and eukaryotic amplification was: 95 C for 2 min, followed by 25 cycles at 95 C for 30 s, 55 C for 30 s, 72 C for 60 s with a final extension at 72 C for 5 min. Each 20 mL reaction mixture included 5 FastPfu Buffer, 2 units of FastPfu Polymerase (Stratagene, USA), 250 mM dNTP mix, 0.1 mM of each primer, and 1e5 ng template DNA. PCR amplicon libraries were prepared by combining three independent PCR products for each sample to minimize the impact of potential early round PCR errors (Ye and Zhang, 2011). The PCR products were purified using the PCR purification kit (Fermentas, USA) and then utilized for pyrosequencing on the 454 Genome Sequencer FLX platform (Roche Diagnostics, Indianapolis, IN, USA).
2.4.
Data analysis
The raw sequences obtained from 454 pyrosequencing were optimized, and low quality sequences were removed using the Mothur (http://www.mothur.org) as described by previous studies (Douterelo et al., 2013; Sturgeon et al., 2013). Briefly, Mothur was used to trim barcode and primer sequences, and to eliminate sequences shorter than 200 bp, with one or more ambiguous bases and quality score inferior to 25. In addition, chimeras were identified with the “chimera.uchime” command. Sequences were clustered into Operational Taxonomic Units (OTUs) at 97% sequence similarity by using Mothur. Taxonomy was assigned to each unique sequence using Bacterial references and Eukaryotic references in the Silva database (http://www.arb-silva.de) for 16S rRNA and 18S rRNA sequences. All samples were normalized to ensure equal number of sequences in each sample (15,000 of 16S rRNA gene sequences and 7800 of 18S rRNA gene sequences, respectively). Species richness, diversity indices (i.e., observed OTUs, Chao1 estimator, Shannon index, Simpson index and abundance-based coverage estimator (ACE)), and rarefaction curves were obtained using Mothur, at a 3% dissimilarity cutoff. To compare the community diversity between samples based on phylogenetic information, the Fast UniFrac online tool (http://unifrac.colorado.edu/) was used to estimate the weighted UniFrac metric (considering the relative proportion of each branch in the community) and to carry out principal coordinate analysis (PcoA). Moreover, heatmap was implemented by R packages heatmap (http://www.r-project.org/). The abundance of the top 10 genera in each sample was log2-
transformed, viz. log2(1000x þ 1), where x is the frequency per genus (Lundberg et al., 2012). The 16S rRNA and 18S rRNA gene sequences were deposited in the NCBI Sequence Read Archive under accession numbers SRA060041 and SRA060705, respectively.
2.5.
Quantitative real-time PCR (qPCR) analysis
QPCR was performed on a Mx 3005P Real-Time PCR System (Stratagene, USA). The PCR mixture (25 mL) consisted of 1 SYBR Green qPCR Mix (Fermentas, USA), primer sets (200 nM each), and 3 ng template DNA. The PCR assay primers, PCR cycling conditions, and their fragments of target genes have been described by former studies (Mendum et al., 2006; Hsu et al., 2009). A negative control was included in all qPCR assays (template DNA replaced by double-distilled water), and all experiments were performed in triplicates. Quantification was based on plasmid-based calibration curves (Kuiper et al., 2006). Clones harboring specific gene fragments were selected, and the carried plasmids were used as a standard template DNA after extraction with a Tianprep mini plasmid Kit (Tiangen Biotech, China). Plasmid DNA concentrations were determined using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, USA). A standard curve was generated by amplification of serial 8-fold dilution of plasmid DNA for each assay (Wery et al., 2008). At the end of each run, a melting curve was used to ensure specificity of amplification. Gene copy numbers (per liter of water and per gram of biofilm) were calculated as described by Declerck et al. (2007). Data analyses were performed using MxPro software (Stratagene, USA).
3.
Results
3.1.
Physico-chemical and microbiological analysis
A subset of the water quality data are shown in Table 1. Turbidity was 30.2 NTU in the reaction tanks, which gradually decreased to 25.4 NTU in the settling ponds, and finally fell to less than 0.5 NTU in the clear water tanks. Total microbial plate count declined by about one order of magnitude in the settling ponds, and then fell below the detection limit in the clear water tanks. CODMn, nitrite, and ammonia gradually decreased as the drinking water was processed. Overall, the water quality parameters in the clear water tanks, except for temperature and pH, were considerably different from the reaction tank.
221
w a t e r r e s e a r c h 5 2 ( 2 0 1 4 ) 2 1 8 e2 3 0
Table 1 e Water quality parameters at each sampling location. Sample Temperature sites ( C)
pH
MF ETC RT SP CWT
7.76 7.68 7.56 7.46 7.36
22.19 23.12 23.08 23.12 23.18
Turbidity Total plate count CODMn Nitrate Nitrite Ammonia (NTU) (CFU mL1) (mg L1) (mg L1, as NO3) (mg L1, as NO2) (mg L1, as NH3-N)
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.elsevier.com/locate/watres
Diversity and dynamics of microbial communities at each step of treatment plant for potable water generation Wenfang Lin a, Zhisheng Yu a,b,*, Hongxun Zhang a, Ian P. Thompson b a
College of Resources and Environment, University of Chinese Academy of Sciences, 19 A Yuquan Road, Shijingshan District, Beijing 100049, PR China b Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
article info
abstract
Article history:
The dynamics of bacterial and eukaryotic community associated with each step of a water
Received 13 July 2013
purification plant in China was investigated using 454 pyrosequencing and qPCR based
Received in revised form
approaches. Analysis of pyrosequencing revealed that a high degree diversity of bacterial
27 October 2013
and eukaryotic communities is present in the drinking water treatment process before
Accepted 29 October 2013
sand filtration. In addition, the microbial compositions of the biofilm in the sand filters and
Available online 7 November 2013
those of the water of the putatively clear tanks were distinct, suggesting that sand filtration and chlorination treatments played primary roles in removing exposed microbial com-
Keywords:
munities. Potential pathogens including Acinetobacter, Clostridium, Legionella, and Mycobac-
454 pyrosequencing
terium, co-occurred with protozoa such as Rhizopoda (Hartmannellidae), and fungi such as
Biofilm
Penicillium and Aspergillus. Furthermore, this study supported the ideas based on molecular
Drinking water treatment process
level that biofilm communities were different from those in corresponding water samples,
Microbial diversity
and that the concentrations of Mycobacterium spp., Legionella spp., and Naegleria spp. in the
qPCR
water samples declined with each step of the water treatment process by qPCR. Overall, this study provides the first detailed evaluation of bacterial and eukaryotic diversity at each step of an individual potable water treatment process located in China. ª 2013 Elsevier Ltd. All rights reserved.
1.
Introduction
The supply of safe drinking water to the public is one of the great technological advancements of the 19th century, as well as a major technological challenge (Berry et al., 2006; Kormas et al., 2010; Poitelon et al., 2010). It is well known that microorganisms are widely present in the drinking water treatment system, e.g. in the storage tanks, filter systems, and the interior of pipe walls (Bonadonna et al., 2009). Uncontrolled and
excessive microbial growth not only leads to the deterioration in water quality and the associated undesirable tastes, odors, and visual turbidity, but can also cause process malfunctioning such as clogging of filters, bio-fouling and bio-corrosion (Hammes et al., 2008). In particular, the occurrence of pathogens such as enteropathogenic Escherichia coli O157, Helicobacter pylori, Legionella pneumophila, and Mycobacterium avium may cause waterborne illnesses (Aw and Rose, 2012). Among these, Legionella and Mycobacterium were most commonly
* Corresponding author. College of Resources and Environment, University of Chinese Academy of Sciences, 19 A Yuquan Road, Shijingshan District, Beijing 100049, PR China. Tel./fax: þ86 10 88256462. E-mail address: [email protected] (Z. Yu). 0043-1354/$ e see front matter ª 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.watres.2013.10.071
w a t e r r e s e a r c h 5 2 ( 2 0 1 4 ) 2 1 8 e2 3 0
detected in the drinking water systems, although typically present in trace quantities (Vaerewijck et al., 2005; Feazel et al., 2009; Felfo¨ldi et al., 2010; Marciano-Cabral et al., 2010). For instance, sequences related to Legionella spp. were detected in our previous studies of the biofilms developing on household taps (Lin et al., 2013). Eukaryotic microorganisms such as protozoa, algae, and fungi are also ubiquitous in freshwater environments. The presence of free-living pathogenic amoebae such as Acanthamoeba, Hartmannella, and Naegleria in potable water, increases the possibility of infections (Poitelon et al., 2009b; Marciano-Cabral et al., 2010; Koubar et al., 2011). Surprisingly, the diversity of eukaryotes in drinking water treatment system has received limited attention based on molecular technology. In general, conventional water treatment system includes coagulationeflocculation, sedimentation, sand filtration, and disinfection processes (Abbaszadegan et al., 1997). However, still comparatively little is known of how each process impacts of the water microbial community and specifically how each step impacts on the species composition and relative abundance particular with regards biofilms. Hence, a thorough understanding of the community structure in biofilms may facilitate the management of drinking water production system and the control of biofilm (Simoes et al., 2007; Elhariry et al., 2012). To date most of what we know with regards microbial community compositions and their dynamics in the drinking water treatment system has come from studies employing traditional culture methods, community fingerprinting techniques, and low-throughput molecular approaches (Eichler et al., 2006; Roeder et al., 2010). Recent advances in sequencing technology have enabled high-throughput sequencing of microbial communities, e.g. 454 pyrosequencing, and this now enables microbial detailed community structure analysis to be achieved at high taxonomic resolution (Andersson et al., 2010). Pyrosequencing is an ideal tool for investigating microorganisms that constitute the bulk of the community (Gobet et al., 2012). However, it is not well suited for quantitatively tracking key functional members of the community (Zhang et al., 2011). Conversely, quantitative realtime PCR (qPCR) has been demonstrated to be a useful tool for quantitative analysis of specific microorganisms in environmental samples (Brinkman et al., 2003). By integrating highthroughput pyrosequencing with qPCR, the composition of whole microbial communities can be assessed whilst quantifying key functionally important members. This study is the first to unveil the dynamics of complex microbial communities in water samples undergoing different stages of treatment as well as in biofilms associated with key treatment steps including coagulationeflocculation, sedimentation, sand filtration, and disinfection by 454 pyrosequencing. In addition, qPCR has been used for quantitative analysis of potentially pathogenic microbes and free-living amoebae.
Mingfeng River (Source 1, supplying about 10%) and the East Trunk Canal (Source 2, supplying about 90%), both of which are branches of Yangtze River. This drinking water treatment plant produces nearly 8000 m3/day of water, and serves almost 40,000 people. In brief, the surface water is first subjected to coagulation in reaction tanks, which then flows into settling ponds, resulting in sedimentation. Water is successively filtered through the sand. To ensure of the treated drinking water is safe to drink, ClO2 (free residual chlorine: >0.1 mg L1 after 30 min contact time) is added at a minimal residual concentration (>0.02 mg L1) prior to distribution to households (Fig. 1). Biofilms can grow on the walls of treatment facilities which are composed of cement, resulting in some nuisance, such as increasing the opportunity for pathogens to survive and persist. Hence, the walls of the reaction tanks and settling ponds are regularly cleaned at intervals of 15 days. In addition, the sand filtration is backwashed every 24 h to maintain downstream water quality.
2.2.
Materials and methods
2.1.
Drinking water treatment process
The drinking water treatment plant is located in a small town of Hubei province, China. The water source came from the
Sampling and nucleic acid extraction
Biofilm and water samples were taken from each of the water treatment steps, including two raw water source areas, reaction tank, settling pond, sand filtration, and the clear water tank (Fig. 1). Each original sample was a mixture of three independent samples (Zhang et al., 2012). Water samples were collected at each treatment step and processed within 12 h. Microbial biomass was harvested from approximately 3 L water samples using 0.22 mm polycarbonate membranes (47 mm diameter, Millipore, USA) (Poitelon et al., 2009a). Meanwhile, the water in the reaction tanks, settling ponds, and clear water tanks was ordinarily drained in order to provide access to biofilms growing on the walls which were mechanically removed with a sterile scraper (Joh and Lee, 2011). To obtain the biofilm samples from the sand filters, backwashed water sample was collected and then treated as detailed above. For each sampling point, the physico-chemical and microbiological water quality parameters were determined. In situ water quality parameters (temperature and pH) were measured using a multi-parameters water quality monitoring sonde (YSI Professional Plus, USA). Other analyses included assessment of turbidity, CODMn, nitrate, nitrite, ammonia and total plate count which were performed according to Chinese standards (GB: 5749-2006) by Pony Testing International Group (Beijing, China). Samples were stored in an ice box before being transported to the laboratory, and then kept at 70 C for further usage. Filtered membranes with microbes were cut into pieces with a sterilized cutter, and total DNA extracted using FastDNA SPIN Kit (MP Biomedicals, USA), following the manufacturer’s protocol. For biofilm samples (w0.5 g), the same kit was used for extraction of total DNA.
2.3.
2.
219
PCR amplification and 454 Pyrosequencing
Bacterial V1eV3 region of the 16S rRNA gene was amplified using forward primer 8F: 50 -AGAGTTTGATCCTGGCTCAG-30 and reverse primer 533R: 50 - TTACCGCGGCTGCTGGCAC-30 . For eukaryotes, a PCR strategy was designed to amplify the variable V4 region of the 18S rRNA gene. The universal
220
w a t e r r e s e a r c h 5 2 ( 2 0 1 4 ) 2 1 8 e2 3 0
Fig. 1 e Schematic diagram of the drinking water treatment process.
forward primer 3NDf: 50 -GGCAAGTCTGGTGCCAG-30 and the reverse primer V4_euk_R2: 50 -ACGGTATCT(AG)ATC(AG) TCTTCG-30 were added into the reaction. The PCR program for both bacterial and eukaryotic amplification was: 95 C for 2 min, followed by 25 cycles at 95 C for 30 s, 55 C for 30 s, 72 C for 60 s with a final extension at 72 C for 5 min. Each 20 mL reaction mixture included 5 FastPfu Buffer, 2 units of FastPfu Polymerase (Stratagene, USA), 250 mM dNTP mix, 0.1 mM of each primer, and 1e5 ng template DNA. PCR amplicon libraries were prepared by combining three independent PCR products for each sample to minimize the impact of potential early round PCR errors (Ye and Zhang, 2011). The PCR products were purified using the PCR purification kit (Fermentas, USA) and then utilized for pyrosequencing on the 454 Genome Sequencer FLX platform (Roche Diagnostics, Indianapolis, IN, USA).
2.4.
Data analysis
The raw sequences obtained from 454 pyrosequencing were optimized, and low quality sequences were removed using the Mothur (http://www.mothur.org) as described by previous studies (Douterelo et al., 2013; Sturgeon et al., 2013). Briefly, Mothur was used to trim barcode and primer sequences, and to eliminate sequences shorter than 200 bp, with one or more ambiguous bases and quality score inferior to 25. In addition, chimeras were identified with the “chimera.uchime” command. Sequences were clustered into Operational Taxonomic Units (OTUs) at 97% sequence similarity by using Mothur. Taxonomy was assigned to each unique sequence using Bacterial references and Eukaryotic references in the Silva database (http://www.arb-silva.de) for 16S rRNA and 18S rRNA sequences. All samples were normalized to ensure equal number of sequences in each sample (15,000 of 16S rRNA gene sequences and 7800 of 18S rRNA gene sequences, respectively). Species richness, diversity indices (i.e., observed OTUs, Chao1 estimator, Shannon index, Simpson index and abundance-based coverage estimator (ACE)), and rarefaction curves were obtained using Mothur, at a 3% dissimilarity cutoff. To compare the community diversity between samples based on phylogenetic information, the Fast UniFrac online tool (http://unifrac.colorado.edu/) was used to estimate the weighted UniFrac metric (considering the relative proportion of each branch in the community) and to carry out principal coordinate analysis (PcoA). Moreover, heatmap was implemented by R packages heatmap (http://www.r-project.org/). The abundance of the top 10 genera in each sample was log2-
transformed, viz. log2(1000x þ 1), where x is the frequency per genus (Lundberg et al., 2012). The 16S rRNA and 18S rRNA gene sequences were deposited in the NCBI Sequence Read Archive under accession numbers SRA060041 and SRA060705, respectively.
2.5.
Quantitative real-time PCR (qPCR) analysis
QPCR was performed on a Mx 3005P Real-Time PCR System (Stratagene, USA). The PCR mixture (25 mL) consisted of 1 SYBR Green qPCR Mix (Fermentas, USA), primer sets (200 nM each), and 3 ng template DNA. The PCR assay primers, PCR cycling conditions, and their fragments of target genes have been described by former studies (Mendum et al., 2006; Hsu et al., 2009). A negative control was included in all qPCR assays (template DNA replaced by double-distilled water), and all experiments were performed in triplicates. Quantification was based on plasmid-based calibration curves (Kuiper et al., 2006). Clones harboring specific gene fragments were selected, and the carried plasmids were used as a standard template DNA after extraction with a Tianprep mini plasmid Kit (Tiangen Biotech, China). Plasmid DNA concentrations were determined using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, USA). A standard curve was generated by amplification of serial 8-fold dilution of plasmid DNA for each assay (Wery et al., 2008). At the end of each run, a melting curve was used to ensure specificity of amplification. Gene copy numbers (per liter of water and per gram of biofilm) were calculated as described by Declerck et al. (2007). Data analyses were performed using MxPro software (Stratagene, USA).
3.
Results
3.1.
Physico-chemical and microbiological analysis
A subset of the water quality data are shown in Table 1. Turbidity was 30.2 NTU in the reaction tanks, which gradually decreased to 25.4 NTU in the settling ponds, and finally fell to less than 0.5 NTU in the clear water tanks. Total microbial plate count declined by about one order of magnitude in the settling ponds, and then fell below the detection limit in the clear water tanks. CODMn, nitrite, and ammonia gradually decreased as the drinking water was processed. Overall, the water quality parameters in the clear water tanks, except for temperature and pH, were considerably different from the reaction tank.
221
w a t e r r e s e a r c h 5 2 ( 2 0 1 4 ) 2 1 8 e2 3 0
Table 1 e Water quality parameters at each sampling location. Sample Temperature sites ( C)
pH
MF ETC RT SP CWT
7.76 7.68 7.56 7.46 7.36
22.19 23.12 23.08 23.12 23.18
Turbidity Total plate count CODMn Nitrate Nitrite Ammonia (NTU) (CFU mL1) (mg L1) (mg L1, as NO3) (mg L1, as NO2) (mg L1, as NH3-N)
