Pyrosequencing Demonstrated Complex Microbial Communities in a ...

Microbes Environ. Vol. 26, No. 2, 149–155, 2011

http://wwwsoc.nii.ac.jp/jsme2/ doi:10.1264/jsme2.ME10205

Pyrosequencing Demonstrated Complex Microbial Communities in a Membrane Filtration System for a Drinking Water Treatment Plant SOONDONG KWON1, EUNJEONG MOON1, TAEK-SEUNG KIM1, SEUNGKWAN HONG1, and HEE-DEUNG PARK1* 1School

of Civil, Environmental and Architectural Engineering, Korea University, Anam-Dong, Seongbuk-Gu, Seoul 136–713, South Korea (Received December 1, 2010—Accepted February 17, 2011—Published online March 18, 2011)

Microbial community composition in a pilot-scale microfiltration plant for drinking water treatment was investigated using high-throughput pyrosequencing technology. Sequences of 16S rRNA gene fragments were recovered from raw water, membrane tank particulate matter, and membrane biofilm, and used for taxonomic assignments, estimations of diversity, and the identification of potential pathogens. Greater bacterial diversity was observed in each sample (1,133–1,731 operational taxonomic units) than studies using conventional methods, primarily due to the large number (8,164–22,275) of sequences available for analysis and the identification of rare species. Betaproteobacteria predominated in the raw water (61.1%), while Alphaproteobacteria were predominant in the membrane tank particulate matter (42.4%) and membrane biofilm (32.8%). The bacterial community structure clearly differed for each sample at both the genus and species levels, suggesting that different environmental and growth conditions were generated during membrane filtration. Moreover, signatures of potential pathogens including Legionella, Pseudomonas, Aeromonas, and Chromobacterium were identified, and the proportions of Legionella and Chromobacterium were elevated in the membrane tank particulate matter, suggesting a potential threat to drinking water treated by membrane filtration. Key words: membrane, drinking water, pathogen, pyrosequencing, microbial community

One main objective of drinking water treatment is to remove pathogenic microorganisms (14). Drinking water contaminated by pathogenic protozoa, bacteria, and viruses can cause diseases (14, 15, 25, 26). Statistics indicate that 126 drinking water-related disease outbreaks, 429,000 cases of illness, 653 hospitalizations, and 58 deaths occurred in the United States during the years 1991–1998 (4). Sand filtration and disinfection are commonly used to purify drinking water. Sand filtration removes particulate matter including microorganisms at the surface or in the middle of the sand bed. Direct collisions, van der Waals force, surface charge attraction, and diffusion are known to be involved in the capture of particulate matter by sand filters (12). Generally, the filtration process is affected by several operational parameters (e.g., linear velocity, backwash rate, etc.) (12) and design conditions (e.g., grains size, depth of sand bed, etc.) (7). Current increased water quality requirements make it more difficult to design and utilize sand filters for drinking water treatment (26). Microfiltration (MF) or ultrafiltration (UF) with membranes is an attractive alternative to sand filtration for drinking water production mainly due to an excellent ability to remove microorganisms as well as suspended solids and colloids, without the need for high concentrations of disinfectants. Because the membranes used to purify drinking water have pores that are smaller (typically 0.04–0.2 μm) than microorganisms (typically 0.5–5.0 μm), microorganisms are effectively rejected through a sieving mechanism (30), although some microorganisms (e.g., ultramicrobacteria * Corresponding author. E-mail: [email protected]; Tel: +82–2–3290–4861; Fax: +82–2–928–7656.

(28)) can pass through the membranes. However, defects on a membrane’s surface can decrease sieving efficiency, allow pathogens to pass through the membrane, and affect public health, and it is important to test the integrity of membranes during the filtration process (1, 9, 10). A membrane integrity test is frequently conducted by counting particles in filtered water and/or checking pressure-induced decay by applying high pressure to the membranes. In addition, it is also important to know which pathogens can persist in membrane systems to prepare for a possible entering of pathogens into the public water supply. Pathogens are usually detected by culture and colony counting methods (13), microscopic observation (35), and PCR (13). In submersed membrane filtration operated in a dead-end mode for drinking water treatment, membrane modules are installed in a tank (the membrane tank), and particulate matter is concentrated in the membrane tank by filtration through the membrane. In addition, aeration is frequently applied from the bottom of the membrane tank to minimize the accumulation of foulants on the surface of the membrane during backwash and/or filtration periods (5, 29). This operation can result in the concentration of particulate organic matter while oxygen is dissolved in the tank, resulting in an environment suitable for the growth of aerobic microorganisms, although biocidal treatment affects the growth of microorganisms in the membrane system. The membrane tank therefore can behave like a bioreactor that facilitates the growth of diverse microorganisms, including some pathogens. If the membrane tank promotes the growth of pathogenic microorganisms and the membrane surface has some defects, it would result in a potential threat to people who consume the water.

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Although the potential exists for the growth of microorganisms in a membrane tank, such growth has not been well reported or characterized. Thus, the objectives of this study were 1) to investigate bacterial community composition and diversity and 2) to identify potential pathogenic bacteria in membrane tanks. To this end, we collected biomass samples from a pilot-scale drinking water treatment plant that operates a low pressure submersible MF system, and characterized bacterial 16S rRNA gene sequences using a high-throughput pyrosequencing technique, and then analyzed the sequences using bioinformatics tools. Materials and Methods The pilot plant and its operating conditions A pilot-scale drinking water treatment plant was set up at the Kuei municipal drinking water treatment plant (Seoul, South Korea) which produces 650,000 m3 d−1 and provides treated water to residents of northern Seoul. The Kuei treatment plant takes water from the Paldang reservoir located in the upper parts of the Han River and treats the water by alum coagulation, flocculation, sedimentation, sand filtration, and chlorination. As shown in Fig. 1, the pilot plant primarily consisted of a membrane tank (working volume=1.2 m3) and a produced water tank (0.1 m3). Raw water was pumped to the membrane tank at a rate of 5.1 m3 h−1. The water was filtered through submersed hollow-fiber membranes by a suction pump at a rate of 4.8 m3 h−1, and the filtered water was delivered to the produced water tank. At the bottom of the membrane tank, the concentrate was withdrawn at a rate of 0.3 m3 h−1. The pilot plant was operated at a flux of 60 LMH (L m−2 h−1). A daily total of 91 cycles of suction (15 min), backwashing (0.5 min), and relaxation (0.17 min) were processed, followed by one cycle of maintenance cleaning (2 min) and relaxation (15 min). During backwash, treated water from the produced water tank was back-flowed across the membrane to the membrane tank at a rate of 7.2 m3 h−1, without addition of a chemical. Maintenance cleaning was practiced similar to the backwash operation except for providing 15 mg L−1 of NaOCl. A total of four horizontal-type membrane modules were installed in the membrane tank (Cleanfil®-S20H, KOLON Industry, South Korea), and air was continuously supplied at the bottom of the membrane modules at a rate of 200 L min−1 during filtration and backwash periods. The module consisted of polyvinylidene fluoride hollow-fiber membranes with a nominal pore size of 0.07 µm. The overall membrane surface area of the four modules was 80 m2. The pilot plant had been operated since April, 2006 and had consistently produced water with low levels of turbidity (0.04 ± 0.01 Nephelometric Turbidity Units (NTU)) irrespective of highly variable raw water quality (turbidity = 10.4 ± 15.9 NTU). Sampling, DNA extraction, and PCR amplification After the pilot plant was operated for 30 months, during August

Fig. 1. A schematic representation of the pilot-scale drinking water treatment plant using membrane filtration.

KWON et al. 2009, particulate matter was collected from the membrane tank using a bucket-type sampler. An attached biofilm sample was also harvested by scraping the biofilm formed on the middle of hollowfiber membranes, using a sterilized spatula after lifting the membrane modules. For a comparison of bacterial communities, raw water was also sampled from the source water equalization basin using a bucket-type sampler. Samples were immediately stored in an ice box before being transported to the laboratory. A total of 20 L of raw water and 2 L of the membrane tank water were passed through a membrane filter (0.2 µm) to collect particulate matter. It took several hours to filter the samples. Immediately after the filtration, the particulate matter was used for the extraction of total DNA. Total DNA was extracted in duplicate using the UltraClean soil extraction kit (Mobio Laboratory, Solana Beach, USA) following the manufacturer’s protocol. For membrane biofilm samples (~0.2 g), the same kit was used for extraction of total DNA. For the amplification of bacterial 16S rRNA gene fragments, the PCR primers 27F (5'-AGAGTTTGATCMTGGCTCAG-3') (8) and 518R (5'-ATTACCGCGGCTGCTGG-3') (18) were used. RDP’s Probe Match utility (http://rdp.cme.msu.edu) demonstrated that 65.1% and 87.9% of sequences within domain Bacteria matched with the 27F and 518R primers, respectively. Each 50-µL reaction mixture included 1X EF-Taq buffer (Solgent, Daejeon, South Korea), 2.5 units of EF-Taq polymerase (Solgent), 0.2 mM dNTP mix, 0.1 µM of each primer and 100 ng of template DNA. The PCR profile was as follows: 95°C for 10 min; 35 cycles at 94°C for 45 s, 55°C for 1 min and 72°C for 1 min, with a final extension at 72°C for 10 min. The duplicate PCR products were pooled and purified using the QIAquick gel extraction kit (Qiagen, Hilden, Germany). The purified products were used for pyrosequencing. Pyrosequencing The ends of the purified PCR products (~1 µg) were blunted, and short adaptors (14-bp long) were ligated onto both ends for sorting sequences by key as well as for providing a priming region. The modified products to be sequenced were attached to DNA capture beads, one fragment per bead, and amplified using emulsion-based clonal amplification. The beads were set into the wells of a PicoTiterPlate device (1 of 8 lanes), with appropriate chemicals and enzymes, and inserted into the Genome Sequencer FLX Titanium Series (454 Life Science, Branford, USA) for pyrosequencing. All of the procedures followed the manufacturer’s directions (454 Life Science) and were conducted at Macrogen (Seoul, South Korea). Data analysis Initially, trimBarcode.pl Perl script (Macrogen) was used to sort the raw 16S rRNA gene sequences obtained from pyrosequencing by key (i.e., sequences from the raw water, membrane tank particulate matter, and membrane biofilm), to discard low quality and short (< 250-bp long) sequences, and to trim the primer sequences. In addition, the sequences were further refined by removing potential chimeric sequences with the Mothur utility (24). The processed sequences were used for the analyses. Taxonomic assignment of the sequences was done using the Classifier (32) provided by the RDP. At a 3% cutoff, richness and diversity indices (i.e., observed OTUs, Chao1 estimator, Shannon index, and ACE), rarefaction curves, rank abundance curves, and Venn diagrams were obtained using the Mothur utility (24). The input file of the Mothur utility was generated by aligning the processed sequences and constructing a distance matrix using the RDP’s Pyrosequencing Aligner and RDP’s Column Formatted Distance Matrix, respectively, according to the developer’s introduction. Differences in bacterial community composition were evaluated by a proportional test (31) using Minitab software (http://www.mintab.com). Phylogenetic and statistical analyses Legionella and Chromobacterium 16S rRNA gene sequences were retrieved from the total sequences obtained in this study using RDP’s Classifier, while their related sequences were retrieved from

Microbial Communities in a Filtration System the NCBI database (http://www.ncbi.nlm.nih.gov/) using the BLAST utility. Then, the retrieved sequences were multiply aligned using ClustalX version 1.83 (27). A neighbor-joining tree (22) was generated and a bootstrap analysis was performed using the same software with 1000 resampling trials. The calculated tree was drawn using TreeView version 1.6.6 (20). Sequence deposition The 16S rRNA gene sequences determined in this study have been deposited in GenBank under accession numbers GU738023 to GU784790.

Results and Discussion Bacterial community composition In order to investigate bacterial community composition, 16S rRNA gene sequences were retrieved using pyrosequencing from three samples: water in the raw water equalization basin (raw water), particulate matter in the membrane tank (membrane tank particulate matter), and biomass on the surface of membranes in the membrane tank (membrane biofilm). A total of 22,275 (average length=418.9 bp), 13,702 (428.6 bp), and 8,164 bacterial 16S rRNA gene sequences (average length=426.5 bp) were obtained from the raw water, membrane tank particulate matter, and membrane biofilm, respectively, and used for the community analyses. Sequences were initially assigned to corresponding bacterial taxa using the RDP classifier with an 80% cutoff value, which identified 8, 12, and 12 phyla for the raw water,

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membrane tank particulate matter, and membrane biofilm, respectively. Community composition by phylum is summarized for each sample in Fig. 2 (A). All three samples had a similar bacterial community composition at the phylum level, with a preponderance of Proteobacteria (62.9–83.3% of each sample’s total sequences) and Bacteroidetes (9.0– 19.4%). Members within Nitrospira, Acidobacteria, OD1, Actinobacteria, Verrucomicrobia, Cyanobacteria, Chloroflexi, TM7, Firmicutes, Plactomycetes, and Gemmatimonadetes were also found, but the percentage of these sequences were relatively low (< 2.9%). It should be noted that significant fractions of sequences (0.2–19.0%) were assigned to the unclassified phylum. The sequences were unlikely to be generated from non-specific PCRs or new organisms due to the high identity with sequences in the NCBI database (data not shown). Rather, the accumulation of 16S rRNA gene sequences in the RDP database is outpacing our ability to classify them as Sanapareddy et al. inferred (23). At the class level of taxonomic classifications, the differences between samples were obvious (P < 0.001). Fig. 2 (B) shows a classification by class within Proteobacteria. The raw water was predominated by Betaproteobacteria (61.1% of total sequences), while both the membrane tank particulate matter (42.4%) and membrane biofilm were predominated by Alphaproteobacteria (32.8%). The differences among samples were more evident at the genus level (P < 0.001). Table 1 shows the 20 genera found frequently in each sample. In the raw water, 48.0% of sequences were attributed to Curvibacter, which was not significant in the membrane tank particulate matter (0.14%) or membrane biofilm (0.12%). Flavobacterium, Pseudomonas, Porphyrobacter, and other minor members followed Curvibacter in prevalence. No sequences constituting more than 10% of all sequences were found in either the membrane tank particuTable 1.

The 20 genera frequently identified in the raw water, membrane tank particulate matter, and membrane biofilm Percentage of sequences (%)

Genus

Fig. 2. Taxonomic assignment of 16S rRNA gene sequences retrieved from the raw water, membrane tank particulate matter, and membrane biofilm classified by (A) phylum and (B) class within the phylum Proteobacteria.

Rheinheimera Polaromonas Gemmatimonas Bdellovibrio Cellvibrio Methylibium Duganella Massilia OD1 Novosphingobium Rhodobacter Nitrospira Flavobacterium GP4 Sphingopyxis Porphyrobacter Sphingomonas Undibacterium Curvibacter Pseudomonas Total

Raw water

Membrane tank particulate matter

Membrane biofilm

1.44 1.23 0.00 0.00 0.01 0.01 0.03 0.01 0.00 0.68 0.12 0.00 14.72 0.00 0.28 3.34 0.51 0.33 48.04 6.48 77.23

0.00 0.06 0.27 0.55 0.59 0.25 0.26 0.46 1.40 0.76 1.07 2.90 0.24 2.44 2.94 2.55 5.78 3.77 0.14 0.15 26.58

0.00 0.00 0.72 0.94 1.32 0.81 1.26 0.85 0.71 0.16 0.16 1.62 0.16 1.08 5.22 1.91 3.75 8.70 0.12 0.06 29.55

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Fig. 3. Venn diagram of shared OTUs among the raw water, membrane tank particulate matter, and membrane biofilm.

late matter or membrane biofilm samples. In the membrane tank particulate matter, Sphingomonas, Undibacterium, Nitrospira, Sphingopyxis, Porphyrobacter, and GP4 were mainly identified, while in the membrane biofilm, Undibacterium, Sphingopyxis, and Sphingomonas were the principal members. The differences in microbial community composition were made obvious by analyzing OTUs shared among the three samples. Mothur’s Venn diagram analysis was used to identify the numbers of shared OTUs among samples (3% cutoff) as shown in Fig. 3. Only 85 OTUs were shared among the three samples, which constituted 6.9%, 4.9%, and 7.5% of the raw water, the membrane tank particulate matter, and the membrane biofilm, respectively. This suggests that each sample had a distinct bacteria community composition possibly due to different environmental and growth conditions. The shared OTUs predominantly belonged to the genera Pseudomonas, Porphyrobacter, Undibacterium, Sphingopyxis, Sphingomonas, and Curvibacter. It is also worth noting that 638 OTUs were shared by the membrane tank particulate matter and the membrane biofilm, while only 149 OTUs were shared by the membrane tank particulate matter and the raw water, and 111 OTUs were shared by the membrane biofilm and the raw water. This indicates that the bacterial community composition of the membrane tank particulate matter was more similar to that of the membrane biofilm than that of the raw water. Freshwater habitats comprise a specific bacterial community in which Proteobacteria (mostly Alpha-, Beta-, and Gammaproteobacteria) predominate, although Actinobacteria, Bacteroidetes, Nitrospira, Chloroflexi, Cyanobacteria, and Verrucomicrobia are also frequently present (6, 36). The bacterial communities found in drinking water treatment plants, using freshwater as a source, appear not to differ much from those in freshwater. Several researchers demonstrated the dominance of Proteobacteria in drinking water treatment systems (2, 6, 33), however, it is likely that particular unit operations employed by drinking water treatment systems affect bacterial composition. Eichler and coworkers (6) studied bacterial communities

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from source water, water treatment plant, and tap water. They demonstrated that flocculation and sand filtration did not influence the major bacterial species of source water, but chlorination changed the composition, as evidenced by an RNA fingerprint analysis. The results of our study also demonstrated distinctive bacterial communities for different water treatment stages. Proteobacteria constituted 83.3% of all bacterial sequences of the raw water (Fig. 2 (A)), while Beta-, Alpha-, and Gammaproteobacteria constituted 61.1%, 11.3%, and 10.2%, respectively (Fig. 2 (B)). However, the proportions of Proteobacteria were 62.9% and 65.7% in the membrane tank particulate matter and membrane biofilm, respectively. Within the phylum Proteobacteria, the bacterial community difference was more significant (P < 0.001). The portions of Betaproteobacteria were 10.8% and 21.5% in the membrane tank particulate matter and in the membrane biofilm, respectively, while the portions of Alphaproteobacteria for each sample were 42.4% and 32.8%, respectively. Although this study demonstrated distinct bacterial communities for the three samples, the differences were not necessarily caused by the environment at each stage due to the long period of the pilot operation (i.e., 30 months) and variations of bacterial community composition in the raw water with time. Several researchers have reported an abundance of Alphaproteobacteria in biofilms from membrane filtration systems. Chen and coworkers used isolation and cloning experiments to demonstrate the dominance of Alphaproteobacteria in biofilms formed on MF membranes used to treat wastewater effluent (2). In addition, Chon and coworkers (3) retrieved 68 clones of Alphaproteobacteria from a total of 120 bacterial clones from foulants attached on a membrane used to treat drinking water. Our study corroborated the dominance of Alphaproteobacteria in the membrane biofilm, and revealed the dominance of Alphaproteobacteria in the membrane tank particulate matter. The increase in Alphaproteobacteria demonstrated the likelihood that the membrane filtration produced an environment conducive for enriching members within Alphaproteobacteria. During membrane filtration, particulate matter tends to be concentrated in the membrane casing for pressurized types and in the membrane tank for submersible types of filtration, and could serve as a growth substrate for some microorganisms. In addition, air is supplied during backwash or air sparging periods. Thus, aerobic microorganisms that can easily utilize the particulate matter would have a selective advantage over other types of microorganisms, though further research is needed to verify whether members within Alphaproteobacteria have such an advantage. Bacterial diversity A total of 1,228, 1,731, and 1,133 operational taxonomic units (OTUs) were identified for the raw water, membrane tank particulate matter, and membrane biofilm, respectively (Table 2). For a comparison of species richness among the three samples, rarefaction curves were generated using a 3% cutoff as shown in Fig. 4. None of the curves became flatter, demonstrating that more sampling events (i.e., more sequences) are required to explain the large fraction of OTUs in the three samples. The particulate matter sample curve

Microbial Communities in a Filtration System Table 2.

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Number of 16S rRNA gene sequences analyzed, observed OTUs, Chao 1, Shannon index, and ACE for the raw water, membrane tank particulate matter, and membrane biofilm samples Sample

Raw water Membrane tank particulate matter Membrane biomass

No. of sequences analyzed. 22,275 13,702 8,164

Observed OTUs

Chao 1

Shannon index

ACE

3%

5%

3%

5%

3%

5%

3%

5%

1,228 1,731 1,133

755 1,281 872

1,665 2,657 1,943

986 1,902 1,395

5.30 5.89 5.58

4.60 5.48 5.15

1,660 2,707 2,458

999 1,877 1,636

Fig. 4. Rarefaction curves of OTUs in the raw water, membrane tank particulate matter, and membrane biofilm evaluated by 3% sequence variation.

was the steepest, reflecting the highest species richness among the samples. The Chao1 index estimated 1,665, 2,657, and 1,943 OTUs at a 3% cutoff for the raw water, membrane tank particulate matter, and membrane biofilm samples, respectively, also demonstrating the highest bacterial diversity for the membrane tank particulate matter. Other nonparametric diversity indices such as Shannon index and ACE gave same result (Table 2). The numbers of 16S rRNA gene sequences analyzed (8,164–22,275 sequences) were significantly larger than those from conventional cloning and sequencing methods, which generally employed less than 200 sequences. The increased numbers made it possible to detect more microorganisms (i.e., 1,133–1,731 OTUs at a 3% cutoff). These numbers are much greater than those in previous studies based on clone libraries in which generally less than 100 OTUs were identified in samples from freshwater (6, 36) and drinking water treatment systems (6, 17, 21, 33). The numbers of OTUs observed in this study were even higher than those from biofilm in two water meters investigated by pyrosequencing (133 and 208 OTUs) (11). The increased numbers of sequences also revealed the presence of rare species including pathogenic bacteria. Rare species contributed a significant portion of the diversity in the tested samples. As shown in the rank abundance curve in Fig. 5, less than 1% of the sequences (i.e., relative OTU abundance = 0.01) constituted more than 75% of the bacterial diversity (i.e., observed OTUs) for each sample. Signatures of pathogens Occasionally, poorly treated water gives rise to outbreaks of waterborne diseases, which emphasizes the importance of controlling pathogens during drinking water treatment (19). In this study, signatures of potential bacterial waterborne pathogens were identified in all samples (i.e., the raw water,

Fig. 5. Rank abundance curves of OTUs in the raw water, membrane tank particulate matter, and membrane biofilm evaluated by 3% sequence variation. The values corresponding to 0.01 of relative OTU abundance were 120, 125, and 229 abundance ranks for the membrane tank particulate matter, membrane biofilm, and raw water, respectively. The abundance ranks constituted the top 6.1, 9.7, and 16.7% of the total observed OTUs for each sample.

the membrane tank particulate matter, and the membrane biofilm) by comparing the retrieved sequences with known sequences of pathogens obtained from the NCBI database. Potential pathogens are listed in Table 3. The pathogens were mostly found in the genera Legionella, Pseudomonas, Aeromonas, and Chromobacterium, although there were differences in numbers for each sample. These bacteria are reported to be responsible for Legionellosis, Pneumonia, Enteritis, and Sepsis, respectively, and are frequently found in contaminated water distribution systems (25). The raw water had 1,443 Pseudomonas sequences, 13 Legionella sequences, and one Aeromonas sequence. The membrane tank particulate matter had 17 Legionella, 20 Pseudomonas, one Aeromonas, and 11 Chromobacterium sequences, while the membrane biofilm had 5 Pseudomonas sequences and one Chromobacterium sequence. The percentage of potential pathogens was 6.54% in the raw water, but decreased to 0.36% and 0.07% in the membrane tank particulate matter and membrane biofilm, respectively. The decreased percentage of potential pathogens in the membrane system might be caused by residual chlorine supplied during the cleaning period when the produced water was back-flowed across the membrane into the membrane tank with 15 mg L−1 of NaOCl. Interestingly, Pseudomonas sequences decreased significantly in the membrane tank particulate matter (6.48% → 0.15%), while Legionella (0.06% → 0.12%) and Chromobacterium (0.00% → 0.08%) sequences increased. These results demonstrated the likelihood that chlorine was effective in reducing the number of Pseudomonas in the membrane tank, but much less effective in reducing the numbers of Legionella and Chromobacterium. The phylogenetic asso-

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154 Table 3.

Bacterial pathogenic signatures from the raw water, membrane tank particulate matter, and membrane biofilm samples Number of sequences1

Genus Legionella

Pseudomonas

Aeromonas

Chromobacterium

Species L. pneumophila L. rubrilucens L. jordansis Others P. aeruginosa P. alcaligenes P. pseudoalcaligenes Others A. hydrophila A. caviae A. veronii A. sorbia Others C. violaceum Others Total Percentage

1

Raw water

membrane tank particulate matter

Membrane biofilm

0 0 0 13 593 14 1 835 0 0 0 0 1 0 0

0 0 0 17 3 3 2 12 0 0 0 1 0 0 11

0 0 0 0 1 1 0 3 0 0 0 0 0 0 1

1,457 6.54

49 0.36

6 0.07

Related disease Legionellosis Legionellosis Legionellosis Unknown Pneumonia Pneumonia Pneumonia Unknown Enteritis Enteritis Enteritis Enteritis Unknown Sepsis Unknown

Classification was based on an identity higher than 97% using the 16S rRNA gene sequences of the designated species.

Fig. 6. Neighbor-joining phylogenetic tree based on Legionella and Chromobacterium 16S rRNA gene sequences recovered from the membrane tank particulate matter and pure cultures of these bacteria obtained from Genbank (http://www.ncbi.nlm.nih.gov/). Bacillus subtilis was used as the outgroup. The scale bar represents 0.1 substitutions per site. Clone sequences exhibiting more than 99% identity are indicated by the number of other sequences in parentheses following the sequence name. Genbank accession numbers for pure culture strains are indicated in parentheses following the strain name. Bootstrap values providing 50% support are indicated at the nodes.

ciation of the sequences of Legionella and Chromobacterium observed in the membrane tank particulate matter with other pure-culture sequences is presented in Fig. 6. Fifteen out of 17 Legionella sequences (88%) showed > 99% sequence identity and were clustered with L. feeleii, L. maceachernii, and L. oakridgensis, while eight out of 11 Chromobacterium sequences (73%) demonstrated >99% sequence identity and were clustered with C. aquaticum. The three Legionella species are commonly found in water and soil and are known to cause lung infections and pneumonia (16). The pathogenesis of C. aquaticum is not clear, but phylogenetically similar to C. violaceum (96.8% identity) which can cause

serious blood poisoning in human (34). Pathogens frequently detected in freshwater such as Escherichia coli, Shigella, Salmonella, Vibrio, Helicobacter, and Mycobacterium were not found in this study. Although this study demonstrated complex microbial communities in a membrane filtration system, it has not clarified how the bacterial community diversity of a drinking water treatment system influences the quality of water produced or how the presence of pathogen signatures threatens the safety of water. In addition, it is not clear why some potential pathogens persisted, or possibly concentrated in the membrane tank; further research is required to resolve these

Microbial Communities in a Filtration System

issues. Nevertheless, this study clearly demonstrated that there were numerous bacterial species in the membrane tank of a submersed membrane system for producing drinking water, and some potential pathogenic bacteria persisted even in the presence of chlorine. If a membrane surface is damaged and potential pathogens can pass through, the treated water may pose an increased chance of contamination. Although membrane filtration is an emerging technology that can replace traditional sand filtration, caution should be paid to monitoring the integrity of the membrane, and water disinfection might be practiced even after membrane filtration, to reduce the potential risk of pathogens in the water produced. Acknowledgements We would like to thank to the engineers of KOLON Industry for providing samples and operational data. This study was funded by grants from the National Research Foundation of Korea to H.-D. Park (K20901001306-09B1200-10310) and by the Korea Science and Engineering Foundation to S. Hong (R01-2006-000-10946-0). References 1. Adham, S.S., J.G. Jacangelo, and J.M. Laine. 1995. Low-pressure membranes: Assessing integrity. J. AWWA 87:62–75. 2. Chen, C.L., W.T. Liu, M.L. Chong, M.T. Wong, S.L. Ong, H. Seah, and W.J. Ng. 2004. Community structure of microbial biofilms associated with membrane-based water purification processes as revealed using a polyphasic approach. Appl. Microbiol. Biotechnol. 63:466–473. 3. Chon, K., J.S. Chang, H. Oh, E. Lee, and J. Cho. 2009. Community of bacteria attached on the PVDF MF membrane surface fouled from drinking water treatment, in Seoul, Korea. Drinking Water Eng. Sci. 2:35–39. 4. Craun, G.F., N. Nwachuku, R.L. Calderon, and M.F. Craun. 2002. Outbreaks in drinking-water systems, 1991–1998. J. Environ. Health. 65:16–23. 5. Cui, Z.F., S. Chang, and A.G. Fane. 2003. The use of gas bubbling to enhance membrane processes. J. Membr. Sci. 221:1–35. 6. Eichler, S., R. Christen, C. Holtje, P. Westphal, J. Botel, I. Brettar, A. Mehling, and M.G. Hofle. 2006. Composition and dynamics of bacterial communities of a drinking water supply system as assessed by RNA- and DNA-based 16S rRNA gene fingerprinting. Appl. Environ. Microbiol. 72:1858–1872. 7. Ellis, K.V. 1985. Slow sand filtration. CRC Crit. Rev. Environ. Sci. Technol. 15:315–354. 8. Frank, J.A., C.I. Reich, S. Sharma, J.S. Weisbaum, B.A. Wilson, and G.J. Olsen. 2008. Critical evaluation of two primers commonly used for amplification of bacterial 16S rRNA genes. Appl. Environ. Microbiol. 74:2461–2470. 9. Guo, H., Y. Wyart, J. Perot, F. Nauleau, and P. Moulin. 2010. Lowpressure membrane integrity tests for drinking water treatment: A review. Water Res. 44:41–57. 10. Hong, S.K., F.A. Miller, and J.S. Taylor. 2001. Assessing pathogen removal efficiency of microfiltration by monitoring membrane Integrity. Water Sci. Technol: Water Suppl. 1:43–48. 11. Hong, P.Y., C.C. Hwang, F.Q. Ling, G.L. Andersen, M.W. LeChevallier, and W.T. Liu. 2010. Pyrosequencing analysis of bacterial biofilm communities in water meters of a drinking water distribution system. Appl. Environ. Microbiol. 76:5631–5635. 12. Huisman, L., and W.E. Wood. 1974. Slow Sand Filtration. World Health Organization, Geneva, Switzerland. 13. Lazcka, O., F.J. Del Campo, and F.X. Munoz. 2007. Pathogen detection: A perspective of traditional methods and biosensors. Biosens. Bioelectron. 22:1205–1217. 14. LeChevallier, M.W., and K.-K. Au. 2004. Water Treatment and Pathogen Control: Process efficiency in achieving safe drinking water. IWA Publishing, London, UK.

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