Population dynamic succession and quantification of ammonia-oxidizing bacteria in a membrane bioreactor treating municipal wastewater

Journal of Hazardous Materials 165 (2009) 796–803

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Population dynamic succession and quantification of ammonia-oxidizing bacteria in a membrane bioreactor treating municipal wastewater Bin Zhang a,b , Baosheng Sun a , Min Ji a,∗ , Huina Liu c a

School of Environmental Science and Technology, Tianjin University, Tianjin 300072, PR China Institute of Hygiene and Environmental Medicine, Academy of Military Medical Sciences, Tianjin 300050, PR China c Tianjin Jinsha Architecture & Planning Co. Ltd., Tianjin 300074, PR China b

a r t i c l e

i n f o

Article history: Received 24 June 2008 Received in revised form 15 October 2008 Accepted 16 October 2008 Available online 6 November 2008 Keywords: Membrane bioreactor (MBR) Ammonia-oxidizing bacteria (AOB) PCR-DGGE Cloning and sequencing Real-time PCR

a b s t r a c t Ammonia-oxidizing bacterial community structures and abundances in membrane bioreactor (MBR), which was utilized to treat municipal wastewater, were analyzed by polymerase chain reactiondenaturing gradient gel electrophoresis (PCR-DGGE), cloning of 16S rDNA fragments and quantitative real-time PCR techniques. DGGE analysis indicated marked change of ammonia-oxidizing bacterial communities’ structure mainly happened during day 62 to day 80. The results suggest that the dynamics of AOB community structure are important and necessary for the improving and stable nitrogen degradation and the removal in MBR. Cloning and sequencing analyses of screened clones revealed multitude nitrosifying organisms and denitrifying bacteria, which suggest that they may coexist several modes of nitrification and denitrification in MBR. Real-time PCR results demonstrated that the abundance of AOB was <0.01% of the total bacteria in inoculating activated sludge. After domestication period, AOB 16S rDNA and amoA gene content in 120th day was 20.44 and 14.38 times those of seeding activated sludge. The analyses suggest a correlation between the quantity of AOB populations and nitrification efficiency. © 2008 Elsevier B.V. All rights reserved.

1. Introduction The combination of membrane filtration with a biological reactor is known as a membrane bioreactor (MBR). MBRs occupy a smaller land area and produce better and more reliable effluent quality than a conventional aerobic biological process [1]. The membranes used a low pore size (typically 0.10–0.22 ␮m), which means that the effluent suspended solids (SS) content is very low and leads to a higher mixed liquor suspended solids (MLSS) concentration [2,3]. Ammonia in water environments can be toxic to fish and other aquatic life at sufficiently high levels, and contributes to eutrophication. So biodegradation and elimination of ammonia in wastewater is one of the main functions of wastewater treatment plants (WWTPs). Nitrification is the conversion of the most reduced form of N (ammonia) to its most oxidized form (nitrate) in aerobic activated sludge systems. Nitrification is carried out by two different groups of organisms, the ammonia-oxidizing bacteria (AOB) and the nitrite-oxidizing bacteria (NOB), respectively. AOB are primarily responsible for the first and often the rate-limiting step in

∗ Corresponding author. Tel.: +86 22 2740 4005; fax: +86 22 2740 6057. E-mail addresses: [email protected] (B. Zhang), [email protected] (M. Ji). 0304-3894/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.jhazmat.2008.10.116

nitrification: conversion of ammonia to nitrite; it is essential for the removal of ammonia from the wastewater [4]. Aerobic autotrophic AOB are found within two phylogenetic groups based on comparative analyses of 16S rRNA sequences [5,6]. One group comprises strains of Nitrosomonas and Nitrosospira spp. within the class of ␤-Proteobacteria, and the other contains Nitrosococcus oceani and Nitrosococcus halophilus within the class of ␥-Proteobacteria. Most AOB are phylogenetically closely related to other activated sludge bacteria within the ␤ subdivision class of Proteobacteria [7]. A number of studies suggest that there are physiological and ecological differences between the different AOB genera and lineages and that environmental factors such as salinity, pH, and concentrations of ammonia and suspended particulate matter (SPM) select for certain species of AOB [8–10]. MBRs have a longer solid retention times (SRT) and a lower F/M ratio as a consequence of complete biomass retention. The higher biomass loading also increases shock tolerance, which is particularly important where feed is highly variable [11,12]. The longer SRT can provide enough time for the growth of organism that need long generation time. All these advantages ensure that MBRs have a favorable culture for the increasing of AOB. The physiological activity and abundance of AOB in wastewater processing is critical in the design and operation of waste treatment systems, particularly since these organisms display low growth rate

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and high sensitivity to environmental disturbances and inhibitors [13]. For this reason, a better understanding of the ecology and microbiology of AOB in wastewater treatment systems is necessary to enhance treatment performance and control. However, compared to the studies of AOB diversity in nature [10,14–17], quantitative studies have been limited, especially for MBR wastewater treatment systems. Most of the related studies have focused mainly on treatment performance and operation conditions without considering the ammonia-oxidizing bacterial communities involved. Although a few researchers have investigated bacterial communities of MBRs [11,18,19], the information on structure, diversity, and abundance of AOB communities in MBR treating municipal wastewater is still largely limited. A better understanding of structure and dynamics of AOB communities is essential to optimize the operating conditions of MBR and improve ammonia removal efficiency. The ecology and abundance of AOB are difficult to study by conventional cultivation techniques, which could result in wrong estimations of their numbers and community structure in the environment [20]. Recently developed molecular tools include polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), cloning-sequencing and real-time PCR to provide a rapid, culture-independent detection technique for AOB, which allow detailed clarification and quantitation of AOB populations in the environments [7,21–23]. The primary purpose of this research is, therefore, to monitor the AOB communities of submerged membrane bioreactor treating municipal wastewater by nested PCR-DGGE, cloning and sequencing of 16S rDNA genes and real-time PCR quantification. We focused on diversity of the AOB population structure, variation of predominant species and changes in the amount of total bacteria and AOB during a long-term operation in MBR. Additionally, we discussed the relationship between the NH4 + -N removal effectiveness and the variation of AOB communities and quantity. 2. Materials and methods 2.1. Samples of activated sludge and description of MBR treatment systems The MBR system has a typical single-stage aerobic activatedsludge process for simultaneous BOD removal and nitrification. Details of the treatment processes and operational parameters were described by Zhang et al. [24]. Activated sludge samples were collected from the aeration tank of MBR system. On the day of sampling, the sludge was immediately sent to laboratory, and mixed liquor suspended solids concentration was determined. The sludge from approximately 200 mg of MLSS was transferred into a 1.5-ml Eppendorf tube and centrifuged at 14,000 × g for 10 min. The supernatant was removed, and the pellet was kept for extraction of genomic DNA. 2.2. DNA extraction and nested PCR Genomic DNA was extracted from the pellets in 2.1 using a Fast DNA spin kit (Bio 101, Qbiogene Inc., Carlsbad, CA) with a small modification at the initial step: 1 ml of sodium phosphate buffer solution was added to and mixed with the sample by a hand-held blender, and then the tube was sonicated for 30 s on ice. The remaining steps followed the manufacturer’s instructions. The product from DNA extraction was verified by electrophoresis in 0.7% (w/v) agarose. To minimize the variation in DNA extraction, the templates used for nested PCR and real-time PCR quantification were prepared from the mixture of DNA, which was extracted in triplicate for a sample.

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To amplify ammonia-oxidizer specific 16S rDNA for DGGE, a nested PCR approach was used. For the first round PCR, the AOBspecific primer pair was used to obtain DNA fragments 465 bp in length with an equimolar mixture of three forward primers (CTO189fA/B and CTO189fC), and with the reverse primer CTO654r, containing a single ambiguous base [25]. After the first round of amplification, PCR amplicons were purified with the QIAquick® purification kit (Qiagen Inc., Valencia, CA) to eliminate the primers and other contaminants so that they would not interfere with the next round of amplification. A nested PCR was performed on the PCR products obtained from CTO primers with a second primer pair (F338GC/R518) allowing the amplification of an internal fragment of 196 bp. All primers, target and PCR conditions are listed in Table 1. All PCR amplicons were examined by electrophoresis in 1.5% (w/v) agarose with ethidium bromide staining to confirm the product size. Using a PCR Authorized Thermocycler (Eppendorf, Hamburg, Germany), the PCR mixture was prepared in a total volume of 50 ␮l in 0.2-ml Axygen PCR tubes. The reaction solution contained 2.5 U of Taq DNA polymerase (Promega, Shanghai, China), 1× buffer (Promega), 2.0 mM Mgcl2 , 2 ␮l template DNA (10–100 ng), 0.2 mM dNTPs, and 0.25 ␮M of each primer. 2.3. DGGE analysis The nested PCR amplicons were separated on polyacrylamide gels (8%, 37.5:1 acrylamide–bisacrylamide) with a 35–55% linear gradient of denaturant (100% denaturant = 7 M urea plus 40% formamide). The gel were run for 7 h at 150 V in 1× TAE buffer (40 mM Tris–acetate, 20 mM sodium acetate, 1 mM Na2 EDTA, pH 7.4) maintained at 60 ◦ C. Denaturing gradient gels were poured and run by using the DGGE-2001 System (C.B.S. 夽Scientific, Del Mar, CA, USA) After electrophoresis, silver-staining and development of the gels were performed as described elsewhere [27], air-dried and scanned. The gel images were analyzed with the software Quantity One, version 4.31 (Bio-rad). The Shannon diversity index H was introduced to examine the structural diversity of the ammonia-oxidizer community [28]. This  index was calculated using the following equation: H = − [(ni/N) log(ni/N)], where ni/N is the proportion of community that is made up by species i (brightness of the band i/total brightness of all bands in the lane). DGGE fingerprints were manually scored by the presence of bands with consideration of the band brightness intensity. This was done at least three times to ensure constant results. 2.4. Cloning, sequencing, and phylogenetic analysis Prominent DGGE bands were excised and dissolved in 30 ␮l Milli-Q water overnight, at 4 ◦ C. DNA was recovered from the gel by freeze–thawing three times. 5 ␮l of these DNA band samples were used as templates, using the primer set F338GC/R518, and the products again were subjected to DGGE to check their migration. The target DNA fragments were then excised and reamplified using the primer set F338/R518 without GC-clamp, thus obtaining a pure sample for the cloning and sequencing step. The PCR products were cloned using the pGEM® -T Easy vector system (Promega, Madison, WI). The positive colonies were amplified with F338/R518. PCR amplicons were submitted for sequencing using ABI 3730 capillary sequencers (PE Applied Biosystems, Invitrogen, Beijing, China). Sequence data from 16S rDNA fragments of ammonia oxidizers were subjected to phylogenetic analyses. Homology searches were conducted using the GenBank server of the National Centre for Biotechnology Information (NCBI) and the BLAST algo-

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Table 1 Primer characteristics and conditions for nested PCR. Primers

Sequence position a

CTO189fA/B CTO189fC CTO654r

189–207

F338-GC R518

341–357a 518–534a

a

Target (specificity)

PCR conditions

References

◦

◦

◦

16S rRNA gene (AOB ␤-Proteobacteria)

3 min at 94 C, followed by 38 cycles of 30 s at 92 C, 30 s at 57 C, and 45 s at 72 ◦ C, followed by a 5-min final extension at 72 ◦ C

[25]

16S rRNA gene (Universal)

5 min at 95 ◦ C, followed by 30 cycles of 1 min at 94 ◦ C, 1 min at 65 ◦ C (−0.5 ◦ C each cycle and 10 cycles at 55 ◦ C), and 1 min at 72 ◦ C, followed by a 5-min final extension at 72 ◦ C

[26]

a

633–654

Escherichia coli numbering.

rithm. After checking the sequence chromatograms for errors, sequences were aligned against a representative selection of published 16S rDNA sequences of AOB or a related ␤-proteobacter as an outgroup sequence. Phylogenetic tree design were operated using the software DNAMAN Version5.2.2 (Lynnon Biosoft), and the neighbour-joining method was used for tree construction. 2.5. Real-time PCR: quantification of total bacteria, AOB and amoA gene Quantification of the 16S rDNA gene of total bacteria was performed using the primer F338/R518. The primers CTO189f/RT1r and amoA-1F/amoA-2R were used to amplify the 116 bp long DNA fragment in the V2-region of the 16S rDNA of total AOB and amoA gene of AOB, respectively. Standard DNA were pGEM® -T Vectors (Promega, Madison, WI) possessing the 16S rDNA gene fragments of genomic DNA amplicons (PCR conditions were described in Tables 1 and 2). As the fragment lengths of the vector and the inserted 16S rDNA gene were known, the numbers of target DNA were directly calculated from the concentrations of the prepared plasmid. The concentrations of the standard DNA for the three primer sets were adjusted as a series of 10-fold dilution in the ranges of 5.8 × 106 to 5.8 × 1011 (total bacteria), 5.8 × 104 to 5.8 × 109 (AOB 16S rDNA), and 5.2 × 104 to 5.2 × 109 (amoA gene) copies ml−1 , respectively. Then three standard curves were constructed after real-time PCR amplification of a dilution series of standard DNA. The PCR mixture was prepared in a total volume of 25 ␮l in 0.2ml Low Tube Strip (eight tubes per strip, Bio-Rad, CA, USA). The reaction solution contained 12.5 ␮l 2× Power SYBR® Green PCR Master Mix (P/N: 4367659, Applied Biosystems, CA, USA), 1 ␮l template DNA, and 0.25 mM of each primer. The template DNA in the reaction mixtures was amplified and monitored with an iQTM five Multicolor Real-Time PCR Detection System (Bio-Rad, CA, USA). The primers, target and PCR conditions are listed in Table 2. The fluorescence intensity was measured after each elongation step and a melting curve analysis was performed upon completion of PCR. The threshold cycle (Ct ) values obtained for each sample were compared with a standard curve to determine the initial copy number of the target gene.

Fig. 1. Variation and removal efficiency of TOC.

Fig. 2. Variation and removal efficiency of NH4 + -N.

3. Results 3.1. MBR performance MBR were operated for about 120 days. Figs. 1–3 show change and removal efficiency of TOC, NH4 + -N and total nitrogen (TN) concentration in the influent and the permeate. TOC and NH4 + -N

Table 2 Primer characteristics and conditions for real-time PCR. Primers

Sequence position

Target (specificity)

PCR conditions

References

F338 R518

341–357a 518–534a

16S rDNA gene (Universal)

94 ◦ C for 3 min followed by 40 cycles of 30 s at 94 ◦ C, 15 s at 55 ◦ C, 30 s at 72 ◦ C

[26]

CTO189fA/B CTO189fC RT1r

189–207 283–304

16S rDNA gene (AOB ␤-Proteobacteria)

95 ◦ C for 10 min followed by 40 cycles of 15 s at 94 ◦ C, 15 s at 59 ◦ C, 10 s at 72 ◦ C

[25,29]

moA-1F amoA-2R

332–349a 802–822a

amoA gene (AOB ␤-Proteobacteria)

95 ◦ C for 3 min followed by 40 cycles of 15 s at 95 ◦ C, 15 s at 59 ◦ C, 10 s at 72 ◦ C

[30]

a

Escherichia coli numbering.

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799

Fig. 3. Variation and removal efficiency of total nitrogen (TN). Fig. 4. Change of MLSS concentration during whole operation.

concentration removal efficiency were over 80% and 60% after day 10, over 90% and 70% after day 30. However, TN removal efficiency slowly elevated, and removal efficiency was merely 52.5% at the end of experience because MBR was a single-phase aerobic process. The mixed liquor suspended solids concentrations were determined on the day of sampling. Fig. 4 shows the changes in MLSS concentration of MBR during the operation, and sample points () are also shown. 3.2. DGGE analysis: AOB community structure changes in MBR We used specific PCR amplification of 16S rDNA genes followed by DGGE to reveal the AOB population change in activated sludge

of MBR. Fig. 5 shows the image of a DGGE gel of the PCR amplicons of the samples taken from MBR systems during whole operation. In the figure, the locations of the predominant bands excised are labeled with the letters. DNA in these bands were reamplified, cloned and sequenced. In the Shannon diversity index, each band corresponded to a unique species, and the density of each band was equivalent to the species abundance [31]. The H was influenced by both the species number and species abundance. This measurement could be influenced by unknown factors related to the DNA extraction and the efficiency of the 16S gene amplification for particular populations

Fig. 5. DGGE profile of ammonia-oxidizing bacteria in the sludge samples. Lane labels along the top show sampling time (days) from startup of the bioreactor; (A) DGGE profile (the principal bands are labeled A–D and a–h (bands that were excised and sequenced)); (B) similarity diagram of sample lanes (the lane day 1 is defined as 100%. Values along the bottom indicate the similar coefficiency with day 1). Bands A–D: predominant species existed in all samples; bands a–h: dynamic species existed in partial samples.

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3.4. Real-time PCR: quantification of total bacteria and AOB The high linear correlation (R2 > 0.99) was observed for all plasmids for six orders of magnitude, ranging from 106 to 1011 or 104 to 109 gene copies per ml DNA extract. Fig. 8 shows the quantitative variation of total bacteria cell number, AOB-cell numbers and amoA copy number of the MBR during whole operation. 4. Discussion Fig. 6. Change of Shannon diversity index of AOB population over 120 days.

[11]. Nevertheless, the DGGE banding patterns do provide a means for measuring the apparent diversity of the community. The Shannon diversity index for each sample is shown in Fig. 6. During earlier period (before day 30), populations with fewer species and an disproportionate distribution of individuals have a lower diversity than populations with either more species or an even distribution of each species, which continued ecological succession in the later stages of the experiment. The value of H achieved peak on day 45 because the number of bands in the sample were most. The trend of H could be linked to variation in the AOB communities and other experimental data. Major variations of H occurred between day 30 and day 80, during which time the NH4 + -N removal efficiency was >70% and the TN removal efficiency increased from 34% to 50%. 3.3. Sequencing results and phylogenetic analyses In this study, we focused on ammonia-oxidizing bacteria, which are responsible for an important step within the ammonia biodegrading and thus are related to wastewater treatment process performance. The principal bands (Band A to Band D and Band a to Band h) were identified. Nucleotide sequences were compared to the GenBank database using BLASTN, all sequences showed between 94% and 100% similarity with previously identified 16S rRNA gene sequences. The results are shown in Table 3. A comprehensive phylogenetic 16S rRNA tree reflecting the relationships of AOB and several non-AOB reference organisms was generated by Purkhold et al. [5]. Fig. 7 depicts a phylogenetic tree based on the results presented in Table 3 and other closely related 16S rDNA genes in Genbank. All of the tree methods used in this study resulted in the same grouping of AOB sequences analyzed. The AOB sequences grouped within the genera of Nitrosospira and Nitrosomonas. In addition, bands a, c, d, f and g, which belong to ␤-Proteobacteria, were found to be closely related to Comamonas sp. and Achromobacter sp. (including denitrificans).

Analysis of the DGGE banding patterns indicated that population shifts were slow prior to day 62 and after day 80, whereas there was a significant change between day 62 and day 80. Collating Fig. 4 with Fig. 5 revealed that the MLSS concentration did not increase while the AOB community structure changed notably. These results suggest that the rapid variation in bacterial community structures had a large impact on biomass concentration in the MBR. Throughout the experiment, the AOB produced characteristic fingerprints on denaturing gradient gels with several dominant bands appearing across all biomass samples. These results demonstrate that bands A–D (Fig. 5) were present in all of the samples, and that band A was weak, while bands B–D were very intense. These findings indicate that the populations represented by these bands were present in high level throughout the experiment. In addition, during the operation of the MBR, the predominance of primary and dominant communities in inoculating activated sludge including bands a–c died out gradually, while secondary microbial communities, which were represented by bands d–h increased. In addition changing the conditions caused even the relatively persistent species to become depleted or temporarily not detectable. Similarly, the presence of the new predominant populations in the sludge and relative persistence in the established biomass indicates that these species were adapted to the MBR process conditions and the character of the influent during normal operations. These changes in AOB communities are valid, considering the bioreactor startup and population stabilization. Taken together, these results suggest that certain subpopulations may have been responsible for the operational success of the MBR, and the accompanying increased NH4 + -N and TN removal efficiency. And the specific microbial structure in this system protects the MBR functions, thereby providing higher quality water permeates. Many previous studies have debated whether Nitrosomonas or Nitrosospira, is the more important ammonia-oxidizing bacteria in wastewater treatment systems [5,13,32–37]. As shown in Table 3 and Fig. 7, sequence analysis revealed that there a multitude of nitrosifying organisms existed in the system evaluated here. Of these organisms, three dominant bands were closely related to

Table 3 Results of some partial 16S rDNA sequences using BLASTN in GenBanka . Bands

A B C D a b c d e f g h

Most closely related sequence Accession

Description

Identity (%)

EU375651 DQ911628 EF042993 EF042992 EU158809 EF042985 EU375650 AF351233 AY744691 AF233876 EU301775 DQ450405

Comamonas sp. y42 16S rRNAgene, partial sequence Uncultured ammonia-oxidizing bacterium isolate DGGE gel band DCC 16S ribosomal RNAgene, partial sequence Uncultured Nitrosospira sp. clone 47-2 16S rRNA gene, partial sequence Uncultured Nitrosospira sp. clone 47-1 16S ribosomal RNA gene, partial sequence Uncultured Nitrosomonas sp. clone LEO 04 16S ribosomal RNA gene, partial sequence Uncultured Nitrosomonas sp. clone H3 16S ribosomal RNA gene, partial sequence Comamonas sp. j41 16S ribosomal RNA gene, partial sequence Uncultured beta proteobacterium clone 4-70 16S ribosomal RNA gene, partial sequence Uncultured beta proteobacterium clone bw1-27 16S ribosomal RNA gene, partial sequence Comamonas denitrificans strain 110 16S rRNA gene, partial sequence Achromobacter denitrificans strain L2 16S ribosomal RNA gene, partial sequence Uncultured denitrifying bacterium clone N4 16S rRNA gene, partial sequence

99 96 94 99 94 98 100 100 94 97 99 95

a The partial sequences of 16S rDNA genes obtained in this study were submitted to the GenBank database under accession numbers EU661949–EU661956 and EU819077–EU819080.

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Fig. 7. Phylogenetic tree of ammonia-oxidizing bacteria in MBR. The scale bar represents 5% estimated sequence divergence.

Nitrosomonas sp., while two were closely related to Nitrosospira sp. However, Nitrosomonas were merely the predominant species in the earlier stages of the experiment (before day 30), while Nitrosospira sp. were the representative of the most intensive bands (bands C and D in Fig. 5(A)) during the entire operation. Furthermore, Nitrosomonas sp. died out gradually as the operation progressed. Thereby these findings indicate that Nitrosospira sp. eventually became the most dominant AOB species in the MBR, even though Nitrosomonas was likely the predominant species in the inoculating activated sludge. Some clusters in comprehensive phylogenetic tree of AOB such as Nitrosomonas oligotropha and Nitrosomonas communis were not detectable [5]. These populations are usually observed in oligotrophic environments and reactors, which occur in systems that are operated with alternate phases of aeration and non-aeration. In the present study, the influent contained sufficient nutrients for the growth of microorganism. In addition, to delay membrane fouling, the aeration intensity and dissolved oxygen concentration were higher in the reactor used in the MBR. Three denitrifying bacteria (Comamonas denitrificans, Achromobacter denitrificans and Uncultured denitrifying bacterium) were found to have evolved into the prominent population during the later period of the operation (after day 62), however, their predominance was not stable. Corresponding with the emergence of the denitrifying bacteria, the TN removal efficiency slowly increased during the later period of the operation. Taken together, these

results suggest that several modes of nitrification and denitrification were occurring in the MBR evaluated here. Understanding the factors controlling nitrification efficiency is important for improving the effluent quality. This study suggests a link between the presence and change in ␤-Proteobacteria-like AOB (including denitrificans) that were detected using nested PCRbased approaches and the quality of effluent from a MBR municipal wastewater treatment system. Although several studies have proposed such a connection in other biological wastewater treatment plants [38,39], the present study, which focuses on municipal wastewater treatment, revealed that this link in the MBR occurs due to the characteristic of the microbial communities present in the system. Both the 16S rDNA and amoA gene provide well-studied genetic markers for the characterization of AOB [5]. However, because the AOB 16S rDNA assay has the potential to produce false positives and the amoA assay has the potential to produce false negatives, the use of the two assays in the samples provides complimentary data for the detection of AOB [7]. Therefore, we quantitated both AOB 16S rDNA and amoA gene and compared them with cell numbers of total bacteria. The results of real-time PCR indicated that the 16S rDNA copy numbers and amoA gene copy numbers per ml of DNA extracts varied from 1.57 × 106 to 3.21 × 107 and 5.43 × 105 to 7.81 × 106 , respectively. The percentage of AOB 16S rDNA and amoA gene in the total amount of bacteria was <0.01% and 0.0001% in seeding activated sludge; After domestication period, the amount

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References

Fig. 8. Population dynamic of total bacteria, AOB 16S rDNA and amoA gene in the MBR.

of AOB increased significantly; AOB 16S rDNA and amoA gene content in 120th day was 20.44 and 14.38 times those of inoculating activated sludge. In numerous researches [39–41], the pertinent question is whether an increase in the number of AOB results in improved ammonia removal efficiency. The results of this study are consistent with those studies in which an increase in the concentration of AOB populations is linked to improved effluent quality in wastewater treatment bioreactors. In the beginning stage of the experiment (before day 8), due to highly sensitivity to environmental variation, AOB population decreased and some species (such as bands a–c in Fig. 5) died out. Along with operation, some communities gradually adapted to circumstance and process parameter of MBR and increased continuingly. Corresponding to the increase of AOB population, removal efficiency of NH4 + -N and total nitrogen rose, respectively. 5. Conclusions In the present study, it has been shown that DGGE of specifically amplified 16S rDNA fragments is a viable technique for the analysis of ammonia-oxidizing populations in MBR. With the help of sequence data from excised DGGE bands, predominant species of AOB could be detected among biomass samples. Such data provide insight into the diversity and distribution of this ecologically important group of organisms. Real-time PCR technology is a valuable tool for quantification of ammonia-oxidizing bacteria in MBR, offering high throughput, analytical sensitivity, and precision. This study has suggested that there is a link between the diversity and quantity of AOB and the effluent quality from a MBR municipal wastewater treatment system. After activated sludge was inoculated, the changes of AOB community can improve nitrifying performance and maintain self-growth. The sequences indicated that Uncultured Nitrosospira sp. was the climax bacteria in AOB population; three denitrifying bacteria were identified at later stage of experiment. Real-time PCR analysis revealed that AOB population size might be a potential important factor for the constant nitrogen degradation. The direct link between AOB communities and membrane fouling is still unclear, and further data should be gathered. Further studies are necessary to develop quantitative techniques for particular AOB, especially for those predominant species, and to better clarify the roles of these ammonia oxidizers in MBR and other wastewater treatment systems. Moreover, the use of functional genes in this approach leads the possibility to investigate gene expression. Acknowledgement This work was financially supported by Natural Science Foundation of Tianjin City (07JCZDJC02100).

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