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Analysis of the bacterial community in a laboratory-scalenitrification reactor and a wastewater treatment plant by454-pyrosequencing

Lin Ye a, Ming-Fei Shao a, Tong Zhang a,*, Amy Hin Yan Tong b, Si Lok b

aEnvironmental Biotechnology Laboratory, The University of Hong Kong, Hong Kong SAR, ChinabGenome Research Center, The Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China

a r t i c l e i n f o

Article history:

Received 25 January 2011

Received in revised form

20 March 2011

Accepted 22 May 2011

Available online 31 May 2011

Keywords:

Activated sludge

Bacterial community

Cloning

454 High-throughput

pyrosequencing

* Corresponding author. Fax: þ86 852 2559 53E-mail address: zhangt@hkucc.hku.hk (T

0043-1354/$ e see front matter ª 2011 Elsevdoi:10.1016/j.watres.2011.05.028

a b s t r a c t

For full understanding of the microbial community in the wastewater treatment bioreac-

tors, one of the feasible and effective ways is to investigate the massive genetic informa-

tion contained in the activated sludge. In this study, high-throughput pyrosequencing was

applied to analyze the 16S rRNA gene of bacteria in a laboratory-scale nitrification reactor

and a full-scale wastewater treatment plant. In total, 27,458 and 26,906 effective sequence

reads of the 16S rRNA gene were obtained from the Reactor and the wastewater treatment

plant activated sludge samples respectively. The taxonomic complexities in the two

samples were compared at phylum and genus levels. According to the pyrosequencing

results, even for a laboratory-scale reactor as simple as that in this study, a small size clone

library is far from enough to reflect the whole profile of the bacterial community. In

addition, it was found that the commonly used informatics tool “RDP classifier” may

drastically assign Nitrosomonas sequences into a wrong taxonomic unit resulting in

underestimation of ammonia-oxidizing bacteria in the bioreactors. In this paper the

reasons for this mistakenly assignment were analyzed and correction methods were

proposed.

ª 2011 Elsevier Ltd. All rights reserved.

1. Introduction library (Matsumoto et al., 2009) as well as direct visualization

Nitrification is an important step for removing ammonium

nitrogen from wastewater. The key role that bacterial

communities play in wastewater treatment has been inten-

sively studied in the past decades in laboratory-scale and full-

scale bioreactors by the use of various molecular methods.

PCR-DGGE (polymerase chain reaction - denaturing gradient

gel electrophoresis) and T-RFLP (terminal restriction fragment

length polymorphism) were used to investigate the diversity

of the bacterial communities of activated sludge from

different wastewater treatment plants (WWTPs) (Boon et al.,

2002; Regan et al., 2002). More recently, analysis of clone

37.. Zhang).ier Ltd. All rights reserve

of bacterial species by FISH (fluorescent in situ hybridization)

(Hao et al., 2009) were employed to determine the community

compositions. Although bacterial species involved in nitrifi-

cation have also been characterized by the aforementioned

approaches, the extraordinary diversity of microorganisms in

the activated sludge exceeds the sensitivity and dynamic

range of those molecular methods and precludes a complete

characterization of the interplay of various components of the

microbial community in nitrification.

Pyrosequencing developed by Roche 454 Life Science

(Branford, CT, USA) is a high-throughput analytical method

that can generate huge amounts of DNA reads through

d.

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 0e4 3 9 8 4391

a massively parallel sequencing-by-synthesis approach

(Margulies et al., 2005). This technology have been usedwidely

to analyze themicrobial community in various environmental

samples, such as marine water (Qian et al., 2011), soil (Roesch

et al., 2007), human distal intestine (Claesson et al., 2009),

wastewater treatment plant influent (McLellan et al., 2010)

et al.. However, few studies have been conducted on activated

sludge by this method. Kwon and colleagues (Kwon et al.,

2010) investigated the microbial diversity in an integrated

fixed-film activated sludge system. Their results showed the

bacterial abundances were quite high, totally 3034 and 1451

operational taxonomic units (OTUs) were identified at the 3%

cutoff for the suspended and attached samples, respectively.

In the present study, we characterized and compared the

bacterial communities in a laboratory-scale nitrification

reactor with that from activated sludge of a wastewater

treatment plant by 454-pyrosequencing. The diversity and

abundance of the nitrifiers in these two samples were also

investigated. Additionally, we found that RDP Classifier,

a commonly used informatics tool for pyrosequencing data

analysis, may have phylogenetically assigned Nitrosomonas

sequences into a wrong order. The key nucleotides leading to

the mistaken assignment have been identified and a method

to overcome this problem was proposed based on the BLAST

results.

2. Materials and methods

2.1. Reactor operation and WWTP description

In the present study, a fermentor (Sartorius Biostat� A plus)

(Goettingen, Germany) with a working volume of 2.6 L was

configured for continuous operation to conduct nitrification

studies. A pH value of 7.5 was held by automated addition of

sodium bicarbonate. Dissolved Oxygen (DO) was maintained

at 0.5 mg/L by stirring and aeration. The influent was made

with deionizedwater (67%) and seawater (33%) to simulate the

typical salinity of sewage found in Hong Kong. NH4Cl was

Fig. 1 e Nitrogen concentration in the influent and effluent (The

samples for cloning and pyrosequencing analysis).

added to the influent to get the ammonia nitrogen concen-

tration of 200 mg/L, In addition, 20 mg/L of KH2PO4 was added

into the influent to provide sufficient phosphorus for the

growth of microorganisms in the Reactor. Without adding

organic matter, the total organic carbon (TOC) of the influent

was as low as 0.64 � 0.05 mg/L. The hydraulic retention time

(HRT) of the Reactor was 18.4 h. The Reactor was shieldedwith

aluminum foil to avoid exposure to light. Prior to the present

study, the Reactor was ran continuously for more than 500

days to investigate the ammonia-oxidizing bacteria (AOB) and

nitrite-oxidizing bacteria (NOB) under different conditions (Jin

et al., 2010; Ye and Zhang, 2010).

ShatinWWTP is a full-scale wastewater treatment plant in

Hong Kong. This WWTP treats saline sewage (salinity 1.2%)

with a four-stage process (anoxiceaerobiceanoxiceaerobic)

that may simultaneously remove organic compounds and

nitrogen. The seed sludge of the Reactor described above was

taken from Tank No.16 of the first aerobic stage. Activated

sludge sample used to perform 454-pyrosequencing analysis

was also taken from the same tank.

2.2. Chemical analysis

Concentrations of ammonium, nitrite and nitrate were

measured according to the Standard Methods (Eaton and

Franson, 2005) by Nesslerizaion Method, Colorimetric Method

and Ultraviolet Spectrophotometric Screening Method,

respectively.

2.3. DNA extraction and PCR

Sludge samples of Day 165, 178, 190, and 201 (as indicated by

the inverted triangle in Fig. 1) were taken from the Reactor for

DNA extraction using FastDNA� SPIN Kit for Soil (MP

Biomedicals, Illkirch, France). For clone library construction,

the above DNA mixture was pooled and amplified by PCR

using primer set EUB8F (50-AGAGTTTGATCMTGGCTCAG-30)(Heuer et al., 1997) and UNIV1392R (50-ACGGGCGGTGTGTRC-30) (Ferris et al., 1996). 30 ml PCR mixture contained 0.2 ml of Ex

4 inverted open triangles indicate the time points of sludge

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 0e4 3 9 84392

Taq TM(TaKaRa, Dalian, China), 3 ml of 10� Ex Taq Buffer, 3 ml of

dNTPmixture, 0.2 mMof each primer, and 20e50 ng of genomic

DNA. The thermocycling stepswere as follows: 95 �C for 7min,

followed by 35 cycles at of 95 �C for 1min, 55 �C for 1min, 72 �Cfor 1 min and a final extension step at 72 �C for 10 min.

For pyrosequencing, the above DNA mixture of the four

samples was amplified with a set of primers targeting the

hypervariable V4 region of the 16S rRNA gene (RDP’s Pyrose-

quencing Pipeline: http://pyro.cme.msu.edu/pyro/help.jsp).

The forward primer is 50-AYTGGGYDTAAAGNG-30 and the

reverse primers are the mixture of four primers, i.e. 50-TACCRGGGTHTCTAATCC-30, 50-TACCAGAGTATCTAATTC-30,50-CTACDSRGGTMTCTAATC-30, and 50-TACNVGGGTATC-

TAATCC-30 (Claesson et al., 2009). Barcodes that allow sample

multiplexing during pyrosequencing were incorporated

between the 454 adapter and the forward primers.

2.4. Cloning

PCR products were purified using PCRquick-spinTM PCR

Product Purification Kit (iNtRON Biotechnology, Sangdaewon-

Dong, Korea). The purified PCR productswere cloned using the

InsTAclone� PCR Cloning Kit (Fermentas, Burlingtong,

Ontario, Canada) following the instructions of the vendor.

White colonies were selected for whole-cell PCR amplification

with the M13F (50-TGTAAAACGACGGCCAGT-30) and M13R (50-CAGGAAACAGCTATGAC-30) primer set. The PCR products

were purified and sequenced on the ABI 3730xl capillary

sequencer (Applied Biosystems, Foster City, CA, USA) using

M13F or M13R primers. The clone library sequences in this

study have been deposited in GenBank under accession

numbers HM117160 to HM117171.

2.5. High-throughput 454 pyrosequencing

The composition of the PCR products of V4 region of 16S rRNA

gene was determined by pyrosequencing using the Roche 454

FLX Titanium sequencer (Roche 454 Life Sciences, Branford,

CT, USA). Samples in this study were individually barcoded to

enable multiplex sequencing. The results are deposited into

the NCBI short reads archive database (Accession Number:

SRA026842.2).

2.6. Sequence analysis and phylogenetic classification

Following pyrosequencing, Python scripts were written to: 1)

remove sequences containing more than one ambiguous base

(‘N’); 2) check the completeness of the barcodes and the

adapter; 3) remove sequences shorter than 150 bps. The “RDP

Align” tool in RDP’s Pyrosequencing Pipeline was used to align

the effective sequences. A cluster filewas generatedwith “RDP

Complete Linkage Clustering” tool. From the cluster file, the

rarefaction curve was generated using the “RDP Rarefaction”

tool. Taxonomic classification of the sequences was per-

formed using the RDP Classifier (Version 2.2) with a set

confidence threshold of 50%.

Python and Biopython (Cock et al., 2009) were used to

create scripts to: 1) extract all sequences that were assigned

into the order of Burkholderiales according to the result of RDP

Classifier (the downloaded assignment detail text file); 2) use

qblast function in Biopython to run BLAST and search against

“nr” database through the internet automatically for all the

above-mentioned extracted sequences; 3) parse the BLAST

results to check the top 10 hits whether there is a hit con-

taining Nitrosomonas or Comamonas in the title; and 4) record

the maximum identities between the query sequences and

the subject sequences identified in last step.

On the other hand, all sequences obtained from pyrose-

quencing in this study were compared with Greengenes 16S

rRNA gene database (DeSantis et al., 2006) using NCBI’s

BLASTN tool (Altschul et al., 1990) and the default parameters

except for the maximum hit number of 100 (Claesson et al.,

2009). Then the sequences were assigned to NCBI taxon-

omies with MEGAN (Huson et al., 2007) by using the Lowest

Common Ancestor (LCA) algorithm and the default parame-

ters, i.e. absolute cutoff: BLAST bitscore 35, and relative cutoff:

10% of the top hits.

3. Results and discussion

3.1. Reactor performance

Prior to this study, the Reactor was operated under a very low

oxygen concentration condition (DO 0.15 mg/L). Accordingly,

the bulk of the ammoniumwas partially oxidized to nitrite. In

present study, the DO level was increased to 0.5 mg/L, the

nitrite was gradually reduced and several molecular methods

(DGGE, T-RFLP and Cloning) have been used to confirm that

nitrite-oxidizing bacteria (Nitrospira) proliferated intensively

in this period (Ye and Zhang, 2010). The operational condition

and the performance of the Reactor were described in detail in

our previous paper (Ye and Zhang, 2010).

3.2. Cloning results

Twelve OTUswere obtainedwith 3% nucleotide cutoff from 61

clones that were sequenced in 16S rRNA gene clone library.

According to RDP Classifier, the 61 sequences examined by

Sanger-dideoxy based sequencing can be assigned to 3 phyla.

Based on both RDP Classifier and BLAST analysis (Table 1),

OTU-2 and OTU-4 are Nitrosomonas and Nitrospira species,

which accounted for 21.3% and 3.2% in total bacterial

community, respectively, suggesting that these species

represent the dominant AOB and NOB in the Reactor. It should

be noted that according to the BLAST results, the most

dominant OTU, OTU-1, is probably a heterotrophic species

that is similar (max identity 99%) to an uncultured bacteria

reported in the marine sediment.

3.3. Taxonomic complexity of the bacterial community

Pyrosequencing of the Reactor sample and the WWTP sludge

sample yielded 27,458 and 26,906 effective sequence tags

respectively. The amount of sequences was comparable to

those of other studies (Kwon et al., 2010; Lee et al., 2010;

McLellan et al., 2010). RDP Classifier was firstly used to

assign these sequence tags into different phylogenetic bacte-

rial taxa. Fig. 2 and Supplementary Table S1 show the relative

bacterial community abundances on the phylum level. Except

Table 1 e The affiliation and closest match of the bacterial OTUs.

OTU Clones Percentage Genus assignment basedon RDP classifier [Probability]

Closest match from BLAST[Max Identity]

1 34 55.7% Coxiella [35%] Uncultured bacterium [99%]

2 13 21.3% Nitrosomonas [56%] Nitrosomonas sp. [99%]

3 3 4.9% Oleiphilus [10%] Uncultured geproteobacterium [99%]

4 2 3.2% Nitrospira [100%] Uncultured Nitrospira sp. [98%]

5 2 3.2% Loktanella [40%] Uncultured bacterium [94%]

6 1 1.6% Aminobacter [56%] Uncultured bacterium [94%]

7 1 1.6% Azoarcus [56%] Denitromonas indolicum [97%]

8 1 1.6% Muricauda [100%] Muricauda sp. [98%]

9 1 1.6% Adhaeribacter [31%] Uncultured bacterium [91%]

10 1 1.6% Roseivirga [74%] Uncultured bacterium [98%]

11 1 1.6% Roseivirga [45%] Uncultured bacterium [97%]

12 1 1.6% Marinicola [21%] Uncultured bacterium [91%]

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 0e4 3 9 8 4393

for a small number of minor phyla accounting for no more

than 0.6% of total community found only inWWTP sludge, the

numbers of tabulated phylum present in the Reactor and the

WWTP sludge were nearly identical. Proteobacteria, Firmicutes

and Bacteroidetes were three phyla that were abundant in both

samples. These three phyla were also ubiquitous in soil (Fierer

et al., 2007). However, Actinobacteria and Chloroflexiweremuch

less abundant in the Reactor than in theWWTP. Heterotrophic

bacteria in these phyla may be depleted in the Reactor under

such an oligotrophic environment. It was also observed that

Nitrospira phylum in the Reactor was significantly more

abundant than that in the WWTP sludge, suggesting that

the elevated level of nitrite and oxygen present in the Reactor

may have favored the propagation of these nitrite-oxidizing

bacteria. Notably, although the organic matter in the

influent of the nitrification reactor was very low, the hetero-

trophic bacteria were still dominant over the autotrophic

bacteria. The carbon source of these heterotrophic bacteria

were probably from soluble microbial products (SMP) in

the Reactor (Barker and Stuckey, 1999; Rittmann andMcCarty,

2001).

Proteobacteria

Unclass

ified Bacte

ria

Firmicu

tes

Bacteroidetes

Nitrosp

ira

Actinobacteria

Planctomycetes

Acidobacte

ria

Verruco

microbia

Deinococcu

s-Thermus

Chlamydiae

TM7

Chloroflexi

Spirochaetes

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

55%

Perc

enta

ge

Phylum name

Reactor WWTP

Fig. 2 e Bacterial community compositions at phylum level

revealed by pyrosequencing.

454-Pyprosequencing provides at least three logs or more

sensitivity over conventional Sanger-dideoxy based

sequencing for assessing microbial diversity. Fig. 3 showed

the diversity of bacteria in the Reactor was significantly

reduced after 500 days’ operation compared with the seed

sludge from WWTP. The diversity reduction from seeding

sludge to lab-scale reactor was usually investigating by DGGE

and cloning previously (Liu et al., 2002). Heterotrophic bacteria

were greatly depleted in the Reactor. Especially, the phylum

Actinobacteria, where most members are heterotrophs (Servin

et al., 2008), were dramatically reduced (Fig. 2). While the

absence of light under Reactor conditions avoids perturbing

the growth of the nitrifiers (Sinha and Annachhatre, 2007),

light dependent bacteria were depleted. A marked decrease of

the phototropic Chloroflexi phylum (Holt et al., 1994) (Fig. 2) in

the Reactor was observed compared with the seed sludge.

In order to further compare the microbial communities of

the two samples from the Reactor and WWTP, all-against-all

comparison was conducted by using the MEGAN software.

The sequences in each of the samples were normalized before

doing the comparison. The tree created byMEGANwas shown

in Fig. 4. The pie charts beside the leaves of the tree indicate

the relative abundance of the genus in the two samples. From

0 5000 10000 15000 20000 25000 300000

500

1000

1500

2000

2500

3000

3500

3% Reactor 5% Reactor 3% WWTP 5% WWTP

OTU

s

Number of sequences

Fig. 3 e Rarefaction curves of OTUs defined by 3% and 5%

distances in Reactor and WWTP sludge samples.

Fig. 4 e Sequences from the Reactor andWWTP assigned into NCBI taxonomies with BLAST andMEGAN. (Pie charts indicate

the relative abundance for each genus. The ratio of gray color area to dark color area in each pie represented the ratio of the

relative abundance of the corresponding genus in WWTP to that in the Reactor.)

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 0e4 3 9 84394

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 0e4 3 9 8 4395

Fig. 4, it could be seen that at the genus level the microbial

communities of the Reactor and the WWTP were quite

different. There were some genera (such as Marinobacter,

Pseudomonas, Aequorivita, Muricauda, etc.) appearing only in

the Reactor. According to the previous reports (Bowman

and Nichols, 2002; Gauthier et al., 1992; Yoon et al., 2005),

these genera are usually halotolerant bacteria and exist in

the marine environment. These bacteria may come from the

seawater of the influent and could adapt themselves to the

conditions of the Reactor. Many other genera, which were

marked by gray color in Fig. 4, exist only in the WWTP. That

indicates the diversity of the bacteria in the WWTP was much

more complex than that in the Reactor. Also, there are some

genera, including Phyllobacteriaceae, Rhodobacteraceae, Chroma-

tiales, Comamonadaceae, Nitrospira, Rhodococcus, etc., exist both

in the Reactor and the WWTP.

According to the cluster files produced by the “RDP

Complete Linkage Clustering” tool, there were 494 and 381

OTUs in Reactor sludge using two cutoffs levels of 3% and 5%,

respectively. By contrast, WWTP sludge had 1986 and 1648

OTUs using the same cutoffs. According to the rarefaction

curve (Fig. 3), the species complexity in the Reactor was six-

fold less than that in WWTP. Judging from the numbers of

OTUs obtained by pyrosequencing in this study, not to

mention the WWTP, even for a laboratory reactor as simple

as that in our study, a small scale clone library was not

sufficient to reflect the whole profile of the bacterial

community, especially for those minor populations.

Furthermore, it can be deduced that the commonly used

molecular methods (such as DGGE and T-RFLP) in environ-

mental biotechnology may also have insufficient resolutions

to characterize the microbial communities in the wastewater

treatment bioreactors. High-throughput sequencing methods

have the potential to be effective means for better under-

standing of the microorganisms in various environmental

engineering facilities.

3.4. Diversity and abundances of AOB and NOB

It was found that in the results of RDP Classifier (Table 2),

the sequences that were assigned into Nitrosomonadales

order were quite few. For the Reactor sludge sample, only

0.65% of the sequences were classified into this order, which

was inconsistent with the limited results from Sanger-

dideoxy sequencing of the clone library. Such low abun-

dance of AOB also conflicted with the performance (high

ammonium removing rate) of the Reactor. A further check

showed that there were large numbers of Nitrosomonadales

Table 2 e Relative abundance of dominant AOB and NOB in th

Sample AOB/NOB RDP Classifier Clone Library

Reads % Reads %

Reactor Nitrosomonadales 179 0.65% 13 21.3%

Nitrospirales 1782 6.49% 2 3.2%

WWTP Nitrosomonadales 14 0.05% e e

Nitrospirales 273 1.01% e e

sequences that were wrongly assigned by the RDP Classifier

into the order of Burkholderiales, a neighbor of Nitro-

somonadales in Proteobacteria phylum. Most of these mis-

assigned sequences were closely related (identity>97%) to

Nitrosomonas species as shown by BLAST analysis. Following

reclassification, 15.54% of the sequences from the reactor

sludge sample were Nitrosomonas related, indicating that

Nitrosomonas-like AOB were remarkably enriched in the

reactor (Table 2).

In both the Reactor and the WWTP sludge, the dominant

AOB species was Nitrosomonas and the dominant NOB species

was Nitrospira, which were affiliated to Nitrosomonadales and

Nitrospirales order, respectively. This result was consistent

with the previous reports of activated sludge from other

researchers (Layton et al., 2005; Logemann et al., 1998).

Except Nitrosomonas and Nitrospira, the other AOB and NOB

genus were very rare in the reactor. Nitrosospira, another

genus belonging to b-Proteobacteria AOB, accounted for only

0.6% and 0.056% in the reactor and the WWTP sludge

samples, respectively. Only one sequence and seven

sequences of Nitrosococcus were found in 27,458 and 26,906

sequences in the Reactor and WWTP sludge samples,

respectively. For NOB, only 13 Nitrobacter sequences were

found in the WWTP sludge and none was found in the

reactor. The present results suggested that except Nitro-

somonas and Nitrospira, all other species of bacterial nitrifiers

play only a very small role in nitrification process in the

wastewater treatment reactors.

3.5. Mistakenly classified Nitrosomonas sequences

In this study, as aforementioned, RDP Classifier may wrongly

assign sequences inNitrosomonadales order into another order,

consequently, leading to an underestimation of the abun-

dance of AOB in the samples. Except for the samples in this

study, we also investigated the pyrosequencing results of

other 14 samples from WWTPs in another study, a lot of

sequences were mistakenly assigned to Burkholderiales.

Thirty sequences, including 10 from mistakenly assigned

Nitrosomonas sequences, 10 from correctly assigned Nitro-

somonas sequences, and 10 from Comamonas (a genus in Bur-

kholderiales) sequences, were selected from the sequences

obtained by the pyrosequencing, and used to draw

a Neighbor-joining phylogenetic tree using Jukes-Cantor

model (Fig. 5). It was found that the Nitrosomonas sequences

and Comamonas sequences can be clearly classified into

different groups, indicating that there are marked differences

e Reactor and WWTP sludge.

BLAST-corrected RDP Classifier MEGAN Assignment

Reads % Reads %

4059 14.78% 4268 15.54%

1782 6.49% 1817 6.61%

14 0.05% 13 0.05%

273 1.01% 273 1.01%

Nitro_M-GPQYMAQ01D2WQ6

Nitro_M-GPQYMAQ01CPALL

Nitro_M-GPQYMAQ01EE6QD

Nitro_M -GPQYMAQ01DCDM8

Nitro_M-GPQYMAQ01BBFHN

Nitro_M-GPQYMAQ01B7TNH

Nitro_M-GPQYMAQ01D96JO

Nitro_M-GPQYMAQ01A2KAW

Nitro_M-GPQYMAQ01EIO8Z

Nitro_M-GPQYMAQ01EI4S3

Nitro-GPQYMAQ01A9K6V

Nitro-GPQYMAQ01E0QA6

Nitro-GPQYMAQ01C68KE

Nitro-GPQYMAQ01DEH7U

Nitro-GPQYMAQ01CCBG2

Nitro-GPQYMAQ01AHLH1

Nitro-GPQYMAQ01AYECJ

Nitro-GPQYMAQ01CXQ2B

Nitro-GPQYMAQ01BQZAW

Nitro-GPQYMAQ01BEAJB

Comam-GPQYMAQ01EM28M

Comam-GPQYMAQ01CGVBO

Comam-GPQYMAQ01BYP7R

Comam-GPQYMAQ01EUM1H

Comam-GPQYMAQ01C0W9M

Comam-GPQYMAQ01ELIAF

Comam-GPQYMAQ01B2DQT

Comam-GPQYMAQ01DJK4R

Comam-GPQYMAQ01DPQIB

Comam-GPQYMAQ01AYTQ6

75

82

25

33

33

62

100

31

67

86

60

98

90

72

43

40

0.01

I

II

III

Fig. 5 e Neighbor-joining phylogenetic tree using Jukes-Cantor model of Nitrosomonas sequences and Comamonas

sequences based on V4 region of 16S rRNA gene sequences (I - Mistakenly assigned Nitrosomonas sequences, II e Correctly

assigned Nitrosomonas sequences, III e Comamonas sequences).

Fig. 6 e The key nucleotides that caused the mistakenly

assigned Nitrosomonas sequences (I - Mistakenly assigned

Nitrosomonas sequences, II e Correctly assigned

Nitrosomonas sequences).

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 0e4 3 9 84396

in the sequences of these two groups. It indicates the possible

unrecognized deficiencies of the RDP Classifier.

Further examination of these sequences reveals that the

correct assignment of Nitrosomonas related sequences into

the Nitrosomonas genus is dependent on a key dinucleotide

position, as show in Fig. 6. If the ‘GC’ dincucleotide at

position 106 of group I was changed to ‘AT’, as it is found

in group II, the RDP Classifier would then assign group I

correctly into Nitrosomonas. It is therefore advised that the

phylogenetic results of Nitrosomonas species obtained using

the RDP Classifier should be cross-validated by other inde-

pendent tools such as Greengenes’ classification tool

(DeSantis et al., 2006) and GAST (Huse et al., 2008).

Accordingly, we have developed a batch BLAST method,

which has been described in the materials and methods

part, to confirm the suspicious sequences.

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 0e4 3 9 8 4397

4. Conclusions

The diversity of the bacterial community in the nitrification

reactor was significantly reduced compared with seeding

sludge from the WWTP after 500 days’ operation. While, the

bacteria both in the reactor and the seeding sludge distributed

almost over the same phyla.

RDP classifier is a powerful tool for pyrosequencing data

analysis but it appears to misassign Nitrosomonas sequences

into wrong taxonomic rank. Some other tools, such as Blast

and Greengenes classification tool, can be used to correct the

results of the RDP classifier. We also developed a batch Blast

method to confirm the suspicious sequences.

According to the pyrosequencing results, for such

a reactor, a small scale clone library is not enough to reflect

the profile of the bacterial community.

Although the influent of the nitrification reactor contained

nearly no organic matter, the heterotrophic bacteria were still

dominant and much more than autotrophic bacteria in the

reactor.

Acknowledgments

Dr. Ming-Fei Shao thanks HKU for the postdoctoral fellowship.

Lin Ye thanks HKU for the postgraduate studentship. We also

would like to thank Hong Kong General Research Fund

(HKU7197-08E) for financial support of this study and W Chan

and CK Wong for technical help in pyrosequencing.

Appendix. Supplementary data

Supplementary data associated with this article can be found,

in the online version, at doi:10.1016/j.watres.2011.05.028.

r e f e r e n c e s

Altschul, S., Gish, W., Miller, W., Myers, E., Lipman, D., 1990. Basiclocal alignment search tool. J. Mol. Biol. 215, 403e410.

Barker, D.J., Stuckey, D.C., 1999. A review of soluble microbialproducts (SMP) in wastewater treatment systems. Water Res.33, 3063e3082.

Boon, N., Windt, W., Verstraete, W., Top, E., 2002. Evaluation ofnested PCReDGGE (denaturing gradient gel electrophoresis)with group-specific 16S rRNA primers for the analysis ofbacterial communities from different wastewater treatmentplants. FEMS Microbiol. Ecol. 39, 101e112.

Bowman, J., Nichols, D., 2002. Aequorivita gen. nov., a member ofthe family Flavobacteriaceae isolated from terrestrial andmarine Antarctic habitats. Int. J. Syst. Evol. Microbiol. 52, 1533.

Claesson, M., O’Sullivan, O., Wang, Q., Nikkila, J., Marchesi, J.,Smidt, H., De Vos, W., Ross, R., O’Toole, P., 2009. Comparativeanalysis of pyrosequencing and a phylogenetic microarray forexploring microbial community structures in the humandistal intestine. PloS One 4, e6669.

Cock, P., Antao, T., Chang, J., Chapman, B., Cox, C., Dalke, A.,Friedberg, I., Hamelryck, T., Kauff, F., Wilczynski, B., 2009.Biopython: freely available Python tools for computationalmolecular biology and bioinformatics. Bioinformatics 25, 1422.

DeSantis,T.,Hugenholtz, P., Larsen,N.,Rojas,M.,Brodie,E.,Keller,K., Huber, T., Dalevi, D., Hu, P., Andersen, G., 2006. Greengenes,a chimera-checked 16S rRNA gene database and workbenchcompatible with ARB. Appl. Environ. Microbiol. 72, 5069.

Eaton, A., Franson, M., 2005. Standard methods for theexamination of water & wastewater. Am Public Health Assn.

Ferris, M., Muyzer, G., Ward, D., 1996. Denaturing gradient gelelectrophoresis profiles of 16S rRNA-defined populationsinhabiting a hot spring microbial mat community. Appl.Environ. Microbiol. 62, 340e346.

Fierer, N., Bradford, M.A., Jackson, R.B., 2007. Toward anecological classification of soil bacteria. Ecology 88, 1354e1364.

Gauthier, M., Lafay, B., Christen, R., Fernandez, L., Acquaviva, M.,Bonin, P., Bertrand, J., 1992. Marinobacterhydrocarbonoclasticus gen. nov., sp. nov., a new, extremelyhalotolerant, hydrocarbon-degrading marine bacterium. Int. J.Syst. Evol. Microbiol. 42, 568.

Hao, X., Wang, Q., Zhang, X., Cao, Y., Mark Loosdrecht, C.M., 2009.Experimental evaluation of decrease in bacterial activity dueto cell death and activity decay in activated sludge. Water Res.43, 3604e3612.

Holt, J., Krieg, N., Sneath, P., Staley, J., Williams, S., 1994. Bergey’sManual of Systematic Bacteriology, nineth ed. Williams andWilkins.

Heuer, H., Krsek, M., Baker, P., Smalla, K., Wellington, E., 1997.Analysis of actinomycete communities by specificamplification of genes encoding 16S rRNA and gel-electrophoretic separation in denaturing gradients. Appl.Environ. Microbiol. 63, 3233e3241.

Huse, S., Dethlefsen, L., Huber, J., Welch, D., Relman, D.,Sogin, M., 2008. Exploring microbial diversity and taxonomyusing SSU rRNA hypervariable tag sequencing. PLoS Genet. 4,e1000255.

Huson, D., Auch, A., Qi, J., Schuster, S., 2007. MEGAN analysis ofmetagenomic data. Genome Res. 17, 377e386.

Jin, T., Zhang, T., Yan, Q., 2010. Characterization andquantification of ammonia-oxidizing archaea (AOA) andbacteria (AOB) in a nitrogen-removing reactor using T-RFLPand qPCR. Appl. Microbiol. Biotechnol. 87, 1167e1176.

Kwon, S., Kim, T., Yu, G., Jung, J., Park, H., 2010. Bacterialcommunity composition and diversity of a full-scaleintegrated fixed-film activated sludge system as investigatedby pyrosequencing. J. Microbiol. Biotechnol. 20, 1717e1723.

Layton, A., Dionisi, H., Kuo, H., Robinson, K., Garrett, V.,Meyers, A., Sayler, G., 2005. Emergence of competitivedominant ammonia-oxidizing bacterial populations in a full-scale industrial wastewater treatment plant. Appl. Environ.Microbiol. 71, 1105.

Lee, T.K., Van Doan, T., Yoo, K., Choi, S., Kim, C., Park, J., 2010.Discovery of commonly existing anode biofilm microbes intwo different wastewater treatment MFCs using FLX Titaniumpyrosequencing. Appl. Microbiol. Biotechnol., 1e9.

Liu, W.T., Chan, O.C., Fang, H.H.P., 2002. Microbial communitydynamics during start-up of acidogenic anaerobic reactors.Water Res. 36, 3203e3210.

Logemann, S., Schantl, J., Bijvank, S., Loosdrecht, M., Kuenen, J.,Jetten, M., 1998. Molecular microbial diversity in a nitrifyingreactor system without sludge retention. FEMS Microbiol. Ecol.27, 239e249.

Margulies, M., Egholm, M., Altman, W., Attiya, S., Bader, J.,Bemben, L., Berka, J., Braverman, M., Chen, Y., Chen, Z., 2005.Genome sequencing in microfabricated high-density picolitrereactors. Nature 437, 376e380.

Matsumoto, S., Katoku, M., Saeki, G., Terada, A., Aoi, Y.,Tsuneda, S., Picioreanu, C., van Loosdrecht, M., 2009. Microbialcommunity structure in autotrophic nitrifying granulescharacterized by experimental and simulation analyses.Environ. Microbiol. 12, 192e206.

wat e r r e s e a r c h 4 5 ( 2 0 1 1 ) 4 3 9 0e4 3 9 84398

McLellan, S., Huse, S., Mueller Spitz, S., Andreishcheva, E.,Sogin, M., 2010. Diversity and population structure of sewagederived microorganisms in wastewater treatment plantinfluent. Environ. Microbiol. 12, 378e392.

Qian, P., Wang, Y., Lee, O., Lau, S., Yang, J., Lafi, F., Al-Suwailem, A., Wong, T., 2011. Vertical stratification ofmicrobial communities in the Red Sea revealed by 16S rDNApyrosequencing. ISME J. 5, 507e518.

Regan, J.M., Harrington, G.W., Noguera, D.R., 2002. Ammonia-andnitrite-oxidizing bacterial communities in a pilot-scalechloraminated drinking water distribution system. Appl.Environ. Microbiol. 68, 73e81.

Rittmann, B., McCarty, P., 2001. EnvironmentalBiotechnology: Principles and Applications. McGraw-Hill,New York.

Roesch, L., Fulthorpe, R., Riva, A., Casella, G., Hadwin, A., Kent, A.,Daroub, S., Camargo, F., Farmerie, W., Triplett, E., 2007.

Pyrosequencing enumerates and contrasts soil microbialdiversity. ISME J. 1, 283e290.

Servin, J., Herbold, C., Skophammer, R., Lake, J., 2008. EvidenceExcluding the Root of the tree of Life from the Actinobacteria.Mol. Biol. Evol. 25, 1e4.

Sinha, B., Annachhatre, A., 2007. Partial nitrificationdoperationalparameters and microorganisms involved. Rev. Environ. Sci.Biotechnol. 6, 285e313.

Ye, L., Zhang, T., 2010. Estimation of nitrifier abundances ina partial nitrification reactor treating ammonium-rich salinewastewater using DGGE, T-RFLP and mathematical modeling.Appl. Microbiol. Biotechnol. 88 (6), 1403e1412.

Yoon, J., Lee, M., Oh, T., Park, Y., 2005. Muricauda flavescens sp.nov. and Muricauda aquimarina sp. nov., isolated from a saltlake near Hwajinpo Beach of the East Sea in Korea, andemended description of the genus Muricauda. Int. J. Syst. Evol.Microbiol. 55, 1015.