Profiling bulking and foaming bacteria in activated sludge by high throughput sequencing

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Profiling bulking and foaming bacteria in activated sludge by high throughput sequencing Feng Guo, Tong Zhang* Environmental Biotechnology Laboratory, The University of Hong Kong, Hong Kong SAR, China

article info

abstract

Article history:

Bulking and foaming bacteria (BFB) are notorious in wastewater treatment although they

Received 1 December 2011

are always presented in the normal activated sludge and playing certain roles other than

Received in revised form

being harmful. Previous studies using microscopy or conventional molecular methods

11 February 2012

could hardly get the full profile of the BFB in the normal activated sludge. In this study, high

Accepted 17 February 2012

throughput sequencing was adopted to investigate the BFB community, which was sub-

Available online 24 February 2012

dominant in activated sludge from 14 global wastewater treatment plants. The fulllength 16S rRNA gene sequences of BFB groups were collected from previous studies to

Keywords:

build a database for local BLAST and subsequent taxonomic assignment. The total BFB

Activated sludge

percentage in each sample ranged from 1.86% to 8.99% according to the 16S rRNA gene V4

Bulking

pyrotags detected at the BLAST similarity cutoff of 97%. The most abundant and frequent

Foaming

BFB groups are ‘Nostocoida limicola’ I and II, Mycobacterium fortuitum, Type 1863, and

Bacteria

‘Microthrix parvicella’. The BFB among the activated sludge samples were both biogeo-

High throughput sequencing

graphically and technological distributed to some extent. An extending application was performed to evaluate and design oligonucleotides probes based on the rich information of high similar sequences. Our study also gave an exemplified case of investigation on the specific sub-dominant functional groups in complex bacterial communities revealed by high throughput sequencing. Crown Copyright ª 2012 Published by Elsevier Ltd. All rights reserved.

1.

Introduction

Bulking and foaming of activated sludge (AS) are two of the most concerned microbial issues in wastewater treatment. Bulking causes deteriorated settleability of bioflocs, which is indicated by high sludge volume index (SVI) and results in serious problems in solideliquid separation (Martins et al., 2004; Wanner et al., 2010). Foaming costs extra operation and housekeeping, as well as lowers the quality of effluent (Soddell and Seviour, 1990; De los Reyes, 2010). Most bulking events result from the overgrowth of filamentous bacteria away from the floc surface, while the foaming is majorly caused by the mycolic acid containing actinomycetes (mycolata group),

many of which are also filamentous. Other than their notoriety, since the ubiquitous presence of filamentous bacteria in normal AS, many studies suggested that the filamentous bacteria in AS are significant builder for the bioflocs (at least for part of the flocs, Wanner and Grau, 1989). They are backbones within flocs and supplementary to the extracellular polymers during the formation of flocs (Jenkins, 1992; Urbain et al., 1993). Moreover, the rapid growth rate of filamentous bacteria implies their competence in removal of organic matter, especially under low COD loading or substrate limiting conditions (van Niekerk et al., 1993; Martins et al., 2004). The pioneer and foundation work on classifying filamentous bulking and foaming bacteria (BFB) in AS was carried out

* Corresponding author. Tel.: þ86 852 28578551; fax: þ86 852 25595337. E-mail addresses: [email protected], [email protected] (T. Zhang). 0043-1354/$ e see front matter Crown Copyright ª 2012 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.watres.2012.02.039

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by Eikelboom (1975). Many other researchers devoted to describe, isolate and find ways to control BFB in AS for the past decades (Slijkhuis, 1983; Chudoba, 1985; Kanagawa et al., 2000; Guo et al., 2010). In early studies, the BFB were classified by direct or dye-stained observation under microscope. In the mid 1990s, upon the initial prevalence of molecular methods, more and more studies began to use fluorescent in situ hybridization (FISH) or other 16S rRNA-based techniques to classify them (Wagner et al., 1994a), and reveal many previously unclassified groups (Beer et al., 2002; Speirs et al., 2009; Kragelund et al., 2011). To our knowledge, there are more than 20 types of bulking bacteria and over 30 cultured species of foaming bacteria (Wanner et al., 2010; De los Reyes, 2010). Although the BFB are playing double-edged roles in AS, the filamentous BFB community has not been comprehensively characterized in normal AS. This is largely due to the low profiling depth and extent of previous studies based on conventional molecular methods. Traditional methods to determine the BFB in sludge samples based on direct (or after staining) microscopic observation are useful but need wellexperienced people, in addition to suffering from low sensitivity. For example, the filamentous bacteria are difficult to detect if they are inside the flocs. Molecular methods, such as FISH or RT (real-time) -qPCR, are highly sensitive and standardized (Kanagawa et al., 2000; Levantesi et al., 2006; Kumari et al., 2009). However, these methods are often limited by the unfitness of probes or primers (Thomsen et al., 2002). More importantly, the specific-probe based methods could not give an overall profile of the BFB. Currently, the high throughput sequencing techniques exhibit overwhelming superiority on profiling complex bacterial community for its unprecedented sequencing depth (Kwon et al., 2010; Ye and Zhang, 2011). The recent mainstream platform, Roche 454 FLX Titanium version, can produce hundreds of thousands of w400 bp sequences for each run. It could give detailed information on bacterial community structure qualitatively and quantitatively. Assuming all bacteria in sludge has the same copy number of 16S rRNA gene in their genomes, the detect limitation is about 0.01% if 10,000 reads were obtained for the sample. It is very helpful in analyzing the sub-dominant groups at abundance of 0.01%e1%, like BFB in normal AS. Moreover, although the cost of current high throughput sequencing is higher than traditional assays, it could provide much more information than other 16S rRNA-based or microscopic methods. To date, the cost and operational time of high throughput sequencing is reduced continuously. It is a promising way to monitor bacterial communities of important environmental samples automatically and precisely, such as activated sludge. In this study, we applied Roche 454 pyrosequencing to investigate the community of BFB in activated sludge from 14 wastewater treatment plants (WWTPs) all over the world. A database of the 16S rRNA gene sequences of BFB bacteria was built as references for local BLAST training. The results showed the diversities and distributions of the BFB in normal AS from 14 WWTPs. It also suggested that the high throughput sequencing could give much more information in BFB qualification and quantification to monitor and control bulking and foaming. An expanding application of evaluation and design

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of FISH probe based on the obtained sequences was also proposed.

2.

Materials and methods

2.1.

Sampling

Activated sludge samples were collected from the aeration tanks of 14 wastewater treatment plants of China (9 plants, 6 from Mainland and 3 from Hong Kong), Singapore (1 plant), Canada (1 plant), and the United States (2 plants). Some detailed information of the WWTPs was summarized in Table S1. At sampling, the activated sludge was 1:1 fixed with 100% ethanol on site and kept in an ice box. They were stored at 20  C at lab until DNA extraction.

2.2. DNA extraction, PCR amplification and pyrosequencing Samples of 10 mL were centrifuged at 4000 rpm for 10 min at 4  C. Two hundred milligrams of the pellet of each sample was collected for DNA extraction in duplicate with the FastDNA SPIN Kit for Soil (Q-Biogene, CA), which was found to be the most suitable (having the lowest contamination) for the samples in this study, compared with the ZR Soil Microbe DNA Kit, the SoilMaster DNA Extraction Kit, the PowerSoil DNA Isolation Kit, and the UltraClean Soil DNA Isolation Kit. The duplicate DNA extracts were then merged together. Then the DNA was examined for yield, purity and suitability for PCR by both electrophoresis and spectrometry (NanoDrop-1000). The hypervariable V4 region (w240 bp) of the bacterial 16S rRNA gene was amplified. The primer set composed of a forward primer, 50 -AYTGGGYDTAAAGNG-30 and an equal molar mixture of four reverse primers, i.e. 50 TACCRGGGTHTCTAATCC-30 , 50 -TACCAGAGTATCTAATTC-30 , 50 -CTACDSRGGTMTCTAATC-30 , and 50 -TACNVGGGTATCTAATCC-30 based on the RDP pyrosequencing pipeline (http:// pyro.cme.msu.edu/pyro/help.jsp). Barcodes that allow sample multiplexing during pyrosequencing were inserted between the 454 adapter sequence and the forward primers. Three replicated 100 ml PCR reaction solutions were prepared for each sample using MightyAmp polymerase (TaKaRa) according to the instruction. PCR was performed in an i-Cycler (BioRad) under the following condition: initial denaturation at 98  C for 2 min; 28 cycles at 98  C for 15 s, 56  C for 20 s, and 68  C for 30 s, and a final extension at 68  C for 10 min. Then the three replicated PCR products were mixed and purified with PCR quick-spin PCR Product Purification Kit (iNtRON Biotechnology, Korea). After quantification using Nanodrop, the PCR products of all samples were mixed to get equal concentration of DNA fragment for each sample and sent out for pyrosequencing on the Roche 454 FLX Titanium platform (Roche, Nutley, NJ, USA) at Genomic Research Center in the University of Hong Kong.

2.3.

Data cleaning before analysis

Before analyzing, the sequences were cleaned by: 1) removing all sequences containing any ambiguous base (‘N’); 2)

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checking the completeness of the barcode and adapter; 3) removing sequences shorter than 150 bp; 4) eliminating the chimera sequences with the software Chimera Slayer (Haas et al., 2011); 5) cutting out the primers in all the pyrotags; and 6) re-sampling all groups randomly at the same depth of 16489 pyrotags.

2.4.

Database for bulking and foaming bacteria

To build the BFB database, the BFB species were selected referring to the summary of other researchers (Blackall, 1999; De los Reyes, 2010). The reference sequences were selected from NCBI database following these criteria and priorities: 1) 16S rRNA gene full sequences of confirmed BFB reported in previous studies; 2) then, if no corresponding sequences for BFB pure culture available, the sequences from sludge samples were adopted; 3) finally, sequences of the BFB species from other sources, including freshwater, soil and etc. were considered if there were no sequences in the first two sources. To avoid repeated hitting, the redundant sequences were filtered out (at 97% similarity cutoff at the V4 region) and only one reference sequence for each type (or subtype) was kept in the database. Finally, a database containing the nearly fulllength of 16S rRNA gene from 22 bulking and 5 foaming bacteria was generated (Table 1). Only five foaming bacteria were considered in the present study because all of the mycolic acid-containing foaming bacteria are derived from a suborder Corynebacterineae in Actinobacteria. They are highly similar in 16S rRNA gene sequences. Moreover, several bulking bacteria in this suborder are also responsible for foaming (De los Reyes, 2010).

2.5.

BFB analysis

Pyrotags of each sample were locally blasted against the BFB database on a PC (Altschul et al., 1990). The e-value was set at 1  1060, under which the lowest similarity between reference sequences and the query pyrotags is about 87%. For each hit pyrotag, only one of the closest reference sequences was listed. Then the results were filtered through the following two steps: 1) remove all results that have similarity less than 95% (except for the analysis of FISH probes that set at 90%) because only close pyrotags are meaningful for analysis; 2) remove pyrotags that have more than 2 uncovered bases (i.e., query coverage is less than 99% for 200 bp pyrotags). Then the sequences were ready for statistics. The bacterial abundance (A) of each subgroup was counted by the formula: A ¼ N/16489, where N was the read number hit on the reference sequence of certain BFB group at different cutoffs (i.e., 100%, 99%, 97% and etc.).

2.6.

Data processing

The neighbor-joining cluster among sequences is performed on MEGA 4.0 software (Tamura et al., 2007). The cluster among samples is based on the BrayeCurtis distance and conducted on PAST v2.11 procedure (Hammer et al., 2001). Heat map was drawn using MATLAB (version R2011a) and other figures using OriginPro 8.5.

3.

Results and discussion

3.1.

Total BFB abundances in 14 WWTPs

Most previous studies gave total filamentous cell length or simplified scales, instead of the total abundance of cell number or 16S rRNA gene copy number of BFB bacteria in AS (Jenkins et al., 2004). In this study, according to the detected pyrotags, the abundances of BFB in the 14 AS samples were shown in Fig. 1. There were 0.76% w 5.03%, 1.86% w 8.99% and 2.71% w 15.65% BFB at 99%, 97% and 95% similarity cutoff, respectively. At 97% cutoff which is the general threshold for the bacterial species level (Stackebrandt et al., 2002), BFB in most plants (12 in 14) are between 2 and 6% of total bacteria. A study only investigating ‘Microthrix parvicella’ showed that usually over 2% of the total 16S rRNA gene copies were the targeting group in the high SVI (>300 ml g1) AS samples (Kumari et al., 2009). Since the sludge samples investigated in this study were not obviously bulking, the abundance of total BFB should be less than bulking sludge. For most samples, the pyrotags with 95e97% similarity to the reference sequences are not high (less than 20% to the total hit pyrotags in 9 samples), suggesting the relative uniqueness of BFB groups and the relative precision of this blast method. However, two samples showed very high abundant pyrotags at 95e97% similarity level (52.2% for sample US-2 and 42.4% for sample CN-HK-1). Detailed investigation showed that these two samples mainly contained Sphaerotilus natans (36.2% for sample US-2 and 44.7% for sample CN-HK-1), Type 1863 Actinobacter (61% for sample US-2) and ‘Nostocoida limicola’ II Tetrasphaera jenkinsii (44% for sample CN-HK-1). The richness of relatively low similar pyrotags indicated that there were closely related species to these target groups although it is still hard to determine whether they are BFB or not. To further evaluate the taxonomic assignment of the pyrotags, the abundances of the pyrotags at various cutoff values for various BFB groups (only >50 hit tags in all samples were considered) were calculated, as shown in Fig. 2. Generally, the left groups were more reliable because they had more tags with high similarity. Most of them are culturable and wellcharacterized species, such as Skermania piniformis, Haliscomenobacter hydrossis and Gordonia amarae. However, it is always a limitation in determining specific bacteria by 16S rRNA gene sequences because some bacteria (e.g., species in one genus) shared very similar sequences although their phenotypes are distinct. For example, the species in genus Trichococcus, Trichococcus floculiformis typed with ‘N. limicola’ I had been reported that shared very similar (>99%) 16S rRNA gene with other species in this genus (Liu et al., 2002), while it is not clear whether they are real BFB type or not. In the present study, this group was the most abundant among nearly all samples. It is possible that they are overestimated due to miscounting of the other species within this genus that are not BFB. On the other hand, the right groups in Fig. 2 had much more low-similar tags. They are mostly poor-characterized BFB (e.g., Type 0092-2 and the groups within phylum Chloroflexi). This may be derived from two possibilities: 1) there are certain loosely-related groups in the samples and they are not

Table 1 e Related groups of bulking and foaming bacterial database. Types

Phylum

Genus or species according to the reference

RDP classifier by the reference sequencea

RDP classifier by the V4 region of reference sequencea

Reference related to bulking

Accession no. in NCBI

Sequence source

Beggiatoa

Beggiatoa

Beggiatoa

Williams and Unz, 1985

NR_041726.1

Cyanobacteria Flexibacter elegans

Cyanobacteria CFB group

Unknown Flexibacter elegans

N.A. Flexibacter

N.A. Flexibacter

N.A. Jenkins et al., 1993

N.A. AB078048.1

Haliscomenobacter hydrossis Leucothrix mucor

Bacteroidetes

H. hydrossis

Haliscomenobacter

Haliscomenobacter

Ziegler et al., 1982

NR_042316.1

Proteobacteria

Leucothrix mucor

Leucothrix

Leucothrix

Williams and Unz, 1989

NR_044870.1

Actinobacteria

‘Candidatus Microthrix’

DQ147288.1

Actinobacteria

‘Candidatus Microthrix’

Rossetti et al., 1997

X93044.1

1 Sequence from reference

Mycobacterium fortuitumc

Actinobacteria

Mycobacterium

Blackall et al., 1991

GU142933.1

Nocardiaform-like organisms ‘Nostocoida limicola’ I ‘Nostocoida limicola’ II ‘Nostocoida limicola’ III

Actinobacteria

Mycobacterium fortuitum Gordonia amarae

Acidimicrobidae (Class) Actinobacteria (Phylum) Mycobacterium

Levantesi et al., 2006

‘Microthrix parvicella’

Acidimicrobidae (Class) Iamia

1 Sequence from different source No sequences 1 Sequence from different source 1 Sequence from different source 1 sequence from different source 1 sequence from reference

Gordonia

Gordonia

Blackall, 1994

AF020331.1

1 sequence from different source 1 Sequence from reference

Firmicutes Actinobacteria Planctomycetes

Trichococcus Tetrasphaera jenkinsii Isosphaera

Trichococcus Tetrasphaera Isosphaera Singulisphaera

Liu et al., 2000 Liu and Seviour, 2001 Liu et al., 2001

AF244372.1 NR_043461.1 AF244748.1 AF244751.1

1 Sequence from reference 1 Sequences from reference 2 Sequences from reference

Rhodococcus globerulusc

Actinobacteria

Rhodococcus globerulus

Rhodococcus

Trichococcus Tetrasphaera Planctomycetaceae (Family) Singulisphaera Rhodococcus

Schuppler et al., 1995

HM217119.1

Rhodococcus ruberc

Actinobacteria

Rhodococcus ruber

Rhodococcus

Rhodococcus

Schuppler et al., 1998

JF895525.1

Skermania piniformisc

Actinobacteria

Skermania piniformis

Skermania

Skermania

Eales et al., 2006

AY788090.1

Sphaerotilus natans

Proteobacteria

Sphaerotilus

Leptothrix

Eikelboom, 1975

GU591793.1

Thiothrix form I Tsukamurella pseudospumaec Type 0041/0675

Proteobacteria Actinobacteria

Thiothrix T. pseudospumae

Thiothrix Tsukamurella

Burkholderiales incertae sedis (Family) Thiothrix Tsukamurella

1 sequence from different source 1 sequence from different source 1 sequence from different source 1 sequence from different source

Howarth et al., 1999 Nam et al., 2004

AF127020.1 AY333425.1

1 Sequence from reference 1 sequence from reference

TM7-like Betaproteobacteria

Unknown Aquaspirillum

N.A.

N.A.

N.A.

No sequences

Type 0092

Chloroflexi CFB

Unknown

Unknown Proteobacteria CFB group Unknown

Unknown Thiothrix eikelboomii Unknown Unknown

Bacteria (Domain) Flavobacterium NA Thiothrix Runella NA

AB445105.1 X85210.1 N.A. L79965.1 X85209.1 N.A.

2 Sequences from reference

Type Type Type Type

Bellilinea Flavobacterium NA Thiothrix Runella NA

Hugenholtz et al., 2001 Thomsen et al., 2002 Thomsen et al., 2006 Speirs et al., 2009 Bradford et al., 1996 N.A. Howarth et al., 1999 Bradford et al., 1996 N.A.

‘Microthrix calida’

0211 021N 0411 0581

b

No sequences 1 Sequence from reference 1 sequence from reference No sequences (continued on next page)

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Proteobacteria

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Beggiatoa

N.A.: not available. a Confidence thresholds were set at 80% for full length and 50% for V4 region of 16S rRNA gene. b The two sequences were eliminated in the final database because they are high similarity to ‘Microthrix parvicella’ and Sphaerotilus natans, respectively. c Five reference sequences of foaming bacteria were collected in the final database.

3 Sequences from reference X95305.1 X95304.1 X85207.1 Acinetobacter Moraxella osloensis Chryseobacterium Type 1863

Type 1851

‘Kouleothrix aurantiaca’ Proteobacteria CFB group

Chloroflexi (Phylum)

Chloroflexi (Phylum) Acinetobacter Enhydrobacter Cloacibacterium

Acinetobacter Enhydrobacter Cloacibacterium

Seviour et al., 1997

1 Sequence from reference AY063760.1 Beer et al., 2002

1 Sequence from reference 1 Sequence from reference No sequences No sequences HQ262529.1 GU808362.1 N.A. L79964.1 Kragelund et al., 2011 Speirs et al., 2011 N.A. Howarth et al., 1998

Caldilinea Chloroflexi (Phylum) Unknown Burkholderiales incertae sedis (Family) Chloroflexi (Phylum) Chloroflexi Chloroflexi Unknown Betaproteobacteria 0803 0914 0961 1701b Type Type Type Type

Caldilinea Unknown Unknown Unknown

Caldilinea Caldilinea Unknown Leptothrix

Reference related to bulking RDP classifier by the reference sequencea Genus or species according to the reference Phylum Types

Table 1 e (continued )

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RDP classifier by the V4 region of reference sequencea

Accession no. in NCBI

Sequence source

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Fig. 1 e The abundances of BFB in 14 WWTPs. The abundances are the percentages of hit pyrotags to the total 16489 pyrotags at various cutoff values.

belonging to this BFB type; 2) there is higher variation of 16S rRNA gene within the same BFB group.

3.2.

BFB community structures in 14 WWTPs

BFB profile at 97% similarity cutoff value in 14 WWTPs was shown in Fig. 3. The 27 reference bacterial sequences belong to six phyla: Proteobacteria, CytophageeFlavobactereBacteroides (CFB group), Chloroflexi, Planctomycetes, Firmicutes and Acitnobacteria. Four types were not hit in any sample. They are Beggiatoa alba, Flexibacter elegans, Type 0411 and species in genus Isosphaera belonging to ‘N. limicola’ III type. Three BFB groups, G. amarae, Mycobacterium fortuitum and Type 1863 Acinetobacter were detected in all samples. Gordonia spp., usually typed as Norcadioforms were responsible for both bulking and forming (De los Reyes, 2010). M. fortuitum is a foaming but not always a filamentous-shaped bacterium (Soddell, 1999). In addition, ‘N. limicola’ II e T. jenkinsii, ‘N. limicola’ I e Trichococcus and S. natans were detected in 13 samples. ‘N. limicola’ I was abundant in all the 13 samples. It consisted of over 2% of total pyrotags in 7 WWTPs. However, as mention above, this group may be overestimated due to the low resolution of 16S rRNA gene sequences. Besides that, ‘M. parvicella’, H. hydrossis, Type 1863 Chryseobacteria and Type 1863 Moraxella osloensis presented at high frequencies and considerable abundances in the samples. Some very frequently-reported BFB groups, such as Type 1851 and Thiothrix form I were rarely found in these normal AS samples. To investigate whether there is a biogeographical pattern for BFB community in globally distributed 14 WWTPs, the samples were clustered by BrayeCurtis distance based on the matrix of percentages of each group in total BFB of each sample (Fig. 3). The results showed the BFB community was geographically distributed to some extent. First of all, the 6 of 7 Chinese mainland plants and 2 Hong Kong samples clustered together, respectively. The main characteristics of the six

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Fig. 2 e Abundance of the various BFB groups at each cutoff value of similarity. Only the BFB groups with over 50 hits were calculated. The left groups had more highly similar pyrotags while the right groups had more relatively remote tags.

Chinese plants had low abundance of G. amarae and very rich ‘N. limicola’ I. The two Hong Kong plants were dominated by M. fortuitum. However, the three North American samples, one Chinese mainland, Singapore and one Hong Kong samples (treating saline sewage) are dispersed in difference clusters. Besides geographical location, the process seemed also responsible for BFB community structure. The three plants (SG, CA and US-1) using conventional activated sludge are clustered together. They have very high coverage of all BFB groups but without an obviously dominant group. The 23 hit BFB groups were ranked by both their frequencies and abundances, as listed in Table 2. The groups with high abundances usually had high frequencies, indicating they are normal populations and playing certain roles in normal AS. For most groups, their abundances are less than 1%, which can hardly be detected by clone library or DGGE methods. High throughput sequencing provides sufficient resolution for these sub-dominant populations. Many previous studies on bulking sludge (Blackall, 1999) showed that the most prevalent species were Nocardiaform-like organisms, Type 021N, ‘M. parvicella’, Type 0092, Type 0041 and some groups that were not included in our database, such as Type 0675 (without reference sequence) and Type 1701 (reference sequence are closely related to S. natans and excluded from the final database). However, our results showed that the ‘N. limicola’ forms, Type 1863 and ‘M. parvicella’ are most common bulking bacteria in the normal AS. The difference results between surveying unbulking and bulking AS suggested that some groups may be opportunistic bulking bacteria (similar to opportunistic

pathogens), which stick outside from flocs and cause high SVI only under certain conditions. The other groups, for example, Type 021N may be more virulent because once emerge, it always overgrow and cause bulking.

3.3. Evaluation and designation of oligonucleotides probes for BFB Other than qualification and quantification, the deep sequencing can obtain a great number (tens to hundreds) of closely related sequences from the sub-dominant or even rare bacteria, which are hardly been found by traditional molecular method. A promising application of these phylogenetically related sequences is to design and evaluate probes or primers targeting the populations. We selected five groups of BFB and collect their related sequences (similarity >90% at least) from all the samples (Table 3). The sequences were then sorted into 4 levels of similarity, >99%, 97 w 99%, 95 w 97% and 90 w 95%. Seven previously reported probes for these BFB groups targeting in V4 region of 16S rRNA gene were evaluated. For the optimal probe, it should hit most tags at high similarity (e.g., >97%) and not match with most remote sequences (similarity <95%). Table 3 shows that it has a good resolution for SNA656-673 targeting S. natans and TE 652-669 targeting Thiothrix eikelboomii. The 2 probes got very high identical coverage among over 97% similar tags, while they were little possible to match with the low-similar pyrotags. The quality of the 2 probes targeting ‘M. parvicella’, MP645-661 and MP650-666 is moderate, because it got similar coverage for

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Fig. 3 e Heat map to profile the BFB community in the 14 WWTPs. Each row is a BFB group with one reference sequence and each column represents an AS sample. The cluster among the rows is based on the full length of 16S rRNA gene reference sequences and drawn on Mega 4.0 software. The cluster among samples is according to BrayeCurtis distance derived from the percentages of each group and drawn on the PAST software. The numbers of hit tags were logarithmized then shown as colored blocks. The white blocks are undetected groups.

Table 2 e Ranks of 23 hit BFB groups in 14 normal AS samples. Types

‘Nostocoida limicola’ I Trichococcus Mycobacterium fortuitum Type1863 Acinetobacter ‘Nostocoida limicola’ II Tetrasphaera jenkinsii ‘Microthrix parvicella’ Gordonia amarae Haliscomenobacter hydrossis Sphaerotilus natans Type0092-1 Bellilinea Type1863 Moraxella osloensis Type0803 Caldilinea Type 021N Thiothrix eikelboomii Type1863 Chryseobacteria ‘Nostocoida limicola’ III-2 Singulisphaera Type0914 Caldilinea Skermania piniformis Type1851 Chloroflexi Rhodococcus ruber Type0092-2 Flavobacterium Rhodococcus globerulus Thiothrix Form I Leucothrix mucor Tsukamurella pseudospumae

Ranka

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 19 20 21 21

Frequency (in %)

92.9 100.0 100.0 92.9 78.6 100.0 85.7 92.9 71.4 85.7 57.1 71.4 78.6 57.1 50.0 50.0 21.4 42.9 35.7 28.6 7.1 7.1 7.1

a Rank is sorted by multiply the frequency and abundance for each group.

Abundance in all samples hit pyrotags Tags

in %

4745 1906 1067 531 558 354 385 270 202 137 178 75 40 52 59 29 47 15 11 11 8 1 1

2.06 0.83 0.46 0.23 0.24 0.15 0.17 0.12 0.09 0.06 0.08 0.03 0.02 0.02 0.03 0.01 0.02 0.01 0.00 0.00 0.00 0.00 0.00

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Table 3 e Evaluation and design of FISH probes on BFB groups. Targets

Probes (50 to 30 )

Sphaerotilus natans

SNA656e673 (Amann et al., 19 95) CATCCCCCTCTACCGTAC

Microthrix parvicella

MP626-644 (Seviour and Blackall, 1999) AGTATCAAATGCAGGCTCA MP645-661 (Erhart et al., 1997) CCGGACTCTAGTCAGAGC MP650-666 (Erhart et al., 1997) CCCTACCGGACTCTAGTC HY655-672 (Amann et al., 1995) GCCTACCTCAACCTGATT TE 652-669 (Amann et al., 1995) TCCCTCTCCCAAATTCTA T1851-1 (Beer et al., 2002) AATTCCACGAACCTCTGCCA

Haliscomenobacter hydrossis Thiothrix eikelboomii

Type1851

T1851-2b CCTGAGCGTCAGATATGGCC

Groups with various similarity to the reference sequencea

Total pyrotags

100% hit pyrotags

One-mismatched pyrotags

>99% 97e99% 95e97% 90e95% >99% 97e99% 95e97% >99% 97e99% 95e97% >99% 97e99% 95e97% >99% 97e99% 90e95% >99% 97e99% 90e95% >99% 97e99% 95e97% 90e95% >99% 97e99% 95e97% 90e95%

19 237 1675 4802 57 365 12 57 365 12 57 365 12 273 40 11 31 42 74 34 12 50 318 34 12 50 318

18 (94.7%) 227 (95.8%) 805 (48.1%) 217 (4.5%) 51 (89.5%) 22 (6.0%) 0 (0%) 52 (91.5%) 355 (97.3%) 11 (91.7%) 52 (91.2%) 358 (96.8%) 11 (91.7%) 31 (11.4%) 13 (32.5%) 7(63.6%) 30 (96.8%) 36 (85.7%) 1 (1.4%) 34 (100%) 12 (100%) 50 (100%) 311 (97.8%) 34 (100%) 10 (83.3%) 0 (0) 0 (0)

0 (0) 0 (0) 594 (35.5%) 821 (17.1%) 0 (0) 298 (81.7%) 0 (0) 1 (1.7%) 1 (0.3%) 0 (0) 1 (1.8%) 4 (1.1%) 0 (0) 241 (88.3%) 27 (67.5%) 0 (0) 1 (3.2%) 6 (13.3%) 51 (68.9%) 0 (0) 0 (0) 0 (0) 2(0.6%) 0 (0) 2 (16.7%) 0 (0) 0 (0)

a Some groups had few or no hit tags at specific cutoff value. Only sequences over 10 hit tags at certain cutoff value were analyzed. Identical sequences to the references were not included in analysis. b The probe is designed based on comparing the conservational region among high similar sequences but different in low similar sequences.

all the 95 w 100% pyrotags. In contrast, probe MP626-644 seemed too harsh to cover all its targets. It was prone to only identically hit the tags with over 99% similarity. On the other side, the probe HY655-672 for H. hydrossis seemed unfit. Only 11.4% pyrotags with over 99% similarity were identically hit and most other tags got one mismatched base, while many remote sequences were fully matched. The 97% similar sequences (40 sequences) all were classified as Haliscomenobacter by RDP classifier even when setting the confidence threshold at 80%. It suggested the probe was low coverage for this genus. It should be improved by replacing the ‘C’ to ‘T’ at the 667-position or use degenerate base ‘Y’. The low coverage may derive from the insufficient sequence source when designing the probe (Wagner et al., 1994b), which were all from cultivable species. The probe for Type 1851 (T1851-1) may be too loose because of its over-hitting against the remote sequences (similarity 90 w 95%). We also found that this probe fully matched with many Type 0803 similar pyrotags (about 30% for 99% similar tags). Due to the lack of enough standard strains, as well as the poorly characterized taxonomic position, designing probes for groups like Type 1851 is problematic with the limited 16S rRNA gene information obtained by traditional molecular methods. By comparing over 40 high similar sequences (including the reference sequence of Ben 52 isolate of Type 1851, Beer et al., 2002) and over 300 low similar sequences, a new probe was proposed in the present study (T1851-2, see Table 3).

A further examination for this probe was performed in RDP Probe Match module. Among all sequences in RDP database (over 1.8 million sequences), only 15, all in unclassified Chloroflexi, were hit without mismatch (including the prototypical Type 1851). However, it is unfit for a suspicious Type 1851-like species, ‘Kouleothrix aurantiaca’ (2 bases mismatch like most 90e97% similar sequences), which exhibits different Gram stain result from the normal Type 1851 cells (Kohno et al., 2002). Our results also suggested ‘K. aurantiaca’ should be a different species closely related to prototypical Type 1851. Although new probe for Type 1851 is theoretically stricter than the old one, however, the unclarity of the taxonomic inclusion of Type 1851 makes them hard to be compared. Bulking activated sludge samples caused by Type 1851 from various time and locations should be examined by FISH for the availability of the probes. In this case, our results showed the benefit and feasibility for probe designation of rare bacterial groups that had little sequence source by high throughput sequencing.

4.

Conclusions

So far, the microscopy-based classification and quantification of BFB are only grasped by few researchers or skilled technicians. Even so, the quantification and profiling of total BFB in AS were usually not considered in previous research.

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The present study showed that the total BFB could be determined well through high throughput pyrosequencing. Although there are some inadequacies in our study, such as not including of all known BFB types in the database, low resolution of 16S rRNA gene in specific groups and inconsideration of uneven distribution of 16S rRNA gene copy number in various bacterial species, the results showed overall BFB community in unbulking AS fairly. The abundance of total BFB (1.86 w 8.99% at 97% similarity cutoff) in unbulking sludge should be less than the bulking AS. The difference between the major BFB in this study and previously reports on bulking sludge samples suggested that some groups of BFB were normal residents and others may be occasional in AS but more dedicated in causing bulking and foaming. Other than profiling, the rich information from sequences could be helpful in designing or evaluating oligonucleotide probes and primers for specific groups. Similar application could be considered in other functional bacterial groups in complex environmental samples.

Acknowledgments Dr. Feng Guo thanks HKU for the postdoctoral fellowship. The authors would like to thank the Hong Kong General Research Fund (HKU7198/10E) for financially supporting this study, thank Mr. Ye Lin and Dr. Cai Lin for helpful discussion, and thank Prof. Gao DW, Prof. Deng BL, Dr. Huang QG, Dr. Zhu HG, Dr. Liang DW, Dr. Duan JZ, Dr. Zhang M, Dr. Zhang XX, Mr. Yu K, and Miss Yang Y, for the sludge sampling.

Appendix. Supplementary material Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.watres.2012.02.039.

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