metagenomic analysis on seasonal microbial variations of activated sludge

7/23/2019 Metagenomic Analysis on Seasonal Microbial Variations of Activated Sludge

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Metagenomic analysis on seasonal microbialvariations of activated sludge from a full-scalewastewater treatment plant over 4 years

Feng Ju, Feng Guo, Lin Ye, Yu Xia and Tong Zhang*

Environmental Biotechnology Lab, The University of

Hong Kong SAR, Hong Kong, China.

Summary

Metagenomic technique was employed to character-

ize the seasonal dynamics of activated sludge (AS)

communities in a municipal wastewater treatment

plant (WWTP) over 4 years. The results indicated

that contrary to Eukaryota (mainly Rotifera andNematoda ), abundances of Bacteria and Archaea

(mainly Euryarchaeota ) were significantly higher in

winter than summer. Two-way analysis of variance

and canonical correspondence analysis revealed that

many functionally important genera followed strong

seasonal variation patterns driven by temperature

and salinity gradients; among them, two nitrifying

bacteria, Nitrospira and Nitrosomonas , displayed

much higher abundances in summer, whereas

phosphate-removing genus Tetrasphaera , denitrifier

Paracoccus and potential human faecal bacteria, i.e.

Bifidobacterium , Dorea and Ruminococcus , showedsignificantly higher abundances in winter. Particu-

larly, occurrence of dual variation patterns beyond

explanation merely by seasonality indicated that

multivariables (e.g. dissolved oxygen, sludge reten-

tion time, nutrients) participated in shaping AS com-

munity structure. However, SEED subsystems

annotation showed that functional categories in AS

showed no significant difference between summer

and winter, indicating that compared with its micro-

bial components, the functional profiles of AS were

much more stable. Taken together, our study provides

novel insights into the microbial community vari-

ations in AS and discloses their correlations with

influential factors in WWTPs.

Introduction

Activated sludge (AS) harbours highly complex microbial

consortium, and heavily relies on them for the removal of

organic pollutants and nutrients (such as nitrogen and

phosphorus) from sewage to protect our environment and

human health, thereby the performance of biological

wastewater treatment plants (WWTPs) using AS is closely

associated with the structure and functions of microbes.

For example, ammonia-oxidizing bacteria and nitrite-

oxidizing bacteria (NOB) are highly dedicated bacteria

groups for nitrogen removal, while the polyphosphate-

accumulating organisms (PAOs) are significant for phos-

phorus condensation (Wagner and Loy, 2002; Venter

et al ., 2004). Although they played key roles in pollutant

removal and biogeochemistry cycling, however, the

dynamics and behaviours of these functional bacteria

under various conditions are still not clear. Furthermore,

the occurrence of certain bacterial species not fully

studied in AS could be harmful to nutrients removal in

WWTPs by outcompeting related functional bacterial

groups (Bassin et al ., 2012).

Study on seasonal variation is an alternative way

for correlating environmental factors with bacterial

community and function. There are a lot of studies on

the microbial dynamics in different ecosystems, like

ocean (Barberán et al ., 2011), lake (Pajdak-Stos andFialkowska, 2012) and soil (Venter et al ., 2004),

which are usually lacked of either sufficient long-term

monitoring data (both chemical and biological) or

powerful tools with adequate coverage for profiling the

whole complex microbial communities. Particularly, AS in

WWTPs is monitored routinely and kept cultured in a

nearly artificially controlled environment, which would

favour our study on the impact of interior environmental

and operational variables on those functionally signifi-

cant microbes. Moreover, with the advent of high-

throughput sequencing (HTS) technology, the diversity

and abundances of microbial communities within AS

could be comprehensively investigated.

Previous studies of AS using HTS mainly adopt 454

pyrosequencing of 16S rRNA gene amplicons to monitor

the bacterial communities in various bioreactors, including

AS (McLellan et al ., 2010; Zhang et al ., 2011), membrane

bioreactors (Guo and Zhang, 2012a) and oxidation

ditches (Ben-Amor et al ., 2005). These results based on

sequencing 16S rRNA gene amplicons, however, may be

limited by efficiencies of primers, Polymerase chain

Received 25 October, 2012; accepted 15 September, 2013. *Forcorrespondence. E-mail [email protected]; Tel. (+852) 28591968 (lab), (+852) 2857 8551 (office); Fax (+852) 2559 5337.

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Environmental Microbiology Reports (2014) 6(1), 80–89 doi:10.1111/1758-2229.12110

© 2013 Society for Applied Microbiology and John Wiley & Sons Ltd

http://onlinelibrary.wiley.com/doi/10.1111/1758-2229.12110/pdf

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reaction (PCR) biases, taxonomic classification effective-

ness of variable regions selected and pyrosequencing

noises (Guo and Zhang, 2012b; Fayle et al ., 2013).

Importantly, these studies merely explored the bacterial

diversity without relevance to their functions and seasonal

dynamics within the AS system.

Shotgun metagenomics, which adopts direct sequenc-

ing of metagenomic DNA instead of 16S rRNA amplicons,results in much larger, more informative and more

in-depth coverage sequencing data sets derived from

various ecosystems, such as soil (Costello et al ., 2009),

ocean water (Freilich et al ., 2010), human gut (Qin et al .,

2010), cow rumen (Hess et al ., 2011) and etc. Analyses of

these data sets shed light on disclosing enormous micro-

bial diversity and plentiful functional genes in these envi-

ronments. To the best of our knowledge, only very limited

work on metagenomic analysis of the functions and com-

positions of microbial communities in AS (Horner-Devine

et al ., 2007; Steele et al ., 2011) have been reported.

Nevertheless, these studies only based on analysis of a

single sample with no observation of seasonal dynamics

of microbial communities.

In the present study, Illumina HTS-based metagenomic

approach was employed to characterize seasonal vari-

ations of microbial compositions and functions in the

AS of a full-scale WWTP using eightAS samples collected

in two seasons (winter and summer) over 4 years.

Metagenomic profiling of seasonal dynamics of AS com-

munities was linked to environmental or operational con-

ditions in a full-scale WWTP by canonical correspondence

analysis (CCA). The results will help us understand the

effects of these parameters on the structure and diversity

of AS communities, as well as the impacts of the diversityof functionally important microorganisms on the stability of

the process. Meanwhile, seasonal dynamics of metabolic

profiles in AS were evaluated.

Results and discussion

The metagenomic data sets in this study

A total of 10 metagenomes were sequenced with DNA

extracted from eight AS samples of Sha Tin WWTP using

Illumina HTS. A total of 204 581 817 paired-end (PE)

metagenomic reads were generated with a length of

100 bps. The number of PE reads in each sample was

normalized to 25 426 000, and averaged 24 645 626

(± 345 341) PE reads remained for each sample after

de-replication (Table S2). The number of tags obtained

after reads overlapping were 18 098 869 (± 523 507), with

an average length of 167 (±

3.3) nt. Among the obtainedtags, 12 624–15 473 were identified as 16–18S rRNA

genes tags using NCBI’s BLASTN (Altschul et al ., 1990)

search against SILVA SSUref database (Pruesse et al .,

2007) at a maximum e-value cut-off of 1e-20. Those hits

with similarity > 95% and alignment length > 100 nt were

annotated by the lowest common ancestor (LCA) algo-

rithm in MEGAN using default parameters (Huson et al .,

2007). The analysis of the three replicated data sets of

sample AS08-7 showed that Illumina HTS has good

reproducibility based on values of slope (approach 1.0)

and high linear coefficient (R2> 0.94) (Fig. S1), suggest-

ing that a sequencing depth of 5 G for each AS sample

adopted in this study was deep enough to obtain analyti-

cal results with high reproducibility.

Microbial composition and seasonal dynamics at

domain and phylum levels

The seasonality of microbial communities in different

ecosystems like ocean (Barberán et al ., 2011), lake

(Pajdak-Stos and Fialkowska, 2012) and soil (Venter

et al ., 2004) have been well documented, with little atten-

tion being paid to the AS system (Kim et al ., 2013), in

which the seasonal dynamics of microbial communities

greatly affects the performance and stability of pollutantsremoval.

As shown in Table 1, microbial communities in AS were

predominated by Bacteria , with abundances between

85.6% and 93.0%, followed by Eukaryota (0.73–7.3%)

and Archaea (0.11–0.40%). Paired t -test indicated that

there was a significant (P -value < 0.05) difference in

domain distribution between winter and summer samples.

The abundance of Bacteria in summer was 87.8 (± 2.8)%,

which was lower than that in winter [91.0 (± 1.6)%]. Similar

Table 1. Domain distribution of the sequences in the eight data sets derived from AS samples.

Summer abundance (%a) Winter abundance (%a)

P -valuesbAS07-7 AS08-7 AS09-7 AS10-7 Average AS08-1 AS09-1 AS10-1 AS11-1 Average

Bacteria 91.7 88.2 85.6 85.8 87.8 93.0 90.4 89.3 91.3 91.0 0.021Archaea 0.13 0.19 0.08 0.11 0.13 0.12 0.40 0.33 0.23 0.27 0.045Eukaryota 1.90 4.74 7.28 6.17 5.02 0.73 2.71 2.52 1.72 1.92 0.020Unassigned 6.29 6.87 7.04 7.92 7.03 6.18 6.55 7.86 6.73 6.83 0.331

a. The abundance in percentage was based on the taxonomic results using the identified 16/18S rRNA gene tags.b. P -value refers to the one-tailed probability value of the paired t -test using summer and winter samples. Paired t -test was conducted to compare

differentials in the domain distribution of microbial communities between summary and winter samples.

Multivariables shape activated sludge communities 81

© 2013 Society for Applied Microbiology and John Wiley & Sons Ltd, Environmental Microbiology Reports, 6, 80–89



results were observed for Archaea , with winter abundance

doubling that of summer. Differently, the average abun-

dance of Eukaryota in summer [5.0 ± (2.3)%] was much

higher than that in winter [1.92 ± (0.90)%]. Previous

studies have widely demonstrated that bacteria-eating

Eukaryota , like Rotifera and Nematoda , inAS could reduce

biomass production via predation of the micro-

organisms, improving the performance of WWTPs

(Yiannakopoulou et al ., 2009). As most of the Eukaryota

found in Sha Tin AS were affiliated with Rotifera and

Nematoda , the great variation of Bacteria and Eukaryota

during summer and winter could be associated with sig-

nificant change of quantities of predator and prey in the

AS system, which could affect the performance of

WWTPs.

At phylum level, a total of 40 phyla were found in all

eight samples, and 22 of them were identified as major

phyla (top 15 in each sample). Among the major phyla,

16 were from Bacteria , 5 from Eukaryota and 1 from

Archaea (Fig. 1). Similar to previous findings on AS using

cloning (Snaidr et al ., 1997), microarray (Xia et al ., 2010)

and 454 pyrosequencing (Zhang et al ., 2011), meta-

genomic data sets in this study found that Proteobacteria

was the most abundant phylum (38–43%, averaging at

40.8%) in all 4-year AS samples, and the subdominant

phyla were Actinobacteria (averaging at 21.9%), Bacte-

roidetes (10.6%), Chloroflexi (8.7%) and Firmicutes

(4.8%) (Fig. 1, Table S3). Notably, most of the Eukaryotal

populations belonged to Rotifera (1.3%) and Nematoda

(0.7%), and their occurrence in the AS is beneficial to the

reduction of the biomass production by predation of the

microorganisms.

Seasonal variation patterns were observed at phylum

level, as shown in Fig. 1. On the one hand, abundance of

Fig. 1. The relative abundances of major phyla in the eight Sha Tin AS samples. The number above each bar represents the abundance ratioof each phylum in summer to winter samples. The phylum names in black, red and green represent phyla that were affiliated to Bacteria ,Eukaryota and Archaea , respectively.

82 F. Ju et al.




Actinobacteria (phylum) was usually higher in winter

26.0 ± (3.1)% than in summer 17.9 ± (6.9)% (Table S3),

with abundance ratio of summer to winter (P1-ratio) of

0.7. Similar seasonal dynamics was also observed for

two other bacterial phyla, that is Verrucomicrobia and

Thermotogae (with P1-ratios of 0.6 and 0.1), two

eukaryotic phyla, namely Arthropoda and Gastrotricha

(with P1-ratios of 0.5 and 0.5), and one archaeal phylumEuryarchaeota (with P1-ratio of 0.6), although their abun-

dances were relatively lower (Table S3). On the other

hand, another four bacterial phyla, that is Nitrospirae ,

Cyanobacteria , Acidobacteria and Spirochaetes (with

P1-ratios of 3.3, 2.4, 2.0 and 2.0), and three eukaryotic

phyla, namely Rotifera , Nematoda and Streptophyta (with

P1-ratios of 4.9, 7.5 and 3.6), showed an opposite vari-

ation dynamics, with abundances in summer 1.0–6.5

times higher than those in winter.

Grouping of the eight AS samples

The similarity patterns of the eight AS samples were

evaluated at class and family levels through two inde-

pendent methods: principal coordinate analysis (PCoA)

and cluster analysis (CA), based on Bray–Curtis distance.

We hypothesized that temperature exerts the most signifi-

cant effect on the differentials in microbial structures

between summer and winter samples.

As shown in Fig. 2A and B, PCoA bases on abun-

dances of classes (A) and families (B) revealed that the

microbial communities in the eight AS samples could be

clustered into four subgroups, i.e. Group I: samples col-

lected in summer of 2007 and 2008 (AS07-7 and AS

08-7); Group II: samples collected in winter of 2008 and2009 (AS08-1 and AS09-1); Group III: samples collected

in summer of 2009 and 2010 (AS09-7 and AS10-7); and

Group IV: samples collected in winter of 2010 and 2011

(AS10-1 and AS11-1). CA of the eight AS samples also

showed the same seasonal grouping patterns using

benchmarks of 0.87 and 0.78 (as indicated by the blue

dotted lines in Fig. 2C and D). As demonstrated by the CA

and PCoA, sludge samples collected in summer or winter

were certainly similar to each other, possibly due to the

seasonal temperature difference, as well as other envi-

ronmental or operational parameters, such as salinity and

sludge retention time.

Apart from the above seasonal patterns, the eight AS

samples were divided into two clusters using a bench-

mark of 0.81 for class level and 0.73 for family level

respectively (as indicated by the red dotted lines in

Fig. 2C and D). Cluster I was composed of four samples

(Groups I and II) collected in the first 2 years; and Cluster

II comprised four samples (Groups III and IV) collected in

the last 2 years, revealing the existence of dual grouping

patterns that were beyond reasonable explanation merely

by seasonality (summer and winter). Similarly, PCoA plots

(Fig. 2A and B) also supported the existence of another

grouping pattern beyond summer and winter, considering

the fact that samples collected in the last 2 years (Groups

III and IV) tended to cluster closer to each other (than

to samples in Group I or Group II) regardless of their

opposite seasonal characteristics as summer and winter

samples. Noteworthy, the grouping patterns of ASsamples displayed by CA and PCoA were quite similar to

the mixed variation patterns of chemical oxygen (COD),

total Kjeldahl nitrogen (TKN-N) and total phosphate (TP)

concentrations (Table S1, Appendix S1), possibly reveal-

ing an intrinsic correlation between the AS microbial struc-

ture and the available nutrients for microbial growth.

Seasonal genus dynamics and genus–

environment relationship

Comparative analysis revealed the core and distinct

genera harboured in the AS of Sha Tin WWTP over a

period of 4 years. As shown in Table S5, 100 out of the

total 643 assigned genera were shared by all eight AS

samples, occupying 79.4% of the classified sequences at

genus level, while 202 rare genera that only appeared in

one sample accounted for merely 1.2% of total assigned

sequences, indicating the existence of considerable

amount of rare species in AS.

The major genera (top 20 in each sample) were

selected (a total of 43 genera for all eight samples) and

compared with their abundances in other samples, as

shown in Fig. S2 and Table S4. Judging from the aver-

aged abundance of each genus in all samples, Mycobac-

terium (8.9 ± 2.4%) and Nitrospira (6.1 ± 4.2%) werefound as the two most abundant genera in AS of Sha

Tin WWTP, followed by Planctomyces (5.4 ± 2.2%) and

Caldilinea (4.3 ± 1.1%). The other abundant genera

(> 1.0%) included one genus of phosphate-accumulat-

ing organisms (PAO), Tetrasphaera (2.4 ± 2.0%); one

hydrolyser-related genus, Lewinella (1.7 ± 0.7%); one

marine nematode, Diplolaimella (2.3 ± 3.3%); two deni-

trifying bacteria, Rhodobacter (2.1 ± 0.5%) and Paracoc-

cus (1.4 ± 0.6%); four fermentative or photo-fermentative

bacteria that could utilize a variety of organic substrates,

including Streptococcus (1.3 ± 0.5%), Bifidobacterium

(1.1 ± 0.7%), Rhodobacter and Rhodobium (1.0 ± 0.4%);

four bulking- and foaming-related genera, i.e. Candidatus

Microthrix (4.0 ± 1.5%), Gordonia (2.0 ± 2.5%), Nocar-

dioides (1.5 ± 0.7%) and Tetrasphaera (Guo and Zhang,

2012a); plus five not well-described genera, i.e. Iami ,

Amaricoccus , Caldithrix , Pirellula and Haliea (Fig. S2

and Table S4). Other less abundant genera (< 1.0%)

includes another four widely reported denitrifying-related

genera, that is Azoarcus , Zoogloea , Thauera and

Hyphomicrobium , and one well-known ammonia-oxidizing





genus, Nitrosomonas , which plays particularly significant

roles in oxidizing ammonia into nitrite during nitrification

process in WWTPs (Raponi et al ., 2004).

The variations of AS communities over the 4-year sam-

pling period were examined based on two-way analysis of

variance. As shown in Fig. 3A and B, 33 genera displayed

significantly changed [P -value < 0.05, false discovery rate

(FDR) < 0.3] abundances over the 4-year sampling

period. Among these genera, 12 (names in blue) mani-

fested significantly changed abundances with seasonal

alternations from summer to winter (P1, Fig. 3A); 15

(names in purple) had significantly changed abundances

from the first 2 years to the last 2 years (P2, Fig. 3B); and

6 (names in green) displayed significantly changed abun-

dances across both P1 and P2. To unravel the underlying

influential factors responsible for the complex variation

patterns observed in the AS communities, those genera

with significantly changed abundances across P1, P2 and

P1P2 were extracted (as shown in Fig. 3A and D), and the

relationships between their abundances and the opera-

tional conditions/wastewater characteristics of the AS

process were explored by CCA using 4-year monitoring

data in Sha Tin WWTP (Table S1) and the PAST software

(Hammer et al ., 2001).

A B

DC

Fig. 2. Principal coordinate analysis (PCoA) and cluster analysis (CA) of eight activated sludge (AS) samples at class and family levels. PCoA(A and B) was conducted using the Bray–Curtis distance and a transformation exponent of 2 (as recommended). Similar grouping patternswere adopted as used in the cluster analysis. CA (C and D) was performed using unweighted pair group mean averages as algorithm and theBray–Curtis distance for similarity measurement. The blue and red dotted lines show the similarity cut-off levels to cluster the eight Sha TinAS samples.

84 F. Ju et al.




AS shown in Fig. 3C, all 12 P1-affiliated genera (the

blue points) were distributed either along or very close to

temperature and salinity lines (or their extension lines).

These strong correlations of P1-affiliated genera with

either temperature or salinity indicated that temperature

and salinity were the major variables that led to the sig-

nificantly different abundances of these populations from

summer to winter, with those abundant in summer being

either positively correlated with temperature or negatively

correlated with salinity, or even both. This positive corre-

lation of abundances of NOB like Nitrospira and

hydrolysers like Lewinella with temperature partially

explains the decreased nitrification and hydrolysis activ-

ities at lower temperature, leading to the incomplete nitri-

fication and hydrolysis problems that commonly occur

within full-scale biological treatment units during winter

season. On the contrary, those P1-affiliated genera

located on negative direction of horizontal axis were posi-

tively correlated with salinity, and simultaneously nega-

tively correlated with temperature, with P1-ratio between

A B

C

Fig. 3. Boxplots showing all genera (A, B) with significantly changed abundances across Pattern 1 (P1) and/or Pattern 2 (P2) based ontwo-way analysis of variance, and genus-conditional triplot (C) displaying variations of these significantly changed genera with respect to theenvironmental variables, based on canonical correspondence analysis (CCA). P1: summer vs winter samples; P2: first-2-years vs last-2-yearssamples. For subfigures A and B, the genus names in bold blue, purple and green represent those genera with significantly changedabundances across P1, P2 and P1P2 (both P1 and P2) respectively. For subfigure C, eigenvalues of horizontal and vertical axes equal topercentage variances of 49.2% and 32.1% respectively; each genus is represented by a coloured point (blue for P1, purple for P2, and blackfor both P1 and P2), accompanied by the genus name. Environmental variables are indicated by thick green lines with variable names (in red)at the end.





0.2 and 0.8 (Table S4). Among them, two genera, that is

Tetrasphaera and Nocardioides , may be associated with

bulking and foaming problems that commonly occurred

in the AS of many WWTPs (Guo and Zhang, 2012a); other

three genera, namely Bifidobacterium , Dorea and

Ruminococcus , have been regarded as human faecal

bacteria (Qin et al ., 2010), which mainly originated from

the sea water used for toilet flushing.Moreover, Fig. 3C showed that all P2-affiliated genera

(the purple points) were distributed between the lines

(or their extension lines) of two clusters of influential

factors: i.e. cluster (I) dissolved oxygen (DO), mean cell

retention time and sludge retention time (SRT); and

cluster (II) COD, mixed liquor suspended solids, TKN-N

and TP, perhaps implicating the combined influences of

multivariables that led to the significantly changed abun-

dances across P2 in P2-affiliated genera. It could also be

found that P2-affiliated genera were independent from the

influences of temperature and salinity, considering the fact

that these genera located somewhere around the vertical

directions of lines of temperature and salinity. Moreover,

those P2-affiliated genera located on positive direction of

vertical axis, such as Clostidium , Pirellula , Amaricoccus

and Pseudorhodobacter , had significantly higher abun-

dance in the first 2 years than the last 2 years, with ratios

of averaged abundances from first 2 years to last 2 years

(P2-ratio) between 1.4 and 5.6. However, for those

P2-affiliated genera located on the negative direction of

vertical axis (e.g. Flavobacterium , Flexibacter , Haliea and

Aeromonas ), their abundances in the first 2 years were

usually lower than the last 2 years, with P2-ratios between

0.03 and 0.4.

Particularly, six genera (the black points in Fig. 3C),including Nitrosomonas , Kosmotoga , Tetrasphaera and

etc., displayed dual variation patterns characterized by

significantly changed abundances across both P1 and P2,

indicating the coexistence of multiple variables shaping

the abundances of these genera in AS.

Global gene functional profiles and seasonal

dynamics in AS

The overall functional profiles were predicted for the eight

AS metagenomic data sets using the SEED subsystem

(Overbeek et al ., 2005) in MG-RAST at the e-value cut-off

of 10−5 (Meyer et al ., 2008). For each data set, 41.1–

51.4% of the 17 001 280–18 608 516 tags contained pre-

dicted proteins assigned to known functions at Level 1.

This was comparable to the percentage of annotated

sequences (40%) in a previous study on AS using

pyrosequencing (Sanapareddy et al ., 2009). The most

dominant functional categories were those involved with

clustering-based subsystems (15.6 ± 0.1%), carbohy-

drates (10.2 ± 0.2%), protein metabolism (8.8 ± 0.2%),

and amino acids and derivatives (8.6 ± 0.1%) (Table S6),

suggesting their significant roles in microbial communities

of AS. These dominant functional categories evident in

our AS metagenomes were also highly represented

in metagenomic surveys in other environments like

grassland soil (Costello et al ., 2009), marine environ-

ment (Chaffron et al ., 2010), freshwater (Pandit et al .,

2009) and enhanced biological phosphorus removal(Horner-Devine et al ., 2007), indicating high similarity of

Level 1 functional categories among these ecosystems.

Moreover, another two categories, namely ‘nitrogen

metabolism’ and ‘phosphorus metabolism’, merely

accounted for 1.38 ± 0.07% and 0.90 ± 0.02% of all the

predicted proteins that were assigned to known functional

categories, although they are of particular importance

for biological nitrogen and phosphorus removal from

wastewater.

Due to the difference in environmental and operational

parameters (e.g. temperature, salinity, DO, SRT), signifi-

cantly different abundances of functional genes were

expected to occur in summer and winter. However, it

seemed that differences in the 28 Level 1 functional

categories between summer and winter were not signifi-

cant, considering the fact that relative standard devia-

tions of those functional categories in all AS samples

were no more than 9.7% (averaging at 3.2%), as shown

in Table S6. Further comparisons at subsystems Level 2

and Level 4 (Fig. 4) showed that except for a small

number of low-abundance functional categories at Level

4, the majority of functional categories showed no sig-

nificant difference (using twofold as the cut-off) in abun-

dance between summer and winter. This implicated that

compared with its microbial compositions, the functionalcategories in AS were much more stable. There are

several possible explanations for this: (i) high proportion

of functional genes related to fundamental metabolism

are shared in summer and winter, (ii) gradients of influ-

ential factors are not strong enough to make significant

changes in abundances of functional genes, and/or (iii)

influential factors exert discrepant effects on abun-

dances of microorganisms carrying similar functional

genes.

Comparison with previous studies on AS

This is the first systematic metagenomic profiling of sea-

sonal dynamics of AS communities by conducting Illumina

HTS. Our analysis demonstrates that some core genera,

especially those belonging to functionally important

groups in AS (such as nitrifying bacteria, bulking and

foaming bacteria, denitrifying bacteria, fermentative bac-

teria, hydrolyser, PAO, etc.), were shared by all the eight

samples, but they had quite different abundances in

summer and winter seasons. Meanwhile, functional

86 F. Ju et al.




analysis based on SEED subsystems reveals that the

functional categories in AS showed no significant differ-

ence between summer and winter.

Previously, most studies on the diversity of AS micro-

bial communities mainly rely on analysis of a single AS

sample using clone library analysis (Sasaki et al ., 1994),microarray (Xia et al ., 2010) or 454 pyrosequencing

(Kwon et al ., 2010), which targeted specific genes (such

as 16S rRNA) for exploring the microbial diversity

without observing their functions within AS. For the

first time, microbial communities in AS were correlated

with 11 environmental and operational parameters in

a full-scale WWTP by metagenomic technique and

CCA analysis, which provides a comprehensive under-

standing of the influences of different variables on the

microbial component and dynamics of functionally sig-

nificant microorganisms. Our findings indicate that

beside temperature and salinity, other variables (such as

SRT, DO, salinity, etc.) also play significant roles in

shaping the overall AS community structure. However,

for two variables with opposite effects on abundances of

the same species, it is not easy to distinguish whether

the species is more positively affected by one variable,

or more affected by the negative effect of the other. For

example, judged from the CCA plots, temperature and

salinity exert almost contrary effects on AS microbes,

although the effect of temperature is more significant.

Unfortunately, it is hard to distinguish the effects of

different variables on microbial community in a full-

scale WWTP since a lot of uncontrollable or even

undetectable influential factors are involved in such a

pollutant-removing process. Further studies, probably

laboratory-scale ones designed with much simpler andmore controllable experimental conditions, are needed

to clarify the effect and significance of each variable on

the AS microbial community dynamics as well as stability

of the process.

Several technical limitations may affect our results.

First, the length of tags obtained in this study was short,

about 167 bps in average, although taxonomic classifica-

tion of this length by LCA in MEGAN indicates that 100 bp

is long enough to identify a species, and 200 bp might

constitute an optimal trade-off between the rate of under-

prediction and the production cost of such reads (Huson

et al ., 2007). Moreover, in this work a sequencing depth of

5G is still not deep enough to accurately explore the rare

species within AS owing to the limited number (12 624–

15 473 for each sample) of the identified 16/18S rRNA

gene tags, although metagenomic sequencing is neither

low throughput nor PCR-based. Finally, biases are also

likely to be introduced during the DNA extraction, although

the optimal extraction kit (after comparison with several

other kits) has been used, and Illumina HTS has been

proved to possess good repeatability.

L o g 2 ( W i n t e r s i g n a l )

L o g 2 ( W i n t e r s i g n a l )

Log2(Summer signal) Log2(Summer signal)

A B

Fig. 4. The signal intensities (reads number) of functional categories from summer ( X -axis) and winter (Y -axis) samples. The results arebased on subsystem annotation at Level 2 (A) and Level 4 (B). The differentially detected genes were identified as signal intensity differenceof ≥ 2 folds, indicated by the two diagonal lines. Each point in the figure represents one functional category at Level 2 or Level 4 determinedby subsystem implemented in MG-RAST. The signal intensities were indicated by the number of sequences that were assigned into eachcategory.





Acknowledgements

The authors would like to thank GRF HKU 7201/11E for

financial support of this research. Feng Ju would like to thank

HKU for the postgraduate studentship. Dr. Guo Feng would

like to thank HKU for postdoctoral fellowship. Technical

support from Ms. Vicky Fung is greatly appreciated.

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Supporting information

Additional Supporting Information may be found in the online

version of this article at the publisher’s web-site:

Table S1. Characteristics of the environmental and opera-

tional parameters in Shatin WWTP from July 2007 to January

2011.

Table S2. Illumina sequencing metadata of the eight acti-

vated sludge samples in this study.Table S3. The percentage abundances (%) of the major

phyla (top 15 in each sample) in the eight AS samples.

Table S4. Abundances of the top 20 genera in each AS

sample.

Table S5. Percentages of the shared genera and their corre-

sponding sequences.

Table S6. The relative abundances of the 28 functional cat-

egories in four ecosystems based on SEED subsystems

implemented in MG-RAST.

Fig. S1. Reproducibility of Illumina high-throughput sequenc-

ing at different sequencing depths.

Fig. S2. Heat map of major genera (top 20 in each sample) of

the eight AS samples.

Appendix S1. Variation of environmental and operationalparameters.



metagenomic analysis on seasonal microbial variations of activated sludge

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