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Metagenomic analysis reveals significant changes of microbial compositions and protective functions during drinking water treatment

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The metagenomic approach was applied to characterize variations of microbial structure and functions in raw (RW) and treated water (TW) in a drinking water treatment plant (DWTP) at Pearl River Delta, China. Microbial structure was significantly influenced by the treatment processes, shifting from Gammaproteobacteria and Betaproteobacteria in RW to Alphaproteobacteria in TW. Further functional analysis indicated the basic metabolic functions of microorganisms in TW did not vary considerably. However, protective functions, i.e. glutathione synthesis genes in 'oxidative stress' and 'detoxification' subsystems, significantly increased, revealing the surviving bacteria may have higher chlorine resistance. Similar results were also found in glutathione metabolism pathway, which identified the major reaction for glutathione synthesis and supported more genes for glutathione metabolism existed in TW. This metagenomic study largely enhanced our knowledge about the influences of treatment processes, especially chlorination, on bacterial community structure and protective functions (e.g. glutathione metabolism) in ecosystems of DWTPs.
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Metagenomic analysis reveals
significant changes of microbial
compositions and protective functions
during drinking water treatment
Yuanqing Chao
1
, Liping Ma
1
, Ying Yang
1
, Feng Ju
1
, Xu-Xiang Zhang
1,2
, Wei-Min Wu
3
& Tong Zhang
1
1
Environmental Biotechnology Lab, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China,
2
State Key Laboratory of
Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, China,
3
Department of Civil
and Environmental Engineering, Stanford University, Stanford, California 94305, United States.
The metagenomic approach was applied to characterize variations of microbial structure and functions in
raw (RW) and treated water (TW) in a drinking water treatment plant (DWTP) at Pearl River Delta, China.
Microbial structure was significantly influenced by the treatment processes, shifting from
Gammaproteobacteria and Betaproteobacteria in RW to Alphaproteobacteria in TW. Further functional
analysis indicated the basic metabolic functions of microorganisms in TW did not vary considerably.
However, protective functions, i.e. glutathione synthesis genes in ‘oxidative stress’ and ‘detoxification’
subsystems, significantly increased, revealing the surviving bacteria may have higher chlorine resistance.
Similar results were also found in glutathione metabolism pathway, which identified the major reaction for
glutathione synthesis and supported more genes for glutathione metabolism existed in TW. This
metagenomic study largely enhanced our knowledge about the influences of treatment processes, especially
chlorination, on bacterial community structure and protective functions (e.g. glutathione metabolism) in
ecosystems of DWTPs.
Modern drinking water (DW) treatment usually is a multistep process, including flocculation, sedimenta-
tion, filtration and disinfection, to reduce the carrying particles and microorganisms in raw water (RW)
1
.
Among these processes, disinfection is a key step in DW treatment plants (DWTPs) to eliminate
pathogenic microorganisms by applying various disinfectants, such as chlorine, monochloramine, and ozone
2
.
Although microorganisms can be effectively removed after treatment, some of them may survive and proliferate
in DW distribution system (DWDS), and subsequently induce several serious problems
3,4
, including biofilm
growth, nitrification, microbially mediated corrosion, and pathogens persistence. Thus, fully investigation of
microbial structure and functions in RW and treated water (TW) of DWTPs is necessary and will facilitate the
enhancement of treatment efficiency, the development of anti-pathogen strategies, and the optimization of
DWDS.
Recently, high-throughput sequencing (HTS) techniques have shown great advantages on analyzing the
microbial community for its unprecedented sequencing depth
5
. Compared with traditional shotgun sequencing
method, HTS techniques, such as 454 pyrosequencing and Illumina sequencing, are proven to be more time-
saving and cost-effective
6
, and have been applied for investigating microbial structure and/or functions in various
complex environments, such as fresh water
7
, sea water
8
, soil
9
, and human guts
10
. Recently, several studies have
applied HTS technique to evaluate microbial community in DWTPs and DWDS
11–13
. These studies successfully
assessed the microbial community before and after treatment and provided useful information for optimizing the
DW treatment processes. However, they did not study the variation of microbial functions, which might be
crucial for understanding treatment processes more comprehensively. Up to now, only one metagenomic work
evaluated the microbial structure and functions after DW treatment
14
. However, their study primarily focused on
the disinfection effects by two independent methods, rather than the comparison of the RW and TW metagen-
omes and evaluation the influences of treatments in DWTPs.
Thus, the aim of the present study was to address the following questions: 1) What would be the impact of
treatment on the DW microbial structure? 2) What kind of protective functions will play roles in protecting the
OPEN
SUBJECT AREAS:
ENVIRONMENTAL
SCIENCES
WATER MICROBIOLOGY
METAGENOMICS
Received
14 August 2013
Accepted
29 November 2013
Published
19 December 2013
Correspondence and
requests for materials
should be addressed to
T.Z. (zhangt@hku.hk)
SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 1
microorganisms against the chlorination? 3) Will the antibiotic res-
istance genes (ARGs) and mobile genetic elements (MGEs) be sig-
nificantly removed in DW treatment processes? To answer the above
questions, we collected the microorganisms in RW and TW of a
DWTP at Pearl River Delta (PRD), China, separately extracted geno-
mic DNA and conducted Illumina sequencing. About 13 Gb DNA
reads were generated and then used to investigate the microbial
community, functional profiles, as well as occurrence of ARGs and
MGEs in RW and TW of PRD_DWTP.
Results
Metagenomes summary and repeatability.After the metagenomic
data were extracted, the reads quality was evaluated by FastQC
pipelines. Although the reads quality in TW was not as good as
that in RW, the overall quality for TW metagenomes was still
acceptable, judged from the quality scores across all bases in TW
reads (Figure S1). For RW, 98 60.55% reads could pass the qua-
lity check (QC) pipelines of MG-RAST (Table 1). However, only 55
628% reads in TW remained after QC. This was consistent with
another study
14
, in which the reads of two TW metagenomes had
lower QC pass ratios (35 and 18%). However, the possible reasons
behind this were still unclear. Among reads passing QC, the contents
of rRNA in DW were between 0.036 and 0.14%. This varying range
was comparable with metagenomes in marine ecosystems
15
and
activated sludge
16
, indicating that waterborne metagenomes might
contain similar rRNA contents. The unknown reads, which failed to
be identified as rRNA genes or annotated as protein with known
functions, occupied quite large portion in both of RW (76 63.3%)
and TW (84 61.8%) samples. This may suggest that a certain
amount of novel reads in PRD_DW samples were captured.
To test the repeatability of DNA extraction and Illumina sequen-
cing, the sample of RW12 was divided into two parts as technical
duplicates (RW12_1 and RW12_2). DNA in these two duplicates
were separately extracted and then sequenced. The results indicated
the repeatability was quite well, since high correlation coefficients
could be obtained between two duplicates, even at species level
(Figure S2).
Taxonomic analysis.Although eukaryotic, archaeal and viral reads
(including predicted proteins and rRNA genes) could be detected in
RW and TW samples, most of the reads were related to the Bacteria
domain (86–96% of total annotated reads, Figure S3). Comparing
with RW (23 62 phyla), significantly few (P50.019) bacterial phyla
were assigned in TW (12 62 phyla), indicating that the diversity of
TW community in PRD_DWTP significantly decreased (Figure S4).
This could be confirmed by the significantly decreased alpha
diversity (P,0.001), based on the calculation by MG-RAST, in
the TW (312 617 species) compared with the RW (1,017 660
species). Similar results could also be obtained by Chao and
Shannon indexes in RW and TW metagenomes of the DWTP
(Table S1).
The bacterial structure in Proteobacteria was further analyzed
(Figure 1). In RW, Gamma, Beta, and Alphaproteobacteria were
the top 3 classes in Proteobacteria (Figure 1). For TW, the abun-
dances of Gamma- (P50.043) and Betaproteobacteria (P50.022)
sharply decreased. While, Alphaproteobacteria increased signifi-
cantly (P50.002) after treatment, as several families in this class
became more abundant, including Sphingomonadaceae, Beijerinc-
kiaceae and Rhizobiaceae. This revealed that these families remained
after treatment and thus became more abundant in TW of
PRD_DWTP, especially for Sphingomonadaceae (from 0.30% to
19% in average, Figure 1).
Functional analysis.Several Level 1 subsystems of SEED had the
largest quantity of annotated reads in RW and TW (Figure S5),
including ‘protein metabolism’, ‘carbohydrates’, ‘amino acids and
derivatives’ and ‘clustering-based subsystems’. Previous studies
also obtained similar major subsystems in metagenomes of soil
9
,
freshwater
7
, and activated sludge
16
. To identify specific functions
for DW metagenomes in PRD_DWTP, PCA analysis of Level 1
subsystems in different ecosystems was conducted (Figure 2).
Metagenomes from different ecosystems separately distributed,
revealing functional differences in different ecosystems. Several
subsystems, including ‘protein metabolism’, ‘RNA metabolism’,
‘respiration’ and ‘membrane transport’, positively correlated to the
DW samples. These functions may play more roles in DW
metagenomes than in other ecosystems. Moreover, DW samples
clustered together, indicating the unique characteristics of drinking
water ecosystems.
Several dominant subsystems of ‘amino acid and derivatives’ and
‘carbohydrates’, which related to the basic cellular processes that are
essential to bacteria, were further analyzed. Their Level 2 subsystems
showed high similarity in relative abundances (Figure 3A), suggest-
ing that the treatment processes might not significantly affect the
synthesis of amino acid and carbohydrates. However, the protective
functions, e.g. ‘stress response’, largely changed after DW treatment
(Figure 3B). Comparing with RW, the abundances of genes in ‘oxid-
ative stress’ (P50.011) and ‘detoxification’ (P50.030) significantly
increased in TW, as the genes related to glutathione synthesis largely
increased after treatment in PRD_DWTP (Figure 3B).
Glutathione metabolism.To further study the glutathione synthesis
in metagenomes of RW and TW in PRD_DWTP, the pathway of
glutathione metabolism was reconstructed by using 1,205 bacterial
species (non-redundant) in KEGG collection
18
. Compared to 40
enzymes in original KEGG pathway constructed by prokaryotes
and eukaryotes, the reconstructed pathway contained 21 enzymes
produced by bacteria only (Figure 4A). Several enzymes, including
isocitrate dehydrogenase, leucyl aminopeptidase and glucose-6-
phosphate 1-dehydrogenase, could be produced by most of bacte-
ria, suggesting they might play significant roles for glutathione
metabolism in many ecosystems. In DW metagenomes of PRD_
DWTP, 12 enzymes were detected (Figure 4B), revealing the
bacteria in PRD_DW system only contained partial reactions for
glutathione metabolism. Interestingly, several differences were
observed when comparing DW samples (Figure 4B) with the
established pathway for bacteria (Figure 4A). For instance, quite
Table 1
|
Metagenome summary of RW and TW
Metagenomes
RW (%) TW (%)
RW11 RW12-1 RW12-2 TW11 TW12
Total Reads 11,907,016 41,924,342 36,078,336 11,918,058 34,555,046
Past QC 11,799,760 (99) 41,109,708 (98) 35,446,306 (98) 8,973,420 (75) 12,326,212 (36)
Ribosomal RNA
a
15,992 (0.14) 51,390 (0.13) 44,052 (0.12) 3,251 (0.036) 7,673 (0.062)
Annotated Protein
a
3,268,476 (28) 8,980,700 (22) 7,847,216 (22) 1,545,699 (17) 1,810,357 (15)
Unknown
a
8,515,292 (72) 32,077,618 (78) 27,555,038 (78) 7,424,470 (83) 10,508,182 (85)
a
taking the reads which past QC pipelines as 100%.
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SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 2
few bacteria contained enzymes of 5-oxoprolinase and pepB amino-
peptidases. While genes for these enzymes could be abundantly
found in DW metagenomes of PRD_DWTP, indicating the gluta-
thione metabolism in DW might be different with that in other
ecosystems.
Two enzymes involving in the glutathione synthesis had relative
higher abundances in DW ecosystem of PRD_DWTP, i.e. glu-
tathione synthase and glutathione reductase. However, the reaction
of glutathione synthesis by glycine and gamma-glutamylcysteine via
glutathione synthase should be the major pathway in DW samples, as
abundant enzymes for biosynthesis of glycine (PepB aminopepti-
dase, leucyl aminopeptidase and aminopeptidase N) and gamma-
glutamylcysteine (glutamate-cysteine ligase) could be detected
(Figure 4B). Moreover, since the enzymes for the production of
NADPH and glutathione disulfide were absent, the importance of
glutathione reductase might significantly decrease for glutathione
synthesis in DW metagenomes of PRD_DWTP. For most of detected
enzymes, significant more genes could be found in TW than RW
(Figure 4B). This was consistent with the above observation that
genes related to glutathione synthesis were significantly increased
after treatment in PRD_DWTP (Figure 3B). The quantification of
glutathione synthesis genes were partially confirmed by qRT-PCR,
which also showed significant higher abundances of glutathione
synthase and glutamate-cysteine ligase genes in TW (Figure S6).
ARGs and MGEs analysis.ARGs were detected in RW and TW
samples of PRD_DWTP by aligning the reads against related
databases. The level (annotated reads number/total reads number)
of 10 and 183 ppm for detected ARGs in RW were discovered
through comparison against ARDB and CARD database, respec-
tively, and abundance of 2 ppm for ARGs were annotated via
ARDB&CARD database. While, more reads in TW could be anno-
tated according to ARDB, CARD and ARDB&CARD databases
(Table 2). To be more rigorous, following analysis were based on
the results annotated via ARDB&CARD database. Although the level
of ARGs showed an increase trend, the diversity of ARGs decreased
from 10 to 7 types, accompanying by the percentages of ARGs varied
significantly after treatment in PRD_DWTP (Figure 5). In RW, the
acridine resistance genes had the highest level, followed by genes
resistant to beta-lactam and tetracycline. In TW, the top ARGs
were genes resistant to sulfonamide and acridine. Acridine
resistance genes showed higher abundance in both DW samples
and owned a slight increase with 5% after disinfection. Noticeably,
as the dominant ARGs, sulfonamide resistance genes increased from
3.5% to the highest level as 33% after treatment in PRD_DWTP,
which may be due to its property as a key component of integrons
(all of the sulfonamide resistance genes detected in this study were
sul1)
19
. This could be further convinced by the increasing abundance
of integrons in RW and TW with 10 and 58 ppm, respectively
(Table 2). This showed much higher abundance than a previous
study, in which sulfonamide resistance genes only occupied 5.5%
in TW
20
.
The mobility of ARGs usually depends on the MGEs, including
integrons, IS, and plasmids
21
. Totally, the levels of 10 and 58 ppm in
Figure 1
|
Percentage (also called relative distribution) of different families in the phylum of Proteobacteria based on the rRNA reads annotated
using SILVA SSU database for RW and TW. The reads number which annotated as Proteobacteria was taken as 100%. The families, which accounted for
more than 0.5% in either RW or TW, are shown in the figure. The clustering among samples was according to Gower distance and drawn on the PAST
software (version 1.99).
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SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 3
RW and TW of PRD_DWTP were annotated to known integronase
genes and gene cassettes, respectively. Their levels showed large
increase after treatment (Table 2). The plasmids and IS owned a
similar trend with larger number detected in TW. However, the level
of annotated VF, in TW (10 ppm) was lower than those in RW
(30 ppm), revealing that the treatment might remove more VF.
Although treatment processes in PRD_DWTP demonstrated differ-
ent removal efficiencies for different MGEs, they all showed a sig-
nificantly lower diversity after disinfection, like ARGs (Table 2).
Discussion
In the present study, the majority of cleaned reads (72–85%, Table 1)
failed to be annotated as known genes. Other metagenomic studies
also obtained similar percentage of unknown reads in various eco-
systems, including desert/non-desert soil (77–87%)
22
, permafrost
(,89%)
23
, grassland (,66%)
9
and so on, suggesting low annotation
ratio of metagenomic reads is quite common in metagenomic
research. This phenomenon might be mainly attributed to several
possible explanations, such as limitation of available rRNA/protein
database for annotation, short length of obtained reads, algorithms
and criterions for alignment, etc. Although minority of obtained
reads could be annotated, previous studies also evaluated the general
functions (e.g. SEED subsystems) as well as several specific functions
(e.g. carbon and nitrogen cycle genes) of microbial communities in
various ecosystems
9,22,23
, revealing that the metagenomes could be
applied, at least to some extent, to analyze the detailed microbial
structure/functions, even with such low annotation portion of meta-
genomic reads. Moreover, in the present study, an individual
method, i.e. qRT-PCR, was also applied to verify the functional
results (glutathione synthesis genes, Figure S6) obtained by metage-
nomic analysis (Figure 4). The good consistency of results obtained
by metagenomic and qRT-PCR methods further proved the validity
of metagenomes to study the microbial structure and functions in
DW ecosystem.
The taxonomic analysis revealed that DW treatment in the
PRD_DWTP could significantly influence the microbial structure,
as indicated by the large drop of several dominant phyla in RW after
treatment except Proteobacteria (Figure S4). Although previous
studies showed that Proteobacteria often dominated in freshwater
ecosystems, including DW systems
13,24,25
, our study revealed the
decrease of other phyla which have not been reported before. Fur-
thermore, the dominant classes in Proteobacteria obviously shifted
from Gamma and Betaproteobacteria in RW to Alphaproteobacteria
in TW (Figure 1), implying bacteria in Alphaproteobacteria may
tolerate more chlorination during DW treatment. This revealed
more than the results reported in previous studies, that, one only
showed the dominant Betaproteobacteria class in RW decreased
significantly after chlorination
24
and another observed the large
increase of Alphaproteobacteria after DW treatment
13
. Among the
dominant families in Alphaproteobacteria of TW, the survival of
Sphingomonadaceae family might be associated with its high resist-
ance to chlorination
26
. Thus, the bacteria in Sphingomonadaceae
family are often abundantly found in DW systems
27,28
, as we observed
in PRD_DWTP (Figure 1).
Previous studies indicated that some bacteria, which might resist
to chlorination at a certain degree, could survive after disinfection
and proliferate in the DWDS
29,30
. This is consistent with the results of
the present study, since the functional analysis strongly suggested the
microorganisms in TW of PRD_DWTP contained higher protective
genes responding to the selective pressure of chlorination, such as
glutathione related genes (Figure 3 and 4). Glutathione has been
proven to directly increase bacterial resistance to chlorine com-
pounds
31
and is also indirectly implicated in the regulation of other
oxidation resistant systems, such as OxyR, SoxR and SOS systems
32
.
Figure 2
|
The principal component analysis of five ecosystems using the percentage of annotated reads in Level 1 SEED subsystems. The ecosystems of
soil, human faeces and ocean were analyzed by using public data on MG-RAST. The metagenomes of activated sludge
17
were also analyzed on MG-RAST.
The metagenomic information of these 4 ecosystems is shown in Table S2.
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SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 4
Noticeably, starvation could stimulate the glutathione synthesis and
subsequently enhance bacterial chlorine resistance
32
. This may
explain the poor efficiency of residual disinfectants in DW to inac-
tivate pathogens
33
, especially considering the oligotrophic conditions
in the DWDS networks. Moreover, it was reported that glutathione
could be primarily found in Gram-negative bacteria and eukar-
yotes
34
. While, the genes for glutathione biosynthesis in eukaryotes
were proposed to have been transferred from bacteria via the pro-
genitor of mitochondria during evolutionary
35
. This widely accepted
theory strongly suggests the Alphaproteobacteria, the modern rela-
tives of the mitochondrial progenitor
34
, may commonly contain
glutathione biosynthesis genes. This might be one of possible expla-
nations for the taxonomic observation that Alphaproteobacteria
became dominant in the TW metagenomes of PRD_DWTP
(Figure 1), rather than other bacterial classes.
After treatment in PRD_DWTP, the levels of most detected ARGs
decreased significantly (Figure 5), suggesting the DW treatment
could significantly remove most of ARGs in RW. This might be
mainly attributed to the effective removal of the corresponding bac-
teria, which carried those ARGs, during treatment
36
. However, as
several specific ARGs largely accumulated after treatment in
PRD_DWTP (Figure 5), the level of total ARGs in TW significantly
Figure 3
|
Average percentages of Level 2 subsystems in ‘amino acids and derivatives’, ‘carbohydrates’, and ‘stress response’ (A) and relative
abundances of Level 3 subsystems in ‘oxidative stress’ and ‘detoxification’ (B) in RW and TW. For Level 2 subsystems (A), the reads number which
annotated to the belonging Level 1 subsystems was taken as 100%. For ‘oxidative stress’ and ‘detoxification’ (B), the reads number which annotated to the
‘stress response’ was taken as 100%. The asterisks indicate the significant differences between RW and TW (*:P,0.05; **:P,0.01). Here, GGAA
represents ‘Glutamine, glutamate, aspartate, asparagine’. LTMC represents ‘Lysine, threonine, methionine, and cysteine’.
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SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 5
increased (Table 2), revealing the chlorination may increase bacterial
resistances for specific antibiotics. This is consistent with the pre-
vious observation that bacteria in swine wastewater displayed higher
antibiotic resistances after chlorination
37
. This phenomenon might
be mainly caused by the co-selection of chlorine/chloride and anti-
biotic resistance
38
. For example, Pseudomonas aeruginosa could join
in the course of benzalkonium chloride to promote resistance to
several antibiotics
39
. The co-selection mechanism was reported that
chlorination could induce the mutation and substitution of bacterial
structure genes, e.g. gyrA, nfxB and mexR, which are important
mechanisms of resistance, encoding different ARGs to ciprofloxacin
and fluoroquinolone
40
.
Similar to ARGs, the levels of MGEs in TW of PRD_DWTP also
increased (Table 2). This might be partially attributed to the bacterial
stress response which can enhance the plasmid production when
facing environmental stresses
41
and thus may increase the plasmids
copy number in the cells of remaining bacteria after chlorination
20
.
For other MGEs, the mechanisms behind are still unclear. Since
MGEs were reported to play important roles in ARGs horizontal
gene transfer
42
, the increase of ARGs and MGEs in surviving bacteria
might enlarge the risks for ARGs horizontal transfer among bacteria
in DWDS and cause serious threats to human being. Therefore, more
efforts should be made in future to assess the occurrence and fate of
ARGs and MGEs in DW systems and comprehensively evaluate their
potential risks for human health.
HTS has been proven to be a powerful technique to characterize
various ecosystems including DW systems
11–14
. In the present study,
we successfully characterized the taxonomic and functional profiles
of bacteria in PRD_DWTP before and after treatment via metage-
nomic analysis. However, there are still some limitations in our
study. First of all, although it was suggested that reads length of
100 bp was long enough to resolve microbial community differ-
ences
5
, Illumina reads with 100 bp length were still relatively short
for accurate identification of the DW community at deeper levels
(genus or species). Second, large amount of dead cells were generated
after disinfection. Although released DNA from broken cells could
dissolve and might not be retained by the applied filters, the DNA
from dead but intact cells might contaminate the TW samples. This is
possible to bring bias to the presented results. Thirdly, it should be
noticed that the rise of functional genes for oxidative stress, detoxi-
fication and glutathione metabolism after treatment were just poten-
tial trends and did not guarantee any increase of the expression of
these genes in TW ecosystems, as the current analyses were based on
DNA instead of RNA. Thus, metatranscriptomic studies based on
Figure 4
|
Modified KEGG pathway for glutathione metabolism in bacteria domain only. The pathway A was constructed by 1,205 bacterial species
(non-redundant) in KEGG. The pathway B contained average relative abundances of annotated enzymes detected in RW and TW metagenomes.
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SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 6
RNA should be conducted in future to accurately evaluate the active
taxonomy and functions in DW systems.
Methods
Water samples.RW and TW were collected from a DWTP which has a production
capacity of 135,000 m
3
/day, located at Pearl River Delta area, China. RW was from a
reservoir and was treated by flocculation, sedimentation, sand filtration and
chlorination (Figure S8). Torayvino high-performance cartridge-type water purifiers
(Toray Industries Inc., Japan) were equipped on the RW and TW taps in the DWTP,
since they are featured for easy operation and effective collection for microorganisms.
After purifiers’ equipment, the RW and TW were filtrated for 3 and 72 h, respectively.
And the microorganisms in the resulting 86 L of RW and 2070 L of TW were then
collected by controlling the flow rate at 8 mL/s. After collection, the purifiers were
immediately transported to laboratory. Upon the arrival, the hollow fiber filter in
purifier was immersed into 200 mL ultrapure water and then treated by
ultrasonication (8200E-1, Branson Ultrasonics Corp., US) for 30 min to detach the
microbial cells. Visual microscopy indicated that the collected microorganisms could
be effectively detached from filter surface by the ultrasonication (Figure S9). The cells
in water were collected by filtration using mixed cellulose esters membrane
(HAWP04700, Millipore Corp., US) with a pore size of 0.45 mm. Previous studies
supported 0.45 mm membrane could effectively collect microorganisms in the
water
20
. The membrane was stored at 220uC before DNA extraction. Five samples of
RW and TW collected in July of 2011 and 2012 were named as RW11, RW12-1,
RW12-2, TW11, and TW12. Biological duplicated were designed by applying RW
and TW samples from different years. Technical duplicates were conducted as RW12-
1 and RW12-2 from the same RW sample.
DNA extraction and Illumina sequencing.Genomic DNA of microorganisms in
RW and TW was separately extracted by FastDNAHSPIN Kit for Soil (MP
Biomedicals, Illkirch, France) according to the instruction. The concentration and
purity of DNA was evaluated by NanoDrop spectrophotometer (ND-1000, Thermo
Fisher Scientific, US). DNA of ,10 mg for each sample was used for library
construction. In detail, DNA fragmentation was carried out by Covaris S2 (Covaris,
01801-1721). The fragments were then processed by end reparation, A-tailing,
adapter ligation, DNA size-selection, PCR reaction and products purification. Finally,
a,180 bp DNA fragment reads library was constructed and then sequenced by
Illumina HiSeq 2000 (BGI, China). The base-calling pipeline (Version Illumina
Pipeline - 0.3) was used to process the raw fluorescence images and call reads. Raw
reads with .10% unknown nucleotides or with .50% low quality nucleotides
(quality value ,20) were discarded
10
.
Table 2
|
The level and diversity of ARGs and MGEs in RW and TW
Database
RW TW
Level (ppm)
a
Diversity
b
Level (ppm) Diversity
ARGs ARDB 10 13 18 9
CARD 183 22 241 16
ARDB&CARD 2 10 8 7
MGEs Integron 10 60 58 18
VF 30 83 10 30
IS 19 77 258 29
Plasmid 1,650 467 8,650 304
a
Level: annotated reads number/total reads number; ppm: parts per million.
b
Diversity: assigned categories of ARGs and MGEs by using annotated reads.
Figure 5
|
Average relative distribution of ARGs in RW and TW against ARDB&CARD database.
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SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 7
Acquired Illumina reads were filtered by using Meta Genome Rapid Annotation
using Subsystem Technology (MG-RAST, http://metagenomics.nmpdr.org) QC
pipelines
43
to remove the replicated reads, since the platforms of HTS occasionally
produce large numbers of reads that are nearly identical
44
. Only one representative
read in the clusters of replicated reads, whose first 50 base pairs were identical, was
preserved. The reads which contained 5 or more ambiguous base were then removed.
The filtered reads were used for the following bioinformatic analysis.
Bioinformatic analysis.Illumina reads of RW and TW samples were annotated by
using MG-RAST online server (version 3.3)
43
. The reads were distributed into
different categories, including rRNA reads, protein reads (with known or unknown
functions), and unknown reads, according to the results of similarity comparison
with rRNA and protein databases.
For taxonomic analysis, SILVA Small Subunit (SSU) database (version 104)
45
was
used as annotation source for 16S rRNA reads to analyze the bacterial populations in
RW and TW samples by using an E-value cutoff of 10
25
, minimum identity cutoff of
60%, and minimum alignment length cutoff of 15 aa.
For functional analysis, SEED Subsystems
46
and KEGG
18
databases were applied to
explore the microbial functions in DW samples. Similarity search between protein
reads and the SEED/KEGG databases was conducted by using an E-value cutoff of
10
25
, minimum identity cutoff of 60%, and minimum alignment length cutoff of 15
aa. The annotated reads were sorted into 28 Level 1 subsystems to provide overall
profile of microbial functions and were then compared with other 42 metagenomes
from 4 ecosystems (Table S2). The Level 2 and 3 subsystems, which belong to sub-
systems of ‘amino acids and derivatives’, ‘carbohydrates’, and ‘stress response’, were
further analyzed to investigate specific shift of microbial functions after treatment.
The KEGG was used to construct glutathione pathway in bacteria domain and to
evaluate the specific reactions for glutathione metabolism in DW metagenomes.
ARGs and MGEs analysis.ARG reads on Antibiotic Resistance Genes Database
(ARDB, 7828 reads, http://ardb.cbcb.umd.edu)
47
and Comprehensive Antibiotic
Resistance Database (CARD, 3380 reads, http://arpcard.mcmaster.ca) were
downloaded. The sub-databases of ARDB and CARD were created according to the
antibiotic categories
42
(Figure S10). Then reads in ARDB were aligned against CARD
using BLAST with an E-value cutoff of 10
26
to develop the core database of ARDB and
CARD, referring as ARDB&CARD. A protein read in the ARDB and CARD was
annotated as a shared resistance gene in the ARDB&CARD, according to its BLAST
hit (blastp) for the alignment with amino acid read identity as 100%. Then reads in
RW and TW were aligned against ARDB, CARD and ARDB&CARD using BLAST
with an E-value cutoff of 10
26
, respectively. A read was annotated as a resistance gene
according to its BLAST hit (blastx) for the alignment with amino acid read identity
above 90% for at least 25 aa
42
. The BLAST results were sorted into the sub-database by
a script. The sorting results of RW and TW were then compared to evaluate the
occurrence, level and elimination of ARGs during treatment.
MGEs, including integrons, insertion sequences (IS), VF and plasmids, which play
important roles in transporting ARGs in environments, were also conducted. The
metagenomes were searched for signatures of known MGEs in the reference data-
bases, including INTEGRALL for integrons
48
, ISfinder for IS
49
, VFDB for VF
50
, and
NCBI RefSeq for plasmids
42
. A read was assigned to an integron, IS, VF, plasmid if the
BLAST hit (blastn) with a nucleotide read identity over 90% or at least 50 bases
42
.
After the level of MGEs being explored, the diversity of integron, IS, VF and plasmid
in RW and TW were also counted by script according to annotated accession num-
bers, and then compared.
Quantitative RT-PCR.The results from metagenomic data were confirmed by
quantitative RT-PCR (qRT-PCR) on a MyiQ Real-Time PCR Detection System (Bio-
Rad, Hercules, CA). The relative concentration of two glutathione synthesis genes, i.e.
gshA (glutamate-cysteine ligase, EC 6.3.2.2) and gshB (glutathione synthase, EC
6.3.2.3), were measured by qRT-PCR using selected primers (Table S3). Reactions
were conducted in PCR tubes strip with a final volume of 25 mL, containing 12.5 mL2
3iQSYBRGreen Super-Mix (Bio-Rad, Hercules, CA), 0.5 mL of each primer (10 mM)
and 1 mL template DNA (,25 ng DNA). Thermal cycling were conducted using the
following protocol: 94uC for 3 min, followed by 40 cycles of 94uC for 5 s, annealing at
56uC for 30 s and 72uC for 1 min. Each reaction was run in triplicate. The
quantification of target genes was done with the software iCycler iQversion 5.0 (Bio-
Rad, Hercules, CA). The cycle threshold (Ct) value was used to calculate and compare
the relative gene concentrations in RW and TW. The RW and TW samples in 6
months of 2012 (Feb., Apr. Jun., Aug., Oct., and Dec.) were applied.
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Acknowledgments
The authors thank the Hong Kong GRF (HKU 7201/11E) for the financial support on this
study. Yuanqing Chao, Liping Ma, Ying Yang and Feng Ju thank HKU for the postgraduate
studentship. Prof. Xu-Xiang Zhang thanks HKU for the postdoctoral fellowship. The
technical assistance of Ms. Vicky Fung is greatly appreciated.
Author contributions
Y.C. and L.M. conducted the experiments, analyzed the data, and wrote the manuscript.
Y.Y. conducted the experiments and analyzed the data. F.J. analyzed the data. X.X.Z.
conducted the experiments. W.M.W. provided important suggestions. T.Z. designed the
experiments and modified this manuscript. All authors reviewed the manuscript.
Additional information
Metagenomes accession numbers: DW metagenomes studied in the present study were
deposited in the NCBI Sequence Read Archive database with accession numbers of
SRR835363, SRR850211, SRR850212, SRR850456 and SRR850459.
Supplementary information accompanies this paper at http://www.nature.com/
scientificreports
Competing financial interests: The authors declare no competing financial interests.
How to cite this article: Chao, Y.Q. et al. Metagenomic analysis reveals significant changes
of microbial compositions and protective functions during drinking water treatment. Sci.
Rep.
3
, 3550; DOI:10.1038/srep03550 (2013).
This work is licensed under a Creative Commons Attribution-
NonCommercial-NoDerivs 3.0 Unported license. To view a copy of this license,
visit http://creativecommons.org/licenses/by-nc-nd/3.0
www.nature.com/scientificreports
SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 9

Supplementary resource (1)

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Viruses are important drivers of microbial community ecology and evolution, influencing microbial mortality, metabolism, and horizontal gene transfer. However, the effects of viruses remain largely unknown in many environments, including in drinking water systems. Drinking water metagenomic studies have offered a whole community perspective of bacterial impacts on water quality, but have not yet considered the influences of viruses. In this study, we address this gap by mining viral DNA sequences from publicly available drinking water metagenomes from distribution systems in six countries around the world. These datasets provide a snapshot of the taxonomic diversity and metabolic potential of the global drinking water virome, and provide an opportunity to investigate the effects of geography, climate, and drinking water treatment practices on viral diversity. Both environmental conditions and differences in sample processing were found to influence the viral composition. Using free chlorine as the residual disinfectant was associated with clear differences in viral taxonomic diversity and metabolic potential, with significantly fewer viral populations and less even viral community structures than observed in distribution systems without residual disinfectant. Additionally, significantly more viral-encoded genes involved in mitigating oxidative stress were observed in systems that use free chlorine, while significantly more viral genes involved in nitrogen metabolism were observed in systems that do not. Through this study, we have demonstrated that viral communities are diverse across drinking water systems and vary with the use of residual disinfectant. Our findings offer directions for future research developing a more robust understanding of how virus-bacteria interactions in drinking water distribution systems affect water quality. Graphical Abstract
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Viruses are important drivers of microbial community ecology and evolution, influencing microbial mortality, metabolism, and horizontal gene transfer. However, the effects of viruses remain largely unknown in many environments, including in drinking water systems. Drinking water metagenomic studies have offered a whole community perspective of bacterial impacts on water quality, but have not yet considered the influences of viruses. In this study, we address this gap by mining viral DNA sequences from publicly available drinking water metagenomes from distribution systems in six countries around the world. These datasets provide a snapshot of the taxonomic diversity and metabolic potential of the global drinking water virome; and provide an opportunity to investigate the effects of geography, climate, and drinking water treatment practices on viral diversity. Both environmental conditions and differences in sample processing were found to influence the viral composition. Using free chlorine as the residual disinfectant was associated with clear differences in viral taxonomic diversity and metabolic potential, with significantly fewer viral populations and less even viral community structures than observed in distribution systems without residual disinfectant. Additionally, drinking water viruses carry antibiotic resistance genes (ARGs), as well as genes to survive oxidative stress and nitrogen limitation. Through this study, we have demonstrated that viral communities are diverse across drinking water systems and vary with the use of residual disinfectant. Our findings offer directions for future research to develop a more robust understanding of how virus-bacteria interactions in drinking water distribution systems affect water quality.
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Fresh potable water is an indispensable drink which humans consume daily in substantial amounts. Nonetheless, very little is known about the composition of the microbial community inhabiting drinking water or its impact on our gut microbiota. In the current study, an exhaustive shotgun metagenomics analysis of the tap water microbiome highlighted the occurrence of a highly genetic biodiversity of the microbial communities residing in fresh water and the existence of a conserved core tap water microbiota largely represented by novel microbial species, representing microbial dark matter. Furthermore, genome reconstruction of this microbial dark matter from water samples unveiled homologous sequences present in the fecal microbiome of humans from various geographical locations. Accordingly, investigation of the fecal microbiota content of a subject that daily consumed tap water for three years provides proof for horizontal transmission and colonization of water bacteria in the human gut. This article is protected by copyright. All rights reserved.
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