ArticlePDF Available

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


Abstract and Figures

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.
Content may be subject to copyright.
Metagenomic analysis reveals
significant changes of microbial
compositions and protective functions
during drinking water treatment
Yuanqing Chao
, Liping Ma
, Ying Yang
, Feng Ju
, Xu-Xiang Zhang
, Wei-Min Wu
& Tong Zhang
Environmental Biotechnology Lab, The University of Hong Kong, Pokfulam Road, Hong Kong SAR, China,
State Key Laboratory of
Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210046, China,
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)
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
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
, 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
Recently, high-throughput sequencing (HTS) techniques have shown great advantages on analyzing the
microbial community for its unprecedented sequencing depth
. 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
, and have been applied for investigating microbial structure and/or functions in various
complex environments, such as fresh water
, sea water
, soil
, and human guts
. Recently, several studies have
applied HTS technique to evaluate microbial community in DWTPs and DWDS
. 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
. 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
14 August 2013
29 November 2013
19 December 2013
Correspondence and
requests for materials
should be addressed to
T.Z. (
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.
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
, 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
activated sludge
, 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
, and activated sludge
. 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
. 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
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
15,992 (0.14) 51,390 (0.13) 44,052 (0.12) 3,251 (0.036) 7,673 (0.062)
Annotated Protein
3,268,476 (28) 8,980,700 (22) 7,847,216 (22) 1,545,699 (17) 1,810,357 (15)
8,515,292 (72) 32,077,618 (78) 27,555,038 (78) 7,424,470 (83) 10,508,182 (85)
taking the reads which past QC pipelines as 100%.
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
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
. 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
The mobility of ARGs usually depends on the MGEs, including
integrons, IS, and plasmids
. 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).
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).
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%)
, permafrost
, grassland (,66%)
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
, 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
, 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
and another observed the large
increase of Alphaproteobacteria after DW treatment
. Among the
dominant families in Alphaproteobacteria of TW, the survival of
Sphingomonadaceae family might be associated with its high resist-
ance to chlorination
. Thus, the bacteria in Sphingomonadaceae
family are often abundantly found in DW systems
, 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
. 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-
and is also indirectly implicated in the regulation of other
oxidation resistant systems, such as OxyR, SoxR and SOS systems
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
were also analyzed on MG-RAST.
The metagenomic information of these 4 ecosystems is shown in Table S2.
SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 4
Noticeably, starvation could stimulate the glutathione synthesis and
subsequently enhance bacterial chlorine resistance
. This may
explain the poor efficiency of residual disinfectants in DW to inac-
tivate pathogens
, 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-
. While, the genes for glutathione biosynthesis in eukaryotes
were proposed to have been transferred from bacteria via the pro-
genitor of mitochondria during evolutionary
. This widely accepted
theory strongly suggests the Alphaproteobacteria, the modern rela-
tives of the mitochondrial progenitor
, 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
. 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’.
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
. This phenomenon might
be mainly caused by the co-selection of chlorine/chloride and anti-
biotic resistance
. For example, Pseudomonas aeruginosa could join
in the course of benzalkonium chloride to promote resistance to
several antibiotics
. 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
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
and thus may increase the plasmids
copy number in the cells of remaining bacteria after chlorination
For other MGEs, the mechanisms behind are still unclear. Since
MGEs were reported to play important roles in ARGs horizontal
gene transfer
, 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
. 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-
, 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.
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.
Water samples.RW and TW were collected from a DWTP which has a production
capacity of 135,000 m
/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
. 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
Table 2
The level and diversity of ARGs and MGEs in RW and TW
Level (ppm)
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
Level: annotated reads number/total reads number; ppm: parts per million.
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.
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, QC
to remove the replicated reads, since the platforms of HTS occasionally
produce large numbers of reads that are nearly identical
. 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)
. 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)
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
, minimum identity cutoff of
60%, and minimum alignment length cutoff of 15 aa.
For functional analysis, SEED Subsystems
and KEGG
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
, 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,
and Comprehensive Antibiotic
Resistance Database (CARD, 3380 reads, were
downloaded. The sub-databases of ARDB and CARD were created according to the
antibiotic categories
(Figure S10). Then reads in ARDB were aligned against CARD
using BLAST with an E-value cutoff of 10
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
, 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
. 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
, ISfinder for IS
, VFDB for VF
, and
NCBI RefSeq for plasmids
. 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
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 and gshB (glutathione synthase, EC, 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.
1. Betancourt, W. Q. & Rose, J. B. Drinking water treatment processes for removal of
Cryptosporidium and Giardia.Vet. Parasitol. 126, 219–234 (2004).
2. Ramseier, M. K., von Gunten, U., Freihofer, P. & Hammes, F. Kinetics of
membrane damage to high (HNA) and low (LNA) nucleic acid bacterial clusters
in drinking water by ozone, chlorine, chlorine dioxide, monochloramine,
ferrate(VI), and permanganate. Water Res. 45, 1490–1500 (2011).
3. Berry, D., Xi, C. W. & Raskin, L. Microbial ecology of drinking water distribution
systems. Curr. Opin. Biotechnol. 17, 297–302 (2006).
4. Pronk, M., Goldscheider, N. & Zopfi, J. Particle-size distribution as indicator for
fecal bacteria contamination of drinking water from karst springs. Environ. Sci.
Technol. 41, 8400–8405 (2007).
5. Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of
sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).
6. Porazinska, D. L. et al. Evaluating high-throughput sequencing as a method for
metagenomic analysis of nematode diversity. Mol. Ecol. Resour. 9, 1439–1450
7. Breitbart, M. et al. Metagenomic and stable isotopic analyses of modern
freshwater microbialites in Cuatro CiEnegas, Mexico. Environ. Microbiol. 11,
16–34 (2009).
8. DeLong, E. F. et al. Community genomics among stratified microbial assemblages
in the ocean’s interior. Science 311, 496–503 (2006).
9. Delmont, T. O. et al. Structure, fluctuation and magnitude of a natural grassland
soil metagenome. ISME J. 6, 1677–1687 (2012).
10. Qin, J. J. et al. A human gut microbial gene catalogue established by metagenomic
sequencing. Nature 464, 59–65 (2010).
11. Hong, P. Y. et al. Pyrosequencing analysis of bacterial biofilm communities in
water meters of a drinking water distribution system. Appl. Environ. Microbiol. 76,
5631–5635 (2010).
12. Kwon, S., Moon, E., Kim, T. S., Hong, S. & Park, H. D. Pyrosequencing
demonstrated complex microbial communities in a membrane filtration system
for a drinking water treatment plant. Microbes Environ. 26, 149–155 (2011).
13. Pinto, A. J., Xi, C. W. & Raskin, L. Bacterial community structure in the drinking
water microbiome is governed by filtration processes. Environ. Sci. Technol. 46,
8851–8859 (2012).
14. Gomez-Alvarez, V., Revetta, R. P. & Domingo, J. W. S. Metagenomic analyses of
drinking water receiving different disinfection treatments. Appl. Environ.
Microbiol. 78, 6095–6102 (2012).
15. Gilbert, J. A. et al. Potential for phosphonoacetate utilization by marine bacteria in
temperate coastal waters. Environ. Microbiol. 11, 111–125 (2009).
16. Ye, L., Zhang, T., Wang, T. T. & Fang, Z. W. Microbial structures, functions, and
metabolic pathways in wastewater treatment bioreactors revealed using high-
throughput sequencing. Environ. Sci. Technol. 46, 13244–13252 (2012).
17. Ju, F., Guo, F., Ye, L., Xia, Y. & Zhang, T. Metagenomic analysis on seasonal
microbial variations of activated sludge from a full-scale wastewater treatment
plant over 4 years. Environ. Microbiol. Rep. DOI:10.1111/1758-2229.12110
18. Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for
integration and interpretation of large-scale molecular data sets. Nucl. Acids Res.
40, D109–D114 (2012).
19. Ma, L. P. et al. Occurrence, abundance and elimination of class 1 integrons in one
municipal sewage treatment plant. Ecotoxicology 20, 968–973 (2011).
20. Shi, P. et al. Metagenomic insights into chlorination effects on microbial antibiotic
resistance in drinking water. Water Res. 47, 111–120 (2013).
21. Partridge, S. R., Tsafnat, G., Coiera, E. & Iredell, J. R. Gene cassettes and cassette
arrays in mobile resistance integrons. FEMS Microbiol. Rev. 33, 757–784 (2009).
22. Fierer, N. et al. Cross-biome metagenomic analysis of soil microbial communities
and their functional attributes. Proc. Natl. Acad. Sci. U.S.A. 109, 21390–21395
23. Mackelprang, R. et al. Metagenomic analysis of a permafrost microbial
community reveals a rapid response to thaw. Nature 480, 368–371 (2011).
24. Kormas, K. A., Neofitou, C., Pachiadaki, M. & Koufostathi, E. Changes of the
bacterial assemblages throughout an urban drinking water distribution system.
Environ. Monit. Assess. 165, 27–38 (2010).
25. Poitelon, J. B. et al. Assessment of phylogenetic diversity of bacterial microflora in
drinking water using serial analysis of ribosomal sequence tags. Water Res. 43,
4197–4206 (2009).
26. Koskinen, R. et al. Characterization of Sphingomonas isolates from Finnish and
Swedish drinking water distribution systems. J. Appl. Microbiol. 89, 687–696
27. Srinivasan, S., Harrington, G. W., Xagoraraki, I. & Goel, R. Factors affecting bulk
to total bacteria ratio in drinking water distribution systems. Water Res. 42,
3393–3404 (2008).
28. Vaz-Moreira, I., Nunes, O. C. & Manaia, C. M. Diversity and antibiotic resistance
patterns of Sphingomonadaceae isolates from drinking water. Appl. Environ.
Microbiol. 77, 5697–5706 (2011).
29. Norton, C. D., LeChevallier, M. W. & Falkinham, J. O. Survival of Mycobacterium
avium in a model distribution system. Water Res. 38, 1457–1466 (2004).
30. Wang, Y. Y., Claeys, L., van der Ha, D., Verstraete, W. & Boon, N. Effects of
chemically and electrochemically dosed chlorine on Escherichia coli and
Legionella beliardensis assessed by flow cytometry. Appl. Microbiol. Biotechnol.
87, 331–341 (2010).
31. Chesney, J. A., Eaton, J. W. & Mahoney, J. R. Bacterial glutathione: A sacrificial
defense against chlorine compounds. J. Bacteriol. 178, 2131–2135 (1996).
32. Saby, S., Leroy, P. & Block, J. C. Escherichia coli resistance to chlorine and
glutathione synthesis in response to oxygenation and starvation. Appl. Environ.
Microbiol. 65, 5600–5603 (1999).
33. Payment, P. Poor efficacy of residual chlorine disinfectant in drinking water to
inactivate waterborne pathogens in distribution systems. Can. J. Microbiol. 45,
709–715 (1999).
34. Copley, S. D. & Dhillon, J. K. Lateral gene transfer and parallel evolution in the
history of glutathione biosynthesis genes. Genome. Biol. 3,
research0025.1–0025.16 (2002).
SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 8
35. Fahey, R. C., Newton, G. L., Arrick, B., Overdankbogart, T. & Aley, S. B.
Entamoeba histolytica - a eukaryote without glutathione metabolism. Science 224,
70–72 (1984).
36. Xi, C. W. et al. Prevalence of antibiotic resistance in drinking water treatment and
distribution systems. Appl. Environ. Microbiol. 75, 5714–5718 (2009).
37. Macauley, J. J., Qiang, Z. M., Adams, C. D., Surampalli, R. & Mormile, M. R.
Disinfection of swine wastewater using chlorine, ultraviolet light and ozone.
Water Res. 40, 2017–2026 (2006).
38. Loughlin, M. F., Jones, M. V. & Lambert, P. A. Pseudomonas aeruginosa cells
adapted to benzalkonium chloride show resistance to other membrane-active
agents but not to clinically relevant antibiotics. J. Antimicrob. Chemother. 49,
631–639 (2002).
39. Mc Cay, P. H., Ocampo-Sosa, A. A. & Fleming, G. T. A. Effect of subinhibitory
concentrations of benzalkonlum chloride on the competitiveness of Pseudomonas
aeruginosa grown in continuous culture. Microbiology 156, 30–38 (2010).
40. Varon, E., Janoir, C., Kitzis, M. D. & Gutmann, L. ParC and GyrA may be
interchangeable initial targets of some fluoroquinolones in Streptococcus
pneumoniae.Antimicrob. Agents Chemother. 43, 302–306 (1999).
41. Wegrzyn, G. & Wegrzyn, A. Stress responses and replication of plasmids in
bacterial cells. Microb. Cell Fact. 1, 2 (2002).
42. Kristiansson, E. et al. Pyrosequencing of antibiotic-contaminated river sediments
reveals high levels of resistance and gene transfer elements. PLoS ONE 6, e17038
43. Meyer, F. et al. The metagenomics RAST server - a public resource for the
automatic phylogenetic and functional analysis of metagenomes. BMC
Bioinformatics 9, 386 (2008).
44. Gomez-Alvarez, V., Teal, T. K. & Schmidt, T. M. Systematic artifacts in
metagenomes from complex microbial communities. ISME J. 3, 1314–1317
45. Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and
aligned ribosomal RNA sequence data compatible with ARB. Nucl. Acids Res. 35,
7188–7196 (2007).
46. Overbeek, R. et al. The subsystems approach to genome annotation and its use in
the project to annotate 1000 genomes. Nucl. Acids Res. 33, 5691–5702 (2005).
47. Liu, B. & Pop, M. ARDB-Antibiotic Resistance Genes Database. Nucl. Acids Res.
37, D443–D447 (2009).
48. Moura, A. et al. INTEGRALL: a database and search engine for integrons,
integrases and gene cassettes. Bioinformatics 25, 1096–1098 (2009).
49. Siguier, P., Perochon, J., Lestrade, L., Mahillon, J. & Chandler, M. ISfinder: the
reference centre for bacterial insertion sequences. Nucl. Acids Res. 34, D32–D36
50. Yang, J., Chen, L. H., Sun, L. L., Yu, J. & Jin, Q. VFDB 2008 release: an enhanced
web-based resource for comparative pathogenomics. Nucl. Acids Res. 36,
D539–D542 (2008).
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
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.
, 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,
SCIENTIFIC REPORTS | 3 : 3550 | DOI: 10.1038/srep03550 9

Supplementary resource (1)

... High-throughput sequencing techniques, such as 16S rRNA metabarcoding, have the potential to provide in-depth information that complements standard bacterial quality parameters, and they can help to generate a more accurate picture of microbial communities at different water treatment stages. Several water microbiome studies based on 16S rRNA amplicon sequencing have described microbial composition in DWTPs and distribution networks, which differs according to the water source (e.g., river [10][11][12][13][14], lake [15], groundwater [13,16] or seawater [17]). ...
... Although most studies characterizing bacterial communities in DWTP stages and drinking water have not detected Cyanobacteria and report Proteobacteria as the dominant phylum [11,[13][14][15]25,55,63,64], other researchers have obtained a high abundance of Cyanobacteria reads in water from distribution networks [8], treatment stages [65] or sludge storage in drinking water processing [65,66]. These variable results may be accounted for by factors such as location, the type and quality of source water, and whether or not disinfectant procedures are used, all of which create a unique habitat in each DWTP or distribution network. ...
Full-text available
Monitoring bacterial communities in a drinking water treatment plant (DWTP) may help to understand their regular operations. Bacterial community dynamics in an advanced full-scale DWTP were analyzed by 16S rRNA metabarcoding, and microbial water quality indicators were determined at nine different stages of potabilization: river water and groundwater intake, decantation, sand filtration, ozonization, carbon filtration, reverse osmosis, mixing chamber and post-chlorination drinking water. The microbial content of large water volumes (up to 1100 L) was concentrated by hollow fiber ultrafiltration. Around 10 million reads were obtained and grouped into 10,039 amplicon sequence variants. Metabarcoding analysis showed high bacterial diversity at all treatment stages and above all in groundwater intake, followed by carbon filtration and mixing chamber samples. Shifts in bacterial communities occurred downstream of ozonization, carbon filtration, and, more drastically, chlorination. Proteobacteria and Bacteroidota predominated in river water and throughout the process, but in the final drinking water, the strong selective pressure of chlorination reduced diversity and was clearly dominated by Cyanobacteria. Significant seasonal variation in species distribution was observed in decantation and carbon filtration samples. Some amplicon sequence variants related to potentially pathogenic genera were found in the DWTP. However, they were either not detected in the final water or in very low abundance (<2%), and all EU Directive quality standards were fully met. A combination of culture and high-throughput sequencing techniques may help DWTP managers to detect shifts in microbiome, allowing for a more in-depth assessment of operational performance.
... The chlorinated water leaving the treatment plant was dominated by Alphaproteobacteria (76.5%, Fig. 7B), in agreement with previous findings showing Alphaproteobacteria to be the dominant group in treated water samples due to their capacity to survive in low nutrient and chlorinated environments (Chao et al., 2013). Some members of Alphaproteobacteria, have been found to commonly contain glutathione biosynthesis genes (Chao et al., 2013). ...
... The chlorinated water leaving the treatment plant was dominated by Alphaproteobacteria (76.5%, Fig. 7B), in agreement with previous findings showing Alphaproteobacteria to be the dominant group in treated water samples due to their capacity to survive in low nutrient and chlorinated environments (Chao et al., 2013). Some members of Alphaproteobacteria, have been found to commonly contain glutathione biosynthesis genes (Chao et al., 2013). Glutathione has been proven to directly increase bacterial resistance to chlorine compounds (Chesney et al., 1996), therefore possibly explaining the abundance in chlorinated water. ...
Monitoring the changes that occur to water during distribution is vital to ensure water safety. In this study, the biological stability of reverse osmosis (RO) produced drinking water, characterized by low cell concentration and low assimilable organic carbon, in combination with chlorine disinfection was investigated. Water quality at several locations throughout the existing distribution network was monitored to investigate whether microbial water quality changes can be identified. Results revealed that the water leaving the plant had an average bacterial cell concentration of 10³ cells/mL. A 0.5 – 1.5 log increase in bacterial cell concentration was observed at locations in the network. The residual disinfectant was largely dissipated in the network from 0.5 mg/L at the treatment plant to less than 0.1 mg/L in the network locations. The simulative study involving miniature distribution networks, mimicking the dynamics of a distribution network, fed with the RO produced chlorinated and non-chlorinated drinking water revealed that distributing RO produced water without residual disinfection, especially at high water temperatures (25-30°C), poses a higher chance for water quality change. Within six months of operation of the miniature network fed with unchlorinated RO produced water, the adenosine triphosphate (ATP) and total cell concentration (TCC) in the pipe biofilm were 4 × 10² pg ATP/cm² and 1 × 10⁷ cells/ cm². The low bacterial cell concentration and organic carbon concentration in the RO-produced water did not prevent biofilm development inside the network with and without residual chlorine. The bacterial community analysis using 16S ribosomal RNA (rRNA) gene sequencing revealed that mesophilic bacteria with higher temperature tolerance and bacteria associated with oligotrophic, nutrient-poor conditions dominated the biofilm, with no indication of the existence of opportunistic pathogenic species. However, chlorination selected against most bacterial groups and the bacterial community that remained was mainly the bacteria capable of surviving disinfection regimes. Biofilms that developed in the presence of chlorine contained species classified as opportunistic pathogens. These biofilms have an impact on shaping the water quality received at the consumer tap. The presence of these bacteria on its own is not a health risk indicator; viability assessment and qPCRs targeting genes specific to the opportunistic pathogens as well as quantitative microbiological risk assessment (QMRA) should be included to assess the risk. The results from this study highlight the importance of implementing multiple barriers to ensure water safety. Changes in water quality detected even when high-quality disinfected RO-produced water is distributed highlight microbiological challenges that chlorinated systems endure, especially at high water temperatures.
... drinking water (Jia et al., 2020), municipal wastewater (Guo et al., 2015), industrial wastewater (Shin et al., 2017) and swimming pool (Tsamba et al., 2020) as the last and most solid barrier that prevents potential pathogens into the natural environments or human bodies. It has been demonstrated that chlorination disinfection can effectively reduce the absolute abundance (copy/mL) of ARGs (Chao et al., 2013), but may contribute to the enrichment of their relative abundances (copy/cell) (Jia et al., 2020), likely induced by the underlying mechanisms of cross-or coresistance to disinfectants and antibiotics (Xi et al., 2009). This could be further aggravated by horizontal gene transfer (HGT) of ARGs via mobile genetic elements (MGEs, e.g. ...
... Many studies had been conducted on chlorination associated AMR concerns mainly referring the removal rates of drinking water and municipal wastewater treatment process (Chao et al., 2013;Guo et al., 2015;Jia et al., 2019), cultivable isolates survived in drinking water (Bergeron et al., 2015) and swimming pool (Wei et al., 2018), and further focused on viable but non-culturable strains (Lin et al., 2017) and HGT frequency (Zhang et al., 2021) under exposure to chlorination, which promoted knowledge on chlorination associated AMR risks. While a systematic knowledge of AMR risks of total microbes and concerned pathogenic strains during chlorination for public health related water supply-drinking water and swimming pool-is still deficient for oriented monitoring and control. ...
Full-text available
As a widely used disinfection technology, the effects of chlorination on antibiotic resistome and bacterial community received great scientific concerns, while the pathogens associated health risks kept largely unknown. With this concern, the present study used metagenomic analysis combined with culture method to reveal chlorination effects on antibiotic resistance genes (ARGs) and their bacterial hosts (total microbes and Escherichia coli) through simulating the chlorination dosage with human health concerns (drinking water and swimming pool). The resistome profiling showed that chlorination process could significantly decrease both abundance and diversity of total ARGs, while with limited removal rates of 6.0–8.7% for opportunistic pathogens E. coli isolates. Of all the observed 515 ARG subtypes, 105 core subtypes were identified and persistent during chlorination for both total microbes and E. coli. Antibiotic susceptibility test showed that chlorination treatment could efficiently remove multi-resistant E. coli isolates but select for tetracycline resistant isolates. Five ARG-carrying genomes (assigned to Bacteroidetes, Firmicutes, Actinobacteria) enriched by 18.1–102% after chlorination were retrieved by using metagenomic binning strategies. Bray-Curtis dissimilarity, network and procrustes analyses all indicated the remained antibiotic resistome and bacterial community were mainly chlorination-driven. Furthermore, a systematic pipeline for monitoring chlorination-associated antimicrobial resistance risks was proposed. These together enhance our knowledge of chlorination treatment associated public concerns, as important reference and guidance for surveillance and control of antibiotic resistance.
... The detailed sample information (e.g., sample ID, sampling location and sampling date) is listed in Table S1. To solve the problem of extremely low biomass in tap water that usually does not meet the requirement of high-throughput sequencing and is an obstacle to profile bacterial diversity, we optimized the sample collection and pretreatment process from our previous study (Chao et al., 2013) to more effectively collect microorganisms from drinking water (see details in Fig. S7). Microorganisms in each sample were collected by filtering tap water through high-performance cartridge-type water purifiers (Torayvino, Toray Industries Inc., Japan). ...
Full-text available
Drinking water at the point of use harbors microorganisms that may pose potential risks to human health. However, the microbial diversity and health impacts of household drinking water are poorly understood, since culture-based methods only target on specific microorganisms and low biomass of drinking water hinders a high-throughput profiling. Here, we used an optimized workflow to efficiently collect microorganisms from low-biomass drinking water and performed deep sequencing of 16S rRNA genes to profile the bacterial diversity and biogeography of 110 household drinking water samples covering 38 cities of 29 provinces/regions in China, and further explored environmental drivers and potential health implications. Our analyses revealed a diverse drinking water community comprising a total of 22,771 operational taxonomic units (OTUs). The spatial turnover of drinking water communities is scale-dependent and appears to be driven largely by rainfall and water source river. The identified potential pathogenic species may have the possibility of causing health risks. Our novel insights enhance the current understanding of the diversity and biogeography of drinking water bacterial communities within a theoretical ecological framework and have further important implications for safe drinking water management and public health protection.
... Previous studies have shown that chlorination is effective to remove ARGs in full-scale DWTPs (Lin et al., 2016;Xu et al., 2016;Zheng et al., 2018), but this removal is not complete. The residual ARGs are still significant in number and are often carried by chlorine-tolerant bacteria that has the potential to survive and proliferate during water distribution (Chapman 2003;Chao et al., 2013;Khan et al., 2016;Garner et al., 2018). In addition to comparing boiling with chlorination, the effect of temperature changes during heating on ARGs is also of interest, and based on this, the performance of pasteurization on ARG removal is also studied. ...
Antibiotic resistance genes (ARGs) in municipal drinking water may not be effectively removed during centralized treatment. To reduce potential health risks, water disinfection at the point-of-use scale is warranted. This study investigated the performance of boiling, a prevalent household water disinfection means, in response to ARGs contamination. We found that boiling was more efficient in inactivating both Escherichia coli and environmental bacteria compared to chlorination and pasteurization. Boiling of environmental bacteria suspension removed a much broader spectrum of ARGs and mobile genetic elements (up to 141 genes) than chlorination (up to 13 genes), such better performance was largely attributed to a stronger inactivation of chlorine-tolerant bacteria including Acinetobacter and Bacillus. Accumulation of extracellular ARGs was found during low-temperature heating (≤ 80°C) of E. coli and at lower chlorination dosages (≤3 min). These extracellular ARGs as well as the intracellular ARGs got removed as the heating temperature increased or the chlorination time prolonged. Under the same treatment time (30 min), high-temperature heating (≥ 90.1°C) damaged the DNA structure more thoroughly than chlorination (5 mg/L). Taking into account the low transferability of ARGs after DNA melting, boiling may provide an effective point-of-use approach to attenuating bacterial ARGs in drinking water and is still worth promoting in the future.
... To produce a snapshot of the virome in drinking water distribution systems, we mined publicly available drinking water metagenomes (Chao et al., 2013;Dai et al., 2020;Douterelo et al., 2018;Garner et al., 2018;Huang et al., 2014;Jia et al., 2015;Ma et al., 2019Ma et al., , 2017Pinto et al., 2016;Potgieter et al., 2020;Sevillano et al., 2021;Zhang et al., 2019). The majority of the samples were collected in Europe (31%) and Asia (35%), with the remainder obtained from North America (23%) and Africa (11%) (Figure 1). ...
Full-text available
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
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.
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.
To reduce the risk of by-products from traditional disinfection technology and to ensure safe water quality, tea polyphenols (TP) were used as disinfectants after the ultrafiltration (UF) process. The disinfectant effect of TP was tested on the total number of bacteria and changes in bacterial community structure, and pathogen virulence factors were detected by Illumina’s high-throughput sequencing technology. The results showed that the recommended dosage of TP for water treatment after UF was 5 mg/L, which can effectively inhibit bacterial growth and maintain the disinfectant effect for up to 48 h later. In the disinfection process, the degradation of the tea polyphenols concentration was fitted to the equation of the second order reaction kinetics. The lower the initial concentration and the higher the reaction temperature, the faster the TP decay. The metagenomic analysis of the microorganisms indicated that disinfection with tea polyphenols reduced the diversity of microorganisms in the water and altered the structure of the bacterial community. The existence of tea polyphenols also significantly inhibited the growth of potential common Gram-negative pathogens, especially Mycobacterium. TP disinfectant can also reduce the diversity and abundance of pathogenic bacterial virulence factors and improve biological safety in drinking water.
Microbial contamination of surface waters is of particular relevance in low-income and middle-income countries (LMICs) since they often represent the only available source of water for drinking and domestic use. In the recent years, a growing urbanization, profound demographic shifts and drastic climate events have greatly affected LMICs capacity to reach access to safe drinking water and sanitation practices, and to protect citizens’ health from risks associated to the exposure and use of contaminated water. Detailed phylogenetic and microbiological information on the exact composition of pathogenic organisms in urban and peri-urban water is scarce, especially in rapidly changing settings of sub-Saharan Africa. In this review we aim to highlight how large-scale water pathobiome studies can support the LMICs challenge to global access to safe water and sanitation practices.
Full-text available
For centuries ecologists have studied how the diversity and functional traits of plant and animal communities vary across biomes. In contrast, we have only just begun exploring similar questions for soil microbial communities despite soil microbes being the dominant engines of biogeochemical cycles and a major pool of living biomass in terrestrial ecosystems. We used metagenomic sequencing to compare the composition and functional attributes of 16 soil microbial communities collected from cold deserts, hot deserts, forests, grasslands, and tundra. Those communities found in plant-free cold desert soils typically had the lowest levels of functional diversity (diversity of protein-coding gene categories) and the lowest levels of phylogenetic and taxonomic diversity. Across all soils, functional beta diversity was strongly correlated with taxonomic and phylogenetic beta diversity; the desert microbial communities were clearly distinct from the nondesert communities regardless of the metric used. The desert communities had higher relative abundances of genes associated with osmoregulation and dormancy, but lower relative abundances of genes associated with nutrient cycling and the catabolism of plant-derived organic compounds. Antibiotic resistance genes were consistently threefold less abundant in the desert soils than in the nondesert soils, suggesting that abiotic conditions, not competitive interactions, are more important in shaping the desert microbial communities. As the most comprehensive survey of soil taxonomic, phylogenetic, and functional diversity to date, this study demonstrates that metagenomic approaches can be used to build a predictive understanding of how microbial diversity and function vary across terrestrial biomes.
Full-text available
A metagenome-based approach was used to assess the taxonomic affiliation and function potential of microbial populations in free-chlorine-treated (CHL) and monochloramine-treated (CHM) drinking water (DW). In all, 362,640 (averaging 544 bp) and 155,593 (averaging 554 bp) pyrosequencing reads were analyzed for the CHL and CHM samples, respectively. Most annotated proteins were found to be of bacterial origin, although eukaryotic, archaeal, and viral proteins were also identified. Differences in community structure and function were noted. Most notably, Legionella-like genes were more abundant in the CHL samples while mycobacterial genes were more abundant in CHM samples. Genes associated with multiple disinfectant mechanisms were identified in both communities. Moreover, sequences linked to virulence factors, such as antibiotic resistance mechanisms, were observed in both microbial communities. This study provides new insights into the genetic network and potential biological processes associated with the molecular microbial ecology of DW microbial communities.
Full-text available
The soil ecosystem is critical for human health, affecting aspects of the environment from key agricultural and edaphic parameters to critical influence on climate change. Soil has more unknown biodiversity than any other ecosystem. We have applied diverse DNA extraction methods coupled with high throughput pyrosequencing to explore 4.88 × 10(9) bp of metagenomic sequence data from the longest continually studied soil environment (Park Grass experiment at Rothamsted Research in the UK). Results emphasize important DNA extraction biases and unexpectedly low seasonal and vertical soil metagenomic functional class variations. Clustering-based subsystems and carbohydrate metabolism had the largest quantity of annotated reads assigned although <50% of reads were assigned at an E value cutoff of 10(-5). In addition, with the more detailed subsystems, cAMP signaling in bacteria (3.24±0.27% of the annotated reads) and the Ton and Tol transport systems (1.69±0.11%) were relatively highly represented. The most highly represented genome from the database was that for a Bradyrhizobium species. The metagenomic variance created by integrating natural and methodological fluctuations represents a global picture of the Rothamsted soil metagenome that can be used for specific questions and future inter-environmental metagenomic comparisons. However, only 1% of annotated sequences correspond to already sequenced genomes at 96% similarity and E values of <10(-5), thus, considerable genomic reconstructions efforts still have to be performed.
Full-text available
Kyoto Encyclopedia of Genes and Genomes (KEGG, or is a database resource that integrates genomic, chemical and systemic functional information. In particular, gene catalogs from completely sequenced genomes are linked to higher-level systemic functions of the cell, the organism and the ecosystem. Major efforts have been undertaken to manually create a knowledge base for such systemic functions by capturing and organizing experimental knowledge in computable forms; namely, in the forms of KEGG pathway maps, BRITE functional hierarchies and KEGG modules. Continuous efforts have also been made to develop and improve the cross-species annotation procedure for linking genomes to the molecular networks through the KEGG Orthology system. Here we report KEGG Mapper, a collection of tools for KEGG PATHWAY, BRITE and MODULE mapping, enabling integration and interpretation of large-scale data sets. We also report a variant of the KEGG mapping procedure to extend the knowledge base, where different types of data and knowledge, such as disease genes and drug targets, are integrated as part of the KEGG molecular networks. Finally, we describe recent enhancements to the KEGG content, especially the incorporation of disease and drug information used in practice and in society, to support translational bioinformatics.
Metagenomic technique was employed to characterize 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 and Nematoda), 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, showed significantly higher abundances in winter. Particularly, occurrence of dual variation patterns beyond explanation merely by seasonality indicated that multivariables (e.g. dissolved oxygen, sludge retention time, nutrients) participated in shaping AS community structure. However, SEED subsystems annotation showed that functional categories in AS showed no significant difference between summer and winter, indicating that compared with its microbial components, the functional profiles of AS were much more stable. Taken together, our study provides novel insights into the microbial community variations in AS and discloses their correlations with influential factors in WWTPs.
To understand the impact of gut microbes on human health and well-being it is crucial to assess their genetic potential. Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of 3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from faecal samples of 124 European individuals. The gene set, approximately 150 times larger than the human gene complement, contains an overwhelming majority of the prevalent (more frequent) microbial genes of the cohort and probably includes a large proportion of the prevalent human intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over 99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and 1,150 prevalent bacterial species and each individual at least 160 such species, which are also largely shared. We define and describe the minimal gut metagenome and the minimal gut bacterial genome in terms of functions present in all individuals and most bacteria, respectively.
The objective of this study was to explore microbial community structures, functional profiles and metabolic pathways in a lab-scale and a full-scale wastewater treatment bioreactors. In order to do this, over 12 gigabases of metagenomic sequence data and 600,000 paired-end sequences of bacterial 16S rRNA gene were generated with the Illumina HiSeq 2000 platform, using DNA extracted from activated sludge in the two bioreactors. Three kinds of sequences (16S rRNA gene amplicons, 16S rRNA gene sequences obtained from metagenomic sequencing, and predicted proteins) were used to conduct taxonomic assignments. 16S rRNA gene tags have less bias for investigating the microbial community. Specially, relative abundances of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) were analyzed. Compared with quantitative real-time PCR (qPCR), metagenomic sequencing was demonstrated to be a better approach to quantify AOA and AOB in activated sludge samples. It was found that AOB were more abundant than AOA in both reactors. Furthermore, the analysis of the metabolic profiles indicated that the overall patterns of metabolic pathways in the two reactors were quite similar (73.3% of functions shared). However, for some pathways (such as carbohydrate metabolism and membrane transport), the two reactors differed in the number of pathway-specific genes.
This study aimed to investigate the chlorination effects on microbial antibiotic resistance in a drinking water treatment plant. Biochemical identification, 16S rRNA gene cloning and metagenomic analysis consistently indicated that Proteobacteria were the main antibiotic resistant bacteria (ARB) dominating in the drinking water and chlorine disinfection greatly affected microbial community structure. After chlorination, higher proportion of the surviving bacteria was resistant to chloramphenicol, trimethoprim and cephalothin. Quantitative real-time PCRs revealed that sulI had the highest abundance among the antibiotic resistance genes (ARGs) detected in the drinking water, followed by tetA and tetG. Chlorination caused enrichment of ampC, aphA2, bla(TEM-1), tetA, tetG, ermA and ermB, but sulI was considerably removed (p < 0.05). Metagenomic analysis confirmed that drinking water chlorination could concentrate various ARGs, as well as of plasmids, insertion sequences and integrons involved in horizontal transfer of the ARGs. Water pipeline transportation tended to reduce the abundance of most ARGs, but various ARB and ARGs were still present in the tap water, which deserves more public health concerns. The results highlighted prevalence of ARB and ARGs in chlorinated drinking water and this study might be technologically useful for detecting the ARGs in water environments.
The bacterial community structure of a drinking water microbiome was characterized over three seasons using 16S rRNA gene based pyrosequencing of samples obtained from source water (a mix of a groundwater and a surface water), different points in a drinking water plant operated to treat this source water, and in the associated drinking water distribution system. Even though the source water was shown to seed the drinking water microbiome, treatment process operations limit the source water's influence on the distribution system bacterial community. Rather, in this plant, filtration by dual media rapid sand filters played a primary role in shaping the distribution system bacterial community over seasonal time scales as the filters harbored a stable bacterial community that seeded the water treatment processes past filtration. Bacterial taxa that colonized the filter and sloughed off in the filter effluent were able to persist in the distribution system despite disinfection of finished water by chloramination and filter backwashing with chloraminated backwash water. Thus, filter colonization presents a possible ecological survival strategy for bacterial communities in drinking water systems, which presents an opportunity to control the drinking water microbiome by manipulating the filter microbial community. Grouping bacterial taxa based on their association with the filter helped to elucidate relationships between the abundance of bacterial groups and water quality parameters and showed that pH was the strongest regulator of the bacterial community in the sampled drinking water system.