AEM Accepted Manuscript Posted Online 10 July 2015 Appl. Environ. Microbiol. doi:10.1128/AEM.01297-15 Copyright © 2015, American Society for Microbiology. All Rights Reserved.
1
Using amplicon sequencing to characterize and monitor bacterial diversity in
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drinking water distribution systems.
3 4
Jennifer L. A. Shawa*, Paul Monisb#, Laura Weyricha, Emma Sawadeb, Mary Drikasb,
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Alan Coopera.
6 7
Australian Centre for Ancient DNA, University of Adelaide, Adelaide, South
8
Australia, Australiaa. Australian Water Quality Centre, South Australia Water
9
Corporation, Adelaide, South Australia, Australiab.
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Running Head: bacterial diversity in drinking water systems
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#Address correspondence to Paul Monis (corresponding author):
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[email protected]
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*Present address: Jennifer L A Shaw, CSIRO, Land and Water Flagship, Waite
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Campus, Urrbrae, Adelaide, South Australia, Australia, 5064.
18 19 20 21 22 23 24
1
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Using amplicon sequencing to characterize and monitor bacterial
26
diversity in drinking water distribution systems.
27 28
ABSTRACT
29
Drinking water assessments use a variety of microbial, physical and chemical
30
indicators to evaluate water treatment efficiency and product water quality. However,
31
these indicators do not allow the complex biological communities, which can adversely
32
impact the performance of drinking water distribution systems (DWDSs), to be
33
characterized. Entire bacterial communities can be studied quickly and inexpensively
34
using targeted metagenomic amplicon sequencing. Here, amplicon sequencing of the
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16S rRNA gene region was performed alongside traditional water quality measures to
36
assess the health, quality, and efficiency of two distinct, full-scale DWDSs: (a) a linear
37
DWDS supplied with unfiltered water subjected to basic disinfection before distribution
38
and (b) a complex, branching DWDS treated by a four-stage water treatment plant
39
(WTP) prior to disinfection and distribution. In both DWDSs bacterial communities
40
differed significantly after disinfection, demonstrating the effectiveness of both
41
treatment regimes. However, bacterial repopulation occurred further along the DWDSs,
42
and some end-user samples were more similar to the source water than the post-
43
disinfection water. Three sample locations appeared to be nitrified, displaying elevated
44
nitrate levels and decreased ammonia levels, and nitrifying bacterial species, such as
45
Nitrospira, were detected. Burkholderiales were abundant in samples containing high
46
amounts of monochloramine, indicating resistance to disinfection. Genera known to
47
contain pathogenic and fecal-associated species were also identified in several locations.
48
From this study, we conclude that metagenomic amplicon sequencing is an informative
2
49
method to support current compliance-based methods, and can be used to reveal
50
bacterial community interactions with the chemical and physical properties of DWDSs.
51 52
Keywords: drinking water distribution system | amplicon sequencing | NGS | bacterial |
53
16S
54 55
INTRODUCTION
56
Transmission of pathogens via contaminated water is a significant cause of illness
57
worldwide. It has been estimated that one third of gastrointestinal illnesses are caused
58
by contaminated drinking water (1), and 4% of all deaths worldwide are due to polluted
59
drinking water and poor sanitation (2). In developed nations water quality assessments
60
and treatment facilities have been introduced to reduce microbial contamination,
61
resulting in a significant reduction in drinking water-related illnesses and deaths. Water
62
treatment commonly involves reduction of organics and other contaminants via
63
coagulation and sedimentation, separation of any remaining solids via filtration, and
64
finally disinfection via chemical oxidants or ultra-violet radiation. The addition of
65
chemical oxidants such as chlorine and monochloramine is the most common method of
66
drinking water disinfection (3). The level of treatment required varies from system to
67
system, with some drinking water distribution systems (DWDSs) receiving only one or
68
two levels of treatment, whilst others require multiple treatments to create water suitable
69
for end-use.
70
Currently, most drinking water quality assessments do not directly taxonomically
71
identify the many multifarious bacterial taxa present in drinking water systems. Instead,
72
a variety of biological, physical and chemical indicators are used to assess the efficiency
73
of water treatment processes and the quality of the product water. These indicators
74
quantify bacterial cells using flow cytometry or heterotrophic plate counts, and measure
3
75
concentrations of nutrients, such as ammonia, organic carbon, nitrates, and nitrites.
76
Quantitative PCR (qPCR) can be used to directly quantify specific bacterial taxa (4),
77
such as fecal indicators or known pathogens, but this does not allow measurements of
78
whole community diversity. Conversely, denaturing gradient gel electrophoresis
79
(DGGE) can be used to estimate the biodiversity of bacterial communities (5), but
80
individual taxa cannot be identified without additional expensive DNA sequencing.
81
Recently, metagenomic amplicon sequencing has emerged as a promising
82
technique for characterizing complex biological communities, as multiple taxa within
83
communities deriving from multiple samples can be sequenced in a relatively rapid and
84
inexpensive manner. Metagenomics has been successfully used to investigate bacterial
85
communities present in end-use tap water, experimental drinking water systems, and
86
bacterial biofilms within pipes (6 – 10). However, very few studies have explored
87
bacterial communities through full-scale DWDSs or investigated community variations
88
between different systems. In the present study, we characterize changes in bacterial
89
community structure at different locations along two full-scale DWDSs, and also
90
determine the viability of metagenomic sequencing as a monitoring tool for assessing
91
system efficiency and water quality. We also measured various water quality parameters
92
at the same sample locations to validate the metagenomic findings. Using these
93
approaches, bacterial diversity, water quality, and treatment efficiency were assessed at
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multiple sites along two distinct DWDSs: (a) a linear DWDS in Western Australia
95
(WA) that undergoes monochloramine disinfection before distribution and (b) a
96
complex, branching DWDS in South Australia (SA) that is treated by a four-stage water
97
treatment plant (WTP) prior to monochloramine disinfection and distribution. From this
98
metagenomic dataset, we aim to guide future management and water-quality monitoring
4
99 100
strategies, and potentially reduce associated human-health risks by extending current knowledge of bacterial communities in DWDSs.
101 102
METHODS
103
Site descriptions
104 105
WA DWDS
106
The WA DWDS was chosen for this study because it is a simple, linear system
107
with minimal catchment run-off present in the source water. A large proportion of the
108
catchment area is state owned and managed forest, and the remaining land is primarily
109
low intensity agricultural land, such as grazing pastures. The water is a blend of ground
110
water, desalinated seawater and is supplemented from the metropolitan system. The
111
source water undergoes disinfection with monochloramine before distribution. Areas
112
local to the reservoir are supplied via several large storage tanks (Fig. 1).
113 114
SA DWDS
115
The SA DWDS is a larger, more complex system with multiple distinct
116
distribution branches (Fig. 2). The source water is supplied from river water with the
117
catchment area passing through state owned forests and agricultural areas that produce
118
wool, cotton, wheat, cattle, pigs, dairy, rice, wine, fruit and vegetables. The source
119
water is treated within a four-stage water treatment plant (WTP), consisting of
120
coagulation, flocculation, sedimentation and filtration, UV disinfection, and
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monochloramine disinfection before being distributed.
122 123
Sample collection
124
For each system, 600 ml water samples were collected from the source water,
125
within the treatment plant (WTP), and also at each sample point indicated in Figures 1
5
126
and 2. Sample locations covered the expanse of the distribution systems from the source
127
water through to various township storage tanks and customer taps. For three of the end-
128
user locations, samples were collected from within the township storage tank (tank) and
129
also from a customer tap (CT) after the water had left the tank (tank outlet tap or a
130
nearby CT) to assess whether bacterial diversity was affected by tank storage. In the SA
131
system, additional WTP and source water samples were collected two weeks later, to
132
determine if bacterial communities are subject to short-term temporal fluctuations.
133
Samples were collected in sterile bottles, immediately placed on ice, and transported to
134
dedicated laboratory facilities, where they were stored at 4°C and processed within 24
135
hours of collection.
136 137
Water quality analyses
138
Traditional, compliance-based water quality analyses, such as nutrient and
139
chemical analyses, flow cytometry, and quantitative PCR amplification of nitrification-
140
associated genes, were conducted for each of the samples. Classical chemical indicators
141
for nitrification, such as free ammonia, nitrate, nitrite, and monochloramine levels were
142
measured using standard protocols (11). Flow cytometry (FCM), in combination with
143
the the BacLight bacterial viability kit (Molecular Probes, USA), was utilized to
144
enumerate the bacterial cells present in the water, as previously described (12). For the
145
FCM analyses, duplicate 500 μL aliquots were sub-sampled from each of the 600 ml
146
samples, with one aliquot stained with SYTO-9 to stain both intact (live) and membrane
147
damaged (dead) cells, while the other aliquot was stained with both SYTO9 and
148
propidium iodide (which only stains membrane damaged cells). . FCM enumeration
149
using a FACS Calibur flow cytometer (Becton Dickinson, San Jose, CA)was performed
150
separately to count the total cells (stained with SYTO9 alone) and live cells (the
151
population of cells stained with SYTO9 but not propidium iodide). In order to calculate 6
152
cell densities, sample tubes were weighed pre and post analysis to allow calculation of
153
the volume analyzed. The resulting FCM data were analyzed using CellQuest software
154
(Becton Dickinson, USA). The % viable cells (in Table 1) was calculated using: live
155
cell count per mL / total cell count per mL x 100.
156
Basic qPCR assays were used for the semi-quantification of the number of
157
ammonia oxidizing bacteria (AOB) and archaea (AOA) in each sample using the DNA
158
extractions (Section 2.4.1). The assays used primers for AOA and AOB as previously
159
described (13), except that the PCR reagents were replaced with the Fast Start Essential
160
Probe Master mix (Roche) with the addition of 3.3 µM SYTO9 (final concentration,
161
Molecular Probes). The amplifications were conducted on a RotorGene 6000 HRM
162
(Qiagen) using the published cycling conditions, followed by DNA melting curve
163
analysis on the HRM channel from 70°C to 95°C in 0.2°C increments. The first
164
derivative melting profiles were visualized with the Digital Filter set to “none”. The
165
presence of non-specific amplicon prevented conventional quantification using the
166
amplification curves. Instead, the presence of AOA/AOB was semi-quantified using the
167
height of the DNA melting peaks relative to the peak in the 104 copies/reaction DNA
168
standard. The standard was nominally scored as +++, with samples with higher melting
169
peaks scored as ++++, weak peaks as + and peaks with a height in between weak and
170
the control as +++.
171 172
Metagenomic sequencing
173
DNA extraction
174
Within 24 hours of collection, three 50 ml sub-samples from each of the 600 ml
175
water samples were centrifuged at 4000 rpm for 30 minutes to pellet the intact bacterial
176
cells, and the supernatant was discarded. The pellet from each of these sub-samples was
177
resuspended in 180 μl of sterile nuclease free water (Sigma-Aldrich, Castle Hill, 7
178
Australia) and transferred to the MoBio Ultraclean Soil DNA Kit (MoBio Laboratories
179
Inc., Solana Beach, CA). DNA was extracted, alongside extraction blanks consisting of
180
180 μl of sterile nuclease water, following manufacturer’s recommendations. Once the
181
extractions were complete, DNA yield was quantified using a Nanodrop
182
spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA), and the replicate
183
sub-samples were combined to reduce sequencing costs, resulting in ten samples for the
184
SA system and eight for the WA system (Table 1).
185 186
PCR amplification and NGS sequencing
187
Amplicon libraries were prepared in dedicated sterile PCR hoods. Prior to adding
188
GoTaq, the PCR mastermix was subjected to ultra-violet radiation for fifteen minutes to
189
reduce contamination from laboratory bacteria (14). Sample DNA was added in a
190
second sterile hood in a separate room. A two-step nested PCR approach was adopted,
191
first amplifying a longer DNA fragment prior to amplifying a shorter fragment
192
appropriate for amplicon-based NGS sequencing. This was done to minimize the
193
contribution of highly degraded, uninformative and contaminant DNA (15). A real-time
194
quantitative polymerase chain reaction (RT PCR) platform was used for the PCR
195
amplification step in order to enable comparison of DNA melt curves and determine if
196
samples were contaminated by DNA present in reagents and plastic ware. In the first RT
197
PCR, universal primers 27F (5’-AGAGTTTGATCCTGGCTCAG-3’) and 1492R (5’-
198
TACCTTGTTACGACTT -3) were used to amplify a 1,500 base pair (bp) fragment
199
within the 16S rRNA gene (12). Reactions of 25µL were prepared in quadruplicate and
200
pooled post-PCR to reduce PCR bias (16). Each reaction contained 5µL of DNA
201
extraction template, 5% dimethylsulfoxide (DMSO), 3.3μM SYTO-9 dye, 0.4U µL-1 Go
202
Taq (Promega), 200 µM dNTPs, 0.5µM of each primer, 1x PCR buffer, 2.5mM of
203
MgCl2 and 4.4 μL of nuclease free water, and was amplified using the following 8
204
parameters: 95°C for 3 minutes, 35 cycles at 94°C for 30 seconds, 50°C for 60 seconds
205
and 72°C for 120 seconds. In the second RT PCR, a smaller 176 bp region from within
206
the 1,500 bp PCR product was amplified using barcoded (‘x’ refers to barcode) Ion
207
torrent
208
CCATCTCATCCCTGCGTGTCTCCGACTCAGxxxxxxxCCTACGGGAGGCAGCA
209
G-3’)
210
CCTCTCTATGGGCAGTCGGTGATATTACCGCGGCTGCTGG-3’). Reactions were
211
prepared as above. The second round of amplification was performed with the following
212
parameters: 95°C for 3 minutes, 35 cycles at 95°C for 20 seconds, 65°C for 20 seconds
213
and 72°C for 30 seconds. PCR products were purified (Ampure, Agencourt Bioscience),
214
quantified, and pooled to equimolar concentrations, before sequencing on an Ion
215
Torrent Personal Genome Machine using the Ion PGM 200 sequencing kit (Life
216
technologies, Carlsbad, CA, USA).
217 218
Data analysis
fusion
primers:
and
341F
518R
(5’-
(5’-
219
Reads that contained zero mismatches within the barcode and primer sequence
220
were demultiplexed into their respective samples, and barcodes and primer sequences
221
were trimmed off using CutAdapt v1.1 (17). Reads were then filtered for quality (Phred
222
threshold of 20 for 90% of sequence bp) and length (>100bp) using FastX toolkit
223
(FASTX-toolkit v0.0.13; http://hannonlab.cshl.edu/fastx_toolkit). The resulting files
224
were converted to a QIIME software compatible .fna file (Script available from:
225
(http://www.u.arizona.edu/~gwatts/azcc/QIIMEfastaFormatter.pl) and imported into
226
QIIME (18).
227
Within QIIME, sequences were clustered to 97% similarity to form operational
228
taxonomic units (OTUs) and aligned using Pynast. Chimeric reads were detected and
229
removed using Chimera slayer (19). The standard default threshold (0.8) was applied to 9
230
assign
231
http://greengenes.lbl.gov). Contaminant OTUs identified in the extraction blanks were
232
removed from samples. All samples were rarefied at 8904 sequences (i.e. the lowest
233
number of reads present within a single sample) to ensure an even read coverage for
234
each sample and minimize skewed diversity estimates. Diversity estimates were
235
obtained for each sample by calculating Chao species richness and Shannon-Weiner
236
diversity indices. The number of operational taxonomic units (OTUs) per sample were
237
also measured. Sequences were also re-clustered at 98% similarity and taxonomy re-
238
assigned to improve phylogenetic resolution for some closely related species. Further,
239
sequences from specific genera of interest were extracted from the sequence files and
240
then blasted against the NCBI database (date accessed: 17/10/2014) to allow further
241
phylogenetic discrimination between closely related taxa. Exploratory and statistical
242
analysis of the data was carried out using QIIME, SPSS (SPSS Inc. Released 2009.
243
PASW Statistics, Version 18.0. Chicago: SPSS Inc.), and Explicet (20). Jack-knifed
244
UPGMA trees based on weighted hierarchal clustering (replicate sequences = 6670; e =
245
10) were constructed to visualize sample similarity based on phylogenetic distance
246
between taxa (UniFrac software; 21) and were viewed using FigTree V1.4 software
247
(tree.bio.ed.ac.uk/software). Heat maps were constructed within Explicet to visualize
248
dominant taxa (> 0.1%) for each sample and determine dissimilarity between samples.
249
Raw
250
(http://www.ncbi.nlm.nih.gov/sra) under the BioProject ID: PRJNA287069.
taxonomy
data
files
using
were
the
deposited
Greengenes
in
the
database
NCBI
(version
sequence
read
gg_12_8;
archive
251 252
RESULTS
253
Treatment effectiveness
10
254
To assess the efficiency of DWDS treatments, bacterial communities pre- and
255
post-treatment were compared. Metagenomic amplicon sequencing revealed that the
256
bacterial communities were significantly altered after treatment in both systems (P =
85%) two
390
of the end-use locations within the SA DWDS: SA-3 tank and SA-5 CT (Figure 3b).
391
Closer inspection revealed that the Burkholderiales taxa in the SA-3 tank sample
392
belonged to the Comamonadaceae family, and in SA-5 CT predominantly
393
Hydrogenophaga, a yellow-pigmented, hydrogen-oxidizing bacterium. These two end-
394
use samples had unusually low diversity and OTU counts compared to all other samples
395
(Mann-Whitney U; P = >0.001) and therefore could indicate locations where
396
monochloramine resistant taxa are outcompeting other bacterial taxa for resources.
397
These taxa could be a target for future microbiological studies as the tanks were
398
relatively unusual and may provide useful insights into bacterial community dynamics
399
within DWDSs that utilize monochloramine. The order Sphingomondales were also
400
identified in relatively high proportions in the SA post-disinfection sample, but not in
401
the WA post-disinfection sample. Nevertheless, their relatively high abundance in
402
samples with high amounts of monochloramine warrants further investigation in future
403
studies.
(3%),
Delftia
(10%),
Pelomonas
(2%),
and
an
unknown
16
404 405
Pathogen detection
406
The OTU tables for each system were screened for common waterborne pathogens to
407
determine if metagenomics is a feasible tool for pathogen identification in DWDSs.
408
Several genera known to contain pathogenic species were identified in the systems;
409
however, in most cases the 16S gene fragment did not allow species- or strain-level
410
resolution of the taxa. Therefore, we utilized an approach similar to current compliance
411
methods, where potential coliform microorganisms are identified as indicators of
412
contamination, even if specific species or strains cannot be resolved (23). The genus
413
containing the coliform bacteria Escherichia and Shigella was identified in relatively
414
large proportions in the WA post-disinfection sample (8%) and the WA-1 inlet sample
415
(5%), but was negligible in the SA post-disinfection sample. This suggests the filtration
416
steps used in the SA WTP are crucial for complete removal of fecal coliform cells.
417
However, as the FCM analyses revealed that the WA disinfection step reduces the
418
percentage of viable bacterial cells to just 3.4%, it likely that the Escherichia and
419
Shigella taxa detected by metagenomic sequencing are intact but non-viable cells (i.e.
420
they are effectively neutralized but the cells have not yet degraded). In an attempt to
421
discriminate between Escherichia and Shigella all associated sequences were extracted
422
from the dataset and blasted separately in NCBI. Results indicated that the closest
423
matches to the taxa found in the WA system were Escherichia coli, Escherichia
424
fergusonii, Shigella dysenteria, and Shigella sonnei, some of which are human
425
pathogens (SM5). Other taxa known to contain pathogenic species were also identified
426
throughout the systems. For example, Burkholderia, a genus known to contain
427
pathogenic species, were identified in SA-3 tank, while other samples contained the
428
genera Campylobacter (SA-1 CT), Mycobacterium (SA source water, SA post-
17
429
disinfection, SA-1 CT, SA-3 tank SA-5 CT), and Legionella (SA source water). In the
430
WA DWDS, Leptospira (WA-4 CT), Mycobacterium (WA-3, WA-4 CT), Clostridium
431
(WA-3 Inlet), and Legionella (WA source water, WA post-disinfection, WA-4 CT,
432
WA-3 inlet and outlet) were identified.
433 434
DISCUSSION
435
Bacterial communities in drinking water systems are complex, and although current
436
monitoring methods are useful to quantify total and live bacterial cell counts and detect
437
specific taxa, they do not allow the intricate and complex nature of these diverse
438
communities to be effectively evaluated. In this study, the addition of metagenomic
439
analyses to currently used compliance-based methods revealed complex interactions
440
between biological communities and various DWDS parameters, such as water
441
treatment, monochloramine tolerance, nitrification, and tank storage effects. To
442
summarize, the water treatment regimes within both the WA and SA systems were
443
initially highly effective at reducing bacterial community diversity and cell counts.
444
However, some sample locations at a further distance from the WTPs displayed
445
bacterial regrowth and increases in diversity. The data also revealed that nitrification
446
was occurring at multiple locations at varying degrees of severity, and that
447
monochloramine
448
Sphingomondales, were present in some locations. The metagenomic analyses
449
successfully identified genera associated with fecal coliforms and pathogens; however,
450
the universality and small size of the gene fragment used in this study prevented
451
specific pathogens from being identified.
tolerant
bacterial
groups,
including
Burkholderiales
and
452
In this study, nitrification was found to be occurring in both systems further along
453
the DWDS. Nitrification refers to the biological oxidation of ammonia to nitrite,
18
454
commonly conducted by the bacterial genus Nitrosomonas (24), followed by the
455
oxidation of nitrite to nitrate, usually by Nitrobacter or Nitrospira (25). Free ammonia
456
is often present in DWDSs as a consequence of the process used to generate chloramine.
457
Nitrospira was detected in WA-4 and SA-2 CT, and water quality analyses revealed
458
little to no ammonia or monochloramine at these locations and elevated levels of nitrate.
459
Nitrospira has been coined the most dominant and ubiquitous group of nitrifying
460
bacteria (26), and was only one of two nitrifying bacteria detected by the metagenomic
461
analysis. However, it is possible that there were other nitrifying bacteria present that
462
have not been classified in genetic databases. The qPCR results indicated that there
463
were AOA and AOB associated genes present throughout the entire systems. However,
464
the metagenomic data only detected nitrifiers when various stages of nitrification were
465
also confirmed by the water quality data. The discrepancy between these results could
466
be due to a number of different reasons; for example, ammonia oxidizers may have been
467
at low abundance and their DNA signal may have been obscured by more highly
468
abundant taxa, preventing detection via metagenomic sequencing. In contrast, it could
469
be that the highly specific nature of the qPCR is detecting AOA and AOB even when
470
the abundance of those taxa is below effectual levels, which would explain their
471
consistent presence throughout the systems. Alternatively, this result may indicate that
472
ammonia-oxidizing genes are present in other bacterial species that may be
473
taxonomically unknown at present. In addition to detecting nitrifying bacteria, the
474
metagenomic data revealed that the end-user locations WA-4 and SA-2 CT had OTU
475
counts, biodiversity measurements and OTU proportions more similar to that of the
476
source water than the post-disinfection samples, suggesting that bacterial population
477
regrowth during nitrification was occurring at these sites. The detection of nitrifying
478
species in DWDS locations in the early stages of nitrification could be used as a
19
479
preventative measure, identifying sections of a DWDS that may require remedial action,
480
such as flushing or additional disinfection, to avoid potential future, more severe
481
nitrification episodes.
482
Taxa identified in samples with high amounts of disinfectant (monochloramine)
483
are likely to be species that are resistant to or tolerant of disinfectants. For example,
484
Burkholderiales, which is known to contain strains resistant to antibiotics (27), were
485
identified in high percentages in the post-disinfection samples in both systems. The
486
sensitivity of Burkholderia to chlorine (28) and monochloramine (29) is known to vary
487
considerably, with some isolates more tolerant than others. Previous research has
488
revealed that co-cultures of the pathogenic Burkholderia species B. pseudomallei with
489
the ubiquitous freshwater amoeba Acanthamoeba astronyxis greatly enhances the
490
survival of B. pseudomallei in the presence of monochloramine, with 100 times more
491
monochloramine required to maintain disinfectant efficacy when the amoeba was
492
present (29). This is due to the ability of Acanthamoeba cysts to resist common
493
disinfectants, such as monochloramine and chlorine, and its ability to bear
494
phagocytosis-resistant bacteria within, thus protecting the bacteria from water
495
treatments (30). While these particular species may not have been present in these
496
systems, it may be the case that similar inter-kingdom interactions can occur for other
497
Burkholderiales species. As eukaryotes were not sequenced in this study, it is not
498
known what amoebae were present in the SA and WA systems. Further, other studies
499
found that viable B. pseudomallei cells can be recovered from water containing up to
500
1000ppm free chlorine and after the disinfectant had previously successfully reduced
501
cells to compliance levels, suggesting that species affected by initial disinfection could
502
recover to harmful levels later in the DWDS (31). Burkholderiales have also previously
503
been identified in drinking water pipe biofilms (32), which may potentially serve as a
20
504
reservoir to seed cells back in to the water post-disinfection. This provides a further
505
possible explanation for the relatively high abundance of Burkholderiales in the post-
506
disinfection samples, as biofilm microorganisms are known to be more resistant to
507
drinking water disinfection than free-living microorganisms (33). Again, although these
508
previous studies have focused on specific pathogenic strains of Burkholderiales, it is
509
possible that other species within this order are able to resist disinfection in the same
510
way. In addition to Burkholderiales, Sphingomonadales are also known to contain
511
chlorine resistant species (34), are found in biofilms (32), and were detected in
512
relatively high proportions in post-disinfection samples. Therefore, it is plausible that
513
the Sphingomonas identified in this study could also be resistant to disinfectants,
514
explaining its high proportions in samples with elevated amounts of monochloromine.
515
Bacterial genera known to contain notable waterborne pathogens were identified at
516
some locations along the DWDSs. Clostridium, Campylobacter, Corynebacterium,
517
Escherichia coli, Mycobacterium, Legionella, Burkholderia and Leptospira were all
518
identified in various samples throughout the systems, some close to the consumer end-
519
use locations. As mentioned previously, Burkholderia contains pathogenic species that
520
have been shown to resist disinfectants and antibiotics. Burkholderia are of particular
521
public health significance in warmer climates (35), with outbreaks of the water-borne
522
disease Meliodosis sporadically occurring in South America, Asia and Australia (36).
523
Campylobacter was present in relatively high abundance in the SA source water.
524
Campylobacter is a known fecal-associated bacteria that usually requires a host to
525
survive and grow (37), the presence of this taxa in the source water combined with its
526
failure to reappear further along the DWDS post-disinfection supports this concept.
527
However, whilst Campylobacter can only survive a few days without a host in water
528
above 15°C, it can survive for many weeks in a viable but non-culturable state in water
21
529
at 4°C (37); therefore it’s presence may be of importance during colder winter months.
530
Cyanobacteria were found in high abundances in the WA post-disinfection sample,
531
likely because chloramine disinfection alone is not capable of destroying filamentous or
532
colonial cells or due to the presence of akinetes, which are environmentally-resistant,
533
dormant cells with thick walls (38). Some cyanobacterial species are of public health
534
significance due to the production of toxins, while others can cause the deterioration of
535
the aesthetic quality of the water due to the production of secondary metabolites that
536
produce an earthy, musty taste and odor (39). Despite the presence of these genera, it
537
must be stressed that the metagenomic data presented here did not confirm the presence
538
of specific pathogenic strains or species and also does not provide information on the
539
viability or infectivity of the taxa detected, making it difficult to quantitatively assess
540
any public health significance. Even so, the ability to identify multiple genera in a large
541
number of samples rapidly and inexpensively demonstrates the usefulness of a
542
metagenomic monitoring approach for screening DWDSs for potential pathogens and
543
other organisms detrimental to water quality. Further to this, increased phylogenetic
544
resolution may be obtainable by targeting loci with greater specificity to target groups,
545
and the inclusion of eukaryotic analyses might reveal important community
546
relationships, such as the symbiotic relationship between Acanthamoeba and
547
Burkholderia discussed above.
548
In future, metagenomics should be used alongside current assessment methods to
549
generate databases that contain information about bacterial taxa typically found in
550
drinking water systems. Such databases would eventually be able provide information
551
about which taxa indicate a healthy, high quality DWDS, and may also help diagnose
552
problems within systems, enabling an appropriate solution to be achieved more rapidly.
553
However, there are limitations associated with metagenomic approaches for assessing
22
554
DWDS health and quality. For example, metagenomics alone does not provide
555
information on the amount of total or viable cells in the system; therefore, intact
556
organisms that are no longer alive and active may be detected (as was seen in the SA
557
post-disinfection sample). Further, choice of loci is critical and should be dependent on
558
the proposed research question. Nonetheless, this study demonstrates that metagenomics
559
used in collaboration with other techniques, such as FCM analyses and nutrient
560
measures, can provide highly useful information about the health status and efficiency
561
of particular DWDSs, and future use will foster a more detailed picture of bacterial
562
community dynamics within DWDSs.
563 564
ACKNOWLEDGEMENTS
565
This work was funded by Australian Research Council (ARC) grants
566
LP110100459 and LP0991985. We gratefully acknowledge the assistance of Renae
567
Philips, Melody Lau, and the Water Treatment and Distribution Research technical team
568
and the support of Ralph Henderson from Water Corporation, Western Australia
569 570
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571
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27. Schweizer HP. 2012. Mechanisms of antibiotic resistance in Burkholderia
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Variability of Burkholderia pseudomallei strain sensitivities to chlorine
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30. Mogoa E, Bodet C, Morel F, Rodier M, Legube B, Hechard Y. 2011. Cellular
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31. Howard K, Inglis, TJJ. 2003. The effect of free chlorine on Burkholderia Pseudomallei in potable water. Water Res. 37: 4425 – 4432.
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686 687
FIGURE LEGENDS AND TABLES
688 689
Table 1. Sample names and descriptions of water quality for each system: pH,
690
turbidity (Nephelometric Turbidity Unit), DOC (dissolved organic carbon), NH2Cl
691
(Monochloramine), AOB (ammonia oxidizing bacteria), AOA (ammonia oxidizing
692
prokaryotes) and % viable cells (described in materials and methods).
693 694
Figure 1. A diagram of the bacterial diversity within the WA DWDS, including pie
695
charts that depict the ten most prolific bacterial taxa. WTP indicates water treatment
696
plant (monochloramine dosing only), and CT indicates customer tap. Red squares
697
indicate sample collection locations. WA reserviour is fed with a mixture of ground
698
water and desalinated water and has no physical filtration system. The WA WTP
699
utilizes monochloramine.
700 701
Figure 2: A diagram of the bacterial diversity within the SA DWDS, including pie
702
charts that depict the eleven most prolific bacterial taxa. WTP indicates the water
703
treatment plant (five stage treatment plant), and CT indicates customer tap. Red
28
704
squares indicate sample collection points. The SA system utilizes river water as the
705
source, and is treated by a four stage filtration process, followed by Ultra-Violet
706
(UV) and monochloramine disinfection.
707 708
Figure 3. Heatmaps depict the bacterial taxa that contributed to >0.01% of the total
709
diversity in at least one sample and cluster dendogram estimates sample similarity
710
based on weighted Unifrac comparisons of bacterial communities for those samples
711
for the (a) WA DWDS, and (b) SA DWDS.
712 713
Figure. 4. Nutrient concentrations for both (a) WA DWDS and (b) SA DWDS. As
714
water quality indices were measured by different water corporations the protocols
715
used differ slightly, with free ammonia measured for the WA DWDS and ammonia
716
as nitrogen measured for the SA DWDS.
29
Table 1. Sample names and descriptions of water quality for each system: pH, turbidity (Nephelometric Turbidity Unit), DOC (dissolved organic carbon), and NH2Cl (Monochloramine). DOC (mg/L)
NH2Cl (mg/L)
Nitrite as N (mg/L)
Nitrate as N (mg/L)
AOB
AOA
% Viable cells
Total cells / mL
Sample name
Sample Description
pH
Turbidity (NTU)
WA system Source Water Post-disinfection WA 1 Inlet WA 1 Outlet WA 2 tank WA 3 Inlet WA 3 Outlet WA 4 CT
Raw untreated inlet Post-treatment Storage tank inlet Storage tank outlet Storage tank Storage tank inlet Storage tank outlet Customer tap
7.6 8.2 8.3 8.2 8.2 8.4 8.4 8.3
0.49 0.83 0.85 0.74 1.01 0.35 0.33 0.24
1.70 2.10 2.20 2.20 2.50 2.20 2.30 2.10
< 0.1 3.53 3.59 3.34 3.11 2.84 2.52 2.46
1
Using amplicon sequencing to characterize and monitor bacterial diversity in
2
drinking water distribution systems.
3 4
Jennifer L. A. Shawa*, Paul Monisb#, Laura Weyricha, Emma Sawadeb, Mary Drikasb,
5
Alan Coopera.
6 7
Australian Centre for Ancient DNA, University of Adelaide, Adelaide, South
8
Australia, Australiaa. Australian Water Quality Centre, South Australia Water
9
Corporation, Adelaide, South Australia, Australiab.
10 11
Running Head: bacterial diversity in drinking water systems
12 13
#Address correspondence to Paul Monis (corresponding author):
14
[email protected]
15 16
*Present address: Jennifer L A Shaw, CSIRO, Land and Water Flagship, Waite
17
Campus, Urrbrae, Adelaide, South Australia, Australia, 5064.
18 19 20 21 22 23 24
1
25
Using amplicon sequencing to characterize and monitor bacterial
26
diversity in drinking water distribution systems.
27 28
ABSTRACT
29
Drinking water assessments use a variety of microbial, physical and chemical
30
indicators to evaluate water treatment efficiency and product water quality. However,
31
these indicators do not allow the complex biological communities, which can adversely
32
impact the performance of drinking water distribution systems (DWDSs), to be
33
characterized. Entire bacterial communities can be studied quickly and inexpensively
34
using targeted metagenomic amplicon sequencing. Here, amplicon sequencing of the
35
16S rRNA gene region was performed alongside traditional water quality measures to
36
assess the health, quality, and efficiency of two distinct, full-scale DWDSs: (a) a linear
37
DWDS supplied with unfiltered water subjected to basic disinfection before distribution
38
and (b) a complex, branching DWDS treated by a four-stage water treatment plant
39
(WTP) prior to disinfection and distribution. In both DWDSs bacterial communities
40
differed significantly after disinfection, demonstrating the effectiveness of both
41
treatment regimes. However, bacterial repopulation occurred further along the DWDSs,
42
and some end-user samples were more similar to the source water than the post-
43
disinfection water. Three sample locations appeared to be nitrified, displaying elevated
44
nitrate levels and decreased ammonia levels, and nitrifying bacterial species, such as
45
Nitrospira, were detected. Burkholderiales were abundant in samples containing high
46
amounts of monochloramine, indicating resistance to disinfection. Genera known to
47
contain pathogenic and fecal-associated species were also identified in several locations.
48
From this study, we conclude that metagenomic amplicon sequencing is an informative
2
49
method to support current compliance-based methods, and can be used to reveal
50
bacterial community interactions with the chemical and physical properties of DWDSs.
51 52
Keywords: drinking water distribution system | amplicon sequencing | NGS | bacterial |
53
16S
54 55
INTRODUCTION
56
Transmission of pathogens via contaminated water is a significant cause of illness
57
worldwide. It has been estimated that one third of gastrointestinal illnesses are caused
58
by contaminated drinking water (1), and 4% of all deaths worldwide are due to polluted
59
drinking water and poor sanitation (2). In developed nations water quality assessments
60
and treatment facilities have been introduced to reduce microbial contamination,
61
resulting in a significant reduction in drinking water-related illnesses and deaths. Water
62
treatment commonly involves reduction of organics and other contaminants via
63
coagulation and sedimentation, separation of any remaining solids via filtration, and
64
finally disinfection via chemical oxidants or ultra-violet radiation. The addition of
65
chemical oxidants such as chlorine and monochloramine is the most common method of
66
drinking water disinfection (3). The level of treatment required varies from system to
67
system, with some drinking water distribution systems (DWDSs) receiving only one or
68
two levels of treatment, whilst others require multiple treatments to create water suitable
69
for end-use.
70
Currently, most drinking water quality assessments do not directly taxonomically
71
identify the many multifarious bacterial taxa present in drinking water systems. Instead,
72
a variety of biological, physical and chemical indicators are used to assess the efficiency
73
of water treatment processes and the quality of the product water. These indicators
74
quantify bacterial cells using flow cytometry or heterotrophic plate counts, and measure
3
75
concentrations of nutrients, such as ammonia, organic carbon, nitrates, and nitrites.
76
Quantitative PCR (qPCR) can be used to directly quantify specific bacterial taxa (4),
77
such as fecal indicators or known pathogens, but this does not allow measurements of
78
whole community diversity. Conversely, denaturing gradient gel electrophoresis
79
(DGGE) can be used to estimate the biodiversity of bacterial communities (5), but
80
individual taxa cannot be identified without additional expensive DNA sequencing.
81
Recently, metagenomic amplicon sequencing has emerged as a promising
82
technique for characterizing complex biological communities, as multiple taxa within
83
communities deriving from multiple samples can be sequenced in a relatively rapid and
84
inexpensive manner. Metagenomics has been successfully used to investigate bacterial
85
communities present in end-use tap water, experimental drinking water systems, and
86
bacterial biofilms within pipes (6 – 10). However, very few studies have explored
87
bacterial communities through full-scale DWDSs or investigated community variations
88
between different systems. In the present study, we characterize changes in bacterial
89
community structure at different locations along two full-scale DWDSs, and also
90
determine the viability of metagenomic sequencing as a monitoring tool for assessing
91
system efficiency and water quality. We also measured various water quality parameters
92
at the same sample locations to validate the metagenomic findings. Using these
93
approaches, bacterial diversity, water quality, and treatment efficiency were assessed at
94
multiple sites along two distinct DWDSs: (a) a linear DWDS in Western Australia
95
(WA) that undergoes monochloramine disinfection before distribution and (b) a
96
complex, branching DWDS in South Australia (SA) that is treated by a four-stage water
97
treatment plant (WTP) prior to monochloramine disinfection and distribution. From this
98
metagenomic dataset, we aim to guide future management and water-quality monitoring
4
99 100
strategies, and potentially reduce associated human-health risks by extending current knowledge of bacterial communities in DWDSs.
101 102
METHODS
103
Site descriptions
104 105
WA DWDS
106
The WA DWDS was chosen for this study because it is a simple, linear system
107
with minimal catchment run-off present in the source water. A large proportion of the
108
catchment area is state owned and managed forest, and the remaining land is primarily
109
low intensity agricultural land, such as grazing pastures. The water is a blend of ground
110
water, desalinated seawater and is supplemented from the metropolitan system. The
111
source water undergoes disinfection with monochloramine before distribution. Areas
112
local to the reservoir are supplied via several large storage tanks (Fig. 1).
113 114
SA DWDS
115
The SA DWDS is a larger, more complex system with multiple distinct
116
distribution branches (Fig. 2). The source water is supplied from river water with the
117
catchment area passing through state owned forests and agricultural areas that produce
118
wool, cotton, wheat, cattle, pigs, dairy, rice, wine, fruit and vegetables. The source
119
water is treated within a four-stage water treatment plant (WTP), consisting of
120
coagulation, flocculation, sedimentation and filtration, UV disinfection, and
121
monochloramine disinfection before being distributed.
122 123
Sample collection
124
For each system, 600 ml water samples were collected from the source water,
125
within the treatment plant (WTP), and also at each sample point indicated in Figures 1
5
126
and 2. Sample locations covered the expanse of the distribution systems from the source
127
water through to various township storage tanks and customer taps. For three of the end-
128
user locations, samples were collected from within the township storage tank (tank) and
129
also from a customer tap (CT) after the water had left the tank (tank outlet tap or a
130
nearby CT) to assess whether bacterial diversity was affected by tank storage. In the SA
131
system, additional WTP and source water samples were collected two weeks later, to
132
determine if bacterial communities are subject to short-term temporal fluctuations.
133
Samples were collected in sterile bottles, immediately placed on ice, and transported to
134
dedicated laboratory facilities, where they were stored at 4°C and processed within 24
135
hours of collection.
136 137
Water quality analyses
138
Traditional, compliance-based water quality analyses, such as nutrient and
139
chemical analyses, flow cytometry, and quantitative PCR amplification of nitrification-
140
associated genes, were conducted for each of the samples. Classical chemical indicators
141
for nitrification, such as free ammonia, nitrate, nitrite, and monochloramine levels were
142
measured using standard protocols (11). Flow cytometry (FCM), in combination with
143
the the BacLight bacterial viability kit (Molecular Probes, USA), was utilized to
144
enumerate the bacterial cells present in the water, as previously described (12). For the
145
FCM analyses, duplicate 500 μL aliquots were sub-sampled from each of the 600 ml
146
samples, with one aliquot stained with SYTO-9 to stain both intact (live) and membrane
147
damaged (dead) cells, while the other aliquot was stained with both SYTO9 and
148
propidium iodide (which only stains membrane damaged cells). . FCM enumeration
149
using a FACS Calibur flow cytometer (Becton Dickinson, San Jose, CA)was performed
150
separately to count the total cells (stained with SYTO9 alone) and live cells (the
151
population of cells stained with SYTO9 but not propidium iodide). In order to calculate 6
152
cell densities, sample tubes were weighed pre and post analysis to allow calculation of
153
the volume analyzed. The resulting FCM data were analyzed using CellQuest software
154
(Becton Dickinson, USA). The % viable cells (in Table 1) was calculated using: live
155
cell count per mL / total cell count per mL x 100.
156
Basic qPCR assays were used for the semi-quantification of the number of
157
ammonia oxidizing bacteria (AOB) and archaea (AOA) in each sample using the DNA
158
extractions (Section 2.4.1). The assays used primers for AOA and AOB as previously
159
described (13), except that the PCR reagents were replaced with the Fast Start Essential
160
Probe Master mix (Roche) with the addition of 3.3 µM SYTO9 (final concentration,
161
Molecular Probes). The amplifications were conducted on a RotorGene 6000 HRM
162
(Qiagen) using the published cycling conditions, followed by DNA melting curve
163
analysis on the HRM channel from 70°C to 95°C in 0.2°C increments. The first
164
derivative melting profiles were visualized with the Digital Filter set to “none”. The
165
presence of non-specific amplicon prevented conventional quantification using the
166
amplification curves. Instead, the presence of AOA/AOB was semi-quantified using the
167
height of the DNA melting peaks relative to the peak in the 104 copies/reaction DNA
168
standard. The standard was nominally scored as +++, with samples with higher melting
169
peaks scored as ++++, weak peaks as + and peaks with a height in between weak and
170
the control as +++.
171 172
Metagenomic sequencing
173
DNA extraction
174
Within 24 hours of collection, three 50 ml sub-samples from each of the 600 ml
175
water samples were centrifuged at 4000 rpm for 30 minutes to pellet the intact bacterial
176
cells, and the supernatant was discarded. The pellet from each of these sub-samples was
177
resuspended in 180 μl of sterile nuclease free water (Sigma-Aldrich, Castle Hill, 7
178
Australia) and transferred to the MoBio Ultraclean Soil DNA Kit (MoBio Laboratories
179
Inc., Solana Beach, CA). DNA was extracted, alongside extraction blanks consisting of
180
180 μl of sterile nuclease water, following manufacturer’s recommendations. Once the
181
extractions were complete, DNA yield was quantified using a Nanodrop
182
spectrophotometer (Nanodrop Technologies, Wilmington, DE, USA), and the replicate
183
sub-samples were combined to reduce sequencing costs, resulting in ten samples for the
184
SA system and eight for the WA system (Table 1).
185 186
PCR amplification and NGS sequencing
187
Amplicon libraries were prepared in dedicated sterile PCR hoods. Prior to adding
188
GoTaq, the PCR mastermix was subjected to ultra-violet radiation for fifteen minutes to
189
reduce contamination from laboratory bacteria (14). Sample DNA was added in a
190
second sterile hood in a separate room. A two-step nested PCR approach was adopted,
191
first amplifying a longer DNA fragment prior to amplifying a shorter fragment
192
appropriate for amplicon-based NGS sequencing. This was done to minimize the
193
contribution of highly degraded, uninformative and contaminant DNA (15). A real-time
194
quantitative polymerase chain reaction (RT PCR) platform was used for the PCR
195
amplification step in order to enable comparison of DNA melt curves and determine if
196
samples were contaminated by DNA present in reagents and plastic ware. In the first RT
197
PCR, universal primers 27F (5’-AGAGTTTGATCCTGGCTCAG-3’) and 1492R (5’-
198
TACCTTGTTACGACTT -3) were used to amplify a 1,500 base pair (bp) fragment
199
within the 16S rRNA gene (12). Reactions of 25µL were prepared in quadruplicate and
200
pooled post-PCR to reduce PCR bias (16). Each reaction contained 5µL of DNA
201
extraction template, 5% dimethylsulfoxide (DMSO), 3.3μM SYTO-9 dye, 0.4U µL-1 Go
202
Taq (Promega), 200 µM dNTPs, 0.5µM of each primer, 1x PCR buffer, 2.5mM of
203
MgCl2 and 4.4 μL of nuclease free water, and was amplified using the following 8
204
parameters: 95°C for 3 minutes, 35 cycles at 94°C for 30 seconds, 50°C for 60 seconds
205
and 72°C for 120 seconds. In the second RT PCR, a smaller 176 bp region from within
206
the 1,500 bp PCR product was amplified using barcoded (‘x’ refers to barcode) Ion
207
torrent
208
CCATCTCATCCCTGCGTGTCTCCGACTCAGxxxxxxxCCTACGGGAGGCAGCA
209
G-3’)
210
CCTCTCTATGGGCAGTCGGTGATATTACCGCGGCTGCTGG-3’). Reactions were
211
prepared as above. The second round of amplification was performed with the following
212
parameters: 95°C for 3 minutes, 35 cycles at 95°C for 20 seconds, 65°C for 20 seconds
213
and 72°C for 30 seconds. PCR products were purified (Ampure, Agencourt Bioscience),
214
quantified, and pooled to equimolar concentrations, before sequencing on an Ion
215
Torrent Personal Genome Machine using the Ion PGM 200 sequencing kit (Life
216
technologies, Carlsbad, CA, USA).
217 218
Data analysis
fusion
primers:
and
341F
518R
(5’-
(5’-
219
Reads that contained zero mismatches within the barcode and primer sequence
220
were demultiplexed into their respective samples, and barcodes and primer sequences
221
were trimmed off using CutAdapt v1.1 (17). Reads were then filtered for quality (Phred
222
threshold of 20 for 90% of sequence bp) and length (>100bp) using FastX toolkit
223
(FASTX-toolkit v0.0.13; http://hannonlab.cshl.edu/fastx_toolkit). The resulting files
224
were converted to a QIIME software compatible .fna file (Script available from:
225
(http://www.u.arizona.edu/~gwatts/azcc/QIIMEfastaFormatter.pl) and imported into
226
QIIME (18).
227
Within QIIME, sequences were clustered to 97% similarity to form operational
228
taxonomic units (OTUs) and aligned using Pynast. Chimeric reads were detected and
229
removed using Chimera slayer (19). The standard default threshold (0.8) was applied to 9
230
assign
231
http://greengenes.lbl.gov). Contaminant OTUs identified in the extraction blanks were
232
removed from samples. All samples were rarefied at 8904 sequences (i.e. the lowest
233
number of reads present within a single sample) to ensure an even read coverage for
234
each sample and minimize skewed diversity estimates. Diversity estimates were
235
obtained for each sample by calculating Chao species richness and Shannon-Weiner
236
diversity indices. The number of operational taxonomic units (OTUs) per sample were
237
also measured. Sequences were also re-clustered at 98% similarity and taxonomy re-
238
assigned to improve phylogenetic resolution for some closely related species. Further,
239
sequences from specific genera of interest were extracted from the sequence files and
240
then blasted against the NCBI database (date accessed: 17/10/2014) to allow further
241
phylogenetic discrimination between closely related taxa. Exploratory and statistical
242
analysis of the data was carried out using QIIME, SPSS (SPSS Inc. Released 2009.
243
PASW Statistics, Version 18.0. Chicago: SPSS Inc.), and Explicet (20). Jack-knifed
244
UPGMA trees based on weighted hierarchal clustering (replicate sequences = 6670; e =
245
10) were constructed to visualize sample similarity based on phylogenetic distance
246
between taxa (UniFrac software; 21) and were viewed using FigTree V1.4 software
247
(tree.bio.ed.ac.uk/software). Heat maps were constructed within Explicet to visualize
248
dominant taxa (> 0.1%) for each sample and determine dissimilarity between samples.
249
Raw
250
(http://www.ncbi.nlm.nih.gov/sra) under the BioProject ID: PRJNA287069.
taxonomy
data
files
using
were
the
deposited
Greengenes
in
the
database
NCBI
(version
sequence
read
gg_12_8;
archive
251 252
RESULTS
253
Treatment effectiveness
10
254
To assess the efficiency of DWDS treatments, bacterial communities pre- and
255
post-treatment were compared. Metagenomic amplicon sequencing revealed that the
256
bacterial communities were significantly altered after treatment in both systems (P =
85%) two
390
of the end-use locations within the SA DWDS: SA-3 tank and SA-5 CT (Figure 3b).
391
Closer inspection revealed that the Burkholderiales taxa in the SA-3 tank sample
392
belonged to the Comamonadaceae family, and in SA-5 CT predominantly
393
Hydrogenophaga, a yellow-pigmented, hydrogen-oxidizing bacterium. These two end-
394
use samples had unusually low diversity and OTU counts compared to all other samples
395
(Mann-Whitney U; P = >0.001) and therefore could indicate locations where
396
monochloramine resistant taxa are outcompeting other bacterial taxa for resources.
397
These taxa could be a target for future microbiological studies as the tanks were
398
relatively unusual and may provide useful insights into bacterial community dynamics
399
within DWDSs that utilize monochloramine. The order Sphingomondales were also
400
identified in relatively high proportions in the SA post-disinfection sample, but not in
401
the WA post-disinfection sample. Nevertheless, their relatively high abundance in
402
samples with high amounts of monochloramine warrants further investigation in future
403
studies.
(3%),
Delftia
(10%),
Pelomonas
(2%),
and
an
unknown
16
404 405
Pathogen detection
406
The OTU tables for each system were screened for common waterborne pathogens to
407
determine if metagenomics is a feasible tool for pathogen identification in DWDSs.
408
Several genera known to contain pathogenic species were identified in the systems;
409
however, in most cases the 16S gene fragment did not allow species- or strain-level
410
resolution of the taxa. Therefore, we utilized an approach similar to current compliance
411
methods, where potential coliform microorganisms are identified as indicators of
412
contamination, even if specific species or strains cannot be resolved (23). The genus
413
containing the coliform bacteria Escherichia and Shigella was identified in relatively
414
large proportions in the WA post-disinfection sample (8%) and the WA-1 inlet sample
415
(5%), but was negligible in the SA post-disinfection sample. This suggests the filtration
416
steps used in the SA WTP are crucial for complete removal of fecal coliform cells.
417
However, as the FCM analyses revealed that the WA disinfection step reduces the
418
percentage of viable bacterial cells to just 3.4%, it likely that the Escherichia and
419
Shigella taxa detected by metagenomic sequencing are intact but non-viable cells (i.e.
420
they are effectively neutralized but the cells have not yet degraded). In an attempt to
421
discriminate between Escherichia and Shigella all associated sequences were extracted
422
from the dataset and blasted separately in NCBI. Results indicated that the closest
423
matches to the taxa found in the WA system were Escherichia coli, Escherichia
424
fergusonii, Shigella dysenteria, and Shigella sonnei, some of which are human
425
pathogens (SM5). Other taxa known to contain pathogenic species were also identified
426
throughout the systems. For example, Burkholderia, a genus known to contain
427
pathogenic species, were identified in SA-3 tank, while other samples contained the
428
genera Campylobacter (SA-1 CT), Mycobacterium (SA source water, SA post-
17
429
disinfection, SA-1 CT, SA-3 tank SA-5 CT), and Legionella (SA source water). In the
430
WA DWDS, Leptospira (WA-4 CT), Mycobacterium (WA-3, WA-4 CT), Clostridium
431
(WA-3 Inlet), and Legionella (WA source water, WA post-disinfection, WA-4 CT,
432
WA-3 inlet and outlet) were identified.
433 434
DISCUSSION
435
Bacterial communities in drinking water systems are complex, and although current
436
monitoring methods are useful to quantify total and live bacterial cell counts and detect
437
specific taxa, they do not allow the intricate and complex nature of these diverse
438
communities to be effectively evaluated. In this study, the addition of metagenomic
439
analyses to currently used compliance-based methods revealed complex interactions
440
between biological communities and various DWDS parameters, such as water
441
treatment, monochloramine tolerance, nitrification, and tank storage effects. To
442
summarize, the water treatment regimes within both the WA and SA systems were
443
initially highly effective at reducing bacterial community diversity and cell counts.
444
However, some sample locations at a further distance from the WTPs displayed
445
bacterial regrowth and increases in diversity. The data also revealed that nitrification
446
was occurring at multiple locations at varying degrees of severity, and that
447
monochloramine
448
Sphingomondales, were present in some locations. The metagenomic analyses
449
successfully identified genera associated with fecal coliforms and pathogens; however,
450
the universality and small size of the gene fragment used in this study prevented
451
specific pathogens from being identified.
tolerant
bacterial
groups,
including
Burkholderiales
and
452
In this study, nitrification was found to be occurring in both systems further along
453
the DWDS. Nitrification refers to the biological oxidation of ammonia to nitrite,
18
454
commonly conducted by the bacterial genus Nitrosomonas (24), followed by the
455
oxidation of nitrite to nitrate, usually by Nitrobacter or Nitrospira (25). Free ammonia
456
is often present in DWDSs as a consequence of the process used to generate chloramine.
457
Nitrospira was detected in WA-4 and SA-2 CT, and water quality analyses revealed
458
little to no ammonia or monochloramine at these locations and elevated levels of nitrate.
459
Nitrospira has been coined the most dominant and ubiquitous group of nitrifying
460
bacteria (26), and was only one of two nitrifying bacteria detected by the metagenomic
461
analysis. However, it is possible that there were other nitrifying bacteria present that
462
have not been classified in genetic databases. The qPCR results indicated that there
463
were AOA and AOB associated genes present throughout the entire systems. However,
464
the metagenomic data only detected nitrifiers when various stages of nitrification were
465
also confirmed by the water quality data. The discrepancy between these results could
466
be due to a number of different reasons; for example, ammonia oxidizers may have been
467
at low abundance and their DNA signal may have been obscured by more highly
468
abundant taxa, preventing detection via metagenomic sequencing. In contrast, it could
469
be that the highly specific nature of the qPCR is detecting AOA and AOB even when
470
the abundance of those taxa is below effectual levels, which would explain their
471
consistent presence throughout the systems. Alternatively, this result may indicate that
472
ammonia-oxidizing genes are present in other bacterial species that may be
473
taxonomically unknown at present. In addition to detecting nitrifying bacteria, the
474
metagenomic data revealed that the end-user locations WA-4 and SA-2 CT had OTU
475
counts, biodiversity measurements and OTU proportions more similar to that of the
476
source water than the post-disinfection samples, suggesting that bacterial population
477
regrowth during nitrification was occurring at these sites. The detection of nitrifying
478
species in DWDS locations in the early stages of nitrification could be used as a
19
479
preventative measure, identifying sections of a DWDS that may require remedial action,
480
such as flushing or additional disinfection, to avoid potential future, more severe
481
nitrification episodes.
482
Taxa identified in samples with high amounts of disinfectant (monochloramine)
483
are likely to be species that are resistant to or tolerant of disinfectants. For example,
484
Burkholderiales, which is known to contain strains resistant to antibiotics (27), were
485
identified in high percentages in the post-disinfection samples in both systems. The
486
sensitivity of Burkholderia to chlorine (28) and monochloramine (29) is known to vary
487
considerably, with some isolates more tolerant than others. Previous research has
488
revealed that co-cultures of the pathogenic Burkholderia species B. pseudomallei with
489
the ubiquitous freshwater amoeba Acanthamoeba astronyxis greatly enhances the
490
survival of B. pseudomallei in the presence of monochloramine, with 100 times more
491
monochloramine required to maintain disinfectant efficacy when the amoeba was
492
present (29). This is due to the ability of Acanthamoeba cysts to resist common
493
disinfectants, such as monochloramine and chlorine, and its ability to bear
494
phagocytosis-resistant bacteria within, thus protecting the bacteria from water
495
treatments (30). While these particular species may not have been present in these
496
systems, it may be the case that similar inter-kingdom interactions can occur for other
497
Burkholderiales species. As eukaryotes were not sequenced in this study, it is not
498
known what amoebae were present in the SA and WA systems. Further, other studies
499
found that viable B. pseudomallei cells can be recovered from water containing up to
500
1000ppm free chlorine and after the disinfectant had previously successfully reduced
501
cells to compliance levels, suggesting that species affected by initial disinfection could
502
recover to harmful levels later in the DWDS (31). Burkholderiales have also previously
503
been identified in drinking water pipe biofilms (32), which may potentially serve as a
20
504
reservoir to seed cells back in to the water post-disinfection. This provides a further
505
possible explanation for the relatively high abundance of Burkholderiales in the post-
506
disinfection samples, as biofilm microorganisms are known to be more resistant to
507
drinking water disinfection than free-living microorganisms (33). Again, although these
508
previous studies have focused on specific pathogenic strains of Burkholderiales, it is
509
possible that other species within this order are able to resist disinfection in the same
510
way. In addition to Burkholderiales, Sphingomonadales are also known to contain
511
chlorine resistant species (34), are found in biofilms (32), and were detected in
512
relatively high proportions in post-disinfection samples. Therefore, it is plausible that
513
the Sphingomonas identified in this study could also be resistant to disinfectants,
514
explaining its high proportions in samples with elevated amounts of monochloromine.
515
Bacterial genera known to contain notable waterborne pathogens were identified at
516
some locations along the DWDSs. Clostridium, Campylobacter, Corynebacterium,
517
Escherichia coli, Mycobacterium, Legionella, Burkholderia and Leptospira were all
518
identified in various samples throughout the systems, some close to the consumer end-
519
use locations. As mentioned previously, Burkholderia contains pathogenic species that
520
have been shown to resist disinfectants and antibiotics. Burkholderia are of particular
521
public health significance in warmer climates (35), with outbreaks of the water-borne
522
disease Meliodosis sporadically occurring in South America, Asia and Australia (36).
523
Campylobacter was present in relatively high abundance in the SA source water.
524
Campylobacter is a known fecal-associated bacteria that usually requires a host to
525
survive and grow (37), the presence of this taxa in the source water combined with its
526
failure to reappear further along the DWDS post-disinfection supports this concept.
527
However, whilst Campylobacter can only survive a few days without a host in water
528
above 15°C, it can survive for many weeks in a viable but non-culturable state in water
21
529
at 4°C (37); therefore it’s presence may be of importance during colder winter months.
530
Cyanobacteria were found in high abundances in the WA post-disinfection sample,
531
likely because chloramine disinfection alone is not capable of destroying filamentous or
532
colonial cells or due to the presence of akinetes, which are environmentally-resistant,
533
dormant cells with thick walls (38). Some cyanobacterial species are of public health
534
significance due to the production of toxins, while others can cause the deterioration of
535
the aesthetic quality of the water due to the production of secondary metabolites that
536
produce an earthy, musty taste and odor (39). Despite the presence of these genera, it
537
must be stressed that the metagenomic data presented here did not confirm the presence
538
of specific pathogenic strains or species and also does not provide information on the
539
viability or infectivity of the taxa detected, making it difficult to quantitatively assess
540
any public health significance. Even so, the ability to identify multiple genera in a large
541
number of samples rapidly and inexpensively demonstrates the usefulness of a
542
metagenomic monitoring approach for screening DWDSs for potential pathogens and
543
other organisms detrimental to water quality. Further to this, increased phylogenetic
544
resolution may be obtainable by targeting loci with greater specificity to target groups,
545
and the inclusion of eukaryotic analyses might reveal important community
546
relationships, such as the symbiotic relationship between Acanthamoeba and
547
Burkholderia discussed above.
548
In future, metagenomics should be used alongside current assessment methods to
549
generate databases that contain information about bacterial taxa typically found in
550
drinking water systems. Such databases would eventually be able provide information
551
about which taxa indicate a healthy, high quality DWDS, and may also help diagnose
552
problems within systems, enabling an appropriate solution to be achieved more rapidly.
553
However, there are limitations associated with metagenomic approaches for assessing
22
554
DWDS health and quality. For example, metagenomics alone does not provide
555
information on the amount of total or viable cells in the system; therefore, intact
556
organisms that are no longer alive and active may be detected (as was seen in the SA
557
post-disinfection sample). Further, choice of loci is critical and should be dependent on
558
the proposed research question. Nonetheless, this study demonstrates that metagenomics
559
used in collaboration with other techniques, such as FCM analyses and nutrient
560
measures, can provide highly useful information about the health status and efficiency
561
of particular DWDSs, and future use will foster a more detailed picture of bacterial
562
community dynamics within DWDSs.
563 564
ACKNOWLEDGEMENTS
565
This work was funded by Australian Research Council (ARC) grants
566
LP110100459 and LP0991985. We gratefully acknowledge the assistance of Renae
567
Philips, Melody Lau, and the Water Treatment and Distribution Research technical team
568
and the support of Ralph Henderson from Water Corporation, Western Australia
569 570
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571
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27. Schweizer HP. 2012. Mechanisms of antibiotic resistance in Burkholderia
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pseudomallei: implications for treatmnet of melioidosis. Future Microbiol. 7:
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28. O'Connell HA, Rose LJ, Shams A, Bradley M, Arduino MJ, Rice EW. 2009.
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29. Howard K, Inglis TJJ. 2005. Disinfection of Burkholeria pseaudomallei in potable water. Water Res. 39: 1085 – 1092.
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30. Mogoa E, Bodet C, Morel F, Rodier M, Legube B, Hechard Y. 2011. Cellular
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34. Sun W, Lui W, Cui L, Zhang M, Wang B. 2013. Characterization and
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identification of a chlorine-resistant bacterium, Sphingomonas TS001, from a
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36. Josephson J. 2001. Meliodosis: An emerging tropical health problem. Environ. Sci. Technol. 35: 454 – 457.
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37. Nachamkin I, Skirrow MB. 1998. Campylobacter, Arcobacter and Helicobacter.
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Microbial Infections: Systematic Bacteriology (9th ed.). London: Arnold.
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38. Adams DG, Carr NG. 1981. The developmental biology of heterocyst and alkinete
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686 687
FIGURE LEGENDS AND TABLES
688 689
Table 1. Sample names and descriptions of water quality for each system: pH,
690
turbidity (Nephelometric Turbidity Unit), DOC (dissolved organic carbon), NH2Cl
691
(Monochloramine), AOB (ammonia oxidizing bacteria), AOA (ammonia oxidizing
692
prokaryotes) and % viable cells (described in materials and methods).
693 694
Figure 1. A diagram of the bacterial diversity within the WA DWDS, including pie
695
charts that depict the ten most prolific bacterial taxa. WTP indicates water treatment
696
plant (monochloramine dosing only), and CT indicates customer tap. Red squares
697
indicate sample collection locations. WA reserviour is fed with a mixture of ground
698
water and desalinated water and has no physical filtration system. The WA WTP
699
utilizes monochloramine.
700 701
Figure 2: A diagram of the bacterial diversity within the SA DWDS, including pie
702
charts that depict the eleven most prolific bacterial taxa. WTP indicates the water
703
treatment plant (five stage treatment plant), and CT indicates customer tap. Red
28
704
squares indicate sample collection points. The SA system utilizes river water as the
705
source, and is treated by a four stage filtration process, followed by Ultra-Violet
706
(UV) and monochloramine disinfection.
707 708
Figure 3. Heatmaps depict the bacterial taxa that contributed to >0.01% of the total
709
diversity in at least one sample and cluster dendogram estimates sample similarity
710
based on weighted Unifrac comparisons of bacterial communities for those samples
711
for the (a) WA DWDS, and (b) SA DWDS.
712 713
Figure. 4. Nutrient concentrations for both (a) WA DWDS and (b) SA DWDS. As
714
water quality indices were measured by different water corporations the protocols
715
used differ slightly, with free ammonia measured for the WA DWDS and ammonia
716
as nitrogen measured for the SA DWDS.
29
Table 1. Sample names and descriptions of water quality for each system: pH, turbidity (Nephelometric Turbidity Unit), DOC (dissolved organic carbon), and NH2Cl (Monochloramine). DOC (mg/L)
NH2Cl (mg/L)
Nitrite as N (mg/L)
Nitrate as N (mg/L)
AOB
AOA
% Viable cells
Total cells / mL
Sample name
Sample Description
pH
Turbidity (NTU)
WA system Source Water Post-disinfection WA 1 Inlet WA 1 Outlet WA 2 tank WA 3 Inlet WA 3 Outlet WA 4 CT
Raw untreated inlet Post-treatment Storage tank inlet Storage tank outlet Storage tank Storage tank inlet Storage tank outlet Customer tap
7.6 8.2 8.3 8.2 8.2 8.4 8.4 8.3
0.49 0.83 0.85 0.74 1.01 0.35 0.33 0.24
1.70 2.10 2.20 2.20 2.50 2.20 2.30 2.10
< 0.1 3.53 3.59 3.34 3.11 2.84 2.52 2.46
