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Multiple Detection of Occurrence of Bacterial Pathogens in Two Rivers in the Kinki District of Japan with a DNA Microarray


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Comprehensive understanding of the occurrence of pathogens in the aquatic environment is important for assessing possible biological health risks associated with water usage. The occurrence of bacterial pathogens in the Yodo and Kita Rivers, Kinki district, Japan, was investigated by using a DNA microarray targeting 1012 species/groups of bacterial pathogens infectious to human, animals, plants, fish, and shellfish. Eighty-seven pathogens were detected in 24 river water samples collected from two rivers, with more than half present in both rivers. The pathogen profile in the river waters varied primarily seasonally. Effluent from wastewater treatment plants, a well-known possible pathogen source, did not significantly affect the occurrence of bacterial pathogens in the monitored basins. Moreover, some of the detected pathogens, particularly non-fecal ones, did not positively correlate with the total coliform count, a conventional hygienic indicator. Therefore, the conventional hygienic indicator for fecal contamination is inadequate for comprehensive determination of the health risks associated with contamination of river water by bacterial pathogens.
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Multiple Detection of Occurrence of Bacterial
Pathogens in Two Rivers in the Kinki District
of Japan with a DNA Microarray
Division of Sustainable Energy and Environmental Engineering, Osaka University
/21 Yamadaoka, Suita, Osaka 5650871, Japan
Vol.45, No.1, pp.3143 (2009) Page 38 Line 616 should appear as shown below.
The correlation between the RSIs of the 30 probes detected in two or more samples in
spring, summer, or winter (shown in boldface in Table 3) and the relative total coliform
count was assessed. The RSI of 18 probes increased with the relative total coliform count
(e.g., Fig. 4 AC). In contrast, the remaining 12 probes corresponding to 11 pathogenic
bacteria did not show a positive correlation with the relative total coliform count (e.g., Fig.
4 DF).
Japanese Journal of Water Treatment Biology Vol.45 No.1 3143 2009
Multiple Detection of Occurrence of Bacterial
Pathogens in Two Rivers in the Kinki District
of Japan with a DNA Microarray
Division of Sustainable Energy and Environmental Engineering, Osaka University
/21 Yamadaoka, Suita 5650871, Japan
 Comprehensiveunderstandingoftheoccurrenceofpathogensintheaquaticenvironment
occurrenceofbacterialpathogens intheYodoandKitaRivers,Kinkidistrict,Japan,was
investigated by using a DNA microarray targeting 1012 species/groups of bacterial
were detected in 24 river water samples collected fromtwo rivers, with more than half
Efuent from wastewater treatment plants, a well-known possible pathogen source, did
not signicantly affect the occurrence of bacterial pathogens in the monitored basins.
Moreover,some of the detectedpathogens,particularlynon-fecal ones, did not positively
correlatewith the total coliform count,a conventionalhygienic indicator.Therefore,the
conventionalhygienic indicatorfor fecal contaminationis inadequatefor comprehensive
 Keywords: bacterial pathogen; DNA microarray; hygienic indicator; river water;
total coliform count
*Corresponding author
Surface freshwater is a primary source of
drinking water for the majority of the world’s
human population. In FY2001, of the
approximately 17 billion m3 of drinking water
consumed in Japan, 72.3% originated from
surface water sources such as reservoirs,
rivers, and lakes (
english/water_en/frame-e02.html). The appro-
priate assessment and management of surface
water quality are of great importance to
protect against potential health risks
associated with unintentional ingestion of
microbiologically contaminated surface water.
Nevertheless, during the last decade, 21
health hazard cases in Japan were caused
by pathogenic microorganisms, including
Escherichia coli, Campylobacter jejuni,
Shigella sonne, Plesiomonas shigelloides,
Yersinia enterocolitica, Leptospira spp.,
Clostridium botulinum, norovirus, and rota-
virus, in drinking water supplied from public
or private water supply systems1). Plant and
sh and shellsh pathogens have also caused
serious damage to agriculture and sheries
and have disrupted natural ecosystems. To
reduce the incidence of disease and problems
caused by pathogenic micro organisms, more
adequate prediction of pathogenic contami-
nation of water sources is required, in
addition to improvement of water resource
management and disinfection systems.
To date, several systematic studies have
32 Japanese J. Wat. Treat. Biol. Vol.45 No.1
been undertaken to determine the occurrence
of bacterial pathogens in environmental
waters26). These investigations targeted well-
known pathogens such as E. coli, Bacteroides,
Campylobacter, and Salmonella. Nevertheless,
emerging and reemerging infectious diseases
have been increasing yearly worldwide, and
in Japan the causative microorganisms of
diseases have included pathogens not targeted
by previous studies1). It is obvious that the
comprehensive detection and monitoring of
multiple pathogens, including those not
targeted in previous surveys, is very
important for assessing the possible health
risks associated with waterborne pathogens;
i.e., it is urgent that the kinds and numbers
of pathogens present in surface waters used
as water sources be fully elucidated.
Microarray analysis has recently emerged
as a promising tool that allows the
simultaneous and specic detection of tens of
thousands of genes on small glass slides. By
application of the microarray technique, the
pathogens present in the aquatic environment
can be efciently monitored and characterized,
providing the informative data necessary for
establishing effective strategies for bacterial
pathogen control. This technique should allow
those pathogens that need special attention,
although ignored by current water safety
assessment methods, to be identied.
Moreover, the correlation of these newly
identied important pathogens with one or
more, possibly novel, hygienic indicators,
would contribute much to advance the
management of the aquatic environment.
However, no DNA microarray analysis data
on the comprehensive monitoring of a very
wide range of pathogens have been published,
and no report has completely characterized
the pathogen risk in an aquatic environment
such as a river basin. Several types of
microarrays for the detection of pathogens
have been designed7
11). However, the target
organisms in these studies were limited to
specic well-known pathogen groups (fewer
than 100 different pathogens); the simul-
taneous analysis of hundreds or thousands of
pathogens in the aquatic environment by the
microarray technique has not been reported.
In this study, we performed a DNA
microarray analysis targeting 1012 species/
groups of bacterial pathogens infectious to
humans, animals, plants, sh, and shellsh
to comprehensively understand the occurrence
and behavior of multiple bacterial pathogens
in surface waters of two rivers in the Kinki
district of Japan as a case study. We then
assessed whether pathogens detected in
multiple samples correlated with total
coliform test results, a hygienic water quality
River water samples A total of 24
subsurface water samples (30 to 50cm
depth) were collected from four stations on
the Yodo River (Y1 to Y4, upstream to
downstream) and two stations on the Kita
River (K1 and K2, upstream and downstream,
respectively) in the Kinki district of Japan
(Fig. 1) in October 2005, August 2006, and
January and May 2007. The Yodo River is
the largest watershed in the Kinki district of
Japan, with a catchment area of 8240 km2. It
is the main drinking water source of the
more than 14 million residents of Osaka City
and its 24 circumjacent cities. The Kita River
is among the Japanese rivers with the best
water quality, based on the biochemical
oxygen demand (BOD) level. The river ows
from northwestern Shiga Prefecture through
southwestern Fukui Prefecture, and its
catchment area is 842 km2. The collected
river water samples were transported on ice
to the laboratory and subjected to water
quality analysis on the same day and DNA
extraction within 12 h of collection.
DNA Microarray We purchased a
microarray for detecting bacterial pathogens
from AMR Inc. (Gifu, Japan). Oligonucleotide
probes for the 16S rRNA genes of 1012
bacterial pathogens infectious to humans,
animals, plants, sh, and shellsh are
mounted on this microarray. The target
pathogens include all biosafety level (BSL) 2
and 3 pathogens in the classication of
Japanese Society for Bacteriology12) and other
opportunistic pathogens. This microarray was
developed by Prof. Takayuki Ezaki and his
coworkers at Gifu University, Japan, for the
comprehensive, accurate, and rapid testing of
the causative pathogens of infectious diseases,
including emerging/reemerging diseases,
Multiple Detection of Bacterial Pathogens in Rivers by DNA Microarray
which are increasing in number and becoming
more globalized. Applicability of the
microarray to environmental samples had
been assured by the developers.
Microarrayanalysis DNA was extracted
from river water samples as described
elsewhere13). The conserved region (ca. 510
bp) of eubacterial 16S rDNA was PCR
amplied using the 8UA (5’AGA GTT TGA
TCM TGG CTC AG3’) and 519B (5’GTA
TTA CCG CGG CKG CTG3’) primer set
with a Mastercycler Standard (Eppendorf,
Tokyo, Japan). The 5’end of the reverse
primer was labeled with Cy3 dye to
uorescently label the PCR products.
Amplied products were puried by ethanol
Microarray hybridization was performed
in accordance with the manufacturer’s
instructions. Cy3labeled target DNA (35 μg)
was dissolved in a 50 μl hybridization buffer
(5× SSC, 0.5% sodium dodecyl sulfate),
denatured at 90 for 1 min, cooled to 55 ,
and hybridized with the prehybridized array
in a hybridization chamber (DNA Chip
Research Inc., Kanagawa, Japan) at 55 for
16 h. Following hybridization, the microarray
slides were scanned with an arrayWoRx (GE
Healthcare UK Ltd., Buckinghamshire,
England). Scanned images were then
processed with Array Vision ver. 8.0 (GE
Healthcare UK Ltd.). After subtraction of the
background intensity, the signal intensities
of the spots were normalized in relation to
the intensity of the positive Cy3 spots. Test
spots whose relative signal intensity (RSI)
exceeded 0.25 were considered positive and
used for further analysis.
Water quality measurements Water
temperature, electrical conductivity, pH, and
dissolved oxygen (DO) were recorded at the
sampling site. Concentrations of dissolved
organic carbon (DOC), total nitrogen (TN),
heterotrophic bacteria, eubacterial 16S rDNA,
and total coliforms were analyzed in the
laboratory. DOC was analyzed with a total
organic carbon analyzer (TOC5000A;
Shimadzu, Kyoto, Japan). Concentrations of
TN were measured by the standard
method14). Heterotrophic bacteria were
determined by plating using a 1/10 diluted
CGY medium15). The eubacterial 16S rDNA
number was quantied from a DNA template
prepared as described above by most-probable-
number (MPN)PCR16) using the EUBf933
and EUBr1387 primer set17). Total coliforms
were quantied by the MPN method using a
slightly modied standard coliform medium
(lactose 5 g/l, bonito extract 3 g/l, peptone 10
g/l, pH 7.0).
Statistical analysis Correlation analysis
between the RSI of each pathogen and the
total coliform count relative to the total
number of heterotrophic bacteria was
performed with Microsoft Excel 2002
(Microsoft Corporation, Redmond, WA, USA).
Principal component analysis (PCA) against
the occurrence pattern (presence/absence) of
the pathogens in the river water samples
was carried out with SPSS 15.0 for Windows
Yodo River
Lake Biwa
Wakasa Bay
Yodo River
Lake Biwa
Wakasa Bay
Fig. 1 Locations of sampling stations.
34 Japanese J. Wat. Treat. Biol. Vol.45 No.1
(SPSS Inc., Chicago, IL, USA).
Physiological and biological water quality
parameters Physiological and biological
water quality parameters in the river water
samples are listed in Table 1. Water
temperature varied according to the season.
Although electrical conductivity was low in
almost all samples, it was exceptionally high
at station K2 in spring, summer, and autumn,
indicating that a brackish water environment
had developed as a result of the backow of
marine water. Concentrations of DOC and
TN tended to increase between Y2 and Y3
in the Yodo River, possibly related to efuents
from wastewater treatment plants (WWTPs)
along the river. Heterotrophic bacteria
occurred in river water samples in quantities
on the order of 103 to 105 CFU/ml. Total
coliform counts in spring, summer, and
winter samples varied from 3.6 × 101 to 9.3
× 103 MPN/100 ml. Spring and summer
samples tended to have higher total coliform
counts than winter samples.
Pathogenproleinriverwatersamples A
total of 87 bacterial pathogen species/groups,
including 21 BSL2 pathogens, were detected
by the DNA microarray analysis of 24 river
water samples (Table 2). Forty-nine of the 87
pathogens (listed in Table 3) were present in
two or more samples, and 45 of these
pathogens were found in both rivers.
Furthermore, 27 of the 49 pathogens were
present in two distinct seasons, whereas the
other 22 were detected in only one sampling
period. The distribution of the other 38
pathogens, which each occurred in only one
sample, was as follows: 3 at Y2, 6 each at
Y1, Y3, and Y4, and 17 at K2. Although most
of the detected pathogens were human or
animal pathogens, two sh/shellsh-infectious
pathogens, Pseudoalteromonas atlantica18)
and Vibrio cholerae/mimicus19), and two plant
Table 1 Physicochemical and biological water quality parameters of the river water samples in this studya
16S rDNA
(MPN/100 ml)
Y1 21.2 8.1 7.9 0.1 1.5 1.1 2.7 × 1042.4 × 105naa
Y2 21.5 7.2 6.8 0.1 1.1 1.4 7.1 × 1031.5 × 105na
Y3 22.0 7.3 7.4 0.2 1.9 2.0 1.3 × 1042.1 × 105na
Y4 21.8 6.6 6.4 0.2 2.0 1.6 3.3 × 1042.4 × 104na
K1 13.4 6.7 9.5 0.1 0.36 2.4 5.7 × 1032.3 × 103na
K2 11.8 6.5 6.5 13.9 1.8 2.6 1.9 × 1057.0 × 103na
Y1 30.0 9.0 6.9 1.4 na na 1.1 × 1042.3 × 1044.3 × 102
Y2 28.6 7.3 4.8 0.1 na na 1.4 × 1042.3 × 1049.0 × 101
Y3 29.8 7.5 6.1 0.2 na na 4.0 × 1049.3 × 1049.3 × 102
Y4 30.2 8.5 6.3 0.1 na na 4.0 × 1049.3 × 1049.3 × 102
K1 21.6 8.2 9.3 0.1 na na 6.3 × 1032.4 × 1041.4 × 102
K2 30.0 7.8 3.3 50.1 na na 1.1 × 1051.5 × 1044.3 × 103
Y1 7.2 7.5 7.8 0.1 6.3 0.87 4.6 × 1049.3 × 1033.6 × 101
Y2 6.5 7.6 8.1 0.1 5.5 0.80 7.8 × 1034.3 × 1033.6 × 101
Y3 13.1 7.1 5.9 0.3 11.3 4.6 1.7 × 1042.4 × 1047.4 × 102
Y4 8.4 7.5 4.2 0.1 6.1 1.7 4.4 × 1049.3 × 1032.1 × 102
K1 6.6 7.2 6.3 0.1 2.8 0.67 4.1 × 1031.5 × 1033.6 × 101
K2 7.1 6.9 6.8 0.1 4.8 0.50 4.4 × 1042.1 × 1032.4 × 103
Y1 19.8 7.7 9.1 0.1 2.4 0.34 4.6 × 1042.4 × 1062.3 × 102
Y2 19.4 7.4 7.6 0.1 2.7 0.69 7.9 × 1032.4 × 1042.9 × 102
Y3 21.0 7.1 7.0 0.2 8.2 1.9 4.7 × 1039.3 × 1061.5 × 103
Y4 20.5 7.4 7.9 0.1 4.5 1.5 1.8 × 1049.3 × 1042.1 × 102
K1 12.8 7.2 8.8 0.1 8.2 0.88 4.5 × 1032.4 × 104na
K2 16.8 6.6 6.2 7.0 3.3 0.90 5.7 × 1049.3 × 1049.3 × 103
 a na, not analyzed.
Multiple Detection of Bacterial Pathogens in Rivers by DNA Microarray
Table 2 Relative signal intensity of positive probes in 24 river water samplesa
Pathogen species/group October, 2005 August, 2006 January, 2007 May, 2007
Y1 Y2 Y3 Y4 K1 K2 Y1 Y2 Y3 Y4 K1 K2 Y1 Y2 Y3 Y4 K1 K2 Y1 Y2 Y3 Y4 K1 K2
Acetivibrio cellulosolvens 0.62 0.25 0.51 0.30 0.60 0.31 1.42
Acinetobacter aceti 0.30
Acinetobacter anitratum 0.56
Acinetobacter baumannii 0.56
Acinetobacter haemolyticus 0.54
Acinetobacter johnsonii 0.61
Acinetobacter junii 0.57
Acinetobacter lwofi 0.41
Acinetobacter radioresistens 0.25
Actinobacillus muris 0.61 0.43 0.64 0.62 0.87 0.65 0.57 0.56 0.63 0.81 0.57
Actinobacillus pleuropneumoniae
1.06 0.76 0.95 0.82 0.82 0.79 1.05 0.58 0.65 0.93 0.63 0.57
Actinomadura spp. 0.71 0.57 0.83
Aegyptianella pullorum 0.29 0.49
Agrobacterium tumefaciens group
Anaplasma marginale/centrale
0.35 0.29
Anaplasma phagocytophila 0.30 0.37
Arcobacter genus 0.68 0.26 0.60 0.61 0.59
Arcobacter sp. 0.28
Bacteroides distasonis 0.47 0.30 0.62
Bacteroides fragilis 0.97
Bacteroides urealyticus 0.66 0.54 0.58 0.69 0.64 0.54
Balneatrix alpica 0.57 0.49 0.90 0.62 0.79 0.63 0.54 0.55 0.64 0.58 0.59 0.97
Bordetella avium group 0.26
Borrelia burgdorferi/valaisiana
Brevundimonas diminuta 0.75 0.37 0.63 0.56 0.66 0.72
Brevundimonas group 0.68 0.43 0.58 0.62 0.63 0.49
Campylobacter concisus 0.71 0.52 0.63 0.30 0.54
Campylobacter fetus group 0.33 0.26 0.34
Campylobacter jejuni group 0.69 0.61 0.27 0.58
Campylobacter rectus 0.57 0.50
Campylobacter sputorum 0.59 0.40
Centipeda periodontii 0.39
Chromobacterium violaceum 0.42
meningosepticum group (1)b0.59 0.33 0.52 0.30 0.56 0.29
meningosepticum group (2)
0.27 0.95
Chryseobacterium proteolyticum
Chryseobacterium scophthalmum
Clavibacter michiganensis 1.19 1.19 0.91 1.42 1.35
Corynebacterium mycetoides 0.31
Eggerthella lenta group 0.33
Eperythrozoon spp. 1.15 1.13 1.30 0.61 1.04
Erysipelothrix spp. 0.31
Erysipelothrix rhusiopathiae 1.29 0.38 0.63 0.81 0.67 0.65
36 Japanese J. Wat. Treat. Biol. Vol.45 No.1
Pathogen species/group October, 2005 August, 2006 January, 2007 May, 2007
Y1 Y2 Y3 Y4 K1 K2 Y1 Y2 Y3 Y4 K1 K2 Y1 Y2 Y3 Y4 K1 K2 Y1 Y2 Y3 Y4 K1 K2
Erysipelothrix tonsillarum 0.65 0.29 0.37 0.66 0.57 0.56
Eubacterium combesii 0.37
Ewingella americana 1.17 1.17 1.03 1.33 1.37
Haemophilus haemolyticus 0.33
Haemophilus inuenzae (1) 0.87 0.82 0.65 0.63 0.73 0.73 0.62 0.64 0.66 0.58 1.04 0.81
H. inuenzae (2) 1.06 0.56 0.86 0.82 0.74 0.79 1.04 0.55 1.05 0.97 0.64 0.76
Haemophilus parasuis (1) 0.89 0.76 0.90 0.56 0.59 0.65 0.98 0.61 1.05 0.61 1.01 0.94
H. parasuis (2) 0.63 0.68 0.62 0.81 0.78 0.78 0.57 0.58 0.63 0.96 0.61 0.58
Hafnia alvei 1.22 0.38 0.38 0.89
Klebsiella oxytoca group 0.38 0.26 0.23
Kluyvera ascorbata 0.68 0.50 0.56 0.51 0.59 0.32
Kluyvera cryocrescens 0.62 0.32 0.28 0.57 0.28
Lactobacillus spp. 0.30 0.26
Legionella brunensis 0.27
Legionella spiritensis 0.31
Leptospira noguchii 0.46 1.22 1.46 1.24 1.36
Leptospira parva 0.28 0.44 0.29
Leptospira santarosai 0.49 0.32
Mannheimia granulomatis 0.97 0.81 0.89 0.53 0.46 0.69 0.57 0.57 0.63 0.57 0.60 0.55
Mannheimia haemolytica 1.06 0.89 0.94 0.85 0.77 0.79 0.63 1.02 1.07 0.61 1.07 0.77
Marinospirillum megaterium 1.03 0.74 0.94 0.77 0.63 0.60 0.60 1.01 1.01 0.80 1.04 0.93
Moraxella caviae 1.06 0.58 0.64 0.94 0.86 0.62 0.65 1.05 1.07 0.90 1.06 0.94
Moraxella lacunata group 1.03 0.89 0.51 0.50 0.41 0.34 1.04 1.02 1.05 0.89 1.06 0.94
Mycobacterium mucogenicum 0.27 0.34 0.33 0.80 0.35 0.69
Mycobacterium nonchromogenicum
Mycoplasma kahnei 0.34
Olsenella uli 1.11 0.31 0.48
Pasteurella bettyae 1.06 0.58 0.97 0.74 0.54 0.71 0.85 1.01 1.05 1.02 1.06 0.85
Pasteurella caballi 0.99 0.85 0.57 0.44 0.65 0.82 0.94 0.91 0.59 1.00 0.85
Pasteurella pneumotropica (1)
1.06 0.92 0.96 0.80 0.78 0.83 1.04 1.02 1.05 0.96 1.06 0.84
Pasteurella pneumotropica (2)
1.03 1.05 0.47 0.58 0.80 0.79 0.99 0.65 1.06 0.64 1.02 0.89
Peptoniphilus asaccharolyticus
Porphyromonas cangingivalis 0.81
Proteus vulgaris 0.26
Pseudoalteromonas atlantica group
Rhodococcus equi 0.40
Selenomonas ruminantium 1.37
Slackia heliotrinreducens 0.39
Sphingobacterium multivorum
Sphingobacterium thalpophilum
Sphingomonas paucimobilis 0.28
Staphylococcus capitis/caprae 0.27
Treponema denticola 0.35 0.51 1.33 0.67 1.14
Vibrio cholerae/mimicus 0.46
aBlank entries indicate a negative result (relative signal intensity 0.25).
bNumbers in parentheses following the species name indicate that probes were designed based on different sequences of the same species.
Multiple Detection of Bacterial Pathogens in Rivers by DNA Microarray
Table 3 Biosafety level of pathogens detected in two or more samplesa
Pathogen species/group BSLbPathogen species/group BSL Pathogen species/group BSL
Acetivibrio cellulosolvens Campylobacter sputorum 1Leptospira noguchii 1
Actinobacillus muris
Chryseobacterium meningosepticum group (1)
2Leptospira parva
Actinobacillus pleuropneumoniae
1C. meningosepticum group (2) 2Leptospira santarosai 1
Actinomadura spp. 2Clavibacter michiganensis Mannheimia granulomatis 1
Aegyptianella pullorum 1Eperythrozoon spp. 1Mannheimia haemolytica 1
Anaplasma marginale/centrale
12Erysipelothrix rhusiopathiae 2Marinospirillum megaterium
Anaplasma phagocytophila 2Erysipelothrix tonsillarum Moraxella caviae
Arcobacter genus 1Ewingella americana 1Moraxella lacunata group 1
Bacteroides distasonis 1Haemophilus inuenzae (1) 2Mycobacterium mucogenicum 2
Bacteroides urealyticus 1H. inuenzae (2) 2Olsenella uli 1
Balneatrix alpica 1Haemophilus parasuis (1) 2Pasteurella bettyae 1
Brevundimonas diminuta 1H. parasuis (2) 2Pasteurella caballi
Brevundimonas group 1Hafnia alvei 1Pasteurella pneumotropica (1) 2
Campylobacter concisus 1Klebsiella oxytoca group 2P. pneumotropica (2) 2
Campylobacter fetus group 2Kluyvera ascorbata 1Treponema denticola 1
Campylobacter jejuni group 12Kluyvera cryocrescens 1
Campylobacter rectus 1Lactobacillus spp. 1
a Pathogen species/groups shown in boldface were assessed for their correlation with total coliforms.
b Biosafety level (BSL) according to the Japanese Society for Bacteriology12).
c Numbers in parentheses following the species name indicate that probes were designed based on different sequences of the
same species.
pathogens, Agrobacterium tumefaciens20) and
Clavibacter michiganensis21), were also de-
tected, each in only one of the 4 sampling
The number of pathogen species/groups
found varied among samples (Fig. 2). Similar
numbers of pathogens (16 to 20 species) were
detected at all six sampling stations in spring
and winter. Even the pathogen prole was
almost identical in the 12 samples collected
in these seasons; the following species were
present in all 12 samples: Actinobacillus
pleuropneumoniae, Balneatrix alpica,
Haemophilus inuenzae and H. parasuis,
Mannheimia granulomatis and M.
haemolytica, Marinospirillum megaterium,
and Moraxella caviae and M. lacunata.
Summer samples contained the lowest
numbers of bacterial pathogen species (9 to
16 species) among the four seasons. In
autumn, 10, 15, and 13 pathogen species
occurred at stations Y2, Y3, and Y4,
respectively. In contrast, in the same season,
24 and 32 different pathogens occurred at
stations Y1 and K2, respectively.
Exceptionally, all 1012 bacterial pathogens
1 2 3 4 1 2
reviR atiKreviR odoY
Number of bacterial
pathogen species/groups
Fig. 2 Spatial and temporal variations in the number of pathogenic bacterial species in the
Yodo and Kita rivers. Probes with relative intensity 0.25 in the microarray analysis
were judged as positive.
38 Japanese J. Wat. Treat. Biol. Vol.45 No.1
targeted were below the detection limit in
autumn at station K1.
In the Yodo River, 16, 5, 9, and 15 species/
groups were detected at all four stations in
spring, summer, autumn, and winter,
respectively (Table 2; Fig. 2). The number of
pathogen species increased from Y2 to Y3
and slightly decreased from Y3 to Y4 in
summer and autumn. In summer, 7 and 5
species/groups that were never detected at
the upstream stations Y1 and Y2 were found
at Y3 and Y4, respectively. In the Kita River,
the pathogens detected at stations K1 and
K2 were completely different between stations
in summer and autumn although almost
identical in spring and winter, as mentioned
In the PCA against the occurrence pattern
of bacterial pathogens in the river water
samples, excluding the autumn sample from
K1, in which no pathogen was detected,
73.0% of the total variation was explained by
the rst (PC1) and second (PC2) principal
components. Scatter plot based on PC1 and
PC2 revealed that the 23 analyzed samples
fell into three distinct groups A, B, and C,
depending basically on the season of sample
collection (Fig. 3); group A consisted of all of
the spring and winter samples, group B
consisted of the autumn samples from all
stations on the Yodo River and station K2 on
the Kita River and a summer sample from
K1, and group C consisted of the summer
samples from all stations on the Yodo River
and from K2 on the Kita River.
Correlation between pathogens detected in
microarrayanalysisandtotalcoliforms The
correlation between the RSIs of the 30
pathogens detected in two or more samples
in spring, summer, or winter (shown in
boldface in Table 3) and the relative total
coliform count was assessed. The RSI of 19
species increased with the relative total
coliform count (e.g., Fig. 4 A–C). In contrast,
the remaining 11 pathogenic bacteria did not
show a positive correlation with the relative
total coliform count (e.g., Fig. 4 D–F). The
latter group included 4 fecal bacteria
(Campylobacter rectus, Leptospira noguchii,
Leptospira parva, Leptospira santarosai) and
7 non-fecal bacteria (Actinobacillus
pleuropneumoniae, Eperythrozoon spp.,
Haemophilus inuenzae, Haemophilus
parasuis, Klebsiella oxytoca group,
Mannheimia granulomatis, and Pasteurella
pneumotropica [2 targets]).
We reported the simultaneous detection of
multiple pathogens in surface waters from
the Yodo and Kita Rivers in the Kinki district
of Japan. Of the two monitored rivers, the
Yodo River is a relatively polluted urban
river, whereas the Kita River is a clean,
rural river. The geographical features of the
two river basins suggest that neither has any
potential fecal sources other than WWTPs.
No unusual abundance of fecal indicator
bacteria in the WWTP efuents or in the
surface waters of either river has been
reported in recent years. Therefore, the
health risk associated with waterborne
pathogens does not appear to be easily
predictable by use of the conventional fecal
indicators in these basins. Nevertheless, we
found a total of 87 pathogen species/groups
in our survey. In addition, more than half
were present in both rivers, and one-third
occurred in two seasons. These results
suggest that specic groups of bacterial
pathogens may be commonly present in
surface waters in our monitoring region.
Furthermore, the detection of sh/shellsh
-0.6 0 0.6 1.2
PC1 (52.7%)
PC2 (20.3%)
Group A
Group C
Group B
Fig. 3 Ordination produced from a principal component
analysis based on pathogen proles of river
water samples collected from the Yodo and Kita
rivers in spring (open circles), summer (closed
diamonds), autumn (open squares), and winter
(closed triangles).
Multiple Detection of Bacterial Pathogens in Rivers by DNA Microarray
and plant pathogens, in addition to human
and animal pathogens, indicates that the
surface waters may pose health hazards to
various organisms, with consequent economic
and ecological damage.
The pathogen prole in the surface waters
of the monitored rivers varied primarily
according to the season. Seasonal variables
are well known to be one of the most
important factors determining bacterial
survival in the natural environment. Many
previous studies have reported that the
composition of the bacterial community in
river environments is season dependent22, 23).
Thus, the pathogen prole in the monitored
rivers can be expected to change in accordance
with the natural, season-dependent appear-
ance/disappearance of microorganisms. It has
been also reported that the incidence of some
kinds of pathogens in river environments is
inuenced by seasonal variables, particularly
water temperature24
Bacterial pathogens enter surface waters
from both point and non-point sources,
including in raw sewage, efuent from
WWTPs, and run-off from agriculture and
livestock farming4, 28
30). Among these potential
sources, efuent from WWTPs is recognized
as an important pathogen source that can
alter the pathogen prole along a river’s
course. In our monitoring area, several
WWTPs are located between stations Y2 and
Y4 on the Yodo River. The number of
pathogen species/groups became marginally
elevated between Y2 and Y3 in summer and
autumn, and several pathogens that were
absent at the upstream stations emerged at
Y3 and Y4 in summer. From these results,
we inferred that efuent from WWTPs
slightly impacted the pathogen prole of the
Yodo River in summer and autumn. However,
no noticeable impact was observed in spring
or winter, suggesting that the inuence of
WWTPs on the pathogen prole in the Yodo
River is marginal, despite the input of a
large amount of WWTP efuent by repeated
use of river water (by the time it reaches the
river’s mouth the downstream, the water has
been used ve times)31), compared with the
predominant impact by seasonal factors. In
-8 -6 -4 -2 0
-8 -6 -4 -2 0
-8 -6 -4 -2 0
-8 -6 -4 -2 0
-8 -6 -4 -2 0
-8 -6 -4 -2 0
Log ((MPN-total coliform count/CFU-heterotrophic bacteria) + 0.0000001)
Log (RSI)
Fig. 4 Examples of correlations between the coliform count and the relative signal intensity of pathogen
probe detected. The total coliform count relative to the total number of heterotrophic bacteria was
used. A, Balneatrix alpica; B, Moraxella caviae; C, Pasteurella bettyae; D, Actinobacillus
pleuropneumoniae; E, Haemophilus inuenzae (1); F, Mannheimia granulomatis. A, B, and C are
examples of a positive correlation, and D, E, and F are examples without any positive correlation.
40 Japanese J. Wat. Treat. Biol. Vol.45 No.1
the Yodo River basin, coverage of the sewer
system was nearly 90% in FY2004. In
addition, WWTPs within the basin are
equipped with satisfactory disinfection units.
These facts suggest that WWTPs are not a
signicant pathogen source in the basin
because the public sewer system has been
sufciently improved, even though the Yodo
River basin is relatively polluted according to
the BOD level.
The bacterial species diversity in river
water increases at the mouth, where a
brackish water environment results from the
mixing of river water and seawater32, 33). Thus,
we speculated that the pathogen diversity
would also be higher at the mouth of a river
than further upstream where no mixing
occurred. As expected, our results showed
that the pathogen species richness in the
Kita River was greater at K2, near the
mouth of the river (nearly 500 m upstream
from the mouth), than at K1. The occurrence
of the highest number of distinct pathogens
at K2 presumably also resulted from the
unusual condition of a brackish water
environment, namely, characteristics inter-
mediate between those of freshwater and
seawater. Therefore, waters at the river
mouth, under brackish water conditions, are
likely to serve as a reservoir of diverse
bacterial pathogens even in the case of a
less-polluted river like the Kita River.
Recent studies have shown that conventional
hygienic water quality indicators are not well
correlated with feces-related bacterial path-
ogens such as the Bacteroides-Prevotella
group34), Campylobacter spp.3), Salmonella
spp.4), and Yersinia spp.35) or eukaryotic
pathogens such as Cryptosporidium spp.3,4, 35).
In this study, we also observed that 11 of
the 30 pathogen species/groups assessed did
not show a signicant positive correlation
with total coliforms. These species/groups
included not only opportunistic but also
BSL2 pathogens (Haemophilus inuenzae,
Haemophilus parasuis, Klebsiella oxytoca,
and Pasteurella pneumotropica), and more
than half were non-fecal. To our knowledge,
this is the rst report suggesting the
possibility that multiple pathogens, including
both fecal and non-fecal ones, show a low
correlation with the conventional hygienic
indicator. The lack of a positive correlation
between these pathogens and the conventional
fecal indicator may reect a dissimilarity in
the environmental behavior between some
bacterial pathogens and the indicator
bacteria3, 4). Moreover, with respect to non-
fecal pathogens, the source and route of their
discharge into surface waters differ from
those of the fecal indicator bacteria, which
may be another important reason for the low
correlation. Because species of Leptospira,
which have in fact caused a human health
hazard via the drinking water supply in
Japan1), were among the pathogens that did
not correlate with total coliforms, it is clear
that the conventional hygienic indicators
cannot necessarily predict the occurrence of
signicant bacterial pathogens. Therefore,
systematization of a new set of indicators
that can comprehensively predict the
occurrence of various pathogens is strongly
required for assessment of the health risks
associated with waterborne pathogens. Among
the pathogens that did not positively correlate
with the total coliforms in this study, high-
risk (BSL2) and non-fecal pathogens may be
candidates for new indicators. From the
viewpoint of preventing damage to agriculture
and sheries as well as ecological damage,
pathogens infectious to sh/shellsh and
plants should also be considered as candidate
indicators for advanced management of the
aquatic environment. Further study on the
simultaneous determination of the occurrence
of the candidate indicator pathogens sug-
gested here and in earlier studies35,34, 35)
should be performed by cost-effective and
reliable molecular tools such as multiplex
real-time PCR36, 37) to establish an ideal set of
hygienic indicators for advanced management
of the aquatic environment.
We are grateful to Prof. Takayuki Ezaki of
the Department of Microbiology, Regeneration
and Advanced Medical Science, Graduate
School of Medicine, Gifu University, for his
valuable and stimulating discussions. This
study was supported in part by a FY2005
feasibility studies grant from the Envi-
ronmental Technology Development Fund of
the Ministry of the Environment, Japan, by a
Multiple Detection of Bacterial Pathogens in Rivers by DNA Microarray
Grant-in-Aid for Encouragement of Young
Scientists (B) no. 18710026 from the Ministry
of Education, Culture, Sports, Science and
Technology, Japan, and by the Nihonseimei
Foundation for Promotion of Environmental
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(Submitted 2008. 12. 1)
(Accepted 2009. 1. 5)
... The applicability of the microarray to environmental samples had been assured by the developers and previously applied to landfill leachets, river water samples, and shallow well groundwater [5,24,25]. In brief, Cy3-labeled polymerase chain reaction (PCR) products were prepared by using a PCR mixture (50 µL) containing 25 µL of SapphireAmp Fast PCR Master Mix (Takara Bio, Kusatsu, Japan), 0.25 µL each of 15 pmol/µL forward and reverse primers [5,[26][27][28], 1 µL of DNA template, and 23.5 µL of ultrapure water. ...
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Because of heavy dependence on groundwater for drinking water and other domestic use, microbial contamination of groundwater is a serious problem in the Kathmandu Valley, Nepal. This study investigated comprehensively the occurrence of pathogenic bacteria in shallow well groundwater in the Kathmandu Valley by applying DNA microarray analysis targeting 941 pathogenic bacterial species/groups. Water quality measurements found significant coliform (fecal) contamination in 10 of the 11 investigated groundwater samples and significant nitrogen contamination in some samples. The results of DNA microarray analysis revealed the presence of 1-37 pathogen species/groups, including 1-27 biosafety level 2 ones, in 9 of the 11 groundwater samples. While the detected pathogens included several feces- and animal-related ones, those belonging to Legionella and Arthrobacter, which were considered not to be directly associated with feces, were detected prevalently. This study could provide a rough picture of overall pathogenic bacterial contamination in the Kathmandu Valley, and demonstrated the usefulness of DNA microarray analysis as a comprehensive screening tool of a wide variety of pathogenic bacteria.
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As a country on the whole, Canada enjoys abundant freshwater resources, yet there remain regions with severe discrepancies between supply and demand. One solution to insufficient water supplies that has been gaining in popularity in other areas of the world is that of water reuse. Reuse or recycling of treated wastewater reduces effluent discharges into receiving waters and offers a reliable alternative supply of water for applications that do not require high-quality water, freeing up limited potable water resources. As compared to other countries worldwide, water reuse is currently practised infrequently in Canada. Use of reclaimed water requires a clear definition of the quality of water required, and while water quality criteria typically focus on pathogen risk to human health, chemical contaminants may also limit suitability for some reuse applica-tions. Both health and environmental risk assessments are important steps in designing criteria for reuse projects. Alberta and British Columbia have recently produced guidance documents for water reuse projects; the permitted applications are discussed and the water quality criteria are compared with other standards and guidelines. Various treatment technologies for on-site and central wastewater reclamation facilities are described. Additional considerations for implementation of water reuse projects include project feasibility and planning, infrastructure needs, economics, and public acceptance.
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A fish pathogen, Vibrio cholerae non-O1, was isolated from diseased ayu fish (Plecoglossus altivelis) collected from rivers in eight prefectural districts of Japan. This organism was found to have biochemical characteristics similar to those of V. cholerae non-O1, except that our isolates were negative for ornithine decarboxylase. Antiserum against an ayu isolate did not agglutinate with the majority of environmental V. cholerae non-O1 isolates, but a major O antigen was common among the ayu isolates. All strains were hemolytic to sheep erythrocytes, and oral administration of culture supernatants induced fluid accumulation in suckling mice. However, the crude toxin was not lethal to adult mice, and no cholera toxin-like enterotoxins were detected.
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In order to develop a rapid and specific detection test for bacteria in soil, we improved a method based on the polymerase chain reaction (PCR). Each step of the protocol, including direct lysis of cells, DNA purification, and PCR amplification, was optimized. To increase the efficiency of lysis, a step particularly critical for some microorganisms which resist classical techniques, we used small soil samples (100 mg) and various lytic treatments, including sonication, microwave heating, and thermal shocks. Purification of nucleic acids was achieved by passage through up to three Elutip d columns. Finally, PCR amplifications were optimized via biphasic protocols using booster conditions, lower denaturation temperatures, and addition of formamide. Two microorganisms were used as models: Agrobacterium tumefaciens, which is naturally absent from the soil used and was inoculated to calibrate the validity of the protocol, and Frankia spp., an actinomycete indigenous to the soil used. Specific primers were characterized either in the plasmid-borne vir genes for A. tumefaciens or in the variable regions of the 16S ribosomal gene for Frankia spp. Specific detection of the inoculated A. tumefaciens strain was routinely obtained when inocula ranged from 10(7) to 10(3) cells. Moreover, the strong correlation we observed between the size of the inocula and the results of the PCR reactions permitted assessment of the validity of the protocol in enumerating the number of microbial cells present in a soil sample. This allowed us to estimate the indigenous population of Frankia spp. at 0.2 x 10(5) genomes (i.e., amplifiable target sequences) per g of soil.
Changes in bacterial diversity during the field experiment on biostimulation were monitored by denaturing gradient gel electrophoresis (DGGE) analysis of PCR-amplified 16S rDNA fragments. The results revealed that the bacterial community was disturbed after the start of treatment, continued to change for 45 days or 60 days and then formed a relatively stable community different from the original community structure. DGGE analysis of soluble methane monooxygenase (sMMO) hydroxylase gene fragments, mmoX, was performed to monitor the shifts in the numerically dominant sMMO-containing methanotrophs during the field experiment. Sequence analysis on the mmoX gene fragments from the DGGE bands implied that the biostimulation treatment caused a shift of potential dominant sMMO-containing methanotrophs from type I methanotrophs to type II methanotrophs.
Two enteric pathogens, Campylobacter jejuni and Yersinia enterocolitica, and the indicator bacterium Eschericiha coli, were investigated for survival in autoclaved lake water and susceptibility to chlorine at 4 and 10°C. In addition, survival of Y. enterocolitica was investigated in oligotrophic lake water. The 3-log reduction (99.9% inactivation) time for Y enterocolitica in lake water was 17–18 days at 4°C and 14–15 days at 10°C. After exposure to 0.2 mg/l Cl2 a 3-log reduction was obtained for C. jejuni in 10–15 s, for Y. enterocolitica in 20–180 s, and for E. coli in 20–25 s, depending on bacterial strain, plasmid content and temperature. The same reduction of C. jejuni was obtained with 0.02-0.04 mg/l of free chlorine in 12 min at 4°C and 2 min at 10°C. These results indicate that C. jejuni is more sensitive to chlorine than most other waterborne pathogens and E. coli, indicating that it may easily be controlled by present disinfection practices. Y. enterocolitica is about as sensitive to chlorine as E. coli, but may survive for a longer time in cold, clean surface waters. E. coli is an adequate indicator for C. jejuni both in water sources and in chlorinated drinking water. E. coli, however, is not a safe indicator for the presence of Y. enterocolitica O:3 under all conditions, and especially not in oligotrophic lake water. Furthermore, the results for Y. enterocolitica O:3 indicate an association between enhanced resistance to chlorine and the presence of a virulence plasmid.
: A procedure for counting viable heterotrophic bacteria in activated sludge was evolved from a study of the effects of modifications to procedures at the different stages of enumeration. Optimal counts were obtained with Casitone-glycerol-yeast extract agar (CGY) with incubation for 6 days at 22°. Homogenization of mixed liquor was conveniently performed, with minimal lethal effect on the bacteria, by treating samples, diluted 1/10 in sodium tripolyphosphate solution (5 mg/1), in a boiling tube immersed in the Kerry ultrasonic cleaning bath for 1 min. Counts were significantly affected by the pH value of diluent and CGY, but not by the homogenization method or by treating homogenized samples with enzymes or N-acetyl cysteine, or by adding colloidal peptizing agents to the diluent. Replicate colony counts showed variances greater than the mean, although precision increased with increasing number of colonies/dish; there was a direct relationship between colony counts and volume plated for up to c. 1000 colonies/dish. Counts on spread plates tended to be higher and more precise than on dilution frequency plates, although the 2 methods showed satisfactory correlation. Counts were not significantly affected by the method of sampling and preparing the initial dilution, and it was considered prudent to examine samples immediately after collection.