Next Article in Journal
Agricultural Productive Carrying Capacity Improve and Water Optimal Allocation under Uncertainty Based on Remote Sensing Data in Lancang County, Southwest China
Previous Article in Journal
Farmers’ Perceptions on an Irrigation Advisory Service: Evidence from Tunisia
Previous Article in Special Issue
Depth-Dependent Concentrations of E. coli in Agricultural Irrigation Ponds
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Application of Coagulation and Foam Concentration Method to Quantify Waterborne Pathogens in River Water Samples

Department of Civil and Environmental Engineering, Faculty of Engineering, University of Miyazaki, MiyazakI 889-2192, Japan
Center for Social and Environmental Systems Research, National Institute for Environmental Studies, Ibaraki 305-8506, Japan
Department of Soil, Water, and Climate, University of Minnesota, Minneapolis, MN 55108-1095, USA
BioTechnology Institute, University of Minnesota, Minneapolis, MN 55108-1095, USA
Author to whom correspondence should be addressed.
Water 2022, 14(22), 3642;
Submission received: 21 September 2022 / Revised: 31 October 2022 / Accepted: 9 November 2022 / Published: 11 November 2022
(This article belongs to the Special Issue Research on Microbiological Water Quality)


One of the major challenges in detecting waterborne pathogens is the low concentration of the target bacteria in water. In this study, we applied the coagulation and foam concentration method to obtain DNA from water samples collected from upstream, near an estuary. The DNA samples were analyzed using 16S rRNA gene sequencing to clarify the microbial community shifts and to identify potentially pathogenic bacteria. Bacterial communities changed as the river flowed downstream, most likely influenced by land use and human activities such as the discharge of wastewater-treatment plant effluent. Based on the 16S rRNA gene amplicon sequencing, potentially pathogenic bacteria were detected with greater than 0.1% of their relative abundances. Among these, Yersinia ruckeri and Pseudomonas alcaligenes were widely detected in the river water. In addition, digital PCR (dPCR) was used to quantify major waterborne pathogens. Shiga toxin-producing Escherichia coli (STEC), Shigella spp., and Campylobacter jejuni were all below the limit of detection. In contrast, general E. coli, which has the beta-D-glucuronidase gene (uidA) were detected by dPCR (copies/100 mL) at similar levels to those measured using the culture-based method (as colony forming units/100 mL). These results suggest that the coagulation and foam concentration method is useful for concentrating microbes and obtaining DNA from river water samples for environmental monitoring.

1. Introduction

Waterborne diseases, which are caused by various pathogenic microorganisms present in polluted water, pose a serious threat to the world’s public health. The World Health Organization [1] estimates that such infectious diseases account for 3.6% of all infections worldwide, causing 1.5 million deaths each year. In Japan, water supply and sewerage treatment systems are widespread (with coverage rates of 98.0% and 79.7%, respectively); however, waterborne infectious diseases still occur frequently [2]. Natural systems such as rivers, lakes, and coastal areas can harbor various pathogens [3,4,5,6]. Land use and human activities such as animal feedlots, manure application to agricultural fields, and the effluent discharge from sewage treatment plants could also influence the occurrence of pathogens in the United States (Arizona, California, and Florida) [7]; however, the occurrences of waterborne pathogens in a river that flows along various land-use patterns are still unclear.
Quantitative PCR (qPCR) is frequently used to detect specific pathogens in various matrices including water samples [8]. Digital PCR (dPCR) is also becoming popular to quantify specific genes, because it is more sensitive and accurate in quantifying low-abundance genes than qPCR [9]. Quantification with dPCR is also less affected by PCR inhibitors than that with qPCR [9]. Another approach to the detection of potential pathogens is to analyze the entire microbial population by sequencing thousands to millions of the PCR amplicons of the 16S rRNA gene fragment, and identify potentially pathogenic taxa afterward [10].
Despite the recent and ongoing technological advancements, one of the major challenges to detecting pathogenic bacteria in natural waterbodies is the low concentrations of the target bacteria, often below the detection limit of PCR analysis [8,11,12]. Therefore, to detect these bacteria in water samples, it is necessary to concentrate them. While membrane filtration is most frequently used to concentrate bacteria in water samples [12], filters could be clogged with various suspended solids present in water samples, hindering the processing of large volumes of water. Another method to concentrate microbes is to make aggregates of cells and recover them by centrifugation; however, it requires 1 to 16 h to process [13,14,15]. Previously, we developed the coagulation and foam concentration method to concentrate bacteria from environmental water samples. This method is fast, simple, and efficient [16]. By using the coagulation and foam concentration method, it is possible to concentrate bacterial cells from 5 L of river water samples, from which 100 µL of DNA can be extracted [17]. The resulting DNA can be used to quantify target pathogens with dPCR, although this method has not been used with sequencing-based pathogen detection.
In this study, based on our previous study [16,17], we carried out a survey from upstream to downstream of an actual river basin with varied water quality, and enlarged the sample volume. The objectives of this study were to (1) apply the coagulation and foam concentration method to concentrate microbial cells and recover DNA from river water samples, and (2) use the DNA samples for dPCR and 16S rRNA gene amplicon sequencing. The water samples were collected along a river that flows from forest to agricultural and urban areas in Japan; we expected to detect the impacts of human activities on the occurrences of pathogens and microbial community structures. This field study is a practical trial of this method for a natural river.

2. Materials and Methods

2.1. Water Sample Collection

Water samples were collected from the Kiyotake River, a 28.8 km-long river that flows in the forest, agricultural areas, and urbanized areas in Miyazaki, Japan (Figure 1) on 12 November 2017, and 18 September 2018. Eight sampling sites (S1–S8) were established along the river. Site S1 is the uppermost stream of the river, and the catchment area mostly covers forests. Sites S2 to S5 are adjacent to forest or agricultural forest areas. There is a local community (population 11,000) between sites S3 and S4. Sites S6 to S8 pass through the urban areas in Miyazaki City. In addition, there are two sewage treatment plants: one between S3 and S4 and the other between S7 and S8. Based on the land use patterns, eight sampling sites were divided into three groups (upper, middle, and lower regions). The S1, S4, and S8 sites were selected as the representatives of each group. A total of 20 samples were collected from the eight sites. At Site S1, three samples were collected per sampling event to analyze the variation due to water sampling at the same site. One sample was collected per sampling event at each of the other sites.
At each site, water samples were collected from the river surface (<30 cm depth) and stored in a 10-L sterile polyethylene bottle. The sample bottles were placed in a heat retaining box, to prevent sample-temperature changes. The samples were transported to the laboratory within 2 h after collection.

2.2. Analysis of General Water Quality

A bench-top pH/water-quality analyzer (LAQUA; Horiba, Japan) was used to measure the pH and electrical conductivity of the water samples. Turbidity was determined using a turbidity meter (SEP-PT-706D; Mitsubishi Kagaku, Japan). The concentrations of total organic carbon (TOC) in the samples were determined using a TOC analyzer (TOC-V Model, Shimadzu Co., Kyoto, Japan). Total coliforms and E. coli were enumerated by using CHROMagar ECC agar plates (CHROMagar, France). In brief, 10 or 100 mL of water samples were filtered through a sterile 0.45-μm-pore mixed cellulose membrane filter (47 mm diameter, Advantec, Japan). The membrane filters were incubated on CHROMagar ECC agar plates for 24 h at 37 °C. Blue colonies were counted as E. coli, while mauve colonies were counted as other coliform bacteria. Enterococcus were enumerated by using the membrane filter method [18], with membrane-Enterococcus indoxyl-β-d-glucoside (mEI) agar plates. The mEI plates were prepared within a week prior to the survey, and stored in a refrigerator for preparation. The samples (10 or 100 mL) were individually passed through a membrane filter, and the filters were incubated on mEI agar plates for 24 h at 41 °C. Blue colonies were counted as Enterococcus. Bacterial counts were expressed in units of colony-forming units (CFU) per 100 mL. The fecal indicator recovery assay was carried out in triplicate.

2.3. Bacterial Cell Concentration and DNA Extraction

Bacterial cells were concentrated by using the coagulation and foam concentration method, as described previously [17]. In brief, the water sample (5 L) was rapidly mixed with a quantity of coagulant (stock solution, 10 g/L FeCl3 in 0.01 M HCl; final concentration, 5 mg-Fe/L.) at 150 rpm for 3 min, using a jar tester (MHS-8; Miyamoto Co., Japan). After coagulation, the stock casein solution (10 g/L milk casein dissolved in 0.01 M NaOH) was added to the samples at a final concentration of 10 mg/L. Each sample was rapidly mixed at 150 rpm for 1 min, and transferred to a cylindrical column (height 100 cm, diameter 9 cm, volume 5000 mL) of a batch flotation device. Dispersed air was supplied from the bottom of the column at 1 L/min for 3 min, to generate foam. Foam generated on the water surface was drawn into a trap bottle, using an aspirator. The recovered foam was defoamed in a suction tube and transferred to a 50 mL centrifuge tube. The tubes were centrifuged at 10,000× g for 10 min, to collect the coagulation flocs (orange pellets). All reagents and materials used were sterile. The column and tarp bottle were washed using alkali detergent for laboratory ware (Clean Ace, AS ONE Corporation, Japan), rinsed with sterile water and 70% ethanol, and dried before use.
The pellets recovered from the foam were resuspended in 2 mL sterile physiological saline (0.85% NaCl) by using a vortex for 1 min, and filtered through a 0.45-μm-pore membrane filter. DNA was extracted from the membrane filters using a PowerWater DNA Isolation Kit (MoBio Laboratories, Carlsbad, CA, USA), according to the manufacturer’s instructions. A final DNA elution volume of 100 µL was used. The DNA concentrations were measured using a Quantus fluorometer (Promega, PA, USA). The efficiency of DNA extraction from the coagulated flocs is high (>87.5% as a relative DNA recovery efficiency obtained from bacteria-spiked test) [16].

2.4. 16S rRNA-Gene Sequencing

To analyze the microbial communities, the V3–V4 region of the bacterial 16S rRNA gene was amplified with an adaptor and index-tagged primers, as described previously [19]. The PCR products were purified using Agencourt AMPure (Beckman Coulter), and sequenced using a MiSeq platform (Illumina) with the v3 Reagent kit (Illumina) for sequencing. The sequencing data were analyzed using Qiime ver. 1.9.0 [20]. The merged reads were clustered into operational taxonomic units (OTUs) at 97% sequence identity by the Qiime analysis. The taxonomic identity of the representative sequence for each OTU was identified using the EzBioCloud 16S database [21]. The raw sequence reads have been deposited in the DDBJ Sequence Read Archive under DRA014560, and DRA014561.

2.5. Digital PCR

Four pathogen genes were quantified using dPCR, including the Shiga toxin genes (stx1 and stx2) of Shiga toxin-producing E. coli (STEC), the invasion plasmid antigen gene (ipaH) of enteroinvasive E. coli (EIEC) and Shigella spp., and the hippuricase gene (hipO) of Campylobacter jejuni. In addition to the genes for pathogens, the beta-D-glucuronidase gene (uidA) of general E. coli was also included. The dPCR reaction mixture (15 µL) contained 1 × QuantStudio 3D Digital PCR Master Mix (Applied Biosystems), primers and TaqMan probes and 2 µL DNA temperate solution. The primer and probe sequences and the PCR conditions are shown in Table S1. Digital PCR was carried out using the QuantStudio 3D Digital PCR system (Thermo Fisher) with a QuantStudio 3D Digital PCR 20K Chip (Thermo Fisher), as described previously [17]. The conditions used for the PCR reactions are shown in Table S1. We used the default settings in the QuantStudio 3D Digital PCR System and QuantStudio 3D Analysis Suite software (Thermo Fisher) to identify the threshold fluorescence intensity necessary to discriminate between positive and negative wells. The measurements were carried out in triplicate and reported as mean copies per mL of water.

2.6. Statistical Analysis

Statistical significances in the quantitative data obtained in this study were tested using R version 4.0.2. Relationships between DNA concentration and total coliforms, E. coli, and Enterococcus counts were examined, based on Pearson’s correlation coefficient after verifying that the data were normally distributed by the Shapiro test. Differences in the bacterial relative abundances among sites and groups were analyzed with the Kruskal–Wallis test. Microbial community structures were analyzed, using R with vegan [22] and phyloseq packages [23]. Principal coordinates analysis (PCoA) with Bray–Curtis distance matrices was used to visualize the dissimilarities in microbial communities among sites and years. Differences in microbial community structures were tested using permutational multivariate analysis of variance (PERMANOVA). Canonical analysis of principal coordinates (CAP) was carried out using Bray–Curtis distance matrices to identify environmental variables associated with the patterns in microbial communities [24]. Variables for the CAP models were selected by the forward selection method, using the ordistep function of vegan.

3. Results and Discussion

3.1. General Water Quality

The water quality of the Kiyotake River is summarized in Table 1. The pH ranged from 6.9 to 7.8, which is a typical river pH [25]. The EC in the water upstream of the Kiyotake River was less than 100 µS/cm, and it increased as the river went downstream. The turbidity ranged from 0.41 to 1.97 degrees. The mean numbers of total coliforms, E. coli, and Enterococcus were extremely low at sites S1 and S2 upstream, ranging from 1.6 × 102 to 7.4 × 102 CFU/100 mL, 3 × 10−1 to 5.0 × 100 CFU/100 mL, and 1.3 × 100 to 3.7 × 100 CFU/100 mL, respectively. These bacterial counts increased as the river went downstream. The concentration of total organic carbon (TOC) was also investigated in 2017; TOC was 0.21 to 0.44 mg-C/L at the sites S1 to S8, which was extremely low compared with the water quality standard for drinking water in Japan [2]. Based on these water quality measurements, the influence of the pollution load is small in the Kiyotake River, although it flows through some urban areas.

3.2. DNA Concentrations along the River

To examine the influence of our coagulation and foam concentration procedure on the DNA concentration, we collected triplicate samples at the same site and used them for cell concentration and DNA extraction. The variation in the DNA concentration due to our sample processing was small: the mean ± SD (standard deviation) DNA concentration in the river water was 125 ± 9.98 ng/L (coefficient of variation 7.96%) and 109 ± 13.8 ng/L (12.7%) at site S1 in 2017 and 2018, respectively. The concentration of DNA changed as the river went downstream (Figure S1), probably reflecting the abundance of bacteria and other small organisms in the river’s suspended components. The mean DNA concentration in the upper sites S1 and S2 was 101 ng/L, and the fluctuation was small. The difference in the DNA concentration was particularly large between sites S3 and S4 (>500 ng/L at S4) and between sites S7 and S8 (>1000 ng/L at S8). A similar trend was also seen in the Tama River that passes through the big city of Tokyo, Japan; the DNA concentration was the lowest (150 ng/L) in the uppermost stream, whereas that in the middle stream after the discharge of treated sewage was high (2830 ng/L) [26]. The DNA concentration of the Kiyotake River in this study was lower than that of the Tama River, most likely reflecting the land use and human activities around the river.
We observed that the DNA concentration significantly correlated with the total coliform count (r = 0.733, p < 0.05) and the E. coli count (0.760, p < 0.05) (Figure S2), further supporting the impact of sewage-treatment effluent on the DNA concentration. This also suggests that the change in the number of coliforms and the number of E. coli was related to the DNA concentration. On the other hand, no significant correlation was found between the DNA concentration and the number of Enterococcus (p > 0.05), probably because of the different growth/survival kinetics of Enterococcus compared with those of coliform bacteria and E. coli [27].
The amount of DNA concentration obtained by using the coagulation and foam concentration method ranged from 375 to 144,000 ng in the final 100 µL extract, which is enough for next-generation sequencing and dPCR.

3.3. Detection of Pathogen Genes

Four pathogen genes (hipO, stx1, stx2, and ipaH) and one E. coli gene (uidA) were quantified by dPCR in the DNA samples obtained from the S1, S4, and S8 sites collected in 2017 and 2018. These sites represent the land-use patterns of the river studied. We used the coagulation and foam concentration method to concentrate 5000 mL of river water to 100 µL (i.e., concentrate at 5 × 104 times). However, all of the target pathogen genes were below the detection limit (the detection limit: hipO, 8 copies; stx1, 13 copies; stx2, 6 copies; ipaH, 21 copies [17]). These pathogens were also not detected by the 16S rRNA gene sequencing approach. Taken together, these results suggest that the occurrences of STEC, Shigella spp, and Campylobacter jejuni, the waterborne bacterial pathogens of most concern, were negligible in the Kiyotake River, even though it receives effluent from two wastewater treatment plants. In contrast, uidA of E. coli was detected from all sites, except from the S1 site collected in 2017, with a range of 27.1 to 90.1 copies/100 mL. These values were relatively similar to the culture-based E. coli counts, indicating that E. coli present in the river were mostly viable.

3.4. Changes in the Bacterial Community along the River

Bacterial community structures were assessed by the 16S rRNA gene-sequencing analysis. A total of 471,244 reads (21,044–57,981 and 19,562–26,749 per sample in 2017 and 2018, respectively) were clustered into OTUs and used for taxonomic assignment. The phyla Proteobacteria and Bacteroidetes predominated the bacterial communities at all sites (Figure 2), although the relative abundance of Bacteroidetes was larger in the lower and middle sites (S4–S8) than in the upper sites (S1–S3) (p < 0.01 by the Kruskal–Wallis test). In both 2017 and 2018, the phyla Acidobacteria, Planctomycetes, and OP3 were only detected in the upper sites (S1–S3), whereas Actinobacteria were only detected in the middle (S4–S6) and lower sites (S7 and S8). Similarly, Cyanobacteria were only detected in the upper and middle sites. Parcubacteria (also kwon as OD1) were detected in all regions (i.e., upper, middle, and lower sites), but their relative abundance was larger in the upper sites than in the middle and lower sites (p < 0.05 by the Kruskal–Wallis test). This shift may reflect the change in the land use of the river catchment from forest to the agricultural and urban environment [28]. Change in the river bacterial community structures was also shown by the PCoA (Figure 3). The difference in the bacterial community structures by site (group) and year was supported by the PERMANOVA (p < 0.05). The CAP analysis showed that the DNA concentration, turbidity, and E. coli levels were strongly associated with the patterns in microbial communities (Figure 4). As discussed above, DNA concentrations and the levels of E. coli might have been influenced by the effluent from the sewage treatment plants located between the S3 and S4 sites and upstream of the S8 site. Therefore, the changes in the bacterial community structures along the river were at least in part influenced by the sewage effluent.

3.5. Occurrence of Potential Pathogens

The influence of sewage-treatment effluent raises a concern about the presence of potentially pathogenic bacteria. Therefore, we extracted the genera that contain potential human pathogens from our OTU data, based on the list of 145 pathogenic bacterial genera presented by Fang et al. (2018) [29]. A total of 87 and 73 potentially pathogenic genera were detected in 2017 and 2018, respectively (Figure S3). They accounted for 1.36–4.52% and 1.15–6.97% of the total bacterial populations detected in 2017 and 2018, respectively. Among those, 16 genera occupied a relatively large fraction (>0.1%) of the population (Figure 5). Acinetobacter, Aeromonas, Pseudomonas, and Sphingomonas were frequently detected across the sites in both 2017 and 2018. In particular, Aeromonas was detected most frequently (4.83% of the population) at the S6 site in 2018. Although Acinetobacter and Aeromonas are not frequently detected in human feces, they are abundant in sewage, soil, and surface water [30,31,32]. Therefore, the potential sources of these bacteria in the river water studied might include sewage and soil. Similarly, Pseudomonas spp. are ubiquitous in various environments including soil, water, and humans [33]. Pseudomonas spp. in the Kiyotake River may originate from these sources. Our previous study also detected P. aeruginosa in various locations along the Kiyotake River, with the concentration ranging from 2 to 25 CFUs per 100 mL [34]. Similar to this study, Sphingomonas has been detected in various water environments such as freshwater [35] and natural mineral water [36]. Legionella was only detected in the upstream sites. Since Legionella is widely distributed in natural environments such as moist soil [37], the potential source of this bacterium would be soil or sediment in the catchment. On the other hand, Arcobacter was frequently detected in the downstream sites. The detection of Arcobacter may be related to the discharge of sewage effluent, because Arcobacter is one of the major genera found in sewage [38]. The influence of human activities such as sewage effluent is also supported by the high levels of E. coli found in the downstream sites. Although these 16 genera were consistently detected both in the 2017 and 2018 samples, their relative abundances to the total population were variable between the two years, probably because of the differences in environmental conditions such as temperature and precipitation. Future research is needed to clarify the factors influencing their abundance. It is also important to note that these data show relative abundances. The absolute abundances of these genera (i.e., copies of bacteria per L water) may be different. For example, the relative abundances of potentially pathogenic genera were relatively low in the S8 site, but these samples had the highest DNA concentration, which means they had the highest biomass. Therefore, the absolute abundances of these genera in the S8 site may be larger than in the other sites.
In addition to the genus level, we tried to detect potentially pathogenic species at the species level by using the list of 538 pathogenic bacterial species [39]. Eighteen potentially pathogenic bacterial species were detected in our OTU data (Figure S4). Among these, Yersinia ruckeri had the highest abundance (up to 0.41%) and was widely detected both in the upstream and the downstream sites over the two years. Pseudomonas alcaligenes was also widely detected, with the highest relative abundance (0.055%) seen at the uppermost S1 site in 2018. These results suggest that Y. ruckeri and P. alcaligenes likely originated from natural environments (e.g., forest soils) in the catchment. In contrast to the genus-level analysis, Acinetobacter spp., Aeromonas spp., and Sphingomonas spp., were not identified at the species level based on the OTU data, most likely because it is difficult to identify these species based on the V3–V4 region of the bacterial 16S rRNA gene information alone. Longer 16S rRNA gene fragments may need to be sequenced to obtain species-level information [40].

4. Conclusions

This study applied the coagulation and foam concentration method to concentrate the DNA from river water to analyze the bacterial community structures and the occurrence of potential pathogens. Our 16S rRNA gene-sequencing analysis indicates the impact of land use and human activities on bacterial communities in the river water. While we did not detect target pathogens (STEC, Shigella spp, and Campylobacter jejuni), E. coli were constantly detected by dPCR in this study. Although 16S rRNA gene information alone cannot distinguish some human pathogens from others (e.g., E. coli O157 vs. general E. coli), this approach is still useful to assess the overall community change and to detect potentially pathogenic bacteria. These results suggest that the coagulation and foam concentration method is useful for environmental monitoring because this method is simple, fast, and easy to use. To further enhance detection sensitivity, it might be necessary to process a larger volume of water (e.g., >10 L). Given the simple nature of this method, we expect that it is relatively easy to achieve, both in the laboratory and on-site [41]. To obtain information on the river environment for improving public health, further research is needed, including the application of this method to investigate various river basins with different characteristics.

Supplementary Materials

The following supporting information can be downloaded at:, Figure S1: Changes in the DNA concentration at each sampling site in the Kiyotake River; Figure S2: Correlations between DNA concentration and the counts of fecal indicator bacteria (total coliforms, E. coli, and Enterococcus) along the river; Figure S3: Heatmap showing the relative abundances of 35 potentially pathogenic genera identified. Only genera that were present at >0.1% of the population are shown; Figure S4: Heatmap showing the relative abundances of 18 potentially pathogenic species identified. Only species that were present at >0.1% of the population are shown; Table S1: Primers, probes, and thermal conditions used for dPCR in this study [42,43,44,45].

Author Contributions

Conceptualization, supervision, investigation, writing—original draft, Y.S.; investigation, formal analysis, methodology, validation, A.J.; visualization, writing—review and editing, S.T.; formal analysis, validation, K.N.; validation, Y.M.; formal analysis, software, validation, visualization, writing—review and editing, S.I.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.


This publication was supported by JSPS KAKENHI Grant Number JP 18K11680.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or used during the study appear in the submitted article or Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.


  1. WHO. WHO Global Water, Sanitation and Hygiene Annual Report 2018. Available online: (accessed on 20 January 2019).
  2. Ministry of Health, Labour and Welfare. Japan. Available online: (accessed on 11 November 2019).
  3. Brookes, J.D.; Antenucci, J.; Hipsey, M.; Burch, M.D.; Ashbolt, N.J.; Ferguson, C. Fate and transport of pathogens in lakes and reservoirs. Environ. Int. 2004, 30, 741–759. [Google Scholar] [CrossRef] [PubMed]
  4. Alegbeleye, O.O.; Sant’Ana, A.S. Manure-borne pathogens as an important source of water contamination: An update on the dynamics of pathogen survival/transport as well as practical risk mitigation strategies. Int. J. Hyg. Environ. Health. 2020, 227, 113524. [Google Scholar] [CrossRef] [PubMed]
  5. Amarasiri, M.; Furukawa, T.; Nakajima, F.; Sei, K. Pathogens and disease vectors/hosts monitoring in aquatic environments: Potential of using eDNA/eRNA based approach. Sci. Total Environ. 2021, 796, 148810. [Google Scholar] [CrossRef] [PubMed]
  6. González-Fernández, A.; Symonds, E.M.; Gallard-Gongora, J.F.; Mull, B.; Lukasik, J.O.; Navarro, P.R.; Aguilar, A.B.; Peraud, J.; Brown, M.L.; Alvarado, D.M.; et al. Relationships among microbial indicators of fecal pollution, microbial source tracking markers, and pathogens in Costa Rican coastal waters. Water Res. 2021, 188, 116507. [Google Scholar] [CrossRef] [PubMed]
  7. Harwood, V.J.; Levine, A.D.; Scott, T.M.; Chivukula, V.; Lukasik, J.; Farrah, S.R.; Rose, J.B. Validity of the indicator organism paradigm for pathogen reduction in reclaimed water and public health protection. Appl. Environ. Microbiol. 2005, 71, 3163–3170. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Aw, T.G.; Rose, J.B. Detection of pathogens in water: From phylochips to qPCR to pyrosequencing. Curr. Opin. Biotechnol. 2012, 23, 422–430. [Google Scholar] [CrossRef]
  9. Salipante, S.J.; Jerome, K.R. Digital PCR-An Emerging Technology with Broad Applications in Microbiology. Clin. Chem. 2020, 66, 117–123. [Google Scholar] [CrossRef]
  10. Deshmukh, R.A.; Joshi, K.; Bhand, S.; Roy, U. Recent developments in detection and enumeration of waterborne bacteria: A retrospective minireview. Microbiol. Open. 2016, 5, 901–922. [Google Scholar] [CrossRef] [Green Version]
  11. Rodrigues, C.; Cunha, M.Â. Assessment of the microbiological quality of recreational waters: Indicators and methods. Euro-Mediterr. J. Environ. Integr. 2017, 2, 25. [Google Scholar] [CrossRef] [Green Version]
  12. Mathai, P.P.; Dunn, H.M.; Magnone, P.; Zhang, Q.; Ishii, S.; Chun, C.L.; Sadowsky, M.J. Association between submerged aquatic vegetation and elevated levels of Escherichia coli and potential bacterial pathogens in freshwater lakes. Sci. Total Environ. 2019, 657, 319–324. [Google Scholar] [CrossRef]
  13. Zhang, Y.; Riley, L.K.; Lin, M.; Hu, Z. Determination of low-density Escherichia coli and Helicobacter pylori suspensions in water. Water Res. 2012, 46, 2140–2148. [Google Scholar] [CrossRef]
  14. Gonzales-Gustavson, E.; Cárdenas-Youngs, Y.; Calvo, M.; da Silva, M.F.; Hundesa, A.; Amorós, I.; Moreno, Y.; Moreno-Mesonero, L.; Rosell, R.; Ganges, L.; et al. Characterization of the efficiency and uncertainty of skimmed milk flocculation for the simultaneous concentration and quantification of water-borne viruses, bacteria and protozoa. J. Microbiol. Methods 2017, 134, 46–53. [Google Scholar] [CrossRef]
  15. Jansson, L.; Eriksson, R.; Hedman, J.; Lavander, M. Evaluation and modification of lanthanum-based flocculation for isolation of bacteria from water samples. Biotechnol. Rep. 2018, 19, e00267. [Google Scholar] [CrossRef]
  16. Suzuki, Y.; Imafuku, Y.; Nishiyama, M.; Teranishi, K.; Jikumaru, A.; Nukazawa, K.; Ogura, Y. A highly efficient method for concentrating DNA from river water by combined coagulation and foam separation. Sep. Sci. Technol. 2019, 54, 3128–3134. [Google Scholar] [CrossRef]
  17. Jikumaru, A.; Ishii, S.; Fukudome, T.; Kawahara, Y.; Iguchi, A.; Masago, Y.; Nukazawa, K.; Suzuki, Y. Fast, sensitive, and reliable detection of waterborne pathogens by digital PCR after coagulation and foam concentration. J. Biosci. Bioeng. 2020, 130, 76–81. [Google Scholar] [CrossRef]
  18. United States Environmental Protection Agency. Method 1600: Enterococci in Water by Membrane Filtration Using membrane-Enterococcus Indoxyl-β-D-Glucoside Agar (mEI); U.S. EPA: Washington, DC, USA, 2002.
  19. Suzuki, Y.; Shimizu, H.; Kuroda, T.; Takada, Y.; Nukazawa, K. Plant debris are hotbeds for pathogenic bacteria on recreational sandy beaches. Sci. Rep. 2021, 11, 11496. [Google Scholar] [CrossRef]
  20. Caporaso, J.G.; Kuczynski, J.; Stombaugh, J.; Bittinger, K.; Bushman, F.D.; Costello, E.K.; Fierer, N.; Gonzalez Peña, A.; Goodrich, J.K.; Gordon, J.I.; et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 2010, 7, 335–336. [Google Scholar] [CrossRef] [Green Version]
  21. Yoon, S.H.; Ha, S.M.; Kwon, S.; Lim, J.; Kim, Y.; Seo, H.; Chun, J. Introducing EzBioCloud: A taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. Int. J. Syst. Evol. Microbiol. 2017, 67, 1613–1617. [Google Scholar] [CrossRef]
  22. Oksanen, J.; Blanchet, F.G.; Friendly, M.; Kindt, R.; Legendre, P.; McGlinn, D.; Minchin, P.R.; O’Hara, R.B.; Simpson, G.L.; Solymos, P.; et al. Vegan: Community Ecology Package, R Package Version 2.5-6. 2019. Available online: (accessed on 20 January 2021).
  23. McMurdie, P.J.; Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 2013, 8, e61217. [Google Scholar] [CrossRef] [Green Version]
  24. Anderson, M.J.; Willis, T.J. Canonical analysis of principal coordinates: A useful method of constrained ordination for ecology. Ecology 2003, 84, 511–525. [Google Scholar] [CrossRef]
  25. Noto, Y.; Yasuda, M. The synthetic evaluation of water quality of rivers. J. JSCE 1983, 338, 79–88. [Google Scholar] [CrossRef] [Green Version]
  26. Liu, M.; Hata, A.; Katayama, H.; Kasuga, I. Consecutive ultrafiltration and silica adsorption for recovery of extracellular antibiotic resistance genes from an urban river. Environ. Pollut. 2020, 260, 114062. [Google Scholar] [CrossRef] [PubMed]
  27. Jokinen, C.C.; Cook, S.R.; Reuter, T.; Tymensen, L. Assessing enterococci as an alternative fecal indicator for irrigation water quality. Agric. Water Manag. 2020, 233, 106098. [Google Scholar] [CrossRef]
  28. Bussi, G.; Whitehead, P.G.; Thomas, A.R.C.; Masante, D.; Jones, L.; Cosby, B.J.; Emmett, B.A.; Malham, S.K.; Prudhomme, C.; Prosser, H. Climate and land-use change impact on faecal indicator bacteria in a temperate maritime catchment (the River Conwy, Wales). J. Hydrol. 2017, 553, 248–261. [Google Scholar] [CrossRef] [Green Version]
  29. Fang, T.; Cui, Q.; Huang, Y.; Dong, P.; Wang, H.; Liu, W.T.; Ye, Q. Distribution comparison and risk assessment of free-floating and particle-attached bacterial pathogens in urban recreational water: Implications for water quality management. Sci. Total Environ. 2018, 613–614, 428–438. [Google Scholar] [CrossRef]
  30. McLellan, S.L.; Huse, S.M.; Mueller-Spitz, S.R.; Andreishcheva, E.N.; Sogin, M.L. Diversity and population structure of sewage-derived microorganisms in wastewater treatment plant influent. Environ Microbiol. 2010, 12, 378–392. [Google Scholar] [CrossRef] [Green Version]
  31. Baumann, P. Isolation of Acinetobacter from soil and water. J. Bacteriol. 1968, 96, 39–42. [Google Scholar] [CrossRef] [Green Version]
  32. Brandi, G.; Sisti, M.; Schiavano, G.F.; Salvaggio, L.; Albano, A. Survival of Aeromonas hydrophila, Aeromonas caviae and Aeromonas sobria in soil. J. Appl. Microbiol. 1996, 81, 439–444. [Google Scholar] [CrossRef]
  33. Peix, A.; Ramírez-Bahena, M.; Velázquez, E. The current status on the taxonomy of Pseudomonas revisited: An update. Infect. Genet. Evol. 2018, 57, 106–116. [Google Scholar] [CrossRef]
  34. Suzuki, Y.; Kajii, S.; Nishiyama, M.; Iguchi, A. Susceptibility of Pseudomonas aeruginosa isolates collected from river water in Japan to antipseudomonal agents. Sci. Total Environ. 2013, 450–451, 148–154. [Google Scholar] [CrossRef]
  35. Tabata, K.; Kasuya, K.; Abe, H.; Masuda, K.; Doi, Y. Poly(Aspartic Acid) Degradation by a Sphingomonas sp. Isolated from Freshwater. Appl. Environ. 1999, 65, 4268–4270. [Google Scholar] [CrossRef] [Green Version]
  36. Vachée, A.; Mossel, D.A.; Leclerc, H. Antimicrobial activity among Pseudomonas and related strains of mineral water origin. J. Appl. Microbiol. 1997, 83, 652–658. [Google Scholar] [CrossRef]
  37. Steele, T.W.; Moore, C.V.; Sangster, N. Distribution of Legionella longbeachae serogroup 1 and other legionellae in potting soils in Australia. Appl. Environ. Microbiol. 1990, 56, 2984–2988. [Google Scholar] [CrossRef] [Green Version]
  38. Fisher, J.C.; Levican, A.; Figueras, M.J.; McLellan, S.L. Population dynamics and ecology of Arcobacter in sewage. Front. Microbiol. 2014, 5, 525. [Google Scholar] [CrossRef] [Green Version]
  39. Li, W.C.; Tse, H.F.; Fok, L. Plastic waste in the marine environment: A review of sources, occurrence and effects. Sci. Total Environ. 2016, 566–567, 333–349. [Google Scholar] [CrossRef]
  40. Wagner, J.; Coupland, P.; Browne, H.P.; Lawley, T.D.; Francis, S.C.; Parkhill, J. Evaluation of PacBio sequencing for full-length bacterial 16S rRNA gene classification. BMC Microbiol. 2016, 16, 274. [Google Scholar] [CrossRef] [Green Version]
  41. Suzuki, Y.; Ahdoh, Y.; Maruyama, T.; Mitsuyama, M.; Shimotsu, Y.; Gomi, K.; Mori, H.; Kunikane, S. Removal of di-2-ethylhexyl phthalate (DEHP) form river water using dispersed bubbles. J. JSWE 2006, 29, 29–35. [Google Scholar] [CrossRef] [Green Version]
  42. Chui, L.; Couturier, M.R.; Chiu, T.; Wang, G.; Olson, A.B.; McDonald, R.R.; Antonishyn, N.A.; Horsman, G.; Gilmour, M.W. Comparison of Shiga toxin-producing Escherichia coli detection methods using clinical stool samples. J. Mol. Diagn. 2010, 12, 469–475. [Google Scholar] [CrossRef]
  43. Vu, D.T.; Sethabutr, O.; Von Seidlein, L.; Tran, V.T.; Do, G.C.; Bui, T.C.; Le, H.T.; Lee, H.; Houng, H.S.; Hale, T.L.; et al. Detection of Shigella by a PCR assay targeting the ipaH gene suggests increased prevalence of shigellosis in Nha Trang, Vietnam. J. Clin. Microbiol. 2004, 42, 2031–2035. [Google Scholar] [CrossRef] [Green Version]
  44. Vondrakova, L.; Pazlarova, J.; Demnerova, K. Detection, identification and quantification of Campylobacter jejuni, coli and lari in food matrices all at once using multiplex qPCR. Gut. Pathog. 2014, 6, 12. [Google Scholar] [CrossRef]
  45. Silkie, S.S.; Tolcher, M.P.; Nelson, K.L. Reagent decontamination to eliminate false-positives in Escherichia coli qPCR. J. Microbiol. Methods 2008, 72, 275–282. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Sampling sites in the Kiyotake River watershed. Land-use patterns (urbanized area, densely inhabited district, forest area, and agricultural area) and the location of wastewater treatment plants are also shown.
Figure 1. Sampling sites in the Kiyotake River watershed. Land-use patterns (urbanized area, densely inhabited district, forest area, and agricultural area) and the location of wastewater treatment plants are also shown.
Water 14 03642 g001
Figure 2. Relative abundance of bacterial phyla that are present at >2% in the populations at each site.
Figure 2. Relative abundance of bacterial phyla that are present at >2% in the populations at each site.
Water 14 03642 g002
Figure 3. Principal coordinate analysis (PCoA) plots showing the Bray–Curtis dissimilarities among the bacterial communities. Sites were divided into three groups based on the land-use patterns: the upper sites (S1–S3), the middle sites (S4–S6), and the lower sites (S7 and S8).
Figure 3. Principal coordinate analysis (PCoA) plots showing the Bray–Curtis dissimilarities among the bacterial communities. Sites were divided into three groups based on the land-use patterns: the upper sites (S1–S3), the middle sites (S4–S6), and the lower sites (S7 and S8).
Water 14 03642 g003
Figure 4. Canonical analysis of principal coordinates (CAP) plots showing the associations between environmental variables and the patterns in bacterial communities. Microbial communities were analyzed using Bray–Curtis distance matrices. All environmental variables shown in the plots had significant effects (p < 0.05) on the microbial community patterns, based on PERMANOVA. Sites were divided into three groups based on the land-use patterns: the upper sites (S1–S3), the middle sites (S4–S6), and the lower sites (S7 and S8).
Figure 4. Canonical analysis of principal coordinates (CAP) plots showing the associations between environmental variables and the patterns in bacterial communities. Microbial communities were analyzed using Bray–Curtis distance matrices. All environmental variables shown in the plots had significant effects (p < 0.05) on the microbial community patterns, based on PERMANOVA. Sites were divided into three groups based on the land-use patterns: the upper sites (S1–S3), the middle sites (S4–S6), and the lower sites (S7 and S8).
Water 14 03642 g004
Figure 5. The relative abundances of the 16 potentially pathogenic bacterial genera at each site. Only major genera that were present at >0.1% of the population are shown.
Figure 5. The relative abundances of the 16 potentially pathogenic bacterial genera at each site. Only major genera that were present at >0.1% of the population are shown.
Water 14 03642 g005
Table 1. Water qualities at each sampling site in 2017 and 2018.
Table 1. Water qualities at each sampling site in 2017 and 2018.
TurbidityTOCFecal Bacteria (Mean CFU/100 mL) **
(°C)(-)(μS/cm)(Degree *)(mg/L)Total ColiformsEscherichia coliEnterococcus
S117.522.47.107.4975.893.20.491.550.331.6 × 1027.4 × 1025.0 × 1003 × 10−13.7 × 1002.0 × 100
S216.823.07.017.6477.486.70.570.740.211.8 × 1026.3 × 1021.0 × 1004.0 × 1001.3 × 1003.3 × 100
S316.124.06.987.8087.687.00.711.140.213.5 × 1021.4 × 1034.0 × 1004.0 × 1002.9 × 1015.8 × 101
S417.225.26.947.79100.8105.60.411.210.331.6 × 1035.0 × 1035.8 × 1013.1 × 1015.7 × 1017.4 × 101
S517. × 1034.9 × 1034.6 × 1014.1 × 1014.2 × 1018.0 × 101
S618.326.47.037.78123.9132.70.891.010.401.3 × 1036.8 × 1039.0 × 1011.9 × 1012.0 × 1018.2 × 101
S719.426.87.227.68123.4128. × 1034.2 × 1035.3 × 1014.7 × 1011.2 × 1021.1 × 102
S819.127.46.877.56720.0166.01.071.970.441.0 × 1032.0 × 1035.7 × 1012.0 × 1011.9 × 1011.6 × 101
Note: *: Turbidity as kaolin unit: 1.0 mg/L of kaolin clay suspension = 1.0 degree as kaolin unit. **: Mean of three replicates.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Suzuki, Y.; Jikumaru, A.; Tamai, S.; Nukazawa, K.; Masago, Y.; Ishii, S. Application of Coagulation and Foam Concentration Method to Quantify Waterborne Pathogens in River Water Samples. Water 2022, 14, 3642.

AMA Style

Suzuki Y, Jikumaru A, Tamai S, Nukazawa K, Masago Y, Ishii S. Application of Coagulation and Foam Concentration Method to Quantify Waterborne Pathogens in River Water Samples. Water. 2022; 14(22):3642.

Chicago/Turabian Style

Suzuki, Yoshihiro, Atsushi Jikumaru, Soichiro Tamai, Kei Nukazawa, Yoshifumi Masago, and Satoshi Ishii. 2022. "Application of Coagulation and Foam Concentration Method to Quantify Waterborne Pathogens in River Water Samples" Water 14, no. 22: 3642.

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop