Article pubs.acs.org/est
Water Quality Monitoring and Risk Assessment by Simultaneous Multipathogen Quantification Satoshi Ishii,* Takamitsu Nakamura, Shuji Ozawa, Ayano Kobayashi, Daisuke Sano, and Satoshi Okabe Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, Sapporo 060-8628, Japan S Supporting Information *
ABSTRACT: Water quality monitoring and microbial risk assessment are important to ensure safe water for drinking, recreational, and agricultural purposes. In this study, we applied a microfluidic quantitative PCR (MFQPCR) approach to simultaneously quantify multiple waterborne pathogens in a natural freshwater lake in Hokkaido, Japan, from April to November, 2012. Tens of thousands of geese stopped over at this lake during their migration in spring and fall. Because lake water is used for irrigation of the surrounding agricultural area, we assessed infection risks through irrigation water usage based on pathogen concentrations directly measured by MFQPCR. We detected various pathogens in the lake water, particularly during the bird migration seasons, suggesting that migratory birds were the main source of the pathogens. However, neither counts of geese nor fecal indicator bacteria were good predictors of pathogen concentrations. On the basis of quantitative microbial risk assessment, concentrations of Campylobacter jejuni and Shigella spp. in water samples were above the concentrations that can potentially cause 10−4 infections per person per year when water is used to grow fresh vegetables. These results suggest that direct and simultaneous multipathogen quantification can provide more reliable and comprehensive information for risk assessment than the current fecal indicator-based approach.
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INTRODUCTION Pathogens that contaminate drinking, recreational, or irrigation water can potentially cause human diseases. Therefore, levels of contamination and their potential risks to human health should be properly assessed and controlled. Currently, microbial water quality is assessed according to levels of fecal indicator bacteria (FIB) such as Escherichia coli or enterococci.1,2 This is based on the assumption that FIB behave similarly to enteric pathogens after they are released from their hosts, such as humans and warm-blooded animals. However, FIB of nonfecal origin are now widely recognized.1,2 In addition, a poor correlation has sometimes been observed between FIB counts and the occurrence of pathogens. 3−5 Therefore, the FIB-based approach may not provide reliable information for assessing microbial risks. The direct quantification of multiple pathogens would enable us to perform more reliable and comprehensive risk assessment than the indirect FIB-based approach. However, only a limited number of reports are available;6 this is most likely be due to the lack of appropriate techniques to quantify multiple pathogens of concern. We had previously developed a novel method to simultaneously quantify multiple pathogens by using microfluidic quantitative PCR (MFQPCR) technology.7 We could quantify pathogens in spiked environmental water samples that were inoculated with pathogens at concentrations as low as 100 cells/L.7 This approach is promising for future water quality monitoring because it can assess up to 96 targets and 96 © 2014 American Chemical Society
samples simultaneously and can detect relatively low pathogen concentrations. However, it has not been tested in natural, nonspiked environmental water samples. In this study, we applied the MFQPCR method to monitor levels of multiple pathogenic bacteria in natural, nonspiked environmental water samples collected from Lake Miyajimanuma watersheds, Hokkaido, Japan, over 1 year of sampling. Lake Miyajimanuma is the largest stopover site for greater white-fronted geese (Anser albif rons) in Japan, with >60 000 geese using this small lake (ca. 30 ha) area during the spring and fall migration seasons (Supporting Information, SI, Figure S1, ).8 Effluent water from the lake enters into irrigation canals, and is subsequently used for agriculture (Figure 1). Therefore, fecal contamination by geese is of concern for safe food production because geese may carry some human pathogens, such as Salmonella spp.9 and Campylobacter spp.10 However, human health risks associated with lake water have not been analyzed. Consequently, the objectives of this study were (i) to apply MFQPCR to quantify multiple pathogens in Lake Miyajimanuma watersheds, (ii) to analyze potential sources of pathogens, and (iii) to perform quantitative microbial risk Received: Revised: Accepted: Published: 4744
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at 10 000g for 15 min. DNA was extracted from the cell pellets using a PowerSoil DNA Isolation kit (MoBio Laboratories, Carlsbad, CA). DNA was also extracted from 0.2 g of fecal samples using a QIAamp DNA Stool Mini kit (Qiagen, Hilden, Germany). DNA samples were stored at −20 °C until use. A specific target amplification (STA) reaction was performed to preamplify target genes prior to MFQPCR. The STA reaction is a previously optimized 14-cycle multiplex PCR that uses the same 34 primers that are used for MFQPCR.7 This reaction is necessary to quantify target genes that are present in DNA samples in small amounts. The STA reaction does not have major impacts on the quantitative results obtained by MFQPCR.7 Both DNA samples and the standard plasmid mixture were subjected to the STA reaction. MFQPCR was performed using a 96.96 Dynaic Array chip (Fluidigm, South San Francisco, CA) and a BioMark HD Reader (Fluidigm). Seventeen genes (eaeA, stx1, stx2, ipaH 7.8, ipaH all, virA, invA, ttrC, cadF, ciaB, cpe, plc, mip, iap, hlyA, ctxA, and tdh) were quantified, in quadruplicate, based on the standard curve method, as described previously in detail.7 By targeting these genes, we can quantify pathogenic E. coli, Shigella spp., Salmonella spp., Campylobacter jejuni, Clostridium perf ringens, Legionella pneumophila, Listeria monocytogenes, Vibrio cholerae, and Vibrio parahemolyticus. In addition to MFQPCR, we also performed conventional qPCR to confirm the results obtained by MFQPCR. Aliquots (2 μL) of DNA samples were directly used (i.e., without the STA reaction) for conventional qPCR in a final volume of 20 μL. Conventional qPCR was performed in duplicate using an ABI Prism 7000 Sequence Detection System (Applied Biosystems), as described previously.7 Data Analysis. Quantitative values were obtained from MFQPCR and conventional qPCR results by using standard curves as previously described.7 Values below the lowest concentration on the standard curve (i.e., detected but not quantifiable) were treated as negative.14 In addition, values obtained from less than 50% of qPCR replicates were also treated as negative. We converted gene copies/μL DNA samples to cells/L water samples, assuming that the cell recovery and DNA extraction efficiency were 100% in all samples, and that only one gene copy was present per cell.7 For statistical analysis, numerical data (e.g., gene quantity, E. coli counts, goose counts) were log transformed to satisfy parametric assumptions of equality of variances and a normal distribution.15 Pearson’s correlation, Spearman’s rank correlation, and Kendall’s rank correlation were calculated between gene quantity, E. coli counts, and goose counts, by using R ver. 3.0.2. QMRA. Health risks associated with lake water used for agricultural irrigation purposes were assessed using the QMRA approach.16−18 We calculated a point estimate for the pathogen concentration (Cinf) that can result in an infection probability of 10−4 infections per person per year. This calculation was based on the pathogen-specific β-Poisson dose−response models (eq 1):
Figure 1. Map of sampling locations. Water samples were collected from sites MJ1, MJ2, MJ3, and MJ4. Legends: white, agricultural fields; light gray, water body; dark gray, forest; black fill, houses; and black lines, waterways. Flow directions of waterways are shown by block arrows.
assessment (QMRA) of lake water used for agricultural purposes.
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MATERIALS AND METHODS Sampling Sites. Water samples were collected from four streams around Lake Miyajimanuma (Figure 1) from April to November, 2012. Site MJ1 (43° 33.47′ N, 141° 71.24′ W) was an influent stream to the lake, which contained water discharged from surrounding agricultural fields. Site MJ2 (43° 33.39′ N, 141° 71.66′ W) was an effluent stream from the lake. Sites MJ3 and MJ4 were located along the same irrigation canal. Site MJ3 (43° 33.71′ N, 141° 71.95′ W) was located upstream from the discharge of effluent water from the lake; whereas site MJ4 (43° 32.88′ N, 141° 71.71′ W) was located downstream from the lake discharge. Water from MJ3 and MJ4 sites were used for irrigation. At each site, ca. 10-L water samples were collected. Electrical conductivity (EC), and pH were measured on-site using a EC and a pH meter, respectively (Horiba, Kyoto, Japan). Total N and total P concentrations were measured by persulfate method and ascorbic acid method, respectively, according to the standard analytical procedure.11 Suspended solids (SS) were collected on glass fiber membrane (Advantec, Tokyo, Japan) and their oven-dried weights were measured.11 E. coli counts were obtained using the most probable number (MPN) method with an EC blue kit (Nissui Pharmaceutical, Tokyo, Japan). Fresh fecal droppings from geese were collected from agricultural fields around Lake Miyajimanuma using a surfacedisinfected spatula. Fecal samples were stored in Whirl-Pak bags (Nasco, Fort Atkinson, WI) and were stored on ice during transportation to the laboratory. DNA Extraction and MFQPCR. Water samples (5 L) were pressure filtered through 0.22-μm-pore poly(ether sulfone) membrane filters (Millipore, Billerica, MA), and cells on the membrane surface were detached with vigorous shaking in phosphate buffered saline (pH 7.2) containing 0.1% gelatin,12 as previously described.13 Cells were pelleted by centrifugation
⎛ ⎞−α d 1/ α Pd = 1 − ⎜1 + (2 − 1)⎟ N50 ⎝ ⎠
(1)
where Pd is an infection probability caused by a single exposure of a pathogen, d is number of pathogens ingested, N50 is the number of pathogens that can infect 50% of the exposed population, and α is a slope parameter for β-Poisson 4745
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distribution.16 The α and N50 values are different by pathogens (SI Table S1, ). In this study, we assumed that (i) one gene copy corresponds to one infection dose; (ii) people consume 100 g fresh vegetables every 2 days; (iii) 10 mL of irrigation water remains on 100 g fresh vegetables; and (iv) pathogen concentrations are reduced by 3 log before consumption by washing the vegetables.17,19 On the basis of these assumptions, d can be expressed as a function of Cinf [copies/L] by (eq 2): 10 d = C inf × × 10−3 (2) 1000
Miyajimanuma (SI Figure S3, ). Enteropathogenic E. coli (EPEC), Shigella spp., C. jejuni, C. perf ringens, and V. cholerae were frequently detected in all sampling sites at various sampling time points (Figure 3). Similar results were also obtained by conventional qPCR. Pathogen concentrations measured by MFQPCR were in good correlation with those measured by conventional qPCR (Figure 4), suggesting that MFQPCR could provide quantitative information that is as accurate and reliable as conventional qPCR. Although the STA reaction was used in the MFQPCR method, no STA reaction was used in conventional qPCR. This finding suggests that the STA reaction did not have major impacts on the results obtained by qPCR, similar to the previously reported spike-in experiments.7 In addition, sequencing analysis of the representative qPCR amplicons confirmed that the target molecules were correctly amplified (data not shown). These results suggest that MFQPCR is also applicable to natural, nonspiked, environmental water samples. Temporal Variation in Pathogen Concentrations. Seasonal variation was observed in pathogen concentrations. In general, pathogen concentrations were high when geese were present in the lake (Figure 3), although no significant correlation was observed between goose counts and pathogen concentrations both by parametric (Pearson’s correlation) and nonparametric (Spearman’s and Kendall’s rank correlations) methods. This is most likely due to the long-term survival of pathogens after fecal deposition by geese, particularly under low-temperature conditions.10 Sunlight can also negatively impact the survival of pathogens,10,23,24 and this can also partly explain the low abundance of pathogens in the summer months. Shigella spp. (ipaH and virA) and V. cholerae (ctxA) were detected in September before the arrival of migratory birds, similar to E. coli. Because Shigella spp. is now recognized as a subspecies of E. coli,25 some Shigella spp. may be able to grow in environments in which other E. coli grow.2 The occurrence of V. cholerae may be attributed to the environmental reservoirs for this pathogen, as described below. We observed poor correlations between FIB (E. coli) and pathogen concentrations, probably due to the different survival and growth kinetics or to the different source origins between FIB and pathogens. Furthermore, we sometimes observed a negative correlation between E. coli and pathogen concentrations (e.g., plc, r2 = 0.39; P = 0.013). These results suggest that it is difficult to predict pathogen concentrations based on E. coli counts alone, similar to a previous study.4 Potential Sources of Pathogens. To further examine if fecal droppings from geese were responsible for pathogens in water samples, we performed MFQPCR as well as conventional qPCR targeting EPEC, Shigella spp., C. jejuni, C. perf ringens, and V. cholerae in fecal droppings from geese (n = 55). As a result, eaeA, ipaH all, virA, ciaB, and plc were detected in 20%, 7.8%, 9.7%, 16.3%, and 3.6% of feces, respectively. The average pathogen densities in the positive samples ranged from 3.5 to 4.2 log copies/g feces (SI Table S2). These results suggest that geese could have contributed to the excretion of multiple pathogens. Because greater white-fronted geese travel nearly 3500 km between Japan and northeastern Russia,8 they may contribute significantly to the distribution of pathogenic bacteria in broad areas, similar to other migratory birds (e.g., cranes).26 Geese have previously been reported to carry several pathogens, such as Salmonella spp.9,27 and Campylobacter
In addition, Pd can be calculated from an annual infection probability (Pyr) by (eq 3): Pd = 1 − (1 − Pyr)1/(365/2)
(3)
−4
In this study, Pyr of 10 infections per person per year was considered acceptable.17 By using (eq 1−3), we can calculate Cinf for each pathogen. The occurrence of pathogens at >Cinf were considered as a threat to human health.
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RESULTS AND DISCUSSION General Water Quality. In general, water quality deteriorated during the bird migration seasons (i.e., late April to early May and late September to October; SI Figure S2). Fecal droppings from thousands of waterfowls most likely caused eutrophication of the lake. However, the influent stream to the lake contained fertilizer runoff, which can also cause eutrophication.20,21 In 2012, an algal bloom was observed due to the eutrophication of the lake. The occurrence of algae probably caused an increase in pH in the lake-impacted water (MJ2 and MJ4) samples during the summer and fall months. Levels of E. coli also increased during the bird migration seasons (Figure 2), indicating the occurrence of fecal
Figure 2. Temporal variations in E. coli and goose counts in Lake Miyajimanuma environments. E. coli counts in water samples collected from MJ1 (○), MJ2 (□), MJ3 (△), and MJ4 (◊) sites were measured using the most probable number (MPN) method. Goose counts reported by the Miyajimanuma Waterbird & Wetland Center are plotted as a dashed line.
contamination due to waterfowls. However, E. coli also increased during August to September before the arrival of migratory birds. This increase may be due to the growth of E. coli in response to nutrients provided by soil, sediments, or algae because E. coli are known to grow in these environments.2,22 Consequently, no significant correlation was observed between E. coli and goose counts. Occurrence of Pathogens. Various enteric pathogens were detected in water samples collected around Lake 4746
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Figure 3. Temporal variations in pathogen concentrations in Lake Miyajimanuma environments. The quantitative results for eaeA (targeting EPEC), ciaB (targeting C. jejuni), ipaH 7.8 and virA (targeting Shigella spp.), plc (targeting C. perf ringens), and ctxA (targeting V. cholerae) are shown. The quantitative results for all genes are shown in Figure S3 (SI). Because these genes are present at one copy per genome, the unit of copies/L is equivalent to cells/L. Concentrations in water samples collected from MJ1 (○), MJ2 (□), MJ3 (△), and MJ4 (◊) sites are shown for each gene. The lower quantification limit was 2 log copies/L water. Geese migration seasons are shaded in gray.
In contrast, ctxA, a marker gene for V. cholerae, was not detected in fecal droppings from geese. This pathogen may have originated from other animal or environmental reservoirs. Benthic animals, algae, cyanobacteria, and other planktons can serve as important reservoirs for V. cholerae.29−31 Spatial Variation in Pathogen Concentrations. The occurrence of pathogens did not vary by sampling sites, although geese spend the majority of their time in the lake. Initially, we expected higher pathogen concentrations in the streams impacted by effluent water from the lake (MJ2 and MJ4). However, we also frequently observed high pathogen concentrations in the streams not impacted by effluent water from the lake (MJ1 and MJ3). This is most likely due to the contamination of the MJ1 and MJ3 streams by geese feces from the surrounding agricultural field. Geese feed on the agricultural field around Lake Miyajimanuma8 and drop their feces on this field (SI Figure S4). Subsequently, pathogens in their fecal droppings may have been transported to the streams by surface runoff. QMRA. We performed QMRA for water samples collected in this study based on the quantitative data obtained by MFQPCR. Pathogen concentration that can cause infection at a probability of 10−4 infections per person per year (Cinf) was calculated as 5.68, 1.38, 0.46, and 0.55 log cells/L for pathogenic E. coli, Shigella spp., C. jejuni, and V. cholerae, respectively. We could not calculate Cinf for C. perf ringens because the β-Poisson parameters for this pathogen were not available. We sometimes observed higher concentrations for Shigella spp. and C. jejuni in water samples collected from Lake Miyajimanuma, suggesting that these water samples may have the potential to cause human diseases if used as irrigation water to grow fresh vegetables. Currently, fresh vegetables are not cultivated for commercial purposes in the study area. However,
Figure 4. Correlation between concentrations measured by MFQPCR and those measured by conventional qPCR. The quantitative results for eaeA (○), ipaH 7.8 (□), ipaH all (◊), virA (△), ciaB (×), and ctxA (+) were used for comparison. The linear regression equation and goodness-of-fit (r2) value are also shown.
spp.10,26,27 Our study showed that geese also carried EPEC, Shigella spp., and C. perf ringens. However, Salmonella spp. was not detected in our fecal samples from geese, in agreement with the absence of Salmonella marker genes in water samples. No detection of Salmonella spp. was also verified using a qPCRbased Salmonella detection kit (Primerdesign, Southampton, U.K.) and by an enrichment culture method.28 These results were in contrast to the report by Jokinen et al.,27 in which Salmonella spp. was isolated from 10% of the feces from Canada geese collected in Alberta, Canada. These results indicate that the detection of Salmonella spp. in fecal droppings from geese may depend on area or host species (greater white-fronted geese vs. Canada geese). 4747
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Environmental Science & Technology care should be taken when farmers want to grow fresh vegetables. Dilution or filtration is recommended if lake water is used to grow fresh vegetables.32 The recent trend for water quality monitoring has been directed toward the use of QMRA.33,34 However, it is difficult to predict pathogen concentrations using the current FIB-based approach.4 In this study, we showed that QMRA was possible based on pathogen concentrations directly measured by MFQPCR. On the basis of the results obtained by QMRA, the water in Lake Miyajimanuma had potential health risks when used as a source of irrigation water to grow fresh vegetables, particularly during the spring and fall months. The assumptions used in this study are based on relatively old literature.17,19 For more accurate QMRA, we need to develop better predictive models because risks can vary significantly by vegetables.18 The behavior of pathogens (e.g., decay rate) in environments can also influence the calculation. In addition, there is a room for improvement in DNA-based gene quantification. For example, we need to eliminate the PCR signals from dead cells, to improve DNA recovery efficiencies, and to evaluate the presence of PCR inhibitory substances.35 The discrimination of live and dead cells by using propidium monoazide36 and the use of sample processing controls or internal amplification controls35,37 can help increase the accuracy of QMRA using DNA-based quantitative data such as those obtained by MFQPCR. In conclusion, the MFQPCR method is applicable in the direct quantification of multiple pathogens in natural, nonspiked environmental water and fecal samples. In water samples collected from Lake Miyajimanuma, several enteric pathogens were frequently detected, the concentrations of which were sometimes >Cinf in water used as irrigation water to grow fresh vegetables. Waterfowl were the most probable sources of many of these pathogens. Direct, simultaneous multipathogen quantification can provide more reliable information for risk assessment than the current FIB-based approach. Instead of searching for an alternative FIB, which has not been very successful so far,3 we would like to propose to directly monitor multiple pathogens in environmental water samples for water quality monitoring and QMRA.
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ACKNOWLEDGMENTS
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REFERENCES
We thank Takahiro Segawa at National Institute for Polar Research for allowing us to use their BioMark facility, and Katsumi Ushiyama at Miyajimanuma Waterbird & Wetland Center for providing geese count data. We also thank Reiko Hirano for technical assistance, and Sumire Sakai for taking beautiful pictures of the geese and Lake Miyajimanuma. This study was supported, in part, by the CREST program from Japan Science and Technology Agency, Grant-in Aid for Scientific Research A (23246094) from Japan Society for the Promotion of Science.
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ASSOCIATED CONTENT
S Supporting Information *
Parameter values for the pathogen-specific β-Poisson dose− response models (Table S1), frequencies of occurrence and average densities of pathogens in fecal droppings from geese (Table S2), photograph of geese in Lake Miyajimanuma (Figure S1), temporal variations in general water quality (Figure S2), quantitative detection of various virulence factor genes in Lake Miyajimanuma water samples (Figure S3), and photograph of geese and their feces on the agricultural field (Figure S4). This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*Email: [email protected]; Phone: +81-11-706-7162; Fax: +81-11-706-7162. Notes
The authors declare no competing financial interest. 4748
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(34) Ashbolt, N. J.; Schoen, M. E.; Soller, J. A.; Roser, D. J. Predicting pathogen risks to aid beach management: the real value of quantitative microbial risk assessment (QMRA). Water Res. 2010, 44, 4692−4703. (35) Kobayashi, A.; Sano, D.; Taniuchi, A.; Ishii, S.; Okabe, S. Use of a genetically-engineered Escherichia coli strain as a sample process control for quantification of the host-specific bacterial genetic markers. Appl. Microbiol. Biotechnol. 2013, 97, 9165−9173. (36) Nocker, A.; Cheung, C.-Y.; Camper, A. K. Comparison of propidium monoazide with ethidium monoazide for differentiation of live vs dead bacteria by selective removal of DNA from dead cells. J. Microbiol. Methods 2006, 67, 310−320. (37) Stoeckel, D. M.; Stelzer, E. A.; Dick, L. K. Evaluation of two spike-and-recovery controls for assessment of extraction efficiency in microbial source tracking studies. Water Res. 2009, 43, 4820−4827.
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Water Quality Monitoring and Risk Assessment by Simultaneous Multipathogen Quantification Satoshi Ishii,* Takamitsu Nakamura, Shuji Ozawa, Ayano Kobayashi, Daisuke Sano, and Satoshi Okabe Division of Environmental Engineering, Faculty of Engineering, Hokkaido University, Sapporo 060-8628, Japan S Supporting Information *
ABSTRACT: Water quality monitoring and microbial risk assessment are important to ensure safe water for drinking, recreational, and agricultural purposes. In this study, we applied a microfluidic quantitative PCR (MFQPCR) approach to simultaneously quantify multiple waterborne pathogens in a natural freshwater lake in Hokkaido, Japan, from April to November, 2012. Tens of thousands of geese stopped over at this lake during their migration in spring and fall. Because lake water is used for irrigation of the surrounding agricultural area, we assessed infection risks through irrigation water usage based on pathogen concentrations directly measured by MFQPCR. We detected various pathogens in the lake water, particularly during the bird migration seasons, suggesting that migratory birds were the main source of the pathogens. However, neither counts of geese nor fecal indicator bacteria were good predictors of pathogen concentrations. On the basis of quantitative microbial risk assessment, concentrations of Campylobacter jejuni and Shigella spp. in water samples were above the concentrations that can potentially cause 10−4 infections per person per year when water is used to grow fresh vegetables. These results suggest that direct and simultaneous multipathogen quantification can provide more reliable and comprehensive information for risk assessment than the current fecal indicator-based approach.
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INTRODUCTION Pathogens that contaminate drinking, recreational, or irrigation water can potentially cause human diseases. Therefore, levels of contamination and their potential risks to human health should be properly assessed and controlled. Currently, microbial water quality is assessed according to levels of fecal indicator bacteria (FIB) such as Escherichia coli or enterococci.1,2 This is based on the assumption that FIB behave similarly to enteric pathogens after they are released from their hosts, such as humans and warm-blooded animals. However, FIB of nonfecal origin are now widely recognized.1,2 In addition, a poor correlation has sometimes been observed between FIB counts and the occurrence of pathogens. 3−5 Therefore, the FIB-based approach may not provide reliable information for assessing microbial risks. The direct quantification of multiple pathogens would enable us to perform more reliable and comprehensive risk assessment than the indirect FIB-based approach. However, only a limited number of reports are available;6 this is most likely be due to the lack of appropriate techniques to quantify multiple pathogens of concern. We had previously developed a novel method to simultaneously quantify multiple pathogens by using microfluidic quantitative PCR (MFQPCR) technology.7 We could quantify pathogens in spiked environmental water samples that were inoculated with pathogens at concentrations as low as 100 cells/L.7 This approach is promising for future water quality monitoring because it can assess up to 96 targets and 96 © 2014 American Chemical Society
samples simultaneously and can detect relatively low pathogen concentrations. However, it has not been tested in natural, nonspiked environmental water samples. In this study, we applied the MFQPCR method to monitor levels of multiple pathogenic bacteria in natural, nonspiked environmental water samples collected from Lake Miyajimanuma watersheds, Hokkaido, Japan, over 1 year of sampling. Lake Miyajimanuma is the largest stopover site for greater white-fronted geese (Anser albif rons) in Japan, with >60 000 geese using this small lake (ca. 30 ha) area during the spring and fall migration seasons (Supporting Information, SI, Figure S1, ).8 Effluent water from the lake enters into irrigation canals, and is subsequently used for agriculture (Figure 1). Therefore, fecal contamination by geese is of concern for safe food production because geese may carry some human pathogens, such as Salmonella spp.9 and Campylobacter spp.10 However, human health risks associated with lake water have not been analyzed. Consequently, the objectives of this study were (i) to apply MFQPCR to quantify multiple pathogens in Lake Miyajimanuma watersheds, (ii) to analyze potential sources of pathogens, and (iii) to perform quantitative microbial risk Received: Revised: Accepted: Published: 4744
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at 10 000g for 15 min. DNA was extracted from the cell pellets using a PowerSoil DNA Isolation kit (MoBio Laboratories, Carlsbad, CA). DNA was also extracted from 0.2 g of fecal samples using a QIAamp DNA Stool Mini kit (Qiagen, Hilden, Germany). DNA samples were stored at −20 °C until use. A specific target amplification (STA) reaction was performed to preamplify target genes prior to MFQPCR. The STA reaction is a previously optimized 14-cycle multiplex PCR that uses the same 34 primers that are used for MFQPCR.7 This reaction is necessary to quantify target genes that are present in DNA samples in small amounts. The STA reaction does not have major impacts on the quantitative results obtained by MFQPCR.7 Both DNA samples and the standard plasmid mixture were subjected to the STA reaction. MFQPCR was performed using a 96.96 Dynaic Array chip (Fluidigm, South San Francisco, CA) and a BioMark HD Reader (Fluidigm). Seventeen genes (eaeA, stx1, stx2, ipaH 7.8, ipaH all, virA, invA, ttrC, cadF, ciaB, cpe, plc, mip, iap, hlyA, ctxA, and tdh) were quantified, in quadruplicate, based on the standard curve method, as described previously in detail.7 By targeting these genes, we can quantify pathogenic E. coli, Shigella spp., Salmonella spp., Campylobacter jejuni, Clostridium perf ringens, Legionella pneumophila, Listeria monocytogenes, Vibrio cholerae, and Vibrio parahemolyticus. In addition to MFQPCR, we also performed conventional qPCR to confirm the results obtained by MFQPCR. Aliquots (2 μL) of DNA samples were directly used (i.e., without the STA reaction) for conventional qPCR in a final volume of 20 μL. Conventional qPCR was performed in duplicate using an ABI Prism 7000 Sequence Detection System (Applied Biosystems), as described previously.7 Data Analysis. Quantitative values were obtained from MFQPCR and conventional qPCR results by using standard curves as previously described.7 Values below the lowest concentration on the standard curve (i.e., detected but not quantifiable) were treated as negative.14 In addition, values obtained from less than 50% of qPCR replicates were also treated as negative. We converted gene copies/μL DNA samples to cells/L water samples, assuming that the cell recovery and DNA extraction efficiency were 100% in all samples, and that only one gene copy was present per cell.7 For statistical analysis, numerical data (e.g., gene quantity, E. coli counts, goose counts) were log transformed to satisfy parametric assumptions of equality of variances and a normal distribution.15 Pearson’s correlation, Spearman’s rank correlation, and Kendall’s rank correlation were calculated between gene quantity, E. coli counts, and goose counts, by using R ver. 3.0.2. QMRA. Health risks associated with lake water used for agricultural irrigation purposes were assessed using the QMRA approach.16−18 We calculated a point estimate for the pathogen concentration (Cinf) that can result in an infection probability of 10−4 infections per person per year. This calculation was based on the pathogen-specific β-Poisson dose−response models (eq 1):
Figure 1. Map of sampling locations. Water samples were collected from sites MJ1, MJ2, MJ3, and MJ4. Legends: white, agricultural fields; light gray, water body; dark gray, forest; black fill, houses; and black lines, waterways. Flow directions of waterways are shown by block arrows.
assessment (QMRA) of lake water used for agricultural purposes.
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MATERIALS AND METHODS Sampling Sites. Water samples were collected from four streams around Lake Miyajimanuma (Figure 1) from April to November, 2012. Site MJ1 (43° 33.47′ N, 141° 71.24′ W) was an influent stream to the lake, which contained water discharged from surrounding agricultural fields. Site MJ2 (43° 33.39′ N, 141° 71.66′ W) was an effluent stream from the lake. Sites MJ3 and MJ4 were located along the same irrigation canal. Site MJ3 (43° 33.71′ N, 141° 71.95′ W) was located upstream from the discharge of effluent water from the lake; whereas site MJ4 (43° 32.88′ N, 141° 71.71′ W) was located downstream from the lake discharge. Water from MJ3 and MJ4 sites were used for irrigation. At each site, ca. 10-L water samples were collected. Electrical conductivity (EC), and pH were measured on-site using a EC and a pH meter, respectively (Horiba, Kyoto, Japan). Total N and total P concentrations were measured by persulfate method and ascorbic acid method, respectively, according to the standard analytical procedure.11 Suspended solids (SS) were collected on glass fiber membrane (Advantec, Tokyo, Japan) and their oven-dried weights were measured.11 E. coli counts were obtained using the most probable number (MPN) method with an EC blue kit (Nissui Pharmaceutical, Tokyo, Japan). Fresh fecal droppings from geese were collected from agricultural fields around Lake Miyajimanuma using a surfacedisinfected spatula. Fecal samples were stored in Whirl-Pak bags (Nasco, Fort Atkinson, WI) and were stored on ice during transportation to the laboratory. DNA Extraction and MFQPCR. Water samples (5 L) were pressure filtered through 0.22-μm-pore poly(ether sulfone) membrane filters (Millipore, Billerica, MA), and cells on the membrane surface were detached with vigorous shaking in phosphate buffered saline (pH 7.2) containing 0.1% gelatin,12 as previously described.13 Cells were pelleted by centrifugation
⎛ ⎞−α d 1/ α Pd = 1 − ⎜1 + (2 − 1)⎟ N50 ⎝ ⎠
(1)
where Pd is an infection probability caused by a single exposure of a pathogen, d is number of pathogens ingested, N50 is the number of pathogens that can infect 50% of the exposed population, and α is a slope parameter for β-Poisson 4745
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distribution.16 The α and N50 values are different by pathogens (SI Table S1, ). In this study, we assumed that (i) one gene copy corresponds to one infection dose; (ii) people consume 100 g fresh vegetables every 2 days; (iii) 10 mL of irrigation water remains on 100 g fresh vegetables; and (iv) pathogen concentrations are reduced by 3 log before consumption by washing the vegetables.17,19 On the basis of these assumptions, d can be expressed as a function of Cinf [copies/L] by (eq 2): 10 d = C inf × × 10−3 (2) 1000
Miyajimanuma (SI Figure S3, ). Enteropathogenic E. coli (EPEC), Shigella spp., C. jejuni, C. perf ringens, and V. cholerae were frequently detected in all sampling sites at various sampling time points (Figure 3). Similar results were also obtained by conventional qPCR. Pathogen concentrations measured by MFQPCR were in good correlation with those measured by conventional qPCR (Figure 4), suggesting that MFQPCR could provide quantitative information that is as accurate and reliable as conventional qPCR. Although the STA reaction was used in the MFQPCR method, no STA reaction was used in conventional qPCR. This finding suggests that the STA reaction did not have major impacts on the results obtained by qPCR, similar to the previously reported spike-in experiments.7 In addition, sequencing analysis of the representative qPCR amplicons confirmed that the target molecules were correctly amplified (data not shown). These results suggest that MFQPCR is also applicable to natural, nonspiked, environmental water samples. Temporal Variation in Pathogen Concentrations. Seasonal variation was observed in pathogen concentrations. In general, pathogen concentrations were high when geese were present in the lake (Figure 3), although no significant correlation was observed between goose counts and pathogen concentrations both by parametric (Pearson’s correlation) and nonparametric (Spearman’s and Kendall’s rank correlations) methods. This is most likely due to the long-term survival of pathogens after fecal deposition by geese, particularly under low-temperature conditions.10 Sunlight can also negatively impact the survival of pathogens,10,23,24 and this can also partly explain the low abundance of pathogens in the summer months. Shigella spp. (ipaH and virA) and V. cholerae (ctxA) were detected in September before the arrival of migratory birds, similar to E. coli. Because Shigella spp. is now recognized as a subspecies of E. coli,25 some Shigella spp. may be able to grow in environments in which other E. coli grow.2 The occurrence of V. cholerae may be attributed to the environmental reservoirs for this pathogen, as described below. We observed poor correlations between FIB (E. coli) and pathogen concentrations, probably due to the different survival and growth kinetics or to the different source origins between FIB and pathogens. Furthermore, we sometimes observed a negative correlation between E. coli and pathogen concentrations (e.g., plc, r2 = 0.39; P = 0.013). These results suggest that it is difficult to predict pathogen concentrations based on E. coli counts alone, similar to a previous study.4 Potential Sources of Pathogens. To further examine if fecal droppings from geese were responsible for pathogens in water samples, we performed MFQPCR as well as conventional qPCR targeting EPEC, Shigella spp., C. jejuni, C. perf ringens, and V. cholerae in fecal droppings from geese (n = 55). As a result, eaeA, ipaH all, virA, ciaB, and plc were detected in 20%, 7.8%, 9.7%, 16.3%, and 3.6% of feces, respectively. The average pathogen densities in the positive samples ranged from 3.5 to 4.2 log copies/g feces (SI Table S2). These results suggest that geese could have contributed to the excretion of multiple pathogens. Because greater white-fronted geese travel nearly 3500 km between Japan and northeastern Russia,8 they may contribute significantly to the distribution of pathogenic bacteria in broad areas, similar to other migratory birds (e.g., cranes).26 Geese have previously been reported to carry several pathogens, such as Salmonella spp.9,27 and Campylobacter
In addition, Pd can be calculated from an annual infection probability (Pyr) by (eq 3): Pd = 1 − (1 − Pyr)1/(365/2)
(3)
−4
In this study, Pyr of 10 infections per person per year was considered acceptable.17 By using (eq 1−3), we can calculate Cinf for each pathogen. The occurrence of pathogens at >Cinf were considered as a threat to human health.
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RESULTS AND DISCUSSION General Water Quality. In general, water quality deteriorated during the bird migration seasons (i.e., late April to early May and late September to October; SI Figure S2). Fecal droppings from thousands of waterfowls most likely caused eutrophication of the lake. However, the influent stream to the lake contained fertilizer runoff, which can also cause eutrophication.20,21 In 2012, an algal bloom was observed due to the eutrophication of the lake. The occurrence of algae probably caused an increase in pH in the lake-impacted water (MJ2 and MJ4) samples during the summer and fall months. Levels of E. coli also increased during the bird migration seasons (Figure 2), indicating the occurrence of fecal
Figure 2. Temporal variations in E. coli and goose counts in Lake Miyajimanuma environments. E. coli counts in water samples collected from MJ1 (○), MJ2 (□), MJ3 (△), and MJ4 (◊) sites were measured using the most probable number (MPN) method. Goose counts reported by the Miyajimanuma Waterbird & Wetland Center are plotted as a dashed line.
contamination due to waterfowls. However, E. coli also increased during August to September before the arrival of migratory birds. This increase may be due to the growth of E. coli in response to nutrients provided by soil, sediments, or algae because E. coli are known to grow in these environments.2,22 Consequently, no significant correlation was observed between E. coli and goose counts. Occurrence of Pathogens. Various enteric pathogens were detected in water samples collected around Lake 4746
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Figure 3. Temporal variations in pathogen concentrations in Lake Miyajimanuma environments. The quantitative results for eaeA (targeting EPEC), ciaB (targeting C. jejuni), ipaH 7.8 and virA (targeting Shigella spp.), plc (targeting C. perf ringens), and ctxA (targeting V. cholerae) are shown. The quantitative results for all genes are shown in Figure S3 (SI). Because these genes are present at one copy per genome, the unit of copies/L is equivalent to cells/L. Concentrations in water samples collected from MJ1 (○), MJ2 (□), MJ3 (△), and MJ4 (◊) sites are shown for each gene. The lower quantification limit was 2 log copies/L water. Geese migration seasons are shaded in gray.
In contrast, ctxA, a marker gene for V. cholerae, was not detected in fecal droppings from geese. This pathogen may have originated from other animal or environmental reservoirs. Benthic animals, algae, cyanobacteria, and other planktons can serve as important reservoirs for V. cholerae.29−31 Spatial Variation in Pathogen Concentrations. The occurrence of pathogens did not vary by sampling sites, although geese spend the majority of their time in the lake. Initially, we expected higher pathogen concentrations in the streams impacted by effluent water from the lake (MJ2 and MJ4). However, we also frequently observed high pathogen concentrations in the streams not impacted by effluent water from the lake (MJ1 and MJ3). This is most likely due to the contamination of the MJ1 and MJ3 streams by geese feces from the surrounding agricultural field. Geese feed on the agricultural field around Lake Miyajimanuma8 and drop their feces on this field (SI Figure S4). Subsequently, pathogens in their fecal droppings may have been transported to the streams by surface runoff. QMRA. We performed QMRA for water samples collected in this study based on the quantitative data obtained by MFQPCR. Pathogen concentration that can cause infection at a probability of 10−4 infections per person per year (Cinf) was calculated as 5.68, 1.38, 0.46, and 0.55 log cells/L for pathogenic E. coli, Shigella spp., C. jejuni, and V. cholerae, respectively. We could not calculate Cinf for C. perf ringens because the β-Poisson parameters for this pathogen were not available. We sometimes observed higher concentrations for Shigella spp. and C. jejuni in water samples collected from Lake Miyajimanuma, suggesting that these water samples may have the potential to cause human diseases if used as irrigation water to grow fresh vegetables. Currently, fresh vegetables are not cultivated for commercial purposes in the study area. However,
Figure 4. Correlation between concentrations measured by MFQPCR and those measured by conventional qPCR. The quantitative results for eaeA (○), ipaH 7.8 (□), ipaH all (◊), virA (△), ciaB (×), and ctxA (+) were used for comparison. The linear regression equation and goodness-of-fit (r2) value are also shown.
spp.10,26,27 Our study showed that geese also carried EPEC, Shigella spp., and C. perf ringens. However, Salmonella spp. was not detected in our fecal samples from geese, in agreement with the absence of Salmonella marker genes in water samples. No detection of Salmonella spp. was also verified using a qPCRbased Salmonella detection kit (Primerdesign, Southampton, U.K.) and by an enrichment culture method.28 These results were in contrast to the report by Jokinen et al.,27 in which Salmonella spp. was isolated from 10% of the feces from Canada geese collected in Alberta, Canada. These results indicate that the detection of Salmonella spp. in fecal droppings from geese may depend on area or host species (greater white-fronted geese vs. Canada geese). 4747
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Environmental Science & Technology care should be taken when farmers want to grow fresh vegetables. Dilution or filtration is recommended if lake water is used to grow fresh vegetables.32 The recent trend for water quality monitoring has been directed toward the use of QMRA.33,34 However, it is difficult to predict pathogen concentrations using the current FIB-based approach.4 In this study, we showed that QMRA was possible based on pathogen concentrations directly measured by MFQPCR. On the basis of the results obtained by QMRA, the water in Lake Miyajimanuma had potential health risks when used as a source of irrigation water to grow fresh vegetables, particularly during the spring and fall months. The assumptions used in this study are based on relatively old literature.17,19 For more accurate QMRA, we need to develop better predictive models because risks can vary significantly by vegetables.18 The behavior of pathogens (e.g., decay rate) in environments can also influence the calculation. In addition, there is a room for improvement in DNA-based gene quantification. For example, we need to eliminate the PCR signals from dead cells, to improve DNA recovery efficiencies, and to evaluate the presence of PCR inhibitory substances.35 The discrimination of live and dead cells by using propidium monoazide36 and the use of sample processing controls or internal amplification controls35,37 can help increase the accuracy of QMRA using DNA-based quantitative data such as those obtained by MFQPCR. In conclusion, the MFQPCR method is applicable in the direct quantification of multiple pathogens in natural, nonspiked environmental water and fecal samples. In water samples collected from Lake Miyajimanuma, several enteric pathogens were frequently detected, the concentrations of which were sometimes >Cinf in water used as irrigation water to grow fresh vegetables. Waterfowl were the most probable sources of many of these pathogens. Direct, simultaneous multipathogen quantification can provide more reliable information for risk assessment than the current FIB-based approach. Instead of searching for an alternative FIB, which has not been very successful so far,3 we would like to propose to directly monitor multiple pathogens in environmental water samples for water quality monitoring and QMRA.
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ACKNOWLEDGMENTS
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REFERENCES
We thank Takahiro Segawa at National Institute for Polar Research for allowing us to use their BioMark facility, and Katsumi Ushiyama at Miyajimanuma Waterbird & Wetland Center for providing geese count data. We also thank Reiko Hirano for technical assistance, and Sumire Sakai for taking beautiful pictures of the geese and Lake Miyajimanuma. This study was supported, in part, by the CREST program from Japan Science and Technology Agency, Grant-in Aid for Scientific Research A (23246094) from Japan Society for the Promotion of Science.
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ASSOCIATED CONTENT
S Supporting Information *
Parameter values for the pathogen-specific β-Poisson dose− response models (Table S1), frequencies of occurrence and average densities of pathogens in fecal droppings from geese (Table S2), photograph of geese in Lake Miyajimanuma (Figure S1), temporal variations in general water quality (Figure S2), quantitative detection of various virulence factor genes in Lake Miyajimanuma water samples (Figure S3), and photograph of geese and their feces on the agricultural field (Figure S4). This material is available free of charge via the Internet at http://pubs.acs.org.
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AUTHOR INFORMATION
Corresponding Author
*Email: [email protected]; Phone: +81-11-706-7162; Fax: +81-11-706-7162. Notes
The authors declare no competing financial interest. 4748
dx.doi.org/10.1021/es500578s | Environ. Sci. Technol. 2014, 48, 4744−4749
Environmental Science & Technology
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