Article Cite This: Environ. Sci. Technol. XXXX, XXX, XXX−XXX
pubs.acs.org/est
Municipal Wastewater as a Microbial Surveillance Platform for Enteric Diseases: A Case Study for Salmonella and Salmonellosis T. Yan,*,† P. O’Brien,‡ J. M. Shelton,† A. C. Whelen,‡,§ and E. Pagaling†,# †
Department of Civil and Environmental Engineering, the University of Hawaii at Manoa, Honolulu, Hawaii 96822, United States State Laboratories Division, Hawaii Department of Health, Honolulu, Hawaii 96782, United States § Department of Microbiology, the University of Hawaii at Manoa, Honolulu, Hawaii 96822, United States ‡
S Supporting Information *
ABSTRACT: Municipal wastewater (MW) contains a conglomeration of human enteric microbiota from a community and, hence, represents a potential surveillance tool for gastrointestinal infectious disease burden at the community level. To evaluate this, the concentration of Salmonella in MW samples from Honolulu, Hawaii, was monitored over a 54-week period, which showed positive and significant linear and rank correlation with clinical salmonellosis case numbers over the same period. Salmonella isolates were obtained from the MW samples and then compared with clinical isolates obtained by the Hawaii Department of Health State Laboratories over the same period. The MW isolate collection contained 34 serotypes, and the clinical isolate collection contained 47 serotypes, 21 of which were shared between the two isolate collections, including nine of the 12 most commonly detected clinical serotypes. Most notably, nine Salmonella strains, including one outbreak-associated Paratyphi B strain and eight other clinically rare strains, were shared and concurrently detected between the MW and the clinical isolate collections, indicating the feasibility of using enteric pathogens in the MW as a timely indication of community enteric disease activity.
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communities.16−18 We hypothesize that raw MW represents a unique source of enteric pathogen information for a community that is independent of the clinical data stream and potentially offers a source of time-sensitive disease information that is otherwise difficult to obtain or is completely inaccessible. In a previous study, we illustrated the use of MW to estimate the disease burden of enteropathogenic E. coli (EPEC) and the genetic diversity of eae gene in a community.19 This work generated otherwise inaccessible information on a nonreportable disease; however, the lack of clinical information on EPEC prevented cross validation of the community disease burden estimated by the MW-based approach. Salmonella enterica consists of a diverse group of serotypes that are ubiquitous in nature and is a common cause of human salmonellosis, which leads to considerable morbidity and mortality.20,21 For example, Salmonella enterica serotype Typhi (S. Typhi) and Paratyphi (S. Paratyphi) are the etiological agents of typhoid fever, which causes 17 to 21 million infections and more than 600 000 deaths worldwide each year.22 Although typhoid fever has been essentially eradicated in the U.S., a significant number of nontyphoidal salmonellosis cases (1.4 million per year) still occur23 and result in 15 000 hospitalizations and 400 deaths a year.21 Salmonellosis is a reportable
INTRODUCTION Microbial infectious diseases continue to be a major cause of human illness in the U.S. and throughout the world. The U.S. CDC estimated that, each year, enteric pathogens alone cause 47.8 million illnesses, resulting in 3037 deaths,1,2 and globally, over 13 million deaths were attributed to microbial infectious diseases.3 This problem is further exacerbated by emerging and resurgent microbial pathogens, including pathogens with heightened virulence and increasing antimicrobial resistance.4,5 To effectively combat this problem, it is critical to have comprehensive and timely surveillance capabilities for microbial infectious diseases. However, most of the existing disease surveillance approaches rely predominantly on clinical diagnostic data, which only cover a limited number of diseases and lack timeliness due to reporting biases and procedural delays.6 Other alternative or supplemental mechanisms, including syndromic surveillance7 and the emerging big data analytics,8 have yet to prove their ability to provide immediate and actionable public health value. Since municipal wastewater (MW) systems continuously collect fecal human waste from communities, MW is expected to contain a collection of human gastrointestinal microbiota. Previous studies have investigated the feasibility of using MW to monitor tobacco and illicit drug use9,10 and to detect contaminants of emerging concern in human communities.11 Other related studies have focused on detecting and/or inactivating pathogens12−15 and determining MW as the source of microbial pathogens in the environment and diseases in the © XXXX American Chemical Society
Received: January 10, 2018 Revised: March 29, 2018 Accepted: April 4, 2018
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DOI: 10.1021/acs.est.8b00163 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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random from the colonies on XLD plates and then examined on LIA and TSA slants for confirmation of Salmonella. Further confirmation was performed using a qPCR assay that targets the Salmonella-specific ttr locus.27 A total of 378 confirmed Salmonella isolates were obtained for the MW isolate collection and were stored in LB broth with 50% (v/v) glycerol at −80 °C for further analyses. The clinical Salmonella isolates were identified by community clinical laboratories and submitted to Hawaii State Laboratories where they were confirmed biochemically and serotyped for routine public health surveillance of salmonellosis.28 PFGE Analysis. The whole cell PFGE method and subsequent data analysis of the Salmonella isolates from the clinical and the MW isolate collections were conducted using the standard operating procedure from PulseNet PFGE.29 Briefly, the Salmonella isolates were grown on TSA with 5% defibrinated sheep blood (TSA-SB) and incubated overnight. Colonies were picked and resuspended in cell suspension buffer (100 mM Tris-HCl/100 mM EDTA, pH 8.0) to achieve a concentration of 0.4−0.45 on a Dade turbidity meter. 200 μL of this suspension was embedded in 0.5% Seakem Gold agarose (Lonza, Allendale, NJ). These PFGE plugs were then incubated in cell lysis buffer (50 mM Tris/50 mM EDTA, pH 8.0, and 1% Sarcosyl) with proteinase K (1 mg/mL) for at least 1.5 h. Restriction digestion of genomic DNA within the agarose plugs was conducted using Xbal (50 U/sample). Electrophoresis was conducted on a CHEF Mapper (Bio-Rad, Richmond, CA). Analysis was performed using BioNumerics version 5.10 (Applied Maths, Austin, TX), and serotype classification was based on clustering analysis with entries in the PulseNet database. Data Analysis. Two derivative parameters of CSalm were calculated, including total weekly Salmonella flux (JSalm = CSalm × Qweek) and the ratio between CSalm and CEnt in the same MW samples (RS/E). Log-transformed CSalm and CEnt were analyzed to identify outliers using the 1.5 interquartile range (IQR) method, which identified two log CSalm outliers (Weeks 4 and 38). After outlier removal, the log CSalm and log CEnt data were tested and confirmed to satisfy the normality assumption based on the Shapiro-Wilk test, and the Student’s t-test was conducted to determine whether statistically significant differences existed in the log CSalm and log CEnt values between the summer months and winter months. The clinical salmonellosis case number (N), which is typically directly used without log transformation in epidemiology studies,30 was also analyzed using the IQR method, which identified one outlier (Week 18). In preparation for computing Pearson’s product-moment correlation coefficients (r), log CSalm, log CEnt, log JSalm, log RS/E, and N underwent pairwise deletion of outliers and were subsequently also confirmed of normality based on the ShapiroWilk test. Pearson’s product-moment correlation coefficients (r) between N and log CSalm, log JSalm, log RS/E, and log CEnt were computed to detect underlying linear relationships, and the significance of the correlation coefficient was determined using the t distribution. The nonparametric Spearman’s rank correlation coefficient (ρ), which is commonly used in epidemiology studies for its robustness toward influential data points and outliers and does not require normality,31,32 was also computed using all data (including outliers). The relative abundance of each Salmonella serotype was calculated as the ratio between the number of isolates classified to the serotype and the total number of isolates in the respective isolate collection. The “Undertermined” serotype is the collection of
disease, and positive clinical specimens or isolates are sent to public health laboratories for characterization. Information on clinical cases of salmonellosis is available through an extensive genetic database of clinical Salmonella isolates called PulseNet,24 which permits validation of the proposed MWbased approach in comparison with the traditional clinical approach. In this study, a 54-week MW sampling campaign was conducted to collect composite raw MW samples each week at the Sand Island Wastewater Treatment Plant (SIWTP) in Honolulu, Hawaii. Salmonella concentrations in the MW samples were determined and subsequently compared with the reported clinical salmonellosis case numbers through correlation analysis. Salmonella isolates were also obtained from the MW samples, and their serotypes were determined by pulsed-field gel electrophoresis (PFGE) and compared with that of the clinical isolate collection. Finally, identical Salmonella PFGE patterns between the MW isolate collection and the clinical isolate collection were identified, and their temporal detection patterns were compared to reveal concurrent detection in both collections.
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MATERIALS AND METHODS MW Sample and Flow Data Collection. MW sampling was conducted at the Sand Island Wastewater Treatment Plant (SIWTP) in Honolulu, Hawaii, over a 54-week period (April 27, 2010 to May 9, 2011). The SIWTP collects sanitary MW from the Honolulu urban center and surrounding areas where major employment centers are located and treats approximately 60% of the MW of the City and County of Honolulu. Each Sunday, raw untreated MW samples (1 L) were collected hourly using an autosampler placed upstream of the primary clarifiers, and 40 mL of homogenized hourly samples was mixed to make the daily composite samples. The samples were stored at 4 °C in the dark and transported back to the laboratory for processing within 24 h. The MW flow data were collected by the SIWTP using a Doppler flow meter and were used to calculate the total weekly MW flow (Qweek). Bacterial Enumeration in MW Samples. A five-tube most probable number (MPN) method modified from the EPA method 1682 for biosolids25 was used to enumerate Salmonella in the MW samples. Briefly, 10-fold serial dilutions (up to 10−4) of the raw MW samples were used to inoculate tryptic soy broth (TSB). After incubation at 35 °C for 24 h, 30 μL of each MPN tube was inoculated onto the modified semisolid Rappaport Vassiliadis (MSRV) media and incubated at room temperature for 1 h and then at 42 °C for 16−18 h. Potential Salmonella-positive tubes were indicated by a ring of motility around the inoculation site on the MSRV plates. Samples from the motility zone were then taken with a sterile loop and streaked onto xylose lysine deoxycholate (XLD) agar and incubated at 35 °C for 24 h. Colonies on the XLD agar that were red, pink, or colorless with black centers were then stab inoculated onto lysine iron agar (LIA) and tryptic soy agar (TSA) slants to confirm that they were Salmonella. The Salmonella concentrations in the MW samples (CSalm) were calculated using the five-tube MPN table and reported as MPN/100 mL. The concentration of a commonly used fecal indicator bacteria enterococci was also determined in the MW samples (CEnt) using the standard membrane filtration mEI agar method.26 Salmonella Isolation. From each of the 49 Salmonellapositive MW samples, five to ten isolates were picked at B
DOI: 10.1021/acs.est.8b00163 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Figure 1. Concentration dynamics of Salmonella (CSalm) and enterococci (CEnt) in daily composite MW samples in Honolulu over 54 weeks (from April 26, 2010 to May 8, 2011). The inset is a box plot showing distribution of the concentration data.
of salmonellosis disease burden, as typically more salmonellosis disease cases are reported in summer months than in winter months in Honolulu. Similar seasonality of salmonellosis is commonly observed in temperate regions of the U.S.,37 which is attributed to higher ambient temperatures38 and intensified precipitation39 in summer months. Correlation between MW Salmonella and Clinical Salmonellosis Cases. To further determine the relationship between Salmonella in MW and salmonellosis prevalence in the community, the CSalm in MW samples was compared with clinical salmonellosis case numbers (N) of the same weeks through correlation analyses (Table 1). Since the CSalm in the
all isolates that were not assigned to any particular serotype. All statistical analyses were conducted in R, and the default significance value cutoff is P ≤ 0.05 unless stated otherwise.
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RESULTS AND DISCUSSION Salmonella Concentration in Raw MW Samples. The concentrations of Salmonella (CSalm) and enterococci (CEnt), a common fecal indicator bacteria used here as a reference, were determined in 54 raw MW samples from SIWTP collected over a 54-week sampling period (Figure 1). Salmonella was detected in 49 of the 54 raw MW samples, and the CSalm fluctuated significantly over the sampling period, as indicated by a wide concentration range (4−105.2MPN/100 mL) and a large coefficient of variation (5.54). The lower quartile, median, and upper quartiles of CSalm were 18, 46, and 256 MPN/100 mL, respectively, with an overall geometric mean of 89 MPN/ 100 mL. This is in contrast with the relatively stable CEnt in the same raw MW samples, which showed a much narrower concentration range (105.5−106.8CFU/100 mL) and a much smaller coefficient of variation (0.74). The large fluctuation of CSalm (compared to the relative stability of CEnt) suggests considerable variation in the number of Salmonella cells excreted into the MW system and potentially fluctuating salmonellosis disease prevalence over time. The CSalm values detected in the raw MW samples in Honolulu were in agreement with some previous estimations of Salmonella in MW (e.g., 102−104 cells/100 mL33) and previous reports from different countries, including 1.5−27 CFU/100 mL in French MW,34 93−104.0 MPN/100 mL in Finnish MW,35 and 4.5 × 105−2.4 × 106 MPN/100 mL in Mexican MW.36 The CSalm values in the summer months (May−Oct., Weeks 1−27; geometric mean: 102.4 MPN/100 mL) were significantly higher (t test, P < 0.001) than those in the winter months (Nov.−Apr., Weeks 28−54; geometric mean: 101.5 MPN/100 mL). In contrast, the CEnt values in the summer months (geometric mean: 106.0 CFU/100 mL) showed no significant difference (t test, P = 0.99) from those in the winter months (geometric mean: 106.0 CFU/100 mL). This seasonality of CSalm in the MW samples corresponds to the seasonality
Table 1. Correlation between Log-Transformed Salmonella Concentration (log CSalm), Salmonella Weekly Flux (log JSalm), Enterococci Concentration (log CEnt), Salmonella/ Enterococci Concentration Ratio (log RS/E), and Clinical Salmonellosis Case Number (N) over the Sampling Period clinical salmonellosis case number (N) Pearson’s r (n = 46) log log log log
CSalm JSalm RS/E CEnt
0.397 0.392 0.386 −0.109
(P (P (P (P
= = = =
0.006) 0.007) 0.008) 0.471)
Spearman’s ρ (n = 49) 0.329 0.325 0.326 −0.208
(P (P (P (P
= = = =
0.022) 0.023) 0.023) 0.150)
MW samples can also be affected by wastewater flow, which can be significantly impacted by fluctuation in water consumption and rainfall-derived infiltration and inflow (RDII),40 the weekly flux of Salmonella (JSalm) and the concentration ratio between Salmonella and enterococci (RS/E) were also calculated and compared with N (Figure S1). Pearson’s correlation coefficients showed that the log transformed CSalm, JSalm, and RS/E of the MW samples all exhibited positive and statistically significant correlation with N (r: 0.386−0.397, P: 0.006−0.008). In comparison, the CEnt of the MW samples did not exhibit either positive or significant Pearson’s correlation with N (r = −0.109, P = 0.471). The Spearman’s rank correlation coefficients, which were computed using all data including outliers, also showed C
DOI: 10.1021/acs.est.8b00163 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
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Environmental Science & Technology both positive and significant correlation between log CSalm, log JSalm, and log RS/E of the MW samples and N. Again, log CEnt showed neither positive nor significant Spearman’s rank correlation coefficient with N. The presence of positive and significant correlation between log CSalm and the clinical salmonellosis disease case number N demonstrates the direct association between Salmonella abundance in the MW samples and salmonellosis disease prevalence in the community. The two derivative parameters of log CSalm (i.e., log JSalm and log RS/E) showed similar correlation with N as log CSalm, suggesting that water consumption changes, and RDII did not significantly impact the correlation. Indeed, an extreme wet peaking factor of 4 would only change log CSalm by a factor of 0.6, which is dwarfed by the large variation in the amount of Salmonella cells excreted by infected individuals (estimated to be 2.5 × 105 to 1 × 109 Salmonella cells per gram fecal material41). It should be noted that the removal of two outlier data points in log CSalm and one outlier in N was necessary in order to meet the normality assumption for Pearson’s correlation analysis. These outliers were very influential in computing Pearson’s r, as their inclusion in the analysis would have increased the P value of correlation coefficients and would no longer have made the correlation significant (data not shown). The outlier data in log CSalm and N may be attributed to measurement errors and several other factors. First, the concentration of Salmonella in patients’ feces can vary widely and cause significant fluctuation in the amount of cells discharged into the MW system by the same number of patients.41 Second, besides fecal waste from infected humans, other sources may also contribute Salmonella to the MW, including kitchen waste from contaminated foods1 and house pets.42,43 Third, the Salmonella discharged into the MW collection systems may undergo growth and decay both in the bulk MW and within biofilms on the sewer walls, further complicating Salmonella concentration dynamics in the MW. Finally, the salmonellosis case number N can be significantly impacted by patients’ reluctance to seek medical assistance and varying clinical practices of disease diagnosis; it was estimated that only about 1/30 of individuals with enteric diseases seek medical assistance while health practitioners can also vary in lab test ordering practices.6,7 MW Salmonella Serotypes. To understand the diversity of Salmonella in the MW samples, the 378 Salmonella isolates from the MW isolate collection were characterized by PFGE. They were grouped into 34 different serotypes, which exhibited significant variation in their relative abundance in the MW isolate collection (Figure 2). S. Derby dominated the MW collection, making up 20.9% of the relative abundance, which is considerably higher than the second most abundant serotype, S. Paratyphi B var. L(+) tartrate(+) (7.7%). Overall, the 15 major serotypes (defined as ≥2.0% relative abundance), which included many of the most frequently reported clinical serotypes such as S. Enteritidis, S. Typhimurium, S. Newport, S. Muenchen, and S. Infantis,30 collectively accounted for 85.4% of the MW isolate collection. The remaining 19 minor serotypes in the MW collection accounted for the remaining 14.6% and were generally not frequently detected in health clinics. A similar distribution of Salmonella serotypes in MW samples was also reported previously in Zaragoza, Spain.44 The serotype distribution of the MW isolate collection was compared with that of the clinical isolate collection of the same period. The clinical isolate collection included 338 Salmonella isolates from clinical cases reported in Hawaii, which were
Figure 2. Relative abundance of Salmonella isolates grouped into different serotypes in the MW and clinical isolate collections. The serotypes are ranked on the basis of their relative abundances in the MW isolate collection.
grouped into 47 serotypes. The MW isolate collection shared 21 serotypes in common with the clinical isolation collection, which included seven of the 11 clinical major serotypes (defined as ≥2.0% relative abundance). Although the MW collection provided good coverage of the major serotypes contained in the clinical isolate collection, there remained 26 serotypes in the clinical isolate collection that were absent in the MW collection. The majority of these serotypes (76.9%) were represented by only one (57.7%) or two (19.2%) clinical isolates. Therefore, the absence of these serotypes in the MW isolate collection could have resulted from the small numbers of Salmonella cells discharged into the MW by a few infected individuals. The limited sampling depth (≤10 isolates per sample) used in constructing the MW isolate collection may also explain this observation. The MW isolate collection also contained many serotypes that were not detected in the clinical isolate collection, including four major serotypes S. Mbandaka (6.3% relative abundance), S. Senftenberg (5.8%), S. Anatum (4.5%), and S. London (2.9%). Among them, only S. Anatum appeared in the top 20 most frequently reported clinical serotypes according to the U.S. CDC Salmonella reports.30,37 The presence of these serotypes in MW but absence in the clinical data could be explained by variable virulence, subclinical or asymptomatic shedding,45 and the presence of other environmental sources (for example, S. Senftenberg is common in D
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Environmental Science & Technology marine sediment and water46). Alternatively, it is also possible that infections caused by these serotypes were not detected by the traditional clinical approach for disease surveillance, which would further indicate the potential benefits of including MW monitoring as part of an integrated surveillance strategy. Concurrent Detection of an Outbreak-Causing Salmonella Strain. To determine whether timely disease information is contained in the MW, PFGE patterns of the Salmonella isolates were compared. Twenty-four different PFGE patterns, which henceforth are referred to as 24 Salmonella strains grouped into 13 different serotypes (Table 2), were shared between the MW and the clinical isolate Table 2. Total Numbers of Unique Salmonella PFGE Patterns (Strains) of Different Serotypes That Were Shared between the Clinical and MW Collections and the Number of Those Showing Concurrent Detection Salmonella serotypes
shared PFGE patterns
concurrent detection
Typhimurium Enteritidis Muenchen Infantis Newport Paratyphi B Derby Agona Amager Uganda Javiana Chester Takoradi total
3 2 4 3 1 1 3 1 1 2 1 1 1 24
0 0 0 0 1 1 1 0 1 2 1 1 1 9
Figure 3. PFGE patterns of clinical (labeled N) and MW (labeled Y) Salmonella Paratyphi B isolates (A) and their temporal detection patterns in the MW and clinical isolate collections over the 54-week sampling period (B).
outbreaks47 and the detection of Cryptosporidium in MW during a waterborne outbreak in the UK.48 A previous study in Sweden also reported that Salmonella strains in sewage sludge were identical to clinical strains recovered from an outbreak that had occurred months to years beforehand.49 To the best of the authors’ knowledge, the present study is the first report where a bacterial pathogen strain was detected in the MW during its outbreak in the community. Later in the study period, there were three sporadic clinical cases caused by the same S. Paratyphi B strain, though no corresponding matches were detected in the MW collection, which again was likely to be caused by insufficient sampling depth used when establishing the MW collection. From week 47 to week 54, a total of 12 isolates belonging to S. Paratyphi B were detected in the MW collection, and in week 53, S. Paratyphi B isolates were dominant in the MW sample. However, no clinical isolates of S. Paratyphi B were detected during that period, suggesting possible low sensitivity (falsenegatives) detection of salmonellosis disease burden in the community by the traditional clinical approach. It is also possible that the lack of correspondence between the MW and the clinical isolate collections was due to the S. Paratyphi B isolates being genetically different strains, since PFGE may be incapable of differentiating individual strains within a certain highly genetically homogeneous serotype.50,51 Other factors that were discussed previously regarding the relationship between CSalm in the MW and community salmonellosis case numbers N (such as potential survival of Salmonella in sewer biofilm) might also have played a role. Concurrent Detection of Clinically Rare Salmonella Strains. Also concurrently detected in the MW and the clinical Salmonella collections were two S. Uganda strains (named S. Uganda 1 and S. Uganda 2) that differed by three PFGE bands (Figure 4A). In Week 21, a single clinical S. Uganda 1
collections. Nine of the 24 shared strains were detected in both isolate collections within 1 week from each other (i.e., concurrent detection), including one S. Paratyphi B strain that caused a major outbreak with 17 confirmed cases in Hawaii from March to May 2010, which coincided with the beginning of our MW sampling campaign by 4 weeks (Figure 3). The PFGE patterns of the Salmonella isolates from the MW and the clinical collections were indistinguishable (Figure 3A), which together with their concurrent detection (Figure 3B) indicate that they are the same strain and have a common origin. The temporal detection pattern showed that the S. Paratyphi B strain was the most frequently isolated strain in the MW samples during the first 4 weeks of the sampling campaign when the outbreak was ongoing. After clinical cases completely subsided in Week 5, detection of the S. Paratyphi B isolates in MW eventually disappeared after persisting at a low level for two additional weeks. Although the outbreak caused by S. Paratyphi B (Figure 3) was the only clinical outbreak during the sampling period and hence did not permit robust statistical inference, the concurrent detection of this Salmonella strain in both MW and clinical samples supports the potential use of MW for time-sensitive outbreak surveillance. The outbreak-causing S. Paratyphi B led to its numerical dominance within the Salmonella population in the MW samples (e.g., six isolates in Week 2). Although high levels of enteric pathogens are intuitively expected in MW during disease outbreaks, only a few previous studies have reported time-sensitive detection, which include the detection of the Hepatitis A virus and Norovirus in Sweden MW during E
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detection of clinical cases and also persisted in MW for three additional weeks. S. Uganda is rarely isolated from human clinical cases in the U.S., with only a median of 48 human isolates per year from 1993 to 2001,52 so the detection of four Uganda clinical isolates in the clinical collection of 338 isolates (1.2%) can be considered rare events. The concurrent detection of two rare Uganda strains in the MW and clinical collections supports the notion that MW-based microbial surveillance has the potential to achieve high sensitivity. The continuous isolation of the same Uganda strains in MW samples for up to 6 weeks after the reported clinical cases suggests that the strains continued to circulate within the community while evading clinical detection. Although it is also possible that the strains were simply surviving in the sewer system, this possibility is considered unlikely because of the observation of a large number of Uganda 1 isolates in the MW samples in Weeks 25 and 26, which was highly unlikely to be primarily caused by surviving in the sewer system. Concurrent detection of the clinical and MW Salmonella isolates also included six additional strains that belong to six different serotypes (Figure 5). The MW and clinical Salmonella isolates were indistinguishable by their PFGE patterns (Figure S2). The S. Amager, S. Chester, S. Takoradi, and S. Newport strains were each detected in one cluster over the entire 54week sampling period in both the MW collection and the clinical collection (Figure 5A−D). S. Javiana (Figure 5E) and S. Derby (Figure 5F) appeared in two separate clusters in the MW collection, though only one cluster for each serotype showed temporal correlation with clinical isolates. S. Newport and S. Javiana ranked third and fourth most abundant among laboratory-confirmed Salmonella infections, while the other serotypes ranked out of the top 20.37 Although the six serotypes
Figure 4. PFGE patterns of two Uganda strains in the clinical (labeled N) and MW (labeled Y) isolate libraries (A) and their detection patterns in the MW and clinical isolate collections over the 54-week sampling period (B).
isolate was detected, while corresponding S. Uganda 1 isolates were detected in the MW sample collected after the clinical detection and persisted in the MW for 7 weeks (Figure 4B). The detection of the S. Uganda 2 strain in three clinical cases occurred in Week 35, 14 weeks after the S. Uganda 1 cluster. Similar to the S. Uganda 1 detection, the S. Uganda 2 isolates were also detected in the MW sample collected after the
Figure 5. Concurrent detection of the Salmonella serotype Amager strain (A), Chester strain (B), Takoradi strain (C), Newport strain (D), Javiana strain (E), and Derby strain (F) in the clinical and MW isolate collections over the 54-week sampling period. F
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intensive and time-consuming would be needed in order to make the proposed MW-based microbial surveillance approach practically feasible for routine monitoring.
typically exhibit different clinical detection frequencies in health clinics nationwide, all six strains were only represented by a single clinical case over the sampling period, indicating the rarity of their occurrence in the local clinical isolate collection. The current detection of these clinically rare Salmonella strains in the MW samples provides strong evidence of their association and being from common sources. The concurrent detection of the nine Salmonella strains in the MW and clinical collections (Figures 3, 4, and 5) clearly demonstrates that there is a correlation between the occurrence of salmonellosis in the community and the presence of Salmonella in MW. The grouping of Salmonella isolates into different serotypes and strains was based on indistinguishable PFGE patterns, which is the prevailing method for bacterial strain subtyping for epidemiological investigations.28,29 It should be noted that the PFGE-based strain subtyping was reported to be inadequate for S. Enteritidis because of its high genetic homogeneity,50,51 which requires methods with higher discriminatory power (such as whole genome sequencing) for strain differentiation.53 However, similar limitations have not been reported for any of the serotypes to which the nine Salmonella strains belong; for example, the serotype S. Paratyphi B is known to be highly genetically diverse.54 Overall, this case study of Salmonella and salmonellosis provides multiple lines of evidence supporting the feasibility of using MW as a microbial surveillance platform for enteric diseases in a community and the possibility of using MW to monitor salmonellosis activity in a time-sensitive fashion. First of all, Salmonella concentration in Honolulu MW exhibited positive and statistically significant linear and rank correlation with clinical salmonellosis case numbers over the same time period. Second, the MW Salmonella isolate collection shared 21 serotypes with the clinical Salmonella isolate collection established over the same time period, including nine of the 12 most common clinical serotypes. Finally, and most significantly, nine Salmonella strains, including an outbreakassociated S. Paratyphi B strain and numerous clinically rare strains, were concurrently detected in health clinics and in the MW samples, supporting the feasibility of monitoring enteric pathogens in the MW as a timely indication of community enteric disease activity. Although enteric disease information on the community is undoubtedly embedded within the MW, to achieve the full potential of the proposed MW-based microbial surveillance approach, one would need to overcome several major technological challenges. Although the presence of a linear correlation between CSalm in MW and salmonellosis case number N in the community (Table 1) suggests that directly quantifying CSalm in the MW for the estimation of disease prevalence is possible, the large fluctuation of CSalm and the presence of data outliers can present practical challenges in data interpretation, especially for predictive applications. Further testing of multiple outbreaks caused by Salmonella and other enteric pathogens is required in order to establish statistical confidence for MW-based microbial disease outbreak detection. Many of the clinical Salmonella strains were either not detected at all or not concurrently detected in the MW samples, which was attributed primarily to low MW sampling frequency, low sampling depth, and potential biases introduced by the cultivation-based approach, which was necessary for obtaining individual MW strains for direct comparison with the clinical isolates. Future development of high throughput and high automation molecular methods that are significantly less labor
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ASSOCIATED CONTENT
S Supporting Information *
The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.8b00163. Additional information on log JSalm and log RS/E and clinical salmonellosis case number N over the 54-week sampling (Figure S1); the PFGE patterns of the Salmonella isolates belonging to the serotypes S. Amager, S. Chester, S. Takoraki, S. Newport, S. Javiana, and S. Derby (Figure S2) (PDF)
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AUTHOR INFORMATION
Corresponding Author
*Phone: 808-956-6024; fax: 808-956-5014; e-mail: taoyan@ hawaii.edu. ORCID
T. Yan: 0000-0001-7500-7993 Present Address #
E.P.: Ecological Sciences, The James Hutton Institute, Craigiebuckler, Aberdeen, Scotland, UK, AB15 8QH. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS The authors thank Mr. Ken Tenno of the Water Quality Laboratory, Department of Environmental Services of the City and County of Honolulu, for collecting sanitary sewage samples and providing wastewater flow data, Dr. Qian Zhang and Dr. Kun Yang for providing laboratory assistance, and Dr. Alexandria Boehm of Stanford Univeristy for sharing the laboratory protocol for Salmonella enumeration. We also thank student investigators Ana-Melissa Kea from the University of Hawaii Science, Technology, Engineering and Mathematics (STEM) program and Sasha Canovali from the Windward Community College Institutional Development Award (IDeA) Network for Biomedical Research Excellence (INBRE) program for performing much of the PFGE testing. This work was financially supported by grants from the National Science Foundation (CBET-0964260 and CBET-1507979 to T.Y.).
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REFERENCES
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DOI: 10.1021/acs.est.8b00163 Environ. Sci. Technol. XXXX, XXX, XXX−XXX
