TY - JOUR
AU1 - Karen, Shapiro,
AU2 - Mary, Silver,
AU3 - A, Byrne, Barbara
AU4 - Terra, Berardi,
AU5 - Beatriz, Aguilar,
AU6 - Ann, Melli,
AU7 - A, Smith, Woutrina
AB - ABSTRACT Pollution of nearshore waters with disease-causing microorganisms impacts ecosystems health through illness and deaths in people and wildlife, as well as negative socioeconomic consequences of impaired marine resources. Insight on pathogen ecology in coastal habitats is crucial for accurately mitigating inputs and impacts of microbial pollution. Three objectives were addressed to (i) compare fecal pollution in proximity to (a) freshwater runoff, and (b) endemic marine wildlife; (ii) evaluate presence and magnitude of fecal microorganisms in marine snow and mussels and (iii) determine if pathogens in mussels and FIB levels in seawater or mussels are correlated. Sampling during the wet season, proximity to freshwater, and FIB levels in mussel homogenates (but not seawater) were associated with pathogen presence in mussels. Pathogens and FIB were enriched in aggregate-rich fractions, further supporting an important role of marine snow in pathogen transmission. The lack of association between FIB in surrounding waters and presence of pathogens in mussels calls into question current regulations for insuring safe seafood to consumers in the United States, and alternative monitoring approaches such as direct testing for select pathogens should be further evaluated. pollution, shellfish, fecal indicator bacteria, zoonotic pathogens, marine snow, water quality INTRODUCTION Human population centers are preferentially situated along waterways, including coastlines (Small and Nicholls 2003; Kummu et al.2011). The concentration of people and associated domestic animals in these regions exerts environmental stressors on nearshore habitats, including the discharge of fecally derived pathogens into coastal habitats on which humans depend. Studies on parasites, bacteria and viruses suggest that pathogens can concentrate in coastal estuaries, marshes and beach sands (Yamahara et al.2007; Shapiro et al.2010a; Hogan et al.2012). Pollution of nearshore waters with disease-causing microorganisms impacts humans directly via sickness and death, as well as indirectly via socioeconomic consequences of lost productivity, impaired recreational opportunities and restrictions on seafood harvest. Collectively, these impacts total nearly $1 billion annually in USA (Ralston, Kite-Powell and Beet 2011; DeWaal and Glassman 2014). Contamination of nearshore habitats with zoonotic pathogens, disease-causing organisms that can infect and cause illness and/or death in both humans and animals, is a risk to human health, and can cause ecological alterations to coastal habitats by harming marine wildlife. For example, in coastal California, one iconic marine species, the Southern sea otter (Enhydra lutris nereis), has suffered mortality and morbidity due to terrestrial pathogen pollution. Deaths of sea otters are relevant for coastal ecology because of the central role that otters play in the sustainability of kelp forest habitats (Dayton 1985; Watson and Estes 2011). The terrestrial pathogen Toxoplasma gondii was a primary or contributing cause of death in more than one quarter of all sea otter carcasses recovered between 1998 and 2001 (Kreuder et al.2003). As felids are the only known definitive hosts that can shed environmentally robust oocysts (Hutchison et al.1969; Dubey, Miller and Frenkel 1970), this finding was suggestive of terrestrial pathogen pollution having significant consequences for marine ecology and wildlife health. Current approaches to evaluating coastal pathogen pollution have largely focused on microbial indicators such as coliform bacteria. For example, in USA the National Shellfish Sanitation Program (NSSP) requires regular monitoring of fecal indicator bacteria (FIB) in seawater for evaluating safety of harvested seafood for human consumption (NSSP 2015). Similarly, the Environmental Protection Agency (US EPA) established water quality monitoring guidelines for safe recreation that are also based on FIB quantification (U.S.EPA 2012). However, relying on traditional FIB for estimating the degree of pollution and therefore risk to beach-goers and food consumers has been increasingly recognized as problematic, because the presence and/or magnitude of FIB concentrations do not consistently predict the presence of disease-causing fecal pathogens (Stewart et al.2008). More sophisticated tools for evaluating the presence of fecal pollution that specifically originates from human waste have been gaining steady recognition for more reliable identification of polluted waters that can pose a risk to people (Wang et al.2010; Bradshaw et al.2016). While extremely valuable, such microbial source tracking assays still do not provide direct data on specific pathogens that may be present in coastal habitats, and may overlook the presence of zoonotic pathogens that can arise from fecal pollution derived from wildlife. In addition to the challenge of determining the presence of specific pathogens polluting nearshore ecosystems, few studies have simultaneously tested different environmental matrices in an attempt to characterize the transport and distribution of harmful microorganisms. Several reports have demonstrated that pathogens can associate with marine snow, (a.k.a. organic macroaggregates >0.5 mm) (Lyons et al.2010; Shapiro et al.2012a,b). Pathogen association with marine snow will subsequently affect the spatial distribution of microorganisms that would tend to accumulate at sites that promote formation of sinking macroaggregates. Moreover, macroaggregate-associated pathogens are more likely to become incorporated into the marine food chain, where they are accessible to higher trophic level marine fauna, as well as to seafood-consuming people (Shapiro et al.2014). The overarching aim of the current investigation was to provide novel insight on the ecology of fecal microorganisms in coastal habitats by addressing three distinct objectives: (i) determine the magnitude of pathogen pollution near two potential sources of fecal contamination along the central coast of California, USA: (a) freshwater runoff, and (b) fecal material released by endemic marine wildlife, specifically California sea lions (Zalophus californianus) (CSL); (ii) evaluate the distribution of FIB and select zoonotic pathogens in different matrices including marine snow (macroaggregates), aggregate-poor seawater and resident mussels (Mytilus californianus) and (iii) test for correlation between the presence of FIB in surrounding seawater or mussels and presence of select zoonotic pathogens in mussels. METHODS Field sites Two geographical regions along the central California coast were selected for field sampling of seawater, marine aggregates and mussels. The study was designed to test for FIB and zoonotic pathogen distribution in different matrices from two potential sources of fecal pollution: overland runoff and CSL. Thus, each region included two sampling sites—one near (<5 km) and the second distant (>5 km) to a freshwater source that was also located near rocks that are routinely utilized by CSL to haul-out. Specific sampling sites were selected based on accessibility and safety for boat and ground personnel, and locations where freshwater runoff and CSL sites were distinctly separated. At Cambria these sites included Santa Rosa Creek as a freshwater discharge site (35.5686 N, −121.1100 W), and White Rock (35.5329, N −121.0884 W) as a CSL site. At Carmel, the freshwater site was Carmel River Beach (36.5368 N, −121.9270 W), and Point Lobos served as the CSL haul-out site (36.5166 N, −121.9583 W) (Fig. S1, Supporting Information). To evaluate the effect of season on microbial ecology, samples were collected at least twice during the wet and dry seasons for 2 years (2011–2013). Sample collection Marine macroaggregates were collected using ten 2 L acrylic cylinders that were open on both ends (Fig. S2, Supporting Information). Cylinders were submerged and capped underwater (30–50 cm below the surface), thus minimizing presence of air that could disrupt the fragile nature of composite marine aggregate particles. All 10 cylinders were collected at the same site, gently transported to the beach where they were placed upright for 30 min, allowing readily visible material to settle. This approach yielded macroaggregates from 20 L of seawater present in 10 separate settling columns. After the settling period, the overlying seawater was syphoned and combined into an 18–19 L sample, henceforth referred to as the ‘aggregate-poor’ seawater fraction. This term acknowledges that some small and/or non-sinking material, including macroaggregates, were likely still present in this fraction. The settled material that included sinking macroaggregates in each cylinder was removed using a serologic pipette, and combined into a ∼2 L sample, henceforth referred to as the ‘aggregate-rich’ seawater fraction. The aggregate-rich and -poor fractions were placed in coolers with ice and transported to UC Davis within 6 h. Cylinders and caps were thoroughly washed and then autoclaved before each sampling effort, with sterilized materials used for each collection and site. For mussel collections, a minimum of 45 mussels (≥3 cm) were randomly collected from rocks located within 50 m of the water collection sites, placed in Ziploc™ bags, and transported on ice to UC Davis where they were processed for FIB and pathogen detection within 24 h as described below. In addition, bulk-surrounding seawater was collected from the same site as the marine aggregates using a sterile 2 L carboy to evaluate the FIB concentrations in samples that represent those routinely taken by regulatory agencies for assessing microbial quality of seawater where seafood is harvested. Fecal indicator bacteria enumeration in seawater and aggregates Standardized microbiological methods were employed for quantification of FIB in bulk seawater, aggregate-poor and aggregate-rich seawater fractions (a laboratory workflow schematic is depicted in Fig. S3a, Supporting Information). A membrane filtration approach was used to enumerate E. coli and total coliforms on 4-methylumbelliferyl-β-D-galactopyranoside (MUGal) and indoxyl-β-D-glucuronide (MI) agar (EPA Method 1604) (U.S.EPA 2002), as well as enterococci on m-Enterococcus agar (SM 9230C) (APHA 1995). For fecal coliforms, a 5-tube most probable number (MPN) method was employed (EPA Method 8001) (U.S.EPA 1998) so that results could be compared with the MPN method approved by the American Public Health Association for fecal coliform quantification in shellfish tissues (NSSP 2015). Salmonella detection in aggregate-rich and -poor seawater Because of the large volume of aggregate-poor seawater fraction (18–19 L), these samples were initially concentrated using hollow-fiber ultrafiltration (HFF) (Fig. S3b, Supporting Information) to increase the likelihood of detection of pathogens that are expected to occur in low concentrations in natural environmental waters (e.g. 0.2–33 (oo)cysts/L; Dreelin et al.2014; Boarato-David, Guimarães and Cacciò 2016). Hollow-fiber ultrafiltration is a closed system that recirculates water through a column packed with polysulfone hollow fibers (Fresenius Optiflux® F200NR, 30 kDa pore size), resulting in a retentate with concentrated particles, including pathogens (Rajal et al.2007). Recoveries of pathogens using hollow-fiber ultrafiltration has been evaluated for T. gondii (8–54%) (Shapiro et al.2010b); C. parvum (51–83%) and G. intestinalis (53–61%) (Hill et al.2009) and Salmonella (59–99%) (Polaczyk et al.2008). The hollow-fiber ultrafiltration method was selected in this study because it is less expensive than the traditional capsule filtration methods and allows for simultaneous concentration of pathogens including viruses, bacteria and protozoa (Hill et al.2009). Water was concentrated by continuously pumping (15–18 psi) water samples through a recirculating system until the volume of retentate was reduced to approximately 150–200 mL. The retentate was collected from the outflow port, and the filter was removed from the system apparatus and flushed (10 cycles) with 50 mL of glycin solution (0.05 M Glycin and 0.1% Tween 80). The glycin wash was combined with the collected retentate, and the exact volume of the final retentate recorded. The retentate was then well mixed and aliquoted for the separate analyses methods as described below. The aggregate-rich fraction consisted of a total volume of 1.5–2 L and was therefore not concentrated prior to pathogen testing. Presence of Salmonella was tested using previously described methods for detection of this bacterial genus in seawater (Morinigo et al.1990; Efstratiou, Mavridou and Richardson 2009). Briefly, 50 mL of concentrated aggregate-poor retentate, or 500 mL of aggregate-rich seawater, was filtered through a 47 mm, 0.45 μm pore size cellulose membrane filter (Millipore Microfil™ V Filtration Device, Thermo Fisher Scientific, Waltham, MA), placed in 100 mL pre-enrichment media consisting of 1X buffered peptone water (Veterinary Medical Biological Media Services, UC Davis (VBMS, UCD)), and incubated on a rotating shaker at 35 ± 2°C. After 18–22 h, 0.1 mL of the pre-enrichment solution was placed in 10 mL of Rappaport-Vassiliadis (RV) Salmonella enrichment broth (Thermo Fisher Scientific, Waltham, MA) and incubated at 43°C in a stationary incubator for 72 h. Selective culture in Rappaport-Vassiliadis broth was performed in duplicate for each sample. Enriched broth was then sub-cultured by streaking on xylose lysine deoxycholate (XLD) agar (VBMS, UCD) and incubated at 35°C ± 2°C for 18–24 h. Suspect positive colonies (three per sample) that produced hydrogen sulfide (black) were inoculated onto separate 5% sheep blood agar plates (VBMS, UCD), incubated at 37°C for 24 h, and inoculated into triple sugar iron agar, Christensen's urea agar, citrate agar, as well as lysine decarboxylase (VBMS, UCD) and ortho-nitrophenyl-beta-D-galactopyranoside (Thermo Fisher Scientific, Waltham, MA) broths for biochemical characterization. The samples were also spot-tested for the production of indole and cytochrome oxidase. Isolates biochemically confirmed to be S. enterica were sent to the National Animal National Veterinary Services Laboratory (Ames, Iowa, USA) for serotyping. Salmonella isolates were compared by pulsed-field gel electrophoresis (PFGE) following the protocol described by PulseNet (Ribot et al.2006). Briefly, the DNA from Salmonella isolates were suspended in agarose plugs, digested with the XbaI restriction enzyme (New England Biolabs, Ipswich, MA), and subjected to multi-directional electrophoresis for 18–19 h. Slight modifications to the PulseNet protocol were made as previously described (Berardi et al.2014). Briefly, (i) the optical density610 values of Salmonella suspension were decreased from 1.3–1.4 to 0.7–0.9 to reduce DNA smearing; and (ii) thiourea (100 µM, Fisher Scientific, Pittsburgh, PA) was added to the running buffer. Gels were stained with Gel star (Fisher Scientific, Pittsburgh, PA) and visualized using a gel doc UV illumination system (Alpha Innotech, Santa Clara, CA). Protozoal pathogen detection in aggregate-rich and aggregate-poor seawater fractions Concentrated aggregate-poor retentate (50 mL—obtained from hollow fiber ultrafiltration) and aggregate-rich seawater (500 mL), were tested for presence of Cryptosporidium, Giardia and T. gondii. Water fractions were vigorously mixed prior to aliquoting into different tubes destined for various pathogen detection methods to insure subaliquots were as homogenous as possible. The overall approach for protozoa detection was to apply sensitive microscopy-based techniques followed by nucleic acid extraction and PCR for samples in which protozoal organisms were visualized (Fig. S3b, Supporting Information). Detection of Cryptosporidium and Giardia was performed following standardized protocols that utilize immunomagnetic separation (IMS) to isolate the parasites, followed by direct fluorescence antibody (DFA) staining for parasite visualization based on Environmental Protection Agency Method 1623 (U.S.EPA 2001) (Fig. S3b, Supporting Information). Application of immunomagnetic separation provides a cleaner sample with reduced environmental debris that has been shown to aid visual as well as molecular identification of these parasites (as compared to PCR on environmental matrices without immunomagnetic separation) (U.S.EPA 2001; Smith and Nichols 2010). For T. gondii, we have further demonstrated that, even in challenging environmental water samples, microscopy (via membrane filtration) is more sensitive for oocyst detection as compared with direct application of PCR (Shapiro et al.2010b). Parasites were identified using light and epifluorescence microscopy (Zeiss™ Axioskop; 400X) as Cryptosporidium oocysts when 4–6 μm spherical to elliptical organisms outlined in apple green were observed with up to four blue-staining nuclei, and as Giardia when 9–14 μm oval cysts were outlined with apple green and contained 2–4 blue-staining nuclei. For both parasites, no atypical staining by fluorescein isothiocyanate (FITC—apple green fluorescence) or 4',6-diamidino-2-phenylindole (DAPI—blue fluorescence) were also confirmed. Any samples in which these parasites were visualized were further processed for molecular confirmation. Briefly, slides were scraped and washed with PBS into a 1.5 mL microcentrifuge tube, and a 100 µL pellet obtained via centrifugation. A single freeze/thaw cycle (4 min boiling water followed by 4 min immersion in liquid nitrogen) was initially performed to fracture (oo)cyst walls, followed by nucleic acid extraction using the Qiagen DNA mini kit® (Qiagen Inc. Valencia, CA) as previously described (Miller et al.2005). DNA amplified targeting two loci in the 18rRNA gene for Cryptosporidium (Morgan et al.1997; Xiao et al.2000), and the gdh (Read, Monis and Thompson 2004) and beta-giardin genes for Giardia (Caccio, De Giacomo and Pozio 2002), as previously described (Adell et al.2014). Positive controls consisted of DNA extracted from 10 000 (oo)cysts of G. lamblia (H3 isolate) or C. parvum (Iowa isolate) obtained from Waterborne Inc (New Orleans, LA 70 118, USA). For detection of T. gondii, aggregate-poor retentate (50 mL), or aggregate-rich seawater (500 mL), were processed for oocyst visualization using membrane filtration as previously described (Shapiro et al.2010b) (Fig. S3b, Supporting Information). Any samples in which suspect oocyst-like structures were visualized were further processed for molecular confirmation by nucleic acid extraction (as described above) and PCR targeting the 529-bp repetitive element, ITS1 and B1 loci of T. gondii using validated protocols (Shapiro et al.2010b; Shapiro et al.2015). Positive controls consisted of DNA extracted from 10 000 T. gondii Type II oocysts obtained from experimentally infected cats (Fritz et al.2012). For all PCR assays, negative controls included extraction reagents with no added DNA template, and PCR reagents with PCR-grade water added instead of DNA template. For all three protozoan parasite targets, any PCR product that yielded an amplicon consistent in size with positive controls (T. gondii, C. parvum or Giardia enterica DNA) was purified using a QIAquick gel extraction kit (Qiagen Inc., Chatsworth, California), and the samples were submitted to the UC Davis core DNA Sequencing Facility. For sequence analysis, the forward and reverse DNA sequences were aligned using a multiple sequence comparison by log-expectation (MUSCLE) method via Geneious software (Biomatters, Auckland, New Zealand), ends trimmed, and the consensus sequence compared with GenBank reference sequences using Basic Local Alignment Search Tool (BLAST) (http://blast.ncbi.nlm.nih.gov/Blast.cgi). Fecal coliform and Salmonella detection in mussels Mussels were initially washed by vigorous scrubbing using a sterile bristle brush under running DI water to remove any organic debris associated with the shell surface. Detection of bacteria was then conducted on whole shellfish tissue by dissecting contents of 15 mussels, adding equal volume of 0.5X BPW (VBMS, UCD), and homogenizing mussels as a ‘batch’ (representing each site and time point) in a blender for 60–90 sec (USDA 1998; NSSP 2015). Quantification of fecal coliforms in mussel homogenates was conducted using a 5-tube MPN method as recommended by the National Shellfish Sanitation Program (NSSP 2015). Briefly, homogenates were inoculated in three dilutions into five replicate tubes containing pre-enrichment lauryl tryptose broth (VBMS, UCD) and incubated for 24–48 h at 35 ± 0.5°C in a stationary water bath. Tubes that produced gas bubbles were sub-cultured for confirmation in Escherichia coli broth (VBMS, UCD), incubated for 24 h at 44.5 ± 0.2°C in a water bath and observed for formation of gas. Presence of Salmonella in mussel homogenates was tested using a similar approach as described above for detection of the bacterium in seawater (Kumar, Surendran and Thampuran 2010). Briefly 50 mL of homogenate (representing 25 g of mussel tissue) was added to 225 mL 1X BPW (VBMS, UCD) and incubated at 35 ± 2°C for 18–22 h. The remainder of the protocol for detection of Salmonella was followed as described above for seawater. Protozoal pathogen detection in mussels Hemolymph was selected for detection of protozoan pathogens in mussels due to the relative time efficiency entrained in obtaining this matrix from large numbers of mussels, and prior studies documenting hemolymph as an effective matrix type for detection of Cryptosporidium and T. gondii (Graczyk et al.1997; Fayer et al.1999; Lindsay et al.2001). Detailed description of the assays used for detection of Cryptosporidium and Giardia have been previously described by (Miller et al.2005), and for detection of T. gondii by Shapiro et al. (2015). Briefly, the surface of 30 mussels was thoroughly cleaned, and the hemolymph (0.5–3 mL) from each individual mussel was combined and centrifuged to obtain a 100 μL pellet that was subsequently subjected to nucleic acid extraction using Qiagen DNA mini kit® (Qiagen Inc. Valencia, CA) as described above. Amplification of protozoal DNA targeted the same genes and followed the same methods as described above for molecular confirmation of protozoal pathogens in water samples. Any samples that yielded amplicon sizes consistent with the protozoal targets were submitted for confirmation via sequence analysis. Amplified DNA was purified using a QIAquick gel extraction kit (Qiagen Inc. Valencia, CA), and the samples were submitted to the UC Davis core DNA Sequencing Facility for analysis. Data analysis The non-parametric Wilcoxon signed-rank test was used to test for significant differences in FIB concentrations in matched samples collected near two distinct sources of fecal pollution (freshwater vs. CSL), as well as association with aggregates (concentrations in the aggregate-rich fraction) versus individual FIB cells suspended in the water column (concentrations in the aggregate-poor fraction). A Mann-Whitney test was used to evaluate possible differences in FIB concentrations between samples taken in the wet and dry seasons. To test for associations between the presence of zoonotic pathogens in mussels and FIB levels in mussels or bulk seawater, a Fisher's Exact test was applied by categorizing FIB data into levels below and above concentrations established as safety limits set by government regulatory agencies, including the Environmental Protection Agency (EPA) that determines guidelines for safety of recreation in marine waters, and by the National Shellfish Sanitation Program (NSSP) operated by the US Food and Drug Administration that determines the safety of marine sites for seafood harvest. Associations between pathogens in mussels and FIB were tested for each pathogen separately, as well as for a combined ‘pathogen’ outcome variable that was designated as ‘present’ if any of the targeted zoonotic pathogens were detected in mussels. A Fisher's Exact test was also used to evaluate possible associations between presence of pathogens in mussels and putative risk factors, including proximity to freshwater runoff and season (wet vs. dry). Logistic regression was used to test for associations between the presence of zoonotic pathogens in mussels and sampling season (wet vs. dry), and source of fecal pollution (runoff vs. CSL). Univariable logistic regression was initially conducted on each parasite as well as a combined ‘pathogen’ variable that indicates whether any of the targeted pathogens were detected in mussels. Multivariable regression was then performed using variables that has an associated significance level of P ≤ 0.2 in the univariable analyses. All statistical analyses were performed using STATA (College Station, Texas, USA), with multivariable model risk factor significance indicated when P ≤ 0.05. RESULTS Sources and distribution of fecal indicator bacteria (FIB) Concentrations of FIB were significantly higher in both aggregate-rich (containing marine snow) and aggregate-poor water fractions in sites near freshwater runoff, as compared with samples collected near CSL haul-out sites (Fig. 1). These results were observed when all samples were combined (data not shown), as well as for samples from the Carmel region alone (Fig. 1a and b). However, FIB were not consistently higher in samples collected near freshwater runoff when data were analyzed for the Cambria region separately (Fig. 1c and d). At this region, no significant difference was observed in concentrations of total coliforms, E. coli, or enterococci in aggregate-rich or -poor samples measured near runoff or CSL sites. Figure 1. View largeDownload slide Fecal indicator bacteria in aggregate-rich (A,C) and -poor seawater (B,D) fractions collected near freshwater runoff or California sea lion (CSL) haul-out sites. Results are depicted separately for the two field sites in Carmel (A,B) and Cambria (C,D). Error bars denote one SD from the mean. (*P ≤ 0.1; **P ≤ 0.05). Figure 1. View largeDownload slide Fecal indicator bacteria in aggregate-rich (A,C) and -poor seawater (B,D) fractions collected near freshwater runoff or California sea lion (CSL) haul-out sites. Results are depicted separately for the two field sites in Carmel (A,B) and Cambria (C,D). Error bars denote one SD from the mean. (*P ≤ 0.1; **P ≤ 0.05). To compare the distribution of FIB between aggregate-rich and -poor fractions, mean quantities of total coliforms, E. coli, and enterococci were compared in these two fractions for all sites combined, as well as for the Carmel and Cambria field regions separately (Fig. 2). When data was analyzed for all sites combined, concentrations of FIB were significantly higher in the aggregate-rich fractions as compared with aggregate-poor fractions for all bacteria tested including total coliforms, E. coli and enterococci (Fig. 2a). When stratified by region, the same trend was observed, but was only significant (P < 0.05) in Cambria (Fig. 2b) for total coliforms and enterococci, and at Carmel (Fig. 2c) for total coliforms and E. coli. Associations between FIB concentrations and season (wet vs. dry) were only significant at specific sites with select bacteria (Fig. S4, Supporting Information). Significant differences indicated higher FIB in wet compared to dry seasons and were present only at Carmel River Beach for total coliforms and enterococci in aggregate-rich fractions, and for enterococci in aggregate-poor fractions. Figure 2. View largeDownload slide Distribution of fecal indicator bacteria in aggregate-rich and -poor seawater fractions analyzed for all sites combined (A) and for each region separately—Cambria (B) and Carmel (C). Error bars denote one SD from the mean. **P ≤ 0.05. Figure 2. View largeDownload slide Distribution of fecal indicator bacteria in aggregate-rich and -poor seawater fractions analyzed for all sites combined (A) and for each region separately—Cambria (B) and Carmel (C). Error bars denote one SD from the mean. **P ≤ 0.05. Sources and distribution of zoonotic pathogens A total of 959 mussels were collected from the field sites and tested in this study. T. gondii DNA was detected in 13 mussels (1.5% prevalence), and Giardia was detected in four mussels (0.4% prevalence). For Salmonella enterica, (which was tested on batched mussels), three batches of mussels tested positive (Fig. 3). Cryptosporidium DNA was not detected in mussels through the duration of this study. Presence of zoonotic pathogens in mussels was associated with sampling during the wet season, and in sampling locations near freshwater runoff (P < 0.05, Table 1). Detailed results of T. gondii and Giardia findings, as well as molecular characterization of these pathogens, were further described by Shapiro et al. (2015) and Adell et al. (2014), respectively. In aggregate-rich or poor seawater fractions, T. gondii and Salmonella were not detected. However, Giardia and Cryptosporidium were detected via microscopy in three aggregate-rich and three aggregate-poor samples. Concentrations of (oo)cysts in the aggregate rich fraction were approximately one order of magnitude greater (range 2–10 (oo)cysts/L) than concentrations in the aggregate-poor fractions (range 0.2–0.4 (oo)cysts/L). Further molecular confirmation for Giardia and Cryptosporidium in these samples was attempted, but PCR did not amplify DNA fragments consistent with positive control amplicons. Absence of molecular detection of the targeted protozoan parasites in environmental matrices harboring low numbers of parasites (observed using microscopy) has been previously described (Shapiro et al.2010b; Adell et al.2014). Figure 3. View largeDownload slide Most probable number (MPN) of fecal coliforms (FC) in bulk surrounding seawater and mussels, and presence of zoonotic pathogens in mussels collected from two sites close to freshwater runoff (Santa Rosa creek (A) and Carmel River Beach (C)) and two sites close to California sea lion haul-out rocks (White Rock (B) and Point Lobos (D)) in central California. Maximum allowable levels of FC in mussel tissues (FC > 230 MPN/100 g; black line) or surrounding seawater (FC > 14 MPN/100 mL; gray line) indicate guidelines set by the National Shellfisheries Sanitation Program for safe harvest of seafood (NSSP 2015). Presence of pathogens in mussels is designated as follows: G = Giardia; T = Toxoplasma gondii; and S = Salmonella. The number preceding each letter indicates the number of mussels that tested positive for Giardia or T. gondii (Salmonella was tested in batches of 30 mussels). Presence of pathogens in mussels was associated with high coliform counts in mussels, but not in surrounding seawater (Table 2). Figure 3. View largeDownload slide Most probable number (MPN) of fecal coliforms (FC) in bulk surrounding seawater and mussels, and presence of zoonotic pathogens in mussels collected from two sites close to freshwater runoff (Santa Rosa creek (A) and Carmel River Beach (C)) and two sites close to California sea lion haul-out rocks (White Rock (B) and Point Lobos (D)) in central California. Maximum allowable levels of FC in mussel tissues (FC > 230 MPN/100 g; black line) or surrounding seawater (FC > 14 MPN/100 mL; gray line) indicate guidelines set by the National Shellfisheries Sanitation Program for safe harvest of seafood (NSSP 2015). Presence of pathogens in mussels is designated as follows: G = Giardia; T = Toxoplasma gondii; and S = Salmonella. The number preceding each letter indicates the number of mussels that tested positive for Giardia or T. gondii (Salmonella was tested in batches of 30 mussels). Presence of pathogens in mussels was associated with high coliform counts in mussels, but not in surrounding seawater (Table 2). Table 1. Association between presence of zoonotic pathogens in mussels and (i) sampling season (wet vs. dry), and (ii) source of fecal pollution (runoff vs. California sea lions (CSL)). Univariable logistic regression was initially conducted on each parasite as well as a combined ‘Any pathogen’ variable that indicates whether any of the targeted pathogens were detected in mussels. Multivariable regression was then performed on variables for which an association at P ≤ 0.2 was obtained through univariable regression. Predictor variable Outcome variable Odds Ratio (95% CI) P value Univariable regression Season: Wet (Reference Dry) T. gondii 12.3 (1.6–94.6) 0.002 Giardia 1.0 (0.1–7.1) 0.687 Salmonella 2.4 (0.18–22.1) 0.501 Any pathogen 4.1 (1.4–12.3) 0.006 Source: Freshwater runoff (Reference CSL) T. gondii 4.9 (1.1–22.2) 0.020 Giardia spp. 0.9 (0.1–6.2) 0.635 Salmonella 1.7 (0.2–19.4) 0.551 Any pathogen 2.7 (1.0–7.4) 0.039 Multivariable regression Season (wet) T. gondii 11.5 (1.5–89.1) 0.019 Source (runoff) 4.5 (1.0–20.4) 0.053 Season (wet) Any pathogen 3.9 (1.3–11.8) 0.015 Source (runoff) 2.5 (0.9–6.7) 0.079 Predictor variable Outcome variable Odds Ratio (95% CI) P value Univariable regression Season: Wet (Reference Dry) T. gondii 12.3 (1.6–94.6) 0.002 Giardia 1.0 (0.1–7.1) 0.687 Salmonella 2.4 (0.18–22.1) 0.501 Any pathogen 4.1 (1.4–12.3) 0.006 Source: Freshwater runoff (Reference CSL) T. gondii 4.9 (1.1–22.2) 0.020 Giardia spp. 0.9 (0.1–6.2) 0.635 Salmonella 1.7 (0.2–19.4) 0.551 Any pathogen 2.7 (1.0–7.4) 0.039 Multivariable regression Season (wet) T. gondii 11.5 (1.5–89.1) 0.019 Source (runoff) 4.5 (1.0–20.4) 0.053 Season (wet) Any pathogen 3.9 (1.3–11.8) 0.015 Source (runoff) 2.5 (0.9–6.7) 0.079 View Large Table 1. Association between presence of zoonotic pathogens in mussels and (i) sampling season (wet vs. dry), and (ii) source of fecal pollution (runoff vs. California sea lions (CSL)). Univariable logistic regression was initially conducted on each parasite as well as a combined ‘Any pathogen’ variable that indicates whether any of the targeted pathogens were detected in mussels. Multivariable regression was then performed on variables for which an association at P ≤ 0.2 was obtained through univariable regression. Predictor variable Outcome variable Odds Ratio (95% CI) P value Univariable regression Season: Wet (Reference Dry) T. gondii 12.3 (1.6–94.6) 0.002 Giardia 1.0 (0.1–7.1) 0.687 Salmonella 2.4 (0.18–22.1) 0.501 Any pathogen 4.1 (1.4–12.3) 0.006 Source: Freshwater runoff (Reference CSL) T. gondii 4.9 (1.1–22.2) 0.020 Giardia spp. 0.9 (0.1–6.2) 0.635 Salmonella 1.7 (0.2–19.4) 0.551 Any pathogen 2.7 (1.0–7.4) 0.039 Multivariable regression Season (wet) T. gondii 11.5 (1.5–89.1) 0.019 Source (runoff) 4.5 (1.0–20.4) 0.053 Season (wet) Any pathogen 3.9 (1.3–11.8) 0.015 Source (runoff) 2.5 (0.9–6.7) 0.079 Predictor variable Outcome variable Odds Ratio (95% CI) P value Univariable regression Season: Wet (Reference Dry) T. gondii 12.3 (1.6–94.6) 0.002 Giardia 1.0 (0.1–7.1) 0.687 Salmonella 2.4 (0.18–22.1) 0.501 Any pathogen 4.1 (1.4–12.3) 0.006 Source: Freshwater runoff (Reference CSL) T. gondii 4.9 (1.1–22.2) 0.020 Giardia spp. 0.9 (0.1–6.2) 0.635 Salmonella 1.7 (0.2–19.4) 0.551 Any pathogen 2.7 (1.0–7.4) 0.039 Multivariable regression Season (wet) T. gondii 11.5 (1.5–89.1) 0.019 Source (runoff) 4.5 (1.0–20.4) 0.053 Season (wet) Any pathogen 3.9 (1.3–11.8) 0.015 Source (runoff) 2.5 (0.9–6.7) 0.079 View Large Association between zoonotic pathogens in mussels and FIB in mussels and seawater Concentrations of fecal coliforms measured in mussel tissue homogenates and surrounding seawater (a bulk surface seawater sample that was not separated into aggregate rich and poor fractions), along with presence of target zoonotic pathogens in mussels, are presented in Fig. 3. The presence of pathogens was associated with fecal coliform levels in mussels that exceeded maximum allowable levels historically set by the NSSP (National Shellfisheries Sanitation Program) for the safe harvest of shellfish as determined by a single sample measurement (Table 2) (NSSP 2015). When the association of specific pathogens was tested with fecal coliforms in mussels, a significant relationship was only present for T. gondii (Table 2). Presence of pathogens in mussels was not associated with fecal coliform concentrations in bulk surrounding seawater (Fig. 3 and Table 2). No significant associations between other commonly employed FIB in surrounding seawater (total coliforms and enterococci) and presence of pathogens in mussels were detected (Table 2). Table 2. Association between zoonotic pathogens detected in mussels and fecal indicator bacteria (FIB) concentrations in mussels or bulk seawater. Data were analyzed using a Fisher's Exact test with FIB set as a binary independent variable, using established safety limits set by government regulatory agenciesa−c. The combined outcome variable ‘any pathogen’ was designated as ‘present’ if any zoonotic pathogen was detected in mussels. Predictor variable Outcome variable P value Mussel fecal coliformsa T. gondii 0.040 Giardia spp. 0.305 Salmonella 0.268 Any pathogen 0.050 Seawater fecal coliformsb T. gondii 1.000 Giardia spp. 0.388 Salmonella 0.704 Any pathogen 0.400 Seawater enterococci T. gondii 0.415 Giardia spp. 0.634 Salmonella 0.298 Any pathogen 0.716 Predictor variable Outcome variable P value Mussel fecal coliformsa T. gondii 0.040 Giardia spp. 0.305 Salmonella 0.268 Any pathogen 0.050 Seawater fecal coliformsb T. gondii 1.000 Giardia spp. 0.388 Salmonella 0.704 Any pathogen 0.400 Seawater enterococci T. gondii 0.415 Giardia spp. 0.634 Salmonella 0.298 Any pathogen 0.716 a Exposure set at FC > 230 MPN/100 g (NSSP 2015) b Exposure set at FC > 14 MPN/100 mL (NSSP 2015) c Exposure set at enterococci > 35 CFU/100 mL (U.S.EPA 2012). View Large Table 2. Association between zoonotic pathogens detected in mussels and fecal indicator bacteria (FIB) concentrations in mussels or bulk seawater. Data were analyzed using a Fisher's Exact test with FIB set as a binary independent variable, using established safety limits set by government regulatory agenciesa−c. The combined outcome variable ‘any pathogen’ was designated as ‘present’ if any zoonotic pathogen was detected in mussels. Predictor variable Outcome variable P value Mussel fecal coliformsa T. gondii 0.040 Giardia spp. 0.305 Salmonella 0.268 Any pathogen 0.050 Seawater fecal coliformsb T. gondii 1.000 Giardia spp. 0.388 Salmonella 0.704 Any pathogen 0.400 Seawater enterococci T. gondii 0.415 Giardia spp. 0.634 Salmonella 0.298 Any pathogen 0.716 Predictor variable Outcome variable P value Mussel fecal coliformsa T. gondii 0.040 Giardia spp. 0.305 Salmonella 0.268 Any pathogen 0.050 Seawater fecal coliformsb T. gondii 1.000 Giardia spp. 0.388 Salmonella 0.704 Any pathogen 0.400 Seawater enterococci T. gondii 0.415 Giardia spp. 0.634 Salmonella 0.298 Any pathogen 0.716 a Exposure set at FC > 230 MPN/100 g (NSSP 2015) b Exposure set at FC > 14 MPN/100 mL (NSSP 2015) c Exposure set at enterococci > 35 CFU/100 mL (U.S.EPA 2012). View Large Molecular characterization of Salmonella The three Salmonella isolates obtained from contaminated mussels were further characterized using PFGE. Two isolates recovered from Carmel River mussels in April and July (2012) were typed as S. enterica serovar Infantis and had indistinguishable digestion patterns (Fig. S5, Supporting Information). In addition, the PFGE digestion pattern of the Salmonella strain isolated from mussels was similar to that found in an isolate obtained from humans infected with Salmonella Infantis during a salmonellosis outbreak that occurred at the same time period ((CDC 2012), Fig. S5). The isolate obtained from Point Lobos mussels (a CSL haul-out site) in March (2013) was typed as S. enterica serovar Enteritidis and had an indistinguishable PFGE digestion pattern compared with isolates derived from CSL feces collected from these sites during the same time period (as previously reported by Berardi et al.2014). DISCUSSION Characterizing fecal pathogen ecology in coastal ecosystems is challenging due to complex mechanisms that impact not only the sources of fecal contamination (people, domestic animals or wildlife), but also the transport and fate of microorganism in a vast habitat. The data obtained in this study provide insight on diverse environmental matrices in coastal habitats for quantifying FIB and detecting zoonotic fecal pathogens. Zoonotic pathogens were most often detected in mussels, as compared with marine snow (macroaggregates) or aggregate-poor seawater. In addition, marine snow harbored zoonotic protozoa at concentrations that were approximately 10-fold higher than in aggregate-poor seawater. Results further demonstrated that monitoring FIB in bulk seawater did not predict pathogen presence in shellfish, calling into question current guidelines set by regulatory agencies for protecting seafood consumers from illness in USA (NSSP 2015). The first step for mitigating the health and associated economic cost of coastal pathogen pollution is to identify contamination sources—thereby enabling identification of the problem for remediation measures. This study compared fecal contamination in spatially paired coastal sites near two potential sources of pollution: freshwater runoff-driven pollution from people and terrestrial animals, and endemic marine wildlife, specifically CSL. Quantitative FIB data demonstrated that FIB were significantly higher near freshwater runoff as compared with CSL haul-out rocks in Carmel, while in Cambria FIB levels did not significantly differ between runoff and CSL haul-out sites (Fig. 1). Qualitative data on the presence of zoonotic pathogens demonstrated that when pathogens were combined, their presence in mussels was associated with proximity to runoff. When stratified by each pathogen separately, T. gondii presence in mussels was associated with proximity to freshwater runoff, but this association was not present for Giardia, or Salmonella (Table 1). The small number of positive samples for the latter two pathogens likely hindered the power of these analyses. The finding that T. gondii contamination in marine habitats is associated with land-derived fecal pollution is consistent with prior reports linking exposure of sea otters to T. gondii with proximity to freshwater runoff (Miller et al.2002). Presence of zoonotic pathogens at distinct coastal sites reflects the distribution of definitive hosts capable of shedding environmentally resistant parasite stages: for pathogens with exclusively terrestrial animals as definitive hosts (T. gondii), higher likelihood of contamination near river mouths is more likely, as compared with pathogens that can also be shed in stool from marine animals (Cryptosporidium, Giardia and Salmonella). Findings presented here indicate that while the risk of pathogen pollution from wildlife is unpredictable and difficult to quantify—it is not negligible (WHO 2010). Coastal regions are likely to experience differing contributions of fecal pollution that are specific to each site and must be evaluated on a regional basis for effective implementation of control measures. A second critical question exists for addressing the timing of coastal pathogen pollution. In coastal California, rainfall (that can mobilize the transport of pathogens from land to sea) is typically restricted to winter months, and hence wet versus dry season was tested as a risk factor for presence of fecal contamination in the nearshore. In Carmel River Beach, FIB concentrations were consistently higher during wet season months, and this association was seen both in aggregate-rich as well as aggregate-poor seawater fractions (Fig. S4). However, at Santa Rosa Beach, an association between FIB and the wet season was absent. The comparatively smaller watershed at this location, and/or overall dilution of pathogens entering marine waters, may explain this observation. For White Rock and Point Lobos, the lack of association between season and fecal pollution at CSL haul-out sites can be more readily explained, as the source of pollution (CSL) is present year-round, and the action of waves washing over the rocks at high tide provides a more constant force of fecal matter mobilization than rain-driven runoff. In fact, the haul-out rocks selected in this study were almost completely submerged at high tide, and thus any feces deposited by CSL during low tide would be washed into coastal waters within hours. Investigating the role of marine snow in transmission of pathogens in the nearshore is a relatively new area of research (Lyons et al.2005, 2007, 2010). Several studies have demonstrated that pathogens—including Cryptosporidium, Giardia, T. gondii and Salmonella—can be present on macroaggregates at concentrations that are several orders of magnitude greater than in surrounding seawater (Shapiro et al.2012a,b). However, these investigations were performed in spiked seawater under laboratory conditions, and evidence for presence of these pathogens in marine snow collected from environmental samples has not been previously described. Of the pathogens targeted in this study, only Cryptosporidium and Giardia were detected in aggregate-rich samples, with concentrations that were 10–50-fold greater than those present in aggregate-poor seawater. The ability of marine snow to serve as hot-spots for pathogen accumulation is further supported by the higher concentrations of FIB quantified in this fraction, as compared with aggregate-poor water (Fig. 2). Failure to detect T. gondii and Salmonella in marine snow may be due to the relatively low concentration of these targets in environmental waters, as compared with bivalves that can accumulate pathogens from surrounding water and retain them in their tissues for days to months (Robertson 2007). Findings for Cryptosporidium and Giardia combined with FIB data reinforce the hypothesis that marine snow can play an instrumental role in the transport and fate of fecal pathogens in the nearshore. Entrainment or capture of pathogens in macroaggregates has been shown to greatly facilitate their uptake by mollusks including bivalves and snails (Kach and Ward 2008; Ward and Kach 2009). Thus, when evaluating the transmission dynamics of pathogens in aquatic environments, marine snow may facilitate bioavailability of these microorganisms by transforming them from suspended particles in water into more available food sources for invertebrates (e.g. clams, mussels and oysters) that serve as food for marine wildlife, as well as humans. An unexpected but intriguing result of this study was the Salmonella serotype detected in mussels. Only twice were mussels found to be contaminated with Salmonella near freshwater runoff, and these isolates were obtained during two consecutive sampling efforts in April and July 2012 from mussels collected near Carmel River Beach. Both isolates were typed as Salmonella enterica serovar Infantis and had indistinguishable banding patterns when subjected to PFGE (Fig. S5). During the same time period, a human outbreak in USA due to Salmonella Infantis was reported by the United States Center for Disease Control (CDC), with contaminated dog food implicated as a suspected source for the outbreak (CDC 2012). When comparing the human outbreak strain-banding pattern from PulseNet (Center for Disease Control), the strains were identical with the exception of two additional fragments in the mussel isolates (Fig. S5). Interestingly, the third Salmonella isolate obtained from a batch of mussels near White Rock (a CSL haul-out site) was typed as S. enterica serovar Enteritidis—with a banding pattern that was indistinguishable to isolates derived from the same population of CSL (Fig. S5, Supporting Information; Berardi et al.2014). Combined, the Salmonella findings demonstrate how molecular epidemiology can be utilized for discriminating sources of fecal pollution entering the nearshore. A notable result of this study—with direct relevance to human public health—was the lack of association between commonly measured FIB in seawater and the presence of pathogens in mussels. Current guidelines for monitoring shellfish quality in USA are issued by the National Shellfish Sanitary Program (NSSP)—a cooperative program among individual states and federal agencies including the US Food and Drug Administration (FDA), Environmental Protection Agency (EPA), National Oceanic and Atmospheric Administration (NOAA), and the shellfish industry (NSSP 2015). The National Shellfish Sanitary Program provides a system for classifying shellfish-growing waters according to levels of either fecal or total coliforms measured in bulk samples of seawater. For example, for a shellfish growing area in a remote location to be classified as ‘approved’, the median fecal coliform levels in a single sample must be <14 MPN/100 mL (Section .02 Microbiological Standards; NSSP 2015). Total coliforms are considered less specific indicators for fecal contamination and are no longer recommended by most monitoring agencies (Hackney and Pierson 1994). While testing microbial quality (using FIB) of surrounding water is mandatory, testing actual shellfish stocks for FIB, or the zoonotic pathogens selected in this study, are not currently performed by regulatory agencies. Our results demonstrate no association between contamination of mussels with T. gondii, Giardia or Salmonella and any of the measured FIB in seawater (including fecal coliforms, total coliforms, enterococci or E. coli). However, a significant association was found between the presence of pathogens in mussels and levels of fecal coliforms in mussel homogenate that exceeded the maximum recommended threshold that is currently employed by the European Union (EU) and that was historically suggested as a guideline by the National Shellfish Sanitary Program (>230 MPN/100 g) (Table 2). When tested for each pathogen individually, that association was still present for T. gondii, but not with the other two pathogens, which may be due to low number of detects and reduced statistical power. One aspect of the current investigation that warrants further research is the lack of data on protozoan parasite viability in the targeted environmental samples. Unlike detection methods that were applied for detection of Salmonella (culture-based), the microscopy and molecular assays used for protozoa detection do not discriminate between viable or infectious parasites and those that are non-viable, and therefore no longer capable of causing infections in susceptible hosts. Assays that incorporate DNA-binding dyes (e.g. PMA-PCR) or those targeting RNA quantification (Reverse Transcription qPCR) show promise for protozoa viability discrimination (Alonso, Amoros and Guy 2014; Travaille et al.2016) and should be applied in future investigations to clarify the potential of detected parasites to cause illness in shellfish consumers. In conclusion, enrichment of fecal microorganisms in marine snow sampled under field conditions supports an important role of these particles in mediating the transmission of biological pollutants in coastal ecosystems. Among the different matrices evaluated here, testing for specific pathogens in shellfish appear to be the most reliable approach for detecting presence of harmful pathogens in nearshore ecosystems. When specific pathogen testing is not possible due to financial or logistical constraints, monitoring fecal coliforms or E. coli levels in shellfish flesh may provide a more accurate association with presence of fecal pathogens in contaminated bivalves, as compared with FIB testing in surrounding water. SUPPLEMENTAL DATA Supplementary data are available at FEMSEC online. ACKNOWLEDGEMENTS We are grateful to the field teams that supported collections and laboratory processing of mussels, and specifically acknowledge Aiko Adell, Don Canestro, Miles Daniels, Mark Kocina, Colin Krusor, Zach Randell, Matt Smith, Tim Tinker, Joe Tomoleoni, Jim Webb and Ben Weitzman. Laboratory processing of the mussels was further assisted by Heather Fritz, Leopoldo Guerrero, Kaitlyn Hanley, Claudia Llerandi, Anna Naranjo and Andrea Packham. Collection of mussels from the sites in Cambria was facilitated through the University of California Ken Norris Rancho Marino Reserve. Kelley B. 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TI - Fecal indicator bacteria and zoonotic pathogens in marine snow and California mussels (Mytilus californianus)
JF - FEMS Microbiology Ecology
DO - 10.1093/femsec/fiy172
DA - 2018-11-01
UR - https://www.deepdyve.com/lp/oxford-university-press/fecal-indicator-bacteria-and-zoonotic-pathogens-in-marine-snow-and-GzgAy7f05c
VL - 94
IS - 11
DP - DeepDyve
ER -