Application of a Microfluidic Quantitative Polymerase Chain Reaction

Nov 17, 2015 - Application of a Microfluidic Quantitative Polymerase Chain Reaction. Technique To Monitor Bacterial Pathogens in Beach Water and...
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Letter pubs.acs.org/journal/estlcu

Application of a Microfluidic Quantitative Polymerase Chain Reaction Technique To Monitor Bacterial Pathogens in Beach Water and Complex Environmental Matrices Muruleedhara N. Byappanahalli,*,† Meredith B. Nevers,† Richard L. Whitman,† and Satoshi Ishii‡,§ †

Great Lakes Science Center, U.S. Geological Survey, 1574 N 300 E, Chesterton, Indiana 46304, United States Department of Soil, Water, and Climate and §BioTechnology Institute, University of Minnesota, 1479 Gortner Avenue, St. Paul, Minnesota 55108, United States

‡

S Supporting Information *

ABSTRACT: Microfluidic quantitative polymerase chain reaction (MFQPCR) and conventional quantitative polymerase chain reaction methods were compared side by side in detecting and quantifying 19 genetic markers associated with Escherichia coli and select bacterial pathogens in algae, beach sand, and water from Lake Michigan. Enteropathogenic E. coli (EPEC), Shiga toxinproducing E. coli, Salmonella spp., Campylobacter jejuni, and Clostridium perf ringens were among the pathogens tested. Of the pathogenic markers, eaeA that encodes intimin in EPEC was detected in all sample types: water (5%), detached/floating algae (42%), exposed/stranded algae (43%), sand below exposed algae (27%), and nearshore sand with no algae (22%). Other pathogenic markers, however, were detected sporadically. Despite comparable results from the two methods for the genetic markers tested in this study, the MFQPCR method may be superior, with the advantage of detecting and quantifying multiple pathogens simultaneously in environmental matrices.

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Recently, Ishii et al.6 evaluated the usefulness of microfluidic qPCR (MFQPCR), an emerging technique in which up to 96 singleplex qPCRs are run simultaneously in reaction chambers, as a tool for monitoring enteric pathogens in environmental waters. The main objectives of this study were (a) to determine the suitability of MFQPCR for simultaneous quantification of enteric bacterial pathogens in nearshore waters and beach substrates, (b) to compare the MFQPCR results with those obtained by the conventional qPCR method for the same targets, and (c) to evaluate if MFQPCR is a viable method for quantitative determination of enteric pathogens and/or their surrogates in the beach environment. Bacterial pathogens, such as Campylobacter, enteropathogenic E. coli, Salmonella, Shigella, and Clostridium perf ringens, were tested by targeting select virulent markers associated with these bacteria.6

INTRODUCTION

Fecal indicator bacteria (FIB; e.g., Escherichia coli and enterococci) have been routinely recovered from a variety of environmental substrates, such as soils and sediments, aquatic and terrestrial vegetation, beach sand, and detrital materials, across a wide geographic range.1−3 There is growing concern that traditional FIB are not effective in predicting human illness risk, especially when these bacteria are not associated with human sewage.4 Ideally, pathogens could be directly monitored in impacted water bodies. However, culturing and identifying many human pathogens with traditional methods is tedious, expensive, and often impractical in routine applications. Developments in molecular biology in the 1990s opened new opportunities by circumventing cultural methods. The molecular methods, especially polymerase chain reaction (PCR) and quantitative PCR (qPCR), as well as the emergence of metagenomics in recent years, have proven to be indispensable tools in environmental microbiology.5 Molecular markers targeting sources have been used with varying degrees of success (see the review by Harwood et al.4), but for improved certainty, multiple markers that are more specific to a source (e.g., sewage) would be appropriate. Most traditional qPCR methods can target only one or several markers per reaction. © 2015 American Chemical Society

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METHODS Locations and Sampling. The principal study area was Jeorse Park (JP) Beach, located on the southwest shore of Lake Michigan in East Chicago, Indiana, USA (41.651110°/− 87.433422°); beach characteristics are explained in greater Received: Revised: Accepted: Published: 347

September 14, 2015 October 23, 2015 November 17, 2015 November 17, 2015 DOI: 10.1021/acs.estlett.5b00251 Environ. Sci. Technol. Lett. 2015, 2, 347−351

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Environmental Science & Technology Letters

Figure 1. Correlations between gene concentrations measured by MFQPCR and conventional qPCR: (A) f tsZ, (B) uidA, and (C) eaeA. The linear regression equations and goodness of fit (r2) values are also shown for each assay.

detail elsewhere.7 Jeorse Park has been identified as one of the most polluted beaches in the nation.8 The beach (∼270 m long) is bounded by two breakwalls that act as substrates for Cladophora (a green alga) growth; throughout the summer, detached algal mats accumulate in shallow water or become stranded on the beach. Portage Lakefront and Riverwalk (PL), located in Portage, Indiana, USA (41.631170°/−87.179369°), was included as an additional study location. Between June 26 and September 25, 2012, triplicate samples of (a) detached algae (i.e., floating approximately 5−10 m from the shoreline; 200−400 g) (DA), (b) nearshore sand (NS) (0.5−1 m shoreward), (c) exposed algae (i.e., stranded; 0.5−1 m shoreward) (EA), and (d) sand beneath the exposed algae (ES) were collected at JP. A single surface water (SW) sample was collected, typically 1 m from the edge of the detached algae each time. A similar sampling design was used at the PL location between June and August 2012, but because of the infrequent presence of algae at this location, not all sample types (i.e., DA, NS, EA, ES, and SW) were collected during each event. Sterile disposable gloves were used for sampling. Samples were processed within 24 h of collection. Enumeration of Culturable E. coli. Culturable E. coli concentrations were determined by defined substrate technology9 using the Colilert-18 and Quanti-Tray 2000 method (IDEXX, Westbrook, MA). Water samples were analyzed without dilution. For algae, an initial bacterial elutriation was used prior to analysis. Briefly, replicates of algal samples were mixed with sterile spatulas, and then a subsample (25 g) was placed into a sterile dilution bottle containing 100 mL of phosphate-buffered water (PBW; pH 7.0 ± 0.2)9 and 0.01% hydrolyzed gelatin.10 The mixture of algae and PBW was shaken twice for 2.5 min, with a 1 min resting period between shakes; the mixture was then transferred to sterile 50 mL tubes and centrifuged (2000 rpm, 622g) for 1 min. The resulting supernatants were decanted into sterile dilution bottles, and aliquots (40 mL) from the three replicate elutriates were composited. A portion of the composited elutriate was used for E. coli analysis. For the fresh sample, dry weight ratios were determined individually after algal subsamples (5 g) had dried at 105 °C for 24 h, and the mean ratio was used for calculating gene copy numbers per gram of dry weight (dw). Sand samples were similarly processed and elutriated for E. coli analysis. Subsamples (35 g) were placed into a 500 mL sterile bottle. A 100 g portion of the mixed sand was combined

with 200 mL of PBW (pH 6.8), and the mixture was vigorously shaken for 2 min. The clear supernatant was decanted into a sterile bottle, and an aliquot was used for E. coli analysis as described above. For the fresh sample, dry weight ratios were determined after subsamples (5 g) had dried at 105 °C for 24 h, and the mean ratio was used for calculating gene copy numbers per gram of dw. DNA Extraction. Samples of water (100 mL) and algal (40 mL) and sand (50 mL) elutriates were filtered through 0.40 μm polycarbonate filters (Millipore, Billerica, MA), and the filters were stored in 2.0 mL centrifuge tubes at −80 °C until analysis was performed. Prior to DNA extraction, filters were thawed and transferred to PowerBead tubes, and DNA was extracted using the PowerSoil DNA extraction kit (MOBIO Laboratories, Solana Beach, CA). To assess the presence of PCR inhibitors, 0.2 μg/L salmon testes DNA was added after the bead beating step of the DNA extraction process.7 MFQPCR and Conventional qPCR Assays. Specific target amplification (STA) reaction and MFQPCR were performed as described elsewhere.6 The STA reaction is a 14-cycle multiplex PCR with the same primers used for MFQPCR.11 STA reactions increase the number of targeted molecules without major impacts on the quantitative information.6 Both DNA samples and the standard plasmid mixture were amplified by STA reaction, prior to MFQPCR. MFQPCRs were performed in a 96.96 Dynamic Array chip (Fluidigm, South San Francisco, CA) and a BioMark HD Reader (Fluidigm). Nineteen genes, namely, f tsZ and uidA (for generic E. coli), eaeA [for enteropathogenic E. coli (EPEC)], stx1 and stx2 [for Shiga toxin-producing E. coli (STEC)], ipaH 7.8, ipaH all, and virA (for Shigella spp.), invA and ttrC (for Salmonella spp.), cadF and ciaB (for Campylobacter jejuni), cpe and plc (for C. perf ringens), mip (for Legionella pneumophila), iap and hlyA (for Listeria monocytogenes), ctxA (Vibrio cholerae), and tdh (Vibrio paraheamolyticus), were quantified in quadruplicate on the basis of the standard curve method previously established.6 In addition to MFQPCR, conventional qPCR was also performed to verify the results. Aliquots (1 μL) of DNA samples were used, without STA reaction, for conventional qPCR in a final volume of 10 μL. Conventional qPCR was performed in duplicate using an ABI Prism 7500 Fast RealTime PCR System (Applied Biosystems), as described elsewhere.6 A negative control (no DNA template) was included in all MFQPCR and qPCR runs. 348

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amplification signals from dead cells14 and, therefore, may allow us to quantify live bacteria only. The correlations for algal samples were not strong: the low slope and high intercept values indicated that E. coli concentrations (from MFQPCR) were underestimated because of PCR inhibitions15 or the occurrence of the false-positive signals from non-E. coli bacteria (e.g., Aeromonas)16 in the Colilert system. We believe the latter was the most likely reason for the poor correlation because (i) an independent qPCR assay targeting the salmon sperm DNA (an internal amplification control) showed no apparent PCR inhibition in our samples7 and (ii) Cladophora mats harbor a wide range of bacteria besides E. coli.17 Occurrence of Pathogens. Several virulence factor genes were frequently detected in the samples collected (Table 2). As there was no large temporal variation during the sampling period, all data were combined and analyzed (Figure S2 of the Supporting Information). Among these, eaeA that encodes intimin of EPEC was detected in all sample types: 5, 42, 43, 27, and 22% of the SW, DA, EA, ES, and NS, respectively (Table 2). In contrast, other virulence factor genes (stx1 from STEC, ipaH all from Shigella spp., invA and ttrC from Salmonella spp., cadF from Campylobacter jejuni, and plc from C. perf ringens) were detected only in a few samples. MFQPCR and conventional qPCR methods performed similarly in detecting the virulence factor genes. Significant correlations were observed between eaeA concentrations measured by MFQPCR and conventional qPCR (Figure 1C), similar to the findings of Ishii et al.12,18 The eaeA gene has been detected in beach water and sand,19,20 as well as irrigation water,12 indicating that EPEC strains may be one of the more common enteric pathogens in the environment. The eaeA gene encodes intimin, which is the central virulence factor of EPEC.21 Because EPEC can cause diarrhea, especially in children,21 the occurrence of EPEC in beach environments elevates the risk of exposure to this pathogen during recreational activities. The infrequent occurrence of Salmonella and Campylobacter virulent markers was unexpected, because these pathogens have been detected in Cladophora mats along these sampling locations in previous years.10,22 A number of factors (e.g., temporal variation, sampling time and source, survival, and genotypic composition) likely contribute to their relative abundance and distribution in environmental matrices as suggested by Ishii et al.10 and Byappanahalli et al.22 Relationship between E. coli and Pathogen Concentrations. Significant correlations were observed between E. coli (as measured by Colilert-18 and f tsZ- or uidA-targeted MFQPCR) and eaeA concentrations (Figure S1), contrary to a lack of correlation between E. coli and eaeA concentrations in irrigation water in Japan.12 These results suggest that EPEC responds to environmental conditions like generic (i.e., nonpathogenic) E. coli strains in the samples analyzed in this study. Although we did not check the presence of eaeA in the Colilert trays, a correlation between Colilert counts and eaeA concentrations is an indication that EPEC detected by cultureindependent methods were likely from viable bacteria, which may increase the health risk associated with these beach substrates. In contrast to eaeA, no correlation was found between E. coli and other virulence factor genes. Thus, predictions of the presence of these pathogens vis-à-vis E. coli occurrence are rather tenuous.

Data Analysis. Quantitative values were considered negative when they were below the lowest concentration on the standard curve [i.e., detected but not quantifiable (DNQ)] or when the observed amplification was 0.7) were Table 1. Correlative Relationships between E. coli Concentrations Measured by MFQPCR and Colilert-18 in Various Substrates substrate

slopea

intercepta

r

SW DA EA ES NS

1.41 0.27 0.24 1.01 1.22

−1.77 3.61 3.59 0.05 −0.65

0.64 0.33b 0.28 0.79b 0.80b

a The slope (a) and intercept (b) of linear equations (y = ax + b) are shown, where x and y are the E. coli concentrations measured by MFQPCR and Colilert-18, respectively. bThe correlation coefficient (r) was significantly different from zero (p < 0.05).

observed for sand samples, with slopes of the linear equations being close to 1.0. DNA-based methods, such as qPCR, measure both live and dead cells, so qPCR and culture-based estimates often do not match. In this study, correlations between the Colilert-18 and MFQPCR results for E. coli in the sand samples indicated that the bacterial population was mostly comprised of viable cells (r = 0.8). In contrast, MFQPCR results for E. coli counts in the SW (r = 0.64) were greater than Colilert-18 results, likely as a result of the mixed population (i.e., live, viable but nonculturable, and dead cells). Use of propidium monoazide could reduce the magnitudes of the 349

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Mean concentrations ± the standard deviation are shown. Units are log10 copies/100 mL for SW and log10 copies/g for DA, EA, ES, and NS. The detection frequency (%) is indicated in parentheses. BQL indicates below quantification limit. Genes not listed in this table were BQL in all samples.

(57) (100) (95) (100) (73) 0.41 0.61 0.58 0.68 0.73

f tsA

± ± ± ± ± 1.02 1.91 1.98 1.51 1.46 substrate

SW (n = 37) DA (n = 24) EA (n = 21) ES (n = 22) NS (n = 37)

Implications for Water Quality and Human Health. A previous study identified MFQPCR as a useful method for testing pathogens in a lake heavily contaminated with avian feces.12 The current findings show that MFQPCR is useful for identifying and quantifying multiple pathogens simultaneously in lake water and other substrates. In addition, quantitative values obtained by MFQPCR were as accurate as those obtained by conventional qPCR. On the basis of our best estimate, MFQPCR is less expensive ($0.17 per assay per sample) than conventional qPCR ($0.45 per assay per sample).18 Further, MFQPCR can be used in developing alternate recreational water quality criteria using quantitative microbial risk assessment (QMRA) or similar techniques. Our results suggest that sand and algae can be important reservoirs of certain bacterial pathogens. However, the impact of their occurrence in these substrates on nearshore water quality and associated health risk is unknown (see reviews by Solo Gabriele et al.23 and Whitman et al.3). Pathogen transport pathways and estimates of health risks are areas that deserve attention in future studies.

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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.estlett.5b00251. Correlations between E. coli concentrations measured by MFQPCR and Colilert (Figure S1), temporal dynamics of E. coli and pathogen concentrations in surface water (SW), detached algae (DA), exposed algae (EA), exposed sand (ES), and nearshore sand (NS) (Figure S2), and performance of the qPCR assays (Table S1) (PDF)

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone: (219) 926-8336, ext. 421. Author Contributions

M.N.B. and S.I. contributed equally to this work. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Ashley Spoljaric, Kasia Przybyla-Kelly, and Dawn Shively for their help with sampling and laboratory analysis, Reiko Hirano for technical assistance, and Takahiro Segawa for allowing us to use his Fluidigm devices. This project was funded in part through a grant from the Great Lakes Restoration Initiative. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. This article is Contribution 1988 of the USGS Great Lakes Science Center.

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REFERENCES

(1) Byappanahalli, M. N.; Ishii, S. Environmental Sources of Fecal Bacteria. In The Fecal Bacteria; Sadowsky, M. J., Whitman, R. L., Eds.; American Society for Microbiology Press: Washington, DC, 2011; pp 93−110. (2) Ferguson, D.; Signoretto, C. Environmental persistence and naturalization of fecal indicator organisms. In Microbial Source Tracking: Methods, Applications, and Case Studies; Hagedorn, C., Blanch, A. R., Harwood, V. J., Eds.; Springer: New York, 2011; pp 379−397.

a

0.34 (9) 0.19 (14)

0.40 (11) 0.22 (8)

plc

0.70 ± 1.67 ± BQL 0.87 ± 0.79 ± cadF

1.42 ± 1.07 (5) BQL BQL BQL 0.35 (3)

ttrC

BQL BQL BQL 1.18 (5) BQL

invA ipaH all

BQL BQL 1.08 ± 1.10 (10) 1.03 ± 0.58 (14) BQL

stx1

0.11 0.49 0.38 0.41 0.76 ± ± ± ± ± 0.33 0.59 0.62 0.71 0.69 0.92 1.76 1.79 1.60 1.47

± ± ± ± ±

uidA

(51) (100) (100) (95) (65)

0.46 0.95 0.94 0.96 1.14

eaeA

(5) (42) (43) (27) (22)

BQL BQL BQL BQL 0.48 (3)

Shigella STEC EPEC generic E. coli

Table 2. Occurrences of Pathogens in Various Substrates Based on the MFQPCRa

BQL 2.28 (4) BQL 1.11 (5) BQL

Salmonella

C. jejuni

C. perf ringens

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