A Microfluidic Quantitative Polymerase Chain Reaction Method for the

Dec 19, 2017 - This method has been previously used to detect foodborne and waterborne pathogens(10) but, to the best of our knowledge, has not yet be...
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Letter Cite This: Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX

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A Microfluidic Quantitative Polymerase Chain Reaction Method for the Simultaneous Analysis of Dozens of Antibiotic Resistance and Heavy Metal Resistance Genes Kyle D. Sandberg,† Satoshi Ishii,‡,§ and Timothy M. LaPara*,†,‡ †

Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, Minneapolis, Minnesota 55455, United States BioTechnology Institute, University of Minnesota, St. Paul, Minnesota 55108, United States § Department of Soil, Water, and Climate, University of Minnesota, St. Paul, Minnesota 55108, United States ‡

S Supporting Information *

ABSTRACT: This study developed, optimized, and demonstrated a microfluidic quantitative polymerase chain reaction (MFqPCR) method for the simultaneous quantification of 39 antibiotic resistance genes (ARGs), five heavy metal resistance genes, three genes encoding the integrase of three different classes of integrons, and 16S rRNA genes (used as a measure of total bacterial biomass). Because the volume of the template is much smaller with MF-qPCR (a few nanoliters) than with conventional qPCR, a preamplification step was needed to improve the sensitivity and the limits of quantification of the MFqPCR method to be similar to those of conventional qPCR. The MF-qPCR method was successfully demonstrated on untreated municipal wastewater, treated municipal wastewater, and drinking water samples. The treated municipal wastewater samples had higher concentrations of all genes compared to those in the drinking water samples. Similarly, the untreated municipal wastewater samples had higher concentrations for all but one of the targeted genes compared to those in the treated municipal wastewater samples. The MF-qPCR method established in this study provides highly accurate quantitative information about numerous ARGs and other genes from environmental samples.

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waters, and drinking water.5−9 Most studies focus on only a few genes out of the thousands of ARGs that are known to exist. Gaining a more complete profile of ARGs, however, can be onerous because conventional quantitative polymerase chain reaction (qPCR) allows for analysis of only one or a few genes at a time, which is slow and expensive. Therefore, a high-throughput qPCR method that is cheaper and faster and allows for the simultaneous quantification of numerous genes is urgently needed. The goal of this study was to develop a microfluidic quantitative PCR (MF-qPCR) method that allows for the simultaneous quantification of numerous ARGs from environ-

INTRODUCTION Antibiotic resistance is an ever-increasing public health problem that threatens the ability to treat once easily manageable infections and diseases.1 Mortality rates for individuals infected with antibiotic resistant infections are almost twice that of individuals infected with susceptible strains of the same organisms.2 In the United States and Europe, resistant organisms cause hundreds of thousands of infections annually, leading to tens of thousands of premature deaths.3,4 Antibiotic resistance also imposes a huge financial burden on the health care industry, costing tens of billions dollars annually.3 While antibiotic resistance genes (ARGs) pose the greatest threat to public health when they are associated with pathogens, ARGs are also of concern regardless of their host because of horizontal gene transfer. ARGs have been studied in numerous environments, including soils, sediments, surface © XXXX American Chemical Society

Received: Revised: Accepted: Published: A

December December December December

7, 2017 18, 2017 19, 2017 19, 2017 DOI: 10.1021/acs.estlett.7b00552 Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX

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Environmental Science & Technology Letters

Figure 1. Standard curves for six representative genes simultaneously quantified by MF-qPCR. Standard curves for the first MF-qPCR assay are represented by the filled circles (no STA); the second MF-qPCR assay is represented by the empty circles (14-cycle STA), and the third MF-qPCR assay is represented by the filled triangles (17-cycle STA). Each curve is labeled with the calculated PCR efficiency (expressed as a percent). Additional plots of standard curves for all genes quantified by MF-qPCR are shown in Figures S1−S4.

were collected at the end of the treatment process, after disinfection but before the wastewater was discharged to the environment. Following collection, wastewater samples were placed on ice (500 L) were collected via a dead-end microfiltration method that typically achieves >70% cell recovery.11 Cells were lysed by subjecting samples to three freeze−thaw cycles followed by a 90 min incubation at 70 °C. DNA was then extracted and purified from the samples using a FastDNA Spin Kit (MP BioMedicals, Santa Ana, CA) and stored at −20 °C until the DNA was needed. Primer Selection and Validation. Prior to performing the MF-qPCR, we found primer sets for each gene of interest in the literature (Table S1). Because all amplifications during MF-qPCR must operate with the same thermal cycle program, primer sets were considered only if they were shown to successfully amplify at an annealing temperature at 60 °C. Standards were created by inputting the primer sequences into the GenBank database,12 finding a DNA sequence from a cultured organism known to harbor the target gene, and creating a synthetic, double-stranded segment of DNA with this sequence (Integrated DNA Technologies, Coralville, IA).13 Primers and standards for each target gene were empirically validated using a CFX Connect Real-Time System (Bio-Rad, Hercules, CA). The volume for these reactions was 25 μL and consisted of 12.5 μL of SsoFast EvaGreen Supermix with Low ROX, 25 μg of bovine serum albumin, optimized quantities of forward and reverse primers, and approximately 1 ng of DNA template. The thermal protocol began with an initial denaturation at 95 °C for 2 min followed by 40 cycles of denaturation at 95 °C for 15 s and an annealing/extending step at 60 °C for 1 min. Melt curves were visually inspected to ensure the absence of the primer dimer and to ensure that the standards and samples had peaks at the same temperature.

mental samples. We used the BioMark real-time PCR system (Fluidigm, South San Francisco, CA), which utilizes a few different arrays (96.96, 48.48, and 192.24) to perform singleplex qPCR at volumes of approximately 10 nL (the actual volume varies slightly depending on the specific platform). One dimension of the array is filled by unknown samples and standards, whereas the other array is filled by different primer sets. The arrays are then mixed using an integrated fluidic circuit controller, enabling thousands of qPCRs to be performed simultaneously. An a priori limitation of the method is that all PCRs must be performed using the same thermal cycling program. This method has been previously used to detect foodborne and waterborne pathogens10 but, to the best of our knowledge, has not yet been applied to simultaneously quantify numerous ARGs. Our approach was to select a wide array of ARGs acting against many different classes of antibiotics as well as genes that encoded resistance to a single class of antibiotics but via different mechanisms of resistance (e.g., enzymatic transformation and efflux). A total of 48 genes were selected for quantification, including 39 genes encoding resistance to most major classes of antibiotics, five heavy metal resistance genes, a gene associated with each of three classes of integrons, and the 16S rRNA gene. A preamplification step was also optimized to improve the sensitivity of the method for samples with low concentrations of specific ARGs. Following the optimization of the preamplification step, this method was successfully used to quantify the target genes in untreated municipal wastewater, treated municipal wastewater, and drinking water samples.

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MATERIALS AND METHODS Sample Collection and DNA Extraction. Untreated municipal wastewater samples as well as drinking water samples were collected as part of this study; treated municipal wastewater samples were collected as a part of a previous study.5 Each sample was collected from a different wastewater treatment facility or public water supply. Untreated municipal wastewater samples (volume of 10 mL) were collected after the bar rack/screen but prior to any other unit operation. Treated municipal wastewater samples (volume of 100 mL) B

DOI: 10.1021/acs.estlett.7b00552 Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX

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Figure 2. Quantities of 48 different genes in untreated municipal wastewater (red), treated municipal wastewater (green), and drinking water (blue) samples for the MF-qPCR with 100-fold dilution of preamplified samples. Points represent the arithmetic mean of log10-transformed quantifications (n = 12) for that sample type; error bars represent one standard deviation of the log10-transformed quantifications. Empty symbols show the quantification limit for those samples that were outside the range of the standard curve (above or below).

Specific Target Amplification. To increase the concentration of template DNA, a preamplification step known as specific target amplification (STA) was performed prior to MF-qPCR. This STA reaction is a multiplex PCR that involves simultaneous use of numerous primer sequences (48 primer sets were used in this study) and a relatively small number of PCR cycles prior to microfluidic PCR. The STA reaction mixtures consist of 5 μL of SsoFast EvaGreen Supermix with Low ROX, 2.5 μL of a mixture containing of each primer at 50 nM, and 2.5 μL of DNA template. The PCR protocol used for the STA consisted of an initial denaturation at 95 °C for 10 min followed by cycles of 15 s at 95 °C for denaturation and an anneal/extension step of 4 min at 60 °C, as described previously.10

Primer and standard sequences and other pertinent details can be found in the Supporting Information (Tables S1 and S2). Microfluidic qPCR. A Biomark Gene Expression 48.48 IFC chip was used to run the samples according to the protocol provided by manufacturer (Fluidigm). An MX IFC Controller (Fluidigm) was used to prepare the chip, and a Biomark HD (Fluidigm) was used to read the chip. The following thermal protocol was used: 95 °C for 60 s, 40 cycles of 96 °C for 5 s and 60 °C for 20 s, followed by 3 s at 60 °C and slow heating to 95 °C at a rate of 1 °C/3 s. ROX was used as a passive dye. Negative control samples (extraction blanks and no template controls) were negative for all assayed genes except for 16S rRNA, which had background levels of ∼2000 genes, which is similar to values from our prior work.5 C

DOI: 10.1021/acs.estlett.7b00552 Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX

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RESULTS AND DISCUSSION Direct MF-qPCR. The first MF-qPCR was directly performed on the DNA extracted from the 12 samples of untreated municipal wastewater samples, 12 treated municipal wastewater samples, 12 drinking water samples, and nine standards consisting of a mixture of double-stranded DNA strands containing targets for each of the 48 target genes. The standard curves typically represented a 10-fold dilution series ranging from 108 to 100 copies per reaction for each gene. Standard curves were produced for each gene by plotting the Cq values versus the logarithm of the number of copies of the gene in each standard (Figure 1). The r2 of each standard curve was at least 0.99 and contained between four and eight points, depending on the gene. The slopes of these standard curves were used to calculate the PCR efficiency for each gene, 14 of which had efficiencies outside the optimal range of 90− 110% (Table S3 and Figures S1−S4). Quantification of the various genes in the treated municipal wastewater and in the drinking water samples, however, was not possible because the majority of these genes were at quantities below the lowest standard that amplified. This suggests that the limit of quantification was poor compared to that of conventional qPCR. This difference in sensitivity was likely caused by the difference in sample volume used for the conventional qPCR (0.5 μL) compared to the volume used for MF-qPCR (2.25 nL). Specific Target Amplification. A preamplification step, known as specific target amplification (STA), was used to improve the detection limits of MF-qPCR. Comparatively low primer concentrations (50 nM) were used in the STA to limit carryover of the primer from the STA reaction to the MFqPCR assay, which could lead to false positives due to nonspecific amplification. Using these primer concentrations for the STA followed by 10-fold dilution of the STA prior to MF-qPCR, the concentrations of nontarget PCR primers in the MF-qPCR would be more than 2 orders of magnitude lower than the concentration of the primers used in the MF-qPCR assay. The STA reaction was empirically optimized by conventional qPCR prior to performing the MF-qPCR assay. The initial STA attempt was 14 cycles, which should have provided >103-fold preamplification of the target genes. Standard curves were prepared for the 16S rRNA gene, blaNDM‑1, mexB, and tet(W), by conventional qPCR before and after the STA to estimate the extent of preamplification provided by the STA. The STA increased the quantity of each gene by at least 102fold but no more than 103-fold. We concluded these results were insufficient to achieve the goal of developing a MF-qPCR method with sensitivity similar to that of conventional qPCR. Another 14-cycle STA was therefore performed with primer concentrations of 100 nM to determine whether the primer concentration was limiting the amplification in the STA. This produced results similar to those from the previous STA run, suggesting primer concentrations were not limiting amplification of the STA. A 17-cycle STA, therefore, was performed with primer concentrations of 50 nM, successfully achieving a >103-fold increase in gene quantities. MF-qPCR of Preamplified Samples. A second MF-qPCR assay was performed on the same genes and the same samples using a 17-cycle STA and primer concentrations of 50 nM. As in the first run, standard curves were constructed to quantify the genes in the samples and to calculate PCR efficiency, giving

standard curves with four to six points (Figure 1). The PCR efficiencies for all genes, except sul1, nikA, and vanA (which did not amplify well), are shown in Table S3 and Figures S1−S4). For this run, 26 of the genes had an amplification efficiency of 0.99 were constructed for each of the 48 genes, each with four to six points (Figure 1 and Table S3 and Figures S1−S4). When the amplification efficiencies of this run were observed, 36 genes had efficiencies of 90−110%, similar to that of the first MF-qPCR assay. The additional dilution of the STA products prior to the MF-qPCR assay, therefore, helped resolve the problem of poor amplification efficiency. Quantification of Resistance Genes in Wastewater and Drinking Water Samples. As a demonstration of its utility, MF-qPCR was used to quantify ARGs and heavy metal resistance genes in samples of untreated municipal wastewater, treated municipal wastewater, and drinking water samples (Figure 2). As expected, the treated municipal wastewater samples had higher concentrations for all genes compared to those of the drinking water samples. Similarly, the untreated municipal wastewater samples had higher concentrations for 47 of the genes compared to those of the treated municipal wastewater samples. The exception of this trend was imp13, for which the treated municipal wastewater samples had statistically similar concentrations (P = 0.27). Comparison with Conventional qPCR. The MF-qPCR method was directly compared to conventional qPCR for four genes [sul1, tet(A), blaSHV, and cadA] on 11 different untreated wastewater samples (Tables S4−S7). For sul1 and tet(A), the results were highly similar (t test, P > 0.4) between the two methods. In contrast, the MF-qPCR method measured a significantly smaller quantity of blaSHV than conventional qPCR did (P < 0.001) but a marginally larger quantity of cadA than conventional qPCR did (P = 0.07). Implications. Since the mass production of sulfonamides in the 1930s, numerous classes of antibiotics have been mass produced to treat infections and diseases.14−16 One unintended result of this widespread use of antibiotics has been the increase in antibiotic resistance in clinical settings and in the environment.8,17,18 To improve our understanding of the behavior of ARGs in the environment, it is important to quantify a wide variety of ARGs. However, because of the numerous classes of antibiotics in use and the variety of resistance mechanisms employed by microorganisms, quantifying ARGs using conventional qPCR is time-consuming and costly. For example, using the 48.48 platform, we performed a single PCR (preamplification) on each sample/standard (48 PCRs, 25 μL each = 1.2 mL of reaction fluid) plus 48 × 48 reactions using the microfluidic device (48 × 48 × 10 nL each = 0.023 mL of reaction fluid). Using conventional qPCR, in contrast, the same PCRs would all be done at full volume (48 × 48 × 25 μL = 57.6 mL). If we assume 2 h per qPCR assay, the MF-qPCR method would require 4 h whereas conventional qPCR would require 96 h to complete. That is, our MF-qPCR method would reduce time and material costs by >95%. The results of this study demonstrate that it is possible to simultaneously quantify at least 48 genes encoding resistance D

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used herein. If we assume that the genome of a typical bacterium has 5 million bp, and the average 16S rRNA gene consists of 1500 bp, approximately 10000 reads would be necessary to detect a 16S rRNA gene. To achieve the same quantification limit as this MF-qPCR method for a target gene with approximately 10−5 gene copy per copy of 16S rRNA, approximately 109 sequencing reads would be necessary per sample. In addition to the improved detection limits with MFqPCR, the bioinformatics tools necessary to analyze metagenomic data are not needed. In contrast, the MF-qPCR method is limited compared to shotgun metagenomics in that only the targeted genes can be quantified (i.e., shotgun metagenomics is an open-ended assay).

to most major classes of antibiotics and heavy metals as well as genes associated with antibiotic resistance (i.e., mobile genetic elements like integrons). This method allows for quality assurance and quality control by allowing for the analysis of amplification curves and melt curves to ensure that no significant inhibition or nonspecific amplification occurred. In addition, this method can be expanded or contracted, as other platforms are available (these are typically marketed for singlecell real-time PCR). A substantial portion of the work presented herein focused on optimizing the preamplification of targets such that MFqPCR would have a sensitivity similar to that of conventional qPCR. The STA improved the sensitivity of the MF-qPCR assay such that it exhibited detection limits similar to those of conventional qPCR. In contrast, the STA can cause overamplification, such that some of the genes (e.g., 16S rRNA genes) exceeded the range of values of the standard curves for most of the samples. This issue generally impacted only genes and/or samples with concentrations of >50000 copies per reaction, while still allowing for quantification to 1−10 copies for other gene targets (i.e., similar to conventional qPCR). Therefore, users need to carefully consider the expected concentration range for each target/sample combination because quantities can be both above and below the quantifiable range. This issue can be resolved, to a certain extent, by identifying those genes that are expected to be above the quantifiable range and not including these primers in the STA (i.e., the range for specific genes to be quantifiable can be empirically controlled). Other studies have claimed to quantify a wide variety ARGs in environmental samples using multiplex qPCR methods.19,20 The methods used in these studies, however, make a critical assumption of questionable validity. In these studies, the 16S rRNA gene was quantified as a reference gene using conventional qPCR and then the remainder of the genes were quantified by the difference between the Cq value of the target gene and the Cq value for the 16S rRNA gene. This socalled ΔΔCq method is problematic because it assumes that the amplification efficiencies of the genes are the same, which is unlikely. Particularly worrisome is the fact that the ΔΔCq method does not easily allow amplification efficiency to be measured, such that the error introduced by different amplification efficiencies cannot be evaluated. The MF-qPCR approach developed herein successfully demonstrated that untreated municipal wastewater has significantly larger quantities of antibiotic resistance, antibiotic resistance-associated, and metal resistance genes compared to those of treated municipal wastewater; similarly, treated municipal wastewater has higher levels of these genes compared to those of drinking water. Although these results were anticipated, they validate the ability of MF-qPCR to accurately quantify numerous genes simultaneously. These results were also consistent with previous studies, which had demonstrated a difference of 1−2 log units for several ARGs between untreated and treated wastewater.21,22 Furthermore, the quantities of resistance genes in the drinking water samples were consistent with those previously detected in drinking water.9 This MF-qPCR method offers substantially better detection limits compared to shotgun metagenomics, which also has been used to detect a myriad of ARGs in other studies.23,24 The limit of quantification for shotgun metagenomics is orders of magnitude higher than that seen with the MF-qPCR method

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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.7b00552. Data regarding PCR primer sequences, sequences of the synthetic DNA molecules used as standards for qPCR, and the amplification efficiencies for all target genes during all three MF-qPCR runs (Tables S1−S3), a direct comparison of MF-qPCR with conventional qPCR (Tables S4−S7), and standard curves for all gene targets from all three MF-qPCR runs (Figures S1−S4) (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, 500 Pillsbury Dr. SE, Minneapolis, MN 55455. E-mail: [email protected]. Telephone: (612) 624-6028. Fax: (612) 626-7750. ORCID

Timothy M. LaPara: 0000-0002-5653-5309 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The research was financially supported by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources. The authors thank Kris Wammer for providing the drinking water samples.

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REFERENCES

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DOI: 10.1021/acs.estlett.7b00552 Environ. Sci. Technol. Lett. XXXX, XXX, XXX−XXX