Framework for Using Quantitative PCR as a Nonculture Based Method

Feb 15, 2011 - ABSTRACT: Measuring the efficiency of virus disinfection with quantitative PCR (qPCR) has been criticized as inadequate due to...
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Framework for Using Quantitative PCR as a Nonculture Based Method To Estimate Virus Infectivity Brian M. Pecson,† Martin Ackermann,‡ and Tamar Kohn*,† cole Polytechnique Laboratory of Environmental Chemistry, School of Architecture, Civil and Environmental Engineering (ENAC), E Federale de Lausanne (EPFL), Station 2, 1015 Lausanne, Switzerland ‡ Department of Environmental Sciences, ETH Zurich, and Department of Environmental Microbiology, EAWAG, 8600 Duebendorf, Switzerland †

bS Supporting Information ABSTRACT: Measuring the efficiency of virus disinfection with quantitative PCR (qPCR) has been criticized as inadequate due to the production of false-positive signals. Such a claim, however, presupposes an understanding of the theoretical qPCR response. Many studies have assumed that the loss in qPCR signal upon disinfection should equal the loss in infectivity, without accounting for the fact that qPCR typically assays only a fraction of the viral genome. This study aimed to develop a theoretical framework to relate viral infectivity with genome damage measured by qPCR. The framework quantified damage to the entire genome based on the qPCR amplification of smaller sections, assuming single-hit inactivation and a Poissonian distribution of damage. The framework was tested and modified using UV254 inactivation studies with bacteriophage MS2 (culturing and qPCR of approximately half the genome). Genome regions showed heterogeneous sensitivities to UV254 treatment, thus deviating from the assumption of Poissonian damage. We offered two modifications to account for these deviations and confirmed that the qPCR-based framework accurately estimated virus infectivity. This framework offers the potential to monitor the infectivity of viruses that remain nonculturable (norovirus). While developed for UV254-inactivated virus, the framework should apply to any disinfection technique that causes inactivation via single genomic lesions.

’ INTRODUCTION Culturing of viruses provides direct information about their ability to cause infection, the most relevant parameter to assess the viral health threat of samples from drinking and wastewater, to food, fomites, and vaccines. Because important waterborne viruses remain unculturable (e.g., norovirus), nonculture based methods are needed to determine infectivity. Quantitative PCR (qPCR) is one of the most powerful molecular techniques that detects viral genomes with high specificity and sensitivity. One of the main criticisms of qPCR is that it produces false-positive signals upon virus disinfection, i.e., the qPCR signal overestimates the concentration of infective viruses. Multiple explanations exist for this overestimation. Certain treatments cause inactivation with little to no damage to the genome, such as pasteurization.1 Therefore, inactivation is not coupled with genome damage, resulting in false-positive PCR signals. To overcome this limitation, complementary methods have been developed to assay the integrity of the viral capsid,2,3 its ability to protect the genome from nuclease degradation,1,4 and its ability to mediate attachment to host cells.5 However, even disinfection treatments that primarily target the genome, such as UV254 irradiation, have been reported to produce false-positives. A frequently applied assumption is that if the ratio of PCR-measured genome damage to inactivation is less than 1:1, then PCR is producing false-positives.6-9 While this outcome is almost universally reported, studies have found that longer templates come closer than shorter templates to achieving a 1:1 ratio, suggesting that target length is a critical factor.6,8,10 r 2011 American Chemical Society

One element that is conspicuously missing is a framework to relate the measured genome damage to inactivation. The goal of this study is to develop a theoretical framework to relate genome damage measured by qPCR to viral infectivity. The framework assumes single-hit inactivation and quantifies damage to the entire genome based on the PCR amplification of smaller sections. This information allows us to relate qPCR results to virus infectivity and gives us new parameters with which to design qPCR assays of viral inactivation. We demonstrate that the claim of false positives is often a matter of misinterpretation of the data. If the proper theoretical framework is used, qPCR can quantify inactivation.

’ MATERIALS AND METHODS Experimental Approach. In brief, the Experimental Section was divided into two sections. In the first, culturing and qPCR were used to assay UV254-induced inactivation and genome damage in 8-log10- inactivated samples of bacteriophage MS2. Approximately 50% of the MS2 coding region was analyzed by qPCR. Second, we conducted a UV254 inactivation time course to obtain a range of levels of virus inactivation. Following inactivation over time allowed us to compare the culturing and qPCR Received: October 15, 2010 Accepted: January 19, 2011 Revised: December 21, 2010 Published: February 15, 2011 2257

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Environmental Science & Technology results to varying levels of inactivation, from completely infective samples to those with approximately 8-log10 inactivation. The ability of the theoretical framework to track viral infectivity was tested and modified using the experimental results. Viruses. The procedures for phage production, RNA extraction, and the development of whole-genome RNA standards have been reported.1 Primers. Twelve primer sets were developed for an earlier study,1 and sets 2, 3, 6, 7, 10, and 12 were used in this study. The location and sequence of the primer sets is included in the Supporting Information. Quantitative PCR. Experimental samples and RNA standards were reverse transcribed (RT) and amplified in parallel using the RotorGene 3000 quantitative PCR platform (Corbett Life Science, Sydney, Australia). Each RT-qPCR sample was run in 15 μL total volume comprising 7.5 μL of 2 One Step SYBR RTPCR buffer III, 0.3 μL of TaKaRa ExTaq HS (5 U/ μL), 0.3 μL of PrimeScript RT enzyme Mix II, 0.3 μL of 10 μM forward and reverse primers, 3.3 μL of water, and 3 μL of RNA sample (Takara Bio, Shiga, Japan). The following thermocycling conditions were used: 10 min at 42 °C, 20 s at 95 °C, 45 cycles of 95 °C for 15 s, 60 °C for 20 s, and 72 °C for 20 s, followed by a melting ramp from 72 to 95 °C, holding for 45 s on the first step (72 °C) followed by 5-s holds on all subsequent temperatures. The genome copies/mL in the original sample were calculated by dividing the qPCR results (in total genome copies, calibrated using RNA standards) by the volume of the extracted sample used in each qPCR reaction (total genome copies per 3 μL of extraction) and accounting for the 4-fold concentration during the RNA extraction (from an initial 200-μL sample to 50 μL). UV Treatments. All inactivating treatments were performed in triplicate in dilution buffer (DB). After inactivation, samples were divided into two for culturing and RNA extraction. Sample manipulation was performed immediately following the inactivating treatment. 8-log10 UV inactivation. 2-mL samples of MS2 at 6  1011 pfu/mL were pipetted into 10 mL glass beakers and placed on a multipoint magnetic stirplate (Poly 15, Thermo Scientific Variomag, Waltham, Massachusetts) below a 30 W, germicidal UV lamp (model G30T8, 2.7 ( 0.3 mW/cm2 irradiance at 253.7 nm wavelength, Sankyo Denki, Tokyo, Japan) for 120 s. The UV irradiance was measured by actinometry, as described elsewhere,11 and corresponded to 2800 J/m2. UV time course. Experiments were conducted as described above, except for the use of MS2 at 1  1012 pfu/mL. Samples were exposed for 10, 25, 50, and 120 s with the goal of achieving 1, 2, 4, and 8-log10 inactivation (equivalent to UV doses of 260, 610, 1200, and 2800 J/m2, respectively). Statistical Analysis. Triplicate samples were run with both the culturing and qPCR assays. Control and experimental qPCR samples were used to determine the fraction of undamaged genomes (N/N0) using one to six different primer sets. Each N/ N0 replicate was used to calculate the number of lesions in the amplicon, which was in turn used to estimate the number of lesions in the genome. The arithmetic mean of these values was used to calculate the proportion of phages with zero genomic lesions. To compare inactivation levels across different segments, we performed a one-way analysis of variance (with ‘segment’ as a random factor) in JMP (Version 8.0.2; SAS Institute Inc., Cary, NC, 1989-2007); the data (N/N0, which is a proportion) was arcsin-sqrt transformed to approach a normal distribution of the residuals.

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’ RESULTS AND DISCUSSION Development of a Theoretical Framework To Quantify Total Genome Damage. To develop the framework, we used

the case of UV254 inactivation. UV254 primarily acts through the production of photodimers between adjacent pyrimidine nucleotides in both DNA and RNA. Their presence blocks DNA and RNA replication and transcription, ultimately leading to inactivation.12-17 Given the vital role of the genome in virus infectivity, a single nucleic acid lesion may be sufficient to cause inactivation.15,18 This damage has also been shown to block the enzymes used in reverse-transcription (RT)-PCR, such as Taq polymerase and reverse transcriptase.15-18 Therefore, the damage that renders a virus inactivated also renders it nonamplifiable by PCR or RT- PCR. It is often assumed that every nucleotide in the genome has an equivalent chance of absorbing UV irradiation and that absorption by a given nucleotide is independent of whether other nucleotides have already been damaged.18 If UV254 causes rare, random, and independent damage, then the number of damaged nucleotides in the genome follows a Poisson distribution defined by the parameter lambda, λ, that is both the mean number and the variance of damaged nucleotides.19 The probability (P) of a virus having n genome lesions in a population of viruses with an average of λ genome lesions is described by19 λn e-λ ð1Þ Pðn, λÞ ¼ n! If each genome lesion inhibits reverse transcription or PCR amplification, then only the intact genomes will be amplified. Therefore, the proportion of intact genomes is equivalent to the ratio of PCR results obtained after and before UV254 treatment, N/N0, for PCR reactions attempting to amplify the whole genome. This proportion N/N0 is equivalent to the probability of detecting a fully intact genome under conditions where the average number of genome lesions in the whole virus population corresponds to λ. This is referred to as the no-hit probability in the Poisson distribution, i.e., n = 0 N ¼ Pð0, λÞ ¼ e-λ ð2Þ N0 The PCR data give us an estimate for N/N0 that can be used in eq 2 to calculate λ   N λ ¼ - ln ð3Þ N0 With an estimate of λ in hand, the number of genome lesions can be calculated for different levels of inactivation and for different length genomes and PCR amplicons. Take for example the case of bacteriophage MS2. If we assume single-hit inactivation by UV254, then only the viruses with intact genomes would be infective. Therefore, the probability of detecting an infective virus with the culture method (C/C0) is equivalent to the probability of detecting an intact genome using qPCR (N/N0). For example, a treatment that reduces infectivity to 1 in 10,000 viruses—equivalent to a C/C0 = 10-4, or a 4-log10 inactivation— would give rise to a N/N0 equivalent to 0.0001; solving eq 3 for λ, we find that such a level of inactivation is expected with an average number of 9.21 genome lesions per virus (λ), regardless of the total genome length. 2258

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Figure 1. Probability of an amplicon containing no lesions as a function of amplicon length, and the corresponding loss in qPCR signal, after a 4 log10-inactivation of MS2. Solid line depicts the probability of no lesions; dashed line depicts log10 loss in qPCR signal.

It should be noted that while we assume a single hit is sufficient to inactivate the viruses, the framework does allow for the possibility that some viruses sustain >1 hit. Such is the case for the 4-log10 inactivated sample, where the average virus has sustained 9.21 genome hits. Because we are interested in quantifying the fraction with zero hits, i.e., the infective viruses, we do not need to distinguish between single- and multiple-hit viruses, though the framework would allow us to calculate the probability of finding a virus with any number of hits (eq 1). For PCR systems that quantify in real-time, the ideal amplicon size is typically smaller than the entire genome.20 Therefore, such methods will detect only a fraction of the total genome lesions. For an amplicon of a given length, the proportion that is undamaged can be calculated as follows  Tot genome lesions   Genome size Proportionðundamaged ampliconÞ ¼ exp

-

Amplicon size

ð4Þ

The probability of finding intact amplicons of different lengths after a 4-log10 inactivation of bacteriophage MS2 (3569-nt genome) is presented in Figure 1, along with the corresponding qPCR signal loss. Because these 9.21 lesions are randomly distributed, only assays that cover the entire genome would detect each of the lesions. Correspondingly only whole-genome assays would yield a qPCR signal loss that corresponds 1:1 to infectivity loss. Shorter targets will contain fewer lesions resulting in a higher probability of finding an intact amplicon and lower qPCR signal loss. Note that while the loss of qPCR signal decreases with decreasing amplicon size, every amplicon provides an accurate measure of the total genome damage. Verification of the Genomic Damage Framework. To test if the framework could accurately predict UV254 inactivation, we carried out an inactivation experiment and compared the observed level of inactivation with the predicted level, based on the PCR amplification of a small amplicon. MS2 was irradiated with UV254 to produce an inactivation of about 7.4-log10 (C/C0 = 3.55  10-8, based on triplicate samples). Performing PCR over a 302-nt segment (using primer set 3), we found the treatment decreased the proportion of intact amplicons, N/N0, in the three replicates to 0.27, 0.13, and 0.15.

The number of lesions in this segment was calculated for the three replicates (eq 3), leading to an estimated number of lesions per segment of 1.30, 2.03, and 1.92. Since the total genome of MS2 is 11.8 times longer than the 302-nt segment, one would expect that the average number of lesions per genome is 11.8 times larger than the number of lesions in this amplicon, leading to an estimate of about 15.3, 24.1, and 22.8 for the three replicates. These numbers can be understood as three realizations of a Poisson process that describes the acquisition of genome lesions. The arithmetic mean of the three numbers, 20.71, is then an unbiased estimator of λ, the expected average number of lesions per genome. The question now is whether this value of λ is compatible with the observed inactivation, C/C0. According to eq 2, if the average number of lesions per genome is 20.71, the proportion of all phages that contain zero lesions is 1.0  10-9. This is similar to the proportion of phage that was found to be infective in the direct infectivity assay (C/C0 = 3.55  10-8). Let us draw attention to the large difference between the proportion of viruses with no damage in this PCR fragment (1.83  10-1) and the proportion of viruses that remain infective (3.55  10-8). These values are equivalent to a 0.76-log10 decrease in PCR signal and 7.4-log10 decrease in infectivity. Such differences have sometimes been interpreted as evidence for false-positive PCR results - i.e., a situation where the rate of successful PCR amplification is too high and does not accurately reflect the high rate of inactivation of viruses. Our analysis suggests a different conclusion: if one analyzes the PCR results correctly, taking into account that the PCR amplicon is much shorter than the whole genome, then one finds that this level of PCR amplification is consistent with the observed level of inactivation. If a single genome lesion is sufficient to inactivate a virus, then this level of genome damage could account for inactivation. However, this conclusion depends critically on assumptions about the homogeneity of damage in different genome regions, and we will now turn to discussing these assumptions in detail. Testing for Homogeneity of the Damage Rate Across the Genome. One critical assumption of this analysis is that different regions of the virus genome acquire damage at the same rate, and that analyzing a particular region allows one to draw conclusions that are representative of the whole genome. This assumption is supported by a number of studies,15,18,21 including that of Simonet and Gantzer (2006) who demonstrated that UV254-induced genome damage of MS2 increased linearly with amplicon size.10 If certain genome regions were more sensitive to damage than others, a nonlinear increase in genome damage would result from increasing target length. In order to test this assumption, we analyzed genome damage in six different regions that together cover about 50% of the total MS2 genome (Figure 2). The average fraction of intact segments was not different from the theoretically predicted value (5.1 ( 0.36)  10-3 lesions/nucleotide vs theoretical value of 4.8  10-3 (eq 2). However, when each segment was considered individually, we found that the proportion of undamaged segments (N/N0) differed between segments (range 0.12-0.5, p < 0.001). These differences between amplicons presumably reflect different vulnerabilities to lesions in different parts of the MS2 genome. Previous studies have shown differences due to varying sensitivities of the 30 vs 50 end, in coding vs noncoding regions and due to the association of nucleic acids with proteins.14,22-24 2259

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Figure 2. Proportion of genome regions that remain undamaged after a UV254 treatment causing 7.4-log10 inactivation (C/C0 = 3.55  10-8). Undamaged proportion based on qPCR amplification (N/N0) of six target regions. Error bars depict standard error of the mean.

For MS2, it is known that the genome is associated with the A-protein at both the 50 and 30 ends, which may play a role in its heterogeneous susceptibility to damage.24 Alternatively, and maybe less likely, they could also reflect regional differences in how sensitive the PCR reaction is to lesions in the amplicon to be amplified. Irrespective of their origin, these differences in vulnerability represent a challenge for the analysis that we present here: so far, we had assumed that the rate at which lesions occur is constant across the phage genome. Deviations from this assumption will lead to biases when we extrapolate from PCR results to genomic lesions. For example, the estimated proportion of undamaged genomes varies over orders of magnitude (range: 3  10-11 to 4  10-5) depending on which of the six MS2 targets is used (Figure 2). We propose two different ways of how one can modify the analysis in response to this deviation from the underlying assumptions. A first approach is to respond to regional differences in the rate at which lesions occur by choosing a number of different amplicons from different parts of the genome and then combining these results to get a genome-wide estimate of the number of lesions. A second approach is to use direct infectivity measurements to calibrate the PCR results. Then, in future experiments, one can use this calibration to extrapolate from PCR amplification to inactivation while accounting for PCR biases. Both approaches rest on the assumption that each single lesion leads to the inactivation of the virus, and prevents PCR amplification. They also assume that all viruses in the sample are equally exposed to the damaging agent, so that the expected number of lesions is equal across viruses. They do not, however, assume that the rate at which lesions occur is constant across the genome; rather, they allow for variation in the lesion rate across different regions of the genome. In the following, we will discuss these two approaches in more detail. Combining PCR Results from Different Amplicons. The basic idea of this approach is that, by analyzing amplicons from different genome regions, one can balance differences in damage rate between these regions and achieve a less biased estimate of the genome-wide damage. To do so, PCR results from different

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amplicons need to be combined into an aggregate measure of damage. This can be done by multiplying the proportions of undamaged amplicons; the PCR signal, N/N0, is a measure of the proportion of viruses in a population in which this amplicon contains no lesions. If we want to know the proportion of viruses in which two or more specific amplicons are undamaged, we can multiply the proportion that contains no damage in the first amplicon with the proportion that contains no damage in the second amplicon, etc. Note that this approach does not assume that the damage rate is equal across these amplicons; it is also appropriate if different amplicons acquire lesions at different rates. Also, and related to this previous point, the length of the individual amplicons is not relevant for the calculation. With this approach, we end up with the estimated proportion of viruses that contain no lesions in any of the n amplicons we analyzed. This value can then be used to extrapolate to the proportion of viruses that are undamaged across the whole genome. This extrapolation assumes that the group of amplicons analyzed is representative of the damage rate to the whole genome. In such a case, one can extrapolate from the damage to the amplicons to the damage to the entire genome by accounting for the total length of the analyzed amplicons. This assumption becomes less problematic as the number of analyzed amplicons increases, since this will tend to make this group more representative of the whole genome (see below). We can extrapolate from the proportion of phages that are undamaged in the n amplicons analyzed to the proportion of viruses with undamaged genomes as follows Proportionðundamaged genomesÞ ¼ Proportionðundamaged over n ampliconsÞtotal

genome length length of n amplicons

ð5Þ

Based on the PCR results for the six amplicons presented in Figure 2, the estimate for the proportion of phages with undamaged genomes lesion is 1.2  10-8, calculated from the product of all undamaged fractions (0.21  0.18  0.32  0.14  0.12  0.50) raised to the power of (3569/1792). This is close to the direct experimental measurement of the proportion of phage that is infective (C/C0 = 3.55  10-8). This result suggests that combining PCR results from different amplicons improves the estimate of virus inactivation by decreasing the effect of the variation in the lesion rate between amplicons. The quality of this estimate increases with increasing number of amplicons analyzed; Figure 3 shows how the estimates of the number of phages without genome damage become less variable with increasing number of amplicons analyzed, and that these estimates converge to one value. This has practical consequences: analyzing more amplicons, and combining the results into an estimate of the genome-wide lesion rate, improves the quality of this estimate. It should be emphasized that this approach does not require culturing, demonstrating that qPCR alone can accurately estimate viral infectivity. Calibrating PCR Results from One Amplicon with Direct Measurements of Infectivity. A second approach is to carry out an initial experiment where one measures the loss of infectivity directly and performs quantitative PCR on the same samples, with a number of replicates. The experimental measurement of inactivation can then be used to calibrate the PCR results, so that one can extrapolate from PCR amplification of one region measured in future experiments to the genome-wide 2260

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Figure 3. Estimated proportion of genomes without lesions, as a function of the number of amplicons on which the estimate is based. The dotted line represents the proportion of infective viruses measured with culturing.

damage. As above, this approach does not assume that different parts of the genome acquire lesions at the same rate. However, it does assume that a single lesion inactivates a virus and prevents PCR amplification, and it also assumes that all viruses are equally exposed to the damaging agent. The PCR result is calibrated with the experimentally measured inactivation as follows Proportionðundamaged genomeÞ ð6Þ ¼ Proportionðundamaged ampliconÞc that can be rearranged into lnðProportionðundamaged genomeÞÞ c¼ lnðProportionðundamaged ampliconÞÞ

ð7Þ

where c is a measure for the lesion rate in the amplicon relative to the lesion rate in the whole genome. If the lesion rate per nucleotide is constant across the genome, then c is simply the ratio between genome length and amplicon length (as in eq 5). If the lesion rate per nucleotide is different between the amplicon and the rest of the genome, then this difference will result in a c that deviates from the length ratio. Once c has been experimentally determined in an initial calibration experiment, it can then be used in future experiments for estimating genome damage based on the measured damage in an amplicon. In order to test this approach, we determined the variable c for the amplicon amplified by primer set 3 (data from Figure 2), based on three replicate measurements of the loss of qPCR signal (N/N0) and of infectivity (C/C0). The resulting estimate of c is 10.1. If this amplicon had the same average lesion rate as the rest of the genome, c would be equivalent to the ratio between the amplicon length and genome length, i.e., 3569/302, or 11.8. The experimentally determined c (10.1) is close to this theoretical value (11.8), indicating that the rate at which this amplicon acquires lesions is close to being representative for the whole genome. With this estimate of c at hand, we carried out an independent experiment where we exposed MS2 to UV254, sampled at different time points and used these samples for PCR and a

Figure 4. Effect of increasing UV254 exposure on MS2 infectivity, qPCR signal, and the estimated proportion of undamaged genomes based on qPCR values. The estimated inactivation was derived by calculating the variable c based on an earlier experiment (Figure 3), and using this variable to extrapolate from the PCR results to the proportion of genomes without lesions. Symbols:  , MS2 infectivity; filled diamonds, qPCR signal (N/N0) from primer set 3; open diamonds, estimated proportion of undamaged genomes.

direct measurement of inactivation by plating. Using the value of c derived above, we extrapolated the PCR result to estimate the proportion of genomes without lesions and compared this estimate with the experimentally derived measure of inactivation. The estimated and measured levels of inactivation are in good agreement (Figure 4). Unlike the first modification, the second does require a culturing assay for calibration. If such an assay exists, however, this second modification would be technically less demanding since only a single primer set would be used instead of multiple sets. Benefits of the Framework To Assay Virus Infectivity. One of the top priorities in water treatment is the creation of nonculture based methods to assess viral infectivity. With this framework, we show how qPCR results can be used to estimate virus infectivity after UV-treatment, even in the absence of a culturing system, by combining PCR results from different amplicons. Studies will be needed to determine which virus classes are inactivated by single UV lesions; assuming this mechanism holds across viruses, the framework offers a newfound potential to assess the infectivity of important human viruses, such as norovirus, for which no infectivity assays currently exist. While the framework was developed using UV irradiation, it should apply to any treatment causing inactivation due to single genomic lesions. Despite the need for refinements, the Poisson distribution accurately modeled the behavior of the average genome section. Therefore, it offers valuable predictive capacities. The most important practical application is the ability to estimate the expected qPCR signal for a given level of inactivation, which offers a metric to evaluate what constitutes a false-positive result. For example, a 100-nt amplicon should only show a 0.11-log10 decrease in signal (N/N0 = 0.77) after a UV treatment causing 4-log10 inactivation of a viral genome of length 3569 (C/C0 = 10-4, eq 4). Without a method to relate qPCR and infectivity 2261

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results, this large difference in signal (0.11 log10 vs 4.0 log10) is difficult to interpret - it might easily be interpreted as a falsepositive signal. However, only average losses less than 0.11 log10 should be considered false-positives (Figure 1). Using the Framework To Design qPCR-Based Assays. A major conclusion of this study is that the detection of genome damage depends on the length of the genome targeted. Therefore, selecting the “best” primer set based on typical or default criteria—e.g., amplicon lengths of 60-100 nt20—will not guarantee that genomic damage will be detected or quantifiable. Given that signal loss decreases with amplicon size, some critical amplicon length exists for which the expected loss in signal is too small to be detected by qPCR. The framework offers a rule of thumb to predict the minimum amplicon length that is needed for detection; the following equation can be used to calculate the PCR target length needed to detect inactivation in different length genomes Amplicon size ¼

-lnðProbzero - hit ðampliconÞÞ Tot genome lesions Genome size

ð8Þ

Using the 4-log10 inactivation of viruses required by the U.S. EPA for the treatment of surface water, we obtain λ = 9.21 for any genome size (eq 3). Furthermore, if we assume a minimum difference in qPCR signal of 0.1- to 0.5-log10 is needed to exceed the signal-to-noise threshold, then amplicons of 100-200 and 500-1000 nt would be needed for viral genomes of 4000 and 8000 nt, respectively (eq 8). These are longer than the ideal qPCR amplicon length (60-100 nt) but still fall within the range of qPCR targets that have been used. Alternatively, damage to multiple short sections could be measured with qPCR and combined, as demonstrated in Figure 3. Other methods for quantifying longer amplicons have been established that do not rely on real-time quantitation and may be of use if longer targets are needed.15 It should be noted that eq 8 gives only a first approximation of the minimum amplicon size needed. Because genome damage is not perfectly Poissonian, the amplicon length may deviate from the theoretical values. In addition, for proper experimental design one would need to determine the method detection limits and to account for measurement variability when selecting a minimum amplicon size. The method presented here does not affect the sensitivity of the PCR assay; accordingly, the method would be applicable for the analysis of food or environmental samples as long as the viral quantities are within the detection limits of the assay. To use this model as a predictive tool, information is needed on the nature of genome damage caused by different treatments. For example, whether viruses undergo single-hit or multihit inactivation has important consequences when determining infectivity based on qPCR results. Knowledge of these different mechanisms is also critical for determining which method to choose to assay virus infectivity. Our experiments have shown that qPCR may be a good measure of virus infectivity after UV254 treatment because genome damage appears to be the dominant inactivating mechanism in the inactivation of MS2. This framework should also work for any other treatment that causes inactivation via single-hit genomic damage. For pasteurization, however, where little genome damage occurs, or for viruses that sustain critical UV254-induced protein damage in addition to

genome damage, targeting nucleic acids alone will undoubtedly underestimate inactivation.1 In summary, if damage occurs across the entire genome, then assaying only a fraction of it by qPCR will never detect all the damage. Consequently, the ratio of inactivation to qPCR signal will not be 1:1 for single-hit inactivation but will approach 1:1 as the qPCR target approaches the full length of the genome. In this study we created a framework to estimate inactivation based on the qPCR amplification of smaller targets and demonstrated that qPCR can be used to quantify UV254 inactivation of MS2 if one uses an appropriate analysis.

’ ASSOCIATED CONTENT

bS

Supporting Information. Table with the primer sequences used for the qPCR assay. This material is available free of charge via the Internet at http://pubs.acs.org.

’ AUTHOR INFORMATION Corresponding Author

*Phone: þ41 (0)21 693 0891. Fax: þ41 (0)21 693 8070. E-mail: tamar.kohn@epfl.ch.

’ ACKNOWLEDGMENT This work was supported in part by the Swiss National Science Foundation (Project No. 200021_118077). Support for B.M.P. was provided in part by Marie Curie Fellowship grant no. 220706. ’ REFERENCES (1) Pecson, B. M.; Martin, L. V.; Kohn, T. Quantitative PCR for Determining the Infectivity of Bacteriophage MS2 upon Inactivation by Heat, UV-B Radiation, and Singlet Oxygen: Advantages and Limitations of an Enzymatic Treatment To Reduce False-Positive Results. Appl. Environ. Microbiol. 2009, 75 (17), 5544–5554. (2) Wigginton, K. R.; Menin, L.; Montoya, J. P.; Kohn, T. Oxidation of virus proteins during UV254 and singlet oxygen mediated inactivation. Environ. Sci. Technol. 2010, 44 (14), 5437–5443. (3) Sano, D.; Pinto, R. M.; Omura, T.; Bosch, A. Detection of Oxidative Damages on Viral Capsid Protein for Evaluating Structural Integrity and Infectivity of Human Norovirus. Environ. Sci. Technol. 2010, 44 (2), 808–812. (4) Nuanualsuwan, S.; Cliver, D. O. Pretreatment to avoid positive RT-PCR results with inactivated viruses. J. Virol. Methods 2002, 104 (2), 217–225. (5) Jiang, Y. J.; Liao, G. Y.; Zhao, W.; Sun, M. B.; Qian, Y.; Bian, C. X.; Jiang, S. D. Detection of infectious hepatitis A virus by integrated cell culture/strand-specific reverse transcriptase-polymerase chain reaction. J. Appl. Microbiol. 2004, 97 (5), 1105–1112. (6) Shin, G. A.; Sobsey, M. D. Reduction of Norwalk virus, poliovirus 1, and bacteriophage MS2 by ozone disinfection of water. Appl. Environ. Microbiol. 2003, 69 (7), 3975–3978. (7) Duizer, E.; Bijkerk, P.; Rockx, B.; de Groot, A.; Twisk, F.; Koopmans, M. Inactivation of caliciviruses. Appl. Environ. Microbiol. 2004, 70 (8), 4538–4543. (8) Sobsey, M. D.; Battigelli, D. A.; Shin, G. A.; Newland, S. RT-PCR amplification detects inactivated viruses in water and wastewater. Water Sci. Technol. 1998, 38 (12), 91–94. (9) Baert, L.; Wobus, C. E.; Van Coillie, E.; Thackray, L. B.; Debevere, J.; Uyttendaele, M. Detection of murine norovirus 1 by using plaque assay, transfection assay, and real-time reverse transcription-PCR before and after heat exposure. Appl. Environ. Microbiol. 2008, 74 (2), 543–546. 2262

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dx.doi.org/10.1021/es103488e |Environ. Sci. Technol. 2011, 45, 2257–2263