The detection efficiency of digital PCR for the virulence genes of waterborne pathogenic bacteria

Waterborne pathogens are the primary concern for the safe reuse of wastewater. Although digital PCR (dPCR) is considered promising for absolutely quantitating genes, the detection efficiency of dPCR is affected by many factors. This study tested eight virulence genes of pathogenic bacteria on a control plasmid and reclaimed water samples with reported primer–probe sets and designed ones on quantitative PCR (qPCR) and dPCR. Probe efficiency, data analysis, and PCR inhibition were found to affect the detection efficiency of dPCR. Firstly, poor probe quality, which is determined by probe quenching and activation efficiencies, was the main cause of PCR failure. Secondly, even if the PCR was successful, the probe quality and signal intensity could still affect the quantitative process. Manual analysis of dPCR data on the weak signal intensity would significantly reduce errors. And lastly, the sensitivity of PCR inhibition was lower in dPCR than qPCR, but inhibition still existed. The dPCR produced various detection efficiencies for different targets in one sample indicating inconstant inhibitory effects. Dilution was still the proper approach to overcome inhibition, but decreased the detection limit. More studies are required to ensure accurate waterborne pathogen quantitation by dPCR.

A biosensor's basic structure contains a substrate of silicon or polymers with specific biological recognition elements such as aptamers, or antibodies of known pathogens. The binding effect will generate corresponding biological responses and be converted into readable signals ready to be analyzed (Leonard et al. ). Despite the advantages of portability and miniaturizations of the techniques, the applications of a biosensor still face the challenges of low sensitivity due to the environmental influences of dirt, pH and temperatures (Bridle & Desmulliez ).
Immunology-based and nucleic-acid-based assays are still considered the most robust technologies nowadays (Hameed et al. ). Immunology-based assays involved the specific interactions between antibodies and antigens on the surface of the target bacteria. They are rapid and specific, but lack sensitivity and have high values of cost, while for nucleic acid-based detection technologies, they depend on the detection of specific DNA or RNA sequences. In this case, the virulence genes of pathogens are widely adopted. Virulence is defined as the ability of the microorganisms to overcome the host defense. A typical nucleic-acid-based detection strategy used in the field is the qPCR technique. The amplification process of the target DNA is simultaneously monitored by the fluorescence produced during each cycle of a PCR reaction. One of the advantages of qPCR is the use of fluorescent dyes or probes of different wavelengths, which can be used in an automated system for multiple detections, although qPCR will result in larger error, when the target sequence is at very low abundance or the differences between samples are less than two-fold. Consequently, it leads to the emergence of dPCR.
The key characteristic of dPCR is that a sample is first partitioned into thousands of individual reactions. After thermal cycling, the target gene copy can be calculated by the positive and negative numbers of partitions and the Poisson distribution law. The main advantage is that it allows absolute quantification of the target sequence rather than the relative quantification of qPCR. The dPCR technique is best suited to applications requiring precision, high sensitivity and reproducibility, such as low abundance target detection. Because of the target concentration effect of partitioning, dPCR is also less susceptible to contaminants that affect PCR detection efficiency (Monteiro & Santos ). The disadvantages of dPCR are that it is costly and time-consuming. The cost of one dPCR reaction is about 6-10 times higher than that of qPCR, and the entire procedure of dPCR takes two or three times as long in operation. Recently, dPCR has been applied in detecting microbial agents associated with waterborne diseases. Ricchi et al. () suggested that dPCR approaches were considered valid based in the testing of waterborne pathogens such as There are several versions of dPCR from different companies, like ddPCR from Bio-Rad and dPCR from Stilla Technologies. Although these digital PCR technologies have distinct ways to generate droplets and count positive ones, their core principles are the same, and they are all capable of performing absolute quantitation. Nonetheless, the quality of primers and probes, the way of analyzing data, and the interference of impurity from DNA samples could still lower the detection efficiency of dPCR. The detection efficiency means the ratio of experimental quantification result to the real gene copy (the specificity of testing different subspecies is not discussed in this study).
This study investigated the influence of PCR reaction conditions, analytical methods, and PCR inhibitions on the detection efficiency of absolute quantification of waterborne pathogenic bacteria by detecting the eight virulence genes in dPCR, and provided corresponding solutions.

Synthesis of the control plasmid
In this study, eight virulence genes of eight common pathogenic bacteria were chosen (Table 1). A plasmid containing eight virulence gene fragments, invA, cadF, ipaH2, regA, eae, hsp, mip, yst, and a human GAPDH gene fragment was synthesized as the external control ( Figure S1). The entire sequence is available in the Supplementary Materials.
The plasmid was linearized to mimic the form of genomic DNA extracted from water. The quantity of plasmid was determined by UV 260 absorbance.

Extraction of bacterial genomic DNA
The DNA purification method was adapted from a recent paper (Shi et al. ). The extraction method was proven to have higher DNA recovery efficiency for the samples on filter membranes than commercial kits. Briefly, water samples from a river, a lake, and an artificial pond (water quality data is available in Table S1) were filtered with 0.45 μM MCE membranes (BOJIN, China), which were stored at À80 C before DNA extraction. The procedure started with adding lysozyme lysis buffer (1 mg/mL in Tris-HCl, pH 8.0) to break the cell wall of gram-positive cells (37 C, 30 min) followed by degradation of proteins by proteinase K (0.4 mg/mL, 60 C, 2 h).
Bath sonication (100 W, 5 min) and syringe shearing were used to assist cell lysis. Then phenol/chloroform/isoamyl alcohol (25:24:1) was added to remove proteinase K. The upper phase was finally precipitated by isopropanol.

qPCR and dPCR amplification
To test the PCR performance of different probes, two sets of primers and probes were synthesized for eight bacterial virulence genes, namely BHQ and MGB. Each set contained forward and reverse primers and a probe. The primers and probes of BHQ sets were all reported from the literature (Table S2)

Assessment of PCR detection efficiency
In all experiments, 2,340 copies of control plasmids were added for each reaction. Inhibition of environmental samples was assessed by comparing their absolute copy numbers (dPCR) to the deionized water control. The detection efficiency of dPCR was calculated by the following equation: where E represents the PCR detection efficiency; Dpe represents the detected target gene copies in the environmental sample together with the plasmid control; De represents the detected target gene copies in the environmental sample; Dp represents the detected target gene copies from the plasmid control only.

Data analysis and statistics
For calculating the standard deviation of dPCR detection efficiency, the positive counts and the upper and lower con-

RESULTS
The influence of primer/probe design on dPCR detection  (Table S2), but only 50% of them worked in this study. This suggests that the successful design of a PCR primer-probe set is also related to the PCR reagents, protocols, and even instruments. All MGB sets were designed by Primer Express 3.0, which was developed by the same company as that of the dPCR reagent and instrument. It is fairly acceptable that six out of eight primerprobe sets were successful. But whether the failure of PCR reaction is due to inferior primers or probes is still not clear. In addition, all the primers were examined by SYBR Green dye independent of probes. All the primers from MGB sets had positive results and the primers of two genes, eae and yst, from BHQ sets did not have significant signal ( Figure S5). The results indicate that probes were the main cause of PCR reaction failure.
Another cause of poor amplification signal could be PCR conditions. The PCR mix (QuantStudio™ 3D Digital PCR Master Mix v2) provided by ThermoFisher has a very special recommended PCR protocol. The protocol has the annealing and extension combined and sets the temperature at 60 C with a duration time of 2 min. It has been optimized specifically for the QuantStudio™ 3D Digital PCR system. Under such pre-set conditions, all the primers and probes were designed to reach the annealing temperature of 60 C. And the long extension time is due to the low extension activity of Taq polymerase at 60 C.
Consequently, it reduced the options for further optimization of PCR conditions. Optimization of reaction temperature or primer/probe ratio for the cadF gene showed that lower temperature could slightly improve the signal ( Figure S6), but the signal was still much lower compared with other genes. Increasing probe to primer concentration ratio did not affect the results ( Figure S7).
The reactions of the two previous unsuccessful genes remained negative regardless of the changing conditions (data not shown). These data also support that the quality of the probe is most important.
The influence of data analysis of dPCR detection efficiency Aside from the well-defined negative-positive separation on the scatter plots (Figure 1), determinations of positive dot cutoff for dPCR analysis is critical since this is one of the  It is noted that the cadF-MGB had significant amplification on qPCR, but with a large Ct value of 31 ( Figure S2(b)), while all the other five successful ones had much smaller Ct within 19-22 for the same template copies ( Figure S2). The result showed that the final signal of the cadF-MGB reaction on qPCR was one order of magnitude lower. And it could be the cause for its poor performance on dPCR. The fluorescence of the amplification curve showed weak positive signals but still rose ten-fold higher than the baseline. In contrast, the dPCR amplification seemed to be a negative result. It appeared that the fluorescence intensity on both qPCR and dPCR instru- amplifications on the environmental samples. When the plasmid was mixed with an environmental DNA sample, which was extracted from the water of a reclaimed-waterreplenished river (Table S1), none of the four positive sets above had significant amplification in the qPCR instrument ( Figure 3(a)). This result indicates that the PCR reaction was Nonetheless, the reactions were all successful in dPCR without observable inhibitory effect (Figure 3(b)). This suggests that dPCR is not as sensitive to PCR inhibitors as qPCR. Furthermore, when three more environmental samples were tested, it showed that the inhibition also happened with dPCR ( Figure 4). Sample 1 was taken from a reclaimed-water-filled river in spring, and did not exhibit apparent inhibitory effect for six genes. Sample 2 was from a mock reclaimed-water-filled pond in summer, and showed significant inhibition. Sample 3 was from a reclaimed-water-filled lake in summer, and had the strongest inhibitory effect on PCR amplification. It seemed that the inhibitory potentials were higher with the samples having smaller A 260 /A 230 or A 260 /A 280 values (Table S1). But the data was too limited to draw significant conclusions, and the changes of these two values were small. It is also challenging to identify the possible inhibitor due to its mixture form and there is not even a feasible method to measure humic acids in the DNA samples.
Although the dPCR data sometimes showed no inhibition with the environmental samples, the inhibitory effect could still be seen from the histogram of dPCR  Table S1). The solid lines represent the spiking controls of 2.34 × 10 3 -copy plasmids in deionized water and the dotted lines represent the spiking controls in the environmental samples in (a). The same reactions in environmental samples were performed with dPCR (b). Data are mean ± SD (n ¼ 3).
severely affects the quantification detection efficiency by losing the positive counts to the negative peak and increasing the error in data analysis. Sample 3 completely inhibited the reaction (Figure 5(e)).

DISCUSSION
From the results, it is clear that poor probe design, inadequate data analysis, and PCR inhibitors could significantly affect the detection efficiency of dPCR. Possible solutions for improving the performance of dPCR are discussed.
The reason why some probes do not produce strong signals is not well understood to date. On the two ends of a probe, a reporter emits fluorescence and a quencher inhibits the fluorescence from the reporter. The fluorescence of the reporter should be quenched before the reaction and activated during the reaction. However, the quenching and activation efficiencies usually are not 100%, which results in a narrowed signal interval ( Figure S8). From Figure S7, it is shown that the background fluorescence signal increased dramatically with the increasing probe concentration, which was even higher than the signal increase during the amplification. This result demonstrated that the quenching efficiency of this probe is significantly low. The quenching efficiency mostly depends on the distance between the reporter and quencher on the probe. Probes containing a hairpin structure have been designed to maximize the quenching efficiency like molecular beacons (Navarro et al. ). In this study, the activation efficiency could not be maximized, since reporter and quencher were separated due to the break of the hairpin structure during the hybridization step of the PCR reaction, and they were still on the probe. Thus, the fluorescence could not be completely activated.
According to this defect, the hydrolysis probe was invented to optimize activation efficiency, like the popular TaqMan probe (Navarro et al. ). By contrast, the quenching efficiency is compromised since the distance between reporter and quencher is much longer than for the hybridiz- There has been no accurate model for the data analysis to now, because the distribution of the peaks is unpredictable. Sometimes the positive droplets will be too few to be considered significant. Approaches using a quantitated plasmids. Samples 1, 2, and 3 represent a reclaimed-water-filled river, pond, and lake, respectively (water quality indices are shown in Table S1 and virulence gene copies in environmental samples are shown in Table S3). For each assay, 2,340 copies of the synthesized control plasmids were spiked. Errors were calculated based on the method section 'Data analysis and statistics'.
positive control and others to prove the detection efficiency should be considered. The ideal way to prove the detection efficiency of dPCR is to test with known copy numbers of templates, whereas the dPCR itself might be the most accurate quantification test. One indirect approach to verify the detection efficiency of the absolute quantification of dPCR is to compare all the reactions within one plasmid template.
When 2,340 copies of plasmid were added for the three independent tests with MGB primer-probe sets, the coeffi-  PCR inhibition is a serious problem for the quantitation of specific targets in environmental samples. If the inhibition effect is similar when amplifying different genes, only one control test is needed to determine the PCR detection efficiency. Samples can be directly applied for the quantification of multiple targets. Unfortunately, the inhibitory effects were clearly distinct for each gene ( Figure 5).
It suggests that the PCR detection efficiency of each gene should be examined for absolute quantification. This might be due to the varied sensitivities of different reactions to the PCR inhibition. The reaction with a perfect peak separation, the invA gene, was supposed to have a larger antiinterference space, but in practice it did not, for unknown reasons. Therefore, it would be better to have all the reaction free of inhibition. Dilution is the most common approach (McKee et al. ). An environmental sample, which had a strong inhibitory effect for some genes, was tested for the dilution effect (Figure 7). Five-fold dilution significantly improved the PCR detection efficiencies, and ten-fold dilution completely eliminated inhibition effects. Although dilution can alleviate the inhibition problem, it significantly lowers the detection limit. It is not recommended for counts ±95% confidence interval for (a, b, c) calculated by the software QuantStudio 3D AnalysisSuite™, and mean ± SD for (d), n ¼ 3.