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Species identification and quantification of silver pomfret using the droplet digital PCR assay
Food Chemistry 302 (2020) 125331
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Species identification and quantification of silver pomfret using the droplet digital PCR assay Weiwei Caoa,1, Yiming Lib,1, Xun Chena, Yanlei Changa, Lili Lia, Lei Shia, Weibin Baia, Lei Yea, a b
T
⁎
Institute of Food Safety and Nutrition, Jinan University, Guangzhou 510632, Guangdong, China College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, Guangdong, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Species identification Silver pomfret Quantification Droplet digital PCR Real-time PCR
Adulteration of the high-value silver pomfret (Pampus argenteus) is a serious problem worldwide, necessitating accurate identification and quantification of the species. In this study, optimisation of the digital droplet PCR (ddPCR) assay for the identification and quantification of the silver pomfret was carried out. The primer and probe concentrations, melting temperature, and PCR cycle number were optimised by combining single-factor experiments with an orthogonal experimental design. The absolute limits of detection and quantification of the ddPCR were 2 copies/μl and 21 copies/μl, respectively. Its sensitivity was 0.1% for meat mixtures and 0.5% for DNA mixtures. The ddPCR was 156 times more sensitive than the real-time PCR, although both methods had similar specificities. However, the overall time needed to complete the ddPCR method was twice that of the realtime PCR. Notwithstanding, the ddPCR methodology established in this study can be a valuable tool for addressing species adulteration issues.
1. Introduction
whole fish is required, making this method unsuitable for the many manufacturers who purchase pre-processed ingredients. Although protein-based methods are convenient, the proteins are denatured or degraded during processing, rendering the results inaccurate (Chen et al., 2014). Numerous types of DNA assays for fish identification have been reported, including the uses of mitochondrial DNA (Koo et al., 2018; Sun & Tang, 2018), restriction fragment length polymorphism (Chen et al., 2014), DNA barcodes (Handy et al., 2011; Valdezmoreno, Quintallizama, Gómezlozano, & Garcíarivas, 2012), and microsatellites (Meer, Gardner, Hobbs, Pratchett, & Herwerden, 2012). Although these methods have high specificity and sensitivity, they are time consuming and complicated. Moreover, the DNA quality can sometimes be poor, again because of processing, which affects the reproducibility of the results. Many assays based on the real-time quantitative polymerase chain reaction (qPCR) have been used for species identification. Iwobi et al. (2016) optimised a modular multiplex qPCR approach, in which a quadruplex system and a pentaplex assay were applied for species identification (horse, beef, pork, and sheep) and quantification. Other researchers have also used qPCR techniques to identify pork (AlKahtani, Ismail, & Asif, 2017), donkey (Chisholm, Conyers, Booth, Lawley, and Hird, 2005), ostrich (Cheng, Chou, Lee, & Sheu, 2016), and chicken and turkey (Köppel, Daniels, Felderer, & Brünennieweler,
The silver pomfret (Pampus argenteus), which belongs to the family Stromateidae, is found off the coast of China (from the Bohai Sea to the South China Sea) and in the Arabian Gulf and Indian Ocean (Wu, Fu et al., 2016). Because pomfret muscle is a source of high-quality protein (Feng, Ping, Chao, Shi, & Zhang, 2010), the silver pomfret is a highly valued species with great market demand worldwide (Wu, Hu, Chen, Liu, & Ye, 2016). Unfortunately, silver pomfret populations have declined in recent years owing to high-intensity fishing, especially in China (Yang, Jian, & Yue, 2006). To maintain profits, some manufacturers have taken to reducing costs by substituting the silver pomfret with cheaper fish, the discovery of which inevitably affects consumer trust as well as the viability of the market and has therefore attracted the attention of regulators. To help curtail these illegal practices, accurate methods for the identification of the silver pomfret are required. There are currently three main methods for identifying specific fish species; namely, traditional morphological analysis, protein analysis, and DNA analysis. The traditional method of analysing the morphological features requires considerable professional knowledge, and the accuracy can be affected by the natural diversity and morphological plasticity of the fish (Zhang & Hanner, 2012). Moreover, access to the ⁎
Corresponding author. E-mail address: [email protected] (L. Ye). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.foodchem.2019.125331 Received 9 July 2018; Received in revised form 30 July 2019; Accepted 4 August 2019 Available online 05 August 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.
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NanoDrop 2000C spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA).
2013). The digital PCR (dPCR) assay has unprecedented sensitivity and precision and measures nucleic acid concentrations without the use of standard curves (Pinheiro et al., 2012). In the droplet digital PCR (ddPCR) assay, the PCR mixture is distributed into tens of thousands of droplets using a droplet generator, where every droplet hosts an independent PCR. Following limiting dilution principles and Poisson algorithms, the absolute copy number of the target DNA is determined on the basis of the number of droplets that are positive for amplification of the target (David, Dejan, Andrej, Dany, & Jana, 2016; Ren, Deng, Huang, Chen, & Ge, 2017). The dPCR assay has been widely used for the detection and quantification of genetically modified organisms (David et al., 2016) as well as for food authentication (Pinheiro et al., 2012). Moreover, many ddPCR methods have been developed for the identification of various species, including beef, pork, chicken, and turkey (Shehata et al., 2017), horse (Floren, Wiedemann, Brenig, Schütz, & Beck, 2014), pork (Amaral, Santos, Oliveira, & Mafra, 2016), and sheep and goat (Wang et al., 2017). However, the ddPCR assay has not previously been used to identify the silver pomfret. In this study, we developed, optimised, and validated a ddPCR assay for the identification and quantification of the silver pomfret. To establish optimal reaction conditions, single-factor experiments were combined with an orthogonal experimental design, primarily for establishing the optimal primer-to-probe ratio, melting temperature, and number of PCR cycles. The specificity, sensitivity, and reproducibility of this method were evaluated. In addition, we established a qPCR method for identifying the silver pomfret and compared the results with those of the ddPCR method. To our best knowledge, this is the first comprehensive report describing ddPCR and qPCR assays for identifying the silver pomfret. The goal of this study was to provide an alternative tool that can be used by food regulators and enforcement agencies to verify the label certification of the silver pomfret and prevent its adulteration.
2.3. PCR amplification and sequencing of the 16S rRNA gene To identify the species of the 30 additional fish samples, sequencing of their 16S rRNA genes was carried out using primer pairs (16S-F and 16S-R) synthesised by Sango BioTech (Shanghai, China). The primer sequences are listed in Suppl. Table S1. The amplification reactions were performed in a 25-μl volume, containing 18 μl of sterile distilled H2O, 2.5 μl of 10 × LA PCR buffer II (Mg2+ Plus, TaKaRa, Tokyo, Japan), 0.5 μl of dNTP (10 mM each), 1 μl of each primer (5 μM), 1 μl of Taq DNA polymerase (1 unit, TaKaRa, Tokyo, Japan), and 1 μl of DNA template. The PCR program was as follows: 94 °C for 2 min; 34 cycles of 94 °C for 20 s, 52 °C for 50 s, and 72 °C for 1 min; and a final extension step of 72 °C for 7 min. All PCR products were sent to Sango BioTech (Shanghai, China) for sequencing. The sequencing results were then compared against the sequences on the NCBI’s GenBank database to identify the fish species. 2.4. Screening of the ddPCR primers and probes Three primer pairs and hydrolysis probes were designed for Pampus argenteus using PrimerExpress software 3.0.1 (Applied Biosystems, Foster City, CA, USA) and synthesised by Sango BioTech (Shanghai, China) at HPLC purification grade. The primer sequences are listed in Suppl. Table S1. The ddPCR assay mixture contained 10 μl of 2 × ddPCR Supermix for Probes (Bio-Rad, Munich, Germany), 300 nmol/l of each primer, 100 nmol/l of hydrolysis probes, 1 μl of template DNA, and enough sterile distilled water to make up a final volume of 20 μl. Droplets were generated using the QX200 droplet generator (Bio-Rad, Munich, Germany) according to the manufacturer’s instructions. The droplets (≈40 μl) were transferred to a 96-well plate, which was then heat-sealed. The PCR amplification was performed in a C100 Touch thermal cycler (Bio-Rad, Munich, Germany) using the following conditions: 95 °C for 10 min; then 40 cycles of 94 °C for 30 s, 55 °C for 1 min, and 98 °C for 10 min. A ramp rate of 2 °C/s was used in all steps. Thereafter, the droplets were analysed in the QX200 droplet reader (Bio-Rad, Munich, Germany). Data were analysed using QuantaSoft software 1.7 (Bio-Rad, Munich, Germany). All tests were repeated twice.
2. Materials and methods 2.1. Test materials The silver pomfret (Pampus argenteus) and the golden pompano (Trachinotus ovatus) were purchased from a local market. After washing the fish with purified water, muscle samples (1–2 g) were obtained using scissors and weighed. Mixtures of both fish species were then prepared to known proportions (i.e. 0.1%, 0.5%, 1%, 10%, and 50% of silver pomfret to golden pompano); specifically, the mixtures contained 0.1 g silver pomfret/99.9 g golden pompano, 0.5 g silver pomfret/99.5 g golden pompano, 1 g silver pomfret/99 g golden pompano, 10 g silver pomfret/90 g golden pompano, and 50 g silver pomfret/50 g golden pompano. Additionally, DNA was extracted from both fish species and mixed at 0.01%, 0.1%, 0.5%, 1%, 5%, and 10% of silver pomfret DNA to golden pompano DNA. To avoid contamination, all samples were homogenised separately in a Grindomix GM200 laboratory knife mill (Retsch, Haan, Germany), using different containers and knives that had been treated with a DNA decontamination solution. Thirty additional fish samples (including the silver pomfret and 16 other different species, Suppl. Table S7) were purchased from several seafood markets and websites (http://www.jd.com and http://www. taobao.com). After washing the fish with purified water, muscle samples (1–2 g) were obtained using scissors and stored at − 20 °C for DNA extraction. The species identity of each fish sample was then verified using the optimised ddPCR and qPCR methods.
2.5. Screening of the concentration ratio between the primers and probes On the basis of previous studies (David et al., 2016; Košir et al., 2017), the primer-to-probe concentration ratios were set to 300 nM:100 nM, 300 nM:200 nM, 300 nM:300 nM, 400 nM:200 nM, and 400 nM:400 nM. All tests were repeated three times. The reactions were carried out in one plate, using the C100 Touch thermal cycler (Bio-Rad, Munich, Germany), with the following reaction conditions: 95 °C for 10 min; then 40 cycles of 94 °C for 30 s, 55 °C for 1 min, and 98 °C for 10 min. A ramp rate of 2 °C/s was used in all steps. 2.6. Screening of the melting temperature The temperature gradient was set up using the built-in function of the C100 Touch thermal cycler (Bio-Rad, Munich, Germany). Each row was run at a different temperature. The gradient protocol was as follows: 95 °C for 10 min; then 40 cycles of 94 °C for 30 s, 55–60 °C for 1 min, and 98 °C for 10 min. A ramp rate of 2 °C/s was used in all steps. The individual row temperatures were 60, 59.7, 59.2, 58.2, 57, 56, 55.3, and 55 °C (from rows A to H). The temperature gradient was set to 55, 56, 57, 58.2, 59.2, and 60 °C (rows H, F, E, D, C, and A). All tests were repeated three times.
2.2. DNA extraction Total genomic DNA was extracted using the Qiagen DNeasy Blood & Tissue Kit (Qiagen, Valencia, CA, USA) and eluted with ultrapure water. The DNA concentrations (ng/μl) were assessed at 260 nm using a 2
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2.7. Screening of the number of PCR cycles The optimal number of PCR cycles was screened by conducting three ddPCR assays with the same reaction components and reagent concentrations but different numbers of cycles (i.e. 40, 50, and 60 cycles). The reaction conditions in the C100 Touch thermal cycler (BioRad, Munich, Germany) were as follows: 95 °C for 10 min; then 40, 50, or 60 cycles at 94 °C for 30, 55–60 °C for 1 min, and 98 °C for 10 min. A ramp rate of 2 °C/s was used in all steps. All tests were repeated three times. 2.8. Optimisation of the ddPCR system To avoid any potential interference caused by interactions between different factors of the ddPCR assay, nine combinations were studied in the ddPCR optimisation experiment by using an L9 (34) orthogonal experimental design, with three factors and three levels (Suppl. Table S2). The fixed primer-to-probe ratios used were 300 nM:200 nM, 300 nM:300 nM, and 400 nM:200 nM. The temperature was set at 56, 57, or 58.2 °C, and the number of PCR cycles was set to 40, 50, or 60 cycles. Each combination was repeated three times.
Fig. 1. Screening of droplet digital PCR primers and probes for the silver pomfret.
as a positive control, whereas that of Siniperca chuatsi was used as a negative control. The CY-1 primers and probe yielded positive amplification of the silver pomfret DNA and were negative for Siniperca chuatsi DNA (Fig. 1). Because the CY-2 and CY-3 primers and probes failed to amplify the DNA from either species, CY-1 was selected for further optimisation of the ddPCR assay.
2.9. Specificity of the silver pomfret ddPCR system To confirm the specificity of the ddPCR assay for detecting the silver pomfret, DNA samples from Trachinotus ovatus, Psenopsis anomala, Pampus punctatissimus, Ephippus orbis, Pampus chinensis, and Pampus cinereus were tested using the optimised assay. Additionally, silver pomfret DNA was used as a positive control, which was verified by sequencing. All tests were repeated twice.
3.2. Optimisation of the ddPCR assay system To optimise the ddPCR system for detecting the silver pomfret, three parameters of the assay were evaluated. One of these was the primer-toprobe concentration ratio, which is known to affect both the ddPCR fluorescence amplitude and sensitivity (David et al., 2016; Rodríguez, Rodríguez, Córdoba, & Andrade, 2015). Of the five concentration ratios tested while keeping the other parameters constant (melting temperature set to 55 °C and number of cycles set to 40), the ratios 300 nM:200 nM, 300 nM:300 nM, 400 nM:200 nM, and 400 nM:400 nM enabled better separation of the positive and negative droplets (Fig. 2). Because the effects of 300 nM:300 nM and 400 nM:400 nM were similar, the 300 nM:200 nM, 300 nM:300 nM, and 400 nM:200 nM concentration ratios were chosen for subsequent experiments. The second parameter assessed was the melting temperature, which according to the Droplet Digital™ PCR Applications Guide is one of the most critical parameters affecting ddPCR specificity. Our results showed that the fluorescence amplitude was lower when the temperature was increased, making it more difficult to separate the positive and negative droplets (Fig. 3). Although lower temperatures generated
2.10. Sensitivity of the silver pomfret ddPCR system To determine the sensitivity of the silver pomfret ddPCR assay, a dilution series of silver pomfret DNA was prepared. Four replicates of the dilution series were measured by ddPCR for the preliminary experiment (two separate runs, two replicates on the first day and two replicates on the second day). The absolute limit of quantification (aLOQ, expressed in copy numbers) and absolute limit of detection (aLOD) of the ddPCR were determined on the basis of the experimental results of a third set of experiments performed on 15 replicates (David et al., 2016). The limit of quantification (LOQ) was determined as the aLOQ in the sample, where the coefficient of variation (CV) of all replicates was below 25%. The limit of detection (LOD) was determined as the aLOD in the sample, where at least 14 replicates generated a positive signal (David et al., 2016). 2.11. Real-time fluorescence PCR The qPCR assay was performed twice for each sample, using the Probe qPCR Mix (RR391A, TaKaRa, Tokyo, Japan). Each qPCR amplification procedure was performed with QuantStudio 6 Flex (Applied Biosystem, Foster City, CA, USA) in a total volume of 20 μl, following the manufacturer’s instructions. The qPCR system for detecting silver pomfret comprised the primers CY-1-FP and CY-1-BP, and the probe CY1-Probe. The thermal cycling conditions were as follows: 95 °C for 30 s; then 30 cycles of 95 °C for 5 s and 60 °C for 34 s. 3. Results and discussion 3.1. Screening of the ddPCR primers and probes To screen for an optimal target gene for use in the ddPCR assays, three sets of primers and probes were designed for the targeting of three silver pomfret genes; namely, cytochrome oxidase subunit I (COI), cytochrome B (cytb), and 16S rRNA. The DNA of silver pomfret was used
Fig. 2. Influence of different primer-to-probe concentration ratios on the droplet digital PCR. 3
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2, 4, 5, 6, and 8 created more rain droplets, and the fluorescence differences between the positive and negative droplets were not sufficient for clear peak separation (Fig. 5). Combinations 3 and 9 yielded suboptimal results in terms of fluorescence peak separation between positive and negative droplets, whereas combination 7 performed better with respect to fluorescence amplitude and droplet separation (Fig. 5). Taking into consideration the previous single-factor experiments and the orthogonal experimental setup, combination 7 was considered to represent the optimal conditions for the silver pomfret ddPCR system. The combination 7 reaction mixture contained 10 μl of 2 × ddPCR Supermix for Probes, 400 nmol/l of each primer, 200 nmol/l of hydrolysis probes, 1 μl of template DNA, and enough sterile distilled water to make up a final volume of 20 μl. For combination 7, the ddPCR program was as follow: 95 °C for 10 min; then 50 cycles of 94 °C for 30 s, 56 °C for 1 min, and 98 °C for 10 min. In previous studies (Grelewska-Nowotko, Żurawska-Zajfert, Żmijewska, & Sowa, 2018; Köppel & Bucher, 2015; Lievens, Jacchia, Kagkli, Savini, & Querci, 2016; Witte et al., 2016), optimisation of the ddPCR assay was accomplished by changing a single reaction factor, with all other components held constant. This method is one-sided and ignores possible interactions between factors (Cao et al., 2017). To avoid this bias, we used an orthogonal experimental design, along with the singlefactor experiments, to determine the optimal conditions for the silver pomfret ddPCR. This is the first report on such use of orthogonal designs to screen the conditions of a ddPCR assay system.
Fig. 3. Influence of different melting temperatures on the droplet digital PCR.
higher fluorescence amplitudes and clearer peak separation, a greater rain effect (intermediate fluorescence of some droplets) was created, which influenced the accuracy of the results. These results were consistent with the conclusions of previous studies, which pointed out that a lower melting temperature was beneficial for droplet separation (Gerdes, Iwobi, Busch, & Pecoraro, 2016; Witte et al., 2016). Taking into account the synthetic fluorescence amplitude and the peak separation of the two droplets (i.e. less intermediate droplets), 56, 57, and 58.2 °C were chosen for further use in the optimisation studies. The third ddPCR parameter assessed was the PCR cycle number. As the number of cycles was increased, the peak separation improved and the fluorescence amplitude of the positive droplets increased (Fig. 4), which was consistent with the results of a previous report (Köppel & Bucher, 2015). The 40, 50, and 60 PCR cycles all produced acceptable fluorescence amplitudes and distinct cluster separations. Therefore, all three cycles were analysed in the following experiments. On the basis of the results of the single-factor experiments, further ddPCR analysis was performed with nine combinations of primer-toprobe concentration ratios, melting temperatures, and cycle numbers, using an orthogonal experimental design (Suppl. Table S2). Han, Chen, Mao, Tang, and Guan (2010) also reported the use of the orthogonal test of three factors to screen the optimal conditions for sucrose:sucrose 1-fructosyltransferase (FST-1) activity. Of these nine combinations, 1,
3.3. Specificity, aLOQ, and aLOD of the ddPCR system To investigate the specificity of the optimised silver pomfret ddPCR system, six additional fish species with similar morphological features, or with highly homologous sequences, were tested using the same ddPCR conditions. The optimised assay system specifically amplified the DNA of silver pomfret, but not that of the other fish species (Suppl. Fig. S1). According to a previous study (David et al., 2016), the aLOQ of the ddPCR is defined as the lowest copy number with a CV of ≤ 25% during quantification. In this present study, the preliminary aLOQ was determined to be at least 22 copies, whereas the preliminary aLOD was 1.4 copies (Suppl. Table S3). In an additional preliminary experiment, the aLOQ was 21.9 copies and the aLOD was 1.7 copies (Suppl. Table
Fig. 4. Influence of different cycle numbers on the droplet digital PCR. 4
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Fig. 5. Reaction results of the silver pomfret droplet digital PCR system based on nine combinations of reaction factors according to an orthogonal design.
(samples 1–9, with six replicates) were simultaneously tested by qPCR. The detection limit of the qPCR system was approximately 313 copies (Fig. 6), indicating that the sensitivity of the optimised ddPCR was 156 times higher than that of the qPCR assay. Wang et al. (2017) also reported that the ddPCR system for detecting Salmonella typhimurium was 10 times more sensitive than qPCR.
S3). The results of experiments performed on 15 individual replicates showed that the aLOQ was at least 21 copies, and the aLOD was 2 copies (Suppl. Table S4). These results were similar to published results on the ddPCR quantification of meat species, where some of the LOD values ranged from 1 to 5 copies/μl (Wang et al., 2017). To compare the sensitivity between the ddPCR assay and the conventional qPCR assay, serial dilutions of the silver pomfret DNA
Fig. 6. Sensitivity of the silver pomfret real-time PCR assay. The concentration of the silver pomfret DNA samples were as follows: sample 1, 3.6 ng/μl; sample 2, 1.2 ng/μl; sample 3, 400 pg/μl; sample 4, 133 pg/μl; sample 5, 44 pg/μl; sample 6, 22 pg/μl; sample 7, 11 pg/μl; sample 8, 5 pg/μl; and sample 9, 1.67 pg/μl. 5
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contained 10 μl of 2 × ddPCR Supermix for Probes, 400 nmol/l of each primer, 200 nmol/l of hydrolysis probes, 1 μl of template DNA, and enough sterile distilled water to make up a final individual sample volume of 20 μl. The reaction program consisted of 95 °C for 10 min, followed by 50 cycles of 94 °C for 30 s, 56 °C for 1 min, and 98 °C for 10 min. The specificity of this ddPCR system was confirmed by negative testing for other fish species. The aLOD and aLOQ were 2 copies/μl and 21 copies/μl, respectively. The method was further validated using fish meat mixtures, DNA mixtures, and commercial samples, showing a sensitivity of 0.1% and 0.5% for meat mixtures and DNA mixtures, respectively. The commercial sample analysis indicated that the system was effective and specific and that 30% of the samples tested were not in agreement with their label statements. The ddPCR and qPCR methods were compared by using them to detect the same DNA samples, with the results showing that the ddPCR was 156 times more sensitive in detecting the silver pomfret, although the two methods had similar specificities. However, the ddPCR technique was more complicated to carry out and required 2.5 times the experimental duration of the qPCR method. Considering that the silver pomfret is a highly economical and valuable fish, its authenticity is a concern for consumers, especially as there is currently no accurate and efficient method to identify fish species. Hence, the established methodology in this study can be a valuable tool for law enforcement agencies and fishery-related institutions, enabling them to identify and quantify the silver pomfret and to address the adulteration issues.
3.4. Comparison of the ddPCR and qPCR assays To assess the accuracy and applicability of the ddPCR system, different muscle mixtures (0.1%–50%) and DNA mixtures (0.01%–10%) of the silver pomfret and the golden pompano were detected with both the ddPCR and the qPCR systems and the results were compared. The sensitivity of the optimised ddPCR assay in detecting silver pomfret in the meat mixtures was 0.1% (0.1 g/100 g) (Suppl. Table S5). This was one order of magnitude lower than that of other reported works with the ddPCR, which achieved 1% (w/w) for detecting sheep meat in sheep and chicken mixtures (Ren et al., 2017) and 1% (w/w) for detecting goat meat in goat and sheep mixtures (Wang et al., 2018). According to the results obtained from the DNA mixture analysis (Suppl. Table S6), the sensitivity of the ddPCR system was 0.5%, which was consistent with that of a previous report (Santurtún, Riancho, Arozamena, López-Duarte, & Zarrabeitia1, M.T , 2017). The ddPCR and qPCR results for the meat and DNA mixtures indicated that the two methods were highly consistent with each other. However, the ddPCR results could be quantified without the need for a silver pomfret standard curve, and the CV was less than 25%, indicating that the assay method exhibited good accuracy and reproducibility. The ddPCR assay was further applied to commercial samples to verify their compliance with label statements. Thirty fish samples were collected from several seafood markets and websites and then analysed with the ddPCR and qPCR systems simultaneously. The results showed that three of these samples were silver pomfret, whereas 27 samples were not (Suppl. Table S7). According to subsequent sequencing, the three positive samples were verified to be silver pomfret, and the negative samples were confirmed as other fish species (Suppl. Table S7). It is noteworthy that 30% of the analysed samples did not correspond to their label statements. According to the tests of the sensitivity, specificity, operation, and experimental time, the ddPCR system was much more sensitive than the qPCR assay (by 156 times), albeit both systems performed similarly in terms of specificity (Suppl. Table S8). Owing to a lack of certified reference material, it was difficult to quantify the silver pomfret by qPCR; therefore, the results of the qPCR assay were in fact qualitative. The ddPCR system was more complicated to perform since it required four steps (viz. preparation of the PCR mixture, generation of the droplets, amplification, and reading of the droplets), whereas the qPCR needed only two steps (viz. preparation of the PCR mixture and amplification). The time taken to complete the ddPCR amplification was nearly 2.3 h owing to the ramp rate of 2 °C/s needed to prevent droplet breakage. After the amplification step, the droplet reading was timeconsuming, requiring almost 1.5 h to complete for a 96-well plate. Therefore, at least 5 h was needed to complete the ddPCR experimentation for one 96-well plate. By contrast, after preparation of the qPCR mixture, the amplification was carried out directly and the whole experiment was completed in only 2 h. Yang, Paparini, Monis, and Ryan (2014) also reported that the ddPCR took two times longer to complete than the qPCR did. Therefore, the ddPCR system is recommended mainly if excellent result accuracy is required, especially for law enforcement agencies. If high detection efficiency is required, then the qPCR system is the better option, especially for the food industry.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The presented work was funded by the National Key Research and Development Program of China (2016YFD0401203), the National Natural Science Foundation of China (31571934), and the Science and Technology Planning Project of Guangdong Province, China (2017B020207004). Data availability statement The datasets generated during the current study are available from the corresponding author upon reasonable request. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodchem.2019.125331. References Al-Kahtani, H. A., Ismail, E. A., & Asif, A. M. (2017). Pork detection in binary meat mixtures and some commercial food products using conventional and real-time PCR techniques. Food Chemistry, 219, 54–60. Amaral, J. S., Santos, G., Oliveira, M. B. P. P., & Mafra, I. (2016). Quantitative detection of pork meat by EvaGreen real-time PCR to assess the authenticity of processed meat products. Food Control, 72, 53–61. Cao, Y., Wang, L., Duan, L., Li, J., Ma, J., Xie, S., ... Li, H. (2017). Development of a realtime fluorescence loop-mediated isothermal amplification assay for rapid and quantitative detection of Ustilago maydis. Scientific Reports, 7, 13394. Chen, S., Zhang, Y., Li, H., Wang, J., Chen, W., Zhou, Y., & Zhou, S. (2014). Differentiation of fish species in Taiwan Strait by PCR-RFLP and lab-on-a-chip system. Food Control, 44, 26–34. Cheng, J. H., Chou, H. T., Lee, M. S., & Sheu, S. C. (2016). Development of qualitative and quantitative PCR analysis for meat adulteration from RNA samples. Food Chemistry, 192, 336–342. Chisholm, J., Conyers, C., Booth, C., Lawley, W., & Hird, H. (2005). The detection of horse and donkey using real-time PCR. Meat Science, 70, 727–732. David, D., Dejan, Š., Andrej, B., Dany, M., & Jana, Ž. (2016). Multiplex quantification of
4. Conclusion This work aimed at developing and validating a sensitive and accurate methodology for the detection and quantification of the silver pomfret, for verifying label statements and regulating markets and orders. For these purposes, an optimised species-specific ddPCR assay was successfully developed. The CY-1 primers and probe were selected for optimisation of the ddPCR assay. The optimal primer-to-probe concentration ratio, melting temperature, and PCR cycle number were screened by combining single-factor experiments with an orthogonal design arrangement. The optimised silver pomfret ddPCR system 6
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Ren, J., Deng, T., Huang, W., Chen, Y., & Ge, Y. (2017). A digital PCR method for identifying and quantifying adulteration of meat species in raw and processed food. Plos One, 12, e173567. Rodríguez, A., Rodríguez, M., Córdoba, J. J., & Andrade, M. J. (2015). Design of primers and probes for quantitative real-time PCR methods. Methods in Molecular Biology, 1275, 31–56. Santurtún, A., Riancho, J. A., Arozamena, J., López-Duarte, M., & Zarrabeitia1, M. T. (2017). Indel analysis by droplet digital PCR: A sensitive method for DNA mixture detection and chimerism analysis. International Journal of Legal Medicine, 131, 67–72. Shehata, H. R., Li, J., Chen, S., Redda, H., Cheng, S., Tabujara, N., ... Hanner, R. (2017). Droplet digital polymerase chain reaction (ddPCR) assays integrated with an internal control for quantification of bovine, porcine, chicken and turkey species in food and feed. Plos One, 12, e182872. Sun, P., & Tang, B. J. (2018). Low mtDNA variation and shallow population structure of the Chinese pomfret Pampus chinensis along the China coast. Journal of fish biology, 92, 214–228. Valdezmoreno, M., Quintallizama, C., Gómezlozano, R., & Garcíarivas, M. D. C. (2012). Monitoring an alien invasion: DNA barcoding and the identification of lionfish and their prey on coral reefs of the Mexican Caribbean. Plos One, 7, e36636. Wang, Q., Cai, Y., He, Y., Yang, L., Li, J., & Pan, L. (2018). Droplet digital PCR (ddPCR) method for the detection and quantification of goat and sheep derivatives in commercial meat products. European Food Research and Technology, 244, 767–774. Wang, M., Yang, J., Gai, Z., Huo, S., Zhu, J., Li, J., ... Shi, F. (2017). Comparison between digital PCR and real-time PCR in detection of Salmonella typhimurium in milk. International Journal of Food Microbiology, 266, 251–256. Witte, A. K., Mester, P., Fister, S., Witte, M., Schoder, D., & Rossmanith, P. (2016). A systematic investigation of parameters influencing droplet rain in the Listeria monocytogenes prfA assay – Reduction of ambiguous results in ddPCR. Plos One, 11, e168179. Wu, C., Fu, S., Xiang, Y., Yuan, C., Hu, Y., Chen, S., ... Ye, X. (2016). Effect of chitosan gallate coating on the quality maintenance of refrigerated (4°C) silver pomfret (Pampus argentus). Food and Bioprocess Technology, 9, 1835–1843. Wu, C., Hu, Y., Chen, S., Liu, J., & Ye, X. (2016). Formation mechanism of nano-scale antibiotic and its preservation performance for silvery pomfret. Food Control, 69, 331–338. Yang, W., Jian, L. I., & Yue, G. (2006). Multiplex genotyping of novel microsatellites from silver pomfret (Pampus argenteus) and cross-amplification in other pomfret species. Molecular Ecology Notes, 6, 1073–1075. Yang, R., Paparini, A., Monis, P., & Ryan, U. (2014). Comparison of next-generation droplet digital PCR (ddPCR) with quantitative PCR (qPCR) for enumeration of Cryptosporidium oocysts in faecal samples. International Journal for Parasitology, 44, 1105–1113. Zhang, J., & Hanner, R. (2012). Molecular Approach to the Identification of Fish in the South China Sea. Plos One, 7, e30621.
four DNA targets in one reaction with Bio-Rad droplet digital PCR system for GMO detection. Scientific Reports, 6, 35451. Feng, Z., Ping, Z., Chao, S., Shi, Z. H., & Zhang, L. Z. (2010). Amino acid and fatty acid compositions and nutritional quality of muscle in the pomfret, Pampus punctatissimus. Food Chemistry, 118, 224–227. Floren, C., Wiedemann, I., Brenig, B., Schütz, E., & Beck, J. (2014). Species identification and quantification in meat and meat products using Droplet Digital PCR (ddPCR). Food Chemistry, 173, 1054–1058. Gerdes, L., Iwobi, A., Busch, U., & Pecoraro, S. (2016). Optimization of digital droplet polymerase chain reaction for quantification of genetically modified organisms. Biomolecular Detection & Quantification, 7, 9–20. Grelewska-Nowotko, K., Żurawska-Zajfert, M., Żmijewska, E., & Sowa, S. (2018). Optimization and verification of droplet digital PCR even-specific methods for the quantification of GM maize DAS1507 and NK603. Applied Biochemistry & Biotechnology, 1–14. Han, Y., Chen, L., Mao, D., Tang, L., & Guan, L. (2010). Expression and activity analysis of sucrose: Sucrose 1-fructosyltransferase from onion. New Biotechnology, 27, 324–329. Handy, S. M., Deeds, J. R., Ivanova, N. V., Hebert, P. D., Hanner, R. H., Ormos, A., ... Yancy, H. F. (2011). A single-laboratory validated method for the generation of DNA barcodes for the identification of fish for regulatory compliance. Journal of Aoac International, 94, 201–210. Iwobi, A., Sebah, D., Spielmann, G., Huber, I., Kraemer, I., Gerdes, L., ... Busch, U. (2016). A multiplex real-time PCR method for the quantitative determination of equine (horse) fractions in meat products. Food Control, 74, 89–97. Koo, D. H., Singh, B., Jiang, J., Friebe, B., Gill, B. S., Chastain, P. D., ... Singh, K. K. (2018). Single molecule mtDNA fiber FISH for analyzing numtogenesis. Analytical Biochemistry, 552, 45–49. Köppel, R., & Bucher, T. (2015). Rapid establishment of droplet digital PCR for quantitative GMO analysis. European Food Research & Technology, 241, 1–13. Köppel, R., Daniels, M., Felderer, N., & Brünennieweler, C. (2013). Multiplex real-time PCR for the detection and quantification of DNA from duck, goose, chicken, turkey and pork. European Food Research & Technology, 236, 1093–1098. Košir, A. B., Divieto, C., Pavšič, J., Pavarelli, S., Dobnik, D., Dreo, T., ... Žel, J. (2017). Droplet volume variability as a critical factor for accuracy of absolute quantification using droplet digital PCR. Analytical & Bioanalytical Chemistry, 409, 6689. Lievens, A., Jacchia, S., Kagkli, D., Savini, C., & Querci, M. (2016). Measuring digital PCR quality: Performance parameters and their optimization. Plos One, 11, e153317. Meer, M. H. V. D., Gardner, M. G., Hobbs, J. P. A., Pratchett, M., & Herwerden, L. V. (2012). Identification of twenty one microsatellite loci for conservation genetic studies of the endemic butterflyfish Chaetodon tricinctus. Conservation Genetics Resources, 4, 243–246. Pinheiro, L. B., Coleman, V., Hindson, C. M., Herrmann, J., Hindson, B. J., Bhat, S., & Emslie, K. R. (2012). Evaluation of a droplet digital polymerase chain reaction format for DNA copy number quantification. Analytical Chemistry, 84, 1003–1011.
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Species identification and quantification of silver pomfret using the droplet digital PCR assay Weiwei Caoa,1, Yiming Lib,1, Xun Chena, Yanlei Changa, Lili Lia, Lei Shia, Weibin Baia, Lei Yea, a b
T
⁎
Institute of Food Safety and Nutrition, Jinan University, Guangzhou 510632, Guangdong, China College of Light Industry and Food Sciences, South China University of Technology, Guangzhou 510641, Guangdong, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Species identification Silver pomfret Quantification Droplet digital PCR Real-time PCR
Adulteration of the high-value silver pomfret (Pampus argenteus) is a serious problem worldwide, necessitating accurate identification and quantification of the species. In this study, optimisation of the digital droplet PCR (ddPCR) assay for the identification and quantification of the silver pomfret was carried out. The primer and probe concentrations, melting temperature, and PCR cycle number were optimised by combining single-factor experiments with an orthogonal experimental design. The absolute limits of detection and quantification of the ddPCR were 2 copies/μl and 21 copies/μl, respectively. Its sensitivity was 0.1% for meat mixtures and 0.5% for DNA mixtures. The ddPCR was 156 times more sensitive than the real-time PCR, although both methods had similar specificities. However, the overall time needed to complete the ddPCR method was twice that of the realtime PCR. Notwithstanding, the ddPCR methodology established in this study can be a valuable tool for addressing species adulteration issues.
1. Introduction
whole fish is required, making this method unsuitable for the many manufacturers who purchase pre-processed ingredients. Although protein-based methods are convenient, the proteins are denatured or degraded during processing, rendering the results inaccurate (Chen et al., 2014). Numerous types of DNA assays for fish identification have been reported, including the uses of mitochondrial DNA (Koo et al., 2018; Sun & Tang, 2018), restriction fragment length polymorphism (Chen et al., 2014), DNA barcodes (Handy et al., 2011; Valdezmoreno, Quintallizama, Gómezlozano, & Garcíarivas, 2012), and microsatellites (Meer, Gardner, Hobbs, Pratchett, & Herwerden, 2012). Although these methods have high specificity and sensitivity, they are time consuming and complicated. Moreover, the DNA quality can sometimes be poor, again because of processing, which affects the reproducibility of the results. Many assays based on the real-time quantitative polymerase chain reaction (qPCR) have been used for species identification. Iwobi et al. (2016) optimised a modular multiplex qPCR approach, in which a quadruplex system and a pentaplex assay were applied for species identification (horse, beef, pork, and sheep) and quantification. Other researchers have also used qPCR techniques to identify pork (AlKahtani, Ismail, & Asif, 2017), donkey (Chisholm, Conyers, Booth, Lawley, and Hird, 2005), ostrich (Cheng, Chou, Lee, & Sheu, 2016), and chicken and turkey (Köppel, Daniels, Felderer, & Brünennieweler,
The silver pomfret (Pampus argenteus), which belongs to the family Stromateidae, is found off the coast of China (from the Bohai Sea to the South China Sea) and in the Arabian Gulf and Indian Ocean (Wu, Fu et al., 2016). Because pomfret muscle is a source of high-quality protein (Feng, Ping, Chao, Shi, & Zhang, 2010), the silver pomfret is a highly valued species with great market demand worldwide (Wu, Hu, Chen, Liu, & Ye, 2016). Unfortunately, silver pomfret populations have declined in recent years owing to high-intensity fishing, especially in China (Yang, Jian, & Yue, 2006). To maintain profits, some manufacturers have taken to reducing costs by substituting the silver pomfret with cheaper fish, the discovery of which inevitably affects consumer trust as well as the viability of the market and has therefore attracted the attention of regulators. To help curtail these illegal practices, accurate methods for the identification of the silver pomfret are required. There are currently three main methods for identifying specific fish species; namely, traditional morphological analysis, protein analysis, and DNA analysis. The traditional method of analysing the morphological features requires considerable professional knowledge, and the accuracy can be affected by the natural diversity and morphological plasticity of the fish (Zhang & Hanner, 2012). Moreover, access to the ⁎
Corresponding author. E-mail address: [email protected] (L. Ye). 1 These authors contributed equally to this work. https://doi.org/10.1016/j.foodchem.2019.125331 Received 9 July 2018; Received in revised form 30 July 2019; Accepted 4 August 2019 Available online 05 August 2019 0308-8146/ © 2019 Elsevier Ltd. All rights reserved.
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NanoDrop 2000C spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA).
2013). The digital PCR (dPCR) assay has unprecedented sensitivity and precision and measures nucleic acid concentrations without the use of standard curves (Pinheiro et al., 2012). In the droplet digital PCR (ddPCR) assay, the PCR mixture is distributed into tens of thousands of droplets using a droplet generator, where every droplet hosts an independent PCR. Following limiting dilution principles and Poisson algorithms, the absolute copy number of the target DNA is determined on the basis of the number of droplets that are positive for amplification of the target (David, Dejan, Andrej, Dany, & Jana, 2016; Ren, Deng, Huang, Chen, & Ge, 2017). The dPCR assay has been widely used for the detection and quantification of genetically modified organisms (David et al., 2016) as well as for food authentication (Pinheiro et al., 2012). Moreover, many ddPCR methods have been developed for the identification of various species, including beef, pork, chicken, and turkey (Shehata et al., 2017), horse (Floren, Wiedemann, Brenig, Schütz, & Beck, 2014), pork (Amaral, Santos, Oliveira, & Mafra, 2016), and sheep and goat (Wang et al., 2017). However, the ddPCR assay has not previously been used to identify the silver pomfret. In this study, we developed, optimised, and validated a ddPCR assay for the identification and quantification of the silver pomfret. To establish optimal reaction conditions, single-factor experiments were combined with an orthogonal experimental design, primarily for establishing the optimal primer-to-probe ratio, melting temperature, and number of PCR cycles. The specificity, sensitivity, and reproducibility of this method were evaluated. In addition, we established a qPCR method for identifying the silver pomfret and compared the results with those of the ddPCR method. To our best knowledge, this is the first comprehensive report describing ddPCR and qPCR assays for identifying the silver pomfret. The goal of this study was to provide an alternative tool that can be used by food regulators and enforcement agencies to verify the label certification of the silver pomfret and prevent its adulteration.
2.3. PCR amplification and sequencing of the 16S rRNA gene To identify the species of the 30 additional fish samples, sequencing of their 16S rRNA genes was carried out using primer pairs (16S-F and 16S-R) synthesised by Sango BioTech (Shanghai, China). The primer sequences are listed in Suppl. Table S1. The amplification reactions were performed in a 25-μl volume, containing 18 μl of sterile distilled H2O, 2.5 μl of 10 × LA PCR buffer II (Mg2+ Plus, TaKaRa, Tokyo, Japan), 0.5 μl of dNTP (10 mM each), 1 μl of each primer (5 μM), 1 μl of Taq DNA polymerase (1 unit, TaKaRa, Tokyo, Japan), and 1 μl of DNA template. The PCR program was as follows: 94 °C for 2 min; 34 cycles of 94 °C for 20 s, 52 °C for 50 s, and 72 °C for 1 min; and a final extension step of 72 °C for 7 min. All PCR products were sent to Sango BioTech (Shanghai, China) for sequencing. The sequencing results were then compared against the sequences on the NCBI’s GenBank database to identify the fish species. 2.4. Screening of the ddPCR primers and probes Three primer pairs and hydrolysis probes were designed for Pampus argenteus using PrimerExpress software 3.0.1 (Applied Biosystems, Foster City, CA, USA) and synthesised by Sango BioTech (Shanghai, China) at HPLC purification grade. The primer sequences are listed in Suppl. Table S1. The ddPCR assay mixture contained 10 μl of 2 × ddPCR Supermix for Probes (Bio-Rad, Munich, Germany), 300 nmol/l of each primer, 100 nmol/l of hydrolysis probes, 1 μl of template DNA, and enough sterile distilled water to make up a final volume of 20 μl. Droplets were generated using the QX200 droplet generator (Bio-Rad, Munich, Germany) according to the manufacturer’s instructions. The droplets (≈40 μl) were transferred to a 96-well plate, which was then heat-sealed. The PCR amplification was performed in a C100 Touch thermal cycler (Bio-Rad, Munich, Germany) using the following conditions: 95 °C for 10 min; then 40 cycles of 94 °C for 30 s, 55 °C for 1 min, and 98 °C for 10 min. A ramp rate of 2 °C/s was used in all steps. Thereafter, the droplets were analysed in the QX200 droplet reader (Bio-Rad, Munich, Germany). Data were analysed using QuantaSoft software 1.7 (Bio-Rad, Munich, Germany). All tests were repeated twice.
2. Materials and methods 2.1. Test materials The silver pomfret (Pampus argenteus) and the golden pompano (Trachinotus ovatus) were purchased from a local market. After washing the fish with purified water, muscle samples (1–2 g) were obtained using scissors and weighed. Mixtures of both fish species were then prepared to known proportions (i.e. 0.1%, 0.5%, 1%, 10%, and 50% of silver pomfret to golden pompano); specifically, the mixtures contained 0.1 g silver pomfret/99.9 g golden pompano, 0.5 g silver pomfret/99.5 g golden pompano, 1 g silver pomfret/99 g golden pompano, 10 g silver pomfret/90 g golden pompano, and 50 g silver pomfret/50 g golden pompano. Additionally, DNA was extracted from both fish species and mixed at 0.01%, 0.1%, 0.5%, 1%, 5%, and 10% of silver pomfret DNA to golden pompano DNA. To avoid contamination, all samples were homogenised separately in a Grindomix GM200 laboratory knife mill (Retsch, Haan, Germany), using different containers and knives that had been treated with a DNA decontamination solution. Thirty additional fish samples (including the silver pomfret and 16 other different species, Suppl. Table S7) were purchased from several seafood markets and websites (http://www.jd.com and http://www. taobao.com). After washing the fish with purified water, muscle samples (1–2 g) were obtained using scissors and stored at − 20 °C for DNA extraction. The species identity of each fish sample was then verified using the optimised ddPCR and qPCR methods.
2.5. Screening of the concentration ratio between the primers and probes On the basis of previous studies (David et al., 2016; Košir et al., 2017), the primer-to-probe concentration ratios were set to 300 nM:100 nM, 300 nM:200 nM, 300 nM:300 nM, 400 nM:200 nM, and 400 nM:400 nM. All tests were repeated three times. The reactions were carried out in one plate, using the C100 Touch thermal cycler (Bio-Rad, Munich, Germany), with the following reaction conditions: 95 °C for 10 min; then 40 cycles of 94 °C for 30 s, 55 °C for 1 min, and 98 °C for 10 min. A ramp rate of 2 °C/s was used in all steps. 2.6. Screening of the melting temperature The temperature gradient was set up using the built-in function of the C100 Touch thermal cycler (Bio-Rad, Munich, Germany). Each row was run at a different temperature. The gradient protocol was as follows: 95 °C for 10 min; then 40 cycles of 94 °C for 30 s, 55–60 °C for 1 min, and 98 °C for 10 min. A ramp rate of 2 °C/s was used in all steps. The individual row temperatures were 60, 59.7, 59.2, 58.2, 57, 56, 55.3, and 55 °C (from rows A to H). The temperature gradient was set to 55, 56, 57, 58.2, 59.2, and 60 °C (rows H, F, E, D, C, and A). All tests were repeated three times.
2.2. DNA extraction Total genomic DNA was extracted using the Qiagen DNeasy Blood & Tissue Kit (Qiagen, Valencia, CA, USA) and eluted with ultrapure water. The DNA concentrations (ng/μl) were assessed at 260 nm using a 2
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2.7. Screening of the number of PCR cycles The optimal number of PCR cycles was screened by conducting three ddPCR assays with the same reaction components and reagent concentrations but different numbers of cycles (i.e. 40, 50, and 60 cycles). The reaction conditions in the C100 Touch thermal cycler (BioRad, Munich, Germany) were as follows: 95 °C for 10 min; then 40, 50, or 60 cycles at 94 °C for 30, 55–60 °C for 1 min, and 98 °C for 10 min. A ramp rate of 2 °C/s was used in all steps. All tests were repeated three times. 2.8. Optimisation of the ddPCR system To avoid any potential interference caused by interactions between different factors of the ddPCR assay, nine combinations were studied in the ddPCR optimisation experiment by using an L9 (34) orthogonal experimental design, with three factors and three levels (Suppl. Table S2). The fixed primer-to-probe ratios used were 300 nM:200 nM, 300 nM:300 nM, and 400 nM:200 nM. The temperature was set at 56, 57, or 58.2 °C, and the number of PCR cycles was set to 40, 50, or 60 cycles. Each combination was repeated three times.
Fig. 1. Screening of droplet digital PCR primers and probes for the silver pomfret.
as a positive control, whereas that of Siniperca chuatsi was used as a negative control. The CY-1 primers and probe yielded positive amplification of the silver pomfret DNA and were negative for Siniperca chuatsi DNA (Fig. 1). Because the CY-2 and CY-3 primers and probes failed to amplify the DNA from either species, CY-1 was selected for further optimisation of the ddPCR assay.
2.9. Specificity of the silver pomfret ddPCR system To confirm the specificity of the ddPCR assay for detecting the silver pomfret, DNA samples from Trachinotus ovatus, Psenopsis anomala, Pampus punctatissimus, Ephippus orbis, Pampus chinensis, and Pampus cinereus were tested using the optimised assay. Additionally, silver pomfret DNA was used as a positive control, which was verified by sequencing. All tests were repeated twice.
3.2. Optimisation of the ddPCR assay system To optimise the ddPCR system for detecting the silver pomfret, three parameters of the assay were evaluated. One of these was the primer-toprobe concentration ratio, which is known to affect both the ddPCR fluorescence amplitude and sensitivity (David et al., 2016; Rodríguez, Rodríguez, Córdoba, & Andrade, 2015). Of the five concentration ratios tested while keeping the other parameters constant (melting temperature set to 55 °C and number of cycles set to 40), the ratios 300 nM:200 nM, 300 nM:300 nM, 400 nM:200 nM, and 400 nM:400 nM enabled better separation of the positive and negative droplets (Fig. 2). Because the effects of 300 nM:300 nM and 400 nM:400 nM were similar, the 300 nM:200 nM, 300 nM:300 nM, and 400 nM:200 nM concentration ratios were chosen for subsequent experiments. The second parameter assessed was the melting temperature, which according to the Droplet Digital™ PCR Applications Guide is one of the most critical parameters affecting ddPCR specificity. Our results showed that the fluorescence amplitude was lower when the temperature was increased, making it more difficult to separate the positive and negative droplets (Fig. 3). Although lower temperatures generated
2.10. Sensitivity of the silver pomfret ddPCR system To determine the sensitivity of the silver pomfret ddPCR assay, a dilution series of silver pomfret DNA was prepared. Four replicates of the dilution series were measured by ddPCR for the preliminary experiment (two separate runs, two replicates on the first day and two replicates on the second day). The absolute limit of quantification (aLOQ, expressed in copy numbers) and absolute limit of detection (aLOD) of the ddPCR were determined on the basis of the experimental results of a third set of experiments performed on 15 replicates (David et al., 2016). The limit of quantification (LOQ) was determined as the aLOQ in the sample, where the coefficient of variation (CV) of all replicates was below 25%. The limit of detection (LOD) was determined as the aLOD in the sample, where at least 14 replicates generated a positive signal (David et al., 2016). 2.11. Real-time fluorescence PCR The qPCR assay was performed twice for each sample, using the Probe qPCR Mix (RR391A, TaKaRa, Tokyo, Japan). Each qPCR amplification procedure was performed with QuantStudio 6 Flex (Applied Biosystem, Foster City, CA, USA) in a total volume of 20 μl, following the manufacturer’s instructions. The qPCR system for detecting silver pomfret comprised the primers CY-1-FP and CY-1-BP, and the probe CY1-Probe. The thermal cycling conditions were as follows: 95 °C for 30 s; then 30 cycles of 95 °C for 5 s and 60 °C for 34 s. 3. Results and discussion 3.1. Screening of the ddPCR primers and probes To screen for an optimal target gene for use in the ddPCR assays, three sets of primers and probes were designed for the targeting of three silver pomfret genes; namely, cytochrome oxidase subunit I (COI), cytochrome B (cytb), and 16S rRNA. The DNA of silver pomfret was used
Fig. 2. Influence of different primer-to-probe concentration ratios on the droplet digital PCR. 3
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2, 4, 5, 6, and 8 created more rain droplets, and the fluorescence differences between the positive and negative droplets were not sufficient for clear peak separation (Fig. 5). Combinations 3 and 9 yielded suboptimal results in terms of fluorescence peak separation between positive and negative droplets, whereas combination 7 performed better with respect to fluorescence amplitude and droplet separation (Fig. 5). Taking into consideration the previous single-factor experiments and the orthogonal experimental setup, combination 7 was considered to represent the optimal conditions for the silver pomfret ddPCR system. The combination 7 reaction mixture contained 10 μl of 2 × ddPCR Supermix for Probes, 400 nmol/l of each primer, 200 nmol/l of hydrolysis probes, 1 μl of template DNA, and enough sterile distilled water to make up a final volume of 20 μl. For combination 7, the ddPCR program was as follow: 95 °C for 10 min; then 50 cycles of 94 °C for 30 s, 56 °C for 1 min, and 98 °C for 10 min. In previous studies (Grelewska-Nowotko, Żurawska-Zajfert, Żmijewska, & Sowa, 2018; Köppel & Bucher, 2015; Lievens, Jacchia, Kagkli, Savini, & Querci, 2016; Witte et al., 2016), optimisation of the ddPCR assay was accomplished by changing a single reaction factor, with all other components held constant. This method is one-sided and ignores possible interactions between factors (Cao et al., 2017). To avoid this bias, we used an orthogonal experimental design, along with the singlefactor experiments, to determine the optimal conditions for the silver pomfret ddPCR. This is the first report on such use of orthogonal designs to screen the conditions of a ddPCR assay system.
Fig. 3. Influence of different melting temperatures on the droplet digital PCR.
higher fluorescence amplitudes and clearer peak separation, a greater rain effect (intermediate fluorescence of some droplets) was created, which influenced the accuracy of the results. These results were consistent with the conclusions of previous studies, which pointed out that a lower melting temperature was beneficial for droplet separation (Gerdes, Iwobi, Busch, & Pecoraro, 2016; Witte et al., 2016). Taking into account the synthetic fluorescence amplitude and the peak separation of the two droplets (i.e. less intermediate droplets), 56, 57, and 58.2 °C were chosen for further use in the optimisation studies. The third ddPCR parameter assessed was the PCR cycle number. As the number of cycles was increased, the peak separation improved and the fluorescence amplitude of the positive droplets increased (Fig. 4), which was consistent with the results of a previous report (Köppel & Bucher, 2015). The 40, 50, and 60 PCR cycles all produced acceptable fluorescence amplitudes and distinct cluster separations. Therefore, all three cycles were analysed in the following experiments. On the basis of the results of the single-factor experiments, further ddPCR analysis was performed with nine combinations of primer-toprobe concentration ratios, melting temperatures, and cycle numbers, using an orthogonal experimental design (Suppl. Table S2). Han, Chen, Mao, Tang, and Guan (2010) also reported the use of the orthogonal test of three factors to screen the optimal conditions for sucrose:sucrose 1-fructosyltransferase (FST-1) activity. Of these nine combinations, 1,
3.3. Specificity, aLOQ, and aLOD of the ddPCR system To investigate the specificity of the optimised silver pomfret ddPCR system, six additional fish species with similar morphological features, or with highly homologous sequences, were tested using the same ddPCR conditions. The optimised assay system specifically amplified the DNA of silver pomfret, but not that of the other fish species (Suppl. Fig. S1). According to a previous study (David et al., 2016), the aLOQ of the ddPCR is defined as the lowest copy number with a CV of ≤ 25% during quantification. In this present study, the preliminary aLOQ was determined to be at least 22 copies, whereas the preliminary aLOD was 1.4 copies (Suppl. Table S3). In an additional preliminary experiment, the aLOQ was 21.9 copies and the aLOD was 1.7 copies (Suppl. Table
Fig. 4. Influence of different cycle numbers on the droplet digital PCR. 4
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Fig. 5. Reaction results of the silver pomfret droplet digital PCR system based on nine combinations of reaction factors according to an orthogonal design.
(samples 1–9, with six replicates) were simultaneously tested by qPCR. The detection limit of the qPCR system was approximately 313 copies (Fig. 6), indicating that the sensitivity of the optimised ddPCR was 156 times higher than that of the qPCR assay. Wang et al. (2017) also reported that the ddPCR system for detecting Salmonella typhimurium was 10 times more sensitive than qPCR.
S3). The results of experiments performed on 15 individual replicates showed that the aLOQ was at least 21 copies, and the aLOD was 2 copies (Suppl. Table S4). These results were similar to published results on the ddPCR quantification of meat species, where some of the LOD values ranged from 1 to 5 copies/μl (Wang et al., 2017). To compare the sensitivity between the ddPCR assay and the conventional qPCR assay, serial dilutions of the silver pomfret DNA
Fig. 6. Sensitivity of the silver pomfret real-time PCR assay. The concentration of the silver pomfret DNA samples were as follows: sample 1, 3.6 ng/μl; sample 2, 1.2 ng/μl; sample 3, 400 pg/μl; sample 4, 133 pg/μl; sample 5, 44 pg/μl; sample 6, 22 pg/μl; sample 7, 11 pg/μl; sample 8, 5 pg/μl; and sample 9, 1.67 pg/μl. 5
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contained 10 μl of 2 × ddPCR Supermix for Probes, 400 nmol/l of each primer, 200 nmol/l of hydrolysis probes, 1 μl of template DNA, and enough sterile distilled water to make up a final individual sample volume of 20 μl. The reaction program consisted of 95 °C for 10 min, followed by 50 cycles of 94 °C for 30 s, 56 °C for 1 min, and 98 °C for 10 min. The specificity of this ddPCR system was confirmed by negative testing for other fish species. The aLOD and aLOQ were 2 copies/μl and 21 copies/μl, respectively. The method was further validated using fish meat mixtures, DNA mixtures, and commercial samples, showing a sensitivity of 0.1% and 0.5% for meat mixtures and DNA mixtures, respectively. The commercial sample analysis indicated that the system was effective and specific and that 30% of the samples tested were not in agreement with their label statements. The ddPCR and qPCR methods were compared by using them to detect the same DNA samples, with the results showing that the ddPCR was 156 times more sensitive in detecting the silver pomfret, although the two methods had similar specificities. However, the ddPCR technique was more complicated to carry out and required 2.5 times the experimental duration of the qPCR method. Considering that the silver pomfret is a highly economical and valuable fish, its authenticity is a concern for consumers, especially as there is currently no accurate and efficient method to identify fish species. Hence, the established methodology in this study can be a valuable tool for law enforcement agencies and fishery-related institutions, enabling them to identify and quantify the silver pomfret and to address the adulteration issues.
3.4. Comparison of the ddPCR and qPCR assays To assess the accuracy and applicability of the ddPCR system, different muscle mixtures (0.1%–50%) and DNA mixtures (0.01%–10%) of the silver pomfret and the golden pompano were detected with both the ddPCR and the qPCR systems and the results were compared. The sensitivity of the optimised ddPCR assay in detecting silver pomfret in the meat mixtures was 0.1% (0.1 g/100 g) (Suppl. Table S5). This was one order of magnitude lower than that of other reported works with the ddPCR, which achieved 1% (w/w) for detecting sheep meat in sheep and chicken mixtures (Ren et al., 2017) and 1% (w/w) for detecting goat meat in goat and sheep mixtures (Wang et al., 2018). According to the results obtained from the DNA mixture analysis (Suppl. Table S6), the sensitivity of the ddPCR system was 0.5%, which was consistent with that of a previous report (Santurtún, Riancho, Arozamena, López-Duarte, & Zarrabeitia1, M.T , 2017). The ddPCR and qPCR results for the meat and DNA mixtures indicated that the two methods were highly consistent with each other. However, the ddPCR results could be quantified without the need for a silver pomfret standard curve, and the CV was less than 25%, indicating that the assay method exhibited good accuracy and reproducibility. The ddPCR assay was further applied to commercial samples to verify their compliance with label statements. Thirty fish samples were collected from several seafood markets and websites and then analysed with the ddPCR and qPCR systems simultaneously. The results showed that three of these samples were silver pomfret, whereas 27 samples were not (Suppl. Table S7). According to subsequent sequencing, the three positive samples were verified to be silver pomfret, and the negative samples were confirmed as other fish species (Suppl. Table S7). It is noteworthy that 30% of the analysed samples did not correspond to their label statements. According to the tests of the sensitivity, specificity, operation, and experimental time, the ddPCR system was much more sensitive than the qPCR assay (by 156 times), albeit both systems performed similarly in terms of specificity (Suppl. Table S8). Owing to a lack of certified reference material, it was difficult to quantify the silver pomfret by qPCR; therefore, the results of the qPCR assay were in fact qualitative. The ddPCR system was more complicated to perform since it required four steps (viz. preparation of the PCR mixture, generation of the droplets, amplification, and reading of the droplets), whereas the qPCR needed only two steps (viz. preparation of the PCR mixture and amplification). The time taken to complete the ddPCR amplification was nearly 2.3 h owing to the ramp rate of 2 °C/s needed to prevent droplet breakage. After the amplification step, the droplet reading was timeconsuming, requiring almost 1.5 h to complete for a 96-well plate. Therefore, at least 5 h was needed to complete the ddPCR experimentation for one 96-well plate. By contrast, after preparation of the qPCR mixture, the amplification was carried out directly and the whole experiment was completed in only 2 h. Yang, Paparini, Monis, and Ryan (2014) also reported that the ddPCR took two times longer to complete than the qPCR did. Therefore, the ddPCR system is recommended mainly if excellent result accuracy is required, especially for law enforcement agencies. If high detection efficiency is required, then the qPCR system is the better option, especially for the food industry.
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The presented work was funded by the National Key Research and Development Program of China (2016YFD0401203), the National Natural Science Foundation of China (31571934), and the Science and Technology Planning Project of Guangdong Province, China (2017B020207004). Data availability statement The datasets generated during the current study are available from the corresponding author upon reasonable request. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foodchem.2019.125331. References Al-Kahtani, H. A., Ismail, E. A., & Asif, A. M. (2017). Pork detection in binary meat mixtures and some commercial food products using conventional and real-time PCR techniques. Food Chemistry, 219, 54–60. Amaral, J. S., Santos, G., Oliveira, M. B. P. P., & Mafra, I. (2016). Quantitative detection of pork meat by EvaGreen real-time PCR to assess the authenticity of processed meat products. Food Control, 72, 53–61. Cao, Y., Wang, L., Duan, L., Li, J., Ma, J., Xie, S., ... Li, H. (2017). Development of a realtime fluorescence loop-mediated isothermal amplification assay for rapid and quantitative detection of Ustilago maydis. Scientific Reports, 7, 13394. Chen, S., Zhang, Y., Li, H., Wang, J., Chen, W., Zhou, Y., & Zhou, S. (2014). Differentiation of fish species in Taiwan Strait by PCR-RFLP and lab-on-a-chip system. Food Control, 44, 26–34. Cheng, J. H., Chou, H. T., Lee, M. S., & Sheu, S. C. (2016). Development of qualitative and quantitative PCR analysis for meat adulteration from RNA samples. Food Chemistry, 192, 336–342. Chisholm, J., Conyers, C., Booth, C., Lawley, W., & Hird, H. (2005). The detection of horse and donkey using real-time PCR. Meat Science, 70, 727–732. David, D., Dejan, Š., Andrej, B., Dany, M., & Jana, Ž. (2016). Multiplex quantification of
4. Conclusion This work aimed at developing and validating a sensitive and accurate methodology for the detection and quantification of the silver pomfret, for verifying label statements and regulating markets and orders. For these purposes, an optimised species-specific ddPCR assay was successfully developed. The CY-1 primers and probe were selected for optimisation of the ddPCR assay. The optimal primer-to-probe concentration ratio, melting temperature, and PCR cycle number were screened by combining single-factor experiments with an orthogonal design arrangement. The optimised silver pomfret ddPCR system 6
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