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Comparing the performance of conventional PCR, RTQ-PCR, and droplet digital PCR assays in detection of Shigella
Molecular and Cellular Probes 51 (2020) 101531
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Comparing the performance of conventional PCR, RTQ-PCR, and droplet digital PCR assays in detection of Shigella
T
Jin Yanga, Nana Zhanga, Jun Lvb, Ping Zhub, Xing Panb, Jiaqingzi Hub, Wenfeng Wua, Shan Lib,∗∗, Hongtao Lia,∗ a
Key Laboratory of Freshwater Fish Reproduction and Development, Ministry of Education, State Key Laboratory Breeding Base of Eco-Environment and Bio-Resource, Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, School of Life Sciences, Southwest University, Beibei, 400715, Chongqing, China b Institute of Infection and Immunity, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, 442000, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Foodborne infections Droplet digital PCR Quantitative real-time PCR Conventional PCR Shigella spp. detection
The incidence of foodborne infections caused by Shigella spp. is still very high in every year, which poses a great potential threat to public health. Conventional quantification methods based on culture techniques, biochemical, and serological identification are time-consuming and labor-intensive. To develop a more rapid and efficient detection method of Shigella spp., we compared the sensitivity and specificity of three different polymerase chain reaction (PCR) methods, including conventional PCR, quantitative real-time PCR (RTQ-PCR), and droplet digital PCR (ddPCR). Our results indicated that ddPCR method exhibited higher sensitivity, and the limit of detection was 10−5 ng/μl for genomic DNA templates, 10−1 cfu/ml for Shigella bacteria culture. In addition, we found that ddPCR was a time-saving method, which required a shorter pre-culturing time. Collectively, ddPCR assay was a reliable method for rapid and effective detection of Shigella spp.
1. Introduction The bacteria of Shigella spp. are heat-resistant gram-negative and human host-specific pathogens that infect the intestinal epithelial cells [1]. Four different subspecies have been identified based on the antigen structure, namely S. flexneri, S. dysenteriae, S. boydii, and S. sonnei [2]. Shigella spp. are responsible for bacillary dysentery (BD) and symptomatically characterized by abdominal cramps, diarrhea, and fever. BD is a highly contagious intestinal disease in which the intestinal lining is damaged, accompanied with watery diarrhea, dehydration, and high morbidity [3,4]. In addition, previous studies have showed that virulence and pathogenicity patterns are highly heterogeneous among Shigella subspecies [5]. Shigella spp. are leading pathogens to cause shigellosis in humans, and the current global strategies are to develop effective methods for early detection to quickly prevent the spread of the disease [6]. Conventional quantification methods based on culture techniques, biochemical, and serological identification are time-consuming, labor-intensive, and have relatively low specificity and sensitivity [7]. Additionally, Shigella can only be detected in highly contagious environments and improper preservation of the specimen can result in
false positive or negative results [8]. To overcome these drawbacks, several molecular-based quantification and identification methods have been proposed [9,10]. RTQ-PCR, a highly flexible molecular-based technique with high sensitivity, especially in the detection of foodborne pathogens and it is currently the most widely accepted detection method in surveys [11]. However, the application of RTQ-PCR is limited by the reference standard curve, which is time-consuming due to the many steps involved during detection [12]. Therefore, it's necessary to establish a highly sensitive detection method with lower limit of detection (LOD) and shorter enrichment time for the detection of Shigella spp. In this study, we applied and optimized a ddPCR assay for detection and quantification of Shigella spp., and we also compared the sensitivity and specificity of three different PCR methods using Shigella genomic DNA or cultured bacterial strains. The results showed that ddPCR method exhibited higher sensitivity, and it took less enriched culture time compared to the other two methods in detecting Shigella from artificial mouse feces. Collectively, ddPCR assay was a promising approach for rapid and effective detection of Shigella spp.
∗
Corresponding author. Corresponding author. E-mail addresses: [email protected] (J. Yang), [email protected] (N. Zhang), lvjunfi[email protected] (J. Lv), [email protected] (P. Zhu), [email protected] (X. Pan), [email protected] (J. Hu), [email protected] (W. Wu), [email protected] (S. Li), [email protected] (H. Li). ∗∗
https://doi.org/10.1016/j.mcp.2020.101531 Received 31 October 2019; Received in revised form 19 January 2020; Accepted 6 February 2020 Available online 13 February 2020 0890-8508/ © 2020 Elsevier Ltd. All rights reserved.
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2. Materials and methods
2.6. Cellular infection
2.1. Bacterial strains
Murine RAW264.7 macrophage cell line obtained from American Type Culture Collection (ATCC, TiB-71, USA) [15,16] were cultured in low glucose DMEM (Gibco) supplemented with 10% FBS (BI). Cells were seeded in 24 well plates at 1 × 105 cells/well. S. flexneri strains were incubated overnight in LB medium at 37 °C while shaking at 220 rpm. The bacteria were then sub-cultured (1:33) in fresh LB medium for 3 h and added to the cells at 10−1 cfu/ml, with a centrifugation at 800g for 5 min to promote infection. Two hours later, cells were washed thrice with PBS and the extra bacteria were killed with 100 μg/ml gentamicin for a further 1 h. However, the dose of gentamicin was reduced to 20 μg/ml when the infection time was extended.
All Shigella strains (S. flexneri and S. sonnei) and control strains including Escherichia coli O126: H27, Salmonella typhimurium SL1344, Klebsiella pneumonia, and Pseudomonas aeruginosa PAO1 were obtained from Shiyan Centers for Disease Control and Prevention. The bacteria strains were stored at −80 °C in LB media (Sigma, USA) containing 20% glycerol (v/v) (Sigma, USA). Pure strains were cultivated for 24 h at 37 °C and inoculated in LB broth. The plate counting method was used to determine the bacterial counts. 2.2. Extraction of genomic DNA
2.7. Western blot and immunofluorescence
Genomic DNA was extracted from 1 ml 1 OD (2.0–5.0E+08 cells) of all the cultured bacterial strains using the bacterial genome DNA extraction kit (DP302, TIANGEN) following the manufacturer's instructions. The concentration and purity of the extracted DNA were determined by Nano Drop 2000c (Thermo Fisher Scientific, USA) (Table S1). A series of 10-fold diluted DNA solutions (ranging from 103 ng/μl to 10−9 ng/μl) were prepared for PCR analysis. The extracts and PCR products were visualized on 1% agarose gel to check for homogeneity. Follow-up experiments were immediately conducted to guarantee the stability of the results.
Infected cells were washed with ice-cold PBS and treated with SDS sample buffer. Then boiled at 98 °C for 15 min. Standard immunoblotting analysis was performed with appropriate antibodies: mouse anti-β tubulin (MA1-850, Sigma), mouse anti-IκBα and p-IκBα (9242s and 2859s, CST), mouse anti-TBK1 and p-TBK1 (3504s and 5483s, CST), mouse anti–NF–κB (p50 and p105, ab494729, Abcam) and p–NF–κB (p65, 4947s, CST). For fluorescence staining, infected cells were washed with PBS and fixed with 4% paraformaldehyde at room temperature for 10 min. Cells were then permeabilized for 10 min in 0.5% Triton X-100, and blocked with 1% FBS for 30 min, followed by the incubation with appropriate primary antibody (Tianjin biochip corporation, IM-EH002) and secondary antibody (with specific Alexa Fluor 488 conjugated anti-mouse antibodies). The nucleus was stained with DAPI (10 μg/ml) for 2 min. Leica SP8 laser scanning confocal microscopy was used to capture the images. All image data shown are representative of at least three randomly selected fields.
2.3. Primers and probe Studies have showed that ddPCR is compatible with TaqMan hydrolysis probes [13]. The primer pair (forward 5′-CGCAATACCTCCGG ATTCC-3′ and reverse 5′-TCCGCAGAGGCACTGAGTT-3′) and probe (5’ -6-FAM- AACAGGTCGCTGCATGGCTGGAA-BHQ1-3′) used in this study targeted the virulence gene ipaH9.8 (from China Food safety standard, cFDA SN/T1870 -2016) of Shigella enterobacter. The primers and probe were synthesized by Sangon Biotech (Shanghai, China).
2.8. Spiking sample 2.4. Real-time quantitative PCR
Artificially contaminated feces were prepared by adding 1 ml of PBS into 1g mouse feces, and a series of 10-fold dilution prepared. Then the number of bacteria was determined by plate counting. To estimate the pre-culture time (Table .1), a concentration range from 103 cfu/ml to 10−1 cfu/ml was prepared. Pre-enrichment processes were performed on all three sample platforms at 0 h, 4 h, 6 h, and 8 h. Time consumption was determined by analyzing 1 μl culture samples for each time period. All data displayed represent at least three randomly selected fields.
RTQ-PCR assay was performed as previously described [14]. The RTQ-PCR efficiency of the amplification was estimated from the slope of the standard curve using the equation: E = −1 +0.11/slope to control the quality and validity of the RTQ-PCR results. The effective amplification was set in the range of 90%–110%, and R2 > 0.99. Bio-Rad CFX Manager v 3.0 was used to build a linear relationship plot of Ct (threshold cycle) and log input for DNA copies. 2.5. Droplet digital PCR
3. Results
QX200™ Droplet Generator, QX200™ Droplet Reader, C1000 Touch ™ Thermal Cycler and PX1™ PCR Plate sealer (Bio-Rad, USA) were used to perform ddPCR. All the experimental components of ddPCR were shown in Table S2. QX200™ Droplet Digital™ System was used to generate droplets. The reaction mixture contained 20 μl of the PCR reaction mix and 70 μl droplet generation oil for probes (186–3005, Bio-Rad). The C1000 Touch thermal cycler was used to perform the ddPCR reactions. Meanwhile, several thermal gradients were used to optimize the assay with the annealing temperature ranging from 52 °C to 68 °C. All the tests were amplified in triplicates and the recommended ramp rate was 2 °C/sec. QX200™ Droplet Reader was used for fluorescence quantitation. The values above the back ground threshold or the negative control were considered to be positive. Discrimination range was set between 2000 and 2200 for accuracy. The initial concentration was calculated in reference to the pattern of Poisson distribution.
3.1. Evaluation of the specificity of three PCR assays Conventional PCR, RTQ-PCR, and ddPCR were performed using S. flexneri, S. sonnei strips, and other 4 control bacterial strains as templates to determine the specificity and amplification efficiency of Table 1 Time consumption of three platforms in S. flexneri contaminated mice feces. Conc.(cfu.ml)
3
1 × 10 1 × 102 1 × 101 1 × 100 1 × 10−1
2
Culture time (h) conventional PCR
RTQ-PCR
ddPCR
0 2 4 6 –
0 0 2 4 6
0 0 0 2 4
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Fig. 1. Assessment of primer specificity. (A). The specificity of primers determined with conventional PCR. (B). Amplification plot of RTQ-PCR. (C). Absolute copy number of amplifiers.
of S. flexneri genomic DNA were used to determine the sensitivity and LOD of the conventional PCR, RTQ-PCR, and ddPCR. In conventional PCR, 10−3 ng/μl genomic DNA was detected (Supplementary Fig. 1A). RTQ-PCR sensitivity was determined by the linearity of CT values of the samples and quantitative detection ranged from 102 ng/μl to 10−4 ng/ μl (Supplementary Fig. 1B). ddPCR was the most sensitive method because it could detect genomic DNA diluted to 10−5 ng/μl (Supplementary Fig. 1C), hence it performed the lowest LOD. PCR assay sensitivity decreases due to the loss of large amounts of samples during genomic DNA extraction. To avoid this problem, 10-fold serial dilution series of diluted S. flexneri suspensions (from 108 cfu/ml to 10−5 cfu/ml) were analyzed to determine the LOD using the three PCR methods. Results indicated that conventional PCR could detect 102 cfu/ml bacteria (Fig. 3A). RTQ-PCR showed good linearity within the quantification range of 105 cfu/ml to 100 cfu/ml with a 0.9917 coefficient of determination (R2) (Fig. 3B). Similar to the detection of genomic DNA, ddPCR exhibited the lowest LOD (10−1 cfu/ml) when compared to conventional PCR and RTQ-PCR (Fig. 3C). These results showed that the ddPCR detection sensitivity was 10-times higher than that of RTQ-PCR detection and 1000-fold higher than that of conventional PCR.
primers targeting ipaH9.8 gene [17] in Shigella spp. The target DNA from Shigella group (S. flexneri and S. sonnei) has been successfully amplified (Fig. 1), while no amplification was detected for the other 4 control bacterial strains tested, indicating that primers of ipaH9.8 gene were specific for Shigella detection. In addition, all PCR products amplified by conventional PCR were sequenced and analyzed for further validation of the specificity of the assay. 3.2. Thermal optimization of the ddPCR assay A total droplets count exceeding 10, 000 is considered reliable in ddPCR method [18]. In this study, a 7-temperature gradient ranging from 52 °C to 68 °C was used to generate more droplets with higher amplification efficiency and to determine the most suitable annealing temperature. The optimal range of annealing temperatures giving the largest difference in fluorescence between negative and positive droplets was between 50 °C and 62 °C (Fig. 2A). As expected, reactions at 58 °C gave a high absolute copy number (Fig. 2B) and a high positivedroplet proportion (Fig. 2C). Therefore, 58 °C was selected as the most suitable hybridization temperature for the detection of genomic DNA or in a bacterial solution. 3.3. Comparison of analytical conventional PCR, RTQ-PCR, and ddPCR assays in detecting genomic DNA and Shigella bacterial suspension
3.4. Pathogenicity of the minimum doses of Shigella detected by ddPCR The ddPCR method exhibited high detection precision with LOD of bacteria. To determine whether the minimum doses of Shigella detected by ddPCR could affect the inflammatory response and host immune state. RAW264.7 cells were infected with 0.1 cfu/ml of Shigella. At 24 h post infection, cellular Shigella was easily detected using
S. flexneri causes 90% of the cases of shigellosis and the main symptoms are dysenteric diarrhea and fever [19]. Therefore, a bacterial solution of S. flexneri and genomic DNA were selected to design a ddPCR assay for Shigella. Serial dilutions (from 103 ng/μl to 10−9 ng/μl)
Fig. 2. Optimization of digital PCR reaction conditions. (A). Amplifiers scatter diagram of ddPCR. (B). Absolute copy number of amplifiers at different annealing gradients. (C). Positive droplets and total droplets of ddPCR. 3
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Fig. 3. Detection limits of the three PCR detection methods for diluted bacteria samples. (A). The lowest detection limit of bacterial strains for conventional PCR. Products were visualized by 1% agarose gel electrophoresis. (B). CT values of different concentrations of bacterial strains detected by RTQ-PCR (C). Absolute copy number of ipaH9.8 obtained from the gradient-diluted strains detected by ddPCR.
Fig. 4. The inflammatory effect of the lowest dose on cells. (A). Fluorescence images of S. flexneri in cells after infection. S. flexneri were stained with a specific antibody (green) or DAPI to mark the nuclei (blue). (B). The phosphorylation level of IκBα, TBK1, and NF-κB following LPS stimulation or 0.1 cfu S. flexneri infection. (C). Release of IL-6, IL-1β, and TNFα in RAW264.7 cells stimulated with LPS or 10−1 cfu S. flexneri.
3.5. Comparison of PCR, RTQ-PCR, and ddPCR assays in detection of Shigella in artificially contaminated feces
immunofluorescence (Fig. 4A). As expect, infection of Shigella activated host NF-κB pathway [20,21], which lead to elevated phosphorylation of IκBα, TBK1, and p65 (Fig. 4B), and subsequently promoted the transcription of cytokines, including IL-6, IL-1β, and TNF-α (Fig. 4C). Lipopolysaccharide (LPS) treatment was set as a positive control. Importantly, this dose of Shigella (10−1 cfu/ml) was difficult to detect by conventional PCR, RTQ-PCR (Fig. 3), and remains undetectable in current clinical practice [22,23]. Therefore, ddPCR is a promising approach for the clinical diagnosis of Shigella infection.
To compare the sensitivity of these three PCR methods in analysis of the practical samples, artificially contaminated feces with S. flexneri were pre-pared and analyzed to estimate the efficiency of the PCR methods. Trace spiking samples were selected to calculate the preculturing time of three platforms. Compared with conventional PCR and RTQ-PCR, ddPCR detected S. flexneri in feces even without preculturing (Table 1). Therefore, ddPCR had an advantage in saving test time and promoted detecting efficiency of S. flexneri in contaminate feces.
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4. Discussion
revision of the manuscript.
Shigella spp. posed a threat to human health due to their low limit of detection and high pathogenicity. Our study established a ddPCR method for detecting Shigella using specific primers and probes. Comparative analysis of three PCR methods revealed that conventional PCR was the most convenient method for detecting Shigella spp. from genomic DNA sample or cultured bacteria strains for as low as 10−3 ng or 102 cfu, respectively. However, the sensitivity of conventional PCR was minimal. RTQ-PCR could detect 10−4 ng of genomic DNA or 100 cfu bacteria. Notably, ddPCR showed higher sensitivity and accuracy compared to conventional PCR and RTQ-PCR in the detection of 10−5 ng genomic DNA or 10−1 cfu bacteria. ddPCR has been widely used in the quantitative determination of bacteria such as salmonella and other pathogens [24,25]. Therefore, our study presents a novel method for Shigella detection. Shigella spp. are among the most common intestinal pathogens that cause acute diarrhea. Due to its highly pathogenic even at a small number of bacteria [26], this makes it difficult to detect [27]. Therefore, rapid and accurate early diagnostic assays with a broad detection spectrum are urgently required, which will improve the prevention and control of BD outbreaks and minimize the spread of Shigella infection. In this study, ddPCR has a short detection time, high specificity, good reproducibility, and accuracy, and these properties are superior to those of the commonly used conventional biochemical culture and RTQ-PCR [28]. Thus, ddPCR is an effective and highly sensitive method for the detection of Shigella. Its high sensitivity circumvents the need to culture the samples, which shortens the time for early detection. Importantly, we demonstrated that the lowest dose detected (10−1 cfu bacteria) can still cause an inflammatory response in immune cells. Moreover, ddPCR decreased the pre-culturing time (saving 2 h) required in artificially contaminated mouse feces. In conclusion, we present a fast and accurate detection tool for the clinical detection of Shigella spp. ddPCR is advantageous because it does not require a standard curve and direct quantification of the DNA copies in trace templates reference to the Poisson distribution pattern [29] with higher sensitivity and requires a shorter period of time [30]. Although ddPCR presents many advantages, it has several shortcomings. For example, this method only contains FAM, HEX, and VIC fluorescent channels, and the samples could only be detected at specific dilutions [31]. Besides, it may be less accurate when the concentration of the target gene exceeds 102 ng/μl or target bacteria exceeds 105 cfu/ml. More importantly, this method often yields false-positive results due to its high sensitivity, and samples tested in triplicates increases the costs [32]. Despite these shortcomings, in this study, we showed that ddPCR was highly sensitive and specific, hence suitable for Shigella detection.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.mcp.2020.101531. References [1] J.A. Carter, J.C. Jimenez, M. Zaldivar, S.A. Alvarez, C.L. Marolda, A. Valvano, I. Contreras, The cellular level of O-antigen polymerase Wzy determines chain length regulation by WzzB and WzzpHS-2 in Shigella flexneri 2a, Microbiology 155 (2009) 3260–3269, https://doi.org/10.1099/mic.0.028944-0. [2] M. Jharna, V. GaneSh, E. Jennifer, S. MahadeVan, C. SubhaSh, The recent trends of shigellosis: a JIPMER perspective, J. Clin. Diagn. Res. 6 (2012) 1474–1477, https:// doi.org/10.7860/JCDR/2012/4157.2536. [3] M. Azage, A. Kumie, A. Worku, A. Bagtzoglou, E. Anagnostou, Effect of climatic variability on childhood diarrhea and its high risk periods in northwestern parts of Ethiopia, PloS One 12 (2017) e0186933, , https://doi.org/10.1371/journal.pone. 0186933. [4] J.I. Lee, S.J. Kim, C.G. Park, Shigella flexneri infection in a newly acquired rhesus macaque (Macaca mulatta), Lab. Anim. Res. 27 (2011) 343–346, https://doi.org/ 10.5625/lar.2011.27.4.343. [5] F. Orrett, Prevalence of Shigella serogroups and their antimicrobial resistance patterns in southern trinidad, J. Health Popul. Nutr. 26 (2008) 456–462, https://doi. org/10.3329/jhpn.v26i4.1889. [6] M.R. Vinas, E. Tuduri, A. Galar, K. Yih, M. Pichel, J. Stelling, S.P. Brengi, A.D. Della, C.V. Ploeg, S. Bruno, A. Roge, M.I. Caffer, M. Kulldorff, M. Galas, M.A. Group, Laboratory-based prospective surveillance for community outbreaks of Shigella spp. in Argentina, PLoS, Negl. Trop. Dis. 7 (2013) e2521, , https://doi.org/10.1371/ journal.pntd.0002521. [7] R. Lindqvist, Detection of Shigella spp. in food with a nested PCR method – sensitivity and performance compared with a conventional culture method, J. Appl. Microbiol. 86 (1999) 971–978, https://doi.org/10.1046/j.1365-2672.1999. 00777.x. [8] A. Camacho, R.J. Souza, J.M. Irache, C. Gamazo, Towards a non-living vaccine against Shigella flexneri: from the inactivation procedure to protection studies, Methods 60 (3) (2013) 264–268, https://doi.org/10.1016/j.ymeth.2012.09.008. [9] P. Van, E.D. Witte, H. Henau, A. Muynck, L. Verstraeten, B. Herendael, S. Weekx, Evaluation of a real-time multiplex PCR for the simultaneous detection of Campylobacter jejuni, Salmonella spp., Shigella spp./EIEC, and Yersinia enterocolitica in fecal samples, Eur. J. Clin. Microbiol. Infect. Dis. 34 (2015) 535–542, https://doi. org/10.1007/s10096-014-2257-x. [10] C. Chai, H. Jang, S.K. Oh, Real-time PCR-based quantification of Shigella sonnei in beef and a modified Gompertz equation-based predictive modeling of its growth, Appl. Biol. Chem. 59 (2016) 67–70, https://doi.org/10.1007/s13765-015-0144-5. [11] M. Pavlovic, A. Luze, R. Konrad, A. Berger, A. Sing, U. Busch, I. Huber, Development of a duplex real-time PCR for differentiation between E. coli and Shigella spp, J. Appl. Microbiol. 110 (2011) 1245–1251, https://doi.org/10.1111/j. 1365-2672.2011.04973.x. [12] B.L. Lopez, M. Bekaert, M. Bakheit, S. Frischmann, P. Patel, E.L. Simon, L. Lambrechts, V. Duong, P. Dussart, G. Harold, C. Fall, O. Faye, A. Sall, M. Weidmann, Development and validation of four one-step real-time RT-LAMP assays for specific detection of each dengue virus serotype, PLoS Neglected Trop. Dis. 12 (2018) e0006381, , https://doi.org/10.1371/journal.pntd.0006381. [13] A. Morley, Digital PCR: a brief history, Biomol. Detect. Quantif. 1 (2014) 1–2, https://doi.org/10.1016/j.bdq.2014.06.001. [14] W. Pfaffl, A new mathematical model for relative quantification in real-time RT–PCR, Nucleic Acids Res. 29 (2001), https://doi.org/10.1093/nar/29.9.e45 e45e45. [15] J.N. Zenevich, S. Stuart, M.A. Nour, S.E. Girardin, J. Mogridge, Listeria monocytogenes and Shigella flexneri activate the NLRP1B inflammasome, Infect. Immun. 85 (11) (2017), https://doi.org/10.1128/IAI.00338-17 e00338-17. [16] I. Cho, J.W. Jeon, S.H. Paek, D.H. Kim, H.S. Shin, U.H. Ha, S.K. Seo, S.H. Paek, Tolllike receptor-based immuno-analysis of pathogenic microorganisms, Anal. Chem. 84 (22) (2012) 9713–9720, https://doi.org/10.1021/ac300668y. [17] D.T. Vu, O. Sethabutr, L.V. Seidlein, V.T. Tran, G.C. Do, T.C. Bui, H.T. Le, H. Lee, H.S. Houng, T.L. Hale, J.D. Clemens, C. Mason, D.D. Dang, Detection of Shigella by a PCR assay targeting the ipaH gene suggests increased prevalence of shigellosis in Nha Trang, Vietnam, J. Clin. Microbiol. 42 (2004) 2031–2035, https://doi.org/10. 1128/JCM.42.5.2031-2035.2004. [18] E. Dupas, B. Legendre, V. Olivier, F. Poliakoff, C. Manceau, A. Cunty, Comparison of real-time PCR and droplet digital PCR for the detection of Xylella fastidiosa in plants, J. Microbiol. Methods 162 (2019) 86–95, https://doi.org/10.1016/j.mimet.2019. 05.010. [19] D.T. George, C.A. Behm, D.H. Hall, U. Mathesius, M. Rug, K.C. Nguyen, N.K. Verma, Shigella flexneri infection in Caenorhabditis elegans: cytopathological examination and identification of host responses, PloS One 9 (2014) e106085, , https://doi.org/ 10.1371/journal.pone.0106085. [20] E.T. Arena, J.Y. Tinevez, G. Nigro, P.J. Sansonetti, B.S. Marteyn, The infectious hypoxia: occurrence and causes during Shigella infection, Microb. Infect. 19 (2017) 157–165, https://doi.org/10.1016/j.micinf.2016.10.011. [21] J.Y. Park, T.W. Chung, Y.J. Jeong, C.H. Kwak, S.H. Ha, K.M. Kwon, F. Abekura,
CRediT authorship contribution statement Jin Yang: Validation, Methodology, Writing - original draft. Nana Zhang: Investigation. Jun Lv: Resources. Ping Zhu: Visualization. Xing Pan: Software. Jiaqingzi Hu: Formal analysis. Wenfeng Wu: Data curation. Shan Li: Supervision, Validation, Project administration. Hongtao Li: Writing - review & editing, Funding acquisition. Declaration of competing interest The authors declare that they have no competing interests. Acknowledgements This work was supported by the Innovative Research Team of Hubei Provincial Department of Education (T201713), the Startup Fund for Talents Research of Taihe Hospital (RCQD002), and the Foundation of Health and Family Planning Commission of Hubei Province (WJ2018H260). We also thank the Free-science Dr. Jerry for language 5
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J. Yang, et al.
[22]
[23]
[24]
[25]
[26]
[27]
S.H. Cho, Y.C. Lee, K.T. Ha, J. Magae, Y.C. Chang, C.H. Kim, Ascofuranone inhibits lipopolysaccharide–induced inflammatory response via NF-kappaB and AP-1, pERK, TNF-α, IL-6 and IL-1β in RAW 264.7 macrophages, PloS One 12 (2017) e0171322, , https://doi.org/10.1371/journal.pone.0171322. J. Liu, M. Almeida, F. Kabir, S. Shakoor, S. Qureshi, A. Zaidi, S. Li, B. Tamboura, S.O. Sow, I. Mandomando, P.L. Alonso, T. Ramamurthy, D. Sur, K. Kotloff, J. Nataro, M.M. Levine, O.C. Stine, C. Houpta, Direct detection of Shigella in stool specimens by use of a metagenomic approach, J. Clin. Microbiol. 56 (2018), https://doi.org/10.1128/JCM.01374-17 e01374-17. W. Luo, S. Wang, X.X. Peng, Identification of shiga toxin-producing bacteria by a new immuno-capture toxin gene PCR, FEMS, Microbiol. Lett. 216 (2002) 39–42, https://doi.org/10.1111/j.1574-6968.2002.tb11411.x. G. Singh, A. Sithebe, A.M. Enitan, S. Kumari, F. Bux, T.A. Stenström, health, Comparison of droplet digital PCR and quantitative PCR for the detection of Salmonella and its application for river sediments, J. Water Health 15 (4) (2017) 505–508, https://doi.org/10.2166/wh.2017.259. D. Porcellato, J. Narvhus, S. Skeie, Detection and quantification of Bacillus cereus group in milk by droplet digital PCR, J. Microbiol. Methods 127 (2016) 1–6, https://doi.org/10.1016/j.mimet.2016.05.012. P. Lapaquette, S. Fritah, N. Lhocine, A. Andrieux, G. Nigro, J. Mounier, P. Sansonetti, A. Dejean, Shigella entry unveils a calcium/calpain-dependent mechanism for inhibiting sumoylation, Elife 6 (2017) e27444, , https://doi.org/10. 7554/eLife.27444.001. Y. Liu, Y. Cao, T. Wang, Q. Dong, J. Li, C. Niu, Detection of 12 common food-borne bacterial pathogens by TaqMan real-time PCR using a single set of reaction
[28]
[29]
[30]
[31]
[32]
6
conditions, Front. Microbiol. 10 (2019) 222, https://doi.org/10.3389/fmicb.2019. 00222. K. Shahin, H. Bao, M. Komijani, M. Barazandeh, M. Bouzari, A. Hedayatkhah, L. Zhang, H. Zhao, T. He, M. Pang, R. Wang, Isolation, characterization, and PCRbased molecular identification of a siphoviridae phage infecting Shigella dysenteriae, Microb. Pathog. 131 (2019) 175–180, https://doi.org/10.1016/j.micpath. 2019.03.037. M. Wang, J. Yang, Z. Gai, S. Huo, J. Zhu, J. Li, R. Wang, S. Xing, G. Shi, F. Shi, L. Zhang, Comparison between digital PCR and real-time PCR in detection of Salmonella typhimurium in milk, Int. J. Food Microbiol. 266 (2018) 251–256, https://doi.org/10.1016/j.ijfoodmicro.2017.12.011. G. Gobert, A. Cotillard, C. Fourmestraux, L. Pruvost, J. Miguet, M. Boyer, Droplet digital PCR improves absolute quantification of viable lactic acid bacteria in faecal samples, J. Microbiol. Methods 148 (2018) 64–73, https://doi.org/10.1016/j. mimet.2018.03.004. C.M. Hindson, J.R. Chevillet, H.A. Briggs, E.N. Gallichotte, I.K. Ruf, B.J. Hindson, R.L. Vessella, M. Tewari, Absolute quantification by droplet digital PCR versus analog real-time PCR, Nat. Methods 10 (2013) 1003–1005, https://doi.org/10. 1038/nmeth.2633. X. Zhou, T. Zhang, D. Song, T. Huang, Q. Peng, Y. Chen, A. Li, F. Zhang, Q. Wu, Y. Ye, Y. Tang, Comparison and evaluation of conventional RT-PCR, SYBR green I and TaqMan real-time RT-PCR assays for the detection of porcine epidemic diarrhea virus, Mol. Cell. Probes 33 (2017) 36–41, https://doi.org/10.1016/j.mcp.2017.02. 002.
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Comparing the performance of conventional PCR, RTQ-PCR, and droplet digital PCR assays in detection of Shigella
T
Jin Yanga, Nana Zhanga, Jun Lvb, Ping Zhub, Xing Panb, Jiaqingzi Hub, Wenfeng Wua, Shan Lib,∗∗, Hongtao Lia,∗ a
Key Laboratory of Freshwater Fish Reproduction and Development, Ministry of Education, State Key Laboratory Breeding Base of Eco-Environment and Bio-Resource, Key Laboratory of Eco-environments in Three Gorges Reservoir Region, Ministry of Education, School of Life Sciences, Southwest University, Beibei, 400715, Chongqing, China b Institute of Infection and Immunity, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, 442000, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Foodborne infections Droplet digital PCR Quantitative real-time PCR Conventional PCR Shigella spp. detection
The incidence of foodborne infections caused by Shigella spp. is still very high in every year, which poses a great potential threat to public health. Conventional quantification methods based on culture techniques, biochemical, and serological identification are time-consuming and labor-intensive. To develop a more rapid and efficient detection method of Shigella spp., we compared the sensitivity and specificity of three different polymerase chain reaction (PCR) methods, including conventional PCR, quantitative real-time PCR (RTQ-PCR), and droplet digital PCR (ddPCR). Our results indicated that ddPCR method exhibited higher sensitivity, and the limit of detection was 10−5 ng/μl for genomic DNA templates, 10−1 cfu/ml for Shigella bacteria culture. In addition, we found that ddPCR was a time-saving method, which required a shorter pre-culturing time. Collectively, ddPCR assay was a reliable method for rapid and effective detection of Shigella spp.
1. Introduction The bacteria of Shigella spp. are heat-resistant gram-negative and human host-specific pathogens that infect the intestinal epithelial cells [1]. Four different subspecies have been identified based on the antigen structure, namely S. flexneri, S. dysenteriae, S. boydii, and S. sonnei [2]. Shigella spp. are responsible for bacillary dysentery (BD) and symptomatically characterized by abdominal cramps, diarrhea, and fever. BD is a highly contagious intestinal disease in which the intestinal lining is damaged, accompanied with watery diarrhea, dehydration, and high morbidity [3,4]. In addition, previous studies have showed that virulence and pathogenicity patterns are highly heterogeneous among Shigella subspecies [5]. Shigella spp. are leading pathogens to cause shigellosis in humans, and the current global strategies are to develop effective methods for early detection to quickly prevent the spread of the disease [6]. Conventional quantification methods based on culture techniques, biochemical, and serological identification are time-consuming, labor-intensive, and have relatively low specificity and sensitivity [7]. Additionally, Shigella can only be detected in highly contagious environments and improper preservation of the specimen can result in
false positive or negative results [8]. To overcome these drawbacks, several molecular-based quantification and identification methods have been proposed [9,10]. RTQ-PCR, a highly flexible molecular-based technique with high sensitivity, especially in the detection of foodborne pathogens and it is currently the most widely accepted detection method in surveys [11]. However, the application of RTQ-PCR is limited by the reference standard curve, which is time-consuming due to the many steps involved during detection [12]. Therefore, it's necessary to establish a highly sensitive detection method with lower limit of detection (LOD) and shorter enrichment time for the detection of Shigella spp. In this study, we applied and optimized a ddPCR assay for detection and quantification of Shigella spp., and we also compared the sensitivity and specificity of three different PCR methods using Shigella genomic DNA or cultured bacterial strains. The results showed that ddPCR method exhibited higher sensitivity, and it took less enriched culture time compared to the other two methods in detecting Shigella from artificial mouse feces. Collectively, ddPCR assay was a promising approach for rapid and effective detection of Shigella spp.
∗
Corresponding author. Corresponding author. E-mail addresses: [email protected] (J. Yang), [email protected] (N. Zhang), lvjunfi[email protected] (J. Lv), [email protected] (P. Zhu), [email protected] (X. Pan), [email protected] (J. Hu), [email protected] (W. Wu), [email protected] (S. Li), [email protected] (H. Li). ∗∗
https://doi.org/10.1016/j.mcp.2020.101531 Received 31 October 2019; Received in revised form 19 January 2020; Accepted 6 February 2020 Available online 13 February 2020 0890-8508/ © 2020 Elsevier Ltd. All rights reserved.
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2. Materials and methods
2.6. Cellular infection
2.1. Bacterial strains
Murine RAW264.7 macrophage cell line obtained from American Type Culture Collection (ATCC, TiB-71, USA) [15,16] were cultured in low glucose DMEM (Gibco) supplemented with 10% FBS (BI). Cells were seeded in 24 well plates at 1 × 105 cells/well. S. flexneri strains were incubated overnight in LB medium at 37 °C while shaking at 220 rpm. The bacteria were then sub-cultured (1:33) in fresh LB medium for 3 h and added to the cells at 10−1 cfu/ml, with a centrifugation at 800g for 5 min to promote infection. Two hours later, cells were washed thrice with PBS and the extra bacteria were killed with 100 μg/ml gentamicin for a further 1 h. However, the dose of gentamicin was reduced to 20 μg/ml when the infection time was extended.
All Shigella strains (S. flexneri and S. sonnei) and control strains including Escherichia coli O126: H27, Salmonella typhimurium SL1344, Klebsiella pneumonia, and Pseudomonas aeruginosa PAO1 were obtained from Shiyan Centers for Disease Control and Prevention. The bacteria strains were stored at −80 °C in LB media (Sigma, USA) containing 20% glycerol (v/v) (Sigma, USA). Pure strains were cultivated for 24 h at 37 °C and inoculated in LB broth. The plate counting method was used to determine the bacterial counts. 2.2. Extraction of genomic DNA
2.7. Western blot and immunofluorescence
Genomic DNA was extracted from 1 ml 1 OD (2.0–5.0E+08 cells) of all the cultured bacterial strains using the bacterial genome DNA extraction kit (DP302, TIANGEN) following the manufacturer's instructions. The concentration and purity of the extracted DNA were determined by Nano Drop 2000c (Thermo Fisher Scientific, USA) (Table S1). A series of 10-fold diluted DNA solutions (ranging from 103 ng/μl to 10−9 ng/μl) were prepared for PCR analysis. The extracts and PCR products were visualized on 1% agarose gel to check for homogeneity. Follow-up experiments were immediately conducted to guarantee the stability of the results.
Infected cells were washed with ice-cold PBS and treated with SDS sample buffer. Then boiled at 98 °C for 15 min. Standard immunoblotting analysis was performed with appropriate antibodies: mouse anti-β tubulin (MA1-850, Sigma), mouse anti-IκBα and p-IκBα (9242s and 2859s, CST), mouse anti-TBK1 and p-TBK1 (3504s and 5483s, CST), mouse anti–NF–κB (p50 and p105, ab494729, Abcam) and p–NF–κB (p65, 4947s, CST). For fluorescence staining, infected cells were washed with PBS and fixed with 4% paraformaldehyde at room temperature for 10 min. Cells were then permeabilized for 10 min in 0.5% Triton X-100, and blocked with 1% FBS for 30 min, followed by the incubation with appropriate primary antibody (Tianjin biochip corporation, IM-EH002) and secondary antibody (with specific Alexa Fluor 488 conjugated anti-mouse antibodies). The nucleus was stained with DAPI (10 μg/ml) for 2 min. Leica SP8 laser scanning confocal microscopy was used to capture the images. All image data shown are representative of at least three randomly selected fields.
2.3. Primers and probe Studies have showed that ddPCR is compatible with TaqMan hydrolysis probes [13]. The primer pair (forward 5′-CGCAATACCTCCGG ATTCC-3′ and reverse 5′-TCCGCAGAGGCACTGAGTT-3′) and probe (5’ -6-FAM- AACAGGTCGCTGCATGGCTGGAA-BHQ1-3′) used in this study targeted the virulence gene ipaH9.8 (from China Food safety standard, cFDA SN/T1870 -2016) of Shigella enterobacter. The primers and probe were synthesized by Sangon Biotech (Shanghai, China).
2.8. Spiking sample 2.4. Real-time quantitative PCR
Artificially contaminated feces were prepared by adding 1 ml of PBS into 1g mouse feces, and a series of 10-fold dilution prepared. Then the number of bacteria was determined by plate counting. To estimate the pre-culture time (Table .1), a concentration range from 103 cfu/ml to 10−1 cfu/ml was prepared. Pre-enrichment processes were performed on all three sample platforms at 0 h, 4 h, 6 h, and 8 h. Time consumption was determined by analyzing 1 μl culture samples for each time period. All data displayed represent at least three randomly selected fields.
RTQ-PCR assay was performed as previously described [14]. The RTQ-PCR efficiency of the amplification was estimated from the slope of the standard curve using the equation: E = −1 +0.11/slope to control the quality and validity of the RTQ-PCR results. The effective amplification was set in the range of 90%–110%, and R2 > 0.99. Bio-Rad CFX Manager v 3.0 was used to build a linear relationship plot of Ct (threshold cycle) and log input for DNA copies. 2.5. Droplet digital PCR
3. Results
QX200™ Droplet Generator, QX200™ Droplet Reader, C1000 Touch ™ Thermal Cycler and PX1™ PCR Plate sealer (Bio-Rad, USA) were used to perform ddPCR. All the experimental components of ddPCR were shown in Table S2. QX200™ Droplet Digital™ System was used to generate droplets. The reaction mixture contained 20 μl of the PCR reaction mix and 70 μl droplet generation oil for probes (186–3005, Bio-Rad). The C1000 Touch thermal cycler was used to perform the ddPCR reactions. Meanwhile, several thermal gradients were used to optimize the assay with the annealing temperature ranging from 52 °C to 68 °C. All the tests were amplified in triplicates and the recommended ramp rate was 2 °C/sec. QX200™ Droplet Reader was used for fluorescence quantitation. The values above the back ground threshold or the negative control were considered to be positive. Discrimination range was set between 2000 and 2200 for accuracy. The initial concentration was calculated in reference to the pattern of Poisson distribution.
3.1. Evaluation of the specificity of three PCR assays Conventional PCR, RTQ-PCR, and ddPCR were performed using S. flexneri, S. sonnei strips, and other 4 control bacterial strains as templates to determine the specificity and amplification efficiency of Table 1 Time consumption of three platforms in S. flexneri contaminated mice feces. Conc.(cfu.ml)
3
1 × 10 1 × 102 1 × 101 1 × 100 1 × 10−1
2
Culture time (h) conventional PCR
RTQ-PCR
ddPCR
0 2 4 6 –
0 0 2 4 6
0 0 0 2 4
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Fig. 1. Assessment of primer specificity. (A). The specificity of primers determined with conventional PCR. (B). Amplification plot of RTQ-PCR. (C). Absolute copy number of amplifiers.
of S. flexneri genomic DNA were used to determine the sensitivity and LOD of the conventional PCR, RTQ-PCR, and ddPCR. In conventional PCR, 10−3 ng/μl genomic DNA was detected (Supplementary Fig. 1A). RTQ-PCR sensitivity was determined by the linearity of CT values of the samples and quantitative detection ranged from 102 ng/μl to 10−4 ng/ μl (Supplementary Fig. 1B). ddPCR was the most sensitive method because it could detect genomic DNA diluted to 10−5 ng/μl (Supplementary Fig. 1C), hence it performed the lowest LOD. PCR assay sensitivity decreases due to the loss of large amounts of samples during genomic DNA extraction. To avoid this problem, 10-fold serial dilution series of diluted S. flexneri suspensions (from 108 cfu/ml to 10−5 cfu/ml) were analyzed to determine the LOD using the three PCR methods. Results indicated that conventional PCR could detect 102 cfu/ml bacteria (Fig. 3A). RTQ-PCR showed good linearity within the quantification range of 105 cfu/ml to 100 cfu/ml with a 0.9917 coefficient of determination (R2) (Fig. 3B). Similar to the detection of genomic DNA, ddPCR exhibited the lowest LOD (10−1 cfu/ml) when compared to conventional PCR and RTQ-PCR (Fig. 3C). These results showed that the ddPCR detection sensitivity was 10-times higher than that of RTQ-PCR detection and 1000-fold higher than that of conventional PCR.
primers targeting ipaH9.8 gene [17] in Shigella spp. The target DNA from Shigella group (S. flexneri and S. sonnei) has been successfully amplified (Fig. 1), while no amplification was detected for the other 4 control bacterial strains tested, indicating that primers of ipaH9.8 gene were specific for Shigella detection. In addition, all PCR products amplified by conventional PCR were sequenced and analyzed for further validation of the specificity of the assay. 3.2. Thermal optimization of the ddPCR assay A total droplets count exceeding 10, 000 is considered reliable in ddPCR method [18]. In this study, a 7-temperature gradient ranging from 52 °C to 68 °C was used to generate more droplets with higher amplification efficiency and to determine the most suitable annealing temperature. The optimal range of annealing temperatures giving the largest difference in fluorescence between negative and positive droplets was between 50 °C and 62 °C (Fig. 2A). As expected, reactions at 58 °C gave a high absolute copy number (Fig. 2B) and a high positivedroplet proportion (Fig. 2C). Therefore, 58 °C was selected as the most suitable hybridization temperature for the detection of genomic DNA or in a bacterial solution. 3.3. Comparison of analytical conventional PCR, RTQ-PCR, and ddPCR assays in detecting genomic DNA and Shigella bacterial suspension
3.4. Pathogenicity of the minimum doses of Shigella detected by ddPCR The ddPCR method exhibited high detection precision with LOD of bacteria. To determine whether the minimum doses of Shigella detected by ddPCR could affect the inflammatory response and host immune state. RAW264.7 cells were infected with 0.1 cfu/ml of Shigella. At 24 h post infection, cellular Shigella was easily detected using
S. flexneri causes 90% of the cases of shigellosis and the main symptoms are dysenteric diarrhea and fever [19]. Therefore, a bacterial solution of S. flexneri and genomic DNA were selected to design a ddPCR assay for Shigella. Serial dilutions (from 103 ng/μl to 10−9 ng/μl)
Fig. 2. Optimization of digital PCR reaction conditions. (A). Amplifiers scatter diagram of ddPCR. (B). Absolute copy number of amplifiers at different annealing gradients. (C). Positive droplets and total droplets of ddPCR. 3
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Fig. 3. Detection limits of the three PCR detection methods for diluted bacteria samples. (A). The lowest detection limit of bacterial strains for conventional PCR. Products were visualized by 1% agarose gel electrophoresis. (B). CT values of different concentrations of bacterial strains detected by RTQ-PCR (C). Absolute copy number of ipaH9.8 obtained from the gradient-diluted strains detected by ddPCR.
Fig. 4. The inflammatory effect of the lowest dose on cells. (A). Fluorescence images of S. flexneri in cells after infection. S. flexneri were stained with a specific antibody (green) or DAPI to mark the nuclei (blue). (B). The phosphorylation level of IκBα, TBK1, and NF-κB following LPS stimulation or 0.1 cfu S. flexneri infection. (C). Release of IL-6, IL-1β, and TNFα in RAW264.7 cells stimulated with LPS or 10−1 cfu S. flexneri.
3.5. Comparison of PCR, RTQ-PCR, and ddPCR assays in detection of Shigella in artificially contaminated feces
immunofluorescence (Fig. 4A). As expect, infection of Shigella activated host NF-κB pathway [20,21], which lead to elevated phosphorylation of IκBα, TBK1, and p65 (Fig. 4B), and subsequently promoted the transcription of cytokines, including IL-6, IL-1β, and TNF-α (Fig. 4C). Lipopolysaccharide (LPS) treatment was set as a positive control. Importantly, this dose of Shigella (10−1 cfu/ml) was difficult to detect by conventional PCR, RTQ-PCR (Fig. 3), and remains undetectable in current clinical practice [22,23]. Therefore, ddPCR is a promising approach for the clinical diagnosis of Shigella infection.
To compare the sensitivity of these three PCR methods in analysis of the practical samples, artificially contaminated feces with S. flexneri were pre-pared and analyzed to estimate the efficiency of the PCR methods. Trace spiking samples were selected to calculate the preculturing time of three platforms. Compared with conventional PCR and RTQ-PCR, ddPCR detected S. flexneri in feces even without preculturing (Table 1). Therefore, ddPCR had an advantage in saving test time and promoted detecting efficiency of S. flexneri in contaminate feces.
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4. Discussion
revision of the manuscript.
Shigella spp. posed a threat to human health due to their low limit of detection and high pathogenicity. Our study established a ddPCR method for detecting Shigella using specific primers and probes. Comparative analysis of three PCR methods revealed that conventional PCR was the most convenient method for detecting Shigella spp. from genomic DNA sample or cultured bacteria strains for as low as 10−3 ng or 102 cfu, respectively. However, the sensitivity of conventional PCR was minimal. RTQ-PCR could detect 10−4 ng of genomic DNA or 100 cfu bacteria. Notably, ddPCR showed higher sensitivity and accuracy compared to conventional PCR and RTQ-PCR in the detection of 10−5 ng genomic DNA or 10−1 cfu bacteria. ddPCR has been widely used in the quantitative determination of bacteria such as salmonella and other pathogens [24,25]. Therefore, our study presents a novel method for Shigella detection. Shigella spp. are among the most common intestinal pathogens that cause acute diarrhea. Due to its highly pathogenic even at a small number of bacteria [26], this makes it difficult to detect [27]. Therefore, rapid and accurate early diagnostic assays with a broad detection spectrum are urgently required, which will improve the prevention and control of BD outbreaks and minimize the spread of Shigella infection. In this study, ddPCR has a short detection time, high specificity, good reproducibility, and accuracy, and these properties are superior to those of the commonly used conventional biochemical culture and RTQ-PCR [28]. Thus, ddPCR is an effective and highly sensitive method for the detection of Shigella. Its high sensitivity circumvents the need to culture the samples, which shortens the time for early detection. Importantly, we demonstrated that the lowest dose detected (10−1 cfu bacteria) can still cause an inflammatory response in immune cells. Moreover, ddPCR decreased the pre-culturing time (saving 2 h) required in artificially contaminated mouse feces. In conclusion, we present a fast and accurate detection tool for the clinical detection of Shigella spp. ddPCR is advantageous because it does not require a standard curve and direct quantification of the DNA copies in trace templates reference to the Poisson distribution pattern [29] with higher sensitivity and requires a shorter period of time [30]. Although ddPCR presents many advantages, it has several shortcomings. For example, this method only contains FAM, HEX, and VIC fluorescent channels, and the samples could only be detected at specific dilutions [31]. Besides, it may be less accurate when the concentration of the target gene exceeds 102 ng/μl or target bacteria exceeds 105 cfu/ml. More importantly, this method often yields false-positive results due to its high sensitivity, and samples tested in triplicates increases the costs [32]. Despite these shortcomings, in this study, we showed that ddPCR was highly sensitive and specific, hence suitable for Shigella detection.
Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.mcp.2020.101531. References [1] J.A. Carter, J.C. Jimenez, M. Zaldivar, S.A. Alvarez, C.L. Marolda, A. Valvano, I. Contreras, The cellular level of O-antigen polymerase Wzy determines chain length regulation by WzzB and WzzpHS-2 in Shigella flexneri 2a, Microbiology 155 (2009) 3260–3269, https://doi.org/10.1099/mic.0.028944-0. [2] M. Jharna, V. GaneSh, E. Jennifer, S. MahadeVan, C. SubhaSh, The recent trends of shigellosis: a JIPMER perspective, J. Clin. Diagn. Res. 6 (2012) 1474–1477, https:// doi.org/10.7860/JCDR/2012/4157.2536. [3] M. Azage, A. Kumie, A. Worku, A. Bagtzoglou, E. Anagnostou, Effect of climatic variability on childhood diarrhea and its high risk periods in northwestern parts of Ethiopia, PloS One 12 (2017) e0186933, , https://doi.org/10.1371/journal.pone. 0186933. [4] J.I. Lee, S.J. Kim, C.G. Park, Shigella flexneri infection in a newly acquired rhesus macaque (Macaca mulatta), Lab. Anim. Res. 27 (2011) 343–346, https://doi.org/ 10.5625/lar.2011.27.4.343. [5] F. Orrett, Prevalence of Shigella serogroups and their antimicrobial resistance patterns in southern trinidad, J. Health Popul. Nutr. 26 (2008) 456–462, https://doi. org/10.3329/jhpn.v26i4.1889. [6] M.R. Vinas, E. Tuduri, A. Galar, K. Yih, M. Pichel, J. Stelling, S.P. Brengi, A.D. Della, C.V. Ploeg, S. Bruno, A. Roge, M.I. Caffer, M. Kulldorff, M. Galas, M.A. Group, Laboratory-based prospective surveillance for community outbreaks of Shigella spp. in Argentina, PLoS, Negl. Trop. Dis. 7 (2013) e2521, , https://doi.org/10.1371/ journal.pntd.0002521. [7] R. Lindqvist, Detection of Shigella spp. in food with a nested PCR method – sensitivity and performance compared with a conventional culture method, J. Appl. Microbiol. 86 (1999) 971–978, https://doi.org/10.1046/j.1365-2672.1999. 00777.x. [8] A. Camacho, R.J. Souza, J.M. Irache, C. Gamazo, Towards a non-living vaccine against Shigella flexneri: from the inactivation procedure to protection studies, Methods 60 (3) (2013) 264–268, https://doi.org/10.1016/j.ymeth.2012.09.008. [9] P. Van, E.D. Witte, H. Henau, A. Muynck, L. Verstraeten, B. Herendael, S. Weekx, Evaluation of a real-time multiplex PCR for the simultaneous detection of Campylobacter jejuni, Salmonella spp., Shigella spp./EIEC, and Yersinia enterocolitica in fecal samples, Eur. J. Clin. Microbiol. Infect. Dis. 34 (2015) 535–542, https://doi. org/10.1007/s10096-014-2257-x. [10] C. Chai, H. Jang, S.K. Oh, Real-time PCR-based quantification of Shigella sonnei in beef and a modified Gompertz equation-based predictive modeling of its growth, Appl. Biol. Chem. 59 (2016) 67–70, https://doi.org/10.1007/s13765-015-0144-5. [11] M. Pavlovic, A. Luze, R. Konrad, A. Berger, A. Sing, U. Busch, I. Huber, Development of a duplex real-time PCR for differentiation between E. coli and Shigella spp, J. Appl. Microbiol. 110 (2011) 1245–1251, https://doi.org/10.1111/j. 1365-2672.2011.04973.x. [12] B.L. Lopez, M. Bekaert, M. Bakheit, S. Frischmann, P. Patel, E.L. Simon, L. Lambrechts, V. Duong, P. Dussart, G. Harold, C. Fall, O. Faye, A. Sall, M. Weidmann, Development and validation of four one-step real-time RT-LAMP assays for specific detection of each dengue virus serotype, PLoS Neglected Trop. Dis. 12 (2018) e0006381, , https://doi.org/10.1371/journal.pntd.0006381. [13] A. Morley, Digital PCR: a brief history, Biomol. Detect. Quantif. 1 (2014) 1–2, https://doi.org/10.1016/j.bdq.2014.06.001. [14] W. Pfaffl, A new mathematical model for relative quantification in real-time RT–PCR, Nucleic Acids Res. 29 (2001), https://doi.org/10.1093/nar/29.9.e45 e45e45. [15] J.N. Zenevich, S. Stuart, M.A. Nour, S.E. Girardin, J. Mogridge, Listeria monocytogenes and Shigella flexneri activate the NLRP1B inflammasome, Infect. Immun. 85 (11) (2017), https://doi.org/10.1128/IAI.00338-17 e00338-17. [16] I. Cho, J.W. Jeon, S.H. Paek, D.H. Kim, H.S. Shin, U.H. Ha, S.K. Seo, S.H. Paek, Tolllike receptor-based immuno-analysis of pathogenic microorganisms, Anal. Chem. 84 (22) (2012) 9713–9720, https://doi.org/10.1021/ac300668y. [17] D.T. Vu, O. Sethabutr, L.V. Seidlein, V.T. Tran, G.C. Do, T.C. Bui, H.T. Le, H. Lee, H.S. Houng, T.L. Hale, J.D. Clemens, C. Mason, D.D. Dang, Detection of Shigella by a PCR assay targeting the ipaH gene suggests increased prevalence of shigellosis in Nha Trang, Vietnam, J. Clin. Microbiol. 42 (2004) 2031–2035, https://doi.org/10. 1128/JCM.42.5.2031-2035.2004. [18] E. Dupas, B. Legendre, V. Olivier, F. Poliakoff, C. Manceau, A. Cunty, Comparison of real-time PCR and droplet digital PCR for the detection of Xylella fastidiosa in plants, J. Microbiol. Methods 162 (2019) 86–95, https://doi.org/10.1016/j.mimet.2019. 05.010. [19] D.T. George, C.A. Behm, D.H. Hall, U. Mathesius, M. Rug, K.C. Nguyen, N.K. Verma, Shigella flexneri infection in Caenorhabditis elegans: cytopathological examination and identification of host responses, PloS One 9 (2014) e106085, , https://doi.org/ 10.1371/journal.pone.0106085. [20] E.T. Arena, J.Y. Tinevez, G. Nigro, P.J. Sansonetti, B.S. Marteyn, The infectious hypoxia: occurrence and causes during Shigella infection, Microb. Infect. 19 (2017) 157–165, https://doi.org/10.1016/j.micinf.2016.10.011. [21] J.Y. Park, T.W. Chung, Y.J. Jeong, C.H. Kwak, S.H. Ha, K.M. Kwon, F. Abekura,
CRediT authorship contribution statement Jin Yang: Validation, Methodology, Writing - original draft. Nana Zhang: Investigation. Jun Lv: Resources. Ping Zhu: Visualization. Xing Pan: Software. Jiaqingzi Hu: Formal analysis. Wenfeng Wu: Data curation. Shan Li: Supervision, Validation, Project administration. Hongtao Li: Writing - review & editing, Funding acquisition. Declaration of competing interest The authors declare that they have no competing interests. Acknowledgements This work was supported by the Innovative Research Team of Hubei Provincial Department of Education (T201713), the Startup Fund for Talents Research of Taihe Hospital (RCQD002), and the Foundation of Health and Family Planning Commission of Hubei Province (WJ2018H260). We also thank the Free-science Dr. Jerry for language 5
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[22]
[23]
[24]
[25]
[26]
[27]
S.H. Cho, Y.C. Lee, K.T. Ha, J. Magae, Y.C. Chang, C.H. Kim, Ascofuranone inhibits lipopolysaccharide–induced inflammatory response via NF-kappaB and AP-1, pERK, TNF-α, IL-6 and IL-1β in RAW 264.7 macrophages, PloS One 12 (2017) e0171322, , https://doi.org/10.1371/journal.pone.0171322. J. Liu, M. Almeida, F. Kabir, S. Shakoor, S. Qureshi, A. Zaidi, S. Li, B. Tamboura, S.O. Sow, I. Mandomando, P.L. Alonso, T. Ramamurthy, D. Sur, K. Kotloff, J. Nataro, M.M. Levine, O.C. Stine, C. Houpta, Direct detection of Shigella in stool specimens by use of a metagenomic approach, J. Clin. Microbiol. 56 (2018), https://doi.org/10.1128/JCM.01374-17 e01374-17. W. Luo, S. Wang, X.X. Peng, Identification of shiga toxin-producing bacteria by a new immuno-capture toxin gene PCR, FEMS, Microbiol. Lett. 216 (2002) 39–42, https://doi.org/10.1111/j.1574-6968.2002.tb11411.x. G. Singh, A. Sithebe, A.M. Enitan, S. Kumari, F. Bux, T.A. Stenström, health, Comparison of droplet digital PCR and quantitative PCR for the detection of Salmonella and its application for river sediments, J. Water Health 15 (4) (2017) 505–508, https://doi.org/10.2166/wh.2017.259. D. Porcellato, J. Narvhus, S. Skeie, Detection and quantification of Bacillus cereus group in milk by droplet digital PCR, J. Microbiol. Methods 127 (2016) 1–6, https://doi.org/10.1016/j.mimet.2016.05.012. P. Lapaquette, S. Fritah, N. Lhocine, A. Andrieux, G. Nigro, J. Mounier, P. Sansonetti, A. Dejean, Shigella entry unveils a calcium/calpain-dependent mechanism for inhibiting sumoylation, Elife 6 (2017) e27444, , https://doi.org/10. 7554/eLife.27444.001. Y. Liu, Y. Cao, T. Wang, Q. Dong, J. Li, C. Niu, Detection of 12 common food-borne bacterial pathogens by TaqMan real-time PCR using a single set of reaction
[28]
[29]
[30]
[31]
[32]
6
conditions, Front. Microbiol. 10 (2019) 222, https://doi.org/10.3389/fmicb.2019. 00222. K. Shahin, H. Bao, M. Komijani, M. Barazandeh, M. Bouzari, A. Hedayatkhah, L. Zhang, H. Zhao, T. He, M. Pang, R. Wang, Isolation, characterization, and PCRbased molecular identification of a siphoviridae phage infecting Shigella dysenteriae, Microb. Pathog. 131 (2019) 175–180, https://doi.org/10.1016/j.micpath. 2019.03.037. M. Wang, J. Yang, Z. Gai, S. Huo, J. Zhu, J. Li, R. Wang, S. Xing, G. Shi, F. Shi, L. Zhang, Comparison between digital PCR and real-time PCR in detection of Salmonella typhimurium in milk, Int. J. Food Microbiol. 266 (2018) 251–256, https://doi.org/10.1016/j.ijfoodmicro.2017.12.011. G. Gobert, A. Cotillard, C. Fourmestraux, L. Pruvost, J. Miguet, M. Boyer, Droplet digital PCR improves absolute quantification of viable lactic acid bacteria in faecal samples, J. Microbiol. Methods 148 (2018) 64–73, https://doi.org/10.1016/j. mimet.2018.03.004. C.M. Hindson, J.R. Chevillet, H.A. Briggs, E.N. Gallichotte, I.K. Ruf, B.J. Hindson, R.L. Vessella, M. Tewari, Absolute quantification by droplet digital PCR versus analog real-time PCR, Nat. Methods 10 (2013) 1003–1005, https://doi.org/10. 1038/nmeth.2633. X. Zhou, T. Zhang, D. Song, T. Huang, Q. Peng, Y. Chen, A. Li, F. Zhang, Q. Wu, Y. Ye, Y. Tang, Comparison and evaluation of conventional RT-PCR, SYBR green I and TaqMan real-time RT-PCR assays for the detection of porcine epidemic diarrhea virus, Mol. Cell. Probes 33 (2017) 36–41, https://doi.org/10.1016/j.mcp.2017.02. 002.